Digital Twin Driven Smart Design [1 ed.] 0128189185, 9780128189184

Table of contents :
Cover
Digital Twin Driven Smart Design
Copyright
Contents
List of contributors
Preface
Part 1: Theory and methodology
1 Digital twin driven smart product design framework
1.1 Introduction
1.2 Development of product design and prospect forecast
1.2.1 Traditional design methods and technologies
1.2.2 New era of data-driven product design
1.2.3 Call for digital twin driven smart product design framework
1.3 Digital twin and its applications
1.3.1 History of digital twin
1.3.2 Concept of digital twin
1.3.3 Applications of digital twin
1.4 Five-dimension digital twin for a product
1.4.1 Physical entity
1.4.2 Virtual entity
1.4.3 Digital twin data
1.4.4 Services
1.4.5 Connections
1.5 Framework of digital twin driven smart product design
1.5.1 Key processes of digital twin driven smart product design
1.5.1.1 Digital twin driven task clarification
1.5.1.2 Digital twin driven conceptual design
1.5.1.3 Digital twin driven virtual verification
1.5.2 Related technologies for digital twin driven smart product design
1.5.2.1 Digital twin driven TRIZ
1.5.2.2 Digital twin driven virtual prototyping
1.5.2.3 Digital twin driven product design evaluation
1.5.2.4 Digital twin driven virtual commissioning
1.5.2.5 Digital twin driven green design
1.5.2.6 Digital twin driven lean design
1.5.2.7 Digital twin driven factory design
1.5.2.8 Digital twin driven process design
1.6 Case study
1.6.1 Digital twin driven smart product design application in bicycle
1.6.2 Digital twin driven smart product design application in landing gear
1.7 Summary
References
2 Digital twin driven conceptual design
2.1 Introduction
2.2 Conceptual design methodology foundation of digital twins
2.2.1 General design theory
2.2.2 Axiomatic design theory
2.2.3 Systematic design process
2.2.4 Function–behavior–structure ontology
2.3 Digital twin based conceptual design
2.3.1 Digital twin based function modeling
2.3.2 Digital twin based concept generation
2.3.3 Digital twin based concept evaluation
2.3.4 Digital twin based contradiction resolution
2.3.5 Digital twin based constraint management
2.3.6 Digital twin based complexity management
2.3.7 Collaborative conceptual design
2.3.8 Digital twin based design affordance
2.4 Case study
2.4.1 Digital twin based robot vacuum cleaner functional domain formulation
2.4.2 Digital twin based robot vacuum cleaner concept generation
2.4.2.1 Step 1: digital twin assisted design parameter generation
2.4.2.2 Step 2: digital twin assisted design parameters integration
2.4.2.3 Step 3: digital twin assisted conceptual evaluation
2.4.2.4 Step 4: review and redesign
2.4.3 Digital twin based robot vacuum cleaner constraints management
2.4.4 Digital twin based robot vacuum cleaner contradiction solving
2.5 Summary
References
3 Conceptual design driven digital twin configuration
3.1 Introduction
3.2 Development of physical entity
3.3 Development of virtual entity
3.4 Development of twin data center
3.5 Development of services
3.6 Development of connections
3.7 Integration of digital twin compositions and working management
3.7.1 Management with working mode
3.7.2 Management with working sequence
3.7.3 Management with power output ratio
3.8 Case study
3.8.1 Step 1: development of autonomous vehicle physical entity
3.8.2 Step 2: development of autonomous vehicle virtual entity
3.8.3 Step 3: development of autonomous vehicle twin data center
3.8.4 Step 4: development of autonomous vehicle services
3.8.5 Step 5: development of autonomous vehicle connection system
3.8.6 Step 6: development of autonomous vehicle working modes, sequences, and output ratio
3.9 Summary
References
4 Digital twin driven virtual verification
4.1 Introduction
4.2 Related works
4.2.1 Related works on traditional product design verification
4.2.1.1 Virtual verification
4.2.1.2 Physical verification
4.2.2 Related works on digital twin driven virtual verification
4.3 Digital twin driven virtual verification method
4.3.1 A model of digital twin driven virtual verification
4.3.1.1 Product design—digital twin—product design
4.3.1.2 Product design—digital twin—manufacturing
4.3.1.3 Product design—digital twin—usage
4.3.1.4 Product design—digital twin—maintenance
4.3.1.5 Product design—digital twin—end-of-life
4.3.2 Iterative framework of digital twin driven virtual verification
4.4 Case study I: digital twin driven virtual verification in design for a commercial coffee machine
4.4.1 Case study background
4.4.2 Working principle of commercial coffee machine
4.4.3 Factors impacting coffee quality
4.4.4 Group gaskets of coffee machine
4.4.5 Collection of key controlling data of commercial coffee machine
4.4.5.1 Temperature of extraction and boiler
4.4.5.2 Pressure of extraction and boiler
4.4.5.3 Flow rate of water
4.4.6 Digital twindriven virtual verification application
4.4.6.1 Cause analysis
4.4.6.2 Potential solutions
4.4.6.3 Addition of a night mode in product design
4.4.6.4 Iteration of digital twin driven virtual verification process
4.5 Case study II: digital twin driven virtual verification in design for 3D printers
4.5.1 Background of 3D printing
4.5.2 Virtual verification of 3D printer design
4.5.3 Virtual verification of 3D printer design, manufacturing, and usage
4.5.4 Virtual verification of 3D printer maintenance and end-of-life
4.6 Summary
References
5 Digital twin driven design evaluation
5.1 Introduction
5.2 Related works
5.2.1 Existing product design evaluation methods
5.2.2 Digital twin driven product design methods
5.3 Digital twin driven product design evaluation methodology
5.3.1 Digital twin driven product design evaluation framework
5.3.2 Digital twin driven product design evaluation workflow
WARNING!!! DUMMY ENTRY
Step 1: Product structure decomposition
Step 2: Evaluation indexes analysis
Step 3: Complex network building
Mapping network
Prediction network
Feedback network
Step 4: Complex network training
Step 5: Consistency judgment
Step 6: Perceived evaluation
5.4 Digital twin driven product design evaluation algorithm design
5.5 Case study: Digital twin driven roll granulator design evaluation
5.5.1 Background
5.5.2 Working principle of roll granulator
5.5.3 Evaluation indicators analysis
5.5.4 Digital twin driven roll granulator design evaluation
5.6 Summary
References
6 Digital twin driven energy-aware green design
6.1 Introduction
6.1.1 Iterative optimization of energy consumption
6.1.2 Energy consumption digital thread
6.1.3 Product life cycle
6.2 Related works
6.2.1 Green design in material selection
6.2.2 Green design in disassembly
6.2.3 Green design in supply chain
6.3 Energy-aware five-dimension digital twin
6.4 Potential applications of digital twin driven green design
6.4.1 Digital twin driven energy-aware green design in material selection
6.4.1.1 Integrated energy consumption digital thread for digital twin driven energy-aware green design in material selection
6.4.1.2 Iterative optimization of energy consumption
6.4.2 Digital twin driven energy-aware green design in disassembly
6.4.2.1 Continuously optimization of disassembly sequence
6.4.2.2 Disassembly feasibility improvement based on digital twin
6.4.3 Digital twin driven energy-aware green design in supply chain
6.4.3.1 Enhanced energy consumption prediction of green supply chain based on digital thread
6.4.3.2 Rapid construction of green supply chain
6.5 Summary
References
7 Digital twin enhanced Theory of Inventive Problem Solving innovative design
7.1 Theory of Inventive Problem Solving–based innovative design
7.1.1 History and applications of Theory of Inventive Problem Solving
7.1.2 Theory of Inventive Problem Solving–based innovative design
7.1.3 Digital twin enhanced Theory of Inventive Problem Solving innovation process
7.2 Digital twin enhanced strategic analysis of Theory of Inventive Problem Solving innovative design process
7.2.1 Digital twin enhanced demand evolution analysis
7.2.2 Digital twin enhanced technology evolution analysis
7.2.3 Digital twin enhanced technology maturity evaluation
7.3 Digital twin enhanced problem statement of Theory of Inventive Problem Solving innovative design process
7.3.1 Digital twin enhanced 9-box method
7.3.2 Digital twin enhanced resource analysis
7.3.3 Digital twin enhanced ideal final result analysis
7.4 Digital twin enhanced problem analysis of Theory of Inventive Problem Solving innovative design process
7.4.1 Digital twin enhanced function model analysis
7.4.2 Digital twin enhanced root cause analysis
7.4.3 Digital twin enhanced contradiction analysis
7.5 Summary
References
Part 2: Application and case study
8 Digital twin driven factory design
8.1 Introduction
8.2 Related works
8.3 Digital twin driven factory design
8.3.1 Framework for digital twin driven factory design
8.3.2 Functions of digital twin in different stages
8.3.3 Modular approach for building flexible digital twin toward factory design
8.4 Case study
8.4.1 Digital twin driven factory design of a paper cup factory
8.4.2 Digital twin driven factory design of a nylon factory
8.4.3 Discussion
8.5 Summary
References
9 Digital twin based computerized numerical control machine tool virtual prototype design
9.1 Introduction
9.2 Related works
9.2.1 Related works on virtual prototype design
9.2.2 Advantages of digital twin based computerized numerical control machine tool virtual prototype
9.3 Framework of digital twin based computerized numerical control machine tool virtual prototype
9.3.1 Functional requirements
9.3.2 Framework of digital twin based computerized numerical control machine tool virtual prototype
9.4 Design of DT-based CNCMT virtual prototype descriptive model
9.4.1 Composition analysis of computerized numerical control machine tools
9.4.2 Mechanical subsystem modeling of computerized numerical control machine tools
9.4.3 Electrical subsystem modeling of computerized numerical control machine tools
9.4.3.1 Implementation of permanent magnet synchronous motor model
9.4.3.2 Implementation of inverter driver model
9.4.3.3 Implementation of sensor and limit switch model
9.4.3.4 Implementation of control module
9.4.4 Coupling relationship between subsystems of computerized numerical control machine tools
9.5 Design of DT-based CNCMT virtual prototype updating strategy
9.5.1 Design of mapping strategy
9.5.2 Design of consistency maintenance strategy
9.6 Case study
9.6.1 Case 1: Design stage
9.6.2 Case 2: Operation stage
9.6.3 Case 3: Maintenance stage
9.7 Summary
Acknowledgment
References
10 Digital twin driven lean design for computerized numerical control machine tools
10.1 Introduction
10.2 Related works
10.2.1 Related works on lean design methods
10.2.2 Related works on digital twin driven design methods
10.3 Framework of digital twin driven lean design
10.3.1 Digital twin driven lean design in digital space
10.3.2 Digital twin driven lean design in physical space
10.4 Design of workload–digital twin model
10.4.1 Analysis of workload
10.4.2 Construction of workload–digital twin model
10.5 Application of workload data
10.5.1 Workload data generation
10.5.1.1 Data preprocessing
10.5.1.2 Data analysis
10.5.1.3 Data storage
10.5.2 Workload data selection
10.5.2.1 Analysis of target performance indicators of computerized numerical control machine tools
10.5.2.2 Analysis of required workload data of computerized numerical control machine tools lean design
10.5.3 Workload–digital twin model instantiation
10.6 Optimization and evaluation for computerized numerical control machine tools
10.6.1 Optimization for computerized numerical control machine tools
10.6.2 Evaluation for computerized numerical control machine tools
10.7 Case study
10.7.1 Problem description
10.7.2 Digital twin driven lean design for the feed system of computerized numerical control machine tools
10.7.3 Results and discussion
10.8 Summary
Acknowledgment
References
11 Digital twin based virtual commissioning for computerized numerical control machine tools
11.1 Introduction
11.2 Related works
11.2.1 Traditional virtual commissioning
11.2.2 Digital twin based virtual commissioning
11.3 Framework of digital twin based virtual commissioning for computerized numerical control machine tools
11.4 Workflow of digital twin based virtual commissioning for computerized numerical control machine tools
11.4.1 Step 1: Keeping virtual and physical computerized numerical control machine tools consistent
11.4.2 Step 2: Dynamic commissioning
11.4.3 Step 3: Kinematic commissioning
11.5 Case study
11.5.1 Construction of platform for digital twin based virtual commissioning
11.5.2 Dynamic commissioning of computerized numerical control machine tools
11.5.3 Kinematic commissioning of computerized numerical control machine tools
11.5.4 Discussion
11.6 Summary
Acknowledgment
References
12 Digital twin driven process design evaluation
12.1 Introduction
12.2 Related works
12.2.1 Process design
12.2.2 Process design evaluation
12.2.3 Digital twin driven process design evaluation
12.2.3.1 Real-time data acquisition
12.2.3.2 Data fusion
12.2.3.3 Digital twin data–driven process evaluation
12.3 Framework for digital twin driven process design evaluation
12.3.1 Process design layer
12.3.2 Data fusion layer
12.3.3 Process evaluation layer
12.4 Reconfigurable process plan creation
12.4.1 3D process models creation
12.4.2 Process information management
12.4.3 Digital twin based process models construction
12.5 Digital twin data generation
12.5.1 Real-time data acquisition
12.5.2 Digital twin data management
12.6 Process plan evaluation based on digital twin data
12.6.1 Process design evaluation framework
12.6.2 Process plan evaluation method
12.7 Case study
12.7.1 Diesel engine connecting rod model description
12.7.2 Real-time data collection and management
12.7.3 Verification of process design evaluation method
12.7.4 Discussion
12.8 Summary
References
Index
Back Cover

Citation preview

Digital Twin Driven Smart Design

Digital Twin Driven Smart Design

Edited by

Fei Tao School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R. China

Ang Liu School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia

Tianliang Hu School of Mechanical Engineering, Shandong University, Jinan, P.R. China

A.Y.C. Nee Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2020 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-818918-4 For Information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Matthew Dean Acquisitions Editor: Brian Guerin Editorial Project Manager: Joshua Mearns Production Project Manager: R. Vijay Bharath Cover Designer: Christian J. Bilbow Typeset by MPS Limited, Chennai, India

Contents List of contributors Preface

xiii xv

Part 1 Theory and methodology

1

1.

3

Digital twin driven smart product design framework Meng Zhang, Fangyuan Sui, Ang Liu, Fei Tao and A.Y.C. Nee 1.1 Introduction 1.2 Development of product design and prospect forecast 1.2.1 Traditional design methods and technologies 1.2.2 New era of data-driven product design 1.2.3 Call for digital twin driven smart product design framework 1.3 Digital twin and its applications 1.3.1 History of digital twin 1.3.2 Concept of digital twin 1.3.3 Applications of digital twin 1.4 Five-dimension digital twin for a product 1.4.1 Physical entity 1.4.2 Virtual entity 1.4.3 Digital twin data 1.4.4 Services 1.4.5 Connections 1.5 Framework of digital twin driven smart product design 1.5.1 Key processes of digital twin driven smart product design 1.5.2 Related technologies for digital twin driven smart product design 1.6 Case study 1.6.1 Digital twin driven smart product design application in bicycle 1.6.2 Digital twin driven smart product design application in landing gear 1.7 Summary References

3 4 4 6 7 8 8 9 10 11 11 12 13 13 13 14 14 17 23 23 25 28 28

v

vi

2.

Contents

Digital twin driven conceptual design

33

Yuchen Wang, Ang Liu, Fei Tao and A.Y.C. Nee

3.

2.1 Introduction 2.2 Conceptual design methodology foundation of digital twins 2.2.1 General design theory 2.2.2 Axiomatic design theory 2.2.3 Systematic design process 2.2.4 Function behavior structure ontology 2.3 Digital twin based conceptual design 2.3.1 Digital twin based function modeling 2.3.2 Digital twin based concept generation 2.3.3 Digital twin based concept evaluation 2.3.4 Digital twin based contradiction resolution 2.3.5 Digital twin based constraint management 2.3.6 Digital twin based complexity management 2.3.7 Collaborative conceptual design 2.3.8 Digital twin based design affordance 2.4 Case study 2.4.1 Digital twin based robot vacuum cleaner functional domain formulation 2.4.2 Digital twin based robot vacuum cleaner concept generation 2.4.3 Digital twin based robot vacuum cleaner constraints management 2.4.4 Digital twin based robot vacuum cleaner contradiction solving 2.5 Summary References

33 34 36 36 37 37 38 38 39 40 41 42 42 43 44 44

Conceptual design driven digital twin configuration

67

45 48 58 60 63 65

Yuchen Wang, Lin Liu and Ang Liu 3.1 3.2 3.3 3.4 3.5 3.6 3.7

Introduction Development of physical entity Development of virtual entity Development of twin data center Development of services Development of connections Integration of digital twin compositions and working management 3.7.1 Management with working mode 3.7.2 Management with working sequence 3.7.3 Management with power output ratio 3.8 Case study 3.8.1 Step 1: development of autonomous vehicle physical entity

67 69 75 79 83 85 88 88 89 91 91 92

3.8.2 Step 2: development of autonomous 3.8.3 Step 3: development of autonomous center 3.8.4 Step 4: development of autonomous 3.8.5 Step 5: development of autonomous system 3.8.6 Step 6: development of autonomous modes, sequences, and output ratio 3.9 Summary References

4.

Contents

vii

vehicle virtual entity vehicle twin data

92

vehicle services vehicle connection

95 98 101

vehicle working

Digital twin driven virtual verification

101 105 106

109

Yiling Lai, Yuchen Wang, Robert Ireland and Ang Liu

5.

4.1 Introduction 4.2 Related works 4.2.1 Related works on traditional product design verification 4.2.2 Related works on digital twin driven virtual verification 4.3 Digital twin driven virtual verification method 4.3.1 A model of digital twin driven virtual verification 4.3.2 Iterative framework of digital twin driven virtual verification 4.4 Case study I: digital twin driven virtual verification in design for a commercial coffee machine 4.4.1 Case study background 4.4.2 Working principle of commercial coffee machine 4.4.3 Factors impacting coffee quality 4.4.4 Group gaskets of coffee machine 4.4.5 Collection of key controlling data of commercial coffee machine 4.4.6 Digital twindriven virtual verification application 4.5 Case study II: digital twin driven virtual verification in design for 3D printers 4.5.1 Background of 3D printing 4.5.2 Virtual verification of 3D printer design 4.5.3 Virtual verification of 3D printer design, manufacturing, and usage 4.5.4 Virtual verification of 3D printer maintenance and end-of-life 4.6 Summary References

109 110 110 112 113 113

Digital twin driven design evaluation

139

117 117 118 119 120 120 121 125 128 129 129 131 134 136 137

Lei Wang, Fei Tao, Ang Liu and A.Y.C. Nee 5.1 Introduction

139

viii

6.

Contents

5.2 Related works 5.2.1 Existing product design evaluation methods 5.2.2 Digital twin driven product design methods 5.3 Digital twin driven product design evaluation methodology 5.3.1 Digital twin driven product design evaluation framework 5.3.2 Digital twin driven product design evaluation workflow 5.4 Digital twin driven product design evaluation algorithm design 5.5 Case study: Digital twin driven roll granulator design evaluation 5.5.1 Background 5.5.2 Working principle of roll granulator 5.5.3 Evaluation indicators analysis 5.5.4 Digital twin driven roll granulator design evaluation 5.6 Summary References

140 140 142 146 146 147 153

Digital twin driven energy-aware green design

165

154 154 156 157 158 161 161

Feng Xiang, Yuan yuan Huang, Zhi Zhang and Ying Zuo 6.1 Introduction 6.1.1 Iterative optimization of energy consumption 6.1.2 Energy consumption digital thread 6.1.3 Product life cycle 6.2 Related works 6.2.1 Green design in material selection 6.2.2 Green design in disassembly 6.2.3 Green design in supply chain 6.3 Energy-aware five-dimension digital twin 6.4 Potential applications of digital twin driven green design 6.4.1 Digital twin driven energy-aware green design in material selection 6.4.2 Digital twin driven energy-aware green design in disassembly 6.4.3 Digital twin driven energy-aware green design in supply chain 6.5 Summary References

7.

Digital twin enhanced theory of inventive problem solving innovative design

165 166 167 168 168 168 170 171 173 175 175 178 181 182 183

185

Chunlong Wu and Youcheng Zhou 7.1 Theory of inventive problem solving based innovative design 185 7.1.1 History and applications of theory of inventive problem solving 185

Contents

7.1.2 Theory of inventive problem solving based innovative design 7.1.3 Digital twin enhanced theory of inventive problem solving innovation process 7.2 Digital twin enhanced strategic analysis of theory of inventive problem solving innovative design process 7.2.1 Digital twin enhanced demand evolution analysis 7.2.2 Digital twin enhanced technology evolution analysis 7.2.3 Digital twin enhanced technology maturity evaluation 7.3 Digital twin enhanced problem statement of theory of inventive problem solving innovative design process 7.3.1 Digital twin enhanced 9-box method 7.3.2 Digital twin enhanced resource analysis 7.3.3 Digital twin enhanced ideal final result analysis 7.4 Digital twin enhanced problem analysis of theory of inventive problem solving innovative design process 7.4.1 Digital twin enhanced function model analysis 7.4.2 Digital twin enhanced root cause analysis 7.4.3 Digital twin enhanced contradiction analysis 7.5 Summary References

ix

186 189 190 191 191 192 194 194 195 195 196 197 198 199 199 200

Part 2 Application and case study

203

8.

205

Digital twin driven factory design Ning Zhao, Jiapeng Guo and Hu Zhao

9.

8.1 Introduction 8.2 Related works 8.3 Digital twin driven factory design 8.3.1 Framework for digital twin driven factory design 8.3.2 Functions of digital twin in different stages 8.3.3 Modular approach for building flexible digital twin toward factory design 8.4 Case study 8.4.1 Digital twin driven factory design of a paper cup factory 8.4.2 Digital twin driven factory design of a nylon factory 8.4.3 Discussion 8.5 Summary References

213 218 218 224 231 234 234

Digital twin based computerized numerical control machine tool virtual prototype design

237

205 205 209 209 211

Tianliang Hu, Tianxiang Kong, Yingxin Ye, Fei Tao and A.Y.C. Nee 9.1 Introduction

237

x

Contents

9.2 Related works 9.2.1 Related works on virtual prototype design 9.2.2 Advantages of digital twin based computerized numerical control machine tool virtual prototype 9.3 Framework of digital twin based computerized numerical control machine tool virtual prototype 9.3.1 Functional requirements 9.3.2 Framework of digital twin based computerized numerical control machine tool virtual prototype 9.4 Design of DT-based CNCMT virtual prototype descriptive model 9.4.1 Composition analysis of computerized numerical control machine tools 9.4.2 Mechanical subsystem modeling of computerized numerical control machine tools 9.4.3 Electrical subsystem modeling of computerized numerical control machine tools 9.4.4 Coupling relationship between subsystems of computerized numerical control machine tools 9.5 Design of DT-based CNCMT virtual prototype updating strategy 9.5.1 Design of mapping strategy 9.5.2 Design of consistency maintenance strategy 9.6 Case study 9.6.1 Case 1: Design stage 9.6.2 Case 2: Operation stage 9.6.3 Case 3: Maintenance stage 9.7 Summary Acknowledgment References

10. Digital twin driven lean design for computerized numerical control machine tools

238 238 240 241 241 242 244 244 245 247 254 254 255 256 257 258 258 259 259 260 260

265

Yongli Wei, Tianliang Hu, Wenlong Zhang, Fei Tao and A.Y.C. Nee 10.1 Introduction 10.2 Related works 10.2.1 Related works on lean design methods 10.2.2 Related works on digital twin driven design methods 10.3 Framework of digital twin driven lean design 10.3.1 Digital twin driven lean design in digital space 10.3.2 Digital twin driven lean design in physical space 10.4 Design of workload digital twin model 10.4.1 Analysis of workload 10.4.2 Construction of workload digital twin model 10.5 Application of workload data 10.5.1 Workload data generation

265 267 267 268 269 269 271 271 271 273 274 274

Contents

10.5.2 Workload data selection 10.5.3 Workload digital twin model instantiation 10.6 Optimization and evaluation for computerized numerical control machine tools 10.6.1 Optimization for computerized numerical control machine tools 10.6.2 Evaluation for computerized numerical control machine tools 10.7 Case study 10.7.1 Problem description 10.7.2 Digital twin driven lean design for the feed system of computerized numerical control machine tools 10.7.3 Results and discussion 10.8 Summary Acknowledgment References

11. Digital twin based virtual commissioning for computerized numerical control machine tools

xi 276 278 278 279 281 282 282 283 284 285 285 285

289

Weidong Shen, Tianliang Hu, Yisheng Yin, Jianhui He, Fei Tao and A.Y.C. Nee 11.1 Introduction 11.2 Related works 11.2.1 Traditional virtual commissioning 11.2.2 Digital twin based virtual commissioning 11.3 Framework of digital twin based virtual commissioning for computerized numerical control machine tools 11.4 Workflow of digital twin based virtual commissioning for computerized numerical control machine tools 11.4.1 Step 1: Keeping virtual and physical computerized numerical control machine tools consistent 11.4.2 Step 2: Dynamic commissioning 11.4.3 Step 3: Kinematic commissioning 11.5 Case study 11.5.1 Construction of platform for digital twin based virtual commissioning 11.5.2 Dynamic commissioning of computerized numerical control machine tools 11.5.3 Kinematic commissioning of computerized numerical control machine tools 11.5.4 Discussion 11.6 Summary Acknowledgment References

289 291 291 292 293 294 295 296 296 297 297 299 301 304 305 306 306

xii

Contents

12. Digital twin driven process design evaluation

309

Xiaojun Liu, Jinfeng Liu, Honggen Zhou and Zhonghua Ni 12.1 Introduction 12.2 Related works 12.2.1 Process design 12.2.2 Process design evaluation 12.2.3 Digital twin driven process design evaluation 12.3 Framework for digital twin driven process design evaluation 12.3.1 Process design layer 12.3.2 Data fusion layer 12.3.3 Process evaluation layer 12.4 Reconfigurable process plan creation 12.4.1 3D process models creation 12.4.2 Process information management 12.4.3 Digital twin based process models construction 12.5 Digital twin data generation 12.5.1 Real-time data acquisition 12.5.2 Digital twin data management 12.6 Process plan evaluation based on digital twin data 12.6.1 Process design evaluation framework 12.6.2 Process plan evaluation method 12.7 Case study 12.7.1 Diesel engine connecting rod model description 12.7.2 Real-time data collection and management 12.7.3 Verification of process design evaluation method 12.7.4 Discussion 12.8 Summary References Index

309 310 310 311 311 313 314 315 315 315 316 316 319 320 320 322 322 322 323 324 325 325 327 328 329 330 333

List of contributors Jiapeng Guo School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, P.R. China Jianhui He School of Mechanical Engineering, Shandong University, Jinan, P.R. China Tianliang Hu School of Mechanical Engineering, Shandong University, Jinan, P.R. China Yuan yuan Huang School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan, P.R. China Robert Ireland School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia Tianxiang Kong School of Mechanical Engineering, Shandong University, Jinan, P.R. China Yiling Lai School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia Ang Liu School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia Jinfeng Liu School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, P.R. China Lin Liu Beijing 7Invensum Technology Co., Ltd, Beijing, P.R. China Xiaojun Liu School of Mechanical Engineering, Southeast University, Nanjing, P.R. China A.Y.C. Nee Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore Zhonghua Ni School of Mechanical Engineering, Southeast University, Nanjing, P.R. China Weidong Shen School of Mechanical Engineering, Shandong University, Jinan, P.R. China Fangyuan Sui School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R. China Fei Tao School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R. China Lei Wang School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan, P.R. China xiii

xiv

List of contributors

Yuchen Wang School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia Yongli Wei School of Mechanical Engineering, Shandong University, Jinan, P.R. China Chunlong Wu National Engineering Research Center for Technological Innovation Method and Tool, Hebei University of Technology, Tianjin, P.R. China; School of Mechanical Engineering, Hebei University of Technology, Tianjin, P.R. China Feng Xiang School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan, P.R. China Yingxin Ye School of Mechanical Engineering, Shandong University, Jinan, P.R. China Yisheng Yin School of Mechanical Engineering, Shandong University, Jinan, P.R. China Meng Zhang School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R. China Wenlong Zhang School of Mechanical Engineering, Shandong University, Jinan, P.R. China Zhi Zhang School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan, P.R. China Hu Zhao School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, P.R. China Ning Zhao School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, P.R. China Honggen Zhou School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, P.R. China Youcheng Zhou National Engineering Research Center for Technological Innovation Method and Tool, Hebei University of Technology, Tianjin, P.R. China; School of Mechanical Engineering, Hebei University of Technology, Tianjin, P.R. China Ying Zuo School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R. China

Preface By virtue of new-generation information technologies (e.g., Internet of Things, cloud computing, and big data analytics), growing data can be obtained directly from all stages of product/service life cycle (e.g., manufacturing, usage, maintenance, and recovery) and then transformed into useful information to support design decision-making. Traditionally, the growing data is mostly obtained from the physical world, with little connection to the corresponding digital models in the cyber world. Such disconnection often leads to design challenges such as incomplete information support, cyber-physical inconsistency, and imperfect digital mapping. To address such challenges, it is critical to achieve deeper cyber-physical fusion, with equal focus on the roles of both physical and cyber worlds, as well as their backand-forth interactions, communications, and coevolvement. Against this background, this book aims to introduce digital twin (DT), a pragmatic way for the cyber-physical fusion, as a new approach to support engineering design. In this book the authors propose to employ DT to reinforce the traditional design theories, methods, and tools on a wide spectrum of design activities such as conceptual design, virtual verification, design evaluation, green design, Theory of Inventive Problem Solving (TRIZ) based innovative design, factory design, virtual prototyping, lean design, virtual commissioning, and process design. The applicability of DT for smart design lies in its unique capability of high-fidelity modeling and simulation, fusion of real and simulated data, integration of product life cycle data, cyber-physical interaction, data learning and analytics, etc. The authors hope that this book will contribute to the pervasive research and application of smart design in the future. This book has 12 chapters, which are classified into two parts. Part 1 includes Chapters 1 7, which present relevant theories and methodologies of DT-driven smart design. Chapter 1, Digital twin driven smart product design framework, proposes a DT-driven product design framework from a holistic perspective and then discusses the related key processes and technologies. Chapter 2, Digital twin driven conceptual design, envisions the DT-driven conceptual design in terms of functional modeling, concept generation, concept evaluation, and contradiction resolution. In Chapter 3, Conceptual design driven digital twin configuration, a systematic conceptual design process is followed to configure DT in order to reduce design complexity, uncertainty, and coupling. Chapter 4, Digital twin driven virtual

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verification, proposes a DT-driven virtual verification framework for simulating product behavior and performance in terms of design, manufacturing, usage, maintenance, and end-of-life cycle. Chapter 5, Digital twin driven design evaluation, proposes a DT-driven modular product design evaluation model and studies its internal network structure. Chapter 6, Digital twin driven energy-aware green design, explores DT-driven green design with respect to material selection, disassembly, and supply chain. Chapter 7, Digital twin enhanced Theory of Inventive Problem Solving innovative design, discusses the applicability of DT for TRIZ-based innovative design in terms of strategic analysis, problem statement and problem analysis. Part 2 includes Chapters 8 12, which focus on practical applications of DT-driven smart design. Chapter 8, Digital twin driven factory design, presents two cases about the design of a paper cup factory and a nylon factory, as a way, to verify the efficiency of the proposed DT-based modular design method. Chapter 9, Digital twin based computerized numerical control machine tool virtual prototype design, presents a virtual prototype of a DTbased computerized numerical control (CNC) machine tool, which includes the descriptive model design and the updating strategy design. Based on the virtual prototype constructed in Chapter 9, Digital twin based computerized numerical control machine tool virtual prototype design, Chapter 10, Digital twin driven lean design for CNC machine tools, introduces a DT-driven lean design method, and Chapter 11, Digital twin based virtual commissioning for computerized numerical control machine tools, showcases a DT-driven virtual commissioning platform for the CNC machine tool. Finally, Chapter 12, Digital twin driven process design evaluation, presents a DT-based process design evaluation method that can respond to real-time machining states, exemplified by the machining process of a diesel engine connecting rod. The authors would like to acknowledge the invaluable support and suggestions from many collaborators, based on their research in DT for smart design. Many thanks for the strong support and cooperative work from Profs. Xiaojun Liu, Ning Zhao, Feng Xiang, Jingfeng Liu, Chunlong Wu, Lei Wang, as well as their group members, to accomplish this book together. In particular, the authors would like to express their gratitude for the invaluable contributions from the members in Digital Twin Research Group at Beihang University: Meng Zhang, Qinglin Qi, He Zhang, Weiran Liu, Xin Ma, Jiangfeng Cheng, Lianchao Zhang, Fangyuan Sui. They actively engaged in the research program on digital twin at Beihang University together with Prof. Fei Tao from 2015. Many thanks to Dr. Meng Zhang for helping Prof. Fei Tao organize this book. Thanks to all the participants who attended the Conference on Digital Twin and Smart Manufacturing Service from 2017 to 2019, who kindly helped promote the research and application of DT.

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Some contents were previously published in the International Journal of Production Research, CIRP Annals—Manufacturing Technology, Journal of Ambient Intelligence and Humanized Computing, Proceedings of the Institution of Mechanical Engineers Part B—Journal of Engineering Manufacture, International Journal of Advanced Manufacturing Technology, IEEE Access, Journal of Intelligent Manufacturing, Computer Integrated Manufacturing Systems (in Chinese), Modular Machine Tool & Automatic Manufacturing Technique (in Chinese), and Procedia CIRP, etc. Thanks go to all the anonymous reviewers who had provided many constructive comments and suggestions. Some contents of this book were financially supported by the following research projects in China: Beijing Natural Science Foundation (No. JQ19011), National Natural Science Foundation of China (No. 51875030), and Industrial Internet Innovation and Development Project. The authors are most grateful to Mr. Brian Guerin, the Senior Acquisitions Editor from Elsevier, who took the initiative to encourage them to publish this book. The authors are equally grateful to the anonymous reviewers who had provided constructive feedback on the book proposal. The strong support from Mr. Joshua Mearns as well as other colleagues from Elsevier is greatly appreciated. Fei Tao, Ang Liu, Tianliang Hu and A.Y.C. Nee September 1, 2019

Chapter 1

Digital twin driven smart product design framework Meng Zhang1, Fangyuan Sui1, Ang Liu2, Fei Tao1 and A.Y.C. Nee3 1

School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R. China, 2School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia, 3Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

1.1

Introduction

The era of data-driven product design is coming with the advancement of new IT. The design process is becoming more digitalized than ever before, supported by the computer-aided tools for product design, e.g., computer aided design (CAD), finite element analysis (FEA), computer aided engineering (CAE), computer aided manufacturing (CAM). In the virtual world, products are created in computers to visualize product structure, simulate product behavior, and validate product performance. In the physical world, in virtue of technologies such as Internet of Things (IoT), cloud, and artificial intelligent, products’ performance, behavior, and interaction with the users can be captured directly and analyzed in real time. However, generally, the virtual and physical products are built, analyzed, and upgraded, separated from each other, which could lead to such problems as incomplete information support, cyber-physical inconsistency, and imperfect digital mapping. Therefore the data-driven product design calls for a new framework that can effectively integrate, synchronize, and converge the increasingly “bigger” data that are related to the virtual product, the physical product, and their back-and-forth interactions [1]. Different from CAD that exclusively focuses on the digital world and IoT that heavily concentrates on the physical world, digital twin (DT) is characterized by the interaction and convergence of the digital and physical worlds, which could possibly bring many benefits [1]. On one hand, the physical product can be made more intelligent to actively adjust its behavior in real time according to the simulation by the virtual product; and on the other

Digital Twin Driven Smart Design. DOI: https://doi.org/10.1016/B978-0-12-818918-4.00001-4 © 2020 Elsevier Inc. All rights reserved.

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hand, the virtual product can be made more realistic to accurately reflect the real states of the physical product [1]. In the past, DT is mostly used for production [2], prognosis and diagnosis [3], predictive maintenance [4], etc. In this chapter, combining with the authors’ previous work [1], a systematic DT-driven smart product design (DTPD) framework coupled with related key technologies is explored from a holistic perspective, in order to guide designers to use the design theories, DT technology, and product life cycle data to facilitate the product-design process. The rest of this chapter is organized as follows. Section 1.2 reviews the development of product design and points out the prospect. Section 1.3 introduces the DT with respect to its concept, background, and applications in product design. In Section 1.4 a five-dimension DT for a product is constructed and its abilities enabled by new IT are introduced. Based on this, Section 1.5 formally presents the DTPD framework by describing the systematic processes and technologies of the design based on DT approach, which aims at helping the readers gain a preliminary understanding of the design process driven by DT. Each process or technology will be discussed in detail in the following Chapters 2 12. Section 1.6 presents case studies of design for the bicycle and landing gear to showcase the possible applications of DTPD in practice. Section 1.7 draws conclusions and outlines future works.

1.2 1.2.1

Development of product design and prospect forecast Traditional design methods and technologies

Design methods have been developed for years. They play a significant role in different stages of product design, including task clarification, conceptual design, embodiment design, detail design, and virtual verification, and they can also guide the designs for factory, process planning, control logistics, etc. Some traditional design methods and technologies are introduced briefly in this section. Theory of inventive problem solving (TRIZ) is one of the most wellknown problem-solving methods for the conceptual design, which is the most significant and creative phase [5 8]. The design thinking behind TRIZ is highly analogous to that behind the big data analytics, as they intend to uncover the hidden patterns, general principles, and unknown correlations, under the surface of an enormous amount of data [1]. The TRIZ matrix that involves recognized pairs of contradictions was manually created by Altshuller based on his personal interpretation and abstraction of hundreds of thousands of patents [1,8]. The matrix can be used to remove conflicts between parameters (e.g., volume, weight, speed, and energy) in a system. Virtual prototyping refers to the process of constructing and testing a realistic and interactive virtual prototype, that is, a 3D digital representation

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5

that can perform various types of simulations iteratively about product performances [9 11]. An initial version of a product can be provided to designers and users via the virtual prototype to find out potential flaws, and then it would be revised and verified iteratively in the digital space until finally reaching the expectation. If the virtual verification is reliable enough, to a large extent, it could be used in product certification in place of the traditional physical verification. Made possible by the virtual prototype, the product development cycle can be significantly shortened. Product design evaluation can be considered as a multicriteria decisionmaking problem. Various design indexes (e.g., cost, quality, production time, and sustainability) need to be assessed and weighted relying on both subjective preference and objective information, in order to select the suitable design from several alternatives [12]. It includes several steps such as evaluation goal definition, evaluation system establishment, evaluation method selection, and data collection [13]. Common algorithms used for design evaluation include analytic hierarchy process [14], fuzzy logic [15], artificial neural network [16], etc. Virtual commissioning aims at testing control logics and codes in digital environment, where physical entities are substituted by virtual models with kinematics, functionality, and electrical properties [17,18]. There are mainly two types of virtual commissioning. One connects the virtual models with a real controller to test the real hardware in terms of cycle time and memory usage, while the other uses an emulated controller and hardware is not required [17]. Operators can debug control codes according to outputs of the virtual models prior to the real commissioning. Generally, virtual commissioning focuses on one of the following levels: machine level, cell level, line level, and production system level [19]. Since degradation of environment has become a global issue, sustainability in the product-development process has attracted more and more attentions. Against such a background, the concept of green design is introduced to help designers balance product development and ecological friendliness through considering material selection, disassembly and recyclability, etc., early at design stage [20,21]. Common strategies for green design include realizing product modularization, choosing reusable materials, reducing material types, simplifying product structure, etc. Lean design is another design concept. Lean thinking is about “creating more value, defined from the customer’s perspective while consuming fewer resources” [22,23]. Lean design applies this idea to product-development process, aiming at “removing waste (in time, material, complexity, and underutilization of resources) from all aspects of the product-development process before it ever gets to the manufacturing floor” [24]. The lean design process can be classified into three stages [25]: (1) value stream analysis, which identifies and analyzes the waste; (2) design implementation guided

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by lean concepts, including preliminary design, basic design, and detailed design; and (3) design verification. Factory design is a huger project than the design for a single product, since a factory involves abundant physical objects, complicated link relations, and various flows of materials, energy and information. Factory design concerns many interrelated factors, such as facility layout, workforce layout, production capacity, machine usage, logistics, and processes [26]. In the past, designers often applied heuristic optimization algorithms to solving problems related to design constraints and objectives [27]. Nowadays, to present more details and to better use designers’ knowledge, virtual simulation coupled with 3D immersive models and environment are employed in a user interface to create and edit the factory design schemes [28]. Process design that is an essential step in the production can create optimal machining plans according to features (e.g., depression features and protrusion features) of the machined parts [29]. Using technologies such as computer-aided process planning and model-based definition, more and more 3D models carrying process information and knowledge have been built for the process design [30]. Since similar parts have similar processes, finding the similar parts and reusing the related 3D models can help to reduce design cost and time [31]. The traditional design methods have contributed a great deal in the past years. In the coming big data era, embracing big data could enable these methods to better facilitate the design process, in terms of identifying highvalue data and converting data to useful information, dealing with unstructured information generated without any predefined models or formats, and responding rapidly to the dynamically changing data and emerging new situations [1].

1.2.2

New era of data-driven product design

Nowadays, data can be accumulated through a product’s life cycle, including design, production, distribution, usage, maintenance, upgrade, and recycle. As illustrated in the authors’ previous work [1], some data are related to the product’s status, behavior, and performance (e.g., state sampling data in utility, maintenance, failure information, upgrade information, degradation status, remaining values, and recycle scheduling records). Some data are related to when, where, how, by whom, and under what circumstances the product is used. Some data are related to customers in regards to their demographics information (e.g., income, education, and gender), behaviors (e.g., online browsing, searching, and purchasing history), and preferences (e.g., product rating, reviewing history, and repeated purchase). The technological backbones of data-driven product design are IoT and big data analytics [1]. The enabling technologies of IoT include sensors, software, electronics, actuators, etc. The applications of IoT can be found in many fields such as

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healthcare [32], transportation [33], agriculture [34], and smart city [35]. IoT can lead to countless benefits such as significantly enhanced automation, accuracy, efficiency, and productivity. It is made possible using the IoT technologies that data can be collected directly from the physical products in real time; that a group of physical products can communicate and collaborate directly with each other; that a physical product connected to IoT can interplay with an intangible service on the Internet; and that a physical product can be monitored, controlled, and upgraded remotely [1]. Big data are valuable information assets that are characterized by high volume, velocity, and variety. Big data analytics aims to abstract useful information to facilitate a certain human creativity out of an enormous amount of highly unstructured and seemingly unrelated data. Design-related big data are richly available on the Internet and the IoT. On one hand, some big data are generated by customers from the Internet. For example, countless text-heavy customer reviews are published on e-commerce platforms (e.g., Amazon.com) and social platforms (e.g., Facebook.com). On the other hand, some big data can be collected from the physical products using IoT and advanced sensor technologies [1]. Despite the sweeping trend, there are some major challenges ahead of data-driven product design [1]: (1) How to effectively integrate a variety of different data about product, customer, and environment, which are collected from diversified sources (both Internet and IoT) using different methods, in order to discover the deeply hidden game-changing patterns? (2) How to effectively convert an enormous amount of data into a small selection of useful information that can be directly queried by designers to support them making design decisions at different phases of the design process? (3) How to rapidly respond to a current event that is happening in the real world based on the real-time data, and how to predict a future event that will occur based on historical data?

1.2.3

Call for digital twin driven smart product design framework

DT is considered as a promising way to deal with the previous problems. First, in the DT, a set of high-fidelity virtual models that always accompany the physical entities can track, transform, pack, store, and process the data collected from the whole product life cycle. Supported by the accumulated data, the DT is able to mine hidden patterns and predict future trends about product usage, failure, and service, to provide more insights for designers. Second, since the virtual models in the DT have the ability to judge and learn via the embedded rules and algorithms, they can remove the redundant data and false data and then find out the key information (e.g., new demands, most asked functionalities, and upgrading strategies) for the designers. Furthermore, the DT can obtain valuable information through comparing, associating, and analyzing data from both the physical and virtual worlds to

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support more accurate decision-making, compared with traditional methods that mainly consider either physical data or simulated data. Third, the DT can present states of products to the designers in real time by realistic 3D modeling, which enables real-time connection and interaction between the designers and the products. Based on this, the designers could respond to the current event rapidly. Moreover, the DT could predict the future event through virtual simulation driven by historical data. Due to these reasons, a DTPD framework is desired.

1.3 1.3.1

Digital twin and its applications History of digital twin

The concept of “twin” came from National Aeronautics and Space Administration (NASA)’s Apollo program, where one spacecraft was launched into the outer space, while the other (called the twin) remained on earth to mirror flight conditions during the mission [36]. Compared with the “twin,” the DT aims at providing more insights of the spacecraft using realistic virtual models, rather than the same physical entity. Some initial thinking on DT can date back to Grieves’s presentation about product life cycle management (PLM) around 2003 according to the Whitepaper [37]. He put forward an idea called “conceptual ideal for PLM,” which focused on linking the physical and virtual spaces throughout the entire product life cycle, so that everything existing in the physical space would be truly reflected in the virtual one [38]. Although the term “DT” was not used then, the proposed idea included the important elements of DT, that is, the physical space, virtual space, and connection for information running. However, at that time, it did not attract enough attentions due to technological limitations, and few related articles or reports were published [39]. Until 2010 the concept of DT was officially proposed by NASA as one of the top technical challenges in the report “Draft Modeling, Simulation, Information Technology & Processing Roadmap” [40]. The DT is defined as “an integrated multiphysics, multiscale, probabilistic simulation of a vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin” [40]. In the following years, along with the development of data-acquisition technology (e.g., IoT), processing technology (e.g., cloud computing and big data), artificial intelligence, and simulation technology, the concept of DT becomes more mature and specific. Especially in aerospace industry, it is considered as an emerging simulation approach that can further optimize decision-making on airframe designing and maintaining [41], vehicle capacity estimation [42], fleet prognosis [43], etc. In 2014 the Whitepaper on DT was published and the proposed threedimension structure of the DT (i.e., physical entity, virtual entity, and

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connection) was widely accepted [37]. Meanwhile, beyond the aerospace industry, the DT was also employed into the fields of electric power generation, maritime, city management, agriculture, construction, manufacturing, oil and gas, healthcare and medicine, environmental protection, security, and emergency [44]. With the development of smart manufacturing, the DT is particularly popular in the manufacturing field, because it is regarded as a promising technology to integrate the manufacturing physical and virtual worlds. Against this background, Tao et al. published the first paper on DT shop-floor in January 2017 [45] and proposed the five-dimension DT concept [3]. They also wrote the first book on DT-driven smart manufacturing to further discuss the key technologies in manufacturing based on DT approach and to explore the seamless combination of DT with new IT [44]. Many famous enterprises (such as Airbus [46], Lockheed Martin Space Systems Company [47], Siemens [48], GE [49], and Huawei [50]) have proposed and even begun to practice the DT-based smart production. Other organizations such as Gartner [51,52] and Smart Manufacturing Association of China Association for Science and Technology [53] have also attached great importance to the DT. Recently, due to the fatal crashes of Boeing 737, improving safety of flights has become the society focus. DT is treated as an emerging way to build live models of the physical airplanes for real-time monitoring, thus to detect latent faults timely and avoid air disasters, which also causes a rapid rise in the concept stocks of the DT [54]. Based on the current trend, it can be expected that the DT will experience a rapid development in the next few years.

1.3.2

Concept of digital twin

Up to now, there is not a universal definition of DT. From the view of NASA, as pointed out before, the DT can be seen as a virtual mirror of the physical counterpart integrating multiple physics and scales, and it employs both dynamic sensor data and historical data from the product life cycle [40]. Based on this, other understandings for different objects are also proposed, some of which are selected and enumerated later: For a product the DT is an equivalent digital image that exists throughout the product life cycle from conception and design to usage and servicing, knows the product’s past, current, and possible future states, and facilitates the development of the product-related intelligent services [55]. For a production asset the DT is a digital representation that contains all the states and functions of a physical asset, having the possibility to collaborate with other DTs to achieve a holistic intelligence that allows for decentralized self-control [56]. For a factory the DT can be described as a digital copy of a real factory, a machine, a worker, etc., which is created authentically and can be

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independently expanded, automatically updated, as well as globally available in real-time [57]. To emphasize the simultaneous existence and continuous interaction between the physical and virtual spaces, Grieves [37] concentrated on three components of the DT, that is, the physical entity existing substantially with real functions and capabilities, the digital mapping accompanying the physical counterpart throughout its lifetime, and the connection which enables data exchange between the previous two parts. Grieves believed that the most valuable tools provided by the DT are conceptualization, comparison, and collaboration [37]. Conceptualization refers to the ability of directly exhibiting actual and virtual products information simultaneously, to eliminate the information transformation process in the users’ brains. Then by comparison, difference between the actual and expected parameters can be identified visually, which contributes to finding potential faults and optimizing future operations. Collaboration aims at enabling ideas from different individuals to be interacted freely and accurately based on the shared conceptualization supported by the DT. To satisfy the new demands for extending applications, embracing new IT, fusing physical and virtual data, generating intelligent services, and constructing ubiquitous connections, Tao et al. proposed the concept of five-dimension DT, which adds DT data and services based on the initial three-dimension structure [3]. The newly added “DT data” fuse all data from both physical and virtual spaces, in order to provide more accurate and comprehensive information through data processing. The “services” make various algorithms, data, and models in the DT standardized and encapsulated, which can help users of different level of professional knowledge in easiness of use. The proposed five-dimension DT is also more efficient to embrace the new IT, since such technologies as big data, cloud, artificial intelligence, and IoT can be applied to different dimensions of the DT directly. In the stage of exploration the five-dimension DT has shown good universality and practicability in different areas, that is, satellite communication network, shipping, vehicles, power plant, aircraft, electromechanical equipment, stereoscopic warehouse, healthcare, shop-floor, and smart city [58].

1.3.3

Applications of digital twin

The DT has been applied into various stages of product life cycle. From the perspective of article number, there are more studies focusing on DT in the production stage (e.g., human machine interaction [59], process planning [30], and energy consumption management [60]), and service stage (e.g., prognostics [3], maintenance [4], and recycling [61]) [44]. It is because, on one side, under the background of smart manufacturing, DT has been highly valued and spread in the manufacturing field as a promising technology for cyber-physical fusion, which attracts lots of attentions; and on the other side,

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since the DT application starts from prognostics and health management [40], it has been developing for a longer period in the service stage. By comparison, there is not much concern for applying the DT in the product creation stage (i.e., the design stage). However, as stated by Dassault, the DT has huge potential in design [62]. What is more, if a product DT model could be established from the design phase, then more related design data, marketing data, and user experience data can be integrated, which will result in better services for the production and after-production stages [1]. Some current DT applications in the design stage have been investigated in the authors’ previous works [1,39,44]. For example, Zhuang et al. [63] explored the connotation, architecture, and trends of DT in terms of product and suggested some relevant theories and tools to implement the DT-based product design. Canedo [64] believed that product design could be notably improved by adding data from DT. Yu et al. [65] proposed a new DT model to manage the 3D product configuration, which could reinforce the collaboration between design and production. Tao et al. [1] explored a DT-driven product design framework to facilitate the design phases such as task clarification, conceptual design, and virtual verification. Schleich et al. [66] put forward a DT model to manage geometrical variations, in order to evaluate part deviations of a product at the early stage. Zhang et al. [67] studied a DT-based approach to design production lines, taking a glass production line as an example. Ferguson et al. [68] yielded a DT-based damage prediction method for water pumps to address certain design challenges. Mavris et al. [69] explored a design method based on the DT and digital thread for an aircraft. It is expected that by using the DT, experimental, actual measurement and calculation can be integrated, and the real-test environment parameters can also be integrated. For designers the DT serves to provide affordance information, identify and diagnose various complexities associated with a product, and guide designers to formulate rational functional requirements. For customers the DT serves to meet customer needs and to deepen designers’ understandings. In addition, the product use in actual conditions could be accurately simulated.

1.4

Five-dimension digital twin for a product

In this section, a five-dimension DT for a product that has been studied in our works [1,3,58] is presented, as shown in Fig. 1.1. Correspondingly, abilities of the DT enabled by new IT are also introduced.

1.4.1

Physical entity

The physical entity is the real product that can be operated by users. It is created from raw materials or parts, through machining, cleaning, assembly,

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PART | 1 Theory and methodology FIGURE 1.1 Five-dimension DT for a product [3]. DT, Digital twin.

testing, and packaging. Through sensor technology and IoT technology, a mass of data generated during the product life cycle (e.g., production running data, environmental data, customer usage data, maintenance data, and interactive data) can be collected in real time. Meanwhile, these data can also be supplemented by other data from the product manual, web page customer browsing records, download records, evaluation feedbacks, etc. All of the data can be stored and processed in the cloud platform that enables both designers and users from anywhere with Internet access to get the data conveniently.

1.4.2

Virtual entity

The virtual entity is the mirror image of the physical product, including mainly four types of models, that is, geometry models, physics models, behavior models, and rule models [3]. The first two models describe geometric properties (e.g., shape, location, and assembly relation) and physics performances (hardness, strength, and wear resistance) of the product, respectively. The behavior models not only analyze behaviors of the product but also focus on behaviors of the users and environment, as well as interactions among them. The rule models include mainly the evaluation, optimization, and forecasting models established following the laws of product operation and maintenance, etc [1]. The virtual product can be developed, deployed, and maintained in the cloud environment, which also provides easy access for designers and users. What is more, in virtue of virtual reality (VR) and augmented reality (AR), designers and users are enabled to interact directly with the virtual product in a completely virtual environment or a mixed cyber-physical environment with high authenticity and real-time feedbacks.

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1.4.3

13

Digital twin data

In the DT, data collected from both physical and virtual spaces can be analyzed, integrated, and visualized [1]. As stated in Ref. [1], first, data analytics is required to convert data into more concrete information that can be directly queried by designers for decision-making. Second, since the product data are collected from diverse sources (e.g., physical product, virtual models, Internet, and product manual), those hidden patterns that cannot be uncovered based on a single data source are able to be discovered through data integration. Third, data visualization technologies are incorporated to present data in a more explicit fashion. Finally, advanced artificial intelligence techniques can be incorporated to enhance the DT’s cognitive ability (e.g., reasoning, problem solving, and knowledge representation), so that certain recommendations can be made automatically.

1.4.4

Services

There are mainly two types of services in the DT, functional service and business service (BS) [58]. The former encapsulates different data, models, algorithms, etc., into services to support the DT working, such as services for model assembly, simulation, and verification; services for DT data analytics, integration, and mining; and services for connection, communication, and interaction, whereas the latter is provided to meet user demands for such as machine fault prediction, product risk evaluation, product energy consumption analysis, employee training, and customer experience. The BS can be presented to users through software interfaces with standard inputs and outputs for clear instruction and easy usage.

1.4.5

Connections

The connections among the previous four parts include CN_PD, CN_VD, CN_SD, CN_PV, CN_PS, and CN_VS, which refer to the connection between the physical entity and DT data, virtual entity and DT data, services and DT data, physical entity and virtual entity, physical entity and service, as well as virtual entity and services, respectively. These connections are enabled by a number of technologies such as network communication, IoT, and network security [1]. Networking technologies, for example, Bluetooth, QR code, barcode, Wi-Fi, Z-Wave, etc., enable the product to send its ongoing data to the cloud platform, which contains various virtual models, services, and DT data. In virtue of IoT, much effort is devoted to connecting the physical and virtual spaces, which can be adapted for the DT research. In addition, since the product data are directly and indirectly related to userproduct interactions, it is critical to guarantee the security of connections [1].

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1.5

Framework of digital twin driven smart product design

The five-dimension DT can facilitate different design activities for designers. To fully make the use of the benefits brought by the DT, a framework of DTPD is proposed. The complete process of DTPD is divided into five parts that correspond to the five design phases, as shown in Fig. 1.2, including task clarification, conceptual design, embodiment design, detail design, and virtual verification. In addition, to support the DTPD, other related technologies, for example, DT-driven TRIZ, DT-driven virtual prototyping, DT-driven product design evaluation, DT-driven factory design, etc., are also indispensable. In this section, key processes and technologies of the DTPD are outlined as a prelude for Chapters 2 12, which will give more detailed illustrations.

1.5.1

Key processes of digital twin driven smart product design

DTPD is characterized by the abilities to inspire new ideas, enrich design information, represent design knowledge, make full use of virtual models, etc. Based on this, roles of DTPD are explored mainly in terms of planning and task clarification, conceptual design, and virtual verification, which have been presented in the authors’ previous work [1]. Details of the work are introduced in this section as follows.

1.5.1.1 Digital twin driven task clarification In the task clarification phase, customer needs are translated into functional requirements under design constraints, where the DT serves as an “interpreter” to facilitate the “translation” process [1]. Planning and task clarification

Conceptual design

Embodiment design

Detail design

Market competition

Market analyzing Proposal formulating

Problems identifying Function definition

Defect data

Error detection

Customer’s requirement

Requirements elaborating

Test data

Optimization of principles, layout and production

Product sales

Task clarifying

Virtual verification

Historical defect data

Virtual experience Detail drawing

Material data Environment data Size data

Parameter Production, data assembly, transport and Principle operating establishing Assembly instruction data Criteria Customer’s evaluating habit Develop and define Mechanics data the construction structure Economic data

FIGURE 1.2 Framework of DTPD [1]. DTPD, Digital twin driven smart product design.

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The DT serves to deepen designers’ understandings of target customers and customer voices. The customer-related information is useful for enhancing product personalization and adaptability. The massive customer voices must be processed to be useful for task clarification [70]. For example, a function recommender system can be incorporated into the DT to recommend exciting new functions for a target product through the analysis of readily available customer reviews published on e-commerce platforms. In addition, the DT serves to guide designers to rationally formulate functional requirements. Functions can be represented as the input output transformation of energy, materials, and signals, all of which can be possibly detected by different sensors and analyzed by the DT. Furthermore, the DT can provide key information such as the frequency of the function being used, the duration of using it every time, the number of customers using the function, etc. Last but not least, the DT serves to clarify various design constraints imposed on a particular product. Since the DT is oriented from PLM, it is potentially capable of synthesizing a comprehensive collection of design constraints imposed by all the relevant stakeholders. Some examples of the constraints include weight, size, budget, schedule, manufacturing capability, environmental standards, safety standards, and government regulations. In the conceptual design phase, designers can further confirm whether and to what extent a new concept complies with various constraints through virtual verification. In the future, it would be intriguing to explore how to collect constraint information by means of sensors directly from the physical product [1].

1.5.1.2 Digital twin driven conceptual design In the conceptual design phase the functional requirements are translated to the design parameters, working principles, and physical structures. Conceptual design involves concept generation and concept evaluation. A good design concept must be logically feasible, functionally simple, and physically certain. The DT plays the role in identifying design opportunities and evaluating new design concepts [1]. Conceptual design is arguably the most critical design phase in the engineering design process. It involves the activities of function formulation, concept generation, concept organization, concept evaluation, and concept improvement. The importance of conceptual design can never be overstated. The effectiveness of conceptual design determines over 50% of a product’s cost and quality. The unwise decisions made in the conceptual design phase are the root causes of complexity in operation. Nonetheless, conceptual design is commonly regarded to be the most challenging design phase, largely because the decisions made in this phase often lack the support of tangible objects and concrete information. At present, conceptual design remains highly experience dependent. In other words, decision-making in

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conceptual design is largely driven by a designer’s knowledge, experience, and insight. In practice, it is commonly acknowledged that conceptual design is a privilege of expert designers. The DT enables designers to understand how a product would function, behave, and perform even before building it. Based on the actual behavior and performance as reflected by the real-time data, the designers can create, evaluate, and improve a new design concept in a much more informed manner. The DT enables manufacturers to track a product’s digital life cycle throughout manufacturing, distribution, usage, maintenance, and recycling. Therefore the designers can constantly assess how the decisions in conceptual design impact the later-stage activities and how to develop new concepts with manufacturability, sustainability, and energy efficiency. The DT serves to assist designers in identifying system contradictions within a product. Compared to the current practice of letting designers identify contradictions manually and purely based on their subjective judgment [71], the DT enables designers to identify contradictions in a more informed fashion. In addition, the DT not only serves to identify and diagnose various complexities associated with a product but also can suggest corresponding actions to resolve the complexities, for example, by resetting functional periodicity (e.g., electrical periodicity and information process periodicity [72]) [1].

1.5.1.3 Digital twin driven virtual verification Compared to current virtual verification technologies, DT-driven virtual verification enables extremely high-reliability simulations, allowing designers to further improve and refine the design based on the simulation results. At the same time, thanks to its high efficiency, designers can get timely feedback to avoid unnecessary waste of time and cost. For virtual verification, designers can debug the product and predict its performance directly in DT models by taking full use of various data. Specifically, the defect of design can be found accurately and fixed rapidly, which means the design can be improved more efficiently by avoiding tedious verification and testing. Yet with the traditional model, the effectiveness and feasibility of the design cannot be evaluated until the small batch manufacturing is finished. That will not only extend the design cycle but also greatly increase the time and cost. If the designers select the DT model, the quality of any accessory will be predicted by direct prediction of the DT model. DT-driven verification of virtual products can take full advantage of the equipment, the environment, the material, and the historical data of the previous generation of products, to detect design flaws and find causes, ensuring a quick and easy redesign. Moreover, it can greatly improve design efficiency by avoiding lengthy verification and testing [1]. In addition, the DT can also propose solutions to real systems. In other words, it can facilitate operations and provide services to optimize the

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auxiliary system and to predict physical objects based on virtual models. Thus by using DT technology, designers can create vivid simulation scenarios, effectively apply simulation tests to prototypes, and accurately predict the actual performance of physical products [1].

1.5.2 Related technologies for digital twin driven smart product design Related technologies for DTPD can be incorporated in different phases of design; not only the design processes formulated for product but also for factory, process planning, control logistics, etc. They help to decide where and in what ways different kinds of data, virtual models, and services are exploited for various design activities based on DT approach.

1.5.2.1 Digital twin driven TRIZ Contradiction, ideality, and patterns of evolution are always the main concepts in TRIZ [2], which can be better understood and handled with the help of DT. There are mainly two kinds of contradictions: physical and technical [73]. The former implies two opposite requirements for a same property of a product, such as a bigger size screen of a mobile phone for clear display and smaller size for convenience. And the latter contradiction arises when a useful action causes a harmful effect. For example, improving the speed of an automobile may deteriorate safety and stability. The DT can evaluate the importance of different product properties (e.g., weight, appearance, running speed, and stability) based on data directly collected from the product and users. Therefore designers can better decide how should the properties be sorted in terms of importance and what the trade-offs are when facing different contradictions. Ideality, a ratio of benefits generated by a product to the unwanted costs and harms, is used to measure how close the product is to the ideal final result (IFR) [74]. Although the IFR is hard to completely achieve, a clear definition is still required, because it guides the development direction of the product [75]. With the DT, on one hand, the IFR can be defined more distinctly, since it can be presented via a high-fidelity virtual model with realistic functions, behaviors, structures, etc. On the other hand, since the DT is a perfect tool to reflect both the physical and virtual worlds, it can be employed to exhibit the real product and the expected ideal product simultaneously, thus to show their difference visibly. Moreover, it eliminates the complicated and tedious mental processing and thus is more conducive to creativity during product design. In addition, the DT can also play a role in analyzing the evolution patterns of user demands and technologies. Since the DT can accumulate lots of

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user-related data (such as satisfaction degree, preferences, and habits) from different individuals, it is able to discover the trend of common needs by mining the big data. Besides, product data through the whole life cycle collected by the DT (e.g., component aging state, energy consumption data, and failure rate) can reflect the level of technology.

1.5.2.2 Digital twin driven virtual prototyping Virtual prototype is critical in design, because it can simulate, test, and validate product performances and behaviors as if the real product was built, which significantly accelerates the product development. To further improve the virtual prototype with respect to accuracy, interactivity, and intelligence, the DT can be incorporated in virtual prototyping. Since the DT is an integrated multiphysics, multiscale, probabilistic simulation combining with dynamic sensor data, historical data, and best available model, it enables the virtual prototype to be more concrete and accurate. On one hand, the virtual prototype can reflect more details in different aspects (e.g., structural, mechanical, thermal, and electrical) and scales (e.g., time scale and spatial scale) while considering more uncertain factors existing in the product life cycle, such as production disturbances, delay in transportation, and various using conditions. On the other hand, real-time data from the physical world can help to update and calibrate the virtual prototype continuously, which makes its performances closer to the real product. In virtue of VR and AR the DT-based virtual prototype can support more realistic and natural interactions. For designers, it becomes possible to modify the product, evaluate alternative designs, check for interferences in the immersive environment created by VR and AR, and get visual, auditory, haptic, and other feedbacks in real time, to improve the current version of the product. For users, they would be able to manipulate the product via natural spoken language and operations and have realistic product experience on detailed attributes, functions, and performances. What is more, made possible by the DT, the virtual prototype could be endowed with intelligent abilities, such as analyzing customer preference, evaluating product performance, and predicting further user demands, as well as learning rules of operation and maintenance. Through these abilities, the virtual prototype can provide designing suggestions proactively, which facilitates the design process. 1.5.2.3 Digital twin driven product design evaluation Product design evaluation involves multicriteria decision-making, which means selecting the most suitable design from several alternatives through evaluating various indicators such as functionality, appearance, accuracy, and cost. However, since the product is still rough or indefinite in design

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stage, especially in the concept design phase, it is hard for designers to perform accurate evaluation [76]. Fortunately, the DT can be employed to change this situation. First, the DT can collect data from the last-generation product, which should share considerable common features with the new product under design. These data can be stored in cloud and provided to designers. Then some properties (e.g., energy consumption, running speed, repair rate, and stability) of the new product can be inferred through analyzing data of the similar functional modules of its predecessor, which makes the new version more specific for evaluation. Moreover, since the DT can collect such indicators as energy consumption and cost from multiple product stages, including production, delivery, usage, and maintenance, the evaluation would be performed from the perspective of product life cycle. Second, the DT helps to reduce uncertainties in evaluation through simulation. For example, the actual performance of a product can be affected by many uncertainties, such as changes in using conditions and presenting in the physical world. The DT could mimic using conditions (e.g., temperature, humidity, using time, and load) in the digital world, so that the product performance under combinations of different conditions can be analyzed thoroughly. Based on this, the evaluation could be fed with complete information to obtain a more definite result. Finally, the DT could serve to put weights on different properties of the product. Generally, this process heavily depends on subjective preferences of designers. Enabled by the DT, product data and user data throughout the life cycle of the last-generation product can be analyzed impartially to generate a list of properties in the order of importance. Based on the list, design alternatives can be graded to select out the best one.

1.5.2.4 Digital twin driven virtual commissioning Virtual commissioning can be used to test control logics in a single machine, a production line, and even a whole production system through connecting a real or an emulated controller with virtual models, aiming at reducing the time spent on corrections during the real commissioning stage [77]. Since the virtual models used are the substitutes of the real devices, higher consistency between them is required to enable the commissioning results to be more reliable. The DT is capable of improving the consistency for virtual commissioning. In virtue of the DT the virtual models for commissioning can be enriched with realistic behaviors, such as dynamic and kinematic behaviors. Then two-way connections between the virtual and physical spaces can be built to support continuous interactions, during which real-time data collected from the physical devices can be transmitted to the virtual counterparts for updating and to reach the latest states. The properties that are

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measured from the physical devices and emulated by the virtual models could be compared with each other. If their difference goes beyond a certain range, the virtual models and physical devices are treated as inconsistency, which might be caused by model defects or sudden disturbances [3]. If it is the first case, the virtual models should be inspected and calibrated through tuning model parameters iteratively to keep consistent with the real objects, which would be conducive to making the virtual behaviors approach the actual behaviors gradually. In addition, during the virtual commissioning, rules accumulated from previous commissioning processes in the DT can provide suggestions to facilitate debugging.

1.5.2.5 Digital twin driven green design Green design requires designers to consider environmental impacts and green factors at the initial design stage [78]. The DT can play roles in green design in aspects of material selection, disassembly, and recycling, through providing abundant data and virtual verification and inspiring innovative design concepts. When designers select materials, energy consumption incurred by material extraction, transportation, manufacturing, assembly, usage, disassembly, and reuse needs to be considered comprehensively [79]. Traditionally, these data scatter across isolated data stores with limited illustrations, which makes it hard for designers to retrieve and make use of them. Under the umbrella of the DT, the related data generated in different phases could be directly collected and uniformly stored in the cloud and then presented in virtual scenarios, which could describe where and how the consumption occurs. In this way the data would be accessed and understood by the designers more easily. Disassembly is a process of removing specific components from the product at the end of its useful life for reusing, recycling, or remanufacturing [80,81]. However, since many products are hard to be disassembled, some components have to be discarded in spite of remaining useful, which produces a lot of waste. The DT may provide some effective solutions to the previous situation. First, it could realistically and interactively showcase similar design samples that consider disassembly to designers, with the help of VR and AR technologies, to arouse better designing inspiration. Second, it could automatically generate a new design through learning the accumulated previous design samples, which may consider product modularization, product structure optimization, fastener selection, and disassembly sequence for easy disassembly. In recycling the DT can provide various realistic simulations to evaluate the recycling effectiveness from multiple aspects by calculating such

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indicators as gas emissions, operational costs, and societal damages of recycling, which are difficult to be measured directly.

1.5.2.6 Digital twin driven lean design Lean design is guided by the lean concept, which aims at avoiding waste and maximizing values [82]. It can be roughly classified into three steps, that is, waste identification, lean concept-based design implementation, and design verification [25]. With the DT the waste of a product could be revealed based on the product life cycle data stored in the cloud. For example, by analyzing key information such as the using frequency of one function and the using duration of each time, DT can identify the functions that are seldom asked as wastes; through checking the production flow, DT can find out time wasted for waiting workers, materials, or decisions; and according to the maintenance data, resources (e.g., spare parts, tools, and labors) consumed due to poor design or production could also be recognized as wasted. In design implementation the DT could serve to rank the identified wasted things according to the loss of money, time, and resources in terms of product life cycle. It helps to locate and eliminate the major waste preferentially. Then, the DT could retrieve rules and experiences from previous lean design samples to guide the current design, such as reducing part number, material types, and unnecessary size tolerances. To make sure whether the design satisfies the lean requirements, DT could perform a realistic verification based on a high-fidelity digital mapping of the product as well as real external conditions (e.g., workloads) collected from the last-generation product. 1.5.2.7 Digital twin driven factory design Compared with the design of a single product, factory design is a much huger project. It needs to consider interrelated objects (e.g., machine, material, workforce, and environment) and various flows (e.g., information, material, and cash flows). The DT can be introduced into the phases such as conceptual design, embodiment design, and detail design for the factory [26]. In conceptual design the DT could provide a design collaboration platform combining with VR to build an immersive environment for designers. First, it can exhibit the design through a unified virtual scene visually, which is favorable for eliminating cognitive differences. Second, it allows designers to exchange ideas conveniently through a shared platform. Third, creativity can be better stimulated via the vivid virtualization. Meanwhile, since DTs for other designs of similar factories can be collected and analyzed, designers could gain insight about sources of water and power, storage and

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transportation of materials, layout of facilities, etc., from previous ideas to reduce repetitive mental works. In embodiment design, more detailed problems need to be solved, such as production line layout, machine configuration, process planning, and material handling [26]. In this phase, simulations spanning different domains and subjects (e.g., simulations on dynamics, thermodynamics, fluid, structure, and mechanic) can be performed by the DT to address those problems. In detail design the DT could connect with systems such as enterprise resource planning, manufacture execution system, and programmable logic controller to simulate and debug control polices for the factory [26]. Besides, since the DT should almost be the same with the real factory in this phase, it can also be presented to different managers and workers to reveal potential problems based on user experience.

1.5.2.8 Digital twin driven process design Process design aims at enabling the whole process chain to generate products with intended quality, cost, and development time [83]. However, due to unexpected manufacturing conditions caused by machines, materials, emergent orders, etc., the initial design of process may become no longer applicable to the current situation. DT-driven process design can solve the problem from the following aspects. The DT can incorporate both real-time data from production (e.g., machine conditions, material storage, and clamping tool states) and process information from computer-aided systems (e.g., machining features, precision requirements, and machining types) to generate a process flow automatically. For example, according to the required machining type, the DT can select several capable machines at first. Then the DT could evaluate each candidate machine based on the machine conditions (e.g., machining accuracy, highest spindle speed, and aging degree of components), to decide which one is most capable of meeting further requirements for precision, cost, and processing time. The DT is also able to predict unexpected events (e.g., machine failure and tool replacement) to reduce their negative effects on process design. For example, relying on data fusion of the DT, both real signals (e.g., speed, variation, and power) and simulated signals (e.g., stress and deformation) of a machine can be combined and considered together to support accurate prediction for machine faults [3]. Taking the prediction results into account, the process design could be more robust against disturbances. In addition, the DT can be used to verify the design before actual execution by conducting collision detection, process parameters verification, quality evaluation, etc. The verified design scheme can also be stored as knowledge to support future work.

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Case study

As stated before, the proposed DTPD can bring countless benefits to design, such as new concepts inspiration, iterative verification, multidimension evaluation, and accurate prediction. In this section the design processes for the bicycle and the landing gear of an airplane are taken as examples to illustrate the applications of some processes and technologies of the DTPD.

1.6.1 Digital twin driven smart product design application in bicycle This section introduces a DT-based design case for the bicycle, which has been used in our work [1]. A new paradigm for bicycle use emerged in China in recent years, called “Bicycle-sharing” as shown in Fig. 1.3. When users want to find a shared bicycle, they need to open the app in the mobile phone. Then they will be told where to get the closest bicycle via the automatic positioning service provided by the cloud. When they find the bicycle, they scan its QR code to make the unlocking password sent to the app and displayed on the app interface. Then, they could unlock the bicycle and ride it to their destination. When they finish using, they just need to lock the bicycle again and pay for the riding via the app [1]. “Bicycle-sharing” is a successful case enabled by New IT in public transportation. The real-time bicycle data (e.g., location, route, and speed) can be collected by IoT during riding. Then all the shared bicycles’ data can be stored in the “Sharing Cloud.” Via the big data technology the data are

FIGURE 1.3 The use flows and interaction mode of shared-bicycles [1].

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processed through cleaning, statistics, clustering, and machine learning to find out the hidden patterns of usage and to predict the future trends. Mobile Internet connects the users, shared bicycles, and cloud together. For instance the users send the riding requests and arrival instructions to the “Sharing Cloud,” then the “Sharing Cloud” would feedback the location, password, riding time, and cost to the users [1]. Besides, the user comments can be collected from the app through smart phones and PADs. As explored in Ref. [1], for the new paradigm of “Bicycle-sharing,” the DTPD can be applied to optimizing the design process for the bicycles as follows. Based on IoT and mobile Internet, the virtual space could obtain a lot of data of a real bicycle, such as the real-time running states (e.g., speed, location, acceleration, and wheel pressure), environment data (e.g., weather, road, and traffic), maintenance data (repair rate, replaced part, and remaining service life), and user comments. With these data a virtual model can be established as the virtual mapping of the real bicycle. During the entire life cycle, when the real bicycle changes, the virtual counterpart would be updated to keep pace with it. Meanwhile, the virtual bicycle would constantly collect, accumulate, and analyze the data from the physical space to generate intelligent recommendations for designers. Some key steps of the DTPD-based bicycle design are illustrated in Fig. 1.4. In the task-clarification phase the designers could easily access the online user comments in the cloud, which is superior to the questionnaire or telephone interview. Through data translation based on the DT, the user comments are interpreted into functional requirements. For example, since most of the shared bicycles do not have backseats, many users may have requests for the backseats that are useful for carrying. After statistical analysis the DT could find this preference and remind the designers that adding backseats should be considered in design. Once confirmed by the designers, the functional requirements would be mapped to design parameters, working principles, and physical structures. Taking the backseat as example, the designers first analyze the overall design-related data, such as distribution of users, use purpose, use environment, and bicycle parameters such as height of seat, diameter of wheel, and maximum load. After that, the designers add the backseat on a virtual bicycle provided by the DT according to ideas in their minds, with expected height, material, area, shape, etc. Then enabled by the DT, the expected version can be compared with a similar actual bicycle that has a backseat. In this way the designers could find out their difference visually and decide whether some revisions are needed to make the new design more practical. In the virtual verification phase, the DT, first, predicts the bicycle performances under different operation conditions. For example, it can predict the tire pressure when more weight is put on the backseat, in order to estimate if the selected tire could bear the load. Second, the DT verifies the manufacturing process of the bicycle. For instance, through simulation in virtual space,

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FIGURE 1.4 DT-driven bicycle design based on DTPD [1]. DT, Digital twin; DTPD, digital twin driven smart product design.

the designers can discover the component that is hard to be processed due to poor design; they could also plan a processing chain and then deliver the plan to the production department as instructions. Third, the designed functions of the bicycle are verified in terms of ergonomics, safety, service life, etc. For example, the brake distance is tested in different weather conditions via the digital models to guarantee that users can stop the bicycles in time at any situation. In the processes mentioned previously, once a flaw is found, the design is given back to designers for improvement.

1.6.2 Digital twin driven smart product design application in landing gear Landing gear, the principal support of the airplane during landing, mainly consists of the shock absorber, wheel, brake system, turning system, undercarriage retractile system, etc. It is used to absorb the landing impact energy

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so as to minimize the loads transmitted to the airframe [84]. Since the landing gear bears heavy load during working, an accurate prediction for the load, especially for the dynamically changing landing impact load, is required to ensure that the designed landing gear is capable of withstanding the landing impact energy. Meanwhile, in consideration of airplane mobility and low energy consumption, lightweight design is also important. The DTPD can be applied to the design for the landing gear to meet the previous two requirements simultaneously. This case has been studied in our previous work [58], and the details are introduced as follows. By means of sensors such as displacement sensor, pressure sensor, accelerometer, temperature sensor, and strain gauge, as well as the embedded flight control system, data of the landing gear during flight mission are collected conveniently. The data include real-time flight data related to the landing gear, such as bow angle or elevation angle of the airplane, landing speed, and acceleration, as well as the environment data, such as temperature, pressure, and wind speed, as shown in Fig. 1.5(A). By incorporating the collected data with other static data supplemented by the design documents and user manuals, a digital mapping of the landing gear is built with models in levels of geometry, physics, behavior, and rule, as shown in Fig. 1.5(B). In order to accurately predict the landing impact load, the DT integrates both physical data from the actual airplane (e.g., weight of the aircraft, bow angle, and vertical velocity at the landing moment) and virtual data that are hard to measure but could be simulated by the digital models (e.g., compression/drawing stroke of the shock absorber and friction coefficient against the runway), as shown in Fig. 1.5(C). Compared to the traditional load prediction method that heavily depends on experiences and computing manuals, the DT-based approach converges multidimension information related to the load from both physical and digital aspects, which makes the prediction method robust against the changing landing conditions. Fig. 1.5(D) shows the workflow for design optimization based on Ref. [3]. When the virtual landing gear is consistent with the physical counterpart, the physical and virtual data can be fused and related to the corresponding landing impact load through the neural network. According to the trained relation, the designers can predict the load under different landing conditions in the design stage. In the precondition that the landing gear has sufficient strength to bear the predicted load, the designers could reduce the size of components in the digital space, such as the diameter of the outer cylinder and the position rod, in order to satisfy the need for lightweight. This process requires iterative verifications. Each time the component size is changed; virtual simulations are needed to test if the revised component will bring damage to the landing gear. In addition, virtual verifications in terms of manufacturability, reliability, maintainability, etc., would also be performed through emulating the manufacturing, usage, and maintenance processes of the landing gear in the digital space.

FIGURE 1.5 DT-driven load prediction and design optimization for the landing gear [58]. (A) Physical landing gear; (B) Virtual landing gear; (C) DT data; (D) Workflow for design optimization; (E) Services. DT, Digital twin.

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In the abovementioned process the DT provides services related to both physical and virtual landing gears, such as states monitoring, real-time data analysis and fusion, load prediction, model updating and calibration, and virtual verification, as shown in Fig. 1.5(E). These services can be invocated by designers according to their demands, which allows the designers to easily access related data, models, data processing methods, and previous knowledge.

1.7

Summary

Over the last decade, although the data collection of physical products and creation of virtual products have both made great progress, the connection and integration of the two sides have lagged far behind their respective development [1]. As a result, problems such as incomplete information, cyber-physical inconsistency, and imperfect digital mapping hinder various activities in product design. Against this background, the chapter presents a comprehensive design framework driven by the DT that converges the physical and virtual spaces seamlessly, to solve the previous problems and to further facilitate the product design. Via the proposed DTPD, DT-driven task clarification, concept design, and virtual verification are introduced, and then technologies for the DTPD are discussed, including DT-driven TRIZ, virtual prototyping, product evaluation, and virtual commissioning. This chapter intends to help the readers gain a preliminary understanding of the DTdriven design process. In the following chapters, more detailed methods and applications will be given to illustrate the DT-driven smart design in regard to the key processes and technologies.

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Chapter 2

Digital twin driven conceptual design Yuchen Wang1, Ang Liu1, Fei Tao2 and A.Y.C. Nee3 1

School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia, 2School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R. China, 3Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

2.1

Introduction

Conceptual design is arguably the most critical design phase in the engineering design process. It involves the activities of functional formulation, concept generation, concept organization, concept evaluation, and concept improvement. Its importance to the design process is critical, with over 50% of a product’s cost and quality determined in this stage alone. Poor decisions made during conceptual design are the root causes of complexity in operation. As these decisions are often made without the support of a tangible object or concrete information, conceptual design is commonly regarded to be the most challenging design phase. Due to the uncertainty of the conceptual design phase, it remains a highly experience-driven process. Therefore decisions in conceptual design depend largely on a designer’s knowledge, experience, and insight. Successful conceptual design at present remains the domain of expert designers. Use of a digital twin (DT), or creation of a realistic, virtual product prototype, can enable a designer to understand a product’s functionality and behavior prior to building it. If real-time data of a product can be collected, and a virtual system generated based on this information, designers can create, evaluate, and improve concepts during the conceptual design phase in a much more informed manner. For example, by tracking the digital life cycle of a product throughout manufacturing, distribution, usage, maintenance, and recycling, a designer can assess how the decisions in conceptual design would impact the later stage manufacturability, sustainability, and performance of the products. A key goal of engineering design is to enhance communication throughout a product’s life cycle, from concept generation to manufacture and then to implementation [1]. However, during conceptual Digital Twin Driven Smart Design. DOI: https://doi.org/10.1016/B978-0-12-818918-4.00002-6 © 2020 Elsevier Inc. All rights reserved.

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design, as ideas are generated focusing on a product’s functionality, only limited information exist for designers to make decisions. Typically, communication of information flows from an upstream—where customers use products—to downstream—how products are made. Following this linear process, which involves converting customer voices to functional requirements (FRs), to design parameters (DPs), and to process variables, is timeconsuming. DT provides clear benefit to the communication of information at each stage by connecting end users with downstream constraints. Furthermore, it is proven that innovative designs involve a coevolution between problem and solution domains [2]. Greater interaction during the design phase will ensure the validation of a design’s efficacy. Traditionally, the creation of a product in a virtual space and its realization in a physical space remain separate. For example, a product’s shape is designed in computer-aided design (CAD), yet its strength and function are tested in a physical space. Hence, coevolution between the virtual and physical spaces is often expensive and time-consuming. DTs are of significant interest to designers, as they offer the capability to bridge the virtual and physical spaces. In theory, DTs allow virtual products to be tested as thoroughly as physical ones. Given the ease of testing a virtual product, solutions to problems can be validated far more efficiently. The following sections outline a framework for the developments of DTs. Section 2.2 presents the theoretical foundations (i.e., a collection of relevant design theories and methodologies) of conceptual design and engagement of DT. Section 2.3 proposes how and in what ways DTs can enhance different activities of conceptual design such as function formulation, concept generation, concept evaluation, complexity management, and constraint management. Section 2.4 presents a case study on how DTs can enhance the conceptual design of a smart product—robotic vacuum cleaner. Section 2.5 summaries works in this chapter and draws some preliminary conclusions. Abbreviations of methods and clarifications used are shown in Table 2.1.

2.2 Conceptual design methodology foundation of digital twins In this section, DTs are applied in the context of design theories. A few design theories are reviewed, though others may be equally applicable. Various design theories and methodologies can be classified into different categories such as descriptive model, prescriptive model, computer-based model, design representation (e.g., geometric modeling, functional modeling, and feature-based modeling), design decision-making (e.g., design optimization), and design for X (e.g., design for assembly, manufacturing, and concurrent engineering). This chapter will concentrate on the prescriptive model, or those models that either assist in or enable decision-making processes for designers. The general design flow is shown in Fig. 2.1.

Digital twin driven conceptual design Chapter | 2

TABLE 2.1 Summary of key terminologies. Key terminologies

Abbreviation

Meaning in the context of conceptual design

Design constraint

DC

Boundaries that limit the performance of a product

Functional requirement

FR

Functions that are carried/achieved by a product

Design parameter

DP

Component of a product and means to achieve an FR

Expected behavior

EB

Characteristic attributes derived from the DPs in the virtual space

Actual behavior

AB

Characteristic attributes derived from the real DPs in the physical space

Functional model

FM

Functional architecture of a product

Design complexity

/

The uncertainty of achieving desirable FRs

Design contradiction

/

A combination of FRs that are opposed to one another

Design coupling

/

The dependency relationship between FRs and DPs

FIGURE 2.1 DT-driven conceptual design flow. DT, Digital twin.

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2.2.1

General design theory

General design theory (GDT) is a design framework based on an entity and an identifier or entity concept. The entity refers to an existing object, and the identifier refers to how this object is perceived by a human. The entity and identifier can be regarded as the physical and virtual entities of the DT, respectively. GDT prescribes two basic axioms: G G

Any entity can be described by attributes. The entity set and the identifier set correspond to each other.

In the context of conceptual design, the abovementioned axioms of GDT can be translated as follows: G G

A physical entity and its attributes can be described by its virtual entity. The set of virtual entities and the set of physical entities have the one-toone correspondence. A pair of mutually corresponding physical entity and virtual entity builds the basis for modeling.

2.2.2

Axiomatic design theory

The axiomatic design theory was proposed by Suh in the 1990s [1]. It is characterized by a two-dimensional design framework (i.e., domain and hierarchy), two design axioms (Independence Axiom and Information Axiom), and a zigzagging concept generation process [1]. The axiomatic design theory is one of the most widely adopted design theories. Its application can be found in many areas such as mechanical design, software design, and manufacturing processes. The data provided by DTs can facilitate several operations of the axiomatic design theory: G

G

G

G

Traditionally, the design couplings between FRs and DPs are manually identified and labeled by designers. As a result, some implicit couplings are ignored, and it is difficult to quantify the degree of coupling. DT’s data enable designers to pair couplings between FRs with DPs, and to track and quantify couplings over time. According to the Information Axiom, the design concept that requires the least information content has the greatest certainty to satisfy FRs in the physical world. To evaluate the information content, designers need knowledge of the design range (the expected performance range of FRs) and the system range (the actual performance range of DPs). The data provided by DTs enable designers to analyze the information content in a more accurate and dynamic manner. Multiple principles of axiomatic design theory are adopted to support the conceptual design of DTs. The zigzagging process is employed to connect physical components to their corresponding virtual components. As each DP is met, it is then

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G

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“zigzagged” or compared against the next FR to ensure they do not have conflict. The Independence Axiom is followed to manage the dependency relationships between physical components and virtual components to control the complexity of DTs. It should be noted that both physical entity and virtual entity correspond to the same functional entity (e.g., a hierarchy of FRs).

2.2.3

Systematic design process

The systematic design process, prescribed by Pahl and Beitz [3], is arguably one of the most widely adopted engineering design processes to date. It is especially applicable for designing mechanical systems. The systematic design process includes four stages: planning and clarification of the tasks, conceptual design, embodiment design, and detail design. The subprocess of conceptual design includes four steps: (1) abstracting the essential problem, (2) establishing a function structure, (3) searching for suitable working principles, and (4) combining the working principles into a working structure. The function structure is most useful for the conceptual design of DTs. Each function is represented as a black box of input and output in terms of three dimensions (i.e., energy, material, and signal). The function structure is modeled using IDEF-0 [4]. For the DT-driven conceptual design, data can be incorporated as the fourth dimension of the EMS model. In other words, when modeling the functionalities of a system, it is necessary to indicate the flow of data across different functional units.

2.2.4

Function
behavior
structure ontology

The function
behavior
structure (FBS) ontology classifies design entities into three classes: function (i.e., the purpose of a physical entity), behavior (i.e., the attributes derived from the structure of a physical entity), and structure (i.e., the components of a physical entity). As suggested by Tao et al. [5], the FBS framework [6] can be used as the theoretical foundation of DTdriven conceptual design. A key motivation of DT-driven conceptual design is to align the expected behaviors of a virtual entity with the actual behaviors of the corresponding physical entity. Hence, the physical entity will become more robust and adaptable, while the virtual entity is made more vivid and authentic. The DT-based conceptual design is a highly iterative process that involves the back-and-forth iterations of both physical entity (PE) and virtual entity (VE) in terms of their functions, behaviors, and structures. Made possible by DTs, designers can simulate how a concept would respond to different situations in the interpreted world (i.e., the simulated environment), pilot adjustments in the physical world, and analyze feedback based on real-world data. Through contrast analysis, inconsistencies between expected and actual

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behaviors will emerge. Understanding these inconsistencies offers new insights to create new and improved designs.

2.3

Digital twin based conceptual design

In this section the ability of DTs to enhance the effectiveness of conceptual design will be explained. The analysis is based on the axiomatic design procedure, with additional complexity, contradiction, and constraints management principles applied. The new data provided by DTs can facilitate designers to make more informed decisions in different stages of conceptual design. The propositions are made based on the assumption that DTs are equipped with the following advanced abilities: G

G

G

G

G

data ability: cloud-based data collection, transmission, storage, and cleaning; simulation ability: simulation of a real-world process in the virtual environment; learning ability: the ability to learn from previous failures and to diagnose future problems; analytics ability: the ability to analyze the data to discover the hidden patterns; and connection ability: the ability to connect to other smart devices, cyberphysical systems, and the Internet of Things.

2.3.1

Digital twin based function modeling

Conceptual design begins with formulating a set of FRs and developing a functional model. Products carry out a set of FRs to satisfy customers’ demands. Typically, FRs can be represented in the format of hverb 1 objecti. Traditionally, FRs are based on designers’ interpretation of customer voices. Various FRs should be classified into different categories to facilitate design strategies. The designers can follow the Kano Customer Model to classify various FRs into “must-have,” “one-dimensional,” “attractive,” “indifferent,” and “reserve” features and follow the Long Tail Model to classify various FRs into popular and unpopular functions [7]. The classification result can help the designers rank the importance of different FRs. Traditionally, designers rely on survey, interview, and focus group to collect information for the classification. By contrast, real-world data provided by DTs are more direct and accurate. For example, the data collected by central processors on a product can provide direct evidence of the popularity regarding a functionality. After a set of FRs is formulated and classified for a product, they must be organized into a functional model. There are many previous studies of functional modeling [8]. Every function can be represented as a black box of

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input, output, mechanism, and control by means of IDEF0. The input and output can be specified in terms of energy, material, and signal. Because of the data, DT makes it possible to describe the input
output relationship in a more accurate manner. Functional models represent the blueprint of a product, used for interpreting a complex system and generating new design concepts. The models will be largely disregarded after the conceptual design. Made possible by DTs, data flow can be traced through the whole conceptual design process. In other words, DT makes functional modeling a dynamic process easier. For example, through the real-time data collected by the DT, the energy generation, consumption, and distribution can be linked to different FRs, based on which designers can make necessary adjustments, in the virtual space, toward a more energy-efficient concept.

2.3.2

Digital twin based concept generation

After a set of FRs is determined, the next step is concept generation, which involves mapping each FR to a physical DP and integrating multiple DP alternatives toward a system solution. Concept generation can be driven by creativity, combination, and modification [9]. DT-based concept generation is most applicable to the modification-based concept generation. In virtue of DTs, designers can generate new concepts, simulate the behaviors, optimize the performance, and validate the feasibility in the virtual space before making any changes to the physical entity. In the virtual environment, it is more convenient to alter the design concept by adding new DPs, replacing an existing DP with a new one, merging two DPs, and removing a redundant DP. This is in contrast to traditional CAD, as data are collected using DTs from the physical world in real time. DT can incorporate a variety of contextual information into concept generation. For consumer products, contextual information can include time (i.e., when the product is used), location (i.e., where the product is used), how (i.e., activities the product is used to perform), who (i.e., profile of the user), and environmental conditions (e.g., temperature, light, sound, and humidity). Through a comparison of the virtual and physical contexts, designers can deepen their understanding of the ideal and real contexts in which a product is used. This is especially important for designers to improve a product’s adaptability. Table 2.2 summarizes a collection of typical contextual information that can be collected by DTs. Through learning ability, DTs can learn the desirable and undesirable behaviors within a context. Through real-time data transmission, more specifics can be gradually added to the simulated context to make it more realistic and holistic. In an ideal scenario the DT can control a smart product, in real time, to react and respond within different contexts.

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TABLE 2.2 Contextual information classification. Physical context

Time Location/territory Weather Temperature Humidity Direction Air/water quality

Social context

Peer products Hostility/danger Hospitality Complementary service Resource supply

User context

User demographics User habit User preference User knowledge User mood/health

Operation context

Power/energy Degree of wear Computing power Intelligence Maintenance record Software update

2.3.3

Digital twin based concept evaluation

After generating multiple design concepts, designers can compare and select the most promising concept to pursue. The axiomatic design theory suggests that the concept evaluation can be performed based on two axioms: the Independence Axiom and the Information Axiom [10]. The Independence Axiom ensures that each FR is affected by a single DP independently. This principle is critical for the DT-based conceptual design because it ensures that every FR
DP pair can be treated as a basic DT unit

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FIGURE 2.2 Candidate concepts evaluation with Information Axiom.

that can be designed, implemented, and managed separately. Traditionally, design couplings are identified by designers based on knowledge and experience. As a result, it is difficult to quantify the couplings and to recognize the hidden ones. The analytics of DTs can help designers understand the degree of coupling. The Information Axiom ensures that the least information content for concepts to achieve the highest physical certainty. As presented in Fig. 2.2, for every FR, designers must specify an acceptable design range. Traditionally, the design range is determined by designers based on subjective interpretation of customer voices. The design range directly affects the amount of design resources. If the design range is narrower than the demand range, it will increase the manufacturing cost, otherwise it will fail to satisfy customer needs in some scenarios. The data provided by DTs can be employed to adjust the design range. The system curve indicates the performance of a DP in terms of its performance of reaching different values of the FR. According to the Information Axiom of the axiomatic design theory, the overlapping area between the design range and system range should be enlarged. In the ideal scenario the system curve should fall inside the design range completely. Traditionally, the system curve is drawn by designers based on empirical experiments. Data provided by DTs can be used to draw the system curve as well as the design curve. Moreover, the Information Axiom can deal with “robustness” of a design. Robustness implies that the system is easy to construct and operate and is reliable. A system that satisfies the Independence Axiom inherently requires less information to construct and to operate.

2.3.4

Digital twin based contradiction resolution

Some previous studies suggested that DTs are especially useful for the prognostics and management of complex systems [11]. For example, the same data used to diagnose the root causes of system issues can also address them

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by altering the design decisions. The design thinking behind TRIZ is that different FRs tend to contradict each other due to design coupling. Performance of the whole system is then affected. Traditionally, design contradictions are identified by experienced designers based on long-term observation of a system’s behavior and performance. Made possible by DTs, data corresponding to different FRs can be analyzed to visualize implicit contradictions. In TRIZ, Altshuller prescribed 39 parameters (e.g., weight, speed, force, pressure, temperature, illumination, and energy), and most of these parameters can now be measured by advanced sensors. Based on the continuous correlation analysis of data collected from different sensors that represent different TRIZ parameters, designers can understand both the location and severity of any contradictions. Compared to the current practice of designers manually identifying contradictions based on their abstract thinking, DTs allow designers to identify contradictions in a more informed fashion. Altshuller also prescribed four separation principles (i.e., separation in time, separation in space, separation in system and component, and separation based upon conditions) to deal with the physical contradictions. DTs make it possible to control the separation in real time and in a predictive manner [12].

2.3.5

Digital twin based constraint management

DTs can help designers identify and manage design constraints (DCs). DCs refer to the limitations that prevent an artifact from achieving a better performance or an ideal state. DCs can be categorized as either internal or external. For example, an engine limits the acceleration and speed of a car from the inside. Hence, it falls into the category of internal DCs. By contrast, speed cameras and road conditions limit the speed of a car from the outside. Hence, it is an external constraint. There are different strategies to manage DCs. Some DCs can be altered through engineering negotiation [13], while others must be strictly complied with. DTs can collect both the operational and environmental data of a product, which identify the internal and external constraints, respectively. For each DC identified in conceptual design, designers can develop and deploy a DT unit to monitor its real-time status. As the DC approaches its practical limit or boundary value, the DT unit can send warnings in advance. Over time, the learning ability of DTs will predict the excess of boundary values, based on the analysis of historical and real-time data. DTs can also simulate the coupling between various DCs.

2.3.6

Digital twin based complexity management

In the context of conceptual design, complexity is regarded as the uncertainty for meeting FRs. As mentioned earlier, DT is able to track, collect, pack,

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and store information and statistics related to functional performance, customer review, and real-time status of attached products. As the data continuously accumulate, DT will highlight the critical information and summarize trends, for example, the utilization sequence of multiple functions and peer products. Meanwhile, the designers’ judgments and strategies, as well as customers’ feedback, will support DT to evaluate specific design features. Over time, those information regarding product status, simulation results, customer review, and designers’ judgment can be summarized using DT to identify critical phenomena and anticipate probability of success. Such self-learning ability of DTs can assist designers to discover implicit patterns behind uncertainties. According to Suh’s classification [1], four types of complexities result from unwise design decisions: time-independent real complexity, time-independent imaginary complexity, time-dependent combinatorial complexity, and timedependent periodic complexity. Since time is used as a differentiating factor to categorize complexities, it is possible for DTs to detect different kinds of complexities based on their correlation with real-time data. For example, if a complexity is experienced by all customers, it is likely to be a time-independent real complexity. In contrast, if a complication is only reported by a limited number of customers all the time, it is likely to be a time-independent imaginary complexity. Based on real-time data, DTs can suggest actions to resolve complexities, for example, by resetting the functional periodicity (e.g., electrical periodicity and information process periodicity [1]). DTs serve to capture various uncertainties associated with products. Uncertainties are caused by incomplete and/or unknown information. A product’s actual performance in the physical world can be affected by many uncertainties that arise at the different phases of a product’s life cycle (e.g., production, distribution, usage, maintenance, and recycle). In particular, the aggregation of many uncertainties may significantly affect a product’s function, behavior, and structure. DTs serve to capture various uncertainties in the physical world and simulate them in the digital world, so that more robust concepts can be generated and virtually validated against the uncertainties. The uncertain information can also be provided by DTs to end users in real time.

2.3.7

Collaborative conceptual design

Modern-day product development is becoming increasingly complex, interdisciplinary, and global. Conceptual design, in particular, is becoming more collaborative, involving decision-making from a range of stakeholders [14]. Despite the advantages of having more diverse opinions, the experiencedriven nature of conceptual design and limited information will lead to inefficiencies, which will then affect outputs. DTs provide great assistance to the ever-increasing collaborative nature of design, by providing clearer and objective data to relevant stakeholders.

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There are three key ways of DTs functioning in this context. First, data collected from a DT product may help aggregate the preferences of customer voices. Arrow’s Paradox suggests that it is difficult to aggregate many individual preferences into a collective preference against the bounded rationality (i.e., the decisions are made with a limited amount of information) [15]. Hence, on occasion decision-making will rely on “dictatorship.” DTs provide designers with more information about how a product is used in practice and enhance their understanding of customer preferences. By providing more objective information, the probability of aggregating individual preferences increases. Second, DTs generate data in real-time and store the data in the cloud, which allows for parallel decision-making between remotely distributed stakeholders. Lastly, due to the rise of the Internet of Things, DTs will enable designers to develop smart products in a more collaborative manner. For example, a smart home includes a smart refrigerator, cleaner, security system, energy management, and air conditioner. The individual DTs of these smart devices can be connected to form a DT network for information sharing and collaborative functionality.

2.3.8

Digital twin based design affordance

Design affordance pertains to possible action on a product performed by users in the real world. Previous studies of design affordance indicated that customers do not always interact with a product as intended by the designer. In practice, it is common that different users would interact with the same product in different ways [16]. For example, books are designed as carriers of texts, but they are sometimes used as decorations for interior renovation. Together with other new technologies such as virtual reality, DTs will enable designers to explore users’ perceived affordances in the virtual space. A fundamental difference between affordance and function is that the former is dependent on a particular artifact, whereas the latter can be artifactindependent. New functions can be formulated purely based on customer needs or artifact behaviors. Traditionally, designers can only obtain affordance information through the time-consuming ethnographic observations and contextual inquiries. Since affordance concerns with product
customer interactions, certain affordance information can be directly collected from various smart products with the help of DTs. For example, as an important enabling technology of DT, VR can obtain affordance information based on customer behaviors in a completely virtual environment.

2.4

Case study

The following section uses a robot vacuum cleaner (RVC) consumer product to illustrate a practical application of DT to conceptual design. As a paradigm of smart home products, an RVC operates in a sophisticated home

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environment. An RVC is equipped with multiple sensors to perceive data for analysis. Although existing RVCs from iRobot, Kogan, Samsung, etc. are not developed enough to support DTs, elements of their design can provide an excellent framework for IoT and big data analysis applications. For example, iRobot Roomba can be controlled not only from its panel but also using the remote control of a dedicated smartphone app and Amazon Alexa. By centralizing control of an RVC on one interface such as an app, such technology can be scaled to coordinate multiple smart home products. In these illustrative examples, it is hypothesized that RVCs have entered the era of DT and will be advanced by further simulation, machine learning, analytics, and interaction abilities. Based on these illustrative examples, this section will envision how DTs will assist to facilitate an optimized conceptual design in each critical procedure. In addition, what kind of data should be collected? What should be included in the virtual simulation? What new services should be created from the simulation? How to evaluate the reliability of new concepts? How to collaborate functions and services physically? And what should be the role of designers?

2.4.1 Digital twin based robot vacuum cleaner functional domain formulation Analysis of DT aims to supplement—not to replace—market research. In the past decade the market has shifted from a demand-solving mode to a demand-exploring mode. The maturation of customer review analysis can summarize existing needs for designers, though it is not yet sophisticated enough to predict future needs. According to the Long Tail Model and Pareto Principle, popular features commonly exist on 80% of products and used by customers at 80% of frequency [17]. Collaborating with the Kano Customer Model, most of popular features in Long Tail Model are represented by basic features and performance features in Kano Customer Model. As these features are essential for products to survive in market competition, and most of the customer reviews are put forward concerning these issues, conventional market research has played an effective role in exploring these popular features and extrapolate products [18]. Common resources such as customer reviews, existing products, customer extrapolation, and low-end customers have occupied the majority of market information. For example, control apps are applied to RVCs such as iRobot, Samsung, and Ecovacs. Meanwhile, some primary models such as Roomba 650 use the traditional panel control. Hence, it can be classified as a popular performance feature. In this case, special DT design for popular features may not produce a better effect due to the marginal utility theory. However, the collaboration of DT and conventional design methods will significantly improve design quality in several aspects. As illustrated in

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FIGURE 2.3 Functional domain formulation based on market and DT. DT, Digital twin.

Fig. 2.3, compared to conventional market research, data analysis from DT transmits feedback timely, traces market situation dynamically, and can cover broader customer groups. Conventional customer research by contrast struggles to collect dynamic customer feedback and market changes. Also, customer reviews may only cover general customer concerns but ignore the more important or novel customer needs. DT technology will allow for more rapid discovery of less useful design features. For example, the speech recognition system in RVCs can be used to generate customer sentiment concerning cleaning performance. Due to ethical concerns, the speech recognition system may not be allowed to record all customer conversation. However, an oral feedback mode could provide users with a convenient way to communicate feedback following completion of cleaning objective. The application of big data analytics to oral feedback could also be used to uncover special patterns. Even the frequency of customer’s speech could deliver valuable insight in addition to the actual language. In addition to instantaneous customer feedback, the interaction between products used in tandem could also reveal new insights. Using IoT, an RVC could “know” which other smart home products are used on similar occasions. By knowing what other functions are used, designers can then adopt similar functionality in updated RVC designs. For example, smart home control is one main function of Google Home and Amazon Alexa. During house cleaning, RVCs and smart home controllers normally work together to

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achieve remote control. As speech recognition and an interactive interface are also available on the RVC, smart home control functionality could be added to the RVC. Similarly, other peer products such as humidifier, air conditioner, and lights can provide interaction as well to adjust even an environment optimal for cleaning. For example, room temperature and humidity may affect dust cleaning on the floor. The virtual simulation and physical test may determine the optimal temperature and humidity for the cleaning effect. Equipped with DT technology, existence of peer products can either be automatically noticed by RVC after several cleaning tasks or manually input by designers. When the RVC is capable of smart home control, it may automatically command air conditioner to adjust temperature and humidity. By comparing the cleaning effect on each occasion, the RVC summarizes a set of cleaning-friendly environmental features. This data will be forwarded to the other RVCs and assist the following design. A DT-developed RVC will be able to track the performance of product features against customer needs. Generally, customer needs become more sophisticated over time, and features of products must be adapted accordingly. From the Kano Customer Model, it is observed that excitement and performance features tend to spread to most products and subsequently become performance and basic features. In addition, according to the Long Tail Model, unpopular features can also gradually transform into popular features [19]. For example, initial RVC designs used a physical “Virtual Wall” device to constrain movement to a specific area, such as one room only. Later, some larger brand RVCs made it possible to create a Virtual Wall on a digital app. The improved digital Virtual Wall made the convenience an excitement feature of the RVCs. Furthermore, the near-universal adoption of Virtual Wall apps for RVCs today has reduced it to a performance feature. Conversely, basic and performance features can also return to excitement features. One recent customer trend in smartwatches is the return of retro, analog-like designs from the basic to excitement feature. Conventional market research is often intermittent, as data cannot be gathered in real time but only periodically. DT on the other hand allows for continuous data tracking and dynamic analysis. A collection of an RVC’s design implementation rate, customer satisfaction rate, and functional utilization rate can help to reclassify design features. For example, voice prompts of RVCs were initially designed to be curt. Later, some brands implemented a more humanized voice to suit various customer groups. The initial curt voice prompts mode was still maintained. At that moment, the humanized design was an excellent and unpopular feature. RVCs would then trace the utilization frequency and popularity of the humanized prompts mode. If the utilization frequency reaches a significant level and the function is adopted by more RVCs, this design can be determined as a performance feature then. As a machine-operated tool, when faced with massive uncertainties, DT may never reach the intelligence level capable of determining features during

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the conceptual design phase. However, once designers have formulated the general conceptual design frame and foundation, analysis of DT may help pioneer component level design features and adjustment of existing FRs. Fig. 2.4 presents a general functional hierarchy of a DT-based RVC. At present, the hierarchy contains basic features and some popular features. As data are collected from the RVC, FRs can be updated to add or remove features. For example, for FR2.3 (brush debris on the wall skirt board), if the infrared scanner detects a significant amount of debris on the skirt board after brushing, but customers do not complain, the FR may be insignificant. Another example could be the collection of information through a peer product, such as knowing what kind of music users listen to through an IoT sound system. Although not always feasible, DTs can reveal customer needs previously hidden from reviews. These novel FR generation mechanisms will also assist in the affordance design, identifying new objectives of products, which designers never intend for them to perform. For example, some users purchase an RVC to entertain their pets. The RVC may detect pet sounds, loading pets on top of the RVC (e.g., a cat or dog sits on the RVC), and excess fur. These activities might also occur outside regular cleaning time. Therefore in this case, an affordance of entertaining pets is observed by the DT system. Construction of a functional model will allow data inputs and outputs of the RVC to be tracked in both the physical entity test and virtual entity simulation. Through analysis of this data flow, a designer can evaluate performance at each step of design and simultaneously adjust FRs and corresponding DPs. Using the hierarchy in Fig. 2.4, a simplified functional model can be generated for RVCs as shown in Fig. 2.5. Hypothetically, if a user highlights an issue of slow movement, a virtual and physical simulation can be created for each step of the “Movement Command” function. Meanwhile, the recording system on the reported RVC should have one data copy of each step. The theoretical expected data of the physical and virtual entities can then be compared, and a procedure is outlined to fix the issue. At a more advanced level, with machine learning and big data analysis, the problem could be solved autonomously by a preinput functional paradigm. Furthermore, using virtual and physical tests during the design phase, the designer can input expected working conditions into the RVCs. The wide network of RVCs can also generate an accurate working range of all parameters. If a parameter is out of the range, the RVC may automatically forward the problem to monitoring hub even before the user notices it.

2.4.2

Digital twin based robot vacuum cleaner concept generation

The design objectives distributed to designers and smart RVCs are shown in Fig. 2.6, and the next sections will follow the procedures flow in Fig. 2.7 to explain how DT assists conceptual design. In the conceptual domain the

FIGURE 2.4 Fundamental general functional requirements hierarchy.

FIGURE 2.5 Functional model of DT-based RVCs. DT, Digital twin; RVC, robot vacuum cleaner.

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FIGURE 2.6 Objectives of the RVC and designer in conceptual design. RVC, Robot vacuum cleaner.

outcome of the functional domain will go through DP generation, DP integration, and concept evaluation to generate a design concept. Conventionally, designers evaluate each DP and concept in several procedures due to constraints of available resources, test apparatus, and time. Through the incorporation of cloud sourcing, machine learning, and virtual simulation, the DT systems will give designers access to more information than ever before. Though the autonomous design becomes possible with data processing, designers will still need to make conceptual and final design decisions. Designers still need to understand the market and uncertainties; hence, the ability of the DT systems to interact with designs is critically important.

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FIGURE 2.7 Procedures flow of the DT-assisted conceptual design. DT, Digital twin.

2.4.2.1 Step 1: digital twin assisted design parameter generation With the case of RVCs, conceptual design begins during generation of DPs. Candidate DPs are generally based on designers’ additions to existing solutions. To understand existing knowledge, designers often embark on a timeconsuming literature search. Then they rely on subjective experience and knowledge to produce innovation. In a DT system, cloud sourcing could gather all existing information and then filter them based on preset contextual inputs. The designers can then review a wide-range information base to produce the conceptual design. For example, to generate DPs for the navigation system, a designer may first define the following contextual information detailed in Table 2.3. If contextual information is relatively flexible, a designer may input more constraints to compress the content such as “maximum 2 minutes path processing time” or delete some constraints to exert design innovation such as

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TABLE 2.3 Example of robot vacuum cleaner contextual information. Physical context

After breakfast (time) Living room (location) 22 C
24 C (temperature) 60% (humidity) From south to north (direction) Normal (air/water quality)

Social context

No excessive word (danger)

User context

Housewife (user demographics)

Room layout (resource supply)

Mop after vacuum (user’s habit) Show the navigation on mobile phone (user’s preference) Experienced (user’s knowledge) No special allergy (user’s health) Operation context

Electricity (power) AR supportive (computing power) Machine learning (intelligence)

Biologically Inspired Design. Using contextual input information, the cloud sourcing techniques will gather all involved existing or developed DPs. The source may come from biological systems such as pigeons and dolphins, existing artificial systems such as GPS and path tracking, or other techniques under development. To formulate a specific DP, the designer may rely on knowledge and experience alone. However, for less complicated or less critical DPs, they can be automatically formulated by DT-based on a functional model and cloud sourcing of existing DPs. In the example of the navigation system, the corresponding flow in the previous functional model is environmental information—managed cleaning path. Within this flow, with the involved input and output of each procedure, required sub-DPs can be automatically searched using infrared sensor, low-resolution camera, 3D virtual space modeling software, and memory stick. Moreover, in the early stage, the selected DPs might be random and unfeasible. The designer is supposed to review and delete those incompetent DPs. With advanced learning ability, gradually, the RVC itself may automatically identify appropriate DPs based on the designer’s judgment and preference from concepts library.

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The DT system is particularly useful for testing alternative DPs. From either the designer’s own knowledge or analysis of customer trends, a number of innovative customer needs could be identified. The DT system allows these new FRs to be tested using existing DPs in the virtual space and gives the designer rapid feedback on its feasibility. For example, DTs could allow a designer to test the effects of adding a solar panel to an RVC and to decide if the benefits outweigh additional costs and risks.

2.4.2.2 Step 2: digital twin assisted design parameters integration The integration of DPs remains a challenge even for experienced designers. The final assembly must satisfy functional demands yet also comply with constraints such as size, weight, and manufacturing costs. The coupling of DPs with FRs relies heavily on the wisdom of designers and cannot yet be managed mathematically [20]. However, with DT technology it is possible to automate this integration process. To begin with, the integration of DPs is highly random and unconstrained, listing all possibilities. The number of integration concepts is reduced as constraints are gradually narrowed. Using the suction power of the RVC as an example, the lower boundary is set to guarantee suction of larger debris, while the upper boundary is constrained by noise levels. As the range between these boundaries is restricted, the number of tenable concepts is reduced. Following this procedure, most of the remaining concepts will be unfeasible from a manufacturing or customer need standpoint. One example that highlights the limits of boundary setting is the shape of the RVC. A round shape is universally adopted considering its turning, wall edge cleaning, and collision protection requirements. A rectangular shape may satisfy the boundary constraints but will fail to deliver on these functions. A method to overcome this issue involves the ranking of physical properties and FRs. To allow the designer to make more sophisticated design decisions, a preprogramed rank of properties and FRs could include basic features . manufacturing cost . performance features. More specifically, other properties could be ranked in order of essentiality: nontoxic . electricity is insulated . cost . weight . high damping ratio. Such ranking specifications will be initially determined by designers. Using DT, virtual concepts can then be evaluated for effectiveness. Once concepts are evaluated based on functionality, the next step is to determine their manufacturability. Many beneficial design features, although innovative, are unfeasible due to manufacturing equipment and technique constraints. With a virtual entity, it is possible for the designer to assess ease and difficulty of assembly and machining by their knowledge, or by linking the DT to manufacturing processes.

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After assessing the feasibility of each virtual concept, the designer is presented with a base set of concepts from which a best option can be selected. The designer will need to use knowledge to modify finer details, integrate features, and apply connection joints. The designer will also need to assess the aesthetic design value, as it is a major customer satisfaction element and is difficult to assess via computation. Though the designer’s human intelligence is critical for selecting the final concept, computational systems can further make this easier. For example, using crowdsourcing methods, it could be possible to identify similar aesthetic preferences of a specific type of customers. They could even detect which other IoT products are purchased and survey the design of a room to identify other unique customer patterns. This information could be processed and used by designers to hone their decisionmaking capability. Meanwhile, as the DT system can adapt to machine learning, the designer’s decision pattern will be dynamically tracked by the system. Gradually, the prediction of DT systems for higher level design may become more accurate. For example, the noise level of the RVC is determined by several factors such as working suction power, damping ratio of the shield material, and internal buffer components. If an automatic suction power adjustment system is applied, an ideal noise level and suction power can be highlighted for future concepts. Similarly, if data analysis reveals a trend toward cost-based competition, the DT can highlight cost-saving concepts during concept generation.

2.4.2.3 Step 3: digital twin assisted conceptual evaluation The DT method will play a decisive role in concept evaluation. Conventionally, assessment of concepts relies on designers’ subjective judgment, prototype tests, sample tests, and customers’ feedback. In this case a full evaluation loop requires completion of many design stages, costing much time and resources. More sophisticated virtual entities therefore allow more reliable evaluation of concepts at an earlier design stage. Using the example of the RVC, a framework for DT-based concept evaluation is outlined. A significant component of the DT model is understanding a product’s environment. For the case of the RVC, some of the factors needed by DT are listed in Table 2.4. As the DT model involves analysis of significant amounts of dynamic data, it is critical that the model be able to extract only the most important information. Minimizing data flow ensures that the DT system can synchronize due to limited computational capacity, also to simplify information such that it can be used. In the functional domain, bottom-level (the very specified FRs disassembled from generally described FRs) FRs are designed to be independent. Therefore it follows that each FR can be measured one-at-a-time,

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TABLE 2.4 Environmental factors of the robot vacuum cleaner test. Air-related

Humidity Temperature Air flow speed Air quality

Floor related

Smoothness Damping ratio Size Dense

Items related

Furniture geometrical shape Sundries position Sundries size Sundries weight

Debris related

Debris size Debris density Debris average amount

Passengers related

Passengers’ activity Passengers’ reaction Passengers’ moving speed Passengers’ size

Supportive products related

Charge speed Performance of dedicated App Virtual dual wall

IoT related

Smart home controller Performance of other smart home products Wireless connection

drastically reducing the need for advanced computation. For example, for the RVC’s cleaning path, the RVC can simply analyze room layout in geometric form. Other factors relating to air, other IoT products, or suction power can be ignored when measuring this factor. Furthermore, by presetting tolerances for room size and ability to navigate around furniture shape, simulations can be performed automatically for this one function.

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Although the core of virtual entity is the product model, the virtual environment can be tested without the engagement of virtual products. As shown in the RVC functional model sample, input items have already existed in the scenario. Environmental factors may have important impact on these input items. For example, higher air humidity makes debris become stickier on the floor and harder to be evacuated. A poorer wireless connection speed will significantly delay the synchronizing of the data processing. The fundamental presimulation of the virtual environment checks the accuracy of virtual entity and assists to adjustment involved algorithm. Unlike the environmental model construction, the RVC itself contains massive couplings and constraints. Achievement of one single FR is performed by its designated DP but affected by other DPs as well. Simulation of the RVC in virtual entity requires the integration of all the DPs. For example, suction power is determined by the motor turbine power and wind tunnel size. This is also affected by the actual size of the RVC unit. For example, being too small will limit motor size while being too large will limit suction capacity. These are typical constraints that occur during conceptual design. Further in-depth strategies for analyzing constraints of DPs will be discussed in the next chapter. The virtual entity will simulate product performance at both component and concept levels. At the component level, candidate DPs generated by cloud sourcing and designers’ thinking are input to a database. Achievements of functions of these DPs will then be tested in a virtual scenario. For example, the brush on the RVC is installed to sweep debris to the chassis of the RVC so that the debris can be suctioned. Most existing RVCs adopt single brush installation, while a few RVCs may adopt double brushes on one side. With the virtual simulation, designers will have more opportunities to implement nonstandard designs such as double wind tunnels and double brushes along diagonals. Each DP will be tested individually in the virtual entity. On this basis, the most feasible DPs will emerge. However, the virtual simulation may not accurately reflect the physical environment. Hence, physical prototype of these DPs will be modeled to perform physical experiments. The precollected environmental information will assist to build the test environment, and the test result of the physical entity will then optimize the virtual entity. Ideally, the accuracy of the virtual entity will approach the real situation. For example, for color sensors on the RVC, candidate DPs may include compound multiple color sensors, individual three primary color sensors, and additional infrared and UV sensors. The initial virtual entity may only contain the target identified color effect, and the result turns out similarly. However, in the real environment, light reflection and transparency of air and temperature will affect color identification. A similar virtual method will be used for the integration of DPs at the concept level. However, to reduce the failure risk, the simulation will not be applied to the complete integration directly. Generally, the functional

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hierarchy is generated with each system as a branch in a tree-like diagram. In Fig. 2.7, functional subsystems include the kinematic system, cleaning system, cleaning management system, detection system, virtual entity simulation system, and communication system. Normally, FRs within a subsystem work together but stay relatively independent of other subsystems. Therefore it is possible to detect errors at the component level only when integrated with others. For example, in the RVC, if the brush that collects debris spins too fast, it may satisfy performance at a component level, yet when integrated into the system, it could affect the RVC’s driving ability. The DT system provides a more efficient method of concept-level validation, by allowing complete integration of subsystems at a virtual level. This would typically be achieved at the prototype stage but requires considerable time, equipment, and material investment. Using the DT system, prototypes will only be required at the final stages of design, eliminating many costs and expediting the conceptual design integration phase. However, as physical entity formulation is required in most conceptual design procedures, there may be questions raised over the validity of DT-assisted design. In early product development periods, establishing its validity will be a major obstacle. The initial virtual entity based on data collection, designer knowledge, and optimization from physical entity testing will have limited capability. However, as the DT gradually accumulates information through physical testing, the virtual model will continuously improve using machine learning techniques. Only when the accuracy reaches a certain high level can design procedures be omitted in favor of DT-based design. DT-based techniques aim not only to improve reliability of concept testing but also to reduce time and costs required.

2.4.2.4 Step 4: review and redesign After finishing the initial product design, the DT techniques will be improved based on two-part performance review: functional review and customer review. The functional review is designed for functional elements such as energy usage, power, and weight, while the customer review covers the satisfaction rate, suggestions, and expectations. The collected data will be compared to the virtual and physical simulation results. Any inconsistencies between real-world collected data and the virtual and physical simulations will be addressed. For example, if customers complain about the RVC knocking over vases, but this is ignored during simulation, the weight, shape, and location of the vases will be updated. As this process is iterated, the DT system will become more reliable. 2.4.3 Digital twin based robot vacuum cleaner constraints management The management of various constraints presents a major challenge in conceptual design. The two crucial relationships are known as constraint
constraint

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(one constraint affects another constraint) and constraint
function (one constraint affects functional achievement). Often difficult to describe mathematically, the impact of constraints is typically limited by a boundary and critical point [21]. For example, 36 V is formulated as the threshold of hazard voltage for human body. The impact of 34, 33, and 32 V on human health is hard to be explicit, but over 36 V must be prohibited by designers. By creating limits in the DT system, designers will have a more reliable constraints management approach in place. The general constraint management frame is shown in Fig. 2.8. For both constraint
constraint and constraint
function relationships, achievements of functions are directly impacted. Unlike the performance of DPs, the impact of constraints only manifests at a point of critical failure. For example, the typical distance range between legs of dining room chairs is 15v
20v0 . For a 17v 3 17v0 chair, if the radius of the RVC exceeds 17v, it will never clean the debris under the chair. On the contrary, if the maximum radius is designed between 15v and 16.5v, this will not be an issue. Some constraints however will involve a compromise between two competing attributes. For example, high suction power of an RVC typically requires a noisy, powerful motor. Though suction performance is important, an excess of 85 dB is

FIGURE 2.8 Procedures of DT-based constraints management. DT, Digital twin.

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considered harmful to aural health. Therefore the noise level must be lower than 85 dB but not reduced to a point where suction is ineffective. DT technology will allow for much more effective analysis of constraining boundaries. Boundary conditions often rely on input criteria such as existing design patterns, customer feedback, and environmental conditions. Through the example of a virtual entity of an RVC model in a household room, different functional and environmental elements can be isolated to understand alternative outcomes. For example, different floor materials such as wood, ceramic, and vinyl may impact the noise level of the RVC. If the noise level on some materials varies significantly, floor material can be then recognized as a constraint. For some constraints the boundaries can change over time. For example, since the first generation RVCs, functions such as mopping, virtual wall, and humanized speech have been added to newer models. However, despite advances in performance, the price of RVCs has not increased due to constraints such as customer’s price expectations. Therefore to increase profitability and remain price-competitive, RVC manufacturers are innovating ways to reduce costs. A DT system will allow cost-cutting measures to be tracked and identify optimal boundaries between cost and performance. Advanced DT systems could even forecast the impact of different changes. For example, battery capacity of the RVC normally supports 160 m2 cleaning for every use. If say 97% of customers only use the RVC to clean once in a housework cycle, the other 3% may recharge the RVC and clean multiple cycles. The path tracking system on the RVC will upload these to database. As time goes by, if the proportion of customers who require multiple cleaning cycles increases to 6%, that may indicate that battery life should be increased. To be more specific, if data collection turns out that the threshold of cleaning area for 97% of customers is 180 m2, this can imply a new battery life threshold, and the simulation in virtual entity will work out the corresponding battery volume. The analysis on constraints may even assist to improve other products. For example, the room layout is one main source of constraints. Furniture placement and room size can affect the cleaning effect and determine RVC design features to some extent. With room information collected from different users, the DT system will select out room designs with best cleaning effect, construct the virtual environment, adjust critical factors, and generate a proper pattern for room layout with high cleaning efficiency. The result can then be shared with architecture and room decoration designers as a reference to design a cleaning-friendly room layout. This approach can be implemented to other IoT products as well.

2.4.4 Digital twin based robot vacuum cleaner contradiction solving Due to the uncertainty and flexibility, contradiction solving is arguably one of the most challenging design procedures. For example, customers expect a

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FIGURE 2.9 Procedures of DT-assisted contradiction solving. DT, Digital twin.

small RVC with long durability, but a large battery inevitably expands RVC size. The general contradiction solving frame with DT is shown in Fig. 2.9. Contradiction solving is unlike constraints, in that contradiction occurs between all design feature properties. However, the importance of properties involved in contradictions varies significantly. For example, the exterior casing of the RVC is designed to be slightly elastic to protect both the RVC and items in collision. However, by increasing elasticity, it is possible that the casing will be weakened. To decide the best outcome, each property must be prioritized based on its importance to functions and consumer needs. The ranking of these needs can then be input to DT database for future use. Properties of the highest level will be saved, while those of lower levels will be discarded. For example, suction power, noise level, and wireless connectivity are most important to customers and hence are classified as high-level properties. By contrast, strength, energy consumption, and complexity are less important and therefore low-level properties. The input can be finished by the RVC according to cloud sourcing of customer reviews and learning from designers’ judgment. A certain threshold should be established by which properties can and cannot be eliminated. The first-round input can be done by the DT system automatically. From a virtual simulation the RVC will highlight a minimum

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number of properties required to achieve all basic features and some performance features. This simulation acts as the boundary for minimum requirement. For example, reducing RVC size to improve agility properties will subsequently require the debris container to shrink as well. Data collection of the DT from sample customers indicates that 80% cleaning collects 0.3 L of debris, setting the minimum boundary of container volume. When the bottom thresholds are set, the DT system will then determine the optimal size. According to the marginal utility, improvements in product properties eventually provide no meaningful performance benefit. That DT can easily perform simulations will provide far greater insight into optimization analysis that is unavailable through traditional design methods. For the improvement of concept to solve contradictions, a well-developed approach has been proposed in the TRIZ methodology. According to the preformulated property importance ranking, contradictions will be identified between two properties of high importance. Candidate solutions will be generated based on cloud sourcing of historical competent design samples and adopted principles. With enough learning from design samples, the DT system may automatically generate a solution. For example, the continuous data interaction of DT will boost the power consumption of the RVC. According to TRIZ matrix [12], the contradiction occurs between 39 (productivity) and 22 (loss of energy). Suggested principles contain 28 (mechanics substitution), 10 (preliminary action), 29 (pneumatics and hydraulics), and 35 (parameter changes). By analyzing other products, the RVC could detect a similar contradiction in smartphones. For some Android smartphones the solution adopts principle 10, updating and downloading from the App Store during battery charging time. The RVC may identify and adopt this approach the same way, which assigns the data uploading and receiving during the battery charging period. It should be noted that the judgment of DT may not be feasible and rational all the time. The designer is still needed for review and adjustment. The DT technology also provides an improved validation approach. Traditionally, the designer cannot prove the effect of improved concepts after TRIZ contradiction solving. As a result, sometimes the improved concepts do not reach the contradiction solving expectation, and the sacrificed properties are weakened. With the virtual entity, these contradiction solving methods can be presimulated before building a physical prototype. For example, as mentioned earlier, the data uploading is arranged during the battery charging period. The method seems applicable but has potential risks, for example, what if some data must be uploaded during cleaning time? What if this reduces the general durability of batteries? What if some other FRs have to occupy the charging time as well? This sort of situations can all be tested in the virtual entity to check feasibility, compatibility and unnoticed contradictions.

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2.5

63

Summary

In summary, this chapter discusses how DT promotes a more effective conceptual design. Some existing design methodologies can be adapted to serve as the theoretical foundation of DT-based conceptual design. A variety of conceptual design activities (i.e., function modeling, concept generation, constraint management, contradiction resolution, and complexity management) can benefit from the data provided by DTs in various ways. Finally, a new design process is prescribed to support the conceptual design of new DT with RVC illustrative examples. In conclusion, the comparison between conventional conceptual design and DT-assisted conceptual design is shown in Table 2.5. Conventionally, the conceptual design is mostly driven by

TABLE 2.5 Comparison between conventional and digital twin (DT)assisted conceptual design. Conceptual design procedures

Conventional design

DT-driven design

Functional modeling

The formulation relies on designers’ interpretation over customer needs. Corroboration of each flow is made by manual survey and tests, which have more bias

DT introduces real-time data to monitor and simulate flows. The big data analysis also improves the validation and saves human effort

Concept generation

It is constrained by designers’ knowledge and experience

DT achieves the automatic comparison among different contextual information. Its learning ability polishes and generates more contextual information to integrate into more candidate concepts

Concept evaluation

The evaluation approach design can be particularly challenging to achieve the concept evaluation comprehensively. The evaluation is critically constrained by apparatus. A validate feedback still relies on customer review

Abilities of simulation and data analysis entitle DT to properly adjust design range and evaluate each concept comprehensively. A validate feedback can be given by simulation

Contradiction Resolution

The contradiction sometimes can be unobvious and there might be new important contradictions introduced in the optimized concept

The contradiction can be highlighted via visualization. The contradiction can be tested iteratively to polish the concept continuously (Continued )

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TABLE 2.5 (Continued) Conceptual design procedures

Conventional design

DT-driven design

Constraints management

The boundary identification relies on designers’ cognition, knowledge, and experience, as well as the functional performance tests. Test of boundary effect will be complex, and some important boundaries might be ignored

Simulation and real-time trace will benefit the boundary identification and boundary adjustment

Complexity management

The designers’ cognition, knowledge, and information constraints increase the couplings. Uncertainties are hard to be judged without massive historical data and practical tests

The big data analysis ability reduces complexity from unknown information and incomplete information. Also, it assists the exploration of implicit pattern of uncertainties

Design collaboration

The collaborative design is mostly determined by designer’s experience with respect to stakeholders

DT provides more unambiguous and objective data to enhance designers’ comprehension about ways, in which customers utilize products and scenarios, in which products engage

Affordance design

The discovery of affordance is occasional and random. Designers ought to go through time-consuming observation to find affordance

Affordance can be explored via information perception of related smart product design

designers’ cognition, knowledge, and experience in products. Therefore designers’ ability will limit the design quality inevitably. Meanwhile, for a complicated project, management of complexity and uncertainties can be particularly challenging without support from a large quantity of historical data and tests. In addition, the validation from experiments and practical tests are normally time-consuming to obtain reliable results. With the support from DT, first, the big data analysis ability support designers to generate, evaluate, and select appreciate concepts in early stage. Then, via the functional simulation and visualization, complexity, couplings, uncertainties, and constraints of design concept become more intuitive to assist designers’ analysis. The concept performance can be evaluated further as well. Also, in the

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product utilization phase, DT will continuously track the status of products and collect relevant real-time data, which helps designers to optimize the products and explore design affordance. As the database volume gradually accumulates, with reference to customer review and designers’ strategies, the learning ability may enable DT to make judgment and improve design concepts actively.

References [1] N.P. Suh, Axiomatic design: advances and applications. Mit-Pappalardo Series in Mecha, 2001. [2] T. Tomiyama, P. Gu, Y. Jin, D. Lutters, C. Kind, F. Kimura, Design methodologies: industrial and educational applications, CIRP Ann.
Manuf. Technol. 58 (2) (2009) 543
565. [3] W. Beitz, G. Pahl, Engineering Design: A Systematic Approach, Springer-Verlag London, Ltd, 1996. [4] L. Qian, J.S. Gero, Function-behaviour-structure paths and their role in analogy-based design, AI EDAM 10 (4) (1996) 289
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Manuf. Technol. 67 (1) (2018) 169
172. [7] Q. Xu, R.J. Jiao, X. Yang, M. Helander, H.M. Khalid, A. Opperud, An analytical Kano model for customer need analysis, Des. Stud. 30 (1) (2009) 87
110. [8] D. Wu, D.W. Rosen, L. Wang, D. Schaefer, Cloud-based design and manufacturing: a new paradigm in digital manufacturing and design innovation, Comput. Des. 59 (2015) 1
14. [9] A. Liu, C.Y.L. Stephen, A new coevolution process for conceptual design, CIRP Ann.
Manuf. Technol. 64 (1) (2015) 153
156. [10] G.A. Hazelrigg, The implications of Arrow’s impossibility theorem on approaches to optimal engineering design, J. Mech. Des. 118 (2) (1996) 161
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48. [12] G.S. Altshuller, The Innovation Algorithm: TRIZ, Systematic Innovation and Technical Creativity, Technical Innovation Center, Inc, 1999. [13] W. ElMaraghy, H. ElMaraghy, T. Tomiyama, L. Monostori, Complexity in engineering design and manufacturing, CIRP Ann.
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370. [16] N.P. Suh, Complexity: Theory and Applications, Oxford University Press, Inc, New York, 2005, ISBN: 0-19-517876-9, . [17] E. Brynjolfsson, Y. Hu, D. Simester, Goodbye Pareto principle, hello long tail: the effect of search costs on the concentration of product sales, Manage. Sci. 57 (8) (2011) 1509
1534.

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[18] T. Wang, J. Ping, Understanding customer needs through quantitative analysis of Kano’s model, Int. J. Qual. Reliab. Manage. 27 (2) (2009) 173
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48.

Chapter 3

Conceptual design driven digital twin configuration Yuchen Wang1, Lin Liu2 and Ang Liu1 1

School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia, 2Beijing 7Invensum Technology Co., Ltd, Beijing, P.R. China

3.1

Introduction

Beginning as a digital 3D modeling and system simulation, the digital twin (DT) concept was introduced by Grieves who concluded DT as a digital entity to represent the physical product [1]. Based on this principle, the DT system initially consisted of three elements: physical product, virtual product, and data transmission between the two products [2]. Later, due to the development of data technologies such as the Internet of Things, big data analysis, and augmented reality, more technical demands were placed on DT concerning real-time modeling, data fusion, intelligent services, and industrial extrapolations. Hence, put forward by Tao’s research team, the configuration of DT was expanded to five compositions: physical entity (PE), virtual entity (VE), twin data center (TDC), service, and connection. Generally, the PE represents the physical product, the physical world environment, actual behaviors, and mechanisms to interface between each. The VE is developed as a digital mirror of the PE, which can predict future behavior through simulation. Twin data contains all data and statistics involved in the DT working process. Service is divided into functional service (FS) and business service (BS): FS provides technical support of DT working, and BS satisfies real customer demands. The connection of DT represents the technical support to achieve interaction among different compositions [3]. Compared to the standard design methodology of generating ideas from unambiguous customer needs (CNs), DT enables designs to be continually evaluated and tested during the early stages of the design process. Hence, its development is highly flexible based on a diversity of practical demands. DT is not developed independently as its design features are directly determined by the served target, which can either be a product or a human. Up to now, most discussions over DT applications have been around fortification of the Digital Twin Driven Smart Design. DOI: https://doi.org/10.1016/B978-0-12-818918-4.00003-8 © 2020 Elsevier Inc. All rights reserved.

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served target via functional enhancement, status monitoring, and creation of functions [4]. Therefore it is arguably challenging for DT designers to generate a universal template for DT development. For existing DT attempts such as the virtual Airbus engine and city development DT introduced by 3DS & Singapore, DT development started from an empty base, which significantly improves the design cost and time [5,6]. However, for more complicated systems with unprohibited couplings, uncertainties, and technical constraints, it is imperative that a systematic framework is developed to enable broader applications. The previous chapter envisioned how DT techniques benefit conceptual design procedures. However, the conceptual design methodology can also be used to develop DT by mapping design features to determine the attributes of products. The general flow of design theory concepts such as axiomatic design theory, general design theory, systematic design process, and functional behavior structural ontology all involve mapping between CNs functional requirements (FRs) design parameters (DPs) design constraints (DCs). The conceptual design quality critically determines the complexity, competence, attributes, and customer satisfaction of the product [7]. Shifting the four factors into the same design level, conceptual design methodology can be applied to either the component or conceptual level. Although the design features of DT are directly related to the practical case, its configuration can be developed using a conceptual design methodology. This chapter will detail how a general DT framework can be developed using conceptual design. The construction of the framework involves mapping each of the five DT elements—PE, VE, twin data, service, and connection—via the conceptual design sequence of CNs FRs DPs DCs. Once each element is developed, a process of integration is then required between each. Sections 3.2 3.4 introduce the configuration development of PE, VE, and TDC individually with the mapping of four design factors. Moreover, discussion about FRs, DPs, and DCs is based on classification. Section 3.5 discusses the service generation, rather than following that sequence, a new mapping that begins at CNs of the served target is applied. Section 3.6 clarifies the issues to consider in the connection development phase, and different connections types are discussed together. Section 3.7 introduces the integration management of DT that involves aspects of working modes, working sequence, and output ratio. Section 3.8 presents illustrative examples of autonomous vehicles (AVCs) to apply the introduced theory to different scenarios. Important concepts used in this chapter and the corresponding abbreviations are presented in Table 3.1, and general contribution of each DT related composition is shown in Fig. 3.1. This chapter focuses on the conceptual level design of general DT configuration. Relevant technical supports will be introduced in subsequent chapters.

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TABLE 3.1 Concepts and abbreviation. Concept

Abbreviation

Implication

Physical entity

PE

A summary of physical product, its behavior, working environment, and mechanism

Virtual entity

VE

The digital mirror of the physical entity which manipulates all essential factors in the physical world

Twin data center

TDC

The core data processor of DT

Service

/

Functions provided by DT

Functional service

FS

Internal technical support provided to support the DT working

Business service

BS

Functions that output to the served target

Connection

/

Technical support to achieve data transmission

Served target

/

The product or human attached and receive support from DT

DT supervisor

/

The platform or engineer who monitors the working of DT

DT developer

/

The engineer who designed the DT system

Working environment

/

The working surrounding of physical product

Service domain

/

The conceptual integration of all services, not physically integrated

Customer need

CN

Customer’s demand and expectation for the product

Functional requirement

FR

The function of the product to achieve customer needs

Design parameter

DP

The technical support to achieve functional requirements

Design constraint

DC

The boundary or factors that affect functional performance

Autonomous vehicle

AVC

The self-driving car

DT, Digital twin.

3.2

Development of physical entity

Physical entity (PE) represents the physical product and its working environment. Also, it generally involves a function to serve a target or purpose and interaction with the physical world. PE has four main objectives. Primarily,

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FIGURE 3.1 Contribution of each DT related composition. DT, Digital twin.

it needs to satisfy product users’ demands directly. Second, it interacts with the physical world to obtain all related data. Third, it observes and records the status and change of interacted compositions including itself [8]. Finally, for all processed and optimized data and functions by DT, these will all be executed by PE eventually. Therefore its related customer demands can be summarized as follows: G G G

G

CNPE1 perceives data of physical world including PE. CNPE2 interacts with other compositions to forward its status. CNPE3 executes the command, optimized functional management, and adjustment from DT, users, and developers. CNPE4 serves the product users.

As the foundation of DT, the quality of PE development critically influences all related DT compositions and services achievement. As shown in Fig. 3.2, from an internal perspective, PE provides the template for modeling and corroborates the simulation results. From an external perspective, from interaction with the working environments, served targets and PE of peer products DT act as an intermediary between feedback, command, and status data from physical surroundings. The collected data can be then analyzed to identify trends, forecasts, and suggestions. The working status and functionality of the PE must also be recorded to assist DT analysis. Interaction with other compositions is usually bidirectional. Therefore generation of engaged FRs is usually defined with both “forward” and “receive.” The general FRs related to PE are shown in Table 3.2. For

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FIGURE 3.2 DT compositions that interact with physical entity. DT, Digital twin.

TABLE 3.2 General functional requirements related to physical entity. Relevant composition

FR number

FRs

VE

FR1.1

Provide the virtual modeling template

FR1.2

Regulate distortion of modeling and simulation

FR1.3

Adjust functional status under the guidance of VE

FR1.4

Retrieve data regarding environmental factors

FR1.5

Record the dynamic working scenario

Peer PE

FR1.6

Coordinate with contemporary PE

FR1.7

Monitor conducts mutually

Data central

FR1.8

Store collected data with respect to the environment, PE, and the served target

FR1.9

Extract data with respect to peer PE, VE, served target, and online resources

FR1.10

Monitor functional achievement performance

FR1.11

Receive feedback and commands

FR1.12

Present status of PE

FR1.13

Forward suggestions, forecast, and warning

FR1.14

Collect data of working parameters, status, and functional performance

FR1.15

Record PE conducts

Working environment

Served target

DT user

PE

FR, Functional requirements; PE, physical entity; VE, virtual entity.

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example, the PE must be capable of self-supervision; its working status, health, conduct, and involve parameters are expected to be recorded and transferred. Such data is forwarded to and adjusted for the VE. The VE can then analyze the data and suggest ways to optimize the working of the PE. Furthermore, the interaction of the PE with its physical environment and working scenarios are also recorded. Functional data from the PE is then forwarded to the VE, and any adjustments are included in the simulation. Data from the revised simulation can then be used to optimize the PE. The process involves constant iterations between the virtual and physical entities, whereby an improvement in one can enhance the other. FRs associated with the served target use DT to compare the real-world performance of the PE with the expectations of the VE. Depending on its application, the served target may either be another product or a customer (sometimes both); for example, a smartphone is required to recognize both human voices and receive a digital signal. Hence, the development of concepts should consider both scenarios. Moreover, the ability for PE to communicate among peer products enables autonomous cooperation and status monitorization. Generally, all collected data and statistics will be stored in a data center. Processed data will be forwarded to other compositions and used to optimize the PE. Throughout the product and DT life cycles, it is essential for the DT user to review, update, and command the DT system constantly. This is particularly essential during the more error-prone, developmental stages of the DT. Technical achievements of DT depend on the explication of its practical use. Each DT composition is assembled using sophisticated subsystems and components. Therefore it is challenging to generate categorical DPs for generally described FRs previously. Hence, functional achievements will be introduced at the technical level rather than component level. In the case study section the concept generation will be described more explicitly in examples. The development of PEs is drawn from a variety of technologies. As each technology is normally defined for a unique purpose, FRs should first be classified to generate applicable concepts. As shown in Fig. 3.3, applied techniques can be classified into four categories: physical cognition, physical control, data interaction, and data processing. Physical cognition is defined as all types of data collection within the physical world such as to conduct recording from PE and feedback from the served target [9]. Physical control mainly refers to the revision on PE. Data interaction contains the communication with VE, peer PE, and DT users. As for data processing, it includes analysis of collected data and generation of suggestions, forecasts, and management plans. Some examples of candidate technical concepts are shown in Table 3.3 [10,11]. FRs, classifications, and

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FIGURE 3.3 Classification examples of functional requirements.

TABLE 3.3 Supportive candidate concepts for physical entity. Classification of FRs

Number of contained FRs

Candidate technical concepts

Physical cognition

FR1.4, FR1.5, FR1.10, FR1.11, FR1.14, FR1.15

Image recognition, laser measurement, speech recognition, scanning measurement, etc.

Physical control

FR1.3, FR1.6

Discrete control, PID control, logical control, system state control, etc.

Data interaction

FR1.1, FR1.7, FR1.8, FR1.9

NFS file system, Raysync, FlashFXP, Synchronous Serial Transmission, etc.

Data processing

FR1.2, FR1.12, FR1.13

SQL, MapReduce, Kylin, Akka, etc.

FR, Functional requirement; PID, proportional integral derivative control

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concepts can be broken down and synchronized. For example, physical parameter collection of VE, a branch of physical cognition, can be specified into “temperature measurement,” “dimension measurement,” “strain measurement,” etc. in correspondence, the candidate technical concepts can be specified as image cognition, conversion measurement, and laser measurement. The development of PEs involves constraints from all compositions. Due to the significant degree to which DCs impact functionality, they are typically mapped against FRs. In this section the generation of DCs will be based on the FRs and classifications shown in Table 3.4. DCs here are generally described as well, and the explication depends on the specific case. DCs come from various sources. Some DCs are objective regulations that cannot be compromised such as “36 V” voltage for human body protection and “80 dB” as an exceeding noise level. Some others exist in the physical environment such as humidity, wind speed, and temperature. Meanwhile, some DCs are generated by designers’ design methods and functional achievement approaches. For example, if a central processor on PE is designed to be a cabinet, then heat radiation might become a challenge. Hence, for the achievements of critical FRs, the adaption of corresponding DCs requires specific analysis.

TABLE 3.4 Examples of physical entity (PE) design constraints. Classification of FRs

Number of contained FRs

Involved DCs

Physical cognition

FR1.4, FR1.5, FR1.10, FR1.11, FR1.14, FR1.15

The accuracy of sensors, the performance of measurement devices, environmental factors, power consumption limit, etc.

Physical control

FR1.3, FR1.6

Compatibility of electronic hardware, fatigue of power transmission system, development of algorithms, the sensitivity of induction device, etc.

Data interaction

FR1.1, FR1.7, FR1.8, FR1.9

Data transmission speed, the volume of data library, the volume of cache, wireless signal strength, etc.

Data processing

FR1.2, FR1.12, FR1.13

Machine learning ability, the complexity of the built-in algorithm, hardware performance, etc.

DC, Design constraint; FR, functional requirement.

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3.3

75

Development of virtual entity

Designed to mirror the PE, the VE reflects the physical parameters, attributes, behaviors, mechanism, and even life cycle of the PE. In addition to replicating the current situation of the PE, the VE allows users to anticipate performance under different conditions and adjustments. This provides early feedback before any changes are made to the PE. While based on the PE, the simulation within VE will primarily optimize the working status of PE in reverse [3,12]. Hence, the three basic CNs of the VE are shown as follows: G G

G

CNVE.1 builds a virtual model of PE. CNVE.2 simulates reaction, mechanism, and life cycle of the product with input factors. CNVE.3 returns simulation results and corresponding suggestions for PE working.

Users’ expectations on the performance of VE will significantly affect the modeling. When the VE is designed to simulate a specific attribute partially, only PE factors related to it will be input to the model. For example, to investigate the aerodynamic performance of a fixed-wing aircraft, the airfoil shape, wind speed, and pressure become the most important factors while other compositions such as material and airframe structure can be omitted. Therefore the modeling of VE is heavily determined by specific customers’ demands and implementation scenarios, which can be a challenging design issue for DT developers. Modeling and working of the VE require the involvement of the PE, TDC, DT supervisor, and peer PE as shown in Fig. 3.4. As the mirror of PE, modeling of VE requires the data collected by PE. In reverse, the simulation within VE will assist the monitor on PE to primarily adjust status. For example, for the monitor of a crane, the VE will simulate the load per turn to balance between working efficiency and crane longevity. If a crane is to carry a heavyweight and hence accelerate fatigue damage, the VE can determine whether the crane should terminate the operation. Supervisors of the DT can input simulation objectives and conditions settings. Supervisors are also responsible for monitoring VE accuracy and for solving errors in real time. Although VE is capable of decision-making and primary anticipation abilities, all collected data from modeling and simulations should be forwarded to TDC, where data will be processed to generate more advanced analysis, forecast, and recommendations. From the DTC, VE may gather supportive data to optimize the modeling and simulation. Also, similar to PE, VE will share data with peer PE to align the modeling bias. Apart from external interaction, VE is responsible for self-monitoring and collection of relevant data. The general FRs generation related to VE is shown in Table 3.5. The above FRs start at a conceptual level and are broken down when technical components are confirmed. Similar to PE, component-level FRs

FIGURE 3.4 Interaction between virtual entity and other compositions.

TABLE 3.5 General functional requirements related to virtual entity. Related composition

Served CNs

FR number

FR

PE

CNVE.1/CNVE.2

FR2.1

Receive the modeling related data

CNVE.1

FR2.2

Correct modeling distortion via the monitor of PE

CNVE.3

FR2.3

Forward primary functional guidance to PE

CNVE.1

FR2.4

Extract relevant historical simulation data

CNVE.3

FR2.5

Record the simulation process

CNVE.3

FR2.6

Return simulation results to DT supervisor

CNVE.1

FR2.7

Receive update and settings from DT supervisor

Peer VE

CNVE.1

FR2.8

Extract/align the VE modeling with peer PE

VE

CNVE.1

FR2.9

Build the virtual model of the physical world

CNVE.2

FR2.10

Simulate input functions and objectives

TDC

DT supervisor

CN, Customer need; DT, digital twin; FR, functional requirement; PE, physical entity; TDC, twin data center; VE, virtual entity.

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can be classified into four categories: modeling, simulation, decisionmaking, and communication. Modeling represents all functions that transfer the PE into digital forms including virtual environment, virtual product, reactions, behavior, attributes, and interactions. In correspondence, modeling requires the collaboration of geometrical modeling techniques, behavior modeling, and mechanism modeling techniques [13]. The simulation includes a virtual test of functions, changes to parameters and reactions, and the collection of data. Decision-making involves the capacity to recognize the status of an FR and adjust accordingly. Although advanced analysis and learning ability are attributed to TDC, it is still critical to entitle VE with primary judgment based on internal scripts to improve the quality of synchronization. Communication consists of interactions with other involved compositions. As the data may exist in the form of digital signal, developed algorithms, images, and sensory perception, communication sometimes requires preconversion to integrate related data. Some examples of candidate techniques are presented in Table 3.6 [14]. As a digital composition, FRs of VE are usually involved with data perception, processing, transferring, and utilization. Interaction is permitted for most of the data-related FRs which affects the functionality of the VE. Hence, the interaction of compositions is a significant source of DCs. As shown in Table 3.7, modeling of VE is initially generated with data forwarded from the PE. The quality of collected data, therefore, affects the quality of the virtual model. DT developers may need to set the parameters

TABLE 3.6 Supportive candidate design parameters for virtual entity development. Classification of FRs

Number of contained FRs

Candidate concepts and technics

Modeling

FR2.2, FR2.9

Interactive feature definition technic, modeldriven architecture, ontology representation method, etc.

Simulation

FR2.10, FR2.5

Stochastic simulation technics, finite state machines, application evolution mechanism, etc.

Decisionmaking

FR2.3, FR2.8

Graphical comparison, grammatical analysis, execution tracking, etc.

Communication

FR2.1, FR2.4, FR2.6, FR2.7,

Raysync, FlashFXP, Synchronous Serial Transmission, etc.

FR, Functional requirement.

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TABLE 3.7 Examples of physical entity (PE) design constraints. Classification of FRs

Number of contained FRs

Related DCs examples

Modeling

FR2.2, FR2.9

Quality of data from PE, content within modeling components library, tolerance of distortion, etc.

Simulation

FR2.10, FR2.5

Quality of rule and mechanism design, content within one simulation cycle, the performance of simulation driver, etc.

Decisionmaking

FR2.3, FR2.8

Quality of modeling, quality of available data, development of the script, etc.

Communication

FR2.1, FR2.4, FR2.6, FR2.7

Amount of data, signal strength, the performance of the interacting composition, etc.

DC, Design constraint.

of the DCs. Such input constraints could include cost, apparatus dimensions, data capacity, energy consumption, and time of operation. Many existing project management strategies regulate these constraints depending on the environment, service targets, and CNs. One of the most critical DCs to consider during the VE development phase is the degree of reduction, or how accurately the VE models the PE. Theoretically, an ideal VE should emulate the PE with 100% similarity. However, constrained by sensor technology and data analysis methods, computational power and budget, modeling distortion cannot be eliminated. The reduction in the VE does not always need to cover all aspects but must focus on DCs necessary to meet the BS. For example, if a DT is built to simulate the aerodynamic design of a helicopter propeller, the VE of the propeller may only require geometrical and motion information. Other properties such as strength and elasticity are not essential for this simulation, given they have minimal impact on aerodynamics. The inclusion of less relevant properties would increase the workload of hardware, power consumption, and expenditure without improving simulation quality. Hence, selecting the most relevant properties for inclusion and measuring the accuracy of the VE are the critical processes in DT design [2]. In this chapter the degree of reduction is classified into four levels as shown in Fig. 3.5. The most basic level is “appearance similarity,” or how well the visual appearance of the VE reflects the PE. Appearance similarity is relatively easy to achieve due to the simple design procedures and ease of comparison. It provides both engineers and customers with some initial feedback about geometrical, esthetic, and assembly design factors. The secondary

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FIGURE 3.5 Degree of reduction classification.

level is classified as “attribute similarity,” where the CE shares the same physical properties with its PE. The ability to accurately model physical properties, such as chemical composition or material structure, is not always achievable. Success depends on modeling techniques, hardware performance, the variability of physical properties, and engineers’ skill. During the initial stages of DT development, engineers will typically model a specific physical attribute before branching out to their integration. The third level is classified as “reaction similarity” that aims to mimic the reactionary behavior of a PE due to certain conditions. The virtual model will analyze input commands and output the optimized result [15]. The fourth level is classified as “experience similarity,” where the VE can forecast the whole life cycle of the physical product such as market popularity, change of customer satisfaction, product durability and even future evolution trends. However, product experience simulation at this stage cannot be achieved due to technical and conceptual constraints. If experience similarity is achieved, the DT will provide engineers and designers with an immediate evaluation concerning the business success of the product, which can significantly enhance design efficiency and quality in the long term.

3.4

Development of twin data center

The TDC is the primary data assembly point, which gathers data from PE, VE, services, online cloud, and developers. Based on these available data sources, information on trends, forecasts, function optimization, and design strategies can be generated via methods of integration, transfer, processing, and regulation. Furthermore, as the big data analysis and machine learning

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methods improve, the TDC will be able to analyze historical data and improve future service quality [3,16]. The primary CNs of the TDC include as follows G G

G G

CNTDC1 stores and pack data collected from interacted resources. CNTDC2 guides and optimizes the functional achievements and working status of VE and PE. CNTDC3 provides forecasts and suggestions to DT developers. CNTDC4 is capable of self-learning ability and gradually improves service quality.

Notably, the TDC is designed as the library and processor of data, not the perceiver. Therefore it can store and analyze trends but not automatically draw conclusions based on inputs. The relationship between the TDC and interacting compositions can be described as junction terminals. As shown in Fig. 3.6, interactive compositions, including PE, VE, DT supervisors, served targets, and cloud data, each forwards raw and scattered data to the TDC, which are then processed and distributed. The result involves the scattered data from each composition becoming organized, explored, and shared. FRs of TDC can be classified into two groups. The first group transfers data, occurring among TDC and interacted compositions. The second is one for processing data, which is executed by TDC internally as shown in Table 3.8. Besides, FRs related to status monitoring and adjustment are mentioned in PE, VE, and TDC development sections. Although PE is the frequent target for these three compositions, there are significant differences among functional achievements attributed to the three compositions. As shown in

FIGURE 3.6 Data processing of twin data center.

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TABLE 3.8 Examples of twin data center (TDC) functional requirements (FRs). Classification

Satisfied CNs

FRs number

FR

Data transferring

CNTDC1

FR3.1

Receive physical world data forwarded from PE

CNTDC1

FR3.2

Receive virtual modeling and simulation data forwarded from VE

CNTDC1

FR3.3

Retrieve relevant supportive data from cloud database

CNTDC3

FR3.4

Record DT developers’ design decisions, adjustments, strategies, and settings

CNTDC1

FR3.5

Retrieve status, settings, demands, and feedback from the served target

CNTDC2

FR3.6

Guide the working status of PE

CNTDC2

FR3.7

Guide the modeling and simulation of VE

CNTDC3

FR3.8

Forward explored trend, critical data, and recommendations to DT developers

CNTDC2

FR3.9

Evaluate current status and risk of VE and PE

CNTDC2

FR3.10

Predict the function achievement, status change, and health condition of VE and PE

CNTDC3

FR3.11

Explore trend based on historical data collection and developers’ design strategy

CNTDC3

FR3.12

Generate the life cycle of the served target

CNTDC4

FR3.13

Improve data processing quality via selflearning from past decisions, online resources, and available data

Data processing

DT, Digital twin; PE, physical entity; VE, virtual entity.

Fig. 3.7, for the PE, its self-monitoring and adjustment focus on its current status, the collected data and subsequent reactions both come from real-time data. For the VE, it is entitled with primary analysis and short-term anticipation ability via its simulation functions. However, due to the lack of big data analysis and learning ability, VE is only capable of phenomenon identification using internal scripts. In comparison, TDC is specially developed as a

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FIGURE 3.7 Difference among physical entity, virtual entity, and twin data center with respect to status monitoring and adjustment.

data junction and processing center. Its entitled predictive analysis and learning ability promote a long-term forecast and trend exploration [17]. For example, a DT system developed for the aircraft landing gear will indicate current stress, strain deformation, and cracking from the PE. The VE will model the crack growth and deformation change after suffering a heavy landing. TDC will then determine the number of cycles before functional failure and manage maintenance procedures accordingly. To generate DPs for the FRs of TDC, a higher degree of FR classification is required. For data transfer the process can be broken down into data storage, data fusion, and data transmission. Data processing can be separated into statistical exploration, prediction, and learning ability. The data forwarded to TDC exists in multiple forms such as digital signals, scripts, images, and codes. Hence, the compatibility of data becomes critical. Also due to the existence of real-time data, technical supports—such as computational languages, platforms, mathematical methods, and technological instruments—need to be synchronized dynamically. Examples of technical support DPs are shown in Table 3.9 [18]. Similar to DCs of VE, DCs of TDC are generated from interacting compositions, the TDC itself and DT developers’ design strategies. As the interaction between TDC and other compositions is mostly in data form, attributes of data effectuate some constraints. For example, data quality, amount, format, transmission, all have an impact on DTC performance. For the TDC itself, design features such as performance setting, power limitation, analysis script, and learning ability all determine output quality. The developers’ design strategies influence design functionality and change depending

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TABLE 3.9 Examples of twin data center technical supports. Classification

Related FRs

Technical supportive DPs

Data storage

FR3.1, FR3.2, FR3.3, FR3.4, FR3.5

DFS, NoSQL, NewSQL, etc.

Data fusion

FR3.1, FR3.2, FR3.3, FR3.4, FR3.5

Raw fusion, feature fusion, decision fusion, etc.

Data transmission

FR3.1, FR3.2, FR3.3, FR3.4, FR3.5, FR3.6, FR3.7, FR3.8

Wireless transmission, wire transmission, Quantum, etc.

Statistical exploration

FR3.9, FR3.10, FR3.11, FR3.12

Support vector machines, multivariate adaptive regression splines, time-series model, etc.

Prediction

FR3.10, FR3.11, FR3.12

Regression technics, discrete choice models, logit versus probit, etc.

Learning ability

FR3.13

Apache MXNet, Pytorch, Theano, etc.

DFS, Distributed file system; DP, Design parameter; FR, functional requirement.

TABLE 3.10 Examples of twin data center (TDC) design constraints (DCs). DC classification

Examples of DCs

Data attributes

Amount of data flow, tolerance of data, quality of data, etc.

TDC design feature

Power limitation, hardware performance, internal algorithm, development of scripts, etc.

Developers’ design strategy

Functional bias, selection of supportive technics, market expectation, etc.

on their application. Examples of TDC DCs are shown in Table 3.10. Also, as data is shared and used by all compositions, each single DC affects all involved FRs.

3.5

Development of services

DT is designed to meet the demands of its users. The quality of the DT’s output is referred to as “service”; specifically, FS and BS. FS is defined as the essential functions which support the DT itself. For example, model assembly, simulation, and synchronization are necessary to construct the VE. Similarly, FS is required for data analysis and interaction. BS generally

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contains outputs and functions to satisfy CNs such as simulation outcome, risk assessment, health forecast, and product design recommendations [3,4]. Unlike the previous four compositions, service is used more as a conceptual method. Its functional achievements depend on the unique combinations of DT subsystems and not a dedicated physical platform. For example, both “risk evaluation” and “status report generation” are BS of DT. Achievement of risk evaluation mainly requires historical data and processing from TDC, while status report generation can be performed mainly by PE. As a result, the achievement of these two BS does not rely on a specially developed platform such as PE, VE, and TDC. Meanwhile, the development of BS is mostly determined by the specific application scenario. The previous CN FR DP DC development sequence becomes invalid here. Though DT itself is a tangible artifact, its purpose is always to serve an existing product, not work independently. Therefore the DT design is directly related to the design of the served product. As the motivation of product design is to satisfy specific CNs, the aim of DT design is also to focus on these needs. For example, a DT is designed to investigate the power consumption of a mobile phone. For the mobile phone’s power system, CNs to include “longer battery duration,” “lower heat generation,” and “sufficient power supply.” The DT system similarly serves to identify factors that influence these CNs. Examples of DT functions are the “power consumption investigation of App,” “power consumption simulation under various signal strength,” and “temperature change simulation under various power output.” Services can then be generated from customer domain step-by-step as shown in Fig. 3.8. Starting with the Customer Domain, here collected CNs are mapped to determine FRs, which include achievement targets, risks to be considered, functional satisfaction, and what trends should be highlighted. Second, FRs are used to guide the virtual model functions and output BS. Third, through virtual model building, data collection categorization, and sample sizing, the required FS can be generated to support the VE [19]. Although service is not physically integrated on a dedicated platform, the achievement of each system still requires support from multiple parts. First, a service should be defined and established with a script, in which its definition, target, function, and achievement operation will be input and used to

FIGURE 3.8 DT service generation flow. DT, Digital twin.

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TABLE 3.11 Examples of service design constraints. DC classification

Examples of DCs

Technical support

Connection quality, hardware performance, the functional bias of supportive software, etc.

Served target

The environmental condition, the category of the served target, the complexity of the served target, etc.

DT developers

Comprehensiveness of service frames, optimization of algorithms, functional bias, etc.

Other services

The number of shared components, working time line, quality of functional services, etc.

DC, Design constraint; DT, digital twin.

guide operations. Second, to execute the service, software and hardware support will be provided by multiple subsystems of DT. For example, if one BS is to investigate the telecommunication efficiency with respect to the change of signal strength, it requires a physical test, virtual simulation, and resource validation. This requires the operation of PE, VE, and TDC. Finally, the execution performance of service should be recorded, using methods such as cameras, sensors, and voice recognition. Moreover, each data collection method must interact via a connection system. As the specific features vary significantly for different services, DCs of service are mostly determined by their practical environment. As shown in Table 3.11, they are classified into four categories: technical support, served target, DT developers, and other services. Technical support directly determines the quality of services. For example, a poor connection quality will impede data transmission. For the served targets the diversity of their attributes and working environment will significantly influence the service quality of DT. For example, voice recognition is one significant data source, and indoor and outdoor conditions apparently result in reconciliation accuracy. Then the developers’ design method will inherently lead to service achievement bias. For example, if a data processing BS is mainly developed to deal with numerical data, then it may not be compatible with image data. Services affect each other sometimes, on some occasions, execution of some services shares common components. Also, the quality of FS directly influences related BS.

3.6

Development of connections

A connection represents the technical support to achieve the data interaction within DT compositions. Most interactions such as perception of physical

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world data, data classification, the command from DT developers, and formulation of product life cycle are all technically based on connection development to achieve detection, embedding, transmission, communication, feedback, command, and storage [3,4,20]. As data flow through the whole DT is expected to be completely synchronized, a connection system must be optimized for real-time data interaction. This is especially important among compositions where data flow is bidirectional, as this places greater demand on connection bandwidth. For example, when PE forwards the environmental data to VE, and VE forwards the simulation results back to PE. Also, due to a large amount of data transfer, the flow of data often takes a long path. Hence, a small change early on can have a significant impact later on. Another issue is the privacy and security of users’ data within DT and how this will be managed [20]. Generally, the CNs of connection can be summarized as follows: G G

G G

CNcon1 achieves data transmission among different compositions. CNcon2 achieves the data synchronization for different compositions within the same data flow. CNcon3 minimizes data distortion. CNcon4 protects the data confidentiality.

As shown in Fig. 3.9, the connection system includes interaction among five compositions: PE, VE, TDC, service domain, and DT developer. For the service domain, although services are not physically integrated and rely on available DT components for execution, each service still requires the data flow to communicate and forward recorded data. For DT developers, a

FIGURE 3.9 Categories of interaction among DT related compositions. DT, Digital twin.

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TABLE 3.12 Examples of design parameters (DPs) for connection development. Classification

Candidate DPs

Data transmission

Bus network, mesh network, wireless network, etc.

Distortion mitigation

Automatic channel transferring, route reposition, route configuration, etc.

Data security

Remote storage, data duplication, regular network reallocation, etc.

Synchronization

Timestamp synchronization, wholesale transfer, mirror, etc.

terminal acts as the connection between DT and developers, where each composition can interact separately. Due to the similarity of connection types between compositions, the same group of FRs can be used for each connection type. Primarily, the connection is expected to transmit data among compositions fluently. The transmission may contain data in several forms and be connected by either cable or wireless. Protecting data security can be another important FR to prevent the leak. Furthermore, to prohibit data, distortion mitigating interference becomes an FR. Moreover, as the data can be perceived and presented in several forms, transforming data can be an FR as well. Finally, synchronizing data is also a critical FR to improve connection quality. According to the CNs and FRs, DP generation is classified into four types: data transmission, distortion mitigation, data security, and synchronization. Examples of technical support concepts are shown in Table 3.12. Technical adoption for each connection varies depending on the real case. Also, tolerance permitting, synchronization, security, and distortion mitigation can be applied to reduce cost and design workload. For example, if the DT supervisor allows for the late feedback, data forwarded to the supervisor can be moderately delayed rather than synchronized. Moreover, the format of data has diversity to each connection so the optimization should be applied uniquely. For example, PE may forward the image and DT developers may forward voice command; the transmission and transformation device at the two terminals will be different. DCs of connection have three major sources: attributes of data, technical support, and surroundings. The functional performance of the connection system is directly affected by the amount, form, and stability of data. Different forms of data transfer differ in performance; for example, wireless transmission is ideal for very long distances. However, its speed and stability are more prone to compromise than a wire connection. Other factors such as the operating environment, weather, and other signals affect the connection quality.

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3.7 Integration of digital twin compositions and working management The previous sections have proposed a general methodology to develop the conceptual framework of DT. Although each of the five major DT compositions is entitled with unique objectives, functions, and concepts, achievement of services normally requires the engagement of multiple compositions. DT is expected to process data in real time, where the interaction of different parts remains dynamic and consecutive. The functional capability of the DT is usually adjusted and guided by DT users. Hence, subsystems of DT work in coordination execute the demands of users. Though due to the complexity of DT—with a high volume of data flow, multiple components, couplings, and uncertainties—integration of subsystems and managing the operation of DT poses a huge challenge for developers [21]. In this section, simplification of DT integration will be discussed in terms of working mode, working sequence, and working power output ratio.

3.7.1

Management with working mode

Achievement of both functional and BSs requires the coordination of multiple DT subsystems. For example, data collection is performed using multiple sensors, data filters, central processors, and data storage. Meanwhile, most subsystems are shared by multiple services, which significantly boost the system complexity. At the bottom level of functionality, components operate independently of others given the low sophistication of performed function. High-level functions often involve integration into services. For example, if an engineer simulates the fatigue of an engine shaft after 2 million cycles, the most convenient way on the control panel is designed as “service-investigate the fatigue-choose models from library-input cycle number,” rather than “choose material-set dimensions-set test environment-choose mechanism-set simulation rules, etc.” Each type of function is defined hereafter as a distinct “working mode,” where separate components behave differently to achieve a specific function. Inside a specific working mode, all the essential components, commands, algorithms, and data interaction are preset by the designers. Each mode determines which subsystems are active and those that are idle. Separation of functions by working mode allows for operators to develop and compare virtual simulations with physical operation. The creation of a working mode involves breaking down functions to their most basic level and methods for their integration. For example, a DT design algorithm of low-levels functions must be stored in the library and integrated according to which working modes are required. DT developers may also input universal functions as the bottom, then insert unique functions and executive commands for a specific sector or product. For example,

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universal services and subfunctions may contain stress detection, deformation detection, and temperature detection. However, for practical applications, diversity in PE is allowed for different users using the same applications, so the applicability of DT working modes varies and may significantly affect the accuracy in VE. Hence, for users who require a particularly accurate simulation, the DT design might need to be customized.

3.7.2

Management with working sequence

Service, or the output performance of a system, is achieved through the integration and coordination of multiple subfunctions. For example, to correct the bias between VE and PE, data interaction will first compare the divergence between each entity. Second, the FS system will identify algorithmic or design errors to highlight the modeling problem. Third, the DT system will then compare results with existing data and forward candidate solutions. Finally, the VE evaluates the candidate solution to forward some reliable suggestions to the designers. However, design couplings cannot be prohibited at this level, which significantly increases the difficulty of working with DT. The independence axiom states that all FRs aim to be independent of others to minimize couplings and inconvenience. However, the existence of DCs limits the achievement of the uncoupling concept. Sequences will be applied to coordinate functional achievements in this condition [1]. This principle plays an important role in DT integration management as well. Fig. 3.10 illustrates a simple sequence management sample. The number in a circle in the figure represents the step number. The mainline design of working sequence starts from the decomposition of services. Those subfunctions which are assembled into the target service will be identified and broken down to the bottom level, where functions are achieved by dedicated components independently. In some cases some bottom-level services may share the same component and therefore cannot operate simultaneously; here subfunctions must be prioritized. There are multiple criteria to rank the importance, for example, some subfunctions can be finished in a very short time, some subfunctions serve the main objective. Next step, the prerequisite relationship should be recognized, some subfunctions reply on others and cannot be achieved independently, the fundamental subfunctions will be achieved at first in this case. In a complicated branch a subfunction may require multiple prerequisites, which support other subfunctions of the same level. In this situation the sequence design should manage the coordination to guarantee the synchronization of the subfunctions at the same level. When the working mode is executing along the mainline, many other subsystems, which may complete the previous objectives or wait for the order, are idle. To fully utilize the whole system, side working sequence can be managed to execute other functions at the same time. The first step is to recognize idle subsystems. Then according to the previous working mode

FIGURE 3.10 Sequence management sample.

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design, engineers will identify available executable service. Without obstructing the mainline, DT will be entitled to provide multiple services during the same period. With the development of DT techniques, the whole recognition procedure can be solved using DT and an operator will select and confirm.

3.7.3

Management with power output ratio

By default, electrical components generally operate at full power output. For a highly complicated DT system with many components, driving at peak performance for extended periods will result in catastrophic damage. To prevent such failures, some DT parts may include inbuilt redundancy measures consisting of sensors in the PE and analysis of prior performance data in the VE. This working principle can be adopted to balance between power consumption and functional achievement. In a practical application the regulation of power output can be achieved by operating in different working modes. Achievement of service normally has a minimal requirement, hence, when the working mode is developed, the working set of each component can be designed to a specific power output ratio to satisfy the minimum requirement. For example, when a working mode is designed for the physical world environmental perception, data retrieval will be the primary objective while data storage aims only to assist. As the performance requirement for data storage is normally less than data analysis, sensors may run at full power, but the TDC may work under roughly 40% power output. If a system contains redundancy design, the objectives can be distributed to each component to split the power consumption of each involved components.

3.8

Case study

This section presents illustrative examples of an AVC to explain the development of five DT compositions introduced in previous sections. Drawing from research in artificial intelligence, AVC development can be traced to the 1980s. Most of the historical research focuses on self-driving capability. In recent years, research has shifted to the autonomous monitoring of driver preferences and behaviors, such as climate control, seat belt reminders, and road swerving prevention. In the future, it is likely that AVCs will enter an era of complete automation; such that they will be able to fully operate and maintain themselves without human intervention. Notably, DT will play an important role in maintenance management, self-driving monitor, power consumption control, and data interaction [22,23]. Illustrative examples in this section will be based on a DT system developed for AVCs. Due to the complexity of engineering an AVC, examples detailed next will involve the explanation of concepts, and not a comprehensive technical investigation.

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Furthermore, the research and application of DT are presently at a developmental stage, so technical issues will not be presented.

3.8.1

Step 1: development of autonomous vehicle physical entity

First, it is important to specify details of the AVC’s operating environment. In this case the AVC is operating in a congested, urban environment. The service target is the passenger, the DT user is the AVC designer, and other peer PEs refer to nearby AVCs. In this scenario the FRs introduced are specified in Table 3.13. Movement control and dodging nearby vehicles remain the most critical tasks of the AVC. Passenger preferences of the interior environment and fuel consumption are also included for FR generation. Depending on the intended use case, the development of PE focuses on designated compositions corresponding to FRs. To satisfy CNs FRs above, concentrate on braking control, interior environment control, fuel management, and path management. Therefore DPs for PE development, in this case, shall be allocated AVC compositions such as telecommunication systems, navigation system, interior control system, and the engine. Nonessential PE components are to be excluded, as they increase development expenditure, increase complexity, and introduce DCs to essential PE compositions. For example, when abundant measurement devices are installed to monitor the health of AVC wheels, this subsystem has a limited influence to deal with traffic congestion while taking up extra data processing capacity and signal transmission bandwidth. As DPs and DCs are mapped from FRs, examples of DP and DC generation are shown in Table 3.14.

3.8.2

Step 2: development of autonomous vehicle virtual entity

Creating the same condition of traffic congestion, the VE development in this scenario generally contains five elements: the kinematic system of the AVC, the interior of the AVC, surroundings (weather, other vehicles nearby, obstructions), the road condition to the destination, and the passenger. Other elements may not critically influence traveling management. The overall objective of VE in this scenario is to adjust and optimize the kinematic system, path management, and interior environment to mitigate trapped time in traffic congestion, adjust the interior environment to passenger’s comfort zone, and reach the destination safely. Some FR examples related to VE are shown in Table 3.15. Achievements of these FRs require the coordination of interacted DT compositions as well. As introduced previously, DPs of VE are generally classified into modeling, simulation, decision-making, and communication. As shown in Table 3.16, some examples of the candidate concepts and associated DCs are presented. In this AVC case, modeling generally covers road conditions, the

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TABLE 3.13 Functional requirements example related to physical entity of autonomous vehicle (AVC). FR number

FRs

FR1.1

Provide the AVC virtual modeling data of vehicle body, power system, braking system, and cab

FR1.2

Regulate distortion of VE with a consecutive data update

FR1.3

Adjust the speed and brake to minimize fuel consumption

FR1.4

Retrieve the data of road condition

FR1.5

Track the vehicle flow in traffic congestion

FR1.6

Coordinate the movement of all nearby AVCs to ease the traffic congestion

FR1.7

Track the movement of other AVCs

FR1.8

Input data of AVC power consumption, road condition, drivers’ comment, etc.

FR1.9

Receive the management plan from backstage regarding navigation and scheduling

FR1.10

Monitor the current speed, power consumption and the interior environment

FR1.11

Recognize passenger’s path preference, request on interior environment adjustment, etc.

FR1.12

Upload data of AVC health and kinematic status

FR1.13

Upload detected passengers’ demand and passenger’s general rating on AVC

FR1.14

Monitor and collect status of the engine, transmission system, fly-by-wire system, etc.

FR1.15

Record the power transmission efficiency, fuel consumption rate, telecommunication signal, etc.

FR, Functional requirement; VE, virtual entity.

surroundings, the AVC, the passenger, and the interior environment. Involved DCs are generated by their physical templates and technical support. For the simulation, as mentioned previously, path management, fuel consumption regulation, and interior environment regulation are the three main objectives. Hence, the simulation concentrates on the effect in these aspects. Notably, the requirement of simulation attributes generates the most DCs. The decision-making role primarily guides the working of PE, and the data used to make the judgment would influence most. Finally,

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TABLE 3.14 Examples of design parameters and design constraints. FR number

Category of FRs

Required DPs

Involved DCs

FR1.1

Data interaction

Transmitter, synchronous transmission, CDMA, etc.

Bandwidth, signal interference, etc.

FR1.2

Data processing

Real-time processing, distributed processing, correlation analysis, etc.

Performance of CPU, heat radiation, power supplement, etc.

FR1.3

Physical control

Fuel gauge, PID control, path manager, etc

Movement of other vehicles, the condition of traffic congestion, etc.

FR1.4

Physical cognition

Image recognition, AVC interaction online platform, a signal receiver, etc.

Signal strength, the performance of image processor, etc

FR1.5

Physical cognition

AVC interaction online platform, online navigation service, etc.

Reliability of received data, carrying capacity of the online platform, etc.

^

^

^

^

FR1.14

Physical cognition

A strain gauge, a thermal sensor, angular speed gauge, etc.

The accuracy of sensors, data transmission lag, placement of sensors, etc.

FR1.15

Physical cognition

Off-line data library, data envelopment analysis, variable reluctance speed sensor, etc.

The volume of cache, parameter change frequency, number of recorded parameters, etc.

DC, Design constraint; DP, design parameter; FR, functional requirement; PID, proportional integral derivative control.

communication is formulated with all the involved compositions such as PE, other AVC, and TDC. Attributes of data such as amounts, quality, and transmission distance all affect the communication quality to some extent. Determined by the users’ demands, the setting of reduction degree is relatively subjective and flexible. Techniques permitting, the level of similarity correlates with the users’ simulation expectation to avoid incompetence or performance waste. As the “experience similarity” remains conceptual envision, it will not be considered in illustrative cases. Hypothetically, envision DT of the AVC is undergoing aerodynamics simulation to investigate its current effect on fuel consumption, examples of VE similarity settings are shown in Table 3.17. Quality of aerodynamics design is mostly determined from the geometry and material of the surface. Therefore the requirement in the reduction degree of chassis, spoiler, and shield will be strict. By contrast,

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TABLE 3.15 Functional requirements examples related to virtual entity of the autonomous vehicle (AVC). FR number

FR

FR2.1

Receive the collected data related to AVC kinematic system, interior, passenger’s feeling, road condition, and surroundings

FR2.2

Consecutively adjust the modeling according to the dynamic data perceived from the physical world

FR2.3

Guide the path management, navigation, kinematic system control and interior control in physical world according to the virtual simulation result

FR2.4

Extract relevant data with respect to vehicle dodging, traffic congestion duration, passenger’s interior environment preference, etc.

FR2.5

Record the simulation data related to parameter setting, functional performance, passengers’ feedback, etc.

FR2.6

Return the data of simulation to the supervisor

FR2.7

Receive update and settings from DT supervisor concerning obstruction dodging algorithm, expanded road data library, the latest customer review about interior control, etc.

FR2.8

Communicate and coordinate with other AVC PE about path management, other passengers’ feedbacks, the health of the engine, etc.

FR2.9

Construct models of the kinematic system, interior, passenger, path to destination and surroundings with data from PE, TDC, and the supervisor

FR2.10

Simulate the function and settings regarding vehicle dodging, signal light identification, interior control, overview fuel consumption, etc.

DT, Digital twin; PE, physical entity; TDC, twin data center.

interior design and device have a negligible impact on aerodynamic performance, so the similarity of these components can be omitted or remain at appearance level.

3.8.3

Step 3: development of autonomous vehicle twin data center

The TDC of an AVC can exist as attached hardware inside the AVC or through a remote platform. In the example next, TDC is assumed to be inside the AVC to achieve real-time rendering. Physical data such as the road condition, surrounding obstacles, nearby vehicles, and engine status will be received by TDC for evaluation of real-time performance. Stored historical data on road conditions will be extracted and analyzed to validate the expected performance of the VE. However, due to a large amount of

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TABLE 3.16 Examples of design parameters and design constraints of autonomous vehicle (AVC) virtual entity development. Classification of DPs

AVC cases

Candidate concepts and technics

Associated DCs

Modeling

Vehicle collision modeling, road condition model, interior environment model, etc.

Interactive feature definition technic, model-driven architecture, ontology representation method, etc.

The number of sensors in the physical world, the number of vehicles on the road, performance of modeling software, etc.

Simulation

Fuel consumption simulation, interior temperature change simulation, vehicle flow simulation, etc.

Stochastic simulation technics, finite state machines, application evolution mechanism, etc.

The complexity of simulated condition, length of the simulation time, quality of models, etc.

Decisionmaking

Fuel consumption control, interior temperature control, speed regulation, etc.

Graphical comparison, grammatical analysis, execution tracking, etc.

Amount of collected data, simulation quality, the complexity of decisions, etc.

Communication

Interaction with the passenger, communicate with other AVC, data exchange with PE, etc.

Raysync, FlashFXP, Synchronous Serial Transmission, etc.

Distance to other AVCs, the complexity of passenger’s feedback, the amount of data from PE, etc.

DP, Design parameter; DC, design constraint; PE, physical entity.

extracted data, TDC will select only the most critical performance and safety-related data for analysis. For example, if a car has a fronton collision, the bulk of TDC capability will be directed to path management. Some examples of TDC FRs, in this case, are shown in Table 3.18. Examples of technical support for TDC in the AVC case are shown in Table 3.19. As the primary function of TDC is to transfer data, DPs introduced previously are applicable. The main issue is to identify the application of the below techniques. Data storage covers the recording and packaging of collected data from surrounding vehicles, the AVC kinematic system, and

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TABLE 3.17 Degree of reduction setting examples of different parts. For virtual aerodynamics simulation

User’s accuracy expectation

Similarity setting

AVC shield geometry

100% same

Reaction similarity

Spoiler

100% same

Reaction similarity

Shield material

100% same

Attribute similarity

Pendants on the shield (antenna, rear-view mirror, etc.)

Geometrically similar

Appearance similarity

Cab

No requirement

Appearance similarity

Tire and wheel

Geometrically similar

Appearance similarity

Chassis

100% same

Reaction similarity

Power system

No requirement

Appearance similarity

Interior

No requirement

Appearance similarity

AVC, Autonomous vehicle.

passenger’s commands. For data fusion, apart from the evaluation of road condition shown in the table—for example, to adjust the interior air quality—the TDC shall integrate the current interior air quality, the external air quality, the air filter performance and identify correlations between each. Data transmission is relatively similar among different cases, which refers to the internal and external interactions. Statistical exploration focuses on the analysis of both the current road conditions and the optimization of such conditions in the future. For example, the TDC may suggest an aversion of the same path next trip or advise a more accurate departure time. For DCs of TDC, in this case, the quality of collected data directly affects the analysis. For example, if thermodynamic sensors attached to the engine fail and return an overperceived temperature, the TDC may overestimate the current power output and reduce engine power unnecessarily. A key issue with TDC design is the space and issue of heat radiation of a vehicle, as it is assumed that it is located inside the AVC. A remotely operated TDC may avoid such problems; however, it will slow transmission speed and impede capability. Some more examples are shown in Table 3.20.

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TABLE 3.18 Functional requirement example of twin data center. Classification

FRs number

FR examples

Data transferring

FR3.1

Receive physical world data such as congestion extent, traveling speed change and interior temperature

FR3.2

Receive virtual modeling and simulation data such as expected fuel consumption, expected arrival time and navigated the path

FR3.3

Retrieve data from cloud database about the current road condition, forehead congestion extent and fuel-save traveling settings

FR3.4

Update the latest working modes with respect to fuel-save, road condition scanning, navigation, etc.

FR3.5

Retrieve feedback from the passenger

FR3.6

Regulate the speed control of AVC

FR3.7

Provide modeling template of vehicle shield

FR3.8

Forward the fuel regulation plan, estimated arrival time, and path chose to the passenger and DT supervisor

FR3.9

Anticipate the duration of traffic congestion

FR3.10

Anticipate the time cost of different paths

FR3.11

Summarize the passenger’s preferred interior environment

FR3.12

Analyze the maintenance cycle of the AVC

FR3.13

Automatically determine the settings of navigation, interior environment, and kinematic system

Data processing

AVC, Autonomous vehicle; DT, digital twin; FR, functional requirement.

3.8.4

Step 4: development of autonomous vehicle services

The final goal of DT service is to satisfy customer demands by enhancing functional achievement. General approaches to achieve this include functional optimization, status monitorization, output control, and guidance. Specifically, this involves measuring the level of service, or CN achievement, with corresponding FRs of AVC. For example, a key CN is “self-driving ability,” where each driving function can be evaluated by its degree of autonomy and accuracy. Another CN is “low maintenance” as customers typically do not like the inconvenience and expense of such tasks. One way a CN may be exceeded would be in the automation of maintenance. In design, the generation of FRs follows from CNs; hence, as shown in Table 3.21, it follows that the generation and evaluation of FS follow from BS.

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TABLE 3.19 Examples of autonomous vehicle (AVC) cases and design parameters. Classification

AVC case example

Technical supportive DPs

Data storage

Store the data of historical traffic congestion, road condition in different time sessions, historical fuel consumption rate, etc.

DFS, NoSQL, NewSQL, etc.

Data fusion

Integrate the eventual effect over path management from vehicles’ flows, road condition change, traffic condition on other roads, etc.

Raw fusion, feature fusion, decision fusion, etc.

Data transmission

Communicate and exchange data with PE, VE, passenger, supervisor, and the cloud database

Wireless transmission, wire transmission, quantum, etc.

Statistical exploration

Summarize the traffic flow in a different session, fastest traveling plan, schedule management, etc.

Support vector machines, multivariate adaptive regression splines, time-series model, etc.

Prediction

Anticipate the fuel endurance in different road conditions, passenger’s demands in a different scenario, maintenance for the kinematic system, etc.

Regression technics, discrete choice models, logit versus probity, etc.

Learning ability

Improve the quality of kinematic control, interior control, navigation, etc.

Apache MXNet, Pytorch, Theano, etc.

DFS, Distributed file system; DP, Design parameter; PE, physical entity; VE, virtual entity.

TABLE 3.20 Examples of autonomous vehicle (AVC) twin data center design constraints. DC classification

Examples of involved DCs

Data attributes

Amount of collected statistics about the kinematic system, implication within passenger’s feedback, the amount of data shared by other AVC, etc.

TDC design feature

The storage volume of TDC, power supply quality from the AVC, technical support concept chosen for TDC, etc.

Developers’ design strategy

Data update cycle, constraints over TDC power consumption, permission setting, etc.

DC, Design constraint; TDC, twin data center.

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TABLE 3.21 Service generation examples of autonomous vehicle (AVC). Customer needs

Functional requirements

BS

FS

Self-driving ability

Navigate the route

Optimize algorithms, AVC position locating, candidate routes simulation, etc.

Route model construction, geographical data detection, etc.

Control the speed

Risk evaluation, speed control command, time estimation, etc.

Speed data analysis, data trend exploration, data interaction, etc.

Brake the AVC

Remote control, brake device development, advance amount calculation, etc.

Command interaction, deceleration data collection, etc.

Recognize signal lights

AVC kinematic plan, signal virtual recognition test, etc.

Color detection, signal transferring, etc.

Recognize the road condition

Driving status setting on different road conditions, automatic path selection, etc.

Virtual model construction of different road conditions, data collection of road condition, etc.

Dodge obstacles and other vehicles

Path management, obstacle recognition, obstacle warning, etc.

Distance measurement, dodge simulation, simulation data storage, etc.

Evaluate the device fatigue

Calculate the corrosion rate, calculate crack growth speed, etc.

Deformation detection, viscosity measurement, crack detection, etc.

Analyze the hazard risk

Calculate device broken probability, generate risk diagnoses report, etc.

Model construction of fatigue device, attributes extraction from the damaged device, etc.

Forecast the overhaul period

The maintenance plan, overhaul schedule, etc.

Model construction of fatigue device, attributes extraction from fatigue device, etc.

Automatic maintenance

(Continued )

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TABLE 3.21 (Continued) Customer needs

Functional requirements

BS

FS

Diagnose the malfunction

Malfunction type judgment, malfunction repair suggestions, etc.

Extraction of malfunctions phenomenon, data comparison, etc.

Contact the 4S shop

Maintenance service booking, maintenance requirement forward, communication, etc.

Extraction of malfunction repair approach, maintenance requirement searching, etc.

BS, Business service; FS, functional service.

3.8.5 Step 5: development of autonomous vehicle connection system Connection development involves improving the performance of data transmission. As the transmission of data in digital form is different most of the time, the preintroduced CNs, FRs, and DPs of connection will not change in the AVC. For a specific application the identification of the data transmission type for different compositions is critical. For example, the wire connection is used for communication between PE and VE. Therefore the number of lines required and the risk of circuit failure become critical problems. However, the communication between TDC and DT systems and operators will be wireless. Here, as shown in Table 3.22, data should be processed and compressed in the AVC DT before transmission to the TDC.

3.8.6 Step 6: development of autonomous vehicle working modes, sequences, and output ratio Examples of the working mode setting for important subsystems of an AVC are shown in Table 3.23. As the engine is the core part of the AVC kinematic system, its data collection, modeling, and functional test are analyzed next. As shown in the table, the working status combination of each involved part forms a specific service. Like the charge of battery, some subsystems of DT are “charged” during operation as well.

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TABLE 3.22 Autonomous vehicle (AVC) connection development examples. Connection type

Data example in AVC cases

Data transmission concept examples

Relevant DCs examples

Dev-SSD

Self-driving status, health monitor result of AVC, etc.

Cloud drive

Storage of online cloud drive, signal transmission distance, etc.

Dev-PE

Current speed, navigation, path, etc.

Wireless

Amount of data, signal strength, etc.

Dev-TDC

AVC maintenance condition, the trend of power/fuel, etc.

Quantum

Stability of the database, the transmission frequency of TDC, etc.

Dev-VE

Simulation result of the cruise, simulation result of clutch fatigue and failure, etc.

Wireless

Size of the packed simulation data, transmission quality between VE and TDC, etc.

SSD PE

Current task of PE, BS execution result of PE, etc.

Dedicated internal data bus

Data exchange sequence, Transmission protocol, etc.

SSD VE

Life cycle of 5% clutch failure risk, brake distance estimation, etc.

The i2c bus with dedicated addresses for each service

Time delay setting between each two services transmission, duration of current simulation task, etc.

SSD TDC

Passengers’ rating of each BS, the functional failure rate of each FS, etc.

An i2c bus with dedicated addresses for each service

Contained components of the service, working mode of the TDC, etc.

PE TDC

Deformation of the shaft, the temperature of the clutch, etc.

Wire connection

The position of the sensor, wire installation, etc.

VE TDC

Cruise simulation result, fuel consumption rate simulation result, etc.

Wire connection

Data compression extent of VE, the bandwidth of the transmission, etc. (Continued )

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TABLE 3.22 (Continued) Connection type

Data example in AVC cases

Data transmission concept examples

Relevant DCs examples

VE PE

Speed regulation commands, fatigue of wheels, etc.

Wire connection

The data transmission frequency of PE, the number of perceivers, etc.

BS, Business service; Dev, DT developer; PE, physical entity; SSD; Service domain; TDC, twin data center; VE, virtual entity.

TABLE 3.23 Examples of working mode management related to the autonomous vehicle engine. Engine data collection mode

Engine modeling mode

Engine functional test mode

Physical parameter sensors

Working

Idle

Idle

Physical scanner

Working

Idle

Idle

Data library

Charging

Working

Charging

Data extractor

Standby

Working

Standby

Signal interactor

Working

Working

Working

Virtual environment generator

Idle

Working

Idle

Environmental factor regulator

Idle

Standby

Working

Virtual product generator

Idle

Working

Idle

Virtual product attributes regulator

Idle

Standby

Working

Conduct library

Charging

Working

Working

Conduct simulator

Standby

Standby

Working

Conduct recorder

Working

Idle

Working

Search engine

Standby

Working

Working

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TABLE 3.24 Examples of capability output ratio setting adjustment for autonomous vehicle engine data collection. Engine data collection mode (primary design sample)

Engine data collection mode (released product)

Physical parameter sensors

90% power output

60% power output

Physical scanner

Full power output

60% power output

Data library

85% charging capability

40% charging capability

Data extractor

Standby

60% transmission capability

Signal interactor

Full power transmission capability

Full power transmission capability

Virtual environment generator

Idle

Idle

Environmental factor regulator

Idle

Idle

Virtual product generator

Idle

Idle

Virtual product attributes regulator

Idle

Idle

Conduct library

50% charging capability

Standby

Conduct simulator

Standby

Idle

Conduct recorder

Full power working

Standby

Search engine

Idle

Standby

The core of sequence management is the identification of service level, prerequisites, and priority. Using content from Table 3.24 as an example, the “functional virtual test of engine” is regarded as the main objective. To execute the virtual simulation vertically—that is, to execute services of different levels—key information must first be extracted from the library database. Then the virtual environmental regulator and virtual product model regulator adjust VE to the set value. Next, when all settings are confirmed and simulation starts, the whole working process will be recorded, processed, and stored in the database. The processed data will finally be analyzed and compared with historical data to highlight critical phenomena or trends. Hence, a general vertical sequence is formulated as: extract conduction-adjust environmental setting—adjust model setting-drive the functional test-record

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the simulation-store the simulation-process critical data-highlight the phenomenon. In contrast, to execute a simulation horizontally—sequence to execute other services of the same level—before the virtual functional test, the engine model and environmental model must be finished beforehand, which is based on data collection from physical engine. Hence, a horizontal sequence is generated as: collect involved engine data-model the engine and test environment-drive the functional simulation test. Meanwhile, “engine data collection” and “engine modeling” have their own vertical sequence as well. Eventually, combine all vertical sequences and the horizontal sequence, the mainline to execute virtual functional test of engine is finished. As shown in Table 3.23, when the DT is executing the functional test of engine, data collection system in the PE is stationary. In this case a side sequence can be added to utilize those idle subsystems. When DT is virtually testing engine functions, another round of data collection in a VE can be triggered. As a result, more data can be collected in one loop which may benefit service execution and development. One critical problem is that “engine data collection” requires some common FS and subsystems with “functional engine test,” the designer shall consider installing more corresponding DPs to satisfy the side sequence. As shown in Table 3.24, diversity of output ratio can be reflected by the different life phases of the AVC composition. In this case, engine monitor service is assigned to two different engine life cycle phases: design prototype and released the finished product. When the engine is still a design prototype, relevant data is lacking. Hence, data collection components are working in a high capability output ratio to fulfill database rapidly. By contrast, when the product has been released to a market and limited modifications have been made, most of the previously collected data is still valid. The dynamic data collection from the VE is slower. Hence, 60% output may suffice. For data extraction, data storage is limited so the extractor may not execute the objectives. When the engine is fully developed, data extractor will start to output data. Moreover, in coordination with the research of working modes, the setting of output ratio can be generated based on corroborated functional demands of working modes.

3.9

Summary

This chapter explores the configuration development of DT via basic conceptual design methodology. The development of five major compositions of DT is presented, which include PE, VE, DTC, service, and connection. The primary customer demands regarding the three core compositions—PE, VE, and DTC—are then discussed. High-level FRs are then generated via mapping to CNs, which are used to guide the discussion over DPs and DCs. Methods of reduction to ensure only the most important FRs are included are also outlined. For the development of service, these are mapped from both

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the CNs and FRs of the served target. And finally, for the connection system, each existing connection in DT is summarized and then explained in terms of CN FR DP DC. To integrate the five compositions, working mode, working sequence, and power output ratio are discussed. To illustrate the developments of DT theory proposed, a case study of an AVC is applied. Hypothetical scenarios involving traffic congestion, maintenance management, and engine monitoring are used to demonstrate DT’s effectivity. Overall, the configuration of DT is more clearly illuminated at a conceptual level, providing a framework for which technical developments can be applied. Technical support will be further explored in other chapters.

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[16] G.N. Schroreder, C. Steinmetz, C.E. Pereira, D.B. Espindola, Digital twin data modelling with AutomationML and a communication methodology for data exchange, IFACPaperOnLine 49 (30) (2016) 12 17. [17] Q. Qi, F. Tao, Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison, IEEE Access. 6 (2018) 3585 3593. [18] Y. Cai, B. Starly, P. Cohen, Y.S. Lee, Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing, Procedia Manuf. 10 (2017) 1031 1042. [19] R.J. Brodie, L.D. Hollebeek, B. Juric, Customer engagement: conceptual domain, fundamental propositions, and implications for research, J. Serv. Res. 14 (3) (2011). [20] S. Yun, J. Park, W.T. Kim, Data-centric middleware based digital twin platform for dependable cyber-physical systems, in: 2017 Ninth International Conference on Ubiquitous and Future Networks, 2017. ,https://doi.org/10.1109/ICUFN.2017.7993933.. [21] M. Grieves, J. Vickers, Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems, in: Transdisciplinary Perspectives on Complex Systems, 2017, pp. 85 113. [22] V. Milanes, D.F. Llorca, B.M. Vinagre, C. Gonzalez, M.A. Sotelo, Clavileno: evolution of an autonomous car, in: 13th International IEEE Conference on Intelligent Transportation Systems, 2010. ,https://doi.org/10.1109/ITSC.2010.5624971.. [23] E. Negri, L. Fumagalli, M. Macchi, A review of the roles of digital twin in GPS-based production systems, Procedia Manuf. 11 (2017) 939 948.

Chapter 4

Digital twin driven virtual verification Yiling Lai, Yuchen Wang, Robert Ireland and Ang Liu School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia

4.1

Introduction

This chapter devises a solution to the issues associated with inefficient, timeconsuming, and expensive methods of physical verification using the more efficient and cost-effective process of digital twin (DT) driven virtual verification. Driving design efficiencies remains imperative for product manufacturers as modern industry becomes increasingly competitive. Manufacturers must continually innovate to boost productivity, quality, reduce costs, and increase profit to stay in business. However, traditional design is a long and expensive process, which significantly hinders manufacturer’s ability to stay competitive. Without a rapid concept-to-market process, designs risk obviation by their market debut. The process typically involves back-and-forth iterations among designers and market research, experimentation, test production, full-scale production, and customer service. Therefore the ability to create a real-world model using a DT framework remains critical for product manufacturers to remain competitive. At present, conventional physical verification methods such as experiments and small batch production are low-efficiency and high-cost endeavors. For example, fatigue tests of a mechanical system can require tens or hundreds of thousands of cycles, taking considerable time. Furthermore, multiple experiments caused by test failure can incur even higher costs. Given the time and cost required for physical product experimentation, shifting this capability to a virtual realm will save significant time and expense. The main obstacle to achieve virtual verification is its limited accuracy in comparison to physical testing. It is difficult to replicate all factors given the complexity of a real-world domain; for example, turbulence of an airplane wing under varied weather conditions or fatigue due to micro-fractures in materials involves a level of variation that is inconsistent and hence difficult Digital Twin Driven Smart Design. DOI: https://doi.org/10.1016/B978-0-12-818918-4.00004-X © 2020 Elsevier Inc. All rights reserved.

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to predict. However, DT involves a comprehensive back-and-forth iteration between virtual and real-world designs to develop self-learning and improved virtual modeling capabilities. This chapter therefore focuses on developing a framework to enable DT-driven design and to demonstrate its application in the design process. DT-driven design is a field in its infancy with limited existing literature on the topic. The development of a framework and case study in this chapter aims to shed new light on the capability of DT. Through the application of DT-driven virtual verification, it is possible to integrate product features and environments to a high degree of reliability within an entirely digital space. This can help designers evaluate product performance and identify defects at more advanced stages of design, allowing optimization before production. This includes modeling a product’s manufacturability, use, and end-of-life (EOL) processes. Efficiency in product design does not only expedite the go-to-market process and reduce costs but also facilitates more thorough design analysis and product improvements. The chapter describes the concept of DT and applies a five-stage framework to an existing coffee machine design. First, background knowledge of relevant design areas such as feature and application integration, the process of product design, and design verification are explained. Second, the definition and key features of DT are provided based on existing knowledge. Third, a detailed framework of DT-driven virtual verification is developed, which can permeate into five main stages of a product life cycle. The five threads of DT-driven virtual verification can be applied in the product design stage. Finally, a case study demonstrates the application of the framework in product design.

4.2 4.2.1

Related works Related works on traditional product design verification

Verification is generally defined as a process of product quality control, which evaluates the extent to which product or service performance is consistent or compliant with customer demand, functional requirements, and design constraints [1,2]. It is a key design activity that bridges conceptual design and embodiment design. First, at the conceptual design stage, verification evaluates discrepancies between mathematical and computational model results and verifies whether the mathematical model can be represented by the computational model [1,3]. Second, at the embodiment design stage, verification involves a process to determine if a product achieves set requirements, by the trial and examination of product performance [4].

4.2.1.1 Virtual verification Finite element methods (FEM) and computational fluid dynamics (CFD) are common techniques for virtual verification. FEM is a numerical approach to

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achieve solutions for linear and nonlinear transient engineering problems [5]. FEM can also be used to predict potential behavior and failure of physical systems in a virtual space [4]. A major challenge of FEM is achieving a high level of fidelity, especially for the prediction of fracture and material damage [4]. CFD refers to the tools and techniques of computer-aided engineering to find solutions to fluid flow problems [6]. Originally developed for use in the aerospace industry [6], CFD is often used to model flows around smoothshaped surfaces [4]. Similar to FEM, CFD is limited in its ability to accurately replicate real-life situations.

4.2.1.2 Physical verification Physical prototyping and testing is a common approach in industrial product performance and quality certification [4]. It can include verification of dimension and shape and mechanical and flow-related testing. Verification of dimension and shape involves comparing the characteristics between the as-built object and model design. According to geometrical product specifications standards as in Fig. 4.1, verification of dimension and shape refers to surface texture, geometric and dimensional characteristics [4]. The methods to conduct the verification of dimension and shape can include direct and indirect measurements, and the objects can be all or selected components [8]. Moreover, mechanical testing applies predominantly to the material properties of an object. It involves understanding how tension, compression, and shear stresses impact fatigue limits, fatigue life span, and creep in material structures [4]. CFD verification involves comparison between computational and experimental results [4,9]. The next stage of physical verification involves inspection, analysis, testing, and demonstration of the manufacturing process [4]. The goal of verification is to ensure that the manufacturing process can complete production to meet required quality standards, which can include design and process control, quality assurance, correction, and prevention [4].

FIGURE 4.1 Characteristic of current GPS standards [7]. GPS, Geometrical product specifications.

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The gap between real situations and computational modeling limits the application of traditional virtual verification, as the key to a reliable and valid virtual verification depends on the accuracy of metrology [10], the consistency and correctness of software [11], and modeling and simulation capabilities [4]. Although physical verification provides a more dependable result than virtual verification, the considerable time and financial input raises development cost and slows production down. Such inefficiencies significantly impact the competitiveness of a product. Therefore it is highly beneficial to verify product design in a virtual domain.

4.2.2

Related works on digital twin driven virtual verification

Compared with the current virtual verification, DT-driven virtual verification is capable of high-fidelity simulations, allowing designers to further improve and refine the design in ways previously unachievable. Due to the ability to model real-life scenarios, designers can obtain feedback on how a concept will fare in later design stages. Tao et al. provided the concept of DT-driven virtual verification, which follows after DT-driven conceptual design [12]. A practical use case of DT was also proposed by NASA to reduce catastrophic consequences of space missions. This involved the development of new materials to withstand extreme conditions and impact assessment of existing defects [13]. The advantage of DT-driven virtual verification compared to the traditional method lies in its ability to utilize the full-scale of product data, incorporating inputs from production, use cases, changes to design, and EOL. This helps detect defects and anticipate the quality of a product before pilot production. Through real-time data generated from products in use and machine-learning capability, it is also possible for DT verification to predict future behaviors or to recommend solutions. Furthermore, unpredictable and problematic outcomes caused by human error, such as incorrect calculations or assumptions, can also be mitigated or eliminated prior to production [14]. Current virtual platforms used in product design are unreliable due to significant differences between the virtual and physical design spaces. For example, most computational programs such as CFD and FEM calculate outputs based on estimated or theoretical calculations of real-world outcomes. A key difference of DT is the collection and analysis of real-time data, which continually enhances the virtual model [12]. The result relies on a far more dynamic and powerful virtual system. DT is a relatively new concept in design, having been around for less than 10 years. Hence, minimal research exists on how DT-driven virtual verification can improve product design and quality. In this chapter, some new research areas are examined, and a general application of DT technology is outlined. However, much of the technology required to realize DT is in the development, such as big data analytics and Internet of Things (IoT) communication.

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Therefore the following sections aim to contribute to the development of DT in two ways. First, expand upon existing DT-driven virtual verification by introducing a framework based on the five core stages of a product’s life cycle. Second, evaluate the framework’s efficiency in the context of a reallife case study. Such insights aim to highlight the importance of DT to future product design and narrow the scope of future research.

4.3 4.3.1

Digital twin driven virtual verification method A model of digital twin driven virtual verification

DT-driven virtual verification, when applied to the integration phase, can have a significant impact on a product’s design and life cycle. This phase involves the integration of features and functions to produce a concept. The concept would then be examined at a holistic level, as features often have competing for needs and priorities. For example, increasing product strength may increase its weight and affect performance or require expensive materials and increase costs. Typically, conceptual analysis depends on the experience of designers and testing of physical prototypes. By using DT, designs can be refined to a far more realistic degree in a virtual space, allowing for more rapid product optimization and improved decision-making capability of designers. By fixing many issues earlier in the design process, the chance of error is reduced throughout the whole product life cycle. Such improvements in efficiency reduce costs and enhance the competitiveness of a product. As shown in Fig. 4.2, the framework of DT-driven virtual verification covers five key stages of a product’s life cycle, namely, product design, manufacturing, usage, maintenance, and EOL. Although a more comprehensive product life cycle framework is possible, these five stages are considered the most important for product design. Each of these stages is listed below and further broken down into three subcomponents. 1. 2. 3. 4. 5.

Product design—DT—product design Product design—DT—manufacturing Product design—DT—usage Product design—DT—maintenance Product design—DT—EOL

The framework above outlines a virtual verification methodology for an entire product life cycle, as defined by Ullman [15]. The process begins at the product design stage then proceeds to change in other versions of the design. The DT system operates as an iterative feedback loop, allowing designers to assess impacts of a particular design on subsequent product life cycle stages and also to use this feedback to improve both the design and the accuracy of the DT model.

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FIGURE 4.2 Framework of DT-driven virtual verification. DT, Digital twin.

4.3.1.1 Product design—digital twin—product design This stage involves the generation of abstract concepts and ideas completed in the conceptual design stage, and a virtual model that is composed of visible models and relevant data. However, the model is not simply a 3D rendering created using CAD software. The projection will be more sophisticated and advanced, including building a database on material properties, characteristics of parts, and environmental factors. Combined with virtual reality and augmented reality technology, DT will produce a vivid virtual product for designers with product manufacturing information (PMI), including dimensions, specifications, structures, and materials. A designer will be able to see a product’s 1:1 dimensions, colors, material flexibility, and extract data to share amongst other product designers. Notably, the designer can interact with the virtual product to determine whether it can perform the designed functions, paying specific attention to movement and coordination of mechanisms, structural interference, and configuration of functions. For example, a desk lamp design can be examined using DT to analyze the movements related to adjusting the height and to provide feedback. PMI of key components, such as surface roughness and tolerance of fit, can be recommended based on current production capability in a factory driven by DT. It is a process of converting abstract design concepts to working concepts far earlier on in the design stage.

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4.3.1.2 Product design—digital twin—manufacturing In product design, consideration of the manufacturing process is essential to ensure that the production process is effective and economical. In fact, production capabilities are a major limitation on the feasibility of product design. Through a virtual simulation of each step of the production process, it is possible for designers to predetect and predict manufacturing issues in particular designs. If any issues are identified, engineers can then improve a design before the manufacturing process, saving the cost and time associated with it. Examples of production processes that DT can assist with include factory logistics, process control, and quality control. Factory logistics include physical space of production processes, storage management, resupply, and deployment [16]. Verification of factory logistics involves analysis of the logistics scheduling system in a factory; typically this is a fixed production line. Optimization of logistics management improves production efficiency as it can reduce unnecessary labor and resources. A holistic and reliable simulation will allow designers to evaluate whether current equipment and techniques can manufacture product parts. This applies to both machine and manual work. For a machine, inputting specifications of machines used in the process can enable the DT system to evaluate whether machines can meet the requirements for tolerance, accuracy, or surface quality of parts. Similarly, for an operator the time required and working conditions affect the efficiency and quality of a product. Such conditions can include high temperatures, unsafe fumes, unsafe lighting or noise, and any other requirements. Moreover, DT can verify if any issues related to the manufacturing capabilities would affect quality control and allow for a final design to be examined virtually. 4.3.1.3 Product design—digital twin—usage Product usage is perhaps the most important stage of product design, as the purpose of design is to meet user requirements. If a product exceeds these requirements, it will be incredibly successful. Otherwise, it will not survive in the market. The virtual verification for product usage is discussed in three aspects: service life, user habits, and safety. By verifying in a virtual space how product can interact with users, it provides designers with a revolutionary capability to evaluate new designs in the concept stage. The virtual verification of a product’s service life, such as analysis of material properties, structural properties, manufacturing techniques, and precision, can be modeled in DT. Traditionally, verification of a product’s service life required experimentation, such as strength and fatigue tests. The money and time invested in such tests—for example, expensive strength testing equipment or lengthy fatigue cycling—can significantly increase development costs. DT technology will allow designers to model various factors

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that influence service life such as structural defects, environmental factors, stress limits, and material deterioration. Another advantage of DT is that designers can optimize such factors against manufacturing costs. Highquality products with a long service life typically are expensive to construct. Therefore a key consideration of designers is the cost benefit analysis between quality and cost. DT allows designers to perform optimization analysis to determine the longest service life possible within cost constraints. User habits and needs heavily influence design characteristics. In a highly competitive market, product function alone cannot please users. Functions must enhance the user experience of the product. Virtual verification in DT can allow designers to simulate user experience. Based on a huge database of product data, user habits, and trends, it is possible to evaluate existing and future products virtually. For example, a refrigerator can easily perform the function of keeping food cold. However, this function is expected by consumers and is readily replicable in the competition. Therefore the goal of designers is to enhance the user experience, such as introducing new features to make storage easier, or new functions such as fully automatic ice-making or water-cooling. Assessing product safety involves the identification, estimation, and evaluation of risk levels [17]. Apart from specific product regulations and standards, design for safety relies on designer experience and customer feedback. DT will provide a more comprehensive prediction model for potential hazards during product use. Using existing data, it can allow for simulation of products under a range of environmental conditions and use cases and earlier detection of risks. Such risks are identifiable via DT, including explosions, the release of toxic chemicals, or physical harm to users. By modeling these factors during conceptual design, it enables designers to respond to future risks and develop solutions more rapidly.

4.3.1.4 Product design—digital twin—maintenance DT-driven virtual verification also aims to confirm the effectiveness and efficiency of maintenance procedures. Through simulation of the maintenance process, factors such as cost, time, location, equipment, and materials can be modeled virtually. A DT system can predict what elements of a product will need to be fixed and when, what tools are needed and whether they are upto-date or need replacement, what skills of maintenance workers are needed and what safety measures might be in place. Virtual verification can also detect potential issues and damage caused by current maintenance practices. 4.3.1.5 Product design—digital twin—end-of-life In the EOL stage, virtual verification via DT provides insight into the recycling and disposal procedures of products. Subcomponents of a product have

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different life cycles and fail under different usage conditions. In addition, whether a part needs to be repaired or replaced can be verified. The effect of environmental factors such as recyclability, health, safety, and sustainability will be modeled throughout a product’s life cycle [18]. Especially given the increasing consumer concerns regarding pollution, virtual verification via DT allows designers to model environmental impacts of products at the EOL stage. There are many ways to dispose of waste, such as incineration, landfill, or chemical decomposition. Designers can model the impact of all these and also model performance or more environmentfriendly materials in conceptual design.

4.3.2

Iterative framework of digital twin driven virtual verification

The DT-driven virtual verification, as one of the key parts of the iterative design process, serves to identify design issues and ensure designs adhere to requirements. During this process, DT provides real-time updated data from physical products to ensure the reliability of the virtual verification. This data is collected by sensors and shared remotely by IoT technology. The data can then be analyzed, stored in a database, and used to improve the accuracy of the virtual design. The ability to integrate information from a comprehensive historical database provides support for virtual verification [12]. The iteration process is shown in Fig. 4.3. For a design that has already been created, the virtual verification process undergoes iterations for each stage of the product life cycle as detailed in Section 4.3.1. At each stage, the DT virtual verification process determines whether a design meets requirements or where defects occur. The number of potential issues varies depending on the maturity of the design. Once one loop is completed, designers can continue to refine this version of design by solving key defects, reiterating the process until the best solution is reached. When applied to each stage of a product’s life cycle, DT-driven virtual verification provides an efficient tool for data collection and design optimization. It allows designers to prevent issues before manufacture, significantly improving efficiency, and reducing costs. Furthermore, it gives designers the revolutionary capability to beta-test designs over many iterations, which would otherwise be constricted by traditional time and cost constraints. Therefore DT-driven virtual verification has the potential to spur leaps in product design innovation in years to come.

4.4 Case study I: digital twin driven virtual verification in design for a commercial coffee machine A case study is detailed below to demonstrate the application of the DTdriven virtual verification framework to a commercial coffee machine.

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FIGURE 4.3 Iteration process of DT-driven virtual verification. DT, Digital twin.

The case study analyzes common issues with coffee machines and proposes potential solutions based on the DT model outlined above.

4.4.1

Case study background

Commercial coffee machines are used to make espresso, a strong black coffee, which forms the base for popular cafe´ beverages such as lattes and cappuccinos. Espresso machines come in super-automatic and automatic forms. Super-automatic machines fulfill the entire extraction process via control systems, whilst automatic machines still require baristas to grind beans in advance and tamp coffee powder into a portafilter. Automatic machines are the standard machines used in most cafe´s. Baristas can obtain the volume of espresso they need by pressing the button under precise controls of extraction parameters. The automatic machines not only can ensure extraction quality of espresso by precisely controlling the key variables such as temperature and pressure of water but also do so quickly and easily. Applying the DT virtual verification process to commercial coffee machines offers many advantages to designers and users. For designers, increased data can assist in developing more user-friendly, more reliable, and

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more cost-efficient machines. For users, such as cafe´ managers and owners, it can provide real-time updates on the operating conditions and performance of the machines. The availability of real-time and statistical sales can also be used as a basis for future planning and strategy and product structural adjustment.

4.4.2

Working principle of commercial coffee machine

Fig. 4.4 shows the schematic of a dual boiler espresso machine. Its main components include boilers, group-heads, and portafilters. The machine has two boilers set with stable pressure and temperature, which provides hot water for espresso extraction and steam for heating milk, respectively. The group-head connects the boilers to the portafilter, which evenly distributes water under pressure to improve the quality of extraction [19]. The portafilter contains tamped coffee powder with a filter that allows water through for extraction. The espresso then flows through a funnel in the portafilter into a cup. There are two paths for water to flow through in an espresso machine, shown as Paths A and B above. Path A pumps water at 15 bar from the water tank to the espresso boiler, which provides hot water for extraction. The pressure of water is then reduced to 9 bar via an overpressure valve, which is the standard pressure for espresso extraction. The water then passes through

FIGURE 4.4 A schematic of a double-boiler espresso machine.

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the pipe to the upper side of the steam boiler for heating via heat exchange and fills the espresso boiler. Once the barista turns on the machine, the water from the espresso boiler then flows through the portafilter, which contains tampered ground coffee, completing the coffee extraction process. Path B is for the water flowing through from the water tank to the steam boiler using a 4 bar pump. The water is then heated in the steam boiler to provide continuous flow of high-pressure steam for frothing and heating milk. The steam is released from the boiler to the milk by turning on a valve [20].

4.4.3

Factors impacting coffee quality

Although there are multiple factors affecting the taste and quality of espresso, the detectable controlling variables for espresso (ES) extraction and the steps are shown in Table 4.1. If parameter values fall outside the standard ranges, the taste of coffee is usually impacted. For example, underextracted coffee occurs when the value of a parameter in Table 4.1 is lower than the minimum value, or if the grind size is too coarse [23]. Underextracted espresso can taste sour, salty, or unsweetened, as acidic compounds are extracted first [24]. Overextraction occurs in opposite conditions, resulting in an overly bitter or astringent espresso taste [25].

4.4.4

Group gaskets of coffee machine

The group seal is one of the most important components of an automatic coffee machine. The group seal is mounted on top of the group head, which connects the water flow to the portafilter. The seal plays a key role in forming enclosure space to maintain the stability of the pressure between a group head and a portafilter as in Fig. 4.5. Due to the high temperature and TABLE 4.1 Parameters impacting the quality of ES with standardized value [21,22]. Steps

Variables

Value range

Grinding coffee beans

Dose of ground coffee

6.5 6 1.5 g

Grind size

Moderate

Extraction temperature

90 C 6 5 C

Extraction pressure

9 6 2 bar

Extraction time

30 6 5 s

Flow rate

25 6 2.5 mL

Injecting water

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FIGURE 4.5 Section view of a group-head with a group gasket.

pressure of the working environment, the seal has a high replacement frequency. Once it is aged or deformed, water leakage and underextraction would occur. Given the importance of the group seal to espresso extraction and the need for frequent replacement, the following case study demonstrates how the virtual verification framework can help solve the issue. A DT system can monitor the variation of the extraction pressure and the age and usage of the gasket. This allows designers to flag product faults in real time and to test new design modifications before production.

4.4.5 Collection of key controlling data of commercial coffee machine Types of real-time data gathered from espresso machines can vary significantly. Data can be classified by components, such as voltage and current of the power supply, temperature, valve operation, extraction pressure, and the flow rate of water. In this section, particular focus is given to data from the temperature and pressure of the boiler and extraction, and the flow rate of water based on respective sensors. Then the data can be collated using a control board and uploaded to a cloud database. The position of each sensor is illustrated in Fig. 4.6, and types of sensors and corresponding parameters are labeled in Table 4.2.

4.4.5.1 Temperature of extraction and boiler The type of temperature sensors chosen for extraction and the boiler is PT100 platinum resistance thermometer (PRT) [26]. It is generally suited to

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FIGURE 4.6 Mounting positions of sensors.

TABLE 4.2 Types of sensors with corresponding measured parameters. Parameters

Types of sensors

Boiler temperature

Platinum resistance thermometer

Extraction temperature

Platinum resistance thermometer with microprobe

Boiler pressure

Gauge pressure sensor

Extraction pressure

Piezoelectric pressure sensor

Flow rate

Flowmeter

a temperature range of 2240 C to 649 C to an accuracy of between 0.1 C and 1 C. Therefore the PRT is selected from among the many types of sensors based on its accuracy and performance. For example, a thermocouple can cover a larger temperature scope of 2267 C to 2316 C [27], yet is only accurate to between 0.5 C and 5 C [28]. The margin of error is small but still can impact the extraction process. Furthermore, cheaper resistance thermometers, made from either copper or nickel, are not selected due to higher likelihood of oxidation and overheating [29]. Furthermore, due to the moderate working temperature in a coffee machine, connecting wire material is silver-plated copper/ fluorinated ethylene propylene (FEP) teflon-insulated wire with a maximum operating temperature of 204.4 C [30]. To determine pressure during extraction, the PRT can also be used. Fig. 4.7 shows that the probe can be mounted on top of the boiler through a

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FIGURE 4.7 A PRT mounted on a boiler (section view). PRT, Platinum resistance thermometer.

TABLE 4.3 Specifications of platinum resistance thermometers for the temperature of boiler and group-head [28,31]. Measured location

Boiler

Group-head

Series

P-M Series

PR-24 Series

Probe type

Closed ended

Closed ended

Sensor accuracy

Class A

Class A

Probe diameter

6 mm

3 mm

Mounting thread

1/2 NPT thread

1/4 NPT thread

Ultimate temperature range

2100 C to 250 C

250 C to 500 C

NPT, National pipe taper.

screw terminal so that it can probe inside the boiler. A potential product suitable for this function is the PR-24 Series of resistance temperature detectors (RTDs) manufactured by OMEGA, which contains a minimum 6-mm diameter screw and the Pt100 sensor [28]. The relative specifications of these PRTs are specified in Table 4.3.

4.4.5.2 Pressure of extraction and boiler Pressure sensors provide an output based on physical pressure input [26]. Types of pressure sensors vary with different functional principles, such as the pressure range and size of objects. A gauge pressure sensor as in Fig. 4.8 measures boiler pressure relative to the ambient atmospheric pressure [32]. Pressure sensors applicable for boilers are often stainless steel. Examples of

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FIGURE 4.8 A gauge pressure sensor mounted on the boiler (section view).

TABLE 4.4 Specifications of sensors for the pressure of boiler and grouphead [32,33]. Measured location

Boiler

Group-head

Model

SSIB010GU4AH5

DAC 102

Sensor type

Gauge

Piezoelectric

Mounting thread

1/4 NPT thread

M7

Ultimate pressure range

200 mbar to 35 bar

0 250 bar

Ultimate temperature range





240 C to 120 C

240 C to 400 C

NPT, National pipe taper.

pressure sensors are shown in Table 4.4 with relevant specifications. One suitable sensor model is the SSIB010GU4AH5 made by the first sensor. The maximum measured pressure can be 10 bar, and the range of operating temperature is from 240 C to 120 C [32]. In consideration of the limited space and the relatively high operating temperature in a group-head, a piezoelectric pressure sensor is ideal for measuring extraction pressure as in Fig. 4.9. The piezoelectric effect allows a piezoelectric membrane to produce a voltage output proportional to the measured pressure [34]. The DAC 102, made by BD sensors [33], has hightemperature tolerance, a broad pressure measurement range, high sensitivity, and a compact size.

4.4.5.3 Flow rate of water Positive displacement flowmeters are widely used for measuring the volume of water in low flow rate situations [35], such as the flow rate of water

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FIGURE 4.9 A pressure sensor and a temperature sensor mounted on a group-head.

FIGURE 4.10 A group-head with a flowmeter.

outside the group-head. The amount of water flow is related to the rotation of an impeller inside. As a flowmeter works in an environment of high pressure and high temperature, tolerances of 9 bar and 95 C should be applied, respectively. A common flowmeter is shown in Fig. 4.10, where water flows through the flowmeter to the group-head.

4.4.6

Digital twindriven virtual verification application

4.4.6.1 Cause analysis Via DT, a virtual model of the espresso machine is remotely accessible by the designers. The DT system will display operational parameters for individual espresso machines and keep a record of all historical data.

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Furthermore, these espresso machines can even become integrated with other IoT devices, with data uploaded and stored in a cloud storage database. Meanwhile, the virtual representation of each espresso machine based on the data can be visualized using computer software. The DT system can also collect data from other relevant sources such as customer feedback on coffee quality, maintenance records, and operational data [12]. The integration of multiple data sources allows designers to better understand the nature and causation of problems, for example, to consider a situation where customers are complaining that an espresso machine’s coffee is weak and sour tasting. To be specific, a sensor detects a low extraction pressure, indicating that underextracted coffee has occurred. Given that the replacement frequency and maintenance of the group gasket is consistent, it can be deduced that the group-head seal is likely the cause of the issue. Correlation between the gasket issues and coffee taste can be stored in the database and used to identify similar issues in the future. Further analysis of the damaged group-head seal from operational data can also shed light on its cause. For example, it could be observed that the reason for the damaged group-head seal lies in the fact that the boiler works overnight even when the extraction section is not in use. The original purpose of continuous operation of the boiler is to ensure a consistent extraction temperature when the extraction section can be started to operate at any time. As the boiler takes time to reheat if switched off, generally cafe´s leave them on overnight to resume operation straight after opening. Furthermore, the electricity is also used overnight for the same reason. However, as the group seal is continuously exposed to high temperatures and it erodes at a faster rate. Such real-time data as mentioned in the previous paragraph is continually stored and updated in the cloud. A key benefit of DT technology is the ability to construct new and improved virtual models based on correlations between product performance and component problems. Therefore it is possible for designers to predict future outcomes during the development of novel designs.

4.4.6.2 Potential solutions In addition to identifying problems, DT can also be applied in conceptual design to create and model new designs. Based on data collected from espresso machines, an accurate virtual model can be created for verification. Continuing with the previous example of the group-head seal, several solutions can be modeled virtually. There are two main ways in which designers can extend the life span of group-head seals: reducing exposure to harsh conditions and increasing heat and pressure tolerances of materials. Using DT, designers could model the two solutions and determine the best option before manufacture. A solution

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analyzed below using the virtual framework is to add a “night mode” to reduce operating temperature when the machine is not in use.

4.4.6.3 Addition of a night mode in product design To add the night mode feature, a printed circuit board (PCB), or control board, could be redesigned to control temperature settings and monitor machine use. Once the conceptual design for the PCB is complete, the design can be modeled via the five-step virtual verification framework detailed in Fig. 4.11. The first stage, design, involves consideration of geometry, material, and function. The geometry and configuration of the PCB can be constructed as a 3D model and tested against dimensional constraints. Material properties of the PCB, such as heat resistance, can be verified by modeling distances between the heat sources. The functionality of the PCB can be modeled based on the input and output voltages and current. Similarly, the manufacturing stage involves analysis of logistics, process control, and quality control. Development of a virtual factory could help determine the logistical requirements of a product, such as time required to manufacture particular shapes, sizes of components, and for supply of materials. Virtual verification of the process line can reveal special constraints, inefficiencies in the order of manufacture, the rate of manufacture, and energy required. Also, any manufacturing processes that increase the rejection rate of the PCBs can also warn designers to optimize the PCB layout or design.

FIGURE 4.11 DT-driven virtual verification of the night mode function. DT, Digital twin.

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Next, in the usage stage, information on life span, customer habits, and safety are examined. Initially, using operational data gathered from existing espresso machines, the life span of components and even the entire concept can be modeled. Night mode can also be tested against different barista habits and working hours. For example, it could be determined that there is no benefit if the machine is in operation for 18 or 24 h/day. Another important feature of the virtual model is the ability to model potentially hazardous situations, such as an overheating boiler causing short circuit to the electronic components or catching fire. The maintenance thread contains the maintenance period, easy maintenance, damage, and failure. The maintenance frequency of components can be predicted based on operational data. In addition, the repair process of each component can be simulated to identify all significant costs of inefficiencies. Virtual verification can also detect damages and failure caused by maintenance, and potential failure that can be found during a regular inspection. In the last thread, virtual verification in the EOL stage can analyze the recycle rate of components to identify high performance components that can serve for a new round of service life. Virtual verification of methods of waste disposal can also help identify sources of pollution and postulate the effect of using other materials. Finally, defects of components that still exist in the final stage can be verified and predicted for the secondary improvement.

4.4.6.4 Iteration of digital twin driven virtual verification process As each design modification might introduce new issues, the DT-driven virtual verification will require multiple iterations to produce a final design. An advantageous modification in the design stage may cause issues in the manufacture stage, or likewise a beneficial feature in the manufacture stage may create problems during the EOL stage. It equips designers with a newfound capability to beta-test feature improvements in the concept stage. Furthermore, real-time feedback data from each product life cycle stage will allow the DT to be updated and improved continually. Further iteration and practical implementation of the product life cycle process will make the DT system more powerful and useful for the designers.

4.5 Case study II: digital twin driven virtual verification in design for 3D printers The 3D printer is an industrial tool used to construct complex 3D structures, typically made of polymers, metals, and ceramics. 3D printers integrate networking, automation, and data processing to create production of customized

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designs. The following case study aims to demonstrate the application of DT-driven virtual verification for 3D printers.

4.5.1

Background of 3D printing

3D printing represents an additive production process that builds an artifact layer by layer. Introduced by Kodama, the first 3D printing method was put forward in the form of vat photopolymerization in 1981. Later, Hull optimized the vat photopolymerization method with stereolithography. Up to 1987 the vat photopolymerization had won significant business success, which promoted the further development of more 3D printing technologies. Material extrusion, sheet lamination, selective laser sintering, binder jetting, and directed energy deposition were invented during this period. In recent years, 3D printing has experienced rapid technological and business adoption [36]. 3D printing plays a critical role in the emerging trend of smart manufacturing. It has notable benefits compared to traditional manufacturing technologies. First, 3D printers can fabricate complex internal structures of varied shapes. Second, each artifact is modeled based on a CAD file, which introduces presimulation before manufacturing. Third, most 3D printers require no tooling, no molding, and minimal postprocessing compared to conventional manufacturing techniques. Finally, as the parameters of a 3D printed design are developed and controlled digitally, product data is readily available. DT enables real-time monitoring of how separate parameters of 3D printers perform, making it easier to specify areas for improvement. Parameters include materials, equipment, and computational processing. Enhancement of each parameter improves product quality, productivity and reduces production costs. Table 4.5 shows some key parameters of powder bed fusion (PBF), a method of 3D printing in which a laser heat source binds powder of a material to create a solid, 3D form [37]. Traditionally, parameters are optimized based on the iterations of simulation, equipment upgrades, and physical tests. DT aims to streamline this process.

4.5.2

Virtual verification of 3D printer design

Existing CAD software allows designers to verify geometrical designs, material usage, and the functional mechanism of a 3D printer and design. However, it cannot evaluate the performance of the 3D printer itself nor the functionality of the 3D printed design in a real-world environment. Application of DT-driven virtual verification aims to bridge the gap between virtual and physical simulations of these designs. A DT-driven approach offers two major benefits to fortify tolerance design compared to conventional CAD. One is that it can easily measure

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TABLE 4.5 Parameters of powder bed fusion. Parameters

Explanation

Equipment parameters Laser power

Applied energy per time unit to the laser beam

Spot size

Intensity of the laser beam, measured in energy per time and area

Quality

Focus ability of the laser beam

Wave length

Wave length of the laser beam

Material parameters Heat conductivity

Rate of heat flows through the material

Specific heat capacity

Energy required to raise the material temperature at constant pressure

Scattering coefficient

Amount of the energy of incident radiation scattered by particles

Reflectance

Strength of the radiation caused by energy reflection

Processing parameters Scan speed

The speed that laser moves across the bed

Layer thickness

Thickness of melted powder after one round scanning

Scan rotation

Angle of rotation that scan adjusts between two consecutive layers

Scan length

Length of the scanning track

physical designs to ensure accuracy and keep a historical database of previous model dimensions for comparison. For example, the size of the nozzle through which filament is printed could be evaluated or even adapted to improve model tolerance. Though CAD software models the geometrical shape, joints, and assembly of an object, it is often difficult to evaluate this sort of tolerances. The other is that DT introduces the simulation in realworld environment. For example, humidity and temperature sometimes keep changing in real-world environment, DT will capture this change and model its effect of cooling for nozzles. Conventional CAD focuses on the modeling for products while modeling for the environment is relatively simple. Functional verification is an especially challenging task for product design. Traditionally, it is based on the customer voice and designer experience. Given the subjectivity of a typical small sample of customers and designers, determination of real-life performance during the conceptual stage often contains errors and biases. For example, a sample of customers by

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FIGURE 4.12 Functional design verification examples of 3D printer.

sheer probability might confer that the size of a 3D printed phone case should be large. However, in practical conditions, most customers might purchase small cases. The advantage of DT is that it allows the usage of products to be tracked, providing far greater numerical and objective data for design decisions. Furthermore, as shown in Fig. 4.12, DT may also incorporate virtual reality techniques to simulate addition of new features to existing products based on current use cases.

4.5.3 Virtual verification of 3D printer design, manufacturing, and usage The 3D printer is both a product and a manufacturing apparatus. Therefore the process of DT-driven virtual verification is explained below in terms of both the 3D printed product and 3D printer functionality. Using the parameters defined in Section 4.5.1, as shown in Fig. 4.13, the PBF printer is used as an example to illustrate the process. A PBF printer operates by fusing layers of powder together by laser thermal energy to construct a 3D object. According to the five-stage framework, the design, manufacturing, and usage are examined to determine how DT-driven design is implemented, followed by the benefits this can achieve.

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FIGURE 4.13 General model of a powder bed fusion machine.

Metal powders are common 3D printing materials used in industry. The major issue of 3D printing with metallic materials is that each layer is forged separated, often resulting in improper grain formulation and tiny fractures within the printed structure. Although these effects can be mitigated by refining metal powder, the cost of fabrication increases dramatically [38]. Therefore if it is possible to improve the microstructures of 3D printed metal objects through modification of 3D printer parameters, the economic and functional feasibility of 3D printing can be improved significantly. At present, quality verification of 3D printed metal products often requires iterations of strength testing. Furthermore, optimizations of the PBF process require iterations of prototyping to achieve a desired result. The high costs associated with materialographic analysis, strength testing, and time required to produce multiple physical models limits the tenability of 3D printing as an alternative to other manufacturing methods. Developing a tenable virtual verification method of 3D printing could eliminate many of the hurdles impeding the advancement of 3D printing metals. Using the example of a 3D printed turbo compressor, the method and potential of DT-driven virtual verification is explored.

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There are two methods by which an iterative DT-driven design process can be developed. The first method involves a method by which the 3D printer analyzes microstructures under different conditions prior to manufacture. For example, a 3D printer can be automated to the point where it can autonomously print a 3D structure under specific conditions, cut into the microstructure, and perform computational analyses on the grain structure of the material. Over thousands of iterations, using machine-learning methods it is possible to identify correlations between printing conditions and material properties. The second method involves generating performance feedback among 3D printing conditions, material structure, and real-life performance of the product. Using the example of the compressor, by equipping the turbo with sensors for all conditions and monitoring feedback of its performance, correlations between 3D printing methods and performance are identifiable. Therefore using this data it becomes possible to develop a self-improving virtual model for both the design of products and manufacturability. Except for data analysis, DT provides an alternative way of verification by amplifying microscopic phenomenon. As introduced in Section 4.2.1, visual observation is an important approach of verification. Many physical phenomena reflect the quality but invisible in normal conditions. Once amplified, experienced engineers can verify them intuitively. Hence, as DT allows engineers to observe virtual products in micro view, quality of products can be verified visually. For example, Fig. 4.14 presents fracturing

FIGURE 4.14 Verification example of 3D printing metal fracturing.

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conditions under different parameter settings. In micro view, engineers can directly observe the fracturing condition and hence identify the better setting. Given the functional limitations of 3D printing for large-scale manufacture, DT-driven virtual verification offers many benefits for its advancement. Providing sensory mechanisms for data collection are in place, in an age of the IoT it is possible to create a large data pool for analysis. Big data processing methods and machine-learning techniques can analyze information from the design, manufacture, and use of compressors to generate selflearning virtual models. Designers can then optimize designs and analyze future performance a priori during the conceptual stage. This capability helps one to overcome two major limitation of 3D printing manufacture. First, it allows for the quality of 3D printed products to be continuously improved, helping one to overcome issues caused by inferior material structures as a result of the process. Second, it expedites the 3D printing process, by eliminating many of the design hurdles caused by product testing. In theory, by providing the capability to provide ease in design, model, and manufacture products to specific requirements, a new era of mass manufacture is achievable. No longer will mass manufacturing systems need to be set up to make the same product; it will be possible for products to be continually innovated upon in real time, significantly expediting the concept-to-market process.

4.5.4 life

Virtual verification of 3D printer maintenance and end-of-

The other key areas of DT-driven design include 3D printer maintenance and EOL design stages. Like the above section in which manufacturing and usage are analyzed simultaneously, maintenance and EOL will be discussed together. The life cycles of 3D printer components vary significantly. For example, Fig. 4.15 illustrates the general structure of a nozzle on a material extrusion printer. Stepper motor, gears, cooling fans, nozzle head, heaters, and thermistors all have failure risks; though due to the low durability of nozzle heads and cooling fans these tend to fail before others. Though material extrusion is a simple technology, maintenance typically only occurs when the functionality of a 3D printer is impaired. Due to the simplicity, it might not be cost-effective to attach a dedicated DT system to each material extrusion nozzle. However, it is possible for sensors to monitor multiple outputs and functions simultaneously, managed by a central DT system. As the DT system learns to understand the causes and effects of issues, its accuracy for self-diagnosis improves. An example of nozzle maintenance and EOL disposal procedure is shown in Fig. 4.16. Once data from the printer has been collated over time, Weibull plots that analyze the failure risk throughout a component’s life cycle can predict failure thresholds. Once the failure risk approaches the threshold, the

FIGURE 4.15 General structure of the material extrusion printer nozzle.

FIGURE 4.16 Maintenance and end-of-life disposal of the nozzle.

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DT will remind engineers to maintain or replace the component. Historical maintenance data can be retrieved using the DT to evaluate the maintenance, repair, and replacement costs. In the case of component disposal, sustainability is an increasingly important factor for consumer products. Since nozzles are fabricated with multiple metals and polymers, it is necessary to adopt different disposal methods. The cloud service of DT will gather the information concerning the disposal of different materials to provide references.

4.6

Summary

A DT-driven virtual verification system gives designers a revolutionary capability to assess design performance at the conceptual design stage. Specifically, through the five-stage framework described above, the application or utility of DT is demonstrated in the following ways. First, it provides an efficient method for designers to iterate designs without the expensive and time-consuming physical tests, especially when products would take generations to improve and refine. This efficiency, coupled with greater resources and simulation functions, creates new opportunities for designers to innovate. Second, use of sensors and IoT technology allows for uploading real-time data. When coupled with advanced machine-learning and big data analytics, data from the physical world can be used to update the virtual world continually. It follows that with every iteration and use case, DT will become increasingly powerful. It is the backand-forth iteration, in combination with existing and new technologies, that will demonstrate and enhance the power of DT. Moreover, given the limited amount of research on DT systems, the framework and case studies provide greater understanding of how DT can be used in design. By expanding the application domain of DT beyond design into product usage, manufacturing, maintenance, and EOL, designers can better examine the full-scale impact of design modifications. However, the completeness of research is limited, in that not all technological factors are assessed in this chapter. The framework forms the foundation for applying DT in design and highlights other areas of research of DT. The five-stage framework can help one to realize the potential of DT for product design. It forms a road map for the significant roles that DT technologies can play in the key stages of product design. Despite the utility of the framework that is evident through its application to the key facets of the product life cycle, its development depends heavily on the integration of multiple technologies. Hence, the subsequent work for developing DT should focus on the analysis of current and future technologies that could make DT-driven virtual verification a reality. For example, analysis on the limits of current 3D modeling technologies, such as CFD and finite element analysis (FEA), could help identify areas that need to be improved to develop DT. Also it is important to further understand what IoT technologies exist now and what is

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not yet feasible to develop DT, and in addition, to know the extent to which machine-learning and big data analysis technologies can interpret data relevant to the product life cycle.

References [1] I. Babuska, J.T. Oden, Verification and validation in computational engineering and science: basic concepts, Comput. Methods Appl. Mech. Eng. 193 (36) (2004) 4057 4066. [2] R. Plant, R. Gamble, Methodologies for the development of knowledge-based systems, 1982 2002, Knowl. Eng. Rev. 18 (1) (2003) 47 81. [3] L.E. Schwer, Guide for verification and validation in computational solid mechanics, in: 20th International Conference on Structural Mechanics in Reactor Technology (SMiRT 20), Espoo, Finland, 2009. [4] P.G. Maropoulos, D. Ceglarek, Design verification and validation in product lifecycle, CIRP Ann.-Manuf. Technol. 59 (2) (2010) 740 759. [5] S. Moaveni, Finite Element Analysis Theory and Application With ANSYS, Pearson Education, India, 2011. [6] F. Moukalled, L. Mangani, M. Darwish, The Finite Volume Method in Computational Fluid Dynamics: An Advanced Introduction With OpenFOAMs and Matlab, Springer International Publishing, 2015. [7] ICS, ISO 17450-1:2011 Geometrical Product Specifications (GPS) General Concepts Part 1: Model for Geometrical Specification and Verification, 2011. [8] G.N. Peggs, P.G. Maropoulos, E.B. Hughes, A.B. Forbes, S. Robson, M. Ziebart, et al., Recent developments in large-scale dimensional metrology, Proc. Inst. Mech. Eng., B: J. Eng. Manuf. 223 (6) (2009) 571 595. [9] W.L. Oberkampf, T.G. Trucano, Verification and validation in computational fluid dynamics, Prog. Aerosp. Sci. 38 (3) (2002) 209 272. [10] International Vocabulary of Terms in Legal Metrology (VIML), International Vocabulary of Terms in Legal Metrology, 2011. [11] US Food and Drug Administration, General principles of software validation, in: Final Guidance for Industry and FDA Staff, Food and Drug Administration, 2002, p. 11. [12] F. Tao, F. Sui, A. Liu, Q. Qi, M. Zhang, B. Song, et al., Digital twin driven product design framework, Int. J. Prod. Res. (2018). Available from: https://doi.org/10.1080/ 00207543.2018.1443229. [13] E.H. Glaessgen, D. Stargel, The digital twin paradigm for future NASA and US Air Force vehicles, in: 53rd Structures, Structural Dynamics, and Materials Conference: Special Session on the Digital Twin: Digital Twin, Hawaii, 2012. [14] M. Grieves, J. Vickers, Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems, Transdisciplinary Perspectives on Complex Systems, Springer, 2016, pp. 85 113. [15] D. Ullman, The Mechanical Design Process, McGraw-Hill Education, 2009. [16] R.H. Ballou, Business Logistics/Supply Chain Management: Planning, Organizing, and Controlling the Supply Chain, Pearson Education, India, 2007. [17] J. Wang, T. Ruxton, Design for safety, Prof. Saf. 42 (1) (1997) 24. [18] J. Fiksel, Design for Environment, second ed., McGraw-Hill Education, 2011. [19] Dave. What is a group head and how does it work? Available from: ,https://knowyourgrinder.com/what-is-a-group-head-and-how-does-it-work/., 2016.

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[20] S. Appliances, Sage ‘dual boiler’ espresso machine - how it works. Available from: ,https://www.youtube.com/watch?v 5 IIFUZZ9ygUs., 2014. [21] A. Illy, R. Viani, Espresso Coffee: The Science of Quality, Elsevier Academic, 2005. [22] Istituto Nazionale Espresso Italiano, The certified Italian Espresso and Cappuccino. Available from: ,http://www.espressoitaliano.org/files/File/istituzionale_inei_hq_en.pdf.. [23] Espresso Machine Company, Under-extracted espresso. Available from: ,https://www. espresso.co.nz/espresso-knowledge/under-extracted-espresso/.. [24] J. Borack, Coffee extraction: sour VS bitter & how to tell the difference. Available from: ,https://angelscup.com/blog/taste/coffee-extraction-sour-vs-bitter/., 2015. [25] M. Perger, Coffee extraction and how to taste it. Available from: ,https://baristahustle. com/blogs/barista-hustle/coffee-extraction-and-how-to-taste-it., 2015. [26] D.S. Nyce, Linear Position Sensors: Theory and Application, Wiley, 2004. [27] Ultra Electronics, RTD vs. thermocouple comparison chart. Available from: ,https:// www.ultra-nspi.com/information-central/rtd-termocouple-comparison/.. [28] OMEGA, Temperature probes. Available from: ,https://www.omega.co.uk/temperature/z/ thermocouple-RTD.html.. [29] W. Nawrocki, Measurement Systems and Sensors, second ed., Artech House Publishers, 2016. [30] Pyromation, How to select and use the right temperature sensor. Available from: ,https:// www.pyromation.com/TechInfo/WhitePapers/ How_to_Select_and_Use_the_Right_Temperature_Sensor.aspx.. [31] OMEGA, Ultra precise RTD sensors for industrial applications. Available from: ,https:// au.omega.com/pptst/P-ULTRA_RTD.html.. [32] FirstSensor, Understanding the difference between absolute, gage and differential pressure. Available from: ,https://www.first-sensor.com/en/products/pressure-sensors/pressure-sensors-and-transmitters/pressure-types.html., 2018. [33] BDSensors, DAC 102 dynamic pressure measurements or precision measurement. Available from: ,https://www.bdsensors.de/en/pressure/piezoelectric-pressure-sensors/ details/produkt/dac-102/.. [34] M.S. Vijaya, Piezoelectric Materials and Devices: Applications in Engineering and Medical Sciences, CRC Press, 2016. [35] F.R. Spellman, The Science of Water: Concepts and Applications, second ed., CRC Press, 2007. [36] K.V. Wong, A. Hernandez, A review of Additive Manufacturing, ISRN Mechanical Engineering, 2012. [37] B. Foster, E. Reutzel, A. Nassar, B. Hall, S. Brown, C. Dickman, Optical, layerwise monitoring of powder bed fusion, in: Solid Freeform Fabrication Symposium, 2015, pp. 10 12. [38] W.J. Sames, F.A. List, S. Pannala, R.R. Dehoff, S.S. Babu, The metallurgy and processing science of metal additive manufacturing, Int. Mater. Rev. 61 (5) (2016) 315 360.

Chapter 5

Digital twin driven design evaluation Lei Wang1, Fei Tao2, Ang Liu3 and A.Y.C. Nee4 1

School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan, P.R. China, 2School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R. China, 3School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia, 4Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

5.1

Introduction

New product development has been considered one of the keys to maintain competitiveness in the fast-growing and highly globalized market [1]. The rapid advances in technology, fast-changing customer needs, and increasingly intense market competition are the impelling factors for companies to develop new products in shorter time, with lower cost and higher quality [2]. It is reported that the product design stage determines as much as 70% of a product’s cost and performance [3]. This is especially true for modularized products, for which the choice of design parameters and configurations will subsequently affect the performance throughout the entire product life cycle (PLC). A poor selection of design parameters for a particular product may increase not only the development cost but also cause additional modifications or even impede the success of the overall new product development [4]. In order to improve the quality and speed up the process of product design, product design evaluation (PDE) has been widely used at the product design stage. PDE at the early stage of product design has been widely recognized as one of the most critical phases in new product development as it determines the direction of the downstream design activities. It is important for designers to assess the impacts of variations of different design configurations on product performance [5]. The costs of PDE are relatively low but can help enterprises grasp the market direction and avoid many decisionmaking mistakes. However, compared with other stages of PLC, it is more dependent on human experience and is difficult to improve through the Digital Twin Driven Smart Design. DOI: https://doi.org/10.1016/B978-0-12-818918-4.00005-1 © 2020 Elsevier Inc. All rights reserved.

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simple application of some technologies. Most of the traditional methods are based on certain fixed rules, such as analytic hierarchy process (AHP) [6], Taguchi [7], failure mode [8], effects analysis [9], and Kano model [10]. Therefore these methods cannot effectively support product evaluation at the design stage before the product is manufactured and utilized. First, the traditional design evaluation methods mostly require that designers must have in-depth domain knowledge to perform iterative assessment of design alternatives. Second, product design in the virtual space and performance evaluation in the physical space are largely separated from each other. As a result, the product development process is stretched by the alternation between design and evaluation. Lastly, traditional evaluation methods are inadequate to process the “big data” generated at the manufacturing and service stages. The connection of the various stages of PLC is severed, which affects the accuracy and dynamism of product evaluation. Against this background, this chapter describes a digital twin (DT)
driven multiobjective PDE method, which integrates the virtual and physical spaces throughout the different stages of PLC. In this method the framework and workflow are established to achieve simulated evaluation in the virtual space, data collection in the physical space, and integration between the virtual and physical spaces. In addition, the PDE algorithms based on artificial neural network are also need to support the nonlinear mapping between the virtual and physical spaces. The remainder of this chapter is organized as follows. Section 5.2 reviews some relevant previous work. Section 5.3 proposes the DT-driven PDE methodology. Section 5.4 introduces the PDE algorithms that can be applied in the methodology. Section 5.5 presents a cased study of roll granulator design evaluation to illustrate the application of the proposed PDE method.

5.2 5.2.1

Related works Existing product design evaluation methods

Generally, PDE involves complex multiobjective decision-making with many factors. In the past, various intelligent models and algorithms have been developed to select the best alternatives at the product design stage. Aya˘g proposed a hybrid method to help decision-makers evaluate the conceptual design alternatives based on the AHP method [11]. With further ¨ zdemir found that the AHP cannot accommodate the research, Aya˘g and O variety of interactions, dependencies, and feedback between elements at higher and lower levels [12]. Hence, they proposed an analytic network process to evaluate a set of conceptual design alternatives in order to reach the best concept and expectations of both customers and company. Anand and Wani presented an evaluation procedure for product life cycle design at the

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conceptual design stage using a digraph and matrix approach [13]. You et al. proposed a multiobjective mixed-integer linear programing model to optimize the design and planning of cellulosic ethanol supply chains with three objectives: economic, environmental, and social factors [14]. Fukushige et al. defined a representational scheme and established a truth maintenance system to evaluate the design, visualization, and the entire PLC [15]. Hosseinijou et al. suggested that material selection decisions must concern not only performance indicators but also economic, social, and environmental impacts. Accordingly, they presented a multiobjective assessment method of PLC [16]. Ajukumar and Gandhi adopted fuzzy preference programing (FPP) and TOPSIS to evaluate the green maintenance aspects of mechanical systems and for ranking design alternatives [17]. Hassan et al. proposed a systematic approach to evaluate the sustainability of configuration alternatives based on the weighted decision matrix and artificial neural network [18]. Most of the abovementioned methods of design evaluation come from experts’ subjective judgments that sometimes are unstable or inconsistent. To prevent inaccuracy of evaluation results due to data inconsistency, some researchers introduced fuzzy logic into the evaluation models and developed various decision-making methods. Kuo et al. proposed a new method to evaluate design alternatives in consideration of environmental factors based on fuzzy logic [19]. Vanegas and Labib presented a new fuzzy-weighted average method for engineering design evaluation [20]. Akadiri et al. came up with a novel fuzzy extended AHP model to facilitate designers to select building materials at the early design stage [21]. Li et al. proposed a fuzzy set
based approach to evaluate design against its tribo-maintainability at the design stage [22]. Zhu et al. presented a systematic PDE method by fuzzy AHP and the VIKOR method as complementary to subjective judgments. In their study the fuzzy AHP based on rough number was presented to determine the weight of the criterion and the VIKOR method was proposed to evaluate design concept alternatives [4]. Wang et al. established a multicriteria decision-making model to address engine health assessment, which combines three fuzzy evaluation methods: AHP, FPP, and TOPSIS [23]. Chang et al. proposed a multiattribute decision-making model based on the fuzzy linguistic quantifier, through which a fuzzy concept was optimized in view of the four objectives: cost, quality, service, and response [24]. To make green supply chains more efficient and effective, Grisi et al. analyzed the supplier performance and proposed a fuzzy AHP model for “green” supplier evaluation [25]. In addition, many other models and algorithms were introduced in product decision evaluation to reach a balance among economic, environmental, service, and other indicators. Peng et al. proposed a web-based visualization system for product design and performance evaluation, which was proven effective in increasing product maintainability and reducing development

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cost [26]. Tao et al. analyzed the energy consumption involved in the three phases of PLC (design, production, and service) and discussed how to employ relevant internet of things (IoT) technologies to reduce energy consumption [27]. Jian et al. holistically considered both inherent attributes and external factors and established a novel evaluation index system to evaluate product maintainability based on the fuzzy AHP [28]. Geng et al. proposed a novel integrated design concept evaluation approach based on vague sets [29]. To improve the quality and effectiveness of concept evaluation at the new product development stage, Song et al. proposed a new decision-making approach for making more rational selections from the alternatives [30]. Wang et al. proposed a static optimization model and a dynamic scheduling model to evaluate and optimize the service cost throughout PLC in the cloud manufacturing environment [31,32]. To structure the literature review of PDE methodologies, a systematic summary is listed in Table 5.1. The codes of Table 5.1 are defined in Table 5.2. QE denotes the qualitative evaluation using fuzzy methods and QO denotes the quantitative optimization using evolutionary algorithms. Following are the suggestions given by structured literature review: 1. The focus of evaluation and optimization lie in indicators such as cost, quality, performance, environment, and service. Nevertheless, there has been little study on the mutual coupling relationships among these indicators. 2. Most of the existing methods are rule-driven, which can hardly adjust the evaluation parameters dynamically according to historical evaluation data. As manufacturing enters the big data era, data is increasing in magnitude. Therefore it calls for more effective data-driven methods to optimize and evaluate the product design process.

5.2.2

Digital twin driven product design methods

In order to realize effective interaction and integration between the virtual space and the physical space, Grieves proposed the notion of DT in 2014 [33]. The rapid advancement of information technologies, which are increasingly being applied in PLC, has paved the way for DT R&D. In light of the great potential of the DT in adapting to the changing market, accelerating product development speed, and saving product development costs, more efforts have been devoted to the application of DT at the product design stage. The DT is mostly used for simulation, optimization, and validation in the virtual space for product design. Tuegel et al. discussed how the DT can be used for predicting the life of aircraft structure and detailed the technical challenges in developing and applying the DT technologies [34]. Tao et al. discussed the application of DT in design, manufacturing, and service stages

TABLE 5.1 Objectives and solutions of reviewed work. Reference

Evaluation objectives C

Aya˘g

P

Q

S

Evaluation methods E

EA

DM

3

AHP

Evaluation category TO

3

FE

MM

ANN

QO

3 3

3

Anand and Wani

3

3

3

You et al.

3

Fukushige et al.

3

3

3

Hosseinijou et al.

3

3

3

3

3

3

Vanegas et al. Akadiri et al.

3

Li et al.

3

3 3

Zhu et al.

3

Wang et al.

3

Chang et al.

3

Grisi et al.

3

3

3 3

3

3

3 3 3

3

3

3

3

3

3

3 3

3

3

3

3

3

3

3

3 3

3

3

3

Kuo et al.

3 3

3

Ajukumar et al.

3

3

3

QE 3

Aya˘g et al.

Hassan et al.

OM

3 3 3

3 3

3

3

3

3

3

3

3 (Continued )

TABLE 5.1 (Continued) Reference

Evaluation objectives C

Peng et al.

P

Q

3

S

Evaluation methods E

EA

DM

AHP

Evaluation category TO

FE

3

Jian et al. 3

Wang et al.

3

This chapter

3

ANN

OM

QO

3

3

3

3

3 3

3

3

QE 3

3

Tao et al.

Song et al.

MM

3

3

3

3

3

3

3

3

3

3 3

3

3

3

Digital twin driven design evaluation Chapter | 5

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TABLE 5.2 Classification codes of reviewed works. Category

Detail

Code

Evaluation objectives

Cost

C

Performance

P

Evaluation methods

Optimization category

Quality

Q

Service

S

Environment

E

Evolutionary algorithm

EA

Decision matrix

DM

Analytic hierarchy process

AHP

TOPSIS

TO

Fuzzy evaluation

FE

Mathematical model solution

MM

Other methods

OM

Artificial neural network

ANN

Quantitative optimization

QO

Qualitative evolutionary

QE

[35] and proposed a DT-driven product design framework [36]. Zheng et al. summarized the related research of DT technology and proposed an application framework for product life cycle management [37]. Zhang et al. proposed a DT-based approach for the rapid customization of a hollow glass production line [38]. Guo et al. proposed a modular approach to help in building a flexible DT to quickly evaluate different designs and identify design flaws in an easy way [39]. In addition to product design, studies of DT technology are also widely conducted in the design and optimization of manufacturing workshops. Tao et al. proposed a novel concept of DT shop-floor and discussed four key components, including physical shop-floor, virtual shop-floor, shop-floor service system, and shop-floor DT data [40]. Liu et al. proposed a DT-driven methodology for rapid individualized design of the automated flow-shop manufacturing system [41]. Uhlemann et al. introduced a DT-based learning factory to demonstrate the potentials and advantages of real-time data acquisition and subsequent simulation [42]. Leng et al. proposed a DT-driven manufacturing cyber-physical system for the parallel control of smart workshops [43]. In order to make CNCMT more intelligent, Luo et al. put forward a DT-driven multidomain unified modeling method and explored a mapping strategy between the physical space and the digital space [44].

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Zhuang et al. proposed a framework of DT-based smart production management and control approach for complex product assembly shop floors [45]. At present, research on DT technologies mainly focuses on several aspects: (1) building the mirror model in the virtual space, (2) implementing the mapping from the physical space to the virtual space, and (3) optimizing product design effects according to the virtual model or data. Comparatively, the research of DT technologies in product evaluation is still lacking. When the model of the virtual space can be well mapped to the physical space, the PDE model in the virtual space can better guide each stage of PLC. In consideration of the four objectives (cost, quality, environment, and service), this chapter describes a DT-driven PDE method. The method not only considers those factors of PLC but can also be applied in the big data context to improve accuracy and dynamism of the evaluation. As a result, designers are enabled to evaluate product design prior to production and service; hence, they can make timely adjustment and optimization at the early stage.

5.3 Digital twin driven product design evaluation methodology 5.3.1

Digital twin driven product design evaluation framework

As shown in Fig. 5.1, the DT divides a PLC into three stages (design stage, manufacturing stage, and service stage) that run separately in the physical space and the virtual space. In the virtual space, various operations in PLC are simulated, monitored, optimized, and verified. In the physical space the corresponding operations are sequentially executed according to the simulation results in the virtual space.

FIGURE 5.1 The process of PLC based on DT. DT, Digital twin; PLC, product life cycle.

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At the design stage the virtual design, verification, and evaluation models based on the DT are established in the virtual space. When the designers input the related parameters, such as design parameters, process parameters, raw materials, and production planning, into the DT model, it will prerun all the processes of PLC in the virtual space. If the users are not satisfied with the evaluation results, the DT can also reoptimize the input parameters. Therefore the designers can rapidly initiate a new product development project in a completely virtual fashion, which increases efficiency significantly. At the manufacturing stage the DT serves as real-time interaction interfaces between the virtual and physical spaces. It collects dynamic data continuously in the physical space and sets up the mirror models in the virtual space. At the service stage the DT enables real-time analysis and evaluation against a variety of performance indicators such as cost, quality, reliability, and energy consumption. Such data is fed back to the virtual models to make more accurate predictions during the product design stage.

5.3.2

Digital twin driven product design evaluation workflow

The DT-driven PDE workflow is shown in Fig. 5.2. In this method the evaluation system is mainly divided into five modules: physical data (PD) in the physical space, virtual data (VD) in the virtual space, and the connection

FIGURE 5.2 DT-driven product design evaluation workflow. DT, Digital twin.

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between the physical space and the virtual space. The PD denotes the actual data (i.e., design data, manufacturing data, service data, and evaluation data) collected from the physical space. Due to inconsistent data sources, there are many downsides in the PD, such as data corruption, large volume, and different dimensions. As a result, the PD cannot be directly applied to the DT system. The VD denotes the standard, lightweight, and pure data stored in the virtual space and is mainly used for archive, learning, prediction, and visualization. In addition, a complex network is established to realize the connection between the physical space and the virtual space. The evaluation method involves the following steps. Step 1: Product structure decomposition For mature or modular products (e.g., smartphones, computers, automobiles, and complex equipment), their functions and structures are largely fixed. To design a product the designers usually design each module without changing the original structure. When modules of different functions are integrated, the evaluation of products will become complicated and repetitive. The DTdriven product decomposition and evaluation method is shown in Fig. 5.3. Given a new product P, it is assumed that n different product architectures (e.g., hardware, software, and appearance) are available, that is, Product structure

New product design

Product architecture 1

...

Part 11

...

Part 1j

Product architecture i

...

Part 1m

Design alternative 111 .. .

Design alternative 1j1 .. .

Design alternative 1m1 .. .

Design alternative 11k .. .

Design alternative 1jk .. .

Design alternative 1mk .. .

Design alternative 11p

Design alternative 1jp

Design alternative 1mp

Product architecture n

...

...

Part n1

...

...

Part nj

Part nm

Design alternative n11 .. .

Design alternative nj1 .. .

Design alternative nm1 ...

Design alternative n1k .. .

Design alternative njk .. .

Design alternative nmk .. .

Design alternative n1p

Design alternative njp

Design alternative nmp

Virtual design

Actual design Learning

Virtual product 1 ... Virtual product i ... Virtual product n

Virtual process evaluation

Actual product i

Actual product 1

Actual product n

Actual process evaluation

Feedback

VPEI 1

VPEI2

VPEI3

...

VPEIl

...

...

VPEIs

VPEIq

Virtual final evaluation

APEI 1

APEI 2

APEI 3

...

APEI l

...

APEI q

Actual final evaluation

Feedback VPEI 1

...

VPEI r

Virtual space

AFEI 1

...

AFEI r

...

AFEI s

Physical space

FIGURE 5.3 A PDE framework of product design for a new product. PDE, Product design evaluation.

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P 5 {P1, . . ., Pi, . . ., Pn}. Any product architecture Pi can be decomposed into a set of product parts, that is, {Pi1, . . ., Pij, . . ., Pim}. For each Pij, it is assumed that a set of design alternatives {DAij1, . . ., DAijk, . . ., DAijp} is available to be evaluated, compared, and selected. Assuming that any DAijkAPij can be selected once, a new product design can be represented by the following formula: ( ) p p p p X X X X PA 5 x11k DA11k ; . . .; x1jk DA1jk ; . . .; xijk DAijk ; . . .; xnmk DAnmk k51

s:t:

k51 p X

k51

k51

xijk # 1

k51

ð5:1Þ

where xijk is a binary variable that equals to 1 if DAijk is selected, and 0 if otherwise. Step 2: Evaluation indexes analysis A two-stage evaluation system is established. It consists of process evaluation indicators (PEIs) influenced by the design decisions, as well as the final evaluation indicators (FEIs) influenced by PEI. Table 5.3 summarizes the PEI and FEI of a typical smartphone. The FEI mainly refers to a comprehensive evaluation indicator of mobile phones given by users and professional reviewers. This study considers a total of four FEIs (cost, environment, performance, and service), which are obtained through a large number of comprehensive scores. The PEI refers to the score of each architecture from every stage of PLC, namely, product design (PDN), product manufacturing (PM), and product service (PS). The PEI is essentially affected by the choice of design alternatives at the design stage. Step 3: Complex network building To integrate various stages of PLC between the virtual and physical spaces, a complex network consisting of three network structures is established: mapping network, prediction network, and feedback network. When the design and evaluation data is collected from the physical space, it should be cleared, compressed, and mapped into the virtual space. The DT system conducts supervised learning continuously and establishes a digital-driven prediction network. If the actual evaluation result is significantly different from the design evaluation result, the feedback network will start to function and relearn. The evaluation indicators are divided into actual evaluation indicators and virtual evaluation indicators. Assuming that the actual PEI (APEI) and actual FEI (AFEI) can be readily collected, PD and VD can be expressed by expressions (5.2) and (5.3), respectively:

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TABLE 5.3 Description of process and final evaluation indicators (FEIs). Indicator category FEI

PEI

PLC

Process evaluation indicators

Code

Description

/

Cost

C

The total cost of the product

/

Environment

E

A comprehensive score of environment indicators

/

Performance

P

The overall performance score from professional organizations

/

Service

S

Comprehensive score of customer’s satisfaction

PDN

Design cost

DE

PDN

Raw material cost

RA

PDN

Volatility of materials

VM

The overall environmental performance of the material selection

PM

Production cost

PC

Comprehensive cost of product manufacturing process

PS

Running speed

RS

The overall score from professional organizations

PS

Photo effect

PE

The overall score of the front and rear cameras

PS

Market cost

MC

PS

Service cost

SC

PS

Energy consumption

EC

Energy consumption during operation

PS

Service life

SL

The average service life data collected from the statistics institution

PS

Repair rate

RR

The percentage collected from the statistics institution

PS

Complaints rate

CR

The percentage collected from the statistics institution

PS

Recyclability

RE

The recyclability evaluated by professional organizations

PDN, Product design; PEI, process evaluation indicators; PLC, product life cycle; PM, product manufacturing; PS, product service.

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151

PD 5 ½APA; APEI; AFEI

ð5:2Þ

VD 5 ½VPA; VPEI; VFEI

ð5:3Þ

where APA is the actual design parameters in the physical space, while VPA, VPEI, and VFEI are the mirror data of APA, APEI, and AFEI. Mapping network A one-to-one mapping network is proposed, mainly to compress data volume, standardize data, and remove improper data. The min
max normalization method is adopted in this chapter to map and normalize the DT-driven data from the physical space to the virtual space: vd 5

ð pd 2 pdmin Þ ð pdmax 2 pdmin Þ

ð5:4Þ

where vd and pd denote the sample data of VD and PD, respectively. pdmin and pdmax denote the minimum and maximum values of pd, respectively. Prediction network When the data is mapped into the virtual space, a complex two-stage prediction network is established, which can be expressed by the following formulae. 2 1 3 FL11 . . . FL1ij . . . FL1tt 6 ... ... ... ... ... 7 6 k 7 k k 7 First layer 5 6 ð5:5Þ 6 FL11 . . . FLij . . . FLtt 7 4 ... ... ... ... ... 5 FLg11 . . . FLgij . . . FLgtt 2

SL111 6 ... 6 k Second layer 5 6 6 SL11 4 ... SLh11

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

SL1ij ... SLkij ... SLhij

3 . . . SL1qq ... ... 7 7 . . . SLkqq 7 7 ... ... 5 . . . SLhqq

ð5:6Þ

where FLkij and SLkij denote the linear mapping from the ith node of the kth layer network to the jth node of the (k 1 1)th layer network, t is the number of input parameters, q is the number of PEIs, g and h are the numbers of the layers of the networks, and k is the number of the(?) hidden layers of First_layer and Second_layer. Assuming that the virtual design input is indicated by VDI, the predicted PEI (PPEI) and predicted FEI (PFEI) can be expressed as follows:   PPEI 5 VDI; First layer ð5:7Þ   ð5:8Þ PFEI 5 PPEI; Second layer

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PART | 1 Theory and methodology

Feedback network Feedback network is similar to prediction network in structure. Taking Second_layer as an example, a recursive algorithm is adopted to calculate the new network node SLk1 ij : Etotal 5

r X 1

2 ðFEI1 u 2FEIu Þ 5

r X

Eu

ð5:9Þ

@outjk11 @netjk11 @Etotal @Etotal 5 3 3 @outjk11 @netk11 @SLkij @SLkij j

ð5:10Þ

@Etotal @Etotal @out1k12 @netk12 1 5 3 3 k11 k12 k12 @out1 @out1 @net1 @out1k11

ð5:11Þ

r X @Etotal @Eu 5 h @outj @outhj u51

ð5:12Þ

u51

2

k SLk1 ij 5SLij 2 η 3

u51

@Etotal @SLkij

ð5:13Þ

k where FEI1 u denotes the actual value of FEIu, @netj denotes the input value k of the jth node of the kth layer network, @outj denotes the output value of the jth node of the kth layer network, and r is the number of FEI.

Step 4: Complex network training In order to realize effective prediction, the structures of First_layer and Second_layer need to be studied. The learning algorithm will be introduced in Section 5.4. Step 5: Consistency judgment Take the historical data as the test set to judge the consistency of the prediction network. If there is a great difference between the predicted evaluation results and the actual evaluation results, the prediction network is not accurate. The feedback network can be adopted and retrained. Step 6: Perceived evaluation After the abovementioned training, the prediction network can be used. Designers input the design parameters into the PDE model and then adjust them in the virtual space until the evaluation result meets the requirements. The evaluation process will also be inputted into the PDE model to enhance the learning ability continuously. In addition, as the evaluation result is a multiobjective combination, a multiattribute decision method is also needed

153

Digital twin driven design evaluation Chapter | 5 First Stage

DA111

Second Stage PA

APEI

AFEI

DA112

...

...

...

...

DAij1

...

...

DAij2

...

...

DAij3

...

...

DAij4

...

...

...

...

Hidden nodes

C E P S

DAnmp Input layer

First Hidden layer

Middle layer

Second Hidden layer

Output layer

FIGURE 5.4 The structure design of prediction network.

to help designers make decisions, which will not be elaborated in this chapter.

5.4 Digital twin driven product design evaluation algorithm design In order to realize the nonlinear mappings between the virtual space and the physical space, the deep artificial neural network needs to be proposed, modified, and optimized. This chapter mainly introduced a common structure of prediction network (mentioned in Section 5.3.2) and some common optimization methods. Fig. 5.4 shows the detailed network structure of the prediction network. The structure consists of the input layer, first hidden layer, middle layer, second hidden layer, and output layer. The input layer refers to a set of design parameters. The output layer consists of four nodes (FEIs). The middle layer, which refers to the PEIs, is the output of the first stage as well as the input of the second stage. There are two design principles of the hidden layers: (1) the number of the hidden layers should be as few as possible. (2) The number of nodes of the hidden layers should not exceed that of the input layer. In general, the number of hidden nodes can be calculated according to the following formulae [46]:

154

PART | 1 Theory and methodology n X

Cki . N

ð5:14Þ

pffiffiffiffiffiffiffiffiffiffiffiffi n1m1t

ð5:15Þ

i50

k5

k 5 log2 n

ð5:16Þ

n1m 2

ð5:17Þ

k5

where k is the number of nodes of the hidden layers, n is the number of nodes of the input layer, m is the number of nodes of the output layer, N is the number of samples, and t is a constant between [1,10]. There are many optimization methods for the deep neural network, such as stochastic gradient descent (SGD), Nesterov, Adagrad, and Adaptive Moment Estimation (Adam). SGD calculates the gradient of small batch through each iteration and then updates the parameters, which is the most commonly used fitting method. However, it is difficult for SGD to select a suitable learning rate and adjust the learning rate according to the sparsity of data. Nesterov added a correction to the SGD to avoid moving too fast and improve sensitivity. Adagrad adds a constraint to SGD to achieve gradient adjustment, which amplifies the gradient in the early stage and constrains the gradient in the later stage. Adam uses gradient first moment estimation and second moment estimation to dynamically adjust the learning rate of each parameter. It applies to most nonconvex optimizations as well as to large data sets and high-dimensional spaces. When selecting the optimization algorithm, it is necessary to choose according to the actual situation. For some complex problems, hybrid optimization algorithm can also be adopted.

5.5 Case study: Digital twin driven roll granulator design evaluation 5.5.1

Background

Roll granulator, an indispensable basic production link of a large number of industrial and agricultural products, is involved in a wide range of national economy. Roll granulator is also a big energy consumer, and the pollution caused by working process is an important source of environmental pollution in China. Therefore the evaluation and optimization of the roll granulator design process is of great significance to both the manufacturers and the consumers. The authors investigated the design, manufacture, and service process of a roll granulator in a building material equipment manufacturing group. It is a high-tech group, which manufactures large complex building material equipment for cement production line, such as cement mill, preheater system,

Digital twin driven design evaluation Chapter | 5

155

rotary kiln, and stacker reclaimer. Due to complex structure, harsh working environment, long production cycle, and distributed manufacturing mode, it is difficult to implement the informatization construction of the building materials and equipment group. The development of new IT technologies brings opportunities for the group, which has established a complete data collection, mining, and analysis system around the PLC. Take the roll granulator as an example, product design, manufacturing, and service mode of the group is shown in Fig. 5.5. When the company receives the order, the technician uses CAD and SolidWorks to design drawings according to customer requirements and imports the product design parameters, processing parameters, and material requirements into the enterprise resource plan (ERP) system through the product data management (PDM) system. Then, the production department organizes production according to the production details. IoT technology is used to collect realtime processing data of equipment and performance test data of products, while handheld terminals are used to collect progress data and quality inspection data. When all the components are processed, they are transported to the construction site for installation. When the product is put into use, the sensors and control system will collect the equipment operation data (such as energy consumption, vibration, and breakdown parameters) in real time and will upload it to the cloud platform. In addition, during the use and maintenance process, the customer also employs the after-sales service system to report problems and evaluations to the group and obtains continuous improvement. In this way, a large amount of data in PLC is collected and

FIGURE 5.5 PLC tracking of a roll granulator.

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PART | 1 Theory and methodology

stored on the cloud platform for real-time monitoring and analysis. New IT has injected fresh energy to large complex equipment life cycle. These data will help build the DT for roll granulator and support designer for design evaluation.

5.5.2

Working principle of roll granulator

Fig. 5.6 shows the schematic diagrams of a roll granulator. Its main components include hydraulic system, upper shell, grinding wheel, and lower shell. After the materials are fed into the roll granulator continuously through the hopper, the grinding wheel is pressed by the hydraulic system, and the materials begin to contact with the roller at A1A2 level, at which point the materials start to be driven by the friction force on the surface of the grinding wheel. Then, single-particle materials are accelerated and move rapidly downward in the vertical direction. When the materials reach the plane B1B2, the gap between the two grinding wheels gets smaller and the pressure gets bigger, it has entered the stage of layer crushing. When the materials are compressed to the plane C1C2, the gap gets the smallest and the compact density reaches the maximum. The pressed shape passes through the plane C1C2 at the circumference of the grinding wheel. Due to the compression and rebound characteristics of the pressed shape, it will rebound in the horizontal direction and finally be excluded from the machine at a speed

Materials Upper shell

v

v A1

Grinding wheel

A2 B1 C1 D1

B2 C2 D2

Lower shell

FIGURE 5.6 Working principle of a roll granulator.

Digital twin driven design evaluation Chapter | 5

157

close to the circumference of the grinding wheel surface through the plane D1D2. Therefore it can be inferred that the design parameters of key parts (e.g., design structure, design size, and material selection) will greatly affect the cost, performance, working life, and energy consumption of the roll granulator. The evaluation index system needs to be built around key working parts.

5.5.3

Evaluation indicators analysis

The evaluation indicator system of the roll granulator is shown in Fig. 5.7, which is mainly established for the previously mentioned four main components. PEI mainly refers to the evaluation index of each part in the manufacturing and operation process of the roll granulator in the manufacturing process, which has a coupling effect on FEI. Cost, energy consumption, work performance, manufacturing cycle, and quality are the basic FEIs that are widely concerned by manufacturers and customers. Cost is the important evaluation indicator of the roll granulator, which is the sum of the cost of all parts in the PLC. It mainly includes design cost, raw material cost, manufacturing cost, labor cost, management cost, transportation cost, and maintenance cost. The cost data is mainly collected and

Hydraulic system

Cooling system

Grinding wheel

Shell

Brand

Waterway structure

Structure design

Structure design

Model

Shape design

Process design

Material selection

Material selection

Structure design

Structure design

Process design

Process design

Manufacturing cost

Supplier evaluation

Processing quality

Energy consumption

Cooling effect

Wheel hardness

Overall weight

Working life

Stability

Manufacturing cost

Structural strength

Particle size

FEIs

Cost

Working performance

Working life

Quality

FIGURE 5.7 The evaluation indicator system of the roll granulator.

.. .

Supplier evaluation

.. .

Material selection

Shape design

Parameter .. .

.. .

PEIs

Design parameters

Roll granulator

Energy consumption

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PART | 1 Theory and methodology

summarized by the ERP system of the group. Working performance mainly refers to the strength and consistency of particles, which largely depends on the key structural design and strength of hydraulic system, cooling system, and roller press mechanism. Working life mainly depends on structural design, material selection, process design, and other factors. The quality is mainly affected by the coupling effects of PEIs such as corrosion resistance, wear resistance, structural strength, and working life, among which the most important factor is the strength and stability of the grinding wheel. Energy consumption mainly depends on the selection of power system, overall structure design, material selection, etc. Through abovementioned analysis, the group has determined relevant data that can affect the evaluation result and realized data collection through PDM, ERP, manufacturing execution systems (MES), energy consumption detection system, etc.

5.5.4

Digital twin driven roll granulator design evaluation

Traditionally, it is extremely difficult for inexperienced designers to balance product cost, performance, quality, customer needs, and energy consumption. DT paves a new way for the design process. The DT-driven design evaluation framework for the roll granulator is presented in this section. As shown in Fig. 5.8, the DT model for roll granulator design consists of three parts: the virtual roll granulator in the virtual space, the real roll granulator in the physical space, and interactions between the virtual and real roll granulator. The design parameters, PEIs data and FEIs data are collected in the physical space, and the virtual roll granulator models are established in virtual space, which is the real mapping and reflection of the real roll granulator. After establishment of DT the DT system keeps learning the connection of the data as well as establishing the mapping network, prediction network, and feedback network. At design stage, DT system continuously analyzes the personalized needs of customers and recommends the most suitable design choices for designers. At manufacturing stage, various IoT technologies are used to collect PEIs data in physical space and compare it with the PEIs in virtual space to consolidate the connection between virtual model and physical entity. At service stage the group remotely collects the operation data and customer feedback of the roll granulators, and forms a comparison with the virtual FEIs. Finally, the abovementioned data is input into the complex network that connects virtual date and physical data, and the deep learning algorithm is used to continuously improve the accuracy of the prediction network. With the continuous accumulation of PLC data, the evaluation function of DT model in virtual space will become more and more powerful, which could be used for the design or redesign of new product. Take the design process of a roll granulator as an example, if a designer receives a roll granulator order with multiple coupling requirements, including cost, energy consumption, working life, and stability, how can he achieve

Digital twin driven design evaluation Chapter | 5

159

FIGURE 5.8 DT-driven roll granulator design evaluation.

this goal? He needs to have rich experience and know the details of the whole PLC from raw materials to delivery to customers, including material performance, various costs, and processing quality. Even so, the final product evaluation results will mostly have a large gap with the design results. The development of IoT technology and information system makes the enterprise collect the PLC data comprehensively and at real time. Fig. 5.9 shows the data collection process of the roll granulators in the information system of the building material equipment manufacturing group over a period of time (from January 10, 2017 to July 15, 2017). Among the data collection system, manufacturing execute system and ERP system, the DT models of different products are established to store PLC data, reflect the condition of the product in real time, and evaluate the each FEIs in virtual

160

PART | 1 Theory and methodology

FIGURE 5.9 Product evaluation page of the roll granulators: (A) input design parameters in the BOM; (B) purchase data collection; (C) Manufacturing process data; (D) equipment operation data; and (E) product comprehensive evaluation data (FEIs). FEIs, Final evaluation indicators.

space. First, the technicians import the design parameters of the product from the 3D design software into the ERP system, such as structure, size, materials, and process. Then, the system takes bill of materials(BOM) as the core and monitors the key information of each node from raw material purchase to production (in Fig. 5.9B and C). When the product is delivered to the customer, sensors installed on the product still monitor the product status in real time and transmit the data back to the system remotely (in Fig. 5.9D). Finally, based on these data, the system establishes DT model in virtual space and gives a comprehensive evaluation (in Fig. 5.9E). As roll granulator is a large and complex product, there are many factors influencing the evaluation results, which are tightly coupled together. Therefore many deep neural networks are built in the DT model, which is mainly used for learning and prediction evaluation. In order to reduce the dynamic calculation pressure of DT model, different evaluation indicators need to be analyzed separately and finally put together. Each deep neural network is mainly responsible for the learning and design evaluation process of an evaluation indicator of a core component. In addition, due to the different emphasis of each indicator, DT needs to first find the core components and key factors that affect the evaluation results and exclude the others.

Digital twin driven design evaluation Chapter | 5

161

For example, grinding wheel is the most important direct working part of the roll granulator; its structure, material, weight, and processing technology have important influence on cost, performance, quality, and energy consumption. The performance and quality evaluation of V/SOK/14 roll granulator in Fig. 5.9E declines continuously after a period of operation, while the roll granulators with other surface treated grinding wheel do not. The DT model will discover that the surface heat treatment process for grinding wheel influences the performance and quality greatly and gives a poor evaluation when designing a new roll granulator. As a result, the designers will have a basic judgment that the surface treatment of grinding wheel needs to be taken into consideration. Energy consumption is another issue that customers are very concerned about. In Fig. 5.9E the energy consumption evaluation of the PM/F175/25 roll granulator is low, DT system will analyze the influencing factors of high energy consumption, such as the quality of parts, the selection of power system, and the design of structure. When designing a new roll granulator, if the designer chooses a similar structure, the DT system will give a poor energy consumption evaluation and put forward suggestions for modification. In this way, the DT for roll granulator would facilitate the iterative evaluation and optimization of design. The design cycle, cost, etc., would be greatly decreased, while the design efficiency, satisfaction, security, etc., would be generally improved.

5.6

Summary

With popularization and application of the new generation of information technologies, the big data
driven manufacturing era has arrived. In order to realize rapid and accurate evaluation at the product design stage, this chapter proposes a PDE approach based on DT technology. First, a DT-driven PDE method is proposed, which integrates the virtual space and the physical space of PLC. To realize the nonlinear mapping between the virtual space and the physical space, an improved multilayer artificial neural network is established. Finally, a practical case of the roll granulator is presented to illustrate how DT is applied to PDE. The future work will mainly focus on more applications of DT technology, such as virtual product design based on DT, product quality tracking and analysis based on DT, and optimization of workshop production scheduling based on DT.

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23.

Chapter 6

Digital twin driven energy-aware green design Feng Xiang1, Yuan yuan Huang1, Zhi Zhang1 and Ying Zuo2 1

School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan, P.R. China, 2School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R. China

6.1

Introduction

Facing the current environmental crisis, energy-aware green design takes energy consumption (EC) as one of the main optimization objectives and emphasizes resources and energy saving without sacrificing functionality during product life cycle. Under given costs and quality requirements, it is a sustainable design mode that systematically considers environmental impacts and economic benefits in each life cycle stage, such as material selection, manufacturing process, disassembly, and reverse supply chain (RSC). Compared with traditional design, energy-aware green design has the following characteristics. First, it extends product life cycle to a complete closed loop, which is from raw material selection, manufacturing, use, and disassembly to the recycle of materials, and then the recycled materials are put into the next round of green design. Second, EC data from virtual and real spaces (e.g., expected EC data, real EC data, and predictive EC data) over the product life cycle are taken into account during systematic decisionmaking on material selection, disassembly planning, and rapid construction for RSC, etc., to achieve low cost, high productivity, easy recycling, and slight pollution to environment. In addition, green design is an interrelated design process, since its various activities are interrelated rather than isolated. For example, if green materials are selected at the initial phase, the EC of subsequent stages such as processing, reuse, and disassembly would also be affected. In the existing works, decision-making (e.g., green materials selection, optimal disassembly sequence, and supplier selection of closed supply chain) of energy-aware green design mostly depends on various virtual models and optimization algorithms to predict how much energy would be consumed in Digital Twin Driven Smart Design. DOI: https://doi.org/10.1016/B978-0-12-818918-4.00006-3 © 2020 Elsevier Inc. All rights reserved.

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Virtual simulation

Green material selection

Physical entities

Iterative optimization

Actual performance

Operation process User requirements Production result

Green disassembly Green design

Product life cycle in physical space

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Product life cycle in virtual space

Virtual models

Product life cycle Reverse supply chain

Simulation process Expected EC data

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EC digital thread

Prediction result Realistic EC data

… Predictive EC data

FIGURE 6.1 The characteristics of DT-driven energy-aware green design. DT, Digital twin.

line with a green design [1]. This method relies on simulation and optimization in virtual space but overlooks interactions between product, user, and environment, thus often leading to deviations from the actual EC results and human requirements. Hence, in order to promote accuracy and comprehensiveness of the predicted EC, rational decision-making should be based on the interaction of virtual and physical spaces, specifically, to interactively compare and optimize the predicted EC of models in virtual space, realistic EC of products in physical space or actual environment, and expected EC according to user requirements. Digital twin (DT) driven energy-aware green design aims to establish virtual models of physical entities based on product lifecycle and digital thread (including EC digital thread). By virtue of models, data and integrated multidisciplinary technology, DT plays an important role in bridging and linking physical and virtual spaces, and providing real-time, efficient, and intelligent green design services. The behaviors (especially EC behaviors) of physical entities will be mapped and predicted in the virtual space. In addition, by means of calculation and comparison among the predicted EC, realistic EC and user required EC, EC is iteratively optimized and decision support capability of green design is improved. As shown in Fig. 6.1, the characteristics of DT-driven energy-aware green design are illustrated as follows.

6.1.1

Iterative optimization of energy consumption

Iterative optimization is realized by combining virtual simulation, actual product performance, and user requirements. In aspect of EC iterative optimization, it includes predicted EC of models in virtual space, realistic EC of products in actual environment, and expected EC from user requirements. The three types of ECs are continuously compared, calculated, and evaluated in physical and virtual spaces until the optimized goal of the design is reached.

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Expected EC according to user requirements: individual users have different product requirements. Under the premise of meeting product functional requirements and cost requirements, green design aims to improve EC indicators throughout the product life cycle. Therefore it is essential to collect user requirements (e.g., national mandatory standard, customer order demand, and enterprise internal regulations), screen user requirements according to certain criteria (e.g., priority, types, and credit rating), determine the preferred needs and ensure the effectiveness of dynamic requirements. In terms of dynamic requirements, taking national emission standard for vehicle as an example, the motor discharge standard has been upgraded from national standard 4 to 5 in China given people’s higher request for the quality of environment [2]. Moreover, different regions of China have different emission standards, and Hainan is the first province in China to set a deadline to ban the sale of fuelpowered cars by 2030. Therefore the expected EC is usually different according to various user requirements. It is necessary to meet EC requirements of customers in different regions and contexts in green design. Realistic EC in actual environment/physical space: different from the predicted EC in the virtual environment, realistic EC data refer to the real EC of physical entities in physical space. It usually includes a variety of energy elements, such as electric energy, water, and raw materials. In the usage phase, realistic EC might vary in different scenarios. For example, EC of a running vehicle would be affected by driving habits, temperature and altitude, and the fuel type. Predicted EC in virtual space: predicted EC is calculated and predicted according to virtual models, which map and reflect the behaviors and rules of physical entities. Traditionally, the accuracy of prediction depends on the authenticity of virtual models. However, the data-driven prediction method depends on both realistic EC data and virtual models in ways that combine virtual and real spaces. Accordingly, better results of EC prediction can be obtained by analyzing the current EC behaviors in the physical space. For example, in order to make full use of the actual operation data of chillers in an energy station to improve the accuracy of EC prediction, Shen et al. [3] presented an EC prediction model based on data mining algorithm with good practicability and reliability. Specifically, the EC behaviors of physical entities are described based on current EC, and the rules of physical entities are formed by analyzing the historical EC data, which are then used to predict potential EC.

6.1.2

Energy consumption digital thread

Digital thread is an extensible and configurable enterprise-level framework. In the life cycle of a complete system, digital thread could inform decision makers by providing access, integration, and the ability of converting different/decentralized data into operation information [4]. Meanwhile, digital

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thread also allows data streams to be connected to provide an integrated view that includes all the isolated functional views of the product life cycle. Furthermore, digital thread provides conditions for transmitting the right information to the right place at the right time [5]. The key data from the various virtual models can be synchronized and communicated in a bidirectional way. In its content the dispersed EC data is integrated into EC digital thread and provides four types of EC data, that is, history EC data, expected EC data, real EC data, and predictive EC data. The history EC data are generated by accumulating various kinds of data over a long period of time. The expected EC data are formulated to meet users’ expectations or national and enterprise standards.

6.1.3

Product life cycle

From the perspective of DT, product life cycle exists in both physical and virtual spaces. In physical space, product life cycle is generally classified into design, material selection, manufacturing, assembly, packing, using, maintenance, disassembly, recycling, etc. Its objects consist of physical entities, operation processes, management processes, production results, etc. Meanwhile, these stages also exist in virtual space, and its objects consist of virtual models, simulation processes, and prediction results, etc. In summary, DT brings innovation and challenge to energy-aware green design. Traditionally, virtual models are built to predict product EC, and balance objectives such as improving product quality, reducing cost, and EC at each stage. DT enhances the capability of iterative optimization of EC in both virtual and real spaces by providing the EC digital thread in the whole product life cycle and improves accuracy of decision-making in green design. This section outlines the differences between traditional design and green design (e.g., closed-loop cycle, systematic decision-making with consideration of EC, and interrelated design stages). The three characteristics of DT for green design (i.e., iterative optimization of EC, EC digital thread, and product life cycle) are introduced. Section 6.2 will present related works of green design. Section 6.3 will apply the five-dimension DT model into green design. Section 6.4 analyzes the way DT enhances the three stages (i.e., material selection, disassembly, and supply chain) of green design.

6.2 6.2.1

Related works Green design in material selection

Green design in material selection is to select suitable materials for a product to minimize environmental impacts (e.g., reducing EC, emissions, and use of resources), while satisfying other requirements, that is, product function, user demands, and cost. In addition to the very phase of raw material selection,

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green design in material selection also has great influence on EC throughout the whole product life cycle, since different materials have different processing methods, recycling strategies, and service cycles. Generally, the design objectives and criteria in the green material selection phase are often in conflicts and involve trade-offs among function, cost, and environmental impacts. Therefore it is difficult to select materials optimally. Many researchers have been devoted to exploring green design in material selection. For example, Chatterjee and Chakraborty [6] presented four preference ranking based multicriteria decision-making (MCDM) methods for gear material selection, with consideration of environmental impacts of materials in the manufacturing phase. Jee and Kang [7] utilized the theories of decision-making to evaluate the weight factor of each material property or performance index to rank the candidate materials. Nevertheless, environmental impacts of the product life cycle were neglected. Liao and Xu [8] presented a fuzzy MCDM method to support material selection. Environmental indexes (e.g., EC, emission, and recyclability) were included in the evaluation system, but these environmental indexes did not cover the product life cycle. Zhang et al. [9] proposed a hybrid MCDM approach for material selection with rubbish bins as an application example. However, the raw data used in MCDM approach in these studies are static and incomplete. Moreover, in terms of environmental impacts, most of the studies only focus on the manufacturing phase, but neglected EC and other stages of the product life cycle. Besides, some researchers paid attention to multiobjectives optimization. For instance, Zhou et al. [10] proposed an integration of artificial neural networks with genetic algorithms based on material database to optimize multiobjective material selection, namely, emissions and recyclability. Tao et al. [11] established a comprehensive optimization model for green material selection with consideration of energy and designed a hybrid optimizing method named chaos quantum group leader algorithm. These studies have well addressed the conflicts among multiple objectives (function, cost and environmental impact, etc.) through various optimization algorithms or simulation models in the virtual space. However, realistic EC in the physical space was not included in validation for green material selection, neither was iterative optimization in both virtual and physical spaces. As a result, the true effects of EC behaviors in a certain context of production are likely to be ignored. To address the aforesaid problems, DT can play a role in the following aspects: (1) DT integrates the EC digital thread in the product life cycle to provide data support for decision-making in material selection. (2) Realistic EC in physical space, predicted EC in virtual space and user demands are all taken into account in the iterative optimization of EC. Design goals and demands of users are achieved through calibration between realistic EC and predicted EC.

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Green design in disassembly

Green design in disassembly aims to find efficient disassembly sequences for waste products, in a way that separates certain components and materials from the product for reuse, recycling, replacement, and maintenance. An effective disassembly sequence can minimize disassembly time, the number of disassembly steps and tools, and EC. Although disassembly is crucial for product recovery, finding the optimal disassembly sequence is a difficult and complex job. Since disassembly sequence has critical impact on the disassembly performance, many scholars have devoted themselves to studying the optimization of disassembly sequence. For example, Smith et al. [12] proposed a novel selective parallel disassembly planning method that is designed to reduce disassembly steps, disassembly time, and EC, and therefore to reduce environmental impacts and improve environmental quality. Kim and Lee [13] presented a sample average approximation algorithm for selecting disassembly sequences to reduce disassembly time and EC. Zhou et al. [14] came up with a product disassembly model based on modularization for generating disassembly sequences, which is required to improve efficiency, reduce cost, and environmental impacts. Gungor and Gupta [15] presented a method to evaluate different disassembly strategies so as to choose the optimal one, which reduces EC of the disassembly process. However, the previous studies obtain the optimized disassembly sequences mainly through various optimization algorithms or simulation models in virtual world. Due to the lack of interaction with physical entities, it is difficult to know the actual states of disassembly (e.g., damage degree of parts and complexity of structure), namely, whether the actual disassembly process is carried out according to the scheduled disassembly sequence. In addition, many studies pay attention to the predictability of disassembly, such as predicting the component accessibility, disassembly posture, and cost. For examples, Soh et al. [16] considered factors in disassembly in the design stage to improve the efficiency of disassembly, which helps to predict the cost of disassembly. Tian et al. [17] established an opportunity constraint programming model of disassembly to conduct quantitative analysis on the uncertain characteristics of disassembly, which predicts the stochastic cost and time in product disassembly. The studies mentioned previously have provided solutions to deterministic disassembly evaluation and planning. However, with the increasing complexity of product types and different levels of waste, disassembly faces more challenges and uncertainties. It is necessary and urgent to combine the virtual space and physical space to accurately predict uncertain factors (e.g., change of parts position and loss of material) in disassembly process. To solve the aforesaid problems DT can play a role in the following aspects: (1) DT obtains real-time disassembly states in the automatic disassembly line

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based on digital thread. Through the interaction between virtual models and physical entities, the obtained information is translated into solutions and thus the existing problems in the disassembly process can be fixed timely. (2) Model-based definition (MBD) is an advanced digital definition method, which attaches all the relevant design concepts, process parameters, product attributes, and management information to the product model. Ahead of the disassembly phase, disassembly information could be added into virtual models at the design stage based on MBD method. In a word, DT promotes the sharing and timely transmission of disassembly information in the product life cycle and ensures that efficient disassembly sequences can be obtained in virtual space.

6.2.3

Green design in supply chain

Green design in supply chain was originally proposed by the Manufacturing Research Association of Michigan State University in 1996 [18]. At that time the purpose of this concept was to consider the development of manufacturing supply chain from the perspective of optimal utilization of resources in terms of impacts on the environment. That is to say, tracking and control are carried out from the beginning of raw material procurement. Then the products follow the environmental protection regulations in the design and development stage to reduce environmental damage caused by products during use and recycling. Many enterprises have realized the importance of green supply chain (GSC). For example, General Motors, Ford, Hewlett-Packard, Procter & Gamble, and Nike have been actively studying and implementing GSC. Among them, General Motors is recognized by the United States Environmental Protection Agency as a demonstration enterprise in GSC management [19]. Traditional supply chain is a forward supply chain (FSC), in which products move from suppliers, manufacturers, retailers to end users. As green design extends the product life cycle to a complete closed-loop cycle, which includes raw material selection, manufacturing, use, disassembly to recycle of raw material, the supply chain also works in a reverse process (i.e., from end users, retailers or third-party recyclers, to suppliers, and manufacturers). In a word, green design in supply chain or GSC is to minimize the negative impacts on the environment and maximize the efficiency of resource utilization in the closed loop formed by FSC and RSC [20]. Once the FSC has independent demands, the corresponding subordinate demands can be calculated and controlled by means of material requirement planning. The whole FSC system is equivalent to a deterministic system. Contrary to the FSC, RSC is affected by government regulation policy, market supply and demand, product quality, etc. There are three types of uncertainty (quality uncertainty, time uncertainty, and quantity uncertainty) in all aspects of the RSC, including product maintenance, reverse logistics, and remanufacturing process. For example, given that returns may occur at anytime and

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anywhere, enterprises cannot accurately predict cost, EC, and benefits, and then make correct decisions on whether and how to build a GSC. With regard to the economic benefits after the GSC is established, some prediction models based on game theory are studied to evaluate cost, benefits, and EC of the GSC. Game theory is divided into incomplete information game and complete information game. Since the information of supply chain nodes is often asymmetric in reality most studies focus on the dynamic game model of supply chain based on incomplete information. For example, Zhao et al. [21] used incomplete information based game theory to analyze the strategies selected by manufacturers in the GSC to reduce environmental risk of materials and carbon emissions throughout the product life cycle. There are also some studies in the construction of static game models based on complete information. For example, Zhang et al. [22] studied GSC management of onestage supply chains by virtue of a game theory analysis model which includes suppliers and managers. In order to build GSC enterprises need as much information as possible, such as demand, recycling, cost, and government incentives, to accurately predict costs and benefits and then decide whether to participate in the GSC. Thus data acquisition and information sharing of the GSC will promote cooperation among the various enterprises in supply chain. Meanwhile, system dynamics (SD) models are proposed for causality and behavior prediction. Vashirawongpinyo [23] established a SD model to analyze and predict the behaviors of manufacturing in the supply chain. Wang [24] established a SD model of the supply chain of perishable products to predict the causality therein. However, there is still a gap between the virtual model and the real system. First, the influencing factors in reality are not fully considered in establishment and simulation of the model. For example, Wang et al. [25] applied game theory to predict the cost of waste products recycling in the RSC assuming that the RSC only has a single manufacturer and a single retailer. Gu et al. [26] used game theory to predict the economic value of the RSC in the case of two manufacturers based on empirical data. In reality, there are many manufacturers and multiple retailers competing in the RSC. Second, SD model parameters are usually set according to personal experience. Mo et al. [27] established an SD model of a two-stage RSC using simulated data and predicted RSC profits and costs at a certain pointin-time by adjusting the parameters. Da et al. [28] used SD methodology to predict the long-term behaviors (e.g., material consumptions) of the RSC to improve reuse ratio and market share based on empirical data. In this case, it is difficult to accurately predict the cost and environmental benefit of GSC based on SD models. In terms of building a GSC, both FSC and RSC belong to the scope of GSC management. Xia [29] compared the similarities and differences between FSC and RSC in terms of structure. FSC and RSC have most similar participants in the supply chain (e.g., manufacturers, retailers, and consumers), and a

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strategic partnership is built among all the node enterprises which have the same goal in the operation of the supply chain. The difference is that RSC is more complicated and involves more links than FSC. In addition, it is generally believed that FSC is formed prior to RSC. Therefore the existing FSC lays a foundation for the construction of RSC and GSC. To solve the aforesaid problems, DT can bring the following advantages: (1) DT provides digital thread for GSC and dynamically acquires real-time data from the physical space. DT makes the analysis and transmission of product information more accurate and fast. The information flow in the RSC system is no longer limited by space and time, and each node in the system can directly share the information with one another to decide whether to join in the GSC. (2) DT promotes the construction of a closed-loop GSC from FSC to RSC. In virtual space, EC of various activities can be predicted using the GSC models, and the overall EC for GSC is also analyzed.

6.3

Energy-aware five-dimension digital twin

Tao et al. [31,32] presented a five-dimension DT model as MDT 5 (PE, VE, SS, DD, CN), wherein PE refers to physical entity; VE refers to virtual entity; DD stands for DT data; SS is services for PE and VE; and CN is connections among PE, VE, DD, and SS. In this chapter, based on the fivedimension DT, a five-dimension DT model considering the EC characteristics is proposed for the first time. Thus the DT model of EC can be expressed as follows: MDT EC 5 {PEC, VEC, DEC, SEC, CEC}, where PEC represents energy-aware physical entities; VEC represents energy-aware virtual models; DEC represents DT data (especially EC data); SEC represents green services; CEC represents the interconnection between PEC and VEC, PEC and DEC, VEC and DEC, PEC and SEC, VEC and SEC, and SEC and DEC. The energy-aware five-dimension DT model is shown as in Fig. 6.2. 1. In the physical entities dimension, PEC is the sum of physical entities, including material resource entities, human resource entities, and mechanical equipment entities, which consume energy in the various activities of green design. Various types of EC data from PEC are collected and integrated into a digital thread. 2. In the virtual models dimension, VEC focuses on high fidelity, multiscale, real-time mapping of PEC from the perspective of EC. Conventionally, virtual models map physical entities in terms of four features: geometry, physics, behaviors, and rules. In green design, behaviors and rules of EC can be reflected. EC behaviors of PEC are monitored in physical space and then mapped to virtual models. EC rules of PEC are optimized using virtual models. VEC can predict the following EC behaviors of PEC and analyze EC rules.

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FIGURE 6.2 Energy-aware five-dimension DT model [30]. DT, Digital twin.

3. In the data dimension, DEC also means the digital thread for EC that reflects energy flow in green design, which can be expressed as DEC 5 {DReal, DPred, DExpe, DServ, DKno, DFus}. DReal represents realistic EC data from PEC, including manufacturing EC data, workshop EC data, enterprise-level EC data, and logistics EC data. DPred represents predicted EC or simulation results of EC from various virtual models. DExpe represents EC from user requirements. DServ represents EC data of services, including real-time EC monitoring service, EC management service, and product service. DK represents EC-related knowledge data, including EC assessment models, intelligent optimization algorithms, and EC assessment rules. DF stands for fused data among DReal, DPred, DExpe, DServ, and DKno. 4. In the service dimension, SEC refers to the services provided by PEC, DEC, and VEC that includes green design service throughout the product life cycle, green material optimization service, EC and energy efficiency assessment, green disassembly, and GSC management. 5. In the connection dimension, CEC is the sum of connections among PEC, VEC, SEC, and DEC according to different interfaces and communication protocols. CEC can be expressed as {CPE DE, CPE VE, CPE SE, CVE DE, CVE SE, and CDE SE}, where CPE DE, CPE VE, CPE SE, CVE DE,

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CVE SE, and CDE SE denote the connections and interactions between PEC and DEC, PEC and VEC, PEC and SEC, VEC and DEC, DEC and SEC, and VEC and SEC. Each connection achieves bidirectional data interaction based on intelligent EC data interaction device and communication interface. Take CPE VE as an example, CPE VE represents the data interaction between the real EC data from the physical space and the EC models from the virtual space.

6.4

Potential applications of digital twin driven green design

6.4.1 Digital twin driven energy-aware green design in material selection During the process of green design the selection of a material has a great impact on the EC of the whole product life cycle. Different materials have different embodied energy and endow products with different properties, which affect EC in manufacturing, usage, maintenance, recycling of products, etc. DT-driven energy-aware green design in material selection is to select suitable materials to deliver optimal EC based on EC digital thread and iterative optimization among realistic EC, expected EC, and predicted EC. This definition reveals two major advantages of DT-driven energy-aware green design in material selection.

6.4.1.1 Integrated energy consumption digital thread for digital twin driven energy-aware green design in material selection Taking a vehicle body as an example, the framework of DT-driven energyaware green design in material selection for the vehicle body is shown in Fig. 6.3. EC digital thread of the vehicle body integrates EC data from both physical space and virtual space. It is used not only to reflect realistic EC in physical space but also to support virtual EC simulation and prediction, iterative optimization, and materials evaluation. In physical space, EC digital thread integrates realistic EC data that reflect EC behaviors in the product life cycle and expected EC data that are acquired according to user demands. In virtual space, some virtual models (EC evaluation models, EC prediction models, EC optimization models, EC rules models, etc.) are used to generate predicted EC data through iterative optimization. 6.4.1.2 Iterative optimization of energy consumption Most optimization algorithms designed for searching the best solution of material selection depend mainly on adjusting the design parameters (e.g., physical property, appearance, and function) and changing material categories, whereas the DT-driven iterative optimization of EC not only relies on the optimization algorithm but also focuses on the interaction among the expected EC, realistic EC, and predicted EC. It refers to the repetitive

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FIGURE 6.3 DT-driven and interaction-enhanced framework for energy-aware material selection [33]. DT, Digital twin.

optimization process between predicted EC and expected EC by adjusting design parameters that enables the predicted EC to approximate the expected EC that is needed. Thus EC can be saved and optimized. Generally, the expected EC, realistic EC, and predicted EC are not consistent. In other words, they are different beyond a certain threshold. Based on the iterative optimization, these three kinds of EC could converge to generate an optimal result. The details of EC iterative optimization are illustrated in the following Step 4. In the process of predicted EC in virtual space, it mainly relies on the combination and selection of design parameters and materials to achieve EC iterative optimization. It is necessary to simulate the realistic EC behavior in virtual space by adjusting design parameters and selection of materials to achieve EC optimization. In the process of iterative optimization of EC, it not only relies on the optimization algorithm to search for the solution space of material selection, but also pays more attention to the interaction between realistic EC data in physical space and the EC model in virtual space. The core of iterative optimization of EC is to calibrate and approximate the predicted EC and expected EC in virtual space by repeatedly adjusting the impact factors of EC.

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The method of DT-driven energy-aware green design in material selection is explained as follow. Designers collect EC data from integrated EC digital thread, including realistic EC data, expected EC data and predicted EC data. Then, based on the analysis of realistic EC data users can propose expected EC which varies with each individual and thus corresponds to different candidate materials. In virtual space, relying on EC prediction models and algorithms, predicted EC data can be acquired. Then predicted EC is iteratively optimized through comparison and calibration to meet the design requirements. After iterative optimization of EC, designers need to consider cost over the product life cycle and product functions. Finally, designers could select the materials that meet user requirements and have the lowest EC by comprehensive evaluation. The steps of DT-driven green design in material selection are shown in Fig. 6.4. Step 1: DT modeling. In virtual space a green material selection model is established, which takes into account the basic material properties such as physical and processing properties (mechanical properties, and process properties) and economic properties and considers EC and environmental factors. DT modeling needs to reflect realistic EC behaviors in high fidelity and predict future EC behaviors. Step 2: Integrated simulation. The constructed DT system performs integrated simulation to predict performance of the product. Not only EC in the product life cycle is predicted, but other properties of the product are also simulated to obtain prediction data for iterative optimization of the performance indicators. Step 3: Consistency judgment. DT-driven EC iterative optimization relies on consistency between expected EC and predicted EC, which is reached

FIGURE 6.4 DT-driven EC iterative optimization process. DT, Digital twin; EC, energy consumption.

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by optimizing EC behaviors. The inconsistency between expected EC and predicted EC can be explained as follows: ek and pk denote expected EC and predicted EC for the ith candidate material, respectively. If Tk 5 :ek 2 pk :=pk # Hk , where Hk is a predefined threshold, predicted EC and expected EC are consistent, which means their difference is within the tolerable range caused by inevitable errors (e.g., low fidelity of models and inaccurate data). Step 4: Iterative optimization. By virtue of DT technology, virtual EC behaviors are analyzed by the designers. Once an abnormal EC behavior is captured, it would be improved through EC iterative optimization in virtual space, where an optimization algorithm is used to repeatedly search the solution space and find the materials that meet the user requirements. After each search the executable result based on selected materials and design parameters are inputs into the DT system for simulation. If the predicted EC, expected EC, and realistic EC are not consistent, the design scheme, including design parameters and selected materials, will be adjusted in the virtual space to proceed to the next generation of EC optimization iteration. The optimal design scheme is iterated repeatedly until a consistency judgment is met, and then the optimal material will be selected. Step 5: Predicted EC output. In the process of iterative optimization only when predicted EC and expected EC are consistent can the optimization process enter the next step. Predicted EC will be output to the evaluation models for designers to evaluate the candidate materials and select the optimal materials. Step 6: Material evaluation. After the predicted performance indicators (e.g., EC in the product life cycle, product functions, and cost in the product life cycle) of the materials are optimized, they will then be evaluated to determine the optimal green material. In this way the proper material is selected using the DT optimization model.

6.4.2 Digital twin driven energy-aware green design in disassembly Disassembly is the premise of product recycling and remanufacturing, and the feasibility of disassembling a product directly impacts the efficiency of subsequent maintenance and recycling activities. DT-based disassembly is driven by data. Through iterative optimization between physical entities and virtual models, it helps to adjust disassembly factors (e.g., disassembly tools and physical location of components) to get the optimal disassembly solution in view of ease and subsequent maintenance or material recovery at the end of the product service life. The DT-driven disassembly has the following two major advantages.

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6.4.2.1 Continuously optimization of disassembly sequence DT-based disassembly can continuously optimize the disassembly sequence that helps to obtain green disassembly features (e.g., parts remanufacturability and environment-friendly materials) in the physical space. In terms of DT-based interactive design for a disassembly system, designers can convert subjective design standards including EC directly into CAD-based design solutions. Through iterative optimization of expected EC and predicted EC the disassembly sequence can be updated continuously by analyzing and comparing with historical data from the EC digital thread until the optimal sequence is generated. In addition, information on disassembly forces, tools, humans, and other factors in the actual disassembly process are collected through various intelligent sensors and stored in databases. In this way, DT can dynamically obtain the disassembly states. EC behaviors can be described with the EC digital thread, which also provides data access, integration, and transformation capabilities to disassembly. When the disassembly process is inconsistent with the scheduled disassembly sequence, the operators can examine the relevant data for comparison and analysis, so as to adjust and optimize the scheduled disassembly sequence, thus achieving the least amount of EC in the disassembly process. The DT-driven energyaware disassembly framework is shown in Fig. 6.5.

FIGURE 6.5 DT-driven and interaction-enhanced framework for energy-aware green disassembly. DT, Digital twin.

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6.4.2.2 Disassembly feasibility improvement based on digital twin MBD is an advanced digital definition method, which attaches all the relevant design concepts, process parameters, product attributes, and management information to the product model. MBD can be regarded as a modeling tool in DT to bring benefits such as a more realistic, real-time, and integrated environment. Through MBD a disassembly model that contains disassembly information of each layer including product layer, subassembly layer, device layer, and part layer is established. Meanwhile, the disassembly features are incorporated into the design stage when designers consider structures that are more conducive to disassembly without affecting product functionality, security, and strength. DT allows for better digital modeling, simulation, and definition for products to be disassembled. The concept of DT-driven energy-aware green design in disassembly is shown in Fig. 6.6. DT promotes the sharing and timely transfer of disassembly information in the product life cycle, which is helpful in predicting the disassembly feasibility of products and is thus conducive to the effective recycle of products. Meanwhile, predictive analysis enables EC data to continuously track disassembly performance and to predict potential solutions of disassembly sequence.

FIGURE 6.6 The concept of DT-driven energy-aware green design in disassembly. DT, Digital twin.

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6.4.3 Digital twin driven energy-aware green design in supply chain DT-driven energy-aware green design in supply chain is to collect real-time data from the supply chain network. The supply chain that complies with green requirements is integrated and constructed quickly based on existing FSC, and EC of the supply chain is predicted, so as to maximize the value of the products and save energy. In this sense, DT-driven energy-aware green design in supply chain has two main advantages discussed next.

6.4.3.1 Enhanced energy consumption prediction of green supply chain based on digital thread As mentioned in Section 6.2.3, the uncertainties in GSC bring difficulty in accurate prediction of EC and comprehensive assessment, which may affect whether all nodes (enterprises) in the supply chain will participate in the GSC. The digital thread provided by DT will promote supply chain collaboration with complete information, and meanwhile enhance accurate prediction of EC and cost. The potential applications of DT-driven green design in the GSC are illustrated as follows. First, the digital thread is constructed, which mainly focuses on the collection of EC data. The EC digital thread of the supply chain integrates all the EC data from physical and virtual spaces. In the physical space, realistic EC data include data for forward processes (raw material acquisition, manufacturing, sales, etc.) and reverse processes (recovery, inspection, remanufacturing, etc.) of the supply chain. In addition, inventory data, sales data, order data, etc., also affect the overall decision on whether to build GSC. In the virtual space, by virtue of some high-fidelity virtual models of the GSC, the expected EC data and cost data are obtained using simulation, providing data support for obtaining optimal GSC. Then the consistency between real EC and expected EC is verified. If not, adjust the virtual models and map the realistic EC of the physical entities to that of the virtual models. In this way the prediction ability of the virtual models is enhanced. 6.4.3.2 Rapid construction of green supply chain The other advantage of DT-driven green design in the GSC is the rapid construction of GSC. The inspiration of rapid construction comes from the relevance of GSC and RSC in node selection, process design, EC comprehensive evaluation, etc. In terms of node selection, FSC and RSC often have the same nodes (end users, manufacturers, etc.). When the recycler of the RSC is a node existing in the FSC, such as the retailer or supplier, the same physical entities, members, EC data, etc., can be mapped to the virtual space using node replication or adjustment. When the recycler of the RSC is from a third party, nodes of the FSC model should be deleted, and then a new node

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model of RSC should be built in virtual space. In terms of process design, similar processes (e.g., distribution and sales) coexist in FSC and RSC. To a certain extent, part of the RSC models can be built with process replication in virtual space. However, the structure of RSC is more complex than that of FSC and involves more elements. Most of the processes are quite different from each other. Therefore the existing virtual FSC models are usually adjusted and deleted to achieve rapid construction of RSC models. For comprehensive evaluation of EC of the supply chain, the existing model in the virtual space is deleted to reconstruct a model that can better reflect the functions of the supply chain in the physical space, and then the EC comprehensive evaluation is made according to the new model. In conclusion, DT creates virtual models of FSC and RSC digitally, and simulates the EC behaviors in the real environment based on the collected dynamic data. By replication, adjustment, and deleting of virtual models, the rapid construction of GSC can be realized. Then the FSC and RSC are integrated to form a closed-loop supply chain.

6.5

Summary

In summary, this chapter discusses the way how DT promotes green design for energy saving. A variety of green design activities (material selection, disassembly, and supply chain management) can benefit from the digital thread (especially EC data) provided by DTs in various ways. The fivedimension DT model is extended in view of EC optimization. Finally, potential applications of DT-driven green design are illustrated to show the advantages over conventional green design. In terms of material selection, DT integrates the EC digital thread in the product life cycle to provide data support for decision-making in material selection. In addition, realistic EC in the physical space, predicted EC in the virtual space, and expected EC of user demands are all taken into account in the iterative optimization of EC. Design goals and user demands are achieved through calibration between realistic EC and predicted EC. In term of disassembly, DT-driven disassembly can find the efficient sequence through optimization of the disassembly model. In the design stage, MBD is regarded as a tool to design the disassembly model. In line with the idea of parallel design the disassembly feasibility of products is considered, which helps to predict the disassembly performance of waste products. In terms of GSC, DT-driven energy-aware green design in supply chain can obtain expected EC using simulation and then ensure the consistency between real EC and expected EC to enhance EC prediction of the GSC. Based on DT-driven energy-aware green design in supply chain, the rapid construction of GSC can be realized by adjusting, replicating, and deleting the existing FSC virtual models.

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References [1] A.K. Mohanty, M. Misra, L.T. Drzal, Sustainable bio-composites from renewable resources: opportunities and challenges in the green materials world, J. Polym. Environ. 10 (1 2) (2002) 19 26. [2] China Starts Implementing Tougher Vehicle Emission Standards. The Xinhua News Agency, (2019). Available from: ,http://www.chinadaily.com.cn/a/201907/02/WS5d1ac566a3103 dbf1432b6d2.html.. [3] J.Q. Shen, H.X. Chen, Y.B. Guo, S.R. Zhou, Energy consumption prediction of chillers based on data mining, HV&AC (2) (2019) 23. [4] E. Kraft, HPCMP CREATEt-AV and the air force digital thread, AIAA J. (2013). [5] C.B. Zhuang, J.H. Liu, H. Xiong, X. Ding, S. Liu, G. Weng, Connotation, architecture and trends of product digital twin, Comput. Integr. Manuf. Syst. 23 (4) (2017) 753 768. [6] P. Chatterjee, S. Chakraborty, Material selection using preferential ranking methods, Mater. Des. 35 (2012) 384 393. [7] D.H. Jee, K.J. Kang, A method for optimal material selection aided with decision making theory, Mater. Des. 21 (3) (2000) 199 206. [8] H. Liao, Z.A. Xu, VIKOR-based method for hesitant fuzzy multi-criteria decision making, Fuzzy Optim. Decis. Mak. 12 (4) (2013) 373 392. [9] H. Zhang, Y. Peng, G. Tian, D. Wang, P. Xie, Green material selection for sustainability: a hybrid MCDM approach, PLoS One 12 (5) (2017) e0177578. [10] C.C. Zhou, G.F. Yin, X.B. Hu, Multi-objective optimization of material selection for sustainable products: artificial neural networks and genetic algorithm approach, Mater. Des. 30 (4) (2009) 1209 1215. [11] F. Tao, L.N. Bi, Y. Zuo, A.Y.C. Nee, A hybrid group leader algorithm for green material selection with energy consideration in product design, CIRP Ann. 65 (1) (2016) 9 12. [12] S. Smith, L.Y. Hsu, G.C. Smith, Partial disassembly sequence planning based on costbenefit analysis, J. Clean. Prod. 139 (2016) 729 739. [13] H.W. Kim, D.H. Lee, An optimal algorithm for selective disassembly sequencing with sequence-dependent set-ups in parallel disassembly environment, Int. J. Prod. Res. 55 (24) (2017) 7317 7333. [14] Z. Zhou, J. Liu, D.T. Pham, W. Xu, F.J. Ramirez, C. Ji, et al., Disassembly sequence planning: recent developments and future trends, Proc. Inst. Mech. Eng., B: J. Eng. Manuf. 233 (5) (2019) 1450 1471. [15] A. Gungor, S.M. Gupta, An evaluation methodology for disassembly processes, Comput. Ind. Eng. 33 (1 2) (1997) 329 332. [16] S.L. Soh, S.K. Ong, A.Y.C. Nee, Application of design for disassembly from remanufacturing perspective, Procedia CIRP 26 (2015) 577 582. [17] G. Tian, M.C. Zhou, P. Li, Disassembly sequence planning considering fuzzy component quality and varying operational cost, IEEE Trans. Autom. Sci. Eng. 15 (2) (2017) 748 760. [18] X.Y. Li, W. Wang, Research on green supply chain management, in: International Conference on Logistics Engineering & Supply Chain, 2008. [19] Y. Chen, R. Simon, C. Reichweiser, J. Woo, Green supply chain, Green. Manuf. (2013) 83 105. [20] B.M. Beamon, Designing the green supply chain, Logist. Inf. Manag. 12 (4) (1999) 332 342. [21] R. Zhao, G. Neighbour, J. Han, M. McGuire, P. Deutz, Using game theory to describe strategy selection for environmental risk and carbon emissions reduction in the green supply chain, J. Loss Prev. Process. Ind. 25 (6) (2012) 927 936.

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Chapter 7

Digital twin enhanced Theory of Inventive Problem Solving innovative design Chunlong Wu1,2 and Youcheng Zhou1,2 1

National Engineering Research Center for Technological Innovation Method and Tool, Hebei University of Technology, Tianjin, P.R. China, 2School of Mechanical Engineering, Hebei University of Technology, Tianjin, P.R. China

7.1 Theory of Inventive Problem Solving
based innovative design 7.1.1 History and applications of Theory of Inventive Problem Solving TRIZ, which is an abbreviation for Teoriya Resheniya Izobreatatelskikh Zadatch, means theory of inventive problem solving in Russian [1]. In 1946 Soviet inventor Altshuller began to deal with the world’s high-quality invention patents and tried to find an inherent general scientific method or law contained in the millions of patents that could be implemented to help people accomplish new inventions or solve technical problems quickly [2]. Over the past 50 years, Altshuller and his team proposed various tools for TRIZ, including analytical and developmental tools, knowledge database, and creativity tools [3]. In the development history of TRIZ the first TRIZ-aided innovation tool “Invention Machine” was born in the Invention Machine Lab. Soon various types of TRIZ-based software were created. Since 1998, a variety of expert organizations have developed their own versions of TRIZ, including I-TRIZ [4], XTRIZ [5], and OTSM-TRIZ [6]. So far, TRIZ has been developed into a set of mature innovative design theories with good engineering applicability [7]. At present, TRIZ is being promoted and widely recognized by the society and industry [8]. Compared with traditional innovative methods such as trial-and-error and brainstorming, TRIZ has distinct features and advantages. It successfully Digital Twin Driven Smart Design. DOI: https://doi.org/10.1016/B978-0-12-818918-4.00007-5 © 2020 Elsevier Inc. All rights reserved.

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reveals the inherent law and principle of invention to some extent. TRIZ focuses on the contradictions of a technical system, trying to solve the contradictions completely rather than just obtaining compromised solutions [9]. Applications of TRIZ used to lack detailed reporting before 1991, because it is said that TRIZ was a state secret of the Soviet Union. After the collapse of the Soviet Union, many TRIZ experts migrated to the United States and some to other western countries, which makes TRIZ spread to the west and receive great attention. TRIZ-related research and practices were rapidly popularized and developed. Currently, TRIZ has spread and been applied in more than 35 countries around the world [10]. In terms of the cultivation of innovation capacity, many universities such as Tianjin University, Hebei University of Technology, Moscow State University of Technology have listed TRIZ as a course of engineering design methodology [11]. And a number of research institutions around the world provide TRIZ theory training and award certificates to the qualified innovation engineers. Through training, engineers are equipped with systematic innovative thinking and ability. For example, Chinese National Engineering Research Center for Technological Innovation Method and Tool trained innovation engineers in TRIZ for enterprises such as China Shipbuilding Industry Corporation and Sany Group, which brings considerable economic benefits to these enterprises [12]. Also, many enterprises have integrated TRIZ into their own product development and design processes to enhance their innovation capacity with the help of TRIZ consulting companies. For example, Samsung group in South Korea regularly invites TRIZ experts to train their engineers and reform product development processes every year, which greatly enhances the innovation capacity of Samsung and strengthens product competitiveness [11]. In terms of solving engineering problems, TRIZ helps designers to identify problems and obtain satisfactory domain solutions [13]. For example, General Electric Company utilized TRIZ to strengthen the concept design stage of Design for Six Sigma, and with the help of TRIZ, Boeing has improved the design of Boeing 737 [14]. In conclusion, TRIZ can be applied to equipping engineers and enterprises with creative thinking and improving their innovation capacity, and providing insights to solve engineering problems in the process of innovative design.

7.1.2 Theory of Inventive Problem Solving
based innovative design Problem is the distance between the intended state and the actual state [15]. According to the types of problems in the design process, design can be divided into conventional design and innovative design, to deal with conventional problems and inventive problems, respectively. If all the steps to implement the transition from the initial to the ideal states are known, it is called a

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conventional problem. If there is at least one unknown step, it is an inventive problem [16]. Fortunately, TRIZ can make the unknown steps clearer gradually by providing some kind of innovation knowledge base, TRIZ-based innovation software, and sets of tools. Therefore the realization of innovation is a process of solving inventive problems based on knowledge, which is introduced and applied to the technical systems where problems occur [17,18]. In chronological order the TRIZ-based innovative design process can be divided into four steps: strategic analysis, problem statement, problem analysis, and problem solving. Following this process, designers can gradually understand the nature of problem and adopt appropriate methods to analyze and solve problems. However, when designers go through the four steps, the tendency of qualitative analysis limits the reliability of the results. Therefore every step of the TRIZ-based innovative design process still needs to be improved to yield more accurate and valid outputs: 1. Strategic analysis aims to help designers adopt suitable strategies to promote enterprise development through analyzing the prospect of a product or the potential of the technology. TRIZ tools for strategic analysis include demand evolution analysis, technology evolution analysis, and technology maturity prediction. Demand evolution analysis is used to discover user’s new demand, such as communication needs, travel needs, and to determine the expected targets of product design, such as low cost, low pollution, and high efficiency. Technology evolution analysis helps designers to predict the possible future state of technology and the technological development trend. For example, Sengupta et al. [19] divided the fitness tracker into form factor, software and hardware. He carried out a study on the technology of fitness tracker device to find its development trend with the help of technology evolution analysis, finding that the form factor should use more colors, software should be more differentiated, and hardware should reduce energy conversion. Technology maturity prediction aims to assess the development potential of the technology [12]. However, existing strategic analysis is short of customer-related data (e.g., the context of use, user preferences, and user comments.) and real-time operation-related data (e.g., spindle speed, power, and working environment). Without these data, designers can only decide their direction of innovative design in a fuzzy way based on their personal experience and knowledge, which makes enterprises difficult to choose suitable development strategies and may lead to function inconsistency between final products and customer expectations. 2. Problem statement helps designers to clarify the problem to be dealt with by analyzing the gap between actual states and the ideal states. Based on the problem statement, designers can decide if they should develop new functions or identify the problems of the existing product. TRIZ tools for problem statement include 9-box method, resource analysis, and ideal

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final result (IFR) analysis. 9-box method means to describe the states and evolution of a product, so as to disclose the gap between the existing state and the future state of the product’s technology system, subsystem or supersystem to drive innovative design. For example, an ordinary pen cannot work properly in space. To deal with this question, Li et al. analyzed the pen’s structure and the space environment and concluded that the key of the pen’s innovative design for use in the space environment lies in the core with steady pressure [20]. Resource analysis is to efficiently discover the potential internal and external resources of a product and its working environment, which will make the gap between the existing product and innovative design clearer. IFR analysis aims to foresee the ideal product solution and discover the obstacles to realize it. For example, when experimenters tested the corrosive effect of strong acids on a particular metal, the containers are corroded simultaneously, which will affect the accuracy of the experiment results. Han made it clear that the obstacle to the ideal solution is the different corrosion resistance between the container material and tested metal. Therefore to overcome the obstacle, he used the tested metal itself as the container directly [21]. However, there are still neither enough direct data of product states nor clear visualization of working condition of a product for the problem statement. Visualization of working condition of the product can help designers know its failure state and its component states. Without precise information on the two aspects, designers cannot fully understand the states of the product technology system, subsystem and supersystem, which makes it difficult for designers to find the obstacles leading to the existing problem. 3. Problem analysis aims to find the nature of the problem and transform this into corresponding contradictions. TRIZ tools for problem analysis include TRIZ function model analysis, root cause analysis, and contradiction analysis. TRIZ function model analysis intends to discover the relationships inside the existing product, and harmful effects leading to the problems. Root cause analysis finds the root cause behind superficial problems [12]. Contradiction analysis is used to transform the root cause into standard contradiction parameters. For example, Ma and Lin [22] found three possible factors leading to wheel burns, and based on the engineering experience and observation of practical situations, he concluded that the deficiency in stiffness of the closed hydrostatic guide is the main root cause. According to the parameters, designers can find corresponding solutions to the contradictions. When designers carry out problem analysis, they still need further support from product initial design data (e.g., structure, components, and 3D model) and operation-related data (e.g., spindle speed, power, and working environment). Without such information, the accuracy of problem analysis will depend on designers’ personal engineering experience and professional proficiency to some extent. As a result, the causes for the problem

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discovered by the designers might be inaccurate or incomplete, which will influence the correctness and reliability of the defined contradictions and further result in the failure of problem solving. 4. Problem solving involves the methods and tools for concept generation and evaluation. Since it has been elaborated in Chapter 2, Digital twin driven conceptual design, this chapter mainly focuses on problem discovery and analysis process and tools. There are still many difficulties in the process of solving inventive problems because of possible deficient preconditions from the first three steps, such as the determination of objects involved in the problem, the function
behavior
structure modeling, and mapping of the product. In short, the TRIZ-based innovative design has already been used to model, analyze, and transform problems into contradictions. Then, designers can solve the inventive problems with TRIZ-related tools to realize innovation. But it still requires information support to help designers make more accurate judgments, achieve efficient processes, and provide high-quality solutions.

7.1.3 Digital twin enhanced Theory of Inventive Problem Solving innovation process Digital twin (DT) is the multiphysical and multidimensional simulation of complex products, aiming to mirror the whole life cycle of their corresponding physical products. Therefore DT enables engineers to master the product state in real time, which provides a precise and comprehensive information base for analysis and judgment in the process of TRIZ innovative design. In general, DT can enhance TRIZ innovation process in the following aspects: 1. Accurate understanding of product and technology development trends can contribute to strategic analysis. With real-time display and accumulated data of DT, designers can form dynamic images of products, which can help them know the product evolution trend. Operation-related data accumulated by DT can indicate product performance through some indexes such as failure rate and service life of product, which can help designers judge the technology maturity. Furthermore, designers can obtain users’ potential demand by analyzing customer-related data to master the market trend and determine the direction of product innovative design. 2. With the aid of DT, designers can fully analyze the technology system because DT can visualize the physical entity in the information domain with high fidelity. And DT can model customer better to help designers know the new direction of product design. State simulation realized by DT can help designers discover the gap between the actual and desired states of the product, which represents the obstacle to realizing the ideal results of innovative design. Meanwhile, operation simulation and failure

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FIGURE 7.1 DT-enhanced TRIZ innovation process. DT, Digital twin; TRIZ, Theory of Inventive Problem Solving.

warning can assist designers in discovering problems underlying the technology system and address them accordingly. 3. DT visualizes the physical entity and presents all the product components to designers, which can help designers construct more accurate TRIZ function models. Through real-time simulation enabled by DT, designers can identify the problems and then carry out further analysis. Besides, the parameters such as velocity and time obtained by sensors may imply the severity of some contradictions. This chapter will provide a detailed description of the TRIZ innovation process enhanced below. The overview of the DT-enhanced TRIZ innovation process is shown in Fig. 7.1.

7.2 Digital twin enhanced strategic analysis of Theory of Inventive Problem Solving innovative design process Facing the ever-changing market, it is important to grasp the general direction of product design in accordance with the TRIZ innovative design process, because the market trend may have influence on the measures taken to solve the corresponding problems. As a front-end process for TRIZ innovative design, strategic analysis includes demand evolution analysis, technology evolution analysis, and technology maturity evaluation. These three methods can

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help enterprises formulate appropriate development strategies from different perspectives. The data accumulated by DT such as user comments and product performance can be of help to the strategic analysis. The three methods enhanced by DT are described in detail in the following sections.

7.2.1

Digital twin enhanced demand evolution analysis

Demands reflect users’ expectations for a product. The starting point and ultimate purpose of innovative design are to meet the users’ needs. Therefore enterprises must fully collect and analyze users’ needs before designing innovative products. Comprehensive and accurate understanding of user needs is the premise of high-quality product design [23]. Although TRIZ researchers have summarized the evolution trend rules of user needs from many case studies, enterprises cannot usually fully grasp user needs. Because it is hard for enterprises to conduct surveys directly with a sufficient number of end users. Even if the surveys are conducted, the survey results are likely to be qualitative and inaccurate. Fortunately, these problems can be solved with the help of DT. In the process of building DT the mirror images of all elements of the physical entity, working environment, and interaction with users are taken into account. Thus DT would accumulate much of the customer-related data including their satisfaction degree, preference, and expectations. For example, how do products satisfy users’ demands in different usage scenarios? How do users evaluate the product? What are the product shortcomings in users’ perspective? Or what functions are used least in different usage scenarios. These data are difficult to be precisely and fully acquired using traditional methods such as user feedback interview and questionnaire survey. Meanwhile, the customer-related data accumulated by DT are more direct and reliable. Based on DT, data mining can help analyze customers’ expectations for the product and discover potential or unmet user needs more easily. In addition, the analysis process enhanced by DT can take various usage scenarios for different users into account, which will make it possible for enterprises to discover users’ individual requirements. All those advantages make demand evolution analysis more accurate. The demand evolution analysis enhanced by DT will enable some new features: (1) the user’s information is reflected more directly and precisely and (2) the results of demand evolution analysis not only pay attention to the common needs of users but will also reflect the unique needs of individual users.

7.2.2

Digital twin enhanced technology evolution analysis

In order to improve product competitiveness, products should be constantly adapted to meet the new user needs and accommodate emerging technologies. Therefore product technology systems need to keep evolving to pursue better performance, lighter weight, less manufacturing resource consumption,

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and more functions. In other words, technology systems need to evolve toward their IFRs [24]. The technology evolution theory in TRIZ can help designers analyze technology development trend, to help enterprises make suitable strategies ahead of other enterprises to make improvements to existing products or develop new products, so as to obtain more user preference and market shares [25]. However, the existing methods to analyze the evolution of technology in TRIZ mainly rely on studying patents related to the technology. Technology evolution analysis remains to be a challenging task and the effectiveness of the results is hardly satisfactory due to the absence of direct big data analysis from the used or in-use products. Nevertheless, DT is able to comprehensively reflect a product throughout its life cycle and visualize the product function realization process, which can provide the needed technologyrelated information for the designers. On one hand, the operation-related data accumulated by DT provide designers with a direct basis for improving the accuracy of technology evolution analysis. For example, aging condition of components, maintenance condition, and failure rate of a product can be manifested based on such data. On the other hand, DT shows the actual states of a product by modeling operation-related data so that the gap between the actual and ideal states of the product can be reflected more intuitively. Through analyzing these data, designers can judge the applicability of the existing technology of the product and determine the feasible technology for future products.

7.2.3

Digital twin enhanced technology maturity evaluation

Technology maturity evaluation in TRIZ is useful to help enterprises establish suitable strategies in different stages of their product life cycle. If the existing technology of a product has potential for further development, enterprises can adopt the strategy to keep improving the existing product. Otherwise, enterprises should devote more efforts to alternative technologies or new working principles. In both cases, it is required to first correctly evaluate the technology maturity of the product. In TRIZ the S-curve is a common method for technology maturity evaluation. It divides product technology maturity into four phases: infancy, rapid growth, maturity, and recession. The S-curve method is to fit the process of product performance improvement [26]. The basic principle is to select the performance indicators that can represent the main functions of the product, trace the points on the coordinate plane, and choose the appropriate S-curve model to fit. So the technology maturity of the current product can be evaluated according to the inflection point generated in the fitting curve. However, it is difficult to directly evaluate the technology maturity through S-curve at present. The major difficulty lies in the capturing and analysis of technology performance data. Various patent analysis methods proposed by TRIZ researchers can only make indirect judgment of technology maturity,

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which may lead to mistakes or lags [27]. The operation-related data presented by DT can help designers learn product performance (e.g., operating speed and energy transfer efficiency). Furthermore, by comparing the operation-related data with customer-related data accumulated by DT, designers may obtain effective clues for adjusting the functions, parameters, and operation information of different generations of products. Aided by the information from DT, a more accurate mathematical model of the S-curve can be established. Designers can preliminarily identify the maturity of the existing technology based on patent analysis and then make a more detailed and accurate judgment based on the direct data of different products from DT. The technology maturity evaluation process realized by the S-curve analysis enhanced by DT is shown in Fig. 7.2.

FIGURE 7.2 S-curve analysis-based technology maturity evaluation enhanced by DT. DT, Digital twin; P, performance; t, time.

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7.3 Digital twin enhanced problem statement of Theory of Inventive Problem Solving innovative design process Problem statement is a fundamental phase in the process of TRIZ innovative design. The 9-box method, resources analysis, and IFR analysis are the three commonly used methods in this phase. Large amounts of information from supervising the real-time state of the product, working environment simulation, and customer modeling realized by DT can be employed in the problem statement phase. This section will introduce these three methods enhanced by DT in detail. The overview of the problem statement process in TRIZ enhanced by DT is shown in Fig. 7.3.

7.3.1

Digital twin enhanced 9-box method

In 9-box method the scope of the problems that designers study is not limited to the product itself but covers the nested systems of multiple levels. The product is seen as a technology system, which is composed of nested subsystems and is also a subsystem subject to its higher level supersystem. For 9box method the subsystem, system, and supersystem will all be divided into three states: past, present, and future. Describing all the 9 states in a coordinate system will produce 9 boxes [28]. The gap between the existing state and the intended future state in the 9 boxes indicates the problems to be dealt with, which thereby implies the targets of innovative design. With existing methods, it is difficult to obtain the relevant information of the states of the technology systems. Nevertheless, DT can record and simulate the nested systems with high fidelity over its life cycle, which will present the states to the designers directly and accumulate large amount of information on the system states and thus help designers better analyze the system development trend and existing problems. The 9-box method takes the product itself and its working environment into account. On one hand, working environment should include elements

FIGURE 7.3 The problem statement process in TRIZ enhanced by DT. DT, Digital twin; TRIZ, Theory of Inventive Problem Solving.

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related to product operation. For example, the working environment of a robot vacuum cleaner includes specific space and obstacles in the space. Accurate information on the working environment needs to be acquired based on DT. On the other hand, users are also included because they can affect implementation of the product functions. In the process of interacting with users, user preference and their habits can be modeled by DT in real time. Furthermore, users’ comments on products which show customers’ real evaluation of the product can be collected directly by DT during use. These comments can help designers identify the new targets of innovative design for future products. Through the visualization of DT, designers can understand more precisely the potential needs of users, identify the product future state, and carry out more effectively the 9-box method to make the problem statement.

7.3.2

Digital twin enhanced resource analysis

In TRIZ, resource analysis allows designers to elaborate comprehensively on the existing resources helpful in solving problems in the later stages, by analyzing the resource composition and utilization of the corresponding systems. Generally, resources have several types, such as physical components, energy, information, redundant function, time, and space. Therefore when carrying out resource analysis, designers should first identify all the available resources and then decide on how to solve problems of the product through integration and transformation of the resources. More accurate analysis of a resource type and function will yield better expected effects of resource analysis. However, the existing methods cannot fully identify all resources relevant to a product and its nested systems and neither can they pay enough attention to the use pattern of the resources and the analysis of working area. Also, there are no detailed and applicable guidelines for identifying and using these resources, which increases the difficulty of resource analysis. This problem could be addressed based on DT. In order to optimize resource analysis, DT conducts real-time modeling of physical entities in the digital world based on product initial design data (e.g., structure, components, and 3D model). The visualization of the product and its components help designers to define the resource composition of the system quickly. Furthermore, DT presents the operation states of every function module of the product, which implies that the effect of resource utilization is quantified. Therefore designers can embody the working area of the resources and specifically determine how to utilize the resources more accurately.

7.3.3

Digital twin enhanced ideal final result analysis

IFR in TRIZ is the perfect state of the product. Low cost, high efficiency, high reliability, and low pollution are usually the ideal states of products [29]. The ideality level of the solution can be obtained from formula (7.1).

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The higher ideality level indicates better performance of a product. Higher level IFR analysis solves problems through making changes within the nested systems, which means to utilize internal and external resources to solve problems, specifically, by implementing corresponding functions, eliminating side effects, and reducing costs while keeping the technology system simple. The solution adopted by the primary IFR usually attempts to use existing resources to solve problems without any additional costs [30]. P Benefits  P Ideality level 5 P ð7:1Þ Expenses 1 Harms where benefits represent the benefits created by the product, expenses indicate the total cost of the product, and harms represent the disadvantages of the product. The IFR analysis analyzes the obstacles that block the realization of the product ideal state and searches for a solution step by step. However, currently the determination of IFR of the product is dependent more on the designers’ experience and thinking mode, such as by brainstorming, than on explicit product-related information. Data accumulated using DT are directly from the product itself and cover the product whole life cycle, which will provide sufficient and effective information to enhance IFR analysis. By virtue of real-time simulation of working environment of the physical product and users based on DT, customer modeling can be more precise. When interacting with users, DT can obtain customer-related data such as their preferences (e.g., preferred functions and utilization environment) and comments (e.g., evaluations on product novelty or deficiencies and expectations). With these data, DT can show the different working conditions of the product to different users, which implies the applicability of the product to target users. Along with the direct information of user evaluation and expectations based on DT, users’ real comments and feedback from product use clearly manifest the potential user demand and purpose to designers, which will help designers further determine the product IFR and modify the IFR dynamically. Meanwhile, with the mirror images of the physical entities, DT displays the product operation states during its whole life cycle, which visualizes how each component works. By comparing the real state with the ideal state, DT can help designers identify the possible obstacles in the realization process of the IFR and find solutions to the obstacles in a more accurate way.

7.4 Digital twin enhanced problem analysis of Theory of Inventive Problem Solving innovative design process After the problem statement, designers can identify the problems in a product. However, the confirmation and analysis of causes for such problems is also necessary for solving the problems. Problem analysis in TRIZ aims to provide designers with a set of methods to analyze the nature of the problem.

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FIGURE 7.4 Overview of the problem analysis process in TRIZ enhanced using DT. DT, Digital twin; TRIZ, Theory of Inventive Problem Solving.

Problem analysis includes mainly TRIZ function model analysis, root cause analysis, and contradiction analysis. In order to analyze the problems, designers will need a mass of relevant information such as product failure and system composition. Here, DT can play a role to collect the related data. The overview of the problem analysis process in TRIZ enhanced by DT is shown in Fig. 7.4.

7.4.1

Digital twin enhanced function model analysis

The technology system of a particular product consists of different components. All the components of the system are connected by effects to form a TRIZ function model of a complex system. The TRIZ function model includes product components, target object, and supersystem components. Product components are the basic elements that make up a system in nested system level. Target object is the action object of the basic (main) function of the technology system. Supersystem components represent all existing elements in the working environment outside the technology system but interact with product [12]. As the first step of problem analysis, TRIZ function model analysis intents to discover inadequate and harmful effects of components, which will be the premise of root cause analysis. However, the existing methods mainly rely on designers’ subjective analysis, which depends intensively on designers’ individual knowledge and experience and thus may not be accurate enough. DT will improve this situation because it enjoys complete information of the product’s function, behavior, and structure, which can help designers to construct a more accurate TRIZ function model. Because of the high fidelity of virtual models, inventory of product initial design data are already existing in DT, which provides designers with the

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information of all the components and their interactive relationships. Designers can obtain the names and numbers of all the components directly from the inventory, which can effectively avoid omissions of components and ensure the accuracy of component analysis. The design parameters describing the realization process of the product function can also be obtained from DT, which will indicate the component geometrical features and material characteristics for describing every function realized by the components in detail. Also, these design parameters can help designers clarify the type and degree of interactions between components and improve the accuracy of interaction analysis in the TRIZ function model. Furthermore, the information of the product’s working environment is also reflected in DT, which will help to recognize the interactions between system-level components and supersystem-level components.

7.4.2

Digital twin enhanced root cause analysis

The root cause analysis is valuable for designers to deepen their understanding of the problems in products, which is the premise of solving the real problems. There are some common methods of root cause analysis, such as cause and effect chain diagram, fishbone diagram, and 5-why method [12]. But in most of the engineering cases, root cause analysis depends heavily on professional knowledge of engineers and designers, which is timeconsuming and not conducive to finding the real causes. To deal with this weak point, DT can offer comprehensive visualization of the relevant factors of the problems, allowing designers to analyze them based on big data analysis and data mining. In DT the real states of tangible products can be displayed on the corresponding virtual entities in real time. All the components of the products and their real working conditions can be presented to the designers. Relying on state simulation of the technology system enabled by DT, designers can obtain visualized dynamic images of the products and discover the changes of characteristics of the products from the past to the present. On this basis, engineers are able to conduct in-depth analysis. Based on real-time acquisition and modeling of the data of physical entities and real-time interaction between physical entities and virtual entities, DT visualizes failure states of each malfunction module of the products and then intuitively presents possible reasons of the superficial problems to designers. For the malfunction module, designers can judge more precisely the location of the problems, the components related to the problems, and the exact abnormalities of the components through analyzing the failure data from DT. With DT, it is easier to present all-sided failure states of the products to designers who could improve the efficiency of the root cause analysis significantly. In addition, similar problems recorded in DT could provide

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references for designers, by providing a shortcut for designers to find the root cause and solution to problems.

7.4.3

Digital twin enhanced contradiction analysis

In this step, designers should use the contradiction parameters to express the key factors found in root cause analysis, so as to choose suitable principles to solve them later [31]. In engineering fields, contradictions would usually mean two contrary requirements for one element which designers may encounter during the design process, or a solution to a problem that leads to another problem [32]. TRIZ intends to discover the nature of the contradictions and thoroughly solve the contradictions instead of finding a compromise. There are three types of contradictions in TRIZ: technical contradiction, physical contradiction, and management contradiction. Since innovative design does not involve management issues, it will not be discussed in this chapter. Technical contradiction refers to an action that results in a useful effect or harmful effect to different subsystems at the same time, or a situation where introduction of a useful action or elimination of a harmful effect will lead to deterioration of the system. The inventive principles in TRIZ can be used to solve them in subsequent problem-solving processes [33]. Physical contradiction means a subsystem or component needs to have two opposite properties for two different purposes. The separation principles in TRIZ could be employed to solve them [12]. Converting the key factors found in root cause analysis into appropriate contradiction parameter pairs is the key challenge. DT is to mirror the physical entities in virtual space, which needs to collect a mass of data related to the operation of the product system. Therefore as the foundation of product, a complex sensor system will be equipped to obtain real-time data to model the physical entities comprehensively. With the sensor system, most contradiction-related parameters such as velocity, time, or pressure can be directly obtained by the users. By analyzing the information of the product states and working conditions obtained from the data, designers can judge the severity of the contradictions and present key problems into standard contradiction parameter pairs more easily and accurately. Furthermore, the product failure states shown using DT indicate what the problem is and where the problem happens, which can help designers quickly confirm the contradiction zone.

7.5

Summary

In summary, this chapter discusses how DT could promote a more effective TRIZ innovative design method. In traditional TRIZ innovative design, designers’ experience and knowledge play an important role and they usually cannot obtain enough information needed in the process without accurate

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data support. Therefore a variety of TRIZ innovative design activities such as strategic analysis (including demand evolution, technology evolution, and technology maturity prediction), problem statement (including the 9-box method, resources analysis, and IFR analysis), and problem analysis (including TRIZ function model analysis, root cause analysis, and contradiction analysis) can benefit from DT in various ways. For strategic analysis, user-related information can help designers analyze users’ potential demand. With the real-time display of relevant data such as technology state or failure state of a product by DT, designers can form dynamic images of the product and judge the technology’s evolution trend. Furthermore, performance-related data in DT can help designers judge the technology maturity. For the problem statement, customer modeling from DT can help designers discover the gap between the actual state and the desired state through the 9-box method and IFR analysis. Based on resource analysis and operation simulation in DT, this can help designers discover problems existing in the technology system. For problem analysis, DT presents all the product components to the designers, which can help designers construct a more accurate TRIZ function model. DT’s real-time mapping to the physical entity also allows designers to follow the product operation state and then identify and locate the problems by root cause analysis. Besides, the parameter values obtained by the sensing system of robot vacuum cleaner (RVC) can also help designers find contradictions. Compared to the traditional TRIZ innovative design process, user demands and technology-related information can be reflected more visibly in the DT-enhanced TRIZ innovative design process. In future work, DT can be used to further extend the ground of TRIZ, such as finding new Inventive Principles, Substance-Field Modeling, and Inventive Standard Solutions.

References [1] J. Terninko, A. Zusman, B. Zlotin, Systematic Innovation: An Introduction to TRIZ (Theory of Inventive Problem Solving), CRC Press, 1998. [2] L. Chechurin, Research and Practice on the Theory of Inventive Problem Solving (TRIZ), Springer International Publishing, 2016. [3] A. Zakharov, TRIZ future forecast, TRIZ J. (2004). [4] R. Fulbright, I-TRIZ: anyone can innovate on demand, Int. J. Innov. Sci. 3 (2) (2011) 41
54. [5] V. Souchkov, R. Hoeboer, R.M.V. Zutphen, Application of RCA 1 to solve business problems, TRIZ J. (2007). [6] D. Cavallucci, N. Khomenko, From TRIZ to OTSM-TRIZ: addressing complexity challenges in inventive design, Int. J. Prod. Dev. 4 (1
2) (2006) 4
21. [7] R.H. Tan, TRIZ and Application, Higher Education Press, 2010. [8] R.H. Tan, J.G. Sun, The Disruptive Innovation Technology Principle of Pre-production, Science Press, 2014.

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[9] C. Nikulin, P. Solis, M. Lo´pez-Campos, A methodology to improve the decision-making process, Int. J. Knowl. Syst. Sci. 9 (1) (2018) 59
81. [10] I.M. Ilevbare, D. Probert, R. Phaal, A review of TRIZ, and its benefits and challenges in practice, Technovation 33 (2
3) (2013) 30
37. [11] Y.H. Liu, et al., International Comparative Research and Case Study on Technology Innovation Methods, Science Press, 2011. [12] H.G. Zhang, Innovation Design: Systematic Innovation Based on TRIZ, China Machine Press, 2017. [13] K. Gadd, TRIZ for Engineers: Enabling Inventive Problem Solving, John Wiley & Sons, 2011. [14] Y.W. Sun, Sergei Ikovenko, TRIZ: The Golden Key to Innovation, Science Press, 2015. [15] M. Sato, Problem Solving, China Renmin University Press, 2010. [16] S.D. Savransk, Engineering of Creativity: Introduction to TRIZ Methodology of Inventive Problem Solving, CRC Press, 2000. [17] M. Zhao, TRIZ Enhancement and Practical Applications, China Machine Press, 2010. [18] W. Liu, R.H. Tan, Y.F Dong, et al., A creative design approach based on TRIZ and knowledge fusion, in: International TRIZ Future Conference, 2018, pp. 167
179. [19] S. Sengupta, J. Kim, S.D. Kim, Forecasting new features and market adoption of wearable devices using TRIZ and growth curves: case of fitness tracking products, Int. J. Innov. Technol. Manage. 15 (1) (2018) 1
19. [20] H. Li, H.G. Yan, Y. Xiao, Several problems with the nine-screen method in TRIZ theory, Ind. Sci. Trib. 17 (2012) 93
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78. [22] L. Ma, X.H. Lin, Optimal design of closed hydrostatic guideway for grinder based on TRIZ theory, J. Xiamen Univ. Technol. 26 (1) (2018) 19
24. [23] K.T. Ulrich, Product Design and Development, Tata McGraw-Hill Education, 2003. [24] L. Fiorineschi, F.S. Frillici, F. Rotini, Enhancing functional decomposition and morphology with TRIZ: literature review, Comput. Ind. 94 (2018) 1
15. [25] Y.K. Wang, W. Zhang, Z.X. Cao, et al., TRIZ encounters with nanophotonics: evolutionary trend to super system as an example, Sci. Technol. Rev. 35 (15) (2017) 45
50. [26] V. Petrov, Laws of dialectics in technology evolution, TRIZ J. (2002). [27] D. Mann, Using S-curves and trends of evolution in R&D strategy planning, TRIZ J. (1999). [28] D. Mann, System operator tutorial: 9-windows on the world, TRIZ J. (2001). [29] E. Domb, The ideal final result: tutorial, TRIZ J. (1997). [30] G. Gasanov, B.M. Gochman, A.P. Yefimochkin, et al., Birth of an Invention: A Strategy and Tactic for Solving Inventive Problems, Interpraks, 1995. [31] C. Lim, D. Yun, I. Park, et al., A systematic approach for new technology development by using a biomimicry based TRIZ contradiction matrix, Creativity Innov. Manage. 27 (4) (2018) 414
430. [32] K. Rantanen, E. Domb, Simplified TRIZ: New Problem Solving Applications for Engineers and Manufacturing Professionals, Auerbach Publications, 2007. [33] J. Hipple, S. Caplan, M. Tischart, 40 Inventive principles with examples: human factors and ergonomics, TRIZ J. (2010).

Chapter 8

Digital twin driven factory design Ning Zhao, Jiapeng Guo and Hu Zhao School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, P.R. China

8.1

Introduction

With the propagation of Industry 4.0, China Manufacturing 2025, and Smart Manufacturing, traditional factories are increasingly transforming into smart factories, and the demands for building new factories are growing at the same time. Because it will be difficult to improve the performance once the factory is built, it is widely recognized that the performance of a new factory is determined at the end of the design phase. It is critical to ensure the factory is designed rightly without hidden defects. Factory design includes shop layout, facility layout, processing flow, material handling system (MHS), manage and control system, and work shift. A well designed factory is the integration of all of the above, which will contribute to production performance in its entire life cycle. Therefore factory design is sophisticated with multidisciplinary and dynamic characteristics. Due to the complexity, traditional factory design approaches always focus on a single discipline and solve it in a static way. Because of lacking approaches and designing tools, designers’ imagination and experience are exceptionally important to traditional factory design. For this reason, digital twin (DT) would be a strong tool to help design and optimize the factory since it could mirror characteristics of a future factory with high fidelity. In this chapter the DT for factory design will be introduced and case studies will be presented.

8.2

Related works

Traditionally, the design of a factory is always considered as a mathematical problem to be solved for an optimal solution. Many researchers have studied this problem with mathematic models and presented algorithms to optimize layout in a factory. Facility layout has been studied as an academic problem Digital Twin Driven Smart Design. DOI: https://doi.org/10.1016/B978-0-12-818918-4.00008-7 © 2020 Elsevier Inc. All rights reserved.

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since the early 1950s. There are a lot of literatures in this area and only a select few of them are reviewed in this chapter. Rosenblatt [1] was the first to develop a formal model and an optimal solution procedure for determining optimal layouts for multiple periods. Recently, Ficko et al. [2] studied a flexible manufacturing system in one or multiple rows and considered the objective to minimize total transport costs. A genetic algorithm is proposed to solve the automated guided vehicle (AGV) transportation problem. Li et al. [3] proposed a parallel hybrid parallel genetic algorithm to solve packing and layout problem. Gonc¸alves Filho and Tiberti [4] presented a new genetic algorithm for cell layout design, which is based on group encoding instead of simple machine encoding, and disclosed the group structure in a dataset. Sahin ¸ and Tu¨rkbey [5] considered the design problem as a multiobjective facility optimization problem that combines the objective of minimization of the total material handling cost and maximization of the total closeness rating scores. A simulated annealing algorithm was proposed to find the Pareto optimal solution set approximately. Khaksar-Haghani et al. [6] presented a novel integer linear programming model for designing multifloor layout of cellular manufacturing systems. Three major decisions are involved for the design, cell formation, group layout, and group scheduling. Lenin et al. [7] aimed to minimize the total distance traveled by products, reduce the number of machines, and decrease the total investment cost of machines used in the final sequence in single-row layout design. A genetic algorithm for solving this multiobjective optimization problem was presented. Kang et al. [8] studied the efficient arrangement of manufacturing cells on MHS and proposed a random-key and cuckoo search based approach to solve the factory design problem. These studies have made great scientific contributions. However, due to the insufficiency of the mathematic model, the layout optimization of these studies merely focused on static aspects. Although researchers have tried to combine layout with MHS recently, other disciplines are still separated and the dynamic behavior of the manufacture system is very hard to be modeled. Moreover, only machines and manufacturing cells layout are considered for optimization. The layout of other smaller facilities such as MHSs, pathways of workers, buffer areas, and tool cabinets are always neglected. However, these facilities might be critical to factory performance. In order to pay attention to greater details in factory design and take advantage of designers’ experience, virtual reality (VR) technology is used to help designers experience the future factory and make decisions in detail. Westka¨mper and Von Briel [9] explored a virtual simulation technology based virtual factory. Based on the data models, the virtual factory generates a simulated performance so that users can reconfigure the factory virtually. This might be the first literature that carries out the prototype of DT-driven factory design. Yap et al. [10] proposed a VR-based robotic work cell layout planning systems, which include VR-based robotic work cell layout and a VR-based robot teaching system. With these systems, users are

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able to try numerous times to attain an optimum solution without damaging the robot or interrupting the production line. Lee et al. [11] presented a mixed reality environment for virtual factory layout planning, which is designed to reduce modeling time and cost in building virtual objects needed for a digital virtual factory. Jiang and Nee [12] presented a novel factory planning system for real-time on-site facility layout planning. Users could construct existing facilities as virtual models and layout the models manually in an augmented reality environment. They also proposed an analytical hierarchy process and genetic algorithm to help with automatic layout planning in this system. Oyekan et al. [13] presented a tool that combines the modeling of discrete event simulation with the 3D visualization to enable decision makers to make informed design choices for future factory layout. These literatures suggest that VR is a valid way to enhance designers’ experience. However, 3D visualization and virtual environment are not sufficient in investigating dynamic behaviors of a factory. For this reason, discrete event simulation is used and integrated with VR. There are many studies focusing on discrete event system simulation (DES) techniques. Patel and Ma [14] presented a discrete event simulation tool to analyze the process layout, operator staffing, and capacity of testing equipment in automobile manufacturing process. Dombrowski and Ernst [15] believed future success is determined by the factory capability to adapt to the dynamic environment. They presented a scenario-based discrete event simulation approach to find out factory layout variants that are adequate for future requirements. Azadeh et al. [16] presented an integrated simulation data envelopment analysis approach to study optimal layout maintenance workshop in the gas transmission unit. First, the maintenance workshop is modeled with discrete event simulation to measure the key performance indicators. Second, a data envelopment analysis is presented to rank all layouts alternatives and to identify the best configuration. Given that discrete event simulation cannot depict spatial changes in a natural way, ElNimr et al. [17] presented a framework for integrating 3D visualization components with discrete event simulations to model the movement of mobile cranes on industrial construction projects and to facilitate tempo-spatial planning of site layout. Zhang et al. [18] presented a framework of simulation-based approach to design plant layout and production process. Consequently, mathematical algorithms and heuristic methods are integrated when applying simulation to balance the operation performance and the planning cost. It is notable that many studies have been dedicated to simulating the factory layout with discrete event simulation and helping the designers experience the designed virtual factory. In order to consider detailed performance indicators (such as capacity calculation, machine utilization, number of machines, and efficiency of the logistics) and depict spatial changes, discrete event simulation has been integrated with VR environment in recent studies

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[13,17]. In this context, the DT is investigated to drive factory design in a new way. Zhang et al. [19] presented a DT-based approach for rapid individualized design of the hollow glass production line. The DT merges with physics-based system modeling and distributed real-time process data to validate an authoritative digital design of the system at the preproduction phase. Uhlemann et al. [20] introduced a DT learning factory as well as a simulation-based data processing method. The comparison between DT and traditional value stream mapping technique in layout shows the benefits of the DT quite clearly. Zhuang et al. [21] proposed a DT-based smart production management and control approach for complex product assembly shopfloors. A detailed implementation process of this approach is demonstrated in a satellite assembly shop-floor scenario. Guo et al. [22] presented a modular approach to help building flexible DT and conducting corresponding changes in different design stages. With DT models, time can be saved by detecting and avoiding design flaws in early stage. In general, factory is a huge and complex system constituted by processing machines, labors, tools, MHSs, control software, and management modes. Comparing with product design, factory design is multidisciplinary and cannot be studied in a static way. Although traditional approaches such as mathematical optimization, VR and DES have made significant contributions on factory design, deficiencies still exist since the physical factory cannot be reflected comprehensively and accurately. Fortunately, DT could generate a high-fidelity mirror to the physical entity in a dynamic way, while considering multiple disciplines, which provides an efficient way to solve the abovementioned problem. Specifically, traditional approaches are compared in the following aspects. Mathematical optimization approach is insufficient to solve the factory design problem, because a factory is too complex to be modeled and many factors might be neglected during layout optimization. For example, facility layout is connected with labor shifts, management modes, and control strategies, which should be designed integrally. But in practice, facility layout is always designed independently with the objective to minimize transport cost. Different to mathematical approaches, the DT offers fidelity to real system and integrates layout design with those aforementioned factors. Therefore the DT can be effective in layout design. Although VR technology has been employed to factory design, but too much focus on visual experience results in longer developing time. Therefore VR technology is usually employed when the design is confirmed. In other words, VR seldom offers help during design stage, let alone when the design time is limited. Different to VR, the DT offers whole life cycle fidelity to the physical factory, and therefore modular approach may be employed. Since modules can be used in the design stage, they can also be employed in the whole life cycle of factory once the design is completed. Consequently, developing time and cost may be saved in this way.

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DES offers a good way to investigate the dynamic behaviors of current as well as future factory. However, professional developers and extra developing time are needed to develop different simulation models in different design stages. Moreover, the developers have to fully understand the perception of the designers who, accordingly, have to understand the simulation results. This may cause inefficiency and constrain the application of DES. Different to DES, the DT offers data-driven simulation models and evolves with physical entities, which may reduce developing time and discover hidden defects with large volumes of data. In general, the DT for factory design can provide high fidelities and help the designers to gain a better understanding of complex and dynamic factory systems. In the following sections, DT-driven factory design approaches are presented.

8.3

Digital twin driven factory design

In this section the framework of DT for factory design is presented. It has been studied in the authors’ previous work [22]. The fidelity between the DT and physical factory is discussed. Because a factory design is changing before getting approved, a modular approach for building DT is proposed to help simplify workload and shorten developing time.

8.3.1

Framework for digital twin driven factory design

Typically, there are three main design stages in factory design, conceptual design, detailed design, and finalized design. The characteristics of these three stages are different, and therefore the functions of the DT are varying in these three stages. Conceptual design is the first stage, focusing on designing the concept of a new factory, which includes plant layout, operation mode, capital investment, and throughput predication. It is widely recognized that roughly 75% of the cost of building a factory is determined in the conceptual design phase. In this phase the DT can help the designers and shareholders verify the design concept via VR environment and predict the throughput and rate of return on investment. In addition, layout problems that arise in older or similar factories can be transferred as knowledge to DT building. Moreover, historical product orders submitted by customers with different demands are also critical in designing the factory in a right way, since these orders can be used as inputs of the DT to verify the related simulation models. Detailed design is the second stage and furthers the conceptual design, including machine configuration, process design, production line or production unit configuration, MHS configuration, and work shift configuration. In most cases, the objective of detailed design is to further and validate the conceptual design. Consequently, the DT in detailed design phase is to help the

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designers gain a better understanding of the connections between configurations and key performance indicators. Based on the understanding, the designers can optimize the configurations and conduct integrated validation with the DT. During optimization, historical configuration problems that arise in old physical factories can be transferred to the DT and then the DT would help the designers optimize the configuration. For this reason, historical production data and historical key performance indicator (KPI) data are critical to build and validate DT. Finalized design is the last stage and is linked to its construction. In this stage the machine and logistics control strategy will be designed and the whole manufacturing system needs to be integrated. Because DT corresponding to the finalized design is most similar to the future factory, the DT offers fidelity to the physical world in this stage. Moreover, the DT connects with multiple control software such as manufacture execution system and programmable logic controller, which enables the DT to emulate the manufacturing and logistics control strategy, and to help the designers to debug the control logic and make decisions. Historical control and logistics problems that arise in old factories are critical to DT building and control optimization. Historical data will be input to the DT and drive the emulation. The framework of applying the DT to the three design stages is shown in Fig. 8.1 [22]. As can be seen in Fig. 8.1, the DT mirrors and feeds back the three physical design stages. Designers connect with suppliers, shareholders and design

Mapping and feedback

Iteration and optimization

Feedback

Approved

Detailed design

Finalized design

Feedback Manufacturing control policy design Logistics control policy design Software design

DES simulation Configure optimization

Emulation Control optimization

Plant layout Capital investment Throughput prediction

Feedback Product line layout Machine configuration Process design Material handling design Work shift configuration

Feedback Manufacturing control policy design Logistics control policy design Software design

FIGURE 8.1 Framework of DT-driven factory design. DT, Digital twin.

Historical layout problems Historical orders

Evolution

Feedback Product line layout Machine configuration Process design Material handling design Work shift configuration

VR Layout optimization

Evolution

Plant layout Capital investment Throughput prediction

Evolution

Approved

Conceptual design

Physical factory

Digital twin Feedback

Historical configuration problems Historical production data Historical KPI data Evolution

Factory design

Historical control problems Historical logistics problems Historical control and logistics data

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documents with consideration of the simulation results of the DT. Based on the output of the DT, the designers evaluate the current design and decide whether it can be approved. In the conceptual design stage the DT mirrors the design concept and visualizes it by animation, which helps the designers to further the concept in a comprehensive way and clearly state the design to the shareholders and suppliers. In the detailed design stage the DT mirrors the configuration details of the factory and validates whether the configurations can achieve higher throughput via simulation. In the finalized design stage the DT mirrors the finally approved design and emulates it with historical process data, and then the control strategy and software design can be confirmed. Moreover, it is possible for the DT in finalized design stage transforming to factory control and monitor system. Apparently, the DT evolves from conceptual design to finalized design. Meanwhile, the fidelity to physical world is also improved stage by stage.

8.3.2

Functions of digital twin in different stages

High fidelity to the physical world is one of the most significant characteristics of DT [23,24]. For factory design the physical design carries the physical world, while the DT carries the virtual world. The virtual world must reflect the physical world with fidelity and therefore can evaluate the physical entities accurately. However, the fidelity to physical design has different meanings in the aforementioned three design stages. In the conceptual design stage the physical world is an uncertain concept hidden in the mind of the designers. It will become definite when the factory layout is produced. Traditionally, the factory layout is manifested as twodimensional diagrams, and investment and throughput can only be estimated using primitive calculation because of uncertainty. To make the design more definite, different knowledge and techniques may be employed in this stage. For example, VR can make the design concept visualization with threedimensional animations and help the designers to conceive the concept more specifically. Moreover, investment and throughput can be predicted with sophisticated algorithms or knowledge-based approaches. Therefore the function of the DT in this stage is to map the uncertain concept design and make it more concrete. Since there are too many uncertainties in this stage and the design might be modified frequently, rough simulation based on VR and embedded algorithms will contribute to the conceptual design. Moreover, object-oriented techniques will be helpful in making the DT more flexible. In the detailed design stage the physical world is more definite than in the conceptual design stage. The facility layout, machine layout, bill of material, manufacturing process, MHS, working shift, and management and control configuration are all defined in this stage. Consequently, the physical world in this stage provides preconditions of manufacturing process in the designed future factory. For example, the product can be processed only if it

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is transported to the designated machine. The corresponding machine, fixtures, tools, and skilled workers need to be available so that the processing conditions are met. Otherwise, the product has to wait in queue, occupy the buffer area, and may jam other products. Obviously, many design parameters are critical in this stage, such as equipment number, worker number, AGV number, and buffer capacity. These parameters are difficult to be validated accurately because the manufacturing process is featured by high dynamic behaviors. Embedded with discrete event simulation, the DT can test different parameter combinations step by step. Consequently, accurate parameters validation can be offered to help make decisions. The function of the DT in detailed design stage is shown in Fig. 8.2 [22], which is mapping the characteristics of configurations such as factory layout, manufacturing process, and shop management. Based on DES technique, performance of these configurations is evaluated and therefore can help optimize the configurations. Apparently, the DT should also be flexible in this stage because the configurations may be changed frequently. In the finalized design stage the physical world refers to those physical entities of the future factory. The features of the physical entities lie not only in the external appearance but also the internal control strategy. Therefore the main connection between virtual models and the physical entities is control. On the other hand, control is decentralized into different physical entities in smart manufacturing. The fidelity of the DT in this stage is to emulate decentralized control of the physical entities and their integration. Based on the emulation, the DT helps the designers to evaluate the control strategies and find the best one to match the designed factory. The fidelity of DT in the aforementioned three design stages is summarized in Table 8.1 [22]. The combination of VR, simulation, and emulation will undoubtedly improve the quality of factory design. Embedded Input configurations Facility layout configuration Making process configuration MHS configuration Working shift configuration Management and control configuraion

Digital twin

Output KPI Throughput

Check and verification

Cycle time Equipment utilization

Test and experiment

MHS utilization Buffer area utilization

Optimization and suggestion

Labor utilization Profit evaluation

FIGURE 8.2 Function of DT in detailed factory design stage. DT, Digital twin.

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TABLE 8.1 Function of digital twin in three design stages. Physical world

Employed techniques

Function

Conceptual design

Factory layout

VR environment Rough simulation

Mapping the uncertain design concept by animation

Detailed design

Facility layout Machine layout BOM Making process MHS Working shift Management and control

Discrete event simulation

Mapping the connections between configurations and KPI

Finalized design

Control strategy of each physical entity Control strategy of the integrated factory

Control emulation

Mapping the connections between control strategies and KPI

BOM, Bill of material; MHS, material handling system; VR, virtual reality.

algorithms help the designers to gain a better understanding of connections between design and KPI and find optimized design solutions. The application of the DT in factory design is different with its other reported applications. The DT should mirror the designed factory that will become concrete in the future. Because there is uncertainty existing in each design stage, the design of the factory might frequently change before the factory is actually constructed. In order to offer detailed simulation and help the designers to make decisions, the DT mirrors not only the final physical factory but also the virtual factory corresponding to each design version. For this reason, the DT has to evolve according to changes of the design. However, developing a DT model may take a considerable amount of time, and accordingly, a lot of DT models will require much more modeling time and workload. Therefore efficient DT building technique is critical to DT applications in factory design.

8.3.3 Modular approach for building flexible digital twin toward factory design Modular approach means to build reusable and parameterized modules corresponding to physical entities and logical functions in advance. When the physical entities change, the modules revise the corresponding parameters

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accordingly. Then they are integrated as a DT model for the physical factory. Through the modular approach, the modeling time and workload can be greatly reduced. When the factory design evolved, the DT can conduct corresponding evolution flexibly. The flexible DT can not only validate current design but also quickly validate other possible design solutions by a number of experiments. The basic feature of the modular approach is parameterization of the modules. Based on parameterization, the model that has the same or similar functions is encapsulated to form functional modules, ensuring that they are reusable and easy to be modified. The modular approach can build a simulation model with multiple independent modules and facilitate the established models for different industries. To build reusable and parameterized modules, it is critical to design the structure and functions of the modules. Four building steps are proposed for the modules as shown in Fig. 8.3. Module structure is the first step where object-oriented technique is employed to make modules reusable. Data configuration is the second step with an objective to establish easily accessible data structure to drive the modules. The third step is interface configuration, to make connections between modules and designers. Based on the former three steps, individual modules can be developed. Based on the individual Module structure

Module definition

Data configuration

Interface configuration

Detail

Detail

Detail

Physical module definition Data module definition Interface module definition Module structures

Product data Order data Equipment data Process data Production unit data

Interface of modules Interface of DT

Key output

Data connections Key output

Interfaces Key output

Module assembly Module building

Detail

Assembly of modules Assembly with parameters

Key output

FIGURE 8.3 Four steps to build flexible DT. DT, Digital twin.

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modules, module building is the last step, which is to assemble the modules efficiently in accordance with predefined parameters. For module definition, typically, modules can be defined in line with each physical entity and the structure of the factory. Except for physical entities, data modules can be defined according to information flow and function of the factory. According to different objectives of the three design stages, the modules can be optimized through changing parameters when necessary, so that the DT can keep fidelity with the physical counterpart during the life cycle. For example, an AGV system module can be separated into path module, berth module, transporter module, route planning module, and traffic control module. The path module can be further separated into straight path module, cross path module, turning path module, and join path module. The structure of the AGV system modules is shown in Fig. 8.4. Based on these modules, an AGV system digital model can be assembled via parameterization. Due to the similarity of the modules, object-oriented techniques can be adopted to build objects and classes. As can be seen in Fig. 8.4, the straight path, cross path, turning path, and join path modules are derived from the path class. Nevertheless, due to the difference in the aforementioned three design stages, the AGV system modules can be modeled in different detail levels. For example, the transport routes and control strategies might be very vague in the conceptual design stage, and the designers are concerned with the number and type of AGVs to calculate investment and throughput. However, the aforementioned factors are very sophisticated in finalized design stage. For this reason, the AGV modules need to be configurable via certain parameters, so as to support the designers to select different detail levels and to gain relevant statistical data. Data configuration will be carried out once the module structure is defined. Historical data of the designed factory is particularly important for factory design. In order to facilitate the data-driven modules, historical data needs to be classified into the following five categories: product data, order

AGV module

Transporter module

Berth module

Straight path module

Path module

Cross path module

Route planning module

Turning path module

Traffic control module

Join path module

FIGURE 8.4 Example of AGV system module structure. AGV, Automated guided vehicle.

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PART | 2 Application and case study

data, equipment data, process data, and production unit data. The module developers need to select input data from these five categories and to establish an accessible data structure. The modules will select and configure useful data from the structure according to different functions. Interface configuration follows data configuration. Each module is independent and connected with others with standard interface. The interface is parameterized and easy for assembly of the modules. For example, Fig. 8.5 [22] shows the configuring dialog of control strategies of AGVs. The control strategies corresponding to different detail levels are preembedded in the AGV modules. The designers can select from the strategies according to different situations. All the strategies are open and can be modified and defined by the users. Model building is finally carried out with the accomplishment of the former three steps. Based on the modules and interfaces, model building is to assemble the selected modules. Once the design is changed, the designers need to adjust the module parameters and the model will be automatically rebuilt. Specifically, the modules are designed for the physical entities through the three design stages. The flexible DT can be divided into two main parts: data module and simulation module. The simulation module includes product module, equipment module, buffer module, automated storage and retrieval system (AS/RS) module, MHS module, and special module. Obviously, these modules, excluding the special module, correspond with the physical entities in the designed factory. The special module is a personalized module developed for physical entities that are not included in the aforementioned modules. The data module, including database and a query interface, is essential

FIGURE 8.5 Example of AGV module interface. AGV, Automated guided vehicle.

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to drive the DT. The objective of this module is to select useful data from the huge amount of historical data and to transform it into input data of the DT. The DT is built by the modules connecting with each other via interfaces. A connection network of the modules is shown in Fig. 8.6 [22]. The data module is the kernel of DT and engine of the simulation. During the simulation, entity modules such as equipment module, buffer module, MHS module, and AS/RS module process and transport virtual products with the guidance of historical data. The MHS module connects with other entity modules, transporting virtual products. Statistics are recorded during simulation. Consequently, not only system performance indicators but also entity performance indicators can be gained from the simulation. Therefore a mass of simulation data can be obtained by the DT with the input of historical data, and thus can provide a thorough examination of the designed factory. Object-oriented techniques can be helpful in building modules. For example, the equipment module is the abstract representative for all kinds of equipment, and can be modified according to equipment type and operations, which include basic operations and special operations. The basic operations such as setup, processing, failure, and changeover relate to all kinds of equipment, while the special operations only relate to special equipment. Packing machine can be modified with the basic equipment module and the special operation “packing.” Once the equipment type is changed in the

Data module Database Data classification program

Historical data

Buffer module Set buffer capacity

Set special operation

Set basic operation

Set storage rules

Statistics

Statistics

AS/RS module Set AS/RS parameter Set AS/RS operation rules

AS/RS operation

Statistics

MHS module Set transport route

Set control strategies

Set transportation parameter

Simulation and experiment

Statistic

Data flow Virtual product flow

FIGURE 8.6 DT building with module connections. DT, Digital twin.

Query dataset

Equipment module Set equipment parameter

Query interface

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design, only special operations would need to be modified and the other parts can be reused. In this way, the basic operations can be reused and thus the developing time is saved. Statistics of the equipment module include total setup time, processing time, changeover time, production rate, changeover frequency, and equipment usage. These statistics can be parameterized according to different situations. With the modular approach, designers can select different modules and configure them in different detail levels to build DT in different design stages. The advantages of the modular approach lie in flexibility and reusability. Moreover, the DT is essentially error-free during development because the reused modules can be validated and encapsulated. Consequently, the workload and developing time of DT can be greatly saved.

8.4

Case study

In this section, two real factory design projects are presented.

8.4.1

Digital twin driven factory design of a paper cup factory

This case study involves design of a paper cup factory in China. It has been studied in the authors’ previous work [22]. The representative products of this factory are single-layer cups, double-layer cups, and corrugated cups. These products are made of raw paper formed in rolls. The single-layer cups are made with single-layer paper, whereas the double-layer cups are made by pasting inner layer paper and outer layer paper together. The corrugated cup is the most complex product, pasted with inner layer paper, middle layer paper, and outer layer paper. The process of making these products includes five steps: printing, corrugating, cutting, gluing, and forming. The specific product routing is shown in Fig. 8.7 [22]. The conceptual design of this plant involves six adjacent workshops numbered 1 5. Workshop 1 is a raw paper warehouse equipped with AS/RS. Workshop 2 is equipped with printing and corrugating machines. Workshop 3 is equipped with cutting machines and gluing machines. Workshop 4 is for

Double-layer cup

Inner layer paper

Cutting Forming

Outer layer paper

Printing

Cutting

Inner layer paper Corrugated cup

Cutting Forming

Middle layer paper Corrugating Outer layer paper

Printing

FIGURE 8.7 Process routing of two products in this factory.

Cutting

Gluing

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forming and workshop 5 is a finished product warehouse equipped with AS/ RS. The rough layout of the workshops and the material flow route between the workshops are shown in Fig. 8.8 [22]. According to throughput capacity and historical orders, the number of machines required for each process can be calculated using empirical calculation and the results are shown in Table 8.2. The materials and the products are transported to each workshop and machine by AGVs. With consideration of bottleneck and changeover time, this conceptual design can meet the objective of annual throughput and obtain the theoretical equipment usage rate. The DT modules can be built with the aforementioned four steps by using simulation software Siemens Plant Simulation. The first step is module definition. Specifically, four basic module classes are defined, data module, equipment module, AGVs module, and buffer module. The data module offers historical data to drive other modules. The printing machines, corrugating machines, cutting machines, gluing machines, and forming machines are derived from equipment machine and are individualized with processing characteristics. The AGVs module offers transportation for virtual products with parameterized transport capacity and rules. The buffer module offers storage capacity in each workshop to temporarily place generated virtual products. The second step is data configuration. Historical orders of the past year are selected as the data source. The processing data is configured as Fig. 8.7 Workshop 1

Workshop 2

Raw paper warehouse

Printing machine

Workshop 3

Workshop 4

Workshop 5

Forming machine

Product warehouse

Cutting machine

Corrugation machine

Single-layer cup

Double-layer cup

Glue

Corrugated cup

FIGURE 8.8 Workshops layout and material flow.

TABLE 8.2 Machine allocation designed with mathematical approach. Process

Number of machines

Empirical usage of machines (%)

Printing

3

76

Corrugating

3

48

Cutting

12

62

Gluing

2

73

Forming

60

61

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PART | 2 Application and case study

and the equipment data is configured as Table 8.2. The production unit data is configured in accordance with production batches and the connections between materials and semifinished products. The third step is interface configuration. The interface of modules is for configuring parameters and monitoring performance indicators. Taking the printing machine module as an example, the parameters include types of production corresponding to processing time and setup time, types of calling AGV corresponding to transportation, and changeover matrix corresponding to changeover time, while machine usage, maximum or minimum number of waiting paper rolls, and setup time ratio are performance indicators. The last step is module building. In this step the working processes of each module are coding, encapsulating, and validating. The process and output of these four steps are shown in Fig. 8.9. According to the conceptual design, machine, AGV, buffer, AS/RS, and data modules are built, respectively, by using simulation software Siemens Plant Simulation. With these modules the DT for the workshops and the

FIGURE 8.9 Module building process of the paper cup factory.

Digital twin driven factory design Chapter | 8

Function area

Function area

Function area Cutting

Raw paper warehouse

Printing machine

Forming machine

machine Corrugation machine

Function area

Glue

Initial layout design

Function area

Data module AGVs module Equipment module

221

Import modules Import data Assembly modules

Buffer module

Check

Building modules

Assembly

Digital Twin Model

FIGURE 8.10 DT building process of the paper cup factory. DT, Digital twin.

factory can be assembled quickly. The building process and interface of the DT is shown in Fig. 8.10. This factory used to plan the production based on due date priority. Therefore the historical orders are sorted according to the due date heuristic rules and these sequenced orders are input to the factory DT model. The orders are allocated to different machines in line with preemptive rules during simulation. With the simulation, performance can be predicted and the conceptual design can be verified. However, it is unexpected that a big gap arises between the predicted results from simulation and the actual results from calculation. The annual throughput and equipment usage from the simulation is much less than the calculated results and cannot meet the design objective. Fig. 8.11 [22] shows the final usage of the three printing machines. It can be observed that the average usage of printing machines is less than 40%, which is much lower than the calculated result in Table 8.2. As can be seen from Fig. 8.11, blocking occupies a large percentage of usage of all the three machines and might be the reason for their lower usage. With investigating the simulation step by step, the usage of three printing machines is not particularly low initially but decreases after a certain period of time of simulation because of a serious blockage. By analyzing the DT model, it is found that the blockage is caused by the jam of corrugated cup orders. According to historical order distribution, the single-layer and double-layer cups account for a majority of annual orders and the corrugated cups only cover a small percentage. However, corrugating is more time-consuming than

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PART | 2 Application and case study

FIGURE 8.11 Blocking effect of three printing machines.

printing. Consequently, the printing machines have to wait for the corrugating machines to corrugate the cups that, as a result, are stuck between the printing machines and corrugating machines. Therefore the blockage often occurs when large corrugated cup orders arrive. Meanwhile, the single-layer cups and double-layer cups will also cause blockage when the printing machines are not available. The analysis results imply a simple and reasonable solution to increasing buffer capacity between the printing and corrugating machines. However, due to the area constraint of workshop 2, the maximum buffer capacity is 10 rolls of raw papers. After maximizing the buffer capacity and testing it by simulation, it is shown that the blockage still exists. Fig. 8.12A [22] shows the dynamic curve of paper rolls staying in buffer before improvement. The blockage happens each time the maximum capacity is reached. Obviously, the blockage happens frequently and it is necessary to find other solutions. According to the aforementioned analysis, the production of corrugated cups encumbers the production of single-layer and double-layer cups because the machines are preemptive. To avoid this situation, one of the printing machines is selected exclusively for corrugated cups. That means only one printing machine is for corrugated cups, while the other two printing machines are available for single-layer and double-layer cups. In other words, multiple types of products are made concurrently in the workshops. With the aforementioned improvements, a simple scheduling program is developed and historical orders of 2 weeks are tested with the DT. With 10 rolls as the maximum buffer capacity, simulations after the improvements are conducted, the dynamic curve of paper rolls waiting in buffer is shown in Fig. 8.12B [22]. As can be seen form Fig. 8.12A and B, the maximum

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FIGURE 8.12 Number of rolls in buffer (A) before and (B) after improvement.

buffer capacity is frequently reached before the improvements and never reached after the improvements. This indicates that the blockage never happens after the improvements. In this way, hidden defects in the design stage are discovered using the DT, which also helps the designers to find appropriate solutions to solve the flaws in advance. The model contains a total of five modules: data module, equipment module, buffer module, AS/RS module, and AGV module. The data module stores the relevant data and data query interface. The equipment module consists of five types of equipment related to five processes. The buffer module includes the buffer area before and after the equipment module. The AS/RS module is used to store raw paper and finished products. The AGV module is a transport module that connects the entire model and controls the operation of the AGVs. The DT can be built easily with these predesigned modules. After model building is completed, only a series of parameters needs to be modified to conduct the simulation under different design scenarios. For example, in order to solve the previously described blockage, three modifications about the buffer, printing machine, and control strategy are conducted on the DT. The modifications can be conducted easily by changing parameters on the dialog. Without the modular approach, these modifications may take several days to develop and the DT would be very stagnant to respond to the physical world. Except for the aforementioned modifications, many other operations can also be conducted by just modifying the input parameters. The effects of the combination of these inputs can be ranked and analyzed using outputs such as throughput, utilization, and cycle time. Consequently, DT equipped with

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modular approach is a strong tool to examine the designed factory and helps designers to conduct optimization by simulation experiments.

8.4.2

Digital twin driven factory design of a nylon factory

In this case the design of a nylon factory is presented. Unlike the aforementioned paper cup factory, this study focuses on traffic optimization inside the factory. Because this new factory is almost a duplication of an old nylon factory, where serious traffic jams often happen. In view of this problem, shareholders decide to eliminate the traffic problem in the early design stage. The layout of this factory has been confirmed and shown in Fig. 8.13. The traffic jam is caused by trucks and AGVs. The trucks are used to import raw materials to the nylon factory and export products out of it. The AGVs are for transporting materials from warehouses to the workshops. In general, there are four types of materials and four types of products in this nylon factory. Each truck is specified with a type of material or product and conducts the Gate 5

Gate 4

Warehouse Workshop

Product B

Workshop

Material E

Workshop

Product B

Product C

Workshop

Material E

Workshop

Product A

Product D

Workshop

Material F

Gate 1

Warehouse Warehouse

Product A

Workshop Material G

Gate 2

Workshop Product B Workshop Gate 3

FIGURE 8.13 Layout of designed nylon factory.

Material E Material H

Warehouse Warehouse Warehouse

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225

transportation exclusively. According to the historical logistic data from similar physical factories, the process route and process time of corresponding trucks are shown in Table 8.3, which are taken as the input data of the DT. As can be seen from Table 8.3, the trucks enter the factory via inbound process and out via outbound process. The product trucks only undergo loading and weighing processes in the factory. Nevertheless, the material trucks have to be weighed and inspected before unloading, in that the materials can only be unloaded if the material quality is approved. After unloading the empty trucks are weighed again to confirm quantity of the materials. The historical numbers of the trucks per day of the physical factory are collected and shown in Table 8.4. As can be seen in Table 8.4, the total number of the product trucks almost equals to the number of the material trucks. The product trucks corresponding to A and B play the most important role in product transportation, whereas the material truck corresponding to E is the most important one in material transportation. The different routes of trucks are shown in Fig. 8.14A. As can be seen, different trucks are designed to enter the factory from different gates to balance traffic. To avoid traffic jam as far as possible, one-third of the trucks are designed to arrive at night. Once a traffic jam happens, the arrived trucks have to stay outside the gates. Different to the trucks, the AGVs conduct the inner transportation without inbound and outbound processes. There is only one AGV loading station in each warehouse and one unloading station corresponding to each workshop. The AGV routes are shown in Fig. 8.14B. Based on the previous information, a DT was built for this factory with the modular approach. The first step is module definition, which is to define four basic module classes, AGVs module, AS/RS module, equipment module, and station module. Trucks and AGVs are both derived from AGVs modules because of the same transport characteristics. The AS/RS module represents the automated storage and retrieval systems located in warehouses. The equipment module simplifies the production and takes each workshop as a piece of equipment. The station module represents a place where the trucks and AGVs stop in the factory, such as the inbound/outbound, loading/unloading, inspection, and weighing locations. The second step is data configuration. One year’s historical data of trucks and AGVs working in the factory is collected. Different from the former case, traffic is the key problem in this case study and the data is not formulated as the former one. The data in this case is classified into material and product transport orders, truck/AGV routes, and truck/AGV processing time. The third step is interface configuration. The parameters include the routes of the trucks and AGVs, transport rules, and the number of loading/ unloading stations. Furthermore, the throughput of AS/RS should correspond with the loading/unloading capacity. For this reason, the parameters of AS/ RS include aisle number, tier number, and column number. In order to mirror traffic jams, the average velocity of trucks and AGVs, number of waiting

TABLE 8.3 Process route and process time of corresponding trucks. Product A

Product B

Product C

Product D

Material E

Material F

Material G

Material H

Inbound (min)

0.8

0.8

0.8

0.8

0.8

0.8

0.8

0.8

Check in (min)

1

1

1

1

1

1

1

1

Weighting (min)

0

0

0

0

3

2

6

12

Inspection (min)

0

0

0

0

15

3

31

16

Loading/unloading (min)

20

22

35

21

57

28

37

38

Weighting (min)

0

0

0

0

2

1

2

3

Check out (min)

1

1

1

1

1

1

1

1

Outbound (min)

0.8

0.8

0.8

0.8

0.8

0.8

0.8

0.8

TABLE 8.4 Number of corresponding trucks per day. Product A

Product B

Product C

Product D

Material E

Material F

Material G

Material H

Number of trucks

48

64

10

33

75

15

38

12

Percentage

15.58

20.95

3.83

10.63

27.39

4.90

13.39

3.33

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PART | 2 Application and case study (A) Gate 5

Deliver

(B) Gate 4

Gate 5

Gate 4

Back

Gate 1

Gate 1

Gate 2

Gate 2

Gate 3

Gate 3

FIGURE 8.14 Routes of trucks and AGVs: (A) routes of trucks and (B) routes of AGVs. AGV, Automated guided vehicle.

trucks outside the gates, service ratio of transport tasks, and average waiting time per task are taken as performance indicators. The last step is module building, where the aforementioned modules are coded, encapsulated, and validated. Based on the former steps, the DT is built with the assembly of modules by using simulation software Siemens Plant Simulation. The DT model is shown in Fig. 8.15. The historical transport and product data of the physical nylon factory over 30 days are collected and taken as the input to the DT. To avoid traffic jams and with consideration of cost, designers prefer to employ 14 AGVs. During the simulation, traffic jams happen in different places dynamically according to predefined rules. The level of traffic jams is measured by the average velocity of trucks and AGVs in different track sections. The final simulation result is shown in Fig. 8.16. As can be seen, the most jammed place is the weighing station, which is caused by material trucks. To solve this problem, more weighing stations should be employed in the DT. According to the simulation of escalating weighing stations (Table 8.5), two weighing stations are selected as the final solution with the consideration of balancing cost and traffic jams. Another problem revealed from the DT is the poor service ratio of AGVs. The service ratio is defined equivalent to the rate of transport tasks completed without waiting. Table 8.6 is the simulation result of AGV transport tasks. Obviously, 33.7% of the tasks are not completed, which means 14 AGVs are not enough. Based on the simulation result, more AGVs are employed but this still results in lower service ratio. The reason is that the AGV loading/unloading station number does not match with the number of AGVs. Therefore the designers should increase the AGV berth area and add loading/unloading

Truck AGV module AGV

Procduct yard AS/RS module Material warehouse

Modules assembling Equipment module

Workshop Inbound/outbound station

Station module

FIGURE 8.15 Module structure of DT corresponding to nylon factory design. DT, Digital twin.

Loading/unloading station Weighting station

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Different color represent different truck/AGV velocity : >10km/h : 5–10km/h : < 5km/h

FIGURE 8.16 Traffic jams in simulation.

TABLE 8.5 Simulation of escalating weighing station. Weighing station number

Waiting trucks

Waiting ratio (%)

1

1566

44

2

68

2

3

50

1.5

TABLE 8.6 Distribution of transport tasks. Task

Service ratio (%)

Completed without waiting

0.3

Completed with waiting

66

Not completed

33.7

stations. Corresponding to the increase of stations, AS/RS is redesigned to increase throughput. Finally different scenarios are designed and tested in the DT. According to the simulation results presented in Table 8.7, the combination of 24 AGVs and 16 loading/unloading stations is selected as the solution.

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TABLE 8.7 Simulation results of different design scenarios. Scenario

AGV number

Station number

Service ratio (%)

Average waiting time per task (min)

1

16

10

94

5

2

19

13

93

8

3

24

16

96

4

AGV, Automated guided vehicle.

However, because the truck routes overlap with the AGV routes in some area, new traffic jams arise after the AGV number is increased. To solve this problem the DT conducts a large number of iterated simulations between the trucks and AGVs. By means of the simulations, the designers gain a better understanding of the factory and therefore can optimize the design. The truck/ AGV routes, loading/unloading station number in different workshops, and berth number in different warehouses keep optimizing during the iterations. Modular approach plays a dominant role to build DTs efficiently for new designs. With the help of efficient simulation, the designers are allowed to make the final optimization and solve the new traffic jams caused by AGVs. To show the effect of the optimization, the number of waiting trucks outside each gate is selected as the performance indicator. One day is selected randomly and divided into 24 hours to test the dynamic value of waiting truck number. The comparison between the simulation results of the DTs for the initial design and the optimized design is shown in Fig. 8.17. As can be seen, the number of waiting trucks decreases sharply after the optimization, which indicates that the traffic jams are distinctly alleviated in the optimized design. The cycle time of the trucks in the factory is another selected performance indicator. The simulation results of the DTs for the initial design and the optimized design are shown in Table 8.8. As shown in Table 8.8, the average and maximum cycle times of the material trucks decrease prominently, while the cycle times of the product trucks present small decreases. The reason is that the product trucks need not be weighed in the factory and therefore are not optimized. The simulation results indicate that the traffic jams around weighing stations are alleviated in the optimized design.

8.4.3

Discussion

In general, the two cases are from real design projects of smart factories. The first case designs a new paper cup factory, which include many design

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PART | 2 Application and case study (A)

Number of waiting trucks 18 16 14 12

Gate5

10

Gate4 Gate3

8

Gate2 6

Gate1 4 2 0 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

Hour

(B) Number of waiting trucks 5

4

Gate5 3

Gate4 Gate3

2

Gate2 Gate1

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

FIGURE 8.17 Number of waiting trucks outside each gate (A) before and (B) after optimization.

elements, such as process, layout, scheduling, and logistics. Data derived from the physical factory such as historical orders and empirical machine usage calculation are taken as the input of the DT. Quite unexpectedly, the bottleneck of this factory is the buffer area of workshop 2. Because the buffer area is the hard constraint of factory design, the solution is to balance workload with scheduling and management. This hidden design flaw is very difficult to be noticed with traditional design approaches. Moreover, the solution is also difficult to be found and validated. The second case designs a new nylon factory. The key design problem of this case is traffic problem, which is rarely noticed in traditional factory design approaches. As the traditional design approaches focus too much on factory layout, they could be helpless to predict traffic situations and offer effective solutions. Nevertheless, the DT plays a dominant role to predict the future traffic situations with historical traffic data from a similar physical nylon factory. The modular characteristic enables the DT to be flexible to offer traffic easing solutions through simulations of multiple design scenarios.

TABLE 8.8 Average cycle time of trucks before and after optimization. Product A

Product B

Product C

Product D

Material E

Material F

Material G

Material H

Before optimization (min)

65.5

86.7

75.2

34.8

102.5

63.4

78.2

81.5

After optimization (min)

64

82.8

71

32

82

40

48

65

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PART | 2 Application and case study

8.5

Summary

With the development of smart manufacturing, traditional factories are transforming to smart factories. Traditional factory design approaches are insufficient to investigate the dynamic behaviors of designed factories. The emergence of DT can effectively solve this problem because of its fidelity to physical factories. A framework for DT’s application in conceptual design, detailed design, and finalized design is proposed in the article. According to different objectives of design stages, a modular approach is further proposed to build reusable modules and enhance flexibility of the DT. By using the DT in factory design, hidden design flaws are discovered and solutions are proposed using different simulation scenarios in two case studies. Moreover, it can be concluded that the cycle time for building DT models can be greatly reduced by using the modular approach, which improves the feasibility for applying the DT to factory design. Different industries have particular characteristics and thus factory design is a sophisticated problem with multidisciplinary and dynamic characteristics. Therefore the multidisciplinary and multidimensional DT can completely mirror the physical world and help designers gain a better understanding than ever before. With the feedback from DT, factory design is more efficient and critical to smart manufacturing. It is envisaged that DT will be employed not only in the design stage but also in the whole life circle of a smart factory by contributing to smart decision-making through layout, pilot run, and full production.

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[8] S. Kang, M. Kim, J. Chae, A closed loop based facility layout design using a cuckoo search algorithm, Expert Syst. Appl. 93 (2018) 322 335. [9] E. Westka¨mper, R. Von Briel, Continuous improvement and participative factory planning by computer systems, CIRP Ann. Manuf. Technol. 50 (1) (2001) 347 352. [10] H.J. Yap, Z. Taha, S.Z.M. Dawal, S.W. Chang, Virtual reality based support system for layout planning and programming of an industrial robotic work cell, PLoS One 9 (10) (2014) 1 15. [11] J. Lee, S. Han, J. Yang, Construction of a computer-simulated mixed reality environment for virtual factory layout planning, Comput. Ind. 62 (1) (2011) 86 98. [12] S. Jiang, A.Y.C. Nee, A novel facility layout planning and optimization methodology, CIRP Ann. Manuf. Technol. 62 (1) (2013) 483 486. [13] J. Oyekan, W. Hutabarat, C. Turner, A. Tiwari, N. Prajapat, N. Ince, et al., A 3D immersive discrete event simulator for enabling prototyping of factory layouts, Procedia CIRP 38 (2015) 63 67. [14] A. Patel, J. Ma, J. Discrete event simulation in automotive final process system, in: Proceedings of the 2002 Winter Simulation Conference, vol. 1, 2002, pp. 1030 1034. [15] U. Dombrowski, S. Ernst, Scenario-based simulation approach for layout planning, Procedia CIRP 12 (2013) 354 359. [16] A. Azadeh, S.M. Haghighi, S.M. Asadzadeh, H. Saedi, A new approach for layout optimization in maintenance workshops with safety factors: the case of a gas transmission unit, J. Loss Prev. Process Ind. 26 (6) (2013) 1457 1465. [17] A. ElNimr, M. Fagiar, Y. Mohamed, Two-way integration of 3D visualization and discrete event simulation for modeling mobile crane movement under dynamically changing site layout, Autom. Constr. 68 (2016) 235 248. [18] Z. Zhang, X. Wang, X. Wang, F. Cui, H. Cheng, A simulation-based approach for plant layout design and production planning, J. Ambient Intell. Humanized Comput. 49 (1 4) (2018) 1 14. [19] H. Zhang, Q. Liu, X. Chen, D. Zhang, J. Leng, A digital twin-based approach for designing and multi-objective optimization of hollow glass production line, IEEE Access 5 (2017) 26901 26911. [20] T.H.J. Uhlemann, C. Schock, C. Lehmann, S. Freiberger, R. Steinhilper, The digital twin: demonstrating the potential of real time data acquisition in production systems, Procedia Manuf. 9 (2017) 113 120. [21] C. Zhuang, J. Liu, H. Xiong, Digital twin-based smart production management and control framework for the complex product assembly shop-floor, Int. J. Adv. Manuf. Technol. 96 (1 4) (2018) 1149 1163. [22] J. Guo, N. Zhao, L. Sun, S. Zhang, Modular based flexible digital twin for factory design, J. Ambient Intell. Humanized Comput. 10 (3) (2019) 1189 1200. [23] M. Grieves, Digital twin: manufacturing excellence through virtual factory replication, in: White Paper, 2014. [24] F. Tao, M. Zhang, Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing, IEEE Access 5 (2017) 20418 20427.

Chapter 9

Digital twin based computerized numerical control machine tool virtual prototype design Tianliang Hu1, Tianxiang Kong1, Yingxin Ye1, Fei Tao2 and A.Y.C. Nee3 1

School of Mechanical Engineering, Shandong University, Jinan, P.R. China, 2School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R. China, 3 Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

9.1

Introduction

Computerized numerical control machine tools (CNCMTs) [1 4] are powerful manufacturing facilities in production processes [5]. The advancement of CNCMTs related technologies also propels the improvement of the entire manufacturing industry [6 8]. CNCMTs are a typical complex system, which is functionally coupled with mechanical subsystem, electrical subsystem, and other control sensors [9]. Direct commissioning and test cutting on CNCMTs is not only time-consuming but can also lead to safety problems. Therefore it is necessary to simulate a virtual prototype of the CNCMTs to improve the efficiency and safety. However, the traditional virtual prototype has two main shortcomings (for detailed analysis, see Section 9.2): 1. In traditional prototyping process of CNCMTs, the mechanical and electrical subsystems are often established by different developers using different analysis software. When simulation is conducted based on the virtual prototype for commissioning or optimizing, these separate simulation platforms cause two problems. One is the increasing time and cost due to various virtual prototypes that focus on only one subsystem [10 12]. The other is the lack of capacity to analyze the performance of CNCMTs in a holistic mode due to the independent simulation patterns. 2. The traditional virtual prototype is built based on parameters calculated through formulas or experience, which can reflect the initial performance of CNCMTs. However, it is difficult to reflect performance changes of

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CNCMTs throughout its life cycle and to make corresponding updates. This discrepancy will cause a deviation between the simulation results and the actual running results. To overcome abovementioned shortcomings, a unified platform is needed for multidomain virtual prototyping that meets the requirements of integrated modeling and simulation. At the same time, the virtual prototype should have the ability to sense and update the timely performance of CNCMTs for more realistic simulation results, which can be achieved through digital twin (DT). DT aims to build authentic mapping of physical objects in the digital space, and to realize intelligent design, manufacturing, commissioning, and maintenance across the life cycle of physical equipment [13 17]. Introducing the DT technology into the established CNCMT virtual prototype can reflect changes of machine performance by perceiving the physical state and mapping it to the virtual prototype. At the same time, this timevarying prototype also provides a loading method for intelligent simulation algorithms to obtain more accurate simulation results. This chapter introduces design methods of DT-based CNCMT virtual prototype, which aims at achieving complex coupling of subsystems through multidomain modeling, and keeping consistency between virtual prototypes and physical CNCMTs with the help of DT. On this basis, time spent on stages such as design, commissioning, and product preparation can be reduced ultimately.

9.2

Related works

According to the abovementioned needs of multidomain modeling of CNCMTs, the virtual prototype should integrate different subsystems into a unified model and have the ability to update this model for performance consistency of CNCMTs. Therefore this section analyzes the shortcomings of previous virtual prototype designs based on related work and introduces the advantages of importing DT to virtual prototype design.

9.2.1

Related works on virtual prototype design

The design and manufacturing processes of industrial products are becoming more and more sophisticated and variable, which leads to long operation cycles, high costs, and high risks of physical experiments on CNCMTs [18]. At this point, emergence of single-field virtual prototype provides a solution to overcome the previously mentioned shortcomings. This technology transfers the process of physical experiments on CNCMTs to virtual simulation by establishing a virtual prototype focusing on a certain field and thus provides an effective, safe, and repeatable way to design and debug

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equipment. Due to these advantages, computer-based virtual prototyping technology is widely used in manufacturing [19]. However, the above single-field virtual prototype [20 25] usually breaks the connection between different subsystems. Although details of different subsystems can be designed well separately, the overall connection between them is neglected, which leads to incomplete representation of CNCMTs. Under this situation, a unified model of CNCMTs is required to analyze its performance with overall consideration. The method of multidomain modeling in virtual prototype starts with analysis of coupling relationship between individual fields. Then, through integrating single-field models into a unified one, a collaborative simulation function of virtual prototype can be obtained. As a result of subsystem collaboration, the multidomain virtual prototype greatly reduces the complexity and cost of simulation. Therefore the virtual prototype is widely used for the simulation of CNCMTs at its design or operation stage [26 32]. At present, multidomain modeling methods applied in the virtual prototype mainly include interface-based method, High Level Architecture and Unified Modeling Language (UML). A typical UML named Modelica is well accepted for virtual prototype building because of those characteristics listed as follows: 1. Supporting object-oriented and modular modeling. Object-oriented and modular modeling organizes structure of the virtual prototype well and contributes to submodule reuse, which improves the development efficiency of the virtual prototype and simplifies subsequent processes, including maintenance, update, and extension of the model. 2. Supporting declarative modeling. Modelica relies on mathematical equations rather than assignment statements or procedural algorithms. Because this modeling language does not focus on causality, complexity and error rate of formula deduction are reduced. Therefore the coupling relationship between various subsystems of CNCMTs can be clearly described. However, even utilizing the multidomain modeling method to build the virtual prototype of CNCMT, problems still exist, which are listed as follows: 1. The virtual prototype cannot reflect real performance of CNCMTs, leading to deviations between simulation results and actual operation results. 2. The virtual prototype cannot provide sufficient data support for CNCMTs along its life cycle due to the lack of mechanism of data collection and data storage, which hinders performance improvement of CNCMTs.

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9.2.2 Advantages of digital twin based computerized numerical control machine tool virtual prototype Traditionally, a virtual prototype of CNCMTs is designed only in accordance with initial design parameters without further updates of performance [33]. The isolation between digital world and real world makes the virtual prototype maintain a rigid state and thus leads to deviation of simulation results [34]. To solve problems mentioned earlier, the concept of DT is introduced into the virtual prototype of CNCMTs to achieve timely and accurate update based on the real-time information interaction between digital and real worlds [35 42]. Unlike traditional virtual prototypes, the DT-based virtual prototype can provide simulation results consistent with realistic operation results because of its real-time renewable characteristics. Detailed advantages of the DT-based virtual prototype are listed as follows: 1. The DT-based virtual prototype is a multidomain and high-fidelity model that integrates different subsystems. This model can support the processes of design, production, operation, maintenance, and recycling along the life cycle of CNCMTs in the digital world [43]. 2. The DT-based virtual prototype can be consistent with physical CNCMTs through real-time data mapping, which is used for dynamic update of the model and data storage [44]. 3. The DT-based virtual prototype can provide data support for subsequent intelligent maintenance and optimization of CNCMTs based on stored data, which come from data mapping [45]. Because of the abovementioned advantages, DT-based multidomain modeling has been widely used in automobile, energy, manufacturing, and other fields [46 50]. Cerrone et al. established a comprehensive model of the specimen from Sandia Fracture Challenge and used DT to predict its crack path [46]. To break through the predicament that information interaction between real and digital worlds is not timely enough, Guo et al. established a multidomain prototype of a workshop for aerospace structural parts manufacturing and achieved model update based on DT to improve productivity [47]. Wang et al. analyzed the characteristics of CNCMTs and used Modelica to build a multidomain virtual prototype, which utilizes DT technology to ensure commissioning accuracy [48]. Zhang proposed a three-layer DT workshop model and described the operation mechanism of DT workshop to complete the interconnection and interaction between the digital and real worlds [49]. Schroeder proposed a modeling method using AutomationML, which can accelerate data exchange between different systems in the model [50].

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It can be concluded from the abovementioned researches that the combination of DT and multidomain modeling can provide accurate modeling and data interaction mechanism, thus it is feasible to use multidomain modeling method to build a CNCMT virtual prototype based on DT. However, because CNCMT virtual prototype is often used for performance simulation rather than process simulation, its model needs to be elaborated down to the component level for simulation. Furthermore, efficient data mapping and consistency maintenance strategies need to be designed to keep the virtual prototype of CNCMTs running as the physical one.

9.3 Framework of digital twin based computerized numerical control machine tool virtual prototype Combination of DT and multidomain modeling shows its potential in the development of CNCMT virtual prototypes and will be explored in this chapter. In this section, requirements for the functions of the DT-based CNCMT virtual prototype are first analyzed. Then, an overall framework of the DT-based CNCMT virtual prototype is proposed to meet these functional requirements.

9.3.1

Functional requirements

CNCMTs is a multivariable, multiparameter, and nonlinear complex system, including coupling relationships between an electrical subsystem and a mechanical subsystem [51 53]. The coupling relationships among structure, performance, and working principle of CNCMTs make it no longer a superposition of several simple subsystems [54 56]. Fig. 9.1 takes the output of spindle speed as an example to show the coupling relationships between the mechanical and electrical subsystems [57]. Electrical subsystem

Mechanical system

Electromechanical coupling

Instruction

Voltage

Force

Current

Torque

Input PI control

Inverter drive

Motor

Mechanical mechanism

Feedback (speed, displacement)

FIGURE 9.1 Electromechanical coupling diagram of CNCMTs. CNCMTs, Computerized numerical control machine tools.

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It can be seen from Fig. 9.1 that there are three main coupling relationships between the mechanical subsystem and the electrical subsystem [58], which are listed as follows: 1. Dynamic conversion of electric energy, magnetic energy, and mechanical energy occurs in the motor. 2. The inverter drive system and the motor interact with each other through voltage change. 3. The electrical subsystem drives the mechanical system, while the feedback from mechanical system acts on the proportional integral (PI) control. To meet the requirements of cosimulation to analyze the performance of CNCMTs in a holistic mode, it is necessary to design a CNCMT virtual prototype using the multidomain modeling method on a unified platform. In this situation, mechanical and electrical subsystems of CNCMTs can be constructed into one model with coupling relationship description, which provides a complete and accurate CNCMT model for simulation. This modeling method not only saves time by avoiding the analysis of different performance of CNCMTs on various simulation software, but also it provides a considerate analysis through simulation on the complete virtual prototype of CNCMTs with various subsystems. The purpose of building the virtual prototype of CNCMTs is to achieve repeatable simulation that saves time and reduces risk through replacing physical experiments with virtual experiments. The accuracy of simulation results is the basis to analyze the performance of CNCMTs. Considering performance attenuation of CNCMTs during its life cycle, traditional virtual prototype without model update will lead to deviation between simulation results and actual operation results. In order to ensure reliability of the simulation results at any stage, it is necessary to design an updating strategy for the virtual prototype. First, a data-mapping strategy is needed to map data from the real world to the digital world. Then, a consistency maintenance strategy is required to update the virtual prototype based on the mapping data. Through the updating strategy, simulation results based on the virtual prototype can be guaranteed to follow closely to the operation results of CNCMTs, which can fully achieve the design purpose of the CNCMT virtual prototype.

9.3.2 Framework of digital twin based computerized numerical control machine tool virtual prototype As described in Section 9.3.1, the DT-based CNCMT virtual prototype must have the following characteristics: (1) mechanical subsystem, electrical subsystem and their coupling relationships are integrated in the same platform and (2) the virtual prototype can dynamically update itself according to the

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performance change of CNCMT. A modeling and application framework of CNCMT virtual prototype is designed as shown in Fig. 9.2. In the physical space, sensors installed on CNCMTs or peripheral entities can collect data that relate to CNCMT operations, which will be mapped into digital space for subsequent applications [32]. Since the physical space is not the focus of this chapter, installation of sensors and their connection to control system will not be elaborated. Research in this chapter is carried out based on the assumption that all necessary data have already been collected. In the digital space the DT-based CNCMT virtual prototype mainly consists of two parts, descriptive model and updating strategy. 1. Descriptive model As shown in Fig. 9.2A, the descriptive model is the most important part of DT-based virtual prototype. It can be considered as a replica of CNCMTs in the digital space with description of subsystems and their coupling relationships. Simulation on the virtual prototype is based on

Digital space DT-based CNCMT virtual prototype

(A)

Electrical subsystem

Update

Mechanical subsystem Simulated perforamnce

Data support for simulation Actual performance (b2) Running status database

Performance differences

Yes

Performance attenuation model

(b3) Consistency maintenance strategy

(b1) Mapping strategy

(B)

CNCMT

Control system

Sensor 1 (Wifi)

Sensor 2 (Bluetooth)

... (RFID)

Sensor n (NB-loT)

Physical space

FIGURE 9.2 Modeling and application framework of CNCMT virtual prototype: (A) descriptive model and (B) updating strategies [32]. CNCMT, Computerized numerical control machine tool.

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this model, so dynamic updates of this model should be guaranteed for more realistic and accurate simulation results. 2. Updating strategy As shown in Fig. 9.2B, the mapping strategy, running status database, and consistency maintenance strategy constitute the updating strategy together. The mapping strategy takes responsibility for the real-time mapping of data between physical space and digital space. The mapping data are stored in the running status database to support simulation on the descriptive model. The consistency maintenance strategy determines whether to update the descriptive model through comparison between performance obtained by simulation and actual performance from the running status database. If the comparison result indicates inconsistency between virtual prototype and CNCMTs, the parameters of the descriptive model will be updated by the performance attenuation model. According to the framework proposed in Fig. 9.2, these two key parts of the DT-based CNCMT virtual prototype will be elaborated in Sections 9.4 and 9.5.

9.4 Design of DT-based CNCMT virtual prototype descriptive model Descriptive model is the most important part of DT-based CNCMT virtual prototype. This section first analyzes the composition of CNCMTs for the design of the corresponding descriptive model at the component level. Then based on the analysis, a mechanical subsystem model and an electrical subsystem model are designed with their coupling relationship to constitute the descriptive model of the CNCMT virtual prototype using a UML named Modelica. The preliminary study of multi-domain modeling method has been explored in the authors’ previous work [59].

9.4.1 Composition analysis of computerized numerical control machine tools As shown in Fig. 9.3, CNCMTs consist of mechanical subsystem and electrical subsystem. The mechanical subsystem acts as the execution unit of CNCMTs, while the electrical subsystem controls the drive unit of CNCMTs. The mechanical subsystem describes CNCMTs at the component or even part level from the structural design perspective, including screw, workbench, and spindle. As the execution unit, mechanical subsystem takes responsibility to complete a certain action based on the transmission and support mechanism between different components or parts.

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CNCMT

Electrical subsystem

Motor and drive

Sensor

Limit switch

Mechanical subsystem

Screw

Workbench

SpindleNut

Rail and sliders

Bed

FIGURE 9.3 Overall structure of CNCMTs. CNCMTs, Computerized numerical control machine tools [59].

The electrical subsystem outputs control signals to the mechanical subsystem based on external inputs and internal algorithms. Then, mechanical subsystem can be driven for safe and accurate actions according to the received control commands.

9.4.2 Mechanical subsystem modeling of computerized numerical control machine tools Mechanical subsystem, sensitivity, accuracy, and stability of which affect workpiece quality greatly, is the execution terminal of CNCMTs. Therefore there are high demands for the stability, response speed, and axial stiffness of the mechanical subsystem in the process of CNCMT operation [60], which need to be analyzed through simulation based on the model of mechanical subsystem. Modelica is selected as the modeling language in this chapter because of its advantages introduced in Section 9.2.1. Mechanics, a kind of component library provided by Modelica, is applied to the mechanical subsystem modeling. It consists of three sublibraries, including MultiBody, Translational, and Rotational. There are various kinds of component connections in the MultiBody sublibrary, which allows encapsulated components connecting to the Translational sublibrary and Rotational sublibrary directly through unified interfaces. Therefore different combination of components in these libraries provides a fast modeling method for the establishment of various mechanical

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subsystem models [44]. The modeling process of the mechanical subsystem of CNCMTs based on the Mechanics library is introduced in detail next. First, a mechanical model of CNCMTs is established using a 3D geometric modeling platform (e.g., SolidWorks). Because mechanical subsystem of CNCMTs is complex at the very detailed level (e.g., at parts level), it is impossible and unnecessary to describe every detail of CNCMTs in the mechanical model. That means the mechanical model should be designed with consideration of research preference (e.g., different simulation objectives) to simplify the minor part moderately. The mechanical model of a three-axis vertical CNCMT is shown in Fig. 9.4, and its main components include a pedestal, a bed, guideways, workbenches, ball screws, bearings, spindles, and accessories. Then the MultiBody sublibrary is utilized to transform this mechanical model into the so-called MultiBody model, which is compatible with Modelica. The MultiBody model is generated based on the three functional modules provided by the MultiBody sublibrary as shown in Fig. 9.5. The world module represents the gravity field of the environment where the model exists in and provides a unified global coordinate system for different models. The fixedTranslation module provides a relative coordinate system for the definition of parts or components to determine their spatial positions and the relationship between them. The bodyshape module is responsible for the geometric representation of the entity model. In addition to modeling entities manually, this module can automatically recognize model in some specific file formats (e.g., mechanical model built in SolidWorks) and transform the model into a format compatible with Modelica. FIGURE 9.4 Mechanical model of CNCMTs (built in SolidWorks). CNCMTs, Computerized numerical control machine tools.

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y

World Fixedtranslation b a

Bodyshape

m

r =r

247

FIGURE 9.5 Basic component elements of the machine multibody model.

r =r x

y

World

base_cs7 b

x

Base a

r={–1.525,–1.97,–7.21238e–015}

r ={–0.375,–1.546,0.33}

FIGURE 9.6 Model of the pedestal.

A pedestal model of CNCMTs is shown in Fig. 9.6 as an application example of the MultiBody sublibrary. A mechanical model designed in SolidWorks is recognized by the BodyShape module and then transformed into MultiBody model automatically. The modeling methods for other components are similar, but the global coordinate system of these components is the same one without reestablishment because all the components in Modelica model are in the unified global coordinate system. Finally, drives and constraints are added to the MultiBody model. Fig. 9.7 shows the drives and constraints definition in the MultiBody model for position, speed, and revolution representation. Through the abovementioned methods, the key components of CNCMTs mechanical subsystem can be modeled using the Modelica language. The overall mechanical subsystem modeled with components such as screw, guideway, and bed is shown in Fig. 9.8.

9.4.3 Electrical subsystem modeling of computerized numerical control machine tools Based on the Modelica language and MWorks platform (a kind of software providing tools for modeling using Modelica), this section introduces the design of important components of the electrical subsystem model to achieve the control logic of CNCMTs.

9.4.3.1 Implementation of permanent magnet synchronous motor model Permanent magnet synchronous motor (PMSM) is widely used in the axes control of CNCMTs due to its compact structure and superior performance. To simplify the modeling process, PMSM can be decoupled by selecting the control mode as “setting current of d-axis equal to zero.” Under this situation, control for PMSM can be equivalent to the control for direct-current

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Position

Speed

Revolute a

phi_ref

Exact= false

W_...

Exact= false

FIGURE 9.7 Related drive and constraint elements.

b

n={0, 0, 1}

FIGURE 9.8 Mechanical subsystem of CNCMTs (built in MWorks). CNCMTs, Computerized numerical control machine tools.

(DC) motor. This equivalent conversion is essentially a process of motor coordinate transformation, which is based on different coordinate systems, including natural coordinate system, stationary coordinate system, and synchronous rotating coordinate system [41]. According to the modeling mechanism of Modelica, PMSM should be abstracted into the form of mathematical model that describes its physical characteristics through equations. Because PMSM is a multiorder and complex system with various variables [41], some ideal assumptions [42] have to be made before abstracting it to mathematical model for simplicity, which are listed as follows: 1. The core reluctance, hysteresis, and eddy current loss of the stator and the rotor are not considered. 2. The damper winding of the rotor is ignored. 3. The magnetic permeability in the permanent magnets is the same to that in the air.

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Electrical interface Resistance

Coordinate transformation

Machine/electric energy conversion

Inductance

Moment of intertia

(B) Mechanical interface

Permanent magnets

FIGURE 9.9 Modelica model of PMSM: (A) internal structure of PMSM and (B) encapsulated model of PMSM [59]. PMSM, Permanent magnet synchronous motor.

4. The waveform of current that goes through the stator is regular sinusoidal. Based on the abovementioned assumptions, PMSM can be decoupled based on the d q coordinate system and its corresponding mathematical model with essential features is built by utilizing electrical components provided by the electrical library of Modelica, as shown in Fig. 9.9. Stator terminal voltage is connected to this model through an electrical interface to provide energy drive for the motor. Then, this voltage goes through the coordinate transformation and electromechanical energy conversion in the model. Finally, torque is output through the moment of inertia, which fully represents the functions of the rotor and stator of the motor. This torque goes through a mechanical interface to realize the function of motor that drives connected components of the mechanical subsystem. The electromechanical energy conversion module plays an important role in the PMSM model because it describes the coupling relationship between mechanical components and electrical components and thus realizes the integration between the mechanical and electrical subsystems. The modeling process of PMSM is shown in Fig. 9.10.

9.4.3.2 Implementation of inverter driver model The three-phase voltage source inverter is a key functional component for power conversion in the PMSM servo system, thus it is selected as the modeling and analysis object in this subsection.

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model AirGap "Basic air gap model" parameter Integer m = 3 "Phase"; parameter Integer p( min = 1) "Number of pole pairs"; output Modelica.SIunits.Torque tau_electrical; Modelica.SIunits.Angle gamma "Rotor angular displacement"; Modelica.SIunits.Current i_ss[2] "Stator current space phasor with respect to the stator fixed frame"; Modelica.SIunits.Current i_sr[2] "Stator current space phasor with respect to the rotor fixed frame"; Modelica.SIunits.Current i_rs[2] "Rotor current space phasor with respect to the stator fixed frame"; Modelica.SIunits.Current i_rr[2] "Rotor current space phasor with respect to the rotor fixed frame"; Modelica.SIunits.MagneticFlux psi_ms[2] "Magnetizing flux phasor with respect to the stator fixed frame"; Modelica.SIunits.MagneticFlux psi_mr[2] "Magnetizing flux phasor with respect to the rotor fixed frame"; Real RotationMatrix[2,2] "matrix of rotation from rotor to stator"; Modelica.Mechanics.Rotational.Interfaces.Flange_a flange_a Modelica.Mechanics.Rotational.Interfaces.Flange_a support "support at which the reaction torque is acting" Machines.Interfaces.SpacePhasor spacePhasor_s Machines.Interfaces.SpacePhasor spacePhasor_r equation gamma = p * (flange_a.phi - support.phi); RotationMatrix = {{+cos(gamma), -sin(gamma)}, {+sin(gamma), +cos(gamma)}}; i_ss = spacePhasor_s.i_; i_ss = RotationMatrix * i_sr; i_rr = spacePhasor_r.i_; i_rs = RotationMatrix * i_rr; spacePhasor_s.v_ = der(psi_ms); spacePhasor_r.v_ = der(psi_mr); tau_electrical = m / 2 * p * (spacePhasor_s.i_[2] * psi_ms[1] - spacePhasor_s.i_[1] * psi_ms[2]); flange_a.tau = -tau_electrical; support.tau = tau_electrical; i_mr = i_sr + i_rr; psi_mr = L * i_mr; psi_ms = RotationMatrix * psi_mr; end AirGap;

FIGURE 9.10 Construction process of PMSM electromechanical coupling function module. PMSM, Permanent magnet synchronous motor.

The input of the three-phase voltage source inverter is the DC bus voltage and control signals, which are six-way bool signals output by a space vector pulse width modulation (SVPWM) device in the control system. Therefore the three-phase voltage source inverter (hereafter called inverter) can be simplified to a three-leg circuit structure that consists of six power switches. These power switches named S1 S6 can constitute eight independent states: (0,0,0), (0,0,1), (0,1,0), (0,1,1), (1,0,0), (1,0,1), (1,1,0), and (1,1,1). Among them, states (1,1,1) and (0,0,0) cannot generate effective voltage and current. The entire inverter circuit can be modeled using power switches, diodes, and related interfaces, as shown in Fig. 9.11. All these components or their alternatives can be found from the electrical library of Modelica.

9.4.3.3 Implementation of sensor and limit switch model As the detecting device for the different signals generated in the CNCMT operation, sensing device plays an important role in the feedback circuit. Sensors of the motor servo system are used for various types of data collection such as rotor position, motor speed, and three-phase winding current to provide feedback information for the control system. The electrical library of Modelica provides different sensor models, such as current sensor, voltage sensor, and angle sensor.

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FIGURE 9.11 Modelica model of the inverter: (A) internal structure of the inverter and (B) encapsulated module.

(A) model machine_x_fu parameter Real a = 1.234; Modelica.Blocks.Interfaces.RealInput x_i Modelica.Blocks.Interfaces.BooleanOutput x_o equation if x_i < a then x_o = false; else x_o = true ; end if; end machine_x_fu;

(B)

machine_x_fu

FIGURE 9.12 Modelica model of limit switch device for negative movement of X-axis feed system: (A) internal expression and (B) encapsulated module.

The limit switches of the feed systems are used to detect the moving range of the workbench to avoid overrange motion, which will result in failure or even safety problems. When the workbench touches the limit switch during movement, the servo system will receive a signal and then interrupt the control command to protect CNCMTs. The model of negative limit switch is shown in Fig. 9.12 as an example.

9.4.3.4 Implementation of control module Coordinate transformation is the basis of applying vector control to PMSM. There are three types of coordinate transformation, which are listed as follows.

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Park

(B) iα

id

i_Park θe

uα

iβ

uq

θe

ud

(alpha,beta)

(d,q)

Clark iα

ia (a,b,c)

(theta,d,q)

(theta,alpha,beta)

iq

(C)

uβ

ib (alpha,beta) iβ ic

(D) model Park Real a[2,2] = {{cos(theta_e), sin(theta_e)}, {-sin(theta_e), cos(theta_e)}}; RealInput theta_e ; RealInput i_alpha; RealInput i_beta; RealOutput id; RealOutput iq; equation {id, iq} = a * {i_alpha, i_beta}; end Park;

FIGURE 9.13 Modelica model of coordinate transformation: (A) Park transformation, (B) Park Inverse transformation, (C) Clark transformation, and (D) the construction process of the Park transformation.

Clark transformation converts the three-phase current and voltage of the motor from natural coordinates into two-phase static coordinates that are established with the reference to the stator. Park transformation converts the three-phase current and voltage of the motor from α β coordinate system into d q coordinate system that is established with reference to the rotor. Park inverse transformation inverts the process of Park transformation. Each coordinate transformation function can be realized by designing the corresponding Modelica model and encapsulating it as the module for subsequent utilization. Fig. 9.13A C shows the three types of coordinate transformation Modelica models, and Fig. 9.13D depicts the implementation details of the Park transformation as an implementation example. As mentioned in Section 9.4.3.2, input of the inverter includes control signals, which are output by SVPWM. The principle of SVPWM for PMSM is described in Ref. [22]. The key point of SVPWM is to calculate the average value of voltage, which is equal to the given voltage vector value, on the basis of the known voltage vector value. Each task of SVPWM can be modeled and encapsulated as an independent function block (FB). Then the complete function of SVPWM can be realized by connecting these FBs in accordance with logic. The SVPWM Modelica model with the logically connected FBs is shown in Fig. 9.14. The Modelica model of the control system can be constructed by connecting the abovementioned encapsulated modules according to the functional logic and actual operation principles of CNCMTs. The inner logic of the model is shown in Fig. 9.15A and the encapsulated module is shown in Fig. 9.15B.

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y u_alpha

realexpression1 300

y1 X

uβ

Y Z

Ts

Ta_Tb_Tc

TI_T2

X_Y_Z uα

X Y Z

T1

Ts N

T2

PWM

Tcm1_2_3

T1

Ta

Ta

T2

Tb

Tb

Ts

Tc

Tc N

T…

sine (input) pwm (output)

T… Tc…

y2 realexpression 0.0002

y3 αβ-N uα N

uβ

y4

u_beta

y5

FIGURE 9.14 SVPWM model built in Modelica. SVPWM, Space vector pulse width modulation.

FIGURE 9.15 Modelica model of the control subsystem: (A) internal structure of control subsystem built in Modelica and (B) encapsulated module.

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FIGURE 9.16 Model structure diagram of the machine tool X-axis feed system [32].

9.4.4 Coupling relationship between subsystems of computerized numerical control machine tools As the X-axis feed system of CNCMTs is taken as an example to show the method of modeling the mechanical subsystem and electrical subsystem, this subsection will elaborate the relationship between these two subsystems. The inner logic of the X-axis feed system model is shown in Fig. 9.16. The control box that realizes the control function is designed in Section 9.4.3.4. Its input is the target speed value, while its output is the six-way bool signals generated by the SVPWM model for the inverter driver. The angular velocity and displacement of the motor detected by the sensing device are fed back to complete the control loop. In the whole system model, power supply of the motor is provided by the inverter driver through a current sensing device. The mechanical energy generated by the motor is output by the rotor through the mechanical interface and transformed to the mechanical subsystem as torque. At the same time, the mechanical subsystem acts as a load to exert reaction to the motor. The energy transformation and reaction describe the coupling relationship between the motor and its driving parts. The model design of the Y-directionand Z-direction feed system and spindle subsystem is similar to the design of the X-direction model, and thus they will not be described in this chapter.

9.5 Design of DT-based CNCMT virtual prototype updating strategy The updating strategy of the DT-based CNCMT virtual prototype mainly includes two parts. One is mapping strategy, which is responsible for real-time

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mapping of data from physical space to digital space. The other is consistency maintenance strategy, which aims at updating the descriptive model based on the mapping data to keep it consistent with CNCMTs.

9.5.1

Design of mapping strategy

To guarantee that the DT-based CNCMT virtual prototype acts as a replica of the physical CNCMTs, the most basic step is to realize data mapping between physical space and digital space. Therefore an efficient real-time mapping strategy, as shown in Fig. 9.17, is required to provide data support for the subsequent updates of the descriptive model. Due to the variety of sensor types, the data collected from CNCMTs are multisource and timevarying. In this situation a mapping strategy with the consideration of data characteristics is needed and should be extensible for new data. In addition, the interaction interface defined for data mapping should be platformindependent and interoperable to adapt to different types of CNCMTs [32]. At present, object linking and embedding (OLE) for process control unified architecture (OPC UA) is widely used for the data transmission between different automation systems because of its standardized, platformindependent and interoperable characteristics. These characteristics can fully meet the data-mapping requirements in DT realization and thus adopt OPC UA as the transport protocol in this research. As shown in Fig. 9.17, the server/client structure based on the OPC UA protocol is the core of the mapping strategy. The OPC UA server and OPC UA client communicate with each other at the information model layer, which can map data directly from the physical space to digital space with

OPC UA client

Physical device

OPC UA server

Running status database

Information model layer Velocity Data parsing layer Data-mapping dictionary

Cutting force

Vibration

...

Protocol driver layer

Physical interface layer

RS485 Physical device

CAN Physical device

Other interface Physical device

FIGURE 9.17 DT-based CNCMTs mapping strategy [32]. CNCMTs, Computerized numerical control machine tools; DT, digital twin.

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the help of data-mapping dictionary. However, considering devices without adoption of OPC UA, the OPC UA server is designed with four layers to integrate data at the underlying layer from different communication interfaces [32]. The collected data are stored in the running status database for further application. This database can either run on the same computer with the OPC UA server or run on another computer, where an OPC UA client runs on for data transmission. Considering the frequency of data acquisition during production, which is usually high for the real-time status update of CNCMTs, the amount of data will be very large. Under this situation, column-oriented database (e.g., HBase) that supports distributed storage which can be extensible is recommended. The functions of the four layers of the OPC UA server are introduced in detail as follows: 1. The physical interface layer aims at collecting data from the various types of sensors or devices. It should be compatible with different interfaces such as RS485, RS232, Wi-Fi, Bluetooth, and CAN. 2. The protocol driver layer provides a common read and write method for data packet utilizing different interfaces. 3. The data parsing layer extracts data from data packet according to different communication protocols. 4. The information model layer converts various data into meaningful information based on the data-mapping dictionary. The data-mapping dictionary provides detailed definition of CNCMTs related data, such as data type, their corresponding meanings, and allowable values.

9.5.2

Design of consistency maintenance strategy

The DT-based virtual prototype reflects not only initial performance of CNCMTs but also performance along its entire life cycle. Therefore in order to ensure the synchronization between the virtual prototype and physical CNCMTs, a consistency maintenance strategy based on the mapping strategy is shown in Fig. 9.18. The mapping strategy provides data support for the implementation of consistency maintenance strategy. Data stored in the running status database play two important roles: offering running status data as the boundary condition for simulation on the descriptive model and offering running performance data for performance comparison. If the comparison result shows difference between the simulated performance and actual performance, which means performance deviation exists between the physical CNCMTs and the virtual prototypes, the descriptive model would need to be updated through the performance attenuation module.

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The performance attenuation module mainly focuses on the attenuation caused by wear and malfunction, because those related attenuation is more obvious and the corresponding parameters of the model are available to be updated. If performance differences appear, the performance attenuation module will call for performance attenuation algorithms (e.g., Archard Adhesion Wear Theory) from the performance attenuation model library. Then the results calculated using the attenuation algorithm will be applied to the parametric dimensions for updating the parameters of the corresponding components. Through this way, each component can keep the consistency with the corresponding physical component and thus consistency between the virtual prototype and the physical CNCMTs can be maintained through assembling these components.

9.6

Case study

As described in Sections 9.4 and 9.5, the coupling relationship between the subsystems and dynamic updating of the models can be realized by constructing a DT-based CNCMT virtual prototype. The accurate simulation results based on the DT-based CNCMT virtual prototype and a large number DT-based descriptive model

Constitue Components model

End

Performance update

CNCMT model

No

Performance differences ?

Assessment and comparison

Simulate

Yes Parametric dimensions Void growth models Continue damage mechanics

Running performance data

Running status data

Performance attenuation model library Archard Adhesion Wear Theory Nonlinear fatigue damage accumulation theory

Running status database

Crack propagation

Performance attenuation module

FIGURE 9.18 Consistency maintenance strategy of DT-based CNCMT virtual prototype. CNCMT, Computerized numerical control machine tool; DT, digital twin.

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of process data stored during CNCMT operations can provide data support for many intelligent applications on CNCMTs among its life cycle. This chapter will display several feasible applications of the DT-based CNCMT virtual prototype at the different stages of CNCMTs life cycle, including design, operation, and maintenance.

9.6.1

Case 1: Design stage

In the design phase of CNCMTs, the DT-based CNCMT virtual prototype can be applied to lean design (Chapter 10: Digital twin driven lean design for computerized numerical control machine tools) and virtual commissioning (Chapter 11: Digital twin based virtual commissioning for computerized numerical control machine tools). Lean design: First, the DT-based CNCMT virtual prototype is established according to physical CNCMTs. Second, some target performance indicators can be analyzed through mining the data stored in the database. Then, new design parameters of CNCMTs are calculated based on target performance indicators and utilized to rebuild the descriptive model of the virtual prototype. Third, workload data (which are defined as the working conditions imposed externally on CNCMTs) can be extracted from the database as boundary conditions of simulation to obtain realistic simulation results. Then the design parameters could be modified and optimized iteratively until the simulation results meet all the target performance indicators. Virtual commissioning: First, the DT-based CNCMT virtual prototype is established according to the physical CNCMTs. Every time before virtual commissioning, the virtual prototype needs to be updated to maintain consistency with physical CNCMT via the updating strategy proposed in Section 9.5. Under this situation, results of both dynamic simulations and kinematics simulations based on the DT-based virtual prototype would reflect the actual performance of CNCMTs.

9.6.2

Case 2: Operation stage

In the operation phase of CNCMTs, the DT-based CNCMT virtual prototype can be applied to fault diagnosis and fault prediction. Fault diagnosis: Due to the mapping strategy and distributed data storage of the DT-based virtual prototype, the historical data stored in the database are sufficient to build a training set for machine learning algorithm application. Fault information during CNCMT operations is also included in these data. Fault diagnosis can be recognized as a process of classification by inputting data caused by some faults (e.g., excessive vibration) to the model that then outputs the fault types. For training the classifier, real-time data from the physical space are taken as inputs, and the corresponding fault types are output timely to realize the fault diagnosis.

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Fault prediction: The process of fault prediction based on the DT-based CNCMT virtual prototype is similar to that of fault diagnosis. However, while fault diagnosis focuses more on unexpected faults, fault prediction usually focuses on long-term process and avoidable faults such as bearing failure. Thus the construction of the training set and selection of input and output data are different from fault diagnosis. In this way, potential failures could be predicted by observing the current state of CNCMTs to avoid heavy losses.

9.6.3

Case 3: Maintenance stage

Maintenance is also based on the historical data stored in the database of the DT-based CNCMT virtual prototype. It involves the classification problem in machine learning, the training set for maintenance consists of fault types and their corresponding disposal methods. Though some disposal methods cannot be collected automatically, the DT-based virtual prototype provides a storage platform for such information recording, which integrates all data to build the training set. Then, after fault types output by the fault prediction or fault diagnosis model, the DT-based CNCMT virtual prototype can offer possible maintenance solutions for users through the trained maintenance model.

9.7

Summary

This chapter combines the multidomain modeling method and DT concept to build a DT-based CNCMT virtual prototype, which improves the simulation accuracy through providing models with coupling relationship description and dynamic updating abilities. The descriptive model and updating strategy are the two important parts that constitute the DT-based CNCMT virtual prototype. The mechanical subsystem and electrical subsystem that constitute the descriptive model are designed using UML Modelica, which integrates these two subsystems into a unified model and expresses their coupling relationship. The updating strategy mainly consists of a mapping strategy and a consistency maintenance strategy. The mapping strategy is designed using OPC UA technology to map data from the physical space to digital space. These collected data are stored in the running status database, which provides data support for implementation of the consistency maintenance strategy and further information extraction. Design of a performance attenuation module is the core of the consistency maintenance strategy. Parameters of component sets in the descriptive model could be updated according to the calculation results provided by attenuation algorithms, which are embedded in the performance attenuation module. Finally, different case studies of design, operation, and maintenance stages of CNCMTs life cycle are

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introduced to demonstrate the application potential of this DT-based CNCMT virtual prototype. Future work will focus on in-depth research of the consistency maintenance strategy. Currently, only wear of mechanical components is considered as the factor that affects the performance of CNCMTs. However, aging of electrical components is another factor that needs to be reflected in the DT-based virtual prototype through the consistency maintenance strategy.

Acknowledgment The work is supported by National Natural Science Foundation of China (Grant No. 51875323) and Key Research and Development Program of Shandong Province, China (Grant No. 2019JZZY010123).

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Chapter 10

Digital twin driven lean design for computerized numerical control machine tools Yongli Wei1, Tianliang Hu1, Wenlong Zhang1, Fei Tao2 and A.Y.C. Nee3 1

School of Mechanical Engineering, Shandong University, Jinan, P.R. China, 2School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R. China, 3 Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

10.1 Introduction As an essential part of manufacturing equipment [1], computerized numerical control machine tools (CNCMTs) greatly affect the whole manufacturing system along its life cycle, and thus it plays an important role in smart manufacturing. Good performance of CNCMTs improves not only manufacturing efficiency but also product quality. The performance of CNCMTs depends on many stages throughout its life cycle such as design, commissioning, operation, and maintenance. Among these stages, design is the most basic one, which determines the initial performance of CNCMTs. In fact, 70% of the product cost depends on the design of CNCMTs [2]. Therefore it becomes a challenge for today’s equipment manufacturers to improve performance of CNCMTs at a relatively small cost at the design stage. Over the years, many researchers have been committed to the exploration of different design methods in the early design stage of CNCMTs. For example, Huo et al. proposed an integrated dynamic design and modeling approach, which supports analysis and optimization of a machine’s overall dynamic performance [3]. Cho et al. used carbon/epoxy composites and resin concrete to design and fabricate a small desktop structure of CNCMTs, which reduces the weight and enhances the structural stiffness and damping capacity [4]. In addition, related research includes innovative solutions for the structure of ultrahigh precision CNCMTs [5,6], design methods for dimension optimization of CNCMTs [7,8], and methods for improving stability of CNCMTs dynamically [9 12]. Digital Twin Driven Smart Design. DOI: https://doi.org/10.1016/B978-0-12-818918-4.00010-5 © 2020 Elsevier Inc. All rights reserved.

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Although traditional design methods can optimize CNCMT’s performance to some extent, design of parameters mainly rely on the experience of engineers, which could lead to imperfect decision-making because of the lack of participation of suppliers or customers. In addition, the abovementioned research only focuses on the design stage without considering later stages, which means that the usage and maintenance of CNCMTs provides little guidance to design. These challenges promote the urgent need for more advanced design methods to guide the design of CNCMTs. At the same time, lean design (LD) method that provides a more powerful tool for lean implementation has been gradually accepted and applied in product design. The term “lean” is derived from “lean production” and is recognized as an extension of lean thinking [13]. Reducing cost without quality sacrifice through eliminating unnecessary functions or components is the purpose of LD [14]. The differences between LD and traditional design can be summarized from three aspects as shown in Table 10.1. Through the abovementioned comparisons, it is obvious that LD method is superior to the traditional design method. However, there are still some unsolved problems in LD, such as inaccurate models for simulation and unrealistic manual-setting simulation conditions (defined as workload in this chapter, which refers to external working conditions imposed on CNCMTs), which will be elaborated in Section 10.2. In this context, this chapter aims at applying the digital twin (DT) driven LD method to CNCMT’s design based on high-fidelity of the DT model and authenticity of workload. The rest of this chapter is organized as follows. The previous works on LD methods and DT-driven design methods are reviewed in Section 10.2. The implementation of DT-driven LD is described TABLE 10.1 Differences between lean design (LD) and traditional design. Traditional design

LD

Realization mode

Design mainly relies on experience of designers with a little help of computer-aided technology [15]

Design is based on team work inline with lean thinking, mainly relying on computer-aided technology [16]

Flexibility

Design scheme is rigid and thus can hardly adjust to external changes easily [17]

Design scheme is dynamic and visualized and thus can adjust to external changes with lower cost in a shorter time [18]

Participants

Decision-making only depends on design engineers

Decision-making depends on any person who understands LD concept (e.g., enterprise managers, design engineers, customers, and suppliers)

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in detail in Section 10.3, followed by the proposed design of workload-DT model in Section 10.4 and demand-oriented application method of workload data in Section 10.5. Section 10.6 elaborates on the evaluation and optimization method for CNCMTs. A case study of LD for CNCMTs feed system is introduced in Section 10.7 to demonstrate the feasibility of the proposed method. Finally, future work is recommended in Section 10.8.

10.2 Related works LD and DT-driven design are the basis of the proposed design method for CNCMTs. Therefore previous research related to these two design methods are reviewed in this section separately.

10.2.1 Related works on lean design methods The volatile market and rapid development of the manufacturing industry has put forward higher requirements for enterprises to provide better products with lower cost. More work needs to be done in the design stage for a good start over the product life cycle, in which customers’ ideas are conceptualized into physical models, and customers’ requirements are defined into procedures, drawings, and technical specifications. Over the years, many scholars and institutions have been committed to the LD related research. Hines et al. introduced lean concept into new product development and discussed the importance of realizing lean concept in design process through cost cutting and time slashing methodology [19]. Dombrowski et al. analyzed a vast range of qualitative design principles for the various approaches [20]. There are also studies on LD theory that reflect the value of lean thinking [21], including a survey on lean practices in the Indian CNCMTs industries [22], product service system LD methodology [23], lean principles for product life cycle management solutions [24,25], etc. Under the guidance of the LD theory, Yang et al. designed a fishing net manufacturing system through simulation optimization [26]. Domingo et al. improved an assembly line using lean metrics, which attempts to reduce the dock-to-dock time and increase the lean rate [27]. Gupta and Kundra studied factors that assess the leanness (wastage removal) in CNCMTs and then proposed a method for both static and dynamic analyses using FEM package for the prevention of overdesign [2]. While the LD theory is widely used across the abovementioned research, there are also many applied studies in related fields, which can provide relevant theoretical support and guidance for LD of CNCMTs. However, the research on LD of CNCMT’s design is still at an early stage, and thus it is inevitable that the current LD-work still has some shortcomings such as inaccurate models and unrealistic workload data (e.g., spindle speed, spindle temperature, and feed speed):

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1. From model aspect. The model in LD is generally used to describe CNCMTs according to the ideal definition given by CNCMT’s designers. The inconsistency between the model and the physical CNCMTs might not only be due to the processing and assembly errors of components, but also due to the performance degradation of subsystems. In this case, the model will not be able to reflect the physical CNCMTs accurately, which will limit the effectiveness of the design for CNCMTs significantly. 2. From workload aspect. When conducting simulation analysis, workload data are usually manually set based on design requirements or empirical data, which cannot truly reflect the actual work conditions of CNCMTs. Furthermore, the traditional workload calculation method does not consider feedback from the product usage and maintenance stages. As a result, the simulation analysis in the design stage cannot reflect the updated requirements timely.

10.2.2 Related works on digital twin driven design methods The definition of DT (see Chapter 1, Digital twin driven smart product design framework, for reference) emphasizes two important characteristics. First, it emphasizes the connection between the physical entity and the corresponding DT model. In other words, the DT model is a replica of the physical entity [28,29]. Second, this connection is established through the collection of real-time data using sensors. The two characteristics show that DT can provide an accurate model and precise workload data through realtime mapping between the physical space and virtual space. At present, the potential application of DT in intelligent manufacturing has attracted increasing attention. Tao et al. presented 6 principles for DT applications and 14 typical applications of DT in view of corresponding key scientific problems or technologies based on the DT concept [30]. In addition to theoretical research, there are also DT-based applications in health maintenance [31], additive manufacturing [32], modeling as-manufactured geometry [33], etc. Many studies on DT-driven design have also been conducted. In order to provide more realistic virtual models for rapid customized workshop design, Schleich et al. proposed a comprehensive reference model based on the concept of skin model shapes [34]. To meet the requirement of customized production line design, Zhang et al. proposed a fast design scheme based on DT, which provides accurate virtual models reflecting the real production lines [35]. There is also research on DT-driven methodology for rapid customized design of automated flow-shop manufacturing system [28]. Dassault Corporation indicated the huge potential of DT in product design [36]. For example, Tao et al. proposed a new method for product design based on the DT technology and analyzed the framework of DT-driven product design [37,38].

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In theory, DT-driven design can contribute to the consistency between simulation results and actual processing results by providing accurate models and realistic workload data from real-time mapping. Combining the advantages of DT technology and LD concept, a new ideal for the optimization design of CNCMTs is obtained, which is called DT-driven LD method. This method will not only save time and cost but also provide more design guidance based on the more accurate simulation results. The DT-driven LD method will be introduced in detail in the following sections.

10.3 Framework of digital twin driven lean design Chapter 9, Digital twin based computerized numerical control machine tool virtual prototype design, has introduced the multidomain model of CNCMTs, mapping strategy between digital space and physical space, and the performance attenuation updating mechanism that provides the theoretical and model basis for this chapter. The implementation of DT-driven LD for CNCMTs will be introduced in terms of digital space and physical space separately in this section.

10.3.1 Digital twin driven lean design in digital space As shown in Fig. 10.1, the implementation of DT-driven LD in digital space takes a more important role, and it mainly consists of three parts: 1. Design of workload-DT model. Workload-DT model involves precategorization of hierarchical representation of CNCMTs working conditions. It facilitates the organization, reconstruction, and invocation of working condition information. 2. Application of workload data. Workload data are used to instantiate the designed workload-DT model, which reflects actual working conditions. The steps of workload data application are shown as follows: Step 1: Workload data generation. Raw data are preprocessed and analyzed to obtain complete workload data (e.g., spindle speed, spindle temperature, and feed speed), which are then stored in a database by categories. Step 2: Workload data selection. Target performance indicators (e.g., precision, stiffness, and thermal deformation) of CNCMTs determine the type of LD simulation (e.g., fluid mechanics simulation, structural mechanics simulation, and thermodynamics simulation), and accordingly, workload data can be selected. Step 3: Workload-DT model instantiation. The selected workload data are filled in the corresponding part of the workload-DT model to

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FIGURE 10.1 Implementation of DT-driven LD systems. DT, Digital twin; LD, lean design.

realize the certain type of simulation, which should meet the requirements imposed by the target performance indicators of CNCMTs. 3. Optimization and evaluation for CNCMTs. Optimization and evaluation are the core of implementing LD for CNCMTs. The specific steps of optimization and evaluation for CNCMTs are enumerated in the following: Step 1: LD simulation. Target performance indicators of CNCMTs determine the LD algorithm [e.g., genetic algorithm (GA), ant colony algorithm, and eigenvalue solving algorithm] that drives LD simulation with the aid of the DT model for CNCMTs and the instantiated workload-DT model. Then optimal simulation parameters (e.g., bed structure parameters, feed system topology parameters, and spindle

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thermal deformation parameters) can be obtained during this process and can be used to update the DT model for CNCMTs as well as to guide the design of next-generation prototypes of CNCMTs. Step 2: LD evaluation. Optimal parameters offered by LD simulation need to be checked to ensure that they meet the target performance indicators. If all the indicators are satisfied, these optimal design parameters are output. Otherwise, products need to be redesigned, which means that the DT model of CNCMTs is adjusted and all these steps for implementing DT-driven LD design should be executed again.

10.3.2 Digital twin driven lean design in physical space Data acquisition and prototype manufacturing are the two main tasks that need to be implemented in physical space for DT-driven LD: 1. Data acquisition. Data collected by sensors not only include status of CNCMTs, but also those related to production such as workpiece and environmental information. These data are the basis for DT-driven LD because they support the update of DT model for CNCMTs and workload loading in simulation. 2. Prototype manufacturing. The manufacturing of next-generation prototypes is guided by the optimal simulation parameters. Then, tests on performance of the new generation prototypes should be carried out to map the actual processing data into the digital space for LD evaluation. Since the data acquisition method has already been introduced in Chapter 9, Digital twin based computerized numerical control machine tool virtual prototype design, and prototype manufacturing does not involve methodology, this chapter only focuses on the implementation for DT-driven LD in the digital space, which will be described in detail in Sections 10.4 10.6.

10.4 Design of workload digital twin model A workload-DT model is for precategorization of hierarchical representation of CNCMT’s working conditions, which facilitates the organization, reconstruction, and invocation of working condition information. A workload-DT model is designed in this section with the analysis of workload for a start.

10.4.1 Analysis of workload Analysis of the workload of CNCMTs starts from the physical entities (e.g., CNCMTs, workpiece, and environment) that participate in the machining process. As a multilayer system, the physical CNCMTs need to be analyzed meticulously at the subsystem and component level to reveal the complex

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workload data. As shown in Fig. 10.2, the CNCMTs can be divided into different functional subsystems (e.g., spindle system, tool system, and feed servo drive system), and these subsystems can be further divided into smaller unit at component level. After classification, data to be collected from the physical entities are determined based on the analysis of factors that influence the performance of CNCMTs during processing. This analysis provides guidance for sensor selection and installation. Taking the thermal stability of the spindle of CNCMTs as an example, the influencing factors include spindle speed and temperature distribution axially along the spindle. However, it is impossible to collect temperature values at continuous points along the spindle. In this case, only the temperature at both ends of the spindle and the spindle speed need to be collected, and then the intermediate temperature can be calculated using formulas. This example suggests that some collected data cannot directly reflect the working conditions of CNCMTs and thus implicit

FIGURE 10.2 Detailed analysis of CNCMTs workload. CNCMTs, computerized numerical control machine tools.

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workload data for subsequent use need to be calculated through processing the raw data (see Section 10.5.1.2 for reference). For LD of CNCMTs, different types of simulation (e.g., fluid mechanics analysis, structural mechanics simulation, and thermodynamic simulation) will be carried out using different workload data to make the design of CNCMTs meet target performance indicators. To collect and utilize the workload data efficiently, these data are divided into several categories (e.g., structural mechanics workload, thermodynamics workload, and fluid mechanics workload). Taking the thermodynamic workload data of spindle for example, the thermodynamic workload contains not only temperature data such as spindle temperature, environment temperature, and tool temperature, but also data related to thermodynamic simulation such as spindle speed. It might be noted that the same data can belong to different workload categories as long as it plays a role in certain types of simulation.

10.4.2 Construction of workload digital twin model In order to integrate the workloads with the CNCMTs DT for simulation in LD, a workload-DT model should be established based on the analysis of workload earlier, as shown in Fig. 10.3. The workload-DT model is a hierarchical representation of the working conditions of CNCMTs with clear classification. For example, to meet the target performance indicators of spindle vibration, structural mechanics simulation for the spindle should be carried out.

FIGURE 10.3 Workload-DT model of CNCMTs. CNCMTs, Computerized numerical control machine tools; DT, digital twin.

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This type of simulation can obtain corresponding workload data efficiently according to the structural mechanics workload-DT model. After the workload-DT model is constructed, it can be stored in an XML file, which is machine readable, well organized, reusable, and capable of transmission. There are two advantages of introducing the workload-DT model into LD as follows: 1. The workload-DT model can be reused to avoid repeated domain knowledge modeling. The ambiguity caused by different definitions and expressions for design concepts and terms can be eliminated by building a unified model with well-organized structure. Therefore data interaction will become more convenient and efficient based on this workload-DT model. 2. As a result of the clear hierarchical presentation of the workload-DT model, data collection and utilization during LD process become more efficient. Furthermore, simulation based on the instance of the workloadDT model will generate more precise and realistic results to guide design of CNCMTs.

10.5 Application of workload data Workload data come from the collected raw data, some of which can be directly used, while some need further calculation. According to the simulation requirements and workload-DT model, suitable workload data are selected from the database and used to instantiate the corresponding part of the workload-DT model for a workload instance generation.

10.5.1 Workload data generation Generation of workload data mainly includes three steps, namely, data preprocessing, data analysis, and data storage. The process and role of each step are described next in detail.

10.5.1.1 Data preprocessing In order to maintain consistency between workload instance and actual working conditions, it is necessary to install a large number of different types of sensing devices on CNCMTs and other physical entities for data collection during operation of CNCMTs. These sensing devices usually have low tolerance for noise and electromagnetic interference. Besides, a high data acquisition rate is usually required to capture instantaneous changes in working conditions. As a result of the facts stated previously, the collected data usually have problems such as missing, abnormal, heterogeneous, and redundant. If these defective data (e.g., abnormal data, missing data, and noisy

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data) are used directly as boundary conditions for simulation in LD of CNCMTs, the accuracy of simulation results will be affected and thus cannot provide effective guidance for the design of CNCMTs. Therefore, data preprocessing needs to deal with the abovementioned problems existing in the collected raw data. Through preprocessing, the data become more accurate, complete, and regular. Data preprocessing includes data cleaning, data integration, data reduction, and data transformation, as shown in Fig. 10.4. Selection of data preprocessing technology depends on the characteristics and application purposes of raw data.

10.5.1.2 Data analysis The preprocessed data are time-discrete data that cannot fully express the entire workload of CNCMTs. Therefore it is necessary to further exploit the implicit information through data analysis. In other words, the implicit workload data can be calculated based on the preprocessed data through relevant rules or formulas (e.g., thrust calculation, Archard adhesion theory, and empirical formula for cutting force). 10.5.1.3 Data storage After data preprocessing and data analysis, the entire workload data are generated and need to be stored for subsequent utilization. Though the amount of workload data is smaller than that of the collected data, column-oriented database (e.g., HBase) with distributed storage capacity is still recommended. Because of the current volatile market, arrangement of equipment in the shop floor usually changes frequently to rebuild production lines for new products, which also leads to the change of workload data. Because the column-oriented

FIGURE 10.4 Data preprocessing.

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database has characteristics of adding columns dynamically, it can adapt to the workload changes quickly to redesign the tables without restarting. Time-discrete and time-continuous data represented by functions are stored separately in two tables of the column-oriented database. To access data effectively, subsystems can be named as column family, while the combination of simulation type and workload data can be named as column. Row key can be defined as the combination of timestamp and a random number after utilizing MD5 (message-digest algorithm) to guarantee its uniqueness and fixed length.

10.5.2 Workload data selection Workload data provide the simulation foundation for LD. This subsection will introduce the two steps of workload data selection before simulation. The first one is the analysis of target performance indicators of CNCMTs. Then, based on the target performance indicators, the required workload data of CNCMTs LD can be analyzed and selected.

10.5.2.1 Analysis of target performance indicators of computerized numerical control machine tools Womack pointed out that proper identification of performance requirements provided by customers is the first step for any LD process [39]. Target performance indicators of CNCMTs generally include precision, stiffness, and thermal deformation as shown in Fig. 10.5. These target performance indicators come from two sources. Namely, some are requested by customers (e.g., thermal deformation, vibration resistance, and precision), and others come from statistical analysis on historical data of the CNCMT DT model by engineers (e.g., moving range of feed axis, common spindle speed, and ultimate spindle speed). For example, the target performance indicators of the machining space of CNCMTs can be obtained through statistical analysis of the data of the moving range of each feed axis. In the LD process of CNCMTs, different LD methods and different parts of workload data are selected for simulation according to different target performance indicators. Assuming that the spindle cannot dissipate heat in time, it will undergo axial expansion deformation and will finally affect the machining accuracy. Thus there should be a target performance indicator for the spindle design to give a threshold for spindle thermal deformation. 10.5.2.2 Analysis of required workload data of computerized numerical control machine tools lean design After target performance indicators of CNCMTs are determined, the next step is to select appropriate workload data for simulation to check whether the designed CNCMTs can meet these indicators. For example, finite

FIGURE 10.5 Target performance indicators of CNCMTs. CNCMTs, Computerized numerical control machine tools.

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element analysis (FEA) is a common simulation method and the type of analysis and simulation boundary conditions have to be determined before starting the simulation. The setting of boundary conditions needs corresponding working condition data to provide data support. Then, the type of analysis is determined according to the target performance indicator. For example, thermal stability of lead screw requires FEA to perform thermodynamic analysis.

10.5.3 Workload digital twin model instantiation After determining the workload data type, the last step is to retrieve the related data from the database designed in Section 10.5.1.3 and instantiate the corresponding part of the workload-DT model. Time-discrete workload data are retrieved according to the start timestamp, since there is no end timestamp of the time-discrete workload data. Nevertheless, time-continuous workload data represented either by a constant value or by a function are retrieved according to start timestamp, end timestamp, and other data related to the function. Multiple XML files will be generated based on different simulation types and each file organizes workload data according to the workload-DT model. An example of the workload-DT model instance in the XML format is shown in Fig. 10.6. Taking thermal deformation of spindle for example, after deciding the target performance indicators, thermodynamic FEA is carried out on the thermal deformation of the spindle by loading relevant workload instance (i.e., an XML file that contains data such as spindle and speed), and then the thermal deformation of the spindle at different rotational speed can be obtained to check whether it exceeds the threshold. If the thermal deformation results do not meet the target performance indicators of CNCMTs, the design parameters of the spindle system should be adjusted for multiple iterations until the thermal deformation results are all within the allowable range. In the DT-driven LD of CNCMTs, workload data come from actual working conditions of CNCMTs during machining and thus provide more accurate and authentic boundary conditions for the simulation to support the design of CNCMTs with more practical guidance. Thereby, subjective errors of workload occurring in manual setting in traditional LD design of CNCMTs can be reduced. Besides, feedback time of design defects becomes shortened due to the real-time data mapping of DT, so that LD of CNCMTs becomes more adaptive to the current volatile market.

10.6 Optimization and evaluation for computerized numerical control machine tools The workflow of DT-driven LD mainly contains two functions, which are optimization and evaluation, as shown in Fig. 10.7. The DT model for

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FIGURE 10.6 Example of workload-DT model instantiation. DT, Digital twin.

CNCMTs, LD algorithm library, LD simulation module, and LD evaluation module are the key parts to guarantee the realization of DT-driven LD.

10.6.1 Optimization for computerized numerical control machine tools When an LD requirement is proposed, a suitable LD algorithm will be selected from the LD algorithm library and the corresponding DT model of

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FIGURE 10.7 Framework of DT-driven CNCMTs optimization and evaluation method. CNCMTs, Computerized numerical control machine tools; DT, digital twin.

CNCMTs will be loaded according to the analysis of LD requirement. The selected LD algorithm and DT model provide algorithm foundation and model foundation for LD simulation separately. At the same time, the required workload data are also loaded into the DT model for subsequent LD simulation. The implementation method for LD simulation guided by the analysis of LD requirement is shown in Fig. 10.8. GA is taken as an example to explain the DT-driven LD simulation in detail. The simulation module and the GA optimization module using GA are the key parts in this simulation process. The main functions of each module are introduced as follows: 1. Simulation module. Simulation analysis is carried out based on the loaded CNCMT DT model and workload data. Before starting simulation, this CNCMT DT model needs to be updated according to the outputs of the GA module (only if there are outputs from the GA module) based on the

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GA optimization module Target performance index of CNCMTs

Output model parameters

Running simulation model Determine target functions and variables Loaded simulation model Generate initial population CNCMTs DT model Individual fitness valuation Performance attenuation updating mechanism Output optimization design parameters

Yes

Are target performance indicator met ? No

Workload instance

Perform selection, crossover and mutation operations

FIGURE 10.8 The workflow of DT-driven LD simulation. DT, Digital twin; LD, lean design.

attenuation updating mechanism introduced in Chapter 9, Digital twin based computerized numerical control machine tool virtual prototype design. Then, if the target performance indicators cannot be met according to the simulation analysis results, design parameters of the model are relayed back into the GA optimization module. 2. GA optimization module. After iterations of optimization based on the GA algorithm, the design parameters that meet the performance indicators can be the output to the simulation module to check their validity based on the DT model. Finally, the optimized design parameters acquired using the DT-driven LD scheme are obtained to guide the next-generation prototype of CNCMTs.

10.6.2 Evaluation for computerized numerical control machine tools Although the optimization process using LD simulation can guarantee all parts of CNCMTs to have reached the target performance indicators, it is still necessary to test the actual performance of the next generation CNCMTs prototype with the optimized design parameters. The performance data obtained during the operation of the CNCMTs can be used to determine whether the target performance indicators are met through LD evaluation

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analysis. If the performance of the CNCMTs prototype is satisfactory, the final design parameters are the output to guide the production of CNCMTs. At the same time, the DT model will be updated according to these new design parameters, so that it can be consistent with the new generation of CNCMTs. To the contrary, if the performance of the CNCMTs prototype is not satisfactory, CNCMTs need to be redesigned in accordance with the workflow illustrated in Fig. 10.7.

10.7 Case study In order to verify the feasibility of the DT-driven LD method for CNCMTs, a feed system is taken as an example in this section. The influence of system parameters on the first order natural frequencies of the feed system have been studied by our team members [40]. This case refers to the parameter settings of the work [40] and achieves the same result through DT-driven LD method.

10.7.1 Problem description At present, the combination of servo motor and ball screw is still one of the most commonly used forms to design CNCMTs feed system. The feed system in the example comes from a certain type of CNCMTs. Configuration of the system parameters is shown in Table 10.2. An end milling cutting tool is selected to machine a plane surface. When the spindle speed reaches 3820 r/min, the cutting frequency becomes closer to the axial first-order natural frequency of the feed system, which is 254.65 Hz. This phenomenon indicates the appearance of resonance, which results in the cutting instability. If a higher cutting speed is needed, it is necessary to improve the firstorder natural frequency of the feed system by modifying its design parameters. The higher the first-order frequency is, the larger will be the TABLE 10.2 Parameter values for feed drive system [40]. System parameters

Parameter values

Left rolling bearing set stiffness (N/m)

1.3 3 109

Ball screw stiffness (N/m)

1.76 3 108

Stiffness of the screw nut and its nut seat (N/m)

5.1 3 108

Right rolling bearing set stiffness (N/m)

8.5 3 108

Ball screw weight (kg)

9.78

Worktable weight (kg)

100

Ball screw length (mm)

1200

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threshold of cutting speed that causes resonance. Then, the occurrence of resonance at the required cutting speed could be avoided.

10.7.2 Digital twin driven lean design for the feed system of computerized numerical control machine tools In order to verify the effectiveness of the proposed method, the effect of position and weight of the machine table on the axial first-order natural frequency of the feed system is analyzed, respectively. 1. Influence of worktable position on the axial first-order natural frequency It is inevitable to move the worktable when analyzing the effect of its position on the axial first-order natural frequency. Therefore, when performing simulation analysis, structural mechanics workload data of the feed system, while the worktable moving from one position to another, are selected and set as the boundary conditions. After selecting the suitable algorithm (e.g., eigenvalue solving algorithm) and carrying out simulation based on the DT model, the position frequency curve can be obtained, as shown in Fig. 10.9. It can be seen that the axial first-order natural frequency turns lower when the worktable moves to the position near the middle of the screw and gradually increases when the worktable moves to the both ends. 2. Influence of worktable weight on axial first-order natural frequency By performing simulation in the same way except that the worktable is kept at the center position and changing the worktable weight, the weight frequency curve can be obtained as shown in Fig. 10.10. It can be seen that

First order natural frequency (rad/s)

1850

1800

1750

1700

1650

1600

1550

200

400

600 800 Worktable position (mm)

1000

1200

FIGURE 10.9 Influence of worktable position on axial first-order natural frequency [40].

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First order natural frequency (rad/s)

2200

1900

1600

1300

1000 50

100

150

200

Worktable quality (kg)

FIGURE 10.10 Influence of CNCMTs table weight on axial first-order natural frequency [40]. CNCMTs, Computerized numerical control machine tools.

the axial first-order natural frequency decreases with the increase of worktable weight. Besides, the rate of the axial first-order natural frequency decrease becomes larger when the weight remains around the original value of 100 kg. When the worktable weight is reduced by 10%, the axial first-order natural frequency increases by 4.94%. It can be concluded that the first-order natural frequency of the CNCMTs feed system can be effectively improved by reducing the worktable weight.

10.7.3 Results and discussion With the aim of increasing the first-order natural frequency and thus improving the upper limit of spindle speed, the structure of the worktable was optimized with a weight reduction by 5% according to the previous analysis. A new generation of three-axis CNCMTs was manufactured based on the optimized parameters, and modal experiments were carried out using modal test equipment including a hammer, a signal conditioner, a data collector, and modal testing software. According to the modal test, the axial first-order natural frequency of the new feed system is 310.35 Hz when the worktable is located in the middle of the screw. It is obvious that the axial first-order natural frequency of the feed system has been improved. When machining the plane with a four-edged end milling cutting tool, the spindle speed can reach 4655 r/min, which increases by nearly 22% compared with the value before LD. All the system parameter values before and after LD are shown in Table 10.3, which reveals the effectiveness of LD.

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TABLE 10.3 System parameter values before and after lean design (LD). System parameters

Before LD

After LD

Amount of change

Rate of change

Axial first-order natural frequency

254.65 Hz

310.35 Hz

350

m22%

Worktable weight

100 kg

95 kg

5

k5%

Maximum spindle speed

3820 r/ min

4655 r/ min

835

m22%

10.8 Summary In this chapter, a DT-driven LD method for CNCMTs has been proposed for the optimization of CNCMT’s performance. The steps for implementing DTdriven LD in digital space are introduced in detail with respect to the workload-DT model design, application of workload data, and optimization and evaluation for CNCMTs. Finally, a case study of feed system optimization design using the DT-driven LD method is introduced to verify the feasibility of this method and indicates that the allowable cutting speed turns higher, which can improve the cutting efficiency and support high-speed cutting. This DT-driven LD method can also be applied to other equipment design. Future work will focus on the lean thinking application in the manufacturing of CNCMTs based on the DT-driven method. In addition, suppliers, designers, manufacturers, and other stakeholders can fully collaborate and communicate to realize the optimum combination of LD and lean manufacturing.

Acknowledgment The work is supported by National Natural Science Foundation of China (Grant No. 51875323) and Key Research and Development Program of Shandong Province, China (Grant No. 2019JZZY010123).

References [1] X.W. Xu, S.T. Newman, Making CNC machine tools more open, interoperable and intelligent—a review of the technologies, Comput. Ind. 57 (2) (2006) 141 152. [2] A. Gupta, T. Kundra, A review of designing machine tool for leanness, Sadhana 37 (2) (2012) 241 259. [3] D. Huo, K. Cheng, F. Wardle, A holistic integrated dynamic design and modeling approach applied to the development of ultraprecision micro-milling machines, Int. J. Mach. Tools Manuf. 50 (4) (2010) 335 343.

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[4] S.-K. Cho, H.-J. Kim, S.-H. Chang, The application of polymer composites to the tabletop machine tool components for higher stiffness and reduced weight, Compos. Struct. 93 (2) (2011) 492 501. [5] Z. Wang, X. Cheng, K. Nakamoto, et al., Design and development of a precision machine tool using counter motion mechanisms, Int. J. Mach. Tools Manuf. 50 (4) (2010) 357 365. [6] F. Aggogeri, A. Merlo, M. Mazzola, Multifunctional structure solutions for ultra high precision (UHP) machine tools, Int. J. Mach. Tools Manuf. 50 (4) (2010) 366 373. [7] C. Brecher, P. Utsch, R. Klar, et al., Compact design for high precision machine tools, Int. J. Mach. Tools Manuf. 50 (4) (2010) 328 334. [8] G.M. Delhaes, A. Van Beek, R.A. Van Ostayen, et al., The viscous driven aerostatic supported high-speed spindle, Tribol. Int. 42 (11 12) (2009) 1550 1557. [9] R.S. Bais, A. Gupta, B. Nakra, et al., Studies in dynamic design of drilling machine using updated finite element models, Mech. Mach. Theory 39 (12) (2004) 1307 1320. [10] T. Kundra, Vibration designing of machine tools, Mod. Mach. Tools (2004) 38 44. [11] B.D. Bhaskar, Studies on Improvement on Dynamic Stability of a Drilling Machine, A Study at IIT Delhi, 2004. [12] S. Apprich, F. Wulle, A. Lechler, et al., Approach for a general pose-dependent model of the dynamic behavior of large lightweight machine tools for vibration reduction, Procedia CIRP 41 (2016) 812 817. [13] M. Holweg, The genealogy of lean production, J. Oper. Manage. 25 (2) (2007) 420 437. [14] J.W. Ohlmann, M.J. Fry, B.W. Thomas, Route design for lean production systems, Transp. Sci. 42 (3) (2008) 352 370. [15] J. Freire, L.F. Alarco´n, Achieving lean design process: Improvement methodology, J. Constr. Eng. Manage. 128 (3) (2002) 248 256. [16] E. Qi, H. Zhang, The frame of factory’s lean design and its realization method, Sci. Sci. Manage. S&T 30 (9) (2009) 167 171. [17] P. Tzortzopoulos, C. Formoso, Considerations on application of lean construction principles to design management, in: Proceedings IGLC, 1999, pp. 26 28. [18] J.M. Jauregui-Becker, N. Perry, Lean design, In: Laperrie`re L., Reinhart G. (eds ), The International Academy for Produ, CIRP Encycl. Prod. Eng. (2015). Springer, Berlin, Heidelberg. [19] P. Hines, M. Francis, P. Found, Towards lean product lifecycle management: a framework for new product development, J. Manuf. Technol. Manage. 17 (7) (2006) 866 887. [20] U. Dombrowski, S. Schmidt, K. Schmidtchen, Analysis and integration of design for X approaches in lean design as basis for a lifecycle optimized product design, Procedia CIRP 15 (2014) 385 390. [21] S. Emmitt, D. Sander, A.K. Christoffersen, Implementing value through lean design management, in: Proceedings of the 12th International Conference, 2004, pp. 361 374. [22] M. Eswaramoorthi, G. Kathiresan, P. Prasad, et al., A survey on lean practices in Indian machine tool industries, Int. J. Adv. Manuf. Technol. 52 (9 12) (2011) 1091 1101. [23] G. Pezzotta, C. Sassanelli, F. Pirola, et al., The product service system lean design methodology (PSSLDM) integrating product and service components along the whole PSS lifecycle, J. Manuf. Technol. Manage. 29 (8) (2018) 1270 1295. [24] R. Navarro, A. Tiwari, C. Turner, Improving product lifecycle management implementations by applying ‘lean’ principles, Int. J. Product. Lifecycle Manage. 6 (4) (2013) 357 380.

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[25] P. Hines, M. Francis, P. Found, A framework for lean product lifecycle management, in: Technology Management Conference, 2016. [26] T. Yang, Y. Kuo, C.-T. Su, et al., Lean production system design for fishing net manufacturing using lean principles and simulation optimization, J. Manuf. Syst. 34 (2015) 66 73. [27] R. Domingo, R. Alvarez, M. Melod´ıa Pen˜a, et al., Materials flow improvement in a lean assembly line: a case study, Assembly Autom. 27 (2) (2007) 141 147. [28] Q. Liu, H. Zhang, J. Leng, X. Chen, Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system, Int. J. Prod. Res. 57 (12) (2019) 3903 3919. [29] M.B. Chhetri, S. Krishnaswamy, S.W. Loke, Smart virtual counterparts for learning communities, in: International Conference on Web Information Systems Engineering, 2004, pp. 125 134. [30] F. Tao, W. Liu, J. Liu, et al., Digital twin and its potential application exploration, Comput. Integr. Manuf. Syst. 24 (1) (2018) 1 18. [31] E.J. Tuegel, A.R. Ingraffea, T.G. Eason, et al., Reengineering aircraft structural life prediction using a digital twin, Int. J. Aerosp. Eng. 2011 (2011) 1 14. [32] G.L. Knapp, T. Mukherjee, J.S. Zuback, et al., Building blocks for a digital twin of additive manufacturing, Acta Mater. 135 (2017) 390 399. [33] J.D. Hochhalter, On the effects of modeling as-manufactured geometry: toward digital twin, Int. J. Aerosp. Eng. 2014 (2014). 1 10. [34] B. Schleich, N. Anwer, L. Mathieu, et al., Shaping the digital twin for design and production engineering, CIRP Ann. 66 (1) (2017) 141 144. [35] H. Zhang, Q. Liu, X. Chen, et al., A digital twin-based approach for designing and multiobjective optimization of hollow glass production line, IEEE Access. 5 (2017) 26901 26911. [36] Digital Twins Land a Role In Product Design [EB/OL]. 2015,,http://www.digitaleng. news/de/digital-twins-land-a-role-in-product-design/X.. [37] F. Tao, F. Sui, A. Liu, et al., Digital twin-driven product design framework, Int. J. Prod. Res. 57 (12) (2019) 3935 3953. [38] F. Tao, J. Cheng, Q. Qi, et al., Digital twin-driven product design, manufacturing and service with big data, Int. J. Adv. Manuf. Technol. 94 (9 12) (2018) 3563 3576. [39] J.P. Womack, D.T. Jones, Lean thinking—banish waste and create wealth in your corporation, J. Oper. Res. Soc. 48 (11) (1997) 1148. [40] Y.Q. Wang, C.R. Zhang, & P. Zhai, Quantitative change rule for axial first-order natural frequency of ball screw feed drive system. Journal of Shenyang University of Technology, 35 (1) (2013) 58 62.

Chapter 11

Digital twin based virtual commissioning for computerized numerical control machine tools Weidong Shen1, Tianliang Hu1, Yisheng Yin1, Jianhui He1, Fei Tao2 and A.Y.C. Nee3 1

School of Mechanical Engineering, Shandong University, Jinan, P.R. China, 2School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R. China, 3 Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

11.1 Introduction The manufacturing industry is facing constant challenges such as the increase of customized products and the requirement for shortening manufacturing cycles, which makes production systems more complex to adapt to the volatile market [1]. Before production, NC programs and servo dynamic parameters need to be tested and optimized in order to guarantee the mass production quality of new products. This process is known as commissioning and is usually conducted on real machine tools, which is time-consuming and costly. Moreover, the increasing complexity of the production systems aggravates the cost and time burden of commissioning and can even result in safety problems. A common approach to deal with commissioning problems is virtual commissioning, which conducts the commissioning process on a virtual model of the physical production system. With the help of virtual commissioning the commissioning time can be reduced by 75% compared with the time
cost using real commissioning method [2]. As shown in Fig. 11.1, there are four different levels in virtual commissioning [3], which are listed as follows: G G

virtual commissioning at machine level, virtual commissioning at cell level,

Digital Twin Driven Smart Design. DOI: https://doi.org/10.1016/B978-0-12-818918-4.00011-7 © 2020 Elsevier Inc. All rights reserved.

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FIGURE 11.1 Four different levels in virtual commissioning. G G

virtual commissioning at line level, and virtual commissioning at production system level.

All these four levels of virtual commissioning have the same purpose that engineers can detect potential errors of NC programs before real commissioning through connecting virtual models with a real or emulated controller [4]. Although virtual commissioning can significantly reduce the time and cost compared with real commissioning, there is still a big challenge for virtual commissioning. Due to the lack of sufficient physical
virtual interaction, traditional virtual commissioning cannot reflect the actual states of physical devices. This inconsistency between virtual models and physical devices will lead to the inaccuracy of commissioning results. Thus the results generated from virtual commissioning cannot guide the production directly and would usually need to be verified through further real commissioning. As the “mother machine” of industry, computerized numerical control machine tools (CNCMTs) are the main manufacturing equipment in the production system, and thus need to be debugged frequently for new products or new production lines. In order to solve the abovementioned problem of virtual commissioning, this chapter proposes a digital twin (DT) based virtual commissioning method for CNCMTs. With the help of DT-based virtual prototype enabled by the multidomain coupling modeling and model updating strategy, the DT-based virtual commissioning method for CNCMTs can overcome drawbacks of traditional virtual commissioning methods.

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The rest of this chapter is organized as follows: related works of virtual commissioning are reviewed in Section 11.2. Section 11.3 presents the overall framework of DT-based virtual commissioning for CNCMTs. Section 11.4 elaborates the workflow of DT-based virtual commissioning method. Based on these designs, a DT-based virtual commissioning platform for CNCMTs and two commissioning cases conducted on this platform are described in Section 11.5. Finally, Section 11.6 concludes this chapter with future directions.

11.2 Related works 11.2.1 Traditional virtual commissioning For virtual commissioning at the production system level a modeling method for transport systems that enables system interaction with all the peripheries was proposed by Wischnewski and Freund [5]. In their research, real controllers are connected to the models of devices for testing and optimizing NC programs. An approach for virtual commissioning on production system was proposed by Drath et al. [6] based on the production system model established through COSIMIR, ABB RobotStudio, and CoDeSyS. A concurrent design procedure of production systems was presented by Ko et al. [7]. This design procedure supports the virtual commissioning through connecting real controller to a virtual production system. These research studies focus more on production process commissioning; thus, the device models established in these studies lack physical description (geometry and kinematics) and logical description, which are very important for virtual commissioning of devices. For the geometric modeling of devices, constructive solid geometry modeling scheme has been employed widely [8,9]. An evolutionary search algorithm, which aims at giving an appropriate geometric description of virtual devices through exploring mass alternatives was developed by Mervyn et al. [10]. For the kinematic modeling of virtual devices, Jacobian Matrix was used to describe the relationship between devices and parts in 3D space [11]. Combining these two aspects, a device model framework that consists of geometric model and kinematic model was proposed by Kang et al. [12]. For the logical modeling of devices a naming rule that enables sufficient information included in Programmable Logic Controller (PLC) symbol names was developed by Park et al. [13]. Log data and PLC I/O signal table are also analyzed to acquire the operation logic of devices for the logical model establishment [14]. Moreover, a method for constructing the logical model of flexible manufacturing systems, which expands Discrete Event Systems Specifications (DEVS), was proposed by Ko et al. [15]. To debug PLC based NC programs with shorter time and lower cost, a virtual commissioning method was proposed by Auinger et al. based on the generic modeling architecture that connects the physical modules with

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simulation modules [16]. A suite of integrated tools named Modular Machine Design Environment, which utilizes 3D graphical simulation and supports virtual engineering, was developed by Adolfsson et al. [17]. A method, aimed at testing logical operations of automatic material handling system, was proposed by McGregor utilizing simulation models [18]. An approach including manual testing, model checking, and virtual commissioning for off-line verification and validation of control logic was proposed by Thapa et al. [19]. At the virtual commissioning phase, virtual devices can be connected to an emulated PLC that runs the to-be-verified program [20]. It can be concluded that models of devices play important role in virtual commissioning, and device modeling requires considerable effort and expertise in these research studies. As one of the most important devices in manufacturing system, CNCMTs require commissioning before mass production of new products. Virtual commissioning for CNCMTs mainly includes servo parameters turning and NC programs check. Therefore the commissioning model for CNCMTs should at least involve the electrical system model and the virtual machining ability [21]. A virtual commissioning method for CNCMTs that includes the automatic collision avoidance method and integrates NC-postprocessor and NCsimulation module was proposed by Kruth et al. [22]. An automatic collision avoidance method for multiaxes milling was proposed by Lauwers et al. [23]. Time-deterministic algorithm is used to describe the dynamic characteristics of CNCMTs and a method called real-time “Hardware in the Loop” simulation was proposed by Stoeppler et al. [24]. The performance of CNCMTs will be affected by wear, lubrication, and vibration during its life span; the lack of updating strategy in the modeling of CNCMTs in the research discussed previously will lead to inconsistency between models (also known as virtual CNCMTs) and physical CNCMTs. Furthermore, this inconsistency will lead to the inaccuracy of commissioning results and thus make the virtual commissioning less reliable. These research studies have shown the advantages of virtual commissioning such as reducing commissioning time and cost [25]; however, models of devices in these studies were established without considering performance attenuation. Although major vendors such as DELMIA and SIEMENS have developed commercial products for virtual commissioning, model inconsistency problem still exists.

11.2.2 Digital twin based virtual commissioning Combining the characteristics of physical
virtual mapping and multidomain coupling modeling, DT provides a new solution for making full use of the virtual commissioning technology.

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Some scholars have proposed the concept of the DT-based virtual test bed, which is similar to the DT-based virtual-commissioning platform. The concept of virtual test bed, which combines the data-processing system with simulation environment, was proposed by Schluse and Rossmann [26]. Traditional simulation for devices or systems only focuses on one specific aspect, while a DT-based virtual test bed enables all aspects of simulation in a unified platform. Then, system can be designed, programmed, controlled, and optimized using simulation for preliminary commissioning rather than commissioning on real devices directly. A workflow for developing the control algorithms of robots using a DT-based virtual test bed was designed by Grinshpun et al. [27]. It can be concluded that DT-based virtual commissioning integrates different disciplines into one platform, which can reduce time and cost to some extent. However, the device models still cannot reflect the actual performance of physical devices during its life cycle because of the lack of model updating strategy. As one of the most important facilities in a production system, CNCMTs need to be debugged frequently for new products and its commissioning efficiency directly affects the production efficiency. Thus this chapter introduces a DT-based virtual commissioning method for CNCMTs, which can conduct simulation on a unified platform using the virtual CNCMTs that not only integrates different disciplines (mechanics and electrics) but also achieves timely update based on the model updates mechanism and huge amount of collected data.

11.3 Framework of digital twin based virtual commissioning for computerized numerical control machine tools As shown in Fig. 11.2, DT-based virtual commissioning for CNCMTs should maintain consistency between models and CNCMTs in both dynamic and kinematic commissioning. Detailed process of establishing a DT-based CNCMTs virtual prototype has been described in Chapter 9, Digital twin based computerized numerical control machine tool virtual prototype design. In order to ensure the consistency between virtual CNCMTs and physical CNCMTs, a DT-based updating strategy for virtual CNCMTs is introduced in virtual commissioning. Then through conducting dynamic and kinematic virtual commissioning, optimal servo parameters and verified NC programs can be obtained and applied on the physical CNCMTs. The functions of DT-based virtual commissioning for CNCMTs are described as follows: G

Maintaining consistency between the virtual CNCMTs and the physical CNCMTs. In order to solve the inconsistency problem between the virtual CNCMTs and the physical CNCMTs, DT data collected from the physical CNCMTs are used to update the virtual CNCMTs according to the updating strategy.

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FIGURE 11.2 Framework of DT-based virtual commissioning for CNCMTs. CNCMT, Computerized numerical control machine tool; DT, digital twin. G

G

Dynamic commissioning. Dynamic performance of CNCMTs plays an important role in product quality and differs according to the load and machining parameters. Servo parameters tuning is the basis of dynamic commissioning for CNCMTs. In dynamic commissioning for CNCMTs, control algorithm that includes servo parameters is loaded in the electrical system model of the virtual CNCMTs to drive the simulation. Then servo parameters can be tuned with the objective of achieving stable dynamic performance according to the simulation result. Kinematic commissioning. Kinematic performance of CNCMTs also plays important role in product quality, efficiency, and even safety. In kinematic commissioning for CNCMTs the motions of all axes are driven by NC programs and then analyzed to check whether collision will happen. This simulation aims at providing an assessment of tested NC programs and checking the possible collision. According to the simulation results, NC program can be modified literally and finally optimized to meet the processing requirements.

11.4 Workflow of digital twin based virtual commissioning for computerized numerical control machine tools Workflow of DT-based virtual commissioning for CNCMTs consists of three steps, which are maintaining consistency between the virtual CNCMTs and the physical CNCMTs, dynamic commissioning, and kinematic commissioning. The first step is necessary, which guarantees the authenticity of commissioning results. While the other two steps reflect the two aspects of commissioning, which are optional and sequentially adjustable. The detail of each step is described next.

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11.4.1 Step 1: Keeping virtual and physical computerized numerical control machine tools consistent The virtual CNCMTs are responsible for describing the geometrical, electrical, and physical characteristics of the physical CNCMTs. To maintain consistency between the virtual CNCMTs and physical CNCMTs, real-time data of the physical CNCMTs are collected to update the virtual CNCMTs according to the updating strategy. As shown in Fig. 11.3, the updating strategy includes data mapping strategy, which consists of data mapping module and data management module, and consistency maintenance strategy, which consists of performance updating module and self-updating DT construction module. G

Data mapping module. As the communication interface between digital space and physical space, data mapping module consists of OPC UA client and OPC UA server, as shown in Fig. 11.3A. Data from the various OPC UA clients (i.e., corresponding sensors) are transmitted to the OPC UA server, which contains the physical interface layer, protocol driver layer, data parsing layer, and information model layer (refer to Chapter 9: Digital twin based computerized numerical control machine tool virtual

FIGURE 11.3 DT-based updating strategy of virtual CNCMTs: (A) data mapping module, (B) data management module, (C) performance updating module, and (D) self-updating DT construction module. CNCMT, Computerized numerical control machine tool; DT, digital twin.

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G

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prototype design.). Through protocol-driven data parsing in the OPC UA server, real-time data (e.g., velocity, vibration, and cutting force) of physical CNCMTs are mapped to the information model that describes data objects, relationship among data objects, and attributes of data objects. Data management module. As shown in Fig. 11.3B, data from the information model (i.e., states of physical CNCMTs) and DT simulation (e.g., angular velocity, angular displacement, and displacement of motor) are cleaned and stored in the database for subsequent utilization in data analysis. Performance updating module. Stored data are used to analyze the performance variations of the physical CNCMTs parts using performance attenuation algorithms (e.g., Archard adhesion wear theory) as shown in Fig. 11.3C. Submodels reflect the physical components of physical CNCMTs, only when the performance of physical components changes and the corresponding submodels are updated and transmitted to the selfupdating DT construction module. Self-updating DT construction module. With the updated submodels a new descriptive model for CNCMTs can be generated by combining unchanged submodels with the updated ones, as shown in Fig. 11.3D.

Based on the implementation of the modules discussed previously, virtual CNCMTs can reflect the real state of physical CNCMTs, so that virtual commissioning results can be close to the real commissioning results.

11.4.2 Step 2: Dynamic commissioning The process of dynamic commissioning for CNCMTs is shown in Fig. 11.4. In this process, with the support of DT data the performance of servo motor in the electric system of virtual CNCMTs can be updated using the workflow presented in Step 1. The control algorithm that includes proportion and integration (PI) parameters is input to the controller to drive the virtual CNCMTs for simulation. According to the simulation results (e.g., response of angular velocity, response of angular displacement, and response of displacement), PI parameters can be adjusted and optimized iteratively in order to achieve stable dynamic performance. The optimal control algorithm is finally transmitted to the physical CNCMTs and executed.

11.4.3 Step 3: Kinematic commissioning Similar to the process of dynamic commissioning for CNCMTs, the DT data server is used to maintain the consistency between the virtual CNCMTs and the physical CNCMTs as presented in Step 1. In the process of kinematic commissioning for CNCMTs the controller compiles NC programs into instructions that involve motion information

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FIGURE 11.4 Process of dynamic commissioning of CNCMTs. CNCMT, Computerized numerical control machine tool.

(e.g., feed direction and feed speed) of all the axes of CNCMTs. Then, these instructions can drive virtual CNCMTs for the simulation. This simulation process is also known as virtual machining, in which the motions of the virtual CNCMTs and the geometric shape of product model are presented visually. Collision or over-limit caused by incorrect NC program can be detected during kinematic commissioning. According to the results of simulation, the NC program can be modified and optimized iteratively until it meets the processing requirements. Finally, the optimized NC program is transmitted to the physical CNCMTs for production.

11.5 Case study 11.5.1 Construction of platform for digital twin based virtual commissioning As shown in Fig. 11.5, the platform for DT-based virtual commissioning is divided into four parts, which are listed as follows.

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FIGURE 11.5 Platform of DT-based virtual commissioning for CNCMTs: (A) control algorithm or NC program, (B) controller—(b1) real controller and (b2) virtual controller, (C) descriptive model, (D) 3D visualization. CNCMT, Computerized numerical control machine tool; DT, digital twin.

1. The to-be-checked control algorithm or NC program is prepared and is loaded into a controller for subsequent simulation. 2. The controller can be either a real hardware controller as shown in Fig. 11.5 (b1) or a virtual controller as shown in Fig. 11.5 (b2). Both types of controllers should compile the NC program and output instructions to drive the virtual CNCMTs for simulation. 3. The descriptive model is the basis of the virtual commissioning platform, which integrates several disciplines (e.g., mechanics and electrical) into a unified model in the form of mathematical equations. Parameters of this model can be updated based on DT data, which are collected from the various sensors and transferred to DT using data mapping strategy. 4. 3D visualization of CNCMTs is used to visualize the simulation process, which includes geometry changes of products and motions of axes.

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Instructions output by the controller are transmitted to DT for CNCMTs through Ethernet (real controller is adopted) or socket (virtual controller is adopted). Based on information exchange interface of socket, DT for CNCMTs can receive control instructions from the virtual controller. The descriptive model for CNCMTs communicates with the 3D visualization through shared memory.

11.5.2 Dynamic commissioning of computerized numerical control machine tools The dynamic performance of CNCMTs plays an important role in product processing because it is closely related to the precision of CNCMTs and thus has impact on product quality. As the dynamic performance of CNCMTs is highly affected by the feed system, this section takes the X-direction feed system as a case study to illustrate the DT-based virtual commissioning method for tuning the PI parameters of a servo system. The commissioning of a motor has been studied in the authors’ previous work [28]. The original parameters of the motor in the virtual X-direction feed system are listed in Table 11.1. As the temperature of the physical motor increases during the operation of CNCMTs, stator resistance, stator leakage inductance, and DQ axis inductance of the physical motor would all change. Based on the interaction between the virtual CNCMTs and the physical CNCMTs, real-time data (e.g., performance parameters of physical motor) of the physical CNCMTs are collected as part of DT data. The parameters that affect the performance of DT for CNCMTs can be updated with the support of DT data, as shown in Table 11.1. Therefore the virtual motor can keep consistency with the physical motor. TABLE 11.1 Parameters of virtual motor [28]. Parameter name

Value

Moment of inertia ðkg m Þ

0.00068

Polar logarithm

3

Back EMF frequency (Hz)

150

Back EMF amplitude (V)

83

Stator resistance (Ω)

0.36

Stator leakage inductance (H)

0.000106

DQ axis inductance (H)

0.0014

Direct-current voltage (V)

150

2

EMF, Electro motive force.

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TABLE 11.2 Origin proportion and integration (PI) parameters [28]. PI parameter name

Parameter value

Proportional coefficient of current loop

2.7356

Integral coefficient of current loop

0.004157

Proportional coefficient of velocity loop

5

Integral coefficient of velocity loop

10

w (rad/s)

System response curve

Control command curve

40 30 20 10 0 –10 –20 –30 –40 0

0.2

0.4

0.6

0.8

1

t (s)

FIGURE 11.6 Response curve of angular velocity [28].

The original PI parameters are shown in Table 11.2. When the target angular velocity of the virtual motor is set at 5 rad/s, the corresponding response curve of angular velocity, angular displacement, and linear displacement of a virtual worktable in the servo system are shown in Figs. 11.6, 11.7 and 11.8, respectively. The data in these figures are collected from the simulation results of dynamic virtual commissioning. These figures show that the X-direction feed system is not stable; thus the PI parameters would need to be further adjusted. Because the instability of the X-direction feed system is caused by the excessive proportional coefficient of the velocity loop, this value is then reduced to 0.5. The new corresponding response curves are depicted in Figs. 11.9, 11.10, and 11.11, respectively. These figures show that the system tends to be stable with high response speed, high control accuracy, and low overshoot. Therefore the new PI parameters after commissioning are more suitable for the X-direction feed system. Performance of the CNCMTs servo system is determined by the tracking error and steady-state error. Constant signal, trapezoidal signal, and sinusoidal signal are used as the input signals to drive the virtual motor, respectively, for generating angular velocity. Then, the corresponding position of X-feed axis can be calculated through the integration of velocity. The angular displacement response curves with constant signal input, trapezoidal signal input, and sinusoidal signal input are depicted in Figs. 11.12, 11.13, and 11.14, respectively. These angular displacement response curves show that the maximum tracking error happens at 0.5 s. The error of X-feed system based on different input signals are listed in Table 11.3.

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pin (rad)

System response curve

301

Control command curve

6 5 4 3 2 1 0 0

0.2

0.4

0.6

0.8

1

t (s)

FIGURE 11.7 Response curve of angular displacement [28].

d (m)

System response curve

Control command curve

–0.188 –0.19 –0.192 –0.194 –0.196 –0.198 –0.2 –0.202 0

0.2

0.4

0.6

0.8

1

t (s)

FIGURE 11.8 Response curve of worktable linear displacement [28].

System response curve

Control command curve

6

w (rad/s)

5 4 3 2 1 0 0

0.2

0.4

0.6

0.8

1

t (s)

FIGURE 11.9 Response curve of angular velocity [28].

11.5.3 Kinematic commissioning of computerized numerical control machine tools Virtual machining of a slot is used as an example to illustrate the method of kinematic commissioning for CNCMTs in this section. Besides the precise DT-based CNCMTs virtual prototype, a machining environment model is indispensable for the entire machining procedure. A workpiece model and a material removal method are designed and included in machining environment model. The material removal method is used to determine whether a part of workpiece has been cut and changed the visualization state of that part.

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pin (rad)

System response curve

Control command curve

5 4 3 2 1 0 0

0.2

0.4

0.6

0.8

1

t (s)

FIGURE 11.10 Response curve of angular displacement [28].

System response curve

Control command curve

–0.19

d (m)

–0.192 –0.192 –0.196 –0.198 –0.2 –0.202 0

0.2

0.4 t (s)

0.6

0.8

1

FIGURE 11.11 Response curve of worktable linear displacement [28].

FIGURE 11.12 Angular displacement response curve with constant signal input.

FIGURE 11.13 Angular displacement response curve with trapezoidal signal input.

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pin (rad)

System response curve 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 –0.01

303

Control command curve 0.0038

0

0.2

0.4 0.5 t (s)

0.6

0.8

1

FIGURE 11.14 Angular displacement response curve with sinusoidal signal input.

TABLE 11.3 Error of X-feed system. Input signal

Following error (rad)

Ratio to target velocity (%)

Constant signal

0.0125

2.5

Trapezoidal wave signal

0.013

4.3

Sinusoidal signal

0.0038

4.9

FIGURE 11.15 Blank model and cutting tool model.

The bodyshape component in the multibody library of Modelica can be used to identify a CAD model and transform it into the special format recognized by Modelica. The position relationship between the blank and the cutting tool is depicted in Fig. 11.15. Before conducing kinematic commissioning the blank is first divided equally into a number of small cuboids. Then the distance vector between the center of the small cuboid and center of the cutting

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tool is calculated. If the distance vector is within a threshold, it can be assumed that the small cuboid has been cut. Based on a virtual controller designed on the CoDeSyS platform, an NC program for machining a slot, as listed, can be compiled into motion instructions. N0 G0 X-88 Y-492 Z448 F50 E10 N01 G1 X-63 Y-492 Z448 F10 E10 N03 G1 X-63 Y-497 Z568 F50 E10 The path of the cutting tool described in this NC program consists of three stages: rapid moving, cutting, and lifting. Based on the updated virtual CNCMTs, the motion of each axis can be reflected accurately, and collision or over-limit caused by incorrect NC program can be detected during virtual machining. The states of the virtual CNCMTs and machining environment model during kinematic commissioning are shown in Fig. 11.16. The visualization state changes of the workpiece are shown in Fig. 11.17.

11.5.4 Discussion Parameters tuning of the servo system and virtual machining of a slot shows the two aspects of DT-based virtual commissioning method for CNCMTs. These two processes are carried out on a commissioning platform, which are constructed based on the DT-based CNCMTs virtual prototype. The biggest difference between DT-based virtual commissioning and traditional virtual

FIGURE 11.16 Model state during virtual machining.

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FIGURE 11.17 Workpiece model at different machining stages: (A) workpiece model before machining, (B) workpiece model during machining, and (C) workpiece model after machining.

commissioning is whether the model used for simulation has update ability and can maintain consistency with the physical equipment. In the case of dynamic commissioning for CNCMTs, PI parameters of control algorithm are adjusted iteratively based on the simulation results until the servo system achieves stable dynamic performance. In the case of kinematic commissioning of CNCMTs, an NC program is compiled into motion instructions by the virtual controller, and these instructions are executed by the virtual CNCMTs for simulation. Simulated motion of each axis and shape changes of workpiece are visualized during virtual machining. Based on the simulation results, the NC program is checked and optimized iteratively until it meets the processing requirements.

11.6 Summary The DT-based virtual commissioning provides a solution to address the problems of inconsistency between models and physical devices that exist in traditional virtual commissioning. It improves the consistency between virtual devices and physical devices through DT data and model updating strategy, which guarantees the authenticity of commissioning results and thus reduces the time and effort spent in real commissioning. The method of DT-based virtual commissioning for CNCMTs includes maintaining consistency between the virtual and physical CNCMTs, dynamic commissioning, and kinematic commissioning. This method can improve the accuracy of simulation results based on the DT-based CNCMT virtual prototype and thus provides more effective guidance for the real manufacturing. To verify the method, PI parameters tuning of a servo system and virtual machining of a

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slot are conducted on the developed DT-based virtual commissioning platform separately to improve the performance of the servo system and check the NC program. The results prove that DT-based virtual commissioning can address the inconsistency problem and provide effective guidance for real manufacturing based on the dynamic commissioning and kinematic commissioning.

Acknowledgment The work is supported by the National Natural Science Foundation of China (Grant No. 51875323) and Key Research and Development Program of Shandong Province, China (Grant No. 2019JZZY010123).

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4943. [3] S. Bangsow, U. Gu¨nther, Creating a model for virtual commissioning of a line head control using discrete event simulation, Use Cases of Discrete Event Simulation: Appliance and Research, Springer, Berlin Heidelberg, 2012, pp. 117
130. [4] C.G. Lee, S.C. Park, Survey on the virtual commissioning of manufacturing systems, J. Comput. Des. Eng. 1 (3) (2014) 213
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746. [9] A. Requicha, S. Chan, Representation of geometric features, tolerances, and attributes in solid modelers based on constructive geometry, IEEE J. Rob. Autom. 2 (3) (1986) 156
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2219. [11] H. Asada, A. By, Kinematic analysis of workpart fixturing for flexible assembly with automatically reconfigurable fixtures, IEEE J. Rob. Autom. 1 (2) (1985) 86
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[13] H.-T. Park, J.-G. Kwak, G.-N. Wang, S.C. Park, Plant model generation for PLC simulation, Int. J. Prod. Res. 48 (5) (2010) 1517
1529. [14] S.C. Park, M. Ko, M. Chang, A reverse engineering approach to generate a virtual plant model for PLC simulation, Int. J. Adv. Manuf. Technol. 69 (9
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2469. [15] M. Ko, S.C. Park, J.-J. Choi, M. Chang, New modelling formalism for control programs of flexible manufacturing systems, Int. J. Prod. Res. 51 (6) (2013) 1668
1679. [16] F. Auinger, M. Vorderwinkler, G. Buchtela, Interface driven domain-independent modeling architecture for “soft-commissioning” and “reality in the loop”, in: Proceedings of the 31st Conference on Winter Simulation: Simulation, 1999, pp. 798
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105, 110.

Chapter 12

Digital twin driven process design evaluation Xiaojun Liu1, Jinfeng Liu2, Honggen Zhou2 and Zhonghua Ni1 1

School of Mechanical Engineering, Southeast University, Nanjing, P.R. China, 2School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, P.R. China

12.1 Introduction In the recent years the computer-aided process planning (CAPP) technology has been challenged worldwide with the amazing advancements in the information and communication technologies (e.g., Internet of Things, artificial intelligence, big data, and cloud computing). These technologies allow the implementation of process evaluation and plan optimization by fusing the historical data and real-time perception data, which forms the basis of smart manufacturing [1]. Achieving the interoperability of the physical and cyber worlds of manufacturing is one of the challenges for promoting and applying the smart manufacturing concept. Since the digital twin (DT), as an effective means to achieve cyber-physical fusion, can break the barriers between the physical world and cyber world of manufacturing, it is becoming the focus of global manufacturing transformation and upgrading. Process planning, as a bridge between design and manufacturing, is one of the key technologies for enterprises to improve their core competitiveness. To meet the requirements of the current 3D manufacturing environment, the model-based definition (MBD) technology, which uses 3D technology to upgrade the current manufacturing capacity, has come into being. MBD is, at its core, a way of gathering and managing product/process data (e.g., geometric data, typology data, and processing parameters) inside a 3D model, in the form of annotations, parameters, and relations. It is treated as an effective strategy for 3D process design with less time and lower cost. During machining process, continuous changes in production resources (e.g., machines, cutting tools, and workpiece) pose a significant obstacle to the accurate and stable process control. For example, the changes in cutting parameters, spindle thermal deformation and vibration, as well as degradation in tool Digital Twin Driven Smart Design. DOI: https://doi.org/10.1016/B978-0-12-818918-4.00012-9 © 2020 Elsevier Inc. All rights reserved.

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performance make the control of the machining process more difficult. Therefore process plan evaluation has become the key to improving product quality and shortening the development cycle. Based on the motivations described previously, a novel DT-driven machining process evaluation method for complex parts process design is presented. First, the design method of reconfigurable process plan (RPP) is introduced, including the process models creation, process information management, and DT-driven process models establishment. Second, the DT data is generated by combining the real-time data and the process design data. The real-time data coming from the processing site includes the status information of the equipment and real-time quality information of the workpiece, which can be managed using the extensible markup language (XML). The data is perceived and monitored based on the constructed management platform. The process design data includes cutting parameters, machining methods, process equipment, and machining quality. Third, the DT-driven process design evaluation method is illustrated based on the DT data. Finally, the key parts of a marine diesel engine are used to prove the validity of the proposed method.

12.2 Related works 12.2.1 Process design With the development of digital manufacturing technology, CAPP technologies and systems are extensively applied in manufacturing enterprises. However, as the manufacturing process of the complex products becomes more complicated, it is difficult to quickly create the machining plans in the traditional way. In order to reduce the process design cost and increase the applicability of the plan, MBD-based methods have gradually attracted the researchers’ attention. According to the MBD technology, the process information (e.g., the geometric information of machining features, the precision information of surface, and the topological relations) can be well organized and managed. Machining features are assumed inherently as a main factor in such an integration effort because of the association of various designs, engineering, and manufacturing data used in CAPP [2]. According to the stored machining knowledge in their repository, MBD-based process planning can be used to construct the process flow with less time. In order to improve the efficiency in process design the strategies for rapid creation of process plan were proposed by the authors’ team, which are illustrated in Refs. [3
5]. However, the planners did not consider the actual processing conditions when designing the process flow. The complex, varied, and dynamic manufacturing conditions could lead to low level of efficiency, sustainability, intelligence in product process execution phases. Therefore automatic

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evaluation and optimization of the process plan becomes an urgent problem to be solved.

12.2.2 Process design evaluation With the promotion and application of the smart manufacturing technology, some evaluation and optimization methods are researched in machining process planning. For process routing evaluation, genetic algorithms [6,7], ant colony algorithms [8,9], simulated annealing algorithms [10], and hybrid algorithms [11
13] are increasingly being used to improve the manufacturing efficiency and manufacturing quality. For process parameter evaluation, virtual simulation methods [14,15] are used to obtain the optimal process parameters. Nevertheless, the applicability of these evaluation methods is greatly reduced due to the continuously changing conditions of production resources. The unexpected events in machining, such as material shortage, tool failures, and emergent orders, require the CAPP system to enhance the modification function of process plan. The process modification is an inevitable process activity for a new machined part. Automatic updating of the associated process models will be quite useful for more efficient process designs when the processes are modified. To respond quickly to the process changing activities the ability of rapidly and efficiently updating the process plan becomes crucial. Thus to improve process responsiveness, Liu et al. [16] demonstrated an automatic updating and propagation method when the process plan needs to be modified. In order to maintain the completeness of process information during process changing, a method that maintains the relationship between geometric construction and associated annotation was proposed in Ref. [17]. However, monitoring the equipment and workpieces and evaluating the process plan based on real-time machining status have not attracted enough attention.

12.2.3 Digital twin driven process design evaluation In order to respond quickly to the unexpected events without replanning, some methods are researched to handle the changing manufacturing environments. Increasing efforts have been devoted to the application of DT-driven process design evaluation, focusing on the real-time data acquisition, data fusion, and DT data
driven process evaluation to estimate the process routes and process parameters [18].

12.2.3.1 Real-time data acquisition Data collection and analysis during the manufacturing operations are the foundation for factory automation and decision-making [19]. Modern manufacturing shop floors are hindered by the bottleneck of capturing and

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collecting real-time field information. Without timely updated information, it is impossible to make accurate shop-floor decisions. Therefore real-time data acquisition has caught much attention and several methods are provided, such as bar coding
based technology [20] and radio frequency identification (RFID) technology [21
23]. Nowadays, the use of RFID technology is widespread, particularly in manufacturing/assembly shop floors [24]. Monitoring the machining processes is important for maintaining quality consistency of the machined parts [25]. To acquire the actual status of the manufacturing resources and the workpiece, the sensor-based monitoring system has attracted much attention. A variety of sensors have been employed to monitor the machine tools [26], for instance, acoustic, vibration, force, etc. The monitoring system which applies the sensory devices to track the state of the machine tools is helpful in adaptive holistic scheduling [27].

12.2.3.2 Data fusion It is well known that not only is the proper monitoring of the shop floor significant and practical, but the fusion of heterogeneous data is also essential for reducing the frequency of incidents and improving the manufacturing efficiency. The principal objective of multisensor data fusion is to improve the quality of information through data fusion and conflict resolution. Different types of equipment have different data capturing standards, which directly results in heterogeneous and uncertain data. Therefore the Internet of Things middleware, feature extraction, and information integration methods have been researched to fuse the heterogeneous data. In different computer aided design (CAD), computer aided engineering (CAE), and computer aided manufacturing (CAM) systems, Yi et al. [28] proposed a heterogeneous model integration method to fuse the features into one model based on the semantic feature. In order to overcome the ineffectiveness of the machining progress information extraction and its application restriction in the workshop monitoring, a fusion method of multisource heterogeneous information that includes machining path, real-time spindle power information, manual input data, and tool position was presented in Ref. [29]; Zhang and Ge [30] designed a fusion system by combining six conventional methods and developed a new diversity measurement index for fault detection to improve the computational efficiency and reliability of the fusion system. 12.2.3.3 Digital twin data
driven process evaluation Generally, process plan evaluation methods can be divided into three categories: model-based methods, knowledge-based methods, and data-based methods. Due to the advantages of having few requirements for the process model and the associated expert knowledge, the data-based methods have recently become the most popular one for process evaluation. To reduce

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subjectivity and improve efficiency of the process design, Zhang et al. [31] proposed a quantitative evaluation method via data-driven performance predictions. On the basis of the extension theory and entropy weight, Yan et al. [32] proposed a sustainability assessment method of machining processes with respect to a consistent set of environmental, economic, and social criteria. Currently, DT-related technologies have been explored and put into practice in the manufacturing field and shown great potential. The combination of smart manufacturing services and DT would radically change product design, manufacturing, usage, and other processes [33]. For design and multiobjective optimization of a hollow glass production line, Zhang et al. [34] presented a DT-based analytical framework that covers both static configuration design and execution system design to support the decision-making. To guarantee good geometrical quality of the final products the DT that contains geometry representation of the assembly, kinematic relations, material properties, and linkage to database was studied in Ref. [35]. A comprehensive reference model, which serves as a DT of the physical product in design and production engineering, was designed for the product life cycle in Ref. [36]. To illustrate the application of DT in the product life cycle, Tao et al. [37,38] proposed a new method for product design, manufacturing, and service driven by DT. The authors’ team proposed a method for reusing and evaluating the multidimensional process knowledge based on big data and DT [39]. Based on the previous description and application, DT technology is considered as a feasible and effective approach to realize the interaction and convergence between the information and physical worlds. (1) According to the real-time status data of the process equipment, the applicability of the reconfigurable process can be greatly improved; (2) according to the realtime data such as vibration, clamping force, spindle thermal deformation and vibration, the processing efficiency and processing quality can be greatly improved; and (3) according to the accumulated process data, process plan can be better evaluated and optimized. Therefore in order to improve the practicability of the process plan, the DT-driven process design evaluation method is proposed.

12.3 Framework for digital twin driven process design evaluation The goal of the proposed framework is to help designers to evaluate the generated process plan based on the DT so as to improve its usefulness. Using the feature recognition technology, MBD technology, object-oriented technology, DT technology, and the cyber-physical system, the dynamic evaluation framework is proposed. In order to evaluate the process plan efficiently,

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FIGURE 12.1 The flowchart of the proposed framework.

the RPP, the DT data and the DT-driven process design evaluation method are established. Fig. 12.1 shows the principle of the DT-driven process design evaluation framework. The framework is divided into three tiers: process design layer, data fusion layer, and process evaluation layer. The implementation process of each layer is shown in the following sections.

12.3.1 Process design layer This layer aims at creating the DT-based process model (DT-PM). It includes three parts: the RPP, the real-time data, and the physical model. First, to automatically update and propagate the process plan, the RPP which includes the 3D process models and the process information is established based on the MBD technology. The 3D process models are created via the feature recognition technology. The process information, seen as the foundation of the process plan, is managed using the hierarchical structure. Second, the process equipment (e.g., tool, fixture, and measuring tool) status data and workpiece quality information (WQI) are acquired based on the acquisition method of real-time data as described in Section 12.5.1. Finally, the DT-PM that can

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truly reflect the real-time status of the processing is created based on the physical model, and serves as the basis for process evaluation.

12.3.2 Data fusion layer This layer aims at generating the DT data. It includes three parts: the perception framework, the real-time data management, and the mapping mechanism. The mapping mechanism that is to fuse the real-time data (such as vibration, clamping force, spindle thermal deformation, and vibration) and the process design data (such as cutting parameters, machining methods, process equipment, and machining quality) is the key of this layer, as illustrated in Section 12.5.2. The real-time data includes the equipment information and workpiece information and is divided into two types: dynamic data and static data.

12.3.3 Process evaluation layer The establishment of the DT-driven process design evaluation method is the main assignment of this layer, while the DT data is the core of the framework. The process design evaluation mainly includes the process route evaluation and the process parameter evaluation. First, the process route is evaluated based on the static data. Then the process parameters are evaluated based on the dynamic data. Finally, the optimization results are generated based on process knowledge, and the RPP is fed back to the designers. According to the elaboration given previously, the framework for DT-driven process design evaluation includes three methods: the creation method of RPP, the generation method of the DT data, and the evaluation method of the process plan. These methods are further explained in Sections 12.3
12.6.

12.4 Reconfigurable process plan creation Continuously changing conditions of production resources (e.g., machines, cutting tools, and workpiece) require the CAPP system to enhance the modification function. For process design, process modification is an inevitable activity for a new machined part. For the accurate and timely updating of the changed process models and their attribute information, the RPP needs to be established. The 3D process models are made up of the removed material volume and the finished parts. The attribute information is inherited from the geometric information and the process constraints. In consideration of the application environment of CAPP in digital manufacturing, a new generation strategy of the RPP is proposed by combining the geometric reasoning knowledge. Partial Content about the RPP has been studied in the authors’ work [2], and the details are introduced as follows.

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Create the 3D process models 3D part model

Combining

Process model-5

Combining

Process model-4

Combining

Process model-3

Process model-2

Combining

Combining

Process model-1

3D rough model

Combining

All the removed material volume

FIGURE 12.2 The creation process of the process models.

12.4.1 3D process models creation Process models serve as the carriers of the process attribute information to guide the downstream manufacturing activities. They are generated by combining the removed material volume with the finished parts. The design process starts from the design model, while the machining starts from the blank. Therefore the creation sequence of the process models is inverse to the machining sequence. Fig. 12.2 shows the creation process of the process models. Nowadays, growing quantities of MBD process models are generated in enterprises based on the 3D process design system. Therefore it is increasingly important for the enterprises to reuse these process models in the competitive global market, as a great amount of time and cost can be saved by doing so. A new method is presented to create the 3D process models rapidly based on the matched design model. The process of the proposed method is shown in Fig. 12.3. First, the process plan of the design model is designed based on the MBD technology. Then, a multilevel machining feature descriptor that captures different levels of information for each process is proposed to search for the matched machining features. Finally, the process plan of the slightly changed design model can be automatically created based on the reusing method, and the corresponding process models are conveniently reconstructed based on the matched similar machining features.

12.4.2 Process information management Better organization and management of the process information are the basis of RPP and the key to improving its efficiency and can help accumulate more process knowledge. The hierarchical structure of the process knowledge can be constructed according to the object-oriented technology as shown in Fig. 12.4.

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FIGURE 12.3 The rapidly creating method of the process plan.

The hierarchical organization model of process knowledge The first layermachining features

Planar

Depression feature

Protrusion feature

Machining type

Inspection tool database

etc.

Parameters database

Other clamping Scribing fixture Alignment fixture Clamping fixture

Clamping tool database

etc.

Grinding

Planer

Tool database

Milling

Equipments database

Other feature types Clamping method

Machining method Turning

Super finishing

Feature information database

Finishing

Semi finishing

The third layermachining elements

Roughing

The second layerprocessing contents

Transition feature

Model database

FIGURE 12.4 The hierarchical organization model of the process knowledge [3].

The hierarchical organization model of the process knowledge is divided into three layers: machining features, processing contents, and machining elements layers. The machining features layer refers to the specific instance features (e.g., planar feature, depression feature, protrusion feature, and transition feature) which are formed in processing. It serves as the carrier and is associated with the other two layers. The processing contents layer refers to the machining requirements (e.g., machining types, machining methods, and clamping methods) which guide the acquired information of each process. The machining method refers to the adopted machining mode such as turning or milling; the machining type refers to the current state of machining such as roughing or semifinishing;

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and the clamping method refers to the required fixture type in current machining such as jaw chuck or special clamping. The machining elements layer, as the basic unit in the hierarchical organization model, contains the prerequisite information of a machining process (e.g., the equipment data, and feature information data and parameter data). The process knowledge organization model is expressed as follows: PKM 5 MFLi , PCLj , MELk

ði; j; k 5 1; 2. . .Þ

ð12:1Þ

where PKM represents the process knowledge model; MFL represents the machining features layer; PCL represents the processing contents layer; MEL represents the machining elements layer; and i, j, and k represent the specific ordinal number in each layer. The type of each machining feature in the machining features layer is expressed as follows: MFLi 5 GPi , TRi

ði 5 1; 2; 3; 4; 5Þ

ð12:2Þ

where GP represents the geometric properties of the machining features and TR represents the topological relations of the feature faces. The processing contents layer is expressed as follows: PCLj 5 PT , PM , CM

ð12:3Þ

where PT represents the processing type; PM represents the processing method; and CM represents the clamping mode. The machining elements layer is expressed as follows: MELk 5 PE , PR

ð12:4Þ

where PE represents the process equipment, and PR represents the process rules. The required machining information of each process is not independent but interconnected with and constrained by others. For example, the feature type determines the geometry parameters and the applicable processing methods; the specific machining method and the geometry parameters determine the equipment to be applied; and the machining tools are selected dependent on the chosen machine. Therefore the link that associates all the necessary machining information of the machining feature is formed based on the machining features layer. Each level of the organization model is not isolated. The relationship among the three layers is established in line with their connection relation and constraint relation as shown in Fig. 12.5. Machining features layer is the core of the process knowledge database, and all the process information is organized and inquired based on this layer. On the one hand, the relationship between the machining feature types and the corresponding geometric information is established. On the other hand, the one-to-many correspondence

Digital twin driven process design evaluation Chapter | 12 Geometric attributes

Topology information

FeatureGeoInf

FeatureTopInf

Size

etc.

Parallelism

etc.

Convexity

Perpendicularity

Feature type

Type

Coplanar

FeatureTypeID

etc.

319

etc.

etc. Machining feature layer

Planer

The machining elements layer MachEquInf

ProEquInf ProKnowInf etc.

Depression feature

Protrusion feature

Geometry and topology information Constraint type of processing layer Constraints content Of processing layer Constraint type of element layer Constraints content Of elements layer Belong

etc.

Processing content layer MachMethodInf ClamMethodInf MachTypeInf etc.

Associate

FIGURE 12.5 Relationship among three layers [3].

between the machining features layer and the processing contents layer is built. In addition, the constraint between the machining features layer and the machining elements layer is established based on the processing contents layer.

12.4.3 Digital twin based process models construction For real-time evaluation of the RPP the DT-PM is created. The DT-PM includes three parts: the MBD-based process models (MBD-PM), process equipment status data (PESD), and WQI. It is expressed as follows: DT-PMi 5

p X l51

MBD-PMil ,

q X m51

PESDim ,

r X

WQIin

ð12:5Þ

n51

where DT-PMi represents the ith DT-PM, MBD-PMil represents the lth process model, PESDim represents the mth process equipment (e.g., tool, machine, and fixture), and WQIin represents the nth quality parameter (e.g., roughness, geometric tolerance, and size). To achieve dynamic reconstruction of the RPP the process design data and real-time data must be

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interrelated. For example, the WQI is associated with the MBD-PM. The PESD and WQI are obtained from the machining site.

12.5 Digital twin data generation The converged data of the real-time data (such as vibration and clamping force) of the physical manufacturing status and the embedded data of the process models is called DT data. The data collected in the physical manufacturing status includes the real-time status of the machine and fixture, the wear state of the tool, and the real-time quality information of the workpiece. The association information of the process models includes the attribute information of the process, the machining methods, and the geometric information of the machining features. Better acquisition, organization, and management of the multisource data of physical machining status are the key to DT data generation. The generation flow of the DT data is shown in Fig. 12.6. It includes three parts: multiagent layout for process equipment, object-oriented data analysis and organization, and mapping mechanism between physical information and virtual information. The real-time data is the foundation of the DT data; the management method of the real-time data is the indemnification of the DT data; the mapping mechanism is the way to generate the DT data. The DT data generation and management methods introduced in this section have been studied in the authors’ work [1].

12.5.1 Real-time data acquisition The data acquisition method can efficiently collect and obtain the multisource data from the processing site. The framework of the acquisition Management of the real-time data

Multiagent layout for process equipment

Dynamic data

Static data

Equipment information

Workpiece information

Objectoriented data analysis and organization

Mapping mechanism of physical information to virtual information Standardized processing technology for acquisition data

Multisource data conversion technology

Multisource data interface technology

Digital twin data for physical and virtual space integration

FIGURE 12.6 Flowchart of physical data mapping virtual data [1].

Digital twin driven process design evaluation Chapter | 12

Data management layer

Size

Precision

length width heigth etc.

roughness

Process step content name order etc.

tolerance shape etc.

Workpiece information

Equipment turning milling drilling etc.

Cutting variable

feed thickness force etc.

321

Tool turning milling drilling etc.

Equipment information

Communications network Wired network, wireless network, sensor network, RFIDs, Bluetooth Data transfer layer

Barcode

ToolScope Size measuring instrument

Inspection Data acquisition layer

Measure Personal

Probe

Scanning machine

FIGURE 12.7 The framework of acquisition method for real-time data [1].

method is shown in Fig. 12.7. It is one of the key technologies to promote the development of intelligent manufacturing shop floor. In order to collect real-time data, an adaptive sensing framework of multiple agents for collecting machining status is designed. With the advancements in sensor network, wireless network, automation technology, and analysis technology, a wide range of applications have been achieved in manufacturing shop floors with RFID and multiagents tool. This meets the requirement of manufacturing shop floors for real-time data acquisition. The adaptive sensing framework is divided into three layers: data-acquisition, data-transfer, and data-management layers. In the data acquisition layer the main objects are the information of workpiece and process equipment, including attribute information, acquired based on the RFID technology, and dynamic information, evolving dynamically with the processing and acquired based on the multiagent tools such as inspection probe, size measuring instrument, tool-scope system, and MDC system. The data transfer layer serves as a bridge between the data acquisition layer and the data management layer. It is seen as the backbone of the entire framework. The data transfer layer consists of different data transmission networks that include the wired network, wireless network, sensor network, RFIDs, Bluetooth and cloud computing platform. In the data-management layer the acquired data is analyzed and managed, and is then classified and associated with the workpiece or process equipment.

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12.5.2 Digital twin data management The DT data includes two parts: the process design data (as shown in Fig. 12.4) and the real-time data. The process design data can monitor and control the physical manufacturing status. Meanwhile, the real-time data of machining can be used to update the process plan. Therefore the mapping mechanism between the process design data and the real-time data should be established. The XML is used to exchange these data. XML is able to describe different kinds of data, and it has been introduced and used in a variety of tools and data models. In order to map the real-time data rapidly to the process design data, the related technologies such as data normalization technology, data transformation and organization technology, data fusion, and decision making technology require some breakthroughs. First, the real-time data of the physical manufacturing status is analyzed and classified into two types: static and dynamic data (as shown in Fig. 12.6); second, the association and matching mechanism of multisource data is studied; and finally, the object-oriented data organization method for the real-time data is established. The static data in machining is mainly composed of attribute information of workpiece and equipment. The dynamic data includes real-time status of the workpiece and equipment. The data possesses complex characteristics such as multisource heterogeneity, distributed storage, real-time changes, and correlations. They are organized and managed in the form of a multilevel structure tree. The DT data allows shop-floor managers to dynamically evaluate the machining activity and to feed back the newly developed machining plan to the physical machining shop floor to guide the second round of production.

12.6 Process plan evaluation based on digital twin data The authors have explored the framework and method for the process plan evaluation in Ref. [1]. The detail are described as follows.

12.6.1 Process design evaluation framework Based on the generated DT data, the framework of DT-driven process design evaluation method is shown in Fig. 12.8. The DT data is the core of the proposed framework and also the basis for process design evaluation. The framework comprises three parts: the real-time data, the process design data, and the DT-driven process design evaluation system. The real-time data coming from the processing site includes the status information of the equipment and real-time quality information of the workpiece; the process design data includes the process execution information; and the system can realize dynamic, real-time evaluation of the process route and optimization of the process parameters in processing.

Digital twin driven process design evaluation Chapter | 12

323

The digital twin driven process evaluation system Machining process evaluation module

Process adjust module

Process route evaluation

Adding process

Deletion process

Process parameters evaluation

Interchange process

Feedback Driven The digital twin data

Dynamic, real-time, and visual monitoring the physical entity

n

ctio

Exe

n ctio

lle

Co

(a) The real-time data (b) The process design data

The management platform of real-time data Method

Milling

Tool name

Cutter

Tool type

12*14*60R1

Size data

10.25/26.36

Rotating speed

10.25

Fixture type

GZ3A-321 etc.

Manufacturing resource evaluation

Dynamic exchange

Process information modification

Dynamic, real-time, and visual mapping the virtual model Sto rag e Ma ppi ng The process design data

< “process 090 - Milling” < “milling the slot”; “no glitches, no sharp corners”; “process planner”/> < “milling” ;“milling cutter”

“cutting parameters” (a) Create process model (b) Associate process information

FIGURE 12.8 The framework of DT-driven process design evaluation method [1]. DT, Digital twin.

12.6.2 Process plan evaluation method At present, machining evaluation mainly concentrates on processing quality and manufacturing cost. Other factors for evaluation include the equipment processing capacity, processing stability, green evaluation, and work-hour. However, these evaluation methods rarely involve the machining planning design stage and are executed under ideal processing conditions. Compared with these methods, the DT-driven dynamic evaluation method has the following characteristics: (1) from single data evaluation to multisource data evaluation. Due to the multisource data in machining, a multistep, multiresource, and multidimension approach is proposed for evaluation and decision-making processes. (2) From passive design to active evaluation. With the real-time data, it is possible and feasible to evaluate and optimize incoming disturbances (e.g., workpiece’s quality problems, equipment failure, and unexpected additional machining tasks) at the beginning of process execution. The operators can take proactive measures to avoid or resolve the possible disturbances in advance. Moreover, the DT-driven evaluation method not only takes advantage of existing information to establish a process model but also utilizes virtual simulation technology to explore and predict the unknown world. This method provides new concepts and tools for machining process design innovation. According to the process information reused method and process model reconstruction method [4,5], the process plan can be rapidly generated. However, the current processing site may not meet the process design requirements. Therefore it is necessary to evaluate and optimize the process

Process route evaluation

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PART | 2 Application and case study

The attributes of workpiece • Size information • Shape information • Process basic information

Process parameter evaluation

The dynamic data in machining • Cutting parameter • Operating parameters • Quality information

The basic structure of the process route

Analyze the process equipment data • Analyze the process design information • Analyzethe processing capabilities • Analyze the manufacturability of parts Meet the design requirements ?

No

Yes

• Adjustment process

• Machining efficiency evaluation • Machining cost evaluation

The history data • Machining data • Op erating data • Quality data

Process path optimization • Adding process • Deletion process

Machining quality evaluation • Machining accuracy evaluation

Mapping

The status of the equipment • Equipment status • Logistics status • Temporary tasks

Meet the machining quality ? Yes Execution

No

Machining parameters optimization

• Cutting parameter • Operating parameters • Replacement equipment

FIGURE 12.9 DT-driven process evaluation method procedure [1]. DT, Digital twin.

plan in combination with the real-time data of process equipment. Fig. 12.9 shows the procedure of the DT-driven evaluation method. This method is divided into two parts. On the one hand, according to the real-time data (e.g., the real-time status of process equipment and workpiece), the machining process route is evaluated by comparing with the process design data. If the current manufacturing status does not meet the process requirements, the processing order needs to be evaluated and optimized by adding processes, deleting processes, etc. If it can meet the process requirements, the machining parameters are analyzed. On the other hand, the machining parameters (e.g., rotating speed and cutting parameters) are evaluated. By integrating and applying these data the DT-driven process design evaluation system is developed for verifying the effectiveness of the proposed method.

12.7 Case study Based on the software modules of ACIS and Hoops, a prototype CAPP system named MPD Processer has been developed by the authors’ team. The system has three modules: process design, process modification, and process evaluation. To demonstrate the effectiveness of the proposed method, some machined parts are chosen to be tested based on this system in the case study.

Digital twin driven process design evaluation Chapter | 12

Num

Process name

05

Milling section

25

Rough boring holes

45

Cut the connecting rod

75

Finishingmilling both ends faces

95

Semifinishing boring holes

115

Finishing the end faces of the connecting rod cover

Process model

Machine name

325

Machine Type

Machining faces

Vertical milling No.7XX/MVXX

Horizontal machining center

Sawing machine

TR65XX

LG26XX

Vertical VMC16XX/ Machining Center VMC21XX

Vertical VMC16XX/ Machining Center VMC21XX

CNC vertical milling

MV6XX

FIGURE 12.10 The key machining processes of the diesel engine connecting rod.

12.7.1 Diesel engine connecting rod model description In order to demonstrate the effectiveness of the proposed method, the diesel engine connecting rod is taken as an instance. It is a small batches complex part. It involves 56 processes, 25 of which are machining processes. For the machining processes, three special machines and five general machines are selected. The corresponding relationship between the key processes and machines is shown in Fig. 12.10. The management of process information is the key to the process plan. Fig. 12.11 shows the creation process of the process plan with (A) on the left showing the process route and the process models. The process route is represented by the machining process hierarchical tree, wherein the tree nodes indicate the machining process. For rapidly creating the process plan the machining information reuse method based on the similar machining features is proposed and applied, as shown in Fig. 12.11B.

12.7.2 Real-time data collection and management The DT data includes the real-time data and the process design data. The former is acquired from the intelligent inspection equipment while the latter is obtained based on the process knowledge database. For dynamically

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PART | 2 Application and case study

FIGURE 12.11 Rapid creation of the process plan based on reuse method: (A) the created reconfigurable process plan and (B) obtained the machining information [5].

The representation mode of the real-time data based on XML < “process 0 5 0 - Milling” /* the process number ID */ < “milling the slot” /*process name*/; “no glitches, no sharp corners”/ * process requirements */; “process planner”/>

< “milling” /*machining methods; “milling cutter” /*machining tools */

“cutting parameters” /* cutting parameters */ />

The dynamic information

< Static_information> < Static_information>