Handbook of Manufacturing Engineering and Technology [1 ed.] 9781447146698, 9781447146704

Table of contents :
Front Matter....Pages i-xlv
Front Matter....Pages 1-1
Front Matter....Pages 3-42
Front Matter....Pages 43-98
Front Matter....Pages 99-124
Front Matter....Pages 125-168
Front Matter....Pages 169-169
Front Matter....Pages 171-230
Front Matter....Pages 231-284
Front Matter....Pages 285-307
Front Matter....Pages 309-410
Front Matter....Pages 411-452
Front Matter....Pages 453-485
Front Matter....Pages 487-566
Front Matter....Pages 567-567
Front Matter....Pages 569-592
Front Matter....Pages 593-615
Back Matter....Pages 617-639
....Pages 641-683

Citation preview

Andrew Y. C. Nee Editor

Handbook of Manufacturing Engineering and Technology 1 3Reference

Handbook of Manufacturing Engineering and Technology

Andrew Y. C. Nee Editor

Handbook of Manufacturing Engineering and Technology With 2123 Figures and 371 Tables

Editor Andrew Y. C. Nee Mechanical Engineering Department Faculty of Engineering National University of Singapore Singapore

ISBN 978-1-4471-4669-8 ISBN 978-1-4471-4670-4 (eBook) ISBN 978-1-4471-4671-1 (print and electronic bundle) DOI 10.1007/978-1-4471-4670-4 Springer London Heidelberg New York Dordrecht Library of Congress Control Number: 2014950446 # Springer-Verlag London 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Foreword

This project started in July 2010 when Anil Chandy and Sandra Fabiani from Springer approached me to take on the editorship of a handbook series on manufacturing engineering. The initial thought that came to me was a colossal task involving hundreds of people and taking tens of years to complete. The decision of the topics to be included is equally daunting. We met a couple of times in person and over the Internet to size up the scope and contact a number of potential volume editors. After many attempts and sounding out prominent authors, I managed to convince three section editors to take on this task, which was still a long way from the 12 section editors planned. In May 2011, I approached the Executive Director of SIMTech, Dr Lim Ser Yong, for his help and a joint presentation together with Anil and Sandra was made to his research group leaders on the significance of such a project. He gracefully agreed. Much to our joy, five section editors agreed and were appointed in 2012, followed by another two editors in 2013. The last two overseas editors joined in mid-2013. In April 2014, we finally saw the project through to completion and the handbook is ready to roll out, even though it has been a lengthy journey!

v

Preface

Innovation and manufacturing capabilities are well known to be the wealth creator of any nation which has strong advanced manufacturing technologies for making high-value-added products and is able to compete globally. Manufacturing is evolving continuously, engulfing more technologies than several decades ago. The rapid development of Internet technology, computer science, materials research, microelectronics, and biosciences has propelled manufacturing activities far beyond mere product fabrication. Manufacturing technology has now entered into the realm of intelligent product creation, and yet at affordable prices, and is highly compatible with environmental concerns. Manufacturing knowledge has been created by both the academia and industry, but unfortunately a great deal of information is scattered over a myriad of published papers, reports, and books – some are publicly available, while others remain proprietary information and are well guarded by the organizations which created them. The raison d’eˆtre of the Handbook of Manufacturing Engineering and Technology is to gather the fundamental and evolving technologies in manufacturing engineering from many experts and practitioners in an attempt to cover as many fields as possible in common manufacturing activities. The collated materials will be updated frequently to capture the latest developments. The six volumes of the handbook cover the following topics: Volume 1 – Forming and Joining • Materials Forming: Forming of Polymer and Composite Materials • Metal Forming • Materials Joining Volume 2 – Machining and Tolerancing Systems • Machining • Tolerancing Systems Volume 3 – Nanomanufacturing and Non-traditional Machining • Nanomanufacturing Using Ion Beam Technology • Non-traditional Machining Processes Volume 4 – Robotics and Automation Volume 5 – Additive Manufacturing and Surface Technology • Additive Manufacturing: Rapid Prototyping, Tooling, and Manufacturing • Surface Technology vii

viii

Preface

Volume 6 – Product Life Cycle and Manufacturing Simulation • Product Life Cycle and Green Manufacturing • Manufacturing Simulation and Optimization It is hoped that these volumes provide useful assistance for both academia and industry with regard to the needed reference and basic knowledge of each process. What is more important is that the knowledge will be updated continuously to keep abreast with the state-of-the-art developments in the world of manufacturing research and practice. Andrew Y. C. Nee, DEng, PhD August 2014 Singapore

Acknowledgments

The Handbook of Manufacturing Engineering and Technology is the collective effort of many distinguished researchers and scientists in the field of manufacturing engineering. Much of the hard work also comes from the section editors who painstakingly contacted all the authors as well as edited and proofread their contributions. The section editors are gratefully acknowledged and are mentioned below along with the names of the respective sections they edited: • Materials Forming: Forming of Polymer and Composite Materials – Suzhu Yu (SIMTech) • Metal Forming – Mehrdad Zarinejad (SIMTech) • Materials Joining – Jun Wei and Wei Zhou (SIMTech) • Machining – Sathyan Subbiah (SIMTech, now at Indian Institute of Technology (IIT) Madras, Chennai) and Hongyu Zheng (SIMTech) • Tolerancing Systems – Ping Ji (Hong Kong PolyU) • Nanomanufacturing Using Ion Beam Technology – Zong Wei Xu and Fengzhou Fang (Tianjin University) • Non-traditional Machining Processes – Hong Hocheng and Hung-Yin Tsai (National Tsinghua University) • Robotics and Automation – Guilin Yang (SIMTech, now at Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences) • Additive Manufacturing: Rapid Prototyping, Tooling, and Manufacturing – David S K Wong (Nanyang Polytechnic) • Surface Technology – Guojun Qi and Sam Zhang (SIMTech) • Product Life Cycle and Green Manufacturing – Bin Song (SIMTech) • Manufacturing Simulation and Optimization – Manoj Kumar Tiwari (IIT Kharagpur) Special thanks go to all the contributing authors, researchers, and students who have made this handbook possible.

ix

x

Acknowledgments

I would like to thank Springer for the great commitment to publish the handbook and, in particular, to the following colleagues from Springer, without whom the project could never be materialized: • Anil Joseph Chandy • Sandra Fabiani • Mansi Seth • Sunali Mull • Neha Thapa Andrew Y. C. Nee Editor

About the Editor

Andrew Y. C. Nee, a Full Professor at National University of Singapore (NUS) since 1989, received his PhD and DEng from University of Manchester, Institute of Science and Technology (UMIST). He has contributed to the fundamental and applied research in the design of molds, dies, and fixtures; manufacturing simulation using augmented reality; and sustainable manufacturing. He was appointed Editor in Chief of Springer’s long-standing International Journal of Advanced Manufacturing Technology in February 2014 and serves on 22 editorial boards. He has published over 500 papers in peer-reviewed international journals and conference proceedings and has authored and edited 12 books and 23 book chapters. He has graduated 40 PhD and 43 master’s students by research. Some of the awards he received include the IEEE Kayamori Award in 1999, IJPR Norman A Dudley Award in 2003, and IMechE Joseph Whitworth Prize in 2009. In NUS, he had served as the Head of Mechanical Engineering, Dean of Engineering, and Director of Research Administration. He was honored with the Engineering Leadership Award by NUS in 2012. Under his leadership, his research team has worked on computer-aided mold design, leading to the setting up of a university spin-off company Manusoft Technologies Pte Ltd and the development of IMOLD. He and his team’s effort in the metal-stamping progressive die design had won them the National Technology Award in 2002. He received the National Day Award Public Administration Medal (Silver) in 2007. xi

xii

About the Editor

He holds honorary professorship from five universities in China: Tianjin, Beijing University of Aeronautics and Astronautics (BUAA), Nanjing University of Aeronautics and Astronautics (NUAA), Shanghai, and Huazhong University of Science and Technology (HUST). He was a recipient of Society of Manufacturing Engineers’s (SME’s) Outstanding Young Manufacturing Engineer Award in 1982, Fellow of SME (1990), and Fellow of The International Academy for Production Engineering (CIRP) (1990). He is a Founding Fellow of the Academy of Engineering Singapore and served as President of CIRP (2011–2012), the International Academy for Production Engineering. He is the first ethnic Chinese in the world to hold this position since CIRP was established in 1951 in Paris. He received the Gold Medal from the US Society of Manufacturing Engineers in Detroit, June 2014. It is an international recognition of his outstanding service to the field of manufacturing engineering through published literature and education.

Section Editors

Yu Suzhu Singapore Institute of Manufacturing Technology, Singapore

xiii

xiv

Mehrdad Zarinejad Singapore Singapore

Section Editors

Institute

of

Manufacturing

Technology,

Jun Wei Singapore Institute of Manufacturing Technology, Singapore

Section Editors

xv

Wei Zhou Churchill College, Cambridge, UK

Sathyan Subbiah Department of Mechanical Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India

xvi

Section Editors

Zheng Hongyu Department of Mechanical Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India

Ping Ji The Hong Kong Polytechnic University, Hong Hum, Kowloon, Hong Kong

Section Editors

xvii

Zong Wei Xu College of Precision Instrument and Opto-electronic Engineering, Centre of MicroNano Manufacturing Technology, Tianjin University, Tianjin, China

Fengzhou Fang College of Precision Instrument & Opto-electronics Engineering, Centre of MicroNano Manufacturing Technology, Tianjin University, Tianjin, China

xviii

Section Editors

Hong Hocheng Department of Power Mechanical Engineering, National Tsinghua University, Hsinchu, Taiwan, Republic of China

Hung-Yin Tsai Department of Power Mechanical Engineering, National Tsinghua University, Hsinchu, Taiwan, Republic of China

Section Editors

xix

Guilin Yang Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences, Ningbo, China

David S. K. Wong Additive Manufacturing Innovation Centre, Nanyang Polytechnic, Singapore

xx

Section Editors

Guojun Qi Singapore Institute of Manufacturing Technology, Singapore

Sam Zhang Shanyong Division of Manufacturing Engineering, School of Mechanical & Aerospace Engineering, College of Engineering, Singapore

Section Editors

xxi

Bin Song Sustainable Manufacturing Centre, Singapore Institute of Manufacturing Technology, Singapore

Manoj Kumar Tiwari Department of Industrial and Systems Engineering (Formerly known as Department of Industrial Engineering and Management), Indian Institute of Technology, Kharagpur, West Bengal, India

Contents

Volume 1 Section I Forming and Joining: Materials Forming - Forming of Polymer and Composite Materials . . . . . . . . . . . . . . . . . . . . . . . . .

1

Yu Suzhu 1

Fundamentals of Polymers and Polymer Composite . . . . . . . . . . . Alok Chaurasia, Nanda Gopal Sahoo, Mian Wang, Chaobin He, and Vishal Tukaram Mogal

3

2

Properties and Applications of Polymer Nanocomposite . . . . . . . . Alok Chaurasia, Yu Suzhu, Cheng Kuo Feng Henry, Vishal Tukaram Mogal, and Sampa Saha

43

3

Polymer Surface Treatment and Coating Technologies Mary Gilliam

........

99

4

Polymer Foam Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiao Hu, Erwin Merijn Wouterson, and Ming Liu

125

Section II

Forming and Joining: Metal Forming

..............

169

Mehrdad Zarinejad 5

Bulk Metal Forming Processes in Manufacturing . . . . . . . . . . . . . Ehsan Ghassemali, Xu Song, Mehrdad Zarinejad, Danno Atsushi, and Ming Jen Tan

171

6

Materials in Metal Forming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sridhar Idapalapati, Xu Song, N. Venkata Reddy, Narasimalu Srikanth, Farshid Pahlevani, Karthic R. Narayanan, and Mehrdad Zarinejad

231

7

Roll Forming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michael Lindgren, Jonas Edberg, and Lars-Erik Lindgren

285

xxiii

xxiv

Contents

8

Metal Casting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anders E. W. Jarfors and Salem Seifeddine

309

9

Incremental Metal Forming Processes in Manufacturing . . . . . . . N. Venkata Reddy, Rakesh Lingam, and Jian Cao

411

10

Combined Sheet and Bulk Forming of High Value Added Components in Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . Atsushi Danno

11

Powder Processing of Bulk Components in Manufacturing . . . . . Andrew Ruys, Oana Gingu, Gabriela Sima, and Saeed Maleksaeedi

Section III

Forming and Joining: Materials Joining . . . . . . . . . . . .

453 487

567

Wei Jun and Wei Zhou 12

Solid State Welding Processes in Manufacturing Junfeng Guo

.............

569

13

Arc Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Kishore Babu, Mahesh Kumar Talari, Sun Zheng, Pan Dayou, S. Jerome, and V. Muthupandi

593

14

High Energy Beam Welding Processes in Manufacturing . . . . . . . Chen Hui-Chi, Bi Guijun, and Sun Chen-Nan

617

15

Solid State Microjoining Processes in Manufacturing . . . . . . . . . . Sharon Mui Ling Nai, Murali Sarangapani, and Johnny Yeung

641

16

Process of Nanojoining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoying Qi, Tey Ju Nie, and Ho Xinning

685

17

Solder Joint Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sharon Mui Ling Nai, Long Bin Tan, and Cheryl Selvanayagam

713

18

Adhesive Bonding Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shantanu Bhowmik, R. Benedictus, and Yu Dan

765

Volume 2 Section IV Machining and Tolerancing Systems: Machining Sathyan Subbiah and Hongyu Zheng

....

785

19

Science of Machining Sathyan Subbiah

...................................

787

20

Machine Tools for Machining . . . . . . . . . . . . . . . . . . . . . . . . . . . . Irving Paul Girsang and Jaspreet Singh Dhupia

811

Contents

xxv

21

Machining Dynamics in Manufacturing Jeong Hoon Ko

....................

867

22

Machinability of Engineering Materials Hongyu Zheng and Kui Liu

....................

899

23

Machining Process Monitoring Huaizhong Li and Yun Chen

...........................

941

24

Coolant and Lubrication in Machining . . . . . . . . . . . . . . . . . . . . . Ramesh Singh and Vivek Bajpai

981

25

Fixed Abrasive Machining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1019 Fu Yucan and Yang Changyong

26

Loose Abrasive Machining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1051 Takashi Sato, Swee Hock Yeo, and Hamid Zarepour

27

Mechanical Micro-machining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1089 Kushendarsyah Saptaji

28

Hybrid Machining Processes Murali Meenakshi Sundaram

29

Environmentally Friendly Machining . . . . . . . . . . . . . . . . . . . . . . 1127 Fu Zhao and Abhay Sharma

30

Simulation in Machining Vis Madhavan

31

Virtual Machining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1185 Peiling Liu and Cheng-Feng Zhu

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1109

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1155

Section V Machining and Tolerancing Systems: Tolerancing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ping Ji 32

1249

Computer Aided Angular Tolerance Charting System: Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1251 Jianbin Xue and Ping Ji

Volume 3 Section VI Nanomanufacturing and Non-Traditional Machining: Nanomanufacturing Using Ion Beam Technology . . . . Zong Wei Xu and Fengzhou Fang 33

1275

Introduction to Nanomanufacturing Using Ion Beam Technology . . 1277 Fengzhou Fang

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Contents

34

State-of-the-Art for Nanomanufacturing Using Ion Beam Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1279 Fengzhou Fang and Zongwei Xu

35

Ion Beam Instruments Used for Nanomanufacturing . . . . . . . . . . 1317 Wuxia Li and Changzhi Gu

36

Ion Beam Figuring Technology Xuhui Xie and Shengyi Li

37

Focused Ion Beam Nanofabrication Technology . . . . . . . . . . . . . . 1391 Zongwei Xu, Fengzhou Fang, and Guosong Zeng

38

Nanometric Cutting of Crystal Surfaces Modified by Ion Implantation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1425 Yunhui Chen and Fengzhou Fang

39

Micro Tools Fabrication by Focused Ion Beam Technology . . . . . 1473 Wei Wu, Wanli Li, Fengzhou Fang, and Zong Wei Xu

40

Nano-gap Electrodes Developed Using Focused Ion Beam Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1513 Takashi Nagase

41

Plasma-Based Nanomanufacturing Under Atmospheric Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1529 Kazuya Yamamura and Yasuhisa Sano

. . . . . . . . . . . . . . . . . . . . . . . . . . . 1343

Section VII Nanomanufacturing and Non-Traditional Machining: Non-Traditional Machining Processes . . . . . . . . . . . . . . Hong Hocheng and Hung-Yin Tsai

1549

42

Electrical Discharge Machining Processes . . . . . . . . . . . . . . . . . . . 1551 Masanori Kunieda

43

Chemical Mechanical Machining Process . . . . . . . . . . . . . . . . . . . 1581 Toshiro Doi

44

Process of Laser Machining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1601 George Chryssolouris, Panagiotis Stavropoulos, and Konstantinos Salonitis

45

Process of Ultrasonic Machining . . . . . . . . . . . . . . . . . . . . . . . . . . 1629 Weilong Cong and Zhijian Pei

46

WaterJet Machining Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1651 Mohamed Hashish

47

Process of Biological Machining . . . . . . . . . . . . . . . . . . . . . . . . . . . 1687 Hong Hocheng and Umesh Jadhav

Contents

xxvii

Volume 4 Section VIII

Robotics and Automation . . . . . . . . . . . . . . . . . . . . . .

1715

Guilin Yang 48

Rigid-Body Motions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1717 Zhenhua Xiong, Chungang Zhuang, and Jianhua Wu

49

Manipulator Kinematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1777 Ashitava Ghosal

50

Manipulator Velocities and Static Forces . . . . . . . . . . . . . . . . . . . 1809 Dan Zhang, Kailiang Zhang, and Andreas Mu¨ller

51

Manipulator Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1855 Shaoping Bai, Lelai Zhou, and Guanglei Wu

52

Trajectory Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1873 Quang-Cuong Pham

53

Motion Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1889 Chien Chern Cheah and Reza Haghighi

54

Force Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1933 Rolf Johansson, Klas Nilsson, and Anders Robertsson

55

Actuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1967 Lorenzo Masia

56

Robot Work Cell Calibration and Error Compensation . . . . . . . . 1995 Pey Yuen Tao, Shabbir Kurbanhusen Mustafa, Guilin Yang, and Masayoshi Tomizuka

57

Grippers and End-Effectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2035 Wenjie Chen, Su Zhao, and Siew Loong Chow

58

Simulation and Offline Programming for Contact Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2071 N. D. Vuong, T. M. Lim, and G. Yang

59

Parallel Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2091 Yan Jin, He´le`ne Chanal, and Flavien Paccot

60

Modular Robots I-Ming Chen

61

Cable-Driven Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2169 Shabbir Kurbanhusen Mustafa, Wen Bin Lim, Guilin Yang, Song Huat Yeo, Wei Lin, and Sunil Kumar Agrawal

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2129

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Contents

62

Compliant Manipulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2229 Tat Joo Teo, Guilin Yang, and I-Ming Chen

63

Autonomous In-door Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2301 Jun Feng Dong, Sean Efrem Sabastian, Tao Ming Lim, and Yuan Ping Li

64

Robotic Assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2347 Heping Chen, Biao Zhang, and George Zhang

65

Robotic Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2403 Wei Lin and Hong Luo

66

Robotic Finishing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2445 Yeow Cheng Sun and Chow Yin Lai

Volume 5 Section IX Additive Manufacturing and Surface Technology - Additive Manufacturing-Rapid Prototyping, Tooling & Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2469

David SK Wong 67

Rapid Manufacturing Using FDM Systems . . . . . . . . . . . . . . . . . . 2471 Allen Kreemer and Zaw Hlwan Moe

68

Reverse Engineering for Additive Manufacturing . . . . . . . . . . . . . 2485 Bill Macy

69

Rapid Prototyping in Manufacturing . . . . . . . . . . . . . . . . . . . . . . . 2505 Jesse Hanssen, Zaw Hlwan Moe, Desmond Tan, and Ong Yong Chien

70

Rapid Tooling in Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . 2525 Chee Kai Chua, Kah Fai Leong, and Zhong Hong Liu

71

Micro Prototyping and Fabrication in Manufacturing . . . . . . . . . 2551 Ian Gibson

72

Micro- and Bio-Rapid Prototyping Using Drop-On-Demand 3D Printing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2567 J. Y. H. Fuh, J. Sun, E. Q. Li, Jinlan Li, Lei Chang, G. S. Hong, Y. S. Wong, and E. S. Thian

Contents

xxix

Section X Additive Manufacturing and Surface Technology: Surface Technology . . . . . . . . . . . . . . . . . . . . . . . . . . .

2585

Guojun Qi and Sam Zhang 73

Foresight of the Surface Technology in Manufacturing . . . . . . . . 2587 Leszek A. Dobrzan´ski and Anna D. Dobrzanska-Danikiewicz

74

Laser Surface Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2639 Jyotsna Dutta Majumdar and Indranil Manna

75

Laser Surface Treatment in Manufacturing . . . . . . . . . . . . . . . . . 2677 Leszek A. Dobrzan´ski, Anna D. Dobrzan´ska-Danikiewicz, Tomasz Tan´ski, Ewa Jonda, Aleksandra Drygała, and Miroslaw Bonek

76

Physical Vapor Deposition in Manufacturing . . . . . . . . . . . . . . . . 2719 Leszek A. Dobrzan´ski, Klaudiusz Gołombek, and Krzysztof Lukaszkowicz

77

Chemical Vapor Deposition in Manufacturing . . . . . . . . . . . . . . . 2755 Leszek A. Dobrzan´ski, Daniel Pakula, and Marcin Staszuk

78

Thermal and Cold Spraying Technology in Manufacturing . . . . . 2805 Jyotsna Dutta Majumdar

79

Electrochemical Processes in Manufacturing Adnan Younis, Dewei Chu, and Sean Li

80

Electrochemical Deposition and Mechanical Property Enhancement of the Nickel and Nickel-Cobalt Films . . . . . . . . . . 2891 Chen-Kuei Chung and Wei-Tse Chang

81

Magnetron Sputtering Technique Manuel Braun

82

ALD: Atomic Layer Deposition – Precise and Conformal Coating for Better Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 2959 Wei He

83

Surface Modification of Semiconductor by Simultaneous Thermal Oxidation and Nitridation . . . . . . . . . . . . . . . . . . . . . . . . 2997 Kuan Yew Cheong and Yew Hoong Wong

84

Surface Treatments for Magnesium Alloys . . . . . . . . . . . . . . . . . . 3031 Xuecheng Dong

. . . . . . . . . . . . . . . . 2851

. . . . . . . . . . . . . . . . . . . . . . . . . 2929

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Thermal Stress Analysis and Characterization of Themo-Mechanical Properties of Thin Films on an Elastic Substrate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3055 Ying Yong Hu and Wei Min Huang

Volume 6 Section XI Product Life Cycle and Manufacturing Simulation: Product Life Cycle and Green Manufacturing Bin Song

.......

3135

86

Remanufacturing and Remaining Useful Life Assessment . . . . . . 3137 Hongchao Zhang, Shujie Liu, Huitian Lu, Yuanliang Zhang, and Yawei Hu

87

Product Design for Remanufacturing . . . . . . . . . . . . . . . . . . . . . . 3195 S. S. Yang, S. K. Ong, and A. Y. C. Nee

88

Product Service Supply-Chain Design . . . . . . . . . . . . . . . . . . . . . . 3219 Zhitao Xu, XG Ming, Tengyun Wu, and Maokuan Zheng

89

Remaining Life Prediction of Cores Based on Data-driven and Physical Modeling Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 3239 Xiang Li, Wen Feng Lu, Lianyin Zhai, Meng Joo Er, and Yongping Pan

90

Use of Embedded Smart Sensors in Products to Facilitate Remanufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3265 H. C. Fang, S. K. Ong, and A. Y. C. Nee

91

Pricing Strategies of Remanufacturing Business with Replacement Purchase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3291 Lei Jing, Boray Huang, and Xue Ming Yuan

92

Diesel Engine Block Remanufacturing: Life Cycle Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3313 Hong-Chao Zhang, Tao Li, Zhichao Liu, and Qiuhong Jiang

93

Sustainable Value Creation in Manufacturing at Product and Process Levels: Metrics-Based Evaluation . . . . . . . . . . . . . . . . . . . 3343 Fazleena Badurdeen, Mohannad A. Shuaib, Tao Lu, and I. S. Jawahir

94

Product Characteristic Based Method for End-of-Life Product Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3377 Yen Ting Ng and Bin Song

95

Life Cycle Management of LCD Televisions – Case Study . . . . . . 3405 Guoqing Jin and Weidong Li

Contents

Section XII Product Life Cycle and Manufacturing Simulation: Manufacturing Simulation and Optimization . . . . . . . .

xxxi

3437

Manoj Kumar Tiwari 96

Resource Scalability in Networked Manufacturing System: Social Network Analysis Based Approach . . . . . . . . . . . . 3439 Vijaya Kumar Manupati, Goran Putnik, and Manoj Kumar Tiwari

97

Improved Intelligent Water Drops Optimization Algorithm for Achieving Single and Multiple Objective Job Shop Scheduling Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3451 S. H. Niu, S. K. Ong, and A. Y. C. Nee

98

Process Plan and Scheduling Integration for Networked Manufacturing Using Mobile-Agent Based Approach . . . . . . . . . . 3475 V. K. Manupati, S. N. Dwivedi, and M. K. Tiwari Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3487

Contributors

Sunil Kumar Agrawal Department of Mechanical Engineering, Columbia University, New York, USA N. Kishore Babu Joining Technology Group, Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research (A*STAR), Singapore Fazleena Badurdeen Institute for Sustainable Manufacturing – ISM, University of Kentucky, College of Engineering, Lexington, KY, USA Shaoping Bai Department of Mechanical and Manufacturing Engineering, Aalborg University, Aalborg, Denmark Vivek Bajpai Machine Tools Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, India R. Benedictus Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands Shantanu Bhowmik Department of Aerospace Engineering, Amrita University, Coimbatore, India Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands Miroslaw Bonek Institute of Engineering Materials and Biomaterials, Silesian University of Technology, Gliwice, Poland Manuel Braun Micromy AB, Taby, Sweden Jian Cao Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA He´le`ne Chanal Institute of France Advanced Manufacturing (IFMA), ClermontFerrand, France

xxxiii

xxxiv

Contributors

Lei Chang Department of Mechanical Engineering, National University of Singapore, Singapore Wei-Tse Chang Institute of Physics, Academia Sinica, Nankang, Taipei, Taiwan Yang Changyong College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, People’s Republic of China Alok Chaurasia School of Materials Science and Engineering, Nanyang Technological University, Singapore Chien Chern Cheah School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore Heping Chen Ingram School of Engineering, Texas State University, San Marcos, TX, USA I-Ming Chen School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore Wenjie Chen Mechatronics Group, Singapore Institute of Manufacturing Technology, Singapore Yun Chen School of Mechanical and Manufacturing Engineering, UNSW, Sydney, NSW, Australia Yunhui Chen The State Key Laboratory of Precision Measuring Technology & Instruments, Centre of MicroNano Manufacturing Technology, Tianjin University, Tianjin, China Sun Chen-Nan Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR), Singapore Kuan Yew Cheong School of Materials & Mineral Resources Engineering, Universiti Sains Malaysia, Penang, Malaysia Ong Yong Chien School of Engineering, Nanyang Polytechnic, Nanyang, Singapore Siew Loong Chow Mechatronics Group, Singapore Institute of Manufacturing Technology, Singapore George Chryssolouris Laboratory for Manufacturing Systems and Automation (LMS), Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece Dewei Chu School of Materials Science and Engineering, University of New South Wales, Sydney, NSW, Australia Chee Kai Chua School of Mechanical and Aerospace Engineering (MAE), Nanyang Technological University, Singapore

Contributors

xxxv

Chen-Kuei Chung Department of Mechanical Engineering, Center for Micro/ Nano Science and Technology, National Cheng Kung University, Tainan, Taiwan Weilong Cong Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, KS, USA Yu Dan Singapore Institute for Manufacturing Technology (SIMTech), Singapore Atsushi Danno Singapore Institute of Manufacturing Technology, Singapore Pan Dayou Joining Technology Group, Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research (A*STAR), Singapore Jaspreet Singh Dhupia School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore Anna D. Dobrzan´ska-Danikiewicz Institute of Engineering Process Automation and Integrated Manufacturing Systems, Silesian University of Technology, Gliwice, Poland Leszek A. Dobrzan´ski Division of Materials Processing Technology, Management and Computer Techniques in Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, Gliwice, Poland Institute of Advanced Manufacturing Technology, Krako´w, Poland Toshiro Doi Art, Science & Technology Center, Kyushu University, Fukuoka, Japan Jun Feng Dong Mechatronics Group, Singapore Institute of Manufacturing Technology, Singapore Xuecheng Dong Singapore Institute of Manufacturing Technology, Singapore Aleksandra Drygała Institute of Engineering Materials and Biomaterials, Silesian University of Technology, Gliwice, Poland S. N. Dwivedi Department of Mechanical Engineering, University of Louisiana at Lafayette College of Engineering, Lafayette, LA, USA Jonas Edberg Department of Applied Physics and Mechanical Engineering, Lulea˚ University of Technology, Lulea˚, Sweden Meng Joo Er School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore Fengzhou Fang The State Key Laboratory of Precision Measuring Technology & Instruments, Centre of MicroNano Manufacturing Technology, Tianjin University, Tianjin, China H. C. Fang Mechanical Engineering Department, Faculty of Engineering, National University of Singapore, Singapore

xxxvi

Contributors

J. Y. H. Fuh Department of Mechanical Engineering, National University of Singapore, Singapore Ehsan Ghassemali Singapore Institute of Manufacturing Technology (SIMTech), Singapore School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore School of Engineering, Jo¨nko¨ping University, Jo¨nko¨ping, Sweden Ashitava Ghosal Department of Mechanical Engineering, Indian Institute of Science, Bangalore, India Ian Gibson National University of Singapore, Singapore Mary Gilliam Department of Chemical Engineering, Kettering University, Flint, MI, USA Oana Gingu University of Craiova, Drobeta Turnu Severin, Mehedinti, Romania Irving Paul Girsang School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore Klaudiusz Gołombek Institute of Engineering Materials and Biomaterials, Silesian University of Technology, Gliwice, Poland Changzhi Gu Beijing National Laboratory for Condensed Matter Physics, The Institute of Physics, Chinese Academy of Sciences, Beijing, China Bi Guijun Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR), Singapore Junfeng Guo Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR), Singapore Reza Haghighi School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore Jesse Hanssen FORTUS/DDM, Stratasys, Inc., Eden Prairie, MN, USA Mohamed Hashish Technology, Flow International Corporation, Kent, WA, USA Chaobin He Institute of Materials Research and Engineering, Singapore Wei He Surface Technology Group, Singapore Institute of Manufacturing Technology (SIMTech), Singapore Cheng Kuo Feng Henry Forming Technology Group, Singapore Institute of Manufacturing Technology, Singapore Hong Hocheng Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan, Republic of China

Contributors

xxxvii

G. S. Hong Department of Mechanical Engineering, National University of Singapore, Singapore Ying Yong Hu School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore Yawei Hu School of Mechanical Engineering, Dalian University of Technology, Dalian, China Xiao Hu School of Materials Science & Engineering, Nanyang Technological University, Singapore Boray Huang Department of Industrial and Systems Engineering, National University of Singapore, Singapore Wei Min Huang School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore Chen Hui-Chi Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR), Singapore Sridhar Idapalapati School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore Umesh Jadhav Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan, Republic of China Anders E. W. Jarfors School of Engineering, Jo¨nko¨ping University, Jo¨nko¨ping, Sweden I. S. Jawahir Institute for Sustainable Manufacturing – ISM, University of Kentucky, College of Engineering, Lexington, KY, USA S. Jerome Department of Metallurgical and Materials Engineering, National Institute of Technology, Tiruchirapalli, TN, India Ping Ji Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Hum, Kowloon, Hong Kong, China Qiuhong Jiang School of Mechanical Engineering, Dalian University of Technology, Dalian, China Guoqing Jin Faculty of Engineering and Computing, University of Coventry, Coventry, UK Yan Jin School of Mechanical & Aerospace Engineering, Queen’s University, Belfast, UK Lei Jing National University of Singapore, Singapore Rolf Johansson Department of Automatic Control, Lund University, Lund, Sweden

xxxviii

Contributors

Ewa Jonda Institute of Engineering Materials and Biomaterials, Silesian University of Technology, Gliwice, Poland Jeong Hoon Ko Singapore Institute of Manufacturing Technology, Singapore Allen Kreemer Stratasys, Ltd., Eden Prairie, MN, USA Masanori Kunieda Department of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan Chow Yin Lai Mechatronics Group, A*STAR Singapore Institute of Manufacturing Technology, Singapore Kah Fai Leong School of Mechanical and Aerospace Engineering (MAE), Nanyang Technological University, Singapore E. Q. Li Department of Mechanical Engineering, National University of Singapore, Singapore Huaizhong Li School of Mechanical and Manufacturing Engineering, UNSW, Sydney, NSW, Australia Jinlan Li Department of Mechanical Engineering, National University of Singapore, Singapore Sean Li School of Materials Science and Engineering, University of New South Wales, Sydney, NSW, Australia Shengyi Li College of Mechatronic Engineering and Automation, National University of Defense Technology (NUDT), Changsha, Hunan, People’s Republic of China Tao Li School of Mechanical Engineering, Dalian University of Technology, Dalian, China Wanli Li College of Precision Instrument and Opto-electronics Engineering, Centre of MicroNano Manufacturing Technology, Tianjin University, Nankai District, Tianjin, China Weidong Li Faculty of Engineering and Computing, University of Coventry, Coventry, UK Wuxia Li Beijing National Laboratory for Condensed Matter Physics, The Institute of Physics, Chinese Academy of Sciences, Beijing, China Xiang Li Singapore Institute of Manufacturing Technology (SIMTech), Singapore Yuan Ping Li Mechatronics Group, Singapore Institute of Manufacturing Technology, Singapore Tao Ming Lim Mechatronics Group, Singapore Institute of Manufacturing Technology, Singapore Wen Bin Lim Institute of Technical Education, Singapore

Contributors

xxxix

Wei Lin Industrial Robotics Team, Mechatronics Group, Singapore Institute of Manufacturing Technology, A*STAR, Singapore Lars-Erik Lindgren Department of Applied Physics Engineering, Lulea˚ University of Technology, Lulea˚, Sweden

and

Mechanical

Michael Lindgren Dalarna University, Falun, Sweden Rakesh Lingam Department of Mechanical and Aerospace Engineering, Indian Institute of Technology Hyderabad, Hyderabad, Andhra Pradesh, India Kui Liu Institute of Manufacturing Technology, Singapore Ming Liu Temasek Laboratories@NTU, Nanyang Technological University, Singapore Peiling Liu Singapore Institute of Manufacturing Technology, Singapore Shujie Liu School of Mechanical Engineering, Dalian University of Technology, Dalian, China Zhichao Liu School of Mechanical Engineering, Dalian University of Technology, Dalian, China Zhong Hong Liu School of Mechanical and Aerospace Engineering (MAE), Nanyang Technological University, Singapore Huitian Lu School of Mechanical Engineering, Dalian University of Technology, Dalian, China Department of Engineering Technology and Management South Dakota State University, Brookings, SD, USA Tao Lu Mechanical Engineering Department, Lexington, KY, USA Wen Feng Lu Department of Mechanical Engineering, Faculty of Engineering, National University of Singapore, Singapore Krzysztof Lukaszkowicz Institute of Engineering Materials and Biomaterials, Silesian University of Technology, Gliwice, Poland Hong Luo Mechatronics Technology, Singapore

Group,

Singapore

Institute

of

Manufacturing

Bill Macy Stratasys, Inc., Eden Prairie, MN, USA Vis Madhavan Department of Industrial and Manufacturing Engineering, Wichita State University, Wichita, KS, USA Jyotsna Dutta Majumdar Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India Saeed Maleksaeedi Singapore Institute of Manufacturing Technology, Singapore

xl

Contributors

Indranil Manna Metallurgical and Materials Engineering Department, Indian Institute of Technology, Kharagpur, West Bengal, India IIT Kanpur, Kalyanpur, Kanpur, Uttar Pradesh, India Vijaya Kumar Manupati Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India Lorenzo Masia Division of Mechatronics & Design, School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore XG Ming School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China Zaw Hlwan Moe School of Engineering, Nanyang Polytechnic, Nanyang, Singapore Vishal Tukaram Mogal School of Materials Science and Engineering, Nanyang Technological University, Singapore Andreas Mu¨ller University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China Shabbir Kurbanhusen Mustafa Industrial Robotics Team, Mechatronics Group, Singapore Institute of Manufacturing Technology, A*STAR, Singapore V. Muthupandi Department of Metallurgical and Materials Engineering, National Institute of Technology, Tiruchirapalli, TN, India Takashi Nagase Department of Physics and Electronics, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka, Japan Sharon Mui Ling Nai Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR), Singapore Karthic R. Narayanan School of Materials Science and Engineering, Nanyang Technological University, Singapore A. Y. C. Nee Mechanical Engineering Department, Faculty of Engineering, National University of Singapore, Singapore Yen Ting Ng Singapore Institute of Manufacturing Technology (SIMTech), Singapore Tey Ju Nie Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR), Singapore Klas Nilsson Department of Computer Science, Lund University, Lund, Sweden S. H. Niu Department of Mechanical Engineering, National University of Singapore, Singapore S. K. Ong Mechanical Engineering Department, Faculty of Engineering, National University of Singapore, Singapore

Contributors

xli

Flavien Paccot University of Auvergne, Clermont-Ferrand, France Farshid Pahlevani Forming Technology Group, Singapore Institute of Manufacturing Technology (SIMTech) A*Star, Singapore Daniel Pakula Division of Materials Processing Technology, Management and Computer Techniques in Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, Gliwice, Poland Yongping Pan School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore Zhijian Pei Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, KS, USA Quang-Cuong Pham School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore Goran Putnik Department of Production and Systems Engineering, University of Minho, Guimara˜es, Portugal Xiaoying Qi Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR), Singapore N. Venkata Reddy Department of Mechanical and Aerospace Engineering, Indian Institute of Technology Hyderabad, Hyderabad, Andhra Pradesh, India Anders Robertsson Department of Automatic Control, Lund University, Lund, Sweden Andrew Ruys School of Aerospace, Mechanical and Mechatronic Engineering, Sydney University, Sydney, NSW, Australia Sean Efrem Sabastian Mechatronics Manufacturing Technology, Singapore

Group,

Singapore

Institute

of

Sampa Saha Department of Chemistry, Michigan State University, Ann Arbor, MI, USA Nanda Gopal Sahoo Institute of Materials Research and Engineering, Singapore Konstantinos Salonitis Manufacturing and Materials, Cranfield University, Cranfield, Bedfordshire, UK Yasuhisa Sano Department of Precision Science and Technology, Graduate School of Engineering, Osaka University, Suita, Osaka, Japan Kushendarsyah Saptaji School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore Murali Sarangapani Heraeus Materials, Singapore Takashi Sato Singapore Institute of Manufacturing Technology (SIMTech), Singapore

xlii

Contributors

School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore Salem Seifeddine School of Engineering, Jo¨nko¨ping University, Jo¨nko¨ping, Sweden Cheryl Selvanayagam Advanced Micro Devices, Singapore Abhay Sharma Department of Mechanical and Aerospace Engineering, Indian Institute of Technology Hyderabad, Yeddumailaram, Andhra Pradesh, India Mohannad A. Shuaib Mechanical Engineering Department, University of Kentucky, Lexington, KY, USA Gabriela Sima Faculty of Engineering and Management of Technological Systems (I.M.S.T.), University of Craiova, Mehedinti, Romania Ramesh Singh Machine Tools Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, India Bin Song Singapore Institute of Manufacturing Technology (SIMTech), Singapore Xu Song Singapore Institute of Manufacturing Technology (SIMTech) A*Star, Singapore Narasimalu Srikanth Energy Research Institute, Nanyang Technological University, Singapore Marcin Staszuk Division of Materials Processing Technology, Management and Computer Techniques in Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, Gliwice, Poland Panagiotis Stavropoulos Laboratory for Manufacturing Systems and Automation (LMS), Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece Sathyan Subbiah School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore J. Sun Department of Mechanical Engineering, National University of Singapore, Singapore Yeow Cheng Sun Mechatronics Group, A*STAR Singapore Institute of Manufacturing Technology, Singapore Murali Meenakshi Sundaram Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH, USA Yu Suzhu Forming Technology Group, Singapore Institute of Manufacturing Technology, Singapore Mahesh Kumar Talari Universiti Teknologi MARA, Shah Alam, Malaysia

Contributors

xliii

Desmond Tan School of Engineering, Nanyang Polytechnic, Nanyang, Singapore Long Bin Tan National University of Singapore, Singapore Ming Jen Tan School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore Tomasz Tan´ski Institute of Engineering Materials and Biomaterials, Silesian University of Technology, Gliwice, Poland Pey Yuen Tao Industrial Robotics Team, Mechatronics Group, Singapore Institute of Manufacturing Technology, Singapore Tat Joo Teo Mechatronics Group, Singapore Institute of Manufacturing Technology, Singapore E. S. Thian Department of Mechanical Engineering, National University of Singapore, Singapore Manoj Kumar Tiwari Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India Masayoshi Tomizuka Department of Mechanical Engineering, University of California, Berkeley, CA, USA N. D. Vuong Mechatronics Group, Singapore Institute of Manufacturing Technology, Singapore Mian Wang Institute of Materials Research and Engineering, Singapore Y. S. Wong Department of Mechanical Engineering, National University of Singapore, Singapore Yew Hoong Wong Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia Erwin Merijn Wouterson School of Mechanical and Aeronautical Engineering, Singapore Polytechnic, Singapore Guanglei Wu Department of Mechanical and Manufacturing Engineering, Aalborg University, Aalborg, Denmark Jianhua Wu State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China Tengyun Wu School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China Wei Wu College of Precision Instrument and Opto-electronics Engineering, Centre of MicroNano Manufacturing Technology, Tianjin University, Nankai District, Tianjin, China

xliv

Contributors

Xuhui Xie College of Mechatronic Engineering and Automation, National University of Defense Technology (NUDT), Changsha, Hunan, People’s Republic of China Ho Xinning Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR), Singapore Zhenhua Xiong State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China Zhitao Xu School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China Zong Wei Xu College of Precision Instrument and Opto-electronics Engineering, Centre of MicroNano Manufacturing Technology, Tianjin University, Nankai District, Tianjin, China Jianbin Xue Department of Mechanical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China Kazuya Yamamura Research Center for Ultra-precision Science and Technology, Graduate School of Engineering, Osaka University, Suita, Osaka, Japan S. S. Yang NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore Guilin Yang Institute of Advanced Manufacturing, Ningbo Institute of Materials Technology and Engineering of the Chinese Academy of Sciences, Zhenhai District, Ningbo, Zhejiang, People’s Republic of China Song Huat Yeo School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore Swee Hock Yeo School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore Johnny Yeung Heraeus Materials, Singapore Adnan Younis School of Materials Science and Engineering, University of New South Wales, Sydney, NSW, Australia Xue Ming Yuan Planning and Operations Management, Singapore Institute of Manufacturing Technology (SIMTech), Singapore Fu Yucan College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, People’s Republic of China Hamid Zarepour School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore

Contributors

Mehrdad Zarinejad Singapore (SIMTech) A*Star, Singapore

xlv

Institute

of

Manufacturing

Technology

Guosong Zeng Department of Mechanical Engineering and Mechanics, P.C. Rossin College of Engineering and Applied Science, Lehigh University, Bethlehem, PA, USA Lianyin Zhai Department of Mechanical Engineering, Faculty of Engineering, National University of Singapore, Singapore Biao Zhang ABB Corporate Research Center, ABB Inc, Windsor, CT, USA Dan Zhang University of Ontario Institute of Technology, Oshawa, Ontario, Canada George Zhang ABB Corporate Research Center, ABB Inc, Windsor, CT, USA Hong-chao Zhang School of Mechanical Engineering, Dalian University of Technology, Dalian, China Department of Industrial Engineering, Texas Tech University, Lubbock, TX, USA Kailiang Zhang University of Ontario Institute of Technology, Oshawa, Ontario, Canada Yuanliang Zhang School of Mechanical Engineering, Dalian University of Technology, Dalian, China Fu Zhao School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA Su Zhao Mechatronics Group, Singapore Institute of Manufacturing Technology, Singapore Hongyu Zheng Institute of Manufacturing Technology, Singapore Maokuan Zheng School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China Sun Zheng Joining Technology Group, Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research (A*STAR), Singapore Lelai Zhou Department of Mechanical and Manufacturing Engineering, Aalborg University, Aalborg, Denmark Cheng-Feng Zhu Singapore Institute of Manufacturing Technology, Singapore Chungang Zhuang State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

Section I Forming and Joining: Materials Forming - Forming of Polymer and Composite Materials Yu Suzhu

1

Fundamentals of Polymers and Polymer Composite Alok Chaurasia, Nanda Gopal Sahoo, Mian Wang, Chaobin He, and Vishal Tukaram Mogal

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Polymer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monomer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Polymerization Reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Addition (Chain Growth) Polymerization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Condensation (Step Growth or Stepwise) Polymerization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Properties of Polymers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deformation of Polymers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Application of Polymers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Polymers Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matrix Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dispersed (Reinforcing) Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effect of Length and Orientation of Reinforcing Material on PMCs . . . . . . . . . . . . . . . . . . . . . . . Particulate Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laminate Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advances of Polymer Composite and Future Trend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 4 5 5 5 7 7 16 17 25 25 26 29 30 31 31 38 39

Abstract

This book chapter is about fundamentals of polymers which emphasize characteristics and applications of polymer and polymer composite, in addition to current progress and future research scope for this class of materials

A. Chaurasia • V.T. Mogal School of Materials Science and Engineering, Nanyang Technological University, Singapore e-mail: [email protected]; [email protected] N.G. Sahoo (*) • M. Wang • C. He Institute of Materials Research and Engineering, Singapore e-mail: [email protected]; [email protected]; [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_19

3

4

A. Chaurasia et al.

and their applications. The general concepts that are readily used in the field of polymer and polymer composite are discussed. This book chapter can provide basic understanding on polymer and polymer composite for newcomers. Then, the physical and mechanical properties of polymer and polymer-composites are described. Discussions on reinforced polymer composite highlighting on fabrication and characterization of polymer composite are provided, and particular importance is placed on the use. Discussions on the various nanofillers in polymer composites and their modification using various techniques have been focused on in this book chapter.

Introduction Many of the terms, definitions, and concepts used in polymers are not generally found in other branches of science, these need to be understood in order to fully discuss the fundamentals of polymers. Application of polymer composites is increasingly important in many different industries, like aerospace, automotive sectors, and areas dealing with corrosion and construction because they are strong, resistant to damage, and easy to install. The global composites market is expected to reach about $62.6 bn in 2012 (Source: The Composites Market 2012–2022: Glass Fibre, Carbon Fibre and Aramid Fibre report, Visiongain 2012). In addition, applications are also found in fuel cells, sensors, electromagnetic interference shielding, human implants, and scaffolds. This book chapter highlights both basic fundamentals and advances toward the understanding of properties of polymers and polymer composites holding various aspects. This chapter also deals with basic characteristics of polymers and polymer composites. In addition, current application and future trend in advances of the polymer and its composite will be discussed.

Polymer The name “polymer” gives an idea of the structure of the materials. The term “polymer” arrived from Greek words in which poly means many and mer means unit. A polymer is a very large molecule formed by successive linking of many monomers into a chain or network structure. The concept of the polymer emerged in the 1920s presented by Hermann Staudinger who received the Nobel Prize in 1953. Generally, a polymer has a chain or network structure made of a carbon backbone with hydrogen. In addition other elements such as O, N, F, Si, S can be arranged on it. Some of the very common polymers are polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyvinylchloride (PVC) (Billmeyer 2007; Fried 1995; Mark and Kroschwitz 1985). The simple structure of polyethylene, for example can be written as given in Fig. 1.

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Fundamentals of Polymers and Polymer Composite

5

Fig. 1 Simple structure of the polyethylene polymer

Monomer Monomers are generally simple forms of organic molecules which can react and give larger molecules in the form of polymer. Figure 2 shows the monomer structure of different polymers.

Polymerization Reactions Polymerization is the process by which polymers are made or “polymerized”. In polymerization, chemical reactions occur between small simple hydrocarbon monomers. Generally there are two main types of polymerization reactions. One is addition polymerization or chain growth and the other is condensation or step growth polymerization.

Addition (Chain Growth) Polymerization Addition polymerization involves a rapid “chain reaction” of chemically activated monomers. It occurs mainly in three stages which are initiation, propagation, and termination. For example, in the case of polyvinylchloride (PVC), the polymerization of vinyl chloride (monomer), initiation can come from a free radical generated on vinyl chloride monomer by initiator. Free radical can also act to initiate and terminate the reaction. This process generally produces linear structures but can produce network structures. A variety of initiators may be used, for example a peroxide or azide containing molecule can be activated by thermal or light.

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Fig. 2 Structure of some common monomers used for preparation polymer

Fig. 3 A simple mechanism of addition (chain growth) polymerization process

Atom-transfer radical-polymerization (ATRP), reversible addition
fragmentation chain transfer polymerization (RAFT), and group transfer polymerization GTP are examples of addition (chain growth) polymerization. Figure 3 shows a simple mechanism of an addition (chain growth) polymerization process from monomer to polymer.

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Formaldehyde H H OH

O

OH

OH H H

OH * + n H2O

Phenols

n

Fig. 4 Synthesis of phenol formaldehyde resins (polymer) through (step growth or stepwise) polymerization process

Condensation (Step Growth or Stepwise) Polymerization In condensation polymerization, individual chemical reactions between reactive functional groups of the monomer that occur one step at a time, are slower than addition polymerization. Whether the final product of the polymer will be linear or network depends on the number of functionality of the monomer (a functionality of two generally gives linear, whereas functionality of three gives network type/ crosslink). Ring-opening polymerization (ROP) and polycondensation are examples of condensation (step growth or stepwise) polymerization processes. Figure 4 shows a simple process, in which phenol and formaldehyde form phenol formaldehyde resins (polymer) through a (step growth or stepwise) polymerization process. There are many techniques available for polymerization, as given in Fig. 5. In addition, polymerization processes are also classified in bulk, suspension, solution, and emulsion.

Properties of Polymers Structure of Polymer A polymer structure can be classified into three possible molecular structures based on architecture which are linear polymer, branched polymer, and crosslinker polymer which is shown in Fig. 6. (a) Linear polymer: A linear polymer consists of a long chain of monomers which are covalently bonded to each other. Some common examples for linear polymers are high density polyethylene (HDPE), PVC, nylon, polyester, and PAN etc. (b) Branch polymer: A branched polymer has small branches covalently attached to the main chain. Some common examples are low density polythene (LDPE), glycogen, and starch. (c) Crosslinker polymer: In cross-linked polymers, polymer chains are linked to each other through a covalent bond. Cross linking results in a giant macromolecule

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Free radicle Addition polymerization (chain growth)

Ionic

Ziegler Natta

Catalytic

Metallocene

Polycondensation

Chromium

POLYMERIZATION PROCESS

Condensation polymerization (step growth)

Ring opening

Polyaddition

Fig. 5 Polymerization techniques

Fig. 6 (a) Linear chain; (b) branched molecule; (c) three-dimensional crosslinked network molecules in which chains are linked to each other through covalent bonds

with a three-dimensional network structure. In elastomers, crosslink density is low or loosely bonded, while thermosets have high density crosslink networks, which make it hard, rigid, and brittle in nature. Bakelites and malamine formaldehyde resins are some example of Crosslinker polymer. Another classification of polymers is based on the chemical type of the monomers used during polymerization process (Fig. 7). Homopolymers are made from the same monomers whereas copolymers are made of more than one different monomer

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a Homopolymer

b Alternating copolymer

c Block copolymer opolymer

d Random copolymer

e Graft copolymer

Fig. 7 (a) Homopolymer; (b) random copolymer; (c) alternating copolymer; (d) block copolymer; (e) graft copolymer

repeating units. In addition, depending on the arrangement of the types of units in the polymer chain, they can also be classified as random, alternating, block, and graft polymers. In random copolymers two or more different monomer units are organized randomly in polymer chain, whereas in alternating copolymers repeating units of the different monomers are arranged in alternating sequences. In block copolymers a long series of the same monomer is followed by a long chain of another monomer. Graft copolymers consist of a polymer chain made from one type of monomer with branches which are made from another type or similar type of monomer. Homopolymers can be classified based on tacticity into isotactic, syndiotactic, and atactic polymers (Figs. 8, 9, and 10). Isotactic Polymer In isotactic polymer all the substituents are located on the same side of the polymer backbone. Polypropylene synthesise using Ziegler-Natta catalysis is an isotactic polymer. Isotactic polymers are generally semicrystalline in nature, e.g., isotactic polystyrene. Syndiotactic Polymer In syndiotactic polymers the substituents are arranged in alternate positions along the polymer back bone. Polystyrene synthesized in the presence of metallocene catalysis during polymerization gives syndiotactic polystyrene which is crystalline in nature and has a melting point of about 161  C, e.g., syndiotactic polystyrene.

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Fig. 8 Isotactic polymer

Fig. 9 Syndiotactic polymer

Fig. 10 Atactic polymer

Atactic Polymer When substituents are positioned randomly along the polymer chain they are known as atactic polymers. This kind of structure is generally formed by freeradical mechanisms. Polyvinylchloride and polystyrene are generally atactic in nature. Due to their random nature atactic polymers are usually amorphous. Tacticity of the polymers is technically very important for application. For example polystyrene (PS) can exist in atactic or syndiotactic form and shows very different properties in the different structures. In atactic PS, polymer chains stack in an irregular fashion, cannot crystallize and form a glass, whereas syndiotactic PS is a semi crystalline material. This is a very general example for many polymers. Tacticity also affects other physical properties, such as melting temperature and solubility. Besides tacticity another classification, based on head-tail configuration of vinyl polymers, should be taken into account when considering polymer defects (Fig. 11). In a head to tail configuration all monomers are normally linked in regular polymer units in which the substituent group on “β” position is separated by three carbon atoms whereas it is two and four carbon atoms for head to head and tail to tail configuration, respectively.

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CH2

CH

Tail

Tail

11

Head R

CH2

CH

Head

R

a

b CH2

CH

CH

R

R

Head-Head

CH2

c CH2

CH

CH2

R

Head-Tail

CH R

CH

CH2

CH2

CH

R

R

Tail-Tail

Fig. 11 Classification, based on head-tail configuration of monomer during polymerization

Random organized: Amorphous region Lamellae structure; High crystallinity region

Fig. 12 Schematic model of a spherulite contains crystalline region and amorphous region. The lamellae part is the crystalline region, the other part is the amorphous region

Generally nuclear magnetic resonance (NMR proton or C13 NMR), X-ray powder diffraction (XRD), secondary ion mass spectrometry (SIMS), and vibration spectroscopy (FTIR) characteristics techniques are used to measure the tacticity of the vinyl polymer.

Microscopic Structure Properties of polymeric materials are very much affected by its microscopic arrangement of molecules or chains. Polymers can have an amorphous or partially crystalline/semicrystalline structure. Generally in amorphous polymers, molecules or chains are arranged in a random manner. In semi-crystalline polymer, molecular chains are partially aligned and chains fold together and form ordered regions known as lamellae which compose larger spheroidal structures named “spherulite”, an example of which is shown in Fig. 12. Formation of spherulite is controlled by many factors such as the number of nucleation sites, structure of the polymer molecules, cooling rate, etc. These factors control the diameter size of spherulite and may vary from a few micrometers to millimeters. Spherulites show higher density, hardness, and brittleness as compared to disordered polymers. The lamellae

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H

R

H

R

H

H

H

H

R

H

R

H

H

H

H

H

H

R

R

H

R = - CH3 , Syndiotactic polymer

Atactic polymer

Fig. 13 Structure of syndiotactic and atactic polystyrene

are connected by amorphous regions which provide certain elasticity and impact resistance. The degree of crystallinity is estimated by different analytical methods such as DSC, XRD, etc. Crystallinity of polymer typically ranges from 10 % to 80 %. That is why crystallized polymers are often called “semicrystalline”. The properties of semicrystalline polymers are determined by the degree of crystallinity, size, and orientation of the molecular chains. Cooling rates, chain structure, and mer chemistry, side branching and chain regularity (isotactic or syndiotactic) are some of the factors which control the degree of crystallinity of the polymer. In general, crystallinity of the polymer increases with slow cooling rate, simple chain and mer structure, and less branching. Polymer structure and intermolecular forces are two major reasons for high crystallinity or high amorpharsity of a polymer. For example if the polymer chain is regular and orderly, it will turn into crystals easily. If it is not, it will not. To better understand, let’s compare the structure of atactic in syndiotactic form of polystyrene when the R group is a phenyl ring (Fig. 13). Figure 13 shows that the syndiotactic polystyrene is very orderly, with the phenyl groups falling on alternating sides of the chain. This means it can pack very easily into crystals. However, atactic styrene does not have such an order. The phenyl groups come on any side of the chain they want and the chains cannot fit for well packing which leads to highly amorphous character in the atactic polystyrene. Other atactic polymers like PMMA (poly(methyl methacrylate)) and PVC (poly (vinyl chloride)) are also amorphous. In general, stereo-regular polymers like isotactic PP (polypropylene) and polytetrafluoroethylene are highly crystalline. PE is another good example and can be crystalline or amorphous. Linear PE is nearly 100 % crystalline where branched PE just cannot pack the way the linear PE can, so it is highly amorphous (Fig. 14).

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Fig. 14 Structure of linear and branched PE O N

N

N O H N

H O

O

H N

N H O

O H

H O

N H

O

H N

N H O

O H N

N H O

O H N

N H

O

O

N H O N H

Fig. 15 In nylon the carbonyl oxygen and amide hydrogen form a hydrogen bond. This allows the chains to form the fiber in orderly fashion

Crystallinity and Intermolecular Forces Intermolecular forces can play a major role in polymer crystallinity. For example, crystallinity of nylon is due to the internal force. It can be seen from Fig. 15, that the polar amide groups of nylon chains are strongly attracted to each other through strong hydrogen bonding. This strong binding results in crystalline behaviors of the nylon. Another example of this is poly(ethylene terephthalate) also known as polyesters. In this case polar ester groups of the poly(ethylene terephthalate) hold polyester into crystals. In addition pi-pi stacking of the phenyl ring is in orderly fashion, making the crystal even stronger (Fig. 16). Polypropylene, syndiotactic polystyrene, nylon, Kevlar, and Nomex are some examples of highly crystalline polymers whereas poly(methyl methacrylate), atactic polystyrene, and polycarbonate are some examples of highly amorphous polymers. There is a way to find out how much of a polymer sample is amorphous or crystalline. Generally, differential scanning calorimetric (DSC) and X-ray diffraction (XRD) are the instruments used to determine the crystalline or amorphous property of the polymer. The crystallinity can affect the physical and thermal properties of polymers. For example, density, mechanical strength, heat resistance, and creep resistance increases with an increase in crystallinity. Melting and Glass Transition Temperatures of Polymers The glass transition is the reversible transition in polymeric materials from a hard and relatively brittle state into a molten or rubber-like state. The term melting

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O

R1

R2

O

O O

R3

R4

R1, R2, R3, R4 = H or any other groups

The polar ester group of polyester, allow chain to organize, responsible for crystallinity.

The phenyl ring of polyester undergoes π-π (pi-pi) stacking, leads crystallinity.

Fig. 16 The polar group and pi-pi stacking can affect the crystallinity of the polymer

temperature for polymers, suggests a transition from a crystalline or semicrystalline phase to a solid amorphous phase. Though the abbreviation of melting temperature is Tm, it should more precisely be called crystalline melting temperature. Crystalline melting is only discussed for crystalline or semi-crystalline polymers among synthetic polymers. Thermoset polymers are closely densely cross-linked in the form of a network, degrade upon heating, cannot be reused (e.g., crosslinked polyisoprene rubber); while thermoplastics, which do not contain cross-links, will melt upon heating, and can be recycled for reuse, e.g., polypropylene, polyethylene, PMMA, etc. Glass transition is a phenomenon that occurs at a specific temperature known as glass transition temperature (Tg), when amorphous materials or amorphous regions within semi crystalline materials go from a hard and relatively brittle state into a rubbery like state or vice versa. This is a reversible phenomenon which very much depends on the nature of the polymer. Tm and Tg usually characterize, respectively, the upper and lower temperature limits for applications of semi-crystalline polymers. Tg may also describe the upper use temperature for amorphous materials. Tm and Tg, are much affected by molecular weight (MW) of polymers, presence of secondary bonding, chain flexibility/chain stiffness, density of branching, and thickness of the lamellae. The melting temperature of a polymer can be over a range of temperatures due to the variation of MW, and generally increases with increasing molecular weight (MW); whereas polar side groups, ether or amide linkages on the main chain, double bonds, aromatic groups, and crystallinity increase the melting temperature. Presence of the bulky, large size non polar groups, branching may lower Tm and Tg because it will decrease crystallinity thickness of the lamellae (crystallizing the solid at a low temperature or annealing just below Tm will do this) and increase the rate of heating.

Viscosity Viscosity is an important property and one of the key issues of the polymer that needs to be considered during manufacturing materials, it is the measured

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Fundamentals of Polymers and Polymer Composite

Specific Volume

15

liquid (viscous region) non-crystalline

supercooled liquid leathery/rubbery (visco-elastic region)

State-glassy/rigid brittle solid (elastic region)

semi-crystalline

crystalline

Temperature

Tg

Tm

Temperature

Fig. 17 State the viscosity, (η) behavior of polymers in these various regions

resistance of the material which is being deformed by either shear stress or tensile stress. Viscosity very much depends on temperature. It is the proportionality constant between the shear stresses and the velocity gradient and can be represent as, Shear stressðτÞ ¼ ViscosityðηÞ  Velocity gradientðdv=dyÞ: Figure 17 can state the viscosity (η) behavior of polymers in different various regions.

Mechanical Properties of Polymeric Materials Viscoelasticity, as a property of materials, is a combination of viscous and elastic behavior. It is both time dependent and temperature dependent. When a polymer is subjected to a step constant stress, polymeric materials experience a time-dependent increase in strain. This phenomenon is known as viscoelastic creep. It is temperature dependent and tests are conducted in the same manner as for metals. Creep modulus is a parameter to quantify this behavior of polymeric materials. Ec ðtÞ ¼ σo =εðtÞ, where ε(t) is time dependent strain and σo is constant stress at a particular temperature. Stress relaxation, which is also a result of viscoelasticity, describes how polymers relieve stress under constant strain, like viscoelastic creep, it is also time dependent. Relaxation modulus is a common parameter to quantify this behavior of polymeric materials and can be given as:

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Er ¼ σðtÞ=εo , where σ(t) ¼ measured time dependent stress and εo is constant stress at a particular temperature. Tensile modulus or elastic modulus or just “modulus” of polymer is identical to Young’s modulus for metals. The value of tensile modulus tends to be much lower for polymers compared to metals. In the case of semi-crystalline polymers the tensile modulus arises from the combination of the modulus of the crystalline and the amorphous regions. Similarly, tensile strength, impact strength, and fatigue strengths of polymers are defined in the same way as for metals. In general these values are much lower for polymers. Ductility values are usually much higher for polymers than metals whereas fatigue curves are the same as for metals, and some polymers may or may not have fatigue limits. The values tend to be lower than for metals and very much affected by loading frequency. Tear strength which is related to the tensile strength, is the energy required to remove a cut specimen that has a standard geometry. Hardness of the polymer is determined by measuring the resistance to penetration, scratching, or marring the surface. Durometer and Barcol are common instruments used for hardness tests for polymers. Polymer properties are very sensitive to temperature and generally with increasing temperature, tensile strength, elastic modulus decreases whereas ductility decreases. Besides temperature, the properties of polymer are very much affected by environment, e.g., moisture, oxygen, UV radiation, organic solvents, etc.

Deformation of Polymers Elastic and plastic deformations of polymers are general properties that are experienced every day. Elastic deformations in thermoplastics, is similar to a metal spring which upon stretching shows uncoiling but returns to its original shape when the stretch force is released. In polymer, elastic deformation behavior comes from coil polymer chains which uncoil upon stretching, and the chains revert to their original conformations when forces are removed. This is a reversible process. During elastic deformation, primary bonds are being stretched but not broken. Plastic deformations come from the chains moving past one another, secondary bonds are being broken and reformed. However, when enough force is applied, the primary covalent-bonds within the chains are also broken. It is not a reversible process like elastic deformation. A polymer chain containing a double bond or bulky group will restrict ability of the chain to rotate freely, making the material more rigid. Typical stress-strain curves for the three different types of polymers are shown in Fig. 18. Figure 18a, shows the properties of brittle polymer which fail during elastic deformation. Figure 18b shows rubber like elasticity property of polymer whereas Fig. 18c shows the typical stress-strain curves of plastic polymer.

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Fig. 18 Typical stress-strain curves for the three different types of polymers

Application of Polymers Commodity Polymer Initially polymers had major applications in manufacturing commodity goods (some of them are shown in Table 1) but with time they expanded to include engineering trough polymer matrix composites (PMCs), which will be discussed later. Amphiphilic Block Copolymer In addition to the above application, it has many other applications with a special kind of polymer known as amphiphilic block which is exploited in various fields. Amphiphilic block copolymers consist of distinct hydrophilic and hydrophobic segments which are able to form micelles in appropriate solvents. The formation of micelle depends on the nature of block, solvent, and concentration (Fig. 19). The size and shape of micelles depend on the chemical structure of block, molecular weight of each block, number of aggregation, and nature of solvent (Hadjichristidis et al. 2002; Raez et al. 2003; Yan et al. 2001a; Cao et al. 2002). Recent progresses in the synthetic techniques have led to the successful synthesis of a wide range of block copolymers containing different types of core and corona blocks with desired properties. Micelles of different shapes and sizes can be obtained. The aggregates can be in the form of rods, spheres or vesicles depending on various factors including the type of solvent and aggregation number (Antonietti et al. 1995; Zhang and Eisenberg 1995; Shen et al. 1999; Zhang et al. 2000; Alexandridis and Lindman 2000; Discher and Eisenberg 2002; Jain and Bates 2003; Wang et al. 2003; Liu et al. 2003). Amphiphilic block copolymers are multipurpose useful materials. In recent years, block copolymers have found wide application in many areas. These are also used as a vehicle for controlling as well as targeting the release of vector agents, for both hydrophobic and hydrophilic (Gref et al. 1994; Allen et al. 1999). Thus these are exploited for applications in drug delivery (Qiu et al. 2009; Lavasanifar et al. 2002; Jones et al. 2003; Riley et al. 2003; Tang et al. 2003), tissue engineering, cosmetic, water treatment, and industrial waste treatment (Hadjichristidis et al. 2002). Biocompatible block copolymers such as PEO

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Table 1 Name of some polymers and their characteristics and applications Polymer Acetal resin

Monomer/polymer unit

O

Acrylonitrile butadiene styrene

Cellulose acetate

Characteristics Good tensile strength and stiffness; resistant to chemical and abrasion

O

Inexpensive, strong, resilient and tough

N

Susceptible to sunlight, heat and high humidity

O CH3

CH3 O O HO O HO O H3C O O CH3 O O

Cellulose acetate butyrate

O O O n

RO RO

O O-R OR OO OR OR

RO

OR

Cellulose nitrate

O

O

O

O2N

O2N

• O

O

CH3 or

•

CH3

Burns with a bright, violent flame; smells of nitrogen oxides

O2N O

O

O

O O2N

Photographic film, varnishes and moldings

R= H or O

n

NO2 O

O NO2

Susceptible to ultraviolet light and contact with alkaline materials

N C O

H2C

CH3

O

Epoxy resin

CH2

OCH2 CH O CH

CH2

CHCH2O

Lacquer, fabric dope, adhesives, paint

n

Chlorinated rubber

Cyanoacrylate resin

Uses In mechanical parts communication equipment, Videocassettes, cosmetic containers, pipes and plumbing parts In automobile parts and fittings, telephones,, pipes and conduits, luggage, boats, toys, and bottles Photographic film, transparent sheeting and fibers

It has high strength, good abrasion and chemical resistance, low water absorption

Used in paints, varnishes, adhesives, inks and paper coatings In gluing glass, ceramics and other hard materials. In suture skin and weld crowns It is used in adhesive, fills, printed circuit boards, molded products and enamel surface coatings

O

Ethyl cellulose

Tough, flexible, transparent film can be prepared

OR

RO O

O OR

n

R = H or CH2CH3

Food containers; hot-melt adhesives, inks, and as protective coatings for paper and textiles (continued)

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Table 1 (continued) Polymer Fluorocarbon

Hydroxyethyl cellulose

Monomer/polymer unit F

F

F

F

C

C

C C

F

F

F

F

OR

RO O

O OR

n R = H or CH2CH2OH

Hydroxypropyl cellulose

OR

RO O

O OR

n

R = H or CH2CH(OH)CH3

Melamine formaldehyde

HN N

N NH N

NH n

Methyl cellulose

OR

RO O

O OR

n

R = H or CH3

Nylon (polyamide)

Phenol formaldehyde resin

O N n H

OH

OH CH2

CH2

n

Polycarbonate

CH3 C CH3

O O C O n

Characteristics Serviceable temperature range ¼
20 to 205; resistant to heat and chemical Discolors and becomes insoluble with thermal aging

Uses Mainly used in aerosols

Excellent photochemical stability, it has poor thermal stability and discolors with age Chemicals and heat resistant hard, durable glossy film Good stability with minimal discoloration or decrease in weight

Emulsifier, stabilizer, thickener, film former in foods, cosmetics, paint removers, paints and glazes; also used as a sizing agent for paper Decorative homeware, circuit breakers, paints and enamels In sizing paper, as an adhesive in textile and paper conservation

Good tensile and flexural strengths, elasticity, and wear resistance and low water absorption Inexpensive, resistant to chemical and heat, sunlight causes discoloration

Fibers, paints, films, foams, and molded parts

High dielectric strength, extremely tough, strong mechanical properties; strong UV absorbent

Emulsifier, stabilizer, thickener, film former

Fibers, adhesives, plywood, textile industry, leather processing, paper, foams, chemical resistant coatings, printed circuit boards Widely used in unbreakable windows, bank screens, police shields, helmet visors, and household appliances (continued)

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Table 1 (continued) Polymer Polycyclo hexanone

Monomer/polymer unit O CH

CH n

Polyester

Polyethylene glycol

H

O

Polyethylene vinylacetate

CH2 CH n O C O CH3

Polyethylene, high density

H H C C H H n

Polyethylene, low density

H H C C H H n

Polyimide

H

O n

Characteristics Oxidation with time makes it brittle and less soluble Inexpensive, easy to fabricate, versatile, resistance to chemicals Very tacky and susceptible to dirt Clear, tough, crack resistant and flexible at low temperatures Inexpensive, tough, lightweight, flexible and chemical resistance Soft and more flexible with very lower tensile strength

R C N C R

Polyisoprene

CH2 C C H3C

Polymethyl acrylates (acrylic resins)

O

n CH2 n H

OCH3 n

Polypropylene

CH3 CH CH2

n

Mainly for sheets, films, autos and boats, pipping boxes

Pore former, solvents, plasticizers Used as paper coatings, shrink-wrap, and hot melt adhesives Containers, packaging films, fibers, pipes, toys, bowls, and bottles

Sheeting, films, paper coatings, toys, bags and packaging Adhesives, binders, fibers; flame-retardant clothing

O

O

Uses Used in picture varnishes and for retouching

Serviceable temperature range ¼
57 to 110 Excellent optical clarity, good weather stability, food chemical resistance Excellent stress and scratch resistance, good chemical and heat resistant, lightweight

Paints, coatings, adhesives, fabrics, textile and leather finishes, windows, optical lenses, glasssubstitute Bottles, fishnets, pipe, clothing, vapor barrier films, road signs, carpet, artificial grass, laminates, food packages, furniture, and photographic enclosures (continued)

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Table 1 (continued) Polymer Polystyrene

Monomer/polymer unit

n

Polyurethane

O

O

O (CH2)n O C NH (CH2)n NH C

Polyvinyl acetate

Odorless, tasteless, nontoxic, slow burning, lightweight, colorless Elastomeric properties

O O

H3C

n

Polyvinyl alcohol Polyvinyl butyraldehyde

n

OH

O

Characteristics Inexpensive; good stability, stiffness, and impact strength; susceptible to UV light Excellent hardness, gloss, and resistance to weathering, abrasion, chemical resistant

O

Uses In food industry; used in insulation, toys, appliances, cabinets, containers, and furniture Elastomer, sealants, adhesives, furniture, mattresses, laminates, carpet cushions, soundproofing, flotation devices, packaging, and filtration Latex house paints, artists media and common household white glues

As an adhesive, films, finishes

Tough, flexible, weather-resistant

Used as shatterproof safety-glass interlayer

Resistant to ignition, corrosion and stains High strength and abrasion resistance, dimensionally stable, good durability Impermeable to gas and susceptible to moisture, weather deteriorates in UV light Serviceable temperature range ¼
70 to 200; heat resistance

Gramophone records, sheeting, gaskets, tubing, raincoats, waterproof coatings Packaging, barrier films, fibers

n

Polyvinyl chloride Cl n

Polyvinylidene chloride

Cl CH2 C Cl

Polyxylylene

H2 C

n

H2 C n

Siloxane

O

R Si R

As a coating to improve mechanical strength and flexibility

Used in electrical appliances and boards, aerospace, gaskets, molds (continued)

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Table 1 (continued) Polymer Sodium carboxymethyl Cellulose

Monomer/polymer unit RO O O-R OR R= H or

RO OO OR

O

OR

ONa

RO

OR

Soluble nylon

Uses Used in detergents, food product and as a sizing agent for textiles and paper

Becomes insoluble and shrinks with time

As an adhesive, coating and sizing agent in paper industry to add strength to wet paper and consolidate friable pigments Foams, insulation, coatings and adhesives

n

O N n H

Urea formaldehyde resin

Characteristics Good stability with negligible discoloration or weight loss

N CH2 N CH2 C O HN CH2 N

Susceptible to heat, acids, and alkalis

Polar solvent Block Polar Block Non-polar Fig. 19 Schematic diagram of formation of micelles in different solvents

Non-polar

(Nojiri et al. 1990), polycaprolactone-b-poly(ethylene oxide), are particularly promising in the field of drug and gene delivery. The synthesis of metal or metal oxide nanoparticles in block copolymer micelles has brought substantial interest as a result of their unique properties. Block copolymer micelles filled with nanoparticles have shown special catalytic (Seregina et al. 1997; Bronstein et al. 2005; Klingelho¨fer et al. 1997; Mayer and Mark 1997; Jaramillo et al. 2003), magnetic, electrical (Platonova et al. 1997; Rutnakornpituk et al. 2002), and optical (Diana et al. 2003) properties. For block copolymers, the selectivity toward the core-forming block is important because of a physico-chemical interaction between the polymer core and the metal precursor that accelerates the metal incorporation into the micelle. A polymer block containing particular functional groups forming the miceller core can be loaded with some specific metal compounds. The micelles can often serve as a nanoreactor for nanoparticles formation. It may also play a role in stabilizing the

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nanoparticles as the core forming polymer block that can be considered to exist in a quasi-solid state (as the core forming block is insoluble in the selective solvent) (Bronstein et al. 2005). Block copolymers are used as colloidal stabilizers to synthesize metallonanoparticle with controlled shape and size. They can provide an environment that can be used to prevent corrosion and leakage of heavy metals. They can also protect catalytic nanoparticles from deactivation (Antonietti et al. 1995; Mayer and Mark 1997; Fo¨rster and Antonietti 1998). Using this concept, block copolymer micelles containing a polystyrene core and a functional corona have been used to produce gold nanoparticles (Mo¨ller et al. 1996). Gold (Au)-labeled micelles have been incorporated using di-blocks of poly(2-vinylpyridine) (P2VP) (Bronstein et al. 2005)/poly(4-vinylpyridine) (P4VP) (Sidorov et al. 2004) and poly(ethylene oxide) (PEO) in water. The size of gold nano-particles obtained is dependent on the nature and number of unit of the two blocks. For example the size of gold nanoparticle is 1–4 nm for P2VP135-bPEO350 whereas gold nanoparticles formed from P4VP28-b-PEO45 ranged in size from 5 to 10 nm. The advantage of gold-labeled micelles with poly(ethylene oxide) PEO corona is that they allow for their preparation in water, an environmentally friendly medium. Zubarev et al. reported a stimulating approach for controlling the interfacial assembly of nanoparticles (e.g., gold nanoparticles) in self-assembled nanostructures of block copolymers, starting from gold nanoparticles covalently attached to V-shaped heteroarm chains (Zubarev et al. 2006). The supramolecular self-assembly of heteroarm star polymers leads to the precise location of gold nanoparticles at the core-shell interfaces of rod-like micelles or vesicles (Zubarev et al. 2006). Park and co-workers investigated the assembly of CdS or CdSe/ZnS quantum dots in vesicles or nanorods of PAA-based block copolymers (SanchezGaytan et al. 2007). The application of block polymer is also reported for nanolithography for the development of biological performance of mineral oil, in biological and pharmaceutical applications (Spatz et al. 1999a; Loginova et al. 2004; Jeong et al. 1997; Otsuka et al. 2003). The synthesis of metal or semiconductor nanoparticles in the cores of amphiphilic block copolymer micelles in organic solvents was reported by many research groups (Antonietti et al. 1995; Moffitt et al. 1995; Saito et al. 1993). Using block copolymer micelle cores as nano reactors allows the synthesis of mono and bimetallic nanoparticles. The size of the nanoparticles, among other factors, depends on the particular reducing conditions as depicted in Fig. 20. Different morphology of the nanoparticles significantly changes the catalytic properties of such systems, even though the size may be similar. The crosslinking of block copolymer micelles provides an additional degree of stabilization to nanoparticles and allows the modification of micelle morphology (Lu et al. 2001; Underhill and Liu 2000; Yan et al. 2001b). The possibility of using nanospheres as nanoreactors for inorganic nanoparticles was demonstrated by the formation of iron oxide magnetic particles (Yan et al. 2001b) and catalytic Pd nanoparticles (Lu et al. 2001). The spatial distribution of nanoparticles in block copolymers was also investigated by many researchers (Hadjichristidis et al. 2002;

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CH2 CH

n

PS-b-P4VP + Pd(CH3COO)2 reduced with N2H4 (IV)

PS-b-P4VP + Pd(CH3COO)2 reduced with NaBH4 (III)

Fig. 20 Metal nanoparticles with different morphology could be obtained depending on the nature of reducing agent

Antonietti et al. 1995; Jinnai et al. 2006). The selective separation of nanoparticles among blocks was achieved as a result of the presence of functional groups in a selected block (Bronstein et al. 2005; Sidorov et al. 2004). A large number of amphiphilic block copolymers forming micelles with a functionalized core in an organic medium are available but when aqueous solutions are favored, the choice of block copolymers is very limited. Here, nanoparticle formation is normally more complicated as the pH of the medium should be taken into consideration. A few examples of such block copolymers include poly(ethylene oxide)-blockpoly(2-vinylpyridine) (PEO-b-P2VP), polybutadiene-block-poly(ethyeleneoxide) (PB-b-PEO), polystyrene-block-poly(2-vinylpyridine)-block-poly(ethyelene oxide) (PS-b-P2VP-b-PEO), and poly-[methoxyhexa(ethylene glycol) methacrylate]-block[2-(diethylamino)ethyl methacrylate] (PHEGMA-b-PDEAEMA). P2VP and PDEAEMA are examples of pH sensitive block as their core forming capability depends on pH (Spatz et al. 1999a). For example, at pH below 5, PEO-b-P2VP forms a molecular solution in water, whereas with a further decrease of pH, the PEO-b-P2VP forms a micellar solution. No micellar decomposition took place during incorporation of metal compounds or metal nanoparticle into micelle solution due to interaction with metal species (Bronstein et al. 1999). Morfit et al. reported the formation of spherical assemblies of CdS containing block copolymer using reverse micelles in aqueous solution (Moffitt et al. 1998). These stable assemblies were created by the slow addition of water into mixtures of the reverse micelles formed by polystyrene-b-poly(acrylic acid) diblock chains. This resulted in micelles containing quantum-confined CdS nanoparticles. This method allows the relocation

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of the CdS nanoparticles formed in the micelle cores in organic medium to aqueous medium without the loss of stability or nanoparticle aggregation (Moffitt et al. 1998). Organization of nanocrystals has been demonstrated by taking advantage of block copolymer self-assembly (Chaurasia et al. 2011). The poly(styrene)-b-poly (2-vinyl-pyridine) (PS-b-P2VP) diblock copolymer was widely utilized as a template for synthesis of hexagonally arranged gold nanoparticles of controlled size (Mela et al. 2007; Spatz et al. 1999b). Non-spherical gold (Antonietti et al. 1996) and cobalt (Platonova et al. 1997) nanoparticles are prepared using poly(styrene)block-poly(4-vinylpyridine) (PS-b-P4VP) diblock copolymer. However, synthesis of nanoparticles of various metal oxides, e.g., TiO2 (Li et al. 2005), SiO2 (Kim et al. 2004), and Fe2O3 (Bennett et al. 2005) with the help of various diblock copolymers have been reported. An attempt has been made to prepare organized ZnO nanoparticles using a Zn(CH2CH3)2 precursor in a PS-b-P2VP block copolymer. However, the procedures are difficult partly due to the highly reactive and moisture sensitive nature of the precursor, which was traditionally used for metal organic chemical vapor deposition (MOCVD) (Braun et al. 2010). Yoo et al. reported synthesis of ZnO nano-arrays using oxygen plasma treatment of PS-b-P4VP/zinc chloride film cast from toluene. ZnO nanoparticles with considerably larger particle size of about 16 nm diameter were obtained. Alok et al. reported a facile method for synthesis and organization of ZnO quantum dots (QDs) in various morphology using zinc acetate as precursor and PS-P4VP as a template (Chaurasia 2012).

Polymers Composites Composite material is a material, composed or made of two or more distinct phases (matrix phase and dispersed phase), having significantly different bulk properties from those of any of the constituents (Campo 2008; Alok et al. 2011; Katz and Milewski 1978; Rosato 2004; Biron 2007).

Matrix Phase Matrix phase is the primary phase, having a continuous character, usually a softer and ductile phase. It helps to hold the dispersed phase. A key role of the matrix is to serve as a binder of the fibers with desired shape and protect them from mechanical or chemical damages. Composite materials can be categorized based on the matrix material (metal, ceramic, and polymer) and the material structure. Based on the nature of the matrix material of the composite it can be classified into metal matrix composites (MMC), ceramic matrix composites (CMC), and polymer matrix composites (PMC). This book chapter is about discussion on polymer matrix composites (PMC). Unsaturated polyester (UP) and epoxiy (EP) are examples of thermoset whereas polycarbonate (PC), polyvinylchloride, nylon, and polysterene are thermoplastic.

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Dispersed (Reinforcing) Phase Dispersed phase is the secondary phase of composite, embedded or dispersed in the matrix in a discontinuous manner. In the composite, the mainly dispersed phase is a greater load carrier than the matrix; therefore it is also known as the reinforcing phase. When small additives like metal alloys, doped ceramics or polymers mix in dispersed phases they are not considered as composite materials since their bulk properties are similar to those of their base constituents. (Physical properties of steel and pure iron are almost the same.) According to the classification of composites, PMC is the material consisting of polymer matrix and reinforcing dispersed phase (glass, carbon, steel or Kevlar fibers). PMCs are widely used due to their simple fabrication methods and low cost. Reinforcement of polymers with a strong fibrous network permits fabrication of PMC which provides advantage over non-reinforced polymers in terms of mechanical properties. Reinforced PMCs provide better tensile strength, high stiffness, high fracture toughness, good abrasion resistance, good puncture resistance, and good corrosion resistance. The main disadvantages of PMCs are associated with low thermal resistance and high coefficient of thermal expansion. Properties of PMCs are determined by properties of the fibers, orientation of the fibers, concentration of the fibers, and properties of the matrix. Two types of polymers are used as matrix materials for fabrication composites, one is thermosets which are generally epoxies or phenolics and the other is thermoplastics (LDPE, HDPE, polypropylene (PP), nylon, and acrylics, etc.). Fiberglass, carbon fiber, and Kevlar (aramid) fibers are widely used to make PMCs.

Fiberglass: Glass Fiber Reinforced Polymers PMC is reinforced by glass fibers (fiberglass is a common name). Use of glass as reinforcing fibers in PMSc shows better corrosion resistance and high tensile strength, which may go up to 590 psi. The making of glass fiber reinforced polymer composite is simple and needs only low-cost technology. Glass fibers are made of molten silica-based or other formulation glass, from which glass extruded and then gathered to strands. The strands are used for preparation of yarns, rovings, woven fabrics, and mat glass fiber products (Fig. 21). Different kinds of glass fibers are used for making PMCs depending on the end requirement. For excellent electrical insulator PMCs, the most popular and inexpensive E-glass fibers are used. The designation letter “E” means “electrical” (E-glass is insulator). The composition of E-glass which is an excellent insulator ranges from 50–57 % SiO2, 10–17 % A1203, 17–26 % CaO, and 9–14 % B203. For high strength PMCs, S-Glass is used. It has applications in military and aerospace areas. S-glass is generally made of silica (SiO2), magnesia (MgO), and alumina (Al2O3). Besides S-glass and E-glass, there are S + R-glass and C-glass. S + R glass is strongest and most expensive and has a diameter half of that of E-glass. C-glass is used for preparation of corrosion and chemical resistant PMCs, widely used for manufacturing storage tanks, pipes, and other chemical resistant equipment.

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Fig. 21 General picture of glass fiber woven roving, Kevlar, and carbon fiber sheet

The widely used matrix materials for manufacturing fiberglass-PMCs are unsaturated polyesters (UP), epoxies (EP), nylon (polyamide), polycarbonate (PC), polystyrene (PS), and polyvinylchloride (PVC). Orientations in the reinforcing glass fibers in fiberglass layers of fiberglassPMCs are very important as they greatly affect the anisotropy behavior of the final materials. Fiberglass normally contains between 42 % and 72 % glass fibers by concentration. Glass fiber reinforced polymer matrix composites are manufactured by open mold processes, closed mold processes, and the pultrusion method. Fiberglass-PMCs show excellent features in terms of high strength-to-weight ratio, high modulus of elasticity-to-weight ratio, corrosion resistance, insulating properties, thermal resistance (with respect to polymer matrix). Fiberglass materials are used for manufacturing, surfboards, gliders, kit cars, sports cars, microcars, karts, bodyshells, boats, kayaks, flat roofs, lorries, K21 infantry fighting motor vehicles, minesweeper hulls, pods, domes and architectural features where a light weight is necessary, high end bicycles, body-parts for automobiles, such as the Anadol (Anadol was Turkey’s first passenger vehicle), Reliant (Reliant was a British car manufacturer), Airbus A320 (A320 is a short- to medium-range, narrow-body, commercial passenger jet airliners manufactured by Airbus), and radome ((combination of word of radar and dome) is a weatherproof enclosure that protects a microwave antenna). Fiberglass reinforced plastics (FRP), also known as glass reinforced plastics (GRP) are a modern composite material, used in chemical plant equipment manufacturing like tanks and vessels by hand lay-up and filament winding processes using BS4994-British Standard. (BS4994 is a British standard related to this application which still remains a key standard for the specification design and construction of vessels and storage tanks using reinforced plastics.) Besides the above applications, fiberglass materials are also used in UHF-broadcasting antennas, large commercial wind turbine blades, velomobiles (a bicycle car), and printed circuit boards used in electronics that consist of alternating layers of copper and fiberglass, which is technically known as FR-4. FR-4 is a grade designation given to glass-reinforced epoxy laminate sheets, tubes, rods and printed circuit boards (PCB). Glass fiber composite is also used in preparation of RF coils used in MRI scanners.

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Carbon Fiber Reinforced Polymer Composites Carbon fiber-PMCs are similar to fiberglass-PMCs, in which carbon fibers are used instead of fiberglass as reinforced materials in polymer matrix. The reinforcing dispersed phase may be in the form of carbon fibers, commonly woven into a cloth. Carbon fibers are used in continuous or discontinuous form during PMCs manufacturing. It is expensive compared to glass fiber but has high specific mechanical properties to weight, with a very high modulus of elasticity, which can match that of steel in terms of tensile strength which may go up to more than 1,000 ksi (7 GPa). In addition it possesses very low density of 114 lb/ft3 (1,800 kg/m3) and high chemical inertness. These properties make carbon fibers one for potential reinforcement. The disadvantage of carbon (graphite) fibers is being brittle, accountable for a catastrophic mode of failure. Some of the various types of carbon fibers available are “ultra-high modulus” (UHM), “high modulus” (HM), “intermediate modulus” (IM), “high tensile” (HT), and super high tensile (SHT). “Ultra-high modulus” (UHM) carbon fibers has modulus of elasticity of about 65,400 ksi (450 GPa) whereas “high modulus” (HM) has modulus of elasticity is in the range 51,000–65,400 ksi (350–450 GPa). For “intermediate modulus” (IM) has modulus of elasticity in the range 29,000–51,000 ksi (200–350 GPa). For “high tensile” (HT) carbon fibers generally have tensile strength of 436 ksi (3 GPa) and modulus of elasticity of 14,500 ksi (100 GPa) whereas for super high tensile (SHT) carbon fiber, tensile strength is about 650 ksi (4.5 GPa). Carbon fibers are manufactured by PAN-based carbon fibers, the most well-liked type of carbon fibers. Polyacrylonitrile (PAN) is used as a precursor for preparing PAN-based carbon fibers. In this method, the polyacrylonitrile precursor goes through several steps to become a carbon fiber (thermal oxidation at 200  C, carbonization in nitrogen atmosphere at 1,200  C for several hours, and graphitization at 2,500  C). Coal tar or petroleum asphalt is used as the precursor in pitchbased carbon fibers. Epoxy, polyester, and nylon are among some of the polymers used as a matrix for preparation of carbon fiber based PMCs. This composite is generally prepared by open mold, closed mold, and the pultrusion method. Carbon fiber reinforcedPMCs are light in weight, show high strength, high modulus elasticity, high fatigue, good electrical conductivity, corrosion resistance, good thermal-stability, and low impact resistance. Carbon fiber reinforced-PMCs are widely used in automotive, marine and aerospace applications, golf clubs, skis, tennis racquets, fishing rods, light weight bicycle frames, artificial light weight legs, etc. Kevlar (Aramid) Fiber Reinforced Polymers Kevlar fibers were originally developed as a replacement for steel in automotive tires, because of its high tensile strength-to-weight ratio, by this measure it is five times stronger than steel on an equal weight basis. Kevlar is a trade name, registered by DuPont Co. in 1965. It is an aramid fiber and its chemical name is polypara-phenylene terephthalamide. It is synthesized from 1,4-phenylene-diamine

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(para-phenylenediamine) and terephthaloyl chloride through a condensation reaction in solution from. Apart from high tensile strength, it has very high modulus of elasticity, very low elongation breaking point, very low coefficient of thermal expansion, high chemical inertness, high fracture and high cut resistance, textile processability, excellent flame resistance, and toughness. It also shows high impact resistance and low density. The disadvantages of Kevlar are its ability to absorb moisture, it is difficult to cut, and has low compressive strength. There are several grades of Kevlar which are developed for various applications. Kevlar 29 – high strength, (~3,700 MPa) used for bullet-proof vests, composite armor reinforcement, helmets, cars, etc. Kevlar 49 has a high modulus about 132 GPa, high strength (~3,810 MPa), low density, and is used in aerospace, automotive, and marine applications. Kevlar 149 which has an ultra-high modulus (about 187 GPa), high strength (about 3,600 MPa), low density highly crystalline fibers, and is used for composite aircraft components. The name of some other modified Kevlar are Kevlar K100 (colored version of Kevlar), Kevlar K119, Kevlar K129, Kevlar AP (has 15 % higher tensile strength than K-29), Kevlar XP (lighter weight resin). Kevlar KM2 is used as enhanced ballistic resistance for armor applications. Most of those Kevlar fibers are used in aerospace armor areas where mechanical, chemical properties, and weight play an important role. UV degradation is the main drawback of Kevlar fiber and the ultraviolet present in sunlight degrades and decomposes Kevlar, so it needs protection during outdoors application. However a combination of Kevlar and carbon fibers, a hybrid fabric, further improves their properties and give very high tensile strength, high impact, and abrasion resistance. Epoxies (EP), vinylester, and phenolics (PF) are the most widely used matrix materials for manufacturing Kevlar (aramid) fiber reinforced-PMCs. Kevlar (aramid) fiber reinforced-PMCs are manufactured by open mold processes, closed mold processes, and the pultrusion method.

Effect of Length and Orientation of Reinforcing Material on PMCs For fibrous composites, dispersed phase in form of fibers improves strength, stiffness and fracture toughness of the material, there is hindered crack growth in the directions normal to the fiber, and strength increases significantly when the fibers are arranged in a particular direction (preferred orientation) and a stress is applied along the same direction. In general PMCs strength is higher in long-fiber compared to that of short-fiber. Short-fiber reinforced composites, consisting of dispersed phase in the form of discontinuous fibers, has a limited ability to distribute the load but is able to share the load. In addition orientation of the fibers in composite also decides the end properties of PMCs.

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Short-fiber can exist in random preferred orientation in composites, whereas long-fiber reinforced composites consist of a reinforced matrix that can exist in the form of continuous fibers with unidirectional or bidirectional orientation.

Particulate Composites Besides reinforcing fibers, a different kind of particle is used to make polymer composite. Choice of particle as a reinforcing agent depends on the end use of composites. Particulate composites consist of a matrix reinforced with a dispersed phase in the form of particles. Effect of the dispersed particles on the composite properties depends on the particles size. Very small particles, (less than 0.25 μm in diameter) finely distributed in the matrix, prevent the deformation of the material by restricting the dislocations movement. This strengthening effect is similar to the metal alloy’s “age hardening”. It is clearly found that, for a given particle amount, the mechanical strength of composite increases with decreasing particle size. As for example, mechanical properties or strength of kaolin filled nylon 6,6 composites increase with decreasing mean particle size (Bradley 1999). There is a large improvement in tensile strength with decreasing particle size. This indicates that the strength increases with increasing surface area of the filled particles through a more efficient stress transfer mechanism (Fu et al. 2008). However, it is noted that for particles with size larger than 100 nm, the composite strength is reduced with increasing particle loading whereas for nanoparticle particles, with size 10 nm or lower, the strength of particle composites trend is reversed with loading. To conclude, particle size and amount of loading clearly has a significant effect on the strength of particulate-filled polymer composites. Interface adhesion quality, between particle (reinforcing) and polymer matrix on fiber-reinforced composites is very important, control the strength and toughness of PMCs. The adhesion strength at the interface decides the load transfer between the components. However the Young’s modulus is not affected by this interfacial adhesion quality because, for small loads or displacements, debonding is not yet reported. Evaluation of adhesion between two different materials can be done by comparing surface properties of the particle (reinforcing) with respect to the polymer matrix. The basic mechanisms related to polymer surface are responsible for adhesion at the molecular level. The strength of micro-particle-filled composites either decreases or increases with particle content. This can be explained by interfacial adhesion, between particle and matrix, which significantly affects the strength of particulate composites. Effective stress transfer is the key factor, contributes to the strength of two-phase composite materials. The stress transfer at the particle-polymer interface is inefficient for weakly bonded particles which leads to discontinuity in the form of de-bonding. As a result composite strength decreases with increasing particle loading. However, for well-bonded or compatible particles addition into a polymer matrix will lead to an increase in strength especially for nanoparticles with high surface areas.

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For example, it is reported that the interface bonding strength between alumina nanoparticles and vinyl ester resin shows decreased strength due to particle agglomeration, but when functionalization alumina nanoparticles are used this leads to a strong interfacial bonding between particle and matrix. This significantly increases both the modulus and strength of the composite. In general, therefore, quality of interfacial adhesion between particles and matrix has a very significant effect on composite fracture toughness. Strong adhesion leads to high toughness in thermoplastic matrices but not necessarily in thermosetting matrices due to different failure mechanisms. To summarize, the strength of particulate composites is determined not only by particle size and quality of interfacial adhesion between particle and matrix but also by the amount of particle loading. Various trends in composite strength have been observed due to the interplay between these three factors (particle size, amount of loading, and interfacial adhesion), which cannot always be separated. Use of hard particles such as ceramic particles prevent wear and abrasion of particulate composites and allow materials designed to work in high temperature applications, whereas copper and silver particles provide composites with high electrical conductivity matrices; for refractory use tungsten and molybdenum are used as dispersed phase to work in high temperature electrical applications.

Laminate Composites Laminate composites are made when a fiber reinforced composite consists of several layers with different fiber orientations, it is also called multilayer (angleply) composite. These layers are arranged in different anisotropic orientations as a matrix reinforced with a dispersed phase in the form of sheets. It directs the increased mechanical strength where mechanical properties of the material are low. Scheme 1 shows the various techniques for the preparation of polymer matrix composites.

Advances of Polymer Composite and Future Trend As discussed previously, conventional reinforced fibers such as glass, carbon fiber have been used to make PMCs. Superior properties of carbon nanotubes (CNTs) such as low-weight, very high aspect ratio, high electrical conductivity, elastic moduli in the TPa range, and much higher fracture strain make them an attractive candidate over conventional reinforced fibers and CNTs, and they are being used to replace conventional reinforced fibers to achieve advanced functional composites which provide excellent properties in terms of strength, aspect-ratio, and thermal and electrical conductivity (Breuer and Sundararaj 2004; Spitalsky et al. 2010; Seymour 1990; Mylvaganam and Zhang 2007). Although CNTs came to light more than a decade ago, preparation of satisfactory polymer composites by CNTs has faced difficulties due to several challenges

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Polymer matrix composite preparation technique

Open Molding methods

Closed Molding methods

Pultrusion

Injection Molding Compression Molding

Spray-up method HandLay-up method

Transfer Molding

Filament Winding Tape Lay-up

Autoclave Curing

Scheme 1 Various techniques for the preparation of polymer matrix composites

such as purification, dispersion, alignment, and adhesion of CNTs. In addition the limitations in interfacial load transfer must be overcome. CNTs are a bit expensive compared to conventional reinforced fiber, but it is worth it to get PMCs which are armed with superior properties, and it is only a matter of time to produce low cost CNTs (Liu et al. 2012; Sahoo et al. 2010). This chapter will discuss the strategies taken by researchers to counter the above challenges, giving particular attention to the CNT-polymer composites (CNT-PMCs). Purification and yield of CNTs are fundamental challenges in terms of cost and time. Generally soot produced through sublimation and recombination for CNTs preparation, contains inherent contaminants and needs a purification process. Most of the adopted processes, to get rid of inherent contaminants, were time consuming and produced small quantities. Latter, an efficient purification method was developed using coiled polymer to extract CNTs from their accompanying material with a high yield. In this method, a toluene containing poly(m-phenylene-co-2,5dioctoxy-p-phenylenevinylene) solution was used to purify nanotube soot by a low power ultrasonic sonicated bath. It allowed inherent contaminants (solid material amorphous carbon) to settle to the bottom of the solution and nanotube composite suspension was then decanted. Dispersion of nanotubes plays a crucial role in controlling the final properties of CNT-PMCs. The effective use of CNTs depends on the uniform dispersion of CNTs into matrix without reducing their aspect ratio. However, CNTs tend to remain in form stabilized bundles due to van der Waals attraction and very low solubility in solvents. To overcome the dispersion problem, many mechanical/physical methods were adopted such as high shear mixing, solution mixing, melt mixing, electrospinning,

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ultrasonication, anionic, cationic, and nonionic surfactants surfactant addition (Pang et al. 2010). In addition in-situ polymerization and chemical modification to functionalize CNTs has been performed. These methods have drawn much attention to the preparation of high performance CNT-polymer composites. In particular, functionalizing nanotube ends with carboxylic groups were reported quite some time ago. Recently, various functionalized CNTs were used to graft polymerization through anionic, ATRP, radical polymerization, click chemistry, and the preparation of CNT composites employing hyperbranched polymers to achieve good CNT dispersion to get polymer grafted CNT materials with improved dispersion ability (Sahoo et al. 2010). Some of the scheme for chemical modification for CNT is given in Fig. 22. In mechanically reinforced composites, one of the most important issues is the interfacial stress transfer as discussed previously. It is responsible for interface failures in shear stress condition. CNTs have an inherent smooth non-reactive surface which limits the interfacial bonding between the CNT and the polymer chains that limits stress transfer. One of the approaches to overcome the above problem is chemical modification and functionalization of CNTs as stated previously, which can give better bonding sites to the polymer matrix, supported by computational calculation. A simple method was followed for integrating CNTs into epoxy polymer via chemical functionalization of CNTs. First SWNTs were treated with oxidizing agent, e.g., concentrated H2SO4/HNO3 mixture, which generated –COOH and –OH group on CNTs surface (Sahoo et al. 2010). These functionalized CNTs were then dispersed in solvents like N, N-dimethylformamide/tetrahydrofuran; epoxy resin/polymer (Forney and Poler 2011). If needed a curing agent was added. This leads either to formation of covalent bonds between CNTs and polymer or better dispersion of CNT in the polymer of CNT-PMCs (Geng et al. 2008). It is reported that composite with 1 wt% functionalized CNTs showed an increase of 18 % and 24 % in tensile strength and modulus respectively over the polymer composites with unfunctionalized CNTs and a 30 % increase in tensile modulus over pure polymer (epoxy resin) (Mylvaganam and Zhang 2007; Geng et al. 2008). In addition, the pi bond present in CNTs structure interaction can be used to make pi-pi compatible CNT-PMCs by choosing a suitable polymer as a matrix. An example of such a CNT-polymer composite is SWNT-polypropylene composite which is made by combining the uniformly dispersed nanotube/solvent mixture with polypropylene matrix/solvent mixture to form nanotube/solvent/matrix mixture. The final composite, with 1 wt% of CNTs, showed more than 50 % increase in tensile strength. This substantial increase in strength was believed to be due to an effect of pi-pi interaction which leads to uniform dispersion of CNTs in the matrix material (Mylvaganam and Zhang 2007). The final properties of the CNT-PMCs can be controlled by using a different polymer and tuning the conditions used in making the composite. Various CNT-polymer composites have been reported to tune electrical properties of composites depending on application as different applications need specific

Fig. 22 Scheme for covalent functionalization of CNTs

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levels of conductivity, such as for electronic goods, semiconductor components, and circuit boards (Heeder et al. 2011). CNTs are excellent candidates for the fabrication of electrically conducting composites due to their high aspect ratio and high electrical conductivity. The electrical conductivity of individual CNTs has been measured and found to be on the order of 106 S/m. The maximum electrical conductivity of SWCNT films has been reported to be in the range of 104–105 S/m due to the contact resistance between the individual nanotubes. Therefore, the range of electrical conductivity of CNT/polymer composites has tremendous potential, and can be tuned to the electrical conductivity of CNT/polymer composites by varying the amount and dispersion of CNTs in the composites considering other factors too. The CNT/polymer composites can be used for a variety of applications including electrostatic dissipation (101 S/m), printable circuit wiring, and transparent conductive coatings. CNTs are being used as fillers for electrically conductive adhesives because of their high aspect ratio, high electrical conductivity, and high oxidation resistance. CNT/composites are widely used in photovoltaic devices and light-emitting diodes. CNT-conducting polymer composites have a potential application in supercapacitors. The PANI/MWNTs composites electrodes showed much higher specific capacitance (328 F g
1) than pure PANI electrodes (Sahoo et al. 2010). Again, the electrical conductivity of CNT/polymer composites is widely defined by the percolation theory. The common factors affecting the percolation threshold of electrical conductivity are similar to mechanical properties such as dispersion, alignment, aspect ratio, degree of surface modification of CNTs, types and molecular weights of the matrix polymer, and composite processing methods. Due to the superior mechanical and thermal properties of CNT/polymer composites, they have drawn great attention to the applications in high end areas such as aerospace and defense industries. The most possible application comes from substituting the metal composite with the significantly lighter weight CNT/polymer composites in the design of airframes which requires materials with low density, high strength, and modulus. O’Donnell et al. reported that CNT reinforced polymer composites can show a profitable effect on the commercial aircraft business due to lighter weight (less fuel consumption). CNTs can be used as additional filler to the carbon fiber-reinforced polymer (CFRP) composite to enhance its interlaminar fracture characteristics (Sahoo et al. 2012). Potential application of PMCs composite can be found in sensing important materials that are critical to the environment, space missions, industrial, agricultural, and medical applications. Detection of NO2 and CO is important to monitor environmental pollution whereas sensing of NH3 is required in industrial and medical environments. Sensors based on individual SWNTs/polymer were demonstrated for sensing NO2 or NH3. During sensing, it is found that the electrical resistance profile of a semi-conducting SWNT changed significantly upon exposure of NO2 or NH3 gas (Penza et al. 2009). The existing electrical sensor materials including carbon black polymer composites operate at high temperatures for

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substantial sensitivity whereas the sensors based on SWNT exhibited a fast response and higher sensitivity at room temperature. A nanotube-poly (dimethylsiloxane) polymer composite film that can be used to form nanosensor, contains at least one conductive channel comprising an array of substantially aligned carbon nanotubes embedded in a polymer matrix, can be used to determine a real time physical condition of a material, and can be exploited in monitoring the physical condition of wing or chassis of a flying airplane or space shuttle (Mylvaganam and Zhang 2007). Composites of conjugated polymers are becoming increasingly used for solar cells because of their great expectations for cheap energy conversion. In addition, low weight, flexibility, and inexpensive preparation procedures of polymer composites for solar cell application make them more attractive than crystalline inorganic semiconductors for future applications. There are many reports on the performance of polythiophene/fullerene solar cells with a hole-collecting buffer layer that was made using composite films of functionalized multi-walled carbon nanotube (f-MWCNT), poly(3,4-ethylenedioxythiphene):poly(styrenesulfonate) (PEDOT:PSS), and ZnO nano particle. P3HT:PCBM bases solar cell reported efficiency as high as 5 %. Among various polymer electrolyte membranes, Nafion is the most suitable candidate for the fabrication of fuel cell membranes owing to its remarkable ionic conductivity and chemical Nafion-based membranes have a high production cost, low conductivity at low humidity and/or high temperature, loss of mechanical stability at high temperature, elevated methanol permeability, and restricted operation temperature. The higher methanol permeability not only decreases the fuel cell efficiency, but also the cathode performance. These problems can be overcome by the incorporation of CNTs into the Nafion membrane to improve the mechanical stability, the proton conductivity and to decrease methanol permeation of the Nafion membrane. Choi’s research group prepared functionalized MWCNTs (oxidized and sulfonated MWCNTs) with reinforced Nafion nanocomposite membranes for PEM fuel cell (Lee et al. 2011; Liu et al. 2012). Fullerenes, a family of carbon allotropes, were discovered in 1985 by Robert Curl, Harold Kroto, and Richard Smalley. Spherical fullerenes are also called buckyballs. The structures of fullerene (C60) and methanofullerene phenyl-C61butyric-acid-methyl-ester (PCBM) are shown in Fig. 23. Fullerene (C60) has attracted continuous attention since its discovery due to its exceptional physical and chemical properties. Fullerene-containing materials have shown wide and promising applications in the field of superconductors, ferromagnets, lubrications, photoconductors, and catalysts (Prato 1997; Wudl 1992, 2002). Organization of fullerene (C60) and its derivatives into nanostructures within polymer systems has potential applications in solar cells and biomedicine (Chen et al. 2009; Po et al. 2010; Sariciftci et al. 1993; Orfanopoulos and Kambourakis 1995). For example, interesting results on polymer solar cells were reported using a blend of fullerene derivative and a block copolymer poly(4-vinyl pyridine) of poly (3-hexyl thiophene) (P3HT-b-P4VP) (Sary et al. 2010). It was also found that

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Fig. 23 Structures of (a) fullerene (C60); (b) methanofullerene phenylC61-butyric-acid-methylester (PCBM)

a

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b

O MeO

fullerene in aqueous solution can generate singlet oxygen under photo-irradiation which has implications in the studies of biomedical and environmental science (Orfanopoulos and Kambourakis 1995; Anderson et al. 1994). One of the biologically most relevant features of C60 is the ability to function as a “free radical sponge” and quench various free radicals more efficiently than conventional antioxidants (Krusic et al. 1991). Fullerene has widespread applications ranging from drug-delivery and tissue-scaffolding systems to consumer products (Markovic and Trajkovic 2008), and it has been explored in the area of biological chemistry, such as enzyme inhibition, antiviral activity, DNA cleavage, and photodynamic therapy (Boutorine et al. 1994). However, many of C60 potential applications have been seriously hampered (Zhu et al. 1997; Ravi et al. 2005) by its extremely low solubility in water. Derivatization of the fullerene molecule with various functional groups and other solubilization procedures such as surfactants or long chain polymers (Ford et al. 2000; Mehrotra et al. 1997) is done through covalent interactions. Alok et al. fabricated functionally unmodified C60-containing nanostructures via a combination of an amphiphilic block copolymer P4VP8-b-PEO105-b-P4VP8 selfassembly and charge-transfer complexation between fullerenes and P4VP segments in organic solvent (Alok et al. 2011). Recently, the development of graphene-based polymer nanocomposites has become a new direction of research in the area of polymer nanocomposites. Graphene is an allotrope of carbon with a two-dimensional structure in which sp2 bonded carbon atoms are densely packed in a honeycomb crystal lattice into a oneatom-thick planar sheet. Graphene possesses high thermal conductivity, superior mechanical strength, and excellent electronic conductivity. As compared with CNTs, graphene has become a relatively cheap nanomaterial because its synthesis procedure is much simpler than those methods used for synthesis of carbon nanotubes. It has been reported that the improvement in mechanical properties of polymers by adding graphene is much more efficient than that by nanoclay or other nanofillers. Therefore, graphene is considered as a good choice of nanofillers for making advanced polymeric nanocomposites (Sahoo et al. 2012). The unique structure and high surface area of graphene sheets allow them to be used as composite fillers in fuel-cell and solar cell applications. Among various polymer electrolyte membranes, Nafion (Nafion is a sulfonated tetrafluoroethylene based fluoropolymer-copolymer) has received significant attention due to its

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remarkable ionic conductivity, and chemical and mechanical stability. However, the disadvantages of Nafion-based membranes are high production cost, low conductivity at low humidity and/or high temperature (over 100  C), loss of mechanical stability at high temperature (around 100  C), elevated methanol permeability, and restricted operation temperature. PEM fuel-cell performance can be improved by increasing the proton conductivity of the membrane by incorporating 10 wt% of sulfonic acidfunctionalized graphene oxide (GO) into the Nafion matrix. These experimental results suggest that the functionalized GO/Nafion nanocomposites offer significant promise as electrolyte membranes for PEMFC applications (Sahoo et al. 2012). When Nafion membrane is replace by poly(ethylene oxide) (PEO), which leads to a GO/PEO membrane, it shows proton conductivity of 0.09 S cm
1, at 60  C, and a power density of 53 mW cm
2 in a hydrogen PEMFC. It is due to partially existing –COOH groups on the GO in the form of –COO
and H+ at room temperature. This provides better ionic/protonic conductivity in the PEO/GO composite membrane (Sahoo et al. 2012). Organic photovoltaic cells (OPV) are of great interest as a potential source of renewable energy and as a promising alternative to traditional inorganic solar cells due to their light weight, ease of manufacturing, compatibility with flexible substrates, and low cost. Currently, the most successful OPV cells are fabricated with a BHJ architecture based on poly(3-octylthiophene) (P3OT) as the donor and the fullerene derivative 6,6-phenyl C60 butyric acid methyl ester (PCBM) as the acceptor. Graphene based PMC has great potential to be used as an acceptor for photovoltaic devices due to its excellent electron transport properties and extremely high carrier-mobility. Most research focuses on replacing or cooperating with PCBM of polymer-based OPVs because its electron mobility is high and its energy level can be tuned easily through controlling its size, layers, and functionalization. However, the power conversion efficiency (PCE) values of the OPVs reported in the literature are only slightly higher than 1 %, indicating that graphene is still far from being qualified to act as this kind of material (Sahoo et al. 2012). Numerous functional filler, reinforcements, function polymer, and fabrication techniques are being exploited by researchers to achieve various kinds of PMCs armed with various properties.

Summary As is known, polymers or polymer composites have various applications. However, there is still a wide scope to explore various paths and ideas to improvement of properties like high-strength, light weight, high performance composites, and electronics to make more convenient, sophisticated, customized tools or products for future application. One of the ways to do this is by synthesizing/modifying polymers, reinforced fibers, functional fillers, and improving or inventing new techniques for making sophisticated products in the future. For example, currently although there are various reinforcing materials, glass fibers and carbon fibers are used most in preparation of high performance

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polymercomposite, but CNT-filled polymers show potential applications due to improved properties, such as high-strength, light weight, and high performance composites; until now, there has not been much industrial successes showing their advantage over traditional carbon fibers. Because of their nanometer scale and high aspect ratio, CNTs usually form stabilized bundles due to van der Waals interactions, despite various methods such as melt processing, solution processing, and in-situ polymerization which are used to counter these problems. However, there are still opportunities and challenges to be found in order to improve dispersion and interfacial properties. The mechanical properties of CNT/polymer nanocomposites may be compromised between the carbon–carbon bond damage and the increased CNT-polymer interaction due to the CNT functionalization. Similarly, electrical conductivity of a CNT/polymer nanocomposite is determined by the negative effect of carbon–carbon bond damage and the positive effect of the improved CNT dispersion. In either case, the choice and control of tailored functionalization sites for chemical modification of CNTs are extremely necessary. It is also necessary to understand the mechanisms involved in the methods used to improve the properties of CNT/polymer composites. This will be helpful to select the appropriate polymers and CNTs as well as maximum adhesion at the CNT-polymer interfaces. Another problem associated with CNT is its high cost. It is one of the major hurdles to accept CNT as a generous reinforcing agent over traditionally existing reinforcing agents like carbon fiber and glass fiber. So, bringing down the manufacturing cost of CNT is one of the aspects toward wide industrial acceptance of CNT as a reinforcing agent. Similar kinds of problems or challenges or scope for improvement are also present in other polymer composite systems for specific use. Other versatile valuable applications are found with block copolymers. They are exploited for applications in drug delivery, tissue engineering, cosmetics, water treatment, and industrial waste treatment. Block copolymer micelles are used in synthesis of metal or metal oxide nanoparticles which have shown special catalytic, magnetic, electrical, and optical properties. The future trend of polymer or polymer composite is or will be decided by market needs or demand and current/future research progress.

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Properties and Applications of Polymer Nanocomposite Alok Chaurasia, Yu Suzhu, Cheng Kuo Feng Henry, Vishal Tukaram Mogal, and Sampa Saha

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Polymer Nanocomposites in the Bio Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biodegradable Polyester Biocomposites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Polypeptide-Based Nanocomposites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other Types of Nano-Biocomposites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Polymer Nanocomposites in the Field of Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conducting Polymer Composite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Potential Electronic Properties and Application of Various Carbon Nanotube (CNT)/Polymer Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conducting Polymer Composites Made from Polymer Brushes . . . . . . . . . . . . . . . . . . . . . . . . . . . . Polymer Nanocomposites for Engineering Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carbon Nanotube/Polymer Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thermal and Rheological Properties of CNTs Base Polymer Composites . . . . . . . . . . . . . . . . . Mechanical Properties CNTs Base Polymer Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Polymer Nanocomposites for Packaging Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Polymer Nanocomposites for Packaging Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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A. Chaurasia (*) • V.T. Mogal School of Materials Science and Engineering, Nanyang Technological University, Singapore e-mail: [email protected]; [email protected] Y. Suzhu • C.K.F. Henry Forming Technology Group, Singapore Institute of Manufacturing Technology, Singapore e-mail: [email protected]; [email protected] S. Saha Department of Chemistry, Michigan State University, Ann Arbor, MI, USA e-mail: [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_22

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Polymer Nanocomposites for Automotive Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Application of Polymer Materials in Automotive Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Approaches of Fabricating Polymer Nanocomposites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Toughened Polymer Nanocomposites Prepared by Melt Compounding . . . . . . . . . . . . . . . . . . . Green Composites for Automotive Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

▶ Chapter 3, “Polymer Surface Treatment and Coating Technologies” mainly discusses the extensive studies which have been carried out on properties and applications of polymer and polymer nanocomposites in the field of bioelectronics. It also highlights on some of the interesting engineering applications such as high-performance composites used in aerospace application. In addition to that, we briefly talked about biodegradable as well as biocompatible polymers which have gained significant attention due to its widespread use in the preparation of biocomposites for various biomedical as well as agricultural applications. Next part of the discussion emphasizes on conducting polymer composite mainly on carbon nanotube (CNT)/polymer composite because of continuous interest in the use of polymers (conjugate) for fabrication of numerous light and/or foldable electronic devices and they are also extremely promising candidates for sensor applications. It also focused on the application of polymer and polymer nanocomposites for packaging areas. The main advantages of plastics as compared with other packaging materials are that they are lightweight and low cost and have good processability, high transparency and clarity, as well as good barrier properties with respect to water vapor, gases, and fats. Our discussion on polymer composite ends with its utility in automotive applications. Because they are lightweight and due to their property tailorability, design flexibility, and processability, polymers and polymer composites have been widely used in automotive industry to replace some heavy metallic materials.

Introduction Polymer nanocomposites have drawn extensive attention due to the scope of tuning of phyisicochemical properties of the materials for high and specific applications. Preparation of polymer nanocomposites gives choice to choose appropriate polymer and type of nanofiller and processing parameter depends on applications. Polymer composites have wide use, from high-end application to low-end application. In this chapter, studies have been carried out on properties and applications of polymer nanocomposites in the field of bioelectronics besides highlighting on engineering applications which include high-performance composites even used in aerospace application.

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Polymer Nanocomposites in the Bio Field In recent years, nano-biocomposites from biodegradable biocompatible polymers have gained significant attention for a wide range of biomedical applications owing to their U.S. Food and Drug Administration (FDA) approval for most of polymers. Nano-biocomposites obtained by adding biopolymers or nanofillers often result in improved material properties without having any toxic products (Bordes et al. 2009; Alok et al. 2011). Such eco-friendly biodegradable polymers are mainly destined to biomedical applications, drug/protein delivery tuning, and formulating biomedical devices. Biopolymers can be either chemically synthesized or biosynthesized from microorganisms. Figure 1 gives a classification with four different categories of biopolymers, depending on the synthesis method employed (Averous and Boquillon 2004). Biopolymers obtained from microorganisms, agro resources, and biotechnology are from renewable resources.

Biodegradable Polyester Biocomposites Many types of polyester have been the predominant choice for materials in biocompatible and biodegradable drug delivery systems. Polyesters such as PLGA,

Fig. 1 Classification of the biopolymers

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PGA, PLA, PCL, etc., are polymers that have gained significant attention for a wide range of applications in the biomaterial field. Owing to their biodegradability and biocompatibility, polyesters have been widely used as carriers for drug, protein, and gene delivery. Fine-tuning of the drug release from polyesters has been extensively studied for many years (Agarwal 2012). Homopolymers and copolymers derived from glycolic acid or glycolide, lactic acid or lactide, and ε-caprolactone are studied extensively for more than a decade for vast biomedical applications. Among all polyesters, aliphatic polyesters are the most investigated degradable polymer for biomedical applications and have been used in sutures, drug delivery devices, and tissue engineering scaffolds. Polyesters are of utmost interest in biomedical applications because these biomaterials can be broken down and resorbed without removal or surgical revision (Fong et al. 2011). Polyesters are susceptible to acid, base-catalyzed hydrolysis, or enzymes present in the body. Fine-tuning of mechanical and drug delivering properties makes polyesters a natural choice towards tissue fixation and controlled drug delivery applications (Vert et al. 1992). Poly(L-lactic acid) (PLLA) is the most prevalent in this category, and though reports of the use of PLLA can be found in the 1960s, exceptional amount of work has been performed and published recently. PLLA is the product resulting from polymerization of L,L-lactide (also known as L-lactide). Being able to degrade into innocuous lactic acid, PLLA has widespread applications in sutures, drug delivery devices, prosthetics, scaffolds, vascular grafts, bone screws, pins, and rods or as plates. Strong mechanical properties and degradation into innocuous end product are the main reasons for such variety of applications (Shikinami et al. 2005). PLLA is U.S. Food and Drug Administration-approved for a variety of applications and is available commercially in a variety of grades. Some of the commercially available products are NatureWorks (Cargill, USA), Lacty (Shimadzu, Japan), PDLA (Purac, the Netherlands), PLA (Galactic/Total, Belgium), and Ecoloju (Mitsubishi, Japan) (Bordes et al. 2009). Some studies suggest the potential use of PLLA as a bone reinforcement material. The mechanical properties of neat PLLA might not be enough for high load-bearing applications. This explains the need to incorporate different elements like oriented fibers, HAP, or clays to form nanocomposites. This result in an increase in the flexural modulus and strength, which corresponds with bone replacement implants (Shikinami et al. 2005). Poly(glycolic acid) (PGA) is another aliphatic biodegradable polyester with applicability in the field of biomaterials. However, unlike PLLA, high water solubility of PGA and fast hydrolysis on exposure to aqueous conditions affect the mechanical properties adversely. Thus, water solubility and its high melting point limit the use of PGA in bionanocomposites. Another biodegradable biocompatible FDA-approved polyester commonly known as poly DL-lactide/glycolide or poly(lactide-co-glycolide) (PLGA), which degrades by hydrolysis of its ester linkages in the presence of water into lactic acid and glycolic acid (Fig. 2; Steele et al. 2012). PLGA has been used to deliver chemotherapeutics, proteins, vaccines, antibiotics, analgesics, anti-inflammatory drugs, and siRNA. Most often PLGA is

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Fig. 2 Hydrolysis of Poly DL-lactide/glycolide into lactic and glycolic acid which later enters Kreb’s cycle through pyruvic and oxalic acid respectively

fabricated into microspheres, microcapsules, nanospheres, or nanofibers to facilitate controlled delivery of drugs. PLGA offers several advantages as delivery devices, for example, site-specific/localized drug delivery through surface functionalization and control of drug release from the matrix by changing its monomer’s ratio, molecular weight, and terminal end groups (Huang et al. 2013). Surface functionalization has been done for various purposes such as PEG to evade secondary immune response, folic acid for tumor targeting, and acrylates for bioadhesion (Tables 1 and 2). Poly(ε-caprolactone) (PCL) is a biodegradable and nontoxic aliphatic polyester exhaustively studied as the biomedical nanocomposite. PCL is obtained by the ringopening polymerization of ε-caprolactone in the presence of metal alkoxides (aluminum isopropoxide, tin octoate, etc.). PCL shows a very low Tg (
61  C) and a low melting point (65  C), which limits some applications. Therefore, PCL is generally blended or modified (e.g., copolymerization, cross-linking). The copolymerization is commonly done with other lactones such as glycolide, lactide, and poly(ethylene oxide) (PEO) or by nanofiller incorporation in order to wide range of properties as per application. The rubbery state because of low Tg permits the diffusion of this polymer species at body temperature, thus making it a promising candidate for controlled release and soft tissue engineering (Vert et al. 1992). This is important for the preparation of long-term implantable devices, as its degradation is even slower than that of polylactide.

Polypeptide-Based Nanocomposites A wide range of possibilities in materials design and application are provided by polypeptide nanocomposites as ability to adopt specific secondary, tertiary, and quaternary structures, a drawback of synthetic polymers (Hule and Pochan 2007). Specific sites of the polypeptide backbone can be modified by incorporating specific amino acid functionality with desired activity. The increased mechanical properties and thermal properties of nanocomposites with addition of fillers are comparable to other widely explored biomedical devices and biomaterials. The secondary conformation of the nanocomposite matrix can be affected by the molecular weight of the polypeptide. Polylysine (ε-poly-L-lysine, PLL) is a small

Applications Fracture fixation, interference screws, suture anchors, meniscus repair

Suture coating, dental and orthopedic implants

Screws, suture, drug delivery

Suture anchors, meniscus repair, medical devices, drug delivery

Biopolymer Poly(lactic acid) (PLA)

Poly(ε-caprolactone) (PCL)

Poly-lactic-co-glycolic (PLGA)

Polyglycolic acid (PGA)

Table 1 Polymer nanocomposites for biomedical applications Monomer structure

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Cosmetic surgery and burns surgery

In bandages and other hemostatic agents, hypoallergenic and has antibacterial properties

Preservative in food products, tissue cultureware coating

Collagen

Chitosan

Poly(L-lysine) PLL

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Table 2 Common poly(lactide-co-glycolide) products Product VicrylTM (Ethicon) PolysorbTM (Covidien) VicrylTM mesh (Ethicon) VyproTM mesh (Ethicon) SeprameshTM (Bard) Codman ® Ethisorb Dura patchTM (Ethicon) Polygraft TrufitTM BGS (Smith & Nephew) DermagraftTM (Advanced Biohealing Inc.) Rapisorb and Rapisorb cranial clamp (Synthes) Lupron Depot ® (Abbot Labs)

Bio-applications Suture Hernia and soft tissue mesh

Dura repair Bone void filler Wound healing scaffolds (ulcers) Fixation Drug delivery (prostate cancer)

natural homopolymer of the essential amino acid L-lysine that is produced by bacterial fermentation (Chaurasia et al. 2012; Gao et al. 2003; Ramanathan et al. 2004; Tasis et al. 2006; Iijima 1991; Ajayan et al. 1994). L-lysine residues generally constitute homopolypeptide ε-polylysine, in which epsilon (ε) refers to the linkage of the lysine molecules. α-Polylysine improves cell adhesion, hence commonly used to coat tissue cultureware. Several secondary structures of PLL are the random coil, α-helix, or β-sheet in aqueous solution, and transitions can be easily achieved using pH, temperature, salt concentration, or use of cosolvent. These different conformations and transitions can be studied using circular dichroism (CD), FTIR, and Raman spectroscopy. PLL preferentially forms β-sheet structure irrelevant to nanocomposite film formation method at high concentrations (Hule and Pochan 2007). These secondary structures of polypeptides aid the design of new nanomaterials for specific desired applications in the biomedical arena. Such nanocomposites with addition of fillers give strength to matrix, and potential applications include drug delivery matrices, tissue engineering scaffolds, and bioengineering materials (Fong et al. 2011).

Other Types of Nano-Biocomposites Polyhydroxyalkanoates (PHAs) are linear polyesters produced in nature by bacterial fermentation of sugar or lipids (Aldor and Keasling 2003; Li et al. 2007a). They can be either thermoplastic or elastomeric material with wide melting range from 40  C to 180  C. Polyhydroxyalkanoates (PHA)-based nano-biocomposites are useful in making bioplastic because of their biodegradability but possess some drawbacks, such as brittleness and poor thermal stability (Aldor and Keasling 2003). This limits their application, and hence, often they are intercalated with clay. PHA/clay nano-biocomposites are prepared by solvent intercalation and/or melt intercalation processes. PHA-based nano-biocomposites have a wide range of applications in sutures, cardiovascular patches, stents, guided tissue repair/ regeneration devices, articular cartilage repair devices, vein valves, bone marrow

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scaffolds, meniscus regeneration devices, ligament and tendon grafts, and in wound dressings and as hemostats (Park et al. 2005; Reddy et al. 2003). Poly(urethane urea) (PUU) block copolymers are used in ventricular assist devices and total artificial hearts as blood sacs. Polyurethane ureas (PUUs) are prepared with the same conditions as polyurethanes, with the difference that diamines are used instead of diols as chain extenders, which constitute the hard segments with improved mechanical and thermal properties. The higher cohesion of urea linkages develops stronger, three-dimensional hydrogen bonding in PUU (Oprea et al. 2013). One of the main disadvantages of PUUs in medical devices is their relatively high permeability to air and water vapor. The majority of the component of copolymer is the soft segment called the poly(tetramethylene oxide), which is responsible for the permeability and diffusive properties of the polymer. Addition of organically modified layered silicates overcomes the permeability and diffusive drawbacks while still maintaining the desired biocompatibility and mechanical properties of the nanobiocomposites (Xu et al. 2000). This additional silicate imparts increase in barrier properties because of intercalated clay layers in the polymer matrix with increases in the modulus and strength of the nanocomposite. More recently polymer-based nanocapsules are being used to design drug delivery system with improved solubility, bioavailability, and controlled release for a specific target. Polymer-based nanocapsules provided stability of drug molecules from degradation by external factors such as light or by enzymatic attack in their transit through the digestive tract (Mosqueira et al. 2001; Ourique et al. 2008). Polymer modification has been done in order to obtain more hydrophilic surfaces or polymer coatings to attain favorable behavior regarding active substance stability in the case of encapsulation (Mora-Huertas et al. 2010). In Fig. 3 micelles, formed from amphiphilic block copolymers (ABCs), with cores and coronas have been demonstrated as a powerful tool for cell imaging, disease diagnosis, and delivery of various water-insoluble materials (including quantum dots, magnetic nanoparticles, and drugs). For example, PEG–PCL and PEG–PLA, are some of the block copolymers, used for nano-encapsulation. This type of polymers are used to encapsulate hydrophobic/ hydrophilic drug or active ingredients depends on application and process. The tri-block copolymers, PCl–PEG–PCl [poly(e-caprolactone)–poly(ethylene glycol)–poly(e-caprolactone)], were used to encapsulate and deliver ibuprofen. The release profile of ibuprofen was significantly affected by the block length of the copolymer composition and the extent of loading. The in vitro profile shows a sustained release of 10 % loading ibuprofen from 3 to 15 days. Release profile depends on the ratio of e-caprolactone to ethylene glycol-derived subunits in copolymer chains. With 5 to 20 wt% ibuprofen loading, release was continued for 2–24 days for copolymer whose e-caprolactone molar ratio to ethylene glycolderived subunits was 2.49 (Yu and Liu 2005). Another example of the effect of nano-encapsulation was reported on reverse multidrug resistance in tumor cells when PEG–PCL was used. This study shows a novel drug delivery system, where an anticancer drug, doxorubicin, was encapsulated by polyethyleneglycol–polycaprolactone (PEG–PCL) using solvent

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Fig. 3 Micelles, formed from amphiphilic block copolymers, with cores and coronas have been demonstrated as a powerful tool for cell imaging, disease diagnosis, and delivery of various water insoluble materials (including quantum dots, magnetic nanoparticles, and drugs)

evaporation method. The size of doxorubicin-loaded polymer nanocomposite was about a diameter of 36 nm and a zeta potential of +13.8 mV. The encapsulation efficiency of doxorubicin was 48.6 %  2.3 %. This drug/polymer nanocomposite showed sustained release profile, increased uptake, and cellular cytotoxicity, as well as decreased efflux of doxorubicin in adriamycin-resistant K562 tumor cells (Diao et al. 2011). Block copolymer nanocomposites are used for bioimaging. One of the primary conditions applicable in bioimaging is micelle-encapsulated superparamagnetic nanocomposites, which should be dispersible and stable in aqueous medium besides other criteria. It was reported that amphiphilic poly(e-carpolactone)-block-poly(ethylene glycol) copolymers were linked to a fluorophore, 2,1,3-benzothiadiazole (BTD). This resulted in new type of bioimaging agent. The polymers form micelles in aqueous solutions with average diameters of 45 nm and 78 nm depending on the polymer structures. So, neutral and hydrophobic biocompatible emitters can be made by using the block copolymer in a rational way (Tian et al. 2010).

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Polymer Nanocomposites in the Field of Electronics Conducting Polymer Composite There has been continuous interest in the use of conjugated polymers for the fabrication of numerous light and/or foldable electronic devices, for example, electrochromic displays, microelectronic devices, protective coatings, and rechargeable batteries. The motivation behind this intense interest is because of their unique electronic and optical properties, ability to be chemically tuned, most importantly their lightweight/foldable mechanical properties, processability, and low cost. They are also extremely a promising candidate for sensor applications because of their conductivity and electrochemical activity that are extremely sensitive to molecular interactions, which provide excellent signal transduction for molecular detection (Gardner et al. 1992; Janata and Josowicz 2003; Chaurasia et al. 2012). The uprising popularity of conducting polymer-based sensors lies in the fact that only specific chemicals can trigger a drastic conductance change. By functionalizing polymer molecules, it can be made more specific (Gao et al. 2003; Ramanathan et al. 2004). However, the limitations such as relative low conductivity and low mechanical and chemical stabilities restrict its use for some practical applications.

Potential Electronic Properties and Application of Various Carbon Nanotube (CNT)/Polymer Composites Nowadays, tremendous efforts have been made to prepare polymer and carbon nanotube composites (because of remarkable electrical as well as thermal conductivities and the superior mechanical properties of carbon nanotubes (CNTs)) with the aim of synergistically combining the merits of each individual (Tasis et al. 2006; Iijima 1991; Ajayan et al. 1994; Dai and Mau 2001; Zengin et al. 2002; Cochet et al. 2001; Sainz et al. 2005; Moniruzzaman and Winey 2006) component. To form a perfect polymer/CNT composite with much more enhanced functionality, in situ polymerization of the desired monomers in the presence of carbon nanotubes would be expected to show best results compared to the post-mixing approaches (Cochet et al. 2001; Sainz et al. 2005; Li et al. 2003). But, well dispersion of the carbon nanotubes into solution is a must for in situ polymerization. There are reports of different dispersal approaches which impart different surface chemistries and electronic structures to the carbon nanotubes such as polymer wrapping, (Zheng et al. 2003; Dalton et al. 2000; Star et al. 2001) noncovalent adhesion of small molecules, (Dai and Mau 2001; Chen et al. 2002) and acidic oxidation (Wang et al. 2005). The key to the expected improvement in the nanocomposites depends critically on the monomer–nanotube interfacial chemical and electronic interactions during polymerization and polymer–nanotube interfacial interactions after polymerization. There are reports on the impacts of the surface chemistry and electronic structure of carbon nanotubes on the kinetics of

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polymerization and the electronic performances of the obtained composites (Cheung et al. 2009). Because of their well-documented unique surface chemistry and electronic structures, they have used single-stranded DNA dispersed and functionalized single-walled carbon nanotubes (ss-DNA-SWNTs) as a representative example. They have also discussed the multiple roles of ss-DNA-SWNTs during and after the in situ polymerization in the fabrication of highly conductive self-doped polyaniline/SWNT composites. The applications of these nanocomposites cover the wide area of biosensing and flexible electronics which will be discussed below. Nowadays, there is increasing efforts for the use of single-walled carbon nanotube (SWNT) networks as sensing materials and conductive flexible electrodes due to its specific advantages. Fabrication of SWNT films can be done quite easily by various room temperature solution-based processes, such as spray coating, (Kaempgen et al. 2005; Artukovic et al. 2005) inkjet printing, (Kordás et al. 2006; Simmons et al. 2007) deposition by a layer-by-layer approach, (Shim et al. 2007; Kovtyukhova and Mallouk 2005) and deposition through a filter (Wu et al. 2004; Zhang et al. 2006a). Due to statistical averaging effects, the obtained network electrodes are highly reproducible and exhibit percolation-like electrical conductivity. A number of applications including electrodes for solar cells, (Rowell et al. 2006) organic light-emitting diodes, (Li et al. 2006) smart windows, (Gruner 2006) sensors (Ferrer-Anglada et al. 2006), and transparent transistors (Artukovic et al. 2005; Chaurasia et al. 2012) where SWNT networks can be very useful. But, all the experimentally measured conductivities of the SWNT networks are considerably lower than the conductivity of a SWNT rope (axial conductivity around 10,000–30,000 S/cm) (Thess et al. 1996). It has also been noted that the conductivity of the SWNT networks decreases as the temperature drops (Bekyarova et al. 2005). The existence of high junction resistance and tunneling barriers between nanotubes (which dominate the overall film conductivity in the network) is the result of its low conductivity and the strong temperature dependence conductivity. Therefore, it can be highly expected that decreasing the inter-tube resistance and lowering the number of these high-resistance junctions could increase the conductivity of the network. Actually, Lee and co-workers (Geng et al. 2007) reported that contact junctions can be improved by treating SWNT networks with a 12 M HNO3 which helps to remove the insulating surfactant in SWNT network, and indeed, it improves the conductivity of the SWNT network by 2.5 times. They have also observed the dramatic decrease in the percolation threshold to the greatly reduced contact resistance between the tubes in the SWNT network.

Electrical Conductivity CNTs Base Nonconducting Polymer Composites As CNTs exhibit high aspect ratio and high electrical conductivity, they are excellent candidates for fabrication of electrically conducting nanocomposites. While the electrical conductivity of individual carbon nanotubes has been measured to be in the order of 106 S/m, (Baughman et al. 2002) the maximum electrical conductivity of SWCNT films has been reported to be in the range of 104–105 S/m

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Table 3 Electrical properties of different CNT/epoxy composites CNT type SWCNT MWCNT-1 MWCNT-2

Vc (vol%) 0.074 0.01 0.0025

β 1.3 – 1.2

Maximum conductivity (S/m) 1.2  10
3 @ 0.2 wt% 5.0  10
1 @ 0.1 wt% 2.0 @ 1.0 wt%

References Shi et al. (2006) Nemati et al. (2013) Nemati et al. (2013)

(Ericson et al. 2004; Sreekumar et al. 2002) due to the contact resistance between the individual carbon nanotubes in the films. Therefore, the range of electrical conductivity of CNT/polymer composites is reported to be tremendously wide. On the other hand, this wide range advises that it is possible to control the electrical conductivity of CNT/polymer composites by varying the amount and degree of dispersion of CNTs in the composites. The CNT/polymer composites can be used for a variety of applications including electrostatic dissipation (101 S/m), printable circuit wiring, and transparent conductive coatings. Again, the electrical conductivity of CNT/polymer composites is widely defined by the percolation theory. The percolation theory predicts that there is a critical volume fraction at which nanocomposites containing conducting fillers in insulating polymers become electrically conductive. According to this theory, σc ¼ A (V
Vc)β, where σc is the conductivity of a composite, V is the CNT volume fraction, Vc is the CNT volume fraction at the percolation threshold, and A and β are constant. So far, there are several publications documented on the progress of electrical conductivity of different CNT/polymer composites (Shaffer and Windle 1999; Sandler et al. 1999, 2003). The percolation threshold has been reported to range from 0.0025 vol% (Sandler et al. 2003) to several vol%. Therefore, it is difficult to draw definite conclusions about the mechanism of electrical conductivity of CNT/polymer composites from the literature. This is because the reported levels of CNT loading to achieve a percolation threshold vary widely. The electrical conductivity and percolation threshold of different CNT/epoxy composite systems are shown in Table 3. It seems that different systems give a wide range of percolation values. However, even for the same system, for example, SWCNT/ epoxy composites (Vc ¼ 0.0025  0.1 %), (Sandler et al. 2003) a wide variation in percolation value was observed. The mechanism for percolation threshold for electrical conductivity of CNT/polymer composites is determined by numerous factors, and a number of publications have reported the factors affecting the percolation mechanism of CNT/polymer composites. The common factors affecting the percolation threshold of electrical conductivity are dispersion, (Sandler et al. 2003; Li et al. 2007b) alignment, (Choi et al. 2003; Du et al. 2003) aspect ratio, (Li et al. 2007b; Bai and Allaoui 2003; Bryning et al. 2005) degree of surface modification (Georgakilas et al. 2002) of CNTs, types and molecular weights of the matrix polymer, (Pan et al. 2010; Ramasubramaniam et al. 2003) and composite processing methods (Liu et al. 2008). The aligned CNTs in epoxy decrease the percolation threshold by one order of magnitude compared to entangled nanotubes (Sandler et al. 2003).

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The electrical conductivity of SWCNT/epoxy composites with SWCNTs aligned under a 25-T magnetic field was increased by 35 % compared to similar nanocomposites without magnetically aligned SWCNTs (Choi et al. 2003). In contrast, Du et al. (2003) found that the electrical conductivity of CNT/PMMA composite with 2.0 vol% CNTs was 10
10 S/cm with the aligned CNTs in the matrix and 10
4 S/cm with unaligned CNTs. This indicates that the alignment of the CNTs in the composite decreased the electrical conductivity. The reason is that there are fewer contact points between the CNTs when they are highly aligned in the composites, so CNT-aligned composites require more nanotubes to reach the percolation threshold. The aspect ratio of CNTs has a tremendous influence on the percolation threshold of CNT/polymer composites without changing other important parameters, such as the polymer matrix or the dispersion and aggregation state of the CNTs. On the other hand, it is well known that chemical functionalization may disrupt the extended conjugation of nanotubes and hence reduce the electrical conductivity of functionalized CNTs. For example, silanefunctionalized CNT/epoxy composites showed a lower electrical conductivity than that of the untreated CNT composites at the same nanotube content (Ma et al. 2007). Cho et al. (2005) reported that the electrical conductivity of the acid-treated MWCNT composites was lower than that of the untreated MWCNT composites at the same content of MWCNTs. This is attributed to the increased defects in the lattice structure of carbon–carbon bonds on the nanotube surface as a result of the acid treatment. In particular, the severe modification of carbon nanotubes may significantly lower their electrical conductivity. However, there are several publications reporting that the functionalization of CNTs can improve the electrical conductivity of the nanocomposites (Tamburri et al. 2005). Tamburri et al. (2005) found that the functionalization of SWCNTs with –COOH and –OH groups enhanced the nanocomposites’ electrical conductivity as compared to the use of untreated SWCNTs.

Electrical Conductivity CNTs Base Conducting Polymer Composites Most of the reported conducting polymer/carbon nanotube composites show conductivity enhancement over polymeric materials but much lower electronic performance compared to CNT films alone (Bekyarova et al. 2005). Few years back, Sun et al. (Wang et al. 2008) demonstrated that bulk-separated metallic SWNTs show superior performance than the as-produced nanotube sample in conductive polymer composites which can be obtained by blending with poly (3-hexylthiophene) and also poly(3,4-ethylene dioxythiophene):poly(styrene sulfonate). They did not show the performance of films prepared from SWNT alone as a control experiment. It was evidenced by Blanchet et al. (2004) that the percolation threshold of a SWNT network was drastically downshifted by replacing the insulating dispersing reagents in the network with a conducting polymer. But, the conductivity of the SWNT network was not increased by the replacement after percolation. However, they have fabricated a water-soluble and highly conductive self-doped polyaniline/SWNT composite (Fig. 4; Ma et al. 2006a) by

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Fig. 4 (a) A proposed SWNT/conducting polymer composite network. A thin skin of conducting polymer wraps around and along each of the SWNTs. (b) An individual composite nanotube coated with a thin layer of functionable conducting polymer. The two “turning knobs” are incorporated to adjust the property of the polymer layer for sensitive and selective molecular detection. (c) A conducting polymer junction in this network (Cheung et al. 2009)

the in situ polymerization of a thin skin of PABA (poly 3-aminophenylboronic acid) along and around ss-DNA-SWNTs. They have also measured the thickness of the polymer layer on the carbon nanotube using transmission electron microscopy (TEM) and it was found to be around 1–3 nm (Fig. 5c; Ma et al. 2006b). The thin conducting polymer layer remarkably improves the contacts between the tubes and hence acts as a “conductive glue” which effectively assembles the SWNTs into a conductive network (Fig. 5b). SWNTs with a PABA layer (referred to composite–SWNT networks) can be fabricated by vacuum filtration and dip coating (same methods used for SWNT alone) (Hu et al. 2004). Also a post mixture can be produced by simple mixing the pre-formed PABA with the same amount of ss-DNA-SWNTs. However, this postmixing process is not as effective as the premixing one in terms of interlinking the tubes (Fig. 5d). Furthermore, it has also been observed that the morphology of the post-mixture composite (Fig. 5d) is akin to the ss-DNA-SWNT network alone (Fig. 5a), except some large particles or aggregates, which could be due to the presence of neat PABA which has not being uniformly mixed with the SWNTs. Surprisingly, they have found that the percolation threshold of the SWNT networks increased by threefold and the conductivity of the post-mixture network significantly decreased (Fig. 6a). Finally, the conductance of the post-mixture composite is five orders of magnitude lower than the network formed from the in situ polymerized PABA composite and is three orders of magnitude lower than the network prepared from SWNT

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Fig. 5 AFM images of the third layers of the films prepared from (a) ss-DNA/SWNTs, (b) in situ polymerized composite, (d) post-mixture and (e) “seed” composite. The concentration of SWNT in all these samples is 10 mg/L. TEM images of in situ polymerized composite (c), “seed” composite (f) and post-mixture (g) (Kobayashi et al. 1984)

alone (Fig. 6a). They have also found from Fourier transform infrared (FTIR) spectroscopic study that the structure of the PABA layer in the composite prepared by in situ polymerization is very different from that of the neat PABA (Fig. 7a) and also drastically different from the PABA in the composite formed by post-mixing with the pre-formed neat PABA (Fig. 7b, purple curve). The FTIR peak of PABA in the composite formed by in situ polymerization at 1,120 cm
1 has been assessed by MacDiarmid et al. (1994, Yan et al. 2007) as the “electronic-like band” and is considered to be a measure of the degree of delocalization of electrons. Therefore, it can be considered as a characteristic peak of polyaniline conductivity. These results are the strong indication towards the conclusion that the PABA has much higher conductivity and existed in the more stable and conductive emeraldine state (Zengin et al. 2002; Sainz et al. 2005), compared to the neat PABA and the post-mixture PABA which were in the nonconductive pernigraniline state (Ma et al. 2006a; Wang et al. 2005). They have also prepared another PABA composite by in situ polymerization in the presence of pre-oxidized ss-DNA/ SWNTs (“seed” method) (Zhang et al. 2004; Zhang and Manohar 2004). The intensity of the “electronic-like” FTIR peak is slightly lower than that of the

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Fig. 6 (a) Conductance of in situ polymerized composite (red square), ss-DNA/SWNTs (black dot), post-mixture (purple triangle), and “seed” composite (green triangle) as a function of layers of the composites and ss-DNA/SWNTs. The conductance was measured by a two-probe approach. Each data point presented here was an average of 18 pairs of electrodes on five silicon chips. (b) Conductance of in situ polymerized composite (red square) and ss-DNA/SWNTs (black dot) measured by a four probe approach. The conductance of “seed” composite and post-mixture was beyond the sensitivity of the measurement setup. Each data point presented here was an average of 10 measurements (Kobayashi et al. 1984)

Fig. 7 Normalized Fourier-transform IR spectra of (a) in situ polymerized composite (red) and pure PABA (blue); (b) “seed” composite (green) and post-mixture (purple) (Kobayashi et al. 1984)

PABA in the in situ polymerized composite with the intact ss-DNA-SWNTs, but much higher than the neat PABA and the post-mixture PABA composite (Fig. 7b). Though the percolation threshold of the composite formed by the seed approach is threefold higher than the in situ composite but similar to the post-mixture composite, the conductance after the threshold is four and six orders of magnitude lower than the SWNT network alone and the in situ composite with intact SWNTs, respectively (Fig. 6a). The morphology of the seed composite film has been studied using AFM and noticed that the PABA in the composite did not interlink the nanotubes into a conductive network. Instead, serious aggregation of the nanotubes into large particles (Fig. 5e, f) has been induced by PABA. The aggregation

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mechanism is still not well understood yet and currently under investigation. The author thinks that it might be related to defects along the tubes (formed by the pre-oxidation process) which remarkably weaken the mechanical strength of the carbon nanotube. Thus, it can be concluded that not only the polymer’s molecular structure but also the arrangement or distribution of the carbon nanotubes in the composites dictates the overall percolation behavior and macroscopic electronic property of the composites. It is also important to mention that the fabrication process significantly impacts the electronic and molecular structure of the PABA formed in the composites as well as the arrangement or lateral distribution of the carbon nanotubes in the composites. To effectively optimize the fabrication parameters and ensure the formation of SWNT networks in a controllable fashion for a variety of potential applications, it is crucial to understand these reaction characteristics. Furthermore, it is worth mentioning that ss-DNA-SWNTs played multiple roles during in situ fabrication of conducting polymer nanocomposites. First, it functioned as catalytic molecular templates during in situ polymerization of ABA as the polymerization process might be 4,500 times faster (Ma et al. 2008a). Additionally, the quality of the resulting PABA was also drastically improved, observed by the fact that the backbone of the self-doped polyaniline had longer conjugated length as fewer short oligomers were produced and they existed in the more stable and conductive emeraldine state, which in turn can be exploited to produce conducting polymer composite materials with a much more enhanced electronic performance. Secondly, the ss-DNA-SWNTs also worked as unique conductive polyanionic doping agents in the resulting polyaniline film with enhanced conductivity and redox activity both in low pH and neutral pH solutions. In addition, it also acted as active stabilizers after the polymerization. The final advantage will be that the large surface area of the carbon nanotubes greatly enhanced the density of the functional groups available for sensitive detection of the target analytes. A wide variety of conducting polymers such as polyaniline (PANI), poly(diphenylamine) (PDPA), polypyrrole, polythiophene, etc., are currently used in different applications including metallic interconnects in circuits, electromagnetic radiation shielding, and chemical sensors (Stutzmann et al. 2003). The conductivity of such polymers arises due to the existence of charge carriers and mobility of those charge carriers along the bonds of the polymer chains. These polymers also show chemical selectivity, which makes them act as ideal candidate for the immobilization of gas molecules, and exhibit highly reversible redox behavior with a distinguishable chemical memory. So, these conducting polymers can potentially act as a gas sensor. Let us define gas sensor first. It is a device which detects the presence of different gases in an area, especially those gases which might be harmful to human beings or animals. The fabrication of ammonia gas sensors via a scanned-tip electrospinning method (Fig. 8) using a single 10-camphorsulfonic acid (HCSA) doped PANI/poly(ethylene oxide) (PEO) nanofiber with a diameter of 100–500 nm on gold electrodes has been reported by Craighead et al. (Liu et al. 2004) in 2004. The characterization of the well-defined single fiber material and the sensor response has been thoroughly studied. They have demonstrated that the sensor showed a rapid and reversible resistance change upon exposure to NH3 gas at

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Fig. 8 (a) A schematic of electrospinning process. (b) A SEM image of typical electrospun fibers (Ding et al. 2009)

concentrations as low as 500 ppb via the protonation and deprotonation of PANI. The performance of nanofiber sensors, for example, response time, can be estimated by considering the diffusion of ammonia into the fiber and the reaction of ammonia with doped PANI. Another aspect was the correlation between response times with fiber diameter. Indeed, the response times with various diameters refer to radiusdependent differences in the diffusion time of ammonia gas into the fibers. Manesh et al. have prepared another type of ammonia gas sensor with a detection limit of 1 ppm (Manesh et al. 2007) using electrospun PDPA/poly(methyl methacrylate) (PMMA) nanofibers as sensing materials. They have demonstrated that the changes in resistance of the nonwoven membrane showed linearity with the concentration of ammonia in the range of 10–300 ppm. Additionally, the detection target was expandable and can be expanded from ammonia to other amines according to Gong et al. (2008) (Fig. 9) using PANI nanotubes which can be easily made using electrospun PVA fiber mat membrane as the template. The small diameter, high surface areas and porous nature of the PANI nanotubes gave considerably better performance with regard to both time response and sensitivity. They have also observed that the responses follow the orders: (C2H5)3 N > NH3 > N2H4. However, the PANI nanotubes showed higher sensitivity and quicker response to (C2H5)3 N compared with PANI prepared without a template. In addition, a reasonable reproducibility has been observed in case of the reversible circulation response change of PANI nanotubes. However, Li et al. (Ji et al. 2008) described coaxial PANI/PMMA composite nanofibers using the electrospinning technique and an in situ polymerization method. The responses of the gas sensors based on these PANI/PMMA composite nanofibers towards triethylamine (TEA) vapor were investigated at RT, and it was found out that the sensors showed a sensing magnitude as high as 77 towards TEA vapor of 500 ppm. Furthermore, the responses were linear, reproducible, and reversible towards TEA vapor concentrations ranging from 20 to 500 ppm. Additionally, it was revealed that the concentration of doping acids only brought changes in resistance of the sensor without affecting its sensing characteristics. For example, the gas sensor with a doping acid

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Fig. 9 (a) Resistance change of PANI nanotubes exposed to 100 ppm of different gases (NH3, N2H4, and (C2H5)3 N). (b) Response of PANI prepared by using PVA fiber mats as the template and without a template upon exposure to 100 ppm of (C2H5)3 N. (c) The reversible circulation response change of PANI nanotubes upon exposure to 100 ppm of (C2H5)3 N (Gao et al. 2008)

(toluene sulfonic acid) exhibited the highest sensing magnitude, which can be explained by understanding its sensing mechanism and the interactions of TEA vapor with doping acids. There are reports of use of polymer as brushes as a composite, intended to make many applications; a significant advantage of polymer brushes compared to other surface modification methods is their mechanical and chemical stability, accompanied by a high level of synthetic flexibility towards the introduction of functional groups. This is in contrast to the physisorbed, non-bound polymer films where chemical modification by using wet chemistry is difficult to conduct. Additionally, it is now possible to grow brushes on virtually every surface (flat surfaces, particles, or macromolecules), to any thickness, of every composition, incorporating a multitude of functional groups and containing series of blocks. More recent applications of polymer brushes include nano-patterned surfaces (Shah et al. 2000), photochemical devices (Whiting et al. 2006), new adhesive materials (Raphael and De Gennes 1992), protein-resistant biosurfaces, (Saha et al. 2012)

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Fig. 10 Optical micrographsof patternedsurfaces: (left image) 10-μm features in a continuous polymer brush showing regions of poly(tert-butyl acrylate) (dark) and poly(acrylic acid) (light) and (right image) interaction of a water droplet with 200-μm features showing an unusual wetting profile and preferential interaction with poly(acrylic acid) brush domains (J. Am. Chem. Soc. 2000, 122, 1844–1845.)

chromatographic devices, (van Zanten 1994) lubricants, (Joanny 1992) polymer surfactants, (Milner 1991) polymer compatibilizers, eight and many more. One of the most attractive applications of surface-initiated polymerizations is the formation of nano-patterned surfaces by soft lithography techniques that combine microcontact printing (μCP) and graft polymerization. An elegant example is that of Hawker et al. who combined photolithography with nitroxide-mediated “living” free radical polymerization to obtain patterned polymer brushes with well-defined hydrophobic and hydrophilic domains (Fig. 10). They extended this concept to synthesize patterned polymer layers by aqueous ATRP (Vidal et al. 1980).

Conducting Polymer Composites Made from Polymer Brushes Recently, Huck and coworkers (Kong et al. 2007) have shown that chargetransporting polymer brushes (polytriphenyl amine acrylate) can be used for a variety of organic electronic device fabrications using composite methodology. These polymer brush films contain a greater level of ordering at the molecular level and display higher charge mobility than spin-coated films of the same polymer, which was attributed to the controlled polymer brush architecture and morphology. As, for example, when CdSe nanocrystals (with diameter in the range of 2.5–2.8 nm) subjected into the polymer brush layers form a polymer composite (Fig. 11), its photovoltaic quantum efficiencies of up to 50 % (Snaith et al. 2005). In another report, Advincula and coworkers (Fulghum et al. 2008) successfully grafted holetransporting PVK (poly(vinyl carbazole)) brushes on transparent ITO electrodes. Using cyclic voltammetry, the PVK brush was electrochemically cross-linked to form a conjugated polymer network film. Covalent linkage of PVK led to a direct electroluminescent PLED device, in which the electroluminescent polymer layer can be simply solution-cast onto the modified ITO.

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Fig. 11 Top image: PTPAA brushes and bottom image: Cartoon of inferred structure for CdSe nanocrystal infiltrated polymer brush photovoltaic device (From bottom to top) ITO-coated glass slide modified by surface attachment of a bromine end-caped trichlorosilane selfassembled-monolayer (SAM) (blue squares), polymer brushes grown from the SAM (red lines), CdSe nanocrystals infiltrated into the brush network exhibiting some degree of phase separation in the plane of the film (small black circles), and caped with an aluminum cathode (Nano Lett. 2005, 5, 1653.)

A more ambitious challenge in surface science is the design of smart surfaces with dynamically controllable properties (Lahann et al. 2003). Such surfaces have characteristics that can be changed or tuned in an accurate and predictable manner by using an external stimulus. Recently, Huck and coworkers have shown that wetting properties of surfaces modified with cationic polyelectrolyte brushes strongly depend on the nature of the counter ion (Fig. 12). Coordination of polyelectrolyte brushes bearing quaternary ammonium groups (QA+) with sulfate anions resulted in highly hydrophilic surfaces, (Moya et al. 2005) whereas coordination of similar brushes with ClO4¯ rendered the surface hydrophobic (Azzaroni et al. 2005). Recent research has focused on the Cu(I)-catalyzed, highly specific, and efficient formation of 1,2,3-triazoles via the 1,3-dipolar cycloaddition of azides and terminal alkynes (“click” chemistry) (Feldman et al. 2004). This methodology has been used to modify surfaces of solid metals and cells, because the reaction provides high yields and stereospecificity and proceeds under mild conditions (Tornøe et al. 2002; Lewis et al. 2002), Click chemistry also has been used for functionalizing polymers in solution (Sumerlin et al. 2005; Gao et al. 2005). Research in nanobiotechnology and

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Fig. 12 Top image: PMETAC brushes. Bottom image: Change in the wetting characteristics of PMETAC brushes (height, h ~20 nm) after exchanging the two contrasting counterions, TFSI (a) and polyphosphate (PP) (b). (c) Representation of θA as a function of counter ion (PP and TFSI). The plot depicts the reversible behavior of PMETAC brushes over repeated cycles of TFSI and PP counter ion exchange. On the right the chemical structures of both counter ions are represented (Angew. Chem. Int. Ed. 2005, 44, 4578.)

biomedical sciences often involves the manipulation of interfaces between man-made surfaces and biomolecules (and cells), which generally requires the construction of surfaces that present chemically active functional groups from non-biofouling supporting materials. Choi and coworkers (Lee et al. 2007) used “click” chemistry to couple azide groups at the terminal of the non-biofouling polymeric film of poly(oligoethylene glycol methacrylate) with incoming molecules of interest containing terminal acetylenes (Fig. 13). As a model for bioconjugation, biotin was immobilized onto the poly(oligoethylene glycol methacrylate) film via click chemistry, and biospecific recognition of streptavidin was demonstrated.

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Fig. 13 Schematic description of the attachment of biotin to polymer brush via click chemistry [acetylene group-containing biotin compound: biotin-PEO-LC-N-pentynoate (1)]

Polymer Nanocomposites for Engineering Applications Polymer nanocomposites, which comprise of additives/fillers and polymer matrices, are considered to be an important group of relatively inexpensive materials for many engineering applications. The polymer matrices may refer to all type of polymers including thermoplastics, thermosets, elastomers, and even polymer blends. Two or more materials are usually combined to produce composites which possess properties that are unique and cannot be obtained by each material alone. For example, high-modulus carbon fibers or silica particles are added into a polymer to produce reinforced polymer composites that exhibit significantly enhanced mechanical properties including strength, modulus, and fracture toughness. Therefore, due to their unique and superior properties as well as ease of production at low cost, polymer-based composites are currently important engineering materials with many applications which include high-performance composites even used in aerospace application, filled elastomers for damping, electrical insulators, thermal conductors, and other special applications in which a particular superior property is needed. Special materials with extraordinary properties are chosen to create composites with desired properties; for example, high-modulus but brittle carbon fibers are added to low-modulus polymers to create a stiff and lightweight composite with a

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Fig. 14 Schematic of nanofillers (Schadler 2004)

reasonable degree of toughness. In recent years, although the highest level of optimization of composite properties with traditional micrometer-scaled fillers has been reached, a large bundle of opportunities has been opened to overcome the limitations of traditional polymer composites by using newly available nanometer-scaled fillers – polymer nanocomposites in which the filler is 1,000) and thus extremely large surface area, their nature of poor

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dispersion in a polymer matrix is rather different from that of other conventional fillers, such as spherical particles and carbon fibers. Therefore, the agglomeration of CNTs in a polymer matrix can be considered as the main reason for the reduced mechanical, thermal, and electrical properties of their nanocomposites as compared with theoretical predictions based on individual CNTs. The critical challenge is, therefore, how to incorporate individual CNTs, or at least relatively thin CNT bundles or disentangled CNTs, into a polymer matrix. In other words, dispersion of CNTs not only is a geometrical problem due to the length and size of the CNTs but also relates to a method for how to separate individual CNTs from CNT agglomerates and stabilize them in a polymer matrix to avoid secondary agglomeration (Ma et al. 2010). In addition, the most suitable processing conditions are required for the efficient transfer of either mechanical load or electrical charge among individual carbon nanotubes in a polymer matrix towards a successful fabrication of CNT/polymer composites (Sahoo et al. 2010). As mentioned above, the agglomeration of CNTs in a polymer matrix and the poor interfacial interaction between the CNTs and the polymer molecules are the most critical issues in the fabrication of CNT/polymer composites. Fortunately, there are several possibilities to improve the dispersion of CNTs in polymer matrices such as solution mixing, melt blending, and in situ polymerization methods. Moreover, several methods are also available to enhance the interaction between the CNTs and the polymer molecules. Especially, the surface modification of CNTs is an effective way to prevent carbon nanotube aggregation by improving their chemical compatibility with the polymer matrixes, which helps CNTs to disperse better and stabilize within a polymer matrix. There are mainly two approaches for surface modification of CNTs, namely, physical modification (noncovalent functionalization) and chemical modification (covalent functionalization). For example, CNTs are used in the development of the stiff and lightweight polymer nanocomposites. CNT/polymer composites show considerably improved mechanical properties even at a low CNT content. For example, with an addition of 0.5 wt% MWCNTs, the tensile strength and modulus for high-density polyethylene nanocomposite films remarkably increased by 30 % and 20 %, respectively (Zhang et al. 2006b). CNTs can also be used as a nucleating agent for crystallization of polymers. Several groups have studied the crystallization of polypropylene in the presence of CNTs (Valentini et al. 2003; Manchado et al. 2005; Bhattacharyya et al. 2003). Assouline et al. (2003) studied the non-isothermal crystallization of MWCNT/isotactic polypropylene (iPP) composites. The crystallization behavior of MWCNT/iPP composite was significantly different from that of the neat iPP. With an addition of 1.0 wt% MWCNTs into iPP, the crystallization rate was increased with evidence of fibril crystal growth rather than spherulite growth. Many research groups have observed the improved thermal stability in CNT/polymer composites. For example, Kashiwagi et al. reported that the addition of MWCNTs into polypropylene enhanced the thermal stability of PP both in nitrogen and in air. Besides, the MWCNTs could significantly reduce the heat release rate of PP. Generally, the thermal stability of the CNT/polymer composites increases due to the higher

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thermal conductivity of MWCNTs that facilitates heat dissipation within the composites (Huxtable et al. 2003). The results show a great potential for the use of CNTs as a flame retardant for polymer materials. Therefore, in the following sections, thermal and mechanical properties and applications of CNT/polymer composites will be covered.

Thermal and Rheological Properties of CNTs Base Polymer Composites The glass transition temperature (Tg) is a measure of the thermal energy required to allow polymer motion involving 10–15 monomeric units and corresponds to the softening of a polymer. Park et al. (2002) reported that Tg did not change for their in situ polymerized SWCNT/polyimide composites. The SWCNT/PMMA composites produced by the coagulation method have the same Tg over a wide range of nanotube loadings (Du et al. 2004). Therefore, it can be concluded that the addition of CNTs does not significantly change the glass transition temperature in CNT/polymer composites, because in the absence of strong interfacial bonds and at low nanotube loadings, the majority of polymer molecules are locally constrained only by other polymer molecules but not by CNTs. On the other hand, in larger districts, carbon nanotubes do obstruct the motion of polymer molecules as measured by rheology. Rheological (or dynamic mechanical) measurements at low frequencies probe the longest relaxation time of a polymer which corresponds to the time required for an entire polymer molecule to change its conformation. Du et al. (2004) found that, although it has little effect on polymer motion at the length scales comparable to or less than an entanglement length, the presence of CNTs has a substantial influence at large length scales corresponding to an entire polymer chain. The storage modulus, G’, at low frequencies becomes almost independent of frequency as CNT loading increases. This shows a transition from a liquid-like behavior (which has short relaxation times) to a solid-like behavior (in which the relaxation times will be infinite) with increasing CNT loading. By plotting G’ versus CNT loading and fitting with a power law function, they reported that the rheological threshold of these nanocomposites was 0.12 wt%. This rheological threshold could be attributed to a hydrodynamic CNT network that impedes the large-scale motion of polymer molecules. A similar phenomenon has previously been observed in nanoclay/polymer composites by Krishnamoorti and Giannelis (1997). They reported that a network of nanoscale fillers restrains polymer relaxations, leading to a solid-like or nonterminal rheological behavior. Therefore, any factor that changes the morphology of the CNT network will influence the low-frequency rheological properties of their nanocomposites. The factors influencing the polymer chain mobility are the aspect ratio of CNTs, dispersion and alignment of CNTs in the polymer matrix, and the molecular weight of the polymer matrix. Du et al. (2004) reported that higher aspect ratio, better dispersion and less alignment of the CNTs, and longer polymer chains would result in more restraint on the mobility of the polymer chains, i.e., the onset of a solid-like behavior

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Fig. 15 Optical micrographs using cross-polarizers of (a) pure PP and (b) a 0.8 wt% SWCNT/PP composite (Bhattacharyya et al. 2003)

occurs at lower nanotube contents. In addition to these factors, the content, size, and interfacial properties of CNTs are expected to influence rheological properties of CNT/polymer composites. For example, at a fixed loading, nanotubes with smaller nanotube diameters and larger aspect ratios will produce a network with smaller mesh size and larger surface area/volume, which might restrain polymer motion to a greater extent (Du et al. 2004). Experimental results support this hypothesis. Lozano et al. (2001) observed a rheological threshold of 10–20 wt% in carbon nanofiber/ polypropylene composites in which the diameter of the carbon nanofiber is 150 nm. The rheological threshold is 1.5 wt% in MWCNT/polycarbonate composites and only 0.12 wt% for the SWCNT/PMMA system (Du et al. 2004). Even if these three systems have different polymer matrices as well as their states of dispersion are unclear, the diameters among carbon nanofibers, MWCNT, and SWCNT differ by orders of magnitude. It can be concluded that if the diameter of filler decreases, the filler loading required for a solid-like behavior increases significantly. The constraints imposed by CNTs on polymer matrices in nanocomposites are also evident in the polymer crystallization behavior. Bhattacharyya et al. (2003) studied crystallization in 0.8 wt% SWCNT/PP composites using optical microscopy (with cross-polars) and differential scanning calorimetry (DSC). From Fig. 15, the spherulite size in PP is much larger than that in SWCNT/PP composites. The authors also reported that upon cooling, the SWCNT/PP composites began their crystallization at the temperature which was about 11  C higher than that for PP’s crystallization, suggesting that nanotubes acted as nucleating sites for PP crystallization. They also observed that both melting and crystallization peaks in the nanocomposite are narrower than those in neat PP. Therefore, they proposed that higher thermal conductivity of the CNT as compared to that of the polymer at least in part should be responsible for the sharper but narrower crystallization and melting peaks, as heat would be more evenly distributed in the nanocomposite samples containing CNTs.

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Mechanical Properties CNTs Base Polymer Composites CNTs exhibit excellent mechanical properties with Young’s modulus as high as 1.2 TPa and tensile strength of 50–200 GPa (Qian et al. 2002). The combination of these exceptional mechanical properties along with the low density, high aspect ratio, and high surface area makes CNTs an ideal candidate of reinforcing fillers for fabrication of stiff and lightweight nanocomposites. Both SWCNTs and MWCNTs have been utilized for reinforcing thermoplastic polymers, such as polyethylene, polypropylene, polystyrene, nylon, and polycarbonate, as well as thermosetting polymers, including epoxy, polyurethane, and phenol–formaldehyde resins. Generally the CNT-reinforced nanocomposites can be considered as particulate composites or short fiber composites with the filler dimensions on the nanometer scale and a high aspect ratio. Therefore, the mechanics of CNT/polymer composites is governed by that of particulate composites or short fiber composites. On the other hand, unlike the macroscopic particulate composites, mechanical properties of CNT/polymer composites mainly depend on the dispersion state of nanofillers, apart from the properties of filler and matrix themselves. In addition to dispersion, there are other important factors that determine an effective reinforcement of CNTs in nanocomposites: they include a high aspect ratio, alignment, and interfacial interactions between CNTs and polymer matrix. The aspect ratio must be sufficiently large to maximize the load transfer between CNTs and the matrix and, thus, to achieve enhanced mechanical properties. For example, polystyrene nanocomposites reinforced with well-dispersed 1.0 wt% CNTs of a high aspect ratio had more than 35 % and 25 % increases in elastic modulus and tensile strength, respectively (Qian et al. 2000). Similar promising results have also been reported, (Coleman et al. 2006; Jiang et al. 2007) but other reports demonstrated only modest improvements in modulus and strength. For example, the impact resistance and fracture toughness of the CNT/epoxy composites containing CNTs of a larger aspect ratio were improved much better than those of the CNT/epoxy composites containing CNTs of a smaller aspect ratio (Hernández-Pe´rez et al. 2008). However, the corresponding tensile modulus and strength showed very limited improvements of less than 5.0 %, probably due to weak bonds between the CNTs and the matrix molecules as well as agglomeration of CNTs. In reality, the dispersion is known as the foremost important issue in producing CNT/polymer composites. Many different techniques, including the functionalization of CNTs and processing of CNT/polymer composites, have been employed for CNT dispersion, as discussed in sections “Other Types of Nano-Biocomposites.” A good dispersion not only makes more filler surface area available for bonding with a polymer matrix but also prevents the aggregated filler from acting as a stress concentrator that is detrimental to mechanical performance of nanocomposites (Liu and Wagner 2005). However, to obtain a uniform CNT dispersion in nanocomposites, some parameters, such as CNT content in nanocomposites, length and entanglement of CNTs, as well as viscosity of matrix, are still needed to optimize. There were many reports (Ma et al. 2007, 2008b, 2009) showing that there is a critical CNT content in the matrix below which the strengthening effect for CNT/polymer composites

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increases with increasing CNT content. Above this critical CNT content, however, the mechanical strengths of CNT/polymer composites decrease, and in some cases, they decrease below those of the neat matrix materials. These observations can be attributed to (i) the problems associated with uniform dispersion of CNTs at high CNT contents and (ii) lack of polymerization reactions that are adversely affected by the high CNT content for an in situ process. The latter effect becomes more pronounced when functionalized CNTs are employed to produce CNT/polymer composites. To a large extent, the technique employed for CNT dispersion can influence the mechanical properties of CNT/polymer composites. It should be noted that the definition of a dispersion state of CNTs in a polymer matrix is totally dependent on the magnification or scale used for the analysis. According to the study by Li et al. (2007b) using the term uniform or good dispersion to evaluate the CNT dispersion without any distinctive description may simply be misleading or inaccurate. This is because, for the conventional composites, uniform or good dispersion generally refers even distribution of fillers in a matrix medium without aggregation. However, for CNT/polymer composites, dispersion has two major aspects: (i) disentanglement of CNT bundles or agglomerates, which is referred as the nanoscale dispersion, and (ii) uniform distribution of individual CNTs or their agglomerates throughout the nanocomposites, which is a micro- and macroscale dispersion. From geometric consideration, the difference between random orientation and alignment of CNTs can result in significant changes in various properties of nanocomposites. The storage moduli of the polystyrene composite films containing random and oriented CNTs were 10 % and 49 % higher than the unreinforced bulk polymer, respectively (Thostenson and Chou 2002). The alignment can be regarded as a special case of CNT dispersion. A few techniques, including mechanical stretching (Jin et al. 1998), melt-spinning (Fornes et al. 2006), dielectrophoresis, and application of an electrical or magnetic field (Park et al. 2006; Steinert and Dean 2009), have been employed during the composite fabrication to align CNTs in a polymer matrix. The degree of CNT alignment in the composite can be governed by two factors: (i) aspect ratio of CNTs and (ii) CNT content. A smaller diameter of CNT can enhance the degree of CNT alignment due to the greater extensional flow, and a higher CNT content decreases their alignment because of the CNT agglomeration and restrictions in motion from neighboring CNTs (Desai and Haque 2005). While alignment is necessary to maximize the strength and modulus, it is not always beneficial because the aligned nanocomposites have very anisotropic mechanical properties, i.e., the mechanical strengths along the alignment direction can be enhanced, whereas these properties are sacrificed along the direction perpendicular to this orientation. In addition, the interfacial properties between CNTs and matrix molecules play an essential role for mechanical properties of such nanocomposites. A strong interfacial adhesion corresponds to high mechanical properties of nanocomposites through enhanced load transfer from matrix to CNT. Chemical and physical functionalizations of CNTs have proven to enhance the interfacial adhesion. Table 4 summarizes the effects of CNT functionalization on the mechanical properties of CNT/polymer composites made from thermoplastic polymers.

Ultrasonication Ultrasonication Ultrasonication

Polystyrene (PS) Poly(vinyl alcohol) (PVA) Poly(methyl methacrylate) (PMMA)

Butyl attachment Polymer grafting Polymer grafting

Maleic anhydride and amine treatment Undecyl radicals attachment

Type of functionalization of CNTs Diamine treatment

0.25 2.5 0.1

1.5

1.5

CNT content (wt %) 1.0

8.3 (25) 35 (40) 57 (104)

55 (84)

22 (75)

2.1 (50) 4.8 (17) 2.7 (86)

10 (13)

17 (33)

Improvement in mechanical propertiesa Modulus Strength (%) (%) 6.1 (42) 5.3 (18)

Koval’chuk et al. (2008) Byrne et al. (2008) Paiva et al. (2004) Kim and Jo (2009)

Yang et al. (2007)

References Meng et al. (2008)

The data are the percentage improvement in the mechanical properties of nanocomposites with pristine CNTs, while the data in the brackets for those with functionalized CNTs compared to the neat polymer

a

Ultrasonication

Fabrication process Twin screw extruder Shear mixing

Polypropylene (PP)

Polyethylene (PE)

Matrix Nylon (polyamide, PA)

Table 4 Effect of CNT functionalization on mechanical properties of CNT/polymer composites

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These results indicate clearly that functionalization of CNTs can greatly enhance the modulus, strength, as well as fracture resistance of CNT/polymer composites.

Polymer Nanocomposites for Packaging Applications Introduction Due to their superior physical, thermal, and mechanical properties, plastics have developed to be the most important class of packaging materials. In Europe, packaging is the largest market for plastics accounting for nearly half of all plastics processed. The main advantages of plastics as compared with other packaging materials are that they are lightweight and low cost and have good processability, high transparency and clarity, as well as good barrier properties with respect to water vapor, gases, and fats. It is estimated that global plastic packaging materials and products market will reach US$262.6 billion by 2015. The huge use of plastics, however, brings in more and more environmental problems; in particular, plastic packaging consists largely of single-use, disposable items. Additionally, plastics are generally made from petroleum, which resources are finite and fast depleting. So, in order to reduce the negative impact to our environment, it is necessary to turn the packaging industry green by using more environment-friendly products. Green packaging is broadly defined as packaging that is designed to lessen environmental impact throughout the whole life cycle; while maintaining accountable performance, it includes packaging with recycled content, reusable packaging, and degradable packaging. Figure 16 shows the green packaging demand by type (The Freedonia Group & Inc 2011) and Fig. 17 shows the green packaging demand by market (The Freedonia Group & Inc 2011).

Fig. 16 Green packaging demand by type

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Fig. 17 Green packaging demand by market

Polymer Nanocomposites for Packaging Applications Polymer nanocomposites, a new class of materials, have shown great potential to enhance the physical, thermal, mechanical, and processing characteristics at low filler loading. In the packaging industry, the use of polymer nanocomposites will not only increase the properties of the packaging polymer materials but also offer additional functions to the packaging. Among the polymer nanocomposites for packaging, nanoclay is one of the nanofiller mostly used and studied. The nanoclay usually not only increases thermal and mechanical properties but also increases barrier properties to moisture, solvents, chemical vapors, gases such as O2, and flavors. Basilia et al. synthesized recycled polyethylene terephthalate (RPET)/organic modified nanoclay (OMMT) nanocomposites by direct melt intercalation method. The mechanical properties increased greatly with the nanofiller fraction as shown in Fig. 18 (Basilia et al. 2002). Hamzehlou and Katbab (2007) also found that modified nanoclay can increase both tensile strength and tensile modulus of the recycled PET (RPET), and permeability of the thin films prepared from RPET/nanocomposites to oxygen gas was also reduced significantly compared with both virgin and neat RPET (Table 5). Emamifar et al. (2011) prepared low-density polyethylene (LDPE) films containing Ag and ZnO nanoparticles by melt mixing in a twin screw extruder. The presence of the nanoparticles increases the antimicrobial activity of L. plantarum; reduced numbers of L. plantarum were observed (p < 0.05) in nanocomposite packages of orange juice containing nanosilver and nano-ZnO. Alamri and Low (2012) reported on water absorption behavior of nanosilicon carbide-filled recycled cellulose fiber (RCF)-reinforced epoxy econanocomposites. Water absorption was found to decrease gradually due to the presence of n-SiC as shown in Fig. 19. It was believed that the high aspect ratio nature of the nanofiller enhances the barrier properties of the materials by creating tortuous pathways for water molecules to diffuse into the composites. Maximum

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Fig. 18 Mechanical properties of RPET-PHIL (OMMT) system at various clay loadings

Table 5 O2 permeation for recycled PET and its nanocomposites Sample FPET Processed PET PET + 1 wt% clay PET + 3 wt% clay PET + 5 wt% clay RPET Processed RPET RPET + 1 wt% clay RPET + 3 wt% clay RPET + 5 wt% clay

O2 permeation (cm3 m
2 day
1 bar
1) 25.33 28.14 19.60 21.70 7.890 32.40 36.80 21.80 22.04 8.540

water uptake of RCF/epoxy composites filled with 5 wt% n-SiC decreases by 47.5 % compared to unfilled RCF/epoxy composites. Biopolymers are promising materials for green packaging applications. Biopolymers are polymers derived from renewable biomass sources, such as vegetable fats and oils, cornstarch, pea starch, or microbiota. Some biopolymers are designed to be biodegradable that are capable of being decomposed by bacteria or other living organisms in either anaerobic or aerobic environments. Most biodegradable polymers are actually designed to be compostable, which means it degrades to carbon dioxide, water, inorganic compounds, and biomass at a rate consistent with known industrial composting conditions. Typical biopolymers from renewable resources and with biodegradable property are polylactic acid or polylactide (PLA), starch, and polyhydroxybutyrate (PHB).

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Fig. 19 Water absorption curves of n-SiC-filled RCF/epoxy eco-nanocomposites

Fig. 20 Illustration of the grafting of PLA onto 3-aminopropyltriethoxysilane (APS)functionalized silica surface

There are many reports that nanofillers increase barrier, thermal, and mechanical properties of biopolymers which are usually have poor mechanical properties, high hydrophilicity, and poor processability (Tang and Alavi 2012; Dean et al. 2008; Park et al. 2004). Wu et al. (2013) reported grafting polymerization of polylactic acid (PLA) on the surface of nano-SiO2 (Fig. 20) and studied properties of PLA/ PLA-grafted SiO2 nanocomposites. It was found that PLA-grafted SiO2 can accelerate the cold crystallization rate and increase the degree of crystallinity of PLA. Shear rheology testing indicated that PLA/PLA-grafted SiO2 nanocomposites have the typical homopolymer-like terminal behavior at low-frequency range even at a content of PLA-grafted SiO2 of 5 wt%. Li and Sun (2011) prepared surface-grafted MgO (g-MgO) by in situ melt polycondensation of lactic acid and surface-hydroxylated MgO nanoparticles and

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Fig. 21 Mechanical properties of PCL/nanoclay nanocomposites as a function of nanoclay fraction

then prepared poly(lactic acid) (PLA) nanocomposites through thermal compounding of PLA and g-MgO/MgO nanoparticles. It was found that PLA/g-MgO nanocomposites exhibited higher tensile strength than neat PLA and PLA/g-MgO nanocomposites with g-MgO fraction lower than 0.05 % show increased thermal stability. Polymer foams, a type of lightweight materials, are very important packaging materials; it provides protection for the products with controllable performance. Generally, uniformity of cell sizes, surface quality, thermal and dimensional stability, and mechanical properties including strength and shock absorption are among the important properties determining the applications of the polymer foams. Using nanofillers into polymer foam will enhance significantly the performance of the foams as packaging materials. Hu et al. (Liu et al. 2010) prepared nanoclay-filled biodegradable poly (e-caprolactone) (PCL) nanocomposites foam with chemical foaming agent. It was found that Young’s modulus of the nanocomposites increased with increasing clay fraction, and elongation at break of the nanocomposites increased with increasing clay fraction at low nanoclay fraction, but decreased at high nanoclay fraction higher than 10 wt% due to the agglomeration of the nanoclays (Fig. 21). Istrate and Chen (2012) also studied nanoclay-filled poly(e-caprolactone) (PCL) foams with a blowing agent. The nanoclay was firstly treated with chemical blowing agents, and the polymer/treated nanoclay nanocomposites were prepared by solution mixing; the pores were foamed by thermal degradation of the blowing agent. The blowing agent played dual roles in this approach: formation of bubbles and facilitation of clay exfoliation. With nanoclay fraction as low as

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2.2 and 2.9 wt%, the compressive modulus and stress at 10 % strain of the porous polymer were found to increase by up to 152 % and 177 %, respectively. Strain improved by up to 69 %, while thermal degradation temperature was also greatly increased. Lee et al. (2008) prepared tapioca starch–poly(lactic acid) nanocomposite foams with four different types of nanoclays (Cloisite 10A, Cloisite 25A, Cloisite 93A, and Cloisite 15A) by melt mixing with extrusion. It was found that the extent of intercalation depended greatly on the nanoclay types, and accordingly, the glass transition temperatures, melting temperatures, and unit density, bulk spring index, bulk compressibility, Young’s modulus, water absorption index, and water solubility index were all influenced significantly with the types of the nanoclays.

Summary With the deteriorating environment and fast-depleting resources, traditional costeffective packaging materials are no longer a guaranteed competitive advantage. Polymer nanocomposites with much improved physical, thermal, mechanical properties and value-added functions will be among the main packaging materials in the future. Bio-based polymers, which are derived from renewable resources and biodegradable which possess the ability to degrade into small molecules upon bioactive environment exposure, are a promising polymer matrix for polymer nanocomposites in green packaging.

Polymer Nanocomposites for Automotive Applications Application of Polymer Materials in Automotive Industries Because they are lightweight and due to their property tailorability, design flexibility, and processability, polymers and polymer composites have been widely used in automotive industry to replace some heavy metallic materials. Table 6 (Szeteiová) and Fig. 22 (Polymers in the automotive industry polymotive 2005) show the typical polymers used in a car. The cars with more components made from polymer composites are lighter. Figure 23 (APME 1999) shows the typical weight saving that can be made in various car parts when using plastics to substitute conventional materials. Cars with more polymers are lighter; in turn, chassis, drive trains, and transmission parts can all be made lighter as a result of having to support a lower overall car weight. The benefits from the weight reduction are the improved efficacy of fuel and less greenhouse gases release. It has been estimated that for every 10 % reduction in a vehicle’s total weight, fuel consumption reduces by 7 %. This also means that for every kilogram of weight reduced in a vehicle, there is about 20 kg of carbon dioxide reduction (Frost & Sullivan 2005). It is estimated that the polymers will account for 18 % of average vehicle weight by 2020, up from 14 % in 2000 as shown in Fig. 24 (A.T. Kearney Inc 2012).

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Table 6 Plastics used in a typical car Component Bumpers Seating Dashboard Fuel systems Body (incl. panels) Under-bonnet components Interior trim Electrical components Exterior trim Lighting Upholstery Liquid reservoirs Total

Main types of plastics PS, ABS, PC/PBT PUR, PP, PVC, ABS, PA PP, ABS, SMA, PPE, PC HDPE, POM, PA, PP, PBT PP, PPE, UP PA, PP, PBT PP, ABS, PET, POM, PVC PP, PE, PBT, PA, PVC ABS, PA, PBT, POM, ASA, PP PC, PBT, ABS, PMMA, UP PVC, PUR, PP, PE PP, PE, PA

Weight in av. car (kg) 10.0 13.0 7.0 6.0 6.0 9.0 20.0 7.0 4.0 5.0 8.0 1.0 105.0

The plastic components have been used from simple interior in the early stage to interior, exterior, powertrain, chassis, engines, electrical systems, and fuel systems in the present. The enormous growth of polymer components in automotives accompanies the demand for high-performance polymer materials to meet the requirements of the components; one of the advanced materials developed to apply in automotive sectors is polymer nanocomposites, which was firstly used by Toyota Motor Co. in 1991 to produce timing belt covers as a part of the engine for their Toyota Camry cars (Polymer nanocomposites drive opportunities in the automotive sector). Polymer nanocomposites, which are among the most widely watched technology areas in the plastics arena, are a new class of materials containing nanoparticles or nanofillers dispersed in the polymer matrix. The commonly used nanofillers for polymer reinforcement include nanoclays, carbon nanotubes, carbon nanofibers, nanosilica, nano-oxides, and polyhedral oligomeric silsesquioxanes; they usually can improve a wide range of properties of polymers at low filler fraction owing to their size and shape.

Approaches of Fabricating Polymer Nanocomposites There are several ways to fabricate polymer nanocomposites; the commonly used are in situ polymerization, solution mixing, and melt compounding. In situ polymerization of polymer nanocomposites includes emulsion, emulsifier-free emulsion, miniemulsion, and dispersion polymerization; nanofillers are usually added directly to the liquid monomer, and a polymerization then starts either thermally or chemically. Significant property improvement can be achieved due to good interaction between the nanofiller and polymer matrix. Figure 25 (Liang et al. 2008) shows the organic-modified nanoclay-filled nylon

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Fig. 22 Polymer breakdown for the BMW 1 series

6 nanocomposites formed by in situ polymerization, Fig. 26 (Liang et al. 2008) shows the barrier property, and Table 7 (Liang et al. 2008) tabulates the mechanical properties and heat distortion temperature (HDT) of the nanocomposites as a function of the filler fraction. The solution mixing to prepare the polymer nanocomposites may consist of several steps: (a) dissolving polymer matrix into an appropriate solvent to make a solution, (b) dispersing the nanofiller into the solution to make a suspension, and (c) casting the new mixture to evaporate the solvent to produce final nanocomposites. A method through solution blending and then compression molding is also usually used by obtaining the mixture powders after procedure (b) and

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Fig. 23 Weight reduction of car using plastics

Fig. 24 Polymers will account for 18 % of average vehicle weight by 2020

compression molding into the mixture powders into the desired panel or prototype. One of the advantages for the solution mixing is the molecular level of mixing. Figure 27 (Bhattacharya and Chaudhari 2013) shows the mechanical properties of nanosilica-filled polyamide composites prepared by formic acid mixing.

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Fig. 25 Nylon 6 nanocomposite formed through in situ polymerization with 12-aminododecanoic acid modified montmorillonite (ADA-MONT, Nanomer ® I.24TL)

Fig. 26 Oxygen transmission rates (OTR) of nylon 6 nanocomposite from in situ polymerization (65 % RH)

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Table 7 Mechanical and thermal properties of nylon 6 nanocomposites Nanoclay ADA-MONT (wt%) 0% 2%

Flexural modulus (MPa) 2,836 4,326 (53 %)

Tensile modulus (MPa) 2,961 4,403 (49 %)

4%

4,578 (61 %)

4,897 (65 %)

6%

5,388 (90 %)

5,875 (98 %)

8%

6,127 (116 %)

6,370 (115 %)

HDT ( C) 56 125 (123 %) 131 (134 %) 136 (143 %) 154 (175 %)

Fig. 27 Comparative tensile properties of nanocomposite films

The composite film exhibits an increased tensile strength with an increase in silica content. However, composite film containing 1.0 wt% nanosilica exhibits much lower tensile properties as compared to the neat polymer due to poor particle distribution. In the melt compounding process, nanofillers are mixed with the polymer matrix at the molten state of polymers in the absence of any solvents. The dispersion of the nanofillers depends largely on the thermodynamic interaction between the polymer chains and the nanofillers. Comparing to other techniques, melt compounding is simple, versatile, and suitable for mass production; the resultant nanocomposites usually have high purity as the process is essentially free of contaminations. Figures 28 and 29 show the mechanical and thermal properties of the polypropylene (PP) nanocomposites as a function of graphene fraction (Pingan et al. 2011). The graphene used in the study was firstly coated with polypropylene latex and then melt-blended with PP matrix.

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Fig. 28 Typical stress–strain curves of PP/PP latex as a function of PP latex loading level with detailed data presented in the figure

Fig. 29 Thermal conductivity of PP and its nanocomposites as a function of grapheme loading

The graphene sheets were well dispersed in the PP matrix and considerable enhancement of the mechanical and electrical properties of PP was achieved by incorporating very low loading of graphene. By addition of only 0.42 vol% of grapheme, about 75 % increase in yield strength and 74 % increase in the Young’s modulus of PP were achieved.

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Toughened Polymer Nanocomposites Prepared by Melt Compounding Tensile strength and impact strength of materials are among the most important properties for the materials applied in automotives. There are a few reports that show nanoclays improve both tensile strength and impact strength for the nanocomposites prepared by melt compounding (Liu and Wu 2002). However, the increase of the impact strength is usually only achieved at low nanoclay fraction; the further increase in the nanoclay fraction results in decrease in impact strength as shown in Fig. 30 (Kelnar et al. 2005). Nanoclays usually can increase the tensile strength and modulus of the polymers due to their rigid inorganic nature, nanoscale dimension, and huge adequate interfacial contact area between the nanoclay and the polymer matrix. The increment of impact strength at low nanoclay fraction is perhaps caused by formation of submicron voids within the intra-gallery of clay layers under impact loading, which prevents crack propagation. On the other hand, nanoclays, particularly exfoliated nanoclays, actually act as stiff fillers which hinder the mobility of the surrounding chains of polymers and thus reduce the impact strength of polymers. Moreover, the size range of individual clay layers is in nanometer scale, which is perhaps too small to provide toughening via mechanisms like crack bridging. The common approach to improve the impact strength of polymers is to add elastomers with long molecular chains. The cavitation of elastomer particles followed by plastic deformation of the matrix is usually the main toughening mechanism in the polymer composites. Nevertheless, the soft nature of elastomers generally decreases the tensile strength of polymers. In order to obtain polymer composites with improved impact strength without sacrificing mechanical strength, ternary polymer nanocomposites with the presence of nanofillers such as nanoclay and elastomer have been designed. It has been found that the mixing sequence of the polymer, elastomer, and nanofiller have great effect on the properties of the

Fig. 30 Tensile strength and impact strength of nanocomposites in dependence on the clay content (■) PA6 matrix, (☐) PA6/EPR-MA 95/5 matrix. PA6: polyamide 6, EPR-MA: maleated (0.6 %) ethylene–propylene elastomer

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nanocomposites, particularly the impact strength of the composites. It has been found that for polyamide 66 or nylon 66 ternary nanocomposite filled with nanoclay of Cloisite ® 30B and elastomer of styrene–ethylene/butylene–styrene tri-block copolymer grafted with 1.84 wt% of maleic anhydride (SEBS-g-MA) (Dasari et al. 2005), the location of the nanoclay in the nanocomposites is different with different mixing sequences based on microstructure study. The two-step mixing–blending nylon 66 and nanoclay initially and later mixing with SEBS-g-MA is the preferred blending sequence to maximize the notched impact strength due to the maximum amount of the exfoliated nanoclay in the nylon 66 matrix. The presence of nanoclay in SEBS-g-MA elastomer phase reduces the cavitation ability of SEBS-g-MA particles. For the polyamide 6 nanocomposites filled with nanoclay of Cloisite ® 93A and elastomer of maleic anhydride-grafted-poly(ethylene–octene) (POE-g-MA), it has been found that the one-step compounding of PA 6 with the nanoclay and the elastomer shows the synergetic effect of the two types of the fillers in improving the tensile modulus and impact strength (Yu 2012; Fig. 31). As compared to PA 6, the impact strength of the nanocomposite is remarkably increased by 96.3 % in one-step mixing, while the impact strength remains the same in two-step mixing–blending of PA 6 with elastomer first and then blending with nanoclay. The nanoclay used in the study has a higher affinity to the PA 6 than to the POE-g-MA; the nanoclays disperse mainly in the PA 6 phase to enhance the tensile property in the one-step process. On the basis of the study on the extrusion sequence, the properties of the ternary composites are further optimized by investigating the filler fraction. Figure 32 (Yu 2012) shows the tensile and impact properties for PA 6 composites with optimized filler fractions. The results are obtained with drying of the test pieces in the oven before testing to eliminate the effect of moisture absorbed on the mechanical properties of the composites. With the optimization in concentration of the fillers, PA 6 composites with much improved impact strength have been obtained; the tensile strength and modulus, at the same time, are not sacrificed. With 7.5 % of nanoclay and 10 % of toughening agent, the impact strength is increased by 110.0 %, modulus is increased by 9.2 %, and tensile strength is similar to that of neat PA 6.

Green Composites for Automotive Applications With the drive of lightweight materials with super performance, improved fuel efficiency, and less CO2 emissions in automotive industry, the usage of polymer nanocomposites in a car will continue to grow. Recently, there is increasing interest about green composites with growing environmental awareness. Green composites are composites that are designed to reduce environmental burden through their life cycles. The examples of green composites are natural fiber-reinforced polymer composites and biopolymer composites. Biopolymer composites are composites in which the polymer matrix is bio-based or biodegradable or bio-based and biodegradable. The research on application of green composites in automotives is

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Fig. 31 Optimization in ternary composite processing (c ¼ nanoclay, t ¼ POE-g-MA)

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Fig. 32 PA 6 composites with much improved impact strength (T ¼ POE-g-MA)

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Fig. 33 Use of natural fibers for composites in the German automotive industry from 1999 to 2005

as early as 1990s, the first car component made from natural fiber composites is believed to be a door quarter panels made of a LoPreFin PP/PET/natural fiber composite appeared on the 1999 Saab 9S. Figure 12 shows use of natural fibers for composites in the German automotive industry from 1999 to 2005 (Karus et al. 2006; Fig. 33). Both natural fiber and biopolymers generally are hydrophilic, easily absorb moisture, and have relatively poor processability and low mechanical properties. These disadvantages restrict their application, particularly as exterior automotive components. Research and development are required to overcome these obstacles to allow more green composites which are eco-friendly, lightweight, and costeffective to be applied in automotive sector. One of the approaches to modify the properties of natural fiber-reinforced polymers and biopolymers is incorporated with nanofillers. Chieng et al. (2012) reported that with the addition of only 0.3 wt% of graphene nanoplatelet, the tensile strength and elongation at break of poly(lactic acid)/epoxidized palm oil blend increased by 26.5 % and 60.6 %, respectively. Nemati et al. (2013)studied mechanical properties of wood plastic composites made from wood flour, recycled polystyrene, and nanoclay. The obtained results indicated that the tensile strength and flexural strength were increased by raising nanoclay content in the composites as shown in Fig. 34. Guigo et al. (2009) prepared lignin and natural fiber nanocomposites filled with sepiolite or organically modified nanoclay by extrusion. It was found that the incorporation of 2 % or 5 % w/w of sepiolite does not influence the mechanical and thermal behavior compared to the reference lignin/natural fibers composite, while nanoclay-based nanocomposites have shown improved properties. Shi et al. (2006) investigated the effect of single-walled carbon nanotubes (SWNTs) and functionalized SWNTs (F-SWNTs) on electrical and mechanical properties of

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Fig. 34 Effect of nanoclay content on flexural strength of wood plastic composites

Fig. 35 Electrical conductivity as a function of nanotube concentration for SWNT and F-SWNT cross-linked nanocomposites. A value of 0.03 wt% is estimated for the electrical percolation threshold of SWNT nanocomposites using the scaling law

unsaturated, biodegradable polymer poly(propylene fumarate) (PPF). It was found that nanocomposites with 0.1 wt% F-SWNTs loading resulted in a threefold increase in both compressive modulus and flexural modulus and a twofold increase in both compressive offset yield strength and flexural strength when compared to pure PPF networks, whereas the use of 0.1 wt% SWNTs gained less than 37 % mechanical reinforcement. The SWNT also increased significantly the electrical conductivity of the PPF polymer matrix as shown in Fig. 35. It has been proven that that the addition of nanofillers is an effective way to improve properties of neat polymers; the green polymer nanocomposites are considered to be the next-generation materials for automotive and other industries.

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Summary Herein, we have highlighted on the facts why polymer nanocomposites becoming popular among researchers, be it in industry or academics. The main reason is its widespread scope of tuning of physicochemical properties of the materials for high end (eg. in aerospace) as well as low end applications which ranges from bio to electronic and commodity to automobile. Furthermore, polymer nanocomposites become dearer due to its wide flexibility in preparation. In addition, it gives choice to choose appropriate polymer, type of nanofiller and processing parameter depending on applications.

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Surface Treatment and Modification of Plastics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Physical Surface Treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liquid Chemical Surface Treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reactive Gas Surface Treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coatings for Plastics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paints and Functional Coatings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Primer Coatings for Adhesion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Metalized Coatings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other Coatings for Plastics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research and Development Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

An overview of surface modification and coating techniques for plastics is presented for changing the surface properties to meet the performance requirements in a variety of applications. Surface modification and coatings are utilized for purposes of adhesion, wettability, biocompatibility, scratch and abrasion resistance, chemical resistance, barrier properties, and more. Methods for modification include physical processes, such as surface roughening and abrading; liquid chemical processes, such as acid etching; and reactive gas chemical processes. The reactive gas chemical processes covered include corona, flame, and low-temperature plasma. Surface degradation from reactive gas exposure is

M. Gilliam (*) Department of Chemical Engineering, Kettering University, Flint, MI, USA e-mail: [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_20

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presented with respect to the sources, chemical mechanisms, and methods for characterization. Coatings for plastics, including paints, functional coatings, and metallization, are summarized.

Introduction Selection of suitable materials for an application not only requires consideration of bulk properties, but surface characteristics play a vital role in their successful application. For plastics, surface treatments and coatings can improve the performance in existing applications, as well as enable the further expansion of plastics for new applications. Surface modification and coating is often performed to improve adhesion, tailor hydrophobic or hydrophilic properties, increase scratch and abrasion resistance, provide decoration, impart electrical conductivity, improve biocompatibility, increase barrier properties, reduce friction, enhance resistance to chemicals, and other goals to target specific applications. Surface modification technologies include physical treatments, wet chemical treatments, and dry process treatments and coatings, such as plasma, corona, and flame. Coating techniques include paint and functional coatings, metallization, printing and decorating, lamination, and chemical vapor deposition. Surface treatments and coatings enable the expansion of plastics into applications otherwise dominated by glass, metal, and ceramic. For applications traditionally using glass, optical quality plastics offer light weight, greater impact resistance, and higher thermal insulation, which can impact energy savings and environmental performance in the long term. However, in order to achieve the required optical clarity, scratch and abrasion resistance, and exposure to weather (depending on the application), a coating or layers of coatings are required for plastics used for glass replacement (Tolinski 2009). Metallization of plastics allows the use of plastics in place of metal components, as well as enhances the decorative possibilities for plastics. Especially in the growing electronics industry, metalized plastics are replacing metal components for connectors, electromagnetic shielding, cell phones, and medical devices. The types of surface treatment or coating selected for a given application depend on the target properties and specifications, the substrate material, the final use and environment in which the part will be applied, all costs associated with the process, and the ability to integrate the treatment with the existing manufacturing processes. When a product traditionally produced from glass, metal, or ceramic is replaced by plastic, oftentimes, new quality control tests are required to capture new failure modes that can occur. This especially holds true for applications in which the plastic replacement has a surface treatment or coating. For example, adhesion tests, accelerated environmental exposure, chemical resistance, optical properties, scratch and abrasion, stress, and other such tests are critical to evaluate the successful application of surface treated or coated plastics.

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Greater environmental and safety considerations as well as cost sensitivities are shaping the technologies for surface treatment and coatings for plastics. Wet chemical treatments that often involve the significant use of harsh chemicals and chemical waste are being replaced by dry processes that offer lower environmental impact. For example, hexavalent chromium, used in wet chemical treatment of plastics prior to metal plating, is carcinogenic when inhaled and requires the use of controls and personal protective equipment to restrict exposure of workers. The European Union prohibits the use of hexavalent chromium in electronics, according to the Restriction of Hazardous Substances Directive. Recent research is focused on using more environmentally friendly chemicals for surface pretreatment prior to metal plating (Nagao et al. 2006). In plastic coatings, radiation curing and water-based formulations have an increasingly greater role with better environmental impact. Radiation curing offers less solvent use, lower energy consumption, rapid curing times, and greater surface hardness and scratch resistance than traditional thermally cured coatings. Despite the advantages offered, radiation-cured coatings still accounted for a small fraction of the total coatings market in 2010 (IHS Chemical Report 2011). It is believed that the high material costs and investment in new equipment are preventing greater penetration of radiation-cured technology for coatings. Powder coating is a fastgrowing coating technique for coating conductive materials, in which highperformance coatings are produced without the use of solvent, reducing VOCs. Recently, powder coating technologies have been adapted for use on plastics using a surface treatment that makes the substrates temporarily conductive using a technique with no VOCs or hazardous by-products (Stay 2012). Table 1 summarizes available surface treatments for plastics regarding purposes and manufacturing processes. Greater detail on surface treatments and coatings are provided in the proceeding sections.

Table 1 Summary of surface treatment processes for plastics Plastic Surface Treatment Surface Modification General Coatings for Plastics Metallization Printing, Decorating, and Polishing

Purposes Wettability, adhesion, printability, paintability, bonding, compatibility with contacting material Durability, UV-protection, chemical resistance, scratch resistance, electrical conductivity, other Decoration, reflectivity, electrical conductivity, EM shielding Aesthetics and decoration, surface smoothing

Manufacturing processes Mechanical roughening, chemical exposure and etching, corona, flame, plasma Paint, themal-cure coating, radiation-cure coating, plasma deposition Vacuum metalizing, arc and flame spraying, plating Printing, in-mold decoration, physical and chemical polishing, vapor polishing

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Surface Treatment and Modification of Plastics Surface treatment and modification techniques are often applied to plastics prior to a coating or finishing process to remove contaminants, generate surface reactive sites, and create new surface functional groups compatible with the subsequent process chemicals. The low surface energy and smoothness of plastic surfaces oftentimes present problems for adhesion of a coating, a metalized layer, decoration, or other finishing processes. Additionally, surface impurities, such as additives, and dust and grime buildup due to static charge may also impact adhesion, finishing, or subsequent application of the plastic part. Cleaning the surface with mild solvents, such as isopropanol, and antistatic processing of the plastic part may not be sufficient to remove impurities and therefore, a surface modification method is oftentimes applied. Several types of techniques have been developed to modify the surface of the plastic in the submicron region to enhance the surface area and sites for bonding, increase surface energy, create reactive sites or targeted chemical functional groups on the surface, and remove surface impurities. Surface modification techniques can be classified into physical processes, such as surface roughening; liquid chemical surface modification, such as acid etching; and reactive gas processes, such as corona, flame, and plasma. Each type of surface modification process has advantages and drawbacks which must be considered for a given application, the type of plastic, and the cost and commercial considerations.

Physical Surface Treatments Physical surface treatments of plastics are useful for bonding a plastic to an adhesive or coating by increasing the surface area of the plastic and creating greater sites for mechanical interlocking. Typical physical surface treatments include surface roughening, such as with sand paper or emery cloth, adhesive abrading, and media blasting. The effect of the physical treatment depends on the type of abrading material, the plastic, the original surface quality, and the process parameters. Surface roughening is a simple process that can be performed prior to applying an adhesive or coating to increase adhesion strength. Surface roughening can be followed by degreasing with a solvent, and recommendations for the types of physical treatments and solvent cleaning depend on the polymer used (SmartAdhesives). Surface roughening can be performed in the presence of an adhesive, referred to as “adhesive abrading,” in which two surfaces are abraded and adhesive coated prior to bonding and curing. Adhesive abrading has shown to increase the bond strengths on PTFE by approximately 700 % (Henkel 2011). Media blasting is a type of physical process that involves propelling small pellets of solid materials to the plastic surface driven by a stream of pressurized gas. The propelled materials can include sand, metallic shot, nutshells, plastic pellets, dry ice crystals, or others (Izzo 2000). While physical surface treatments are relatively simple and low cost, they are not effective for many types of plastics and adhesive or coating. For many

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applications, increasing surface area is not enough for the adhesion requirements, and chemical surface modification is required. The next sections outline chemical surface modification processes, in which chemical reactions drive the surface changes to bring about the desired surface properties. Chemical surface treatments are performed in the liquid phase, as well as the reactive gas or plasma phase.

Liquid Chemical Surface Treatments Wet chemical surface modification involves exposing the surface of a polymer to a chemical or chemical mixture in the liquid phase. The chemicals react with molecules on the surface to create new surface functional groups. Chemicals in the liquid phase can penetrate pores more effectively than other types of surface modification techniques. However, significant amounts of chemicals and chemical waste are involved in wet chemical surface modification for large-scale use, compared to reactive gas surface treatments. Acid etching of polymer surfaces involves application of acid to a surface of a polymer to induce surface oxidation and increase the surface energy. It typically is applied to enhance the bond strength to an adhesive or to a metal surface or coating. Chromic acid is commonly used for acid etching of plastics, which introduces oxidized functional groups, such as hydroxyl, carbonyl, and carboxylic acid. In addition, the process may alter the surface morphology and increase the surface area and, thus, increase the sites for mechanical interlocking. The type of plastic, the etch time, and the process temperature can affect the degree of oxidation and the etch depth. For instance, polypropylene shows an increase in etch depth with increasing etch time and temperature, while the degree of oxidation and etch depth increase with etch time for polyethylene (Henkel 2011). For polypropylene, chromic acid etching was shown to increase the adhesion level of the surface with an epoxy adhesive (Sheng et al. 1995). Other types of liquid surface treatments include the use of relatively milder chemicals, for example, iodine treatment of nylon, which increases the surface crystallinity and enhances adhesion to a metal coating. Sodium and sodium compounds have been shown effective at treating polymer surfaces for biomedical use, including reducing inflammation and infection induced by synthetic materials used inside the body, such as polypropylene meshes (Regis et al. 2012). Many of the liquid chemical surface treatments have considerable drawbacks. For example, wet chemical treatment typically involves many additional processing steps such as washing, rinsing, and drying. Furthermore, wet processes produce a considerable amount of waste, oftentimes requiring hazardous waste disposal. Regulations are driving manufacturing away from using some of the chemicals for liquid surface treatment. On the other hand, reactive gas and plasma discharge processes provide versatile, reproducible, and environmentally benign methods for surface modification of plastics. In addition, reactive gas or plasma processes can be applied to modify very thin surface layers or deposit single- or multilayer coatings without altering the bulk characteristics of materials.

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Reactive Gas Surface Treatments Reactive gas and plasma processes (dry processes) involve the generation of a luminous gas or plasma that contains high-energy neutrals and ionized species. The major types of reactive gas processes that have been employed to modify plastic surfaces include corona discharges, flame treatments, low-pressure nonequilibrium plasmas, and atmospheric nonequilibrium plasmas (Yasuda 2005; Wertheimer et al. 2002; Wolf 2010; Bardos and Barankova 2010). Reactive gas processes have been used for the past several decades to chemically modify the surfaces of plastic parts to increase wettability, enhance adhesion to inks, coatings, and adhesives and to bring about compatibility with a chemical or contacting material in subsequent processes and the final application. The processes are effective at modifying the surface of the plastic while keeping the bulk properties unchanged. In addition, dry processes typically have a much milder environmental impact than liquid chemical processes with respect to hazardous waste and emissions. The most dry surface treatment processes can produce very effective outcomes using only oxygen, nitrogen, or inert noble gases. When other, less inert reactive gases are required to create highly functional tailored surface chemistries, inherently low flow rates of the process gases produce minimal effluent. Reactive gas modification processes involve the exposure of the plastic surface to energetic species with much greater energy than that of the covalent bonds of the organic polymer molecules. This exposure to high-energy species can result in removal of organic surface contaminants, surface cross-linking, reaction with species in the air to form new surface functional groups, and surface ablation and damage. Therefore, for a given plastic and final application, consideration must be made on the types of treatments, process conditions, and potential drawbacks in conjunction with the goals of the final application. In addition, the polymer additives may affect the treatability of the surface, stability of the treated surface, and the compatibility of the treated surface with the final coating or application.

Corona Discharges Corona treatment is a type of atmospheric pressure air electric discharge that is the most widely used surface treatment method for plastic surface modification. Figure 1 shows a corona process for treating polymer films. Most extrusion lines for films use in-line corona processes for surface treatment prior to printing. The basic components of corona processes include a power supply

Fig. 1 Image of corona treatment equipment courtesy of Enercon Industries Corporation

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and a treatment device. The power supply converts standard utility power to a single phase, higher frequency power that is supplied to the treatment device. The treatment device consists of two electrodes, one at high potential that is separated from the substrate surface by air, which is referred to as the “air gap.” The other electrode is at ground potential and is usually the surface on which the substrate is placed. When the voltage is applied to the electrode, energy is transmitted to the air, creating small filamentary discharges that consist of ionized air. High voltage is required to ionize air and corona processes typically operate around 10 kV. A substrate scans through the ionized air, contacting the energetic species that break surface covalent bonds to create radicals, cross-link surface macromolecules, and oxidize surface functional groups. Oxidation can create new surface polar groups to depths greater than 10 nm, such as hydroxyl, carbonyl, amide, and carboxylic acid, resulting in an increase in surface energy and wettability (Wolf 2010). Corona treatment is widely used to treat the surface of webs or rolls of plastic film. Many advances have been made in corona processing equipment and configuration since the invention in 1951 (Vetaphone). The types of rolls used today include bare rolls, covered rolls, and universal rolls. Bare rolls do not have an electrically insulating coating on one or more of the electrodes. Covered rolls have dielectric materials covering the electrode. A variety of dielectric materials are available, including silicone, Hypalon ®, epoxy, ceramic, and glass. The choice of the type of roll covering for a specified plastic and application depend on the properties of the covering, including dielectric strength, dielectric constant, resistance to heat, ozone, cleaning tools, wear, good heat dissipation, low surface porosity, and, of course, cost (Wolf 2010). Universal rolls consist of proprietary ceramic coatings which is useful for treating a wide variety of materials. The extent and effectiveness of corona surface treatment is dependent on the watt density, line speed, and the substrate surface. Consideration of the factors that affect watt density should be made when sizing a corona system. The watt density is proportional to the power and inversely proportional to the station size (web width). The response of a material to corona treatment depends on the type of material and the surface quality (surface energy, crystallinity, etc.). For example, some polyesters show a significant increase in surface tension for corona treatment using low watt densities. On the other hand, polyethylene requires moderate watt densities, and polypropylene requires relatively high watt densities for significant increase in surface tension (Markgraf). While corona treatment is used successfully to increase wettability and adhesion of plastics to inks, coatings, and adhesives, some effects of corona can be disadvantageous for certain applications and plastics. These potential negative effects include detrimental increase in surface roughness, creation of pinholes, and instability of the surface changes over time. The filamentary discharges in the corona process create discrete point locations of high energy in the air gap, which can bring about damage to the surface and heterogenous surface treatment effects. In addition, significant surface morphology changes, and damage of surface macromolecules can occur from reaction with such high-energy, nonuniform ionized air.

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Fig. 2 Image of flame treatment attached to robot courtesy of FTS Technologies

Flame Treatment Flame plasma treatment involves a flame generated by combustion of a gaseous fuel, such as methane or propane that is applied to the surface of a substrate. Exposure of a plastic to a flame brings about surface oxidation, producing increased wettability and surface energy for enhanced adhesion and printability. Flame treatment processes were initially used in the 1950s, and one of the first applications was improving the printability of low-density polyethylene (LDPE) (Brewis and Mathieson 1999). Flame treatment offers some distinct advantages over corona, including uniformity of treatment, a smoother surface after treatment, and a more stable surface for a longer time after treatment. In addition, flame treatment brings about oxidation to depths of only 5–10 nm, while corona can affect the plastic to depths much greater than 10 nm (Wolf 2010). Figure 2 shows an example of a flame process attached to a robot to treat three-dimensional parts. Flame treatment enhances adhesion by increasing the surface energy of a polymer surface through the surface oxidation. Treatment can result in the creation of new polar functional groups, such as hydroxyl, carbonyl, carboxyl, and in some cases, nitrogen groups. For polyolefins, flame treatment can introduce 5–15 % oxygen to the surface (Brewis and Mathieson 1999). It is believed that the chemical mechanisms involve hydrogen abstraction initially to form surface radicals, followed by reaction of the radicals with oxygen atoms and molecules (Strobel et al. 1996). The surface radicals formed can react with other molecules in the flame plasma or the polymer surface to form a variety of functional groups. The main exothermic reaction in the flame plasma discharge for the combustion of methane is as follows: CH4 þ 2O2 ! CO2 þ H2 O in the presence of N2 Flame plasmas also contain highly reactive side products and species from combustion, including ethers, esters, carbonyls, carboxyls, and hydroxyls (Wolf 2010). All of these species can react with the surface of the polymer during

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treatment to create new surface functional groups. In addition, vaporized chemicals can be added to the surface of the plastic during treatment to add specific functional groups. One such system developed by FTS Technologies was tested on DaimlerChrysler’s 2004 AN Dodge Dakota Wheel Flare prior to painting (Brynolf). The patented ATmaP™ technology exceeded DaimlerChrysler requirements and demonstrated superior performance over the control sample. The effects of the treatment are influenced by the design of the equipment, the operational parameters of treatment, and the substrate polymer surface. The operational parameters that affect the level of treatment include the thermal output and flame temperature, the stoichiometric ratio of air/fuel, the total gas flow rate, the specific power (W/area), the gap between the substrate and the flame (“air gap”), and the flame size and shape. The design of the burner can influence the thermal output and flame temperature, as well as the flame size and shape. The main type of burner used for flame treatment is a ribbon burner, which is designed for high heat release and a continuous flame. Modifications with orientation, width, and depths of the ribbons can affect the flame geometry and size, while the flame shape is affected by the depth and width of the stacked ribbons and flats (Wolf 2010). An alternative burner is the enhanced velocity (EV) burner, which was designed for high mass flow and velocity (Markgraf 2004). Compared to the standard ribbon burner, the EV burner can produce increased surface energy and adhesion while allowing for a greater air gap.

Plasma Surface Modification In contrast to corona discharge, low-temperature plasma discharges can produce uniform surface treatments with controllability and flexibility to minimize damage and create numerous possible surface functionalities. Plasma discharges can chemically modify a polymer surface by surface functionalization using reactive gas plasmas, in which new surface functional groups are created, or by surface crosslinking, which includes the CASING effect (cross-linking via activated species of inert gases) with a plasma of a noble gas (Gilliam and Yu 2008; Strobel et al. 2003). The reactions that occur on the polymer surface during a plasma treatment involve the production of surface free radical sites that can react with the surrounding polymer molecules and the plasma-phase species. Radical sites that are not consumed during plasma exposure can be quenched by components in the ambient air that become incorporated into the polymer surface, mainly oxygen, moisture, and nitrogen, upon exposure to atmosphere. Various types of plasma processes for manufacturing are used for surface modification of plastics. The types of processes are basically distinguished by the operating pressure, electrode and power configuration, and energy parameters. Low-pressure plasma processes require a vacuum chamber with pumps and can be operated as batch or semi-batch continuous processes. For semi-batch continuous operation, the system is equipped with loading and unloading stations that can be quickly isolated from the main chamber for pumping down to operating pressure or increasing to the atmospheric pressure. Alternatively, plasmas operating under atmospheric pressure are also used for many plastic surface treatment applications.

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In one such application, atmospheric plasma is used to pretreat polypropylene (PP) parts of tumble dryers to bring about strong adhesion and a durable seal (MIELE: Quality assurance on tumble dryers through plasma). The choice of power includes direct current or alternating current, in which the frequency may vary from the kHz range to microwave (MW) frequencies. In general, the higher frequency brings about a greater degree of ionization in the plasma. High frequency in the microwave region results in the generation of heat that may be destructive to plastics. The configuration of electrodes, such as capacitive or inductive coupling, addition of a magnetic field, and the overall geometry of the chamber can result in varying degrees of effects of treatment. Plasma processes can involve capacitive coupling or inductively coupling of the electrodes. Within the vacuum range, higher pressure operation requires less operational costs and is more forgiving of leaks of the ambient into the chamber. In order to operate with higher pressure vacuum, higher power and frequency is needed. Operation in the radio frequency (RF) range generally offers the best combination of pressure and power (Yasuda 2005). Advantages of using low pressure include low flow rate of gases, the flexibility to use a variety of types of gases, and the uniformity of the surface treatment. Disadvantages of low-pressure processes generally relate to the manufacturability aspects, including the limitation of part size and shape based on the size of the process chamber, the high capital investment cost, and maintenance of the vacuum pumps and peripheral equipment. In contrast, atmospheric pressure plasma processes do not require expensive vacuum equipment or chamber, are less energy intensive, and can be easily placed on existing plastic processing lines. Low-pressure processes entail considering capital costs and requiring expensive pumps and vacuum equipment. Atmospheric pressure plasmas, on the other hand, offer considerable decrease in capital and working cost requirements and provide a true in-line manufacturing process for plasma treatment. Not every outcome produced using a vacuum plasma process can be replicated with atmospheric plasma, however, and thorough background review should take place before selecting a suitable atmospheric plasma process.

Chemical Effects of Reactive Gas Treatment on Polymeric Surfaces This section provides an overview of the chemical effects of reactive gas treatment on polymeric surfaces. In this section, the term “plasma” refers to any reactive gas discharge, including corona, flame, and low-temperature plasma. Low-temperature discharges contain many reactive species including ions, electrons, free radicals, metastable neutral species, ultraviolet (UV) and vacuum ultraviolet (VUV) photons, and ground-state neutrals. Once a plastic substrate is placed in a plasma environment, the surface is subject to continuous bombardment by these plasma species that can react with the surface elements, change the surface chemistry, and modify the surface characteristics. Figure 3 illustrates the interactions of the various plasma species with the plastic surface. Energy transfers from the plasma species to the polymer can cause ablation of hydrogen or side-group species or chain scission,

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Fig. 3 Interactions between the high-energy reactive gas species and the polymer surface

depending on the energy levels and the polymer structure. Free radicals that exist in reactive gas plasma can diffuse to the polymer surface and cause various chemical reactions, including abstraction of hydrogen or other side-group species, and incorporation of chemical species into the polymer. Four main effects of reactive gas treatment on polymer surface include surface cleaning, ablation or etching, surface chemical functionalization, and cross-linking. Each of the effects is present to some degree in a reactive gas process; the extent and degree of the effects depend on the process, energy and frequency, gas chemistry, reactor design, and operating parameters (Yasuda 2005). Surface cleaning involves the removal of organic contamination of the surface from additives in the plastic, previous processing steps, ambient plant air, or contact with another surface. Ablation and etching of surface material can increase the surface area, create reactive surface sites, and remove a weak boundary layer that can be detrimental for adhesion. Cross-linking and branching of surface macromolecules can cohesively strengthen the surface layers. Modification of the surface chemical structure involves the creation of new surface functional groups, which opens up numerous possibilities for surface properties. Ablation and etching of polymeric surfaces can increase the surface area, enhancing adhesion, and create reactive sites for bonding with a subsequent chemical or material. During ablation and etching, elements or small molecules are removed from surface macromolecules and surface radicals are generated. Chemical functionalization reactions can occur from the reactions of the surface radical sites with reactive gas species in the plasma. The surface radical sites can be created

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through ablation of hydrogen, removal of small molecules from the polymer chain side groups, or chain scission as shown below: Hydrogen ablation: RH ! R • þ H • Side  group ablation: RX ! R • þ X • Chain scission: R1 R2 ! R1 • þ R2 • The radicals created on the polymer surface from ablation and etching can then react and form covalent bonds with plasma-phase species. As a result, new functional groups are created on the surface. In addition, when the treated polymer is exposed to the atmosphere, any reactive sites that remain on the surface may react with oxygen, moisture, and nitrogen in the air. Plasma treatment of polymers can introduce a wide variety of functional groups on the polymer surface depending on the chemical precursors or mixtures of gas or vapor added to the plasma. Consequently, numerous surface properties are possible, and tailoring of the surface properties is performed through the tuning of plasma process and conditions. Surface oxidation from plasma treatment can create various oxygen groups in the polymer to increase surface energy, improve wettability, and enhance adhesion to a subsequent coating or material (Strobel et al. 2003; Momose et al. 1992). Other process gases and vapors can include fluorine-containing chemicals for surface fluorination (Jama et al. 1999; Rangel et al. 2003) to increase hydrophobicity of a polymer and nitrogen-containing chemicals for surface nitradation (KlembergSapieha et al. 1991; Tatoulian et al. 2004) to create basic groups for dyeability, printability, or biocompatibility. In the 1960s, Hansen and Schonhorn introduced the CASING effect theory (cross-linking via activated species of inert gases) for polymer surfaces exposed to ions and metastable species of inert gas plasmas (Schonhorn and Hansen 1967). In addition to noble gas plasma species, investigations have shown that VUV/UV photons and electrons that exist in a plasma can induce surface cross-linking on a polymer (Fozza et al. 1999). Surface cross-linking produces many desirable effects on the polymer, including stability of the polymer surface, improved adhesion, improved surface bond strength, and resistance to solvents, heat, and moisture (Gilliam and Yu 2008). Thus, surface cross-linking of a polymer is an important process that can be applied to many mechanically weak polymers to enhance their surface properties. Cross-linking of a polymer surface from inert gas plasmas and VUV/UV irradiation occurs through ablation of hydrogen or other atoms in the side groups from the interactions with metastable noble gas atoms, noble gas ions, electrons, or photons to produce radical sites on the polymer surfaces. The radicals can form bonds with other radicals on nearby macromolecules, resulting in a cross-linked network on the surface layers of the polymer. Photons can be absorbed into the polymer surface and induce cross-linking to depths where other plasma species are physically inhibited. For plasmas that do not contain gas or plasma species that can bond with the

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polymer molecules, the formation of new functionalities on the radical sites is inhibited, and cross-linking with other polymer radicals is the significant consequence in noble gas plasmas. Polymers treated with plasma always contain unreacted or residual surface free radicals that can incorporate oxygen, moisture, and nitrogen upon exposure to atmosphere. Consequently, noble gas plasmas without reactive gas addition can result in a stable, cross-linked top layer with mainly oxygen and some nitrogen functional groups anchored at the surface. Depending on the susceptibility of the polymer to plasma exposure, the presence of additives at the surface, and the energy levels and extent of exposure, plasma treatment could result in polymer degradation and surface instability. Surface instability can result from the degradation of the top layer of macromolecules into nonvolatile, low molecular weight oxidized material (LMWOM), which typically can be removed from the polymer surface with a polar solvent (Strobel et al. 2003; Weikart and Yasuda 2000). The LMWOM gives rise to a loosely bonded weak material surface that can be detrimental for surface stability and adhesion. Hence, the polymer chemical structure, the energy levels and abundance of the reactive plasma species, the wavelengths and intensities of the photons, and the exposure time can determine the relative occurrences of ablation and etching, chemical surface functionalization, cross-linking, and degradation. Most polymers are susceptible to oxidative degradation, which generally occurs by a free radical mechanism that is initiated by plasma ablation reactions and yields peroxy and hydroperoxy intermediates (Fried 1995). The peroxy/hydroperoxy route to degradation is a mechanism that can lead to the formation of LMWOM on plasma-treated polymer surfaces (Fried 1995). The peroxy pathway to LMWOM formation begins with the formation of a radical on the polymer surface that reacts with various plasma species to form peroxy intermediates, as shown in the reactions below: Peroxy formation: R • þ O2 ðor H2 O2 Þ ! ROO • ðþH2 Þ Hydroperoxy formation: ROO • þ R1 H ! ROOH þ R1 • R • þ HO2 ! ROOH Decomposition of the hydroperoxy group leads to the formation of an alkoxy radical, which degrades to form LMWOM as follows: Hydroperoxy decomposition: ROOH ! RO • þ OH Alkoxy degradation: R1 CCðO • ÞR2 ! R1 C • þ R2 C ¼ O Strobel et al. (2003) have offered an alternative theory to the peroxy mechanisms, arguing that the peroxy pathways to LMWOM are too slow to cause the significant LMWOM damage that has been reported in the time frame of a plasma treatment. They concluded that reactions involving atomic oxygen and ozone to form alkoxy radicals are the major routes to the formation of LMWOM on polymer

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Fig. 4 Possible mechanisms that can occur on a polymer surface exposed to a reactive gas discharge process

surfaces in plasma discharges. The atomic oxygen and ozone pathways are shown by the reactions below: Direct alkoxy formation: R • þ O ! RO • R • þ O3 ! RO • þ O2 Alkoxy formation from peroxy and oxygen atom: ROO • þ O ! RO • þ O2 Much is still unknown about the complex reaction mechanisms that occur on the polymer surface during plasma treatment. Figure 4 presents possible reaction pathways that can occur at the plasma-polymer interface during plasma surface treatments of polymers and bring about various outcomes. Many plasma species including ions, photons, electrons, atoms, and free radicals can cause chain scission or hydrogen or side-group ablation, which results in the formation of surface radicals. However, high-energy ions have a greater tendency for chain scission and subsequent degradation. The plasma-activated surface macromolecules can covalently bond with surrounding species to create new functional groups, crosslink with other surface macromolecules, or further react to degrade into volatile etch products or LMWOM. However, the lack of selectivity during a plasma treatment makes it difficult to optimize specific reaction pathways. Surface degradation of a polymer can depend on the polymer’s vulnerability to the particular plasma environment, which is related to the polymer chemical structure. In addition, the extent of degradation is determined by the concentration and energy levels of plasma species, such as oxygen, and the wavelengths and

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intensities of VUV/UV photons emitted from the plasma. For example, the presence of oxygen in the structure of an organic polymer has been shown to enhance the polymer’s susceptibility to plasma etching (Gilliam and Yu 2008; Weikart and Yasuda 2000). On the other hand, aromatic rings in a polymer (both in the backbone and in pendant groups) provide some resistance to surface etching (Gilliam and Yu 2008; Momose et al. 1992). Some organic polymers are sensitive to VUV/UV radiation in plasmas, mainly due to their ability to absorb photons in the VUV/UV wavelength range (Wertheimer et al. 2002). However, the absorption spectrum for a polymer depends on its chemical structure, which causes different photochemical effects on the various polymers. Polymethylmethacrylate (PMMA) is a polymer that easily undergoes oxidative degradation in a plasma environment, while polystyrene (PS) is highly stable toward degradation (France and Short 1998; Moss et al. 1986). Silicon-containing polymers are particularly resistant to photodegradation and oxidative degradation, yet degrade very easily in fluorinecontaining plasmas, because of the formation of stable and volatile Si-F compounds (Brewis and Mathieson 1999). A treated polymer surface can contain mobile functional groups, in which dynamic changes can occur driven by interfacial tension or other surface forces. In the 1930s, Langmuir first pointed out that the surface properties of a solid are determined by the surface configuration (orientation of the surface functional groups) rather than the chemical configuration of the bulk molecules (Langmuir 1938). In addition, a polymer with mobile surface functionalities can undergo surface configuration changes with changing contacting media (Brewis and Mathieson 1999; Gilliam and Yu 2008; Weikart and Yasuda 2000). Surface configuration changes are driven by the thermodynamic requirement to minimize interfacial tension, whereby the interface changes to establish new equilibrium with a new set of conditions. Hydrophobic recovery and loss of wettability can occur in plasma-treated polymers that are stored in ambient air for extended periods of time (Yasuda 2005; Gilliam and Yu 2008; Weikart and Yasuda 2000; Guimond and Wertheimer 2004). Hydrophobic recovery is an indication of polymer surface instability in which the hydrophilicity decreases with time stored in ambient air due to surface configuration changes. Weikart and Yasuda (Weikart and Yasuda 2000) demonstrated that, in some cases, long-term hydrophobic recovery can be reversed by immersing the treated sample in water for 24 h. This indicates that over time, the hydrophilic surface moieties created from plasma treatment rearranged away from the polymer surface. Upon changing the surrounding conditions from air to water, the hydrophilic moieties reoriented toward the polymer surface, thus making the surface wettable again.

Surface Tension and Wettability Testing A variety of techniques have been utilized to characterize surface configuration, wettability, and morphology changes, including atomic force microscopy (AFM), scanning electron microscopy (SEM), and various surface tension and wettability

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Fig. 5 The Wilhelmy force loop obtained from RF plasma treatment of LDPE with two immersion cycles shows significant overshooting, indicating that LMWOM was created on the polymer surface during treatment

tests. Surface tension techniques can be used to assess the quality of the surface changes that take place from plastic surface exposure to a reactive gas process, including the presence of a layer of LMWOM, which impacts morphology. Contact angle is a static surface tension method of quantifying the surface wettability and surface energy changes that take place as a result of plasma exposure. Dynamic surface tension techniques, such as the Wilhelmy plate method, can evaluate dynamic wettability by observing the hysteresis, which is affected by roughness, chemical heterogeneity, surface deformation, surface configuration changes, and adsorption and desorption. The Wilhelmy balance method is one of the simplest and most useful techniques available for analyzing dynamic wettability and surface stability, because of its sensitivity to the surface characteristics of the polymer (Yasuda 2005; Gilliam and Yu 2008; Weikart and Yasuda 2000). The technique involves immersing the substrate in the form of a plate into water and measuring force exerted on the plate surface with a tensiometer as the plate is immersed over time to a preset depth. Retracting the plate and repeating the immersion cycle can reveal dynamic surface changes and the presence of a weak boundary layer or LMWOM. Force loops can be generated on the immersion/retraction cycles that can be used to calculate the dynamic contact angles. During immersion, LMWOM that may be present on the sample surface is removed in water, revealing the underneath, intact polymer surface that is usually more hydrophobic. The exposure of a more hydrophobic surface is indicated by “overshooting” of the force loops in subsequent immersion/ retraction cycles. Figure 5 shows the Wilhelmy force loops obtained from a sheet of low-density polyethylene (LDPE) that was treated with RF plasma of Ar + O2 mixture. The force loop measured in the second immersion cycle shows significant overshooting

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Fig. 6 The Wilhelmy force loop of untreated LDPE shows a stable, hydrophobic surface with no intrinsic hysteresis (the second and third immersion lines trace the first immersion line)

from the first immersion cycle. This result clearly shows the presence of LMWOM on a plasma-treated LDPE surface, which was removed by water in the first immersion to expose a more hydrophobic layer in the second immersion. The Wilhelmy method can also reveal mobile surface functionalities that can undergo rearrangement when driven by interfacial forces, or a surface configuration change. The Wilhelmy force loop for a polymer with hydrophilic functional side groups, such as nylon-6, would show an apparent increase in hydrophilicity from the first advancing cycle to the next. This phenomenon is a demonstration of intrinsic hysteresis, which is caused by surface configuration changes of the hydrophilic moieties near the polymer surface. During the first immersion, the hydrophilic surface moieties rearranged to bend toward the water-polymer interface, thus making the surface more wettable during the second advancing cycle. On the other hand, a polymer that has a dynamically stable surface, such as low-density polyethylene (LDPE), exhibits no intrinsic hysteresis in the Wilhelmy force loop, as shown by Fig. 6. Due to the mobility of polymer chains at the surface, polymers that have been surface treated without a coating may undergo hydrophobic recovery after treatment. Hydrophobic recovery should be considered when implementing a new surface treatment in order to quantify the lifetime of the effects of surface treatment. Low-pressure plasma treatment has been shown to induce the formation of LMWOM on a variety of polymer surfaces, in addition to changing the surface morphology and increasing surface roughness (Weikart and Yasuda 2000). Atmospheric pressure plasma treatment can also impact surface morphology and roughness. One study comparing the effects of air corona treatment to nitrogen atmospheric pressure glow discharge showed that air corona produced much higher quantity of LMWOM in the form of small nodules or droplets (Guimond and Wertheimer 2004). Another study found that corona treatment produced LMWOM, while flame treatment did not (Strobel et al. 2003).

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Coatings for Plastics Coatings enable the expansion of plastics into new applications by imparting protection necessary for performance under harsh conditions of weather, exposure, wear and abrasion, chemicals, and other. Advances in coatings for plastics in the twenty-first century have been driven in part by the design freedom offered by plastics and greater environmental performance, such as lightweight materials for transportation. Coated plastics are used in a multitude of applications spanning such industries as automotives, electronics, packaging, construction, furniture and appliances, fabrics, toys, sporting goods, and others. This section provides a brief summary of various coatings and finishings used on plastics, including paints and functional coatings, metallization, and others. For more in-depth information on coatings for plastics, the reader is directed to available literature (Wicks et al. 2007; Tracton 2006a; Ryntz and Yaneff 2003).

Paints and Functional Coatings Paint and functional coatings discussed in this section are those organic or hybrid coatings that use wet process technology, in which the coating is applied in a liquid form before curing. The functions imparted onto the plastics from such coatings include scratch and abrasion resistance, UV protection, surface quality and appearance, chemical resistance, antimicrobial properties, and other specially targeted properties. Numerous types of paints and functional coatings are available on the market for plastics with a wide range of resins, formulation, and processing options, the choice of which should be selected based on the given application and substrate. The types of coating systems can be classified as thermoplastic or thermoset. Thermoplastic systems involve only a physical change during the cure process, in which the film hardens over time due to loss of solvent. Thermoset systems, on the other hand, undergo chemical reaction during curing, which is initiated by thermal energy, radiation, or oxidation. The resins must contain reactive functional groups to participate in reaction and cross-linking during the curing process, such as hydroxyl, carboxyl, amino, epoxy, and isocyanate. The system may contain a single component (or resin) or two components and a catalyst. The variety of resins available for coatings includes acrylic, polyester, urethane, epoxy, siloxane-based, alkyd, cellulosic, and polyester, among others, a few of which are briefly discussed here. A wide variety of properties can be achieved depending on the choice of resin. Acrylic polymers are those that are typically formed by chain growth polymerization of acrylic or methacrylic acid monomers. Acrylic resins can be used in thermoplastic coatings or thermoset coatings with a functional group, such as a hydroxyl group. Coatings formulated with acrylic resins are typically used for applications requiring photooxidative durability, resistance to hydrolysis, hardness, chemical resistance, and others (Wicks et al. 2007; Nordstrom 2003). Polyesters, polyurethanes, and epoxies are polymerized by step-growth polymerization, which involves reaction of two different monomers. Polyesters used for

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coatings are generally low MW, amorphous, branched, and are cross-linked during curing (Wicks et al. 2007). They are formed by carboxyl and hydroxyl reaction to form the recurring ester group –COO–. Coatings made with polyester resins offer greater flexibility and impact resistance than those prepared with acrylic resins, as well as good impact performance and scratch resistance. Polyester formulations can be prepared as solvent-borne, waterborne, high solids, or solvent-free (Huber and Stoye 2006). In addition, polyesters can be formulated in two-component systems. Polyurethanes are also polymerized by step-growth polymerization, involving an isocyanate and alcohol to make the urethane group R-NHCOOR0 . Polyurethanes can impart flexibility, toughness, and chemical resistance to the substrate surface. Available polyurethanes include aromatic and aliphatic, which are more expensive, but offer better durability and less susceptibility to turn yellow upon exposure to sunlight. As a result, aromatic urethanes are typically only used in primers or other undercoats (Nordstrom 2003). Silicone hard coatings are those made of polysiloxane and offer superior scratch and abrasion resistance, resistance to chemical attack, and durability under harsh conditions. As a result, silicone hard coatings are used in numerous demanding applications, such as auto and train windows, headlamps, safety glasses, face shields, and many more (Bernheim 2006). Many plastic substrates require pretreatment, annealing, or a primer in order to achieve adhesion to a silicone hard coating. Most coatings contain other components, including pigments and other functional additives to achieve the desired properties, that are formulated in a solvent. Reactive monomers are sometimes added as part of the carrier liquid. The use of solvents in a coating formulation enables control of viscosity and flow properties, as well as leveling and film formation of the coating. Greater environmental performance has been driving the developments in formulations with lower solvent (high solids), waterborne formulations, and solvent-free coatings. In order to reduce the amount of solvent used, a conventional approach is to lower the molecular weight of the resin (Nordstrom 2003). Radiation curing allows for high solids in the coating formulations. Waterborne formulations require water-soluble polymers or dispersed polymers using surfactant or surface-modified particles. Powder coatings require no solvent, but entail high temperatures, which limit the use for plastic substrates. Paints and functional coatings are typically applied by flow coating the solution onto the substrate, dip coating the substrate into solution, or spray coating. Sometimes a “flash-off” period is necessary before the curing step to remove some of the solvent. Thermal curing involves the use of thermal energy to initiate reaction in an oven at some prescribed length of time. Curing by radiation, in contrast, can be carried out under room temperature with a fast curing time. Radiation curing requires significantly less energy consumption, reduces emissions, and increases productivity compared to thermal curing. Formulations for radiation curing contain a chemical functional group that is activated by radiation, such as methacrylate and epoxy. The most common sources for radiation curing include electron beam and UV (Koleske 2006).

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Primer Coatings for Adhesion Primers are sometimes applied prior to coating with a material that is particularly problematic for adhesion to the plastic. Poor adhesion can result from incompatibility between the coating chemicals and polymer, poor chemical bonding, lack of mechanical interlocking, stresses induced at the interface, and a considerable difference in thermal expansion properties. In addition to improving adhesion, primers can provide increased resistance to wear, scratch, and abrasion or protection from chemical attack by a solvent that is present in a subsequent coating (Izzo 2000). Other functionalities can be added to primers if desired in designing a coating system. Although primers are technically coatings, the thickness is usually low, on the order of a couple hundred nanometers, and the primary purpose is to treat a surface prior to adhesion to a coating, an adhesive, or another surface. Primers typically contain chemical functional groups that react and bind with the substrate surface, as well as functional groups with high attraction to the subsequent coating, adhesive, or surface. Primer solutions can be water-based or solvent-based (sometimes called “oil-based” or “alkyd-based”). Water-based primers have less environmental impact with respect to waste and effluent handling and are therefore preferred for manufacturing. Common types of primers include polyurethanes, acrylics, epoxies, and polyesters. Primer coatings can consist of one component, which does not require a hardener or activator, or two components, which need to be mixed with a hardener. Primers may also contain additives for functional purposes, such as UV absorbers to prevent photodegradation of the underlying polymer. Primers involve dissolving a reactive chemical species in a solvent or a mixture of solvents. The solution is applied to the surface by spray, flow, or dip application, followed by a flash-off period to allow the solvent to evaporate. Depending on the type of chemical in the primer, the method of curing can be thermal or radiation, such as UV. Silane and isocyanate primers must react with moisture in the ambient air prior to application of an adhesive (Henkel 2011). A wide variety of primer solutions are commercially available and the choice of resins, additives, and processing methods should be considered with respect to the type of polymer, coating, and final application.

Metalized Coatings Metallization of plastics has been used for over a century, with the first commercial application recorded in 1905 (Liepins 2006). Applications for metalized plastics span a wide variety of industries, including automotives, building and appliances, electronics, cosmetics, packaging, toys and sporting goods, and more. Metallization can be applied to plastics to increase the gloss, reflectivity, surface conductivity, and the abrasion resistance. Metallization of plastics has enabled the use of plastics in applications traditionally dominated by metals, such as in some packaging and electronics components. Metalized plastics offer lower weight, better corrosion

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resistance in most cases, and lower cost than metals. Furthermore, the metallization process can be controlled to tailor the electrical conductivity of the finished part. In electronics applications, metalized plastics can be used for attenuation of EMI/RF signals. For aircraft windshields, a nanolayer of transparent gold is used for defogging and deicing. Metallization can provide decoration for plastics in packaging and other application. The most common techniques of metalizing plastics include vacuum metallization, flame and arc spraying, and plating. Vacuum metallization is a type of physical vapor deposition (PVD) process that is performed under vacuum as a batch process. The plastic part is placed in a vacuum chamber, usually on a rotating substrate holder to achieve a uniform coating. Metal in the form of powder or slug is placed in a tray on resistance heaters. The resistance heaters evaporate the metal, releasing a stream of atoms directed away from the heaters in a line of sight. The evaporated metal atoms condense on the surface, which initially form drops that eventually coalesce to form a uniform coating. Common metals for vacuum deposition include aluminum, copper, chromium, gold, silver, and nickel. Vacuum metallization can produce a uniform coating free of pinholes (Athey 2006). Flame and plasma arc spraying are liquid metal processes that require a heat source to melt the metal. Flame spraying involves a flame that melts a metal wire or powder and propels the molten metal to the substrate with compressed air. Arc spray uses metal wires to create an arc that melts the metal in the wires before propelling the molten metal to the substrate with compressed air. Plasma spray involves a plasma jet the melts metal powder and propels the molten metal to the substrate surface. Drawbacks of liquid metal spray processes include losses of 25 %, relatively poor adhesion, and a low-density porous coating (Athey 2006). Conventional electroplating involves placement of a conductive substrate into a solution of metal ions and an electric field to drive the metal ions to the substrate surface. Because most plastics are inherently nonconductive, the technique of electroless plating is typically applied initially to make the surface conductive. The basic process steps for electroless plating typically involve (1) acid etching, typically chromic and sulfuric acid; (2) activation with precious metals, such as tin and palladium, to catalyze growth of the metal layer; and (3) electroless deposition of a metal film from a solution (Hart 1996). An electroless plating solution typically contains the salt of the metal to be deposited, a reducing agent, ligands, buffers, and stabilizers that enhance autocatalysis (Vaskelis 2006). Other less common processes for metalizing plastics include sputtering and lamination of a metal film. Considerations that should be addressed for a metallization process of plastic include adhesion, the coefficient of thermal expansion, and corrosion of the metal. Surface treatments, such as acid etching, typically can enhance adhesion to the metal layer. A high disparity between the coefficient of thermal expansion of the substrate and the metal layer can result in delamination of the metal coating when exposed to thermal cycles. An initial thick copper layer can be applied to help offset this effect. Furthermore, a thick layer of nickel or microporous chromium can reduce susceptibility of the metal layer to corrosion (Hart 1996).

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Fig. 7 Image of PECVD equipment for PC glazing courtesy of SABIC Innovative Plastics and ULVAC Technologies, Inc

Other Coatings for Plastics Other types of surface coatings are used, including printing for decorative purposes, in-mold decoration (IMD), coextruded films, and plasma-enhanced chemical vapor deposition (PECVD). Some solutions involve multilayer treatments and coatings, such as the PECVD equipment shown in Fig. 7 that is used to deposit a protective layer on top of a wet coating on polycarbonate (PC) used for automotive glazing (SABIC and ULVAC). The major printing processes include flexography, gravure, screen printing, letterpress, pad printing, lithographic printing, and ink jet printing. The reader is referred to several sources for more comprehensive information on other coating processes (Yasuda 2005; Wolf 2010; Proell; Tracton 2006b).

Research and Development Trends As mentioned in the Introduction, environmental considerations are shaping the trends in coatings and surface treatments for plastics. Wet chemical treatments typically produce large amounts of hazardous waste and are being replaced with dry processes. Regulations that ban certain chemicals are forcing companies to

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change chemicals and processes, such as the European Union ban on hexavalent chromium. Radiation curing is growing, although still a small fraction of the coatings market in 2010 (IHS Chemical Report 2011). Powder coatings which are solvent-free have been adapted for use on plastics with no VOCs or hazardous by-products (Stay 2012). Coating and surface treatment technologies are used to enable the use of plastics to replace glass and metal parts to reduce the weight in transportation vehicles and improve fuel economy and emissions. For example, SABIC has developed polycarbonate glazing products using a combination of wet coatings and plasma coatings to replace glass and reduce vehicle weight (SABIC and ULVAC). The use of plastics in medical applications continues to grow and is expected to reach 4.4 billion pounds globally by 2015 (Schlechter 2010). Research on surface enhancements and techniques has also followed the trends in medical plastics to enable greater application of plastics in implants, tissue engineering, drug delivery, diagnostics, and other medical and life sciences applications. For biomedical polymers used in implants and tissue engineering scaffolds, cell adhesion, growth, and proliferation are critical for the successful application. Plasma treatment of biomedical polymers has been shown to significantly influence the interactions between the polymer surface and cells (Jacobs et al. 2012). Plasma treatment of polymers is also being investigated for antibacterial applications, cellular-based therapies, loadbearing implants, immobilization of biomaterials, biosensors, and others (Althaus et al. 2012; Garrido et al. 2010; Ogino et al. 2011; Landgraf et al. 2009).

Summary The bulk properties of a plastic alone cannot determine the performance of a plastic part in a given application. The surface properties are oftentimes critical to the success of the application. Surface properties can be enhanced and tailored using various surface modification techniques, as well as coatings and finishes. Surface modification methods include physical treatments, liquid chemical modifications, and reactive gas surface treatments. Physical treatments may bring about surface damage and liquid chemical treatments produce significant chemical waste. Reactive gas treatments include corona, flame, and plasma processes. Low-temperature plasma treatments offer an effective and versatile surface modification method for various polymeric materials by generating new surface functionalities and thus introducing the new surface properties required for many applications. Plasma processes applied to polymers have the merits of being economically efficient, dry, and environmentally benign in tailoring surface characteristics while maintaining their desirable bulk properties. However, plasma treatment of polymers could result in significant surface damage through plasma degradation reactions, which depend on the polymer sensitivity to plasma, the reactive chemicals and gases used, and the processing conditions. Dynamic surface tension techniques, such as the Wilhelmy balance, are effective tools for characterizing treated plastic surfaces. Coatings for plastics can be used for protection of the plastic in the final application or for imparting targeted properties. A wide variety of paints and coatings are available,

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and the choice of coating depends on the substrate, desired properties, and environment intended for the part. Metalization can provide decorative functions, conductivity, and increase abrasion resistance of plastics. Various techniques have been developed and widely used for plastics, including vacuum metallization, flame and plasma arc spray, and electroplating (via electroless plating). Other methods for imparting functional layers onto plastics include printing, IMD, coextrusion, and PECVD.

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Klemberg-Sapieha JE et al (1991) Dual-frequency N2 and NH3 plasma modification of polyethylene and polyimide. J Vac Sci Technol A 9:2975–2981 Koleske JV (2006) Radiation-cured coatings. In: Tracton A (ed) Coatings technology handbook, 3rd edn. Taylor & Francis, Boca Raton, p 97-1 Landgraf R, Kaiser MK, Posseckardt J, Adolphi B, Fischer WJ (2009) Functionalization of polymer sensor surfaces by oxygen plasma treatment. Proc Chem 1:1015–1018 Langmuir I (1938) Overturning and anchoring of monolayers. Science 87(2266):493–500 Liepins R (2006) Conductive coatings. In: Tracton A (ed) Coatings technology handbook, 3rd edn. Taylor & Francis, Boca Raton, p 91-1 Markgraf D (2004) Analysis of new flame treatment technology for surface modification and adhesion promotion. In: TAPPI PLACE conference proceedings, Indianapolis, IN Markgraf D Corona Treatment: An Overview. Enercon Industries Corporation. http://www. enerconind.com/mediaLib/stml/techPaper/Enercon-corona-treating-overview.pdf?ext¼.pdf. Accessed 2 September 2012 MIELE: Quality assurance on tumble dryers through plasma. http://www.sabic-ip.com/gep/en/ NewsRoom/PressReleaseDetail/october_11_2012_sabicandulvacannounce.html. Accessed 12 July 2013 Momose Y, Tamura Y, Ogino M, Okazaki S (1992) Chemical reactivity between teflon surfaces subjected to argon plasma treatment and atmospheric oxygen. J Vac Sci Technol A 10:229–238 Moss SJ et al (1986) Plasma oxidation of polymers. Plasma Chem Plasma Process 6(4):401–416 Nagao T et al (2006) Challenge to chromium-free plastic plating method. Galvanotechnik 97 (9):2124–2130 Nordstrom JD (2003) Polymers for coatings for plastics. In: Ryntz RA, Yaneff PV (eds) Coatings of polymers and plastics. Marcel Dekker, New York, pp 121–155 Ogino A, Noguchi S, Nagatsu M (2011) Low temperature plasma treatment for immobilization of biomaterials on polymer surface. Adv Mat Res 222:297–300 Proell. Pad printing – theory and practice. http://www.proell.de/_files/pdf/Pad%20Printing% 20Theory%20and%20Practice.pdf. Accessed 21 Oct 2012 Rangel EC et al (2003) Enhancement of polymer hydrophobicity by SF6 plasma treatment and argon plasma immersion ion implantation. Surf Interface Anal 35:179–183 Regis S et al (2012) Altering surface characteristics of polypropylene mesh via sodium hydroxide treatment. J Biomed Mater Res A 100A(5):1160–1167 Ryntz RA, Yaneff PV (2003) Coatings of polymers and plastics. Marcel Dekker, New York SABIC and ULVAC announce availability of new ULGLAZE system for high-volume plasma coating of automotive PC glazing components. http://www.sabic-ip.com/gep/en/NewsRoom/ PressReleaseDetail/october_11_2012_sabicandulvacannounce.html. Accessed 12 July 2013 Schlechter M (2010) Medical plastics market. BCC Research, Wellesley, MA Schonhorn H, Hansen RH (1967) Surface treatment of polymers for adhesive bonding. J Appl Polym Sci 11:1461–1474 Sheng E et al (1995) Effects of the chromic acid etching on propylene polymer surfaces. J Adhes Sci Technol 9(1):47–60 SmartAdhesives. Surface prep guide for adhesives. http://www.adhesive.com/instructions_detail_ surfaceprep__adhesives_application.html. Accessed 2 Sept 2012 Stay KE (2012) InnoVoc Solutions™ technology enables powder coating for plastics, composites and other non-metallic substrates. http://www.innovocsolutions.com/pressreleases.html. Accessed 15 Mar 2014 Strobel M et al (1996) Flame surface modification of polypropylene film. J Adhes Sci Technol 10 (6):515–539 Strobel M, Jones V, Lyons CS, Ulsh M, Kushner MJ, Dorai R, Branch MC (2003) A comparison of corona-treated and flame-treated polypropylene films. Plasma Polym 8(1):61–95 Tatoulian M et al (2004) Plasma surface modification of organic materials: comparison between polyethylene films and octadecyltrichlorosilane self-assembled monolayers. Langmuir 20:10481–10489

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Tolinski M (2009) Coating alternatives for plastics: optical coatings and VOC-free technologies are becoming clearer options. Plast Eng 65(5):6–8 Tracton A (2006a) Coatings technology handbook, 3rd edn. Taylor & Francis, Boca Raton Tracton A (2006b) Coatings technology handbook, 3rd edn. Marcel Dekker, New York Vaskelis A (2006) Electroless plating. In: Tracton A (ed) Coatings technology handbook, 3rd edn. Taylor & Francis, Boca Raton, p 27-1 Vetaphone. http://www.vetaphone.com/?page¼History&id¼7. Accessed 2 Sept 2012 Weikart CM, Yasuda HK (2000) Modification, degradation, and stability of polymeric surface treated with reactive plasmas. J Polym Sci A 38:3028–3042 Wertheimer MR, Martinu L, Liston EM (2002) Plasma sources for polymer surface treatment. In: Glocker DA (ed) Handbook of thin film process technology, vol 2. Institute of Physics Publishing, Bristol, pp E3.0:1–E3.0:38 Wicks ZW, Jones FN, Pappas SP (2007) Organic coatings: science and technology, 3rd edn. Wiley, Hoboken Wolf RA (2010) Plastic surface modification. Hanser, Munich Yasuda H (2005) Luminous chemical vapor deposition and interface engineering. Marcel Dekker, New York

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Polymer Foam Technology Xiao Hu, Erwin Merijn Wouterson, and Ming Liu

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classification of Polymer Foams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Blowing Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chemical Blowing Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Physical Blowing Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General Consideration on BA Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Foaming Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Injection Molding Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reaction Injection Molding (RIM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Expandable Polymer Pellets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extrusion Foaming Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nanocomposite Foams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Processing of Nanocomposite Foams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Foam Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Microcellular Foams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Processing of Microcellular Foams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Foam Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . High-Performance Polymeric Foams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Polyimide Foams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phthalonitrile Foams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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X. Hu (*) School of Materials Science & Engineering, Nanyang Technological University, Singapore e-mail: [email protected] E.M. Wouterson School of Mechanical and Aeronautical Engineering, Singapore Polytechnic, Singapore e-mail: [email protected] M. Liu Temasek Laboratories@NTU, Nanyang Technological University, Singapore e-mail: [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_23

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Syntactic Foam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Processing of Syntactic Foam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mechanical Behavior of Syntactic Foam Versus Filler Volume Fraction . . . . . . . . . . . . . . . . . Summary and Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

This chapter covers two major classes of polymer foams, the conventional foams formed by foaming agents and syntactic foams. The first part presents the basics of polymer foaming through the use of blowing agents including a brief introduction on blowing agents and the common foaming methods used by both laboratories and industries. Current technologies used for specialty foam fabrication are included in the discussion. The multiple roles played by nanoparticles during foam formation and the effects on the foam properties are addressed in detail for nanocomposite foams. The various factors affecting the formation of microcellular foams and the properties of microcellular foams are then discussed. Two detailed examples for high-performance polymer foams are highlighted in the article. An exceptional section is devoted to syntactic foams, covering both the processing and the mechanical behaviors. It starts with general preparation of syntactic foam in a laboratory-based environment. The discussion highlights the typical mechanical behavior as well as the change in mechanical behavior observed when the content of microspheres is changed. Details on how the content of microspheres affects the mechanical and fracture properties of syntactic foams are presented. Besides looking at various content of microspheres, the existence of various toughening mechanisms in syntactic foams and the kind of toughening strategies can be used to improve the toughness of syntactic foams are also included in the section. Finally, some of the issues concerning polymeric foams and the latest developments in the field including future trends are addressed.

Introduction It is almost unavoidable to come across some sort of polymer foam in daily life in the modern world. The excellent characteristics such as superior acoustic absorption, strength-to-weight ratio, cost-effectiveness, and ease of being processed in various forms make polymeric foams an attractive material to be found virtually everywhere. High-density polymer foams are found to replace traditional materials in transportation and building constructions for weight reduction, while low-density foams are commonly used in shock absorption and rigid packaging. The flexible and soft polymer foams provide comfort when used for furniture and bedding. The low heat transfer and sound transmission inherited from the porous structures make them optimal thermal and sound insulators. New insights and innovations continue to drive the field of polymer foams forward and are expected to witness increasing attentions (Fig. 1).

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Fig. 1 Photograph of products made from polymer foams. Top left: packaging and disposal drinking cups. Top right: running shoes. Bottom: protective cycling helmet

Various industries are on a constant search for these lighter structures. One of the major factors that drive the effort to reduce the weight of structures is the increase in fuel prices. In particular, the transportation industries, i.e., automotive, marine, and aerospace industry, spend a significant amount of money to fund their R&D activities with the aim to reduce the weight of cars, boats, and aircraft, respectively. The progresses made on polymer nanocomposite laid a solid foundation for nanocomposite foams fabrication. These foams take advantage of the size compatibility of the nanofillers to achieve microscopic reinforcements where conventional fillers failed to do so. Novel porous materials are fabricated by incorporating functional nanofillers and the applications are extended to fields of biomedical, tissue engineering, electronics and areas require high temperature resistance. The technologies for polymer foam fabrication are constantly steered by environmental considerations and cost-effectiveness. Recent research is focused on using more environmentally friendly chemicals for foaming and less stringent conditions such as lower temperature or pressure. Microcellular foams attracted much attention since the invention in the 1980s. Common gas releasing agents such as CFCs were replaced by inert gases such as supercritical CO2 and N2 which almost cause zero environmental impacts.

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Syntactic foams represent a special kind of composite materials comprising of a matrix and a hollow filler. The filler is often also referred to as hollow spheres with diameter ranging from a couple of nanometers to millimeters and can be made of various types of materials, including polymers, glass, metals, and carbon. Glass microspheres are most frequently used due to their mechanical strength, smoothness, regularity of the surface, good wetting characteristics, and low viscosity of the resulting foam. The extremely low density of the hollow spheres effectively reduced the density without severe mechanical performance deteriorations. The inclusion of spherical particles also eliminates the problem posed by the anisotropic bubbles in conventional foams and acted as a good moisture barrier due to the highly closed-cell nature.

Classification of Polymer Foams The nature of the polymer resin being used categorized the foams into thermoset and thermoplastic foams, which are then further divided into rigid and flexible foams. A rigid foam is defined as one in which the polymer exists in the crystalline state or, if amorphous, is below its Tg. Following from this, a flexible cellular polymer is a system in which the matrix polymer is above its Tg. The foam cell size classified foams into macrocellular (>100 μm), microcellular (1–100 μm), ultramicrocellular (0.1–1 μm), and nanocellular (0.1–100 nm). It has to mention here that such a definition is necessary because according to IUPAC recommendation, materials with pores in the nanometer scale are also termed as microporous (McCusker et al. 2001). Another way of classifying polymer foam is through the connectivity of the cells. Closed-cell foams consisted of cells which are isolated from each other by solid cell struts, a feature that helps to improve the insulation and dimensional stability and prevents moisture absorption. Normally the closedcell foams have higher compressive strength and are generally denser comparing to opened cell foam. Open-cell foams contain cells that are connected to each other and formed an interconnected network. Individual cells in opened cell foams are interconnecting which allows the cells to be filled with whatever they are surrounded with. However, the opened cell foams are generally more inferior in mechanical performances.

Blowing Agents Introducing gas into a polymer system is a crucial part of polymer foam fabrication process since gaseous phase defined the voids for such porous structures. During foaming, gas can be physically incorporated into the polymer matrix or released through chemical reactions. In either case, the gas-forming compound is termed as blowing agents (BAs). BAs are traditionally classified into two broad classes, chemical blowing agents (CBAs) and physical blowing agents (PBAs); in recent years, inert gas and gases in the supercritical state are also used.

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Chemical Blowing Agents CBAs are organic or inorganic compounds or mixtures of compounds that liberate gas as a result of chemical reaction such as thermal decomposition or through interactions with other components within the formulation. Chemical blowing agents are generally being characterized by: 1. The gas number: the volume of gases being liberated by 1 g of the blowing agent per unit time (1 min) at the maximum gas liberation temperature 2. The decomposition temperature 3. The temperature range of the maximum rate of decomposition 4. The rate and kinetics of the gas liberation 5. The pressure of the foaming system developed by gas The abovementioned characteristics provide an approximate data, and adjustments have to be made for real foam development. Organic CBAs are sometimes preferred than their inorganic counterparts due to the better compatibility and ease in dispersion. However, the residual components from incomplete decomposition may act as plasticizers and reduce the foam performances. Table 1 lists some commonly used CBAs for polymer foaming, the decomposition temperature, gas released, and the estimated gas yield. Activators can be used to lower the decomposition temperature and rate.

Physical Blowing Agents PBAs refer to compounds which change the physical state upon heating or pressure change and do not affect the chemical or physical properties of the host matrix. Common PBAs include volatile organic compounds such as low boiling hydrocarbons and alcohols and compressed gases or gases in the supercritical state such as supercritical carbon dioxide. Highly porous solids such as activated carbon and silicates are often used as absorbent to facilitate the handling of volatile liquids. Table 1 List of commonly used CBAs for polymer foaming CBA 20 20 -Azobis(isobutyronitrile) 1,10 -Azobis (1-cyclohexylcyanide) Azodicarbonamide Sodium bicarbonate 5-Phenyltetrazole p-Toluenesulfonylhydrazine 2,4,6-Trihydrazino-1,3,5-triazine

Decomposition temperature (
C) 95–104 110–115

Gas released N2 N2

Gas yield (cm3/g) 137 85

200–230 150–230 240–250 110–140 245–285

N2, CO2, NH3 CO2, H2O N2 N2, H2O N2, NH3

120 165 200 120 185

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Chlorofluorocarbons (CFCs) are the most widely used PBA due to their incombustibility, low toxicity, and well-defined gas release temperature and rate. Despite the advantage, CFCs are phased out due to their negative effects in ozone depletion. The process of replacing CFCs speeded up after the signing of “Montreal Protocol on Substance That Deplete the Ozone Layer” in September 1987. Hydrogen-containing CFCs with shorter atmospheric lifetimes are considered as a potential replacement. Although they contribute much less to stratospheric ozone depletion, they are viewed only as temporary replacements. Intensive researches are ongoing to find an environmental friendly candidate. Supercritical fluids such as carbon dioxide and nitrogen attracted much attention in recent years as BA for polymer foam processing for the strong advantages exhibited. Usually the polymer matrixes are saturated with the fluid under pressure and later subject to further pressure increase or heating for bubble nucleation. Such process allows the production of polymer foam with cell size diameter smaller than 100 μm and population density larger than 109 cells per cm3 which is almost unachievable by the addition of conventional BAs. Using supercritical fluid also provides a “clean” foaming process without the use of harmful organic solvent and eliminates the hassle of residual from unreacted BAs from the final products which may deteriorate the final foam performances. However, such foaming method is yet to be employed by the industries due to the high pressure and stringent equipment required.

General Consideration on BA Selection There are several general guidelines to follow while selecting a suitable blowing agent: 1. The decomposition or reaction temperature should be close to the melting temperature and the hardening temperature of the polymer. 2. Chemical compatibility enhanced the dispersion of BA in the matrix. 3. Gas must be liberated within a narrow temperature range. 4. Highly exothermic reaction should be avoided to prevent the polymer matrix from thermal damage. 5. The liberated gas and residual component should not have negative effect on the polymerization process of the polymer matrix. 6. The gas released should be readily dispersed and dissolved in the polymer melt. 7. Cost-effectiveness. 8. The final cell structure required.

Foaming Processing Since the invention of polystyrene (PS) foam in the 1930s, foaming technologies progressed and matured. Nowadays, it is possible to produce foams of nearly every polymer resin through one or more of the following processes:

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Injection Molding Process Polymer matrix and blowing agents (BA) are processed conventionally in the screw injection molding equipment equipped with gas leakage preventive measures such as nozzle shutoff valve or additional external control gate. Gases released from BA through thermal decomposition are dissolved in the polymer melt and injected directly into an enclosed mold cavity to produce foam products as the pressurized gases expand when subjected to atmospheric pressure. BA decomposition should occur during plasticizing process and remain dissolved in the polymer melt until the gas-melt mixture is injected to the enclosed mild. This process often produces foams with a sandwich structure consisting of a continuous foam skin and cellular core and is commonly used to produce thermoplastic polymer foams such as high-density polystyrene (PS) foams, both rigid and flexible, poly(vinyl chloride) (PVC) foams, acrylonitrile butadiene styrene (ABS) structural foams, and thermoset foams such as polyurethane foams.

Reaction Injection Molding (RIM) Reaction injection molding systems combine two or more liquid components that chemically react in a closed mold to form polymer foam, taking the intricate shape of the mold. Figure 2 shows a simple schematic illustration of the RIM process. The components are precisely controlled at stoichiometric ratio before combined at the

Fig. 2 Schematic diagram of reaction injection molding process

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mixing head by high velocity impingement mixing. The process is usually carried out at low temperature involving comportments of low viscosity which greatly reduce the pressure required to drive the components into the mold. RIM is widely used in industries for the production of large, complex parts such as bumpers for vehicles, panels for electrical equipment, enclosures for medical devices, and housings for computer and telecommunications equipment. Recently applications are extended to microcellular foam fabrications.

Expandable Polymer Pellets The polymer pellets are first impregnated with BA during the polymerization process. These pellets are then subject to heat for free expansion or confined expansion. For example, freely expanded PS pellets are commonly used in packaging and as cushion fillings. For confided expansion, the expandable pellets filled up the entire predesigned mold and fused together forming foam products with well-controlled density, shape, and sizes. Disposable foam cups are an excellent example of polymer foams produced by confined expansion from expandable PS pellets.

Extrusion Foaming Process STYROFOAM™ is perhaps the most popular type of polymer foams known to everyone. However, it should not be mistaken as the white disposable foam cups made from expansion process explained in the previous paragraph. STYROFOAM™ is a registered trademark for a line of extruded polystyrene foam products made exclusively by The Dow Chemical Company for thermal insulation and craft applications (Company DC. It is not a cup). The typical extruders as shown in Fig. 3 used for bulk polymer products are commonly used with necessary modifications which mix BA into the polymer melt homogeneously with applied pressure. The sudden drop in pressure led to rapid cell growth as soon as the melt is being extruded out from the die’s orifice. Continuous extrusion is preferred from the economical point of view due to the higher throughput and versatility in the properties and shapes of the products obtained. Besides the traditionally used polymer resins for foam fabrication, trends are moving toward the use of nonconventional foam materials such as biodegradable polyhydroxyalkanoates (Liao et al. 2012). In general, an ideal foaming system consists of a polymer with a gelation time that coincides with the time required for rapid gas liberation; hence the solidifying polymer will be able to “trap” the gas bubbles inside the matrix. Prior to foaming, it is necessary to characterize the BA and to obtain the viscosity profile in order to establish a balanced system for proper foam fabrication. An example is given in Fig. 4 which illustrates that 220
C is the “ideal” foaming condition in which gas liberation and polymer cross-linking occurred within the same time frame. In contrast, when the polymerization process took too long a time, the rising foam may collapse before the

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Fig. 3 Typical extrusion machines used for polymer processing

Fig. 4 Diagram showing the relationship between gas liberation of azodicarbonamide and viscosity change of resorcinol-based phthalonitrile at 190
C and 220
C

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polymer matrix failed to gain enough gel strength to form a stable foam system. Similarly, if polymerization occurred before gas liberation, the resulting foam may have a density much higher than expected. Cell growth was inhibited when the gas generated was unable to overcome the rapidly increasing gel strength.

Nanocomposite Foams There are many emerging applications involving the dispersion of nanoparticles into a polymer matrix with the aim to enhance the properties of the composite material. One of the main reasons why nanoparticles are able to improve the properties of polymer resins is their large surface-to-volume ratio. The large ratio increases the number of particle–matrix interactions, thus increasing the effects on the overall material properties even at rather low filler loadings. The nanoscaled fillers are especially beneficial for foam property enhancements primarily because the foam cell walls are normally within the submicron regime which conventional fillers are incompatible in terms of size. Improvements in thermal, electrical, and mechanical properties could be achieved by synergistically combining the properties of the matrix and the fillers without altering the desired density or the foam morphology. Traditionally three types of nanofillers of distinct geometries as being illustrated in Fig. 5 are used. 0D nanofillers are being characterized by having all the three dimensions in the nanometer scale. Typical examples include spherical silica particles, nanocrystals, and metal particles. Typical examples of 1D nanofiller are nanotubes and nanofibers which feature two dimensions in nanoscale. The last type of nanofillers, 2D, has a lateral dimension in the range of several hundreds of nanoto micrometers and nanoscale thickness. Conventional nanofillers, such as organoclay, are one of the most popular nanofillers being used for nanocomposite fabrication. However, these fillers cannot be used as cell nucleating agents in high-processing-temperature polymers due to

Fig. 5 List of nanofillers, the geometries, and surface area to volume ratio

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thermal degradation of the organic modifiers. For such applications, nonconventional organic modifiers or inorganic nanofillers should be used. Graphene has demonstrated to be an excellent candidate for its thermal stability and has shown its potential in improving the mechanical, thermal, and electrical properties. To produce single graphene layers for mass manufacturing of nanocomposite foams still poses challenges in both the fabrication method and the economic aspects. Recent development at Michigan State University on the production of robust graphene sheets of one to five layers thick and diameters ranging from less than 1 μm to over 100 μm at cost-competitive prices could be an effective solution.

Processing of Nanocomposite Foams In general, two separate steps are involved in the processing of nanocomposite foams: the synthesis of nanocomposite and subsequent foam formation through different foaming techniques as explained in the previous paragraph. It is well understood that particles, especially in the nanorange, tend to agglomerate due to the dominant intermolecular van der Waals interactions between them. Possible incompatibility between the nanofillers and the polymer matrix makes it even more challenging to achieve uniform dispersion. Hence, surface modifications are usually carried out to promote interactions between the nanofiller and the matrix in order to overcome the strongly bonded nanofiller aggregates. Numerous studies and research were carried out to break up the nanofiller agglomerates for homogeneous nanocomposite preparation and were well documented in various academic and industrial publications and patents. A detailed survey on nanocomposite preparation is beyond the scope of this chapter. The focus of this part of the chapter is placed on the effects of nanofillers on the foaming process and foam properties. Besides the performance enhancement exhibited in nanocomposite, nanofillers may serve as heterogeneous nucleation centers facilitating the formation of bubbles, improving the cell density, homogenizing the cell size, and altering the rheological properties of the foaming polymer affecting bubble stability, foam morphology and the foam density.

Nucleating Effect Adding nucleation fillers to improve the cell density, which is defined as the number of bubbles per cm3, is a common practice in foam manufacturing processes. It is generally understood that nanofillers with low surface energy will reduce the free energy (DF) (see Eq. 1). Reduction of free energy is required for bubble initiation in liquid by lowering the surface tension at the liquid–solid interface (Klempner and Frisch 1991): ΔF ¼ γ  A

(1)

where γ is the surface tension and A is the total interfacial area. Figure 6 shows how the cell size in polystyrene (PS) foam was reduced by a great extend and the cell density was increased by at least two orders of magnitude comparing to the pure foam by adding a small amount of nanofillers.

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Fig. 6 Cell morphologies of (a) PS foams, (b) PS/1 % CNFs, and (c) PS/0.1 % CNTs (Lee et al. 2005) (PS polystyrene, CNFs carbon nanofibers, CNTs carbon nanotubes)

A fine dispersion of nanofillers will greatly enhance the nucleation efficiency, resulting in increased cell density and reduced cell size. This can be easily understood as more gas was being consumed by bubble nucleation at the nanoparticle sites. Simultaneously, less gas would be available for bubble growth, hence reduced the final cell size. The classical steady-state nucleation theory is often used to qualitatively describe the number of nucleation sites (see Eq. 2): ΔNo ¼ Co fo exp


ΔG

=kB T

crit



(2)

where ΔGcrit is the critical nucleation formation energy and is described as per Eq. 3. kB is the Boltzmann factor, and T is the absolute temperature. Co is the number of gas molecules dissolved per unit volume of the primary phase; fo is a kinetic pre-exponential factor.

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ΔGcrit ¼ 16πσ

.

3

(3)

3ΔP2

ΔP is the pressure difference inside and outside the nucleating bubble. However, it has to be pointed out that the data obtained by the theory shows a great deal of discrepancy from actual experiments because the nucleation formation energy is dependent on the critical nucleus size, which is an inaccessible parameter during actual foaming process. Lee et al looked into heterogeneous nucleation based on the classical steady-state nucleation theory to qualitatively describe the number of nucleation sites (Ni) generated by nanoparticles (Spitael et al. 2004; Zettlemoyer 1969; Colton and Suh 1987a, b) by introducing fi, a frequency factor of gas molecules joining the nucleus, and Ci to account for the concentration of the heterogeneous nucleation sites (see Eq. 4).   hert Ni ¼ Ci fi exp ΔGcrit=kB T (4) where the energy required to form a nucleus is considered proportional to the energy required in a homogeneous system by a factor dependent on the contact angle between the gas and polymer and particle surface: 16πσ ΔGhert crit ¼

.

3

3ðPG PL Þ

2

SðθÞ

  1 SðθÞ ¼ ð2 þ cosθÞ ð1  cosθÞ2 4

(5)

(6)

A general selection guideline for nucleating agents was proposed by McClurg and Leung (McClurg 2004; Leung et al. 2008): • The nucleation agents should be able to lower the surface energy barrier needed for bubble initiation relative to homogeneous nucleation and unintentional heterogeneous nucleation caused by contaminants in the polymer matrix. • Ideal nucleating agents should have uniform sizes, surface geometries and surface properties and should be easily dispersible. • A rugged surface as illustrated by Fig. 7 that contains many conical crevices of small semi-conical angles (β) is preferred.

Qualitatively, nucleating agent with small contact angle and high surface curvature causes more effective critical energy reduction and results in higher efficiency (Fletcher 1958). However, high nucleation efficiency will be achieved not only by choosing the right geometry and surface but, more importantly, through a good dispersion process (Colton and Suh 1987a, b; Lee et al. 2005). Carbon nanofibers are more effective nucleating agents compared to carbon nanotubes and nanoclay due to their increased homogeneous distribution and their favorable surface and geometrical characteristics (Lee et al. 2005).

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Fig. 7 Surface geometry of nucleating agents with rugged surface, bindicate the semiconical angle (Adapted and redrawn from Leung et al. (2008))

Bubble Stabilization By nature, liquid foams before solidification are thermodynamically unstable. Movements of the liquid cell through capillary actions and gravity further promote the collapse of the foams due to cell thinning. Bubble stabilization is generally achieved in two ways. At first the melt viscosity increases or stabilizes by the adsorbed particles on the foam cell surface. When the viscosity or melt strength of the liquid cell wall has increased, the force required to overcome pressure difference increases. This effect combats excessive liquid cell movement and hence slows down the cell thinning process and stabilizes the cell structure. There have been several recent examples of foams being stabilized by particles adsorbed at air/liquid interface forming a rigid shell that protects the bubbles against coalescence (Dickinson et al. 2004; Hunter et al. 2008; Gonzenbach et al. 2006).

Foam Properties Thermal Properties Well-dispersed nanofillers generally improve the thermal properties of polymer foams. Several mechanisms have been proposed to explain the phenomenon: (i) the barrier effect delayed the escape of volatile decomposition products during degradation; (ii) the nanofillers created a tortuous path for air, delaying the thermooxidative degradation of the material; and (iii) the thermally conductive nanofillers could ease the heat dissipation within the matrix. Figure 8 shows that the addition of 0.1 wt% of functionalized graphite sheets and carbon nanotubes shifted the onset of degradation to higher temperature and improved the char yield by about 50 % compared to the control silicone foam. Compression Properties The compressive properties of foam are highly dependent on their apparent density; hence, the compressive parameters are usually normalized to exclude the effect of density variations. Verdejo et al. demonstrated that the addition of CNTs and functionalized graphene sheets (FGS) into flexible silicone foams caused drastic changes in the compressive behavior (Verdejo et al. 2008) (see Fig. 9). The combination of density change and the reinforcement effect of the nanofillers increased the normalized Young’s modulus by over 200 % through the addition of only 0.25 wt% of FGS. Similar behavior was observed for the case of rigid

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Fig. 8 Weight loss as a function of temperature obtained by thermogravimetric analysis (TGA) (Verdejo et al. 2008)

foams; see Fig. 10 which shows the relationship of compressive parameter of rigid polyurethane foams reinforced by multiwall carbon nanotubes (MWNT) and carbon nanofiber (CNF), respectively. It should be pointed out that not all nanofillers lead to property improvements. For the case of a rigid foam system of PU, hydrogen bonds within the structure network play a prominent role in the mechanical properties. The addition of organic fillers, such as organoclay, may interfere with the formation of hydrogen bonds, hindering the structural formation and causing property deterioration. Hu and co-workers studied the effects of nanofiller content on the compression properties of rigid phthalonitrile foams. Results, as shown in Fig. 11, indicate that the specific compressive stress improvement is dependent on the type of filler incorporated. Both MWNT and expanded graphite seem to be able to improve the compressive stress. However, excessive amount of filler resulted in negative results caused by aggregation of the nanofiller creating a point of failure initiation. On the contrary, fumed silica caused the foam property to drop regardless of the loading content caused by poor interfacial interactions between the filler and the matrix.

Microcellular Foams While the conventional foaming techniques produced foams with cell size in the order of 100 or more micrometers, the idea of microcellular foams emerged in the early 1980s as a means to reduce the cost of mass-produced polymer items

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Fig. 9 Compressive stress–strain behavior of silicone foam nanocomposites (Verdejo et al. 2008)

(Martini-Vvedensky et al. 1984). Microcellular foams are typically rigid structures consisting of closed-cell or open-cell sizes ranging from a few to tens of micrometers and a cell density greater than 109 cells/cm3. The environmental friendly products are usually produced without the use of BAs such as CHCs and are widely

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Fig. 10 Normalized compressive modulus and strength of rigid polyurethane foam (Dolomanova et al. 2011)

used in the food packaging industries. The automotive industries start to replace parts with the low shrinkage, weight-reducing microcellular foams; this high strength-to-weight ratio material even lands itself in the aeronautical and transportation industries. Recent progress made on open-cell microcellular foams widen the

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Fig. 11 Specific compression stress as a function of nanofiller content at foam density of 0.12 g/cm3 (FS fumed silica, MWNT multiwalled carbon nanotubes, GH expanded graphite)

potential applications to tissue engineering. The unique optical performances exhibited by a sheet developed by microcellular foaming technology triggered considerable interest in using these materials as reflector and diffusion sheet in liquid crystal displays by tuning the cell morphologies (Lee et al. 2011).

Processing of Microcellular Foams Solid-state batch foaming process (see Fig. 12) is a straightforward foaming process commonly used for microcellular foam preparation. The process is carried out in two main steps. The free volume of a semisolid or solid polymer is first saturated with inert foaming gas, such as N2 or CO2, or a physical blowing agent inside a high pressure vessel. According to Henry’s law, the amount of gas dissolved in the polymer is directly proportional to the applied pressure; hence pressure is usually applied to improve the relatively low solubility of the gases. Besides pressure, sufficient time must be given to the system to attain an equilibrium concentration that is consistent with the solubility of gas in the polymer and the gas pressure. The fully saturated system is then driven to the supersaturated state by depressurization or increasing the temperature. Both strategies will reduce the solubility of the gas resulting in a thermodynamic unstable system which provokes a phase separation as the driving force for gas nucleation and growth. One consequence of dissolving gas in the polymer is the reduction of the polymer’s glass transition temperature due to plasticization effect. If heating is employed, the temperature of the gas-saturated polymer only needs to be raised to the glass transition temperature of the gas–polymer system to nucleate bubbles. The resultant microcellular structure and properties are affected by the properties such as the presence of nucleation sites, polymer matrix crystallinity, polymer

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Fig. 12 Schematic of the batch process to make solid-state microcellular foams

melt strength, and gas solubility. To further complicate the process, these properties are heavily dependent on the processing conditions such as temperature, pressure, and gas saturation time. Well-dispersed nanoparticles, such as fumed silica, clay, carbon nanotubes, zinc stearate, and titanium oxide, can promote bubble initiation by acting as heterogeneous nucleation centers. The nucleation efficiency can be predicted from the classical nucleation theory as described earlier. Amorphous polymers are preferred over the highly crystalline structures since gas solubility in the crystal structure is substantially lesser than that in the amorphous region. The solubility of gas in a semicrystalline polymer matrix decreases as the crystallinity increases. As a result, the void fraction and cell size will be reduced. Liao et al used long-chain polypropylene to show the effects of spherulite on cell density at different stage of foaming. When first formed, the spherulite caused structural heterogeneity in the polymer matrix and increased the cell density by heterogeneous bubble nucleation. The growing spherulite then increased the polymer melt viscosity and lowered the possibility of cell coalescence. The cell density further increased through homogeneous nucleation when the larger spherulite excluded the bubble-forming gas out of the crystalline regions. Figure 13 shows the change in morphology under different saturation pressure by keeping the temperature constant. Increasing the pressure will force more gas into the polymer matrix, hence increasing the number of nuclei available for bubble initiation; as a result, cell size decreases and cell density increases. Foaming temperature is an important parameter in microcellular foaming process because nearly all the physical properties, such as viscosity, surface tension, gas solubility and diffusivity, and so forth, are temperature sensitive. While trying to increase the diffusion rate of gas into the polymer matrix by increasing the

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Fig. 13 Porous PS foamed at 80
C; pressure at (a) 180, (b) 230, (c) 280, (d) 330, and (e) 380 bar; and depressurization time σy, then the material plastically deforms. If σv < ¼ σy, then the material only deforms elastically. 2  2  2  1  σxx  σyy þ σyy  σzz þ ðσxx  σzz Þ2 þ 6 σxy þ σyz þ σzx σv ¼ σ ¼ 2 rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2  2 1  σx  σy þ σy  σz þ ðσx  σz Þ2 ¼ 2 (35) where σ1σ2σ3 are the principle stresses (normal stresses in the directions without any shear stresses) and 

σv  σy , elastic deformation σv > σy , plastic deformation

It is worth noting that in this case, if uniaxial stress is considered, σ1 6¼ 0, σ2 ¼ σ3 ¼ 0, therefore the von Mises criterion simply reduces to σ1 ¼ σy, which is the yield point of uniaxial loading.

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Yield Function: Hill’s The quadratic Hill’s yield criterion has the form  2  2 F σxx  σyy þ G σyy  σzz þ Hðσxx  σzz Þ2 þ 2Lσ2yz þ 2Mσ2zx þ 2Nσ2xy ¼ 1 (36) Here F, G, H, L, M, and N are constants that are needed to be determined experimentally. The quadratic Hill yield criterion depends only on the deviatoric stresses and is volumetric stress independent. It predicts the same yield stress in tension and compression. It is especially useful when the material has strong texture, for instance, for rolled plates or hot-extruded billets. In materials science, texture is the distribution of crystallographic orientations of a polycrystalline sample. A workpiece in which these orientations are fully random is said to have no texture. If the crystallographic orientations are not random, but have some preferred orientation, then the sample has a weak, moderate, or strong texture depending on the number of crystallites sharing the sample orientation. The material properties show a strong anisotropy because of the texture, and the phenomenon of anisotropic yield can be accounted for using Hill’s yield criterion. The disadvantage associated with the criterion is that there are quite a number of coefficients (six in the quadratic form) to be determined (hence multiple tests are required) before it can be applied to finite element simulation.

Lankford Coefficient Earlier we briefly discussed the anisotropy in elasticity and anisotropy in yield. The anisotropy in plasticity is in no way simpler than that of elasticity or yield. A commonly used plastic anisotropy indicator is the Lankford coefficient (also called Lankford value or R-value). This scalar quantity is used extensively as an indicator of the formability of recrystallized low-carbon steel sheets. Its definition follows: If x and y are the coordinate directions in the plane of rolling and z is the thickness direction, then the Lankford coefficient (R-value) is given by R¼

εpxy εpz

(37)

where εpxy is the plastic strain in-plane and εpz is the plastic strain through the thickness. In practice, the R-value is usually measured at 20 % elongation in a tensile test. For sheet metals, the R-values are usually determined for three different directions of loading in-plane (0 , 45 , and 90 to the rolling direction) and the normal R-value is taken to be the average 1 R ¼ ðR0 þ 2R45 þ R90 Þ 4

(38)

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Fig. 29 Optical micrograph of AA6061 aluminum alloy showing polycrystalline structure

The planar anisotropy coefficient or planar R-value is a measure of the variation of R with angle from the rolling direction. This quantity is defined as 1 Rp ¼ ðR0  2R45 þ R90 Þ 2

(39)

It has been widely recognized that anisotropy is closely linked with the material microstructure. The evolution of material microstructure during bulk forming process may greatly influence the end product’s structural integrity. Hence, understanding the microstructural behavior, before and during the forming process, is one of the main focuses.

Simulation of Microstructure Evolution During Bulk Metal Forming Processes The finite element simulation of microstructure evolution is a very hot topic in research communities. Divided opinions exist in many areas, even on the definitions of some fundamental mechanisms. Therefore, in this section, only the most popular definitions and generalized equation forms are provided. To start, an introduction on the metal microstructure has to be provided. Metal alloys are unusually polycrystalline solids, which consist of many crystallites that are small, often microscopic crystals that are held together through highly defective boundaries. Metallurgists often refer to these crystallites as grains (grain size 30 μm). Figure 29 provides an example of the grain structure in the steel. They are normally of different orientations and separated by grain boundaries. Grain boundaries are interfaces where crystals of different orientations meet. Grains

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in the metal change shapes and orientations during forming process, which in turn influence the material mechanical properties. Modeling such changes may not be easy as there are thousands of grains in the workpiece and capture of individual behavior becomes computationally impossible. Therefore, such changes are always modeled using statistical methods. To represent the grain structure before deformation, methods like cellular automata (Wolfram 1983) and voronoi tessellation (Voronoi 1908) are commonly employed. A value of average grain size is of course another option (although very rough). For the grain structure evolution during deformation, numerous phenomenological models have been developed in this area, and controversies exist on the definitions of various recrystallization mechanisms. However, the computational algorithms behind them are similar: in each time step, local temperature, strain, strain rate, and evolution history, the mechanism of evolution is determined, and then the corresponding grain variables are computed and updated. In the condition that all the phenomena can be divided into the following three microstructural evolution groups, then in each group the corresponding mathematical function can be used to describe such evolution. Dynamic recrystallization (DRX) occurs during deformation and when the strain exceeds the critical strain. The driving force is dislocations annihilation. Static recrystallization occurs after deformation and when the strain is less than the critical strain. The driving force for static recrystallization is dislocations annihilation. The recrystallization begins in a nuclei-free environment. Grain growth occurs before recrystallization begins or after recrystallization is completed. The driving force is the reduction of grain boundary energy.

Dynamic Recrystallization The dynamic recrystallization is a function of strain, strain rate, temperature, and initial grain size, which change in time. It is very difficult to model dynamic recrystallization concurrently during forming as this has the possibility of creating numerical instability. Instead, the dynamic recrystallization is computed in the group immediately after the deformation stops. The average temperature and the strain rate of the deformation period are used as inputs of the Equations. Activation Criteria The onset of DRX usually occurs at a critical stain εc εc ¼ a 2 εp

(40)

where εp denotes the stain corresponding to the flow stress maximum: εp ¼ a1 dn01 ε_m1 eðQ1 =RTÞ þ c1

(41)

in which d0 is the initial grain size, R is the gas constant, T is the temperature in Kelvin, and Q is activation energy.

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Kinetics The Avrami equation (Avrami 1939) is used to describe the relation between the dynamically recrystallized fraction X and the effective strain. "

Xdrex

  # ε  a10 εp hd ¼ 1  exp βd ε0:5

(42)

where ε0.5 denotes the strain for 50 % recrystallization: ε0:5 ¼ a5 dn05 ε_m5 eðQ5 =RTÞ þ c5

(43)

Grain Size The recrystallized grain size is expressed as a function of initial grain size, strain, strain rate, and temperature drex ¼ a8 dh08 εn8 ε_m8 eðQ8 =RTÞ þ c8

(44)

ðif drex  d0 then drex ¼ 0Þ

Static Recrystallization When deformation stops, the strain rate and critical strain are used to determine whether static recrystallization should be activated. The static recrystallization is terminated when this element starts to deform again. Activation Criteria When strain rate is less than ε_sr , static recrystallization occurs after deformation. ε_sr ¼ Aexpðb1  b2 d0  Q2 =TÞ

(45)

Kinetics The model for recrystallization kinetics is based on the modified Avrami equation. " Xsrex ¼ 1  exp βs



t

hs #

t0:5

(46)

where t0.5 is an empirical time constant for 50 % recrystallization: t0:5 ¼ a3 dh3 εn3 ε_m3 eðQ3 =RTÞ

(47)

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Grain Size The recrystallized grain size is expressed as a function of initial grain size, strain, strain rate, and temperature drex ¼ a6 d0 h6 εn6 ε_m6 eðQ6 =RTÞ þ c6

(48)

ðif drex  d0 then drex ¼ 0Þ

Grain Growth Grain growth takes place before recrystallization starts or after recrystallization finishes. The kinetics is described by equation dr ¼



dm rex

Q9 þ a9 texp RT

 1=m (49)

Retained Strain When recrystallization of a certain type is incomplete, the retained strain available for following another type of recrystallization can be described by a uniform softening method: εi ¼ ð1  λXrex Þεi1

(50)

Temperature Limit The temperature limit is the lower boundary of all grain evolution mechanisms. Below this temperature, no grain evolution occurs. Average Grain Size The mixture law is employed to calculate the recrystallized grain size for incomplete recrystallization: d ¼ Xrex drex þ ð1  Xrex Þd0

(51)

Based on the abovementioned equations, the evolution of the microstructure during bulk forming process can be estimated.

Fracture Prediction in Bulk Metal Forming Perhaps one of the most important questions that mechanical engineers would like to ask is when the materials will fracture/damage during the forming process. The answer to this question depends on the geometry of the workpiece, the boundary

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condition, and the material properties. As the first two factors have already been taken into consideration during simulation using finite element method, the focus here will be placed on the materials. There are many numerical models available which intend to provide the material damage criteria under different loading conditions. They may consider damage as a progressive process with initiation and evolution at different stages of loading. Indeed, in most of the metals, the ductile damage dominates, which is a process due to nucleation, growth, and coalescence of voids in ductile metals. However, in our case, the damage is treated as an instantaneous event with a single-value indicator to determine that particular damage for ease of application. Below listed are some of the most commonly used ones in the bulk forming areas:

Maximum Principle Stress/Ultimate Tensile Strength Perhaps the easiest criterion that one can immediately think of is the comparison between the current stress (σ) state and the maximal principle stress or UTS (σUTS). The critical value is given by the ratio between them as α¼

σ σ  αc or α ¼  αc σ1 σUTS

(52)

Cockcroft and Latham This is the most commonly used fracture criterion with bulk deformation (Cockcrof and Latham 1968), which states ð εf σ
dε  C (53) 0

where σ
is the maximum tensile stress in the work piece, εf is the strain at fracture, and C is the C&L constant. This method has been used successfully to predict fracture in edge cracking in rolling and free-surface cracking in upset forging under conditions of cold working.

Rice and Tracy This model is defined as a function of mean stress and effective stress. α is the model coefficient. ð εf ασm e σ dε  C (54) 0

Brozzo Brozzo model is defined as a function of principal stress and mean stress: ð εf 0

2σ
dε  C  σm Þ

3ðσ


(55)

The disadvantage associated with above criteria is that all these methods predict the damage based on a certain critical value, which can only be determined

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experimentally. Moreover, the damage point in the experiment is not easy to identify as it is a progressive process. To make the situation even worse, the critical value varies from material to material, and sometimes different setups may also contribute to such deviation. Therefore, these fracture/damage values can only be used as a rough guideline for the process design. In this section, we had a brief overview of the challenges in utilization and application of FE simulation to design and optimize the forming processes. Issues such as the material friction behaviors, mechanical properties, microstructural evolution, and fracture prediction were covered. Surely the challenges in the simulation are far greater than those presented here. However, this provides a flavor to the readers of this handbook on the complexity the bulk metal forming processes have in terms of modeling and simulation.

Lubrication in Bulk Metal Forming Introduction Friction in bulk metal forming is defined as the resistant force against relative movement of the die and workpiece. This can affect the metal flow, surface quality, and stresses on dies. In most cases, friction is undesirable, except in some rare cases such as rolling, which could not proceed without friction. The characteristics of frictional condition in bulk metal forming are as follows: • • • •

Sliding under high pressure, larger than yield stress of the workpiece Plastic deformation in the workpiece Surface expansion of the workpiece High temperature of the workpiece (hot forming)

Although there have been a lot of reports on the subject of friction in metal forming processes, its mechanism is still not fully understood. It is preferred to reduce the friction in bulk metal deformation by using proper types of lubricants to lower the frictional resistance between the die and the workpiece and to present wear as well as galling on the tools. Based on the form of the contact between the tool and the workpiece, the various friction and lubrication conditions can be summarized as in Table 11. The relative friction coefficient of various friction regimes is typically shown in Fig. 30. This so-called Stribeck curve is useful for the determination of the optimum lubricant based on the process parameters. Various types of friction regimes at the interface have been shown schematically in Fig. 31. Boundary lubricants have significant effects especially in microscale forming processes, since they can trap the lubricants in this scale and reduce the forming load.

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Table 11 Different friction regimes (Mang et al. 2011) Friction regime Solid friction (dry) Boundary friction

Fluid film friction

Mixed friction

Definition When there is no separating layer (except oxide layers) between two solids in direct contact in metal forming There is a molecular layer of chemical substances covering the contacting surfaces. The lubricant layer is created from surface-active substances and their chemical reaction products When a thick layer of a hydrodynamically formed lubricant is present between contacting partners Combination of the fluid and boundary frictions

Remarks Desirable in only rare situations such as hot rolling of plates and slabs

Relative friction level High

Practical when thick long-lasting lubricant films are technically impossible to achieve in a variety of geometrical and thermal conditions

Medium

Only works when the interfacial normal pressure, temperature, and relative speed of die and the workpiece are low Using the appropriate lubricants containing special organics, the machine elements experience mixed friction when starting and stopping their operation

Medium

Low

Fig. 30 Typical Stribeck curve for evaluation of liquid lubricants

Lubricant Selection The selection of the lubricant type depends on many factors, hence is empirical, with a very little analysis-based information. For instance, the lubricant choice in cold forging depends on the process parameters such as normal pressure and surface expansions. In that sense, upsetting of small specimens does not need the same

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Fig. 31 Schematic of differences between mixed and boundary lubrication

Table 12 Typical lubricants being used for different forming processes Type of lubricant Liquid lubricant for cold working Solid lubricant for cold/hot working Chemical conversion coating for cold working

Typical lubricant Mineral, synthetic, and vegetable oil; wax Graphite; MoS2; BN; metallic soap, glass Zinc phosphate + metallic soap Aluminum fluoride + metallic soap

Fig. 32 Schematic of disadvantages of using improper lubricants

lubricant efficiency as conventional backward extrusion. Scaling down the bulk forming process to microscale sometimes brings up the size effect on the friction behavior, which makes the matter more complicated. There are many types of lubricants in use in the industry. The most common types may be categorized as listed in Table 12. Poor selection or poor application of the lubricant type may cause partial direct contact with high pressure between tool and workpiece (Fig. 32). This arises microscale adhesion which leads to galling or seizure or scratch on the final part’s surface.

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Table 13 Different methods for applying lubricants on the surface (Mang et al. 2011) Application method Dripping

Roll coating

Physical/ chemical coating Spraying

Definition Dripping the liquid lubricant on the blank during the process Lubricant is applied on the blank moving between two rollers with a certain pressure Physical/chemical or electrical deposition of a layer of solid lubricant on the blank Spraying a controlled amount of liquid lubricant on the blank during the process

Remarks Cheap and simple, but difficult to control the proper amount of desired lubricant Precise control of the amount of lubricant, but applicable only for rolling processes No lubricant wastage, but relatively more expensive Minimal lubricant wastage, but it does not work for high viscous lubricants

Application of Lubricants The most commonly used lubricant application methods can be summarized in Table 13.

Post-Metal Forming Considerations in Lubricant Selection With increasing demand from industries, it is vital to protect the environment from polluting chemical lubricants during or after metal forming. To do so, some procedures for using biodegradable lubricants have been proposed especially for Euro-zone countries. As an alternative, technologies towards reusing of the waste lubricants have been developing. Furthermore, lubricants remained on the part surface may affect the subsequent processes such as welding and painting. Thus, the lubricants must be easily removable from the formed parts after the process, without environmentally hazardous effects.

Methods for Evaluation of Lubricants To have the optimum lubricant selection, it is essential to evaluate the lubricants. There are many methods that have been proposed for lubricant evaluation. These methods have been designed based on the nature of the forming process (e.g., ring compression test is the best for evaluation of the upset forging process, since both methods involve compression stresses on the workpiece, while for more severe deformation conditions in extrusion, the double-cup backward extrusion test is a proper choice). Here only two of the most common tests are presented.

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Fig. 33 (a) Schematic of the friction effects on the final inner radius in the ring compression test; (b) determination of the shear friction factor using ring compression test and finite element simulation

In general, for evaluation of the friction in hot metal forming, it is advised to consider the die chilling effect, contact time, and forming speed. In ring compression test, a flat ring shape specimen is compressed to a defined reduction. The change in internal and external diameters of the ring depends significantly on the friction at the interface. Thus, changing the friction factor would change the behavior of the internal radius dimension during compression test. To obtain the friction value for a specified lubricant, the change in the internal diameter of the pin in the experiment is compared with that obtained from finite element simulation (i.e., FE simulation of the ring compression test in similar dimensions using different friction factors). One typical example is plotted in Fig. 33. The outcome could be considered as universal as changes in material properties (i.e., strain hardening) have no significant effect on the result. For more severe deformation conditions (e.g., extrusion), the double-cup backward extrusion test may provide a more accurate evaluation for lubricant behavior. As shown in Fig. 34, this test consists of a forward and backward extrusion processes. The ratio of the cup heights (H1/H2) is very sensitive to the frictional behavior. With increasing friction, the cup heights ratio increases. Again here the FE simulation helps in finding the exact value of the friction coefficient. It is important to note that the upper punch is moving while the lower punch is stationary during the test. This leads to a relative velocity between the upper punch and the container, while there is no relative movement between the container and the lower punch. Consequently, the material flow behavior is different in upper and lower portions, which explains the reason behind the difference in the upper and lower cup heights. This test is relatively more dependent on the material properties.

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Fig. 34 Schematic of the double-cup backward extrusion test

As mentioned, many factors may affect the value of the friction factor. Thus, it is important to know the region where the friction test has been done.

Tool Manufacturing and Material Selection for Bulk Metal Forming Introduction Since a considerable portion of the bulk forming process cost is from die manufacturing, the tool (die and punch) must be manufactured by modern manufacturing methods using appropriate material to provide a reasonable tool lifetime at an affordable cost. For a given forming process, the type of the tool material depends mainly on the maximum tool stress and the temperature of forming. Dies need to be changed before their lifetime expires. The lifetime of a die is affected by wear, plastic deformation, galling/seizure, corrosion, and fatigue cracking, etc. Many of these defects occur due to high temperature, high stress, and severe friction. Thus, to prevent severe deterioration of dies in bulk metal forming, special materials must be used and proper tool design employed. Using a proper lubricant can also help increase the die lifetime.

Tool Material Selection In general, the material properties that determine tool material selection for the metal forming process can be summarized as listed in Table 14. In cold bulk metal forming, tool material selection depends mainly on the stress levels (type of deformation). For instance, in forward extrusion, the punch needs to

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Table 14 Tool material selection criteria Material properties Hardenability

Wear resistance

Yield strength Toughness

Resistance to heat cracking

Definition The depth to which a metal can be hardened (it is not related to the hardness value) A gradual change in tool dimensions or shape due to corrosion, dissolution, or abrasion Resistance to plastic deformation measured by yield strength Ability to absorb the forming energy without cracking – combination of strength and ductility Caused by nonuniform thermal expansion at the surface and center of the tool

Remarks Higher alloying elements increases the hardenability A high hot hardness value is required for wear resistance in hot forming – Mo and W alloys improve the wear resistance Higher hardness leads to higher strength, but lower toughness Higher hardness lowers the impact strength; thus medium-alloy steels are the best in this case It is critical for hot metal forming process to have a die material with high thermal conductivity; Mo alloying element increases this resistance

have high compressive strength, whereas in backward extrusion, it needs to have high wear resistance as well. Normally, in conventional cold bulk forming, based on the deformation type and tool stress level in the process, the cold working die steels (such as D2, D3), or high speed steels (such as M2), are used for die material. Tool materials that are used for hot bulk metal forming processes can be summarized as listed in Table 15. Besides the material selection, other parameters such as tool design and the workpiece properties may affect the tool lifetime. For instance, it is advised to prevent sharp corners in tool making. This prevents stress accumulation on the corners, reducing the possibility of cracking. The proper heat treatment to the tool is of importance for increasing the tool life. The hard coatings of tools by PVD, CVD, and plasma nitriding are the useful methods to improve tool life by reducing the galling/seizure and wear.

Manufacturing Techniques After computer-aided design of the tool geometry, the bulk metal forming tools are usually fabricated by machining processes. The main die manufacturing process may be divided into die design, rough machining, heat treatment, finish machining, manual finishing (polishing), or benching and hard coating (if necessary).

High Speed and Hard Machining In conventional die-making techniques, the die is hardened after rough machining. This could cause distortion. To resolve the issue, the hard machining method has

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Table 15 Typical tool material selection for hot metal forming processes (Altan et al. 1983a) Designation 6G, 6F2, 6F3 (ASM)

Alloy type Low alloy steel

6F5, 6F7 (ASM)

Low alloy steel – higher amount of nickel (2–4 %)

6F4 (ASM)

Low alloy steel – higher amount of molybdenum Chromium-based steel alloy

H10, H11, H12, H13, H14, H19 (AISI)

H21, H22, H23, H24, H25, H26 (AISI)

Tungsten-based steel alloy

Main Characteristics Good toughness Shock resistant Reasonable wear resistant Good hardenability Good toughness Age hardening capability High resistance to softening High resistant to heat cracking and wear High resistance to softening Adequate toughness

Application Not good for higher forming temperatures of 500  C

Could be used for more severe applications than the first group Good for warm forming up to around 600  C Good for hot metal forming in higher temperatures than 600  C

Good for hot metal forming at severe forming load and speed

been replaced nowadays. Instead, the hardened metal (45–62 HRC) is machined, preventing the possible distortion, and provides better surface finish.

Electro-Discharge Machining (EDM) EDM is a process that consists of a current-passing-through electrode which provides a voltage potential between itself and the workpiece. Decreasing the gap between the electrode and the workpiece creates the spark that is required for vaporizing the workpiece surface. The removed material is flushed away by the EDM fluid. The hardness of the metal does not influence the efficiency of the process. Due to its good accuracy and its relative higher required process time, EDM functions on much smaller scales than conventional machining. The only disadvantage of this method may be the time needed for electrode design. Moreover, the sparking process consumes the electrode, limiting the repeatability of the process. The surface of tool sometimes needs to be polished after EDM for removing the surface damages caused by EDM and to improve surface roughness. Based on the operation, this process is divided into two types of sink and wire EDMs. In the sink EDM, the internal cavities are made by a copper of graphite electrode. The die cavity gets its shape from the electrode (Fig. 35). The wire EDM (Fig. 36) is similar to the sink EDM in case of functioning. The primary difference is that the electrode is a wire ranging in diameter from 0.05 to 0.3 mm.

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Fig. 35 Schematic of the sink EDM process

Fig. 36 Schematic of the wire EDM process

In the case of superalloy dies, since the hardness value is high, occasionally it is better to cast these dies and subsequently obtain the final shape by EDM.

New Technologies in Bulk Metal Forming Processes Press Technologies The application of servo-presses goes back to the 1950s where they were used for the cutting processes. With the development of transistor controllers, their power capacity was improved for forming technologies during 1990s. Prior to that,

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Table 16 Typical conventional presses according to the performance (Altan et al. 1983a) Strokecontrolled presses Energycontrolled presses Forcecontrolled presses

Feature The rotational movement of the motor is mechanically stored in the flywheel, and by starting the forming it is converted into the linear slide movement The rotational movement of the flywheel is changed to the linear motion with a screw, and the slide stops when the energy stored in the flywheel is consumed completely The pressure of the working oil is raised by the motor, and the press velocity and position is controlled by the oil pressure

b

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Balance Tank

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a Flywheel Capacitor

Ram or slide Servo Motor

Brake

Drive Shaft

Fig. 37 (a, b) Comparison of the conventional mechanical press and mechanical servo-presses

conventional mechanical presses were broadly used for metal forming. These presses are categorized in three categories as summarized in Table 16. All the above mentioned features can be driven by a servomotor without requiring a flywheel and clutch. In other words, the mechanical servo-presses offer a combination of the flexibility of hydraulic presses with speed, accuracy, and reliability of mechanical presses. In Fig. 37, the operation mechanism of both types of presses is shown. Normally the major part of the overall forming energy is required during punch acceleration. In conventional mechanical presses, a portion of this energy is stored in flywheels, while in hydraulic presses, this energy is wasted. In servo-presses, however, this energy is stored in electronic capacitors, with relatively higher efficiency.

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Table 17 Comparison of the different types of presses

Speed control Flexibility Accuracy Energy consumption Noise Maintenance cost

Conventional mechanical press Low Low Medium High

Conventional hydraulic press Medium Good Medium Medium

Mechanical servopress High High High Low

High High

High High

Low Low

By precise control of the punch speed, the hitting velocity of the punch to the specimen can be controlled. This can help in decreasing the noise due to punchworkpiece impact. Furthermore, it improves the die lifetime and results in higher efficiency of the presses. Moreover, with a programmed punching velocity throughout the process, heat generation due to high strain rate can be controlled which provides better die lifetime with consistent product quality. Furthermore, the control over punch velocity can increase the forming process speed, in case a progressive forming process is desired. Table 17 compares different presses.

Bulk-Sheet Metal Forming Processes With the growing competitive industrial vibe, it is important to develop into costeffective production processes. Especially for some automotive components, it is suggested to incorporate bulk metal forming processes into sheet metals to produce high-quality sheet metal components commercially. Sheet-bulk metal forming (SBMF) processes are defined as sheet metal forming where the flow occurs in three dimensions similar to bulk metal forming. The main characteristic of these processes is that the final product has the dimensions of a magnitude similar to the sheet thickness, projecting out of the plane of the sheet. Based on this characteristic, only some special bulk forming processes can be applied on sheet metals as summarized in Table 18.

Micro-Bulk-Sheet Metal Forming Production of very small parts is a trend in many technical areas such as electronics and medical industries (Chinesta et al. 2007). In general, metal forming is well suited for efficient production of micro-parts. Near-net shape and excellent mechanical properties of the final product, together with the mass production capability and lower manufacturing cost, have made this route an interesting alternative. A few years after the introduction of microforming processes by Geiger

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Table 18 Different types of SBMF processes SBMF Process Upsetting/ ironing/flow forming Forging

Application Sheet thinning, sizing

Orbital forming

Sheet thinning and thickening

Coining/ embossing

Sheet thickening

Sheet thinning and thickening

Remarks Combined upset/forging/drawing of sheets can be used for production of features such as nails, screws, and flanges Usually done in closed-die manner to have deformation flow normal to the sheet surface. Notebook cases or cell phone shields can be named as product examples Relatively smoother surfaces, lower forming load, and less noise compared to forging and extrusion, but longer process time. Bevel gears and hollow ring gear parts are some product examples Efficient process for production of small features at the sheet surfaces

et al. (1996), many studies have been conducted towards the development of microparts manufacturing (Engel and Eckstein 2002; Okazaki et al. 2004; Ghassemali et al. 2011). Despite the relatively wide range of research in this field, microforming processes have not been adopted extensively for mass production in the industry. This is related to issues such as handling of micro-parts and even removal of the formed parts without damage which require further process and manufacturing system design and development. It was stated by Engel et al. (2007) that handling of parts is less difficult in sheet-bulk metal forming processes, since the parts usually remain connected to the strip. This is a big advantage for the sheet metal forming processes as they are scaled down to the micron level. Hirota (2007) suggested the use of sheet metal for production of micro-billets. However, a disc specimen with a predefined diameter was used as the initial raw material for the process in his study. A counterpunch was used under the formed pin to the push up after the pin forming process. However, as the pin diameter decreases in size and the surface area-to-volume ratio increases, such a pin removal method will become a challenge due to the greater risk for damage caused to the pin. Based on the concept by Hirota, a progressive micro-bulk-sheet pin forming system was designed and developed by Ghassemali et al. (2012). The system has the following advantages: (i) it can circumvent the handling issues of small billets needed for extruding pins of very small diameter; (ii) such a process uses a strip as the workpiece and is more productive as a progressive process can be implemented; and (iii) instead of ejecting the formed pin, the system uses a blanking process to remove the formed pin from the strip material, eliminating the possibility of buckling damage if a counterpunch is used as an ejector. Figure 38 shows the schematic of the process. As can be seen in this figure, the process setup consists of two stages: (i) pin forming by forward extrusion and (ii) blanking. In the first step, the strip is deformed by a punch of a defined diameter and specified displacement. As a result,

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Fig. 39 Micro-pins produced by progressive microforming process: (a) before and (b) after the blanking process. (c) FESEM micrograph of the final micro-pins

a portion of the material is forward extruded into the die cavity. The strip is inserted via guides on the setup. To ensure the precise positioning, guide holes are created on the edge of the strip and guide pins are used along the different stages of the die setup. The spring-loaded blank holder was used in this lab-scale study to distinguish the forming load from the blank-holder load. In industrial applications, the blank holder is usually attached to the press. After extrusion in stage I, the strip with the attached extruded pin is ejected by springs and can be moved to the next stage. It is noteworthy that the forming process only occurs in stage I of the process. In other words, stage II is only used for detaching the pins from the strip. Thus, the formed micro-pin at stage I is still attached to the strip which makes it easier for handling and transferring to the next stage either for blanking or for subsequent measurements. No counterpunch is used in this process, and the ejection by the springs on the strip only leads to the withdrawal of the formed pin from the forming

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Fig. 40 Load-stroke curve for the 0.8 mm pin produced by 3.2 mm punch

die. The feasibility of this process for production of micro-pins of diameters between 100 μm to macro-scale has been proven (Ghassemali et al. 2012, 2013a). It is important to note that due to its axisymmetric geometry, the progressive microforming process has a good capability of producing other symmetrical micro-parts such as hollow pins, stepped pins, or cups. After stage I, no significant damage in terms of pin fracture and buckling was observed in the experiments, as can be seen from Fig. 39. This was indicative that the ejection process of the formed micro-pin by the ejecting springs on the strips does not cause any damage on the formed pin in this process, although there were galling effects on the pins’ surface. Developing hard-coating techniques seems essential to be able to coat the very small die orifice (Ghassemali et al. 2013a). The three stages in the process can be seen also in the load-stroke curves. Figure 40 shows a typical load-stroke curve for the progressive microforming process. Almost a similar behavior was observed in the punch reaction of other processes, in which the first stage was contributed to the elasticity and the last two stages in the curve was related to the plasticity behavior of material in the process (Jiang and Chen 2011). In the first stage, loads are relatively low, which corresponds to the elastic forming initiation and indentation process. The portion of this stage is relatively small. At stage II, upsetting is the main phenomenon mainly due to the less force required for this phenomenon compared to that of the extrusion. Till this stage, the material mainly flows towards the outside of the punching area rather than moving towards the die cavity. After reaching stage III, the load increases rapidly. In this stage, the required extrusion force is less than the upsetting, and therefore, the extrusion becomes the dominant phenomenon occurring in the process. Therefore,

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the metal will mostly flow towards the die cavity rather than moving outwards, after this stage. The unloading portion of the curve presents the elastic deflection of the material and punch essentially due to the force in stage III. This shape of the loadstroke curve is similar to what happens in the common impression-die forging process (Altan et al. 1983b) as reported also for bending process (Jiang and Chen 2011). Based on this phenomenological study, this process has been optimized using upper bound theory (Ghassemali et al. 2013b). Using the developed model, it is possible to predict the material behavior during the process, with the least material wastage and competitive production rate.

References Altan TO, Oh S-I, Gegel HL (1983a) Metal forming: fundamentals and applications. American Society for Metals, Metals Park Altan T, Oh S-I, Gegel HL (eds) (1983b) Metal forming: fundamentals and applications. American Society for Metals, Metals Park, pp 156–158 Altan T, Ngaile G, Shen G (2004) Cold and hot forging; fundamentals and applications. ASM International, Materials Park ASTM (2009) Standard test methods of compression testing of metallic materials at room temperature. ASTM International, West Conshohocke Avitzur B (1968) Metal forming: processes and analysis. McGraw-Hill, New York Avrami M (1939) Kinetics of phase change. I general theory. J Chem Phys 7(12):1103–1112 Bay N, Wanheim T (1976) Real area of contact and friction stress at high pressure sliding contact. Wear 38(2):201–209 Chinesta F et al (2007) Microforming and nanomaterials advances in material forming. Springer, Paris, pp 99–124 Cockcrof MG, Latham DJ (1968) Ductility and workability of metals. J Inst Met 96(Part 2):33–39 Engel U, Eckstein R (2002) Microforming–from basic research to its realization. J Mater Process Technol 125–126:35–44 Engel U, Rosochowski A, Geißdo¨rferfer S, Olejnik L (2007) Microforming and nanomaterials. Springer, Paris, pp 99–124 Geiger M, Vollertsen F, Kals R (1996) Fundamentals on the manufacturing of sheet metal microparts. CIRP Ann Manuf Technol 45(1):277–282 Ghassemali E et al (2011) Dead-zone formation and micro-pin properties in progressive microforming process. In: 10th international conference on technology of plasticity (ICTP2011), Steel Research International, Germany Ghassemali E et al (2012) Progressive microforming process: towards the mass production of micro-parts using sheet metal. Int J Adv Manuf Technol 66:611–621 Ghassemali E et al (2013a) On the microstructure of micro-pins manufactured by a novel progressive microforming process. Int J Mater Form 6(1):65–74 Ghassemali E et al (2013b) Optimization of axi-symmetric open-die micro-forging/extrusion processes: an upper bound approach. Int J Mech Sci 71:58–67 Hirota K (2007) Fabrication of micro-billet by sheet extrusion. J Mater Process Technol 191 (1–3):283–287 Jiang C-P, Chen C–C (2011) Grain size effect on the springback behavior of the microtube in the press bending process. Mater Manuf Process 27(5):512–518 Kalpakjian S (1997) Manufacturing processes for engineering materials. Prentice Hall, New York Mang TB, Bobzin K, Bartels T (2011) Industrial tribology; tribosystems, friction, wear and surface engineering, lubrication. Wiley-VCH, Weinheim

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Mielnik EM (1991) Metalworking science and engineering. McGraw-Hill, New York Okazaki Y, Mishima N, Ashida K (2004) Microfactory–concept, history, and developments. J Manuf Sci Eng 126(4):837–844 Taylor GI (1934) The mechanism of plastic deformation of crystals. Part I. Theoretical. Proc Royal Soc Lond Ser A 145(855):362–387 Tschaetsch H (2005) Metal forming practise; processes, machines, tools. Springer, Berlin Venugopal Rao A, Ramakrishnan N, Krishna kumar R (2003) A comparative evaluation of the theoretical failure criteria for workability in cold forging. J Mater Process Technol 142(1):29–42 Voronoi G (1908) Nouvelles applications des parame`tres continus à la the´orie des formes quadratiques. Deuxie`me me´moire. Recherches sur les paralle´lloe`dres primitifs. J Reine Angew Math Crelles J 1908(134):198–287 Wolfram S (1983) Statistical mechanics of cellular automata. Rev Mod Phys 55(3):601–644

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Materials in Metal Forming Sridhar Idapalapati, Xu Song, N. Venkata Reddy, Narasimalu Srikanth, Farshid Pahlevani, Karthic R. Narayanan, and Mehrdad Zarinejad

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Formability of Engineering Alloys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Formability and Workability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stress Cracking Under Uniaxial Tension and Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Formability in Sheet Metal Forming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Die and Mold Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classification and Properties of Tool and Die Steels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nonferrous Tool and Die Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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S. Idapalapati (*) School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore e-mail: [email protected] X. Song • M. Zarinejad Singapore Institute of Manufacturing Technology (SIMTech) A*Star, Singapore e-mail: [email protected]; [email protected] N.V. Reddy Department of Mechanical and Aerospace Engineering, Indian Institute of Technology Hyderabad, Hyderabad, Andhra Pradesh, India e-mail: [email protected] N. Srikanth Energy Research Institute, Nanyang Technological University, Singapore e-mail: [email protected] F. Pahlevani Forming Technology Group, Singapore Institute of Manufacturing Technology (SIMTech) A*Star, Singapore e-mail: [email protected] K.R. Narayanan School of Materials Science and Engineering, Nanyang Technological University, Singapore e-mail: [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_42

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Selection of Material for Bulk Metal-Forming Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selection of Material for Sheet Metal-Forming Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lubricants for Metal Forming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Friction Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lubrication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Forming Lubricants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Additives for Lubricants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Testing Standards for Lubricants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Solid Lubricants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manufacturing of Raw Materials for Forming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Casting Defects that Affect the Forming Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Segregation During Metal Casting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Surface Cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heat Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modified High-Temperature Heat Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

In this chapter, a review of the materials involved in the metal-forming processes and some of the processing required before forming on the materials are provided. At first the details of the materials formability definition and applications in different forming processes are discussed. Formability is one of the most important characteristics of the engineering alloys. Subsequently, information on the materials used for metal-forming tools and dies and their selection criteria are provided. These tool and die materials are categorized based on the process details and their limitation is further discussed. A brief look at the lubricants used in metal forming covers a subsequent topic in the current work and focuses on the effectiveness, characterization (friction reduction), types, and general applications as well as additives used in the lubricants. Lastly, a concise summary of the raw material preparation for forming processes is covered, with main focus on casting and heat treatment. These are the main preprocessing routes for preparing the preform in the industry. This chapter serves as a quick reference of forming process material selection for researchers, engineers, and students in the mechanical and materials engineering field.

Introduction The transformation of raw ingot to required complex geometry, under large plastic deformation, using sophisticated tooling, is known as metal forming. For the last century, metal forming has been one of the most widely employed processes, to mass manufacture engineered products, with little or no scrap. The technology know-how is one of the oldest and very mature. Metal-forming process can be classified into bulk and sheet metal forming. The bulk metal forming includes forging, rolling, extrusion, and wire-drawing processes, while shearing,

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Fig. 1 Antiques of third millennium BC made from copper and gold metals

punching, blanking, bending, and deep drawing techniques comprise sheet metalforming processes. The history of metalworking process date backs to the era of third millennium BC when copper and gold metallurgy was invented in the Middle East, primarily for making weapons, ornamentation, and coins as shown in Fig. 1. They were manually swaged using an iron hammer to produce a variety of products. Around second millennium BC, processes such as smelting, which helps to purify the impurities in metals from its ore, were explored and understood. This important discovery motivated a combination of pure metals to form new alloys which satisfied the quest for strength. This was also very evident during the Bronze Age in 4000 BC, when copper and tin were effectively used. Mainly, nonferrous metals and its alloys were explored until the Iron Age. This delay was due to the lack of understanding on high-temperature metalworking and on achieving iron purification. Similarly, the processes also changed systematically from crude hammering to more mechanized forging and rolling. During the Industrial Revolution, at the end of the eighteenth century, witnessed major development in various types of metalworking techniques and materials with special properties and applications, due to the demands of the manufacturing industry. The need to forge large steels led to the discovery of higher tonnage metalworking equipments such as mechanical (screw type) and hydraulic presses. High-speed tandem rolling mills were also used to mass produce strengthened steels. Due to the increasing need of high-strength materials in the automobile industry, steels, such as low carbon and advance high strength, largely benefited because of its ductility and high working temperature. The demands of the aerospace industry turned attention towards materials with high strength to weight ratios, and focus on forming aluminum- and magnesium-based alloys was considered relevant.

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The formability (addressed in the section “Formability in Sheet Metal Forming”) of materials is an important criteria during material selection without compromising mechanical properties. The need for higher-quality products was driving the search for different kinds of materials. With new production processes and technologies, the quality of the materials, thereby the products, has improved significantly. With the advent of technological advancements, different metal forming processes have been keenly studied and are still being explored to improve the productivity. Moreover, the process gives a distinct advantage in terms of operational cost, over joining and cutting, while manufacturing near-net shape components. Research in new materials, pertinent to bulk forming techniques, always has a huge potential, considering the future end products, which aims at higher quality. The materials used in forming tools (addressed in section “Die and Mold Materials”) are being designed to withstand adverse environments, which is a challenge. Moreover from the economic constraints, this plays a crucial role and influences the cost of the product. The material catastrophe also interrupts the production engineering and machine downtime. Properties such as high compressive strength, surface hardness, ductility, wear resistance, and reliability are desired in materials used for forming and have to be altered to meet specialized applications. Lubricants in metal forming (section “Lubricants for Metal Forming”) and raw materials (section “Manufacturing of Raw Materials for Forming”) and state of the art in bulk metal-forming process have developed considerably. The development of new heat treatment and surface coating processes to meet the needs of the forming tool and the end product has to be customized.

Formability of Engineering Alloys Most of the engineering alloys during a uniaxial tensile test, after some degree of stretching, tend to become unstable, due to the onset of necking. With further stretching, there is neck growth in the specimen, and finally a fracture is formed in the neck, so that the specimen breaks into two pieces. When compression testing was discussed, it was stated that for ductile metals, larger strains can in general be obtained in this test than in tensile testing, because neck formation is avoided in compression. But also in the compression test, there are limitations on how much the specimen can be deformed. This is because various cracking phenomena may occur, especially when low-ductility materials are tested. In metal forming, it is a common problem that the material of the workpiece breaks down during forming, in a manner like that for materials subjected to technological tests. In this chapter, different phenomena that cause material failure during metal-forming operations will be discussed, with special emphasis on failure due to necking, tensile stress cracking, and shear cracking.

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Formability and Workability Formability and workability of a material is a measure to which it can be deformed in a specific manufacturing process without any surface or subsurface defects or breakage. Traditionally, formability has more to do with the sheet metal forming, and workability is associated with the bulk forming processes such as wire drawing, forging, extrusion, etc. Formability of a material is also related to materials ductility defined as the maximum tensile elongation (on percentage basis) at fracture. Different phenomena that cause material failure are necking, tensile cracking, and shear cracking. Formability of a material also depends up on manufacturing process parameters such as temperature, loading strain rate, friction between the die and workpiece, reduction ratio, etc. Some of the factors influencing the formability are explained below: (a) Yield Strength: Materials with low flow stress are easy to deform and are said to be malleable. The higher the yield strength of the material, the higher the forces and energy are required to accomplish the deformation. (b) Material Work-Hardening: The flow stress of a range of engineering alloys can be expressed as σ ¼ Ken, where K is the strength coefficient in MPa and n is the strain-hardening exponent. Higher values of n imply the necessity of higher forces with increasing deformation, which can lead to tool wear and tear. Low values of strain-hardening may lead to localized necking during the deformation, which results in nonuniform deformation. (c) Modulus of Elasticity: Having a high elastic modulus leads to the increased elastic recovery after the deformation forces are removed. For precise dimensions of the part, the spring back should be compensated. (d) Operating Temperature: When the forming temperature is greater than 0.3 Tm (where Tm is the melting point of the material in K), it is said to be hot working, and it is easier to form the material due to lower stresses. Sometimes failures occur at very low strains during hot working due to hot shortness, which is due to the presence of liquid phase at the grain boundaries. (e) Hydrostatic Stress: High hydrostatic pressure suppresses the void growth in engineering alloys, thereby retarding the fracture. Formability of materials with limited ductility can be improved if the exit conditions from the die are under hydrostatic pressure. Edge cracks in rolling and central bursts in extrusion are likely due to the presence of tensile stresses at the exit section rolling press or extrusion die, respectively. (f) Grain Size: At low temperatures, it is well known that the strength of engineering alloys is inversely proportional to grain size: smaller-grain-size materials have larger grain-boundary area giving rise to the higher resistance to plastic deformation by dislocation flow. If coarse-grain material is deformed, the plastic deformation leads to “orange peel” type of surface due to the different texture and directional orientation of the microstructure in various grains.

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Fig. 2 Surface cracks in (a) steel, (b) aluminium alloy and (c) metal matrix composite

(g) Chemical Composition: Slag inclusions in the castings, traces of foreign elements, and nonmetallic compounds in the raw material change the chemical composition and degrade the further formability by secondary processes such as forging, extrusion, etc. by becoming the sources for crack initiation. Also, the change in chemical composition alters the surface finish. Detectable surface or undetectable subsurface defects (e.g., central burst cracks in extruded components) appear in the formed component whenever the workability or formability of a material is exceeded. By using either fail-safe design principles or failure analysis diagrams (FADS) for a given load-bearing situation, a decision on the acceptance of the component is made during quality check. Further, some of the internal cracks in the load-bearing structures grow during service, and hence their continuous monitoring using nondestructive methods such as radiography, ultrasonic testing, magnetic particle testing, or eddy current testing is recommended. Some cases of plastic deformation where the limit of ability to form has been exceeded until the material has lost its integrity are shown in Figs. 2 and 3.

Stress Cracking Under Uniaxial Tension and Compression Ductility of an engineering alloy is the ability to deform plastically without fracture and expressed as a measure of the strain at fracture in a uniaxial tension test. However, the percentage elongation in a tensile test is often dominated by the uniform deformation. The end of uniform elongation coincides with the onset of plastic instability accompanied by void nucleation, their growth and coalescence, and final fracture by formation of shear lips on the outer surface. Because of the

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Fig. 3 Shear stress cracks formed in aluminium alloys under (a) cold and (b) hot compression

nonuniform deformation, Cockcroft and Latham (1968) suggested a criterion based on both stresses and strains by arguing that the plastic work must be an important factor. The total amount of plastic work done per unit volume at the fracture point can be formed from Ð e 0

σ 0 de ¼ C

ð1Þ

It appears that the elongation value is too complex to be regarded as a fundamental property of a material, and it seems reasonable to assume that any criterion of fracture will be based on some combination of stress and strain rather than on either of these quantities separately. When the accumulated damage as given by the integral in Eq. 1 reaches a critical value, C, failure will occur. Most of the commercial finite element programs are able to predict the damage parameter through the calculation of stress and strain fields, thereby the energy-based damage parameters. Figure 2 illustrates predominantly three different kinds of surface defects generated due to tensile stress cracking in the worked steel, aluminum, and a metal matrix composite samples: barrelling during uniaxial compression loading causes circumferential hoop tensile stresses which may lead to the cracking. Figure 2a depicts a low-ductility steel sample with several circumferential cracks across the periphery due to high compressive strain of 95 % in hot compression. An aluminum alloy sample in Fig. 2b was compressed to 1.5 strain and a defect led to the cracking at its location on the circumference; elsewhere it has good surface finish. Figure 2c shows severe transverse surface cracking during the extrusion of a metal matrix composite due to fracture when the stress reached a critical value. Another predominant failure in the billet compression of engineering alloys is the formation of shear cracks due to strain-induced material softening. Figure 3 shows such shear failures in the compression of aluminum alloys AA 7108 produced through extrusion process. The presence of friction between the die and workpieces produces nonhomogeneous deformation and barrelling of the sample leading to a deformation state in which a shear cross forms. The largest

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Fig. 4 Forming Limits as defined in the upset forging relating the hoop strain to axial compressive strain Bend tests

Up

set

tes

ts

0.4

Ho

mo

gen

eou

sc

om

pre

Tensile strain, e 1

0.8

ssi

1045 steel −0.8

on

−0.4 Compressive strain, e 2

0

deformations will appear in the shear cross. When the cylinder has been compressed down to a certain stage, the initial strain-hardening part of the flow curve has been exceeded for the material in the shear layer. With continued compression, the shear layer will then experience strain-induced softening. Because of strain softening, this layer will then easily become overstrained, and a shear fracture will form here. It is also to be noted that barrelling during upset forging causes hoop tensile stresses that may lead to cracking. Kuhn (1978) plotted the hoop strain (e1) at fracture as a function of the applied uniaxial compressive strain (e2) as shown in Fig. 4 for 1045 steel. By means of loading punch lubrication with the sample and varying the height to diameter ratio of the test specimens, a number of points are obtained. The data points fit to a linear line as e1f ¼ C  ð1=2Þe2f

ð2Þ

where C is the value of e1f for plane strain. This line parallels e1 ¼ 0.5 e2 for homogeneous compression. In cylinder compression, the shear cross appears three-dimensional, with a complex shape. Even though the shear fracture is initiated somewhere along the plane of the shear cross, propagation of the fracture during further growth most commonly follows a plane path, so that a plane fracture surface is obtained. Upon cracking, a bit of the material may be detached from the rest of the cylinder.

Formability in Sheet Metal Forming Sheet metal forming is an important manufacturing process in automotive, shipping, and aerospace industries. Knowledge of the formability of sheet metal is critical to the success of sheet metal stamping process. The ability to form sheet

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metals into desired shapes is often limited by the occurrence of material instability leading to localized necking. For a stretched sheet metal, the necking could be either diffused or localized. Diffuse necking of sheet metal involves contraction in both the lateral and width directions. In sheet specimens, localized necking occurs after diffuse necking. During local necking, the specimen thins without further width contraction. The forming limit of a sheet metal is defined to be the state at which a localized thinning of sheet initiates when it is formed into a product shape in a stamping process. Formability of sheet metals is at present characterized by the forming limit diagram (FLD) introduced in the 1960s by Keeler and Backofen (1964). The forming limit is conventionally described as a curve in a plot of major principal strain vs. minor principal strain. It must cover as much as possible the strain domain which occurs in industrial sheet metal-forming processes. The curves are established by experiments that provide pair of the values of the limiting major and minor principal strains obtained for various loading patterns (equi-biaxial, biaxial, plane strain, and uniaxial). Hecker (1975) developed an experimental method to determine the forming limit curves. A widely used technique is to print or etch a grid of small circles of diameters on the sheet before deformation. The principal strains can be found by measuring the major and minor diameters after straining. These values at the neck or fracture give the failure condition, while the strains away from the failure indicate safe condition. As the experimental measurement of these strains is time consuming and an expensive process, it would be useful if the forming limit strains can be predicted using theoretical models. Researchers employed two main strategies to predict forming limits: firstly, a group of models that represent strain instabilities as a bifurcated state in an initial homogeneous material and, secondly, models where the strain instability appears in the deformation process due to an imperfection already present in the material.

Forming Limit Diagram Hill (1952) developed the theory for localized necking in sheet metals assuming that localization band develops along the zero extension direction in a sheet metal. This analysis predicted that localized necking would not occur in a sheet subjected to positive biaxial stretching, for which no zero extension direction exists. Therefore, Hills criterion is only applicable to left-hand region of FLD. Swift (1952) developed a diffuse necking theory for biaxially stretched sheets introducing the concept of the maximum in-plane force condition in the necking and localization prediction. According to him, neck initiates when the total differentials of force become zero or negative at the same instant of deformation. However, the Swift diffusive necking criterion is often too conservative and it underestimates the experimentally observed forming limit strains. In order to improve the above model, Hora et al. (1996) considered the experimentally observed fact that the onset of necking depends significantly on the strain ratio and proposed a model called Modified Maximum Force Criterion (MMFC). Aretz (2004) addressed an important singularity in Hora’s MMFC model. It was observed that MMFC fails if the yield locus exhibits straight line segments often observed in

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commercial alloys like Al which are properly described by non-quadratic yield functions. Brunet and Morestin (2001) coupled Hora’s model with Gurson’s (1977) porous plasticity model and obtained good predictions for forming limits. Marciniak and Kuczynski (1967) considered sheet metals subjected to in-plane biaxial loading and proposed a model taking into account that sheet metals are nonhomogeneous from both geometric and structural points of view. The RHS of the FLD for a material was determined by introducing a preexisting material imperfection that lies perpendicular to the major stress axis to explain the development of localized necking during biaxial loading. The imperfection assumed in M-K model need not be perpendicular to the major axis; here the above model was extended to the LHS region of FLDs by Hutchinson and Neale (1978) by considering imperfection at an angle to the major stress axis. The angle selected is the one that minimizes the limit strain. Storen and Rice (1975) proposed an alternative concept for localized necking, caused by the vertex developed on the subsequent yield surface. Recognizing that the shape of the yield surface could cause localization near the vertex, this model predicted a bifurcation corresponding to localized necking under biaxial tension. This method can predict the localized necking over the entire range of forming limit curve (FLC). However, it underestimates the limit for localized necking at the LHS of the FLC. Zhu et al. (2001) considered the moment equilibrium in addition to the force equilibrium adopted by Storen and Rice (1975) for the prediction of FLC over the entire region (LHS& RHS). They also found that the discontinuity of shear stress inside and outside of the localized band is zero. The modified method achieves a good prediction on the LHS of FLCs. Since this method depends on deformation theory of plasticity, it is valid under the proportional loading condition. Friedman and Pan (2000) investigated the influence of different yield functions on FLD predictions using the M-K model. Their results indicate the significance of the yield function used in the FLD analysis. Further, they introduced a parameter (angle) to characterize the influence of the yield locus’ shape on FLD. Yao and Cao (2002) predicted FLCs using M-K model taking into account the effects of pre-strains and kinematic hardening. The exponent of the yield function used in this work was assumed to decrease with increasing pre-strain. One major drawback of FLC models presented above till now is its dependence on strain path, as the straining path of the FLC in sheet metal stamping is not known with any certainty. However, both experimental and numerical results have indicated that FLDs are very sensitive to strain path changes (Ghosh and Laukonis 1976; Graf and Hosford 1993). There is no single curve in strain space that represents the forming limit. Therefore, finding a single path-independent curve to characterize forming limits is of considerable interest. Knowing the drawback of the conventional FLDs, Arrieux et al. (1982) represented formability based on the state of stress rather than the state of strain. They constructed a Forming Limit Stress Diagram (FLSD) by plotting the calculated principal stresses at necking. Stoughton (2000) extended the original idea of Arrieux et al. (1982) and showed that the forming limit for both proportional and nonproportional loading can be explained from a single criterion which is based on the state of stress rather than the

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a 0.4

c1

0.3

0.2

0.1

0 −0.3

b

Graf- Hosford 0% Graf- Hosford 4% Graf- Hosford 7% Graf- Hosford 12% Graf- Hosford 17%

−0.2

−0.1

0 c2

0.1

0.2

0.3

1

Major True Stress

0.8

0.6

0.4

0.2

0.2

0.4

0.6

0.8

1

1.2

1.4

Minor True Stress

Fig. 5 (a) Forming limit curve in strain space after pre-strain (Graf and Hosford 1993). (b) Transformed forming limit curve in stress space (Stoughton 2000)

state of strain by mapping the strain values from different strain paths into a stress space assuming plane stress condition. But it is quite difficult to measure the state of stress experimentally as compared to the state of strain. Using FEM it is possible to estimate the state of stress with good accuracy, but a criterion has to be used to construct the Forming Limit Stress Curve (FLSC) in stress space. To validate this, experimental FLSC is possible only with mapping of experimental FLC in strain space to stress space (Fig. 5). Stoughton (2001) studied the influence of material model on the stress-based forming limit criterion. Stoughton and Zhu (2004) reviewed the theoretical strainbased models of Swift, Hill, and Storen and Rice and their relevance to the stressbased FLD using plane stress conditions. Smith et al. (2003) studied the influence of

Major prin_ stress

Equivalent stress

S. Idapalapati et al. Major prin_ strain

242

Uni-axial stress Plane strain Bi-axial stress Minor prin_ strain εFLC

Minor prin_ stress

Mean stress (Tension)

σFLC

XSFLC

Fig. 6 Transformation of FLC to extended SFLC (Simha et al. 2007)

transverse normal stress on strain space forming limit by assuming that the stress space forming limit is relatively insensitive to transverse normal stress. This model predicts an increase in formability in strain space that varies nonlinearly with an increasing magnitude of compressive transverse normal stress ratio. Smith and Matin (2004) extended their previous model by assuming that the strain ratio is defined to be constant even with the influence of transverse normal stress, instead assuming that the stress space forming limit is relatively insensitive to transverse normal stress. This proposed model is independent of the type of yield function and it is fundamentally different than their original model. Further, the proposed model is much simpler than the original model. Stoughton and Yoon (2004) extended the original idea of Arrieux (1995) and proposed the concept of anisotropic forming limit curve. They proposed that the forming limit is no longer defined by a curve but requires the definition of a surface in strain or stress space, and therefore, it is no longer appropriate to view these limits with a convenience of two-dimensional diagrams. A solution to the challenge of assessing formability for a planar anisotropic material is proposed by rescaling the stresses by a factor; here the scaled stresses have the same relationship to a single forming limit curve in a 2D plot in stress space, as the actual stresses have to the true anisotropic forming limit in 3D space. Simha et al. (2007) proposed an Extended Stress-Based Limit Curve (XSFLC). The stress-based limit curve is then transformed into equivalent stress and mean stress space to obtain XSFLC. However, both FLD and stress-based forming limit diagram (SFLD) are measured and derived, respectively, for plane stress loading conditions. In some metal-forming processes, such as hydroforming and stretch flange forming, the onset of necking occurs under loading conditions that are not plane stress (Fig. 6). Hagbart et al. (Alsos et al. 2008) proposed a new analytical criterion for predicting FLD and SFLD known as BWH criterion by combining Hills localized necking theory for LHS of FLD and Bressan Williams (1983) shear instability criterion for RHS of FLD.

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Bai and Wierzbicki (2008) proposed a new concept of “cumulative forming severity” for predicting neck formation in sheets under nonproportional loading. Fahrettin and Lee 2004; (Ozturk and Lee 2004) investigated the forming limits of sheet metal using ductile fracture criteria by performing the finite element simulation of an out-of-plane formability test. The predictions for LHS of the FLD are good in agreement with experimental results. However, the predictions were not successful for RHS of FLD. They have concluded that ductile fracture criteria should not be used directly to calculate forming limits. Due to advancements in Continuum Damage Mechanics (CDM), Chow and his co-workers (Chow and Yu 1997; Chow et al. 2002, 2003; Chow and Jie 2004) developed a unified damage approach for predicting forming limit diagrams. This theory is extended to predict FLD on damage coupled kinematic–isotropic hardening model under nonproportional loading. Later, the damage theory is coupled with the modified vertex theory to deduce a generalized localized necking criterion. Based on this necking criterion, the forming limit strains of sheet metals with material damage consideration are computed.

Die and Mold Materials In this section, an overview of the die and mold materials used in the metalworking industry is presented. Initially, comprehensive lists of ferrous and nonferrous materials are provided. Subsequently, the application of these materials based on the forming processes is covered, and the guidelines for the die and mold material selection for different metal-forming applications are provided.

Classification and Properties of Tool and Die Steels Steel used for die and mold applications covers a wide range of ferrous alloys. The most commonly used forms are wrought tool steels. Other steels used for metalworking applications include powder metallurgy (P/M) steels, medium-carbon alloy steels, and maraging steels (Davis 1995, 1990).

Wrought Tool Steel For wrought tool steels, a list of the principal types of tool steels with their properties, processing, and service characteristics is provided in Table 1. High-speed steels are tool materials developed largely for high-speed cutting tool applications. There are two classifications of high-speed steels: molybdenum high-speed steels, also called group M (see Table 1), and tungsten high-speed steels, called group T. Groups M and T are equivalent in performance. The main advantage of group M is the lower initial cost. This is due to the lower atomic weight of molybdenum, which is about half that of Tungsten. Based on the weight percentage, only about half as much molybdenum as tungsten is required to provide the same atom ratio. Compositionally, group M contains molybdenum, tungsten, chromium,

AISI designation Wear resistancea Molybdenum high-speed steels M1 7 M2 7 M3 8 M4 8 M7 8 M10 7 M30 7 M33 8 M34 8 M35 7 M36 7 M41 8 M42 8 M43 8 M44 8 M46 8 M47 8 M48 8 M62 8 Tungsten high-speed steel T1 7 T2 8 T4 7 T5 7 Working hardness (HRC) 63–65 63–65 63–66 63–66 63–66 63–66 63–65 63–65 63–65 63–65 63–65 66–70 66–70 66–70 66–70 66–70 66–70 66–70 66–70 63–65 63–66 63–65 63–65

Toughnessa

3 3 3 3 3 3 2 1 1 2 1 1 1 1 1 1 1 1 1

3 3 2 1

5 5 5 5

5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5

Machinabilitya

3 3 3 3

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

Amount of distortiona

Table 1 General properties, processing, and service characteristics of tool steels (Cockroft and Latham 1968)

9 9 5 5

5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

Resistance to crackinga

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T6 8 1 T8 8 2 T15 9 1 Intermediate high-speed steel M50 6 3 M52 6 3 Chromium hot-work steel H10 3 9 H11 3 9 H12 3 9 H13 3 9 H14 4 6 H19 5 6 Tungsten hot-work steel H21 4 6 H22 5 5 H23 5 5 H24 5 5 H25 4 6 H26 6 4 Molybdenum hot-work steel H42 6 4 Air-hardening, medium-alloy, cold-work steel A2 6 4 A3 7 3 A4 5 4 A6 4 4 A7 9 5

3 5 3 5 5 7 7 7 7 5 5 5 5 5 5 5 5 5 5 5 3 3 1

63–65 63–65 64–68 61–63 62–64 39–56 38–55 38–55 40–53 40–54 40–55 40–55 36–54 38–48 40–55 35–45 50–58 45–62 57–62 58–63 54–62 54–60 58–66

10 10 10 10 10

5

9 9 9 9 9 9

10 10 10 10 10 9

5 5

5 5 5

(continued)

Materials in Metal Forming

5 5 7 7 5

5

3 3 5 3 3 3

1 1 1 1 1 3

3 3

3 3 3

6 245

AISI designation Wear resistancea A8 4 A9 4 A10 3 D2 8 D3 8 D4 8 D5 8 D7 9 Oil-hardening cold-work steels O1 4 O2 4 O6 3 O7 5 Shock-resisting steels S1 4 S2 2 S5 2 S6 2 S7 3

Table 1 (continued) Working hardness (HRC) 48–57 40–56 55–62 58–64 58–64 58–64 58–63 58–66 57–62 57–62 58–63 58–64 50–58 50–60 50–60 50–56 58–64

Toughnessa 1 8 3 2 1 1 2 1

3 3 3 3

8 8 8 8 8

5 7 7 5 5

9 9 10 9

Machinabilitya 5 5 7 1 1 1 1 1

5 7 5 5 1

1 1 1 5

Amount of distortiona 5 5 7 5 5 5 5 5

9 3 9 9 9

9 9 9 9

Resistance to crackinga 10 10 10 10 9 10 10 10

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Rating range from 1 (low) to 9 (high)

a

Low-alloy special-purpose steels L2 1 L6 3 Low-carbon mold steels P2 1 P3 1 P4 1 P5 1 P6 1 P20 1 P21 1 Water-hardening steels W1 2–4 W2 2–4 W5 2–4 45–62 45–62 58–64 58–64 58–64 58–64 58–61 30–50 36–39 58–65 58–65 58–65

7 6

9 9 9 9 9 8 8

3–7 3–7 3–7

10 10 10

7 5 3 5 5 7 5

7 5

7 7 7

1 1 1 3 3 1 1

3 1

5 5 5

9 9 9 9 9 9 10

7 9

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vanadium, cobalt, and carbon as principal alloying elements, while group T contains tungsten, chromium, vanadium, cobalt, and carbon. Hot-work steels are the tool materials developed to withstand combinations of heat, pressure, and abrasion associated with punching, shearing, or forming metals at high temperatures. They are in group H and have medium carbon contents (0.35–0.45 wt%) and chromium, tungsten, molybdenum, and vanadium contents of 6–25 wt%. They can be divided into three subgroups: chromium hot-work steels (type H10 to H19), tungsten hot-work steels (type H21 to H26), and molybdenum hot-work steels (types H42 and H43). The chromium hot-work steels are the most commonly used ones for their good resistance to heat softening. The tungsten hot-work steels are better in heat resistance but worse in toughness. Molybdenum hot-work steels are similar to that of tungsten hot-work steels, but only with lower initial cost. Cold-work steels are restricted in forming applications that do not involve prolonged or repeated heating above 205–260  C. There are three categories of cold-work steels: air-hardening steels (group A); high-carbon, high-chromium steels (group D); and oil-hardening steel (group O). Group A contains manganese, chromium, and molybdenum alloying elements to enable the steels to achieve full hardness in sections up to about 100 mm in diameter upon air cooling from the austenitizing temperature. Typical applications for these steels include shear knifes and forming and coining dies. The inherent dimensional stability of these steels makes them suitable for gages and precision measuring tools. Group D steels have from 1.5 to 2.35 wt% C and 12 wt% Cr and commonly 1 wt% Mo in their compositions. Typical applications for group D includes long-run dies for blanking, forming, thread rolling, and deep drawing. Group O steels have high carbon contents, plus sufficient other alloying elements in which full hardness can be obtained in small-to-moderate sections upon oil quenching from the austenitizing temperature. The most important service-related property of group O steels is high resistance to wear at normal temperatures, a result of high carbon content. On the other hand, group O steels have low resistance to softening at elevated temperatures. Group O steels are extensively used in dies and punches for blanking, trimming, drawing, flanging, and forming. In shock-resisting steels (Group S), the principal alloying elements are manganese, silicon, chromium, tungsten, and molybdenum in various combinations. The carbon content is about 0.5 wt% for all group S steels, which provides a combination of high strength, high toughness, and low-to-medium wear resistance. Group S steels are used primarily for chisels, rivet sets, punches, driver bits, and other applications requiring high toughness and resistance to shock loading. The low-alloy special-purpose steels (group L) contain small amounts of chromium, vanadium, nickel, and molybdenum. They are generally used for machine parts, such as arbors, cams, chucks, and collets, and their special applications require a combination of good strength, good toughness, and low price.

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Table 2 List of the principal types of P/M tool steels and their compositions (Cockroft and Latham 1968) Constituent elements, wt% C Cr W Mo Trade name Cold-work tool steels CPM 9V 1.78 5.25 1.3 CPM 10V 2.45 5.25 1.3 CPM 15V 3.4 5 1.3 CPM 440V 2.15 17.5 0.5 Vanadis 4 1.5 8 1.5 Vanadis 10 2.9 8 1.5 Hot-work tool steels CPM H13 0.4 CPM H19 0.4 CPM H19V 0.8

5 4.25 4.25

4.25 4.25

1.3 0.4 0.4

V

Co

9 9.75 14.5 5.75 4 9.8

1.05 2.1 4

S

Others

0.03 0.07

1.0 Sn 0.5 Mn

4.25 4.25

Hardness HRC 53–55 60–62 62–64 57–59 59–63 60–62

42–48 44–52 44–56

Mold steels, also called group P steels, contain chromium and nickel as principal alloying elements. Group P steels are used almost exclusively in low-temperature die-casting dies and in molds for the injection or compression molding of plastics. Water-hardening steels, also called group W steels, contain carbon as the principal alloying element. Small amounts of chromium are added to most of group W steels to increase their hardenability and wear resistance. Group W steels are very shallow hardening and usually have a hard case over a tough and resilient core. They are suitable for cold heading, striking, coining, and embossing tools.

P/M Tool Steels Recently, powder metallurgy (P/M) becomes a major process for manufacturing high-performance tool steels products. The P/M process was used primarily for the production of advanced high-speed tool steels and now also being used in the manufacture of improved cold-work and hot-work tool steels. Table 2 provides a list of the principal types of P/M tool steels and their compositions. A number of improved, high-vanadium P/M tool steels designed for high-wear and cold-work applications are commercially available, which are listed in Table 2. The more uniform microstructure the P/M cold-work steels have, the better toughness they possess. This is crucial in cold-work tooling as it allows higher hardness to be achieved with associated improvements in yield strength and wear resistance. Furthermore, substantial improvements in wear resistance can be realized by using higher vanadium contents in P/M cold-work tool steels than in conventional coldwork tool steels. The no-segregation nature of P/M tool steels makes them very attractive for hot-work tool and die applications, because a common cause of premature failure of

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large die-casting dies is thermal fatigue attributed to segregation. Powder metallurgy processing is an alternative method for producing segregation-free hot-work tool steels of both standard and improved compositions, and it offers near-net shape capability. The compositions of the three P/M hot-work tool steels now commercially available are given in Table 2.

Medium-Carbon Low-Alloy Steel The medium-carbon low-alloy steels commonly used for metalworking applications include AISI 4130, the higher-strength 4140, and the deeper hardening, higher-strength 4340 and 6150. These steels are not normally used as dies or other such demanding applications because of their low hardness values, but they are frequently used for auxiliary tooling, such as containers for hot-extrusion dies or holder blocks for molds for processing of plastics. Chemical compositions for these steels are given in Table 3. AISI 4130 is a water-hardening alloy steel of low-to-intermediate hardenability. It retains good tensile, fatigue, and impact properties up to about 370  C. AISI 4140 is similar in composition to 4130 except for a higher carbon content. It is used in applications requiring a combination of moderate hardenability and good strength and toughness, but in which service conditions are only moderately severe. AISI 4340 is considered the standard to which other ultra-high-strength steels are compared. It combines deep hardenability with high ductility, toughness, and strength. It also has high fatigue and creep resistance. It is often used where severe service conditions exist and where high strength in heavy sections is required. It also exhibits good retention of strength. AISI 6150 is a tough, shock-resisting, shallow-hardening chromium-vanadium steel with high fatigue and impact resistance in the heat-treated condition. Maraging Steels Maraging steels comprise a special class of high-strength steels. These steels differ from conventional steels in that instead of being hardened by a metallurgical reaction involving carbon, they are strengthened by the precipitation of intermetallic compounds at temperatures of about 480  C. Commercial maraging steels are designed to provide specific level of yield strength from 1,030 to 2,420 MPa. These steels typically have very high nickel, cobalt, and molybdenum contents and very low carbon contents. Such characters make the maraging steels relatively soft after annealing. During age hardening, there are only very slight dimensional changes. Therefore, fairly intricate shapes can be machined in the soft condition and then hardened with a minimum of distortion and good weldability and fracture toughness. This makes maraging steels unique in many demanding applications, including aircraft and aerospace components and tooling components such as die-casting dies, plastic molds of intricate design, and casings for cold-extrusion tools. Table 4 lists the chemical compositions of the more common grades of maraging steels.

Designation or trade name 4130 4140 4340 6150

Compositions in wt% C Mn 0.28–0.33 0.4–0.6 0.38–0.43 0.75–1.0 0.38–0.43 0.6–0.8 0.48–0.53 0.7–0.9 Si 0.2–0.35 0.2–0.35 0.2–0.35 0.2–0.35

Cr 0.8–1.1 0.8–1.1 0.7–0.9 0.8–1.1

1.65–2.0

Ni

Table 3 List of the principal types of medium-carbon low-alloy steels and their compositions (Cockroft and Latham 1968) Mo 0.15–0.25 0.15–0.25 0.2–0.3

0.15–0.25

V

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Table 4 Nominal compositions of commercial maraging steels (Cockroft and Latham 1968) Composition in wt%, all grades contain no more than 0.03 % C Grade Ni Mo Co Ti Al Nb Standard grades 18Ni (200) 18 3.3 8.5 0.2 0.1 18Ni (250) 18 5 8.5 0.4 0.1 18Ni (300) 18 5 9 0.7 0.1 18Ni (350) 18 4.2 12.5 1.6 0.1 18Ni (Cast) 17 4.6 10 0.3 0.1 12-5-3(180) 12 3 0.2 0.3 Cobalt-free and low-cobalt-bearing grades Cobalt-free 18Ni (200) 18.5 3 0.7 0.1 Cobalt-free 18Ni (250) 18.5 3 1.4 0.1 Low-cobalt 18Ni (250) 18.5 2.6 2 1.2 0.1 0.1 Cobalt-free 18Ni (300) 18.5 4 1.85 0.1

Nonferrous Tool and Die Materials This section describes the nonferrous materials used for metalworking and plasticforming applications, including cemented carbides (which are the most commonly used nonferrous tool and die material), steel-bonded carbides, ceramics, graphite, diamond, plastics, and nonferrous alloys.

Cemented Carbides Cemented carbides are employed in metal-forming applications because of their combination of high compressive strength, good abrasion resistance, high elastic modulus, good impact and shock resistance, and ability to take and retain excellent surface finish. Typical applications in this category include drawing dies, hot and cold rolling of strips and bards, cold heading dies, forward and back extrusion punches, swaging hammers and mandrels, and can-body punches and dies. Table 5 lists nominal composition and properties of representative WC grades and their applications. Steel-Bonded Carbides Steel-bonded carbides are powder metallurgy materials that are intermediate in wear resistance between tool steels and cemented carbides based on WC-Co. They consist of 25–45 vol.% TiC homogeneously dispersed in a steel matrix. They have the following advantages over the conventional tool steels: (1) machinable in the annealed condition with conventional cutting tools, (2) hardenable with conventional equipment without decarburization and without experiencing an undue change in size, and (3) wear well after hardening on tough applications, giving performance equivalent or superior to that of conventional cemented WC. Common grades of steel-bonded carbides include C, CM, CM-25, CHW-45, CHW-25, SK, CS-40, PK, and MS-5A.

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Table 5 Nominal composition and properties of representative cemented carbides and their applications (Cockroft and Latham 1968) Binder content in wt% 20–30

Grain size Medium

Hardness, HRA 85

11–25

Medium to coarse Medium Fine to medium Fine

84

11–15 10–12 6 10–12 12–16 10 Co with TiC and TaC

88 89 92

Fine to medium Medium

90

Medium

91

88

Typical application Heavy blanking punches and dies, cold heading dies Heading dies (severe impact), hot forming dies, swaging dies Back extrusion punches, hot forming punches Back extrusion punches, blanking punches and dies for high shear strength steel Powder compacting dies, Sendzimir rolls, strip flattening rolls, wire flattening rolls Extrusion dies (low impact), light blanking dies Extrusion dies (medium impact), blanking dies, slitters Deep drawing dies (non-galling), tube sizing mandrels

Ceramics Similar to cemented carbides, ceramics are used in metalworking applications due to their properties: combination of temperature resistance, corrosion resistance, hardness, chemical inertness, and wear resistance. Ceramics offer unique advantages for metalworking applications as they provide very high stiffness-to-weight ratios over a broad temperature range. The high hardness of structural ceramics can be used in applications where mechanical abrasion is expected. The ability to maintain mechanical strength and dimensional tolerances at high temperatures makes them also suitable for high-temperature use (i.e., for isothermal forging dies). Typical applications for ceramics include bearings and bushings, closetolerance fittings, extrusion and forming dies, spindles, metalworking rolls, can-making tools, wire-drawing machine components, and coordinate-measuring machine structures. Commonly used monolithic ceramic materials and their properties are given in Table 6. Graphite, Diamond, and Plastics Graphite is the most commonly used material for constructing the die assembly in hot presses for applying both thermal and mechanical energy to affect the densification of the metal and ceramic powders. Graphite is easily machined, is relatively inexpensive, has moderate room-temperature strength properties that increase with temperature, and has good creep resistance up to 2,500  C. Another important metalworking application for graphite is its use as a mold material during gravity casting. Both natural and synthetic (polycrystalline and single-crystal) diamonds are widely used in the wire industry, especially for cold drawing of small-diameter

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Table 6 Representative properties of ceramics used for metalworking applications (Cockroft and Latham 1968)

Material 90 % Al2O3 94 % Al2O3 96 % Al2O3 99.5 % Al2O3 99.9 % Al2O3 Sintered SiC Reactionbonded SiC Si3N4 ZTA Mg-PSZ Y-TZP Al2O3 – SiCw Si3N4 – SiCw

Bulk density, g/cm3 3.6 3.7 3.72 3.89 3.96 3.1 3.1

Flexure strength (MPa) 338 352 358 379 552 550 462

Fracture toughness pffiffiffiffi (MPa m) 3–4 3–4 3–4 3–4 3–4 4 3–4

Elastic modulus (GPa) 276 296 303 372 386 400 393

Hardness (GPa) 10 12 11 14 15 29 25

3.31 4.1–4.3 5.7–5.8 6.1 3.7–3.9 3.2–3.3

906 600–700 600–700 900–1200 600–700 800–1000

6 5–8 11–14 8–9 5–8 6–8

311 330–360 210 210 330–380 330–380

15 15–16 12 12 15–16 15–16

round wire. Both natural and synthetic diamonds can be used to produce nonferrous, ferrous, and high-temperature alloy wires. Plastics are used extensively for dies and molds, primarily for forming aluminum alloys, low-carbon steels, and stainless steels. The most widely used materials are polyesters, epoxies, and polyurethanes. Plastics are generally used for small-to-moderate production runs of simple or moderately shaped parts.

Nonferrous Alloys Beryllium-copper alloys that nominally contain 0.4–2.0 wt% of Be and small amount (>SSAi)

Fig. 4 Relationship between the specific surface area and the internal structure of a particle: (a) particle shapes with constant mean size and various SSA: spherical (SAA1), irregular (SAA2), dendritic (SAA3), (b) compact and porous spherical particles (Redrawn from (Rouquerol et al. 1999))

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Table 4 Methods for porosity determination Porosity determination Gas adsorption Mercury porosimetry TEM SEM Small-angle X-ray scattering Small-angle neutron scattering

Remarks Easy and frequently used technique for (0.4–50 nm) open pores, using BET method, Fig. 5 (redrawn from Lowell and Shields (1991)) Seldom used for interconnected pores ( R* as shown in the figure, and the reliability at the future moment t2, estimated by online data and the state model, is less than the reliability threshold value, that is, t2 > T, R(t2) < R*, and remanufacturing of the components is needed to increase the reliability. However, when both the reliability value at the current time and the estimated reliability of the future time are larger than the threshold, the components can be continuously in service without parts remanufacturing. When the reliability value at the current time and the reliability at the future time are both less than the predefined reliability threshold, processing costs and technical feasibility analysis need to be further carried out to determine whether the parts are to be remanufactured or to be recycled. Online deciding the time for a part to retire for manufacturing requires a comprehensive model and analysis. Based on the above two types of methods discussed, a comprehensive assessment procedure has been proposed to decide the optimal time for the part to retire for remanufacturing based on the resultant evaluation on cost, performance, technical process, energy sustainability, and environmental impact. The flowchart is shown in Fig. 4.

RUL and the Mathematical Model Security issues have been arousing worldwide concern nowadays, since the system failure always causes severe accidents, leading to significant economic losses. System safety is of great importance for civil and defense security in many areas, such as electric power, petrochemical processing, metallurgy, aerospace, weapons, vehicles, and other major or important mechanical products and infrastructure. Timely and accurate prognostic of the remaining useful life of key components for the system can not only reduce accidents and losses but also provide the information for preventive maintenance, decision for remanufacturing, and remanufacturing process planning. Effective remaining useful life modeling and prediction and the online performance reliability assessment have been catching more and more research attention. There are two major methods for the remaining life modeling – physical model-based method and data-driven statistical method. The following describes the development status and trends about these two models.

Physical Model-Based Method It is the life model based on theoretical formulas of physical mechanics and dynamics. It is an important research direction for life prediction. From the initial stress-based approach to assuming that fatigue life depends on the development process of crack initiation and expansion till fracture, the physical model for life prediction has gained a long-term development with the application of fracture

economic evaluation

technology assessment

environmental assessment

N

Y

remanufacturing

RUL is enough

N Y

Comprehensive evaluation of each index.(analytic hierarchy process (ahp))

recycle

Reliability calculation

Fig. 4 The flowchart of the comprehensive assessment for the decision on the time to remanufacture

The used parts

RUL assessment

Appropriate remanufacturing time

Remanufacturing timing

Comprehensive select the optimal remanufacturing time

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mechanics and crack propagation theory methods. From the 1850s to 1860s, the concepts of S-N (stress-rpm) curve and fatigue limit were first introduced by the German engineer August Wohler (1867) in his study of damaging fatigue test of rail vehicle axle. Later, effects of average stress on life got research attention. Americans Paris and Erdogan (1961) proposed the laws of crack spreading, i.e., Paris law, using fracture mechanics method in 1963. Paris law provided a new method for the fatigue life estimation, developed the “damage tolerance design,” and made an amazing breakthrough of the study of fatigue in theory. Then, researchers made more meaningful research based on Paris’s work. Ray and Tangirala (1996) put forward a nonlinear stochastic model of crack propagation, estimating crack length, crack growth rate, and residual life by the measured sensor signals. The formula about the equivalent theoretical fatigue limit and stress fatigue life was derived based on fatigue limit of room temperature by Xiaohua Yang (1998) and modified by introducing frequency factor to predict the fatigue life of 2Cr13 steel at different frequencies. Through fracture mechanics analysis, Feng Zhang (1997) estimated the wheel damage tolerance crack size and durability and also analyzed the causes of fatigue damage based on Hertz theory. Xiaoyang Zheng (1999) proposed a new cumulative damage rate method to estimate fatigue life under spectrum loading. Based on the statistical analysis on constant amplitude fatigue, logarithmic probability density function of fatigue life under any stress condition was derived; thus, the components’ fatigue life under fatigue load spectrum can be estimated with the help of the cumulative failure probability. Xiulin Zheng (1994) proposed that the size of load spectrum can be represented by overloading caused by maximum load in the load spectrum. Miner’s rule could be applied to predicting the cumulative fatigue damage value of the fatigue crack initiation (FCI) of elements under variable amplitude loading after the load interaction. Miner’s rule can also be used to predict the fatigue life and the critical value of cumulative fatigue damage under variable amplitude loading. Glodez (2002) presented a method to calculate the stress cycle numbers needed for crack initiation by using the strain-life method. Oppenheimer and Loparo (2002) established a physics-based approach for rotor crack diagnostics and prognostics using integrated observers and life models. The observers could be used to estimate the length of the crack, assuming that the velocity and forces on end shafts could be calculated with measured data according to the dynamic model of rotor. And a life model based on the Forman crack growth law of linear elastic fracture mechanics was developed to determine the remaining cycle numbers of the shaft, until a failure occurs. Table 1 shows the comparison and analysis of advantages and disadvantages among all kinds of physical approaches according to the developing sequence of remaining life prediction (Lee et al. 2014). In recent years, more in-depth experimental research on physical mechanics methods for life prediction is conducted. The new methods are featured in deep understanding and development of fatigue theory and comprehensive modeling. Experimental data are expected to be more approximated to the actual situation and specialized research achievements on vital structures or components are gotten,

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Table 1 The comparison of physical model-based methods Approach Life prediction based on stress

Life prediction based on strain

Advantage Simple and easy to analyze Parameters required are few and easy to get

Rich data sources with accumulated experience can be obtained Direct access to the strain parameters by means of measurement Able to conduct notched fatigue analysis

Disadvantage Low accuracy Poor adaptability for the parameters depends on geometry and loading forms Unable to analyze the gap effect without considering crack Complex calculations Insufficient gap analysis Only the crack initiation is considered

Able to judge the impact of the loads order Able to express the cyclic stress–strain response Conducive to fatigue – creep mixture analysis

Life prediction based on accumulative fatigue damage

Life prediction based on fracture mechanics

The impact of actual magnitude and the order of loads has been taken into consideration

Matured and widely used The mechanism of the crack propagation can be physically interpreted since the crack propagation is taken into consideration Able to control the initial damage, the examination period

Only parts of the influence factors have been considered; failed to conduct a comprehensive analysis of the complexity of life prediction Narrow scope of application Difficult to measure and estimate the initial crack size; no research on the initial crack Uneasy to calculate the stress intensity factor of the components with complex geometry Elastic–plastic fracture mechanics is used since linear elastic fracture mechanics can hardly

Application The initial design estimation Long-life components such as the spring shaft and gears and other high-strength materials Whole-life analysis combined with linear elastic fracture mechanics Fatigue test with strain as control condition Situation of high temperature, large strain, and high stress concentration Components with less load cycles, large plasticity, such as low-strength structural steel Whole-life analysis combined with linear elastic fracture mechanics The material and mechanical parts subjected to cyclic loading

More used in engineering Large and important parts and structures, such as aircraft and nuclear reactors Metal materials with metallurgical defects in themselves and components with pores, slag, and weld defects created in welding

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Table 1 (continued) Approach

Life prediction based on damage mechanics

Life prediction based on energy

Advantage

Disadvantage

and working loads, etc., to ensure safety Good agreement with the fatigue mechanism in experimental observations Easy to measure the fatigue damage

meet requirements in general cases Complex calculation and analysis

Unified representation and strong universality

Allowed to take the mean stress and the impact of multidirectional load into account

Insufficient research on damage mechanics of some materials and components Insufficient research

Few applications

Application

Only applied for some metal materials, composite materials, and concrete materials

Composite panels of composite tissue, such as alloy laminate, coating structure Piezoelectric material and the composites

which thereby greatly expand the scope of the study, leading to more specific research results. However, due to the high degree of complexity in the theory of the life prediction method based on physical mechanics and dynamics, the modeling becomes more difficult. On the one hand, modelers’ professional backgrounds are highly demanded; on the other hand, the model’s prediction error highly increases as the increasing of the model complexity. Therefore, the prediction based on the theory and physical models hardly meets the life research for remanufacturing in spatial state. Lifetime prediction in terms of new equipment’s working conditions can help formulate a set of reasonable inspection and maintenance standards, enabling the equipment to operate at its best condition and to achieve its secured service life extension. The assessment process for remanufactured products’ remaining life at a given time can be expressed as follows: (1) build a three-dimensional solid model according to its geometry, size, and/or the assembly state of products; (2) acquire performance data of materials through empirical or experimental data, such as material physical performance parameters, fatigue properties, and S-N curve; (3) based on steps 1 and 2, conduct the finite element stress and strain analysis; (4) make a second service load spectrum analysis based on historical records and the measured data of the product, such as remanufacturing residual stress and secondary assembly stress; (5) given environmental factors, establish a cumulative damage model for life prediction. Because the initial conditions of components will change after servings, the model and the prediction for parts in traditional manufacturing have difficulties without knowing the changed initial conditions for remanufactured parts. Thus, modeling of the initial conditions of remanufactured products plays an essential role in the remaining life modeling and prediction.

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Data-Driven Stochastic Model Method Analytical method based on probability statistics is a traditional reliability theory, mostly regarding lifetime information as objects for statistical analysis. Researchers first get failure data through a large number of tests and then use statistical criterion to select the most appropriate statistical distribution model to obtain lifetime distribution. Stribeck, R. (1907) used a probabilistic approach to predict the life of mechanical parts in 1924. Based on the test results, they suggested that the acceptable life should be defined as the lifetime when the sample parts have 10 % failure or 90 % survival left. Goode noted that the failure process of many devices (such as hot rolling mill pump) could be divided into stationary and nonstationary process (Goode et al. 2000). Also he proposed statistical control method to distinguish the two processes. In the stationary process, life could be predicted by using the reliability data (the length distribution of the steady and non-steady-state process). While in the nonstationary process, assuming the device’s status monitoring data grows exponentially, and using condition monitoring data and reliability data together to predict the life. Subsequently, autoregressive moving average model (ARMA) and autoregressive integrated moving average model (ARIMA) were widely used in time series modeling and predicting. Yan (2004) recommend using logistic regression to establish relationships between characteristic variables and failure probability of the device, in the meantime suggested also applying ARMA model to predict the characteristic variables, and then using the trained logistic model for residual life prediction with the predicted value input. Volk recommended proportional intensity model (PIM) for life prediction and evaluating the effect of preventive maintenance (Volk et al. 2004). Liao compared the predicted performance of logistic regression methods with PHM models of a single device’s remaining life in the example of bearing (Liao et al. 2006). In the paper, root-mean-square value and Camber value of the monitored vibration signal are set as covariates of the PHM in the life prediction, and experimental results show that the prediction accuracy of PHM is better than that of logistic regression model. However, the life model based on statistics does neither consider the difference of failure mechanisms and changing of operational conditions nor fully utilize the relevant information before failure. Moreover, this kind of life analysis and prediction methods are based on probability statistics, supported by a large number of experimental data, without emphasizing on the individual subtle change. So the results of life prediction are the “average property” under the given conditions. Although this life analysis and prediction method can be used to effectively calculate the distribution of the product’s life through accumulated massive data, it can hardly meet individual device life research such as online bearing in a spatial state. More and more research work focuses on modeling of online performance reliability and RUL assessment of a critical system or machinery from real-time performance data sensing.

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Remaining useful life modeling and prediction based on condition monitoring data is defined as predicting the time from the current time to the time that system failure occurs or to the time when a defined the system failure to reach. Jardine (2006) expressed remaining useful life as Xt ¼ {xt:Tt|T > t, Z(t)}, where T represents a random variable of life time, t represents the current time, and vector Z(t) represents the observation data available to the current time. Engel (2000) showed the prediction of the remaining useful life of the helicopter gearbox using polynomial model to extrapolate characteristic variables. Besides, he also pointed out that an effective life prediction method required not only the expected value of remaining useful life but also the uncertainty of predictions (expressed with confidence interval). Baruach and Chinnam recommended HMM, hidden Markov model, for diagnosis and prediction establishing independent HMMs for each health state during equipment failure, and then estimating the state-changing point of each sample, and finally figuring out the conditional distribution of state-changing points (Chinnam and Baruah 2005). Camci and Chinnam regarded hierarchical HMM as dynamic Bayesian network for diagnosis and prediction (Camci and Chinnam 2010). The methods can be used to estimate transition probabilities directly of health status of equipment. Considered the continuity of the state identification and residual life prediction, Mullen applied the improved algorithm – hidden semi-Markov model – to build a framework for equipment’s residual life prediction which was verified by simulation and realized the residual life prediction on the basis of device degraded state recognition (Gokhale and Mullen 2004). In the paper (Bie and Wang 1997), analysis and comparison between the Monte Carlo method and the analytical method are elaborated, and then it gives a comprehensive introduction of the application of Monte Carlo method in the power system reliability assessment, such as the way to improving the convergence rate, the combination of Monte Carlo method with the analytical method, and how to deal with the loads in the Monte Carlo method. In recent years, the state-space model is widely used to model and predict the residual life of mechanical products and reliability evaluation in economic time series analysis. In general, state-space models consist of two equations: one is the state equation and the other is the observation equation. The state equation represents the transition from the current state to the next state, namely, mutual transformational relation, and the observation equation stands for the interrelation between the actual observed vectors and the state vector. The establishment of the two equations provides a consistent modeling framework for adequately describing the motion characteristics of the dynamic system. However, there is a lag in the research of state estimation problem based on state-space model, and for quite a long time basically remains at a level of linear models based on Kalman filtering. In the 1990s, the particle filtering theory had gained a great development since Gordon put forward the recursive process of resampling (Gordon et al. 1993). The reference (Zhang et al. 2005) proposes a fault detection and isolation algorithm based on SIR (sequential importance resampling) particle filter likelihood function value. The algorithm divides the fault system into several fault subsets; every fault subset uses a particle filter to estimate, while all

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particle filters run in parallel. The algorithm is simple and applicable to general nonlinear systems. Using the state-space method to establish the model for mechanical product life prediction is a hot research work in recent days. In terms of state estimate and model parameter estimate in a state-space model, there are limitations of the existing methods, causing prediction accuracy severely reduced. When applying state-space model in application for RUL assessment, there are still many technical problems to be solved. As for nonlinear non-Gaussian state-space model of RUL, either the theory or applied algorithm is still in the beginning stage. Comparative analysis among a variety of life prediction methods is made in Table 2. Advantages and disadvantages of each method together with corresponding scope of applications are also listed (Lee et al. 2014). Table 2 The comparison of data-driven stochastic model Approach Life prediction based on probability theory

Advantage Simple and easy to analyze

Few parameters required and easy to get

Able to obtain confidence limits Life prediction based on ARMA or ARIMA

Life prediction based on logistic regression (LR)

Simple to model, methods mature, and widely used No need for historical data or fully understanding the fault mechanism High efficiency in computing and allowed to be used online No need to set the critical threshold

Able to obtain the time dynamic characteristics without making too much

Disadvantage Too much accumulated experience data required Only a general estimation without regard to the specific cases of working loads and failure Inaccuracy prediction in terms of the individual Unable to be used for long-term prediction Unable to synthesize prior knowledge

Sensitive to noise and the initial state; poor effects on the dynamic process of nonstationary Not feasible unless normal feature domain description and unpredictable behavior are both available Unable to do real-time prediction with off-line data

Application The initial design estimation

Less important, small sized, mass produced components and parts

Suitable for linear time, invariant systems when the performance of its characteristics are smoothly changing

Service performance evaluation for degradation failure equipment like machine tools

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Table 2 (continued) Approach

Advantage

Disadvantage

Application

Life prediction based on proportional hazards

assumptions for equipment failure process and the distribution function Variety of status information are integrated into account The characteristic index able to change with time No need for fault history data

Complex computation of parameter estimation involving numerical integration

Risk analysis of the joint characteristic variables

Assumption that risks change with variables proportionally

Analysis of data distribution, residual distribution unknown, or censored data Pattern recognition or some certain signal processing

Life prediction based on neural network

Life prediction based on Bayesian networks

Life prediction based on support vector machine (SVM)

Strong self-learning ability, nonlinear fitting ability, and good robustness Excellent memory ability and nonlinear mapping skill

Able to deal with uncertain problems Able to avoid data overfitting Effective multisource information fusion and expression Able to visualize the dependency links among each pair of variables Concrete realization of the structural risk minimization criterion Simple structure, good promotion performance, and fast learning Only one minimal in optimization solution

No standard method to determine the structure of the neural network Sufficient sample data required for the parameter calibration Uncertainty of structure and weight Unable to model the unknown fault Difficulty in computing for the unknown network Relies on reliability of the prior knowledge Results are sensitive to the selection of prior distribution Not suitable for largescale data processing Theoretical defects in kernel function for nonlinear classification problems Slow solution speed

Used to solve uncertainty and relevance fault of complex equipment Widely used in the intelligent systems of computer intelligence science, industrial control, medical diagnostics, and other fields Small sample, nonlinear, and highdimensional mode recognition, such as predicting porosity and clay content in oil well logging

(continued)

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Table 2 (continued) Approach Life prediction based on Markov model/ hidden Markov model (HMM)

Advantage Able to model different stages of degradation

Disadvantage Unable to model the unknown fault

No need for prior knowledge of fault mechanism, able to process incomplete data

Complex hidden semiMarkov model is required if the fault time is not exponentially distributed The model calculation increases with the number of states State equation and measurement equation need to be defined

Predictions with confidence intervals State-space prediction based on Kalman filter

State-space prediction based on particle filter

Able to do the linear unbiased minimum mean square error estimation Prediction accuracy does not change with the prediction time interval; good robustness Able to estimate the current state and also predict the future state By finding a random sample to carry out the approximate calculation for the probability density function to obtain the state minimum variance distribution High accuracy

Noise level could influence the performance and stability of the algorithm Only works with linear system and Gaussian noise Large amount of sampling are required to avoid degradation Computation is more complex than Kalman filtering

Application Unable to directly observe the state, but able to observe the vector sequence Widely used in pattern recognition, speech recognition, behavior recognition, character recognition, and fault diagnosis and other fields

Real-time online prediction for linear Gaussian system

Real-time online prediction of strongnonlinear, non-Gaussian noise system, such as radar tracking and robot localization

High-dimensional system and increasing particles complex computation

RUL Online Assessment The online assessment of RUL of machinery usually implies that real-time performance observations are available across the time, and the RUL is estimated by the conditional probability of the performance observations of the unit. The online assessing algorithms are recursive, depending on preceding state observations only. The state-space model is widely used with recursive algorithms in application.

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Support Vector Machine (SVM) Methods Support vector machine (SVM) is a machine learning algorithm proposed in the 1990s and based on Vapnik’s statistical learning theory. Support vector machine is not based on traditional empirical risk minimization principle, but VC (VapnikChervonenkis) dimension theory and structural risk minimization principle in statistics. SVM can solve the structural problems of high-dimensional model with a limited number of samples and has good predictive performance. The initial SVM is mainly applied to pattern recognition problems. Through short-term research and development SVM also makes a good result in regression (Steinwart and Christmann 2008). SVM techniques have developed greatly in recent years, giving a rise of a new kind of learning machines that use the central concept of SVM techniques for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by using different kernel function and the base algorithm. They are used in a variety of fields, including biomedicine and bioinformatics (Furey et al. 2000), image analysis and artificial vision (Guo et al. 2001), and other engineering fields (Taboada et al. 2007). SVMs have been used by researchers to solve classification and regression problems. In this research study, SVM for regression (SVR) is used as an automated learning tool with a different focus to successfully predict the SOC (state of charge) of a high-capacity lithium iron phosphate battery cell as a function of cell voltage, cell current, and cell temperature. SVM model is used an alternative to the traditional regression approaches. As a nonlinear estimator, SVM is more robust than a least squares estimator because it is insensitive to small changes.

Support Vector Regression (SVR) Theory Vapnik (1998) introduced insensitive loss function into support vector machine, which can solve the problem of nonlinear regression. Support vector regression (SVR) is divided into linear regression and nonlinear regression. In most cases, the sample shows a nonlinear relationship. For the nonlinear case, the basic idea of support vector regression machine is that the sample points are mapped to highdimensional feature space by a nonlinear mapping Φ : Rn ! H, and linear regression is applied in high-dimensional feature space to get the nonlinear regression estimation in the original space (Hong et al. 2005). For nonlinear regression presented in Fig. 5, the estimation function is y ¼ f ðxÞ ¼ hω, ΦðxÞi þ b

(1)

where ω is weight vector and is the bias, , is the operation of dot product. The unknown coefficients ω and b are estimated by a minimization of the following function: l X 1 Γ ð ωÞ ¼ k ωk 2 þ C ζ ð f ðxi Þ  yi , xi Þ 2 i¼1

(2)

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Fig. 5 Support vector regression

y

y=( w . x )+b e e

0

x

Fig. 6 The e-insensitive loss function

In the above objective function, the first term is the regularization item, the second term is the empirical risk, and C is a positive number which determines the balance between empirical risk and the regularization parts. Empirical risk is measured by Vapnik’s e-insensitive loss function, which is shown in Fig. 6, defined as (Xu et al. 2005).  0 , jy  f ðxÞj < e   ζ ð f ðxÞ  y, xÞ ¼ jy  f ðxÞje ¼ (3) jy  f ðxÞj  e, jy  f x j  e where e is the width parameter of the differential gap (dead band) in Vapnik’s insensitive loss function. In Eq. 4, slack variables ξi and ξ*i are introduced, and the above minimization problem can be converted into the following problem, namely, min s:t:

l  X  1 ΓðωÞ ¼ kωk2 þ C ξi þ ξi 2 i¼1 hω, Φðxi Þi þ b  yi  ξi þ e yi  hω, Φðxi Þi  b  ξi þ e ξi , ξi  0, i ¼ 1, 2, . . . , l

(4)

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The above constrained optimization problem is a typical quadratic programming problem and can be solved by Lagrange multiplier method. The Lagrange multiplier α*i and η*i are introduced, then l l    1 X X  ðÞ L ω, b, ξi αi ðe þ ξi þ yi  hω, Φðxi Þi  bÞ þ C ξi þ ξi ¼ kω k2  2 i¼1 i¼1 l l  X X    αi e þ ξi  yi þ hω, Φðxi Þi þ b  ηi ξi þ ηi ξi  i¼1

i¼1

(5) Function L is minimized to ω, b,

ξ(*) i ,

then the extreme conditions of function L is

l  X  @L ¼ω αi  αi Φðxi Þ ¼ 0 @ω i¼1 l   @L X ¼ αi  αi ¼ 0 @b i¼1 @L ðÞ ðÞ ¼ C  αi  ηi ¼ 0 ðÞ @ξi

According to Wolf dual theory, the original problem is transformed into its dual problem, namely,

min

s:t:

l    1X     ðÞ W αi αi  αi αj  αj Φðxi Þ, Φ xj þ ¼ 2 i, j¼1 l  l X  X   e αi þ αi  yi αi  αi l  X i¼1

i¼1

i¼1

(6)

 αi  αi ¼ 0 ðÞ

0  αi

 C, i ¼ 1, 2, . . . , l

Solving the dual problem, suppose the solution is α* ¼ (α*1, α*2, . . ., α*1), α ¼ (α1, l  X  αi  αi Φðxi Þ. According to the Karush-Kuhn-Tucker α2, . . ., α1) then, ω ¼ i¼1

(KKT) conditions (Fletcher 1987) of quadratic programming, αiα*i ¼ 0, that is to say, of αi and α*i , at least one of them is zero. Only a few are not zero, and their corresponding sample points are the support vector. The regression estimation function is decided by the regression vector and has nothing to do with the support vector. The definition of inner product kernel function is introduced, and support vector regression estimation function can be written as

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f ðX Þ ¼

l  X

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 αi  αi K ðXi , XÞ þ b

(7)

i¼1

where b ¼

8 l  X >    > >  αi  αi K Xi , Xj þ e y > < j

  αj  0, C

l  X >    > > > αi  αi K Xi , Xj þ e : yj 

  αj  0, C

i¼1

i¼1

SVM Model and Forecasting In support vector regression, the choice of kernel function and other parameters determines model’s regression results and generalization ability. There are several parameters in the solution algorithm of SVR and need to be determined. One is the type of kernel function and its parameters, the other is the penalty factor C, and another is the insensitive function parameter e. Kernel functions have a very important impact on the solution process of SVM. SVM model is characterized by the training set. Kernel function and different forms of kernel functions can generate different SVM regression models, and commonly used kernel functions are linear kernel, polynomial kernel function, radial Gaussian kernel function, and so on. Penalty factor C is used to balance the complexity of the model and empirical risk values making its generalization performance the best. The optimal C in different data subspace is different. In order to control the complexity of the model, the value of C should be generally small, but the value cannot be too small to avoid a big model experience error. e determines the minimum allowable fitting error of the learning machine. e can control the regression approximation error, so as to control the number of support vectors and model’s generalization ability. As the above parameters also determine the forecasting accuracy of support vector regression model, the most appropriate parameters need to be selected to obtain the best prediction results. It is an optimization problem, and methods such as the cross-validation method, the grid search algorithm, particle swarm algorithm, and genetic algorithm can be used to seek the optimal value (Chapelle 2002). Forecast is predicting the future based on the past and present state. In other words, it is to make scientific predictions and reasonable inference to the direction of the future developing trend and the possible future state (Xu et al. 2007). For a given time series {xi}, i ¼ 1, 2, . . ., N. Take the first r (r < N) data as training samples, and the rest of the data set as the test samples. In order to make more effective use of the data, phase space of the one-dimensional time series is reconstructed, and the series is transformed into a matrix form. The one-dimensional time series {xi} can be transformed into the following matrix form: 1 0 X1 x1 B X 2 C B x2 C B X¼B @ ⋮ A¼@ ⋮ Xrm xrm 0

x2 x3 ⋮ xrmþ1

  ⋱ 

1 xm xmþ1 C C, ⋮ A xr1

1 xmþ1 B xmþ2 C C Y¼B @ ⋮ A xr 0

(8)

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Fig. 7 The concept of support vector machine supervised training process xr

xm xm+1

x1

Training group 1

Forecasting points

xm+1 xm+2

x1 x2

Training group 2

xr

Forecasting points

where Xi ¼ Xm i ¼ (xi, xi+1, . . ., xi+m1) and m is the forecasting embedding dimension. Figure 7 (Xu et al. 2007) is the actualization of support vector machine supervised training – (X, Y) is the training pair for the machine input and output. Then the forecast regression function is f ðX t Þ ¼

rm  X

 αi  αi K ðXi , Xt Þ þ b

(9)

t¼1

In Eq. 9, t ¼ m + 1, m + 2, . . ., r; K(,) is kernel function. Lagrange multiplier α(*) i and offset b can be obtained from the following two quadratic programming problems: min

s:t:

r m    1X     ðÞ αi  αi αj  αj K Xi , Xj W αi ¼ 2 i, j¼1 r m  rm  X  X  þe αi þ αi  xi αi  αi Nm X i¼1

 

i¼1

i¼1

(10)

αi  αi ¼ 0 ðÞ

0  αi

 C,

i ¼ m þ 1, m þ 2, . . . , r

One-step ahead forecasting is the forecast of xr+1, x^rþ1, given input vector Xrm+1. Data vector used for forecasting of the next moment is the actual observed value with the m moment up to current time t, rather than the forecasting data, while the forecasting data points will be used as input moment for multiple-step ahead forecasting. According to Eq. 9 and Xrm+1 ¼ (xrm+1, xrm+2, . . ., xr), the 1-step ahead forecasting is

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x^rþ1 ¼

rm  X

 αi  αi K ðXi , Xrmþ1 Þ þ b

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(11)

i¼1

In addition to forecast unknown points, it is important to be able to give a confidence interval under a certain confidence level. Here is the forecasting interval estimation based on t-distribution (Chen and Zhou 2008). For any real value x and the forecasting value x^, the relationship between them is x ¼ x^ þ e, and e is the error. Assume the overall error e  N(0, σ 2), forecasting error set can be obtained from the training set: E ¼ {e1, . . ., en}, where E is a sample of e. Take enþ1 ¼ xnþ1  x^nþ1 as the forecasting error of a single point, which is from the error overall and is independent from the elements in E, then rffiffiffiffiffiffiffiffiffiffiffi n  1 enþ1  e   tðn1Þ η¼ nþ1 S

(12)

where e¼

n 1X ei n i¼1

S2 ¼

n 1X ðei  eÞ2 n i¼1

Given that α is the probability of type I error, through solving P(|η|  λ) ¼ 1  α, so |η|  tα/2(n  1), and then rffiffiffiffiffiffiffiffiffiffiffi rffiffiffiffiffiffiffiffiffiffiffi nþ1 nþ1 e  tα=2ðn1Þ S  enþ1  e þ tα=2ðn1Þ n1 n1

(13)

Besides enþ1 ¼ xnþ1  x^nþ1 , then the forecasting interval of xn+1 is "

rffiffiffiffiffiffiffiffiffiffiffi rffiffiffiffiffiffiffiffiffiffiffi# nþ1 nþ1 , x^nþ1 þ e þ tα=2ðn1Þ S x^nþ1 þ e  tα=2ðn1Þ S n1 n1

(14)

Case Study A case study is conducted for the milling tool’s RUL assessment. The test is carried out in OKUMA vertical three axis milling machine at the Molding Tool Graduate School of Dalian University of Technology shown in (Fig. 8). The tool to test is 7792VXD cow nose cutter, and the diameter, overhanging length, and blade number are 32 mm, 200 mm, and 3, respectively. Spindle speed, cutting depth, and feeding speed are 400 mm/min, 0.4 mm, and 1,000 rpm, respectively. The acoustic emission signal and the force signal are measured at the same time. The acoustic emission (AE) signal is collected every 10 s. The sampling frequency is 2,048 kHz, and the sampling length is 512,000 of pulses.

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Fig. 8 OKUMA vertical three axis milling machine for the test

Both amplitude and distribution of the acoustic emission signals change along with tools’ state from fresh to worn. Some signal features are noticeable and closely related to the wear, but the others are not. The acoustic emission signals are decomposed into 64 bands by wavelet packet (WP) transform, and the WP energy of different sampling time is calculated and normalized that is shown in Fig. 9. It can be clearly observed that the signal energy mainly concentrates on low frequency band and the maximum energy concentrates on Band 2. WP energy of Band 2 is used to estimate the tool wear and its change is shown in Fig. 10 with a nonmonotonically increasing trend. This trend may be caused by the increase of tool wear, which results in increasing contact area between the tool and the work piece. The calibration and the subsequent forecasting work of SVR in this study were performed by resorting to the LIBSVM software package. The LIBSVM software is freeware, and the source codes, written in C++, are open to public. This study has modified the codes for this tool wearing assessment. The grid search method with a cross-validation technique (Hsu et al. 2003) was used to derive the SVR model parameters, including the penalty parameter C and the kernel parameter γ. The error tolerance e is set to 0.01. The search range is 28  28 and the search step is set at 1.0. A threefold cross-validation was used, and the parameters were obtained by the minimum root-mean-square error (RMSE) regarding each fold cross-validation. The model parameters (C, γ) ¼ (27.86, 0.06) were then determined by averaging the derived three sets of parameters from cross-validation, and the RMSE was 0.03 by

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Fig. 9 The WP energy spectrums at different time

0.5 0.45

Normalized energy

0.4 0.35 0.3 0.25 0.2

0

50

100

150

Time ( mins )

Fig. 10 Normalized WP energy change of Band 2

200

250

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Fig. 11 The estimation result of SVR parameters

simulating the calibration events. The estimation result of SVR parameters is shown in Figs. 11 and 12. Figure 13 presents the result of 1-step ahead forecasting after t ¼ 165. At time t, several realizations of x^t, namely, x^tðjÞ ( j ¼ 1,. . ., r), can be obtained from the SVR residuals. A sample of r realizations of the forecast error ej can be obtained and used to “address” the deterministic forecast value obtained from the trained SVR model. The result is a distribution of r forecasts x^tðjÞ , whose average is the deterministic prediction x^t . The empirical distribution of x^tðjÞ represents the probabilistic forecast. The probability distribution pertaining to each t variable can be constructed using a plotting position formula (Vogel 1986). This work applied Hazen plotting position relationships to describe the WP energy distribution of t. A threshold for the normalized energy is set at 0.4. When the normalized energy value exceeds the threshold, the tool is considered failed. The results of reliability estimation at t ¼ 165, t ¼ 185, and t ¼ 200 are shown in Figs. 14a–c, respectively. Figure 15 is the forecast confidence interval based on t-distribution (α ¼ 0.05), and it can be seen that the reliability reduces gradually with the increase of forecast step. Pð x  X Þ ¼ (a) t ¼ 165 (b) t ¼ 185 (c) t ¼ 200

2m  1 2N

(15)

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Best c=27.8576 g=0.0625 CVmse=0.030419

1

MSE

0.8 0.6 0.4 0.2 0 5 5

0 −5

0

−5

log2g

log2c

Fig. 12 The estimation result of SVR parameters (3D)

0.46 real data predict data

0.44

Normalized energy

0.42 0.4 0.38 0.36 0.34 0.32 0.3 165

170

175

180

185 190 195 Time ( mins )

200

205

Fig. 13 The result of 1-step ahead forecasting in SVM model at t ¼ 165

210

215

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Fig. 14 Predictive probability distribution

0.44

0.42

Normalized energy

0.4

0.38

0.36

0.34

0.32

0.3 0

0.1

0.2

0.3

0.6 0.7 0.4 0.5 Exceedance Probability

0.8

0.9

1

0

0.1

0.2

0.3

0.4 0.5 0.6 0.7 Exceedance Probability

0.8

0.9

1

0

0.1

0.2

0.3

0.4 0.5 0.6 0.7 Exceedance Probability

0.8

0.9

1

0.46

0.44

Normalized energy

0.42

0.4

0.38

0.36

0.34

0.32

0.5

0.48

Normalized energy

0.46

0.44

0.42

0.4

0.38

0.36

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0.5 real data predict data

Normalized energy

0.45

0.4

0.35

0.3

0.25 165

170

175

180

185 190 195 Time ( mins )

200

205

210

215

Fig. 15 Forecasting confidence intervals based on t-distribution

State-Space Method State-Space Model (SSM) State-space model (SSM) is a method of typical correlation analysis, which was first proposed by Akaike (1975) and further evolved by Mehra (1979). The applications in the analysis of mechanical products’ remaining useful life using SSM are increasing rapidly because the method can make some fairly complex issues into simple form. SSM provides a consistent analytical framework for processing practical problems on account of the similar model structure. The ideas to create an SSM are as follows: (l) introduce the concept of unobserved state variables; (2) establish a model for describing the status changes, which is called state equation; and (3) determine the observation equation, which contains the state transfer information. An SSM is built by two equations: one is the state equation and the other is the observation equation. The state equation presents the relationship between the current state and the next state, and the observation equation reflects the intrinsic relationship between the observations and the state of the system. The mathematical definitions of SSM are given below: Definition l: state vector xi, the system state variables, used to reflect the intrinsic characteristics of the dynamic system at time i, is random and unobservable generally. xt in N-dimensional Euclidean space that is xi  RNx , where RNx is the

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state space. In general, the state sequence at time t ¼ 1, . . ., s is denoted by x1:s and x1:s ¼ {x1, x2,   , xs}. Definition 2: observation vector yt of the dynamic systems, which can be observed, is also random because of the noise. The observation sequence at time t ¼ 1, . . ., n is denoted by y1:n and y1:s ¼ {y1, y2,   , yn}. Assume the system states can seem as a first-order Markov process and observations under states are independent with each other. The general state-space model of the system (may be a nonlinear and non-Gaussian system) can be written as xt ¼ ft ðxt1 , ut , ηt , θÞ, t  T

(15)

yt ¼ ht ðxt , ut , «t , θÞ, t  T

(16)

where Eqs. 15 and 16 are the state and observation equations, respectively. xi  RNx is the system state vector, yi  RNx is the observation vector, ui  RNx is the input vector of the system, and θ is the static parameters of the model. «i («i  RNx) and ηi (ηi  RNx) denote the observation noise and state noise, respectively; they are independent with each other. f 1 : RN x  RNe 7!RNx is state function and h1 : RNx RNη 7!RNy is observation function. These two functions assumed known, depend on μt (sometime it is omitted for simplicity). Prior distribution of the initial state x0 is assumed to be P(x0).

Model Estimation and Particle filtering Estimation can be divided into three categories based on the given information. They are prediction, filtering, and smoothing. For the state sequence x1:s: (1) if t > s, the future status can be predicted by the available state information, namely, prediction; (2) if t ¼ s, the current status can be predicted by the obtained information, namely, filtering; (3) if t < s, the past state can be estimated by the available information, namely, smoothing. Bayesian filtering is a method of statistical inference. The principle of Bayesian filtering is to construct the posterior probability density of the state variables by using all the known information. The way is predicting the prior probability density of the state using state transition function, then get posterior probability density by updating it using the recent observation. The purpose of establishing SSM is to estimate the current state variables xt using observed information y1:t. The posterior probability density p(xt|y1:t) will be updated once a new observation becomes available, so it will make the calculation inconvenient. However, recursive Bayesian filter, the basic state estimation theory of dynamic system, was introduced to update the recursive estimates, instead to store and reprocess the previous measurement data, which can save storage space and improve the operation speed. Recursive filtering sequence estimation can be used to update the state vector for discrete-time and parameter time-varying system. That is estimating the current state vector according to the current observation vector and several previous state

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vectors without depending on all of previous data (Markov memoryless property). Generally, this process is carried out in two steps. Predictions: to estimate the priori probability density of current state vector using the system state functions. Because of the effect of noise, there will be some deviation between the prior probability density and its actual status. Updated: to incorporate at time t + 1, the new observations yt+1 into the estimator of the state vector to correct the obtained prior probability density and get the posterior probability density of the state vector by Bayesian method. The recursive formula of the conditional probability density function is Initial value: p(x0jy0) ¼ p(x Ð 0) Prediction: p(xtjy1:t1) ¼ p(xtjxt1)p(xt1jy1:t1)dxt1 Þ Update: pðxt jy1:t Þ ¼ pðytpjxðtyÞpjyðxt jy1:t1 Þ t

where p(ytjy1:t1) ¼

Ð

1:t1

p(ytjxt)p(xtjy1:t1)dxt is called as normalized factor ð pðyt jy1:t1 Þ ¼ pðyt jxt Þpðxt jy1:t1 Þdxt :

As the probability density function in application is often not in a closed form, it is difficult to get the exact solution by an analytical method in practice. Only in linear constant coefficient system with zero mean and Gaussian white noise, Kalman filter method provides an optimal solution to the problems of prediction and updating. The n-dimensional state equation and m-dimensional observation equation of the system can be expressed by xk ¼ Φk, k1 xk1 þ Γk, k1 W k1

(17) yk ¼ Ck xk þ V k Assume that both the state noise Wk and measurement noise Vk are independent, standard white noise (sequence of uncorrelated random variables with zero mean), that is, E[Wk] ¼ 0, cov[Wk, Wj] ¼ E[WkWjT] ¼ Qkδkj, E[Vk] ¼ 0, cov[Vk, Vj] ¼ E[VkVjT] ¼ Rkδkj, and cov[Wk, Vj] ¼ E[WkVjT] ¼ 0., where Qk and Rk are the covariance matrixes fornoise Wk and Vk respectively. The statistical properties h of the initial state xi0 is

E½x0 ¼ x0 , Var ½x0 ¼ E ðx0  x0 Þðx0  x0 ÞT ¼ P0, cov[x0, Wk] ¼ E[x0WkT] ¼ 0,

and cov[x0, Vk] ¼ E[x0, VkT] ¼ 0. The optimal linear filtering recursive procedure of the state xk can be obtained as follows: The filter equation is   (18) xkjk1 x^k ¼ x^kjk1 þ K k yk  C^

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where x^kjk1 ¼ Φk, k1 x^k1 and the prior value is x^0 ¼ x^0j0 ¼ x0 . The gain equation is

where Pkjk1

 1 (19) K k ¼ Pkjk1 Ck T Ck Pkjk1 Ck T þ Rk h i  T ¼ E xk  x^kjk1 xk  x^kjk1 is the prediction error variance

matrix. Prediction error variance equation is Pkjk1 ¼ Φk, k1 Pk1 ΦT k, k1 þ Γk, k1 Qk, k1 Qk1 ΓT k, k1 :

(20)

The filtering error variance equation is Pk ¼ ðI  K k Ck ÞPkjk1

(21)

and the prior value is P0 ¼ Var[x0]. A recursive procedure commences at time t0 ¼ 0 by choosing x0 and P0 to be the best estimates of the mean and the covariance of x0, respectively. The estimate for P1|0 is governed by the Eq. 20, then through Eq. 19, K1 can be calculated. The state estimation x^1 at t ¼ 1 can be obtained through Eq. 18. Next, P1|0 was brought into Eq. 21 to calculate P1. By repeated application of the recursive procedure, the realtime state estimation can be obtained. Particle filter is an approximation Bayesian filtering algorithms based on Monte Carlo simulation. Recursive Bayesian filtering can be done through nonparametric Monte Carlo simulation method, and the posterior probability can be effectively calculated for non-Gaussian, nonlinear, and high-dimensional data. The main idea is to use random independent samples called particles with weights to approximate the probability density p(xk|zk). The mean of the samples can instead integral operation to obtain the minimum variance estimation of the state. As each sample (particle) representing a possible state of the system, the probability density function of the particles gradually approaching that of the state with the increased number of particles, then the optimal Bayesian estimation results will be possibly obtained. Particle filter can be applied in nonlinear and non-Gaussian state-space model without constraints. Because of the advantages of the particle filtering technology, particle filter has a wide range of applications in time-varying parameter modeling and becomes a hot research topic and application. The mathematical description of the particle filter algorithm is the following: for a stationary random process, assuming the posterior probability density of the system at time k  1 is p(xk1|zk1), select n random sample points according to certain principles. When the measurement information at time k is available, the posterior probability density p(xk|zk) of the n particles can be updated. The process of particle filtering can also be divided into the two phases of the state estimation and the estimation correction. In the state estimation, a large number of samples will be selected from the probability distribution of xk1. The state of each particle

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can be changed based on the state equation and the controlled variable; the original particles become predicted particle. In the correction phase, the predicted particle values are substituted into the observation equation, update the particle weights after noise processing, and make weight normalization. Hammersley, Morton, and Rosenbluth put forward the sequential importance sampling (SIS) method in the 1950s (Chen 2007). In the late 1960s, SIS method was applied in the control system applications. Scholars in various fields continued to study the SIS modes in the 1970s. The sequential importance sampling (SIS) algorithm is as follows: 1. Initial value t ¼ 0, sampling particles {x(0 j )}Ni ¼ 1 from prior distribution p(x0), and the weight of each particles is wi0 ¼ N1 . for i¼1,2,. . .,N. 2. For t ¼ 1,2,. . ., T, i. For i ¼ 1,2,. . ., N, extracting particles x(i) q(xt|x(i) y1:t) from important t  0 : t1 ðiÞ

ðiÞ

ðiÞ

^t function q(xt|x(i) 0:t1y1:t), and assuming x0:t ¼ x0:t1 , x

.

j Þ ð j Þ ðiÞ w . , Þ t1 ð j N X ðiÞ ðiÞ ðiÞ ~ t ¼ wt = wt . iii. For i ¼ 1,2,. . .,N, normalizing the importance weight by w ð iÞ

ii. For i ¼ 1,2,. . .,N, updating the weight by wt ¼

pð

ði Þ ðiÞ ðiÞ yt xt p xt xt1 ðiÞ ðiÞ q xt x0:t1 y1:t

i¼1

3. The updated particle set {x(i) 0:t : i ¼ 1, . . ., N} is to approximate the posterior N   X ðiÞ probability distributions of the state pðx0:t jy1:t Þ

wt δ x0:t  x0:t and the i¼1

expectations of some functions such as E½gt ðx0:t Þ ¼

N   X ð iÞ ðiÞ gt x0:t w~ t :δ(.) is the t¼1

Dirac delta function. After a number of iterations of the algorithm, the variance of the particle weights will gradually increase over the time, and a small number of particles are with large weight and most of the particles with small weights, which can be neglected, resulting in a type of particles’ “degradation.” The degradation of the particles in iteration process will lead to that the observed value Y of the system couldn’t be gotten after all the predictions. To this end, Gordon proposed the concept of resampling until 1993 (Gordon et al. 1993), which laid the base for the particle filter algorithm, and degradation was effectively inhibited (Storvik 2002). The concept of effective sampling scales is defined as N eff ¼ N N  where wt(i) : X 2 wt ðiÞ i¼1

i ¼ 1, . . ., N are unnormalized weights of the particles. This formula is difficult to accurately estimate the actual calculation; the following approximated formula is ~ t ðiÞ : i ¼ 1, . . . , N are normalized generally used as N eff ¼ N  1  where w X 2 ~ t ð iÞ w i¼1

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(j) {x t-1 }j=1 with the weight 1/N

Initial particles

SIS process

(j) N with t updated particles {xt } j=1 ~ (j) he weight wt (j) N re-sampling particles {xt } with the j=1 weight 1/N

Re-sampling process

Fig. 16 The diagram of resampling process

weights. If Neff < Nth, then resampling should be adopted. In general, Nth ¼ 2 N/3 as a defined threshold number for sampling scales. The most commonly used resampling method can be introduced as follows: First, generate n random numbers {μl: l ¼ 1,2,. . .,n} in the [0, 1] uniform distribution, then use search method to find m1 m X X ~ j. The new samples an integer number m satisfying the formula w~ j  μl  w j¼0

j¼0

xk(m) are joined in the new set of sampling particles. The interval [0, 1] is divided i X into n intervals by λj ¼ w~ j ði ¼ 1, 2,   , nÞ. When the random number μl belongs j¼0

to the mth the interval Im ¼ [λm1, λ], the samples are copied. Obviously, the samples with larger weight can be copied repeatedly, and some samples with smaller weight will be abandoned in the case of n sample number all the time. The weight of particles is now reassigned as 1/N, and the resampling process is realized. The standard particle filter algorithm process with resampling is introduced and Fig. 16 (Zhao et al. 2013) is the schematic of this process: N (i) Initialization: t ¼ 0, choosing particles {x(i) 0 }i ¼ 1 from prior distribution p(x0), i 1 and the weight of each particles is wð0Þ ¼ N for i¼1,2,. . .,N. (i) (ii) Importance sampling: for i  ¼ 1,2, . . ., N, extracting   particles  from q(xt|x0:t1y1:t),

ðiÞ

ð iÞ

ðiÞ

ðiÞ

ðiÞ

that is, x^t  q xt jx0:t1 y1:t . Assuming x^0:t ¼ x0:t1 , x^t

for i ¼ 1, 2, . . ., N.

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Updating the weight by wt ¼ ðiÞ

ðiÞ

normalized by w~ t ¼ wt =

N X

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pðyt jx^t

iÞ Þpðx^ðt iÞ jxðt1 Þ ðiÞ wt1 . The importance weight is ði Þ qð jx0:t1 , y1:t Þ ði Þ

ðiÞ x^t

ð iÞ

wt .

i¼1

(iii) Resampling: calculating N eff ¼

1

N  2 X ~ t ðiÞ w

. If Neff < Nth, then resampling

i¼1

n oN n oN n oN ðiÞ ðiÞ ðiÞ from particle set x~t , w~ tðiÞ , else xt , wðt iÞ particles xt , N1 i¼1 i¼1 i¼1 n oN ðiÞ ðiÞ ~t ¼ x~t , w . i¼1

(iv) The posterior probability distributions of the state Although the particle filter algorithm has been widely adopted in many applications, there are still some shortcomings. To improve the performance and stability of the particle filter further, many improvements have been proposed in recent years, mainly in two aspects:selecting the importance function and maintaining the particles diversity. Auxiliary particle filter (APF), local linearization methods, evolutionary particle filter algorithm, etc., are examples. In data-driven statistical modeling, the model structure and the mode parameters for a particular application are required to set through the understanding of the application and the available data. Then the model parameters’ estimation of a state-space model is necessary. Maximum likelihood estimation and maximumposterior-likelihood estimation are the methods widely used in the aspect of parameter estimation due to its asymptotically optimal properties. However, there are few analytical approaches in solving the likelihood function, and it is calculated by means of mathematical optimization methods in practical application. But in some cases it is difficult to get the gradient of the likelihood function. To this end, the common method in the presence of latent variables is the expectationmaximization (EM) algorithm. The EM algorithm was first proposed by Dempster, Lair, and Rubin in 1977 (Dempster et al. 1977). EM algorithm can make a more complex optimization problem of likelihood function into a series of relatively simple functions by data augmented technology, which provide a framework for maximum likelihood estimation of unknown parameters with incomplete data (missing data) set, while the traditional methods such as Newton–Raphson estimation more complicated. EM algorithm belongs to iterative algorithm and consists in two steps for each iteration algorithm: expectation step called the E-step and maximization step called the M-step. State-space model with algorithm of particle filtering (modified MCMC method) is currently the most studied method for online RUL modeling and assessment due to the HMM property of the state model and MCMC effectiveness for nonlinear, non-Gaussian system. For a particular application, the state-space model (the state function and the observation function) is built based on the given system degradation dynamics and failure modes and failure mechanisms. The system

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initiation conditions are also determined by the application. The available observation data will be used for model coefficients estimation through the EM algorithm by Monte Carlo simulation. The state of the space model and its estimate are used for the assessment of the degradation, the hidden stochastic process. The particle filter is applied to perform Bayes recursive estimation of posterior probability density function of the system state, given every updated observation. This is a recursive simulation approximation. The numerical calculation yet is very expensive at each step, once a new update observation is available. The accuracy of the algorithm is highly depending on the number of particles. As digital computer system has been improved greatly in power and speed, it makes the particle filter and state-space model possible in RUL online application.

Online Degradation and RUL Predictions The purpose of estimating the model parameters and joint status of the state is to predict the future degradation of the individuals, that is the estimation of the state (i) N (xt + 1 : t + ljy1 : t). Assuming the posterior sampling {x(i) 0:t, θt }i ¼ 1 is obtained, the joint posterior distribution at time t: pðx0:t , θjy1:t Þ ¼

N 1X δ ðiÞ ðiÞ ðx0:t , θÞ N i¼1 ðx0:t , θt Þ

(22)

The particles generated during the filtering estimation also represent the behavior of the individual unit degradation. Consequently, the continued evolution should be able to characterize the future degradation of the working unit. With the recursive estimation the l-step ahead prediction of the degrading state, i.e., (xt + 1 : t + l|y1 : t), can be obtained by the following steps: For j ¼ 1, . . ., l, (i) (i) (i) (i) (i) For i ¼ 1, . . ., N, sample x(i) t + j  p(xt + j|xt + j  1, θt ) and xt : t + j ¼ (xt : t + j  1, xt + j) N At the end of iteration, a sample set {x(i) t : t + j}i ¼ 1 available, and an estimation of p(xt + 1:t + lj y1 : t) can be estimated as

pðxtþ1:tþl j y1:t Þ ¼

N 1X δ ði Þ ðxtþ1:tþl Þ N i¼1 ðxtþ1:tþl Þ

(23)

Using the predictive distribution at time t + 1 as input for recursive estimation, the corresponding degradation prediction, for l-step ahead, can be calculated, namely x^tþl ¼

N 1X ðiÞ x N i¼1 tþl

(24)

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The object of prediction is to obtain time-to-failure (TTF) from current time, i.e., the RUL, which is the time from the current to the time the system fault indicator reaches its corresponding failure threshold. The RUL is a random variable, the result for RUL estimation is presented in the form of probability and probability distribution. Referring to (Orchard et al. 2005), given a predetermined failure threshold λ for a state x, the two-sided criterion for system failure is defined as

 Cf ¼ Hlow  x  Hup Where, Hlow and Hup are the upper and lower boundaries of the interval failure respectively. As shown in Fig. 17, Hup and Hlow are set symmetrically on both sides of λ. The sample particle swarms of state estimation from tk to tk + l overlap with the hazard zone (the light shaded area). The sum of the normalized weights of all sample particles which locate in the light shaded area at any time step between tk and tk + l represents the probability of system failure occurring at the corresponding time step. The normalizing constant is the sum of weights of total sample particles which locate in the light shaded area from time tk to tk + l. Therefore, an approximation of the probability distribution of RUL can be obtained through a set of an equal interval discrete samples with their corresponding probabilities, that is

The true value The predictive value

Degradation characteristics

The confidence interval

H up l H low t exp Probability mass function of TTF tk

t k+1 Time

Fig. 17 The estimation principle of RUL (Orchard et al. 2005)

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N     X ~ ikþj pTTF tkþj ¼ p H low  xjkþj  H up w

ði ¼ 1, 2, . . . , N; j ¼ 1, 2, . . . , lÞ

i¼1

(25) ~ ikþj is the normalized weight of each particle at each prediction time, Where, w p() is the probability to failure when the particle value is within the range of the defined failure band. The mean of remaining useful life is therefore estimated to be: ERUL ¼ texp  tk

(26)

where, texp is the expected failure time. There are much research worked on RUL assessment based on the degradation assessment with online observations, namely p(x0:t|y1:t) in Bayesian estimation. However, there are various concerns about online remaining useful life (RUL) assessment based on the monitoring data in practice. The brief discussion is summarized below: 1. The definition of failure by a performance threshold is basically by the experience of the engineers or the analysis of old data, which is inaccurate because the life of the identical products is different even under the same working environment. It is very subjective in the defining the threshold in current RUL assessment practice. 2. The RUL is caused by system degradation. The underlying degradation of the system is a stochastic process, It is considered monotonic (no decreasing for small is better case), time-varying, and random. But the performance or the performance variable of the system could be up and down with fluctuation. Online signal in data acquisition usually shows a strong fluctuation in real application. 3. It is difficult to measure the degradation signal directly in practical applications. Usually other signals highly related to the degradation of the system are measured to assess the remaining useful life. The variables for online monitoring are regarded as covariates, and proportional hazards model are applied to assess the residual life. The advantage of this method is no need to define the failure threshold; however, when dealing with a variety of covariates, the model needs to be deeply study. 4. The degradation of the system performance couldn’t be measured directly and usually some measurable physical variables that highly related to decline of the system degradation will be measured. There are two stochastic processes: system underlying degradation and the measurable performance. Hidden Markov model (HHM) is proposed to study the hidden process from the observation of the measurable process. HMM has been study and applied widely, but there are no uniform approach to determined the direct relationship between observations and states of the hidden process. And how to determine the parameters of the model is still remaining to discuss.

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5. State space model (SSM), belongs to HMM category, is most studied method in predicting the remaining useful life. Kalman filtering method, widely used for constant coefficient linear systems with Gaussian noise, provides optimal estimation and forecasting results, but not directly for those nonlinear systems with non-Gaussian noise. No theoretical form can be applied in model parameter estimation and state estimation, and only Monte Carlo simulation method can be adopted for recursive estimate online. The definition of the baseline model is a difficult point in the term of SSM, the relationship between the observations and the state variables need to totally study and be modeled for an application. The state variables should be linearly independent and can express the system dynamics completely in an SSM.

A Case Study Experimental Design and Setup The data set in this case study is taken from PHM Society (2010). Monitoring signals were obtained from the sensors (acceleration sensors, force sensors, acoustic emission sensors) mounted on the workpiece. Six cutters with three blades of the same type (C1, C2, C3, C4, C5, and C6) were used in the milling test. All the six cutters were experimented on the same test workpiece for 315 times, milling cutting operations under the same setting condition. The vibration signals, force signals, and acoustic emission signals were measured during each milling process; the abrasion losses of C1, C4, and C6 were also measured. Experimental data were saved in 315 sample files for each cutter; there are 6 sets of files in total for six cutters (C1–C6) respectively. The maximum wear values of tool C1 were used to establish the state-space model. As shown in Fig. 18, it is a nondecreasing process. Since the section of the data before milling time 125 showed great volatility, the data of milling cycle from 125 to 315 were set as validation data.

Modeling and Parameter Estimation According to the general degradation model about the cutting tool, the individual degradation process is modeled in a Wiener process with an unknown drift constant β, that is, XðtÞ ¼ βt þ σ B  BðtÞ

(27)

where B(t) is a Brownian motion, σ B is the corresponding diffusion coefficients, and both β and σ B are unknown. The true value of the degradation cannot be measured directly; the observation Y(t) contains the error σ R, i.e.,

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Fig. 18 The maximum wear value of toolC1

Y ðtÞ ¼ XðtÞ þ σ R  eðtÞ

(28)

where e(t) is a Gaussian white noise and σ R is the measurement error. Since the measurement system is generally known with a controllable precision, the value of σ R in the model is assumed to be known. In order to use the particle filtering method for recursive estimation of model parameters and of prediction of degradation, a state-space model is established for the process, and the state of the model represents the unobservable degradation dynamics. Since the unit of interest is monitored through a digital computer DAQ system, a discrete-time state-space model is applied (Jin et al. 2013): Xnþ1 ¼ Xn þ βn þ σ B, n  W n

(29)

Y n ¼ Xn þ σ R, n  V n

(30)

Here, βn, σ B,n, and σ R,n are the values of the model parameters at step n, and Wn and Vn are normal random noise terms. In the Bayesian inference framework, a reasonable prior distribution is first assigned for the unknown parameters of the model. Since both the system dynamics and the degradation process are affected by some of the common factors, including the same working environments, there is likely some dependency existing between β and σ B. Therefore, the joint distribution π 0(β, σ 2B) of β and σ 2B is considered as the prior distribution of the unknown parameters in the model.

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Let m0 ¼ u^β , ν0 ¼ σ^2β , νB ¼ σ^2B; from the modern parametric Bayesian inference principle, the conjugate prior distributions for these two parameters are applied, i.e., a normal inverse-gamma distribution, which is conjugate to the Gaussian likelihood:     π 0 βjσ 2B  N m0 , σ 2B =n0   π 0 σ 2B  IGðaB , λB Þ where n0 ¼ vB/v0, IG(a, λ) is the inverse-gamma density, a is the shape parameter and λ is the scale parameter, and its probability density function is f ðxja, λÞ ¼

λa ðaþ1Þ λ=x x e , x > 0, a > 0, λ > 0 Γ ð aÞ

(31)

In order to get the prior distribution of the model parameters, the hyperparameters in the prior distribution have to be estimated, which can be obtained through the distribution fitting. The bootstrap method is applied to generate multiple sampling for the estimation of the hyperparameters in the prior distribution. According to the degradation model assumption, δi ¼ xi  xi  1, i ¼ 1, . . ., n is the degradation increment, which is an i.i.d. sample from N(β, σ 2B). Because σ R in the model is assumed to be known, the degradation increment can be considered as an approximation of the observations, i.e., δi ¼ xi  xi  1 yi  yi  1, i ¼ 1, 2, . . ., n. For a Wiener process, y0 ¼ 0; therefore, an estimation of β and σ 2B can be obtained by normal distribution fitting based on the n degradation increments. By the bootstrap method, it can generate k bootstrap samples based on the history data, i.e., k degradation process, and k estimations of β and σ 2B are available. The hyperparameters in the distribution can then be attained through distribution fitting, and the prior distributions of the model parameters are therefore calculated. The corresponding algorithm is described as follows: 1. Generating k bootstrap samples based on historical data yi, i ¼ 1, 2, . . ., n 2. For each sample, according to the assumption δi  N(β, σ 2B), getting an estimab tion of β and σ 2B through distribution fitting, i.e., β^ and σ^2B, b b ¼ 1, 2, . . ., k 2 3. According to the assumption π 0,1(β|σ B)  N(m0, σ 2B/n0), π 0,1(σ 2B)  IG(aB, λB), getting the hyperparameters in the distribution through distribution fitting and obtaining the prior distribution of the model parameters Based on the discrete state-space model, the method of sufficient statistics is used to estimate the state and parameters jointly. The sufficient statistics and the conditional posterior distributions of the unknown parameters need to be identified. From the Bayesian inference theory, given the prior distributions of β and σ 2B, the

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corresponding posterior distribution can be calculated based on the conjugate nature. After having obtained n observations, the corresponding posterior distribution of β and σ 2B is normal inverse gamma, with parameter values: mn ¼

n n m0 þ δn n þ n0 n þ n0

σ 2n ¼ ðn þ n0 Þν2B c aB, n ¼ aB, 0 þ

n 2

 1 n0 n  λB, n ¼ λB, 0 þ S2δ, n þ δi  δn 2 2ð n þ n0 Þ where δn ¼ 1n

n X i¼1

δi , S2δ, n ¼

n  X

2 2 δi  δn , and δn and Sδ,n follow that sufficient

i¼1

statistics and the parameters of these posterior distributions can be updated iteratively. The flow chart of the proposed method is shown in Fig. 19. Starting from the current degradation data of the tool, resampling samples are generated using the bootstrap method. According to the state-space model and model parameter distribution assumption, the priori estimation of model parameters can be obtained. Then the posteriori estimation of the model parameters can be performed by combining particle filter algorithm and the sufficient statistics. The state-space model with updated parameters is then identified based on the current degradation data. The tool future degradation can be predicted based on the model. Following the method of RUL prediction discussed above, the random variable of the tool RUL can be systematically evaluated.

Results At milling cycles of K1 ¼ 35, K2 ¼ 75, K3 ¼ 105, and K4 ¼ 135, 900 bootstrap samples are generated respectively. In the particle filtering process, particle number is 1,000, the prior probability density function is adopted as the importance function, and the polynomial resampling method is applied. The parameter estimation results are listed in Table 3. The multistep prediction results are attained from the stochastic degradation model. The threshold for the soft failure is set at 0.15 mm. The prediction of the cutter degradation at K1 ¼ 35, K2 ¼ 75, K3 ¼ 105, and K4 ¼ 135 are shown in Fig. 20. The red lines represent the degradation process of each particle, and the two horizontal lines are the threshold band. The black dotted line is the estimated value

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Fig. 19 The flowchart of the proposed method

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Tool degradation observations Bootstrap method The state space model

The priori estimation of model parameters Sufficient statistics method The posteriori estimation of the model parameters

Degradation prediction Failure threshold

RUL prediction

Table 3 The parameter estimations at different milling cycles Milling cycle K1 ¼ 35 K2 ¼ 75 K3 ¼ 105 K4 ¼ 135

μβ 0.3768 0.3779 0.3456 0.3505

a 45.5507 328.2323 743.8327 533.9785

λ 0.421 0.252 0.113 0.128

σB 0.0094 0.00072 0.00015 0.00024

σR 0.5 0.5 0.5 0.5

of the degradation, and the blue line is the degradation prediction. The 95 % confidence intervals for the predictions are also shown. When the wear value exceeds the predefined threshold, the tool is considered to fail, and the corresponding milling cycle is the TTF. The RUL probability density function at different milling cycles is shown in Fig. 21. Shown in Table 4 are the RUL and TTF results at different forecast origins and in Fig. 22 is the contrast of the predicted and the real values of RUL. It can be seen that the predicted value of the remaining life and the real value is very close, and it proved the model validity.

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Fig. 20 Performance of the model at (a) K1 ¼ 35, (b) K2 ¼ 75, (c) K3 ¼ 105, and (d) K4 ¼ 135

Summary Remanufacturing engineering has demonstrated a positive impact on energy sustainability, environmental protection, and global economic development. The theory and technology of remanufacturing has developed rapidly, and the market for remanufactured products continues to grow worldwide. The remaining useful life (RUL) of in-service machinery is a direct determinant of its remanufacturability. The selection of time to retire for remanufacturing is not only derived from the RUL but also from a comprehensive balancing of all effects regarding technology,

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Fig. 21 The probability density function of RUL at different milling cycles

Table 4 RUL and TTF results at different forecast origins Milling cycle K1 ¼ 35 K2 ¼ 75 K3 ¼ 105 K4 ¼ 135

Predicted TTF 155 148 155 151

Real TTF 146 146 146 146

Predicted RUL 120 73 50 16

Real RUL 111 71 41 11

economics, and environmental impact. This holistic approach optimizes the remanufacturing process. Remanufacturing is a complex system process. Remanufacturing theory provides support for the following steps in the process: (1) determining the “remanufacturability” of an old part; (2) comprehensive balancing and judgment for remanufacturing process planning (RPP) design; (3) and assessing the quality and reliability, or lifetime, of the remanufactured part. An accurate and effective assessment of the RUL will result in a reasonable and correct evaluation of remanufacturability. RUL modeling can be categorized as physical model based and data-driven model based. The major RUL forecasting methods are support vector machine (SVM) and state-space model (SSM), as illustrated in the case studies.

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Fig. 22 The contrast of the predicted and the real values of RUL

The accuracy of RUL assessment in the data-driven model is affected by not only the model itself but multiple factors also such as data acquisition, signal processing, and measurement errors. In the search for a more accurate model for RUL assessment, a robust data acquisition system design is recommended to reduce noise effectiveness in RUL modeling.

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Contents Introduction to Remanufacturing and Design for Remanufacturing . . . . . . . . . . . . . . . . . . . . . . . . . DfRem Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Desired Product Characteristics for DfRem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tools and Methodologies for DfRem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guidelines for DfRem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quantitative and Qualitative Assessment Tools for DfRem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life Cycle Thinking for DfRem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DfRem Through Disassembly Strategy Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integrating DfRem with Existing Product Design Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integration of DfRem in Product Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Task Identification and Prioritization Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Product Concept Generation and Solution Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design Concept Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Challenge and Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Previous research studies have indicated that barriers to the remanufacturing process can be traced to the initial product design stage, and these have ignited the concept of Design for Remanufacturing (DfRem) as a much pursued design

S.S. Yang (*) NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore e-mail: [email protected] S.K. Ong • A.Y.C. Nee Mechanical Engineering Department, Faculty of Engineering, National University of Singapore, Singapore e-mail: [email protected]; [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_72

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activity. In this chapter, the definition and scope of DfRem activities are firstly introduced to provide the readers with an understanding of the topic. Next, a review on the design tools and methodologies for DfRem is presented. Five DfRem approaches have been identified and the strengths and limitations associated with each approach are analyzed. Although various tools and methodologies have been proposed for DfRem, few of them have been adopted by the industry. Therefore, a study on the factors that can help successful integration of DfRem into product development has been conducted. The identified factors are explained and organized according to the impact they have on different product development stages. Finally, future research activities and directions for DfRem are suggested for promoting the remanufacturing industry.

Introduction to Remanufacturing and Design for Remanufacturing Faced with stringent environmental legislations and motivated by growing customers’ awareness of environmental issues, many organizations and companies have adopted the practice of sustainable development. To achieve sustainability, a closed-loop material flow needs to be formed. Reusing, remanufacturing, and recycling are currently the most commonly adopted end-of-life (EOL) strategies in a closed-loop system. Among these strategies, remanufacturing is gaining popularity. Remanufacturing is the process of bringing products back to sound working status, through the process of disassembly, sorting, inspection, cleaning, reconditioning, reassembly, and testing, as shown in Fig. 1 (Lund 1984). The idea of remanufacturing as an academic research topic began to emerge only in early 1980s, with Robert Lund’s original remanufacturing study. Since then, there has been increasing academic interest in remanufacturing arising from its recognized benefits and potential role in changing our society. Previous research studies have indicated that barriers to the remanufacturing process can be traced to the initial product design stage (Amezquita and Bras 1996).

Fig. 1 Remanufacturing processes

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Table 1 DfRem activities Design for core collection Design for disassembly/reassembly Design for inspection Design for cleaning Design for access Design for durability

Design for restoring Design for multiple lifecycles Design for standardization Design for handling Design for upgrade Eco-design

Product features and characteristics may have positive or negative impacts on the efficiency of remanufacture, depending upon decisions made during the design process (Charter and Gray 2008). These have ignited the concept of Design for Remanufacturing (DfRem) as a much pursued design activity (Sundin 2004). The imperative for connecting design and remanufacture is further reinforced by Nasr and Thurston (2006), who stated that the full societal benefits of remanufacturing cannot be achieved unless DfRem is integrated with the product development process.

DfRem Activities The definition of DfRem, as presented by Charter and Gray (2008), is “a combination of design processes whereby an item is designed to facilitate remanufacture.” DfRem is not only a part of “Design for X” (DfX) mewthodology, where X represents one of the aims of the methodologies, it incorporates a series of DfX strategies, such as design for core collection, design for upgrade, design for disassembly, etc. (Charter and Gray 2008). Sundin (2004) suggested that DfRem stands for a collection of many tasks or considerations which prioritization may vary depending on the process needed of the products. Table 1 summarizes the design activities involved in the DfRem methodology.

Desired Product Characteristics for DfRem Remanufacturing is often practiced by the original equipment manufacturers (OEMs), who remanufacture their own products; contracted remanufacturers, who remanufacture the products under contract from the OEMs or customers; or independent remanufacturers (IR), who buy used products to remanufacture and resell them. However, the ability to resolve the difficulties in remanufacturing is most often owned by the OEM, since they control the product design stage and can potentially control remanufacture. Before an OEM considers designing their products for remanufacturing, they should examine whether their products possess the following qualities:

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• Product is made up of standard interchangeable parts (Lund 1998). • The cost of obtaining and reprocessing the core is low compared to the remaining value-added (Lund 1998). • Technology exists to restore product (Nasr and Thurston 2006). • Product technology is stable over more than one life cycle (Lund 1998). • Sufficient customer demand for the remanufactured product (Ayres et al. 1997). • The core is durable and has high value (Charter and Gray 2008). • Potential to be upgraded (Shu and Flowers 1999). • There are channels for reverse flow of used product (Ayres et al. 1997).

Tools and Methodologies for DfRem The goals of product design for remanufacturing are to alleviate the problems which may occur during remanufacturing operations and improve the efficiency and effectiveness of product remanufacturing. Various design tools and methodologies have been proposed to facilitate product design for remanufacturing. Based on their approaches, these tools and methodologies have been classified into five categories (Fig. 2). Each of them will be discussed in the following sections.

Design guidelines

Adaptation from existing design tools

Life cycle thinking Design for remanufacturing

Assessment tools

Fig. 2 Approaches for design for remanufacturing

Disassembly strategy planning

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Table 2 References used in compiling list of guidelines for DfRem

Reference Amezquita et al. (1995) Mabee et al. (1999) MtGlothlin and Kroll (1995) Shu and Flowers (1999) Sundin and Bras (2005) Sundin and Lindahl (2008) Charter and Gray (2008) Ijomah et al. (2007a, b, 2009) Yuksel (2010)

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Guidelines for DfRem The most commonly used and effective approach to facilitate product design for remanufacturing is through providing design guidelines to steer a design toward higher remanufacturability. It is noted that the design guidelines proposed from various literature and research articles have presented a complementary but sometimes overlapping insight. An overview of the design guidelines for successful product remanufacturing is therefore conducted. The collated design guidelines will be presented in a generic and general manner and categorized according to the six steps that constitute the remanufacturing process, namely, core collection, disassembly, inspection and sorting, cleaning, refurbishment, and reassembly and testing. The results can be used to identify the opportunities for enhancing remanufacturing design, setting goals, and measuring progress. Table 2 summarizes the literature sources drawn for composing these guidelines.

Design for Reverse Logistics End-of-life products usually need to be returned to the specific remanufacturing factory in order for remanufacturing to take place. If this process is not well dealt with, a large cost barrier could occur:

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• Product structure. To facilitate core collection, the structure should be designed in such a way so as to minimize the occurrence of damage during transit. For products which movement requires the use of forklifts, sufficient clearance and support at the base should be provided. In addition, structures that protrude outside a regular geometric volume should be avoided, since they are prone to become damaged during the transportation and may also hinder stacking during storage (Shu and Flowers 1999). • Product description. Labels, graphical communication, and the form of the product should be placed on the exterior or interior surface of the product to communicate the information of the product. For example, radio-frequency identification (RFID) is frequently regarded as a form of label to allow a vast array of information to be held (Charter and Gray 2008).

Design for Disassembly Disassembly is not a simple reversal of assembly. Many permanent techniques which have been developed to realize and fasten the assembly process, such as plugging, pressing, forming, sonic welding, and adhesive, can cause problems for the disassembly process (Mabee et al. 1999). Basically, there are four areas that need special attention in design for disassembly: • Joint selection. The selection of the types of joints would critically affect the efficiency of the disassembly process. Nonpermanent joints are generally preferred since they are simple to loosen (Mabee et al. 1999), e.g., bolt joints are usually preferred over adhesives. • Plan for nondestructive disassembly. Disassembly should not be destructive (Bras and McIntosh 1999). After the disassembly, the components are expected to be separated without being damaged or cause damage to other parts of the product. In addition, it is desirable for the fasteners to be reused. • Prevent corrosion/rust. Corrosion and rust are the greatest hindrance reported in an automotive industry survey (Charter and Gray 2008). Prevention of corrosion and rust will lead to better isolation of parts from the elements, using the less or non-corrosive materials or switching to other fastening mechanisms. • Clear instructions for disassembly steps. The disassembly instructions should be properly displayed on the returned core to facilitate the disassembly process. This is particularly important for third-party remanufacturers, who do not have detailed specifications of the products.

Design for Sorting and Inspection Depending on the various inspection results, parts are sorted into three classes, namely, reusable without reconditioning, reusable after reconditioning, and not reusable. Design guidelines to facilitate sorting and inspection include: • Features for easy identification. Parts fulfilling the same function should have identical or distinctly dissimilar features. For example, to differentiate the gears

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that fulfill different functions, gears could be made of different color-coding systems or have a specific number on them to easily identify them (Mabee et al. 1999). • Features for easy assessment. Sundin and Bras (2005) stated that determining and accessing the point for testing should be made easy and the time required for the inspection of the parts should be minimized. Design features such as the sacrificial parts for indicating the component’s condition over time should be encouraged. Sensors can also be embedded to record the useful data and communicate the information over time (Mehmet and Surendra 2010).

Design for Cleaning Cleaning is the most energy- and labor-intensive process in remanufacturing (Gonzalez 1983). Therefore, it is important to take the cleaning process into consideration during design; otherwise, a simple cleaning operation can become too laborious, expensive, or even impossible: • Selection of texture and geometrics. Texture and geometrics that facilitate easy cleaning are encouraged, such as a relatively flat surface which has a lower tendency to trap dirt or collect residue from cleaning (Amezquita et al. 1995). • Minimization of cleaning methods. Structures that require fewer variation of cleaning methods are always preferred. In this way, the cleaning process can be simplified. The material of the product that requires special cleaning methods should be avoided as much as possible, so as to minimize the cleaning cost as well as waste generation (Shu and Flowers 1999). • Labels that can withstand cleaning processes. During the cleaning process, labels and instructions which carry the product information on the component should be prevented from being washed away, since this may cause problems in subsequent refurbishment and reassembly processes (Sundin and Bras 2005).

Design for Reconditioning During the refurbishment process, parts will be restored geometrically and properties to be restored with surface treatment. For those parts which cannot be reused, they will be replaced by new spare parts. The following aspects should be focused while designing for refurbishment: • Durability design. Bulky and slightly overdesigned components are preferred than products with thin and less material, as the former could provide more margin of materials to be worked on with during refurbishment of components (Shu and Flowers 1999). Surfaces should also be designed in such a way that they have strong wear resistance, since the product may need to go through several use cycles. Moreover, it is appropriate to increase the dimensions to maximize usage cycles since part wear tolerance and material removal must be considered in these areas (Mabee et al. 1999). • Minimize the impact of wear and failure. Failure and wear are to be isolated in small parts such as inserts wherever possible. It is desirable that wear and failure

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can be concentrated in detachable, consumable parts to avoid undue expense (Shu and Flowers 1999). • Platform and modularity design. Platform design is used to cluster the components based on the technical and use life; the importance of the components, technology trends, changes in aesthetic preference; or the possibility of being defunct, which will allow the defunct aspects to be removed while retaining the useful aspects of the product. Moreover, platforms may also be created such that the next-generation product can use these platforms. Therefore, the interface and the part should be standardized (Charter and Gray 2008).

Design for Reassembly and Testing Designing products for reassembly and final testing can be improved from the following two aspects: • Adjustments. During reassembly, the number of the adjustments should be kept low, with adjustments being easy to make and are independent from each other. • Upgradability. The design should be flexible enough to be able to adapt to future technology migration as well as accommodate new configurations of the part.

Discussion The lists of design guidelines have provided an understanding of the barriers that may be encountered during remanufacturing processes, as well as directions to enhance the efficiency of product remanufacturing. Table 3 provides more detailed remanufacturing requirements, and their related design criteria are summarized. Remanufacturing requirements are gathered from the feedback of remanufacturers with respect to improving the efficiency of the remanufacturing process. The design criteria are interpreted and “translated” from the remanufacturing requirements, bringing abstract requirements to concrete design specifications. Table 3 aims to provide the product designers with the most comprehensive guidelines to enhance product design for remanufacturing. However, the designers may still need to make proper judgment during the design of each individual product. Though straightforward and comprehensive, the approach of design guidelines for DfRem has been criticized as overly daunting, since it is impossible for designers to consider all these criteria simultaneously and some of the remanufacturing design requirements are intrusive on traditional design (Zwolinski et al. 2006). In addition, there are other issues that the design guidelines do not fully address, such as the subjectivity and customization guidelines (Hatcher et al. 2011).

Quantitative and Qualitative Assessment Tools for DfRem Much work has been reported on developing new quantitative and qualitative assessment tools to evaluate the remanufacturability-related product properties and provide design feedback to the product development team.

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Table 3 Product design guidelines for remanufacturing Remanufacturing process Reverse logistics

Remanufacturing requirement Basic description of the product

Avoid damages during transportation Disassembly

Easy access to internal regions Easy to loosen joints/fasteners Reduce the variation of the tools used Prevent part damage during the disassembly process Prevent the corrosion of parts Clear instruction of the products disassembly process Easy access to the fastener/joints Easy identification of the fastener Using one disassembly direction Multi-disassembly should be possible with one operation

Sorting and inspection

Ease of classification of the components Ease of assessing the condition of the components Request for more objective testing methods Tools to facilitate the sorting process Ease in detecting wear and corrosion Component information are clearly indicated (life cycle, composition, wear indicator, etc.) Testing points are easy to access

Design criteria Labels, graphical communication, packaging, or even the form of a product could be positioned on the packaging Sufficient clearance and support at the base Avoid structures extruding outside Time to remove items for access Number of items to remove for access Number of fastener to remove Number of different tools to unlock the joints Number of permanent joints Number of parts damaged Number of fasteners damaged Isolate the part from the elements Use non-corrosive materials Disassembly layout/instructions provided Position of the parts Type of fasteners/joints Types of parts Position of the fasteners/joints Standardization of the fasteners/ joints Parts are identical or grossly dissimilar Standardization of the parts Small number of components and connections Color-coding/numbering system for similar parts Small number of inspection tools Simple part test

Description of life cycle, composition, and wear indicator is provided (continued)

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Table 3 (continued) Remanufacturing process Cleaning

Remanufacturing requirement Accessibility of the internal parts Simple method for cleaning Simple inner and outside surfaces Standard cleaning methods Less wastes and health concerns Less variation of the cleaning methods Instruction for cleaning methods Labels and instruction to withstand cleaning processes

Reconditioning

Parts are robust Avoid subjective criteria Fewer parts for replacements Avoid technological or aesthetical obsolescence Modularity updatable Clear information of the product displayed Texture areas are refurbishable

Reassembly and testing

Ease for adjustments Capable and adaptable for upgradability Simple methods for testing

Design criteria Number of cavities/corners difficult to clean Surface roughness Total waste generated Time to clean Total cleaning material used Specify cleaning methods Labels and instruction are able to withstand the cleaning process Type of materials Shape of the parts Bulky – overdesign Wear-resistant surface design Number of the usage cycles Number of wear and failure prone positions Number/cost of reparable components Technological cycle of core components Aesthetical cycle of core components Component modularity Upgradability of components Contains a tracking method for life Number of discarded components Number of parts refurbished Number of parts replaced Number of adjustments Time to reassemble Time of final testing Upgraded configurations assembly without modification

Bras and Hammond (1996) have developed a number of matrices, including assembly and disassembly matrices, testing and inspection matrices, cleaning matrices, and part refurbishing and replacement matrices, to evaluate the properties of a product design toward remanufacturability. The indices of these matrices were strategically combined into a single remanufacturability assessment index to provide feedback with respect to product designers on the remanufacturability of the product. Various case studies have been conducted, ranging from customer

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electronics to automotive products, to validate the effectiveness and efficiency of the proposed assessment model. Shu and Flowers (1998) proposed a reliability analysis model for comparing different design alternatives for product remanufacturing. The proposed reliability model is capable of predicting system failure under the condition that failed parts are replaced with components of the same type or different types and the failure characteristics of the constituent parts in the series. Furthermore, an integration of this reliability model with life cycle cost optimization has been achieved to demonstrate the effects of component specifications on product life cycle performance and the use of this information to enable remanufacturing to be more cost-effective and viable. The “RemPro Matrix” proposed by Sundin (2004) has successfully identified the correlation between product attributes and the specific remanufacturing steps. This matrix suggests that designers of new products should address product properties, such as “accessibility,” “wear resistance,” “ability to disassemble,” as well as “controllability” through appropriate product design. The matrix also suggests that the prioritization of the product properties would differ depending on the process needs of the product. Zwolinski et al. (2006) have built a remanufacturable product profile (RPP) which encapsulates the knowledge of both remanufacturing context and remanufactured product properties. A quantitative assessment of the product properties would be made to guide the product designers toward an existing product profile which properties are well adapted to remanufacturing. The specific information to improve the internal design of the product can also be provided by RPP. Du et al. (2012) have developed an integrative model that includes matrices for assessing the technology feasibility, environmental benefit, and economic benefit. The technology feasibility is evaluated based on all the remanufacturing processes. The economic benefit is assessed from the aspect of remanufacturing cost. Energy saving, material saving, and pollution reduction are used to evaluate the environmental benefit. A machine tool is used as a case study to illustrate the feasibility and validity of the proposed methodology. The result obtained from the proposed model can assist the decision maker to decide whether the core should be remanufactured, and serves as a feedback for product designers to improve the weak points that have been identified. While the trend of using quantitative or qualitative assessment tools for improving product design for remanufacturing has received increasing research attention, most of these models and tools remain within the academic realm and have hardly been utilized in the industry today. Some of the reasons, as indicated by Hatcher et al. (2011), are that these design tools are quite complex and are only applicable at the late design stage when most of the decisions have already been made. Moreover, the holistic life cycle thinking is often ignored in these methods, or the design tools may lead to a suboptimization situation. For example, a screw insert is favored in remanufacturing product design. However, introducing a new material, such as a metal part into the original material like plastics, may counter the recycling efforts (Shu and Flowers 1999).

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Life Cycle Thinking for DfRem Life cycle thinking for product design is a concept of addressing problems from a systematic or holistic life cycle perspective. In this concept, product design is conducted with a goal of reducing the undesirable impact throughout its life cycle. The EOL options, such as disposal, recycling, reuse, and remanufacturing, are fully addressed in life cycle thinking, in addition to the manufacturing and usage stages (Fava 1993). Among the methodologies that have been used to quantify the life cycle impact of a product design, life cycle analysis (LCA) and life cycle cost (LCCA) analysis are the most commonly adopted ones. LCA has been widely used to assess the environmental benefits of remanufacturing as compared with original manufacturing. A comprehensive LCA remanufacturing study conducted by Smith and Keoleian (2004) shows that a remanufactured engine could be produced with 68 % to 83 % less energy and 73 % to 87 % fewer carbon dioxide emission, as compared to original manufacturing. The LCA remanufacturing study by Kerr and Ryan (2001) found that remanufacturing could reduce resource consumption and waste generation over the life cycle of a photocopier by up to a factor of three and greater reductions could be achieved if a product is designed for remanufacturing. Meanwhile, the monetary benefit of remanufacturing has been reported through the use of LCC analysis, e.g., remanufactured alternators (Erwin et al. 2012) and engine remanufacturing (Sahni et al. 2010). Besides being used to demonstrate the benefit of remanufacturing, life cycle thinking has also been proposed to evaluate and select different design alternatives. Shu and Flowers (1999) have proposed a DfRem framework for the selection of product fastening and joining methods by using the LCC analysis. The impact of joint selection on the remanufacturing stage relative to other life cycle stages was estimated in cost, which provides a quantitative and straightforward feedback to the product designers. With the emergence of sustainable development, some products are designed to be used for several life cycles before being finally retired from use. This has led to the change of life cycle thinking from a linear life cycle model to a multiple life cycle model. Sutherland et al. (2008) proposed a methodology to estimate the embodied manufacturing/remanufacturing energy across multiple use cycles. This model has demonstrated the potential energy benefits achievable through product remanufacturing as compared with new manufacturing, throughout multiple product life cycles. In addition, it has been used to analyze and compare the impact of different engine head designs over multiple life cycles. It should be noted that DfRem is only one of the factors that determines a product design. Design improvements interpreted from the remanufacturing point of view may have different impacts on other life stages of a product. Therefore, a proper assessment of the impact over the entire life cycle is necessary during product design for remanufacturing. For example, if the remanufacturing enhancement of a redesign has made a product more difficult to manufacturing, the redesign should not be adopted because commercial considerations would not allow it to be

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produced in the first place. However, life cycle analysis has frequently been criticized to be very detailed, expensive, and time consuming. In addition, during the initial design stage, the product information is still incomplete, and this makes life cycle analysis difficult to be conducted.

DfRem Through Disassembly Strategy Planning Disassembly, which allows the separation of the reusable and nonreusable components for further processing, is closely related to EOL strategy determination and regarded as a new frontier of product design for remanufacturing. Many researchers have proposed different methodologies to measure the disassembability of a product and generate an optimum disassembly sequence. For example, Gungor and Gupta (1997) have proposed a disassembly sequence generation heuristic which could generate the optimum disassembly sequence for a product. Turowski and Tang (2005) have developed a Fuzzy Petri Net (FPN) model to represent mathematical uncertainty management and human factor in disassembly. The model could dynamically estimate the impact of these factors on the disassembly process and find an optimum disassembly path with the highest economic value. The factors that affect the EOL strategy include the disassembly sequence, the disassembly time, the disassembly cost, and the benefits from reusing and recycling the components (Jun et al. 2007). There is, therefore, a growing amount of research work on proposing methods for generating “recovery plans,” which attempts to balance the value of the reclaimed parts with the disassembly cost. Gonzalez and Adenso-Diaz (2005) have introduced a model which could determine the optimal EOL strategy for each component and the subsequent disassembly strategy that led to the highest profits. The result from the proposed method could be used to enhance the EOL design from the early stage of product development. Besides economic profits, other works have included the environmental benefit as a criterion for assessing the EOL strategy. Lee et al. (2010) have developed an EOL decision model for remanufacturing options. The maximization of the economic value and environmental benefit has been achieved in this model through an integrative approach. Possible design changes could be interpreted from the EOL decision model. The returned product/component quality, which is an essential consideration for EOL strategy determination, has been taken into account in the model proposed by Krikke et al. (1998) to generate a quality-dependent recovery and disassembly strategy. The optimization of the disassembly strategy was achieved through maximizing the overall net profit using a two-phase dynamic programming algorithm. In addition, active disassembly design has been proposed by Chiodo and Ijomah (2012) to enable rapid, nondestructive self-disassembly of products at the EOL stage. The potential fusing of active disassembly and DfRem is discussed to address the goal of sustainable manufacturing. The applicability of the proposed method has been verified using an automotive electronic control unit as a case study.

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The results obtained from disassembly strategy planning can serve as a feedback to enhance product design for remanufacturing. For example, if two easily remanufactured components are disposed of, redesign should be considered if there is a high disassembly cost to separate them. However, it should be noted that DfRem through disassembly planning is only a stepwise approach. To be more comprehensive, integration with product life cycling thinking can be a possible future research direction.

Integrating DfRem with Existing Product Design Tools Some researchers have used existing design tools to improve the remanufacturability of products. King and Burgess (2006) have adapted platform design for remanufactured products. Platform design is “the strategic architecture of common and parametric components that forms the basis for a product family.” To apply this concept to remanufacturing design, potential multiple life components will be regarded as common components which form the base platform, while single life components will form the parametric components, which increase product design variability. The Failure Mode and Effects Analysis (FMEA) has been modified to facilitate product design for remanufacturing (Sherwood and Shu 2000). The data from the waste-stream analysis of automotive remanufacture are used to derive values of the indices of occurrence, detectability, and repairability. These values are used as the input for FEMA. The outputs are the priorities of the factors that impede the reuse of parts, thus providing the designer with the insight on how to improve product design for remanufacturing. QFD is a proven methodology for translating consumer demands into appropriate technical characteristics and specifications for product development and production. Yang et al. (2013) proposed a QFD model for remanufacturing based on three key modifications to the traditional QFD. The first modification expands the conventional scope of the “customers” to include the remanufacturers, environment concerns, cost factors, as well as product users. The second modification involves a hierarchical structuring of the engineering requirements and computing their weights. The third modification uses the fuzzy set theory to overcome the vagueness and impreciseness involved in the QFD decision-making process. Through this fuzzy QFD approach, the remanufacturing requirements would be mapped to the engineering requirements that the designers need to focus on in order to improve product remanufacturability. A case study for the automobile remanufacturing industry was used to illustrate the applicability of the proposed methodology. The DfRem trend has moved from developing abstract solutions, e.g., the DfRem matrix (Bras and Hammond 1996), to using the existing design tools for DfRem to provide detailed suggestions, e.g., platform design (King and Burgess 2006). These problems associated with the integration of existing design tools with DfRem as most of them are not developed for DfRem purposes and the use of these

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tools as a guide to carry out DfRem will need to be further explored (Hatcher et al. 2011). The review of the design tools has indicated that DfRem is most effective in the early design stage when few design decisions have been made and less technical data is available (Amezquita et al. 1995; Zwolinski et al. 2006).

Integration of DfRem in Product Development Design for remanufacturing implies actions to be taken during the product development stage to enhance the remanufacturing efficiency of a product without compromising other essential product characteristics, e.g., cost and performance. It is practical and realistic to adapt DfRem for existing product development strategies rather than to expect an original product design model to change to accommodate DfRem (Hatcher et al. 2013). Therefore, it will be useful to identify factors for successful integration of DfRem into product development and provide an overview of addressing these factors to facilitate the integration of DfRem. Various research teams have embarked on the investigation of the factors that lead to this successful product development. The factors generally acknowledged to affect the success of product development are examined. A flowchart for a generic design process is shown in Fig. 3, which is adapted from Pahl and Beitz’s model (1996). It consists of three stages, namely, task identification and prioritization, product concept and solution generation, and design concept evaluation, which the development team must iterate and follow closely. Brown and Eisenhardt (1995) have developed a comprehensive model of factors affecting the success in product development and classified these factors into following areas, namely, market, customer, senior management, project leader,

Fig. 3 Product development and its related deciding factors

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communication, team organization, team composition, supplier involvement, and development process. This model has provided a good starting point and reference for investigating the factors that lead to the integration of DfRem into product development. Factors that have been considered as essential when integrating DfRem into product development are introduced in the next section according to the major impact they have on the product development processes.

Task Identification and Prioritization Stage In the product development stage, the design team clarifies the design targets to be achieved by considering the various aspects, such as customer demand, legislation, environmental concern, technology trend, and cost consideration; the team assigns the importance to each of these aspects based on company business decision and priority. Evaluating and analyzing the performance of the existing products or competing products can add to knowledge input in this stage. In the task of integrating DfRem into the product development process, the following factors need to be addressed during the task identification and prioritization stage: • Investigate customer demand and market competitiveness Sufficient customer demand is always the kick-start factor for the integration of DfRem in product development. Market investigation should be carried out on the demand from the new product users and the types of market for remanufactured products, such as the market for low-cost spare parts, the market for “green consumers,” the market for “product service business system,” etc. (Seitz 2007). The needs identified through market investigation should be put as the forefront of the product design considerations. Besides, companies can take a proactive approach to inculcate the mind-set of their customers for remanufactured products and provide well-documented evidence to them that remanufactured products have equal, if not more superior, quality as compared with the original product, so as to grow a larger remanufacturing market (Subramoniam et al. 2009a and 2010). • Identify barriers in remanufacturing processes There is often a misalignment between product designers and remanufacturers on the product design requirements for remanufacturing, which results in wasted effort during product remanufacturing (Hammond et al. 1998). Therefore, effective communication between remanufacturer and OEM design engineer needs to be promoted, so that the development team can remain informed of any barriers during remanufacturing processes and alleviate these problems through proper product redesign. In addition, frequent external communication can expose the project team to new information, e.g., latest remanufacturing technology, which can impact the way that designers design the product (Johansson 2002).

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• Plan DfRem strategically This factor implies that DfRem should be addressed at the operational level and the strategic level. The indicators for determining the performance of a product remanufacturability need to be determined, e.g., the successful remanufacturing rate, processing cost, etc. These factors should be synthesized and balanced well with the existing product performance indicators, e.g., product lifetime, profit rate, etc., to ensure a smooth integration (Charter 1997; Ritzen 2000; Takata et al. 2003).

Product Concept Generation and Solution Stage This step is critical for product development, where product designers will determine the mechanisms, principles, and solutions, based on the tasks defined in the first stage. To ensure the successful implementation of DfRem in this stage, the following points need to be addressed: • Education and training of product designers The mind-set that DfRem is considered throughout the concept development stage should be established through proper education and training (Hatcher et al. 2013; Magnusson and Johansson 1999; Karlsson 1997). Training and education can be carried out in various forms, e.g., plant visits to remanufacturing sites to raise the designer’s consciousness of the link between product development and remanufacturing activities, the training of the designers of using different eco-design methods and remanufacturing design-related tools, and providing remanufacturing design guidelines and good design solutions to support the learning of the product designers on remanufacturing issues (Subramoniam et al. 2009b). • Management support for DfRem Senior management support is acknowledged to be critical for the integration of DfRem. This support can garner strong financial and corporate backing for the project and the recruitment of professional team members to ensure the successful implementation of DfRem (Johansson 2002). Besides the senior management support, mid- and lower-level supervision is also vital for carrying out DfRem operations, for example, the involvement of remanufacturing experts in the product development activities to advise the design team on the ways to alleviate the barriers in the remanufacturing processes through proper design, analyze the impact of design alternatives on remanufacturing efficiency, or even inspire the design team to consider DfRem issues (Roy 1999). • Close supplier relationships A close relationship with suppliers can have a positive impact while the designers are searching for product design that is beneficial for remanufacturing. It is unlikely that the OEMs are responsible for producing all the components of their products, and therefore the expertise of the suppliers in the outsourced components can be a valuable input for product remanufacturing design (Johansson 2002; McAloone 1998).

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• Cross-functional design team Product development requires communication and collaboration among different design teams. The design considerations include productivity, aesthetics, cost, environmental performance, functional performance, and especially remanufacturability in this context. The complex product development process requires frequent cross-functional communication, which includes the sharing of data and information, design trade-off, and feedback of design from different perspectives. Although most of the time remanufacturing may not be on the top of the design priority list (Hatcher et al. 2013), the presence of the remanufacturing requirements in this stage is critical for ensuring that remanufacturing concerns are not neglected (Charter and Gray 2008). • DfRem tools are applied Different design tools and approaches have been proposed to facilitate product design for remanufacturing. Examples include the RemPro Matrix, DfRem guidelines, QFD for remanufacturing, REPRO2, etc. (Sundin 2004; Ijomah 2009; Yuksel 2010; Zwolinski et al. 2006). Introduction of the specific tools into product development or adaptation of existing design tools to include remanufacturing perspectives can be an effective way to support product DfRem integration.

Design Concept Evaluations The design concept should be evaluated from the various aspects, such as economic, environmental, remanufacturing, etc., by using the indicators established in the task identification and prioritization stage. This is the last stage to ensure the satisfaction of the task for remanufacturing design and alter the product design before the final implementation stage. The factors to ensure that the DfRem issues are considered in this stage include the following: • Management support on the final design decisions Most importantly, the prioritization of DfRem with other design issues, such as functionality, cost, and manufacturability, is significantly influenced by management support, since the final decision of product design is usually made by senior management (Hatcher et al. 2013). Therefore, a firm support on product design for remanufacturing from the senior management is indispensable for successful implementation of DfRem, especially in the final evaluation stage. • Tools for DfRem reviews The well-developed and established tools for evaluating life cycle performance can be adopted, e.g., LCA and LCC. However, modifications of these tools are necessary for evaluating the product performance for closed-loop life cycle, instead of the traditional single life cycle. Examples of modified versions of LCA tools have been reported by Sutherland et al. (2008) and Gehin et al. (2009). One of the advantages of adopting these existing evaluation tools is the familiarity that the product designers have with them and thus allowing for easy implementation of remanufacturing evaluation in this stage.

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Fig. 4 Factors that affect the integration of DfRem with product development

Figure 4 summarizes the deciding factors for the integration of DfRem with product development. As compared with Fig. 3, it can be observed that the factors that are important for product development have great impact on the integration of DfRem into product development, e.g., customer and market, management support, close supplier relationship, cross-functional design team, etc. This implies that the likelihood that the integration of DfRem can be high when a company’s product development management is successful. However, there are some specific factors in DfRem integration, e.g., the identification of special requirements from the remanufacturing sites, development of DfRem tools, and education and training of the product developers, which are the factors that a company would need to pay special attention to, if integration of DfRem is to be carried out.

Challenge and Future Research Directions Despite the appealing benefits in carrying out DfRem, there are additional barriers and complications that a company may face. First of all, compared with other DfX issues, e.g., DFA, DfRem is usually not given priority since most OEMs’ main focus is on the manufacturing and usage phases. Whenever there is a conflict between DfRem and other issues, e.g., assembly and manufacturing, DfRem is usually placed in a lower priority as it is viewed as less useful in terms of time and

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cost due to the lack of awareness among designers. Secondly, some OEMs deliberately play down on remanufacturing through product design to stifle the independent remanufacturing activities. This is because OEMs do not have strong desire to enhance remanufacturability for benefitting the independent remanufacturers, who are viewed as strong competitors of their own products. Thirdly, DfRem guidelines involve a variety of design issues, which will form a new set of challenges that the manufacturers may not be ready to deal with. Lastly, the reluctance of a company to share their in-house methods, tools, and knowledge with the outside world also leads to the barrier between the academic and the industry world (Hatcher et al. 2011). It can be observed that though the number of tools and methodologies developed for DfRem is large, few of them have been adopted by the industry. Some of the reasons are that these tools require significant knowledge of remanufacturing, which are only available at the later design stage. In addition, these tools have been criticized to be complex, time consuming, and difficult to apply (Willems et al. 2008). Therefore, there is a strong need to develop effective methods and tools. Incorporating life cycle thinking into DfRem is a future research direction that has been addressed by many researchers (Shu and Flowers 1999; Ijomah 2009). Ignoring the holistic life cycle thinking in DfRem tools can sometimes lead to suboptimization situations, since DfRem is often in conflict with other DfX methodologies. To improve the effectiveness and applicability of DfRem tools, there is a need to consider the impact of remanufacturing enhancement of product features on all the other life cycle stages, such as manufacturing and usage stages. DfRem is often viewed to be under the umbrella of Design for Environment (DfE). As compared with DfRem, DfE is a more well-explored and developed research area. Therefore, the literature on DfE may provide the insight on the approaches that are likely to be applicable in DfRem development. However, it should be noted that not all the requirements for DfE are mutually compatible with DfRem. For example, there are conflicts between DfE and DfRem, e.g., DfRem may require components to be overdesigned such that subsequently remanufacturing operations such as machining and grinding can be performed, while DfE may require components to be designed with minimum use of materials so as not to waste resources. In this chapter, the methodologies and tools for DfRem have been mainly focused on the product design level, which is the level that is directly affected by the mechanical properties of the design. The process-related design issues, e.g., remanufacturing production, supply chain, core collection mechanism, etc., are not considered since they are usually beyond the control of product designers and are more influenced by the company organization structure and business strategy. Discussion on the opportunity, barrier, and effort for improving the process design for remanufacturing could be found in the work by Bras and McIntosh (1999) and Umeda et al. (2012). Another future research direction is the focus on the integration of product and process design for remanufacturing.

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Product Service Supply-Chain Design

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Zhitao Xu, XG Ming, Tengyun Wu, and Maokuan Zheng

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Product-Service Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Is Product-Service Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structure of Product-Service Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Features of Product-Service Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design of Product-Service Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Product-Service Supply Chain Strategy and Network Structure . . . . . . . . . . . . . . . . . . . . . . . . . . Service Facility Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selection of the Outsourced Service Supplier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Configuration of the Service Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Process Design of the Product-Service Supply Chain Management . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The service industry has become an engine of regional and global economic development, and the employment and revenue of the service industry are increasing dramatically in recent years. This chapter discusses product-service supply chain (PSSC) design by outlining the PSSC implications and focusing on the crucial elements of PSSC design. Viewing the subject from the perspective of manufacturers, several presentations are used in practice, such as the supply chain for after-sales service; maintenance, repair, and operations; and productservice systems providing. It first explains what is the PSSC and then moves on to the prominent features and the structure of the PSSC. In a general sense, PSSC

Z. Xu (*) • XG Ming • T. Wu • M. Zheng School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China e-mail: [email protected]; [email protected]; [email protected]; [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_83

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design is designed toward the value of all supply chain members by configuring the service resource reasonably. From the viewpoint of supply chain design, five elements are elaborated in this chapter, which include the PSSC strategy, service facility location, outsourced service supplier selection, service network configuration, and process design for PSSC. The approaches for the PSSC design are reviewed, suggested, and elaborated.

Introduction One of the dominant trends in manufacturing industry is that the manufacturers are trying to extend the value chain and transform themselves from manufacturing and goods-oriented organizations to service-oriented organizations (Wang et al. 2011; Gebauer 2009; Gebauer et al. 2011). In order to keep up with the trend, the innovative product-service strategy is increasingly popular to manufacturers (Phumbua and Tjahjono 2012). Enterprises have proclaimed and tried to undergo this transition, but great difficulties were found in reality (Salminen and Kalliokoski 2008). Although the product service has been paid attention, the product-service supply chain (PSSC) has demonstrated new features. The longer-lasting responsibility for their offered solutions and the willingness to react on changing demands of the customers lead to high demand for the service organization and the belonging supply chain (Sanchez and Mahoney 1996). Otherwise, it is difficult to ensure that partners have the necessary knowledge and resource to perform the service adequately, especially more advanced service for small partners (Ojanen et al. 2011). The product-service providers have serious intent to establish the PSSC to integrate resources of the supply chain members. PSSC management is becoming more and more important. There are huge opportunities for organizations to make an improvement in this area in terms of cost and value (Ellram et al. 2007). During the transformation, the PSSC needs to emphasize the benefits of suppliers’ and customers’ satisfaction simultaneously in actual applications. The successful PSSC relies heavily on the initial design of the service supply chain. Chopra and Meindl (2007) claimed that a supply chain design problem is comprised of the decisions regarding the number and location of production facilities, the amount of capacity at each facility, the assignment of each market region to one or more locations, and the supplier selection for subassemblies, components, and materials. PSSC design extends this definition to include the development of PSSC strategy, selection of the outsourced supplier, allocation of the service facility, configuration of the service network, and design of the PSSC process. This chapter aims at providing an introduction to PSSC and guiding you to understand the main elements of PSSC design. This chapter discusses the subject by outlining the PSSC and focusing on the crucial elements of PSSC design. Viewing the subject from the perspective of manufacturers, several presentations are used in practice, such as the supply chain for after-sales service; maintenance, repair, and operations; and product-service systems providing. It first explains what is the PSSC and then moves on to the prominent features and structure of the PSSC. In

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a general sense, PSSC design is designed toward the value of all supply chain members by configuring the service resource reasonably. From the viewpoint of supply chain design, five elements are elaborated in this chapter. Firstly, PSSC strategy is introduced, which indicates the demand frequency of a product service has significant influence on the service supply chain strategy. Secondly, the approaches for service facility location are discussed. Thirdly, the suggested principles and most widely used methods for the outsourced service supplier selection are presented. Fourthly, considering the difference between the deliveries of a series of product service, it makes sense to configure the service network for any special product-service deliveries. Finally, the critical activities in PSSC are introduced and elaborated.

Product-Service Supply Chain What Is Product-Service Supply Chain With regard to the product providers, especially the equipment providers, the PSSC providing the service related to product is always exiting and described as supply chain for after-sales service (Jalil et al. 2010; Cohn 2006); supply chain for maintenance, repair, and operations (MacInnes and Pearce 2003; Driessen et al. 2010); and supply chain for PSS providing in literature (Erkoyuncu et al. 2010; Johnson and Mena 2008; Wang and Fu 2010). Erkoyuncu et al. (2010) defined the PSSC as “the network of suppliers, service providers, consumers and other supporting units that performs the functions of transaction of resources required to produce services; transformation of these resources into supporting and core services; and the delivery of these services to customers.” However, the motivation mechanism for the traditional SC has changed to the new value instead of customer demand (Baltacioglu et al. 2007). Customer value is the trigger of the SC for PSS. Porter and Millar (1985) define the customer value as a product or service that the customers are willing to pay for. In industrial applications, the things that customers are willing to pay for can be described as productivity, which is able to create added value for them in the productions. Given this, the chapter primarily proposes that: With the purpose of offering productivity to customers, the PSSC is a network which consists of manufacturers, service providers, customers, and other supporting units and performs the functions of service demand forecast, service resource allocation, and service delivery management, as well as supportive activity management.

Structure of Product-Service Supply Chain Prior to proceeding with the design of PSSC, it is necessary to identify the role of the SC members. In the product-service providing, the customers, main provider,

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Fig. 1 Structure of product-service supply chain

other providers, and suppliers constitute the network for PSS delivery. The PSSC structure is drawn up in Fig. 1 to illustrate the role of the members. One of the distinguishing features of the PSSC is the flat structure of the service network, which determines that all the partners have the chance to provide service to customers directly. The customers, who pay for the service and help to realize its value, are the receivers of the product service in the first layer. The customers include not only the end users but also the product owners. An example is the energy saving service provided by Siemens Group, who offers the energy saving equipment for energy saving company. Meanwhile, the company provides energy saving service to factory or community. Therefore, the customers of Siemens Group include the energy saving company, factory, or community. In the complicated process of service delivery, the customers, who cooperate with other units on training, information sharing, and other aspects, play an active role but not just a passive receiver. The second layer of the PSSC is the providers including the main provider and other providers. The providers offer product service directly to customers. The main provider is the product manufacturer and also is the service strategy developer. The customer’s requirement and feedback are gained and applied to the life-cycle management of the service strategy for the continuous improvement. The other providers undertake the service outsourced by the main provider, such as warehouse service, logistics service, technical support, and other supporting activities. The third layer is the suppliers who engage in the service strategy development and offer support to providers. Some of them offer support to customers directly and then they are transformed to be providers. As an example, the GE Aviation sells

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the aircraft engines to aircraft manufacturers and renders innovative service to the airlines. The GE Aviation acts as the supplier and the service provider in air service delivery.

Features of Product-Service Supply Chain With the adoption of the innovative service strategies, such as product-service systems (PSS) (Wang et al. 2011) and servitization (Johnson and Mena 2008), the PSSC demonstrates new features. A thorough investigation into the characteristics of the PSSC is helpful to deliver the service efficiently and effectively. The prominent features can be summarized as: 1. Value co-creation. The innovative service strategies act as a solution for customers on the basis that the value proposition for customers, providers, suppliers, and other stakeholders are identified and realized. The value chain will be more clear and reconfigured under new business models. Value co-creation requires that benefits and risk should be shared exhaustively between the members. 2. Activeness. The feature is manifested not only in active service offered by the providers but also in the active participation of the customer. Providing service actively will benefit both the providers and customers mutually. The customer value creation is more active rather than responsive. Moreover, the outsourced service supplier selection is subject to the customers. 3. Uncertainty. It involves managing resource availability, capacity, and capability along the service network (Erkoyuncu et al. 2010). The uncertainty is due to the following reasons: (1) the interaction between the providers and the customers in a variety of ways, (2) the service complexity and the delivery urgency (Erkoyuncu et al. 2010), (3) the resource availability owing to the perishability of service, and (4) the customer’s participation because the resource of the customer makes contribution to the service delivery. 4. Dynamic. The value chain reconfiguration and optimization will lead to changes in PSSC organization. The reusability of some resource, such as expert or skilled worker, leads to dynamic service resource management.

Design of Product-Service Supply Chain Product-Service Supply Chain Strategy and Network Structure Fisher has divided the products into functional products and innovative products and indicated the need to establish an efficient supply chain and reaction supply chain according to the characteristics of products (Fisher 1997). The similar rule also exists in PSSC. A particular customer’s demand frequency for some product service differs from others during the whole life cycle of the product. For example, as to a customer, some services are required once for several years, like major

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Fig. 2 Matching product service with product-service supply chain strategy and network structure

repairs for equipment, but some maybe required once every month or a few months, such as oil changing and painting maintenance for some products. During the product life cycle, customer demand frequency of product service has a profound effect on the service supply chain design strategy and network architecture, as shown in Fig. 2. If the demand for a product service is of high frequency, the development of efficient PSSC can reduce the service cost through the economies of scale; if the demand frequency is low, a rapid response PSSC can minimize customer service delay. In a general sense, the high-frequency service demands often are of unexpectedness or can be planned but with a long time interval. The low-frequency service demands are usually can be planned or forecasted with short time intervals. Efficient PSSC mainly performs the physical function of the service supply chain by allocating service resources and transforming them into service ability at the lowest cost. As to the rapid response PSSC, it performs the market mediation function of a supply chain, i.e., to make quick response to the unpredictable customer service demand. The comparison between the rapid response and the efficient PSSC is shown in Table 1. Remarkably, the collaboration of the key partners is significant to meet the customer demand for the unpredictable product service. Moreover, because the demand for this kind of product service is often very little and usually customized, it is necessary to establish the cooperation framework with the goal of improving the speed and flexibility of the PSSC in the early stage.

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Table 1 Comparison between rapid response and efficient product-service supply chain Elements Objective

Service resource management strategy Process management

Rapid response product-service supply chain Respond to product service with long time intervals or unpredictable product service as quick as possible and minimize customer wait or loss simultaneously Avoid the necessary service resources lying idle or being wasted, such as reduce spare parts inventory Flexible process management in order to improve the response speed

Service delay

Require a large number of service resources to shorten the service delay

Suppliers

Highlight speed and flexibility

Network architecture

Single- or two-level network

Efficient product-service supply chain Provide product service with short time intervals and predictable product service at the lowest cost Maximize the efficiency of service resource, such as improve the spare parts inventory turnover ratio Standardized process management to ensure the quality of service Shorten the service delay as much as possible without any increase in service cost Highlight service quality and service cost Multilevel network

Centralized service network and decentralized service network are the two basic structures of PSSC network. The centralized service network is often characterized by single or two levels. It improves the efficiency of the service resources through the centralized management. But the average distance from the service centers to the end customers is too long to make a rapid response. The decentralized service network has multilevel structure and improves service resource availability by placing it close to the customers. However, this will reduce the decision-making efficiency of PSSC and increase the management cost at the same time. In terms of the product service with high demand frequency, a lot of service resources are required. The service resources can be transported to customers with reasonable quantities, such as the spare parts, equipment, or other material resources, even the field service engineers. With regard to product service with low demand frequency, the necessary service resource is relatively small. The centralized management can avoid or reduce the idle and waste of service resources. The rapid response PSSC matches the centralized service network, and the efficient PSSC is the right type for the decentralized network. A case in point is the company IR which advocates providing advanced compressed air solutions for customers. One of the services offered by the company is the compressed air, and the customers are charged by the use of compressed air instead of the equipment. And the service supplier is responsible for the air compressor maintenance, repair, and even the operation. The frequency of the product service referring to a series of activities is continuous or infinite. Efficient PSSC should be established for this kind of product service. Its corresponding service network should be decentralized and multilevel.

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Service Facility Location Requirement and Analysis for Service Facility Location As a critical part of the service network, the service facilities offer detection, maintenance, spare parts, customer experience, product modification, and other customized service to customers. The number and the location of service facilities have significant influence on customers’ satisfaction and provider’s competitiveness. In many service and industrial applications of the facility location problem, the number of required facilities and the allocation of the customers to the facilities are the two major questions that need to be answered (Pasandideh and Niaki 2012). The decision is subject to a set of changing elements during the decision. The factors leading to a reasonable service facilities location solution include: 1. The service revenue and the service quality are suggested to be considered simultaneously. The service provider pursues the cost minimization or profit maximization, while the customer attaches importance to service quality which largely depends on the distance between the service facility and customers. 2. Owing to the flexible service tactics borrowed by different service facilities, such as different prices of the same kind of spare part, a demand point is allowed to receive service from just one service facility in a time period. 3. Once a service facility is opened, it should not be closed unless that customer demand reduces significantly, because a lot of money is required to build a CSC. 4. With regard to the industrial equipment, the service capacity of a service facility is restricted by the space, tools, personnel, and other resources. 5. The service demand is dynamic in the planning horizon. The factors posing influence on the service demand include not only the number of the product in use but also the added product sale. 6. Because of the dynamic customer demand, the total numbers of the service facilities are not predetermined and should be adjusted to real requirements. 7. The potential locations for service facilities are different with each other in the service demand, building cost, operation cost, and other aspects. A reasonable solution is subject to those discrepancies. The special requirements constitute the preconditions of the solution for the service facility location formulation.

Method for Service Facility Location Cost and time are the two main criteria affecting the decision to determine the right location for a facility (Boloori Arabani and Farahani 2012; Pasandideh et al. 2013). The dynamic models related to service facility location problems can be sorted into two groups: the single-objective dynamic models and the multiobjective models. Furthermore, in light of the prerequisite that whether a facility is allowed to be closed, the dynamic models also can be divided into explicitly dynamic models and implicitly dynamic models. The aforementioned survey reveals that the focus is on single-objective facility location problems and that

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the mixed integer programming is the most popular approach in the formulation. Minimizing the total cost is the primary criterion in both the single-objective and multi-objective programming models. However, due to the different application backgrounds, the models should be developed referring to specified assumptions, such as the scenario with a facility serving more than one demand point or the closable facilities.

Selection of the Outsourced Service Supplier Requirement and Analysis for the Outsourced Service Supplier Selection The contradiction between the limited service resource and the multiple geographically distributed customers is increasingly acute. As a response to the phenomenon, service outsourcing is getting more popular with manufacturers (Reeves et al. 2010; Ellram et al. 2004). In order to improve the profitability and competitiveness, the manufacturers should rely on a high level of outsourcing and work closely with a few qualified strategic business partners. The service outsourcing enables companies to offer new services more quickly and promote them more efficiently (Bustinza et al. 2010). Consequently, outsourcing service supplier selection has been acknowledged as one of the crucial problems in service supply chain management. To produce a service is to organize a solution to a problem which does not principally involve supplying a good (Gadrey et al. 1995). Under the PSSC environment, it is important to select the most suitable service providers in order to enhance the service efficiency (Zhang and Chen 2009). The features of the outsourced service supplier selection can be derived by the general comparison with the product supplier in manufacturing industry, as shown in Table 2. The outsourced service supplier selection is a strategic decision and aims for long-term success in business. The evaluation is of subjectivity but not only depends on the historical performance. That means there is not too much previous performance data available in the evaluation. The features of the outsourcing service suppliers should be highlighted in the evaluation criteria development. Moreover, the selection method should distinguish the current performance and the potential performance on service providing in the future. Criteria for the Outsourced Service Supplier Selection The manufacturers and the customers are with the same hope that the outsourced service supplier can provide services effectively, efficiently, and flexibly. Following the analysis of the requirement of the service supplier selection, six criteria are suggested and elaborated in Fig. 3. 1. Service capability. Three elements determine the service capability: technical capability, multiservice providing capability, and service management capability. In general, the preferred service supplier should be with the advanced

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Table 2 Comparison of the product supplier and the outsourcing service supplier Elements Final deliverables

Product supplier Standardized and quantified

Delivery process controllability Supplier available for special requirement Supplier controllability Supply chain relationships

Controllable

Supplier replacement Main criteria

Many in general More controllable Transaction relationship or strategic partner relations Low cost and low risk Historical performance

Fig. 3 Suggested criteria for the outsourced service supplier selection

Outsourcing service supplier Unstandardized and hard to quantify Uncertain, less controllable Very limited or only a few Less controllable Strategy partner relations High cost and high risk The ability for promised performance

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technology, capable of offering more kinds of service to customers, and be of strong ability in service innovation and service delivery management. Service resource. Service resource is the foundation of service providing. Firstly, the service infrastructure including the software, like the application of IT, and the hardware, like the equipment, should be considered. Secondly, the special accumulated knowledge as well as the experience in the field is also essential. Finally, the service network, such as the number and the location of service facilities, would influence the service level. Service quality. As a potential strategic supplier, the supplier’s service quality can be assessed by the service flexibility, service process management, continuous improvement of service quality, and customer satisfaction management. Service cost. The service cost consists of the explicit cost,.such as price; the inexplicit cost, such as cost resulted from collaboration; the flexibility of payment, such as the payment terms; and the potential of continuous cost reduction. Service supply chain collaboration. The manufacturers, outsourced service suppliers, and customers have to work closely with each other on service delivery. The profit contribution of the outsourced service business determines the provider’s willingness of engaging in the service supply chain collaboration. The cultural compatibility and the knowledge sharing, such as the infrastructure for knowledge gathering, analyzing, and sharing, have great effect on service supply chain integration. Service risk. The risk focuses on the customer’s risk, like the service quality risk and safety risk; the manufacturer’s risk, like the risk of service being outsourced again; the financial risk resulted from the payment terms; as well as the social and environmental risk, like the pollution in service providing and waste disposal.

Method for the Outsourced Service Supplier Selection In general, the method for the supplier selection can be divided into two groups: the individual approaches and the integrated approaches. The individual approaches were slightly more popular than the integrated approaches in the outsourced service supplier selection. As the individual approach, the data envelopment analysis (DEA) method is the most popular, followed by the mathematical programming, analytic hierarchy process (AHP), case-based reasoning (CBR), and the fuzzy set theory (Ho et al. 2010; Wu and Barnes 2011). With regard to the integrated approach, it was noticed that the integrated AHP and fuzzy set are the two most prevalent approaches. Fuzzy set theory does allow simultaneous treatment of imprecise and precise variables. But the AHP does not consider the interactions among the various factors and also cannot effectively take into account risk and uncertainty in estimating the partner’s performance (Wu and Barnes 2011). Compared with AHP, ANP allows for complex interrelationships among decision levels and attributes by the feedback mechanism. The service supplier selection involves kinds of strategies, quantity, and uncertain criteria. The methods employed in the outsourced service supplier selection should be able to deal with various criteria and their complicated relations effectively.

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Configuration of the Service Network Product-service network is not a simple collection of service centers. Since the product services differ with each other, the product-service network supporting the service delivery should be developed and should be configured individually. Product-service network configuration is a process of selecting the appropriate service network elements and developing the service network that matches a specific product service. In a general sense, the four parts, including the product service, service facility, network management, and the network support, are involved in configuring the product-service network, as shown in Fig. 4. Two service network configuration examples for the compressed air and the air compressor installation are shown in Fig. 4.

Fig. 4 Configuration of product-services network

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Product service includes the identification of the customer requirement and use models. Customer demand is the driving force behind the product service and can be grouped as continuous productivity, such as compressed air for 24 h; intermittent productivity, for instance, very limited compressed air for a day; and product, for example, a little but necessary compressed air for a week. The customer demand requirement determines that the use model is result oriented or availability oriented or function oriented (Meier et al. 2011). It is evident that the continuous productivity involves the result-oriented or availability-oriented use model while the product involves the function-oriented use model. Referring to the area covered by a service center, it can be classified into global, regional, local, and the special service facility. The product service provided by the special service facility often involves the patent, proprietary technology or other knowledge related to intellectual property right protection. The special facility reflects the advantages of the product-service providers and can bring them extra profits. In addition, a fully qualified service center can provide all the product services, for instance, the global service center. Some service center only can provide limited product services, such as the special service center. In the case mentioned above, a fully qualified local service center is responsible for the compressed air providing, but the air compressor installation is outsourced to a service supplier who is able to offer limited product services. Product-service network management includes two aspects. One is business management which is also called administrative management, and the second is process management. Different business processes have different management methods that can be divided into fully global centralized management, partly global centralized management, self-management with agreement, and fully selfmanagement. However, the higher in the degree of integration of product-service network management, the higher the cost of management and the lower efficiency of decision-making. Whereas the higher autonomy of the product-service network management, the lower the management cost accompanied by higher service risk. Therefore, with the changes in the service center management methods, the processes of the service centers fall into four types: standardized and monitored process, standardized process, customized and monitored process, and the customized processes. The local service center providing the compressed air is governed partly by the global service center or the main provider. The operations are standardized and monitored, especially the service delivery activities. The outsourced service supplier, who provides the air compressor installation, is selfmanagement with an agreement which indicates that the supplier should follow the guidelines required by the main provider. Although the activities of the installation are customized, the product-service delivery process is monitored by the provider. Product-service network support includes technical support, engineering support, information support, partnership relation support, and customer collaborative relationship. Since the compressed air providing belongs to the result-oriented use model, the kinds of activities should performed by the provider. Therefore, it is essential to get the technical and engineering support from the global, regional, and

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the local service centers. Owing to many partners being involved, the kinds of partnership relationship can be developed. The customized or localized information system or particular functional module should be developed because it is a customized product service, and long-term or strategic customer relation should be established. Similarly, the critical elements of the service network are highlighted in Fig. 4.

Process Design of the Product-Service Supply Chain Management According to whether a process is involved in direct service providing, the process of PSSC can be clarified into two groups: the functional process and the enabling process (Fig. 5). The functional process, which has a direct effect on the customer value realization, includes the demand management, service resource and capacity management, and service delivery management. Aiming for offering the managerial and technological support, the enabling process covers the performance management, customer relationship management, service provider and supplier relationship management, information technology application management, and service network management.

Fig. 5 The main activities in product-service supply chain

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Functional Process Management Demand management is to understand and react to current and potential customer demand (Rainbird 2004). The product related service is provided actively but not just responded to failures, which mean that service providers should make full preparation for service provision beforehand rather than afterwards. Because keeping the service available at any moment is in the interests of both the providers and the customers. For example, any delay in replacing the broken part would cause extra expenses for both the providers and the customers. Therefore, accurate demand forecasting is incredibly important for preparing and optimizing service resource (Akkermans and Vos 2003). The service demand features identification is to distinguish the deliverables for a better understanding and more accurate forecasting of the customer demands. Generally speaking, the deliverables can be classified as three types. The first type is physical unit, such as spare part, detecting device, and tools. The second is the service unit, such as training, consultancy, technical support, detection, logistics, and stock. While the third is the combination of physical unit and service unit, like providing installation service for free when selling spare parts. In order to avoid any misunderstanding, if there is no specification, the word “service” refers to all the three deliverables in the following part of the chapter. Therefore, it has the same importance as the service demand forecast. Hence, the service demand planning is an ultimate result, indicating the requirements of deliverables on strategic, tactical, and operational plans. The purpose of service resource and capacity management is to balance the customer needs and service capacity. Service outsourcing is a way of maximizing the benefits of the service resource and extending the service capacity. Because of the perishability of services, service resource management plays the role of the buffer. Effective and profitable product service depends on managing service assets and fulfilling service demands in a flexible and integrated manner (Cohen et al. 2006). Generally speaking, the service resources can be sorted as follows: material resource (e.g., spare parts, tools, and material), human resource (experts, technicians, installation staff, etc.), information resource (e.g., schedule information, equipment status information, and logistic information), knowledge resource (e.g., technology and experience), and other resource. The appropriate combined service resources form the service capability, and the resources have to be coordinated for the right time and right place (Meier et al. 2010). Since the service resource results from the service deliverables demand, their differences among the resources and features should be identified initially for planning the engineers, tools, and materials. And then, on the basis of the service demand forecast, the maximal service capacity would be obtained by allocating the service resource on time and space optimally. Service delivery is a providers and customers’ involvement process fulfilling the commitments to customers (West 2004). The service delivery may involve one provider and one customer or involve many providers and many customers simultaneously. The deliverables determine the way of service delivery. It includes

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on-site delivery, like air compressor installation; online delivery, for example, remote technological training; offline delivery (delivered by service factory), such as air compressor cleaning and upgrading. Therefore, the features identification of the deliverables is the first step for any customer satisfaction. Even for the air compressor overhaul, there are options for repairing the product in customers’ company or shipping back to the service factory. The service delivery plan should be formulated according to the service demand and the service resource plan. However, the customers’ participation increases the uncertainty of the service delivery, especially for the outsourced service. The application of the modular technique in PSS development and standardization of PSSC process management would ensure a high level of customer satisfaction (Wang et al. 2011). Whether the service is provided by the main provider or not, the service quality is controllable via monitoring the supply chain process.

Enabling Process Management The customer is no longer regarded as a passive transaction-oriented actor but rather an active relationship-oriented actor with a long-term perspective in the interaction (Kindstro¨m and Kowalkowski 2009). Customer relationship management is concerned with the development of profitable, long-term relationships with customers and other key stakeholders (Payne and Frow 2005). Identifying the customer value and building trust are the key steps in customer relationship management. Therefore, it would be reasonable to assort the customers in light of their value propositions. In company IR which is an air compressor manufacturer in China, the customers are classified according to the customer value: the compressed air, the function of the air compressor, and the air compressor. It is evident that the customers requiring the compressed air have tight connections with the service providers. With regard to this kind of customers, the emphasis of the customer relationship management is not only the product life-cycle management but also the customer life-cycle management, which covers the stages from the service relationship establishment to the time when the customer would not need the service or turns to other service providers. With effective customer interaction, the providers can find out more potential needs and present more innovative services. It not only improves the customer satisfaction but also wins their trust. As mentioned above, providers contribute to service delivery and contact with customers directly. The suppliers are indirect supporters and contribute to the supply chain by sharing their technologies, expertise, and other useful resource. Supplier relationship management involves setting up, developing, stabilizing, and dissolving relationships with suppliers (Moeller et al. 2006). The purpose of the service provider and supplier relationship management is to share the information, technologies, tools, and other necessary service resources freely, as well as the benefit and risk. As a result, the recognition of the partners’ value proposition would be helpful to build long-term mutual confidence and deepen the cooperation. On the one hand, appropriate selection and evaluation approach will promise the

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performance of the partners. On the other hand, the performance of the suppliers and providers could be assessed by the feedback from customers and the service delivery process. The PSSC depends on the information being shared efficiently (Baltacioglu et al. 2007). The information technology (IT) is helpful to build the fundamental information flow of the PSSC. Decreasing the service cost and improving the service response time are the primary drivers for IT application in service delivery, such as the remote diagnosis and maintenance and online training. On the one hand, IT has proved to be an effective tool for PSSC, such as the demand forecast and logistics management (Baltacioglu et al. 2007; Ellram et al. 2004). On the other hand, it facilitates the service providing and enhances the transparency of the service delivery process. Furthermore, it provides a useful tool for several providers offering service corporately and timely, especially when dealing with the thorny problems in the emergency. The contradiction between the decentralized customers and the limited service resource is one of the main challenges in PSSC. An appropriate service network would decrease the service cost and improve customer service significantly. The service network consists of various terminal service facilities, such as the service centers, spare parts warehouse, and the service stations of partners. As for the main providers, the service facilities may be self-built, partner owned, or distributer owned. The service network management performs the network structure design, service facility location selection, service facilities function and capacity design, and the forward and reverse logistics management. Performance management has a profound influence on the improvement of customer satisfaction, service quality of providers, and the behavior of suppliers and other stakeholders. Appropriate performance metrics and measurement can help to implement the principle of value co-creation and strategic objectives of the PSSC. The performance management can be conducted at two levels: the service delivery level and the PSSC level. In the service delivery level, the customer value and customer satisfaction are the core criteria for evaluating the efforts of the providers. In the PSSC level, the customer satisfaction and the service cost are equally important considering the providers’ value proposition. But the time interval of the assessment of the latter is longer than the former.

Summary In this chapter, the author introduces the implications, structure, and roles of the supply chain members, as well as the prominent features of PSSC. To realize PSSC design, the five crucial elements are explained, which include the PSSC strategy, service facility allocation, outsourced service supplier selection, service network configuration, and the process design for PSSC. The approaches for the PSSC design applications are reviewed, suggested, and elaborated.

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Pasandideh SHR, Niaki STA (2012) Genetic application in a facility location problem with random demand within queuing framework. J Intell Manuf 23(3):651–659 Pasandideh SHR, Niaki STA, Hajipour V (2013) A multi-objective facility location model with batch arrivals: two parameter-tuned meta-heuristic algorithms. J Intell Manuf 24(2):331–348 Payne A, Frow P (2005) A strategic framework for customer relationship management. J Mark 167–176 Phumbua S, Tjahjono B (2012) Towards product-service systems modelling: a quest for dynamic behaviour and model parameters. Int J Prod Res 50(2):425–442. doi:10.1080/ 00207543.2010.539279 Porter ME, Millar VE (1985) How information gives you competitive advantage. Harvard Business Review, Reprint Service Rainbird M (2004) Demand and supply chains: the value catalyst. Int J Phys Distrib Logist Manag 34(3/4):230–250 Reeves KA Jr, Caliskan F, Ozcan O (2010) Outsourcing distribution and logistics services within the automotive supplier industry. Transport Res E: Logist Transport Rev 46(3):459–468. doi:10.1016/j.tre.2009.10.001 Salminen V, Kalliokoski P (2008) Challenges of industrial service business development. In: Hefley B, Murphy W (eds) Service science, management and engineering education for the 21st century service science: research and innovations in the service economy. Springer, New York, pp 41–48. doi:10.1007/978-0-387-76578-5_7 Sanchez R, Mahoney J (1996) Modularity, flexibility, and knowledge management in product and organization design. Strateg Manage J 17:63–76, doi:citeulike-article-id:3758594 Wang Li P, Fu J (2010) Analysis on supply chain of manufacturing enterprise product service system. In: Emergency management and management sciences (ICEMMS), 2010 I.E. international conference on, 8–10 Aug 2010, pp 126–129, doi:10.1109/icemms.2010.5563484 Wang PP, Ming XG, Li D, Kong FB, Wang L, Wu ZY (2011) Status review and research strategies on product-service systems. Int J Prod Res 49(22):6863–6883. doi:10.1080/ 00207543.2010.535862 West DM (2004) E-Government and the transformation of service delivery and citizen attitudes. Public Adm Rev 64(1):15–27. doi:10.1111/j.1540-6210.2004.00343.x Wu C, Barnes D (2011) A literature review of decision-making models and approaches for partner selection in agile supply chains. J Purch Supply Manag 17(4):256–274. doi:10.1016/ j.pursup.2011.09.002 Zhang R, Chen R (2009) The Dempster-Shafer synthesis rule for service supplier selection. In: IE&EM’09. 16th international conference on, 2009. IEEE, pp 1625–1630

Remaining Life Prediction of Cores Based on Data-driven and Physical Modeling Methods

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Xiang Li, Wen Feng Lu, Lianyin Zhai, Meng Joo Er, and Yongping Pan

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weibull Model for Analysis of Time-to-Failure Data in Product Life Cycle Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weibull Model for Reliability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Basics of Weibull Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weibull Analysis of Life Data: An Illustrative Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Condition Prediction Using Enhanced Online Learning Sequential-Fuzzy Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Architecture of Fuzzy Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Online Sequential Learning Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multistep Prediction Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simulation Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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X. Li (*) Singapore Institute of Manufacturing Technology (SIMTech), Singapore e-mail: [email protected] W.F. Lu • L. Zhai Department of Mechanical Engineering, Faculty of Engineering, National University of Singapore, Singapore e-mail: [email protected]; [email protected] M.J. Er • Y. Pan School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore e-mail: [email protected]; [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_57

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Abstract

This chapter presents development of enabling technologies that are able to assess the reliability of remanufactured products based on predictive modeling methods, to describe fast and accurate prediction algorithms that are able to predict condition of critical components or parts of manufactured products based on historical data. Machine health condition prediction of critical components under the situation of insufficient data, missing prior fault knowledge, and noisy measurement are studied using an enhanced online sequential learning-fuzzy neural network. Meanwhile, Weibull model-based reliability analysis is investigated in this chapter. Performance of various Weibull parameter estimation methods is compared using case studies. Results of this part of research have enabled the development of a product reliability analysis tool that is able to characterize the product failure modes, failure rate, and reliability profile.

Introduction As the primary goal of remanufacturing is part reuse, understanding of the quality/condition of the returned cores is very important for decision-making in remanufacturing processes. Hence, condition assessment and fault isolation of the returned cores becomes one of the most critical activities that determine the success of a remanufacturing process. Existing practices in remanufacturing typically carry out defect inspection and fault diagnosis only for isolated parts/components after the returned cores are disassembled. This may impose additional challenges and cost on the remanufacturing process such as fluctuation of schedule for remedial processes or treatments in the shop floor due to unexpected defects/faults identified after disassembly. In addition, it depletes the opportunity to assess the condition and performance of the products systematically based on their field operational data in each lifecycle before they are returned as cores, which is very important to establish reliability models of the products (Mazhar et al. 2010). On the other hand, for valuable machineries, such as mining trucks, a large amount of operational data is already being collected, typically on log sheets or by a control system. This process is usually not regarded as part of a condition monitoring or diagnostic program. However, there is a lot of valuable machinery performance and condition information buried in such operational data. In practice, the only challenge is how to extract useful information from such data. It is believed that the operational and inspection data collected on a machine, when properly interpreted, can produce an accurate picture of the machines health. In reality, the cores returned for remanufacturing may have experienced very different working conditions, and their components/parts may have diverse ages and different stress and strains arisen by the users, but remanufacturing companies usually do not involve the historical and field operational data of the cores when they make decisions for remanufacturing processes in the shop floor. Taking the engine of a mining truck as an example, operational data often is adequate to

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allow for calculations of engine efficiency and detection of its deviations, which can then be extended to evaluate the condition of the engines. Analysis such as the emerging operational trends of operational data not only can tell us that there is a performance problem with this engine, but it also can help to isolate fault sources well before the engine is sent for inspection in the remanufacturing workshop. Measures of this sort can greatly assist with prioritizing remanufacturing decisions and balance the time between reman cycles, and more importantly, it may simplify the reman workshop inspection procedure and minimize the inspection cost. It should be noted that most of the current practices in remanufacturing rely on rules of thumb or expert knowledge and lack rigorous reliability-based evaluation models to support remanufacturing shop-floor decision-making. Although recent years have seen some applications using visual and/or statistical analysis tools to assist the lifecycle assessment of remanufactured products, such tools remain inadequate in coping with large amount of field operational data with inherent variability, uncertainty, and nonlinearity. The proposed approach attempts to address critical issues for improving remanufacturing processes through effective analysis of field operational data. This research will fill up the gap in the current state of the art where existing remanufacturing practices lack rigorous and reliable analysis of operational data for support of remanufacturing shop-floor decisionmaking, despite the fact that an effective analysis of field operational data in various aspects of the products will provide invaluable information to facilitate sound decision-making and continuous improvement of remanufacturing processes. More specifically, the novelty of this research includes. A comprehensive approach to condition assessment and fault isolation through rigorous reliability-based evaluation models with progressive model learning capabilities; Fusion of statistical and machine learning techniques to form a fast real-time RUL prognosis tool that can scale well against large operational data with inherent variety and uncertainty in remanufacturing processes.

Weibull Model for Analysis of Time-to-Failure Data in Product Life Cycle Management Management of products and materials at the end-of-life (EOL) is being recognized as an integral part of the product life cycle engineering. Among various EOL management strategies, remanufacturing as a sustainable manufacturing process has received more attention in recent years. In remanufacturing practices, understanding and communicating the failure risk and reliability of a critical part, component, or subsystem plays a crucial role as it has a significant impact on the lifecycle management of the product and also determines the success of the remanufacturing process. In such a context, it is envisaged that many of the life cycle engineering techniques will have a significant impact on remanufacturing practices such as reliability and remanufacturability analysis of valuable parts and components, remaining useful life prediction and warranty cost of remanufactured products etc (Mazhar et al. 2007).

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In life cycle engineering as well as in remanufacturing industry, estimation of product mean life is an important task as it provides valuable information for effective life cycle management, in particular, the core management and inventories in remanufacturing. Usually a product’s mean life is determined by analyzing its time-to-failure data from a wide range of the same category of products operated under the same conditions of use (Li 2004). Another important issue in life cycle engineering and remanufacturing is the quantitative analysis of product or component reliabilities, based on which the product or the component’s expected useful life can be estimated. Understanding the probability of product or component failures at different stages of its life can be very useful to make optimized decisions in life cycle management and remanufacturing practices (Mazhar et al. 2007).

Weibull Model for Reliability Analysis In life cycle management, one of the simplest approaches to predicting failure is based on statistical reliability models of past failures (Gu and Li 2012). Reliability is defined as the probability that a product will continue to perform its intended function without failure for a specified period of time under stated conditions (Pham 2006; Calixto 2013). Usually, reliability predictions are used to estimate future failure based on past failure records by applying a probability distribution such as the exponential distribution. However, one of the principal shortcomings of using the exponential distribution is that it imposes a “Markov” assumption, meaning that the future prediction of a failure is independent of the history of the unit given the current measurement (Lourenco and Mello 2012). In this respect, Weibull distribution (Weibull 1951) for prediction provides an alternative reliability method as it relaxes the assumption of constant failure rates as well as the Markov assumption (Groer 2000). In fact, the most common distribution function in EOL management is Weibull distribution due to its ability to fit a greater variety of data and life characteristics by changing its shape parameter (Artana and Ishida 2002). Today, Weibull analysis is the leading method in the world for fitting and analyzing life data. In most cases of application, Weibull distribution is able to provide the best fit of life data. This is due in part to the broad range of distribution shapes that are included in the Weibull family. Many other distributions are included in the Weibull family either exactly or approximately, including the normal, the exponential, the Rayleigh, and sometimes the Poisson and the Binomial (Abernethy 2006). Compared with classic statistical methods, Weibull analysis uses failure reference and mean-time-to-failure (MTTF) to forecast failures, whereas statistical pattern analysis uses test data to identify a statistical pattern such as trend lines (Fitzgibbon et al. 2002). Another most salient feature to be noted for Weibull analysis is its ability to provide reasonably accurate failure analysis and failure forecasts with extremely small samples of life data, where most of other distributions fail to give meaningful result (usually when the sample size is smaller than 20) (Abernethy 2006). This feature of Weibull analysis makes it very valuable in remanufacturing decision-making practices because it is a common

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case that the life data of very big and especially very expensive parts/components collected in remanufacturing process are either incomplete or small in size.

Basics of Weibull Distribution In general, a typical Weibull probability distribution function (PDF) is defined by f ðt Þ ¼


 β t β1 ðηt Þβ e η η

(1)

where t  0 represents time, β > 0 is the shape or slope parameter, and η > 0 is the scale parameter of the distribution. Equation 1 is usually referred to as the 2-parameter Weibull distribution. Among the two parameters, the slope of the Weibull distribution, β, is very important as it determines which member of the family of Weibull failure distributions best fits or describes the data. It also indicates the class of failures in the “bathtub curve” failure modes as shown in Fig. 1. The Weibull shape parameter β indicates whether the failure rate is increasing, constant, or decreasing. If β < 1, it indicates that the product has a decreasing failure rate. This scenario is typical of “infant mortality” and indicates that the product is failing during its “burn-in” period. If β ¼ 1, it indicates a constant failure rate. Frequently, components that have survived burn-in will subsequently exhibit a constant failure rate. If β > 1, it indicates an increasing failure rate. This is typical for products that are wearing out. To summarize: • β < 1 indicates infant mortality. • β ¼ 1 means random failures (i.e., independent of time). • β > 1 indicates wear-out failures. The information about the β value is extremely useful for reliability-centered maintenance planning and product life cycle management. This is because it can provide a clue to the physics of the failures and tell the analyst whether or not scheduled inspections and overhauls are needed. For instance, if β is less than or equal to one, overhauls are not cost effective. With β greater than one, the overhaul period or scheduled inspection interval can be read directly from the plot at an

Failure rate

Fig. 1 The “bathtub curve” failure modes

β1:wear-out failures

Time

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acceptable or allowable probability of failures. For wear-out failure modes, if the cost of an unplanned failure is much greater than the cost of a planned replacement, there will be an optimum replacement interval for minimum cost. On the other hand, the scale parameter, or spread, η, sometimes also called the characteristic life, represents the typical time-to-failure in Weibull analysis. It is related to the mean-time-to-failure (MTTF). In Weibull analysis, η is defined as the time at which 63.2 % of the products will have failed (Pasha et al. 2006). There are basically two fitting methods for parameter estimation in widespread use in reliability analysis, namely, the maximum likelihood estimation (MLE) and regression methods. MLE involves developing a likelihood function based on the available data and finding the values of the parameter estimates that maximize the likelihood function. Regression method generally works best with data sets with smaller sample sizes that contain only complete data (i.e., data in which all of the units under consideration have been run or tested to failure). This failureonly data is best analyzed with rank regression on time, as it is preferable to regress in the direction of uncertainty. In Weibull analysis, median-rank regression (MRR) method which uses median ranking for regression fitting is often deployed to find out the shape and scale parameters for complete life data (Abernethy 2006). The probability of failure at time t, also referred to as the Weibull distribution or the cumulative distribution function (CDF), can be derived from Eq. 1 and expressed as t β

FðtÞ ¼ 1  eðηÞ

(2)

Thus, the Weibull reliability at time t, which is 1  F(t) ¼ R(t), is defined as t β

RðtÞ ¼ 1  FðtÞ ¼ eðηÞ

(3)

This can be written as t β 1 ¼ eðηÞ 1  FðtÞ

(4)

Taking two times the natural logarithms of both sides gives an equation of a straight line:
ln ln

1 1  FðtÞ

 ¼ βlnt  βlnη

(5)

Equation 5 represents a straight line in the form of “y ¼ ax + b” on log/log(Y) versus log(X), where the slope of the straight line in the plot is β, namely, the shape parameter of Weibull distribution. Through the above transformation, the life data samples can be fitted in the Weibull model and the two Weibull parameters can be estimated.

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Table 1 Life data of a critical component in a diesel engine

No. 1 2 3 4 5 6 7 8 9 10

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Failure time (h) 38,456 48,334 50,806 51,521 61,544 66,667 72,605 75,521 80,785 84,894

The mean of the Weibull PDF, T , which is the MTTF in Weibull analysis, is given by


1 T ¼ηΓ þ1 β

 (6)

where Γ is the gamma function. It is noted that when β ¼ 1, MTTF is equal to η. In fact, as a rough approximation, in practices of Weibull analysis where β is equal to or slightly larger than 1, the characteristic life can be approximated as MTTF. However, for β that is much larger than 1, MTTF should be calculated using Eq. 6. This will be further discussed in the example elaborated in the next section.

Weibull Analysis of Life Data: An Illustrative Case Study In the life cycle management of a certain type of heavy-duty diesel engine, it is required to quantify the life characteristics of a critical component in order to understand its reliability and remanufacturability. The engine manufacturer has provided a past record of 10 failure cases of the said component under normal use conditions. The complete life data, i.e., the failure time of each sample, is shown in Table 1. Assume that our objectives in this case study include: 1. Determine the Weibull parameters and derive the Weibull distribution model for the data given. 2. Estimate the average life of the component (i.e., the MTTF or mean life). 3. Estimate the time by which 5 % of the components will fail or the time by which there is a 5 % probability that the component will fail. 4. Estimate the reliability of the components after a given number of hours of operation.

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Probability - Weibull

99.000

Point A

10.000

X = 35221.228 Y = 5.053

1.000

0.100 10000.000

Time (Hr)

100000

Fig. 2 Weibull probability plot

5. Estimate the warranty time for the component if the manufacturer does not want failures during the warranty period to exceed 5 %. In the following sections, Weibull analysis will be conducted to address the above objectives. In this case study, the 2-parameter Weibull analysis is deployed to analyze the life data characteristics of the diesel engine component. First of all, the parameters are estimated based on the 2-parameter Weibull analysis, in which the standard ranking method and median-rank regression are used to fit the given data in Table 1. As discussed earlier in section “Basics of Weibull Distribution,” regression method should be selected to fit the data when the data sample is small and contains complete life data. The fitting plot is shown in Fig. 2 and the two Weibull distribution parameters are calculated as follows: β ¼ 4.40 and η ¼ 69,079.89 (hours). After the two parameters of β and η are determined, the Weibull PDF expressed by Eq. 1 can be obtained as shown below: f ðtÞ ¼

3:4 4:4 t 4:4  t eð69079:89Þ 69079:89 69079:89

After simplification the Weibull PDF is plotted as shown in Fig. 3.

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Probability Density Function

3.000E-05

2.400E-05

1.800E-05

1.200E-05

6.000E-06

0

0

60000.000

120000.000

180000.000

Time (Hr) Fig. 3 Weibull probability density function

As discussed earlier, the MTTF or mean life is a very important indicator of the life data characteristics in life cycle engineering. MTTF can be either approximated by the value of η in cases where β is slightly larger but close to 1 or calculated using Eq. 6 if β is much larger than 1 or a more accurate value is required. In this case study, β ¼ 4.40 and therefore Eq. 6 is used to calculate the exact MTTF instead. The MTTF calculated is 62,952.73 h, and it is much smaller than the value of characteristic life η, which is 69,079.89 h. As shown in Fig. 2, in the Weibull probability fitting plot of the case study, the x-axis represents time using a logarithm scale, and the probability of failure is displayed on the y-axis using a double log reciprocal scale. Such a Weibull probability plot is able to tell very important information about the characteristics of the failures. From the plot, the probability of failure at a given time, or vice versa, can be obtained. For example, it may be of interest to determine the time at which 1 % of the population will have failed. For more serious or catastrophic failures, a lower risk may be required, for instance, six-sigma quality program goals often equate to 3.4 parts per million (PPM) allowable failure proportion. Such important information can be easily obtained from the Weibull probability plot. In this case study, for the red dot (point A) shown on the plot in Fig. 2, the x and y coordinates are x ¼ 35,221.228 h and y ¼ 5.053, respectively, which can be interpreted in the following way: the failure probability of the component at the time of 35,221.228 h

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1.000

Reliability vs. Time

Poin t B 0.800

0.600

0.400

0.200

0

0

40000.000

80000.000

120000.000

Time (Hr) Fig. 4 Weibull reliability plot

is 5.053 %, or the average time by which 5.053 % of the components will fail is 35,221.228 h. It is known that reliability analysis is a very important issue in life cycle engineering. In this case study, the Weibull reliability function can be calculated based on Eq. 3 and its plot is shown in Fig. 4. Figure 4 can be easily used to answer the estimate of reliability of the component after a certain number of hours of operation. For example, for point B shown on the plot in Fig. 4, the x and y coordinates are x ¼ 32,090.306 h and y ¼ 0.966, respectively, which can be interpreted in the following way: the reliability of the component after 32,090.306 h of operation is 96.6 %. This is in fact the reverse interpretation of the coordinates of x and y in Fig. 4. For example, if the manufacturer does not want failures during the warranty period to exceed 3.4 % (i.e., the required reliability is 96.6 %), then the maximum warranty time promised to customers should not exceed 32,090.306 h, as shown by point B in Fig. 4. In life cycle engineering, failure rate is another important indicator of life data characteristics. Failure rate is usually defined as the frequency with which a product or component fails, and it is often expressed in failures per unit of time

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Failure Rate vs.Time

0.003

0.002

0.002

0.001

6.000E-04

0

0

60000.000

120000.000

180000.000

Time (Hr) Fig. 5 Failure rate versus time

(e.g., per hour in this case study). The failure rate of a product usually depends on time, with the rate varying over the life cycle of the product, as shown in the “bathtub curve” failure modes in Fig. 1. The failure rate of the case study is calculated and shown in Fig. 5. The increasing failure rate shown in the figure confirms that the life data in Table 1 follow a wear-out failure mode.

Condition Prediction Using Enhanced Online Learning Sequential-Fuzzy Neural Networks Machine health condition (MHC) prediction is useful for preventing unexpected failures and minimizing overall maintenance costs since it provides decisionmaking information for condition-based maintenance (CBM) (Vachtsevanos et al. 2006). Typically, MHC prediction methods can be divided into two categories, namely, model-based data-driven methods (Jardine et al. 2006). Due to the difficulty of deriving an accurate fault propagation model (Yu et al. 2012;, Yu et al. 2011), researches have focused more on the data-driven method in recent years (Si et al. 2011). The neural network (NN)-based approach, which falls under the category of the data-driven method, has been considered to be very promising for MHC prediction due to the adaptability, nonlinearity, and universal function

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approximation capability of NNs (Tian and Zuo 2010). Batch learning and sequential learning are two major training schemes of NNs. MHC prediction is essentially an online time-series forecasting problem which should perform realtime prediction while updating the NN. Thus, to save updating time and to maintain consistency of the NN, the sequential learning should be employed in such a problem. The most popular NNs applied to MHC prediction are recurrent NNs (RNNs) and fuzzy NNs (FNNs). In Gu and Li (2012), an extended RNN which contains both Elman and Jordan context layers was developed for gearbox health condition prediction. In Zhao et al. (2009), a FNN in Brown and Harris (1994) was applied to predict bearing health condition. In Wang et al. (2004), an enhanced FNN was developed to forecast MHC. Next, in Wang (2007) and Liu et al. (2009), a recurrent counterpart of the approach in Wang et al. (2004) and a multistep counterpart of the approach in Wang (2007) were presented to predict MHC, respectively. An interval type-2 FNN was also proposed to predict bearing health condition under noisy uncertainties in Chen and Vachtsevanos (2012). Note that the batch learning was employed in Tian and Zuo (2010), Zhao et al. (2009), and Chen and Vachtsevanos (2012). Common conclusions from Tian and Zuo (2010), Zhao et al. (2009), and Wang et al. (2004), Wang (2007), Liu et al. (2009), and Chen and Vachtsevanos (2012) are that the RNN usually outperforms the feedforward NN and the FNN usually outperforms the feedforward perceptron NN, feedforward radial basis function (RBF) NN, and RNN. Recently, to improve prediction performance under measurement noise, an integrated FNN and Bayesian estimation approach was proposed for predicting MHC in Chen et al. (2012), where a FNN is employed to model fault propagation dynamics offline and a first-order particle filter is utilized to update the confidence values of the MHC estimations online. In Chen et al. (2011), a high-order particle filter was applied to the same framework of Chen et al. (2012). A question in the approaches of Chen et al. (2011, 2012) is that the FNNs should be trained by the system state data (rather than the output data) which are assumed to be immeasurable. Extreme learning machine (ELM) is an emergent technique for training feedforward NNs with almost any type of nonlinear piecewise continuous hidden nodes (Huang et al. 2006). The salient features of ELM are as follows (Huang et al. 2006): (i) All hidden node parameters of NNs are randomly generated without the knowledge of the training data; (ii) it can be learned without iterative tuning, which implies that the hidden node parameters are fixed after generation and only output weight parameters need to be turned; (iii) both training errors and weight parameters need to be minimized so that the generalization ability of NNs can be improved; and (iv) its learning speed is extremely fast for all types of learning schemes. ELM demonstrates great potential for MHC prediction due to these salient features. Nonetheless, the original ELM proposed in Huang et al. (2006) is not appropriate for predicting MHC since it belongs to the batch learning scheme. To enhance the efficiency of ELM, online sequential ELM (OS-ELM) was developed in Liang et al. (2006) and was further applied to train the FNN in Rong et al. (2009).

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Due to its extremely high learning speed, the OS-ELM-based FNN in Rong et al. (2009) seems to be suitable for MHC prediction. Yet, there are two drawbacks in Rong et al. (2009) as follows: (i) It is not good to yield generalization models since only tracking errors are minimized and (ii) it may encounter singular and ill-posed problems while the number of training data is smaller than the number of hidden notes. To further improve the efficiency of MHC prediction, a novel FNN with an enhanced sequential learning strategy is proposed in this paper. The design procedure of the proposed approach is as follows: Firstly, a ellipsoidal basic functions (EBFs) FNN is proposed; secondly, the FNN approximation problem is transformed into the bi-objective optimization problem; thirdly, an enhanced online sequential learning strategy based on the ELM is developed to train the FNN; and finally, a multistep direct prediction scheme based on the proposed learning strategy is presented for MHC prediction. The developed enhanced online sequential learning-FNN (EOSL-FNN) is applied to predict bearing health condition by the use of real-world data from accelerated bearing life. Comparisons with other NN-based methods are carried out to show the effectiveness and superiority of the proposed approach.

Architecture of Fuzzy Neural Network For MHC prediction, the n-input single-output system is considered. Yet, the following results can be directly extended to the multi-input multi-output (MIMO) system. The FNN is built based on an EBF NN. It is functionally equivalent to a Takagi-Sugeno-Kang (TSK) fuzzy model that is described by the following fuzzy rules (Wu et al. 2001): Rule Rj : IF x1 is A1j and  and xn is Anj THEN y^ is wj

(7)

where xi  ℝ and y^  ℝ are the input variable and output variable, respectively; Aij is the antecedent (linguistic variable) of the ith input variable in the jth fuzzy rule; wj is the consequent (numerical variable) of the jth fuzzy rule, i ¼ 1, 2, . . ., n, j ¼ 1, 2, . . ., L; and L is the number of fuzzy rules. As illustrated in Fig. 6, there are in total four layers in the FNN. In Layer 1, each node is an input variable xi and directly transmits its value to the next layer. In Layer 2, each node represents a Gaussian membership function (MF) of the corresponding Aij as follows: h  i   2 μAij xi j cij , σij ¼ exp  xi  cij =2σ2ij

(8)

where cij  ℝ and σ ij  ℝ+ are the center and width of the ith MF in the jth fuzzy rule, respectively. Note that the MF in Eq. 8 is an EBF since all its widths σij are

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Fig. 6 Architecture of fuzzy neural network

different (Wu et al. 2001). In Layer 3, each node is an EBF unit that denotes a possible IF-part of the fuzzy rule. The output of the jth node is as follows: h Xn  i   2 ϕj xjcj , σ j ¼ exp  i¼1 xi  cij =σ 2ij (9) where x ¼ ½x1 , x2 ,  , xn T  ℝn , cj ¼ [c1j, c2j,   , cnj]  ℝn, and σ j ¼ [σ 1j, σ 2j,   , σ nj]  ℝn. In the last layer, the output y^is obtained by the weighted summation of ϕj as follows: y^ ¼ f^ðxjW, c, σÞ ¼ Φðxjc, σÞW

(10)

where f^ðÞ : ℝnþLð1þ2nÞ 7!ℝ , Φ ¼ [ϕ1, ϕ2,   , ϕL]  ℝL, c ¼ [c1, c2,   , cL]T  ℝL  n, σ ¼ [σ 1, σ 2,   , σ L]T  ℝL  n, and W ¼ [w1, w2,   , wL]T  ℝL. For the TSK model, the THEN-part wj is a polynomial of xi which can be expressed as follows: wj ¼ α0j þ α1j x1 þ    þ αnj xn

(11)

where α0j, α1j,   , αnj  ℝ are weights of input variables in the jth fuzzy rule. The following lemma shows the universal function approximation property of the proposed FNN. Lemma 1 (Lin and Cunningham 1995) For any given continuous function f ðxÞ : D7!ℝ and arbitrary small constant e  ℝ+, there exists a FNN in Eq. 10 with proper parameters W, c, and σ such that supx  D jf ðxÞ  f^ðxjW, c, σÞj < ε where D  ℝn is an approximation region.

(12)

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Online Sequential Learning Strategy For training FNNs, consider a data set with N arbitrary distinct training samples: N N ¼ fðxl , yl ÞgNl¼1 , where xl ¼ [xl1, xl2,   , xln]T  ℝn, yl  ℝ, and l is the number of the sampling point. If a FNN with L hidden nodes can approximate these N samples with zero error, then there exist proper parameters W,c, and σ such that Φðxl jc, σÞW ¼ yl

(13)

for all l ¼ 1, 2, . . ., N. Since wj in Eq. 11 can be rewritten into wj ¼ xTle αj with  T xle ¼ 1, xTl  ℝnþ1 and αj ¼ [α0j, α1j,   , αnj]T  ℝn + 1, one gets  T W ¼ xTle α1 , xTle α2 ,   , xTle αL :

(14)

Substituting Eq. 14 into Eq. 13 for all l ¼ 1, 2, . . ., N, applying the definition of Φ, and making some manipulations, one gets 3 2 3 y1 xT1e ðϕ1 α1 þ ϕ2 α2 þ    þ ϕL αL Þ 6 xT ðϕ1 α1 þ ϕ2 α2 þ    þ ϕL αL Þ 7 6 y2 7 7 ¼ 6 7: 6 2e 5 4⋮5 4 ⋮ T yN xNe ðϕ1 α1 þ ϕ2 α2 þ    þ ϕL αL Þ 2

From the above expression, it is easy to show that 32 3 2 3 y1 α1 xT1e ϕ1 , xT1e ϕ2 ,   , xT1e ϕL 6 xT ϕ1 , xT ϕ2 ,   , xT ϕL 76 α2 7 6 y2 7 2e 2e 76 7 ¼ 6 7 6 2e 54 ⋮ 5 4 ⋮ 5 4 ⋮ αL yN xTNe ϕ1 , xTNe ϕ2 ,   , xTNe ϕL 2

which can be written into the following compact form: H ðX,c,σÞQ ¼ Y

(15)

where X ¼ ½x1 , x2 ,  , xN T  ℝNn , Y ¼ [y1, y2,   , yN]T  ℝN  1, and Q ¼ [αT1 , αT2 ,   , αTL]T  ℝ(n + 1)L  1 is the consequent parameter matrix and H  ℝN  (n + 1)L is the hidden matrix weighted by the fired strength of fuzzy rules given by   3 xT1e f 1 ðx1 , c1 , σ1 Þ,   , xT1e f L x1 , cL , σL  6 xT f 1 ðx2 , c1 , σ1 Þ,   , xT f L x2 , cL , σL 7 2e 2e 7: H ðX,c,σÞ ¼ 6 5 4 ⋮   T T xNe f 1 ðxN , c1 , σ1 Þ,   , xNe f L xN , cL , σL 2

(16)

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From ELM theory, the parameters c and σ in Eq. 16 can be randomly generated and fixed after generation, i.e., the updating of antecedent parameters is not necessary. Usually, the equality in Eq. 15 cannot be obtained due to the limitation of FNN scale. Consider the following minimizing problem: min ðjjHQ  Yjj2 þ λjjQjj2 Þ

(17)

Q

where ||  || denotes the Euclidean norm and λ is a real positive constant. The leastsquares solution of Q in Eq. 17 is as follows:   ^ ¼ H T H þ λI 1 H T Y: Q

(18)

0 Now, give an initial data set N 0 ¼ fðxl , yl ÞgNl¼1 . From Eq. 18, one immediately gets

^0 ¼ K 1 H T Y 0 Q 0 0

(19)

K 0 ¼ HT0 H 0 þ λI

(20)

 T where Y 0 ¼ y1 , y2 ,   , yN 0 , H0 ¼ H ðX0 , c, σÞ, and X0 ¼ ½x1 , x2 ,  , xN0 T . Let y^l be the estimation of yl with l ¼ 1, 2,   . The FNN output at the initial phase is as follows: ^0 Y^0 ¼ H 0 Q

(21)

 T where Y^0 ¼ y^1 , y^2 ,   , y^N0 . Then, present the (k + 1)th chuck of new observations: N kþ1 ¼ fðxl , yl Þg with l ¼ ∑ jk ¼ 0Nj + 1, ∑ jk ¼ 0Nj + 2,   , ∑ jk ¼+ 10Nj, where Nj denotes the number of observations in the (k + 1)th chunk. From Liang et al. (2006), one obtains the RLS solution for Q in Eq. 17 as follows:

^kþ1 Q

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xX k

,  , xXkþ1

(22) (23) T

where H kþ1 ¼ H ðXkþ1 ,c,σÞ , Xkþ1 ¼ N þ1 N j , and j¼0 j j¼0

T yXk , yXk ,   , yXkþ1 Y kþ1 ¼ þ1 þ2 N N N j . The FNN output at the j j j¼0 j¼0 j¼0 learning phase is as follows:

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y^Xk , y^Xk ,  , y^Xkþ1 where Y^kþ1 ¼ þ1 þ2 N N Nj j j j¼0 j¼0 j¼0

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T .

To avoid the singular problem for the matrix inversion of Kk + 1in Eq. 23 while 1 N0 < L, one makes P0 ¼ K and applies the Woodbury identity to calculate P0 as 0 follows (Huynh and Won 2011):  1 P0 ¼ I=λ  H T0 λI þ H 0 H T0 H0 =λ:

(25)

Similarly, to avoid the ill-posed problem so that the computational cost for the matrix inversion of Kk + 1 in Eq. 23 while Ni  L can be reduced, one makes 1 1 ^ and Pk + 1 ¼ K Pk ¼ K  k k + 1 and applies the updating law of Qkþ1 as follows:  1 Pkþ1 ¼ Pk  Pk H Tkþ1 I þ H kþ1 Pk H Tkþ1 H kþ1 Pk ,   ^k þ Pkþ1 H T Y kþ1  Hkþ1 Q ^kþ1 ¼ Q ^k : Q kþ1

(26) (27)

Multistep Prediction Scheme MHC prediction is essentially an online time-series prediction problem which should carry out updating and prediction concurrently. To carry out multistep direct prediction, consider the nonlinear autoregressive with exogenous input (NARX) model as follows: y s ðk þ r Þ

       nr , ¼ f ys ðkÞ, ys k  r , ys k  2r ,   , ys k   xs ðkÞ, xt k  r , ys k  2r ,   , xs k  nr

(28)

step, n + 1 is the maximum lag, i.e., the order of the system. Then, give a timen0 series data set T ¼ fðxs ðiÞ, ys ðiÞÞg1 i¼1 , its initial set T 0 ¼ fðxs ðiÞ, ys ðiÞÞgi¼1 with n0 > (n + 1)r, and choose the root-mean-square error (RMSE) as the performance index. Based on the proposed learning strategy, the multistep direct prediction scheme of time-series is presented as follows: Step 1) Offline initialization: Obtain the initial 0 N 0 ¼ fðxl , yl ÞgNl¼1 , where N0 ¼ n0  (n + 1)r and      xl ¼ xs ðlÞ, xs l þ r ,   , xs l þ nr ,

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yl ¼ ys ðl þ ð1 þ nÞr Þ:

(30)

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(a) Randomly generate parameters c and σ. (b) Calculate H 0 ¼ HðX0 ,c,σÞ by Eq. 16, where X0 ¼ ½x1 , x2 ,  , xN0 T . ^0 using Eq. 19 with Eq. 20 (if N0  L ) or with Eq. 25 (c) Calculate Q (if N0 < L ).   (d) Calculate the initial training performance: RMSE train Y^0 , Y 0 with Y^0 ¼ H 0  ^0 and Y0 ¼ y , y ,   , y T . Q 1

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(e) Predict the next r step’s time-series:   ^0 : y^N0 þr ¼ H xTN0 þr ,c,σ Q   ^0 . (f) Let Y 10 ¼ yN0 þ1 and Y^10 ¼ y^N0 þ1 ¼ H xTN0 þ1 ,c,σ Q (g) Set the training step: k ¼ 0. Step 2) Online sequential prediction: Present the (k + 1)th training data set   N kþ1 ¼ xN0 þkþ1 , yN0 þkþ1 , where xN0 þkþ1 and yN0 þkþ1 are given by Eqs. 29 and 30, respectively.   (a) Calculate H kþ1 ¼ H xTN 0 þkþ1 ,c,σ by Eq. 16.

 (b) Update the prediction performance by Eq. 31: RMSEPred Y^ðkþ1Þk , Y ðkþ1Þk h iT h T iT ¼ Y Tkðk1Þ , yN 0 þkþ1 , Y^ðkþ1Þk ¼ Y^kðk1Þ , y^N0 þkþ1 , and ^k . y^N0 þkþ1 ¼ Hkþ1 Q ^kþ1 using Eq. 23 with Eq. 22 (if Nk + 1  L ) or by Eq. 27 with (c) Update Q Eq. 26 (if Nk + 1 < L ).   (d) Update the training performance by Eq. 31: RMSEtrain Y^kþ1 , Y kþ1 , Y kþ1  T  ^kþ1 , and Xkþ1 ¼ XT , xN þkþ1 T . ¼ Y T , yN þkþ1 , Y^kþ1 ¼ H ðXkþ1 ,c,σÞQ 0 k

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Simulation Studies The applied MHC monitoring data were collected from PRONOSTIA, an experimental platform dedicated to test and validate bearings fault detection, diagnostic, and prognostic approaches (Nectoux et al. 2012). As shown in Fig. 7, the PRONOSTIA is composed of three main parts: a rotating part, a degradation generation part, and a measurement part. The main objective of PRONOSTIA is

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to provide real experimental data that characterize the degradation of ball bearings along their whole operational life. This platform allows accelerating bearing degradation in only few hours. An example of the vibration raw signal gathered during a whole experiment is shown in Fig. 8. The non-trendable and nonperiodical

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statistical properties of this type of signals increase the difficulty of MHC prediction (Porotsky 2012). In this study, two bearing data sets are chosen under the operating conditions: 1,800 rpm speed and 4,000 N load to carry out simulation. For the NARX model in Eq. 28, set n ¼ 1 and r ¼ 1, 2, 5, or 10 and select xs as the standard deviation (STD) of each vibration data set which consists of 2,560 vibration signals and ys as the 5 % trimmed mean of the vibration signal. The prediction procedure is as follows: First, the offline initialization is carried out based on one data set to obtain an intimal FNN model; second, the online prediction is carried out based on another data set to forecast time-series of r steps ahead. To demonstrate the superiority of the proposed EOSL-FNN, the OS-ELM in Liang et al. (2006) and the NARX-NN are selected as the compared methods, where 10 notes are applied to the NARX-NN and 100 notes with λ ¼ 0.001 are applied to the EOSL-FNN and OS-ELM. Two performance indexes, namely, the RMSE and the mean absolute percentage error (MAPE), are defined as follows:   h   i1=2 ^ Y ¼ E Y^  Y 2 RMSE Y, ,

(31)

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MAPE ¼

 1 Xn ^ ð y  y Þ=y t t t  100%: t¼1 n

(32)

The accuracy index is defined as (100 %  MAPE). The initial training and online prediction performance of the proposed EOSLFNN are depicted in Figs. 9, 10, 11, and 12. One observes that high training and predicting accuracy is obtained under small ahead step, and satisfied training and predicting accuracy is still obtained under large ahead step. The performance comparisons of all prediction methods in terms of the time, RMSE, STD, and accuracy are shown in Table 2. Note that the results are obtained from averaging 10 times’ simulation results. One observes that both the EOSL-FNN and the OS-ELM are extremely faster (with small training and predicting time) and more stable (with small STD) than the NARX-NN, the EOSL-FNN performs similar or better (with small RMSE and accuracy) than the NARX-NN and OS-ELM, and the EOSL-FNN performs a little slower (with larger training and predicting time) than the OS-ELM since it contains more adjusting parameters.

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Summary In this chapter, both enhanced online sequential learning-fuzzy neural network (EOSL-FNN) and Weibull modeling are presented for predicting machine health condition and life cycle reliability analysis. The Weibull distribution is among the most popular in the field of life cycle engineering and reliability analysis because it is able to accommodate various types of failure data by manipulation of its parameters. The case study presented in this chapter has successfully shown that Weibull analysis can provide a simple and informative graphical representation of characteristics of life data, especially when the life data sample is small and other statistical tools fail to given useful information. Weibull analysis is able to answer many life cycle engineering problems such as mean life estimation, reliability of products at any operational time and warranty cost estimation, etc. The advantages of Weibull model in life data analysis can be extended to facilitate decision-making processes in many remanufacturing practices such as prediction of the number of cores returned for remanufacturing, estimation of spare parts or remedy resources needed for each failure mode, and so on. Future work

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of this study will include (1) the analysis of incomplete life data for the support of decision-making in product EOL management and remanufacturing processes and (2) comparison and optimization of parameter estimation methods in Weibull model. It is envisaged that the extension of this research will see more robust capabilities of Weibull analysis in life cycle engineering applications. The novel EOSL-FNN models have been developed and successfully applied to predict machine health condition. An online sequential learning strategy based on the ELM is developed to train the FNN. A multistep time-series direct prediction scheme is presented to forecast bearing health condition online. The proposed approach not only keeps all salient features of the ELM, including extremely fast learning speed, good generalization ability, and elimination of tedious parameter design, but also solves the singular and ill-posed problems caused by the situation that the number of training data is smaller than the number of hidden nodes. Simulation studies using real-world data from the accelerated bearing life have demonstrated the effectiveness and superiority of the proposed approach. Further work would focus on bearing long-term condition and remaining useful life prediction using online dynamic FNNs.

r ¼ 10

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Step r¼1

NN type ESL-FNN OS-ELM NARX-NN ESL-FNN OS-ELM NARX-NN ESL-FNN OS-ELM NARX-NN ESL-FNN OS-ELM NARX-NN

Training Time (s) 0.0352 0.0312 1.5506 0.0334 0.0250 1.535 0.0388 0.0324 1.6427 0.0295 0.0264 1.5085 RMSE 0.0832 0.0865 0.1153 0.0987 0.1056 0.1220 0.1054 0.1181 0.1644 0.1250 0.1441 0.1255

Table 2 Performance comparisons of all methods STD 54.010e-4 34.100e-4 25.200e-4 5.6765e-4 6.9462e-4 197.00e-4 4.7654e-4 3.7799e-4 1474.0e-4 9.3490e-4 5.6543e-4 101.00e-4

Accuracy (%) 97.197 95.195 94.631 97.120 94.585 94.363 95.078 94.044 94.326 94.418 93.317 94.285

Prediction Time (s) 2.1145 2.0159 4.1824 2.2387 2.1141 4.2151 2.2416 2.1541 4.1434 2.3015 2.2784 4.0014 RMSE 0.2343 0.2641 0.3345 0.2645 0.2837 0.4744 0.3879 0.4562 0.4683 0.4561 0.5441 0.6344

STD 0.0354 0.0254 0.0191 0.0083 0.0232 0.2707 0.0141 0.0342 0.1815 0.0355 0.0341 0.1684

Accuracy (%) 98.565 97.548 96.744 98.018 97.453 95.970 97.365 95.343 95.832 95.096 93.992 94.630

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Rong HJ, Huang GB, Sundararajan N et al (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man Cybern B Cybern 39(4):1067–1072 Si XS, Wang W, Hu CH et al (2011) Remaining useful life estimation – a review on the statistical data driven approaches. Eur J Oper Res 213(1):1–14 Tian ZG, Zuo MJ (2010) Health condition prediction of gears using a recurrent neural network approach. IEEE Trans Reliab 59(4):700–705 Vachtsevanos GJ, Lewis FL, Roemer M et al (2006) Intelligent fault diagnosis and prognosis for engineering systems. Wiley, Hoboken Wang W (2007) An adaptive predictor for dynamic system forecasting. Mech Syst Signal Proc 21:809–823 Wang W, Golnaraghi MF, Ismail F (2004) Prognosis of machine health condition using neurofuzzy systems. Mech Syst Signal Proc 18(4):813–831 Weibull W (1951) A statistical distribution function of wide applicability. J Appl Mech Trans ASME 18(3):293–297 Wu SQ, Er MJ, Gao Y (2001) A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans Fuzzy Syst 9(4):578–594 Yu M, Wang D, Luo M, Huang L (2011) Prognosis of hybrid systems with multiple incipient faults: augmented global analytical redundancy relations approach. IEEE Trans Syst Man Cybern A 41(3):540–551 Yu M, Wang D, Luo M, Chen Q (2012) Fault detection, isolation and identification for hybrid systems with unknown mode changes and fault patterns. Expert Syst Appl 39(11):9955–9965 Zhao FG, Chen J, Guo L et al (2009) Neuro-fuzzy based condition prediction of bearing health. J Vib Control 15(7):1079–1091

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H. C. Fang, S. K. Ong, and A. Y. C. Nee

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensor-Embedded Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Product Life-Cycle Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Embedding Smart Sensors in Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensor Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensor Placement and Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensor Data Representation, Fusion, and Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensor Data Representation and Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Fusion and Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework Based on Sensor Data for Product Remanufacturing Decision-Making . . . . . . . . Collection and Presorting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Disassembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inspection and Grading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Repair and Reconditioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reassembly and Reliability Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design Feedback to Manufacturers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Challenges and Issues on the Use of Embedded Smart Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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H.C. Fang (*) • S.K. Ong • A.Y.C. Nee Mechanical Engineering Department, Faculty of Engineering, National University of Singapore, Singapore e-mail: [email protected]; [email protected]; [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_85

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Abstract

Unlike in traditional manufacturing, remanufacturers face uncertainty in quality, quantity, and frequency of returned products, making the remanufacturing processes less predictable and remanufacturing decision-making more difficult. The research on the use of embedded smart sensors in products to facilitate remanufacturing through monitoring and registering information associated with the products, e.g., their state-of-health, remaining service life, remanufacturing history, etc., has received increasingly high level of interests. This chapter first introduces the background of sensor-embedded products, including the essential parts of a typical smart sensor and product information model. Next, the current practices toward the development of embedded smart sensors in products are reviewed in detail in two aspects, namely, (1) embedding smart sensors in products and (2) representing and interpreting sensor data. A conceptual framework is presented to illustrate how sensor data gathered using smart sensors can be managed to facilitate product remanufacturing decision-making. Lastly, future research trends are given to address the challenges efficiently in using embedded smart sensors for facilitating remanufacturing processes and planning.

Introduction Product remanufacturing is regarded as one of the most beneficial product end-oflife (EoL) alternatives for manufacturers to meet increasingly more stringent environmental regulations and government legislations for sustainable product development. It presents an environmentally more attractive option than material recycling as it retains the intrinsic values of a product, e.g., material and energy consumed during manufacturing. It differs from component reuse as it aims to return a used product to a condition with equivalent or even better performance than a new product. Uncertainty in the quality, quantity, and frequency of product returns has been identified as one of the prevalent issues faced by the remanufacturers, and it has significant impact on the decision-making for product remanufacturing. Various research studies have addressed this issue, e.g., the effect of quality categorization of product returns (Aras et al. 2004; Zikopoulos and Tagaras 2007), customer incentives to promote core returns (Liang et al. 2009), optimal core acquisition quantities under quality uncertainty (Galbreth and Blackburn 2010), etc. Apart from this perspective, it has been identified that the presence of uncertainty in product returns is mainly due to a lack of information associated with the usage of these products (Klausner et al. 1998; Parlikad and McFarlane 2007; Hribernik et al. 2011). Therefore, embedding smart sensors to gather useful information of a product has received considerable research interest, presenting a desirable solution such that the condition of the returns is traceable and predictable. Such information could be product identity, constituent components, remaining service life, remanufacturing history, etc. At the time of a product return, all the

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information can be made available to the remanufacturers or recyclers for sound product EoL decision-making. There are two main areas of research on the use of smart sensors in facilitating EoL product recovery. The first area addresses issues in embedding smart sensors in products, and the second area investigates sensor data interpretation and management and the use of such data in EoL decision-making. In this chapter, section “Sensor-Embedded Products” briefly introduces sensor-embedded products, including the essential parts constituting a typical smart sensor, product information model essential for data management, and examples of smart sensors that are commercially available. Sections “Embedding Smart Sensors in Products” and “Sensor Data Representation, Fusion and Interpretation” review the research on the development of embedded smart sensors in products from two aspects, namely, embedding smart sensors in products and sensor data representation and interpretation. In section “Framework Based on Sensor Data for Product Remanufacturing Decision-Making,” a conceptual framework is presented to illustrate the use of sensor data gathered using smart sensors to facilitate product remanufacturing decision-making. Section “Challenges and Issues on the Use of Embedded Smart Sensors” summarizes the challenges in using embedded smart sensors to facilitate remanufacturing processes and planning, followed by section “Summary” the future research trends.

Sensor-Embedded Products A sensor-embedded product contains sensors that monitor the product during its life cycle and collect the product life-cycle data. Such data have significant positive impact on closed-loop product life-cycle management, which benefits all the stakeholders in the entire product life cycle. For example, original equipment manufacturers (OEMs) can receive the design feedback to improve the current design; the maintenance teams can schedule for the potential maintenance tasks and prepare the spare parts accordingly; independent remanufacturers (IRs) can access the conditions of used equipment for effective remanufacturing processes planning; and end users or customers could be notified of potential equipment failure to prevent the production downtime.

Product Life-Cycle Information Information required for effective EOL decision-making can be generally classified into internal and external information (Klausner et al. 1998; Harrison 2003; Parlikad and McFarlane 2007). External information includes information such as the market trends, legislative policies, corporate policies, etc., which are not directly related to a product but have considerable impact on the choice of the recovery options. Internal information can be categorized into static information and time-stamped historical information. Static information is associated with the

3268 Fig. 1 Product data in a product life cycle

H.C. Fang et al. Product Life-cycle

Information Requirements

Production • Design • Manufacturing

Product ID Design info Production info

Usage • Distribution • Use

Disassembly info Reliability info Disposal info

EOL

Usage/age Maintenance history

Collection

Remanufacturing

Remaining useful life of components Part replacement or repair history Remanufacturing history

Recycle/Disposal

intrinsic characteristics of a product, e.g., bill of material (BOM), design information, production processes, disassembly sequence, designed life-span, etc. Such information is normally determined at the product design and manufacturing stages, but subject to changes during the remanufacturing stage when modifications may have been made to the product design. Time-stamped historical information has dynamic characteristics associated with the use of a product and refers to product conditions that can be represented by a sequence of data with respect to time, e.g., the remaining life-span of a product/component in its current use cycle, cumulative service time, number of times a product has been remanufactured or reused, part repair/replacement history, etc. In particular, the remaining service life of a product can be deduced from data obtained through embedding sensors to monitor product use. The part repair/replacement history, which is normally made available during maintenance or servicing, can help identify the typical failures of a product due to factors such as design flaws, patterns of usage, etc. Figure 1 presents a summary of product information available during a product’s life cycle.

Smart Sensors The development of microelectromechanical systems (MEMS) technology has enabled the manufacturing of smart sensors for various applications in smaller sizes and lower cost. A generic smart sensor for product life-cycle data acquisition contains a set of essential components (Zeid et al. 2004; Vadde et al. 2008), namely, (a) a sensing element to register the environmental parameters, e.g., temperature,

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Table 1 Summary of smart sensors for product EoL recovery Sensor Electronic data log (EDL) – prototype

References Klausner et al. (1998)

RFID tags – prototype

Parlikad and McFarlane (2007), (2010); Ilgin and Gupta (2010); Ilgin et al. (2011); Ferrer et al. (2011); Ondemir and Gupta (2013)

Life-cycle unit (LCU) – prototype

Seliger et al. (2003)

Watchdog agent – commercial

Lee et al. (2006)

Autonomous monitoring device – commercial

Cheng et al. (2010)

Description/application Self-contain data acquisition Usage pattern interpretation Reuse potential of components Real-time localization of returned product/ component Component identification Component classification Wireless data transmission Self-contain data acquisition Actuators supporting intelligent disassembly Maintenance scheduling Comprehensive data processing capability E-maintenance RFID tag as sensor ID Wireless data transmission Electronics health monitoring and management

pressure, etc., and convert them into suitable signal forms; (b) a microprocessor to process the received signals; (c) a memory to store the received sensor data and output from the microprocessor; (d) a data transmitter to transmit the data from the smart sensor to the communication network; (e) a power supply from product power source or a separate battery; and (f) a sensor identification (ID). There are cases where the sensing elements are not physically integrated with the data processing unit, e.g., an engine’s electric control unit (ECU) receives data from various engine sensors to monitor and regulate the engine’s performance (Fleming 2001, 2008). Existing research has explored the use of embedded sensors to improve the efficiency of product life-cycle management. Table 1 summarized some examples of embedded smart sensors. An example is a data-recording device known as electronic data log (EDL) (Klausner et al. 1998). It measures and stores the parameters that indicate the degradation of the motor of a consumer product during the use stage. The data

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obtained through EDL can provide valuable insights into the usage patterns of the products. Simon et al. (2001) reported the use of self-contained data acquisition units in washing machines for monitoring the usage parameters and error conditions, which can be accessed either during servicing or at EoL stage. Seliger et al. (2003) developed a microsystem prototype called the life-cycle unit (LCU) for product and component monitoring. The LCU can acquire and analyze the data of the status of a product or a component and inform both the users and servicing personnel of the maintenance schedules in advance. Watchdog AgentTM (Djurdjanovic et al. 2003; Lee et al. 2006) is a commercially available smart sensing system known for its applications in predictive condition-based maintenance, where the performance degradation in a process, product, or component is assessed through multiple sensor-based diagnostic and prognostic techniques. Pecht (2008) has conducted a comprehensive survey on the commercial sensor systems used in prognostics and health management for electronic products and systems. Radio-frequency identification (RFID) tags have been used to replace barcodes as product/component IDs to provide easy access to retrieving, updating, and managing product information in an entire life cycle (Kiritsis et al. 2003; Parlikad and McFarlane 2007). Kulkarni et al. (2007) investigated the practical and economic impact of using RFID in alleviating the quality uncertainty associated with the remanufacturing processes. Ferrer et al. (2011) reported an application of RFIDs where active RFID can be used for easy identification and localization of components within a remanufacturing facility and passive RFID can be permanently tagged onto components of remanufacturable products at the beginning of their service life. The limitation of the RFID is the storage capacity as it cannot store the large amount of data that has been gathered over a period of time. An alternative is to use a RFID tag as an ID to identify a specific smart sensor with embedded memory chip for data storage. Cheng et al. (2010) introduced an autonomous monitoring device for prognostics including RFID tags for sensor identification and wireless data transmission and sealed processor and memory chip for signal processing and storage.

Embedding Smart Sensors in Products The conditions of the returned products would affect the viability and profitability of remanufacturing. Sensors can be installed onto a product for condition monitoring and data collection such that these data can be analyzed for EoL decisionmaking. Embedding smart sensors in products requires considerable domain expertise. The general principle is that the performance of the product should not be compromised in terms of functionality and reliability when sensors are embedded in the product. The two primary concerns of embedding smart sensors are (1) sensor selection and (2) sensor placement and installation. Table 2 summarized some recent research work on these two directions.

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Sensor Selection A selection of sensors to be installed in a product is a prerequisite for product lifecycle data monitoring and collection. Some essential considerations for selecting the sensors are the parameters to be monitored, the electrical and physical characteristics of the sensors, reliability and performance, data transmission options, protective measures on the sensors, and cost (Zeid et al. 2004; Cheng et al. 2008). It is desirable to have a smart sensor system with multiple sensing abilities, miniature size and light weight, low power consumption, considerable long Table 2 Research work on embedding smart sensors for condition monitoring Area of research Parameter identification/ sensor selection

Authors (year) Al-Habaibeh and Gindy (2000)

Mushini and Simon (2005)

Santi et al. (2005)

Cheng et al. (2008); Kumar et al. (2010) Borguet and Leonard (2008)

Wang et al. (2012)

Research issues/ considerations Number of sensors The cost and time for the design of monitoring system Parameter uncertainty Cost of the selected sensors Diagnostics performance Risk reduction potential of selected sensor set General guidelines

Sensor noise (sensitivity) Sensor redundancy/ orthogonality Multiple monitoring points Dimension of data set

Methodologies Automated sensory and signal processing selection Taguchi’s orthogonal arrays Pattern recognition

Applications/ validation Milling cutter

Optimization algorithm: exhaustive search, probabilistic search, genetic algorithm

Aircraft gas turbine engines

Failure modes and effects analysis Genetic algorithm

Boost stage rocket engine

Failure modes and effects analysis

Electronics products

Information theory

Turbofan engine

Fisher information matrix Naı¨ve brute force technique Principal component analysis

Wind turbine

Multivariant analysis (continued)

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Table 2 (continued) Area of research Sensor placement/ installation

Authors (year) Sheng et al. (2006)

Zhang and Vachtsevanos (2007)

Chen et al. (2012)

Pourali and Mosleh (2012)

Al-Habaibeh et al. (2005); Alkhadafe et al. (2012)

Research issues/ considerations Data acquisition quality Sensor coverage Sensor reliability Fault detectability/ isolation Cost for installation Sensor sensitivity Sensor coverage Location of sensors Logical or functional placement Sensor stability Sensor reliability

Methodologies Finite element analysis

Applications/ validation Rolling bearings

Effective-independence method Failure modes and effects analysis Fault detectability metric

Five-tank system

Graph-based model for fault propagation Genetic algorithm Particle swarm optimization Fault propagation graph Bayesian belief networks Logical diagram representation of system Initial optimization procedure Spectral kurtosis analysis Fourier transformation and wavelet

Gearbox (gears and bearings)

Simulation

Gearbox; milling cutter

range, large onboard memory capacity, fast onboard data processing and high rate data transmission, high reliability, and low cost. Pecht (2008) reviewed the common sensors and their sensing principles as well as the required attributes of sensor systems for health monitoring of electronics products. Fleming (2001, 2008) conducted a comprehensive survey on the most significant sensors used in present-day automotive applications. Figure 2 outlines a general procedure for the selection of suitable sensors. The design of the components defines their functionality and physical properties, which will help in the identification of the potential causes of components failures during their use stage. Subsequently, suitable parameters to be monitored can be determined accordingly for the identification of these potential failures. Finally, the type, operating range, size, and life-span of the sensors can be determined based on the parameters to be monitored and the expected life-span of the components.

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Fig. 2 A general procedure for sensor selection

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Components to be reused Identification of component types

Components to be replaced High-frequency service items

Identification of failure causes and mechanisms Environmental parameters Determination of data to be monitored

Physical condition parameters Sensing principle

Selection of sensors

Operating range Size Life-span Reliability Cost

Component Classification Not all components in a product have equal life-span. Some components can last considerably longer than other components. For example, in an automotive engine, components with longer life-span, e.g., engine block, crankshaft, and connecting rods, which normally have significant residual values that can be retained, are usually reused after proper reconditioning. Critical components with shorter lifespan, e.g., engine pistons, will always be replaced with new parts (Smith and Keoleian 2004). In addition, not all components in a product have equal intrinsic value. For example, washer and bearings have lower intrinsic values. However, these components isolate wear and absorb vibrations from the more expensive shaft or housing, which are considered as “ideal” components from a remanufacturing viewpoint (Kalyan-Seshu and Bras 1997). For components which have longer lifespan and relatively high intrinsic value, it is important to track the number of times they have been reused and update their cumulative service history. For components with relatively shorter life-span and lower intrinsic value, they may need to be monitored continuously to determine whether they have reached a stage for replacement or remanufacturing. The components of a product can be classified accordingly based on their useful life-span and intrinsic value. This classification system leads to appropriate EoL options for the various classes in this system, which in turn facilitates the identification of suitable life-cycle parameters to be monitored and the sensors for monitoring these parameters. Parameters Identification The parameters to be monitored should be able to demonstrate the mechanisms of an anticipated failure mode reliably or indicate the effects of the failure. These parameters can be classified into two categories, namely, ambient conditions and operating status. Ambient conditions, such as temperature, humidity, pressure, load, etc.,

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are parameters that have direct or indirect impact on the resulting condition. The operating status, e.g., vibration, speed, etc., indicate directly the current state-ofhealth of the product. The relationship between the parameters and the failures can be established through various methods, e.g., the Taguchi’s method (Al-Habaibeh and Gindy 2000), failure modes and effects analysis (FMEA) related methods (Santi et al. 2005; Vichare and Pecht 2006; Kumar et al. 2010), etc. Some examples of using the FMEA approach to identify the parameters for assessing the state-of-health of electronic products are hard drives (Vichare and Pecht 2006) and computer server systems (Kumar et al. 2010). Once the parameters for condition monitoring have been determined, a set of sensors can be defined accordingly.

Optimal Sensor Selection Sensor selection for product condition monitoring is an optimization problem, particularly for complex systems or processes in which multiple monitoring points are present and a large number of sensors have to be installed (Subrahmanya et al. 2008; Joshi and Boyd 2009). Many research studies have been reported on optimal sensor selection for higher diagnostic performance with justifiable cost. Al-Habaibeh and Gindy (2000) proposed an automated sensor selection approach for milling cutter monitoring in which the number and types of sensors required can be optimized without compromising the performance in the identification of the cutter faults. Mushini and Simon (2005) compared three search and optimization methods, namely, exhaustive search, probabilistic search, and genetic algorithm (GA), to find an optimal set of sensors that can offer the best measurement set with minimized parameter uncertainty for aircraft gas turbine engines. Santi et al. (2005) developed a sensor selection approach for robust health diagnostics. This approach adopts FMEA to determine candidate sensor sets by identifying system critical faults and fault signatures, and applies GA to search for a suitable set of sensors for optimal heath diagnostics based on criteria such as speed of detection, probability of correct fault source isolation, and overall risk reduction potential. Wang et al. (2012) introduced the principal component analysis (PCA) method for sensor selection optimization addressing the presence of multiple monitoring points. The PCA method was utilized to identify an essential number of sensors sufficient for wind turbine condition assessment.

Sensor Placement and Installation The placement and installation of smart sensors is an issue that needs to be addressed for condition monitoring. Embedding a smart sensor would result in modification to the current product design and may weaken its physical strength and structural stability. Many research studies deal with structural health monitoring while addressing optimal sensor placement. Few recent studies listed in Table 1 have been reported on sensor localization optimization by addressing other sensor attributes that have considerable impact on the performance in fault isolation and condition monitoring, e.g., sensing reliability (Al-Habaibeh et al. 2005; Alkhadafe

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et al. 2012), installation cost (Zhang and Vachtsevanos 2007), sensing sensitivity (or signal-to-noise ratio) and sensing coverage (Sheng et al. 2006; Chen et al. 2012), etc. A graph-based model can be used to depict the cause–effect relationship for fault propagation (Zhang and Vachtsevanos 2007). Finite element analysis can be used to model the changes in the mechanical and structural properties of the components due to smart sensor installation (Sheng et al. 2006; Chen et al. 2012). The optimization methods include GA (Chen et al. 2012), Bayesian belief networks (Pourali and Mosleh 2012), etc. Sensors are usually made of different materials from that of the components to be monitored. Currently, no study has addressed sensors embedding methods from the disassembly point of view. It would be desirable for sensors to have plug-anduse mechanism, allowing for fast and safe disassembly in the case of product EoL recovery or faulty sensor replacement.

Sensor Data Representation, Fusion, and Interpretation With the embedded smart sensors, product usage data can be monitored, collected, and sent to the data server located at the remanufacturing center. Similarly, when a used product is returned to a remanufacturing plant, the associated product information should be made available to the remanufacturers. However, to make use of such data in facilitating product EoL decision-making, two primary concerns need to be addressed, namely, (2) sensor data representation and (2) data fusion and interpretation.

Sensor Data Representation and Management An efficient information system should be available so that relevant data can be retrieved in real time and authorized parties can access data of interest safely and conveniently. Different data storage methods can be used for different types of data to achieve easy data retrieval and update (Kiritsis et al. 2003). XML tools can be used to access and manipulate the static attribute data as the existing industry-standard XML schema allows unambiguous access to particular properties of an object and can be applied across all industry sectors. Relational databases can be used to handle historical data (Harrison 2003; Parlikad and McFarlane 2007). Yang et al. (2009) developed an XML-based tree-view structure for product life-cycle data acquisition and provided an information engine for online data analysis and data retrieval. The unintuitive representation of product information to the remanufacturers is a critical issue. For example, disassembly during remanufacturing is often labor intensive with low level of factory automation. Therefore, to improve the efficiency of the disassembly process, intuitive representations of such information can be provided to the disassembly technicians to guide the disassembly operations. Augmented reality-assisted visualization tools have been explored, where virtual

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Voltage, Current, Torque, Vibration, Speed, Number of starts and stops, and Load

Reliability model Sensor fusion techniques Component with shorter life-span

Current use cycle Remaining useful life

Replacement

Operating status Sensor data

Processing/Fusion

Diagnosis/ Prognosis

Component with longer life-span

Cumulative service life

Ambient condition Temperature, Pressure, Humidity

Future use cycle

Remaining number of times that can be reused

Fig. 3 Fusion and interpretation of sensor data

cues in the forms of texts, images, CAD drawings, video clips, etc., can be provided to assist the manual assembly tasks (Yuan et al. 2008; Zhang et al. 2011). This concept can be applied in disassembly, where the disassembly sequence can be organized in a disassembly tree structure.

Data Fusion and Interpretation A product can have multiple sensors embedded. The sensor data are often in different units, requiring robust multisensor fusion for assessing the actual condition of the product, i.e., fault diagnosis and failure prognosis. The difference between the two lies in that the former involves identifying and quantifying the damage that has occurred, while the latter is concerned with trying to predict the damage that is yet to occur (Sikorska et al. 2011). The outcome of fault diagnosis and failure prognosis would serve as an anchor point for any decision made for product EoL recovery. Figure 3 illustrates a generic procedure for sensor data fusion and interpretation.

Sensor Data Fusion Multisensor fusion has been an intensive research area, and many previous studies and reports have focused on condition monitoring in machine tools, manufacturing processes, etc. (Du et al. 1995a, b). It is reported that data fusion structures can be generalized into three types, namely, raw signal-level, feature-level, and decisionlevel fusion (Niu et al. 2010). • Raw signal-level fusion: Raw data from all sensors are collected and combined directly and a feature/signature is extracted from the data set. The data acquisition and fusion are performed in a centralized manner, provided that the sensors are only responsible for sensing and have no processing capability (Spencer et al. 2004).

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Fusion at this level may produce good results since the data could be redundant and has not been processed/compressed; however, it may fail to generate relevant results due to the presence of noises and other imperfections in the measurements. One limitation of signal-level fusion is that data should be of the same or similar physical quantities, e.g., vibration signal cannot be fused with temperature signal. In addition, the data should be calibrated under the same measuring unit. This would restrict the applications of signal-level fusion in complex real environments which can have many physical quantities (Niu et al. 2010). • Feature-level fusion: Features from the same sensor type are first extracted, processed, and represented by feature vectors. These feature vectors are normalized and combined into a compound feature vector, which forms a basic representation of the data set. At each instance, the compound feature vector is passed to a pattern classification model for subsequent decision-making. Feature-level fusion allows individual sensor to perform simple computations, e.g., feature extraction, which reduces the computational burden of the central processor for data storage and processing. • Decision-level fusion: A decision would first be made based on the single-source data obtained from each sensor with necessary data processing techniques (e.g., feature extraction and pattern recognition). A final decision can be generated by fusing individual decision made with respect to each sensor using decision-level fusion techniques, e.g., Bayesian method, Dempster–Shafer theory, etc. Fusion at this level allows the sensors to have self-contained data processing capability, representing the most distributed fusion architecture, in which ubiquitous computing techniques can be well suited. There is no universal approach capable of solving all data fusion problems. Hence, the data fusion algorithms should be selected according to the requirements and issues encountered in the specific applications. Jardine et al. (2006) reviewed the signal processing techniques for waveform data in different domains, e.g., time domain, frequency domain, and time–frequency domain. Niu et al. (2010) listed a number of signatures of signals that can be used for pattern recognition with respect to these data domains. Khaleghi et al. (2013) conducted an analytical survey of recent developments in the data fusion domain. Common but challenging issues pertaining to data fusion, such as imperfect data due to the presence of noise and measurement uncertainty, spurious data from faulty sensor, conflicting data from different sensor types, etc., were studied. Various data fusion algorithms have been reviewed, e.g., probabilistic-based fusion, evidential belief reasoning, Dempster–Shafer evidence theory, and hybrid fusion techniques, and comparisons have been made to analyze their suitability in addressing the various data fusion issues.

Fault Diagnosis and Failure Prognosis Diagnosis is to detect the failure that has occurred in a component (or subsystem) and isolate and identify the root of the failure, based on the data collected by the embedded sensors. Prognosis is to estimate the time at which a component will fail to operate at its stated specifications based on its current condition as well as the

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future load and environmental exposure, i.e., the prediction of the remaining useful life (RUL) of the component. RUL is a commonly used parameter to assess the reliability (or reusability) of a used component. The mean lifetime of a product (TPM) is governed by the critical component with the shortest life-span. Commonly used reliability indices are mean-time-to-failure (MTTF), mean-time-to-repair (MTTR), etc., which are normally provided by the product manufacturers. However, experimental conditions used to determine these indices may not reflect the actual working conditions. Therefore, these indices may need to be rated to consider the actual working conditions. The lifetime of a product/component can be defined in other forms, e.g., the mileage of a car, the amount of petrol consumed for an automotive engine (Smith and Keoleian 2004), the number of starts and stops for an electric motor (Kara et al. 2008), etc. At the component level, it can be defined by the number of reusable cycles, e.g., the camera core for single-use cameras (Geyer et al. 2007). The RUL can be defined in similar forms accordingly. Figure 4 outlines a general procedure for RUL estimation of a component based on the monitored condition. If there is no fault signal, the RUL can be approximated based on reliability models (e.g., MTTF or MTTR) empirically. If a fault signal has been detected, the causes will first be isolated and identified through fault diagnosis, and the failure prognosis process will be triggered at the same time. There has been a substantial amount of research work dedicated to failure diagnostics based on data fusion (Engel et al. 2000; Schwabacher 2005; Roemer et al. 2006; Schwabacher and Goebel 2007; Pecht 2008; Saxena et al. 2008; Heng et al. 2009; Si et al. 2011; Sikorska et al. 2011). In general, prognostic approaches can be divided into three categories, namely, statistical data-driven, physics-offailure (PoF)-based, and hybrid approaches.

Condition monitoring and data fusion

A fault is detected?

No

Empirical reliability model (MTTF/MTTR)

Remaining useful life

Yes Fault isolation/ identification

Likely current/future failure modes

Remaining useful life estimation Fault diagnosis

Failure modes and effects models Confidence interval estimation

Failure prognosis

Fig. 4 A generic diagnosis–prognosis process for RUL estimation

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• Statistical data-driven approaches: This set of approaches estimate the RUL of a component in a probabilistic manner based on the past observed data and statistical models without relying on any physics or engineering principles. The observed data includes past recorded failure data and condition monitoring data, e.g., degradation signals, operational, performance, environmental data, etc. The advantage of statistical data-driven methods is that the mathematical properties of the estimated RUL can be analyzed. However, the effectiveness of these methods is restricted by the availability of the observed data, particularly for newly commissioned systems that lack such observed data. Si et al. (2011) reviewed the data-driven approaches for RUL estimation, including regression-based, Brownian motion-based, and Markovian-based models. Goebel et al. (2008) compared three regression techniques, namely, neural networks, relevance vector machine, and Gaussian process regression; they showed that the estimation outcome significantly relies on the quality and the availability of the observed data. The same research group has developed metrics for evaluating various prognostic techniques and the suitability in various prognostic applications (Saxena et al. 2008, 2009). • Physics-of-failure-based approaches: Physics-of-failure (PoF)-based RUL estimation involves building the mathematical models of the system physics, the knowledge of the product’s life-cycle loads, and relating them with the potential failure modes, failure mechanisms, and failure sites (Pecht 2008). The PoF-based approaches require less data than that of data-driven-based approaches; however, their performance highly depends on the accuracy of the established physics models. In addition, these models normally are defect specific and often too stochastic and complex to model. This makes PoF-based RUL estimation less practical in cases when destructive intervention is required to identify the failure modes, mechanisms, and effects. Heng et al. (2009) reviewed some PoF-based prognostics approaches, e.g., crack-growth modeling, fatigue spall initialization and progression modeling, etc., and the requirements to apply these models. • Hybrid approaches: A hybrid approach fuses the PoF-based prognostics models with the condition monitoring data to produce a more reliable and robust RUL estimation. It aims to utilize the strength of both types of approaches and draw a more comprehensive picture by integrating the PoF model, empirical failure data, and past and current operational and environmental data being monitored. Goebel et al. (2006) adopted Dempster–Shafer regression techniques to fuse the independent RUL estimates to achieve a more accurate and robust RUL prediction.

Framework Based on Sensor Data for Product Remanufacturing Decision-Making Existing research studies have started to investigate the use of product information from embedded smart sensors in product EoL recovery decision-making. Table 3 summarized some recent developments in this direction. For instance, RFID technologies can be implemented to address disassembly-related issues,

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Table 3 Recent development in product EoL decision-making with aid of embedded sensors Theme EoL recovery decision

References Klausner et al. (1998); Simon et al. (2001) Parlikad and McFarlane (2007)

Parlikad and McFarlane (2010)

Ondemir and Gupta (2013)

Disassembly

Ilgin and Gupta (2010); Ilgin et al. (2011)

Maintenance

Lee et al. (2006)

Remanufacturing jobshop management

Ferrer et al. (2011)

Description/issues addressed Usage pattern monitoring Component reuse and cost–benefit analysis Product information categorization Modeling the impact of product info on EoL decisions EoL vehicles (ELV) recovery Sensor network for product life-cycle data sharing Probabilistic model for cost–benefit analysis Recovery decisions without disassembly/inspection EoL decision subject to disassembly-to-order and repair-to-order Cost–benefit analysis EoL decision subject to disassembly-to-order Smart prognostics algorithms “Peer-to-peer” communication Condition-based maintenance Real-time localization of returned product/component Discrete-event simulation

Application Electric motors

Photocopier

Automobile clutch

Air-conditioner

Washing machine Roller bearings, industrial network Electronics maintenance facility

e.g., disassembly-to-order, in remanufacturing jobshop operations (Ferrer et al. 2011; Ondemir and Gupta 2013); EDL enables the decision-makers to access the usage patterns of the returned products or components (Klausner et al. 1998). Figure 5 presents a conceptual framework on the use of product information to assist remanufacturers during remanufacturing operations, i.e., products collection, disassembly, cleaning, sorting, grading, reconditioning, machining, component replacement, and reassembly. If the time-to-failure data of a component is available, the mean lifetime of a component (TCM) can be estimated by using the Weibull analysis (Kara 2010) given in Eq. 1, where η is defined as the life at which 63.2 % of units will fail and β identifies the mode of failure, i.e., β < 1 means infant mortality, β ¼ 1 indicates random failure, and β > 1 describes wear-out failure. β can be obtained from the Weibull distribution function given by Eq. 2, where F(t) represents the fraction of components failing and t is the time-to-failure (Kara 2010).

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Used products collection Product ID Design info Production info

Remanufacturing facility

Pre-sorting

Recycle/Disposal

Disassembly

Disassembly info Disposal info Reliability info Physical condition

Identification of critical component

Cleaning Inspection and grading

Classification of parts

Remaining useful life of components Maintenance history Part replacement history

Reconditioning

Reuse New parts

Remanufacturing history

Reassembly

Corporation policy and criteria

Reliability test

Product Information

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Layout, Equipment

Product reverse logistics solution

Transportation, Storage

Remanufacturing facility planning

Remanufacturing Processes

Fig. 5 Conceptual framework on the use of sensor data to facilitate product remanufacturing decision-making


 1þβ T CM ¼ η  Γ β "
 # t β FðtÞ ¼ 1  exp η

(1)

(2)

The actual usage life (TO) of a component of a returned product can be estimated based on the data obtained during the use phase of the product. Some frequently used estimation techniques (Kara et al. 2005; Kara 2010) are the regression analysis, Kriging techniques, artificial neural networks, etc. Once the actual usage life has been determined, the remaining useful life of a component can be obtained using Eq. 3 (Kara 2010). It can also be obtained based on the number of remanufacturable (reusable) cycles Nreman, given in Eq. 4: T RUL ¼ T CM  T O   T RUL N reman ¼ Floor T PM

(3) (4)

Assuming that the historical data of a large number of returned products is available, (1) the mean lifetime of a critical component (TPM) and (2) the mean lifetime of the core (TCM) can be derived based on the analysis in Eqs. 1 and 2.

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Based on the life-cycle data of a returned product and its components, the following information can be determined and used to assist in decisions on recovery, inspection, assembly, disassembly, etc. They are (1) the number of times a component has been remanufactured (Nreman) or directly reused (Nreuse), (2) the maximum number of times a component can be remanufactured (Nmreman) or directly reused (Nmreuse), and (3) the remaining useful life of a component (TRUL).

Collection and Presorting Both OEMs and independent remanufacturers have their own criteria for accepting cores and the corresponding core refund guidelines to facilitate used products acquisition. OEMs normally exhibit a more generous acceptance threshold, while independent remanufacturers may have more stringent core acceptance criteria, e. g., they may not accept a used product if the product condition is beyond remanufacturing. The end users will receive alerts to return their products when these products are reaching their EoL stage whenever the lifetime data of the products is uploaded to the server. The remanufacturers will be able to determine the quality of the incoming returns based on the sensor data. A general rule for core acceptance is given by Eq. 5, where Dcol ¼ 1 means the acceptance of the core, while Dcol ¼ 0 refers to rejection.  Dcol ¼

1, if ðT RUL > T PM Þ andðN mreman > N reman Þ 0, otherwise

(5)

Once the cores have been accepted, the product type, the technology used in the product, and the production date will be identified to sort these cores for storage and other subsequent operations.

Disassembly Actual disassembly of a return is not necessarily an exact reversal of its assembly sequence due to various factors, e.g., irreversible welded joints may have been used, degradation of components and damages to components during use, missing components, product upgrade during maintenance and remanufacturing tasks, etc. Therefore, the disassembly sequence should consider the changes that have occurred to the product during its entire lifetime. The index for ease of disassembly of a product is determined by the joining methods used, e.g., mechanical fastening, welding, gluing, riveting, etc., and the physical damages to the product, e.g., corrosion, deformation, etc. In addition, the choice of product recovery plays an important role in disassembly. Some components, e.g., electric motors, can be reused directly without the need for further disassembly (Kaehernick and Kara 2008). The cores, e.g., the crankshaft and engine block, require proper reconditioning so that they can be reused in a remanufactured product, such that

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they have to be disassembled completely. The disassembly level of a component based on different recovery options can be determined using Eq. 6, in which Ddis ¼ 0 means no further disassembly is needed, Ddis ¼ 1 refers to complete disassembly, and Ddis ¼ 2 refers to the case where the component should be disassembled properly for disposal or material recycling. 8 < 0, if ðT RUL > T PM Þ and ðN mreuse > N reuse Þ Ddis ¼ 1, else if ðT RUL > T PM Þ and ðN mreman > N reman Þ (6) : 2, otherwise

Cleaning Cleaning is one of the most environmentally unfriendly processes in remanufacturing. The contamination level of a component and the cleanliness to be achieved affect the cleaning technology and cleaning equipment that can be used (Schweinstig 2010). It is important to know the compatibility between the materials used in the components and the cleaning agents. Hence, product information needed for cleaning include (a) the bill of material of a component, (b) design features of the components, (c) required cleanliness level, and (d) the company policies on the disposal of wastes generated during cleaning. The static information can be retrieved as they are available during the design stage. The contamination level of a component can be graded based on technician’s expertise through which suitable cleaning methods can be adopted.

Inspection and Grading Inspection is required to measure and detect the current condition of a component. For components with significant physical defects that cannot be recovered, they can be sorted for material recycling. Other information related to material fatigue, functional degradation, etc., which cannot be detected through simple visual inspection, can be monitored using embedded smarted sensors and assessed to determine the RUL of the components. Table 4 shows a simple method for components grading based on the physical defects, the RUL of the components, and the number of times they have been reused or remanufactured. In general, the components can be graded into three categories (Steinhilper 1998), namely, Table 4 Grading of components based on conditions Conditions of components Significant identifiable physical defects No obvious physical defects TRUL >¼ TPM TRUL < TPM

Tmruse > Truse Tmreman > Treman

Grading decision Not remanufacturable Directly reusable Reusable after repair Not remanufacturable

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(a) directly reusable, (b) reusable after proper repair or reconditioning, and (c) cannot be repaired or reconditioned. Based on these classifications, companies may apply different sorting criteria. The maintenance history can help identify components or parts that require frequent maintenance. In addition, the cores can be further classified based on the number of times they have been directly reused or remanufactured. However, there exists a trade-off between the accuracy of the grading and the economic profitability of remanufacturing (Zikopoulos and Tagaras 2008).

Repair and Reconditioning To restore a used part to a like-new condition, the specifications of the corresponding new part, e.g., geometric features, material, surface property, reliability, etc., should be known a priori by the remanufacturers. The current condition and the failure mode of the used parts affect the reconditioning strategies. For example, a damaged or worn part can be restored by removing the damaged area or adding new material to the worn area, depending on the severity of the damage or wear (Bras 2008). The remanufacturing history as well as the performance and reliability of the previous remanufactured versions of a component will provide feedback on the effectiveness of the reconditioning methods.

Reassembly and Reliability Test The reassembly sequence may be the same as the original new product if there is no significant upgrade during remanufacturing. The original standards and reliability of the product should be known in advance. OEMs can have access to these data, and the independent remanufacturers will be able to access these data if they are within the closed-loop supply chain, where such information may be readily available. Otherwise, detailed inspection and significant expertise will be needed to extract such information from a new product.

Design Feedback to Manufacturers Through collecting used products, manufacturers can obtain feedback on the reliability and durability of their products (Bras and McIntosh 1999). The maintenance record and remanufacturing history can help identify components and parts that require frequent maintenance under certain working and environmental conditions. First, these information can be accumulated and used by the remanufacturers during subsequent inspecting and sorting of the used components and parts. Second, the information can assist the remanufacturers identify critical components that have the most significant impact on the useful life of a product. Through analyzing the failure causes, the failure rates, and their relations with the working conditions,

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remanufacturers can provide feedback to the design teams on the weaknesses of the current design. Kara et al. (2008) proposed to assess the useful life of products based on the critical design parameters, which can be identified at the early design stage, as a means for the products to be redesigned. However, it should be noted that components should not be designed only for remanufacturing, and other requirements, e.g., product functionality and initial manufacturability, etc., should be considered as well (Bras 2008).

Challenges and Issues on the Use of Embedded Smart Sensors The advances in sensor technologies as well as data fusion and interpretation algorithms pave the way for the use of embedded smart sensors in products to facilitate effective EoL decision-making. Nevertheless, there are challenges and issues which may prevent the use of these sensors, as summarized in Fig. 6. As discussed in section “Embedding Smart Sensors in Products,” smart sensors should be selected carefully. The data transceiver should comply with the communication protocol depending on whether the data needs to be transmitted via wired or wireless communication networks. The selection of the memory chip needs to consider the data volume to be stored between two consecutive data uploading intervals. The cost of embedding sensors in products should be economically justifiable (Klausner et al. 1998; Ilgin and Gupta 2010; Parlikad and McFarlane 2010) as it is normally borne by the manufacturers since there is no direct benefit offered to the end users. The challenge lies in the modular design of these sensors

• Selection of sensors • Type, operating range, size • Sensor components • Communication protocol • Installation of sensors • Justification of investment Production • Design • Manufacturing

Usage • Distribution • Use

• Data interpretation • Remanufacturing history • Remaining lifetime • Data representation • Representation method • Possible change in disassembly • Data security and privacy

• Data transmission • Communication network • Automatic/manual • Cooperation among all parties of interest • Data coordination • Data security and privacy Returns Disposal Remanufacturing • Disassembly • Cleaning, inspection, machining • Re-assembly • Disposal options for embedded sensors • Disassembly depth

Fig. 6 Challenges in using embedded smart sensors in product life-span monitoring

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such that they can be disassembled and replaced easily. A faulty sensor should not affect the function and reliability of the products. In product distribution and use stages, data from the embedded smart sensors have to be harnessed by the manufacturing or remanufacturing sites. For stationary equipment or machines, e.g., CNC machines, power generators, etc., wired data transmission can be adopted. For other products, e.g., automotive engines, data can be transmitted to the servers at the manufacturing or remanufacturing sites whenever the products are brought back to these sites with data transmission facilities. There are two data transmission modes, namely, manual mode and automatic mode. The manual mode will need the end users to set up the connection between the sensors and the server. In this mode, proper authorization and access control need to be established to protect the data from unauthorized viewing, modification, etc. In addition, ensuring data validity incurs cost (Rostad et al. 2005). In the automatic mode, the transmission can be initiated whenever a sensor is detected and recognized by the transmitter. Sensors may need to be disassembled and disposed of, e.g., a malfunctioned embedded sensor has to be replaced, or a component has reached its end of life. In the second situation, the disposal or recycling of the component may require the embedded sensor to be separated from the component since sensors are often made of different materials than that of the component. This would require specific disassembly steps. In addition, the disposal of the sensors should not violate any environmental regulations, which may incur additional cost for the remanufacturers/recyclers.

Summary The remanufacturing process is often labor intensive and relies heavily on the expertise of the employees due to wide-ranging variations in the return conditions of the cores. The use of embedded sensors has presented the potential to assist the remanufacturers in making more reliable decisions at each stage of the remanufacturing process. However, product condition monitoring using embedded sensors, particularly sensor data fusion and interpretation, remains challenging in the remanufacturing industry. This chapter has reviewed the current practices toward the development of embedded smart sensors in products in two primary aspects, namely, embedding smart sensors in products and representing and interpreting sensor data. Sensor selection and sensor placement/installation are the two most relevant issues requiring careful considerations to meet the target performance for condition monitoring. Multisensor data fusion and interpretation for efficient fault diagnosis and failure prognosis are reviewed briefly. A conceptual framework has been developed for the use of the sensor data in facilitating remanufacturing operations and decision-making at each remanufacturing stage. With the advancement and development in smart sensor technologies, individual sensors can have powerful computation capacity and the ability to communicate with other sensors or the server via wireless networks. With these features, future

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smart sensor systems can adopt ubiquitous computing in product condition monitoring and management. By allowing all decision-makers to access the product lifecycle data easily and safely, the application of embedded smart sensors to facilitate product EoL recovery decision-making can be further investigated and understood.

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Pricing Strategies of Remanufacturing Business with Replacement Purchase

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Lei Jing, Boray Huang, and Xue Ming Yuan

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Identical Yield Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Different Yield Rate of Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Numerical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effects of Random Yield Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quality-Dependent Rebate Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

A special feature of remanufacturing business is the existence of large proportion of replacement customers. This is due to the fact that many durable product markets are highly saturated and customers who return their

L. Jing (*) National University of Singapore, Singapore e-mail: [email protected] B. Huang Department of Industrial and Systems Engineering, National University of Singapore, Singapore e-mail: [email protected] X.M. Yuan Planning and Operations Management, Singapore Institute of Manufacturing Technology (SIMTech), Singapore e-mail: [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_104

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end-of-life products need to do replacement purchase. At the same time, pricing strategies have been widely adopted by remanufacturing companies to balance supply and demand. In this study, the joint decision of acquisition, trade-in, and selling price is considered. The objective is to maximize the expected profit. It is shown that a remanufacturing firm should offer higher rebates to replacement customers when this customer segment has high return quality and high price sensitivity. The optimal pricing policies under uncertain return yield rate are studied. The profitability of different pricing schemes is also investigated.

Introduction In recent years, there has been an increasing concern on closed-loop supply chains and green supply chains. Due to both economic incentives and legislation regulation, more and more companies are involved in product recovery business. Remanufacturing is one of the various product recovery options. Successful practices of remanufacturing can be found in industries like automotive, construction, mining, aerospace, etc. A special feature of remanufacturing business is the correlation between supply and demand. This phenomenon is due to the large proportion of replacement customers. As reported by Lund and Hauser (2010), many remanufactured products are used for replacement. A possible explanation would be that customer wants to avoid the switching cost of changing to a different product. This study is motivated by these special characteristics of remanufacturing practices. A remanufacturing system is considered with the existence of replacement customer segment and uncertain return yield. The remanufacturing company acquires used products from previous customers through acquisition programs. The supply of return flow is price dependent. Demand comes from both replacement customer and first-time buyer which is also price dependent. Replacement customer will return their old products and get trade-in rebates for new purchases. The demand can be satisfied by either remanufacturing used products or manufacturing new ones. This model represents the remanufacturing practice of many durable products. For highly saturated markets, a significant portion of purchase could be replacement. A practical example can be found in Caterpillar, which is the world’s largest manufacturer of construction and mining equipment and diesel and natural gas engines. Customers who return their end-of-life products will get a cashback from Caterpillar. The company also offers trade-in rebates to those replacement customers. In this article, the problem with deterministic and random yield rate is studied. The effect of different pricing schemes is also investigated. The rest of the study is organized as follows. The relevant literature is reviewed in section “Related Literature.” In section “Model,” the model is described in detail and the optimal pricing policy is presented. To get managerial insights, numerical study is provided in section “Numerical Study.” Finally, conclusions and future research directions are discussed in section “Conclusions.”

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Related Literature This study is mainly related to the three streams of operations research: consumers’ replacement decisions, return acquisition in remanufacturing, and systems with random yield. Some studies in remanufacturing assume supply and demand are independent. However, a notable feature of remanufacturing business is the correlation between returns and sales. For durable products like engines or transmissions, many customers need to do replacement after the products they are using reach the end-ofservice life. Consumers’ replacement or repurchase behavior has been widely discussed in marketing research. Customers’ replacement decisions not only depend on their own attitudes and perceptions (Bayus 1991) but also are affected by companies’ advertisements and product developments (Winer 1997). It is suggested that marketers can mitigate consumers’ loss aversion by accepting the old product as a trade-in (Novemsky and Kahneman 2005). Unlike these studies which focus on descriptive and empirical analysis of consumer’s replacement behavior, in this work, it is assumed that companies can use trade-in rebates as a pricing tool to differentiate replacement customers and first-time buyers. Optimal pricing strategies are then discussed with different yield conditions. Due to the increasing concern on closed-loop supply chain, there is an extensive literature on remanufacturing, reverse logistics, and other related problems. Detailed review of quantitative models and business aspects can both be found Fleischmann et al. (1997) and Guide and Van Wassenhove (2003). More recent reviews are also available (Souza 2008; Guide and Van Wassenhove 2009). One of the important issues in closed-loop supply chain is the product acquisition management, which has been widely discussed in both practice and academia. To stimulate product return, firms can either facilitate the reverse channel or provide monetary incentives to existing customers. In an early work of Guide and Jayaraman, a framework for product acquisition management is investigated (Guide and Jayaraman 2000). Game theory model is used to study the efficiency of different reverse channels in a supply chain setting (Savaskan et al. 2004). Guide et al. consider a remanufacturing planning problem in which returns can have different quality levels (Guide et al. 2003). They assume that return supply from each quality class and product demand are both price dependent. A singleperiod framework is developed to determine the optimal pricing policy. Recently, the joint acquisition, pricing, and inventory management problem is studied in a multiperiod setting (Zhou and Yu 2011). But both papers ignore the fact that higher acquisition price may lead to higher demand due to the existence of replacement customers. Despite the extensive discussion of product acquisition management, few studies investigate the effect of replacement purchase on remanufacturing business. Some researchers consider infinite-horizon model in which previous customers can make repeated purchase in future periods (Debo et al. 2006). Others study the joint pricing problem of new and remanufactured products under the existence of green segment customers (Atasu et al. 2008). They assume return from previous sales can affect future demand. But they take return quantity as a fixed

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fraction of previous sales and do not consider the acquisition decisions. In a closely related work to this study, Ray et al. assume firms can influence customers’ return and repurchase decisions by offering different rebates and prices (Ray et al. 2005). However, instead of considering return products as supply for future production, they model return revenue as a deterministic function of products’ remaining lifespan. This work is also related to the research stream on systems with random yields. Yano and Lee provide a comprehensive review of this problem (Yano and Lee 1995). More recent work includes Hsu and Bassok (1999), Bollapragada and Morton (1999), Li and Zheng (2006), Inderfurth and Transchel (2007), and Tang et al. (2012). In remanufacturing planning, there are several works considering the effect of uncertain yield. Inventory system with deterministic demand and random yield is firstly analyzed (Ferrer 2003). Zikopoulos and Tagaras study a remanufacturing system where return supply comes from two collection sites, both with uncertain yield rate (Zikopoulos and Tagaras 2007). It is shown that in some situations, it is optimal to collect from only one site. Bakal and Akcali develop a single-period model to determine the optimal acquisition and selling price (Bakal and Akcali 2006). Mukhopadhyay and Ma study the joint procurement and production problem of a hybrid system, and both demand and return yield rate are random (Mukhopadhyay and Ma 2009). Zhou et al. adopt a different approach where return flows can have different quality levels, but the remanufacturing process is perfectly reliable (Zhou et al. 2011). This work differs from the existing studies in that replacement customers are considered as a different customer segment. Unlike those models which consider repeated purchase as an uncontrollable process, in this study, it is assumed that replacement demand can be actively controlled by the trade-in rebates.

Model Assumption In this analysis, a single-period remanufacturing business model is considered. A remanufacturing company acquires end-of-use products from existing users and sells remanufactured products to both new and replacement customers. It is assumed the market is monopolistic and the company has pricing power. Furthermore, to make the price discrimination policy possible, it is required to assume there is no efficient secondary market. For new customers who are first-time buyers, their demand is modeled as a linear function of selling price p, ω( p) ¼ abp, where a, b > 0. Replacement customers are current users who need to replace their end-oflife products. Their repurchasing decisions also depend on prices. Since end-of-life product can be used for remanufacturing, companies usually offer trade-in rebates for those replacement purchases. Therefore, the demand of replacement customers is considered as a linear function of repurchasing price f, θ( f ) ¼ δγ f, where δ, γ > 0. The difference between p and f is the trade-in rebates offered to the

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Fig. 1 Problem environment

replacement customers. There is also a return flow from end-of-use products from existing users. It is assumed that acquisition return depends on acquisition price r and can be modeled as η(r) ¼ α + βr, where α, β > 0. For simplicity, η, ω, and θ are used to represent the corresponding functions. Figure 1 illustrates the material flow of such a hybrid system. After return products are acquired (through both trade-in and acquisition), they are disassembled to check whether they can be remanufactured; the cost is denoted as inspection d. Since acquisition return and replacement return are from different customer segments, the yield rate of return products can be either identical or different. The case when yield conditions are identical is firstly considered; and the aggregate yield rate is denoted by ρ, which is a random variable observed only after inspection. In this case, the remanufacturing quantity is min{ω + θ, ρ(θ + η)}, with unit remanufacturing cost cr. Worn-out returns and excess reusable returns are disposed with zero disposition cost. When returns are insufficient to satisfy demand, the company needs to manufacture new products at unit cost c, where c > cr. Later the case when acquisition return and replacement return are of different yield conditions is also discussed. Table 1 summarizes the notations which will be used.

Identical Yield Rate Given the model described above, in this section, the pricing problem when the yield rate is identical between acquisition return and replacement return is formulated. Firstly the case with deterministic yield is considered, which means that the percentage of remanufacturable cores is fixed and known. The assumption is then relaxed to incorporate random yield condition. The optimal decisions are characterized for both cases.

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Table 1 Notations

Notations r f p η(r) ω( p) θ(f) d cr c ρ G(), g()

Explanations Acquisition price Selling price for replacement customer Selling price for new customer Supply of returns, η(r) ¼ α + βr Demand of new customer, ω( p) ¼ abp Demand of replacement customer, θ( f ) ¼ δγf Unit inspection (preprocessing) cost of returns Unit remanufacturing cost Unit manufacturing cost Aggregate yield rate of return products CDF and PDF of random yield rate ρ

Deterministic Yield Rate It is assumed that firm always recognizes that there exists replacement customer segment. When the firm decides not to offer trade-in programs, both replacement and new customers buy products at price p. Meanwhile, replacement and acquisition customers will sell their old products to the firm at price r. In this case, demand function of replacement customers can be characterized as θ( p, r) ¼ δ  γ( p  r). The company decides p and r simultaneously to maximize its profit. Such a pricing strategy is named as uniform pricing. This pricing strategy represents the case when product sales and return collection lack coordination, for example, the reverse channel is outsourced to a third-party collector. It is not the main focus of this study but serves as a benchmark for the price discrimination strategy. For more details of such a uniform pricing, readers can refer to Ray et al. (2005) and Savaskan et al. (2004). The pricing problem can be formulated as follows:




 Max ∏U ðr, pÞ ¼ ω p p þ θ p, r p  r  d  η r r þ d  cr ρðηðr Þ þ θðp, r ÞÞ r, p  cðωðpÞ þ θðp, r Þ  ρðθðp, r Þ
þ ηðr ÞÞÞ subject to ρðηðr Þ þ θðp, r ÞÞ  θðp, r Þ þ ω p The optimal pricing decisions have two possible forms:


 

r U , pU

8
þ 2γ ðc  cr Þþ < ðr U0 , pU0 Þ when ða  bc  ðc þ d
Þγ þ δÞ
¼ d ðβ þ γ Þ  δ  α ρ  c  cr β þ γ ρ2 > 0 : ðr U1 , pU1 Þ otherwise:

where (rU0, pU0) solves the first-order condition and (rU1, pU1) is the optimal solution when constraint is binding. When the firm decides to offer trade-in to replacement customers, it charges p to new customers and f to replacement customers and pay r for each acquisition return. Since the company can choose whether to manufacture or not, two different scenarios are obtained. First, if the company chooses to manufacture,

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remanufacturable return is then less than total demand, ω(p) + θ( f ) > ρ(η(r) + θ( f )), and part of the demand will be satisfied by manufacturing. The profit is the sales revenue minus acquisition, inspection, remanufacturing, and manufacturing cost. Second, when demand is only filled by remanufacturing, i.e., ω( p) + θ( f ) ¼ ρ(η(r) + θ( f )), then there is no manufacturing cost. Additionally, the company should assure pf  r to make the trade-in price attractive to replacement customers. Consequently, the pricing problem is formulated as follows: Max ∏ðr, f , pÞ ¼ r, f , p subject

to








 ωðpÞp þ θ f f  d  η r r þ d  cr ρ η r þ θ f c f Þ  ρ
ðθðf Þ þ η
ðrÞÞÞ
ðωðpÞ þ
θð ρ ηðr Þ þ θ f  θ f þ ω p rþf p

The first constraint makes sure that manufacturing cost occurs only when reusable return is insufficient. The second constraint means that trade-in rebates should be greater than or equal to the acquisition price. Otherwise replacement customers would sell their end-of-life products at price r and purchase new products at price p. When the second constraint is binding, the above problem becomes equivalent to the uniform pricing case. We first solve the relaxation problem without considering the second constraint. After that, we identify the condition for which the pricing policy violates this constraint. Proposition 1 Without considering the second constraint, the optimal pricing policy under deterministic return yield rate is 8 

 


c þ d γ
þ δ þ 2γ c  cr þ < ðr 0 , f 0 , p0 Þ when a  bc  ð r  , f  , p Þ ¼ d ðβ þ γ Þ  δ  α ρ  c  cr β þ γ ρ2 > 0 : ðr 1 , f 1 , p1 Þ otherwise:   cγþdγþδ 1 where ðr 0 , f 0 , p0 Þ ¼  αþdβ  12 ðc  cr Þρ, aþbc solves the 2b 2β þ 2 ðc  cr Þρ, 2γ first-order condition and (r1, f1, p1) is the solution when the first constraint is binding. Proposition 1 shows that either the optimal solution satisfies first-order condition or the first constraint is binding. Corollary 1 Uniform pricing policy should be chosen when δγ  ab  αβ  0. Proof By checking the optimal solution in Proposition 1, we can find the condition when the second constraint is binding. According to Corollary 1, price discrimination policy is implementable only when δγ  ab  αβ  0 . Since γ , b, and β represent the price sensitivity of each customer segments, the result suggests that a higher rebate to replacement customers is preferable when this segment has higher price sensitivity, while the

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sensitivities of new customers and acquisition customers are low. An interesting question is that given δγ  ab  αβ  0, what is the profit improvement of price discrimination? Define value of price discrimination as ∏(r*, f*, p*)  ∏U(r*U, p*U). Corollary 2 gives the result under deterministic yield rate. Corollary 2 Given δγ  ab  αβ  0, the value of price discrimination is ðbαγ þ aβγ  bβδÞ2 4bβγ ðβγ þ bðβ þ γ ÞÞ Corollary 2 can be easily proved by substituting (r*, f*, p*) and (r*U, p*U) into the corresponding profit functions. This corollary shows that the value of price discrimination is independent of unit manufacturing cost c, unit remanufacturing cost cr, and yield rate ρ. We can verify that for both pricing strategies the demand and return volume are the same. Since unit remanufacturing cost, unit manufacturing cost, and yield rate only affect the production cost, the production cost will keep unchanged. This explains why the profit difference is independent in c, cr, and ρ. The implication is that benefits of price discrimination come from better targeting at different customer segments, instead of the cost savings from production.

Random Yield Rate When yield rate is random, depending on the pricing decisions and the realization of yield rate, the firm’s profit has two expressions:  ∏¼



 ωp þ θf  ηr  dðθ þ ηÞ  cr
ω þ θ , ω þ θ  ρ0 θ þ η
 when
ωp þ θf  ηr  dðθ þ ηÞ  c ω þ θ þ ρ0 c  cr θ þ η , otherwise

where ρ0 denotes the realization of yield rate. The expected profit function under uncertain yield rate will be

 


E½∏ðr, f , pÞ ¼ ωp þ θ f  d  η r þ d  cr E min ω þ θ, ρ η þ θ

 cE ððω þ θÞ  ρðη þ θÞÞþ


 ¼ ωðp  cr Þ þ θ f  cr  d  η r þ d  c  cr E½ω þ θ  ρðθ þ ηÞÞþ 

The random yield rate distributes on [A, B] (0  A  B  1), with CDF G(), PDF g(), and mean value μ. Depending on the pricing decisions, the relation between return and sales has two different cases: Case 1: (θ + η)A  θ + ω  (θ + η) B


 E ½∏1 ðr, f , pÞ ¼ ωp þ θ f  d  η r þ d  cr ω þ θ ð θþω θþη ðθ þ ω  ρðθ þ ηÞÞ gðρÞ dρ  ðc  cr Þ A

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Case 2: (θ + η)B  θ + ω E ½∏2 ðr, f , pÞ ¼ ωp þ θðf  dÞ  η ðr þ d Þ þ μðc  cr Þðθ þ ηÞ  cðω þ θÞ Note that the case (θ + η)A > θ + η is omitted. In practice, such situation is obviously suboptimal. When reusable return is greater than the total demand, the company can always reduce the acquisition price to increase the profit. For Case 2, reusable return is less than the total demand for any yield realization. Hence, manufacturing is always needed. The optimization problem is similar to the situation of deterministic yield rate, and the optimal decision only depends on the mean value of yield rate. For Case 1, the expected profit function is similar to that of a classical newsvendor problem with price-dependent demand. However, there is a major difference between this model and newsvendor problem. Classic newsvendor model usually assume random demand and perfectly reliable supply. While in this remanufacturing problem, we assume a deterministic demand and uncertain yield rate. Because of this difference, the profit function in Case 1 shows a different property compared to that of a newsvendor model. Lemma 1 E [∏(r, f, p)] is differentiable. Proposition 2 The expected profit function is jointly concave in r, f, and p. Lemma 1 and Proposition 2 shows that there exists an optimal pricing decision and the optimal solution can be found determined efficiently by gradient methods. Corollary 3 Given two different yield conditions ρ1 and ρ2 which are distributed on [A, B] with ρ1  st ρ2, the optimal expected profit E [∏* (ρ1)]  E [∏* (ρ2)]. For the uniform pricing problem, it is equivalent to add a linear constrain r + f ¼ p to the above problem. Since the concavity of the profit function has been proved, the optimal (r*u p*u) can be obtained similarly. By definition, it is indisputable that E [∏(r*, f*, p*)]  E [∏(r*u, p*u)]. However, because of the complexity of the problem, a closed-form solution is not obtainable. An interesting question is that whether Corollary 2 still holds for the random yield rate problem; numerical results are presented in section “Numerical Study.”

Different Yield Rate of Returns Zikopoulos and Tagaras investigate a reverse supply chain with two collection sites and different return qualities (Zikopoulos and Tagaras 2007). They derive the condition under which is optimal to use only one site. In this study, acquisition returns come from end-of-use products, which means costumers no longer need such product. On the other hand, replacement returns occur only when the products

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Table 2 Integration area Range of ω and η ω  ηB1 ηA1  ω  ηB1

ω  ηA1

Range of θ θ>0 θ(1  A2)  ηB1  ω

Integration limits A1  ρ1  B1, A2  ρ2  B2

θ(1  B2)  ηB1  ω θ(1  B2)  ηB1  ω  θ(1  A2)

A1  ρ1  B1 for A2  ρ2  B2 θð1  ρ2 Þ þ ω ηB1  ω for 1   ρ2  A1  ρ1  η θ B2 , and A 1  ρ1  B1 for A2  ρ2  ηB1  ω 1 θ A1  ρ1  B1 for A2  ρ2  B2 Null

θ(1  B2)  ηB1  ω θ(1  A2)  ηA1  ω θ(1  B2)  ηA1  ω  θ(1  A2)  ηB1  ω ηA1  ω  θ(1  B2)  θ(1  A2)  ηB1  ω θ(1  B2)  ηA1  ω  ηB1  ω  θ(1  A2) ηA1  ω  θ(1  B2)  ηB1  ω  θ(1  A2)

A1  ρ1  θð1ρη2 Þþω for A 2  ρ2  B2

A1  ρ1  θð1ρη2 Þþω for A 2  ρ2  1  ηA1θω Þþω A1  ρ1  θð1ρ2 for A 2  ρ2  B2 η

θð1  ρ  2Þ þ ω ηB1  ω for 1   ρ2  η θ B2 , and A 1  ρ1  B2 for A2  ρ2  ηB1  ω 1 θ

A1  ρ1 

fail or excess their useful life. Due to the difference in return causation, a reasonable conjecture would be that acquisition return and replacement return may have different quality conditions. In this subsection, the assumption of identical yield rate is relaxed to investigate how it affects firm’s profitability. Let ρ1 and ρ2 denote the yield rate of acquisition return and replacement return, respectively. When remanufacturing company adopts price discrimination policy, it is possible that acquisition returns have higher yield rate but receive lower rebates. It is assumed that this will not change the supply function of acquisition returns. The reason is twofold. Firstly, yield condition only comes after inspection; customers cannot get this information in advance. Secondly, the higher rebates to the replacement customers are used to encourage repurchase, and pure return customers should not enjoy this benefit. The expected profit function then becomes
 E½∏ðr, f , pÞ ¼ ωp þ θð f  d Þ  η r þ d  cr E½minfω þ θ, ρ1 η þ ρ2 θg

 cE ðω þ θ  ρ1 η  ρ2 θÞþ

¼ ωðp  cr Þ þ θð f  cr  d Þ  ηðr þ d Þ  ðc  cr ÞE ðω þ θ  ρ1 θ  ρ2 ηÞþ

Assume ρi is distributed on [Ai, Bi], (0  Ai < Bi  1), with CDF Gi (), PDF gi(), and mean value μi(i ¼ 1, 2). Depending on the pricing decisions, the integration area of E[(ω + θ  ρ1θ  ρ2η)+] is shown in Table 2.

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Due to the complexity of the expected profit function, it is not straightforward to show the concavity of the profit function. Therefore, numerical results are reported using MATLAB with interior-point optimization technique.

Numerical Study Unlike most manufacturing systems, in remanufacturing, the quality level of supply flow is highly variable and uncertain. In this section, computational experiments are conducted based on the model described above. The purpose of the numerical study is twofold. Firstly, since it is difficult to attain closed-form solution for random yield rate problem, numerical results are used to investigate the advantages and limitations of different pricing policies. Secondly, sensitivity analyses are carried to find how the optimal decisions change according to different parameter settings. This would help managers make decisions when facing different market conditions. Uniform distribution is used to describe the uncertainty of yield rate condition. However, it is not to claim that uniform distribution is more suitable to model the usability of return products. In literature, several distributions have been adopted for study. Weibull distribution is used by Lo et al. and Wee and Chen (Lo et al. 2007; Wee et al. 2007). Bakal and Akcali use normal distribution in their analysis (Bakal and Akcali 2006). Uniform distribution has been used by Mukhopadhyay and Ma (2009 and Tang et al. (2012). For the numerical study, the following data sets are assigned as base value throughout this section: a ¼ 150, b ¼ 3, δ ¼ 100, γ ¼ 3, α ¼ 10, β ¼ 10, cr ¼ 5, d ¼ 2, c ¼ 30:

Effects of Random Yield Rate Identical Yield Rate Firstly, sensitivity analysis is conducted with identical yield rate. In this situation, acquisition return and replacement have the same yield rate condition. Analytic results are presented in section “Identical Yield Rate.” For the sensitivity analysis, the standard deviation of yield rate σ is fixed at 1/75, and the mean value μ varies from 0.3 to 0.8. Figure 2 shows how the pricing decisions change accordingly. According to Fig. 2, the optimal acquisition price first increases with μ, then after a threshold, the price slightly decreases as μ further increases, while the optimal replacement purchase is always decreasing in μ within the range of computational experiment. When yield rate is low, the optimal selling price for new customers is independent of μ, but as μ further increases, the optimal price decreases to attract more first-time buyers. The result suggests that when expected return yield is low, the firm would choose to acquire less cores ((θ + η)B  θ + ω) and demand is satisfied by both manufacturing and remanufacturing. More specifically, if the

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Fig. 2 Expected yield rate and pricing decisions

reusable return is less than the replacement, then all the new demands are satisfied by manufacturing. Consequently, in this case, selling price p is independent of yield condition. When μ is high, supply of reusable return is ample, and the firm then reduces the selling price to attract more customers. Corollary 2 shows that when yield rate is identical and deterministic, the value of price discrimination is independent in c, cr, and ρ. However, the conclusion under random yield is not easy to draw. Hence, computational experiments are used to verify whether this result still holds in random yield situation. Firstly, the yield variance σ 2 is fixed at 1/75 to study the effect of expected yield rate μ. Afterward, μ is fixed at 0.5 and σ is varied to see how standard deviation affects expected profit. Figure 3 illustrates the profit difference of these two pricing schemes under different yield rate conditions. Under both pricing schemes, the expected profit is increasing with μ and decreasing with σ. These two observations are consistent with intuitions as higher yield rate saves acquisition cost and lower randomness leads to higher profits. It can be also observed that to offer a trade-in program is especially favorable when the expected yield rate is low and the variance of yield rate is large, as the percentage of profit improvement is higher in such cases. On the other hand, managers should also take into account the related cost of such a market decision. Another observation is that, under random yield rate, the profit difference between the two pricing strategies is stable with respect to both μ and σ. Moreover, although not shown here, numerical results also reveal that the profit difference is independent of c and cr, which is consistent with the case of deterministic yield rate.

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Fig. 3 Value of price discrimination

Different Yield Rates As stated in section “Different Yield Rate of Returns,” the difference in return causation may lead to different return quality conditions. This section investigates the situation when ρ1 and ρ2 are independent random variables. Table 3 summarizes how pricing decisions are affected by different yield rate conditions. When μ1 is low, the acquisition price r is unchanged to different levels of μ2. When μ1 is in the

3304 Table 3 Pricing decisions with respect to different yields

L. Jing et al. μ1 0.4

0.6

0.8

μ2 0.4 0.6 0.8 0.4 0.6 0.8 0.4 0.6 0.8

(r, f, p) (3.50, 27.67, 40.00) (3.50, 25.17, 40.00) (3.50, 22.67, 40.00) (5.07, 27.17, 38.74) (5.10, 25.06, 38.70) (4.61, 22.49, 38.14) (5.16, 25.68, 36.42) (5.00, 23.85, 36.22) (4.68, 22.08, 35.83)

E[Π] 598.83 702.58 843.83 862.82 960.03 1090.06 1107.96 1198.79 1305.09

middle level, r first increases in μ2 and then decreases. When μ1 is high, r decreases in μ2. The replacement price f is decreasing in both μ1 and μ2. The selling price p behaves similar to the identical yield case, which remains unchanged when return supply is insufficient to fulfill replacement returns, and then decreases as return yield further increases. Define the profit difference with and without a trade-in rebate as the profit gain from price discrimination. Figure 4 shows that when ρ1 and ρ2 are independent random variables, the profitability of price discrimination policy is affected by not only return yield condition but also replacement customers’ price sensitivity. As indicated by Fig. 4, the profit gain is increasing in the expected yield rate of replacement return while decreasing in that of acquisition return. This result is different from the case when these two yield rates are identical. As Corollary 2 and Fig. 3 show that when yield rates are identical, the profit gain remains the same with respect to the different yield rate levels. The result highlights the importance of identifying return yield conditions of different customer segments. Another observation is that the profit gain is affected by replacement customers’ price sensitivity γ . This is consistent with Corollary 1, which suggests that when γ is low, the company has less incentive to offer higher rebates to replacement customers. The numerical study implies that when μ2 and γ are high, a greater rebate to replacement customer can stimulate the replacement sales and acquire more reusable returns. Therefore, remanufacturing managers are recommended to use price discrimination policy under such circumstances. Although not shown here, the numerical study also reveals that the yield randomness does affect the profitability, but the effect is minor compare with the above two factors.

Quality-Dependent Rebate Policy Up to this point, it is assumed that remanufacturing company will rebate all the return customers without considering the inspection results of return products. While in practice, some companies do check the reusability of return products and pay the rebates based on inspection results. Ray et al. discuss the relation

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Fig. 4 Value of price discrimination, yield rate, and price sensitivity

between products’ residual value and replacement decisions in a deterministic environment (Ray et al. 2005). They assume the perceived residual value of product depends on the remaining useful lifespan, and both customers and the remanufacturing firm are fully aware of this information. In this study, the actual yield condition can only be observed after inspection; it is hard to imagine that return customers would know this information in advance,

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while it is assumed that the remanufacturing firm is well experienced and knows the yield distribution. Since return customers do not have knowledge on the yield distribution, their return decisions only depend on the nominal acquisition price r. It is assumed that the acquisition return function is the same as the case when rebates are paid to all return customers. For the replacement customers, their demand may have two possible situations. Firstly, if replacement customers behave the same as acquisition return customers, the replacement demand function is also unchanged. In this case, the expected profit for the remanufacturing company becomes E½∏R1 ðr, f , pÞ ¼ ωp þ θðμ2 f þ ð1  μ2 Þp  dÞ  ηðμ 1 r þ dÞ

 cr E½minfω þ θ, ρ1 η þ ρ2 θg  cE ðω þ θ  ρ1 η  ρ2 θÞþ where μ1 and μ2 are the expected yield rate of acquisition and replacement returns. Secondly, although the return decisions are induced by the nominal rebates, the replacement customers can make their purchasing decisions after yield information is revealed. In addition, we assume the customers are homogeneous in price sensitivity with respect to the return yield condition. Therefore, the expected profit for the remanufacturing company becomes
 



E½∏R2 ðr, f , pÞ ¼ ωp þ μ2 θðf Þ f  cr þ 1  μ2 θ p p  θ f d  η μ1 r þ d

 cr E½minfω þ ð1  ρ2 ÞθðpÞ, ρ1 ηg  cE ðω þ ð1  ρ2 ÞθðpÞ  ρ1 ηÞþ

Figure 5 demonstrates how different rebate policies and yield conditions affect the firm’s expected profit. If both replacement and acquisition customers only look at the nominal prices, then a quality-dependent rebate policy will improve the firm’s profit significantly. Furthermore, the firm will offer higher rebates to attract more replacement customers and increase the selling price p to generate more profit from customers whose return cannot be remanufactured. This effect makes the firm more profitable when μ2 is low. While for the second situation, the expected profit increment is much less. In this case, the replacement purchasing decision differs regarding to the yield realization. Although the company saves rebate costs for unusable return, part of the replacement customers can only buy at price p and their demand is deterred. Our numerical results suggest that the profitability of such quality-based rebate policy is largely affected by the return customers’ response. In remanufacturing research, few studies consider the effects of different rebate policies. The actual reaction of return customers may fall between the above two scenarios. Further empirical studies are required to justify the assumption of customers’ return decisions.

Summary Matching supply and demand is the major concern of managers who are dealing with remanufacturing business. Most studies in remanufacturing systems have assumed that supply and demand are two independent flows. This assumption is

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Fig. 5 Performance of quality-dependent rebate policy

reasonable for new product manufacturing and sales. However, due to the existence of replacement customers, it does not hold for remanufacturing business. This study investigates the pricing decisions of a remanufacturing firm who are facing both new and replacement demands. A single-period model is developed to evaluate the benefit of adopting a price discrimination policy. It is the first attempt to study the effect of replacement customers in remanufacturing business.

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For deterministic yield condition, it is shown that the price discrimination policy is applicable if replacement customers have high price sensitivity, while new customers and acquisition customers have low price sensitivity. When yield rate is uncertain, due to the complexity of the problem, a closed-form solution is not attainable. Computational experiments are conducted to compare the profits of different pricing schemes. Factors like yield rate conditions and customers’ price sensitivities are investigated. The numerical results show that both factors are crucial for the firm. The price discrimination policy to replacement customer is no worse than the uniform pricing policy in every case. Furthermore, price discrimination policy is significantly better off when the yield rate of replacement return is high. The payment scheme of return rebates also affects firm’s profit. According to the numerical study, when rebates are only offered to reusable returns, the firm is significantly better off if replacement and acquisition customers both make decisions only based on the nominal repurchasing price. The present model has assumed deterministic demand function; in practice, however, the demand information is usually imperfect. Consequently, it is meaningful to incorporate random demand into the model. The company will then decide on both pricing strategy and production quantity. It would increase the complexity of the model, but such a model is similar to the newsvendor problem with endogenous demand, which has been extensively studied. The existing results will facilitate the analysis with a remanufacturing problem setting. There are several other possible extensions for this model. One is to relax the assumption of independence of new customer and replacement customer. In practice, replacement customers may choose to purchase a new product without returning their old one. The demand from this customer segment will then depend on both f and p. It is expected that the optimal pricing policy would be different, but the price discrimination policy should preserve its profitability. A limitation of this model is that the yield rate is taken as the fraction of reusable returns. In practice, return products are usually under different quality conditions and require different remanufacturing costs. The current model would be more realistic if multiple type returns can be incorporated. Besides, one can also consider that the remanufactured products are imperfect substitutes of brand-new products. It is interesting to see how cannibalization effect will change the pricing decisions in such cases.

Appendix Proof of Lemma 1 Define H(x, ρ) ¼ θ( f ) + ω( p)  ρ(η(r) + θ( f )) and K ðxÞ ¼ θðf ÞþωðpÞ θðf Þþηðr Þ , where x ¼ (r, f, p). It is obvious that E[Π(x)] is differentiable for H(x, B) < 0 and H(x, B) > 0. The only thing that needs to be proven is whether E[Π(x)] is differentiable at H(x, B) ¼ 0. Let x0 ¼ {x | H(x, B) ¼ 0}; it can be shown that the partial derivatives at x0 exist and are continuous. Denote Δ as a vector so that H(x0 + Δ, B) > 0 and H(x0Δ, B) < 0. Consider the special case where Δi ¼ tei ¼ (0, . . ., t, . . ., 0), i  {r, f, p}.

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E½∏ðx0 Þ  E ∏ x0  Δr ¼ η r 0  t r 0  t þ d  η r 0 r 0 þ d ! ð Kðx0 Þ þ ðcr  cÞ H ðx0 , ρÞgðρÞdρ A



ð Kðx0 Δr Þ

Hðx0  Δr , ρÞgðρÞdρ



A

Let |Δr| ! 0: ð Kðx0 Þ

¼

ðA Kðx0 Þ A

¼

H ðx0 , ρÞgðρÞdρ 

ð K ð x0 Þ A

ð K ðx0 Δr Þ

H ðx0  Δr , ρÞgðρÞdρ ð Kðx0 Þ ðH ðx0 , ρÞ  H ðx0  Δr , ρÞÞgðρÞdρ þ H ðx0 , ρÞgðρÞdρ A

K ðx0 Δr Þ


 tH 0r ðx0 , ρÞgðρÞdρ þ oðtÞ þ ðK ðx0 Þ  K ðx0  Δr ÞÞH x0 , ξ

where K(x0  Δr) < ξ < K(x0) and ξ ! K(x0) as |Δr| ! 0. Then we can obtain E½∏ðx0 Þ  E½∏ðx0  Δr Þ j Δr j ! ð 
 Kðx0 Þ 0

0 0 Hr ðx0 , ρÞgðρÞdρ þ K r ðx0 ÞH ðx0 , K ðx0 ÞÞ ¼ ηðr 0 Þ  ηr r 0 r 0 þ d þ cr  c A


 
¼ ηðr 0 Þ  η0r r 0 r 0 þ d þ c  cr η0r r 0 μ lim

jΔr j!0

The last equality comes from the fact that K(x0) ¼ B. It is easy to show that E½∏ðx0 þ Δr Þ  E½∏ðx0 Þ jΔr j!0

jΔr j 

 ¼ ηðr 0 Þ  η0r r 0 r 0 þ d þ c  cr η0r r 0 μ lim

Similarly, it can be proven that the partial derivatives exist and are continuous with respect to f and p. Hence, E[∏(x)] is differentiable at x0. Poof of Proposition 2 It is easy to show that E[∏(r, f, p)] is concave when (θ + η)A  θ + ω  (θ + η)B. For (θ + η)A  θ + ω  (θ + η)B, it is necessary to show E[∏ (r, f, p)] is convex in r, f, and p. Applying Sylvester’s criterion, it is equivalent to prove 1. H 1 ¼ @

2

E½∏ðr, f , pÞ @r2

> 0;

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  2  @ E½∏ðr, f , pÞ @ 2 E½∏ðr, f , pÞ      @r 2 @r@f  > 0;  2. H 2 ¼  2 2  @ E ½ ∏ ð r, f , p Þ  @ E ½ ∏ ð r, f , p Þ      2 @r@f @f and   2  @ E½∏ðr, f , pÞ @ 2 E½∏ðr, f , pÞ @ 2 E½∏ðr, f , pÞ      @r 2 @r@f @r@p   2  @ E½∏ðr, f , pÞ @ 2 E½∏ðr, f , pÞ @ 2 E½∏ðr, f , pÞ   > 0:  3. H 3 ¼   @f @r @f @p @f 2    @ 2 E½∏ðr, f , pÞ @ 2 E½∏ðr, f , pÞ @ 2 E½∏ðr, f , pÞ      @p@r @p@f @p2 r Define C ¼ cc ηþθ g



ωþθ ηþθ

 ωþθÞ ηωÞ , and y ¼ γðηþθ . Since c > cr, it is straightforward , x ¼ βðηþθ

that C > 0. It can be obtained that H1 ¼ 2β + Cx2 > 0, H2 ¼ 4βγ + 2Cβy2 + 2Cγx2 > 0, and H3 ¼ 4Cby2β + 4Cbx2γ + 8bβγ + 4Cb2βγ > 0. Therefore, the expected profit function is concave on (θ + η)B  θ + ω. Combing Proposition 1, it can be concluded that E[∏(r, f, p)] is concave.

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Hong-chao Zhang, Tao Li, Zhichao Liu, and Qiuhong Jiang

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life Cycle Assessment (LCA) Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technical Processes of Engine Remanufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Study: LCA-Based Evaluation of Diesel Engine Block Remanufacturing . . . . . . . . . . . . . Goal and Scope Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life Cycle Inventory Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life Cycle Impact Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparisons with Newly Manufactured Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

There has been a growing interest in remanufacturing during the past decade, since it offers many advantages to our economy. However, the qualification and quantification of the benefits of remanufacturing compared to original manufacturing remain confusing to us due to the difficulties of data collection in complex production processes and the lack of accurate and convinced evaluation method. Life cycle assessment (LCA) is a “cradle to grave” approach for

H.-c. Zhang (*) School of Mechanical Engineering, Dalian University of Technology, Dalian, China Department of Industrial Engineering, Texas Tech University, Lubbock, TX, USA e-mail: [email protected]; [email protected] T. Li • Z. Liu • Q. Jiang School of Mechanical Engineering, Dalian University of Technology, Dalian, China e-mail: [email protected]; [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_111

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assessing industrial products and systems, which enables to estimate the cumulative environmental impacts resulting from all stages in a product life cycle. In this book, taking a diesel engine as a case study, a comprehensive LCA is conducted for remanufactured diesel engines, aiming to identify the negative impact on the environment during the whole life cycle and to analyze the potential that remanufacturing had in terms of energy savings and environment protections. In order to demonstrate the environmental benefit of remanufacturing, the environmental impacts achieved in the study are compared with a newly manufactured counterpart. The results show that remanufacturing of a diesel engine has lesser contribution to all the environmental impact categories when compared to its original manufacturing; the greatest benefit is EP which is reduced by 79 %, followed by GWP, POCP, and AP which can be reduced by 67 %, 32 %, and 32 %, respectively.

Introduction Background Resource and Environmental Problem Global emissions of carbon dioxide (CO2) – the main cause of global warming – increased by 3 % in 2011, reaching an all-time high of 34 billion tons in 2011. In 2011, China’s average per capita CO2 emissions increased by 9 % to 7.2 t CO2 (Jos et al. 2012). The International Energy Outlook 2013 (IEO2013) projects that world energy consumption will grow by 56 % between 2010 and 2040, and the industrial sector continues to account for the largest share of delivered energy consumption; the world industrial sector still consumes over half of global delivered energy in 2040 (IEA 2013). Statistics show that according to the present automobile growth, the volume of the end-of-life automobiles will reach up to six million by 2015, and the large quantity of the discarded cars and engines will lead to resource waste and environment pollution. Increasingly serious resource consumptions and environment problems have attracted more and more attention by the society and businesses. The government is establishing legislations and policies to encourage manufacturers to conduct green design and to explore methods for minimizing the effects of their activities on the environment (Zhang and Yu 1999; Kaebemick et al. 2003; MlastasPaul and Zimmemm 2003). The rapid depleting metal resources bring about a rigorous challenge to car components manufacturers and halt economic development of China. Statistics show that most of the emissions are given out by the processes associated manufacturing industry, among which the metal processing operations have a major share in the energy consumption. For instance, heavy-duty truck engines with a large amount of steel and aluminum contribute significantly towards CO2 emissions. Besides, more than 80 % of industrial raw materials are dependent on the supply of mineral resources from within China, and resource reserve shortage has become a major restriction for the development of the equipment manufacturing industry.

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Modern manufacturing industry is facing the problem of how to reduce the environmental impacts of manufacturing. Some experts have put forwards four steps: understand the sources of the impact, quantify the environmental impact, identify improving opportunities, and then apply impact reduction strategies and assess the effectiveness. Quantifying the environmental impact bridged the preceding and the following when considering reducing the environmental impacts and quantifying the differences between a new strategy and traditional mode. There are two major problems in quantifying an impact in manufacturing: (1) manufacturing is not a stand-alone system and (2) both inputs and outputs of manufacturing are closely linked to other systems and processes. During environmental impact control, the impact may shift from one process to another or from one life cycle stage to another. Due to the two complexities, life cycle assessment (LCA) has to be used for a comprehensive and reliable assessment.

Development of Remanufacturing Before remanufacturing, material recycling is always applied as product end-of-life strategy. Material recycling could return the consumed product to their original raw material form to be used again, but it requires added labor, energy, and processing capital to recover the raw materials. Normally, the relative costs of material, labor, energy and the contribution of plant and equipment are the major concerns in product manufacturing. Remanufacturing could preserve much of this value while adding a second life to the product. In contrast, recycling shreds the product in an attempt to recover only the material value. Little or none of the other residual values in the product are retained. Reuse could save the labor from original processing and also retain the function and the design. The material recovery value chain of recycling, remanufacturing, and reuse is shown in Fig. 1. Remanufacturing could repair degraded components and put the product back into service, thus retaining the value of the extracted and refined materials (Kumar et al. 2007). Steinhilper said remanufacturing can avoid between 38 % and 53 % of carbon dioxide generated from new production in the 2010 International BIG R Show (Abby 2011). The remanufacturing of vehicles dates back to the 1940s. In 1947, a take back scheme called “Exchange Parts Program” was launched by Volkswagen to meet raw material shortages after World War II. The program significantly reduced the material and energy consumption for a large proportion of the components. Engine remanufacturing is a process of recovering the performances of the used engine after serious remanufacturing processes based on remanufacturing standard. Being different from the original engine manufacturing and traditional engine overhaul, engine remanufacturing begins with used engine reverse logistics, taking repairable components as processing objects, going through disassembly, cleaning, inspection, repairing, and reassembly processes. Once the product is disassembled and the parts are recovered, the process concludes with an operation not too different from the original manufacturing. Disassembled parts are inventoried, just like purchased parts, and made available for final assembly. It is being realized that a diesel engine remanufacturing too has better environmental performance than its original manufacture because of the fact that the materials’ shaping processes

Reuse

Distribution

• Product residual value • Energy from casting, machining, etc.

• Labor from original process • Function/design intention

Product Assembly

Remanufacturing

Component Fabrication

• Material value

Recycling

Material Processing

Fig. 1 The material recovery value chain of recycling, remanufacturing, and reuse

Material Extraction

Use

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Inventory analysis

Interpretation

Goal and scope definition Application : • Product development and improvement • Strategy planning • Public policy making • Marketing • Others

Impact assessment

Fig. 2 Framework of product life cycle assessment

such as molding, casting, etc. can be avoided. Professor Xu from the National Key Laboratory for Remanufacturing said that in essence, parts remanufacturing can save over 70 % of material costs, cut energy consumption by 60 %, and lower overall cost by 50 % (Xu 2007). Since 2008, China has been trying to set up several auto parts remanufacturing bases under the direction of the National Development and Reform Commission. However, qualification and quantification of the benefits of diesel engine remanufacturing compared to original manufacturing remain unsolved due to the difficulties of data collection in complex production processes and the lack of accurate and convincing evaluation methods. Remanufacturing a qualitative transition of engine, it could give a second service life to an engine with advantages of high quality and efficiency and low cost and pollution. Remanufacturing improves sales volume and profit for enterprises as well as brings about considerable environmental benefits.

Life Cycle Assessment (LCA) Method Life cycle assessment (LCA) is a “cradle to grave” approach for assessing industrial products and systems, which enables the estimation of the cumulative environmental impacts resulting from all stages in a product life cycle, often including impacts not considered in more traditional analyses (EPA 2006). According to the ISO 14040 and 14044 standards, an LCA consists of the following four components (see in Fig. 2): 1. Goal and scope definition – Determine the type of information that is needed to add value to the decision-making process. From EPA 2006, the following six basic decisions should be made at the beginning of the LCA process to make effective use of time and resources: (a) Define the goal(s) of the project.

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(b) Determine what type of information is needed to inform the decisionmakers. (c) Determine the required specificity. (d) Determine how the data should be organized and the results displayed. (e) Define the scope of the study. (f) Determine the ground rules for performing the work. 2. Life cycle inventory analysis – Quantify energy and raw material requirements, atmospheric emissions, waterborne emissions, solid waste, and other releases for the entire life cycle of a product, process, or activity. EPA 1995 defined the following four steps of a life cycle inventory: (a) Develop a flow diagram of the processes being evaluated. (b) Develop a data collection plan. (c) Collect data. (d) Evaluate and report results. Life cycle inventory (LCI) analysis is the most labor-intensive, time-consuming, and costly process. Inventory analysis involves the collection of data and calculations in order to quantify the inputs and outputs to the product system over its entire life cycle (ISO 1999). Currently, the most commonly used inventory analysis methods include simplified LCI, process-based LCI, matrix-based LCI, economic input–output LCI, hybrid LCI, etc. The comparisons of the major LCI approaches are shown in Table 1. 3. Life cycle impact assessment – Assess the potential human and ecological effects of energy, material usage, and environmental releases, as identified in the inventory analysis. The following steps comprise a life cycle impact assessment: (a) Selection and definition of impact categories – identifying relevant environmental impact categories (e.g., global warming, acidification, terrestrial toxicity) (b) Classification – assigning LCI results to the impact categories (e.g., classifying carbon dioxide emissions to global warming) (c) Characterization – modeling LCI impacts within impact categories using science-based conversion factors (e.g., modeling the potential impact of carbon dioxide and methane on global warming) (d) Normalization – expressing potential impacts in ways that can be compared (e.g., comparing the global warming impact of carbon dioxide and methane for the two options) (e) Weighting – emphasizing the most important potential impacts In conclusion, LCA is conducted to calculate the final environmental impacts indicator by 0X m

EI ¼

n X j¼1

1

EI i  Gi C B C B i¼1 Vk  B C A @ Rk

(1)

Data reliability Data uncertainty System boundary

Items Data sources

Depends

Medium to low

Medium to high

Complete

Low

Incomplete

Complete

Depends

Hybrid approach Tiered hybrid analysis Commodity and environmental flows per sector and process

EIO-based LCI Commodity and environmental flows per sector

Process-based LCI Mass and environmental flows of each process High

Table 1 Comparison between the different LCI approaches

Complete

Medium to low

Medium to high

IO-based hybrid analysis Commodity and environmental flows per sector and processbased LCI

Complete

Low

High

Integrated hybrid analysis Commodity and environmental flows per sector and process

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where EI is the final environmental impact indicator, Gi is the value of ith substance in life cycle inventory, EIi is characterization factor of ith substance to kth indicator, k ¼ 1 ~ 5, Rk is the reference value of kth indicator, Vk is the weight factor of kth indicator, m is the number of substance related to kth indicator, and n is the number of the indicators. 4. Life cycle interpretation – Identify, quantify, check, and evaluate information from the results of LCI and LCIA and communicate them effectively (ISO 1998). Within the ISO standard, the following steps to conducting a life cycle interpretation are identified and discussed: (a) Identification of the significant issues based on the LCI and LCIA (b) Evaluation which considers completeness, sensitivity, and consistency checks (c) Conclusions, recommendations, and reporting An LCA can help decision-makers select the product or process which results in the least impact to the environment. In this paper, a comparative life cycle assessment is conducted for an originally manufactured diesel engine and compared with its remanufactured counterpart, aiming to identify the negative impact on the environment during the whole life cycle and analyze the potential that remanufacturing possesses in terms of energy savings and environmental protections.

Technical Processes of Engine Remanufacturing Technical process flows of engine remanufacturing (shown in Fig. 3) includes disassembly, classification and cleaning, inspection, repairing, reassembly, etc. 1. Full-Scale Disassembly Disassembly can be defined as the systematic separation of an assembly into its components, subassemblies, or other groups (Lambert and Gupta 2005). It is an important process in material and product recovery since it allows for the selective separation of desired parts and materials. During the engine disassembly, the quick wear parts, such as the piston assembly, main shaft bushing, oil seal, rubber hose, and cylinder head gasket, are discarded directly. These components always cannot be remanufactured or with no remanufacturing value, and they will be substituted by the new parts when reassembling. The major components after engine disassembly are shown in Fig. 4; some quick wear parts are shown in Fig. 5. 2. Components Cleaning All the parts coming from the disassembly process are cleaned, and the cleaning process involves washing away dirt and dust from the parts as well as degreasing, deoiling, derusting, and freeing the parts from old paint (Steinhilper 1998). Several cleaning methods can be applied according to the different materials and contaminations, including pyrogenic decomposition, chemical cleaning, ultrasonic cleaning, and liquid spraying.

Disassembly

Fig. 3 Technical process flows of engine remanufacturing

Coating

Recycling

Wearing parts

Remanufactured engine

Used engine

Testing

New parts

Cleaning

Reassembly

Intact parts

Inspection

Recycling

Parts can not be remanufactured

Parts can be remanufactured

Inspection

Repairing

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Fig. 4 Major components after disassembly

Fig. 5 Quick wear components

3. Inspection and Identification Inspection of disassembled and cleaned parts is required to determine their reusability and reconditionability. According to Steinhilper (Steinhilper 1998), there are two important aspect of inspection in remanufacturing:

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Fig. 6 Used diesel engine

• Specification of criteria and condition characteristics required for the determination of the condition of the components • Development and application of suitable and affordable testing equipment The testing equipment used for the testing of new parts is generally used after reconditioning the disassembled parts. During inspection, the components are sorted after testing, those which can be reused directly, such as inlet pipe assembly, manifold, oil pan, and timing gear covers etc., are loaded into the warehouse for reassembly; the failure components which can be repaired, such as cylinder block assembly, connection rod assembly, crankshaft assembly, fuel injection pump assembly, and cylinder head assembly etc., are prepared for remanufacturing. 4. Repairing for the Components Which Can Be Remanufactured Several methods and technologies can be applied when repairing the failure parts, for example, the advanced surface technology applied in surface dimension restoration to achieve a better performance compared with original parts, or mechanical manufacturing technology applied to reprocess the remanufactured parts to satisfy the tolerance scope for assembly. 5. Reassembly The parts are reassembled into a remanufactured product using the same power tools and equipment used in the assembly of new parts (Steinhilper 1998). Then the remanufactured engine will go through testing, coating, and package processes. The effect drawings of the used engine before and after remanufacturing are shown in Figs. 6 and 7.

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Fig. 7 Remanufactured diesel engine Table 2 Technical parameters of WD615.87 diesel engine

Parameter Weight Volume Rated power Rated speed Torsion Torque speed

Quantity 850 9,726 213 2,200 1,160 1,100–1,600

Unit Kg ml kw r/min N·m r/min

Case Study: LCA-Based Evaluation of Diesel Engine Block Remanufacturing Goal and Scope Definition The goal of this study is to analyze the energy consumptions and environmental impacts of original remanufacturing of a diesel enginewith the perspective of total life cycle. Resource and energy consumptions and air/water emissions are carried out and five environmental impacts which are Global Warming Potential (GWP), Acidification Potential (AP), Eutrophication Potential (EP), and Abiotic Depletion Potential (ADP) are assessed in this LCA. The engine evaluated in this study is WD615.87 with in-line 6-cylinder, watercooled, and turbocharged engine having a total displacement of 9.726 L. In this LCA, functional unit is defined as “300,000 km driven using a WD615-87 diesel engine.” The major technical parameters of the diesel engine under analysis are shown in Table 2. A cradle to gate boundary scope was selected when analyzing the life cycle of the remanufactured diesel engine, beginning with the used engine recycled back to the

Additional materials production

Components replaced by new

End of life disposal

Energy

Fig. 8 A simplified life cycle of diesel engine remanufacturing processes, indicating the system boundary

Reassembly

Testing

Usage

Cleaning

Disassembly / sorting

Used Engine Recycling

Natural resources

Air/water emissions

Components refurbishing

Inspection

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workshop through disassembly, cleaning, refurbishing, and reassembly. Due to time constraint and technical restrictions, it is difficult to track the usage information of a remanufactured diesel engine; it is assumed according to the remanufacturer’s assurance. A remanufactured engine has the quality as good as a new engine and, therefore, meets the same fuel requirements as an originally manufactured engine. Moreover, as for the period of end-of-life disposal, the remanufactured engines are recycled back for another remanufacturing period; therefore, the phase end-of-life disposal is excluded from the evaluation scopes. The components considered in our analysis include the six parts which can be manufactured in the workshop, including cylinder block, cylinder head, crankshaft, connection rod, gearbox, and the accessories which are purchased from outside but can also be remanufactured. It was investigated that an engine can be remanufactured three to five times. As the failure modes and repair methods are usually different each time and the remanufactured engine has not reached service life, the given remanufacturing cycle has been considered as the first one. Figure 8 shows the system boundary of this life cycle assessment.

Life Cycle Inventory Analysis Data Resources Materials Production The materials consumed in the engine components manufacturing are mainly steel, cast iron, and aluminum. As for remanufacturing, there are some additional materials such as kerosene, copper, nickel and diesel for refurbishing the components. The respective quantities of the main materials used in manufacturing/ remanufacturing are shown in Table 3. The raw materials need to be extracted and refined from the minerals and then undergo various remanufacturing processes to rebuild the engine parts. Energy and resources are used for this purpose. Aluminum, cast iron, and diesel are the three major materials of diesel engine remanufacturing, which bring about large amounts of energy consumptions and environmental emissions. The data related to energy requirements, air/water emissions of materials, mining, and production phases are referred from the Chinese Life Cycle Database (CLCD) developed by IKE, China (Liu et al. 2010). The CLCD database can reflect the average production levels existing currently. The inventory of unit material production is shown in Table 4. Table 3 Main materials used in manufacturing/remanufacturing Materials for manufacturing Steel Cast iron Aluminum Alloy /

Quantity (kg) 188.19 578.83 39.9 32.92 /

Materials for remanufacturing Nickel Cast iron Aluminum Diesel Kerosene

Quantity (kg) 0.388 9 10 14.91 8.8

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Table 4 Inventory of unit material production Inventory (kg) Coal Crude oil Natural gas CO CO2 SO2 NOx CH4 H2S HCL CFCs BOD COD NH4

Production of nickel 1.48E+01 1.60E+00 1.25E-01

Production of diesel 8.58E-02 1.21E+00 6.04E-04

Production of aluminum 1.25E+01 5.11E-01 1.68E-01

Production of kerosene 1.27E+01 1.38E+00 1.04E-01

Production of cast iron 1.11E+00 4.78E-02 1.11E-03

2.23E-02 2.85E+01 1.00E+00 1.25E-01 8.33E-02 6.25E-02 5.69E-03 2.15E-08 6.02E-02 6.25E-02 2.35E-04

4.02E-04 3.75E-01 2.62E-03 6.04E-04 2.05E-02 4.71E-06 3.04E-05 6.60E-10 7.38E-03 8.65E-03 2.01E-04

5.76E-03 2.25E+01 7.74E-02 5.56E-02 6.37E-02 4.69E-04 4.84E-03 3.16E-10 9.77E-03 1.48E-02 1.62E-04

1.91E-02 2.45E+01 8.65E-01 1.03E-01 7.16E-02 4.68E-02 4.89E-03 1.84E-08 5.18E-02 5.33E-02 2.27E-04

4.84E-04 2.21E+00 4.68E-03 2.33E-03 5.15E-03 1.11E-05 5.22E-05 1.33E-02 5.72E-03 5.98E-03 3.02E-05

Table 5 Inventory of the truck transportation process/tkm Inventory

Coal

Mass (kg)

4.04E03 CH4 8.53E04

Inventory Mass (kg)

Crude oil 4.91E02 H2S 1.96E07

Natural gas 8.13E-04 HCL 1.48E06

CO

CO2

SO2

NOx

1.79E02 CFCs 9.48E05

1.26E01 COD 3.93E04

2.03E04 NH4 1.79E05

2.03E03 Dust 9.350E05

Reverse Logistics of the Used Diesel Engines According to the investigation, the used diesel engines for remanufacturing are all recycled back from the CNHTC 4S shop by truck (carrying capacity: 10 t); there are about 170 4S shops in the mainland; the average distance Davg covered for the old engine recycling is estimated by Eq. 2: m X

Davg ¼

i¼1

Di  N i

(2) Rtotal where Rtotal is the total recovery number of the used engines, Di is the recycling distance of the one used engines in the ith 4S shop, and Ni is the number of the used engines recovered by the ith 4S shop. Then, the average distance can be obtained by the investigation, and Davg ¼ 800 km. It is assumed that the truck consumes gasoline only and the transportation inner the plant is ignored, the energy consumption and emissions of unit distance when recycling can be obtained by CLCD (shown in Table 5).

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0.933kwh

Diesel Incineration by pyrolysis

Shot blasting

Electricity

Electricity 0.375kwh

Cleaning Cast iron 5kg

9kg

Reman.2

Reman.1 20.585kwh Electricity

2.3kwh

Coating material

21.111kwh

subsequent process 1

Electricity

subsequent process 2

Cleaning

Water 3.75m3

0.7kwh

Inspection

Cylinder block Fig. 9 Detailed flow diagram of the cylinder block remanufacturing process (Reman.1 and Reman.2 refer to the cylinder liner substitution and the cylinder liner brush plating.)

Engine Disassembly When the used engines are recycled back to the workshop, they are usually disassembled by high-pressure air rifle; the average time for one engine disassembly is 300 min, and it will consume 30 m3 compressed air, which equals 1.2 kg when converted to standard coal. Parts Remanufacturing The volumes of the materials for the component remanufacturing are quantified in the section of materials production. The energy consumptions for the six parts are measured during their remanufacturing processes. The detailed method for data gathering is stated in section “Data Collection in Parts Remanufacturing.” Air/Water Emissions The data for the air/water emissions have been discussed in detail in the data collection sheets. The different gases involved are CO2, CO, H2S, N2O, and chlorofluorocarbons (CFC). The water pollution emissions contains ammonia

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0.2kg kerosene

0.998kwh

Clean 1 2.67m3

0.443kwh

Inspection

Magnetic powder Coating material 5kg

1.169kwh

Energy

Water (1,2)

Compress air 30m3

Reman. 1

Reman. 2

Clean 2

Post processing

0.613kwh

Inspection 0.443kwh

6.615kwh

2.1kwh

Remanufactured crankshaft

Fig. 10 Detailed flow diagram of the crankshaft remanufacturing process (Clean 1, 2: highpressure water jet cleaning; Reman. 1: silk hole repairing, polishing; Inspection: magnetic powder inspection; Reman. 2: crankshaft neck, connecting rod journal laser plating; Post-processing: crankshaft neck milling)

nitrogen, Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD). The data about the energy demand and environmental emissions are all obtained from the CLCD fundamental database.

Data Collection in Parts Remanufacturing Remanufacturing processes are generally composed of several stages: disassembly, cleaning, testing, repair, inspection, updating, component replacement, and reassembly (Sherwood and Shu 2000). The flow diagram of the six parts remanufacturing processes are illustrated for data gathering and the resource and energy consumption of each part are collected from its remanufacturing line. Figures 9, 10, 11, 12, and 13 illustrate the data collection process of the cylinder block, cylinder head, crankshaft, connection rod, gearbox, and flywheel. Table 6 summarized the electricity and material consumptions of engine components remanufacturing; the main materials consumed in the engine remanufacturing are nickel, aluminum, cast iron, and kerosene and diesel. Energy and natural recourse consumption and environmental emissions generated in these materials production can be obtained by CLCD.

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Kerosene

Clean 1

Water (1,2) 0.8m3 3.75g Magnetic Inspection powder

1.4kwh

Nickel 3.64kwh Energy

Electricity 48g

1.5kwh

Reman. 1

Reman. 2

Clean 2

Post processing

0.184kwh

0.105kwh

Inspection 1.4kwh

Remanufactured connection rod Fig. 11 Detailed flow diagram of the connection rod remanufacturing process (Clean 1, 2: highpressure water jet cleaning; Reman. 1: bush substitution, boring, and milling; Inspection: magnetic powder inspection; Reman. 2: big hole nano brush plating; Post-processing: polishing and quilted grinding of the big hole)

Usage According to the remanufacturer’s assurance, a remanufactured engine has the quality as good as a new engine and, therefore, meets the same fuel requirements as an originally manufactured engine. It is assumed that the diesel engine is used in a truck, the energy consumed in the usage is mainly diesel fuel production, and the emissions are generated in the diesel engine operation. The diesel fuel consumed in the usage is calculated as follows: Driving distance: 300,000 km, as is defined in the functional unit Fuel efficiency: 24 ~ 26 L/100 km, using the average 25 L/100 km (Lambert and Gupta 2005) Density of diesel: 0.85 kg/L Mass of the diesel: 3,000  25  0.85 ¼ 63,750 kg The energy inputs and emission outputs of 63,750 kg diesel production is cited from the unit “diesel production” in CLCD, and the air/water emissions of the engine operation is cited from the unit of “operation, passenger car, diesel” in the public ecoinvent 2.0 database.

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4.5kg

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0.56kwh High temperature decomposition

Polishing

Electricity

Electricity 0.225kwh

Clean 1 161.9kwh

Reman. 13.44kwh

Inspection

Energy

1m3

2.3kwh

Clean 2

Water

Remanufactured cylinder head Fig. 12 Detailed flow diagram of the cylinder head remanufacturing process (Clean 1: hightemperature decomposition; Clean 2: dedicated cleaning machine; Reman.: valve pipe substitution, valve processing, and surface grinding)

Life Cycle Inventory The energy consumptions for remanufactured diesel engines along with different life cycle stages are shown in Table 7. Figure 14 is illustrated to show the inventory results more vividly, and logarithmic processing is conducted in order to normalize the result to a more tractable range. Three kinds of natural resources – coal, crude oil, and natural gas – are considered in the production of remanufactured diesel engines. It is obvious that the usage period will consume the most energy and generate the most air emissions; comparatively the used engine reverse logistics will bring about little environmental load. The crude oil and CO2 are the biggest inventory substances, followed by coal, CH4, NOx, COD, and SO2.

Life Cycle Impact Assessment Although much more can be learned about the processes by considering the life cycle inventory data, an LCIA provides a more meaningful basis to make comparisons. Based on the life cycle inventory data, LCIA is conducted for the environmental impacts mentioned above according to ISO 14042 (Yang et al. 2002).

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Diesel

High temperature decomposition

Polishing

Electricity

Cleaning 1.19kwh

Reman.

Remanufactured gear box and fly wheel Fig. 13 Detailed flow diagram of the gearbox and flywheel remanufacturing process

Table 6 Energy and resource consumption of engine components remanufacturing Components Crankshaft Connection rod Cylinder block Cylinder head Gearbox Flywheel Else

Cleaning (kWh) 1.611 0.483 3.608 3.085 0.1 0.162 24.76

Inspection (kWh) 0.886 2.8 0.7 13.44 / / /

Reman. (kWh) 3.269 5.14 2.8875 161.9 0.595 0.595 2.243

Post-processing (kWh) 6.615 0.105 17.694 / / / /

At each process in remanufacturing, the inventory data sets, including resource extraction and air/water emissions, were collected and classified into the impact categories. Subsequently, through characterization and normalization processing, the environmental impacts were calculated for each category.

Classification The LCI results are organized and combined into the impacts categories by classification. The main impact categories to be investigated under this project are Global Warming Potential (GWP), Acidification Potential (AP), Eutrophication Potential (EP), Photochemical Ozone Creation Potential (POCP), and Abiotic Depletion Potential (ADP). Characterization Characterization provides a way to directly compare the LCI results with each impact category. Based on the inventory data, the results of LCI, such as raw

Inventory (kg) Coal Crude oil Natural gas CO CO2 SO2 NOx CH4 H2 S HCL COD NH4

0.01 5.59 0.04 0.01 0.31 7.02E-05 4.54E-04 0.13 3.00E-03

0.01 11.07 0.40 0.08 0.03 0.02 2.21E-03 0.02 9.13E-05

0.06 224.91 0.77 0.56 0.64 4.69E-03 0.05 0.15 1.62E-03

Production of diesel 1.28 18.03 0.01

Production of materials Production of Production aluminum of nickel 5.74 124.95 0.65 5.11 0.08 1.68 0.17 215.31 7.61 0.9 0.63 0.41 0.04 0.47 2.00E-03

Production of kerosene 111.63 12.16 0.92

Table 7 Inventory of life cycle stages of engine remanufacturing

4.00E-03 19.66 0.04 0.02 0.05 9.86E-05 0 0.05 2.69E-04

Production of cast iron 9.86 0.43 0.01 9.92 69.85 0.11 1.13 0.47 1.09E-04 8.18E-04 0.22 0.01

Used engine reverse logistics 2.24 27.17 0.45 0.16 703.94 2.45 2.03 2.08 3.37E-04 0.2 0.06 1.31E-03

Components remanufacturing 452.01 2.7 4.02 27.72 23910.76 168.64 39.83 1306.98 3.00E-01 1.94 552.90 13.32

Diesel fuel production 5468.48 77105.46 37.13

Usage

701.54 201300 6.375 595.34 3.767 / / / /

Operation / / /

739.59 226461.09 186.44 639.90 1314.96 0.74 2.23 554.00 13.34

Total 6176.19 77171.71 44.30

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Coal NOx

CH4

H2S

HCL

Usage

SO2

Components remanufacturing

CO2

Used engine reverse logistics

CO

Production of materials

Crude Natural oil gas

Fig. 14 Log scale results of energy inputs and emission outputs by unit process

0.0001

0.001

0.01

0.1

1

10

100

1000

10000

100000

1000000

COD

NH4

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Table 8 Characterization results of diesel engine remanufacturing Environmental impacts ADP CML2002

GWP IPCC2007

AP CML2002

EP CML2002 POCP CML2002

Substances Steel and cast iron Aluminum Nickel Coal Crude oil Natural gas CO2 CH4 NOx CO SO2 NOx H2S HCL NH4 COD CO

Remanufacturing quantity (kg) 9

Characterization factor 1.66E-6 Kg Sbeq

10 0.388 567.16 60.06 5.40

2.53E-5 4.18E-3 8.08E-7 9.87E-6 7.02E-6

1266.26 4.32 4.98 10.33 11.43 4.98 0.44 0.29 0.02 1.15 10.33

1 25 320 2 1 0.7 1.88 0.88 3.44 0.23 0.03

Remanufacturing 2.98E-03

Kg CO2eq

2886.24

Kg SO2eq

15.82

Kg NO3eq

0.32

Kg C2H4eq

0.31

material consumption, energy consumption, and air/water emissions, were converted into impact indicators by multiplying the characterization factor with IPCC, CML, and WMO methodologies (Yang et al. 2002; WMO 1992). Table 8 shows the results of characterization of the remanufacturing processes.

Normalization and Weighting Normalization expresses the potential impacts in ways that can be compared with an equivalent value, and weighting assigns weights to the different impact categories based on their perceived importance or relevance, which are based on the characterization results. Normalization and weighing of five environmental impacts of remanufacturing are shown in Table 9. The results show that the environmental impacts of manufacturing and remanufacturing are 1.72 and 0.86, respectively (not including ADP).

Interpretation Life cycle interpretation is a systematic technique to identify, quantify, check, and evaluate information from the results of the LCI and LCIA.

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Table 9 Normalization and weighing of environmental impacts of remanufacturing Environmental impacts GWP AP EP POCP

Equivalent valuea 8,700 36 62 0.65

WFb 0.83 0.73 0.73 0.53

Remanufacturing 0.34 0.44 5.16E-03 0.48

Result 0.862

a

Equivalent value of the national standardization, 1990, China Weighting factors according to the reduction target, 2000, China

b

Table 10 Environmental impacts of different life cycle stages of remanufacturing after normalization Environmental impacts GWP AP EP POCP

Processes of engine remanufacturing Materials Old engine reverse production logistic 0.12 0.05 0.30 0.03 3.39E-03 1.37E-03 0.01 0.46

Component remanufacturing 0.16 0.11 2.90E-04 0.01

Usage 53.18 17.28 2.79 33.66

100.00 10.00 1.00 GWP

AP

EP

POCP

0.10 0.01 0.00

Fig. 15 Environmental impacts of the different remanufacturing life cycle stages

0.00 Materials Production Old engine reverse logistic

Components remanufacturing Usage

Contribution analysis is conducted in order to quantify the contribution of the life cycle stages or groups of processes compared to the total result and examined for relevance (EPA 2006). Environmental impacts of different life cycle stages after normalization are shown in Table 10.

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Figure 15 illustrates the results of the environmental impacts as are presented in Table 10. From the results, it can be seen that during the life cycle of diesel engine remanufacturing, the usage brings about most environmental impacts, especially GWP and AP; production of materials brings about larger environmental impacts with regards to GWP and AP; and old diesel engine reverse logistics can bring about less environmental impacts except POCP.

Comparisons with Newly Manufactured Engine In order to demonstrate the environmental benefit of remanufacturing more vividly, the results of the study are compared with an LCA case of new manufactured diesel engine with the same type (Li et al. 2013). The total energy inputs and emissions outputs during the life cycle of diesel engine manufacturing and remanufacturing are shown in Table 11. Figure 16 illustrates the environmental emissions of diesel engine manufacturing and remanufacturing before usage more vividly. Remanufacturing offers significant savings in coal and natural gas consumptions, which are 73.85 % and 71.1 %, respectively. On the other hand, it causes a little more crude oil consumption due to the production of kerosene, diesel materials, and gasoline fuels which are consumed in used engine remanufacturing. Table 11 compares the environmental emissions during diesel engine manufacturing and remanufacturing, which shows that the remanufacturing process results in significant reductions in the most relevant air/water emission categories. For example, the production of a new diesel engine produces 4.84 t of carbon dioxide, while diesel engine remanufacturing produces only 1.25 t of CO2. It should be noted that remanufacturing brings about more H2S emissions from fuel combustion in old engine reverse logistics. Table 11 Total energy inputs and emissions outputs during the life cycle of diesel engine manufacturing and remanufacturing Categories Resources (kg)

Air emissions (kg)

Water emissions (kg)

Coal Crude oil Natural gas CO CO2 SO2 NOx CH4 H2S HCL BOD NH4

Manufacturing 2,703.74 104.13 24.81 15.37 4,844.01 14.44 11.83 13.42 0.03 0.84 5.23 0.05

Remanufacturing 707.71 66.24 7.17 10.33 1,250.33 11.43 4.72 4.21 0.44 0.29 0.95 0.02

Energy savings 1,996.03 37.89 17.64 5.04 3,593.68 3.01 7.11 9.21 0.41 0.55 4.28 0.03

Coal

Crude Natural oil gas CO SO2

Remanufacturing

CO2

CH4

Manufacturing

NOx

Fig. 16 Environmental emissions of diesel engine manufacturing and remanufacturing

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

H2S

HCL

BOD

NH4

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100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% GWP

AP Manufacturing

EP

POCP

Remanufacturing

Fig. 17 Environmental impacts of manufacturing and remanufacturing

The environmental impacts of the manufacturing and remanufacturing strategies are compared and presented in Fig. 17. It is evident that remanufacturing of a diesel engine has lesser contribution towards all the environmental impact categories when compared with its manufacturing equivalent. The greatest benefit regarding environmental impacts is EP, which is reduced by 79 %, followed by GWP, POCP, and AP which can be reduced by 67 %, 32 %, and 32 %, respectively.

Summary This study conducted a comparative LCA for a remanufactured diesel engine produced by China SINOTRUK. The results obtained could be used in the future for engine designing from a life cycle perspective. The energies consumed in the engine component remanufacturing processes are collected in the remanufacturing line, and all of them are showed in detailed process flows. Due to time constraints and technical restrictions, it is difficult to track the usage information of a remanufactured diesel engine; therefore, the usage of a remanufactured engine is regarded as the same with a new manufactured diesel engine, and accurate energy consumptions during the usage period of remanufactured engines require more detailed investigation and survey to guarantee the quality of the LCA data. During the life cycle of diesel engine remanufacturing, the usage brings about most environmental impacts, especially GWP and AP; production of materials brings about larger environmental impacts with regards to GWP and AP; and old diesel engine reverse logistics can bring about less environmental impacts except POCP. Being different from material recycling, remanufacturing “recycles” the value originally added to the raw material, including the cost of labor, energy, and

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manufacturing operations. However, recycling requires added labor, energy, and processing capital to recover the raw materials. Remanufacturing could make greater economic contribution per unit of product than recycling by cutting down energy consumption and resources used for processing. From the analysis provided in this paper, it can be concluded that remanufacturing of a diesel engine has lesser involvement towards all the environmental impact categories when compared to its manufacturing alternate. The greatest reduction is EP, which is reduced by 79%. The results in Fig. 15 show that usage and production of the materials, mainly aluminum and cast iron, brings about serious environmental problems. Future work will focus on building greater efficiencies into the remanufacturing processes and greener energies, reusing a greater percentage of end-of-life components, and developing more sustainable and energy-efficient materials for diesel engine. In the life cycle of remanufactured diesel engines, the environmental impacts are largely determined by diesel consumption, electric power, and material consumptions; thus, subsequent analyses should focus on these aspects for further optimization.

References Abby T (2011) Auto parts remanufacturing to be new emphasis in China. www.chinaautoreview. com/pub/CARArticle.aspx? ID ¼ 6137. Accessed June 2011 EPA Environmental Protection Agency (2006) Life cycle assessment: principles and practice. EPA 600/R-06/060. National Risk Management Research Laboratory, Cincinnati IEA (2013) International energy outlook 2013 (IEO2013). International Energy Agency, Paris, Release Date: July 25, 2013 ISO (1999) Environmental management-life-cycle assessment-goal and scope definition and inventory analysis. Standards Australia, Australia ISO International Standards Organization (1998) Life cycle assessment-impact assessment ISO 14042. International Organization for Standardization, Geneva, Swizerland Jos GO, Greet JM, Jeroen AP (2012) Trends in global CO2 emissions, 2012 Report, Background studies. PBL Netherlands Environmental Assessment Agency, The Hague/Bilthoven, PBL publication number: 500114022 Kaebemick H, Kara S, Sun M (2003) Sustainable product development and manufacturing by considering environmental requirements. Robot Comput Integr Manuf J 19(6):461–468 Kumar V, Shirodkar PS, Camelio JA, Sutherland JW (2007) Characterizing value flow during the product life cycle including the effects of reuse, remanufacturing and recycling. Int J Prod Res 45(18–19):4555–4572 Lambert AJD, Gupta SM (2005) Disassembly modeling for assembly, maintenance, reuse and recycling. CRC Press, Boca Raton Li T, Liu ZC, Zhang HC, Jiang QH (2013) Environmental emissions and energy consumptions assessment of a diesel engine from the life cycle perspective. J Clean Prod 53:7–12 Liu XL, Wang HT, Chen J (2010) Data acquisition method for LCA database and basic life cycle model [J]. J Environ Sci 30(10):2136–2144 MlastasPaul T, Zimmemm JB (2003) Design through the 12 principles of green engineering. Environ Sci Technol J 37(5):94–101 Sherwood M, Shu LH (2000) Supporting design for remanufacture through waste-stream analysis of automotive remanufacturers. CIRP Ann 49(1):87–90 Steinhilper R (1998) Remanufacturing-the ultimate form of recycling. Fraunhofer IRB Verlag, Stuttgart

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WMO World Meteorological Organization (1992) Scientific assessment of ozone depletion 6.1–6.17. World Meteorological Organization, Geneva, Swizerland Xu BS (2007) Theory and technology of equipment remanufacturing engineering. National Defense Industrial Press, Beijing Yang JX, Xu C, Wang SR (2002) Methodology and application of life cycle assessment. China Meteorological Press, Beijing Zhang HC, Yu S (1999) A quantitative approach in environmentally conscious product design support. In: Proceedings of the 1999 I.E. international symposium on electronics and the environment, ISEE-1999, Danvers, 11–13 May 1999, pp 280–285

Sustainable Value Creation in Manufacturing at Product and Process Levels: Metrics-Based Evaluation

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Fazleena Badurdeen, Mohannad A. Shuaib, Tao Lu, and I. S. Jawahir

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sustainable Value Creation through Sustainable Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Economic, Environmental, and Societal Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total Life-Cycle Consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6R Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hierarchical Approach to ProdSI and ProcSI Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Product Sustainability Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Process Sustainability Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ProdSI and ProcSI Evaluation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Study Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Study Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding Remarks for the Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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F. Badurdeen (*) • I.S. Jawahir Institute for Sustainable Manufacturing – ISM, University of Kentucky, College of Engineering, Lexington, KY, USA e-mail: [email protected]; [email protected] M.A. Shuaib Mechanical Engineering Department, University of Kentucky, Lexington, KY, USA e-mail: [email protected] T. Lu Mechanical Engineering Department, Lexington, KY, USA e-mail: [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_52

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Abstract

Conventionally, corporations focused on economic value creation for shareholders. However, sustainable business practices require considering sustainable value added to all stakeholders. The overall sustainable value added can be evaluated by measuring economic, environmental, and societal values created for all the stakeholders. While economic value assessment methods are commonly used and well established, there are challenges in defining and establishing methods for environmental and societal value assessment. Manufacturing is one of the key sectors for achieving economic growth. Applying the sustainable value framework in manufacturing applications requires a total product life-cycle approach that considers the four product lifecycle stages (pre-manufacturing, manufacturing, use, and post-use) and the 6R (Reduce, Reuse, Recycle, Recover, Redesign, and Remanufacture) approach to create sustainable value for all stakeholders through sustainable manufacturing. In order to evaluate how effectively sustainable manufacturing creates sustainable value, there is a need for a structured approach for sustainability assessment. This chapter focuses on developing a sustainability performance evaluation methodology for manufacturing through the introduction of sustainability metrics that quantify and measure sustainable value in a comprehensive manner incorporating numerous factors related to creating sustainable values in sustainable manufacturing activities. The methodology defines sustainability metrics that cover economic, environmental, and social value added for products and manufacturing processes. The methodology also presents the process of normalizing, weighting, and aggregating the measurements for the sustainability metrics to evaluate the overall product sustainability index (ProdSI) and process sustainability index (ProcSI). The application of the ProdSI and ProcSI methodologies is demonstrated by a case study to evaluate the sustainability performance of an automotive component.

Introduction Sustainable development is defined as development that meets “the needs of the present without compromising the ability of future generations to meet their own needs” (UNWCED 1987). Meeting the needs of the present depends on the use of our resources, and being able to meet the needs of future generations requires sustaining these resources. Sustainable development is also shown as the “process of achieving human development . . . in an inclusive, connected, equitable, prudent, and secure manner” (Gladwin et al. 1995). Accordingly, a sustainable corporation is one that “contributes to sustainable development by delivering simultaneously economic, social, and environmental benefits” also known as the triple bottom line (TBL) (Hart and Milstein 2003; Elkington 1998). Corporate contribution to sustainable development is known as sustainable value (Figge and Hahn 2004).

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Although there is universal agreement on the importance of sustainable development, developing methods for applying sustainable development and assessing sustainable value are still a challenge. Laszlo (2008) states that sustainable value addition must take into account the overall benefits or costs to both the shareholders and other stakeholders (i.e., anyone that can help or hurt a business including producers, consumers, the society, and the environment). Accordingly, sustainable value is generated only when business practices deliver value to shareholders without transferring it away from other stakeholders (Badurdeen et al. 2009). Any other cases resulting in transferring benefits from one group to another, or away from both, are considered unsustainable business practices. Ueda et al. (2009) presented three models for value creation based on the nature of interactions between two major stakeholders (producers and consumers) and the environment, which they also consider as a stakeholder. The three models are class I, providing value model; class II, adaptive value model; and class III, co-creative value model. The class III model shows sustainable value creation based on the premise of the producers interacting with the consumers and the environment (both natural and social) to create sustainable value. Although these two models consider the principles of sustainable development by addressing value creation for all stakeholders and consider the environment, they present a conceptual approach and focus only on products. The definition of sustainable value must incorporate several different domains. One domain defines who the value is created for. In this context, value must be created for shareholders and other stakeholders. Accordingly, sustainable value is generated only when value is created simultaneously for shareholders and other stakeholders (Laszlo 2008; Badurdeen et al. 2009). Another domain defines what type of value is being created. The type of value created can be in terms economic value, environmental value, and societal value. Sustainable value creation requires increased value in all these categories. The third domain defines where the value is being created. Sustainable value is created at different levels: the product level, the process level, the enterprise level, and the system (or supply chain) level. Another domain can be defined in terms of area of application of sustainable development and can be classified, for example, along different industrial sectors such as agriculture, transportation, construction and building, energy, and manufacturing. This chapter focuses on the application of sustainable development in manufacturing for sustainable value creation. Manufacturing contributes to 16.5 % of total GDP worldwide and 12.4 % within the USA according to the World Bank data (The World Bank 2013). Aside from being a major value-adding contributor, manufacturing has been the engine for economic growth and has the highest effect on economic growth in the industry. Thus, to promote sustainable development, the value-generating capability through manufacturing should become a major focus. As mentioned previously, sustainable development must not only focus on economic growth, but also consider sustainable value creation (incorporating economic, environmental, and societal values) for all stakeholders. Sustainable manufacturing is defined as “the creation of manufactured products that use processes that minimize negative environmental

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impacts, conserve energy and natural resources, are safe for employees, communities, and consumers and are economically sound” (U.S. Department of Commerce 2009). Consequently, sustainable manufacturing requires a total product life-cycle approach that considers the four product life-cycle stages (pre-manufacturing, manufacturing, use, and post-use) and the 6R (Reduce, Reuse, Recycle, Recover, Redesign, and Remanufacture) (Jawahir et al. 2006) approach to create economic, environmental, and societal values for all stakeholders. In order to evaluate how effectively sustainable manufacturing creates sustainable value, there is a need for a structured approach for sustainability assessment. According to the domains of sustainable development, quantitative sustainability assessment must incorporate the economic, environmental, and societal aspects of sustainable development. Although economic value assessment methods are well established, there are still challenges in defining and establishing quantitative methods to assess environmental and societal values. In addition, the quantified sustainability assessment must be done at different levels in manufacturing by assessing product sustainability, process sustainability, enterprise sustainability, and system sustainability. This chapter focuses on developing a sustainability performance evaluation methodology for manufacturing through the introduction of sustainability metrics that quantify and measure sustainable value in a comprehensive manner incorporating all the different factors related to creating sustainable value in sustainable manufacturing activities, as presented in Fig. 1. Feng et al. (2010) presented a review of prominent metrics and indicators for sustainability assessment in manufacturing. They classified the different methodologies based on the level of technical detail (from low to high) and the application domain (product, process, facility, corporation, sector, country, and world). This work summarized the various methodologies that have been developed by a wide range of entities including corporations (e.g., Ford), international organizations (e.g., OECD), government organizations (e.g., NIST), and standards organizations (e.g., ISO). The categorization of these different methodologies is presented in Fig. 2. Sustainable value assessment can be done at the product, process, plant, and system levels (Badurdeen et al. 2013). However, there could be difficulties in applying the sustainability assessment methods reviewed by Feng et al. (2010) at these levels. Most methods presented are not comprehensive as they focus on only part of the product’s life-cycle or a limited part of the TBL aspects. To have a comprehensive assessment, the sustainability content for both shareholders and all other stakeholders from the three aspects of the TBL must be considered without any aspect being overlooked or repetitive accounting. Furthermore, sustainability performance evaluation must generate measures to assist decision makers to more effectively assess manufacturing improvement efforts that can increase sustainable value; it must lend itself for integration with other tools and techniques used to improve the manufacturing performance. This is only possible through a quantitative assessment approach that can evaluate sustainable value creation along all the aspects covered in Fig. 1. To assess the sustainable value creation in manufacturing, one needs to evaluate the sustainability performance of the manufacturing processes and products.

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Sustainable manufacturing

Takeback logistics

…

6R approach

Economy environment society

Global sourcing

System value chain

Total lifecycle

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…

Sustainable value created through sustainable manufacturing

System

…

…

Main concepts in sustainable manufacturing

Sustainable value

Ownership cost

Material efficiency

Product

Process

Safety and health hazards

End-oflife options

Functional performance

Process emissions

…

…

Fig. 1 Sample of factors related to sustainable value creation in the manufacturing system

Fig. 2 Categorization of sustainability evaluation methodologies (Feng et al. 2010)

Conforming to the definition of sustainable manufacturing from NACFAM (National Council for Advanced Manufacturing) addressing the product and manufacturing process (NACFAM 2012), the target of the research work summarized in this chapter is to develop a set of metrics and a framework through which those metrics can be used to evaluate the sustainability performance of products and

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Metrics Aggregation

Supply Chain Level

Plant Level

Enterprise Level

Line Level

Product Level

Machine Level

Process Level

Metrics Segregation

Fig. 3 Sustainable manufacturing metric hierarchy (Badurdeen et al. 2013)

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manufacturing processes. In the methodology proposed here, a set of comprehensive and quantifiable measurements considering the economic, environmental, and societal aspects of various factors in manufactured products and their manufacturing processes is identified. The total life-cycle behavior of both the product and the intermediate material flows is considered in this comprehensive sustainability evaluation where the 6Rs for closed-loop material flow are also considered. The metrics are quantitative, and the measured data need to go through a process of data collection, normalization, weighting assignment, and aggregation to generate local or global conclusions. The sustainability evaluation of products and manufacturing processes is the major focus in this chapter. However, such an evaluation can be expanded or aggregated, as needed, as shown in Fig. 3, to assess sustainability performance at narrower levels within the organization (e.g., machine level) or at a much broader level (e.g., enterprise and supply chain levels). The remainder of this chapter is organized as follows. The section “Sustainable Value Creation Through Sustainable Manufacturing” explains how sustainable value is generated through sustainable manufacturing and the key aspects that must be incorporated in evaluating sustainable value. The section “Methodology” presents the methodology to define sustainability metrics that cover economic, environmental, and social values added for products and manufacturing processes. This section also presents the process of normalizing, weighting, and aggregating the measurements for the sustainability metrics to evaluate the overall product sustainability index (ProdSI) and process sustainability index (ProcSI). The section “Case Study” demonstrates the application of the ProdSI/ProcSI methodology using a case study to evaluate the sustainability performance of an automotive component. Concluding remarks are presented in the section “Conclusions.”

Sustainable Value Creation through Sustainable Manufacturing The U.S. Department of Commerce defines sustainable manufacturing as “the creation of manufactured products that use processes that minimize negative environmental impacts, conserve energy and natural resources, are safe for

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employees, communities, and consumers and are economically sound” (U.S. Department of Commerce 2009). In addition to the US Department of Commerce definition on sustainable Manufacturing, NACFAM emphasizes the need for considering manufacturing of “sustainable” products and the “sustainable manufacturing” of all products (NACFAM 2012). Adapting the U.S. Department of Commerce definition and the NACFAM modification, Jawahir and Jayal (2011) stressed that sustainable manufacturing must demonstrate reduced negative environmental impact, offer improved energy and resource efficiency, generate minimum quantity of wastes, and provide greater operational safety and personal health, while maintaining and/or improving the product and process quality. Sustainable manufacturing creates sustainable value by applying three key concepts: economic, environmental, and societal consideration; the total lifecycle approach; and the 6R approach. The following sections provide an explanation of how the three concepts are related to sustainable value creation and how they should be considered in quantifiable sustainable value assessment methods, along with the total life-cycle consideration and the 6R approach.

Economic, Environmental, and Societal Aspects Sustainable manufacturing extends beyond the conventional practices that focus on economic performance (e.g., costs) to also focus on environmental (e.g., resource use and wastes) and societal (e.g., health and safety) aspects. These three aspects are in line with creating sustainable value, which can be evaluated by measuring the economic, environmental, and the societal values added (Badurdeen et al. 2009). Manufacturing requires resources. According to sustainable development principles, these resources must be conserved for use by future generations. In addition, manufacturing generates wastes and emissions which impact both the environment and the society. Sustainable manufacturing must minimize these negative impacts. Therefore, quantifying sustainable value generation through sustainable manufacturing requires the quantification of environmental and societal impacts in addition to economic performance. The higher the economic benefit and the lower the adverse environmental and societal impacts, the greater the sustainable value created through manufacturing.

Total Life-Cycle Consideration When evaluating the impacts of economic, environmental and societal aspects in discrete product manufacturing, the total life cycle, covering the four life-cycle stages (pre-manufacturing, manufacturing, use, and post-use), as illustrated in Fig. 4, must be considered. Various stakeholders are involved during different life-cycle stages of a product. Since sustainable value creation must consider impacts on all stakeholders,

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Fig. 4 Total life-cycle approach for sustainability assessment

comprehensive assessment of sustainable value must consider the total product lifecycle. Thus, the sustainability assessment of each factor related to product or process sustainability should be an aggregate of the overall benefits or impacts that occur throughout the four life-cycle stages.

6R Approach The 6R approach (Reduce, Reuse, Recycle, Recover, Redesign, and Remanufacture) promotes a multiple life-cycle concept (Jawahir et al. 2006), as shown in the closedloop material flow system in Fig. 5. Recover is the activity of collecting end-of-life products for subsequent post-use activities. Redesign of the product in view of simplifying future post-use processes is another important element that incorporates environmental considerations at the design stage of both products and processes. It also offers an opportunity for redesigning the next-generation products using recovered materials and residues. Remanufacture involves the manufacturing processes utilizing recovered and reconditioned materials and components. It can be used to restore old products to like new condition, offering similar or even better performance than that of the original products, thus saving natural resources, energy, and cost and reducing the waste generation (Steinhilper 1998). The 6R approach is important because it allows moving from the cradle-to-grave concept, which involves only a single life-cycle and the first three stages, to a multiple life-cycle emphasis (Jawahir et al. 2006). Through the 6R activities and by consideration of multiple product life-cycles, closed-loop material flow is achieved to recover the economic, environmental, and societal value remaining in products at

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Fig. 5 The 6R concept for a near-perpetual material flow (Jawahir et al. 2006)

the end-of-life. In addition, applying reduce throughout the product life-cycle minimizes the adverse environmental and societal impacts of manufacturing operations and maximizes the economic benefits. Therefore, there is a direct relationship between 6R activities and creating sustainable value; hence, the effectiveness of the 6R activities must be evaluated in the quantified assessment of sustainable value. Overall sustainable development not only focuses on economic growth, but also considers sustainable value creation (incorporating economic, environmental, and societal values) for all stakeholders. In addition, the total product life cycle must be considered to aggregate the value creation for all stakeholders across the four stages. Finally, the 6R approach must be incorporated to evaluate sustainable value creation across multiple life-cycles. Using a framework that integrates these aspects, sustainable value can be quantified and assessed in sustainable manufacturing applications. The following section presents a framework that applies a metrics-based approach for quantified sustainable value assessment in sustainable manufacturing. The framework focuses on sustainability assessment at the product and process levels and incorporates the above aspects.

Methodology Following the generic framework presented in the previous section, the methodology to quantify and measure the sustainability performance of products [the product sustainability index (ProdSI)] and processes [process sustainability index (ProcSI)]

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in sustainable manufacturing practices is presented here. The hierarchical approach to identify product and process sustainability metrics incorporating the three key aspects of sustainable manufacturing and the complete sets of product and process sustainability metrics are presented. The process of normalizing, weighting, and aggregating these metrics to compute the ProdSI and ProcSI is also presented.

Hierarchical Approach to ProdSI and ProcSI Development While generic definitions of sustainable products and sustainable manufacturing provide general guidance in identifying factors or elements that can evaluate product sustainability, their identification and quantitative evaluation for assessing sustainability performance of a specific product or manufacturing process are challenging and complex tasks (Fiksel et al. 1998). This is due to the wide range of aspects to be considered in evaluating product and process sustainability, the difficulties in quantifying many sustainability aspects (especially the social aspects), and the inherently heterogeneous nature of the data needed for sustainability evaluation which makes it difficult to combine them for an overall assessment. At this point, it is important to make a distinction must be made between performance metrics and indicators. While an indicator provides qualitative or quantitative information about performance of a specific phenomenon, environment, or area, a performance metric provides a quantitative measure that is required for overall product sustainability assessment. The approach presented here considers sustainability performance metrics which must be measurable, relevant and comprehensive, understandable and meaningful, manageable, reliable, accessible, and measurable in a timely manner (Feng et al. 2010). The proposed ProdSI and ProcSI methodologies have a hierarchical structure that breaks product and process sustainability down to individual metrics through a five- and four-level process, respectively. These levels are index (ProdSI/ProcSI), sub-index, cluster, sub-cluster (for ProdSI only), and individual metric, as presented in Fig. 6. To address the challenges of defining product sustainability metrics, a top-down approach was followed. This hierarchical approach ensures that the individual metrics are comprehensive and cover all major aspects of product sustainability. The identified sustainability metrics are however generic and can be applied to any type of product or manufacturing process by customization. The five-level hierarchical structure developed can be described as follows: • ProdSI – the overall aggregated product sustainability performance index • Sub-index – the three aspects of the TBL: economy, environment, and society • Cluster – major elements or factors of product sustainability within each of the three TBL categories • Sub-cluster – decomposition of clusters to more specific aspects of product sustainability

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Fig. 6 Hierarchical structure of ProdSI and ProcSI

• Individual metric – a quantifiable and measurable attribute or property related to a single parameter or indicator in each sub-cluster that is measured through out the total product life-cycle The evaluation of the overall ProdSI and ProcSI is done using a bottom-up approach to aggregate the individual metrics to provide an overall product and process sustainability assessment. The ProdSI and ProcSI are calculated through a series of operational steps including data collection for individual metrics measurement and data collection and data normalization, weighting, and aggregation. The product and process sustainability metrics are presented next.

Product Sustainability Metrics By expanding the six previously identified major product sustainability elements of environmental impact, societal impact, functionality, resource utilization and economy, manufacturability, and recyclability and remanufacturability, a more comprehensive set of 13 clusters was developed for product sustainability evaluation. These clusters are categorized under the three categories of the TBL: economy, environment, and society, as illustrated in Fig. 7. The complete set of individual product metrics under the economy, society, and environment subclusters is presented in Tables 1–3, respectively. These metrics can be customized for the product being evaluated, considering its functionality and performance. Further, in order to comprehensively evaluate the product sustainability, the measurement of each of these metrics must be made across the four product life-cycle stages (pre-manufacturing, manufacturing, use, and post-use), depending on their applicability.

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Initial Investment Economy

Direct/Indirect Costs and Overheads Benefits & Losses Material Use and Efficiency Energy Use and Efficiency

Product Sustainability Index (ProdSI)

Environment

Other Resources Use and Efficiency Wastes & Emissions Product End-of-Life Product Quality and Durability Functional Performance

Society

Product EOL Management Product Safety and Health Impact Product Societal Impact Regulations and Certification

Fig. 7 Product sustainability clusters

Process Sustainability Metrics Following the criteria mentioned before, a comprehensive set of metrics for manufacturing process sustainability assessment was identified. The chosen metrics are categorized under six clusters that represent the process-related elements of sustainable manufacturing: manufacturing cost, energy consumption, waste management, environmental impact, operator safety, and personnel health. A description of the complete set of individual process metrics under each cluster is presented in Tables 4–9. Similarly, these metrics can be customized for a specific manufacturing process.

ProdSI and ProcSI Evaluation Process The product and process sustainability metrics provide individual measures, but do not directly provide an overall assessment of product or process sustainability. The proposed ProdSI and ProcSI methodologies aggregate the metrics to provide an

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Table 1 Economy sub-cluster: individual metrics Cluster Initial investment

Subcluster Capital cost

Direct/indirect costs and overheads

R&D cost Employee training Labor cost Materials Energy Logistics Product operational cost Legal costs

Benefits and losses

Market value Quality losses

Individual metrics Equipment cost Facility cost R&D cost Initial training cost Labor cost Material cost Packaging cost Energy cost Transportation cost Warehouse cost Recovery cost Product ownership cost Average disassembly cost Environmental regulations violation Other costs related to legal issues Sales price Profit Defective/returned products loss Warranty cost

overall product and process sustainability assessment, respectively. The details of data normalization, weighting, and aggregation methods are presented in the following subsections.

Normalization Due to the heterogeneous nature of the sustainability metrics, the physical measurements of individual metrics cannot be directly aggregated. Therefore, all the individual metrics must be converted to a single normalized scale. In the ProdSI and ProcSI methodology, the individual metrics are normalized to a single scale from 0 to 10, where 0 represents the worst case and 10 represents the best case. Generally, a score of 0–4 would indicate a “poor” status, “average” with a score of 4–6, “good” with a score of 6–8, and “excellent” with a score of 8–10. A single standard normalization method that can be applied for all metrics does not exist; the normalization of each individual metric is case specific and depends on several factors including the unit of measure, the limits of the measured value, whether the individual metric is positively or negatively correlated with overall product sustainability, and the existence of benchmarks or standard reference points for normalization. Establishing reference points is essential to normalize the different units of measurement. Benchmarks can be set up based on earlier generations of the same product, standards, regulations, or expert opinions. The normalization can be done using a continuous scale or a discrete scale from 0 to 10.

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Table 2 Society sub-cluster: individual metrics Cluster Product quality and durability

Subcluster Product repair and maintenance Product reliability Return and recall

Functional performance

Product EOL management

Major product specifications Product customizability Product functional Effectiveness Ease of operation Ease of EOL activities

Product safety and health impact

Product EOL societal impact Safety

Product societal impact regulations and certification

Health Product EOL regulation compliance Product EOL certification

Individual metrics Reparability Maintainability Failure rate Life span Return rate for product defects Product recall rate (Product specific) (Product specific) (Product specific) (Product specific) Ease of EOL product disposal for the user Ease of EOL product recovery Product EOL societal impact Injury rate Product safety specifications (Product specific) Product EOL regulation compliance Product EOL certification

Weighting and Aggregation Weightings are assigned for each element in the ProdSI and ProcSI (individual metrics, subclusters, clusters, and subindices) to balance the normalized values based on their relative importance or level of impact. Typically, a higher weighting is assigned to elements with a higher importance or impact level which must be determined considering many aspects such as regional variations in legislation, expert opinions, monetary valuation, and consumer value requirements. Weighting is a very sensitive process, and it affects the accuracy of the sustainability assessment. Therefore, it is important to be objective when assigning weights to the elements when computing the ProdSI and ProcSI. Currently, there are no universal or standard weighting methods that can accurately capture the relative importance of sustainability metrics or indicators. As such, the most suitable of several weighting methods can be applied. The first is assigning equal weighting, which is considered simple and transparent. However, the shortcoming of equal weighting is that it does not truly reflect the relative importance of the aggregated elements. The second is soliciting experts’ opinions using surveys and questionnaires. Once the surveys and questionnaires are

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Table 3 Environment sub-cluster: individual metrics Cluster Material use and efficiency

Sub-cluster Product material content

Material utilization Regulations and certification Energy use and efficiency

Energy from renewable sources

Energy from nonrenewable sources

Energy regulations/certification

Other resources use and efficiency

Wastes & emissions

Energy efficiency Water use Recycled water use Other natural resources Natural resource regulations/ certification Gaseous emissions Solid waste

Liquid waste

Other waste & emissions

Waste management regulations/ certification

Individual metrics Total product material use Recycled material ratio of product Mass of restricted/ hazardous material Total packaging material use Recycled packaging material ratio Material utilization Regulation compliance Certification Solar Hydro Wind Other Coal Petroleum Nuclear Natural Gas Other Energy regulation compliance Energy certification Energy efficiency Mass of water used Ratio of recycled water used Other natural resources used Natural resource regulation compliance Natural resource certification Greenhouse gases Hazardous gaseous emissions Mass of solid waste landfilled Reused/recycled hazardous waste Disposed hazardous solid waste To hydrosphere Reused/recycled liquid waste Disposed hazardous liquid waste Heat Noise Light Radioactive emissions Waste management regulation compliance Waste management certification

(continued)

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Table 3 (continued) Cluster Product end-of-life

Sub-cluster EOL product/material Recovery

EOL product Reuse EOL product remanufacturing

EOL product recycling

Product EOL regulations/ certification

Individual metrics Ease of product disposability Product disassemblability Ratio of EOL product recovered Product reusability Ratio of EOL product reused Product remanufacturability Product redesign Ratio of product remanufactured Product recyclability Ratio of product/material recycled EOL regulation compliance EOL certification

Table 4 Manufacturing cost cluster with its sub-clusters and individual metrics Subcluster Direct cost

Individual metric Labor cost Operation energy cost Consumable-related cost Cutting tool-related cost Packaging-related cost Scrap cost

Indirect cost

Cost of by-product treatment Training cost Indirect labor cost Maintenance cost Audit and legal cost

Capital cost

Cost of PPE and safety investment Cost of depreciation Cost of jigs/fixtures investment

Measurement method Total employee payment to machining positions/total number of product units made Total cost for energy consumed in machine operation/ total number of product units made Total cost of consumables/total number of product units made (Total cost for purchasing new tools + cost for regrinding used tools – cost of recycling used tools)/total number of product units made (Total cost for purchasing new packages + used package treatment fee)/total number of product units made Total cost of scrapped product units/total number of product units made Total cost for by-product treatment (which is not covered above)/total number of product units made Total training cost/number of employees Total indirect labor cost/total number of product units made Total cost for equipment maintenance/total number of product units made Total cost of audits, legal services, and litigation/total number of product units made Total cost of PPE and equipment/total number of product units made Total depreciation of storage and fixed facilities/total number of product units made Total cost of jigs and fixtures/total number of product units made

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Table 5 Energy consumption cluster with its sub-clusters and individual metrics Subcluster Production

Individual metric In-line electricity consumption In-line fossil fuel consumption

Transportation

Transportation electricity consumption

Transportation fossil fuel consumption

Facilities

Electricity consumption on maintaining facility environment Fossil fuel consumption on maintaining facility environment

Production supply system

Electricity consumption of concentrated supply system Fossil fuel consumption of concentrated supply system

Maintenance

Electricity consumption on maintenance Fossil fuel consumption on maintenance

Efficiency

Energy efficiency

Renewable energy

Percentage of renewable energy used

Measurement method Total electricity consumption of all units and equipment in the line/total number of product units made Total fossil fuel consumption of all units and equipment in the line/total number of product units made Total energy consumption of all transportation equipment in the beginning or end of the line/total number of product units made Total fossil fuel consumption of all transportation equipment in the beginning or end of the line/total number of product units made Total energy consumption of all environmental maintenance units and equipment/total number of product units made Total energy consumption of all environmental maintenance units and equipment/total number of product units made Total energy consumption of all supply system equipment/total number of product units made Total fossil fuel consumption of all supply system equipment/total number of product units made Total electricity consumption for maintenance operations/total number of product units made Total fossil fuel consumption for maintenance operations/total number of product units made Useful equivalent energy output from the process/total energy input Total consumption of renewable energy/total energy consumption

collected, weighting can be assigned by simply averaging the weights assigned by different experts or by following more complex statistical mechanisms such as the analytic hierarchy process (AHP), previously developed for product sustainability evaluation (Gupta et al. 2010). Once the weights are assigned, the normalized metrics are aggregated to calculate the scores for the subclusters, clusters, subindices, and ProdSI/ProcSI. The aggregation follows a bottom-up approach as presented in (Eqs. 2–4).

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Table 6 Waste management cluster with its sub-clusters and individual metrics Subcluster Consumables

Packaging

Used raw material (chips)

Scrap parts

Individual metric Ratio of consumables recovered Ratio of consumables reused Ratio of consumables recycled Mass of disposed used consumables Ratio of used packaging recovered Ratio of used packaging reused Ratio of used packaging recycled Mass of disposed used packaging Ratio of used raw material recovered Ratio of used raw material reused Ratio of used raw material recycled Mass of disposed used raw material Ratio of scrap parts recovered Ratio of scrap parts remanufactured Ratio of scrap parts recycled Mass of disposed scrap parts

Measurement method Mass of recovered consumables/total mass of used consumables Mass of reused consumables/total mass of used consumables Mass of recycled consumables/total mass of used consumables Mass of used consumables going to landfill/total number of product units made Mass of recovered packaging/total mass of used packaging material Mass of reused packaging/total mass of used packaging material Mass of recycled packaging/total mass of used packaging material Mass of used packaging going to the landfill/total number of product units made Mass of used raw material recovered/total mass of used raw material Mass of used raw material reused/total mass of used raw material Mass of used raw material recycled/total mass of used raw material Mass of used raw material going to landfill/total number of product units made Mass of scrap part recovered/total mass of scrap parts Mass of remanufactured scrap part/total mass of scrap parts Mass of recycled scrap part/total mass of scrap parts Mass of scrap part going to the landfill/total number of products made

Sub-cluster Level Y ij ¼

n P m¼1

wijm

Z ijm N ijm

(1)

where Zijm is the mth individual metric under sub-cluster Yij Yij is the jth sub-cluster under ith cluster Xi wijm is the weighting factor for individual metrics Zijm n is the number of individual metrics under the sub-cluster Yij N ijm is the normalized constant for the mth individual metrics under sub-cluster Yij

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Table 7 Environmental impact cluster with its sub-clusters and individual metrics Subcluster Energy

Water Restricted material

Disposed waste

Noise pollution Heat

Individual metric GHG emission from energy consumption of the line Percentage of renewable energy used Total water consumption of the line Mass of restricted materials in disposed consumables Mass of restricted material in disposed packaging Mass of restricted material in disposed raw materials

Measurement method Total energy consumption/total number of product units made Total renewable energy used/total energy consumption Total water consumption/total number of product units made Mass of restricted materials in disposed consumables/total number of product units made Mass of restricted material in used packaging/ total number of product units made Mass of restricted materials in raw material going to landfill/total number of product units made Mass of restricted material in scrap parts going to landfill/total number of product units made Total mass of non-collected solid wastes/total number of product units made Total mass of non-collected liquid wastes/total number of product units made Total mass of non-collected gaseous wastes/total number of product units made Total mass of solid wastes going to landfill/total number of product units made Total mass of liquid wastes going to landfill/total number of product units made Noise level measured outside the plant

Mass of restricted material in scrap parts going to landfill Mass of non-collected solid wastes Mass of non-collected liquid wastes Mass of non-collected gaseous wastes Mass of solid wastes going to landfill Mass of liquid waste disposed Noise level outside the plant Heat generation

Heat generated by the manufacturing line/total number of product units made

Cluster Level Xi ¼

k P

wij Y ij

(2)

j¼1

where Xi is the ith cluster wij is the weighting factor for the sub-cluster Yij k is the number of sub-clusters under the cluster Xi

Subindex Level (for Product Only) QP ¼

s P i¼1

wi Xi

(3)

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Table 8 Operator safety cluster with its sub-clusters and individual metrics Subcluster Working environment conditions (safety)

Individual metric Exposure to corrosive/ toxic chemicals

Measurement method Number of points with corrosive or toxic chemicals/total number of employees (break down to chemical list) Total number of high-temperature points exposed to the operator/total number of employees Total number of points with high-speed components exposed to the operator/total number of employees Total number of points with high-voltage electricity exposed to the operator/total number of employees Total other exposed points with hazardous effects (splash, sparks, high-energy laser, etc.)/total number of employees Total injuries/total number of product units made

Exposure to hightemperature surfaces Exposure to high-speed components and splashes Exposure to highvoltage electricity Other threatening exposure Injuries

Injury rate

Table 9 Personnel health cluster with its sub-clusters and individual metrics Subcluster Working environment conditions (health)

PLI Absentee rate

Individual metric Chemical concentration Mist/dust level Noise exposure Temperature Other hazardous exposure Physical load index Health-related absenteeism rate

Measurement method Chemical concentration in the working environment (break down to the chemical list) Microparticle concentration in the working environment Noise level in the working environment Temperature level in the working environment Hazardous exposure level in the working environment Measured physical load index (Hollman et al. 1999) Health-related absenteeism rate

where QP is the pth subindex wi is the weighting factor for the cluster Xi s is the number of clusters under sub-index QP

ProdSI Level ProdSI ¼

t P

wp Qp

p¼1

(4)

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where wp is the weighting factor for the sub-index QP t is the number of sub-index The normalization and weighting processes are usually associated with subjective judgments (Singh et al. 2012). This affects the sensitivity and accuracy of the product sustainability assessment. Sensitivity analysis and expert evaluations can help reduce the effects of the subjectivity of normalization and weighting and increase the accuracy of the assessment.

Case Study Case Study Overview The case study aims at comprehensively identifying and developing the metrics needed for evaluating sustainability performance of manufactured products and their manufacturing processes. The objectives of the case study are summarized as follows:

Manufacturer • Evaluation of sustainability rating for selected component and process • Identification of areas with potential for sustainability improvements Academic Team • Validation of the initial set of metrics for product and process sustainability • Identifying areas where current metrics are lacking and expanding to a comprehensive set of metrics for sustainability assessment The product under investigation is an automobile power train component. The manufacturing process flow chart is shown in Fig. 8. Not all the data were provided by manufacturer, either due to the lack of measurement or due to confidentiality, and were estimated based on information available in public sources like automotive industry reports (Dreher et al. 2009; Environmental Affairs Co-ordination Office 2002; Schmidt and Taylor 2006; Toyota North America 2010), government agencies, and scientific literature.

Case Study Results Product Sustainability Assessment This section presents the results from applying the total life-cycle-based product sustainability assessment methodology to the automobile component and the resulting ProdSI. Due to lack of information, most data collected for the automobile component relates to its manufacturing stage. Information for other stages are extracted from public resources as mentioned in the previous section and then adapted to the component.

Finish grinding Each journals 230

Finish washing 290

measuring Outer diameter, front & rear, pins 220

Paper lapping Each pins & journals 280

grinding pins 210

final balance (Test & drilling) 270

grinding Pulley fitting axis 200

Initial balance (Test & drilling) 260A

Fig. 8 Process flow chart for the component manufacturing line

Air blow 295

Milling & chamfering Key ways 240

Semi-finish grinding Each journal 170

Deburring Oil hole cross points 100

Milling journal counter weight side face & journals 040

Measuring & stamping Pins & journal diameter 300

Press fitting Keys 250

grinding Thrust bearing contact surface 180

washing Before hardening 110

linear broaching journals, relief cut pin shoulders 050

Automobile Component Manufacturing Process Flow Chart (regenerated from Manufacturer’s materials)

Recentering, finishining pilot hole 160

Drilling, Tapping & reaming Font & rear faces 150

Drilling Front & rear faces 140

Tempering 125

balance (Test & drilling) 260B

Finish turning pin side faces 095

chamfering oil holes 090

drilling journal & pin oil holes 080

lathe turning front axis groove for steady rest. Backside of rear flange 035

milling pin counter weights side face & pins 070

lathe turning outer circumference of each counter weight 030

lathe turning rear oil seal & spigot 020

centering & spot facing both ends 010

Process type Product features Process No.

Material

Complete

Final visual check 310

grinding Oil seal contact axis 190

Induction hardening 120

milling datum surface for pin turning 060

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The results of sustainability measurements and the corresponding scores are presented and discussed next based on each cluster of the metric elements: economy, environment, and society. Then, a summary of product sustainability assessment is presented. Economy: Cost Because cost data were withheld by the manufacturer, values estimated based on public sources or estimation are used in this category. Therefore, the costs related to labor and to training are based on average wage estimation. The information on labor hours consumed for each of the tasks and public data of average automobile worker pay rates are known. The energy cost is estimated by multiplying the amount of energy consumed and price per unit of energy. The costs for materials, transportation, and warehousing are based on estimates as well. Costs for operation and maintenance/repair are based on published data about a medium-size sedan (IntelliChoice 2011). All cost measurements are in dollars spent per unit of product manufactured and are aggregated to the subcluster level without normalization. The normalized scores are given at the subcluster level and equal weights are assigned to these scores. Metrics with no data, namely, capital and legal cost, were not included in the calculation. A final score of 7.53 has been calculated for the cluster Cost based on equal weight. Economy: Innovation In this cluster, the material consumption efficiency is based on in-line measurement. The other two individual metrics – R&D cost and average disassembly cost – are left out due to lack of data. Linear normalization is applied to the material consumption efficiency. Since the material consumption efficiency is 79.1 % in general, a final score of 7.91 is given to the cluster Innovation. Economy: Profitability In this cluster, the profit is based on published data for a medium-size sedan. The rest of the metrics are not considered, as data for the reuse, remanufacturing, or recycling of the component is not available. Economy: Product Quality In this cluster, 12 years is considered as a benchmark for the average life span of a vehicle provided by the U.S. Department of Transportation. Reliability score of 6.09 is given according to market ranking. Data for the other metrics within this cluster were not provided. Environment: Material Use and Efficiency In this cluster, the scores of both recycled material use ratio and recycled packaging material use ratio are rated on a scale from 0 to 10 for 0 % to 100 %, respectively. A score of 10 is given based on the use of 100 % recycled material, according to the supplier. Also, there are no restricted materials used to make the component; thus, a

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full score is given to this metric. The mass of material used measured was the average mass of the raw workpiece. Since there are no plastic materials integrated into the product, no data are given to the associated individual metric. It should be noted that the mass of packaging material is estimated and the mass of transportation plastic tray is unknown. Environment: Energy Use and Efficiency In this cluster, the data for manufacturing energy use is from a direct measurement, in kilowatt hour per unit (kWh/unit) of product manufactured. It should be noted that only the manufacturing stage is considered for this metric. Data for the product energy use (e.g., use stage of life-cycle) is allocated based on the total energy use across the four life-cycle stages of the automobile and the weight for the component as a ratio of the overall weight of the automobile. The energy reduction ratio is from published reports, considering overall manufacturing operations. Again, product recycling, reuse, and remanufacturing are not properly traced by this case company, and the associated information is unknown. The score of 6.64 is given to manufacturing energy use and product energy use as a reference value. The final score is aggregated and generated from those three individual metrics. Environment: Water Use and Efficiency In this cluster, all the data are for a medium-size sedan based on published reports. Linear normalization is applied to the metric of recycled water use. The three metrics equally share the weight leading to a final score of 7.30. Environment: Residues In this cluster, full scores (10 in score column) are given to the metrics solid waste stream and liquid waste stream as there is no waste disposed (0 in measurement column). Furthermore, both landfill reduction and environmental regulation compliance receive full credit because the research team was informed that no waste is sent to landfill and no environmental regulations have been violated, respectively. The measurement for the gaseous emissions during the use stage is calculated from the total gaseous emission for a medium-size sedan provided by the UK Department for Transport (United Kingdom Department for Transport 2013). Since the post-use data are unknown, the metrics for product recycling, reuse, and remanufacturing are not aggregated to the final score. A final score of 9.33 is calculated without considering the end-of-life (EOL) metrics, which are left blank. To have a more sound and comprehensive product sustainability assessment, considering product EOL is a necessity. Environment: Product End-of-Life Management All measurements in this cluster come from published reports. It must be emphasized that low scores are given due to no cosideration of the product EOL.

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Table 10 Score aggregation under the sub-index of economy SUB-CLUSTER Sub-Cluster Value Manufacturing 6.13 Cost Use Cost 6.46 Post-management 10.00 Cost Product 7.91 Development Profit 5.49 Product Life Product Quality Losses

CLUSTER Weight

Cluster

SUB-INDEX

Value Weight

Sub-Index

Value

Weight

Economy

6.75

0.33

0.33 0.33

Cost

7.53

0.25

Innovation

7.91

0.25

0.33 1.00 1.00

Profitability

5.49

0.25

6.09

1.00

Product Quality

6.09

0.25

–

–

Color Coding 0

5

10

Society: Education and Customer Satisfaction Data related to customer satisfaction and product repairs and returns are not available. Therefore, the final score of 7.70 is based on the metric of employee training and development. Society: Product End-of-Life Management and Product Safety and Societal Well-Being Most data are unavailable as they relate to the product EOL stage, for which the company did not have information readily available. There are no product processing injuries for the manufacturing line, and a full score is given to the corresponding metric and the perfect score of 10 is generated solely based on that.

ProdSI Economic subindex sustainability performance is visually presented by the colorcoded table shown in Table 10. As most data are extracted from public sources, there is an opportunity to improve the accuracy of the ProdSI by using actual data related to the component and the automobiles bearing the component. Environmental sub-index sustainability performance is visually presented by the color-coded table shown in Table 11. Results indicate that the product EOL management is the weakest subcluster that needs further consideration and improvement. Societal sub-index sustainability performance is visually presented by the colorcoded table shown in Table 12. The three sub-indices, combined with equal weighting, give a final score for the ProdSI that is 7.59 (out of a full score of 10). The final product sustainability index (ProdSI) in Table 13 once again reveals that the element of product end-oflife management is the area that requires the most efforts for improvement.

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Table11 Score aggregation under the sub-index of environment SUB-CLUSTER Sub-Cluster Value Weight Product 8.06 0.50 Materials Packaging 9.55 0.50 Materials Manufacturing 7.37 0.50 Energy Use Use-phase 5.90 0.50 Energy Use Post-use Phase Energy Use water Use 7.30 1.00 and Efficiency Residues 10.00 0.33 Residue 8.00 0.33 Reduction Post-use Phase Emissions 10.00 Compliance 0.33 Product Endof-Life 2.93 1.00 Management

CLUSTER Cluster

Value

Material Use and Efficiency

8.83

Weight

SUB-INDEX Sub-Index Value Weight

0.20

0.20 Energy Use and Efficiency

6.64

Water Use and Efficiency

7.30

0.20

Environment

7.15

0.33

0.20

Residues

9.33

Product End-of-Life Management

2.93

0.20

Table 12 Score aggregation under the sub-index of society SUB-CLUSTER SubCluster Education Customer Satisfaction Product Repairs and Returns Product Recovery Incentives Product Safety and Societal Well-being

CLUSTER

SUB-INDEX

Value

Weight

Cluster

Value

Weight

7.70

1.00

Education

7.70

0.50

SubIndex

Value

Weight

Society

8.85

0.33

Customer Satisfaction

Product End-of-Life Management

10.00

1.00

Product Safety and Societal Well-being

10.00

0.50

For better visualization, the results can also be represented in the form of spider charts, as shown in Figs. 9 and 10 below. The discontinuity in the charts is due to nonavailability of data for those subclusters/clusters.

Process Sustainability Assessment Machining Cost The research team did not have access to most of the cost data related to the component studied. The labor cost and training cost are estimated based on the number of labor

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Table 13 Score aggregation for ProdSI CLUSTER Cluster

SUB-INDEX Value

Weight

Cost

7.53

0.25

Innovation

7.91

0.25

Profitability

5.49

0.25

Product Quality

6.09

0.25

Material Use and Efficiency

8.83

0.20

Energy Use and Efficiency

6.64

0.20

Water Use and Efficiency

7.30

0.20

Residues Product End-of-Life Management

9.33

0.20

2.93

0.20

7.70

0.33

Education

Value

Weight

Economy

6.75

0.33

Environment

7.15

0.33

Society 10.00

ProdSI

7.59

Customer Satisfaction Product End-of-Life Management Product Safety and Societal Well-being

ProdSI

Sub-Index

8.85

0.33

0.33

Sub-clusters Society 8.85

Product Safety and Societal…

Manufacturing Cost

Product Recovery Incenves

Product Repairs and Returns

Customer Sasfacon

Educaon

Product End-of-Life Management

10.0 9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0

Post-management Cost Product Development

Economy 6.75

Profit

Product Life

Product Quality Losses

Compliance

Product Materials

Post-use Phase Emissions Residue Reducon

Environment 7.15

Use Cost

Residues water Use and Efficiency

Packaging Materials Manufacturing Energy Use Use-phase Energy Use Post-use Phase Energy Use

Fig. 9 Spider chart for sub-clusters within ProdSI

hours (known) consumed for each of the tasks and public data on average automobile worker pay rate. The operation energy cost and coolant-related costs are estimated by multiplying the amount consumed and the unit price of purchasing. Scrap loss is calculated based on number of scrap parts made and raw material price.

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Clusters Cost Product Safety and Societal Well-being

Society 8.85

10.0 9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0

Product End-of-Life Management

Customer Satisfaction

Innovation

Economy 6.75 Profitability

Product Quality

Material Use and Efficiency

Education

Product End-of-Life Management

Energy Use and Efficiency Residues

Water Use and Efficiency

Environment 7.15

Fig. 10 Spider chart for clusters within ProdSI

All cost measurements are in dollars spent per unit of product manufactured. Thus, they are aggregated without any weighting. The normalization takes place at the subcluster level. Since financial data were not available, a score of 7 was assumed for the machining cost cluster. Benchmark data can be applied for more accurate normalization. Energy Consumption Most of the energy consumption data are directly measured in kilowatt hours per unit (kWh/unit) of product manufactured. The data are not normalized until the cluster level. A reference energy consumption amount was set for manufacturing one piece of the component (in $), based on the total amount of energy consumed to manufacture a car as described in published reports. Based on the reference points selected, the energy consumption data are normalized and then aggregated to generate the final score of this cluster. Waste Management The major solid waste streams of the manufacturing line are the machining chips and scrapped parts. While a significant coolant losses were present the exact coolant loss streams were not identified because the coolant system was shared with other manufacturing lines. The company adopts a “zero landfill” policy which makes for a good recovery and recycling practice. Therefore, full scores are given for the corresponding

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individual metrics. Benchmarks are needed for a more objective scoring of reuse and waste generation metrics. Environmental Impact GHG emissions are tracked in terms of the carbon content for the electricity consumed. The only stream of restricted material use related to the coolant loss. Solid waste is tracked, but the liquid waste and gaseous waste streams are not identified. “Zero landfill” policy results in full scores for all the corresponding individual metrics. A reference for the water usage is set by value allocation based on the total amount of water consumed to manufacture a car as reported in published reports. The result shows that manufacturing the component is not a heavy waterconsuming process. The major problem in this cluster is the high greenhouse gas emission (GHG), due to the fact that the major source of electrical power in the local electricity grid being coal. The plant does not utilize any form of renewable energy. The reference point is given by monetary value allocation based on the total amount of GHG emission during the manufacturing of a car as stated in published reports. Personal Health Measurements concerning the working conditions are tracked, based on the benchmarks set by safety regulations from agencies or organizations including EPA, OSHA, and NIOSH (NIOSH 1992, 1998a, b). The records of operator absenteeism were referred to and no health-related absenteeism was found. Noise at the manufacturing site is close to the threshold limit of 85 dB, while hearing protection is not enforced. In this case, the reduction effect of the personal protective equipment (PPE) is not considered; thus, a poor score is given. Aside from that, it was unexpected to see from the physical load index (PLI) questionnaire results that a highly automated manufacturing process still involves considerable physical stress to the operators. The operators often had to bend their bodies to lift heavy workpieces. It is likely that the bodyguards on the equipment are restrictive and limit the ability of operators to access workpieces and tools. Operator Safety All exposures to hazards are well shielded. According to the safety records of the line, no injuries occurred during the period of investigation. As a result, full scores are given to all the measurements within this cluster. ProcSI Results The aggregated scores for the ProcSI clusters are shown in Table 14. The results are also represented in the form of spider chart, as shown in Fig. 11 below. The final score for the ProcSI is 7.61 out of 10.

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Table 14 Score aggregation for ProcSI CLUSTER

ProdSI

CLUSTER

VALUE

WEIGHT

Machining cost

7.00

0.17

Energy consumption

4.73

0.17

Waste management

7.98

0.17

Environmental Impact

8.42

0.17

Personal health

7.12

0.17

Operator safety

10.00

0.17

VALUE

7.61

Color Coding 0

5

10

Sub-clusters Machining cost 10.0 9.0 8.0 7.0

Operator safety

6.0 5.0

Energy consumption

4.0 3.0 2.0 1.0 0.0

Personal health

Waste management

Environmental Impact

Fig. 11 Spider chart for clusters within ProcSI

Concluding Remarks for the Case Study The component under investigation and its manufacturing processes received a fairly good score, considering that a score of 8 is given indicating industry-leading

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performance, based on the value creation evaluation, market behavior and customer impressions. However, there are some concerns that must be addressed. First, the uncertainty and difficulty of the assessment should be considered. Data collection was not easy, and many measurements lack detailed data from the manufacturer. Estimating from other sources and ignoring the influences of the metrics due to missing data can reduce the reliability of the evaluation made. This emphasizes the significance of data collection and management effort needed for a realistic evaluation. On the other hand, the scope of this objective sustainable value creation assessment has not been taken by current manufacturers. one indication is that, in this study the data provided focus on the manufacturing stage while the data in other life-cycle stages are inadequate. Here is an example of the resulting effects. The influence of energy consumption on the economic value creation might be relatively small due to the inexpensive local price of electricity. Under this situation, the manufacturer showed inferior performance in energy related metrics, especially when compared with their performance in other metrics. It implies that even the market-leading manufacturers have difficulties taking the ideas of sustainable value creation and sustainable manufacturing concepts into their applications, thus the full potential of sustainable value is not retained.

Summary Sustainable development is driving corporations to shift from conventional business practices that focus on value creation for shareholders to sustainable business practices that focus on value creation for all stakeholders. Corporate contribution to sustainable development is known as sustainable value. Sustainable value can be divided to economic, environmental, and societal value, which is in line with the triple bottom-line concept introduced by Elkington (1998). This chapter presented the ProdSI and ProcSI methodologies for sustainability assessment at the product and process levels. The ProdSI and ProcSI methods are metrics-based and have ninety and seventy-five metrics categorized into thirteen and six clusters, respectively. The sustainability clusters for product and manufacturing processes were summaries of past research. These metrics were introduced and the process to normalize, weight, and aggregate the measurements for these metrics to compute the ProdSI and ProcSI was explained. Finally, the application of the methodology was demonstrated through a case study to evaluate ProdSI and ProcSI for an automotive component. During this case study, an automobile component and its manufacturing processes are taken into consideration, where the whole procedure of preliminary target setting, data collection and allocation for each of the metrics, normalization of measurements, weighting, and aggregation is demonstrated. Such a case study can be taken as an example of selfassessment for sustained improvement, and benchmarked comparison requires further effort at each of the steps in the procedure to generate universal standards/ baseline of evaluation. Future work will focus on applying the ProdSI and ProcSI

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methodologies to refine and customize the individual metrics’ sets for specific industries and application. It will also address weighting and normalization by involving industry experts, exploring different weighting methods and performing sensitivity analysis to examine the effect on overall sustainability evaluation.

References Badurdeen F, Goldsby TJ, Iyengar D, Jawahir IS (2013) Transforming supply chains to create sustainable value for all stakeholders. In: Jawahir IS (ed) Treatise on sustainability science and engineering. Springer, Dordrecht, pp 311–338 Badurdeen F, Iyengar D, Goldsby TJ, Metta H, Gupta S, Jawahir IS (2009) Extending total lifecycle thinking to sustainable supply chain design. Int J Prod Lifecycle Manag 4(1/2/3):49–67 Brundtland, Gro Harlem et al. (1987) Our common future: the world commission on environment and development. Oxford: Oxford University Press Dreher J, Lawler M, Stewart J, Strasorier G, Thorne M (2009) General motors metrics for sustainable manufacturing, MIT Sloan school of management report. MIT Sloan School of Management, Cambridge, MA Elkington J (1998) Cannibals with forks: the triple bottom line of the 21st century business. New Society, New York Environmental Affairs Co-ordination Office (2002) Car recycling: Europe. Published by Toyota Motor Marketing Europe Environmental Affairs Co-ordination Office Feng SC, Joung CB, Li G (2010) Development overview of sustainable manufacturing metrics. In: Proceedings of the 17th CIRP international conference on life cycle engineering, Hefei, China, pp 6–12 Figge F, Hahn T (2004) Sustainable value added – measuring corporate contributions to sustainability beyond eco-efficiency. Ecol Econ 48(2):173–187 Fiksel J, McDaniel J, Spitzley D (1998) Measuring product sustainability. J Sustain Prod Des 6:7–18 Gladwin TN, Kennelly JJ, Krause TS (1995) Shifting paradigms for sustainable development: implications for management theory and research. Academy of management Review 20(4):874–907 Gupta A, Vangari R, Jayal AD, Jawahir IS (2010) Priority evaluation of product metrics for sustainable manufacturing. In: Proceedings of the 20th CIRP design conference, Nantes, France, pp 19–21 Hart SL, Milstein MB (2003) Creating sustainable value. The Academy of Management Executive 17(2):56–67 Hollman S, Klimmer F, Schmidt K, Kylian H (1999) Validation of a questionnaire of assessingphysical work load. Scandinavian Journal of Work, Environmental Health 25(2):105–114 IntelliChoice Source Interlink Media Inc (2011) Toyota Camry 4Dr Sedan cost of ownership. http://www.intellichoice.com/1-12-2011-44021-1/2011-toyota-camry-4dr-sedan-cost-of-own ership.html. Retrieved 14 Nov 2011 Jawahir IS, Jayal AD (2011) Product and process innovation for modeling of sustainable manufacturing process. In: Seliger G, Khraisheh M, Jawahir IS (eds) Advances in sustainable manufacturing. Springer, Berlin/Heidelberg, pp 299–305 Jawahir IS, Rouch KE, Dillon Jr OW, Joshi KJ, Venkatachalam A, Jaafar IH (2006) Total Lifecycle considerations in product design for manufacture: a framework for comprehensive evaluation, (Keynote paper). In: Proceedingss TMT 2006, Lloret de Mar, Barcelona, September 2006, pp 1–10 Laszlo C (2008) Sustainable value: how the world’s leading companies are doing well by doing good. Stanford University Press, Stanford

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National Council for Advanced Manufacturing (NACFAM) (2012) Sustainable manufacturing. www.nacfam.org/PolicyInitiatives/SustainableManufacturing/tabid/64/Default.aspx. Retrieved 29 Oct 2012 National Institute for Occupational Safety and Health (NIOSH) (1992) Recommendations for occupational safety and health: compendium of policy documents and statements, U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, National Institute for Occupational Safety and Health, Cincinnati, DHHS (NIOSH) Publication No 92–100 National Institute for Occupational Safety and Health (NIOSH) (1998) Criteria for a recommended standard: occupational exposure to metalworking fluids. U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Cincinnati, DHHS (NIOSH) Publication No 98–102 National Institute for Occupational Safety and Health (NIOSH) (1998) Criteria for a recommended standard: occupational noise exposure. U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Cincinnati, DHHS (NIOSH) Publication No 98–126 Schmidt WP, Taylor A (2006) Ford of Europe’s product sustainability index. In: Proceedings of 13th CIRP international conference on life cycle engineering, Leuven, pp 5–10 Singh RK, Murty HR, Gupta SK, Dikshit AK (2012) An overview of sustainability assessment methodologies. Ecol Indicators 15(1):281–299 Steinhilper R (1998) Remanufacturing: the ultimate form of recycling. Fraunhofer IRB Verlag, Stuttgart, pp 1–24 The World Bank (2013) Manufacturing, value added (% of GDP). http://data.worldbank.org/ indicator/NV.IND.MANF.ZS/countries/1W-US?display¼graph. Retrieved 12 Dec 2013 Toyota North America (2010). Toyota 2010 North America environmental report. Toyota Motor North America Ueda K, Takenaka T, Váncza J, Monostori L (2009) Value creation and decision-making in sustainable society. CIRP Ann Manuf Technol 58(2):681–700 U.S. Department of Commerce Website (2009) http://trade.gov/competitiveness/ sustainablemanufacturing/how_doc_defines_SM.asp. Retrieved 5 Oct 2012 United Kingdom Department for Transport (2013) Car fuel data, CO2 and vehicle tax tools. http:// carfueldata.direct.gov.uk/. Retrieved 18 Jan 2013

Product Characteristic Based Method for End-of-Life Product Recovery

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . End-of-Life Product Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classification of EoL Product Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Methodology for Product Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EoL Product Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Types of Product Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . New Product Information to Support Product Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design for EoL Product Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reusability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Remanufacturability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recyclability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Residue Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marketability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cost and Environmental Impact Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life Cycle Cost Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life Cycle (Environmental Impact) Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decision Making in Selecting EoL Product Recovery Option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decision-Making Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

This chapter introduces a method for deciding optimal options in end-of-life (EoL) product recovery. It utilizes multiple factors on EoL product condition and EoL product recovery values for better decision making in the planning of

Y.T. Ng (*) • B. Song Singapore Institute of Manufacturing Technology (SIMTech), Singapore e-mail: [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_84

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EoL product recovery for optimal eco-performance. These factors are measurable and closely tied to the product characteristics. The merits of the method can be seen from three perspectives. Firstly, embedded information and resources of returned products are fed back to the product life cycle chain as a closed loop for continuous improvement in product design and manufacturing. Secondly, EoL product recovery options for reusability, remanufacturability, and recyclability can be optimally determined. And thirdly, it quantifies the benefits of incorporating EoL product recovery into manufacturing processes in terms of manufacturing costs, material utilization, and energy consumption. A case study on a crankshaft from a refrigerator reciprocating compressor is presented to demonstrate the merits of the method.

Introduction By the end of this year, there will be more than 100 million personal computers and more than 500 million mobile phones being disposed globally. Thanks to growth of middle-class population around the world, demand for industrial product has gained momentum throughout the world especially in emerging market. At the same time, the life span for these products has been reduced gradually over the years due to consumer behavior. For example, people in developed countries tend to change personal computer as fast as 4 years of average lifetime, while those in developing countries change in 5–6 years of average lifetime (Yu et al. 2010). According to the UNEP, 20–50 million tons of e-waste is generated each year. The amount is estimated to double in the next decade. Moreover, generation of e-waste is growing three times faster than any other type of municipal waste on global level. The rapid growth of e-waste in the last few decades is mainly because of prosperous growth in electronic and electrical industries and the fast advancement of technologies. Consequently, the quick growing development affects consumption habits. Life cycle of products gets shorter and results in escalating e-waste. This has resulted in replacing electrical and electronic equipment (EEE) products which still have a long life span. Furthermore, majority of the e-waste are ended up in landfills or incinerators. In the USA, about 75–80 % of e-waste ready for EoL management ended up in landfills (Kahhat et al. 2008). It has now become one of the fastest growing waste components in municipal solid waste stream, and it could be a source of hazardous waste that adds to environmental burden and human health risk (Gaidajis et al. 2010; Ongondo et al. 2011; Terazono et al. 2006; Widmer et al. 2005; UNE Programme 2007a, b). To make things worse, 50 % more resources are being extracted than our planet can replenish today (Almond et al. 2012; UNE Protection 2012). What does human do with millions of obsolete products? Most of the obsolete products are being recycled in one way or another. However, the methods are unsystematic, not effective (in terms of energy and cost), and hazardous due to mishandling and end up as landfills. This is because the team who is responsible for

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the recycling does not have the knowledge of the product and has no recycling process, no support of advance technology, no established recycling channel, and no connection with the manufacturer. All the reasons mentioned above are critical criteria for an effective recovery process which can retrieve values from the end-oflife (EoL) product. Therefore, regulatory and manufacturer have been racing to propose product and process to counter this trend, which the trend supports toward sustainable manufacturing. To save the resources and serve the planet better, an effective solution is proposed to predict the life cycle status of the product, determine the product condition, and design a management system to handle it at EoL stage.

Definitions • EoL product condition is referring to the state or quality of the product at the point of return. Usually, there are few critical parameters which can indicate the product condition, and this has to be determined during the stage where the product is designed and manufactured by the manufacturer. For most mechanical parts, the remaining life can be estimated by comparing the date of manufactured and predicted life span modeled by the manufacturer from reliability test. The information such as wear-out life also gives hint to predict remaining useful life. This parameter shall be able to be measured accurately and precisely by certain equipment. Some manufacturers even design or customize this equipment in order to achieve certain efficiency when measuring the EoL product condition. The measured parameters could be used to simulate the EoL product condition by using certain model and assumptions. Then, user can categorize the product according to the product condition. The silver lining behind the cloud of problems are journals which develop methods to quantify the value of returned products – in the form of either recovery value or EoL value. In a case study on television sets, product and environmental costs (converted from carbon emissions) are summed to attain an overall product life cycle cost (PLCC). This value is then put through Weibull analysis by means of historical data. Ultimately, it allows users to compare between making a new product and reusing a returned piece (Anityasari et al. 2005). While the formulation is comprehensive, the method falls short in developing quantity-based comparison between new production and other recovery options like remanufacturing and recycling. • EoL value refers to the quality value at EoL phase and includes technical condition, remaining useful life, and material selling price. • Recovery value is the value gained from EoL treatment as compared to making new product. It includes cost savings, GHG emission savings, and technical condition savings. • Residue value is the remains value after recoverable value has been retrieved from EoL product.

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• Product recovery is the process of restoring EoL product to a former and better state or condition. A product that can recover certain range of value at EoL stage is called product recovery value. • Product recovery cost is the cost required to recover EoL product, and this cost varies depending on the product condition. Both the product recovery value and product recovery cost are determined to support product recovery decision. • Product recovery decision is the decision-making process of selecting the best recovery option. The recovery options are, namely, reuse, remanufacture, recycle, and dispose (incinerate).

End-of-Life Product Recovery End-of-life (EoL) product recovery enables an organization to help reduce the environmental impacts of their products while maintaining profit margin. Some common ways of treating EoL product are landfill and recycling. However, these recovery treatments are more for short-term solution because they achieve low-value recovery efficiency. Recovery treatments such as landfill and recycle are usually adopted by an organization who intends to solve the EoL product without early planning during the product design or manufacture. Therefore, it is proven that high-efficiency product recovery can be achieved when EoL product treatment is being considered or planned during the product development stage. In consequence, product manufacturer has to be involved starting from the birth of the product till EoL stage. In other term, it is called design for EoL product recovery. In order to design for EoL product recovery, it is important to understand the possible types of EoL products, which can be categorized according to the product condition. Based on the EoL product condition, one can determine the product recovery options appropriately. The objective of this topic is to: • Describe the possible types of EoL products • Evaluate the product recovery options • Identify new product information to support product recovery types of EoL product return

Classification of EoL Product Condition Returning of EoL products is acquired from production rejects, supply chain return, end-of-lease return, warranty return, and consumer return: i. Production rejects are the defective products that might be caused by manufacturing process or assembly errors. Thereby, these erroneous parts are not suitable for further assembly into product.

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Table 1 EoL product condition

Types of return Production reject Supply chain return End-of-lease return Warranty return Consumer return

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Product condition Unused (with error) Unused (new) Predictable (inaccurate) Predictable Unpredictable

ii. Supply chain return is the unsold products returned from distributor or retailer. It might cause product obsolescence due to excess purchase or shipment because of errors in sales estimation. iii. End-of-lease return refers to the returning of leased product when the lease schedules are expiring. At the end of the leasing period, the old equipment is often replaced by a new unit and usually comes with maintenance service in the contract. iv. Warranty return refers to the return of products by consumers due to defects in material and workmanship or fail units within a certain period of time specified by the manufacturer or retailer. v. Consumer return mainly refers to the end of product usage. Consumers might return the old unit as trade-in when they purchase a new unit for replacement. In other case, consumer sells it as raw material to the third-party waste recycler. Product condition of the EoL product is classified based on the types of return. The products are mainly categorized in unused, predictable, and unpredictable status as shown in Table 1. i. Unused (with error) product condition is typically caused by inconsistent manufacturing process that resulted in inaccurate part dimension. The defective unused product could be also due to mistake in assembly or destructive dismantling when the product performance is rejected as a whole. ia. Unused (new) product condition, on the other hand, refers to the condition of the parts that are not affected when the product is rejected from product assembly. Similarly, products return from supply chain (distributor) are unused and in new condition with remaining shelf life. ii. Predictable product condition for a warranty return product is possible since the product has been used in a limited timeframe. The condition is predicted with reference to the historical data on failure rates of similar products in the earlier stages of product development. The condition of leasing product is somehow predictable as the up-to-date product information is available during product maintenance. However, the complete identity of the product could not be confirmed as there is a mixture of old and new parts in the reconditioned equipment. Therefore, it is rather difficult to accurately predict overall product performance. iii. Unpredictable product condition is identified from the consumer returns which there are unknown operating condition and environment during usage stage.

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Thereby, the condition of the product could have large variation. For instance, the condition of a 5-year-old machine could be better than a similar 2-year-old machine under different operating frequency. With the source of product returns, EoL product condition can be further streamline so that it can be quantified more accurately.

The Methodology for Product Recovery Figure 1 shows the methodology of product assessment at the point of return. When a product is first received, the date of return must be recorded so that such information can be referred to the date of manufacture to determine its used life. The used life of the product is an important indicator to judge product reusability. If the used life has over the designed life, where the reliability of the part is not covered in design phase, the part is not appropriate for reuse. Otherwise, remaining useful life of the part shall be determined based on the historical or statistical data in product development phase. To better assess the part for recovery, specific type of material needs to be identified for the right treatment. Valuable parts are chosen so that the embedded resources are worthwhile to retrieve. Subsequently, the returned part shall be classified based on the types of return channel. The condition of returned product from different channel is explained in the following section. Consumer returns include end-of-lease return, warranty return, and end-of-use return. The operating condition is commonly known for the leased product, which provides the details of usage trend. It eases the observation effort to identify the product condition. On the other hand, if there is lack of usage information, product condition can be determined through indentifying the critical degrading area, measuring the level of degradation, and then comparing with the manufacturing data. Several recovery options can be explored such as reuse, remanufacture, or recycle. Finally, recovery value is computed with the particular processes based on the recovery option. The EoL products can be categorized according to the few key characteristics that are used to describe the condition of the product. For example, when an EoL product is collected, it will be sorted out based on the types of return and product model. Then, the EoL product will be going through verification, assessment, and determination of critical breakdown component and severity as referred to the data available in design stage. If the EoL product meets the reusable specification, the product will be reused. Otherwise, the EoL product will be decomposed into subassembly parts – mechanical part, electrical part, and consumables as shown in Fig. 2. Then, each of the subassembly part can be further tested to determine the quality. Finally, the component can be graded as good, moderate, poor, and very poor with reference to the characteristics generated in design and development stage. The result of “good” reflects that significant value can be recovered from EoL product. On the other hand, “very poor” regards to zero value; in fact additional resources are required for handling the EoL product.

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Identify date of part return Compare date of manufacture Determine part used life Identify type of material - Metals - Glass - Polymer - Composites - Semiconductor Identify value of part Single out valuable part Classify types of returned part

Production rejects

Distribution

Consumer

Without user input

Inspect damage area

Inspect critical degrading area

With user input

Identify operating condition

Identify critical degrading area Measure level of degradation Compare result with manufacturing data Discover recovery option Compute recovery value

Fig. 1 Product recovery methodology

EoL Product Measurement Product condition in this study implies the state of product’s health; more importantly it indicates the potential cause of product failure. The purpose of scrutinizing the product condition is mainly to identify how significant the remaining product value can be recovered and what is the best alternative recovery option to salvage

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EoL stage Collection

Returned product assessment

New product assessment

Sorting Identify product configuration Accelerated life testing Identify critical breakdown component Determine relationship/ severity

Verify product configuration Assess product status Identify critical breakdown component Good Determine relationship/ severity

Product reuse

Bad Dismantle

Generate product/ component characteristics Mechanical

• Cost(fuel, electricity, consumables, machine, manpower, overhead) • Environmental impact • Quality and reliability

Moderate Remanufacture

Good Reuse

Electrical

Consumables Very poor

Poor Recycle

Disposal

Fig. 2 EoL product assessment flow

Fig. 3 Crankshaft (http:// www.asia.ru/en/ProductInfo/ 490872.html)

the product. Types of products such as consumer durables (household appliances, cars, personal computer, etc.) are used frequently for short interval of time (within a day), while industrial and commercial products (equipment) are generally continuously operating for 24 h a day. However, this analogy does not confirm longer usage life in either case until measurement is executed at the point of return. Most of the products mentioned above are commonly made of electronics and mechanical component. This study focuses on the condition of mechanical component, where there is a list of failure mechanism, such as fatigue, corrosion, wear, and fretting, to name a few. Crankshaft of refrigerator compressor is presented to illustrate the concept of determining EoL product condition as shown in Fig. 3. The crankshaft is part of the compressor that functions to convert the linear piston motion into rotation. The condition of the above mechanical parts are measured in wear-out life (i.e., in terms of cycle), change of dimension from the original measurement, and cleanliness level. Figure 4 shows the part assessment flow, where the wear-out life of the product is first determined. If the usage life has over the design life, the

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Dismantle product into part Determine wear-out life Within design life?

No

Yes

Cost / Carbon emission

Measure part dimension

Yes

Recycle

High

Within reusable limit?

No Dispose

Determine part cleanliness level

Determine part cleanliness level

Determine cost and carbon emission

Cost / Carbon emission

Reuse

Low

High

Low Remanufacture

Fig. 4 Mechanical part assessment flow

part will be sorted out as nonreusable part, which the recovery options are recycle or dispose. Otherwise, the part will be checked for the dimension changed. If the part dimension is within the reusable limit (design specification), the part is suitable for reuse after cleaning. On the other hand, the part will be considered for remanufacturing if the dimension changed is over the limit. Determination of cleanliness level of the part is necessary for cost analysis. Besides, carbon emission from recovery activities is computed to interpret the impact of particular recovery option to environment. All the information allows the decision maker to decide on the recovery options based on cost and carbon emission impact to the environment.

Wear-Out Life Wear-out life measures the age of the product at the point of return; moreover, the information allows manufacturer to estimate remaining life of a product or component after it is collected from the user. The bathtub curve is widely used in reliability engineering. It describes a particular form of the hazard function which comprises three parts. The first part is a decreasing failure rate (early failures), the second part is a constant failure rate (random failures), and the third part is an increasing failure rate (wear-out failures). The life spans of a product or component usually follow one of the curves described above. This curve can be predicted and plotted by manufacturer during product development phase by collecting and analyzing data through running reliability testing. The manufacturer can also do the same for a subsystem. The widely used methods to determine the reliability of the product are tests such as accelerated life test, endurance, temperature cycling, and so on.

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This information will help manufacturer to determine the remaining “safe to use” period when the component is collected from known period of usage. Every product has a unique serial number which can be traced back to the manufactured date. However, to accurately predict the remaining life of the product, it is important to consider also the usage behavior, frequency of usage within a period of time, and unexpected operation of the product outside product specification. The outcome from wear-out life valuation enables the decision maker to make quick decision on whether to reuse the product or component.

Change of Dimension Change of dimension is one of the common mechanisms observed in failure machines or machine parts, where it results in loss of material by mechanical removal. This information not only informs the condition of the product; more importantly it provides a clue to cost computation based on different recovery option and thus estimates the optimal profit. The critical problem to solve here involves searching for the best method to detect loss of material and how to patch the loss of material. There are many methods to detect loss of material and many are costly. In any case, the method selected must be the most cost-effective and accurate. A simpler method to measure dimension change is using profilometer, where the stylus moves relative to the contact of surface. This method could be economical; however, it is time taken. Another advanced method for thickness measurement called electron energy loss spectroscopy (EELS) is introduced. The theory behind this method is electron scattering and energy loss. With the known original dimension, analysis method could be programmed and absolute dimension changed values can be accurately determined in milliseconds. Nevertheless, the equipment could be several times more expensive than profilometer. Taking recovery of high value product and large volume into consideration, return on investment of the advanced machine will be recovered in the long term. Cleanliness Level Cleanliness level of a returned product is another important indicator to quantify a product condition. The contaminated or dirty surface causes bad heat dissipation, lowers flow rate, and thus affects the internal pressure and so on. With a layer of contaminant or dirt covers on the surface, it is impossible to patch the loss material for reuse. Moreover, the dirtier a part is, the longer time and higher cost to clean the part is required. In industry practice, all parts that require cleaning are treated as the worst case. Thereby, the part cleaning line in production is planned in such a way that higher chemical washing concentration is used to clean all parts. In another case, manufacturer might fix a longer washing time in order to make sure all dirt is totally cleaned up. Nonetheless, the concentration of chemical agent and time taken to wash associated with cost. Hence, inspection of cleanliness level is introduced so that part cleaning process could be done cost efficiently. A manual way to identify part cleanliness level is via visual inspection, which the labor sorts out the dirty part according to his

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Table 2 Part cleanliness measure Cleanliness level 1. Very dirty 2. Dirty

Cleanliness measurement and definition Total weight
part weight (3 or more units of gross contaminant) Total weight
part weight (2 unit or gross contaminant)

3. Clean

Total weight
part weight (1 unit of gross contaminant)

Action taken 2 wiping time + wash 1 wiping time + wash Wash

experience. A more accurate and direct method to recognize the cleanliness of part could be using gravimetric measurement, where a highly sensitive scale can detect gross contaminant. To automate the process, this scale can be integrated with EELS to measure the change of part dimension and at the same time attain the cleanliness status. As shown in Table 2, the lowest level of cleanliness represents very dirty, while the highest level of cleanliness stands for clean. A wiping step is introduced to clean the part with the lower level of cleanliness instead of having multiple wash cycle.

Types of Product Recovery There are numerous strategies for the treatment of EoL products. A list of the potential treatment options is as follows: • • • • • • • •

Reuse Repair Remanufacturing Refurbishing Recycling Composting Incineration Landfill

The options to choose depend on the conditions of the EoL products as described in the previous section. Each option shall be designed and optimized in the objective to recover as much value as possible from the EoL product at a minimum cost and minimum environmental impact. Figure 5 as follows shows the assessment flow for EoL reciprocating compressor. A crankshaft is used to examine the method to decide product recovery options among reuse, remanufacture, recycle, and dispose: i. Reuse In reuse, the part is inspected and channeled to assembly and distribution. Reuse only involves evaluation cost; however, the recovered value is lower than remanufacture part as it is recovered as a functional used product.

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Design

Distribution

Manufacturing Pre-cast NEW

Storage

Rough process

Fine process

Disposal

Recycling

Re-machining

Transport

Transport

Clean

Clean

Use

EoL

Inspection

Collection Transport Storage Disassembly Part-level Test

DISPOSE RECYCLE

REMAN

REUSE

Fig. 5 Assessment flow for compressor part

ii. Remanufacture In remanufacturing, the part is cladded before undergoing fine machining, cleaning, and inspection. The cladding process is affected by the amount of wear experienced by the part, and it is averaged for the purpose of this study. After these, the part is finally assembled and distributed. Remanufacturing costs involve EoL product evaluation cost and also cost to repair/remanufacture the EoL product so that it is comparable to a new product. iii. Recycle In recycling, the part is transported to be recycled, before it gets a new lease of life and follows the production of a new part. iv. Disposal In disposal, the part is transported to be landfilled, before it gets a new lease of life and follows the production of a new part. The part selected is iron-casted, thereby disposal will follow the process of actual recycling, as opposed to traditional incineration. The value that can be recovered from disposal is the least as there is cost involved to manage the disposal, while the only benefit is reduced environmental impact.

New Product Information to Support Product Recovery Identifying the environmental aspects and fulfilling all the performances throughout the life cycle may be complex. However, with the closed-loop product life cycle framework in Fig. 6, knowledge that feedback to manufacturing engineers will able to define the changes for efficient and cost saving process, while design engineers

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Design

Manufacturing

Usage

EoL

Product spec.

Mfg. date

Sales data

Usage pattern

Returning date

Product quality

Mfg. process

Customer feedback

Usage env.

Product status

Process quality

Location

Reliability • Product obsolescence

Product conf. Product model

• Market demand • Product spent life • Failure mode • Product lifespan • Product quality

Fig. 6 Product design information for EoL product recovery

define the degree of change in succeeding the integration of all aspects in producing attractive product cost and maintaining product functionality. As referred to the framework below, when a used product is collected, the return date will be captured and the product will be tested for its performance for product remaining life estimation. While at the design stage, useful information such as product specification, product design quality, and reliability data play relevance to this cycle. Product specification dictates the acceptance level for EoL product recovery options, product quality corresponds to the recovery value, and reliability data are to confirm if the functions of the product are statistically significant. Therefore, discovery of new product design data and EoL product status enables decision makers to predict product design life span and desired quality. As, for example, taking into consideration the returned product status and match to the product designed quality, it enlightens product designer the depth of innovation suitable for upcoming designs. On another note, the design-EoL cycle also sees varied decisions made depending on the recovery option chosen. As brought up by Thierry et al. (1995), through sustainability, products can be designed for simplified disassembly – so that relatively transient components are isolated for easy access during dismantling and reassembly. Compatible parts can also be modularized. This framework can be extended and implied in determining which of the recovery options is more appropriate for the given product. If the remaining quality is relatively good, remanufacturing could be desired over discard. Hence, the particular product should be designed for remanufacturability. In other words, this closed-loop method inherently eases the recovery option in the product design.

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Y.T. Ng and B. Song

Product recovery process

Recovered product

Fig. 7 EoL product recovery steps

Design for EoL Product Recovery Figure 7 outlines the steps for product recovery. With the input of product condition, decision maker decides on selecting the recovery process to obtain the optimal recovery value. Finally, the product is restored, where the recovered product is the output of the recovery process. EoL product information is not merely a decision input; more importantly, the knowledge learned enables better design for EoL product recovery. If the EoL product information is utilized wisely, product recovery process could achieve more efficiently and productively. The design of product recovery process shall enable each product recovery cost to be highly efficient so that one can optimize resources required to recover the EoL product according to the product nature. This section shall examine the design for each of the following process: • Reusability • Remanufacturability • Recyclability

Reusability Reusability indicates that the EoL product can be reused from evaluation of EoL product condition, though the EoL product is lagging behind a new product in terms of quality and reliability. In this case, the EoL product does not require any refurbish process; it only requires test to determine that the EoL product condition passes the threshold of reusability. Therefore, the cost involves only the EoL product condition test. The design of EoL product condition test in most cases requires the following to achieve high optimization: • • • •

Accuracy Precision Short test time Less material or manpower required

Automation becomes an obvious option, and thus the test has to be planned, designed, and maintained. Today, there is a lot of high-end equipment which allows

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user to do different kinds of measurement quickly without much manpower required as long as the setting is preset. Besides, these machines can communicate with other machines to enable data sharing or data analysis. This will enable the user to quickly interpret the EoL product condition as reusable.

Remanufacturability Remanufacturability specifies that the EoL product is appropriate for reprocess to become as new product. The cost incurred here is higher than reusability as mentioned above due to the extra process required to reprocess the EoL product, and therefore the value recovered has to be higher too to make economic sense. In most of the cases, remanufacturability is only viable if the EoL product can be repackaged as a new product as the customer is unwilling to pay good price for a used product. In order to design a product for remanufacturability, one can apply the automation strategy of assessing the EoL product condition as mentioned in the section on reusability above. This assessment shall be able to provide information such as which component is not working according to the specification. After the assessment, the EoL product has to go through reprocess so that it can recover the functionality, quality, and reliability comparable to a new product. In order to achieve this, one has to consider the following steps (Fig. 8).

Recyclability Recyclability in this context refers to the decomposition of the EoL product and conversion of the subcomponent back to raw material. The EoL product is recycled in this case, is largely due to irreparable or rectifiable condition. Another reason for recycling is when the cost for repair is too high that it does not make any economic sense. Therefore, the most critical process design for recyclability would be determining the EoL product condition, decomposition, and conversion. During the EoL product condition determination, one has to design the key parameters or conditions that can quickly indicate the high cost of repair. For example, the X-ray machine that shows the crack on mechanical parts can be costly to repair. Therefore, first to detect the costly component will cut short the process to decide on recycling. Secondly, decomposition of the EoL product can be time consuming and resulted in high cost. This can be improved if manufacturer has considered decomposition during product development and manufacturing. For example, manufacturer can design the product to be in modular form so that certain easy to fail parts can be grouped together for easy isolation or replacement during decomposition process. Lastly, the subcomponents shall be converted to raw materials if it is confirmed not to be repaired or reused. The conversion usually involves high cost as a lot of energy is required to convert end product to raw material. To overcome this, one has

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A highly efficient way to disassemble the product into sub component

A quick way to identify the faulty part or part which cause the EoL product not comparable to a new product

Replace or repair the part

Assemble the product

Test the product and ensure the standard is on par with new product

Fig. 8 Design steps for remanufacturability

to design this process so that the conversion is being done in high volume to achieve the economy of scale. Besides, adopting high technology method can also help to reduce the effort of conversion.

Residue Value As explained earlier, residue value is the remains value after recoverable value has been retrieved from EoL product. In any case, residues are the waste resources. Thereby, residue value should keep as minimum as possible. As for the EoL product that cannot be further recovered by using the three methods mentioned above, it has to be discarded. This conclusion can be due to the following: • The cost or time required to recover does not make economic sense. • There isn’t a good method to recover the EoL product due to technology constraint. • The recover method will cause high impact to the environment. Therefore, it is important to design the management of residue value. Usually there will be cost incurred to manage the residue value in order not to cause impact to the environment.

Marketability The commercialization of recovered EoL product is equally important as the design of product recovery process, and this has to be considered or designed as early as possible during the development of the product. One can plan the market of

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recovered EoL product for each category including reuse, remanufacture, and recycle to maximize the recovered value. In practical, one has to consider the potential demand, market, and profits that can be generated from each category of recovered EoL product. Thus, it is important to differentiate the recovered EoL product characteristic so that one can market or price it accordingly. For reusability, the right market should be the secondhand market because it is a used product and the quality grade is lower than a new product. It is important to counter the two biggest challenges to serve this market, which are pricing and quality. Customer will refuse to spend above certain level for a used product, and thus one has to strike a balance between the pricing and the quality guaranteed. For example, from data collection in the lab, one can determine the remaining life of EoL recovered product under reusability category and propose an optimized warranty period to comfort the anxiety of customer buying a secondhand product. The difference between recovered EoL product from reuse and remanufacturing has to be carefully crafted so that customer sees values in both options. Manufacturer has to position of recovered EoL product from remanufacturing at higher grade compare to reuse. This is because more cost is usually spent during the EoL product recovery process for remanufacturing such as replacement of faulty or degraded subcomponent. Therefore, manufacturer could market recovered EoL product from remanufacturing close to new product in terms of pricing and quality. As for the recycled product, it is usually recovered in the form of raw material or at a very low subassembly level. Therefore, one has to observe the raw material market prices and demand to justify the investment or operation of recycling EoL product. At the same time, the cost of disposal and local regulatory can help to support the recycling option. Recycling sometimes can do more harm to environmental impact as it requires reverse engineering and high energy consumption.

Cost and Environmental Impact Analysis This section will be focusing on saving in terms of cost and CO2, which is used as the indicator for environmental burden. In this study, the product ending in disposal is the default path in manufacturing; given OEMs act in accord with the take-back regulations.

Life Cycle Cost Analysis Recovering the EoL product always involves a cost, and this cost can vary depending on the choice of product recovery process. A simple rule of thumb is that the product recovery cost shall not exceed the cost of manufacturing a new product or the cost of disposal. Thus, it is critical that one considers all the costs incurred for recovering the EoL product from EoL product collection, logistic, EoL product evaluation, recovery process, recovered EoL product packaging and marketing, etc.

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The cost calculation for a new product can be seen in Eq. 1, where the cost of manufacturing a new part is one of the cost components in the disposal cost (Eq. 2) and recovery cost (Eq. 3). Operational cost in Eq. 4 is one of the cost components in manufacturing and recovery cost: Cnew ¼ Cop
new þ Coh
new þ Cproc
new þ Cdep
new

(1)

Cdis ¼ Cop
dis þ Coh
dis þ Cproc
dis þ Cdep
dis þ Cnew

(2)

Crec
i ¼ Cop
i þ Coh
i þ Cproc
i þ Cdep
i þ ðCnew Þ

(3)

Cop ¼ Cmat þ

n X

Cprocess
j þ Clabour

(4)

j¼1

I where: Cnew ¼ New product cost Cdis ¼ Disposal cost Crec-i ¼ Recovery cost of option i, i ¼ 1 (Reuse); i ¼ 2 (Reman.); i ¼ 3 (Recycle) Cop ¼ Operational cost (including direct material, process, direct labor) Coh ¼ Overhead cost (including indirect labor, rent, utilities) Cproc ¼ Procurement cost (including collection, transport, take back) Cdep ¼ Machine depreciation cost (assume straight line depreciation over 5 years) Cmat ¼ Material cost Cprocess-j ¼ Cost of process j, j ¼ 1, 2, 3. . .

Life Cycle (Environmental Impact) Assessment Life cycle assessment (LCA) is a widely used technique for assessment of environmental impacts throughout the entire product life cycle. Of the entire life cycle of crankshaft (from refrigerator compressor) as a case study, LCA is applicable to the material extraction, transportation, and manufacturing process of the product. The main contributors to ecological burden are energy usage (in the form of electricity) in material extraction and manufacturing process, as well as fuel consumption in transportation. These inputs are shown in Table 3. In view of the above inputs, the following values, unique to this case study, are keyed into the SimaPro program for each of the reprocessing options: 1. 1 kWh of electricity in Singapore is comprised of (Tan et al. 2010): i. 0.758 kWh natural gas, burned in power plant ii. 0.219 kWh electricity, oil, at power plant iii. 0.023 kWh electricity, waste, at municipal waste incineration plant

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Table 3 Inputs for resource consumption

Input Types Electricity Material Transport type Fuel

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Unit kWh Kg tkm l

2. Power consumed for recycling using induction furnace: 700 kWh/tonne (Gandhewar et al. 2011) 3. Transportation used: transport, lorry, 3.5–7.5 tonne, EURO 3 Global warming is used as the impact category; thus the environmental impact to be obtained is the sum of the carbon dioxide (CO2) emission from all types of energy consumed. The environmental impact caused by recovery activities is calculated using equation (5) Total CO2 emission ¼ ðElectricity consumption  electricity grid emission factorÞ þ ðFuel consumption  GHG emission factorÞ (5) where the electricity grid emission factor (Singapore NEA 2012) is 1 kW electricity ¼ 0.5716 kg CO2e/kWh and the direct GHG emission factor (Guidelines to Defra 2012) is 1litre of diesel ¼ 2.6763 kg CO2e/l The environmental impact caused by carbon emission from making new product is calculated using Eq. 6, carbon emission from product disposal is calculated with Eq. 7, and carbon emission from product recovery options is determined using Eq. 8. Carbon emission from recovery operation in Eq. 9 is one of the carbon components in all of the above equations: EI new ¼

n X EI op
new þ EI proc
new

(6)

j¼1

EI dis ¼

n X EI op
dis þ EI proc
dis þ EI new

(7)

j¼1

EI rec
i ¼

n X EI op
i þ EI proc
i þ ðEI new Þ

(8)

j¼1

EI op
i ¼

n X j¼1

EI process
j

(9)

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1.0E−02

9.0E−03 8.0E−03 7.0E−03 6.0E−03 5.0E−03 4.0E−03 3.0E−03 2.0E−03 1.0E−03 0.0E+00

n n n n n ty icity icity tion tion tion tion ion tion io io io io io ci at at at at at xi otox tox upa upa rma ple plet eple ic ic ic m di f o r h i h a t o o o r p d p de cc cc de l d o e an ec ci nf ec rf ro ro e at ng ec ter ine nd o nd o tra ater etal ossi ut ut la um idan atte si on l e i e a lim i H a z r d a n r r F la M e W C a m n w O ll ox ria Io st te M rin ra te an al la st esh al rre wa Ma la tu rb ic Te esh ul ur rre Fr U m cu t i c e i t e a r r T h Fr N Pa Ag oc ot Ph n

ge

an

ch

tio

le

p de

ty

or tf

n

io

ci

xi to

at

m

Comparing 1 p ‘Crankshaft REUSE’, 1p ‘Crankshaft REMAN’, 1p ‘Crankshaft RECYCLE’ and 1p ‘Crankshaft DISPOSE’: Method: ReCiPe Midpoint (E) V1.06 / World ReCiPe E / Normalisation Crankshaft REUSE

Crankshaft REMAN

Crankshaft RECYCLE

Crankshaft DISPOSE

Fig. 9 The normalized chart of environmental impact model for crankshaft

where: EInew ¼ Environmental impact caused by making new product EIdis ¼ Environmental impact caused by product disposal EIrec
i ¼ Environmental impact caused by recovery option i, i ¼ 1 (Reuse); i ¼ 2 (Reman.); i ¼ 3 (Recycle) EIprocess
j ¼ Environmental impact caused by process j, j ¼ 1, 2, 3. . . The LCA is done using SimaPro program, and ReCiPe (midpoint) is utilized as the assessment method. Among the three cultural perspectives, egalitarian is chosen for it represents long-term and conservative environmental mindset. Furthermore, the environmental impact calculation is reflected in kg CO2 equivalent, which is located under the “climate change” impact category (Flowers et al. 2003). LCA is done for all options – reuse, remanufacture, recycle, and dispose. In the end, the procedure generates various environmental impact indicators, as shown in Fig. 9.

Decision Making in Selecting EoL Product Recovery Option Rapid changing technologies on the product front has aroused consumer pursue for the latest goods. If the assertion holds true, more products would have shorter life span and this resulted in unwanted products surging high. Therefore, caution is needed for an equal fast response from the waste products. On top of that, products such as consumer durables are at the competitive edge in the market. Manufacturer could merely earn a narrow profit or even lose without a prudent manufacturing

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planning. To meet the challenges, manufacturers have to select appropriate manufacturing strategies including recovery options, manufacturing processes, workpiece, machinery, and so on. The decision-making process is more challenging today as more aspects are taking into consideration. One of the most common problems faced by decision maker is assessing wide range of alternative options and choosing one based on the conflicting criteria. The section as follows illustrates the process of decision making using simple and logical methods to select the most optimal option by eliminating the unsatisfactory alternatives to better strengthen the existing decision-making procedures.

Decision-Making Process Decision making is one of the most important actions engaged in planning and selecting strategies to accomplish company goal. Firstly, decision making involves selecting an option from a number of alternatives – reuse, remanufacture, recycle, or discard the EoL product to achieve cost-effective manufacturing. Secondly, decision making is a process that implicates more than simply a final decision among the alternatives – what is the product reliability and impact to the environment if EoL product is reused, remanufactured, recycled, or discarded. Lastly, the decision made is related to action taken that the decision maker engages in to achieve the goal – to develop EoL product recovery production line. A general decision-making process is explained in the following steps (Fig. 10).

Step 1: Defining Problem Original equipment manufacturer (OEM) is required to manage the EoL product (with the premise of take-back regulations enforced). Within the company each subunit is expected to have targets, such as reducing raw material by adding recycled material from EoL product, finding new market for the recovered product, developing new approach of logistic planning, and so on. Generating these targets becomes the basis for identifying the problem, deciding on the actions taken, and evaluating the outcomes. Overall, understand the problem situation is important as it affects the quality of the decision. Step 2: Identifying Requirements Requirements are the conditions that must meet in accordance with the problems set. These requirements are the boundary describing the possible solutions to the decision problem. In order to retrieve the maximum embedded value from the EoL product efficiently, one should determine the condition of the product at point of return. Information such as product design specification and product reliability data are used as benchmark so that the exact quantitative form of requirement can be stated. The quality of a reusable product should be as good as new, however less reliable. As for a remanufactured product, remain or change of dimension is allowed as long as it meets as new condition. With the exact quantitative form of requirement on hand, it can prevent the ensuing debates on judgmental evaluation.

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Defining the problem Identifying requirements

Revision (if necessary)

Establishing goal Generating alternatives Generating criteria Selecting decision analysis method

Developing value or business model against alternatives

Analyzing sensitivity and uncertainty Evaluating decision effectiveness

Fig. 10 Decision-making steps

Step 3: Establishing Goal Goals are the broader statement of desired outcomes toward which effort is directed. In this context, the end goals in decision making are maximizing the profit (via optimal retrieve EoL product value), minimizing the environmental impact, and maximizing (to meet) the product technical condition. Step 4: Generating Alternatives Once the goals have been identified, the next step in the decision-making process is to generate alternatives to the goal. Manufacturer has to search for alternative means of reaching the goals. In this step, relevant information and the likely consequences must be gathered. As such, manufacturer must seek as much information as possible pertaining to the likelihood that each alternative would result in the achievement of various outcomes. For example, manufacturer should consider the solution of setting up a recycling line or engage the third-party recycler could result more cost-efficient. Moreover, the extent of generating alternatives is bounded by the importance of decision, cost, and value of additional information needed to evaluate the alternative and number of people affected by the decision (Zopounidis 2011). The more important, extensive, and greater number of people involve in the decision, the higher cost of evaluating, and lengthy time are required. In this context, the recovery alternatives for EoL product are reuse, remanufacture, recycle, and dispose.

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Best value

Goal: Profitability

Criteria: Sub-criteria:

Price

Environmental impact

Cost

• Company strategy • Market demand

Return volume

• Fuel • Electricity • Consumables • Machine • Manpower • Overhead

Product condition

• Wear out life • Physical life • Company strategy • Dimension • Cleanliness level • Regulation • Collection channel

Resource consumption • Fuel • Electricity • Consumables • Machine • Manpower • Overhead

Product condition • Wear out life • Dimension • Cleanliness level

Fig. 11 Hierarchy of generating criteria and sub-criteria

Step 5: Identifying Criteria With the end goals in mind, criteria among the alternatives can be identified. This step is necessary as the criteria indicate how well each alternative accomplishes the goals. As shown in Fig. 11, the sub-criteria are grouped into a set of related criteria. Grouping the criteria is particularly helpful in scrutiny whether the set of criteria is rightly adapted to the problem. Besides, it is straightforward for the process of calculating criteria weights using some decision analysis methods. Another advantage of grouping the criteria is it aids in visualizing the emergence of higher-level affair. Step 6: Selecting Decision Analysis Method There are a number of decision analysis methods for solving a decision problem. Nonetheless, choosing the particular method depends on the complication of the problem as well as the objective of the decision maker. Sometimes it requires integration of few methods to solve a problem. References of further readings are recommended (Munier 2011). In the case study, two subgoals are identified; thereby multi-attribute utility theory (MAUT) is applied to the decision-making analysis. Step 7: Evaluating Value or Business Model Against Goals After getting the criteria for each alternative, manufacturer could develop value or business model to evaluate the alternatives. In the process of evaluating the business model, manufacturer should verify (1) feasibility of the alternative,

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(2) satisfactory of the requirement, and (3) impact to the people. In order to validate the first aspect, manufacturer must confirm that the costs of handling product recovery are always lower than making a new product. The handling process costs for each alternative are considered based on the process flow shown in Fig. 5. Besides, costs that include operational, overhead, procurement, and machine depreciation are calculated using Eqs. 1, 2, 3, and 4. It is necessary to specify the calculations on different cases. For example, Alternative A: Value ¼ Revenue
Costs Alternative B: Value ¼ Revenue
Costs
Taxes If the problem involves certain time period, it is appropriate to use the notion of net present value (NPV) to model the value:

NPV ¼

n X cash flow at time t t¼1

ð1 þ r Þt

where r is the discount rate and n is the number of years. In addition to feasibility study on each alternative, manufacturer must achieve the cost-benefit requirement. For instance, cost of remanufacture has to be 50 % lower than a new product price. Finally, the chosen alternative must be acceptable to the person who has to commit to the consequences of the decision.

Step 8: Analyzing the Sensitivity and Uncertainty Next, sensitivity analysis is worked on the business model to assess the sensitivity of potential changes and errors. This analysis helps decision maker to identify which parameters are the main drivers of the model’s outcome. In the case study, one-way sensitivity analysis is done to assess the impact change in certain parameter that will have on each alternative’s outcome. The model’s results are shown graphically in the form of tornado diagram (Fig. 12). To achieve the most profit by integrating the recovery option to production line, remanufacturing cost, recycling cost, remanufactured product selling price, and recycled material selling price are the key influential parameters. Deterministic analysis is acquired to determine the most influence parameters, followed by stochastic analysis to further optimize the result. To simplify the process, Monte Carlo simulation is applied to study the uncertainty conditions, followed by plotting the cumulative probability graph. The analyses ease the decision maker to understand the risk profile of each alternative (Fig. 13). The results in the figure show that alternative 1 has the least uncertainty condition, which is also the least risky option. Alternative 2 dominates alternative 3; in other words, the returns (NPV) of alternative 2 are always better than alternative 3. However, alternative 3 tends to have the largest fluctuation (risk) as compared to the other 2 options. Overall, if the decision maker is a risk taker, remanufacture of EoL product will be the best option. As for the risk averse decision maker, setting up the reuse recovery production line should be the safest choice.

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2000

4000

6000

8000

10000

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14000

Selling price: Reuse Cost: Reuse Number of reuse unit

Cost: Remanufacture Selling price: Remanufacture Number of reman unit

Cost: Recycle Selling price: Recycle Number of recycle unit

Fig. 12 Combined tornado diagram

Step 9: Evaluating Effectiveness on Decision The last step in the decision-making process is evaluating the effectiveness (quality) of the decision. All the process can be done using simulation tool based on user’s input, which the simulated results assist in observing the trend. However, in actual case, when an implemented decision does not produce the desired outcomes, revision in part of the process is needed. Typically, inadequate definition of problem is the major flaw. After readdressing the problem formulation, user will go through the same process, thus generating new perspective of analysis.

Summary With an eye on sustainable manufacturing, attention is shifted to reverse supply chains to reduce the organizations’ carbon footprint and preserve our physical environment. But perhaps above all, from a market perspective, sustainability requires gathering more business logic and favorable bottom lines to trigger the change. The proposed framework which allows reuse, remanufacture, and recycle of EoL product can boost the value retrieved from return goods through proper

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Y.T. Ng and B. Song Cumulative Chart for All Alternatives 1.0 0.9 Cumulative Probability

0.8 0.7

Alternative 1 Alternative 2 Alternative 3

0.6 0.5 0.4 0.3 0.2 0.1 0.0 −$9,000 −$6,000 −$3,000

$0

$3,000 $6,000 $9,000 $12,000 $15,000 NPV

Fig. 13 Combined risk diagrams for all alternatives

planning and design. Each of the options discussed above essentially increases product knowledge, creates channels for information feedback, lowers production costs and selling prices, improves customer relationships, and widens presence in new and old markets. In conclusion, the proposed methodology is able to stimulate and support product and process innovation, thus enhancing bottom-line performance, delivering cost benefits to the company, and ensuring sustainability. It serves to create new knowledge, increase failure detection, and reduce risks, as well as provide alternative solutions with integration of public policy.

References Almond R, Grooten M, McLellan R, Dudley N, Duncan E, Oerlemans N, Stolton AS (eds) (2012) Living planet. WWF Z.S.o. London, and G.F. Network. World Wide Fund for Nature; International (collaboration work of Europe and US), p 164 Anityasari M, Han Bao, Kaebernick H (2005) Evaluation of product reusability based on a technical and economic model: a case study of televisions. In: Electronics and the environment. Proceedings of the 2005 I.E. international symposium, New Orleans, Louisiana, USA Flowers L, Bilyk K, Banerjee J, Greiner T, Mehrotra S, Pitt P (2003) 21st century sustainability metrics: an introduction to life cycle analysis. Int J Life Cycle Eng 8(6):324–330 Gaidajis G, Angelakoglou K, Aktsoglou D (2010) E-waste: environmental problems and current management. J Eng Sci Technol Rev 3:193–199 Gandhewar VR, Bansod SV, Borade AB (2011) Induction furnace – a review. Int J Eng Technol 3(4):277–284 Guidelines to Defra/DECC’s GHG conversion factors for company reporting (2012) Department for Environment, food and Rural Affairs (defra), London, United Kingdom Kahhat R et al (2008) Exploring e-waste management systems in the United States. Resour Conserv Recycl 52(7):955–964

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Munier N (2011) A strategy for using multicriteria analysis in decision-making. Springer, Dordrecht, Heidelberg, London, New York Ongondo FO, Williams ID, Cherrett TJ (2011) How are WEEE doing? A global review of the management of electrical and Electronic wastes. Waste Manag 31(4):714–730 Singapore NEA (2012) Information on emission factors. National Environment Agency, Singapore Tan RBH, Wijaya D, Khoo HH (2010) LCI (Life cycle inventory) analysis of fuels and electricity generation in Singapore. Vol 35(12), Elsevier Terazono A et al (2006) Current status and research on e-waste issues in Asia. J Mater Cycles Waste Manag 8(1):1–12 Thierry MSM, Van Nunen J, Van Wassenhove L (1995) Strategic issues in product recovery management. Calif Manag Rev 37(2):114–135 UNE Programme (2007a) E-waste. In: Inventory assessment manual 2007. United Nations Environmental Programme, Division of Technology, Industry and Economics, International Environmental Technology Centre, Osaka/Shiga, p 127 UNE Programme (2007b) E-waste, In: E-waste management manual 2007. International Environmental Technology Centre, p 128 UNE Protection (2012) Resource efficiency. United Nations Environment Programme, Kenya Widmer R et al (2005) Global perspectives on e-waste. Environ Impact Assess Rev 25(5):436–458 Yu J et al (2010) Forecasting global generation of obsolete personal computers. Environ Sci Technol 44(9):3232–3237 Zopounidis C (2011) Handbook of multicriteria analysis. New York, NY: Springer

Life Cycle Management of LCD Televisions – Case Study

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Guoqing Jin and Weidong Li

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Space Interference Matrix and Matrix Analysis Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Customizable Decision-Making Model and PSO-Based Selective Disassembly Planning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Studies on LCD Televisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Solution Space Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selective Optimizations and Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Waste Electrical and Electronic Equipment (WEEE) is one of the most significant waste products in modern societies. Disassembly is a critical step to reduce Electrical and Electronic Equipment (EEE) waste. In the past two decades, despite disassembly has been applied to support recycling and remanufacturing of WEEE products worldwide, full disassembly of WEEE is rarely an ideal solution due to high disassembly cost. Selective disassembly, which prioritizes operations for partial disassembly according to the economic considerations, is becoming an important but still a challenging research topic in recent years. In order to address the issue effectively, in this chapter, space interference matrix is generated based on a product model to represent the space interference relationship between each component, and all feasible disassembly sequences can be obtained by analyzing the space interference matrix with a matrix analysis

G. Jin (*) • W. Li Faculty of Engineering and Computing, University of Coventry, Coventry, UK e-mail: [email protected]; [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_18

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algorithm. Then, a particle swarm optimization (PSO)-based selective disassembly planning method embedded with customizable decision-making models is applied, which is capable to achieve optimized selective disassembly sequences for products. Finally, industrial cases on liquid crystal display (LCD) televisions are used to verify and demonstrate the effectiveness and robustness of the developed research.

Introduction The mounting demand for new products has brought more production activities worldwide in recent years. The rapid development, however, has been hindered by the increasing concerns of the scarcity of natural resources and environmental issues. It has been estimated that the required bio-capacity of two Earths is necessary to satisfy the need of the development in 2050 according to current production and consumption trends (Jovane et al. 2008). On the other hand, more and more products after services are filled up in landfills. Among them, Electrical and Electronic Equipment (EEE) after services, that is, Waste Electrical and Electronic Equipment (WEEE), is becoming one of the major and challenging waste streams in terms of quantity and toxicity. For instance, there is approximately seven million tons of WEEE generated in Europe per year (Walther et al. 2010). In China, 1.1 million tons of WEEE is generated per year (Hicks et al. 2005). Due to the rapid technical innovations and shorter usage life cycle of EEE, WEEE is growing much faster than any other municipal waste streams. In order for the Earth to be cleaner, end-of-life (EoL) recovery strategies are critical to shape the future of WEEE life cycle management patterns. Among the strategies, remanufacturing is viewed as a “hidden green giant” and attracting escalating attentions of researchers and practitioners (Kopacek and Kopacek 1999; Duflou et al. 2008; Kernbaum et al. 2009; Hatcher et al. 2011). Remanufacturers seek to bring some components of products after their services back into “as new” conditions by carrying out necessary disassembly, overhaul, and/or repairing operations for reuse to extend life cycles. There are two driving forces for industries in adopting the relevant technologies and practices, i.e., stricter legislative pressure for environmental protection and better profit margins from remanufacturing. The explanations are expanded below: • The WEEE Directive has been enacted and implemented from 2003 in Europe, and the equivalent Directives have been developed in different countries of the world. Further proposals for the tighter WEEE Directives have been suggested to regulatory bodies with an aim to make products and components after services more recyclable, reusable, and remanufacturable. According to the WEEE Directives, a producer (manufacturer, brand owner, or importer)’s responsibility is extended to the postconsumer stage of WEEE, instead of stopping at selling and maintenance (i.e., extended producer responsibility – EPR; Mayers 2007;

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Sander et al. 2007). The EPR is aimed at encouraging producers especially manufacturers to provide cradle-to-grave support to reduce environmental impacts such that they work closely with remanufacturing industries to recover maximum values and reduce environmental toxicity/hazardousness. For instance, the remanufacturing legislative initiatives are underway in the EU and the USA to ensure original equipment manufacturers (OEMs) and suppliers to provide free access to remanufacturing information facilities in global chains (Giuntini and Gaudette 2003). • Good remanufacturing planning and management can effectively balance economic and environmental targets and close gaps between the shorter innovation cycles of EEE and the extended lives of components of WEEE. Remanufacturing industries in the EU and worldwide have been recently growing quickly because of better economic return values. There are a number of successful cases in industries, including single-use cameras (Eastman Kodak and Fujifilm), toner cartridges (Xerox), personal computers (IBM, HP, Toshiba, Reuse network – Germany), photocopiers (Fuji Xerox – Australia, Netherlands, and UK), commercial cleaning equipment (Electrolux), washing machines (ENVIE – France), mobile phones (Nokia, ReCellular, USA; Greener Solution, UK), etc. Disassembly planning, which is used to determine sensible disassembly operations and sequencing, is critical in remanufacturing. Effective disassembly planning can significantly improve the recycling and reuse rates of components and materials from WEEE to ensure maximum value recovery. For a set of WEEE, there could be a number of different sequences of disassembly operations constrained technically and geometrically between the components of the WEEE, leading to the different decision-making models according to the perspectives and criteria of stakeholders (Kara et al. 2006). As thus, it becomes difficult for remanufacturers to solely depend upon their experiences to plan disassembly operations so as to recover a larger proportion of components and fulfill environmental targets at a reasonable cost. In the past years, research has been carried out to address the issues of disassembly. The previous research can be generally summarized as the following two categories: • Disassembly for design. Disassembly approaches for EEE such as consumer electronic products have been developed to use smart materials like shape memory polymers (SMPs) in the design of embedded releasable fasteners to facilitate the disassembly processes of the products (Masui et al. 1999; Chiodo et al. 2001; Jones et al. 2004; Braunschweig 2004; Hussein and Harrison 2008; Ijomah and Chiodo 2010). Design for remanufacturing/disassembly principles have been spread among Japanese manufacturers since products with the principles are more profitable in this context than those that were not designed with this purpose (Duflou et al. 2008; Sundin et al. 2009; Dindarian et al. 2012). • Disassembly planning and operation sequencing. Typical disassembly operations based on manual, semiautomatic, and automatic processes and the

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Solution space generation

Disassembly planning method

Space interference matrix

Customisable decisionmaking model

Matrix analysis algorithm

Particle Swarm Optimisation (PSO)-based selective disassembly planning approach

Optimised disassembly sequence of WEEE

Fig. 1 A main flow of the developed approach

associated toolkits were summarized (Duflou et al. 2008). Based on disassembly operations and the precedence constraint relationships among the disassembly operations, sequencing rules and intelligent and/or meta-heuristic reasoning algorithms were applied to deduce an optimal plan from a large pool of candidate solutions (Kara et al. 2006; Santochi et al. 2002; Lambert 2002; Kuo 2012). In recent years, remanufacturers are facing many challenges to disassemble WEEE due to their high customization and diversity, high integration level, and more complex assembly processes. Current economic analyses have demonstrated that full disassembly is rarely an optimal solution and necessary owing to high disassembly cost. Selective disassembly, which prioritizes operations to implement partial dismantling of WEEE so as to take account of the legislative and economic considerations and meet the specific requirements of stakeholders, is a promising alternative and has therefore become a new research trend (Duflou et al. 2008; Renteria et al. 2011; Ryan et al. 2011). Attributing to booming personalized and mass-customized EEE, there is still challenge in applying the developed methods to the increasingly diversified and personalized WEEE to make sensible decisions and meet different stakeholders’ perspectives. This chapter presents a new method conducted in the area, and the main flow of the method is shown in Fig. 1. A summary of the developed approach is given below: • A space interference matrix is used to represent the space relationship of each component of WEEE in six directions in a Cartesian coordinate system. By this way, all the space interference relationships between components of WEEE can be digitally recorded and can be analyzed in the next step. • A matrix analysis algorithm is developed to find out all the feasible disassembly sequences of WEEE by analyzing the six space interference matrices in a 3D environment. It is capable to find out all the feasible disassembly sequences of WEEE, and the result can be used as a solution space to support a disassembly planning method to search optimized result. • A particle swarm optimization (PSO)-based selective disassembly planning method with customizable decision-making models has been

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developed. The method is adaptive to various types of WEEE, flexible for customized decision modeling and decision making for different stakeholders, and capable for handling complex constraints and achieving better economic value and environmental protection requirements during disassembly planning. • In the end, industrial case studies on liquid crystal display (LCD) televisions are used to verify and demonstrate the effectiveness of the developed method in different application scenarios.

Methodology Space Interference Matrix and Matrix Analysis Algorithm In the past two decades, there are many research articles published for disassembly research of WEEE. In the literature (Ying et al. 2000; Lambert 2003; Carrell et al. 2009), some detailed reviews on the research were made. Almost all those researches focused on the optimal disassembly solution searching. However, before applying disassembly planning and optimization techniques in real industrial cases such as LCD televisions, which is one of the main products of WEEE nowadays, it cannot evade the issue of the feasible solution space generation for further search and optimization (Wang et al. 2014). There are two reasons: (1) in real practice, if the disassembly sequence is obtained by searching all the disassembly sequences instead of the solution space, the result could hardly be used as there are some geometrical constraints to specify precedent relationships between disassembly operations, and (2) LCD televisions are normally assembled by many components with complex shapes. For an assembly LCD television with N components, the total disassembly sequences could be as much as N! ¼ N  (N1). . .2  1. It is too difficult to search the best disassembly sequence within an acceptable runtime. In this section, an effective approach was developed to address the issues of the solution space generation of WEEE.

Space Interference Matrix Firstly, based on a CAD product model, six space interference matrices are generated in six directions separately in a 3D environment. It can be used to represent the space interference relationship of components of a product:

E1 E2 ⋮ En

2 E1 t11 6 t21 6 4⋮ tn1

E2 t12 t22 ⋮ tn2

   En 3    t1n 7 7 ⋱ ⋮5 tnn

(1)

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(1) Disassembly in X direction X+

Y+

X+ A B

A 0 0

B 1 0

X− A B

A 0 1

B 0 0

(2) Disassembly in Y direction A

B

Y+ A B

A 0 0

B 0 0

Y− A B

A 0 0

B 0 0

(3) Disassembly in Z direction Z+ A B

A B 0 0 0 0

Z− A B

A B 0 0 0 0

Fig. 2 Matrices in six directions to represent the space interference relationship

Fig. 3 Product with four components

In the matrix, the element Ei in each row and column is one of the components in the product. The element tij shows the space interference relationship between components i and j in six directions (X+, X-, Y+, Y-, Z+, Z-) in a 3D environment. If space interference exists between components i and j in one direction, the element tij in the matrix is “1” in this direction. Otherwise, it is “0.” An example is used here to explain the space interference relationship between “A” and “B” (shown in Fig. 2). As the object “B” is in the X+ direction of the object “A,” and the object “A” is in the X- direction of the object “B,” the element tAB in the X+ direction matrix is “1,” and the element tBA in the X- direction matrix is “1,” all other results are “0.” For example, Eqs. 2, 3, 4, 5, 6, and 7 are used to represent the space interference relationship between four components of a product (shown in Fig. 3):

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SXþ

SX

SY þ

SY 

SZþ

SZ

A ¼ B C D

A ¼ B C D

A ¼ B C D A ¼ B C D A ¼ B C D A ¼ B C D

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A B 0 0 61 0 40 0 1 1

C D 3 0 1 0 17 0 15 1 0

(2)

A B 0 1 60 0 40 0 1 1

C D 3 0 1 0 17 0 15 1 0

(3)

A B 0 0 60 0 40 0 1 1

C D 3 0 1 0 17 0 15 1 0

(4)

A B 0 0 60 0 40 0 1 1

C D 3 0 1 0 17 0 15 1 0

(5)

A B 0 1 61 0 40 0 0 0

C D 3 1 0 1 07 0 05 0 0

(6)

A B 0 1 61 0 41 1 0 0

C D 3 0 0 0 07 0 05 0 0

(7)

2

2

2

2

2

2

Matrix Analysis Algorithm Based on the obtained space interference matrices in six directions in above, a matrix analysis algorithm is then developed to find all the feasible disassembly sequences of the product. Figure 4 shows the flow of the algorithm. An example is used here to explain the details of the developed matrix analysis algorithm. Firstly, Eq. 8 is generated by combining Eqs. 2, 3, 4, 5, 6, and 7 in six directions:

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Start

Combine six space interference matrices (t=1)

The obtained combined matrix (i=1) t=t+1 Boolean operator ‘OR’ of combined matrix

Store the disassembly sequence to St as a feasible sequence

Calculate the order number of combined matrix (Norder)

Norder equal to ‘1’

Disassembly sequence repeat

Y

N

Y

Calculate the number of row equal to ‘0’ (Nrow)

Nrow equal to ‘0’

Y

End

N i=i+1 The result of row ri include ‘0’

N

Y Delete component Ei and generate a new matrix

Fig. 4 Flowchart of the matrix analysis algorithm

N

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Fig. 5 Feasible disassembly sequence analysis for the product

A S¼ B C D

A 000000 6 100011 4 000001 111100 2

B 010011 000000 000001 111100

C 000010 000010 000000 111100

D 3 111100 111100 7 111100 5 000000

(8)

The Boolean operator “OR” is used here for the above equation at any row to determine whether a component can be freely disassembled in a direction. Equation 9 is obtained below: A S¼ B C D

A 000000 6 100011 4 000001 111100 2

B 010011 000000 000001 111100

C 000010 000010 000000 111100

D 3 111100 111100 7 111100 5 000000

Result 111111 111111 111101 111100

(9)

The result “111111” represents the relationship between one component and all remaining components of the product in six directions (X+, X-, Y+, Y-, Z+, Z-). If the result is always “1,” it means the component could not be disassembled in any direction; if the result includes “0,” it means the component can be disassembled from that direction. The example in Fig. 5 can be used to explain the concept. In Eq. 2, components “A” and “B” could not be disassembled in any direction as the result is all “1”; component “C” can be disassembled in Z+ direction as the result is “0” in this direction; and component “D” can be disassembled in both Z+ and Z- directions. Here, component “C” is disassembled in Z+ direction firstly, and the remaining combined space interference matrix is shown below: A S¼ B D

2 A 000000 4 100011 111100

B 010011 000000 111100

D 3 111100 111100 5 000000

Result 111111 111111 111100

(10)

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Fig. 6 All the feasible disassembly sequences for the product

From Eq. 10, only component “D” can be disassembled in both Z+ and Zdirections. Here, component “D” is disassembled in Z+ direction, and the remaining combined space interference matrix is shown below: S¼ A B



A B  000000 010011 100011 000000

Result 010011 100011

(11)

From Eq. 11, components “A” and “B” can be disassembled in three directions. After disassembling “A” in X+ direction, the product has been disassembled completely. Loop the above analysis processing until all the feasible disassembly sequences of the product are obtained. Based on the above analysis and the developed matrix analysis algorithm, the total feasible disassembly sequences for the product is 192 (30 + 30 + 30 + 30 + 30 + 30 + 6 + 6) (shown in Fig. 6). The obtained result of all feasible disassembly sequences for the product can be used as a solution space to support our developed PSO-based selective disassembly planning method to search the optimized disassembly sequence based on customer requirements. The details are shown in the following section.

Customizable Decision-Making Model and PSO-Based Selective Disassembly Planning Approach Customizable Decision-Making Modeling for Selective Disassembly Planning Disassembly of WEEE involves different stakeholders, such as environmental regulators and remanufacturers. The different levels of targets will lead them to

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Life Cycle Management of LCD Televisions – Case Study Environmental protection requirement

Disassemble/ recycle at least 75% components from WEEE (Weight)

LCD panel (with liquid crystal)

Printed Circuit Boards

(Hazardous components/ materials)

(Hazardous components/ materials)

CCFL tubes (with Mercury/Phosphorus) (Hazardous components/ materials)

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Technical requirement

Prioritise components/ materials by value

Technical feasibility

(Value)

(Constraints)

WEEE regulators WEEE remanufacturers

Fig. 7 Criteria used to develop different decision-making models to address various users’ needs

adopt or develop different decision-making models. For instance, according to the WEEE Directive, WEEE regulators will check whether remanufacturing companies are able to recycle at least 75 % of WEEE by weight and remove/recover all the hazardous materials. In other words, at least 75 % of WEEE are required to be dismantled to a component level, and all the components containing hazardous materials need to be taken apart from WEEE for further recycling and processing. Apart from fulfilling these fundamental environmental targets, remanufacturers would also improve the economic efficiency by prioritizing valued components during disassembly. In Fig. 7, an example of LCD WEEE is used to illustrate the above scenario. In order to develop a selective disassembly planning method that is suitable for stakeholders to process various types of WEEE and meet their specific requirements, it is imperative to define customizable decision-making models. The models (disassembly indices and objective) developed in this research are described below. Disassembly Indices In the following formulas, two symbols will be used frequently and they are explained here first: n The number of the total disassembly operations in a plan of a set of WEEE m The number of the disassembly operations in a selective disassembly plan Position(Oper(i)) The position (sequence) of the ith disassembly operation in a disassembly plan • Selective Disassembly Plan (DP) and Disassembly Operation (Oper(i)) A set of WEEE can be fully disassembled using a disassembly plan. The number of all the operations in the plan is n. A selective disassembly plan (DP), which consists of a set of disassembly operations, is a part of the above complete operations. The number of the selected operations is m, and the i th operation is denoted as Oper(i). DP can be represented as

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  m  DP ¼ [ OperðiÞ, Position OperðiÞ i¼1

(12)

where [ represents the set of disassembly operations and m  n. For instance, there are a set of disassembly operations Oper(1), Oper(2), Oper (3), Oper(4), and their positions in DP are 4, 2, 1, 3 (e.g., Position(Oper(1)) ¼ 4), so that the sequence of the operations in DP is Oper(3), Oper(2), Oper(4), Oper(1). Meanwhile, Oper(i) has some properties related to the environmental and economic targets defined as follows. • Hazardousness (H(Oper(i))) and Hazardousness Index (Index_H ) H(Oper(i)) of the i th disassembly operation is to indicate the level of hazardousness contained in the component(s) removed by the operation from the WEEE. It can be represented in a qualitative means, i.e., high, relatively high, medium, and low, and converted to a quantitative means accordingly, such as (5, 3, 1, 0) for (high, relatively high, medium, low). Index_H of a set of WEEE is to indicate the accumulated hazardousness contained in the component(s) removed by the disassembly operations in the WEEE. Index_H can be computed as below: Index_H ¼

m X

ðH ðOperðiÞÞ  PositionðOperðiÞÞÞ

(13)

i¼1

A smaller Index_H will be beneficial. The function of multiplying H(Oper(i)) and its position Position(Oper(i)) in DP is to ensure that the disassembly operations with higher hazardousness (i.e., H(Oper(i))) are arranged earlier in DP to achieve a smaller Index_H. For instance, the hazardousness of Oper(1), Oper(2), Oper(3), Oper(4) is (high, low, medium, relatively high), respectively, which can be converted to (5, 0, 1, 3). The positions of the operations in DP are (4, 2, 1, 3). Therefore, the hazardousness index of DP is (5*4 + 0*2 + 1*1 + 3*3) ¼ 30. If the positions of the operations are rearranged as (1, 4, 3, 2), then the hazardousness index is (5*1 + 0*4 + 1*3 + 3*2) ¼ 14. The latter is lower than the earlier since the operations with higher hazardousness are arranged earlier in the latter. In objective defined later on, a weighted minimum hazardousness index will be pursued to ensure the operations to remove the most hazardous components will be arranged as early as possible to improve the efficiency of hazardousness removal in a selective disassembly plan. • Potential Recovery Value (V(Oper(i))), Disassembly Time (T(Oper(i))), and Potential Value Index (Index_V ) V(Oper(i)) of the i th disassembly operation is to indicate the potential recovery value of the component(s) disassembled from the WEEE by the operation. The disassembled component(s) could be reusable so that V(Oper(i)) can be represented as the depreciation value of the equivalent new component(s). T(Oper(i)) represents

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Life Cycle Management of LCD Televisions – Case Study

3417

the time spent for the disassembly operation Oper(i). Index_V of a set of WEEE is to indicate the accumulated potential value index by the disassembly operations in the WEEE. Index_V can be computed as below: Index_V ¼

m  X

  V ðOperðiÞÞ=T ðOperðiÞÞ  Position OperðiÞ

(14)

i¼1

A smaller Index_V will be beneficial. V(Oper(i))/T(Oper(i)) represents the potential value recovery efficiency of Oper(i). The function of multiplying V(Oper(i))/T(Oper(i)) and its position Position(Oper(i)) in DP is to ensure that the disassembly operations with higher V(Oper(i))/T(Oper(i)) are arranged earlier to achieve a smaller Index_V so as to achieve a higher efficiency of potential value recovery for a selective disassembly plan. • Weight Removal (W(Oper(i))) and Weight Removal Index (Index_W ) W (Oper(i)) is to indicate the level of the removed weight by the i th disassembly operation from the WEEE. It can be represented by the weight of the component (s) disassembled by the operation. Index_W of a set of WEEE is to indicate the accumulated weight removal index by the disassembly operations in the WEEE. Index_W can be computed as below: Index_W ¼

m X

ðW ðOperðiÞÞ  PositionðOperðiÞÞÞ

(15)

i¼1

Similarly, a smaller Index_W will be beneficial. The function of multiplying W (Oper(i)) and its position Position(Oper(i)) in DP is to ensure that the disassembly operations with higher W(Oper(i)) are arranged earlier to achieve a smaller Index_W in order to improve the efficiency of weight removal in a selective disassembly plan. Disassembly Constraints During the process of disassembly, there are some technical constraints to specify precedent relationships between disassembly operations. An example in Fig. 8 is used to illustrate the concept. There is a single disassembly direction for components Disassembly direction 1

Joining mechanisms Component A Component B Fig. 8 Examples of constraints during disassembly

Housing

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G. Jin and W. Li

A and B. Geometrically, it can dismantle either the joining mechanism between component B and housing first (Oper(1)) or the joining mechanism between components A and B first (Oper(2)). However, from the technical point of view, it is recommended to remove the joining mechanism between component B and housing first, considering that the disassembly of the second joining mechanism needs more operation space. Therefore, Oper(1) is constrained to be prior to Oper(2) technically. Decision-Making Objective Disassembly decision-making will be modeled as a constraint-based optimization problem. The objective can be customized to address the different requirements of stakeholders through providing weight setting by users. The objective is represented below: MimimiseðIndex_H, Index_V, Index_W Þ ¼ Minimiseðw1  Index_H þ w2  Index_V þ w3  Index_W Þ

(16)

where w1  w3 are the weights. The setting of weights can be used to reflect importance. A higher weight means more attentions will be paid to that index, and a zero value means such the index will not be considered. In order to rationalize the model, the three indices are required to be normalized to be in the same measurement scale. The late case studies can illustrate the normalization process.

Improved Particle Swarm Optimization Algorithm The different selection and optimization sequencing of disassembly operations for a set of WEEE usually brings forth a large search space. Conventional algorithms are often incapable of optimizing the problem. To address it effectively, some modern optimization algorithms, such as genetic algorithm (GA) and simulated annealing (SA), have been developed to quickly identify an optimized solution in a large search space through some evolutional or heuristic strategies. In this research, an improved algorithm based on a modern intelligent algorithm, i. e., PSO, has been applied to facilitate the search process. Moreover, the improved PSO has been also compared with GA and SA for this disassembly planning problem to show the characteristics of the algorithms. For more details of GA and SA implementation, refer to (Li et al. 2002; Li and McMahon 2007; Reddy et al. 1999). A classic PSO algorithm was inspired by the social behavior of bird flocking and fish schooling (Kennedy and Eberhart 1995). Three aspects will be considered simultaneously when an individual fish or bird (particle) makes a decision about where to move: (1) its current moving direction (velocity) according to the inertia of the movement, (2) the best position that it has achieved so far, and (3) the best position that all the particles have achieved so far. In the algorithm, the particles

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Life Cycle Management of LCD Televisions – Case Study

3419

form a swarm, and each particle can be used to represent a potential disassembly plan of a problem. The velocity and position of a particle (disassembly plan) will be computed below:     V tþ1 ¼ w  V ti þ c1  Randð1Þ  Pti  Xti þ c2  Randð1Þ  Ptg  Xti (17) i ¼ Xti þ V tþ1 Xtþ1 i i

(18)

Xi ¼ ðXi1 , Xi2 , . . . , XiN Þ

(19)

V i ¼ ðV i1 , V i2 , . . . , V iN Þ

(20)

Here, i is the index number of particles in the swarm, t is the iteration number, and V and X are the velocity vector and the position vector of a particle, respectively. For an N-dimensional problem, V and X can be represented by N particle dimensions as formulas Eqs. 14 and 15 show. Pi is the local best position that the i th particle has achieved so far; Pg is the global best position that all the particles have achieved so far; W is the inertia weight to adjust the tendency to facilitate global exploration (smaller w) and the tendency to facilitate local exploration to fine-tune the current search area (larger w); Rand(1) returns a random number in [0, 1]; and c1 and c2 are two constant numbers to balance the effect of Pi and Pg. In each iteration, the position and velocity of a particle can be adjusted by the algorithm that takes the above three considerations into account. After a number of iterations, the whole swarm will converge at an optimized position in the search space. A classic PSO algorithm can be applied to optimize the disassembly planning models in the following steps: 1. Initialization: • Set the size of a swarm, e.g., the number of particles “Swarm_Size” and the max number of iterations “Iter_Num.” • Initialize all the particles (a particle is a disassembly plan DP) in a swarm. Calculate the corresponding indices and objective of the particles according to formulas Eqs. 12, 13, 14, 15, and 16 (the result of the objective is called fitness here). • Set the local best particle and the global best particle with the best fitness. 2. Iterate the following steps until “Iter_Num” is reached: • For each particle in the swarm, update its velocity and position values. • Decode the particle into a disassembly plan in terms of new position values and calculate the fitness of the particle. Update the local best particle and the global best particle if a lower fitness is achieved. 3. Decode the global best particle to get the optimized solution. However, the classic PSO algorithm introduced above is still not effective in resolving the problem. There are two major reasons for it:

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• Due to the inherent mathematical operators, it is difficult for the classic PSO algorithm to consider the different arrangements of operations, and therefore the particle is unable to fully explore the entire search space. • The classic algorithm usually works well in finding solutions at the early stage of the search process (the optimization result improves fast), but is less efficient during the final stage. Due to the loss of diversity in the population, the particles move quite slowly with low or even zero velocities, and this makes it hard to reach the global best solution. Therefore, the entire swarm is prone to be trapped in a local optimum from which it is difficult to escape. To solve these two problems and enhance the capability of the classic PSO algorithm to find the global optimum, new operations, including crossover and shift, have been developed and incorporated in an improved PSO algorithm. Some modification details are depicted below: 1. New operators in the algorithm: • Crossover. Two particles in the swarm are chosen as parent particles for a crossover operation. In the crossover, a cutting point is randomly determined, and each parent particle is separated as left and right parts of the cutting point. The positions and velocities of the left part of parent 1 and the right part of parent 2 are reorganized to form child 1. The positions and velocities of the left part of parent 2 and the right part of parent 1 are reorganized to form child 2. • Shift. This operator is used to exchange the positions and velocities of two operations in a particle in a random position so as to change their relative positions in the particle. 2. Escape method. During the optimization process, if the iteration number of obtaining the same best fitness is more than 10, then the crossover and shift operations are applied to the best particle to escape from the local optima. A general diagram to show the above flow is shown in Fig. 9.

Case Studies on LCD Televisions Televisions can be generally classified into five groups: CRT, LCD, PDP, OLED, and RP. The LCD televisions have been developed quickly over the past decades, and they are now sharing the biggest market (e.g., the global market figures for the LCD televisions are forecasted to surpass $80 billion in 2012; Ryan et al. 2011). An LCD television produces a black and colored image by selectively filtering a white light. The light is typically provided by a series of cold cathode fluorescent lamps (CCFLs) at the back of the screen, although some displays use white or colored LED. The LCD televisions studied here are produced by the Changhong Electronics

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Life Cycle Management of LCD Televisions – Case Study

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Initialisation - A disassembly plan (DP) is modelled as a particle

The iteration number is more than 10?

Y

N Fitness computation of the particle based on customisable decision making models according to formulas (12-16) Generation of a new particle based on the following two measurements: (1) Application of velocity and position of the particle using formulas (11-15) (2) Application of crossover and shift to the particle

Optimised disassembly plan Fig. 9 The general workflow of the PSO-based disassembly plan optimization

Company, Ltd. from China, which is the biggest television producer in China. The company provides information about LCD televisions of the type of LC24F4, such as the bill of materials (BoMs), the exploded view, the mass of each part, and the detailed assembly processes. The structure of the LCD television is shown in Fig. 10a, b. The typical exploded view of an LCD television is shown in (c). As shown in (d), an LCD television is typically assembled by three main parts: front cover assembly part, back cover assembly part, and base assembly part. All feasible disassembly sequences for these three parts are generated in the following section.

Solution Space Generation Base Assembly Part The base assembly part of the LC24F4 LCD television is shown in Fig. 11. It is composed of nine parts: (A) metal fixing plate, (B) metal washer 1, (C) metal washer 2, (D) top metal support, (E) cylindrical metal support 1, (F) cylindrical metal support 2, (G) toughened glass seat, (H ) steel plate, and (I) rubber gasket. The space interference matrices to represent the base assembly part in six directions are shown below:

3422

a

c

G. Jin and W. Li

b

d Complete machine Front cover assembly part (1) Surface cover (1-1) Remote control receiver board (1-2) Control buttons board (1-3) Main board (1-4) Power supply board (1-5) LNB board (optional) (1-6) DVD rom (optional) (1-7) Back cover assembly part (2) Base assembly part (3)

Fig. 10 The LC24F4 LCD television and its structures (a) Front view of the LCD television framework (b) Back view of the LCD television framework (c) Typical exploded view of the LCD television structure (d) Part of the BoMs of the LCD television

Fig. 11 The base assembly part of the LC24F4 LCD television: (a) base assembly part, (b) components A, B, C, (c) components D, E, F, and (d) components G, H, I

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Life Cycle Management of LCD Televisions – Case Study

A B C D SXþ ¼ E F G H I

A B C SY þ ¼

D E F G H I

A B C SZþ ¼

D E F G H I

A 2 0 60 6 6 60 6 60 6 6 60 6 60 6 61 6 6 41

B C D E F G H I 3 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 17 7 7 0 0 0 0 0 1 1 17 7 0 0 0 1 1 0 0 07 7 7 0 0 1 0 1 0 0 07 7 0 0 1 1 0 1 1 17 7 1 1 0 0 0 0 1 07 7 7 1 1 0 0 0 1 0 15

A B C D SX ¼ E F G H I

0 0 0 0 0 0 0 1 0 A 2 0 60 6 6 60 6 60 6 6 60 6 60 6 61 6 6 41

B C D E F G H I 3 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 17 7 7 0 0 0 0 0 1 1 17 7 0 0 0 1 1 0 0 07 7 7 0 0 1 0 1 0 0 07 7 0 0 1 1 0 1 1 17 7 1 1 0 0 0 0 1 07 7 7 1 1 0 0 0 1 0 15

A B C SY  ¼

G H I

0 0 0 0 0 0 0 1 0 A 2 0 60 6 6 60 6 60 6 6 60 6 60 6 60 6 6 40

B C D E F G H I 3 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 07 7 7 0 0 1 1 1 1 1 07 7 0 0 0 0 0 0 0 07 7 7 0 0 1 0 0 0 0 07 7 0 0 1 1 0 1 1 07 7 0 0 1 1 1 0 0 07 7 7 0 0 1 1 1 1 0 05

D E F

A B C SZ ¼

0 0 0 0 0 0 0 0 0

D E F G H I

A 2 0 60 6 6 60 6 60 6 6 60 6 60 6 61 6 6 41

3423 B C D E F G H I 3 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 17 7 7 0 0 0 0 0 1 1 17 7 0 0 0 1 1 0 0 07 7 7 0 0 1 0 1 0 0 07 7 0 0 1 1 0 1 1 17 7 1 1 0 0 0 0 1 07 7 7 1 1 0 0 0 1 0 15

0 0 0 0 0 0 0 1 0 A 2 0 60 6 6 60 6 60 6 6 60 6 60 6 61 6 6 41

B C D E F G H I 3 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 17 7 7 0 0 0 0 0 1 1 17 7 0 0 0 1 1 0 0 07 7 7 0 0 1 0 1 0 0 07 7 0 0 1 1 0 1 1 17 7 1 1 0 0 0 0 1 07 7 7 1 1 0 0 0 1 0 15

0 0 0 0 0 0 0 1 0 A 2 0 61 6 6 61 6 61 6 6 61 6 61 6 61 6 6 41

B C D E F G H I 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 07 7 7 1 0 0 0 0 0 0 07 7 1 1 0 1 1 1 1 07 7 7 1 1 0 0 1 1 1 07 7 1 1 0 0 0 0 0 07 7 1 1 0 0 0 0 1 17 7 7 1 1 0 0 0 0 0 05

0 0 0 0 0 0 0 0 0

After combining the above six matrices and using Boolean operator “OR” in rows, the obtained result is as follows:

A B C D S¼ E F G H I

A 000000 6 000001 6 6 6 000001 6 6 000001 6 6 6 000001 6 6 000001 6 6 111101 6 6 4 111101 2

B 000010 000000 000001 000001

C 000010 000010 000000 000001

D 000010 000010 000010 000000

E 000010 000010 000010 111101

F 000010 000010 000010 111101

G 111110 111110 111110 111101

H 111110 111110 111110 111101

000001 000001 111101 111101

000001 000001 111101 111101

111110 111110 000010 000010

000000 111110 000010 000010

111101 000000 000010 000010

000001 111110 000000 111110

000001 111110 111101 000000

I 3 111100 111100 7 7 7 111100 7 7 000000 7 7 7 000000 7 7 111100 7 7 000001 7 7 7 111100 5

000000 000000 000000 000000 000000 000000 000010 111100 000000

Result 111110 111111 111111 111101 111111 111111 111111 111111 111110

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G. Jin and W. Li

Fig. 12 The front assembly part of the LC24F4 LCD television: (a) front assembly part, (b) components J, K, L, M, (c) components N, O, P, Q, and (d) components R, S, T

Based on the developed matrix analysis algorithm, there are a total of 918 feasible disassembly sequences for the base assembly part.

Front Cover Assembly Part The front cover assembly part of the LC24F4 LCD television is shown in Fig. 12. It is composed of 11 parts: (J) control button, (K) power switch, (L) side loudspeaker, (M) control receiver board, (N) positive loudspeaker, (O) power supply board, (P) main board, (Q) metal board, (R) metal mounting plate, (S) surface frame, and (T) LCD screen. The space interference matrices to represent the front cover assembly part in six directions are shown below: J K L M N SX þ ¼ O P Q R S T

J K 2 0 6 0 6 6 6 0 6 6 0 6 6 6 0 6 6 0 6 6 0 6 6 6 0 6 6 0 6 6 4 0 0

L M N O P Q R S T 3 1 1 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 7 7 7 0 0 1 0 0 0 0 1 0 0 7 7 0 0 0 0 0 0 0 1 0 0 7 7 7 0 0 0 0 0 1 0 1 0 0 7 7 0 0 0 0 0 1 1 1 0 0 7 7 0 0 0 0 0 0 0 1 0 0 7 7 7 0 0 0 0 0 1 0 1 0 0 7 7 1 1 1 1 1 1 1 0 1 1 7 7 7 0 0 0 0 0 0 0 1 0 1 5 0 0 0 0 0 0 0 1 1 0

J K L M N SX  ¼ O P Q R S T

J K 2 0 6 1 6 6 6 1 6 6 1 6 6 6 0 6 6 0 6 6 0 6 6 6 0 6 6 1 6 6 4 0 0

L M N O P Q R S T 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 7 7 7 1 0 0 0 0 0 0 1 0 0 7 7 1 1 0 0 0 0 0 1 0 0 7 7 7 0 0 0 0 0 0 0 1 0 0 7 7 0 0 0 0 0 0 0 1 0 0 7 7 0 0 0 1 1 0 1 1 0 0 7 7 7 0 0 0 0 1 0 0 1 0 0 7 7 1 1 1 1 1 1 1 0 1 1 7 7 7 0 0 0 0 0 0 0 1 0 1 5 0 0 0 0 0 0 0 1 1 0

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Life Cycle Management of LCD Televisions – Case Study

J K 2 0 6 0 6 6 6 0 6 6 0 6 6 6 0 6 6 0 6 6 6 0 6 6 0 6 6 1 6 6 4 1 1

L M 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1

N 0 0 0 0 0 0 0 0 1 1 1

O 0 0 1 0 0 0 0 0 1 1 1

P 0 0 1 0 0 0 0 0 1 1 1

Q R S T 3 0 1 0 0 0 1 0 0 7 7 7 0 1 0 0 7 7 0 1 0 0 7 7 7 0 0 0 0 7 7 SY  ¼ 0 0 0 0 7 7 7 0 0 0 0 7 7 0 0 0 0 7 7 1 0 0 0 7 7 7 1 1 0 1 5 1 1 0 0

J K 2 0 J 6 0 K 6 6 0 L 6 6 M 6 6 0 N 6 6 0 6 SZ þ ¼ O 6 6 0 P 6 6 0 6 Q 6 0 6 R 6 6 1 6 S 4 0 T 0

L M 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N 0 0 0 0 0 1 0 0 1 0 0

O 0 0 0 0 0 0 0 0 1 0 0

P 0 0 0 0 0 0 0 0 1 0 0

Q R S T 3 0 1 0 0 0 1 0 0 7 7 7 0 1 0 0 7 7 0 1 0 0 7 7 7 0 1 0 0 7 7 SZ  ¼ 0 1 0 0 7 7 7 0 1 0 0 7 7 0 1 0 0 7 7 1 0 1 1 7 7 7 0 1 0 1 5

J K L M N SY þ ¼ O P Q R S T

0 1 1 0

3425

J K 2 0 6 0 6 6 6 0 6 6 0 6 6 6 0 6 6 0 6 6 6 0 6 6 0 6 6 0 6 6 4 0 0

L M 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

N 0 0 0 0 0 0 0 0 0 0 0

O 0 0 0 0 0 0 0 0 0 0 0

P 0 0 0 0 0 0 0 0 0 0 0

Q R S T 3 0 1 1 1 0 1 1 1 7 7 7 0 1 1 1 7 7 0 1 1 1 7 7 7 0 1 1 1 7 7 0 1 1 1 7 7 7 0 1 1 1 7 7 0 1 1 1 7 7 0 0 1 1 7 7 7 0 0 0 0 5 0 0 1 0

J K 2 0 J 6 0 K 6 6 0 L 6 6 M 6 6 0 N 6 6 0 6 O 6 6 0 P 6 6 0 6 Q 6 0 6 R 6 6 1 6 S 4 0 T 0

L M 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0

N 0 0 0 0 0 0 0 0 1 0 0

O 0 0 0 0 1 0 0 0 1 0 0

P 0 0 0 0 0 0 0 0 1 0 0

Q R S T 3 0 1 0 0 0 0 0 0 7 7 7 0 0 0 0 7 7 0 0 0 0 7 7 7 0 1 0 0 7 7 0 1 0 0 7 7 7 0 1 0 0 7 7 0 1 0 0 7 7 1 0 1 1 7 7 7 0 1 0 1 5

J K L M N O P Q R S T

0 1 1 0

After combining the above six matrices and using Boolean operator “OR” in rows, the obtained result is shown below:

J

2

J K L M N O P Q R S T 3 000000 100000 100000 100000 000000 000000 000000 000000 101111 000100 000100 010000 000000 100000 100000 000000 000000 000000 000000 111110 000100 000100 7 7 7 010000 010000 000000 100000 000000 001000 001000 000000 111110 000100 000100 7 7 010000 010000 010000 000000 000000 000000 000000 000000 111110 000100 000100 7 7 7 000000 000000 000000 000000 000000 000001 000000 000000 110111 000100 000100 7 7 000000 000000 000100 000000 000010 000000 100000 100000 110111 000100 000100 7 7 7 000000 000000 000000 000000 010000 010000 000000 010000 110111 000100 000100 7 7 000000 000000 000000 000000 000000 010000 100000 000000 110111 000100 000100 7 7 011011 111001 111001 111001 111011 111011 111011 111011 000000 110111 110111 7 7 7 001000 001000 001000 001000 001000 001000 001000 001000 111011 000000 111011 5

6 6 6 6 6 6 6 N 6 6 6 S¼ O 6 6 P 6 6 6 Q 6 6 R 6 6 6 S 4 T 001000 001000 001000 001000 001000 001000 001000 001000 111011 110111 000000 K L M

Result 101111 111110 111110 111110 111111 110111 110111 110111 111111 111011 111111

Based on the developed matrix analysis algorithm, there are a total of 7096320 feasible disassembly sequences for the front assembly part.

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Fig. 13 The back cover assembly part of LCD television

Back Cover Assembly Part The back cover assembly part of an LC24F4 LCD television is composed of three parts: (U ) back cover, (V ) cover plate, and (W ) support (shown in Fig. 13). The space interference matrices to represent the back cover assembly part in six directions are shown below:

SX þ

J ¼ K L

2J K 0 1 41 0 0 0

L3 1 15 0

SY þ

J ¼ K L

2J K 0 0 41 0 1 0

L3 1 05 0

SZþ

J ¼ K L

2J K 0 1 41 0 1 0

L3 1 05 0

SX

J ¼ K L

2J 0 41 1

K 1 0 1

L3 0 05 0

SY 

J ¼ K L

2J 0 40 1

K 1 0 0

L3 1 05 0

SZ

J ¼ K L

2J 0 41 1

K 1 0 0

L3 1 05 0

After combining the above six space interface matrices and using Boolean operator “OR” in rows, the combined matrix is as follows:

U S¼ V W

U V 000000 110111 4 111011 000000 011111 010000 2

W 3 101111 100000 5 000000

Result 111111 111011 011111

Based on the developed matrix analysis algorithm, the number of feasible disassembly sequences for the back cover assembly part is 4.

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Life Cycle Management of LCD Televisions – Case Study

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Table 1 Comparison between our developed method and others Our developed method: 450  7096320  4 ¼ 2.6058e+10 (all feasible disassembly sequences) Others: 23! ¼ 23  22. . .2  1 ¼ 2.5852e+22 (all disassembly sequences) Searching range reduced: 2.5852e+22/3776400 ¼ 9.9209e+11 times

3% 2% 4%

Metal Plastic

18%

PCB 19%

Glass

49% Fig. 14 The component/ material composition of the LCD television

LCD screen

Loudspeaker 5%

Others

Based on the above analysis, the developed solution space generation approach can find that the value of all the feasible disassembly sequences of the LC24F4 LCD television is 2.6058e+10 ¼ 918  7096320  4(base assembly part  front cover assembly part  back over assembly part). Compared with all disassembly sequences, which is 23! ¼ 23  22. . .2  1 ¼ 2.5852e+22, the searching range for a disassembly planning algorithm to find the optimized disassembly sequence of the LC24F4 LCD television is reduced to about 9.9209e+11 times (shown in Table 1). All the results from the above have been generated using the algorithm in MATLAB language. It is obvious that the developed approach can dramatically reduce the searching range and obtain all feasible disassembly sequences of the LC24F4 LCD television, which can be used as a solution space to support our developed PSO-based selective disassembly planning method to achieve better economic value and environmental protection requirements within an acceptable runtime.

Selective Optimizations and Comparisons The mass of the LC24F4 LCD television is 5963.8 g, and the main component/ material composition is shown in Fig. 14, in which the percentage is represented in terms of the ratio of mass. Among the component/material compositions, the PCBs (printed circuit boards, which are mainly main boards and power supply boards) and LCD screens are quite complex. Other components/materials include cables, wires, pins, switches, and rubbers. The cables, wires, pins, and switches consist of plastics that are usually polyvinyl chloride (PVC) and nonferrous mainly copper (Cu) and aluminum (Al).

3428

G. Jin and W. Li

Start

1

2

3

18

19

20

6

4

5

7

8

11

9

10

16

17

12

13

14

15

Fig. 15 The disassembly constraint graph

Based on the BoMs of the LC24F4 LCD television, the process of disassembly can be planed. Figure 15 is used to represent the constraints of the disassembly plan and called the disassembly constraint graph. Except for the disassembly constraint graph, there are several other methods to represent the disassembly constraints, such as disassembly tree, state diagram, and and/or graph (Lambert and Gupta 2005). In the graph, nodes represent operations and arcs represent the precedence constraint relationships between operations. Meanwhile, each operation is defined with several properties, such as disassembly operation number, disassembly operation time, component(s) (name, amount, and mass) to be disassembled by each operation, and potential recovered component(s)’ mass, value, and hazardousness. Firstly, one of the disassembly plans of the LC24F4 LCD television is (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20). This plan is called “an initial plan” to be used in the following scenarios for the comparisons with an optimized plan for a better understanding of the optimization process. Table 2 lists the properties of the disassembly process according to the disassembly operation number.

Scenario 1 for Selective Optimization It is aimed to determine a selective optimization disassembly plan (part of the full disassembly plan) to meet the environmental protection targets (100 % hazardousness removal and 75 % component disassembled for the whole WEEE) and achieve the optimized potential recovery value (all the three weights in formula Eq. 15 were set 1). The input data is shown in Table 2. In Fig. 16a, the disassembly planning selection and optimization process is shown. During the computation process, results were normalized, i.e., the index result of each operation was converted as the percentage of the overall results of all the operations. The results in the Y-axis were also accumulated for the operations.

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Life Cycle Management of LCD Televisions – Case Study

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Table 2 Disassembly operations and some properties of the LC24F4 LCD television Disassembly operations 1. Unscrew and remove base part

Time (s) 86.4

2. Unscrew and remove cover plate

86.4

3. Remove back cover part 4. Disassemble back cover part

43.2

5. Remove wire with pin 6. Remove power switch part 7. Remove control button part 8. Unscrew and remove main board

21.6

86.4 43.2 43.2

129.6

9. Unscrew and remove loudspeaker part

86.4

10. Unscrew and remove power supply board and insulating board

86.4

11. Unscrew and remove metal support

86.4

12. Unscrew

86.4

13. Remove loudspeaker 14. Remove remote control receiver board

43.1 21.6

Components Base part M4x12 4x10BTECh Cover plate 3x10KTHCh Support structure Back cover Insulation board Wire with pin

Mass (g) 1.8 1.6 11.2 23.0 0.6 15.6

Potential value (Yuan) 0.0119 0.0106 0.0739 0.1840 0.0004 0.1248

723.8 25.0

1.7904 0.2280

Low

50.0

0.1000

Low

Power switch part Control button Control button part Main board M3x8GB/ T9074.4 Insulating washer Loudspeaker part M3x8GB/ T9074.4 Power supply board Insulating board M3x8GB/ T9074.4 M4x8GB/ T9074.4 Metal support M4x8GB/T818 4x8BTHCh Clamping bush Loudspeaker

5.0

0.0100

Low

3.7 5.5

0.0050 0.0050

Low

196.0 3.0

0.7908 0.0021

Relatively high

3.0

0.0100

60.0

1.3000

2.0

0.0040

118.0

0.6466

25.0

0.1520

0.5

0.0033

0.6

0.0004

183.0 2.4 7.2 24.0 77.8

1.2078 0.0158 0.0475 0.1584 0.0600

Low

3.0

0.4000

Medium

Remote control receive board

Hazardousness removal Low Low

Low

Low

Medium

Low

Low

(continued)

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Table 2 (continued) Disassembly operations 15. Separate surface frame and LCD screen

Time (s) 21.6

16. Disassemble power switch part

64.8

17. Disassemble loudspeaker part

64.8

18. Disassemble base part

86.4

19. Disassemble brace part

20. Disassemble seat part

86.4

64.8

Components Surface frame LCD screen Metal mounting plate Power switch Power wire Wire with pin Loudspeaker Support Washer 4x8BTHCh Metal washer 1 Metal washer 2 Metal fixing plate M4x12GB/ T818 Metal support Plastic support 1 Plastic support 2 M4x12GB/ T818 Toughened grass seat Steel plate Rubber gasket

Mass (g) 270.8 2900.0 639.0

Potential value (Yuan) 1.1000 9.6684 1.2170

5.0 75.5 5.0 152.0 95.0 2.0 2.4 10.0 10.0 15.0

0.0100 0.1000 0.0100 0.6000 0.0200 0.0070 0.0158 0.0660 0.0660 0.0990

2.4

0.0158

25.0 30.0

0.1650 0.2400

20.0

0.1600

2.4

0.0158

150.0

0.3300

50.0 20.0

0.0640 0.0200

Hazardousness removal High

Low

Low

Low

Low

Low

The hazardousness removal, weight removal, and potential recovery value for the initial plan and an optimized plan are shown in (b), (c), and (d), respectively. In (b), a 100 % hazardousness removal target will be achieved after 13 disassembly operations for the optimized plan. In (c), a target to achieve 75 % component disassembled by weight (of the total weight of the WEEE) took 6 operations for the optimized plan. In (d), the result of potential recovery value divided by spent time for each operation is shown, which is a target to achieve the most potential recovery value within the shortest time. To meet the environmental protection targets of removing 100 % components with hazardous materials and 75 % components by weight to be disassembled, the first 13 disassembly operations were selected from the optimized plan as the selective optimized plan. Meanwhile, the potential recovery value and spent time for this plan were optimized in this selective plan.

Life Cycle Management of LCD Televisions – Case Study Optimisation Process

a 2000 1800 1600 1400 1200 1000

1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190

Weighted indices

2200

100 90 80 70 60 50 40 30 20 10 0

Iterations

86.7% by value 85.8% by value Optimised plan

38.8% by value Initial plan

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Operations

Potential recovery value during disassembly

50 40 30 20 10

Optimised plan Initial plan

40 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Operations

Potential recovery value/spent time during disassembly

f Percentage of spent time

Percentage of Potential value

Potential recovery value 100 90 80 70 60 50 40 30 20 10 0

Potential value/time 60

Weight removal during disassembly

e

Initial plan

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Operations

d

100 90 80 75% by weight 70 60 50 40 30 Optimised 20 plan 10 Initial plan 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Operations

Potential value/time

Percentage of hazardousness

Weight Removal

Optimised plan

Hazardousness removal during disassembly

The disassembly planning optimisation process

c

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b Percentage of hazardousness

95

Spent Time 100 90 80 70 60 50 40 30 20 10 0

69.4% by time

77.6% by time

Initial plan 62.7% by time Optimised plan 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Operations

Spent time during disassembly

Fig. 16 Disassembly planning optimization with customizable decision-making models (all weights are 1)

In (b) and (c), it is shown that the initial plan will take 15 disassembly operations to achieve 100 % hazardousness removal and also 15 operations for 75 % components by weight to be disassembled. Therefore, 15 operations are necessary to achieve the environmental protection targets. Therefore, the optimized plan will have 2 less operations. The potential value/time in (d) can be separated and interpreted in (e) and (f). It shows that with the selective optimized plan, the potential recovery values during the disassembly process are 86.7 % (of the total potential value of all the disassembled components in the WEEE) for 13 operations and 38.8 % and 85.8 % for the initial plan after 13 and 15 operations, respectively. With the selective optimized plan, the time spent during the process was 62.7 % (of the total time spent to disassemble the WEEE) for 13 operations and 69.4 % and 77.6 % for the initial plan after 13 and 15 operations, respectively.

3432 Hazardousness Removal 100 90 80 70 60 50 40 30 20 10 0

w1=1,w2=0.5, w3 =1

b Percentage of weight

Percentage of hazardousness

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G. Jin and W. Li

w1,w2,w3 =1

Initial plan 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Weight Removal 100 90 75% by weight 80 70 w1=1,w2= 60 0.5,w3 =1 w1,w2,w3 50 40 =1 30 20 Initial plan 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Operations

Operations

Weight removal during disassembly

Hazardousness removal during disassembly

100 90 80 70 60 50 40 30 20 10 0

w1=1,w2=0.5, w3 =1 50.0% by time w1,w2,w3 =1

Initial plan

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Operations

Spent time during disassembly

Percentage of potential value

Percentage of spent time

d

Spent Time

c

Potential recovery value 100 90 80 70 60 50 40 30 20 10 0

77.4% by value w1=1,w2=0.5, w3 =1

w1,w2,w3 =1

Initial plan 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Operations

Potential recovery value during disassembly

Fig. 17 Disassembly planning optimization with customizable decision-making models (weights are 1, 0.5, 1)

Therefore, if the first 13 operations are selected for both plans, it can be observed that significant potential value is recovered (86.7 % vs. 38.3 %) while less time spent with the optimized solution (62.7 % vs. 69.4 %). If the first 13 operations and 15 operations are selected for both plans respectively, a better potential recovery value (86.7 % vs. 85.8 %) while about 15 % time of the total disassembly time can be saved with the optimized solution (62.7 % vs. 77.6 %). Fifteen percent labor time of disassembling a single set of LCD WEEE stands for 200 s and about 6 h for 100 sets of the LCD WEEE.

Scenario 2 for Selective Optimization It is aimed to prioritize the environmental protection targets (100 % hazardousness removal and 75 % component disassembled for the whole WEEE) (the weights for the hazardousness index and weight removal index in formula Eq. 15 were set 1 and the weight for potential recovery value 0.5). The input data is shown in Table 2. In Fig. 17a, a 100 % hazardousness removal target will be achieved after 10 disassembly operations for the optimized plan with this weight setting. In (b), a target to achieve 75 % component disassembled by weight (of the total weight of the WEEE) took seven operations for the optimized plan with this weight setting. Therefore, 10 disassembly operations are needed for the selective optimized plan, compared to 13 operations in scenario 1. In (c), the time spent for the 10 operations is 50.0 % of the total time for the WEEE, which can be compared to the related results in scenario 1, which were 62.7 % and 69.4 % of the total time spent to

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disassemble the WEEE for the optimized plan with all the weights set to 1 and the initial plan for 13 operations, respectively. In (d), the potential recovery value is 77.4 % of the total potential value of the WEEE for this setting, while the potential recovery values are 86.7 % and 38.8 % of the total potential value of all the disassembled components in the WEEE for the optimized plan and the initial plan in scenario 1, respectively. It can be clearly observed that with the prioritized considerations of hazardousness and weight removal, less operations and time are needed accordingly, while the potential recovery value has to be traded off (from 86.7 % to 77.4 %).

Summary WEEE has been increasingly customized and diversified, and the selective disassembly planning of WEEE to support remanufacturing decision-making is an important but challenging research issue. In this paper, an effective selective disassembly planning method has been developed to address the issue systematically. The characteristics and contributions of the research include: • Space interference matrix has been used to represent the space interference relationship of each component in six directions in a Cartesian coordinate system for WEEE. By this way, all the space interference relationships between components of WEEE can be digitally recorded and can be analyzed in the next step. • A matrix analysis algorithm has been developed to obtain all the feasible disassembly sequences of WEEE by analyzing the six space interference matrices in a 3D environment. It is capable to obtain all the feasible disassembly sequences of WEEE, and the result can be used as a solution space to support a disassembly planning method to achieve better economic value for WEEE within an acceptable runtime. • An improved PSO algorithm-based selective disassembly planning method with customizable decision-making models has been developed. In the method, the customizable decision-making models embedded with adaptive multi-criteria to meet different stakeholders’ requirements have been designed to enable the method flexible and customizable in processing WEEE effectively. • Based on the intelligent optimization algorithms, the developed method is capable to process complex constraints for different types of WEEE based on a generic and robust process and achieve selective optimized disassembly plans efficiently. • Industrial cases on the LC24F4 LCD television have been carried out to verify the effectiveness and generalization of the developed research. Different application scenarios and targets have been set to validate and demonstrate that this research is promising for practical problem solving. Future work will include developing an intelligent automated selective disassembly system with industrial robotic manipulator, cameras, and sensors for WEEE such as LCD televisions.

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References Braunschweig A (2004) Automatic disassembly of snap-in joints in electromechanical devices. In: Proceedings of the 4th international congress mechanical engineering technologies’04, Varna, pp 48–56 Carrell J, Zhang HC, Tate D, Li H (2009) Review and future of active disassembly. Int J Sustain Eng 2(4):252–264 Chiodo JD, Harrison DJ, Billett EH (2001) An initial investigation into active disassembly using shape memory polymers. Proc Inst Mech Eng Part B J Eng Manuf 215(5):733–741 Dindarian A, Gibson AAP, Quariguasi-Frota-Neto J (2012) Electronic product returns and potential reuse opportunities: a microwave case study in the United Kingdom. J Clean Prod 32:22–31 Duflou JR, Seliger G, Kara S, Umeda Y, Ometto A, Willems B (2008) Efficiency and feasibility of product disassembly: a case-based study. CIRP Ann Manuf Technol 57:583–600 Giuntini R, Gaudette K (2003) Remanufacturing: the next great opportunity for boosting US productivity. Business Horizons 46(6):41–48 Hatcher GD, Ijomah WL, Windmill JFC (2011) Design for remanufacturing: a literature survey and future research needs. J Clean Prod 19:2004–2014 Hicks C, Dietmar R, Eugster M (2005) The recycling and disposal of electrical and electronic waste in China – legislative and market responses. Environ Impact Assess Rev 25:447–459 Hussein H, Harrison D (2008) New technologies for active disassembly: using the shape memory effect in engineering polymers. Int J Prod Dev 6(3/4):431–449 Ijomah WL, Chiodo JD (2010) Application of active disassembly to extend profitable remanufacturing in small electrical and electronic products. Int J Sustain Eng 3(4):246–257 Jones N, Harrison D, Billett E, Chiodo J (2004) Electrically self-powered active disassembly. Proc Inst Mech Eng Part B J Eng Manuf 218(7):689–697 Jovane F, Yoshikawa H, Alting L, Boer CR, Westkamper E, Williams D, Tseng M, Seliger G, Paci AM (2008) The incoming global technological and industrial revolution towards competitive sustainable manufacturing. CIRP Ann Manuf Technol 75:641–659 Kara S, Pornprasitpol P, Kaebernick H (2006) Selective disassembly sequencing: a methodology for the disassembly of end-of-life products. CIRP Ann Manuf Technol 55(1):37–40 Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, IV. Perth, Australia, pp 1942–1948 Kernbaum S, Heyer S, Chiotellis S, Seliger G (2009) Process planning for IT-equipment remanufacturing. CIRP J Manuf Sci Technol 2:13–20 Kopacek B, Kopacek P (1999) Intelligent disassembly of electronic equipment. Annu Rev Control 23:165–170 Kuo TC (2012) Waste electronics and electrical equipment disassembly and recycling using Petri net analysis: considering the economic value and environmental impacts. Comput Ind Eng 65 (1):54–64 Lambert AJD (2002) Determining optimum disassembly sequences in electronic equipment. Comput Ind Eng 43(3):553–575 Lambert AJD (2003) Disassembly sequencing: a survey. Int J Prod Res 41(16):3721–3759 Lambert AJD, Gupta SM (2005) Disassembly modelling for assembly, maintenance, reuse, and recycling. CRC Press, Boca Raton Li WD, McMahon CA (2007) A simulated annealing-based optimization approach for integrated process planning and scheduling. Int J Comput Integr Manuf 20(1):80–95 Li WD, Ong SK, Nee AYC (2002) Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. Int J Prod Res 40(8):1899–1922 Masui K, Mizuhara K, Ishii K, Rose C (1999) Development of products embedded disassembly process based on end-of-life strategies. In: Proceedings of the EcoDesign’99: 1st international symposium on environmentally conscious design and inverse manufacturing, Tokyo, pp 570–575

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Mayers CK (2007) Strategic, financial, and design implications of extended producer responsibility in Europe: a producer case study. J Ind Ecol 11:113–131 Reddy SVB, Shunmugam MS, Narendran TT (1999) Operation sequencing in CAPP using genetic algorithm. Int J Prod Res 37:1063–1074 Renteria A, Alvarez E, Perez J, Pozo D (2011) A methodology to optimize the recycling process of WEEE: case of television sets and monitors. Int J Adv Manuf Technol 54:789–800 Ryan A, O’Donoghue L, Lewis H (2011) Characterising components of liquid crystal displays to facilitate disassembly. J Clean Prod 19:1066–1071 Sander K, Schilling S, Tojo N, van Rossem C, Vernon J, George C (2007) The producer responsibility principle of the WEEE Directive, DG ENV. Study Contract N 07010401/ 2006/449269/MAR/G4, https://ec.europa.eu/environment/waste/weee/pdf/final_rep_okopol. pdf. Accessed 13 Nov 2013 Santochi M, Dini G, Failli F (2002) Computer aided disassembly planning: state of the arts and perspectives. CIRP Ann Manuf Technol 51(2):507–529 Sundin E, Lindahl M, Ijomah W (2009) Product design for product/service systems – design experiences from Swedish industry. J Manuf Technol Manag 20(5):723–753 Walther G, Steinborn J, Spengler TS, Luger T, Herrmann C (2010) Implementation of the WEEEdirective – economic effects and improvement potentials for reuse and recycling in Germany. Int J Adv Manuf Technol 47:461–474 Wang H, Rong YM, Xiang D (2014) Mechanical assembly planning using ant colony optimization. Comput Aided Des 47:59–71 Ying T, Zhou MC, Zussman E, Caudill R (2000) Disassembly modelling, planning, and application: a review. In: Proceedings of the 2000 I.E. international conference on robotics & Automation, San Francisco, pp 2197–2202

Section XII Product Life Cycle and Manufacturing Simulation: Manufacturing Simulation and Optimization Manoj Kumar Tiwari

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Resource Scalability in Networked Manufacturing System: Social Network Analysis Based Approach Vijaya Kumar Manupati, Goran Putnik, and Manoj Kumar Tiwari

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework of the Proposed SNAM Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Network Analysis Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Network Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Network Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scalability with Social Network Analysis Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

This paper seeks to address an approach called the social network analysis method (SNAM) to evaluate the effect of resource scalability on networked manufacturing system. Considering the case of networked manufacturing mode, we have proposed a framework of SNAM for generating the collaborative networks. The collaborative networks have been obtained by transferring the input data in the form of an affiliation matrix to the UCINET and Netdraw software packages. Subsequently, we have conducted various tests to analyze the collaborative networks for finding the network structure, size, complexity and its functional properties. In this paper, a social network based greedy k-plex

V.K. Manupati • M.K. Tiwari (*) Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India e-mail: [email protected]; [email protected] G. Putnik Department of Production and Systems Engineering, University of Minho, Guimara˜es, Portugal e-mail: [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_116

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algorithm has been applied to evaluate the scalability effect on different data sets of networked manufacturing system. Experimental studies have been conducted and comparisons have been made to demonstrate the efficiency of the proposed approach.

Introduction Recently scalability in a manufacturing system is considered as an area of research for enhancing the techniques and methodologies to meet the challenges of the emerging manufacturing paradigm. In other words, manufacturing systems scalability can further enhance the manufacturing systems operations by providing further optimization. According to the nature of the undertaken problem, we have considered scalability as “the design of a manufacturing system and its machines with adjustable structure that enable system adjustment in response to market demand changes,” (Koren 2010). However, most of the existing manufacturing organizations still exhibit rigid organizational structures and their deterministic approach cannot support the above mentioned requirements. Researchers’ attention to a large extent have been focused on an alternative to the traditional manufacturing system which can meet high flexible manufacturing operations. Several next generation manufacturing systems such as holonic manufacturing systems (Valckenaers et al. 1994), fractal factory (Okino 1993), networked manufacturing systems and bionic manufacturing systems (Ueda 1993; Liu et al. 2002) can be conceptualized as a network of elements that are adaptable to environmental changes in particular when market demand causes turbulent fluctuations. Over the past few years, many studies focused on the newly emerged manufacturing paradigm, networked manufacturing or network based manufacturing, which has the capability to achieve the requirements and functionalities of global manufacturing (Zhou et al. 2010). Networked manufacturing encapsulates the information and knowledge from product design to manufacturing which enables resource sharing between geographically distributed enterprises to achieve competitive advantages that would be difficult to attain with an individual enterprise (Wiendahl et al. 2007). Networked manufacturing has the ability to change its production mode from make-to-stock to make-to-order. Due to the customized manufacturing environment and competition of delivery times between different manufacturing enterprises, the objective of resource scalability and its effect on the manufacturing system is becoming a critical task. Many manufacturers nowadays try to further push optimizing the performance of the manufacturing system by implementing the scalability function. Different issues related to scalability and its relationship with some of the critical features of recent manufacturing systems are adaptability, flexibility, reconfigurability, etc. (Putnik et al. 2013). A detailed literature review on scalability as an area of research on manufacturing systems is detailed in (Wasserman 1994).

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Although many approaches and models have been developed in the recent past, we have identified that there is still a need to address some more issues particularly relevant to the networked manufacturing systems. First, in networked manufacturing systems (NMS) the structure of the network does not affect the performance of the manufacturing system. In other words, the network structure has no influence on analyzing the performance of the manufacturing system. With the obtained networks from social network analysis method (SNAM) on manufacturing systems, there is a possibility to analyze the scalability and its performance on the manufacturing system (Newman 2002). Second, in NMS, the size, scope, and complexity of the network are not defined. On the contrary, with the SNAM on NMS, there is a possibility to define the network size and its functional properties such as centrality measures and network complexity in a much better way (Mendes et al. 2004). Moreover, there is hardly any information regarding the communication flow inside the network structure and the descriptive statistics that can be used to extract some information about the speed/ nature of the structure. Various types of topologies and how these topologies affect the search space for exploiting the desired solution is discussed in (Neukum and Ivanov 1994). In their work, authors have presented the descriptive statistics such as average distance, diameter, and distribution sequence of various topologies and found that the series of statistics directly affects the performance of the topologies. However, much work has been done on a wide range of problems ranging from natural phenomena to military (Lu and Hamilton 1991; Crovella and Bestavros 1996; Roberts and Turcotte 1998; Zhang et al. 2013). A framework to predict the missing quality of service values of the manufacturing services by combining social network and collaborative filtering techniques is presented (Newman and Park 2003). However, there is limited work that has applied a social network kind of analysis on manufacturing problems, in particular NM-problems. In order to give voice to the challenge, in this paper we have analyzed the existing NMS with social network method (SNM) to find the reconfiguration effect of various performance measures of the system. The detailed description of the analysis, method, and framework is presented in later sections. Since efficiency is a significant part of networked manufacturing problems, the proposed methodology and its characteristics better serves the existing traditional networked manufacturing approach in many ways. The fundamental difference between social networks and non-social networked systems with two important properties are discussed in (Newman 2005). First, the degrees of adjacent vertices in networks are positively correlated in social networks but negatively correlated in most other networks (Watts and Strogatz 1998). Second, high levels of clustering are possible with social networks, whereas in many non-social networks clustering would be expected on the basis of pure chance (Heddaya 2002). In this paper, a case in the context of networked manufacturing is taken. Later, we have shown how a manufacturing execution system data can be extracted and viewed as a network connected with a number of nodes. Later, we map the attributes of the manufacturing system as elements of connecting nodes and the connections between the elements act as interactions where the actual material flows on different resources. Moreover, a framework has been developed and a social network analysis method

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has been conducted to find the effect of resource scalability and its effect on networked manufacturing system. The remainder of this paper is organized as follows. In section “Problem Description,” we give a detailed description of a case with the basic assumptions. In section “Framework of the Proposed SNAM Approach,” we presented a framework and the logical steps of the execution of a case with proposed SNM. The detailed SNAM to find the functional properties of the network has been discussed in section “Social Network Analysis Model.” The scalability feature to the networked manufacturing system has been introduced and with the help of clique based social network algorithm the time scale has been measured and its results are presented in section “Scalability with Social Network Analysis Algorithm.” The paper concludes with section “Conclusion and Future Work” which suggests the directions of the future work.

Case Study We consider a customized manufacturing environment where different customers order multiple products and it is denoted as n. Each product corresponds to a different sequence of operation steps and set of alternative process plans. Consequently, the products with alternative process plans constitute different operations which are to be processed on a set of alternative machines. However, in networked manufacturing environment, the machines with different capabilities are distributed geographically to perform various operations of the products. Hence, the transportation time between the two corresponding machines take part as a significant role for assignment of machines to production tasks. Due to alternative process plans, machines, and operations sequences the problem is much more complex and it is considered as a challenging problem in today’s manufacturing environment. As part of our objective in this paper, network size, scalability, and modularity are considered as one such performance measure to conduct SNAM to find the effect of resource scalability on the above mentioned networked manufacturing system. The above mentioned problem makes several assumptions that are worth highlighting. (a) Products preemption is not allowed; (b) The operation of a product on a machine should not be interrupted until it is finished; (c) We have considered the transportation time between the machines. With this kind of manufacturing system, after an immediate completion of the operation of a job on a machine it is immediately transported to the succeeding machine for its process. (d) A machine can handle only one product at a time. (e) All machines, products with operations and process plans are simultaneously available at time zero.

Framework of the Proposed SNAM Approach It is evident from the literature that not much work has been done on finding the scalability issues in networked manufacturing systems particularly by considering SNAM. In order to respond to the aforementioned issues, it is necessary to conduct

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Resource Scalability in Networked Manufacturing System: Social. . .

Fig. 1 Scalability with respect to resouce and capacity (Heddaya 2002)

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Economic stability

Capacity

System scalability

Not scalable

Resources

further optimization for economic stability. From Fig. 1 the details of scalability and its influence in relation with capacity and resources on system behavior are shown. The X-axis in the graph represents resources and Y-axis represents capacity, where resources and capacity increase the graph follows a straight line which means the mentioned performance measures are linearly increasing. In this paper, we try to prove the system stability with the help of a case by increasing the resource scalability. Moreover, we try to find the linear pattern by increasing the resources scalability to find the effectiveness of the proposed SNAM model. In the study, we propose a step by step process of SNAM and its implementation on a networked manufacturing problem with a framework as illustrated in Fig. 2. The flowchart has been divided into three steps: (1) network modeling, (2) social network analysis method, and (3) evaluation of manufacturing system by considering. Details of the collected data and the description of the method steps are elaborated in the following sections.

Social Network Analysis Model The method shows, how the manufacturing execution data can be extracted and viewed as a network with nodes. However, it is very difficult to get the real world data of process planning and scheduling problem. Therefore, we have used input data from (Zhou et al. 2010) for conducting various tests with SNAM to obtain different characteristics of the network. The SNAM is categorized into two steps: (a) network modeling, and (b) network analysis. The detailed description of these two steps is mentioned in the following sections.

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Start Input Data: Manufacturing Execution Data . Work Orders, Jobs, Operations, Machines/resources, etc.

Representation of Complex Manufacturing Network with the manufacturing data

Conduct tests to analyze various performances of the manufacturing social network. . Centrality . Power law ... . Identify communities with obtained manufacturing social network .etc Evaluation of resource scalability and its effect on networked manufacturing system performance Stop

Fig. 2 Framework to find the resource scalability effect on networked manufacturing system

Network Modeling A network consists of a set of nodes connected with ties indicating interaction (Newman 2005). This section presents how the manufacturing system execution data can be conceived as networks. The collected data from the literature is listed as an affiliation matrix, whose rows and columns represent the attribute (machines, jobs, operations, alternative process plans) information. The detailed case and its description is shown in Table 1. Later, the matrix is analyzed using the modeling algorithm in the Ucinet software package and for visualizing, the obtained results are submitted to the Netdraw software package. The obtained network from the affiliation matrix is called a collaboration network. This collaboration network is more interesting and meaningful than the simple network in terms of its characteristics, size, etc. The above procedure has been repeated for the remaining scenarios and obtained different collaboration networks. The collaborative network and its details are depicted in Fig. 3. The nodes in the network represent different attributes of the manufacturing system and for distinguishing each attribute we mentioned nodes with different colors. For example, in Fig. 3 the nodes with blue color indicate different operations, and the red-accent color indicates different machines participating in performing the task. We showed each different job node with a different color. Before analyzing the network, there is a need to run some preliminary analysis to describe the overall nature of the network. As part of that, we map the network with the elements in the manufacturing system. The detailed description of the preliminary and the detailed statistical analysis of the obtained networks are specified in the following section.

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Table 1 Input data for the 6
6 problem (Zhou et al. 2010) Job J1

PP PP1,1 PP1,2

J2

PP2,1

J3

PP3,1 PP3,2 PP3,3

J4

PP4,1 PP4,2

J5

PP5,1 PP5,2

J6

PP6,1 PP6,2 PP6,3

O1 {1, 2} [6 5] {1, 3} [4, 5] {2} [4] {2, 3} [5, 6] {1} [9] {2, 3} [7, 6] {1, 2} [7, 8] {1, 3} [4, 3] {1} [3] {2, 4} [5, 6] {1, 2} [3, 4] {1, 3} [4, 4] {1, 2, 3} [3, 5, 8]

O2 {3, 4, 5} [7, 6, 6] {2, 4} [4, 5] {1, 3} [2, 3] {1, 4} [6, 5] {3, 4} [8, 8] {4} [7] {3, 4} [7, 6] {2} [4] {2, 4} [4, 5] {5} [7] {3, 4} [4, 3] {2, 3} [5, 6] {4, 5} [7, 10]

O3 {6} [8] {3, 5} [5, 6] {2, 4, 6} [4, 3, 5] {2, 5} [5, 6] {5} [9] {3, 5} [4, 6] {6} [9] {3, 4} [4, 5] {3} [4] {3, 6} [9, 8] {2, 5} [5, 3] {2, 4} [6, 7] {3, 6} [9, 9]

O4

O5

O6

{4, 5, 6} [5, 5, 4] {3, 5} [2, 4] {3, 6} [6, 5]

{2, 4} [3, 4]

{4, 6} [3, 5]

{4, 6} [5, 5]

{2, 4} [6, 4]

{5, 6} [3, 5] {5} [3]

{4, 6} [5, 4]

{3} [4] {6} [7]

{4, 5} [4, 6]

{3, 6} [5, 4]

Network Analysis The goal of the network analysis is to reveal the information of the structure of the collaboration networks for potential synergies. In order to obtain the information of the structure, important properties of descriptive statistics such as average distance, diameter, and modularity of different networks have been tested. Moreover, with the probability distribution the size and complexity of the network has been identified.

Distance of a Network In this paper, we have submitted the input data to the Ucinet and then obtained the results of average distance for different sets. Based on properties of the network and their descriptive statistics, i.e., average distance, it is clear that the graph structure and its complexity increases with an increase in resources. In Table 2 below, the measures of average distance for different scenarios is shown, which depend on the

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Job 1

Mac 3

Job 2

Job 6 Opr 1

Opr 2

Mac 4

PP1

Mac 1

Mac 6 PP2

Opr 3

Mac 2

Opr 4

Job 3 Job 4 Job 5

PP3

Opr 5 Mac 5

Fig. 3 Collaborative networks of 6 by 6 problem Table 2 Scenarios used in the study and the associated graph statistics

Data 6 by 6 6 by 10 6 by 14 6 by 18 6 by 22 6 by 26 6 by 30 6 by 34

Average distance 1.343 1.353 1.417 1.475 1.512 1.554 1.598 1.624

information about the structure and the speed of communication flow. The average distance measures the average number of edges between any two nodes where the average number of cycles of influence is needed to broadcast information throughout the graph.

Complexity Analysis Once a network is generated, it can be proven to be complex if the connections between the work systems follow a well known power law distribution. In this section, the mathematics behind the power law distribution and its implementation to the obtained network has been presented. The probability distribution function for a normalized degree centrality of the collaborative networks follows power law and it is represented in Eq. 1. pðxÞ ¼ Cxα

(1)

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Where, C is constant and α is an exponent and the value of α is assumed as zero with α > 0. We observe that while x approaches zero, the probability of x diverges. Hence, there must be some lowest value at which the power law function should be obeyed. From the descriptive statistics the value of xmin is found for normalization. Normalizing the constant C and its solution in Eq. 1 gives: 1 ð

1¼

1 ð

pðxÞdx ¼ C xmin

x1 dx ¼

xmin

C  αþ1 1 x xmin 1α

α1 C ¼ ðα  1Þxmin

pð x Þ ¼

(3)

  α  1 x α xmin xmin "

n X xi α¼1þn x i¼1 min

(2)

(4)

#1 (5)

In Eq. 2, we can observe that the value of x changes to xmin with α > 1, otherwise the right side of the equation would diverge. If the value of α > 1, then Eq. 2 gives Eq. 3. Thus the correct normalized expression of power law is represented in Eq. 4 and we have plotted the power law distribution p(x) in a log-log graph with the exponent α with the quantities xi, i ¼ 1. . .n are the observed values of parameters x and xmin. Figure 4 shows, the log-log plots of different data sets with normalized distribution of the connections strength, i.e., the number of connections the nodes occurs. From the plots it is evident that the distribution obeys the power law having observed exponents of α ¼ 1.628 to 1.855 for 6 by 6 to 6 by 35 of all data sets. For in-depth analysis of the power law distribution one can refer to (Newman 2005). 101

p(x)

100

10−1

Fig. 4 Power law distribution of 6 by 6 with 1.628

10−2 100

101 x

102

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Table 3 Comparison of different scenarios and their different performance measures Data 6 by 6 6 by 10 6 by 14 6 by 18 6 by 22 6 by 26 6 by 30 6 by 34

Average distance 1.343 1.353 1.417 1.475 1.512 1.554 1.598 1.624

Size of the network (power law) 1.628 1.670 1.691 1.712 1.731 1.743 1.823 1.855

Scalability (greedy K-plex (clique) algorithm) Scalable Scalable Scalable Scalable Scalable Scalable Scalable Scalable

Modularity 0.007 0.008 0.022 0.058 0.076 0.084 0.102 0.122

Scalability with Social Network Analysis Algorithm The proposed SNAM is implemented with a case that is expressed with different manufacturing scenarios. Different tests were performed on several graph sizes and data sets, by fixing the number of multiple jobs to six and by increasing the number of resources to each data set. Table 3 shows the different performances of generated collaborative graphs, whose size complexity increases with the increase in resource scalability. In this work, we try to find the increase in number of cliques according to size of the graph. Thereby, we use social network based greedy k-plex algorithm to find the scalability with respect to time complexity. From Table 3, column 4, it is clear that the number of cliques increases rapidly with the size of the graph and thereby the time complexity, i.e., O(n) to execute the graph also increases. An algorithm for detecting community structure of social networks based on priority knowledge and modularity is used. Column 5 in Table 3 clearly depicts the increase in modularity with increase of resources in the data.

Summary and Future Work The paper presents a social network analysis method (SNAM) that can evaluate the effect of resource scalability in the context of a networked manufacturing system. To prove the effectiveness of the proposed method we have described a case with various complex scenarios. More importantly, we have defined a conceptual model with the help of a framework that fulfills the desired objective. In particular, solutions to queries involving SNAM such as “How can the collaborative networks be acquired from the manufacturing execution data?”, “How can the size of the network and its functional properties be extracted?”, and “How can the extracted properties influence the behavior of the network?” are provided.

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To implement the above proposed method, first the manufacturing execution data has to be converted into an affiliation matrix to be inputted to the UCINET software package. Later, with the obtained results, the Netdraw software package has been used to generate the collaborative networks. It is critical to conduct various tests such as scalability tests and complexity analysis on the network to identify the characteristics, nature, and size of the collaborative network. Moreover, we have mapped the structure of the network with the attributes in the manufacturing system. Essentially, with different experimental settings, the effect of resource scalability on a networked manufacturing system has been tested on different performance measures. To validate the role of scalability and to find the effectiveness of the proposed methodology we used a social network based greedy algorithm to see whether different scenarios follow the same pattern. Results from Table 3 clearly show as the resources increase, the size, complexity, and modularity of the graph increase, thus following a linear increment in time complexity. In future work, one can find the gain in performance measures that can be achieved with resource scalability. Moreover, some issues which are critical for manufacturing system designs such as cost of sharing resources (contention), diminishing returns at higher loads (saturation), and negative return on investment (coherency delays) can be identified with the proposed method.

References Crovella ME, Bestavros (1996) A self-similarity in world wide web traffic: evidence and possible causes. In: Gaither BE, Reed DA (eds) Proceedings of the ACM SIGMETRICS conference on measurement and modeling of computer systems. Association of Computing Machinery, New York, pp 148–159 Heddaya AS (2002) An economically scalable internet. IEEE Comput 35:93–95 Koren Y (2010) The global manufacturing revolution – product–process–business integration and reconfigurable systems, vol 80. Wiley, Hoboken Liu F, Liu J, Lei Q (2002) The Connotation and research development trend of networked manufacturing. In: Proceedings of China mechanical engineering annual conference, Beijing, pp 22–27 Lu ET, Hamilton RJ (1991) Avalanches of the distribution of solar flares. Astrophys J 380:89–92 Mendes R, Kennedy J, Neves J (2004) The fully informed partical swarm: simpler, maybe better. IEEE Tran Evol Comput 8:3 Neukum G, Ivanov BA (1994) Crater size distributions and impact probabilities on Earth from lunar, terrestialplanet, and asteroid cratering data. In: Gehrels T (ed) Hazards due to comets and asteroids. University of Arizona Press, Tucson, pp 359–416 Newman MEJ (2002) Assortative mixing in networks. Phys Rev Lett 89:208701 Newman MEJ (2005) Powerlaws: Pareto distributions and Zipf’s law. Contemp Phys 46:323–351 Newman ME, Park J (2003) Why social networks are different from other types of networks. Phys Rev E 68(3):036122 Okino N (1993) Bionic manufacturing systems. In: Peklenik J (ed) Proceedings of the CIRP seminar on flexible manufacturing systems past-present-future, Bled, pp 73–95 Putnik G, Sluga A, ElMaraghy H, Teti R, Koren Y, Tolio T, Hon T (2013) Scalability in manufacturing systems design and operation: state-of-the-art and future developments roadmap. CIRP Ann Manuf Technol 62(2):751–774

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Roberts DC, Turcotte DL (1998) Fractality and selforganized criticality of wars. Fractals 6:351–357 Ueda N (1993) A generic approach toward future manufacturing system. In: Peklenik J (ed) Proceedings of the CIRP seminar on flexible manufacturing systems past-present-future, Bled, pp 211–228 Valckenaers P, Bonneville F, Brussel H, Brussel V, Bongaerts L, Wyns J (1994) Results of the holonic control system benchmark at the K.U. Leuven. In: Proceedings of the computer integrated manufacturing and automation conference, Rensselaer Polytechnic Institute, Troy, pp 128–133 Wasserman S (1994) Social network analysis: methods and applications (Vol. 8). Cambridge University Press Watts DS, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442 Wiendahl HP, ElMaraghy H, Nyhuis P, Zah MF, Wiendahl HH, Duffie N, Brieke M (2007) Changeable manufacturing – classification, design and operation. CIRP Ann Manuf Technol 56(2):783–809 Zhang WY, Zhang S, Chen YG, Pan XW (2013) Combining social network and collaborative filtering for personalised manufacturing survive recommendation. Int J Prod Res 51 (22):6702–6719 Zhou GH, Xiao Z, Jiang PY, Huang GQ (2010) A game-theoretic approach to generating optimal process plans of multiple jobs in networked manufacturing. Int J Comput Int Manuf 23:1118–1132

Improved Intelligent Water Drops Optimization Algorithm for Achieving Single and Multiple Objective Job Shop Scheduling Solutions

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S. H. Niu, S. K. Ong, and A. Y. C. Nee

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Solution Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of the OIWD Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Schemes for Improving the OIWD Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problem Representation Using Modified Disjunctive Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Enhanced IWD Algorithm (EIWD) for SOJSSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modified IWD Algorithm Based on Scoring Function for MOJSSP . . . . . . . . . . . . . . . . . . . . . Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Experimental Evaluation of EIWD for SOJSSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Experimental Evaluation of MOJSSP-IWD for MOJSSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Job shop scheduling problem (JSSP) is a typical scheduling problem that aims to generate an optimal schedule to assign all the operations to the production equipments. JSSPs can be categorized into single objective JSSP (SOJSSP) and multiple objective JSSP (MOJSSP) based on the optimization objectives considered. SOJSSP involves generating schedules to allocate operations to different machines considering only one objective, while MOJSSP considers more than one objective in the scheduling process. SOJSSP and MOJSSP are typical NP-hard optimization S.H. Niu (*) Department of Mechanical Engineering, National University of Singapore, Singapore e-mail: [email protected] S.K. Ong • A.Y.C. Nee Mechanical Engineering Department, Faculty of Engineering, National University of Singapore, Singapore e-mail: [email protected]; [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_25

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problems which have significant values in real production. Intelligent Water Drops (IWD) is a new type of meta-heuristics which shows excellent ability of solving optimization problems. In this research, IWD is improved and customized to solve SOJSSP and MOJSSP problems. Experiments have been conducted, and the results show that the enhanced algorithms can solve these two types of problems better compared with current literature. To the best of the authors’ knowledge, this is among the first research employing IWD for solving SOJSSP and MOJSSP.

Introduction Scheduling is an optimization process of allocating limited resources or machines over time to perform a set of tasks while satisfying multiple constraints and goals. Scheduling plays an important role in the manufacturing realm. It can be used by high-level production planning systems to check their capacity; it also provides visibility of future plans in the job shops for the suppliers and customers to adjust their actions; it can be used to evaluate the performance of job shop personnel and management; besides, it can provide greater degrees of freedom to avoid future problems (Aytug et al. 2005). Scheduling is well recognized by the academia as well as the practitioners, and it has been extensively studied in recent years. In a job shop, machines or resources are structured according to the processes they perform, where machines with the same or similar material processing capabilities are grouped together to form work-centers. The machines are usually general-purpose machines that can accommodate a large variety of part types. A part moves through different work-centers based on its process plan. Normally, job shops are most suitable for small lot size production (Chryssolouris 2006). There are many advantages of job shop processing, and these advantages become more obvious when there is greater variety in the jobs, and these jobs have different processing sequences. This research focuses on job shop scheduling. The advantages of job shop scheduling are as follows: (1) Each operation can be assigned to a machine to achieve the best production rate or the best quality. (2) The load can be distributed to the machines evenly. (3) It is easier to accommodate machine breakdowns. The scheduling problem is proven to be typically NP-hard; the computation time increases exponentially with the problem size. It is time consuming to search for an optimal solution in the huge solution space, especially when the problem is complex. Therefore, JSSP is among the most difficult (Reza and Saghafian 2005). JSSP is a typical NP-hard optimization problem which is difficult to find the exact solution within a reasonable computation time (Garey et al. 1976). There are two widely used approaches for solving JSSP, namely, the exact methods and the heuristic methods. Exact methods, such as mathematical approaches and dynamic programming, are computationally intensive and can only solve small-scale problems. Heuristic

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methods are used to find near-optimal solutions within limited computational time. They usually aim to find a “good” solution instead of an optimal one. Meta-heuristics are high-level heuristics. For solving difficult combinatorial optimization problems, meta-heuristics has been proven to be one of the most powerful heuristic approaches (Hartmann and Kolisch 2000). The most popular meta-heuristics used to solve the JSSP in recent years include the Tabu search method (TS) (Pezzella and Merelli 2000), genetic algorithm (GA) (Hong et al. 2009; Pan and Han-Chiang 2009; Vilcot and Billaut 2008; De Giovanni and Pezzella 2010), simulated annealing (SA) (Suresh and Mohanasundaram 2006), ant colony optimizer (ACO) (Blum and Sampels 2004; Seo and Kim 2010), shifting bottleneck (SB) (Balas and Vazacopoulos 1998), artificial neural networks (ANN)(Adibi et al. 2010), and particle swarm optimization (PSO) (Zhang et al. 2009; Lin et al. 2010; Sha and Lin 2010; Ge et al. 2008). In 2007, another promising meta-heuristics called intelligent water drops (IWD) algorithm was proposed (Shah-Hosseini 2007). IWD algorithm is the most recent swarm-based nature-inspired optimization algorithm. IWD algorithm has found successful applications in several optimization problems, such as the travelling salesman problem (TSP) (Shah-Hosseini 2007, 2009a), robot path planning problem (Duan et al. 2008, 2009), n-queen puzzle (Shah-Hosseini 2009a), and the multidimensional knapsack problem (MKP) (Shah-Hosseini 2009a, b). The experimental results of these research work demonstrate that the IWD algorithm is very promising for solving optimization problems, and more research is required to improve its efficiency or/and adapt it to other engineering problems. In this research, the OIWD algorithm is successfully customized to solve the SOJSSP and MOJSSP. To the best of the author’s knowledge, it is the first research work on the application of the IWD algorithm to solve SOJSSP and MOJSSP. In this research, the OIWD algorithm is improved through five schemes, namely, (1) diverse soil and velocity initialization is employed to increase the diversity of the solution space; (2) conditional probability computation scheme is designed to further improve the diversity of the solution space; (3) bounded local soil update is proposed to make full use of the guiding information and control the convergence rate of finding a path; (4) elite global soil update is proposed to retain the good information of the results obtained; and (5) a combined local search is used for improving the search quality. The enhanced IWD algorithm is employed to solve the SOJSSP and MOJSSP. The quality and the efficiency of the enhanced IWD algorithm are tested in the experiments. The rest of the paper is organized as follows: section “Problem Formulation” presents the problem formulation. Section “Solution Methodologies” presents the solution methodologies. Section “Experimental Evaluation” describes experimental evaluation. Section “Conclusion” concludes the paper.

Problem Formulation In a JSSP problem, a set of machines M ¼ {Mj | j ¼ 1, 2, . . ., m} and a set of jobs J ¼ {Ji | i ¼ 1, 2, . . ., n} are considered. Each job has a sequence of operations O ¼ {Ok|k ¼ 1, 2, . . ., l}, and these n jobs (i.e., all the operations of these n jobs)

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have to be processed on m machines. Job splitting is not allowed, and the operations are non-pre-emptible, which means temporary interruption of an operation is not allowed after it has started. Each machine only performs one operation at a time, and each operation is performed only once on one machine. JSSP aims to find a feasible assignment (schedule) of all the operations on the given machines with optimized objectives. Depending on the goals of the decision makers, different objectives are used. In the single objective JSSP problem, only one criterion (objective) is considered, while multi-objectives are considered in the MOJSS problem. In this paper, makespan is the objective considered for SOJSSP. Unlike SOJSSP, more than one objective is explored simultaneously in the MOJSSP as merely considering one objective is not sufficient for some situations. The optimization goal for MOJSSP is to find a set of best compromising solutions in the form of alternative trade-offs instead of generating a single optimum. Simultaneous consideration of several objectives in MOJSSP is more challenging. The optimization goals for SOJSSP and MOJSSP considered in this research are as follows: • Makespan (Cmax): This is the time interval between the time at which the schedule begins and the time at which the schedule ends. Thus, the makespan of a schedule is equal to max [Ci], where i ¼ 1, . . ., m. • Tardiness (Ti): The tardiness Ti of a job Ji is the nonnegative amount of time by which the completion time exceeds the due date di, Ti ¼ max[0, (Ci  di)]. The difference between the completion time and due date for each job. • Mean flow time (F): is the average flow time of a schedule, and it is defined XThis n as follows: F ¼ 1n F . This criterion implies that the cost is directly related to i¼1 i the average time to process a single job. The flow time (Fi) is also referred to as the cycle time. It is the amount of time job Ji spends in the shop floor. It is the time interval between the release time ri and the completion time Ci of job Ji: Fi ¼ Ci  ri.

Solution Methodologies In this section, the original IWD (OIWD) is customized to solve SOJSSP and MOJSSP. An overview of the OIWD algorithm is first given in section “Overview of the OIWD Algorithm.” The schemes for improving the OIWD algorithm are presented in section “Schemes for Improving the OIWD Algorithm.” A brief description of the disjunctive graph is then given in section “Problem Representation Using Modified Disjunctive Graph” as the IWD algorithm for scheduling is represented on the disjunctive graph in this research. The enhanced IWD algorithm, EIWD for SOJJP, is presented in section “Enhanced IWD Algorithm (EIWD) for SOJSSP,” and the proposed MOJSS-IWD algorithm for MOJSSP is introduced in section “Modified IWD Algorithm Based on Scoring Function for MOJSSP.” The MOJSS-IWD algorithm is based on the IWD algorithm and a score function is embedded into its local search process.

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Overview of the OIWD Algorithm The IWD algorithm is inspired by the movement of natural water drops which flow in rivers, lakes, and seas. It is a population-based meta-heuristics where the IWDs construct a better solution through cooperation with each other. This algorithm can be applied to solve optimization problems (Shah-Hosseini 2009a). As pointed out by Shah-Hosseini, a stream can find an optimum path considering the conditions of its surroundings to reach its ultimate goal, which is often a lake or a sea. In the process of reaching for the destination, the water drops and the environment react to each other as the water drops move through the river bed. The water drops can change the environment (river beds) in which they are flowing; the environment can also influence the moving directions of the water drops. The gravitational force of the earth powers the IWDs moving toward the destination. If there are no barriers or obstacles, the IWDs will move in a straight path to the destination. However, in the real scenario, as there are different types of obstacles when IWDs are forming their paths, the real path of the IWDs may be different from the ideal path. In a river path, many twists and turns (meanders) can be observed. However, by considering the distance to the destination and the environmental constraints, the constructed path seems to be an optimal one (Duan et al. 2008, 2009). In the OIWD algorithm, the IWDs are associated with two attributes, namely, the amount of soil and the velocity of the IWDs. The velocity enables the water drops to transfer soil from one place to another. Faster water drops can gather and transfer more soil from the river beds. Besides, the velocity of the IWDs is also affected by the path condition. The amount of soil in a path has impact on the IWDs’ soil collection and movement. A path with less soil allows the IWDs to move faster along that path, and the IWDs can attain a higher speed and collect more soil from that path, while a path with more soil is the opposite. In the IWD algorithm, the movement of IWDs from the source to the destination is performed in discrete finite-length time steps. When an IWD moves from one location to the next one, the increase in its velocity is proportional (nonlinearly) to the inverse of the soil of the path between the two locations, and the soils of the IWDs increase because the IWDs remove some soil from the path they travel. The soil increase is in inverse proportion to the time needed for the IWDs passing between the two locations. The time duration to travel from one location to the second location depends on the distance between these two locations and the velocity of the IWDs. In the OIWD algorithm, the undesirability of a path is reflected by the amount of soil in the path. When an IWD has to choose a path among several candidate paths, it would prefer an easier path, i.e., a path with less soil than with more soil. The IWDs select a path based on a probabilistic function. The IWD algorithm uses a parameterized probabilistic model to construct solutions, and the values of the parameters are updated in order to increase the probability of constructing high-quality solutions. The IWD algorithm has been tested using several standard optimization benchmark problems. It can find good solutions for TSP (Shah-Hosseini 2007, 2009a),

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and it can also solve robot path planning (Duan et al. 2008, 2009), the n-queen puzzle (Shah-Hosseini 2009a), and the MKP (Shah-Hosseini 2009a, b) with optimal or near-optimal solutions. In this research, the OIWD algorithm is further improved with five schemes; the rationale behind the proposed schemes is to increase the diversity of the solution space as well as improve the search quality of the IWDs. The detailed description of the schemes is given in section “Schemes for Improving the OIWD Algorithm.”

Schemes for Improving the OIWD Algorithm As a meta-heuristic algorithm, IWD suffers from two problems, viz., (1) it has an earlier convergence and (2) the initial solution and the diversity of the solution space often affect its search quality. The OIWD algorithm is enhanced through five schemes to form the EIWD for solving SOJSSP and the MOJSSP-IWD to solve MOJSSP in this research. (1) Scheme 1: Diverse Soil and Velocity Initialization In the OIWD algorithm, all the edges are set with the same amount of initial soil, and all the IWDs have the same initial velocity. In the modified algorithm, the initial amount of soil of each edge is randomly set, and the initial velocity of every IWD is also randomly chosen. This different initial soil and velocity setting provides the modified IWD algorithm with a diverse initial solution space. (2) Scheme 2: Conditional Probability Computation When an IWD is at node i in the disjunctive graph, the probability of choosing node j is represented by pIWD ( j). The OIWD algorithm computes this probability i based on the soil on the edges. In the EIWD algorithm, to increase the convergence speed of the IWDs, namely, the speed of finding a best path, the probability is computed based on the soil of the edges and the processing time pt( j) of the candidate nodes (operations). To further improve the diversity of the search process, a piecewise function (Eq. 1) is employed to determine this probability (conditional probability computation). A random number φdec  (0, 1) is used to determine the method to be used for computing the probability. φdec is compared with φ0 ¼ 0.5; the probability of choosing node j is determined by comparing the results of φdec and φ0. rn  (0, 1) is a random number to add randomness to the probability. τ is a variable which represents the relative importance of the soil of the edge to the processing time of the next operation, its default value is 1, which indicates these two variables are equally important. The rationale behind the conditional probability computation lies in broadening the possible solution search space

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pIWD ðj Þ i

¼

8 > > > >
f ðsoilði, jÞÞ  ð1=pt ðjÞÞ > > X þs > : min 1, f ðsoilði, kÞÞ

φdec > φ0 !!

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(1)

φdec  φ0

k  schedulable

1 where f ðsoilði, jÞÞ ¼ es þgðsoil ði, jÞÞ and

gðsoilði, jÞÞ ¼

8 < soilði, jÞ : soilði, jÞ 

if

min

j  scheduable

min

j  scheduable

soilði, jÞ  0

soilði, jÞ

else

:

es ¼ 0.01 is to prevent a possible division by 0. By repeatedly applying the above rule, each IWD builds its own path. (3) Scheme 3: Bounded Local Soil Update The soil updating model is one of the most important components of an IWD algorithm. To make full use of the guiding information and controlling of the convergence rate of finding a path, a bounded soil-updating model is proposed. This model differs from the soil-updating model in the OIWD algorithm by applying a lower and an upper bound to the soil-updating process. Let Δsoilmax and Δsoilmin be the upper and lower bound values of the soil changes when the IWDs pass through any edge in the disjunctive graph. The lower bound (a small positive constant) prevents the algorithm from slow convergence, while the upper bound prevents the algorithm from getting to the local optima too quickly. More precisely, the edge (i, j) soil updating and IWD soil updating use the following formulas: 8 if Δsoilði, jÞ < Δsoilmin < ð1  ρL Þ  soilði, jÞ  ρL Δsoilmin soilði, jÞ ¼ ð1  ρL Þ  soilði, jÞ  ρL Δsoilmax if Δsoilði,jÞ > Δsoilmax (2) : ð1  ρL Þ  soilði, jÞ  ρL Δsoilði, jÞ otherwise 8 < soilIWD þ Δsoilmin if Δsoilði, jÞ < Δsoilmin IWD ¼ soilIWD þ Δsoilmax soil (3) if Δsoilði, jÞ > Δsoilmax : soilIWD þ Δsoilði, jÞ otherwise soilIWD represents the soil that an IWD carries. ρL ¼ 0.9 is the local soil-updating parameter. The upper bound and the lower bound of the soil updating are set based on the value of the soil on the edge and the soil in the IWDs. (4) Scheme 4: Elite Global Soil Update In the OIWD algorithm, only the soil in the best iteration solution SIB is updated. In the modified algorithm, the path of the best iteration schedule SIB and the paths corresponding to the IWDs in an elite IWD group are also updated. By doing so,

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better information in each iteration is retained. This elite group contains Nelite IWDs which solutions are among the best Nelite in all IWDs. The number of elite IWDs is determined by a coefficient α%, and the number of elite IWDs, Nelite, is calculated as Nelite ¼ α%  NIWD. When an iteration is completed, the soil on the disjunctive graph is updated using Eq. 4. soilði, jÞ ¼ ð1 þ ρIWD Þ  soilði, jÞ  ρIWD

1 soilIWD k ð l  1Þ

8ði, jÞ  Selitek , k  ½1 . . . N elite 

(4)

In Eq. 4, ρIWD is a global soil updating parameter, and l is the number of operations in each job. For the k-th IWD in the elite group, soilIWD is the soil it k elitek carries and S is its corresponding schedule. α is set by the author; a larger α leads to more information being retained on the path of the IWDs. Through this scheme, more information of the latest iteration can be used to increase the search efficiency. (5) Scheme 5: Combined Local Search After an iteration, all the IWDs can find feasible schedules. A scheme, which is a local search that combines both breadth and depth searches, is proposed to improve the obtained feasible schedules. The combined local search methods for SOJSSP and MOJSSP are introduced in detail in sections “Enhanced IWD Algorithm (EIWD) for SOJSSP” and “Modified IWD Algorithm Based on Scoring Function for MOJSSP,” respectively.

Problem Representation Using Modified Disjunctive Graph To implement the IWD algorithm for SOJSSP and MOJSSP, the JSSP is represented as a modified disjunctive graph Gdis which resembles rivers as in the IWD algorithm. A disjunctive graph Gdis ¼ < N, C, D > can be used to represent a JSSP (Yamada 2003; Balas 1969). A disjunctive graph Gdis has a node set N, a disjunctive edge set D, and a conjunctive edge set C. Each operation has a corresponding node in the node set N. Besides, N contains two dummy operations (source node and sink node) with zero processing time. For the conjunctive edge set C, it contains directed edges connecting the neighbor operations of the same job. Such edge links can represent the precedence constraints of the l operations of the same job. The disjunctive edge set D contains undirected edges which connect consecutive operations processed on the same machine. These edges are undirected ones against each other, which represent the unsolved precedence of the operations. Both the disjunctive edges and conjunctive edges emanate from the operation nodes, and their lengths represent the processing times of the operations where they emanate. Thus, the lengths of the outgoing edges from the same node are the same. In the schedule construction process, one direction of the disjunctive edge pairs should be determined in order to change each undirected disjunctive edge to a

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Table 1 The processing time (in unit time) for each operation (3  3 job)

Table 2 The machine to process each operation (3  3 job)

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Job 1 Job 2 Job 3

1st operation 86 68 33

2nd operation 60 28 67

3rd operation 10 38 96

Job 1 Job 2 Job 3

1st operation 2 2 3

2nd operation 3 1 1

3rd operation 1 3 2

Fig. 1 Disjunctive graph for the 3  3 job described in Tables 1 and 2

directed conjunctive edge. In this research, the IWDs will follow the next node using the probability of the next node that is calculated using IWD algorithm. The processing order of all the conflicting operations that require the same machine is determined by fixing the directions of all the disjunctive edges and a complete schedule is obtained. The optimization objective is the length of the longest path (critical path) in the newly constructed graph. This path is acyclic with the source node as the start node and the sink node as the ending node. For a 3  3 job indicated in Tables 1 and 2, its disjunctive graph representation is shown in Fig. 1. There are two dummy nodes, namely, s and t, which represent the source node and the sink node, respectively. Each operation is represented by a node, and the nodes in one row form a job, e.g., the nodes in the first row (node 11, node 12, and node 13) represent job 1, and the node (operation) with number 12 (O12 for ease of representation) represents the second operation of job 1. The operations belonging to the same job are connected by the conjunctive edges according to their processing order. The first operation of each job is connected to node s, while the last operation of each job is

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Fig. 2 Modified disjunctive graph for the 3  3 job in Tables 1 and 2

connected to node t. The operations which are performed on the same machine are connected by disjunctive edges. For example, O12, O23, and O31 are connected by disjunctive edges as they are performed on machine 3. In this research, the objective is to solve the SOJSSP and MOJSSP depicted in the disjunctive graph using the modified IWD algorithm. The disjunctive graph depicts the environment for the IWDs, and the IWDs flow on the edges of the graph. Each IWD travels on the graph gradually along the edges from source to sink. After the completion of the iterations, all the IWDs will reach the sink. The solutions are represented by the edges that the IWDs have visited. The basic idea of the IWD algorithm is to set up a graph and let the IWDs travel through the graph. IWDs travel from a start node to a destination node. During the travel, the soils of the edges are modified as the IWDs pass through these edges. The soil and velocity of the IWDs are modified as well. To apply the modified IWD algorithm to SOJSSP and MOJSSP, a modified disjunctive graph is used. Figure 2 shows the modified disjunctive graph for the 3  3 job described in Tables 1 and 2. Besides the edges in a standard disjunctive graph (as shown in Fig. 1), new edges are added (shown as dash edges). For each node (operation) Ox, all the operations that are possible subsequent to Ox in the schedule are identified. Dash edges are formed to connect Ox and the potential subsequent operations in the schedule. For example, the possible subsequent operations of O22 in a schedule are O11, O12, O13, O23, O31, O32, and O33, and O22 is connected to O31, O11, O12, and O33 by dash edges (for the rest of the operations, no dash edges are formed as they are already connected to O22.). Figure 3 shows the dash edges of O22. In the modified disjunctive graph, two soil values are attached to each disjunctive edge, one for each direction. The IWDs choose the next edge to visit based on the probability calculated from Eq. 1 using the soil on the path and the processing time. In the modified algorithm, a group of IWDs is used. In each iteration, each IWD starts from node s and visits every node in the modified disjunctive graph until

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Fig. 3 Modified disjunctive graph for the 3  3 job in Tables 1 and 2 (dash edges for O22)

it reaches node t. The path an IWD has passed produces a schedule. The visiting sequence of the nodes in the path corresponds to the order of the operations in a schedule. Each IWD will find its path on the modified disjunctive graph. For an IWD to select the next node to visit, the algorithm which is called G&T algorithm proposed by Giffler and Thompson (Giffler and Thompson 1960) is used, and Scheme 2, i.e., the conditional probability computation scheme described in section “Schemes for Improving the OIWD Algorithm,” is employed for priority computation. The G&T algorithm is used to ensure an active schedule is obtained. Assume IWD1 starts from node s. The set of operations that can be scheduled is Ω {O11, O21, O31}. O31 will be scheduled as it is the only operation which meets the requirement of the G&T algorithm. O31’s successor O32 will be added and Ω becomes {O11, O21, O32}. O21 has the smallest finishing time and thus all the operations that are performed on machine 2 and with start time less than O21’s finishing time will be the candidates. The candidate operations are {O11, O21}. The priority (probability) of each candidate operation will be determined using Scheme 2. The priority of an operation Ox (either O11 or O21) is correlated with (1) the soil on the edge between the latest scheduled operation (O31) and Ox and (2) the processing time of Ox. Assume O11 is selected, the soil of edge (O31, O11) will be updated using the Scheme 3, i.e., the bounded local soil update described in section “Schemes for Improving the OIWD Algorithm.” The velocity and soil of IWD1 will be updated as well. From O11, IWD1 continues its path (select next node to visit, update soil and velocity) until it reaches the sink node.

Enhanced IWD Algorithm (EIWD) for SOJSSP To facilitate the operation of the EIWD algorithm, the JSSP is represented as a disjunctive graph Gdis which resembles rivers as in the OIWD algorithm. The entire procedures of the EIWD are shown in Algorithm 1. As shown in Algorithm 1, the

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EIWD algorithm contains NIWD_iter iterations (Line 3–Line 21). In each iteration, NIWD IWDs travel from the source node to the sink node in Gdis. The path of an IWD can produce a feasible solution (schedule). The soils on the edges where the IWDs pass, the soils of the IWDs, and the velocities of the IWDs are updated during the travelling of the IWDs (Line 6–Line 10). After each iteration, the soils on Nelite IWDs’ paths are updated (Line 13–Line 15). Next, a group of best solutions SBD are chosen, and a combined local search is performed to further improve these solutions (Line 16–Line 17). After a local search, a best iteration solution SIB is identified, and the global best solution STB is updated (Line 18–Line 19). After all the iterations, another local search is performed on STB (Line 22). The brief descriptions of the functions in Algorithm 1 are presented next. Algorithm 1 EIWD for single objective JSSP (JSSP disjunctive graph Gdis) 1: Initialize an IWDs group A, // A population of IWDs 2: initialization (); //scheme 1 3: while (k < NIWD_iter) do 4: for (each time step t) do 5: for (each IWDg  A which feasible solution has not been discovered) do 6: (i, j)¼ selectNextEdge(IWDg); //scheme 2 7: VelIWDg ¼ updateVelocity (VelIWDg ); 8: Δsoil(i, j) ¼ computeDeltaSoil((i, j), IWDg); 9: soil(i, j) ¼ updateEdgeSoil((i, j), Δsoil(i, j)); //scheme 3 10: soilIWDg ¼ updateIWDSoil(soilIWDg , Δsoilði, jÞ); //scheme 3 11: end for 12: end for 13: for (Nelite IWDs) do 14: globalSoilPropagation(); //scheme 4 15: end for 16: SBD ¼setupBestSolutionGroup (); 17: SIB¼combinedLocalSearch(SBD); //scheme 5 18: updatePathSoil(SIB); 19: update(STB); 20: k++; 21: end while 22: STB¼ combinedLocalSearch(STB); (1) initialization (): This function initializes the static and dynamic parameters, such as the soil of each edge and the velocity of each IWD. A scheme (Scheme 1) is proposed to increase the diversity of the initial solution space, and it has been discussed in section “Schemes for Improving the OIWD Algorithm.” (2) selectNextEdge (IWDg): For the g-th IWD, choose the next node to visit in the schedule operation list according to the probability calculated. A conditional

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probability computation scheme (Scheme 2) is designed to replace the probability computation part in the OIWD algorithm (section “Schemes for Improving the OIWD Algorithm”). (3) updateVelocity (VelIWDg ): For the g-th IWD moving from node i to node j on the disjunctive graph, update its velocity as follows: velIWDg ðt þ 1Þ ¼ velIWDg ðtÞ þ

av bv þ cv  soil2 ði, jÞ

velIWDg ðt þ 1Þ is the velocity of the IWDg after updating, soil(i, j) is the soil on the edge linking node i and node j, and av, bv and cv are the updating parameters to ensure that the value of the velocity is increased in the same scale of magnitude as the original velocity. If the value of the velocity increase is too big, the IWDs may be trapped in the local optima; if the value of the velocity increase is too small, the IWDs may need more time to obtain a schedule. Besides, bv also guarantees that the equation is not divided by 0. (4) computeDeltaSoil ((i, j), IWDg) For the g-th IWD, calculate the amount of soil that it loads from the edge (i, j) as follows: Δsoilði, jÞ ¼

as   bs þ cs  time2 i, j; velIWDg

  jÞ is the time taken for an IWD to travel on time i, j; velIWDg ¼ max e p,t ðvel ð v IWDg Þ the edge (i, j) with the velocity velIWDg , and pt( j) is the processing time of operation j. ev ¼ 0.0001 guarantees that the equation is not divided by 0. as, bs, and cs are the updating parameters to ensure that the value of the soil is increased in the same scale of magnitude as the original soil. If the value of the soil is increased significantly, the IWDs may be trapped in the local optima; if the value of the soil is increased marginally, the IWDs would need more time to obtain a schedule. Besides, bs also guarantees that the equation is not divided by 0. (5) updateEdgeSoil ((i, j), Δsoil(i, j)) and updateIWDSoil (soilIWDg , Δsoilði, jÞ): For the g-th IWD, update the soil of the edge it traverses and the soil contained in the IWD. Scheme 3 is proposed to utilize the guiding information, i.e., the amount of soil on the path, and control the convergence rate (section “Schemes for Improving the OIWD Algorithm”). (6) globalSoilPropagation (): Update the soils of the edges included in the current elite IWDs’ solutions (Nelite elite IWDs). This is Scheme 4 and has been discussed in section “Schemes for Improving the OIWD Algorithm.” (7) setupBestSolutionGroup (): This is used for local search; a solution group SBD is set up for recording the best NBD solutions during the local search process. After each iteration in the EIWD algorithm, the schedules generated using the NIWD IWDs are chosen to conduct the local search; these are chosen either

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randomly or based on the quality of the IWDs, where schedules of IWDs with the top quality are selected. Here, the quality of an IWD is determined by the makespans of the schedules that it can produce, and the IWDs which paths can result in a shorter makespan have a higher quality. A random number φ0 dec  (0, 1) is used in the selection of the IWDs. φ0 dec is compared with φ0 0 ¼ 0.5. φ0 0 is set as 0.5 as two IWD selection methods are provided. If φ0 dec > φ0 0, NBD IWDs are randomly picked up to conduct the local search, otherwise, the top NBD IWDs are chosen. The parameter NBD is set by the author; a larger NBD leads to more IWDs being selected to conduct the local search. (8) combinedLocalSearch (): This is a local search method (Scheme 5) which combines breadth search and depth search schemes in the search neighborhood. The input of this function can be a group of solutions (as SBD) or a single solution (as STB), and the output is an improved solution. After the IWDs for the local search have been selected, a local search is performed on these IWDs. As mentioned above, after each iteration in the EIWD algorithm, NBD IWDs are chosen to conduct the local search. To facilitate the local search, the neighborhood structure and the Tabu list designed by Nowicki and Smutnicki (1996) are adopted, where the operations in the critical path are exchanged. The iteration number Niter_LS and the Tabu list size Nsize_tabu can be specified by the decision makers based on the size of the job. A global solution group SBD is maintained, which keeps the best NBD solutions (one for each IWD) discovered during the local search. The initial solutions in SBD come from the solutions of the abovementioned NBD IWDs. Within each iteration in the combined local search, for each solution, sx solution in SBD, its neighborhood is searched using two search schemes, namely, the breadth search and the depth search. The neighborhood is generated by swapping the first two and the last two operations in a block except the first and the last block on the critical path. For the first block on the critical path, only the first two operations are swapped, and for the last block on the critical path, only the last two operations are swapped. Every block on the critical path consists of operations being processed on the same machine, and the two consecutive blocks contain operations being processed on differ0 0 ent machines. There are NDepth of rounds of depth search, and NBreadth neighbors of the solution sx are identified within each round, and the best one is used to update sx. After the combined local search is completed, the best solution in 0 0 SBD is used to update SIB. The values of NDepth and NBreadth are determined based on experiments; too large or too small a value will result in low-quality results. (9) updatePathSoil (SIB): To update the soil of the path associated with the best iteration solution SIB. (10) update (STB): Update best solution STB by the best iteration solution  TBthe global  TB    S if q S < q SIB . q() is a quality function which is SIB using STB ¼ SIB otherwise defined as the makespan of the given schedule. A schedule with a smaller makespan is a better-quality schedule.

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Modified IWD Algorithm Based on Scoring Function for MOJSSP The objective of MOJSS is to generate feasible schedules that attempt to optimize several objectives, and these schedules form the Pareto optimal solution set. Different objectives have been studied for the MOJSS problem, and the techniques to handle multiple objectives can be classified into two categories: (1) Transform the multi-objective problem into a mono-objective problem by aggregating the different objectives into a weighted sum. The weighted combination of several scheduling objectives serves as the performance criterion. For the generated schedule s, the weighted sum fitness function F can be k X represented as F ¼ wi Fi ðsÞ. Fi(s) is the ith criterion of schedule s, and wi i¼1

is the weight of the ith criterion. It is possible that the weights among the criteria are known before generating the schedules, and these weights are usually given by the decision makers according to the situations when decisions are required. (2) Converge toward the Pareto front while achieving diversified solutions scattered all over the Pareto front. For the MOJSS problem, some basic concepts are introduced to discuss the solution methodologies:

(a) Pareto dominance: A feasible solution x1 is said to Pareto dominate over another feasible solution x2, denoted as x1 x2, if and only if 8j  f1, 2, . . . , ng,

f j ðx1 Þ  f j ðx2 Þ

∃k  f1, 2, . . . , ng,

f k ðx1 Þ  f k ðx2 Þ

(b) Pareto optimal solution: A feasible solution x1 is said to be a Pareto optimal solution if and only if there is no feasible solution x2 satisfying x2 x1. (c) Pareto optimal set: The set containing all the Pareto optimal solutions is defined as the Pareto optimal set. (d) Non-dominated solution set: The set containing all non-dominated solutions obtained from a certain algorithm is defined as the non-dominated solution set. (e) Optimal Pareto front: The optimal Pareto front (in the objective space) is formed by those objective vectors corresponding to the solutions in the optimal Pareto set. In this section, the OIWD algorithm is customized to meet the characteristics and requirements of MOJSS, and a Pareto schedule-checking process is embedded into the customized IWD algorithm, which is referred to as MOJSS-IWD. The OIWD algorithm is modified to solve the MOJSS problem. The modified algorithm, MOJSS-IWD, is shown in Algorithm 2. In MOJSS-IWD, a global Pareto set P is maintained. For each objective considered, an external iteration cycle is

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executed to select the best schedules. In this iteration cycle, an internal iteration cycle with NIWD _ iter iterations is executed (Line 6–Line 26). Each internal iteration contains two steps, namely, (1) identification of the initial schedules and (2) a Pareto local search on the schedules identified in Step (1). In Step (1), NIWD IWDs travel from the source node to the sink node in Gdis. The path generated by an IWD is a feasible solution (schedule). The soils on the edges where the IWDs travel, the soils of the IWDs, and the velocities of the IWDs are updated during the travel of the IWDs (Line 9–Line 13). Next, the soils on Nelite IWDs’ paths are updated (Line 16–Line 18). After these updates, a group of good solutions SBD are chosen, and a Pareto local search is performed to further improve these solutions (Line 19–Line 20). After the execution of the Pareto local search, the best solution SIB is identified among the group of good solutions SBD, a dominance check is conducted, and the global best solution STB is updated (Line 22–Line 24). After the completion of the internal iteration cycle, a new Pareto local search is performed on STB (Line 27). The Pareto local search in MOJSS-IWD is based on a scoring function. The schedule that yields the lowest value based on this scoring function is selected during the local search. After this Pareto local search, a dominance check is performed on STB to check whether it can be added into the Pareto set P. Next, the Pareto set P is updated and reported as the final results. ALGORITHM 2 MOJSS-IWD (disjunctive graph Gdis) 1: Initialize a Pareto set P; // A population of IWDs 2: for each optimization objective do 3 Initialize an IWDs group A; 4: initialization (); 5: k:¼0; 6: while (k < NIWD_iter) do 7: for (each time step t) do 8: for (each IWDg  A which feasible solution has not been discovered) do 9: (i, j)¼ selectNextEdge(IWDg); 10: VelIWDg ¼ updateVelocity(VelIWDg ); 11: Δsoil(i, j)¼ computeDeltaSoil((i, j), IWDg); 12: soil(i, j) ¼ updateEdgeSoil((i, j), Δsoil(i, j)); 13: soilIWDg ¼ updateIWDSoil(soilIWDg , Δsoilði, jÞ); 14: end for 15: end for 16: for (NeliteIWDs) do 17: globalSoilPropagation(); 18: end for 19: SBD ¼setupBestSolutionGroup (); 20: SIB¼ParetoLocalSearch(SBD); 21: updatePathSoil(SIB); 22: dominanceChecking(SIB); 23: update(P);

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24: update(STB); 25: k++; 26: end while 27: STB¼ ParetoLocalSearch (STB); 28: dominanceChecking(STB); 29: update(P); 30: end for 31: return P; Pareto local search and Pareto non-dominated solution set generating method are introduced in sections “Pareto Local Search” and “Pareto Non-dominated Solution Set Generating Method.”

Pareto Local Search The Pareto local search combines a breadth search scheme and with a depth search scheme to search the solution space, where the search is based on a scoring function to evaluate the schedules. For each schedule, the sum of the three objective values is computed, and this sum serves as the score to rank the schedules. (a) Selection of IWDs for Performing the Local Search The Pareto local search is conducted for a group of solutions SBD (line 20 in Algorithm 2) (SIB ¼ParetoLocalSearch (SBD)) and a single solution STB (line 21 Algorithm 2) (STB ¼ ParetoLocalSearch (STB)). In case when the input of the Pareto local search is a group of solutions (schedules) SBD, the schedules are selected randomly or based on the scores of the schedules from the scoring function where schedules with the smallest value (a smaller score means a higher quality) are selected. To select schedules, a random number φ0 dec  (0, 1) is used. φ0 dec is compared with φ0 0 ¼ 0.5. If φ0 dec > φ0 0, NBD schedules will be randomly selected for the local search, where NBD ¼ φ% * NIWD. Otherwise, the top NBD schedules are selected. (b) Performing the Local Search After all the schedules have been selected, a local search is carried out. The neighborhood structure and the Tabu list designed by Nowicki and Smutnicki (1996) are adopted. A neighborhood is formed by exchanging the operations in the critical path. Based on the size of the job, the decision maker specifies the iteration number Niter _ LS and the Tabu list size Nsize _ tabu. During the local search, a global solution group SBD is maintained to keep the good NBD solutions (one for each IWD) discovered. For each solution sx in SBD during the iterations of the Pareto local search, two search schemes are used to search its neighborhood, namely, a breadth search and a depth search. To generate the neighborhood, the first two and the last two operations in a block are swapped except the first block and the last block on the critical path. Every block on the critical path consists of operations being processed on the same

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machine, and two consecutive blocks contain operations being processed on different machines. Only the first two operations in the first block on the critical path and the 0 last two operations in the last block are swapped. There are NDepth rounds of depth 0 search, and NBreadth neighbors of the solution sx are identified within each round, with the best solution being used to update sx. After the Pareto local search, the best solution in SBD is used to update SIB. Based on experiments, the value of N 0Depth and 0 NBreadth are determined; too large or too small a value will result in low-quality results.

Pareto Non-dominated Solution Set Generating Method The makespan (Cmax), tardiness (Ti), and mean flow time (F) are the objectives considered in this research. The aim is not to generate a single optimum, but to find a set of solutions which are in the form of alternative trade-offs. A Pareto set P is maintained to store the non-dominated schedules. During the execution of the MOJSS-IWD algorithm, whenever a new schedule s is generated, this new schedule is checked to determine whether (a) it is dominated by any existing schedule in P or (b) it dominates any existing schedule in P. For (a), s is rejected, and for (b), those dominated schedules in P are removed. When checking the new schedule, its scoring function is called. The value of the scoring function is used to conduct the dominance checking. Thus, the newly generated schedule s will be stored in the Pareto set P when it is not dominated by any schedule in the Pareto set P. After checking, P is updated and reported as the result.

Experimental Evaluation The EIWD and MOJSSP-IWD algorithms have been implemented on a PC with an Intel Core 2 Duo L7700 1.8GHz CPU and 2GB RAM. Experiments have been conducted on the benchmark data for JSSP in the OR-Library (Beasley 1990); 43 instances were tested among which three instances (FT06, FT10, FT20) are designed by Fisher and Thompson (FT instances) (Fisher and Thompson 1963) and 40 instances (LA01-LA40) are designed by Lawrence (LA instances) (Lawrence 1984).

Experimental Evaluation of EIWD for SOJSSP The parameters (with their values) used in the experiments are listed in Table 3. For the parameters, through experiments and theoretical study, some observations can 0 0 be obtained. For NIWD, NIWD_iter, NBreadth , NDepth , and NBD, a larger value will result in better solutions but longer computation time. Trade-off values are obtained based on experiments for these parameters. For Nsize_tabu, experiments show that a Tabu list that is too large or too small will result in low-quality results. Niter_LS is set to be

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Table 3 Parameters in the experiments for EIWD Parameter NIWD es Nsize_tabu av bs

Value 20 0.01 10 1 0.01

Parameter NIWD_iter ev Niter_LS Bv Cs

Value 100 0.0001 3,000 0. 01 1

Parameter N0 Breadth Δsoilmax NBD Cv ρL

Value 50 90 10 1 0.9

Parameter N0 Depth Δsoilmin

Value 200 10

as ρIWD

1 0.9

Table 4 Computational results of FT and LA test instances

Average deviation Relative average deviation BKS found/ total instances

HGA-Param

HIA

MPSO

EIWD 0.000584148

TSSB Pezzella and Merelli (2000) 0.001014975

Goncalves et al. (2005) 0.003915762

Ge et al. (2008) 0.003151033

Lin et al. (2010) 0.001378487

1

1.74

6.70

5.39

2.36

37/43

36/43

31/43

32/43

35/43

3,000 as experiments showed that for most cases where the value is larger than 3,000, the results will not improve further, but the computational time is increased. The parameters in the last two rows of Table 3 ensure that the values of the velocity updated and the soil updated are changed in the same scale of magnitude as the original velocity and the original soil. The EIWD algorithm is compared with the TS algorithm (Pezzella and Merelli 2000), GA algorithm (Gonc¸alves et al. 2005), and the PSO algorithm (Lin et al. 2010; Ge et al. 2008). Table 4 shows the comparison results for FT and LA test instances. TSSB, HGA-Param, HIA, and MPSO are the names of the algorithms from Pezzella and Merelli (2000), Goncalves et al. (2005), Ge et al. (2008), and Lin et al. (2010), respectively. In this table, the relative average deviation represents the ratio of the average deviation for TSSB, HGA-Param, HIA, and MPSO with respect to that of EIWD. EIWD can find 37 best known solutions among the 43 instances, and the optimal results are better than that of TSSB, HGA-Param, HIA, and MPSO. From Table 4, it can be seen that the results of EIWD is closest to the best known solutions. Its deviation is about 1.74 times smaller than TSSB, 6.70 times smaller than HGA-Param, 5.39 times smaller than HIA, and 2.36 times smaller than MPSO. Thus, EIWD can find better results as compared with the TS Algorithm (Pezzella and Merelli 2000), GA (Gonc¸alves et al. 2005), and PSO (Lin et al. 2010; Ge et al. 2008). It does not only find more best known solutions (BKS) but also results with a smaller average deviation.

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Table 5 Parameters in the experiments for MOJSSP-IWD Parameters NIWD NIWD NIWD_iter N0Breadth N0Depth es ev Δsoilmax Δsoilmin Nsize_tabu Niter_LS NBD av bv cv as bs cs ρL ρIWD

Number of IWDs Number of IWDs Number of iterations in algorithm MOJSS-IWD Number of neighbors (schedules) generated in a single round of breadth local search Number of rounds of breadth search in depth local search A parameter prevents a possible division by 0 A parameter prevents a possible division by 0 Upper bound for soil changes in any edge (i, j) Lower bound for soil changes in any edge (i, j) Tabu list size The iteration number of local search The number of good solutions chosen to conduct a Pareto local search IWD velocity updating parameters IWD velocity updating parameters IWD velocity updating parameters IWD soil updating parameters IWD soil updating parameters IWD soil updating parameters Local soil updating parameter Global soil updating parameter

Value 50 50 100 50 200 0.01 0.0001 90 10 40 3,000 10 1 0. 01 1 1 0.01 1 0.9 0.9

Experimental Evaluation of MOJSSP-IWD for MOJSSP Experiments have been conducted using the same test instances for MOJSSP, and the research results are compared with the research conducted by Suresh and Mohanasundaram (2006). The makespan (Cmax), tardiness (Ti), and the mean flow time (F) are considered in the experiments in this research. These three objectives are conflicting, and achieving good results with respect to one objective may degenerate the results with respect to the other objectives. Suresh and Mohanasundaram used Pareto archived simulated annealing (PASA) to solve the JSSP with the objectives of minimizing the makespan and the mean flow time of jobs. The schedules generated by PASA does not consider the tardiness (Ti) objective. Simultaneous consideration of the three objectives in this research is more challenging than considering two objectives in the case of PASA. The parameters (with their values) used in the experiments to test MOJSSP-IWD for MOJSSP are listed in Table 5. The values of the parameters are set based on trial and error experiments and theoretical study. The experimental results for MOJSSPIWD are shown in Table 6. In Table 6, “NA” means “not applicable” and it indicates those cases where the number of schedules in the Pareto optimal set obtained by MOJSS-IWD is less than that obtained by PASA. There are a total of six test instances among all the 43 benchmark instances in the category that “the number of schedules in the Pareto optimal set obtained from MOJSSP-IWD is less

Instance PASA MOJSSIWD Instance PASA MOJSSIWD Instance PASA MOJSSIWD Instance PASA MOJSSIWD

LA37 LA38 LA39 LA40 43082.06 19194.19 16469.67 16742.9 44145.53 19137.2 17583.4 16687.73

LA27 23887.6 23882.9

LA26 NA NA

LA01 12094.9 11987

LA24 LA25 10874.06 7530.06 11009.07 7481.07

FT20 NA NA

LA13 LA14 25044.8 4124.7 24982.05 4191.5

FT10 NA NA

LA11 LA12 14010.15 15291.4 14050.4 13869.5

FT06 529.51 529

LA04 13975.9 13966.9

LA16 LA17 40673.33 24489.2 39639.6 24254.6

LA03 20716.8 20704.3

LA06 9528.2 9482.07

LA07 LA08 LA09 14948.33 24526.53 7909.35 14902.13 25722.6 7908.2

LA10 12859.87 13177.2

LA32 NA NA

LA33 NA NA

LA34 LA35 LA36 63284.62 26181.99 24944.94 63265.73 26724.63 25397.4

LA18 LA19 LA20 LA21 LA22 LA23 28758.46 26471.93 25526.24 16927.14 12746.66 13060.34 26203.8 24331.3 23993 18083.27 12681.94 12849.87

LA05 7243.1 7229

LA28 LA29 LA30 LA31 37260.95 41473.35 26577.5 3085.37 37201.4 40833.65 26507.95 3102.27

LA15 NA NA

LA02 14031.9 13951.3

Table 6 Computational results of FT and LA test instances

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than that obtained from PASA.” For the rest of the 43 benchmark test instances (37 Xin total), the  makespan and mean flow time of their schedules are summed up Cmax þ F . Among the 37 instances in Table 6, the MOJSSP-IWD algorithm outperforms the PASA algorithm for 27 test instances in terms of the quality of the Pareto non-dominated set, and the PASA performs better than MOJSSP-IWD for 10 test instances. MOJSSP-IWD outperforms PASA when the sum   X Cmax þ F for the Pareto non-dominated set generated from MOJSSP-IWD is smaller than that generated from PASA. In general, the MOJSSP-IWD algorithm can generate better results than PASA. When the three objectives (makespan Cmax, tardiness Ti, and mean flow time F) are considered such that the problem becomes more challenging, it becomes more obvious that MOJSSP-IWD is a promising approach for solving the multi-objective scheduling problem.

Summary In this paper, a new meta-heuristics, viz., the IWD algorithm, is enhanced to solve SOJSSP and MOJSSP, where the JSSP is modelled as a modified disjunctive graph that resembles rivers in the IWD algorithm. In this research, the OIWD algorithm is improved by introducing five schemes, viz., (1) diverse soil and velocity, (2) conditional probability computation, (3) bounded local soil, (4) elite global soil update, and (5) a combined local search. The enhanced algorithms are the EIWD and the MOJSSP-IWD algorithms. The optimization objective considered for SOJSSP is makespan in this research, while MOJSSP with the consideration of three objectives, namely, the makespan, tardiness, and mean flow time, has been studied. The research goal for MOJSSP is to find a set of solutions in the form of alternative trade-offs in the Pareto optimal set, and a new method is proposed to generate the Pareto non-dominance set. A scoring function and a Pareto schedule-checking process are embedded in the MOJSS-IWD algorithm. Experiments have been conducted using 43 standard benchmark instances from the OR-Library to validate the effectiveness and efficiency of the enhanced algorithms. Experimental results show that EIWD can generate better results by finding additional best known solutions and generate schedules with a smaller deviation from the best known solutions. Experimental results also indicate that MOJSSPIWD can generate better results in general. Considering that the three objectives have been explored, the question under study is very challenging, and MOJSSPIWD is a promising algorithm for solving MOJSSP.

References Adibi MA et al (2010) Multi-objective scheduling of dynamic job shop using variable neighborhood search. Expert Syst Appl 37:282–287

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Process Plan and Scheduling Integration for Networked Manufacturing Using Mobile-Agent Based Approach Vijaya Kumar Manupati, S. N. Dwivedi, and Manoj Kumar Tiwari

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework of the Proposed Mobile Agent-Based Negotiation Scheme . . . . . . . . . . . . . . . . . . . . . The Manager Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Task Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Mobile Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Resource Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Negotiation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Illustrative Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The network-based manufacturing offers various advantages in current competitive atmosphere by way to reduce the short manufacturing cycle time and to maintain the production flexibility. In this paper, a multi-objective problem whose objectives are to minimize the makespan and maximize the machine utilization for generating feasible process plans of multiple jobs in the context of

V.K. Manupati • M.K. Tiwari (*) Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India e-mail: [email protected]; [email protected] S.N. Dwivedi Department of Mechanical Engineering, University of Louisiana at Lafayette College of Engineering, Lafayette, LA, USA e-mail: [email protected] # Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4_117

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network-based manufacturing system has been addressed. A mobile agent-based negotiation approach is proposed to the integration of manufacturing functions in a distributed manner, and the fundamental framework to support the functionality of the approach is presented in detail. With the help of an illustrative example along with varied production, environments that include production demand fluctuations are described, and the proposed approach has been validated. Finally, the computational results are analyzed to the benefit of the manufacturer.

Introduction Global competition renders adequate return on investment, only to those who can provide and develop, the innovative and intricate products with high quality, less process iterations, and cost effective. An effective practice observed by enterprises across the globe in the past decades is to collocate the design, manufacturing, and marketing engineer teams into close physical proximity. Recent advances in information technology and communication technology have profoundly influenced the manufacturing research and its applications. In order to inculcate dynamism in manufacturing and its functions, a dynamic adaptive control of a manufacturing system is indispensable. Thus, it is essential to change the trend of traditional manufacturing system to integration approach. This change in phenomena in manufacturing system reduces the complexity and cost and thus increases its flexibility and enhances the fault tolerance. To support the above requirements and their functionalities, a recently emerged manufacturing paradigm known as networked manufacturing or network-based manufacturing has been adopted. Process planning and scheduling are the two significant functions to be engaged to process various operations of the jobs in a manufacturing system. These functions specify the decision maker of how, when, and in which sequence the operations of the parts are allocated to the manufacturing resources. To realize this, some researchers have realized that there is a greater need to integrate both the functions to achieve better performance of the system. The fundamental idea of the integration of process planning and scheduling functions has been introduced by Chryssolouris and Chan (1985). The abovementioned integration approach has been used by Khoshnevis and Chen (1990) to enhance the shop floor performance. Consequently, a feedback mechanism has been introduced for effective coordination between various resources. The mobile agent-based approach in a distributed manufacturing environment is distinct from agent and multi-agent systems. Rather than having all the features of agent and multi-agent systems, it has also some additional character such as mobility attribute which helps to transport the messages throughout the network (Chou et al. 2010). Thus, the mobile agent gains leverage over other class of agents. In the present research, first, a mathematical model has been developed to present various performance measures of the system, and then, a framework of a mobile

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agent-based negotiation scheme for integrating process planning and scheduling in a network-based manufacturing environment is proposed. The functionalities of these architectures are detailed in later sections. Subsequently, to verify the feasibility of a proposed approach different simulation experiments and comparisons have been made by varying the production demand. In section “Problem Description,” a detailed description of the problem with the basic assumptions and a developed mathematical model along with the constraints is given. In section “Framework of the Proposed Mobile Agent-Based Negotiation Scheme,” a framework of mobile agent-based negotiation scheme to integrate the process planning and scheduling functions is presented. Section “Negotiation Process” explains the mapping of negotiation-based schedule scheme. The experimentation with an illustrative example having different complex scenarios is illustrated, and their results are presented in section “Illustrative Example.” In section “Results and Discussion,” the results and their discussions are detailed. Finally, conclusions are drawn and future work delineated.

Problem Description A series of jobs ordered by different customers are considered and denoted by n. Each job consists of a set of strategies which corresponds to its alternative process plans, and each process plan contains a series of sequential operations. Consequently, the jobs with alternative process plans are processed for different operations on a set of alternative machines. However, in the networked manufacturing the machines are geographically distributive which can perform different operations of the jobs; this can be one of the complex tasks of the present problem. Although, the transportation time between two corresponding machines acts as a crucial role for process planning and scheduling tasks. For large-scale problems, it is difficult to find perfect solutions in a reasonable time. Moreover, the abovementioned problem is further extended by increasing the number of products with product mix and production demand fluctuations. This extension in the problem leads to a much larger search space; thus, it is quite difficult to find the optimal/near-optimal solutions in a reasonable time. Due to flexibility in networked manufacturing, the integration approach has the potential to generate the near optimal process plans. Assumptions for the abovementioned problem: (1) Job preemption is not allowed; (2) when an operation of a job is being processed on a machine, it cannot be interrupted until finished; (3) each machine can handle only one job at a time; (4) transportation time is considered. The system is designed in such a way that, after an immediate completion of the operation of a job on a machine, the job is immediately transported to the succeeding machine on its process; (5) all jobs and machines are simultaneously available at the time zero. Based on the abovementioned problem with several assumptions, the proposed model and its mathematical model are represented:

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Parameters N K Gj Ujop Qjp Tj Prtjopm Trt M V W st ( j, o, p, m1)

trt ( j, p, m1, m2) wt ( j, o, p, m) f( j, t) A Cj Cjopm

Total number of jobs Total number of machines Total number of alternative process plans of job j oth operation in the pth alternative process plan of job j Number of operation in the pth alternative process plan of the job j Makespan of jth job from the set of possible process plans Processing time of the operation Ujop on machine m Transportation time Any machine Maximal number of generations Number of generation for job scheduling game Starting time of operation jth job and pth process plan and oth operation on machine1 (one of the machines on which oth operation of pth process plan of jth job is done) Transporting time for jth job and pth process plan from machine1 to machine2 Waiting time of operation jth job and pth process plan and oth operation on machine Fitness function for each individual in the population A very large positive number Completion time of job j Earliest completion time of operation Ujop on machine m

Decision Variables Xjp

1 0

the pth flexible process plan of job j is selected otherwise

Y jopQrsm 1 the operation U jop precedes the operation U Qrs on machine m 0 otherwise Zjopm

1 if machine m is selected for Ujop 0 otherwise

Objectives: 1. Maximum time required to complete all the jobs: Min makespan
T j ¼ Max
Cjopm  j  ½1, N , o  1, Qjp , p  1, Gj , m  ½1, K 

(1)

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2. Maximization of machine utilization: m X

Max machine utilization ¼ Max mu ¼ X m

mptk

k¼1

(2)

ðmctk  mstk Þ

k¼1

Subject to: 1. For the first operation in the alternative process plan p of job j:   Cj1 pm þ A 1 
Xjp   Ptj1 pm j  ½1, N , p  1, Gj , m  ½1, K 

(3)

2. For the last operation in the alternative process plan p of job j:   CjQjp pm  A 1  Xjp  Cjopm
 j  ½1, N , p  1, Gj , m  ½1, K 

(4)

3. The different operations of one job can not be processed simultaneously:   Cjopm  Cjðo1
Þpm1 þA 1 
Xjp   P tjopm j  ½1, N , o  1, Qjp , p  1, Gj , m, m1  ½1, K 

(5)

4. Each machine can handle only one job at a time:   P tjopm Cjopm  CQrsk þ
A 1  X q þ AY jopQrsm
j, Q  ½1, N , r  1, Qjprs , o, p, S  1, Gj, p , m  ½1, K 

(6)

5. Only one alternative process plan can be selected of job j: X

Xjp ¼ 1
 p  1, Gj 1

(7)

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6. Each machine can only process one operation at a time: k X

Zjopm ¼ 1

 j,  ½1, N , o  1, Qjp , p  1, Gj m¼1

(8)

Framework of the Proposed Mobile Agent-Based Negotiation Scheme Contrary to the traditional classic message passing-based negotiation, the proposed mobile agent-based negotiation scheme has several great advantages (Lange and Oshima) (1998). It has the ability to send an agent to a target system for performing the job locally, thereby enhancing the flexibility of agents by exchanging huge number of messages. Moreover, it can adapt dynamically corresponding to the changed environment and also can combine with the other systems without any difficulty. Figure 1 shows a general representation of the application of mobile agent framework to system integration. The physical links can be the interactions between agents that interconnect each other for proper negotiation. The proposed framework has been broadly divided into four types of functional modules: (1) manager, (2) task agent (T-agent), (3) mobile agent (M-agent), and (4) resource agent (R-agent), which communicates the information among them for distributed problem solving. The detailed functionalities of these agents have been described in the following sections:

The Manager Agent A situation of resource-oriented bidding mechanism is assumed where machine control agents generate a bid based on resource capability to part order (Baker 1998). The process starts with submission of jobs by various customers whose data, i.e., number of jobs, operations to be performed for each job, process plans, and processing time for performing each operation etc., has been stored in a shop database. Upon arrival of jobs, the knowledge manager registers all the resources in the shop database and reports the information to the negotiation scheduler agent whenever required. However, the function of negotiation scheduler is to generate a schedule for the initialization of negotiation process. It is the core function of the shop manager and one of the complicated tasks due to the presence of several concurrent events from distributed systems. The complete information about the resource data is converted into an extensible markup language (XML) based on ontology, and the knowledge manager interprets the information through an XML-parsing system to enhance the interoperability between distributed systems.

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Fig. 1 Flowchart of mobile agent-based scheme

The Task Agent The task agent encapsulates the information for negotiation, i.e., the type of negotiation, the goal of the negotiation process, to whom the agent has to coordinate, etc., for execution of a job submitted by the manager. Rather than encapsulating the information, the task agent has some more functions such as creating a mobile agent to travel through the network according to routing schedule and assign the bids to a bid evaluator for reviewing the best bid. After identifying the best bid, it is reported to the task agent. The task agent receives the bids submitted by the bid evaluator and sends the awarding messages to the resource agents; in response of the awarding message, the task agent receives the conformation message from the resource agent, and then the task agent is destroyed.

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The Mobile Agent The actual negotiation process starts with the creation of a mobile agent, capable of travelling through the entire network, i.e., it acts both as a virtual controller and as a resource controller. Before it begins to travel, it inspects the status of the resources and delivers the generated bids while it travels from one resource agent to another. The mobile agent travels the network of resource controllers with the help of a routing schedule to collect the bids from resource agents. When it enters into the resource agent, it does not get the status of corresponding resource directly due to security issues. Hence, the communicator, which acts as a bridge between various agents, starts the conversation with the resource agent to examine the status of the resources. Depending upon their status, the communicator transfers the diverse information to the target agent. The mobile agent, after completion of its travel according to the specified routing schedule, reports the bid list to the task agent; then it destroys itself.

The Resource Agent The resource/machine agent plays a major role in the performance of a shop floor. In the present problem, the machines are distributed geographically to perform different operations of the task agent. Moreover, it receives the awarding messages from the mobile agent, and in response the resource agent communicates the task acceptance message to the mobile agent. In the proposed negotiation scheme, only the task agent is concerned about the negotiation process where as the resource agent concentrates on providing necessary information to the part agent. The information of preassigned process plans, status of the resources, buffer capacity, etc., are stored in a resource database having information in a common database (XML-based) format using ontology. In case of machine failure due to tool break, motor failure etc., the tasks scheduled at the failed machine will have to be rerouted. This can be achieved through resource agent by sending the resource fault message to the manager who reschedules and cancels the tasks with the help of ontology knowledge base at its perusal.

Negotiation Process Considering the above-described problem, a negotiation-based schedule scheme is presented and developed to realize the effectiveness of different performance measures. Figure 1 illustrates the logical framework of the negotiation schedule whose process is broadly divided into the following three kinds of phases: (1) initialization phase, (2) routing phase, and (3) awarding phase. The scheduling process starts with the initialization phase; here, the bid manager checks whether all the resource agents are participating in the ongoing negotiation process or not. In the routing phase, the N-agents made by the previous phase travel across the resource agents to collect the bids. Thereafter, it returns back to the task

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agent and provides the information about the collected bids from the resource agents for making list of bids. Later, the N-agents generate a bid according to the given policy of negotiation and filters out the bid by pre-evaluation. The completed list of bids is reported to the task agent. The third and the final stage of the negotiation process starts with the T-agent which evaluates the bids received from the N-agents, and it selects the ones that are determined to be most appropriate. With these best bids, the T-agent makes a task and offers the awarding message to the R-agents that process the selected bid. Then, the T-agent revises information related to resource of the shop database in order that the resource may take part in a new negotiation process. The selected R-agents assume the responsibility of execution of the awarding task and send the task acceptance message to the T-agent. When the T-agent receives the message, it destroys itself, and the negotiation process is completed.

Illustrative Example To illustrate the effectiveness and performance of the proposed approach in this paper, three representative instances (represented by problem n  m) based on practical data have been selected as a test bed to compute. Three problem instances (problem 6  6) are taken from (Zhou et al. 2010) and (Manupati et al. 2012) is used here, and these have been extended with varied production demand fluctuations. Table 1 shows the setting of the production demand fluctuations in which three terms of production are used and in which the product mix is set differently in each term.

Results and Discussion In this paper, the two conflicting objectives – makespan and machine utilization – are chosen. In order to find the process plans and scheduling plans for minimum makespan, one needs to run the heuristic algorithms for multiple generations. The two algorithms have been coded in MATLAB software, and the problem is tested on Intel® Core™2 Duo CPU T7250 @2.00GHz, 1.99 GB of RAM. Figure 2 summarizes the Gantt charts obtained from the simulation results of the objective function, i.e., average makespan for nine terms. Each production environment in each term was simulated 20 times on average. Figure 2a–c illustrate the maximum completion time of all six products with different product mix which has been processed with six machines which are situated geographically. Figure 3 portrays percentage of machine Table 1 The makespan values, process plans, and job schedules for 6  6 problem

Term 1 2 3

Production amount in each job Job J1 J2 J3 66 10 10 10 66 10 15 10 66 05 10 15

J4 10 15 05

J5 10 10 10

J6 10 0 15

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Fig. 2 Gantt charts for the average makespan values for all the three terms

Fig. 3 Average machine utilization for all the three terms of a 6  6 problem

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that has been used effectively utilized for processing of different jobs. The obtained simulation results show its consistency for each term; therefore, on an average for all terms, 20 simulation experiments have been considered.

Summary In this paper, a mobile agent-based approach has been developed to generate optimal process plans in the context of network-based manufacturing environment. A mathematical model has been developed and the analysis of different performance measures of the system has been observed. Illustrative examples with different variations have been demonstrated to conduct experimentation with the proposed mobile agent-based approach. Results show that the proposed approach is very effective for integration of process planning and scheduling problem in particular for distributed manufacturing environment.

References Baker A (1998) A survey of factory control algorithms which can be implemented in a multi-agent heterarchy: dispatching, scheduling, and pull. J Manuf Syst 17:297–317 Chou YC, Ko D, Cheng HH (2010) An embeddable mobile agent platform supporting runtime code mobility, interaction and coordination of mobile agents and host systems. Inf Softw Technol 52(2):185–196 Chryssolouris G, Chan S (1985) An integrated approach to process planning and scheduling. AnnCIRP 34(1):413–417 Khoshnevis B, Chen Q (1990) Integration of process planning and scheduling functions. J Int Manuf 1:165–176 Lange DB, Oshima M (1998) Programming and Deploying Java Mobile Agents Aglets. AddisonWesley Longman Publishing Co., Inc. Manupati VK, Sujay D, Cheikhrouhou N, Tiwari MK (2012) Optimal process plan selection in networked based manufacturing using game-theoretic approach. Int J Prod Res 50(18): 5239–5258 Zhou GH, Xiao Z, Jiang PY, Huang GQ (2010) A game-theoretic approach to generating optimal process plans of multiple jobs in networked manufacturing. Int J Comput Integr Manuf 23:1118–1132

Index

A Abnormal grain growth, 582 Abrasive flow machining (AFM), 1056–1058 Accessibility matrix, 2136–2138 Accuracy in IF, 434, 436 Acid etching, 103, 119 Acid pickling, 3037 Ackermann steering, 2310 Acoustic emission (AE) technique, 1074 Acquisition return, 3295, 3300 Actuator, 2053, 2117 Adaptive control (AC), 1899–1903, 1920–1921, 2119 Adaptive Jacobian tracking controller, 1909–1915, 1922–1925 Addition polymerization, 7 Additive manufacturing (AM), 2486, 2508–2510, 2526–2527, 2552–2555, 2568 Adept courier, 2341 Adept Lynx, 2327 Adept Smart VisionTM, 2000, 2002 Adjoint representation, 2002–2003 Aerial robots, 2302 Ageing, 385 Alloy steel, 401–403, 2971 Aluminium, 913 Aluminum alloys, 314, 377–388 classification, 379 Cu in, 381 defect formation, 386–388 Fe in, 382 furnaces types, 314 impurities, 382 mechanical properties, 380 microstructure, 378–386 pure Al, 377 solidification, 378

Amorphous polymers, 143–144 Anode, 598–599 Anodizing methods, 3043–3044 Anomalous codeposition, 2893, 2897, 2906 Approximate Jacobian set-point controller, 1903–1909, 1921–1922 Arc column, 598 Arc discharge, 1553, 1562 Arc plasma, 1562 Arc welding, 594 Argon-oxygen-decarburization converter, 327 Artificial ageing, 386 Assembly automation, 2353, 2368–2372 Assembly configuration evaluation function (ACEF), 2155 Assembly incidence matrix (AIM), 2133 ASTM F42, 2498, 2510 Asus Xtion, 2323 Asymptotically optimal method, 1881–1882 Atmospheric-pressure plasma, 1529 Atomic layer deposition (ALD), 2959 Automated guided vehicles (AGVs), 2303, 2438 Autonomous guided robot, 2303 Automatic robotic welding programming, 2438–2442 Autonomous indoor vehicles (AIV), 2302, 2319, 2324, 2328, 2336–2337, 2340–2344 Care-O-Bot, 2338–2339 exteroceptive sensors, 2319–2324 industrial applications, 2340 localization, 2328–2333 map construction, 2333–2334 path planning, 2334–2335 Pioneer 3 DX platform, 2338 PR2, 2339–2340

# Springer-Verlag London 2015 A.Y.C. Nee (ed.), Handbook of Manufacturing Engineering and Technology, DOI 10.1007/978-1-4471-4670-4

3487

3488 Autonomous indoor vehicles (AIV) (cont.) proprioceptive sensors, 2324–2328 Roomba robot, 2336–2337. (See also Wheeled mobile robots (WMRs)) Autonomous randomized robots, 2303 B Ball grid array (BGA), 738 Ball wheel, 841, 2308 Ball nose mill, 835 Band offset, 3001 Barrel finishing, 1063–1064 Batch ALD reactor, 2980–2983 Bending test, 3113 Best fit alignment method, 2499–2500 Biaxial stretching, 421, 423, 429 Bilayer structures, 3077–3080, 3099 Binder jetting, 2512 Bioactive coating, 2575 Biomachining, 1687 Biomedical applications, 45, 48 Biopolymers classification, 45 Bitmap patterning method, 1288–1289, 1395 Bonding mechanism, 2814–2815 Boring machine, 819–820 Brittle materials, 927–928 Brittle removal modes, 1083, 1085 Broaching machine, 839 Broad beam instruments, 1337 Brockett theorem, 2318 Bubble,1562 Bulk forming, 483, 484 Burr, 1098–1101 C Cable-driven robots, 2170, 2172 Cable tension planning, 2215–2220 CAD model, 2493–2495 Calibration, 2005, 2010, 2012, 2028 Calibration fixture, 2478–2480 benefits of, 2480–2842 design for DDM, 2478–2479 dual control stick system, 2479 performance of, 2480 Cam actuation, 2045–2046 Capacitive sensor, 2057 Carbon nanotubes (CNTs), 55, 68–70, 699–700 Carbon steels, 399 Care-O-Bot, 2338–2339 Car-like drive WMR, 2310

Index Casting, 275–280 defects, 276 heat treatment, 279–281 surface cleaning, 278–279 segregation, 277, 279 Casting/pouring, 313 cost, 407–408 materials, 366 Cast iron, 319–325, 366–368, 375 defect formations, 375 melting, 319 Castor wheel, 2305–2306 Cathode, 597–598 Cell survival rate, 2581 Cemented carbides, 252 Centered steering wheel, 2305 Centre lathe, 816 Centreless grinding machine, 845–847 Ceramics, 253, 930–931, 2841, 2844–2845 Chain growth, 6. See also Addition polymerization Channeling effect, 1291 Chemical conversion coating, 3038–3039 Chemical mechanical polishing (CMP), 1053–1055 Chemical vapor deposition (CVD), 2761–2764 coating, 2797 Chip-interconnect metallization, 2877, 2878 Chronoamperometry, 2916–2918 Cladding, 608–609 Clamps, 2423 Classification of adhesives, 770–772 Cleaning, 3033 Cleaning process, 3008–3009, 3033–3034 Closed cell foams, 128 Closed-loop kinematics, 859. See also Parallel kinematic machine tools (PKMTs) Coatings, 116, 2834–2835, 2841, 2844 Coefficient of thermal expansion (CTE), 3065, 3067, 3069 Cold forging, 457 Cold metal transfer (CMT), 2411 Cold spray, 2808, 2828–2829, 2831 Cold welding, 571 Collaborative network, 3444, 3446 Column-and-knee-type machines, 831 Column mounted robot, 2420 Compensations, 440, 443, 446, 2016–2017, 2019 Compliance, 2456, 2461 Compliant gripper, 2059

Index Compliant manipulator, 2239, 2246, 2281, 2289 fatigue failure, 2242–2245 high positioning resolution actuators, 2281–2283 history, 2239 topology optimization approach, 2275–2281 Composites, 932, 2248 Compound linear spring mechanism, 2248 Comprehensive assessment, 3152 Compression properties, 138–139 Compressive residual stress, 2893 Computed torque control (CTC), 2118 Computer aided engineering (CAE) systems, 289, 853 Computer numerical control (CNC), 853 Conceptual modeling, 2519 Conducting polymers, 2868–2872 Configuration optimization, 2153–2154 Configuration space, 1876 Configuration state space model, 2316 Conformal cooling, 2530, 2538 Connecting port, 2134 Connex process, 2557 Container, 184, 199, 218 Continuous inkjet printing, 2569–2570 Contour tool path, 415, 444–446 Conventional face milling, 835 Coordinate measurement machines (CMM), 318, 392, 2000, 2489 melting, 318–319 Copper alloys, 917 Corona, 104 Corrosion and ware resistant, 2856–2857, 2990 Cost, 654 Cost design, casting, 407–408 Creep feed grinding machine, 847 CrossBeam systems, 1319 Cross-section geometry inspection (CGI), 2489 Crystalline polymer, 128, 143–144 Curvature, 3072–3073, 3106, 3108 Cutting edge radius, 1092 Cutting fluids, 985, 998 contact conditions and geometry, 986, 997 coolant flow rates, 997 Cutting speed drilling, 825 lathe, 817 milling, 833 Cutting tools multipoint, 814, 824. (See also Multipoint cutting tools; Single-point cutting tools) single-point, 814. (See also Multipoint cutting tools; Single-point cutting tools)

3489 Cycle time reduction, 842, 2516 Cylindrical grinding machine, 842 D Data acquisition (DAQ), 956–959 Debris, 1100, 1564, 1569, 1573, 2446–2448, 2454, 2461, 2463 Decision making, 3149, 3397–3401 Dedicated manufacturing systems (DMS), 375, 381, 386 aluminum alloys, 377, 386–388 cast irons, 375 Defects, 570, 586–587, 2418 Delta3 compliant manipulator, 2238 Denavit-Hartenburg (D-H) method, 2138–2139 Dendritic segregation, 277–278 Density of states, 688–690 Deposition, 2707 Deposition rate, 609–610 Design for remanufacturing, 3196–3197 Detonation gun spraying, 2827 Diamond robot, 2095 Dielectric coatings, 2844–2845 Dielectric constant, 1589, 2290, 2291, 3000–3001 Dielectric liquid, 1556, 1563, 1568 Diesel engine, 3324 Dies, 827–828 Differential drive WMR, 2309–2312 car-like drive, 2310 instantaneous center of rotation, 2311–2312 omnidirectional, 2312–2314 synchronous drive, 2311 tricycle, 2311 Differential kinematics, 1811 Diffractive optical element (DOE), 1486 Diffusion bonding, 587–588 Diffusion coefficient, 2918 Digital compass, 2319–2320 Digital thread, 2486 Dilution, 632 Dimensional synthesis 2102 Direct-current, 2894–2897, 2899–2900 Direct digital manufacturing (DDM), 2472–2473, 2476, 2478–2479 in aerospace, 2475 in automotive, 2476–2478 calibration fixture, 2478. (See also Calibration fixture) camera mount, 2475 Direct drive pumps, 1657 Directed energy deposition, 2514

3490 Direct hard tooling, 2537–2538 Direct kinematics, 1789–1790 Direct manufacturing, 2530 Direct numerical control (DNC), 853 Direct rapid tooling, 2541 Direct soft tooling, 2533 Disassembly planning, 3407, 3415, 3432 Discharge crater, 1568 Discharge current, 1557, 1559, 1565 Discharge delay time, 1571–1572 Discharge duration, 1553, 1559, 1560, 1566, 1571, 1576–1577 Discharge energy, 1560, 1566 Discharge gap, 1559, 1575 Discharge interval, 1560 Discharge location, 1553, 1570, 1572–1573 Discharge voltage, 1557, 1562 Displacement modeling, 2100 DoD Cell printing, 2578–2579 2-DOF CDR, 2205–2207, 2211 2-DOF orientation, 2206–2207 3-DOF orientation, 2207–2209 3-DOF CDR, 2207 Double compound linear spring mechanism, 2248 Double sided incremental forming (DSIF), 416–417, 441 Dow 17, 3044–3045 Drag finishing, 1065 Drilling, 1644–1645 Drilling machine, 824 Drop-on-demand 3D inkjet printing, 2568–2569, 2883 Drug delivery, 2883–2885 Dual control stick system, 2479 Ductile cast iron, 371–372 Ductile mode machining, 928–929 Ductile removal modes, 1083–1085 DYNACUT, quick milling optimization software, 892 Dynamic calibration, 2114–2115 Dynamic fuzzy neural networks, 3250. See also Fuzzy neural networks Dynamic model, 2149 Dynamics equation, 1891–1894 E Ejected scaffold, 2575 Elastic emission machining (EEM), 1055–1056 Elastic-plastic analysis, 3082–3087 Electrical discharge machining (EDM), 1553, 1556

Index Electric-arc spray process, 2822 Electric gripper, 2037 Electric motor, 2053 Electrochemical microfabrication, 2872–2880 Electrochemical process, 2853 Electrocrystallization, 2918–2919 Electrodeposition, 2894, 2902–2904 Electrohydrodynamic jet printing (E-jetting), 2571–2573 Electromagnet, 2039 Electroplating, 2895–2896, 2343 Elettric80 system, 2343 Embedded smart sensors, 3270, 3285–3286 End-effector, 2035–2053 End milling, 835 End-of-life (EoL), 3380 Energy sustainability, 3141–3142 Environmental impact, 3141–3142, 3315, 3318, 3336 Environment friendly machining, 1129, 3141, 3315, 3318, 3336 Errors, 1998 E-spinning, 2571 Eulerian simulation, 1170–1171 Euler’s theorem, 1719–1722 Explosion welding, 589–590 Extended Kalman filters (EKF), 2333 Exteroceptive sensors, 2319–2324 digital compass, 2319–2320 infrared and sonar, 2320–2321 vision camera, 2321–2324 Extrusion, 182–186, 191, 193–194, 199, 216–220, 225, 227–228 Extrusion foaming process, 132–134, 2559–2560 Extrusion systems, 2559 F Face mill, 836 Failure analysis, 3140 Feed drives, 851–853 Feeding, 365–366 Fettling, 314 Fiber orientation, 2574–2575 FIB induced deposition (FIBID), 1409–1411 Fibre optic gyros (FOG), 2326 Filtration, cutting fluids, 1000–1001 cyclone separator, 1001 settling tank, 1000 Finishing, 2446–2450, 2454, 2456, 2465 Finite element analysis, 748 Finite element method (FEM), 1159

Index Finite element simulation, 299 Fixed-path time minimization, 1882–1883 Fixture, 2422 Flame, 106–107 Flame spray, 2820–2822 Flexible manufacturing systems (FMS), 861 Flexible roll forming, 159, 303–306 Flexure properties, 158–160 Flow forming, 455 Fluid dynamics, 354 Fluidized ALD reactor, 2979, 2981 Fluoride anodizing, 3037–3038 Focused ion beam (FIB) technique, 1104 Focused ion beam (FIB) technology, 1277, 1392–1394, 1409–1412 advantages, 1403–1409 imaging, 1393–1395 induced deposition, 1409–1411 irradiation, 1412–1419 sputtering, 1395–1403 Force-closure analysis, 2181–2186, 2189 Force control, 2058, 2184, 2186, 2189, 2348–2349, 2353, 2355, 2367, 2373–2374, 2376, 2380, 2382–2383, 2398, 2447, 2449–2450, 2453, 2465 Force-torque sensor, 2058 Forge welding, 570 Forging, 175–183, 191, 193–194, 197–198, 201, 213, 215, 217, 225, 229 Formability, 234, 238–243 Formability in IF, 421–422 Forward kinematics, 2139 Forming limit diagram (FLD), 239, 2139 Fracture properties, 163 Fracture toughness, 908 Frequency, 606 Friction stir welding (FSW), 587, 2416–2418 Friction welding, 572–574 Fuel cells, 2880 Functionally gradient materials (FGMs), 2846 Functional models, 2521–2522 Furnace types, 314, 316, 319–321, 327 channel furnace, 321 coreless induction furnace, 327 crucible furnaces, 316 cupola furnaces, 319–322 electric arc furnaces, 327 electric furnaces, 320–322 medium frequency coreless furnaces, 314 for melting cast irons, 314, 319–326 reverberatory furnaces, 316 shaft furnace, 316

3491 Fused deposition modeling (FDM), 2472, 2479–2480, 2560 Fuzzy neural networks, 3249–3252 G Galvanostatic mode, 2915–2916 Gantry mounted robot, 2420 Gas field ion source (GFIS), 1321–1323 Gas metal arc welding (GMAW), 2409–2412 Gas tungsten arc welding (GTAW), 604, 606, 609, 2409–2412 Gear and rack actuation, 2042–2044 Generalized force, 1848–1849 Generalized predictive control (GPC), 2119 General 6R manipulator, 1800 Geometric constraints, 1874, 1877–1878, 2109 Geometrical calibration, 2109 Geometry-independent models, 2138–2139 Glasses, 928–929 Global conditioning index (GCI), 2103 Global time minimization, 1883 Glue effect, 2973 Gold wire, 653 Gough platform, 2092 Grain refinement, 383 Grain size, 687, 2899–2900, 2919 Grain-boundary strengthening, 687 Graphite, 253–254 Graphite cast iron, 372–374 Graphitization potential, 321 Gravity segregation, 278 Grease removal, 3036–3037 Green packaging, 75 Grey cast iron, 368–371 Grid search, 1878–1879 Grinding wheels, 841–842, 1646, 3035 actuator, 2053–2056 cylindrical grinding, 842–845 sensor, 2056–2058 surface grinding, 841–842 transmission mechanism, 2041 types, 2037–2041 Gripper, 2037, 2041, 2053, 2056 Groove test for IF, 421–423 Guarded tele-operated robot, 2303 H HAE process, 2536, 3045 Hardness, 585, 904, 2906, 2909–2912, 2919–2920 Hard tooling, 2536

3492 Heat flux, 1553, 1566 Heat input, 606 Heat transfer and solidification, 354–360 high pressure die casting, 358–360 sand mold, 280, 354–357 Heat treatment, 279–281 Helium ion microscope (HIM), 1321, 1323, 1327, 1331–1333 High energy beam, 618 High mix low volume production, 2472 High-velocity oxy-fuel (HVOF), 2825–2826 Holonomic WMR, 2316–2317 Horizontal boring machine, 819 Horizontal shaping machine, 820 Hot isostatic pressing (HIP), 544–547 Hot pressing (HP), 543–546 Hot spots, 404 Hybrid, 612 Hybrid kinematic machine tool, 860 Hybrid laser arc welding (HLAW), 2415–2416 Hybrid machining process (HMP), 1109–1124 types, 1112 Hydroxyapatite (HA) coating, 2499, 2578 I IGES, 2499 Incremental sheet metal forming (ISMF), 413–420, 434 Indirect hard tooling, 2538–2539 Indirect rapid tooling, 2540–2541 Indirect soft tooling, 2534–2536 Indoor positioning system (iGPS), 2329–2332 advantages, 2329 applications, 2329 landmark markers, 2331 RFID reader, 2329 trilateration, 2329 ultrasonic transmitter and receivers, 2330 wireless networks, 2331–2332 Inductive sensor, 2056–2057 Industrial robots, 855–288, 1885, 2349–2350, 2355, 2360, 2367 Industrial robot programming, 2078, 2087 methods, 2078 process, 2087 Inertial measurement unit (IMU), 2325–2326 Infrared sensor, 2320 Ingot preheating, 280 Injection molding, 131 Inserts, 2541, 2545 copper tool, 2545

Index die, 2541 hotwork steel tool, 2545 Inspection, 586–587 Integrated remanufacturing design, 3209–3210 Integrated robot vision, 2000 Intelligent control, 2120 Intelligent water drops optimization, 3452 Intensifier pumps, 1656 Intermetallic layer/Intermetallics, 717 Inverse kinematics model (IKM), 1807, 1846, 2110, 2141 Investment casting process, 2535, 2544 Ion beam deposition systems, 1338 Ion beam etching, 1519–1520 Ion beam etching systems, 1337–1338 Ion beam figuring (IBF), 1278, 1343, 1344 Ion beam instruments, 1318 Ion beam implantation systems, 1340 Ion beam nano-lithography instruments, 1333 Ion beam nanomanufacturing (IBNM), 1277 Ion beam technology, 1280 Ion implantation, 1431 Ion optics, 1323–1326 IRB340 Flex Picker, 2092 Iron, 919 Iteration, 2151 backward, 2151–2152 forward, 2151 Iteration convergence method, 1171 J Jacobian analysis, 2100–2101 Jacobian computation, 1829–1834 forward, 1829 Jacobian matrix, 1831, 1835 parallel manipulator, 1834 serial manipulator, 1829–1834 Jacobian matrix, 1895 JC flow-stress model, 1175 Job shop scheduling, 3452 Joining process, 571, 589 Joint space control vs. Cartesian space control, 2120–2121 Joint speed linear control, 2116 K Kinematic design, WMRs, 2304–2308 castor wheel, 2305–2306 centered steering wheel, 2305 fixed wheel, 2304–2305

Index spherical wheel, 2308 Swedish wheel, 2306–2307 Kinematic redundancy, 1846, 1875, 1884 Kiva system, 2341 L Lagrange’s equation, 1859 Lagrangian simulation, 1167–1169 Land robots, 2302 Lapping, 1053, 1054 Laser, 612–613 Laser beam, 2684 Laser beam welding (LBW), 2412–2415 Laser composite surfacing, 2662–2667 Laser conduction welding, 621, 1605 Laser machining, 1605 Laser-material interactions, 1603–1604 Laser processing, 1617 Laser scanners, 2324 Laser surface alloying, 2657–2262 Laser surface cladding, 2667–2669, 2673 Laser surface engineering, 2641, 2672 Laser surface melting (LSM), 2651–2656, 2658 Laser technique, 2678 Laser tracker, 2489–2490 Laser transformation hardening, 2643–2651 Layered manufacturing, 743, 2472 Lead-free solder, 724 Leaf-spring compound linear spring mechanism, 2235 Leap frog, 2491 Levitrack reactor, 2985 Life cycle assessment (LCA), 3317–3320 Light-weight robot, 1861 Li-ion battery, 2883 Linear actuator, 2054 Linear computed torque control, 2116 Linear control, 2116–2118 Linear cutting motion, 794–795 Line vector, 1752 Linkage actuation, 2042 Liquid crystal display (LCD), 3420–3421 Liquid metal ion source (LMIS), 1319–1321 Lithium ion batteries (LIB), 2991 LIthography, Galvanoformung, Abformung (LIGA), 2877 Load transfer vehicle, 2343 Long edge mill, 836 Loose abrasive machining, 1052 Low-temperature deposits, 2915 Low temperature plasma, 107

3493 Lubricants for forming, 273–274 Lubrication/cooling, 981. See also Cutting fluids M Machinability, 936 Machine health condition (MHC) prediction, 3249 Machine tools, 812, 849–850, 855–860 feed drives, 851–853 industrial robot, 855–858 parallel and hybrid kinematic machine tools, 859–860 PKMTs, 859 RMS, 861 spindle drives, 850–851 Machine utilization, 3483–3484 Machining, 801–802, 813, 945 heat flow in, 802 measurands, 945 nomenclature, 801 Machining centres, 847–849 Machining dynamics, 867–896 DYNACUT, quick milling optimization software, 892 input parameters/database information, 892 long depth milling stabilization, mold base, 894 overall geometry and finished product quality, 895 quick milling vibration solver configuration, 893 schematic representation, 870 S45 cutting with 16 mm diameter/four flutes end mill, 891 Machining economics, 934–935, 983, 1127 Machining process, 982, 1129 environmentally friendly machining, 1129 regions of heat generation, 983 Machining time, 817, 826, 834, 840 broaching, 840 drilling, 826 lathe, 817 milling, 834 Magnesium alloys, 317–318, 388–391 melting, 317–318 Magnetic actuator, 2055 Magnetic compound fluid (MCF), 1062–1063 Magnetic field assisted finishing (MAF), 1058 Magnetic float polishing, 1059–1060 Magnetic fluids (MFs), 1059 Magnetic gripper, 2039–2040

3494 Magnetic recording heads, 1057, 2879 Magneto-rheological abrasive flow finishing (MRAFF), 1057–1058 Magneto-rheological finishing (MRF), 1060–1061 Magneto-rheological fluid-based slurry, 1061–1062 MAGOXID-COAT, 3046–3047 Makespan, 3483 Malleable cast iron, 375 Manual tele-operated robots, 2303 Manufacturing, solid state welding processes in, 569 Manufacturing yield design, 404 Map-based localization approaches, 2332–2333 Maraging steels, 250, 252 Mass scaling, 1165 Material and energy flows, 1130 Material engineering, 2704 Material extrusion, 2510–2511 Material jetting, 2511–2512 Material removal mechanisms and modes, 1078–1079 Material removal rate, 903, 1040, 1556, 1576 Materials removal behavior, 1040 Materials selection, 255, 261 bulk metal forming, 255–260 sheet metal forming, 260–265 MATLAB/SIMULINK, 1915 Matrix exponentials, 2003 Measurement system, 1999 Mecanum wheel, 2307 Mechanical gripper, 2037–2038 Mechanical properties, 586 Media, 2449–2450, 2456–2459, 2465 Medium-carbon low-alloy steel, 250 MEMs, 2989, 3453 MEMS-based micro-actuator, 2239 Meta-heuristics, 328, 3453 Metal casting processes, 328 permanent, 328 Metal forming, 172–229, 473 Metal inert gas (MIG) welding, 605 Metallization, 3010–3011 Metal nitrides, 2967, 2970–2971 Metal oxide, 2967 Methodology of research, 2680, 2688 Metrology, 2488–2489, 2491 considerations for part measurement, 2491–2493 destructive internal, 2489 non destructive internal, 2488

Index Microcellular foams, 142 Micro-cutting, 1094–1096 cutting force, 1095 grain size, 1095 vs. macro-cutting, 1094 steel, 1096 Micro-dispensing, 2577 Micro-electrical-mechanical systems (MEMs) gyroscope, 2326–2327 Micro electro mechanical system (MEMS), 1090, 2879 Microfabrication, 2874–2880 Micro grooves, 1454, 1498 Micro optical elements (MOE), 1403 Micro-machining, 1090, 1577 Micro-milling, 1090, 1101 Micro-segregation, 277 Microsoft Kinect, 2323 Microstructure, 574, 578, 1095 Micro tools, 1101–1104, 1473–1509 SEM image, 1493 Micro ultrasonic machining configurations, 1073 development of, 1075–1078 ductile and brittle removal modes, 1083–1085 machining of microfeatures, 1071, 1078 material removal mechanisms and modes, 1078–1079 monitoring and control, 1074–1075 SEM image, 1073 tools, 1073 vs. USM, 1071 workpiece holding method, 1075 Micro-valve dispenser, 2576 Microwave sintering (MWS), 538–540, 828, 1645 Milling, 828–839, 1645 Minimum depth of cut, 1104 Mobile robot, 2302. See also Wheeled mobile robots (WMRs) Mobile-agent, 3476 Modeling, 173, 210, 214 Model predictive control (MPC), 2119 Modular CDR, 2175–2176, 2181–2184, 2186 components, 2134 design, 2132 representation, 2133 simulation software, 2158–2159 Modular robot, 2132–2134, 2158 Modulus of microcellular foams, 145 Mold casting mold processes

Index Mold casting mold processes, disposable, 339–349 Mold/Die manufacturing, 311–313 Mold filling and solidification, 349–353 Molecular layer deposition (MLD), 2972 Monocrystalline, 1429 Monolithic compound linear spring mechanism, 2235 Monte Carlo localization (MCL), 2333 Morphology of Ni-Co film, 2909–2912 Morphology of Ni films, 2906–2909 Motion planning, 1874 Moving robot, 2419 Multi-bodied CDRs, 2178–2181, 2186–2197 Multilayer thin film, 3074–3077, 3483 Multi-objective, 3483 Multiple objective job shop scheduling, 3452 Multipoint cutting tools, 824, 827–828, 839 broaching machine, 839–840 cutting edge, 824 dies, 827–828 drilling machine, 824–827 milling machine, 828–839 taps, 827–829 Multi stage forming, 431–432 N Nanocomposite foams, 134–135 Nano-fabrication, 1407–1409, 2571 Nanofibers, 2571 Nanofillers, 67, 134–136, 138 Nano-hole fabrication, 1416 Nanojoining, 708 Nano machining of ion implanted materials (NiIM), 1278 Nanomanufacturing, 1280 Nanomaterial reinforced composite, 708 Nanomaterials assisted joining, 708 Nanostructured coatings, 2846–2847, 2778 Nanotechnology, 2778 Neobotix system, 2344 Netdraw software package, 3444 Networked based manufacturing, 3475–3485 Networked manufacturing, 2149 Newton-Euler equation, 1859, 1863, 2149 Nickel, 922–923 Ni-Co films, 2906–2909 Ni films, 2906, 2919–2920 Nitridation, 3005–3006, 3009–3010 Non-ferrous alloys, 254–255 Nonholonomic WMRs, 2316–2319 Brokett Theorem, 2318

3495 definition, 2317 kinematic constraints, 2317 posture stabilization, 2319 trajectory tracking, 2318–2319 Non linear control, 2118–2120 Notch flexure joints, 2249 Nucleating agents, 137–138 Nucleation, 135, 137, 143 Numerical control, 853 Numerically controlled plasma chemical vaporization machining (NC-PCVM), 1298 Nylon 6 nanocomposites, 84, 85 O Occupancy grid maps, 2334 Octree hierarchical space decomposition, 1209–1211 Odometry, 2324–2325 Off-centered steering wheel, 2305 Off-line programming, 1997 OLED, 2990–2991 Omnidirectional WMR, 2312–2313 OmniMove, 2342 Omni-wheel, 2307 On-demand manufacturing, 2472, 2474 On-line pre-processing implementation, 2014–2016 Online sensing data, 3150 Open cell polymer foam, 165–166 Optical applications, 2990 Optical trackers, 2490 Optimization, 1874, 1881–1882, 3452–3454 Organic finishing, 3048 Orthogonal cutting, 1157–1159 Orthogonal vs. oblique cutting, 794 Osteointegration, 2578 Oxidation, 2997–3207. See also Nitridation Oxide inclusions, 2818 P Packaging materials, 2992 Parallel kinematic machine tools (PKMTs), 859 Parallel linear spring mechanism, 2247 Parallel robot (PR), 2122, 2383 Parameter optimization, 2383, 2385, 2388–2389, 2391–2392, 2394–2396, 2399 Partial heating method, 719 Particle filters, 2332

3496 Path shortcut, 1876–1881 definition, 1877 optimization, 1881 planning, 1877–1878 Path matrix, 2136–2138 Path shortcut, 1882 PD plus gravity controller, 1896–1899, 1918 Perfactory system, 2558 Permanent magnet, 2040 Phenol formaldehyde resins, 7 Photoelectric/opto-electronic sensor, 2057 Photogrammetry, 2490, 3010 Photolithography, 3010–3011 Physical-fit models, 2519–2521 Physical vapour deposition (PVD), 2727–2735, 2737–2750 adhesion, 2740 AFM, 2746 application, 2737–2739 deposition method, 2727 GDOS, 2743 mechanical properties, 2737 SEM, 2743–2744 synthesis of coatings, 2734, 2743 TEM, 2743 Piezo-actuated printhead, 2579 Piezoelectric actuators, 2055–2056 Pillow effect, 436–437, 2338 Pioneer 3-DX differential drive robot, 2309 Pioneer 3 DX platform, 2338 Plain strain stretching, 429 Planarization CMP, 1591 Planar motion compliant manipulators, 1591 Planar motion compliant manipulators, 2237 Planing machine, 823–824 Plasma, 106–108, 596, 598, 611–612, 1565 cladding, 611–612 deposition, 107 low temperature, 107 spraying, 2822–2824 treatment, 105, 1409 Plasma ALD, 2976–2977, 2979 reactor, 2979 Plasma-assisted polishing (PAP), 1299 Plasmonic lens, 1408–1409 Plastics, 101, 116 in car, 81 coatings, 101, 116 surface treatment and modification, 101 Ploughing, 1093 Plunger, 322–323 Pneumatic actuation, 1984–1987 Pneumatic gripper, 2037

Index Pocket milling, 835 Point cloud registration, 2019, 2021 Poisson’s ratio, 3066, 3069 Polarity, 599–600, 1577 Polishing, 1053, 2652, 3035 Poly(lactide-co-glycolide) (PLGA), 46–47 Polyaniline (PANI), 60 Polycaprolactone (PCL), 2574 Polygon mesh, 2495–2496 Polymer, 924–925, 2972 Polymer brushes, 63–66 Polymer coatings, 2845–2846 Porosity(ies), 2816–2818 cast iron, 375 Post casting processing, 366 Potential field method, 1878 Potentiostatic mode, 2915 Powder bed fusion, 2514 Powder metallurgy (P/M) tool steel, 249 Powder systems, 2428, 2558–2559 Power sources, 601–602, 2428 PR2, 2339–2340 Precipitates, 579–580 Precision, 604 Preston’s equation, 1052 Price discrimination, 3049, 3298, 3304, 3307 Primers, 3049 Process Sustainability Index (ProcSI), 3351–3353, 3371, 3372 case study, 3368 evaluation process, 3354–3355 hierarchical approach, 3352–3353 Product condition, 3381 Product customization, 2473 Product development, 3213 Productivity, 608–609 Product-of-exponentials, 2003–2005 Product recovery, 3377–3402 Product service, 3224, 3231, 3233 Product Sustainability Index (ProdSI), 3351–3352, 3354–3363 case study, 3363–3367 evaluation process, 3354–3363 hierarchical approach, 608, 3352 Profile milling, 835 Properties, laser, 2683 Proprioceptive sensors, 2324–2328 Adept Lynx, 2327–2328 gyroscope, 2326–2327 IMU, 2325–2326 odometry, 2324–2325 Proximity sensor, 2056, 2434 Pulse current, 2903, 2906

Index Pulse discharge, 1561–1564 Pulse generators, 1557–158 Pulsing, 606 Q Quantum effects, 686 Quattro robot, 2092 Quenching, 385 QuickCNC GUI, 1217 R Random yield, 3298–3299, 3301–3304 Rapidly-exploring random trees (RRT), 1879–1881 Rapid manufacturing (RM), 2472–2473, 2530. See also Direct Digital Manufacturing (DDM) Rapid prototyping (RP), 2472, 2507–2510, 2516, 2518, 2527–2528, 2568–2582 Rapid tooling (RT), 2528–2530 6R approach, 3350–3351 Reaction injection molding (RIM) 132 Reactive plasma spraying, 2824 Reconfigurable machine tools, 860–864 Reconfigurable manufacturing system (RMS), 861–862 Reconfigurable modular manipulator, 2130–2131 Reconfigurable robotic workcell, 2159–2162 Recursive dimension reduction algorithm, 2150, 2184 Recursive Newton-Euler algorithm, 2150 Recursive Newton-Euler method, 1856, 1871, 2149–2152 Redundant manipulators, 1779, 1791, 1803 Regressor matrix, 1893–1894, 1899, 1909 Reliability, 722, 3152 Remaining useful life (RUL) assessment, 3137–3190 Remanufacturability, 3140, 3146 Remanufacturing, 3196–3197, 3265–3287 Remanufacturing decision-making, 3279–3282 Remanufacturing engineering, 3140 Remanufacturing time point, 3142, 3148–3150, 3196, 3266, 3292, 3315, 3320, 3324 Repairing, 2830, 2843–2844 Residual stress, 1569, 2819, 2835 Resistance spot welding (RSW), 2406–2409 Resistance welding, 588–589 Return acquisition, 3300. See also Acquisition return

3497 Reverse engineering, 2485–2503 Reverse post processing, 1193 Revolute and prismatic actuator modules, 2096 RiansWeldTM, 2440 Roboforming, 443 Robot, 2448–2453 Robotino robot 2313, 2315 Robot joint control, 1943 Roll forming calibration method, 291–292 definition, 286 design of, 290 finite element simulation, 299–303 flexible, 303–306 flower design, 292–295 machine configuration, 296–299 process in, 286–289, 290 strategies in, 290–291 strip width calculation, 291–292 tool design, 296–299 Rolling, 173–177, 186, 189, 191, 193–194, 197–199, 201, 208–209, 214–215, 217 Rolling contact fatigue failure modes, 2835 Roll-to-roll spatial ALD reactor, 2993 Roomba robot, 2336–2337 Rope and pulley actuation, 2044–2045 Rotary ALD reactor, 2979, 2982 S Sampling-based algorithms, 2335 Sand-blasting, 278 Sandwich composites, 148 Sandwich ladle, 322 Scheduling, 3452, 3465 Screw actuation, 2042 Sealants, 2833 Sealing, 3047 Selective laser sintering, 637, 2559 Self-assembled monolayers (SAM), 306, 2975 Self-driving blank holder system, 306 Semiconductor ALD applications, 2989 Semi-synthetic cutting fluids, 1131 SEMORS, 2158 Semi-synthetic cutting fluids, 2158 Sensor, 2056–2058 Sensor-based error compensation method, 2017 Sensor fusion, 2368, 3276 Sensor selection, 3271–3273 Serial manipulators, 1807 Service supply chain collaboration, 3229

3498 Service supply chain (cont.) enabling process management, 3234–3235 functional process management, 3233–3234 strategy and network structure, 3224 structure of, 1560, 3222 Servo feed control, 1560–1561 Shake-out/part removal, 314 Shaping machine, 820–823 SHARP sensor, 2320 Shear, 738, 757 Sheet lamination, 2512 Sheet metal, 484 Shielding gas, 600–601, 625 Shoulder milling, 835 Side milling, 832, 975 structure of, 975 Signal processing, 959–960 chatter detection, 973 Silica mold, 2544 Silicon, 929–930, 2857–2860 Simulation, 188, 191, 197, 200–203, 209–214, 218 Simultaneous localization and mapping (SLAM), 2333 Sine law for IF, 420 Single crystal, 1322, 1435 Single ion beam system, 1318 Single objective job shop scheduling, 3451–3472 Single particle impact, 1083 Single-point cutting tools, 814–815, 824, 828, 839 boring machine, 819–820 centre lathe, 815–819 closed-loop mechanisms, 1843–1845 geometry, 814 planing machine, 823–824 shaping machine, 820–823 Single point incremental forming (SPIF), 414–415, 430 Single walled carbon nanotube (SWNT), 57 Singularities, 1842–1845 cuspidality concept, 1845 higher-order analysis, 1845 parallel manipulators, 1844–1845 serial manipulors, 1842 Sinking EDM, 1554–1556, 1570 Sinter forging (SF), 548–553 Sintering, 553 Size effect, 1092–1094 Slab milling, 832 Slip sensor, 2058

Index Slot milling, 832 Slotting machine, 821–822 Social network analysis, 3439–3449 Soft tooling, 2532 Software, 2495, 2497 Software packages, 2438 Solar cells, 2882, 2991–2992 Solid end mills, 836 Solidification, 314 Solid model, 2496–2497 Solid state /Batch foaming process, 142, 143 Solid state welding/bonding, 590 Soluble oils, 1131 Spark plasma sintering (SPS), 548 Spatial ALD reactor, 2983–2988 Spatial S-R-U CDR, 2191 Spherical parallel manipulator, 1864–1871 Spindle drives, 850–851 Spin forming, 468–470 Spiral tool path, 419, 444, 446 Spring back in IF, 434, 436 Sputtering coating, 2943, 2945, 2948 decorative coating, 2945 deposition, 2937, 2938, 2945 functional coating, 2945 magnetron, 2955 metallization, 2946 PVD, 2953 reactive sputtering, 2937, 2940 unbalanced magnetron sputtering, 2937 Stamping, 457 State-space model (SSM), 3173–3174 Static calibration, 2114 Static forces, 1848–1849 Statics, 1850–1851 Statics and dynamics models, 2103–2104 Stationary robot, 2418–2419 Steel, 919, 2499 Steel alloys, 399 Steel-bonded carbides, 252 Steel casting deoxidation practice, 325 equipment, 326 gas porosity, 326 reoxidations, 326 sulphides, 326 STEP, 2499 Stereolithography (STL), 2498, 2556–2557 Stewart platform, 2092, 2198 Stiffness, 2906 Stiffness analysis, 2198–2199, 2203

Index Stopping and range of ion in matter (SRIM), 1287, 1392 Straddle milling, 832 Strain, 3074, 3107 Strain hardening modulus, 3074, 3103, 3107 Strain-rate testing, 733 Strengthening mechanism, 2920–2924 Stress, 3055–3129 Substrates, 2973 Summit robot, 2310 Supercapacitors, 2880–2881 Superchair robot, 2313, 2315 Supply chain design, 3219–3235 Support vector machine model, 3162 Surface cleaning, 278–279 Surface coating techniques, 2807 Surface contouring, 835 Surface cross-linking, 104, 107, 112 Surface degradation, 112 Surface engineering, 2798 Surface-enhanced Raman scattering (SERS), 1303–1305 Surface finish in IF, 440 Surface grinding machine, 841–842 Surface modification, 107–108 Surface morphology, 2577–2578 Surface quality, 1096–1098 Surface roughness, 1556, 1576, 1706 Surface tension, 113–115 Surface treatment, 102–103 chemical, 103 physical, 102 reactive gas, 103 Survivability test, 2580–2582 Sustainable manufacturing, 3348–3350 Swedish wheel, 2306–2308 Swisslog robot, 2303 Synchronous drive WMR, 2311 Syntactic foam, 148–150 Synthetic cutting fluids, 1131 System integrators, for robotic welding, 2441 T Tactile sensing, 2434 Tactile sensor, 2058 Tagnite surface treatment, 3045–3046 Tangential acceleration, 1722 Taps, 827–828 Task-space PD plus gravity, 1920 Technologies, 2704 Temperature cycling, 746–747 Temperature distribution, 1565–1567

3499 Tensile properties, 85, 155–158 Thermal ALD, 2976 Thermal properties, 138 Thermal spraying, 2808–2810, 2819, 2846 Thermo-mechanical properties, 3064–3065, 3098 Thin coatings, 2763 Thin films, 3055–3129 Three-dimensional printing (3DP), 2508, 2552, 2554. See also Additive manufacturing 3D roll forming, 303 Through thickness shear, 425, 2971 TiN, 2971 Tissue regeneration, 2575 Titanium, 922 TOF camera, 2323 Tolerance charting, 1269 Tool, 2447, 2449–2450, 2455–2462, 2464 Tool and die materials 243, 1576–1577 Tool electrode, 1552, 1577 Tool electrode wear, 1576–1577 Tool materials, 2789–2792 Topological synthesis, 2096 Top paints, 3049 Total heating method, 718–719 Touch sensing, 2001 Track mounted robot, 2420 Trajectory, 1876–1877, 1881 definition, 1877 optimization, 1881 Transferred plasma arc process, 2825 Transformation Matrices, 2001–2002 Triangulation, 2320 Transport of ions in matter (TRIM) program 1392, 2320 Tricycle WMR, 2311 Triple beam system, 1330 Triple bottom-line (TBL), 3344 Tundish cover, 322 Tungsten carbide, 931 Turning, 1644 Turntable, 2424 Twin-wire, 609 Twist, 440 Two-column planing machine, 823 Two point incremental forming (TPIF), 415–416, 440 Two step sintering (TSS), 540–543 U Ucinet software package, 3444 Ultra-precision machining, 1497

3500 Ultrasonic machining (USM) advantages, 1070 basic elements, 1066–1068 capabilities and applications of, 1069–1070 micro-USM, 1070–1073. (See also Micro ultrasonic machining) process parameters and perfomance measures, 10681069 working principle, 1066–1067 Ultrasonic sensor, 2057, 2320 Ultrasonic welding, 571–572 Underactuated gripper, 2046 Underwater robots, 2302 Unicycle, 2318 Universal wheel, 2307 Unmanned ground vehicles (UGVs), 2302, 2313 V Vacuum gripper, 2038–2039 Vacuum suction, 2054–2055 Variable stiffness device, 2199–2200 Velocity propagation, 1816 Vat photopolymerization, 2513 Velocity differential movement, 1811–1814, 1820 differential relationships, coordinate frames, 1820–1824 inverse kinematics, 1846 Jacobian computation (See Jacobian computation) kinematic characteristics, 1838 kinematic redundancy, 1846 propagation, 1816–1817 relationships, 1814–1816 representation, 1811 singularities. See Singularities Vertical boring machine, 819–820 Vertical shaping machine, 820–823 Vibratory finishing, 1064–1065 Vision-guided robotic tow tracker, 2342 Visual odometry, 2322 W Waste electrical and electronic equipment (WEEE), 3406, 3414–315, 3432 Water-based cutting fluids, 1131 Wave soldering, 719, 2837 Wear resistance, 2837

Index Weibull plot, 747 Weld/arc data monitoring, 2437 Welding process, 569–590 Weld seam tracking, 2434 Wettability, 113–115, 717 Wheeled mobile robots (WMRs) castor wheel, 2305 centered steering wheel, 2305 degree of maneuverability, 2314–2316 description, 2302 differential drive (See Differential drive WMR) fixed wheel, 2304–2305 nonholonomy (See Nonholonomic WMRs) spherical wheel, 2308 Swedish wheel, 2306–2307 White cast iron, 374 Wire EDM (WEDM), 1554 Wire feeders, 2432 WMRs. See Wheeled mobile robots (WMRs) Workpiece clamping system, 1075–1076 Workpiece handling system, 2422 Workpiece holding method, 1075 Workspace analysis, 2101–2102, 2204 Workspace performance measures, 2212 Workspace representation, 2205–2209 Workspace volume, 2210–2212 Wrought tool steel, 243 X X-ray photoelectron spectroscopy (XPS), 3013, 3015–3017 Y Yield start stress, 3083, 3103 Yield strength, 905–906 Young’s modulus, 3066–3067, 3073 Z Zinc alloys, 319, 396–398 melting, 319 Zinc oxide (ZnO), 2970 ZnS, 2972 Zr film on SiC electrical properties, 3020–3021 growth mechanism, 3021–3023 physical characteristics, 3011–3020 vs. Si substrate, 3023–3026