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Industrializing Additive Manufacturing: Proceedings of AMPA2020 [1st ed.]
 9783030543334, 9783030543341

Table of contents :
Front Matter ....Pages i-xiv
Front Matter ....Pages 1-1
Generative Design Optimization and Characterization of Triple Periodic Lattice Structures in AlSi10Mg (Patrik Karlsson, Lars Pejryd, Niclas Strömberg)....Pages 3-16
Structured Approach for Changing Designer’s Mindset Towards Additive Manufacturing: From Theory to Practice (Gustavo Menezes de Souza Melo, Gerret Lukas, Johannes Willkomm, Stephan Ziegler, Günther Schuh, Johannes Henrich Schleifenbaum)....Pages 17-25
An Interactive, Fully Digital Design Workflow for a Custom 3D Printed Facial Protection Orthosis (Face Mask) (Neha Sharma, Dennis Welker, Shuaishuai Cao, Barbara von Netzer, Philipp Honigmann, Florian Thieringer)....Pages 26-36
Opportunities of 3D Machine Learning for Manufacturability Analysis and Component Recognition in the Additive Manufacturing Process Chain (Tobias Nickchen, Gregor Engels, Johannes Lohn)....Pages 37-51
Review on the Design Approaches of Cellular Architectures Produced by Additive Manufacturing (Marco Pelanconi, Alberto Ortona)....Pages 52-64
Front Matter ....Pages 65-65
Multi-material 3D Printing of Thermoplastic Elastomers for Development of Soft Robotic Structures with Integrated Sensor Elements (Antonia Georgopoulou, Bram Vanderborght, Frank Clemens)....Pages 67-81
Solution Approaches and Process Concepts for Powder Bed-Based Melting of Glass (Susanne Kasch, Thomas Schmidt, Fabian Eichler, Laura Katharina Thurn, Simon Jahn, Sebastian Bremen)....Pages 82-95
Additive Manufacturing of Ti-Nb Dissimilar Metals by Laser Metal Deposition (Di Cui, Briac Lanfant, Marc Leparoux, Sébastian Favre)....Pages 96-111
Investigation of Plastic Freeformed, Open-Pored Structures with Regard to Producibility, Reproducibility and Liquid Permeability (Andre Hirsch, Christian Dalmer, Elmar Moritzer)....Pages 112-129
Novel 4-Axis 3D Printing Process to Print Overhangs Without Support Material (Michael Wüthrich, Wilfried J. Elspass, Philip Bos, Simon Holdener)....Pages 130-145
Hybrid Manufacturing: A New Additive Manufacturing Approach for Closed Pump Impellers (Robin Rettberg, Thomas Kraenzler)....Pages 146-159
Adaptive Slicing and Process Optimization for Direct Metal Deposition to Fabricate Exhaust Manifolds (Daniel Eisenbarth, Alessandro Menichelli, Fabian Soffel, Konrad Wegener)....Pages 160-173
Front Matter ....Pages 175-175
Drift Detection in Selective Laser Melting (SLM) Using a Machine Learning Approach (Pinku Yadav, Olivier Rigo, Corinne Arvieu, Emilie Le Guen, Eric Lacoste)....Pages 177-191
Influence of the Inert Gas Flow on the Laser Powder Bed Fusion (LPBF) Process (Florian Wirth, Alex Frauchiger, Kai Gutknecht, Michael Cloots)....Pages 192-204
Artificial Intelligence for Monitoring and Control of Metal Additive Manufacturing (Giulio Masinelli, Sergey A. Shevchik, Vigneashwara Pandiyan, Tri Quang-Le, Kilian Wasmer)....Pages 205-220
Front Matter ....Pages 221-221
Development of a Process Model for Bead Deposition Rates and Cooling Behavior of Large Scale Additive Manufacturing Parts (Michel Layher, Lukas Eckhardt, Andreas Hopf, Jens Bliedtner)....Pages 223-240
Estimations of Interlayer Contacts in Extrusion Additive Manufacturing Using a CFD Model (Raphaël Comminal, Sina Jafarzadeh, Marcin Serdeczny, Jon Spangenberg)....Pages 241-250
Influence of Fibers on the Flow Through the Hot-End in Material Extrusion Additive Manufacturing (Marcin Serdeczny, Raphaël Comminal, David Bue Pedersen, Jon Spangenberg)....Pages 251-267
Deploying Artificial Intelligence for Component-Scale Multi-physical Field Simulation of Metal Additive Manufacturing (Ehsan Hosseini, P. Gh. Ghanbari, F. Keller, S. Marelli, Edoardo Mazza)....Pages 268-276
Front Matter ....Pages 277-277
Experimental Investigation of Filament Behaviour in Material Extrusion Additive Manufacturing (Mark Golab, Sam Massey, James Moultrie)....Pages 279-292
Debinding and Sintering of Dense Ceramic Structures Made with Fused Deposition Modeling (Frank Clemens, Josef Schulz, Lovro Gorjan, Antje Liersch, Tutu Sebastian, Fateme Sarraf)....Pages 293-303
Feasibility Investigation of Gears Manufactured by Fused Filament Fabrication (Hans-Jörg Dennig, Livia Zumofen, Andreas Kirchheim)....Pages 304-320
Qualification of Additively Manufactured Blood Vessel Models for the Evaluation of Braided Stent Designs (Juliane Kuhl, Ngoc Tuan Ngo, Jan-Hendrik Buhk, Andreas Ding, Andrés Braschkat, Jens Fiehler et al.)....Pages 321-333
Front Matter ....Pages 335-335
Additive Manufactured and Topology Optimized Flexpin for Planetary Gears (Anton Höller, Frank Huber, Livia Zumofen, Andreas Kirchheim, Hanspeter Dinner, Hans-Jörg Dennig)....Pages 337-356
A Review of Optimised Additively Manufactured Steel Connections for Modular Building Systems (Zhengyao Li, Konstantinos Daniel Tsavdaridis, Leroy Gardner)....Pages 357-373
Application of Topology Optimisation to Steel Node-Connections and Additive Manufacturing (Moustafa Mahmoud Abdelwahab, Konstantinos Daniel Tsavdaridis)....Pages 374-390
The AM Dowel – A Novel Insert for the Integration of Threads into Additive Manufactured Polymer Components (Daniel Omidvarkarjan, Peter Balicki, Harry Baumgartner, Ralph Rosenbauer, Filippo Fontana, Mirko Meboldt)....Pages 391-398
Novel Pressure Swirl Nozzle Design Enabled by Additive Manufacturing (Michael Umbricht, Kaspar Löffel, Marc Huber, Patrick Lüscher, Janine Bochsler, Daniel Weiss et al.)....Pages 399-414
Design of an Additively Manufactured Customized Gripper System for Human Robot Collaboration (Nikolai Hangst, Stefan Junk, Thomas Wendt)....Pages 415-425
Enhanced Cooling Design in Wire Drawing Tooling Using Additive Manufacturing (Joakim Larsson, Patrik Karlsson, Jens Ekengren, Lars Pejryd)....Pages 426-436
Aortic Model in a Neurointerventional Training Model – Modular Design and Additive Manufacturing (Nadine Wortmann, Andreas M. Frölich, Anna A. Kyselyova, Helena I. De Sousa Guerreiro, Jens Fiehler, Dieter Krause)....Pages 437-454
Integration of Additive Manufacturing into Process Chain of Porcelain Preservation (Bingjian Liu, Fangjin Zhang, Xu Sun, Adam Rushworth)....Pages 455-466
Front Matter ....Pages 467-467
Decision Support System for a Metal Additive Manufacturing Process Chain Design for the Automotive Industry (Markus Johannes Kratzer, Julian Mayer, Florian Höfler, Nikolaus Urban)....Pages 469-482
Business Models for Additive Manufacturing: A Strategic View from a Procurement Perspective (Andreas H. Glas, Matthias M. Meyer, Michael Eßig)....Pages 483-499
A Performance Upgrade of an Industrial Gas Turbine Based on Additive Manufactured Components (Pankaj Bajaj, Fulvio Magni, Peter Flohr)....Pages 500-509
Back Matter ....Pages 511-513

Citation preview

Mirko Meboldt Christoph Klahn   Editors

Industrializing Additive Manufacturing Proceedings of AMPA2020

Industrializing Additive Manufacturing

Mirko Meboldt Christoph Klahn •

Editors

Industrializing Additive Manufacturing Proceedings of AMPA2020

123

Editors Mirko Meboldt Product Development Group Zurich pd|z ETH Zürich Zürich, Switzerland

Christoph Klahn Design for New Technologies ipdz Inspire AG Zürich, Switzerland

ISBN 978-3-030-54333-4 ISBN 978-3-030-54334-1 https://doi.org/10.1007/978-3-030-54334-1

(eBook)

© Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The 2nd AMPA Conference (Additive Manufacturing in Products and Applications) held at ETH Zurich from September 1 to 3, 2020, is again focusing on industrial series product applications and value chains enabled by additive manufacturing. Three years after the first AMPA Conference, additive manufacturing is leaving the hype behind and is growing into established industrial segments. Additive manufacturing is gaining ground and problems relating to value chains, design and production, quality assurance and cost models are becoming increasingly important. In recent months of the COVID-19 pandemic, additive manufacturing impressively demonstrated what the technology can do when supply chains break down. Worldwide, spare parts, consumables and new products were manufactured locally by additive manufacturing. Additive manufacturing is taking the next step on its journey toward a broad range of series production, and we are facing many new scientific challenges. Transforming the potential benefits of additive manufacturing into a successful industrial or end user product is a challenge to all disciplines along the product development process. The topics of Additive Manufacturing in Products and Applications cover all fields necessary to develop and produce successful products. Design Tools & Methods: Identifying and designing AM parts, Process Chain Integration: Setting up a safe and efficient production infrastructure, Business Cases of AM Applications: Quantifying the benefits of AM, Unique Customer Benefits: Learning from good and unusual examples and Teaching and Training: Bringing knowledge and experience to new users.

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Preface

All scientific contributions are double-blind peer-reviewed by industrial committee members for industrial relevance and by members of the scientific committee for scientific quality. In a two-staged process, 35 contributions were selected out of 55 submitted abstracts. We thank everyone who contributed to the success of the Additive Manufacturing in Products and Applications conference: Thanks to the authors for their valuable papers and talks, to the members of the industrial and scientific committees for their hard but fair reviews and for chairing sessions, to the participants of the sessions for the fruitful discussions and last but not least to all those who supported the conference in the background. Christoph Klahn Mirko Meboldt

Organization

Organizing Committee Christoph Klahn Mirko Meboldt Martin Stöckli Petra Kahl Daniel Omidvarkarjan Urs Hofmann

inspire AG, ipdz, Switzerland ETH Zürich, pd|z, Switzerland inspire AG, Switzerland inspire AG, Switzerland inspire AG, ipdz, Switzerland inspire AG, ipdz, Switzerland

Industrial Committee Dominique Beuchat Jeannette Clifford Michael Cloots Hans Gut Arno Held Lorenz Herrmann Steffen Jung Christoph Kiener Harald Kissel Martin May Klaus Müller-Lohmeier Maximilian Munsch Patrizia Richner Ralph Rosenbauer Marco Salvisberg Thomas Scheiwiller Oliver Schlatter Martin Schöpf Ralf Schumacher

3D Precision SA, Switzerland Sika Automotive AG, Switzerland IRPD AG, Switzerland Güdel Group AG, Switzerland AM Ventures Holding GmbH, Germany ABB Schweiz AG, Switzerland Nova Werke AG, Switzerland Siemens AG, Germany Sandvik Machining Solutions AB, Sweden Schunk GmbH & Co. KG, Germany Festo SE & Co. KG, Germany AMPOWER GmbH & Co. KG, Germany Sonova AG, Switzerland ALPA Capaul & Weber, Switzerland GF Precicast SA, Switzerland Bühler AG, Switzerland Injex AG, Switzerland Robert Bosch GmbH, Germany Medartis AG, Switzerland

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Tobias Weber Eric Wycisk

Organization

TRUMPF Laser- und Systemtechnik GmbH, Germany AMPOWER GmbH & Co. KG, Germany

Scientific Committee Rosa Ballardini Klas Boivie David Butler Olaf Diegel Jens Ekengren Richard Hague Russel Harris Andreas Kirchheim Christoph Klahn Christian Lindemann Bingjian Liu Kaspar Löffel Mirko Meboldt Dimitris Mourtzis

Alberto Ortona Eujin Pei Maren Petersen Manfred Schmid Marianne Schmid Daners Jon Spangenberg Adriaan Spierings Panagiotis Stavropoulos

Tao Sun Klaus-Dieter Thoben

University of Lapland, Faculty of Law, Finland SINTEF Manufacturing AS, Norway University of Strathclyde, UK Lund University, Product Development, Sweden Örebro University, Mechanical Engineering, Sweden University of Nottingham, EPSRC Centre for Additive Manufacturing, UK University of Leeds, Future Manufacturing Processes Research Group, UK ZHAW, Zentrum für Produkt- und Prozessentwicklung (ZPP), Switzerland inspire AG, ipdz, Switzerland University Paderborn, DMRC, Germany University of Nottingham Ningbo, Product Design and Manufacture, China FHNW, Institut für Produkt- und Produktionsengineering, Switzerland ETH Zürich, pd|z, Switzerland University of Patras, Laboratory for Manufacturing Systems and Automation, Greece SUPSI, Hybrid Materials Laboratory, Switzerland Brunel University London, UK University of Bremen, Institute Technology and Education, Germany inspire AG, icams, Switzerland ETH Zürich, pd|z, Switzerland DTU, Section of Manufacturing Engineering, Denmark inspire AG, icams, Switzerland University of Patras, Laboratory for Manufacturing Systems and Automation, Greece University of Virginia, USA University of Bremen, BIK, Germany

Organization

Anna Valente Oğuzhan Yilmaz

Michael Zäh Markus Zimmermann

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SUPSI, Automation, Robots and Machines Laboratory, Switzerland Gazi University, Additive Manufacturing Technology Application and Research Center (EKTAM), Turkey TUM, Werkzeugmaschinen und Fertigungstechnik, Germany TUM, Produktentwicklung und Leichtbau, Germany

Contents

Design for AM Generative Design Optimization and Characterization of Triple Periodic Lattice Structures in AlSi10Mg . . . . . . . . . . . . . . . . . . . . . . . . Patrik Karlsson, Lars Pejryd, and Niclas Strömberg Structured Approach for Changing Designer’s Mindset Towards Additive Manufacturing: From Theory to Practice . . . . . . . . . . . . . . . . Gustavo Menezes de Souza Melo, Gerret Lukas, Johannes Willkomm, Stephan Ziegler, Günther Schuh, and Johannes Henrich Schleifenbaum An Interactive, Fully Digital Design Workflow for a Custom 3D Printed Facial Protection Orthosis (Face Mask) . . . . . . . . . . . . . . . . . . . Neha Sharma, Dennis Welker, Shuaishuai Cao, Barbara von Netzer, Philipp Honigmann, and Florian Thieringer Opportunities of 3D Machine Learning for Manufacturability Analysis and Component Recognition in the Additive Manufacturing Process Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tobias Nickchen, Gregor Engels, and Johannes Lohn Review on the Design Approaches of Cellular Architectures Produced by Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marco Pelanconi and Alberto Ortona

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Process Chain Multi-material 3D Printing of Thermoplastic Elastomers for Development of Soft Robotic Structures with Integrated Sensor Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Antonia Georgopoulou, Bram Vanderborght, and Frank Clemens

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Contents

Solution Approaches and Process Concepts for Powder Bed-Based Melting of Glass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Susanne Kasch, Thomas Schmidt, Fabian Eichler, Laura Katharina Thurn, Simon Jahn, and Sebastian Bremen Additive Manufacturing of Ti-Nb Dissimilar Metals by Laser Metal Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Di Cui, Briac Lanfant, Marc Leparoux, and Sébastian Favre

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Investigation of Plastic Freeformed, Open-Pored Structures with Regard to Producibility, Reproducibility and Liquid Permeability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 Andre Hirsch, Christian Dalmer, and Elmar Moritzer Novel 4-Axis 3D Printing Process to Print Overhangs Without Support Material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 Michael Wüthrich, Wilfried J. Elspass, Philip Bos, and Simon Holdener Hybrid Manufacturing: A New Additive Manufacturing Approach for Closed Pump Impellers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 Robin Rettberg and Thomas Kraenzler Adaptive Slicing and Process Optimization for Direct Metal Deposition to Fabricate Exhaust Manifolds . . . . . . . . . . . . . . . . . . . . . . 160 Daniel Eisenbarth, Alessandro Menichelli, Fabian Soffel, and Konrad Wegener Quality Drift Detection in Selective Laser Melting (SLM) Using a Machine Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Pinku Yadav, Olivier Rigo, Corinne Arvieu, Emilie Le Guen, and Eric Lacoste Influence of the Inert Gas Flow on the Laser Powder Bed Fusion (LPBF) Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 Florian Wirth, Alex Frauchiger, Kai Gutknecht, and Michael Cloots Artificial Intelligence for Monitoring and Control of Metal Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 Giulio Masinelli, Sergey A. Shevchik, Vigneashwara Pandiyan, Tri Quang-Le, and Kilian Wasmer Simulation Development of a Process Model for Bead Deposition Rates and Cooling Behavior of Large Scale Additive Manufacturing Parts . . . 223 Michel Layher, Lukas Eckhardt, Andreas Hopf, and Jens Bliedtner

Contents

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Estimations of Interlayer Contacts in Extrusion Additive Manufacturing Using a CFD Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Raphaël Comminal, Sina Jafarzadeh, Marcin Serdeczny, and Jon Spangenberg Influence of Fibers on the Flow Through the Hot-End in Material Extrusion Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Marcin Serdeczny, Raphaël Comminal, David Bue Pedersen, and Jon Spangenberg Deploying Artificial Intelligence for Component-Scale Multi-physical Field Simulation of Metal Additive Manufacturing . . . . . . . . . . . . . . . . 268 Ehsan Hosseini, P. Gh. Ghanbari, F. Keller, S. Marelli, and Edoardo Mazza Prototyping and Testing Experimental Investigation of Filament Behaviour in Material Extrusion Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Mark Golab, Sam Massey, and James Moultrie Debinding and Sintering of Dense Ceramic Structures Made with Fused Deposition Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 Frank Clemens, Josef Schulz, Lovro Gorjan, Antje Liersch, Tutu Sebastian, and Fateme Sarraf Feasibility Investigation of Gears Manufactured by Fused Filament Fabrication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 Hans-Jörg Dennig, Livia Zumofen, and Andreas Kirchheim Qualification of Additively Manufactured Blood Vessel Models for the Evaluation of Braided Stent Designs . . . . . . . . . . . . . . . . . . . . . . 321 Juliane Kuhl, Ngoc Tuan Ngo, Jan-Hendrik Buhk, Andreas Ding, Andrés Braschkat, Jens Fiehler, and Dieter Krause Innovative Use Case Additive Manufactured and Topology Optimized Flexpin for Planetary Gears . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Anton Höller, Frank Huber, Livia Zumofen, Andreas Kirchheim, Hanspeter Dinner, and Hans-Jörg Dennig A Review of Optimised Additively Manufactured Steel Connections for Modular Building Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Zhengyao Li, Konstantinos Daniel Tsavdaridis, and Leroy Gardner Application of Topology Optimisation to Steel Node-Connections and Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374 Moustafa Mahmoud Abdelwahab and Konstantinos Daniel Tsavdaridis

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Contents

The AM Dowel – A Novel Insert for the Integration of Threads into Additive Manufactured Polymer Components . . . . . . . . . . . . . . . . . 391 Daniel Omidvarkarjan, Peter Balicki, Harry Baumgartner, Ralph Rosenbauer, Filippo Fontana, and Mirko Meboldt Novel Pressure Swirl Nozzle Design Enabled by Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Michael Umbricht, Kaspar Löffel, Marc Huber, Patrick Lüscher, Janine Bochsler, Daniel Weiss, and Tom Duda Design of an Additively Manufactured Customized Gripper System for Human Robot Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 Nikolai Hangst, Stefan Junk, and Thomas Wendt Enhanced Cooling Design in Wire Drawing Tooling Using Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426 Joakim Larsson, Patrik Karlsson, Jens Ekengren, and Lars Pejryd Aortic Model in a Neurointerventional Training Model – Modular Design and Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 Nadine Wortmann, Andreas M. Frölich, Anna A. Kyselyova, Helena I. De Sousa Guerreiro, Jens Fiehler, and Dieter Krause Integration of Additive Manufacturing into Process Chain of Porcelain Preservation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455 Bingjian Liu, Fangjin Zhang, Xu Sun, and Adam Rushworth Business Cases Decision Support System for a Metal Additive Manufacturing Process Chain Design for the Automotive Industry . . . . . . . . . . . . . . . . 469 Markus Johannes Kratzer, Julian Mayer, Florian Höfler, and Nikolaus Urban Business Models for Additive Manufacturing: A Strategic View from a Procurement Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483 Andreas H. Glas, Matthias M. Meyer, and Michael Eßig A Performance Upgrade of an Industrial Gas Turbine Based on Additive Manufactured Components . . . . . . . . . . . . . . . . . . . . . . . . . 500 Pankaj Bajaj, Fulvio Magni, and Peter Flohr Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511

Design for AM

Generative Design Optimization and Characterization of Triple Periodic Lattice Structures in AlSi10Mg Patrik Karlsson, Lars Pejryd, and Niclas Str¨ omberg(B) ¨ ¨ Orebro University, 701 82 Orebro, Sweden [email protected], http://www.oru.se

Abstract. In this work, generative design optimization and characterization of triple periodic lattice structures in AlSi10Mg are considered. Structures with Gyroid, Schwarz-D and G-prime lattices are designed optimally by utilizing a generative design optimization approach. The approach is based on topology optimization, support vector machines (SVM), radial basis function networks (RBFN), morphing operations, design of experiments and metamodels. Firstly, topology optimization solutions are generated which are represented using SVM, secondly, sizing solutions obtained by setting the SIMP parameter equal to one are represented with RBFN. Thirdly, graded lattice structures using the RBFN are morphed together with the SVM to final conceptual designs. Fourthly, design of experiments of the conceptual designs are performed using non-linear finite element analyses (FEA) and, finally, metamodel-based design optimization is conducted using convex combinations of Kriging, RBFN, polynomial chaos expansion and support vector regression models. In order to validate the optimal designs, new tensile test specimens that include the periodic lattice structures are suggested. The specimens with all three lattices are manufactured in AlSi10Mg using direct metal laser sintering with an EOS M290 machine. Tensile tests of these specimens are then performed and validated using nonlinear FEA. The test specimens are also characterized with respect to geometry and defects by means of computed tomography, optical microscopy and scanning electron microscopy. The study demonstrates the high potential of using the proposed generative design optimization approach with triple periodic lattice structures for producing robust lightweight designs using additive manufacturing. In order to demonstrate the industrial relevance the established GE engine bracket is studied in the paper and discussed at the conference. Keywords: Generative design

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· Lattice structures · AlSi10Mg

Introduction

In the last ten years or so, additive manufacturing (AM) has become a topic of intensive research and development work in both industry and academia. c Springer Nature Switzerland AG 2021  M. Meboldt and C. Klahn (Eds.): AMPA 2020, Industrializing Additive Manufacturing, pp. 3–16, 2021. https://doi.org/10.1007/978-3-030-54334-1_1

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P. Karlsson et al.

The high interest stems from e.g. short lead time and the possibilities that the technology may offer to generate highly complex structures with “no or limited extra cost of manufacturing”. The development of several processes and equipment dedicated to the additive manufacturing of metal based components is of course also one of the major reasons for this increased interest, especially from industry. This have resulted in expectations that this technology may bring upon a “complete shift in the manufacturing industry”. The high expectations that is quite often seen, may result in significant disappointments (“just another fancy and expensive technology”) if the full potential of the possibilities that AM offers in the design of components with additional functionalities is not utilized. In order to do so, the design process also needs to be developed in order to be able to handle new design elements and process steps in an efficient way. One of the potential show stoppers for increased use of AM in industry, identified by Swedish industry in their Strategic Research Agenda (SRA), is the lack of engineers educated and trained in design for additive manufacturing (DfAM). The need for new and/or improved design methodology and the education of engineers on these methods has therefore been identified as important. One type of design elements that is now possible to use in the manufacture of light weight components is lattices. Lattice structures may provide a high stiffness to weight ratio and can be included anywhere in parts, due to the flexibility of the AM methods. This, together with topology optimisation (TO), may provide significant flexibility in design options, but may also be a challenge for the efficiency of the design process. Especially in the steps from TO-based concept generation to CAD description that is necessary for the generation of machine code for AM equipment. Much of lattices that have been used so far in components are based on struts and vary from simple cubic lattices to topology optimized lattices that alter their shape to increase the stiffness to weight ratio [1]. A new contender for the strut lattices has in recent years appeared in the form of periodic surface based lattices, even though the concept has been around for a long time [2]. These triply periodic minimal surfaces (TPMS) are made up from a single surface that wraps around itself to create a cubic surface lattice structure. The TPMS lattices appears to be stiffer than the strut lattices and they could also provide a higher redundancy to fabrication errors [3,4]. In this work, a two-steps pragmatic approach for combining topology optimization (TO) concepts with optimal graded triply periodic minimal surfaces (TPMS) to final design concepts is presented. The approach is based on a twosteps procedure according to Fig. 1. In the first step, topology optimization is performed by setting the SIMP factor to four. The TO-solution is classified using support vector machines as proposed in [5]. In such manner, an implicit surface representation of the TO-solution is obtained. In the second step, size optimization is performed by setting the SIMP factor equal to one. The optimal thickness distribution is represented by radial basis function networks which in turn is utilized to functionally grade our implicit representations of the TPMS: Gyroid, G-prime and Schwarz-D. After these two steps, the implicit SVM-based TO-surfaces and the optimal graded implicit TPMS are combined by computer

Generative Design Optimization

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Fig. 1. The basic idea of the generative design optimization approach.

graphics operations, such as e.g. boolean, blending and morphing operations, in order to set up design of experiments of final design concepts of TO-based layouts and optimal graded TPMS. In order to evaluate material properties utilized in the design optimization method, tensile test specimens were manufactured by AM, validated by tensile testing and characterized by means of optical microscopy, scanning electron microscopy and computed tomography. Functional graded TPMS have become most popular in tissue engineering. Already in 2008, Gabrielli et al. [6] developed methods for functionally graded TPMS designs to be used as bone substitutes. Functionally graded SchwarzD titanium structures for bone implants were manufactured by selective laser melting in [7]. A review of functionally graded lattice structures in additive manufacturing of orthopedic implants can be found in [8]. A study on the mechanical properties of functionally graded Gyroid structures fabricated by selective laser melting was performed by Yang et al. [9]. Compressive tests of functionally graded TPMS were conducted in [10]. Most recently, functionally graded hybrid TPMS lattices, by blending using a Sigmoid operation, were simulated and tested by Al-Keta et al. [11]. Studies on optimal graded lattice structures appear less frequently. Most of the work so far on optimal graded lattice structures is performed on strut-based designs, see e.g. [1,12,13]. However, last year, an interesting approach for optimal graded TPMS was proposed by Li et al. [14]. Their work is indeed most relevant for comparison of the presented generative design optimization approach in this paper.

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The Generative Design Optimization Approach

In this section, a generative design optimization approach based on implicit surfaces of topology optimization concepts and graded TPMS-based lattice structures is presented, see Fig. 1.

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Gyroid f1 , G-prime f2 and Schwarz-D f3 lattices are considered by the following implicit surfaces (see Fig. 2): f1 = sin(γx) cos(γy) + sin(γy) cos(γz) + sin(γz) cos(γx) ± κ, f2 = [sin(2γx) sin(γz) cos(γy) + sin(2γy) sin(γx) cos(γz) + sin(2γz) sin(γy) cos(γx)] + 0.2 [cos(2γx) cos(2γy) + cos(2γy) cos(2γz) + cos(2γz) cos(2γx)] ± κ,

(1)

f3 = sin(γx) sin(γy) sin(γz) + sin(γx) cos(γy)cos(γz) + cos(γx) sin(γy) cos(γz) + cos(γx) cos(γy) sin(γz) ± κ, where γ and κ are parameters controlling the period and the thickness.

Fig. 2. The triple periodic lattice structures.

The implicit surfaces in (1) representing our TPMS-based lattice structures are graded by setting the SIMP-factor n equal to one1 in the SIMP-based topology optimization formulation reading: ⎧ min c(d) = F T d ⎪ ⎪ ⎪ ( ⎨ ρ ,d ) ⎧ ⎨ K(ρ)d = F , (2) ⎪ s.t. ⎪ V (ρ) ≤ Vˆ , ⎪ ⎩ ⎩  ≤ ρ ≤ 1, where the vector ρ contains density variables  ≤ ρe ≤ 1, where  is a small positive number representing zero density in order to avoid difficulties with singular stiffness matrices. The value ρe = 1 represents of course a completely filled element with material and ρe =  represents no material. Furthermore, d is the displacement vector, F is the external force vector, where  ρne ke (3) K(ρ) = e

1

A SIMP factor close to two might be a better choice in order to represent the stiffness as function of the density of the TPMS lattice. In a near future, we will implement proper material interpolation laws derived by numerical homogenization.

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 is the SIMP-based stiffness matrix, where is an assembly operator. The total volume of the design is obtained as  ρe Ve , (4) V (ρ) = e

where Ve represents the volume of element e and it is constrained by the upper limit Vˆ . The meaning of setting n = 1 is thickness optimization is 2D. The topology optimization is performed by setting n = 4.

Fig. 3. The basic idea of the proposed SVM-based postprocessing approach [5].

The topology optimization solution obtained by solving (2) using n = 4 is transformed to an implicit surface by using the support vector formulation of Mangasarian [15,16]. That is, the topology optimization solution of zeros and ones is classified using the following linear programming problem: ⎧ N N N    ⎪ ⎪ min qi + pi + C vi ⎪ (q ,p ,b,v ) ⎪ ⎪ i=1 i=1 ⎧ i=1 ⎨ N  ⎪ ⎨ (5) i 1 − v − y ( k(xi , xj )y j (qj − pj ) + b) ≤ 0, i = 1, . . . N, i ⎪ ⎪ ⎪ s.t. ⎪ j=1 ⎪ ⎪ ⎩ ⎩ qi ≥ 0, pi ≥ 0, vi ≥ 0, i = 1, . . . N, where k(xi , xj ) represents a kernel function, the sampling point xi is the center point of a finite element e, y i = 1 corresponds to a density ρe = 1 and y i is set to −1 for densities ρe = 0. A geometric model of a topology optimization solution is then defined by the following implicit surface: ⎧ N ⎨ < 0, outside the solid,  y j (qj − pj )k(x, xj ) + b = = 0, boundary of the solid, Φ(x) = (6) ⎩ j=1 > 0, inside the solid,

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where q, p and b are given by the optimal solution of (5). Thus, (6) serves the purpose to be the “CAD” model of the TO concept and it is established completely automatically without any interaction with CAD engineers. The basic idea of the support vector machine (SVM) approach is illustrated in Fig. 3. A major benefit of having the topology optimization concept represented as an implicit surface in (6) is that standard computer graphics operations, such as e.g. boolean, blending and morphing operations, are applicable. In such manner, the triple periodic lattice structures given in (1) can easily be integrated with the topology concept as presented in Fig. 1. Letting g be the implicit surface of the topology optimization solution and h be the graded lattice structure, then we first generate a cover by g˜ = min(g + α, −g + α) and, secondly, merge this cover with the graded lattice structure using max(˜ g , h) in order to generate the final concept. By changing the scalar α we of course change the thickness of the cover and this can also be done locally by morphing operations. In such manner, detailed design optimization of our TO concepts with TMPS-based lattice structures can be done by setting up design of experiments, executing non-linear finite element analyses and establishing metamodels. The latter is obtained by letting the computer experiments from the finite element analyses be represented by optimal convex combinations of metamodels. The taxicab norm of the leave-one-out cross validation errors is minimized for an ensemble of metamodels consisting of Kriging models, radial basis function networks and support vector regression models. This is performed by solving the following linear programming problem [17]: ⎧ N  ⎪ ⎪ pi + qi ⎪ (wmin ⎪ ,p ,q ) ⎨ ⎧i=1 ⎨ Y w − fˆ = p − q, (7) ⎪ T ⎪ s.t. 1 = 1, w ⎪ ⎪ ⎩ ⎩ wi , pj , qj ≥ 0, i = 1, . . . , M, j = 1, . . . , N, where Y consists of the leave-one-out cross validation errors and wi are the weights in the convex combination of metamodels, which is given by yen = yen (x) =

M 

wi yi (x),

(8)

i=1

where yi (x) represents a Kriging model, a radial basis function network or a support vector regression model. Another useful property of the SVM-based implicit surfaces is the treatment of multiple load cases. The standard approach for handling multiple loads is to minimize the weighted compliance. But by using the SVM-based implicit surfaces, we can simply blend together the solutions from the load cases taken independently. This is illustrated for the GE bracket in Fig. 4. Letting g1 , g2 and g3 represent the implicit surface for the three load cases presented in the figure, then the presented blended design is given by max(max(g1 , g2 ), g3 ). The critical load for the structure is then studied by J2-plasticity analysis using Abaqus/Standard.

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Fig. 4. Topology optimization of the GE bracket for multiple load cases by blending support vector machines.

3

Validation of Tensile Tests

In order to perform design optimization using TPMS-based lattice structures as presented in the previous section, reliable material data and constitutive material models are needed. The design optimization process would be easier if standard bulk properties and models could be used instead of finding new material parameters and constitutive equations. It is well-known that structural properties drop

Fig. 5. Tensile tests of lattice structures.

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for thin members. So, the question is if or when an assumption of bulk behaviour is appropriate for the TPMS-based lattice structures. In this work this is investigated by designing, printing, testing and analyzing tensile test specimen of triple periodic lattice structures according to Fig. 5. The purpose of the study is to validate if J2-plasticity with linear isotropic hardening using bulk properties is a proper model for the TMPS-based lattice structures when performing design optimization. The plasticity model is first set up using material data for AlSi10Mg from EOS and calibrate the model for these data using the tensile tests for the test specimen without lattice structures. It is clear as usual that defining the yield stress using the Rp0.2 value implies a non-conservative model. Therefore, the yield stress is adjusted to match the tensile test curves. Next, the calibrated J2-plasticity model is used to simulate the TMPS-based lattice structures and the simulation is compared to the tensile test results for the corresponding lattice structure. The validation procedure is presented for the Schwarz-D structure in Fig. 6. In general, the tensile tests of the lattice structures and the FEA predictions match very well. However, larger standard deviations of the tests of the lattice structures compared to bulk data are observed.

Fig. 6. Non-linear finite element analyses of tensile tests.

4

Characterization of Tensile Tests

Test specimens were, subsequently to tensile testing, characterized by means of scanning electron microscopy (SEM), optical microscopy (OM) and computed tomography (CT). Material characterizations by means of microscopy techniques

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were done by using an Zeiss FEG-SEM Zigma 300 VP and a Zeiss Imager M2m stereo microscope. CT analysis of test samples was done using a Bruker Skyscan 1272 scanner. Reconstruction of CT data was performed using InstaRecon software and utilizing an optimization for beam hardening, ring artefacts and postalignment correction. The postprocessing were performed using VG Studio Max from Volume Graphics and the custom method VGDefX for porosity analysis. In Fig. 7, the results from fractography of bulk specimens by means of SEM are shown. The results generally indicated ductile fracture and dimples in size range of about 0.2–1 µm were observed, Fig. 7 a). Typical defects detected in SEM were pores with size of approximately 20–50 µm, as seen in regions marked by dashed red circles in Fig. 7 b). Some pores contained particles, exemplified in the region marked with dashed red circle in Fig. 7 c) and magnified in Fig. 7 d), with particle size of about 1–10 µm. The results from fractography of samples with lattice structures by means of OM and SEM are seen in Figs. 8 and 9. In order to get an overview of the fractured samples surfaces, surface analysis by means of OM, utilizing focus stacking, was performed. Downskin were observed in OM and a typical surface region containing downskin is marked by dashed lined square in Fig. 8 a). The same fracture surface region, marked in Fig. 8 a), was analyzed by SEM and it was found that the downskin regions contained accumulation of powder particles, Fig. 8 b)–d). In the right lower part of Fig. 8 b), the fracture surface is seen and in the right upper part of the figure, accumulation of powder particles is evident. The interface between the fracture surface and the as printed surface contained partly melted powder, Fig. 8

Fig. 7. Typical fracture surfaces of the bulk specimens. Dimples a) and pores, marked by dashed red circles in b)–c) and magnified in d), were observed in SEM.

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Fig. 8. Fracture surfaces of samples with lattice structures observed in OM a) and SEM b)–d). Arrows indicates build direction.

c), and larger pores with size of approximately 50–100 µm in regions of particle clusters, as seen in Fig. 8 d). Similar to bulk specimens, Fig. 7 b)–d), pores were also observed in the fracture surface analysis of the samples with lattice structures in SEM, Fig. 9 a)–b). The pores varied in size, from nano- to micro sized pores, as seen in the marked regions in Fig. 9 b), and some pores contained particles with sizes of approximately 10–50 µm, Fig. 9 a). It has been shown that pores in parts produced by AM may originate from oxide inclusions and entrapped gas [18–20]. Thus, the particles found in pores in this study, Fig. 1 c)–d) and Fig. 9 a), may be inclusions. However, chemical analysis of the particles in SEM did not indicate foreign elements and, thus, the particles are probably partly melted powder particles, resulting in lack of fusion porosity. Additional pores observed in the present study may though be a result of entrapped gas during manufacturing of samples by AM. The defects identified in specimens with lattice structures by means of SEM were generally pores. However, the gyro samples contained additional defects. Cracks with clusters of micron-sized particles, marked with red arrows in Fig. 9 c), were detected in SEM. Some of these particles were also found in cluster formations on open fracture surface regions. The particles observed in SEM are magnified in Fig. 9 d) and as can be seen in the figure, the particles appears to be partly melted particles. However, more research on this is needed in order to fully explain the particle formation found in Gyro samples.

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Fig. 9. Typical defects in specimens with lattice structures in general a)–b) and Gyro samples specifically c)–d).

In Fig. 10, the results from 3D analysis of specimens by means of CT are shown for the Schwarz D sample. The CT analysis of specimens enabled 3D-view, Fig. 10 a), and 3D porosity analysis, Fig. 10 b), of the samples. Downskin areas observed in OM and SEM were confirmed by CT, which additionally enabled an easy overview of the downskin positions. Clusters of particles were observed on both curved regions and on the outer shell of the tensile test specimens with lattice structures. Downskin may be a result of lattice design challenging the limits of AM design [21]. The results from porosity analysis performed by CT indicated pore sizes of approximately 20–300 µm. This is seen for a fractured Schwarz D specimen in Fig. 10 b), which is a transparent view of the sample in Fig. 10 a). The larger pores may be a result of the large voids between particles downskin areas, where accumulation of particles was observed in SEM, Fig. 8 d). These pores may act as crack initiation sites, causing material failure. Defects such as pores are generally found in AM produced parts and it is commonly known that the defects influence mechanical properties of the material negatively [22]. Additionally, size, amount and position of defects influence mechanical properties of the material and is probably the cause of the tensile test data scattering as seen for the Schwarz-D samples in Fig. 6. Thus, the material in the present study may be optimized by reducing the defects observed in bulk and lattice samples.

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Fig. 10. 3D-view a) and porosity analysis results b) of the Schwarz-D sample, investigated by means of CT.

5

Concluding Remarks

In this work, a generative design optimization approach for functional grading of triply periodic minimal surface lattice structures by using topology optimization, support vector machines and radial basis function networks is proposed and implemented. In a very near future, the approach will be improved by using a proper material interpolation law for the lattice derived by using numerical homogenization instead of using SIMP or RAMP. Established bulk data is the starting point, but this will be needed to be further tested and validated in order to improve the design optimization approach. AM processing, especially of thin structures and/or structures with thin elements included in the component, is still in need for further development and investigations in order to understand and possibly remove flaws and porosity that may possess weaknesses and thereby reducing strength and life of components. In this work, test specimens were characterized by means of CT, OM and SEM. Porosity, possibly caused by entrapped gas during manufacturing of samples by AM, and lack of fusion porosity were observed for both bulk samples and specimens with lattice structures. Size of the pores ranged from nano- to micro sized pores and pores were mainly found in fracture surfaces and in downskin regions, where accumulation of powder particles was evident and larger pores were observed. Additionally, fractography by means of SEM of samples with Gyro lattice structure reviled other defects such as partly melted particles.

References 1. Daynes, S., Feih, S., Lu, W.F., Wei, J.: Optimisation of functionally graded lattice structures using isostatic lines. Mater. Des. 127, 215–223 (2017) 2. Schwarz, H.A.: Gesammelte Mathematische Abhandlungen. Springer, Berlin (1890) 3. Jansson, A., Ekegren, J., Pejryd, L.: Numerical analysis of compression strength in network structures based on trusses, and periodic surfaces, aimed for additive manufacturing. In: The Proceedings of Progression in Additive Manufacturing (2018)

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4. Jansson, A., Pejryd, L.: In-situ computed tomography investigation of the compression behaviour of strut, and periodic surface lattices. In: The Proceeding of the ICT2019, Paper id: 2366 (2019) 5. Str¨ omberg, N.: Efficient detailed design optimization of topology optimization concepts by using support vector machines and metamodels. Eng. Optim. 52, 1136– 1148 (2019) 6. Gabbrielli, R., Turner, I.G., Bowen, C.R.: Development of modelling methods for materials to be used as bone substitutes. Key Eng. Mater. 361–363, 903–906 (2008) 7. Han, C., Li, Y., Wang, Q., Wen, S., Wei, Q., Yan, C., Hao, L., Liu, J., Shi, Y.: Continuous functionally graded porous titanium scaffolds manufactured by selective laser melting for bone implants. J. Mech. Behav. Biomed. Mater. 80, 119–127 (2018) 8. Mahmoud, D., Elbestawi, M.A.: Lattices structures and functionally graded materials applications in additive manufacturing of orthopedic implants: a review. J. Manuf. Mater. Process. 1(2), 1–19 (2017) 9. Yang, L., Mertens, R., Ferrucci, M., Yan, C., Shi, Y., Yang, S.: Continuous graded gyroid cellular structures fabricated by selective laser melting: design. Manuf. Mech. Prop. Mater. Des. 162, 394–404 (2019) 10. Afshar, M., Anaraki, A.P., Montazerian, H.: Compressive characteristic of radially graded porosity scaffolds archiectured with minimal surfaces. Mater. Sci. Eng. 92, 254–267 (2018) 11. Al-Ketan, O., Lee, D.W., Rowshan, R., Al-Rub, R.K.A.: Functionally graded and multi-morphology sheet TPMS lattices: design, manufacturing, and mechanical properties. J. Mech. Behav. Biomed. Mater. 102, 1–17 (2020) 12. Cheng, L., Bai, J., To, A.C.: Functionally graded lattice structure topology optimization for the design of additive manufactured components with stress constraints. Comput. Methods Appl. Mech. Eng. 344, 334–359 (2019) 13. Jin, X., Li, G.X., Zhang, M.: Optimal design of three-dimensional non-uniform nylon lattice structures for selective laser sintering manufacturing. Adv. Mech. Eng. 10(7), 1–19 (2018) 14. Li, D., Dai, N., Tang, Y., Dong, G., Zhao, Y.F.: Design and optimization of graded cellular structures with triply periodic level surface-based topological shapes. J. Mech. Des. 141, 071402-1-13 (2019) 15. Mangasarian, O.L.: Generalized support vector machines. In: Advances in Large Margin Classifiers, pp. 135–146. Cambridge, MA (2000) 16. Mangasarian, O.L.: Exact 1-norm support vector machines via unconstrained convex differentiate minimization. J. Mach. Learn. Res. 7, 1517–1530 (2006) 17. Str¨ omberg, N.: Comparison of optimal linear, affine and convex combinations of metamodels. Eng. Optim. 52, 1–17 (2020) 18. Thijs, L., Van Humbeeck, J., Kempen, K., Yasa, E., Kruth, J.-P.: Investigation on the inclusions in maraging steel produced by selective laser melting. In: 5th International Conference on Advanced Research in Virtual Rapid Prototyping, pp. 297–304 (2011). https://doi.org/10.1201/b11341-48 19. Surreddi, K.B., Oikonomou, C., Karlsson, P., Olsson, M., Pejryd, L.: In-situ microtensile testing of additive manufactured maraging steels in the SEM: influence of build orientation, thickness and roughness on the resulting mechanical properties. La Metallurgia Italiana 3, 27–33 (2018) 20. Tang, M., Chris, P.: Pistorius, oxides, porosity and fatigue performance of AlSi10Mg parts produced by selective laser melting. Int. J. Fatigue 94, 192–201 (2017)

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21. Adam, G.A.O., Zimmer, D.: On design for additive manufacturing: evaluating geometrical limitations. Rapid Prototyp. J. 21, 662–670 (2015). https://doi.org/10. 1108/RPJ-06-2013-0060 22. Yasa, E., Kempen, K., Kruth, J.: Microstructure and mechanical properties of maraging steel 300 after selective laser melting. In: Proceedings of the 21st International Solid Freeform Fabrication Symposium, pp. 383–396 (2010)

Structured Approach for Changing Designer’s Mindset Towards Additive Manufacturing: From Theory to Practice Gustavo Menezes de Souza Melo1(&), Gerret Lukas2, Johannes Willkomm1, Stephan Ziegler1, Günther Schuh2, and Johannes Henrich Schleifenbaum1 1

2

Chair for Digital Additive Production (DAP), RWTH Aachen University, 52074 Aachen, Germany [email protected] Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, 52074 Aachen, Germany

Abstract. Additive Manufacturing (AM) has a great potential of disrupting product design and supply-chain in many industries by means of its unique capabilities. Regarding the product design, the potential benefits comprise functional integration, reduced assembly efforts, reduced weight and increased performance. Although AM has been around for decades, designers still think in the restrictions imposed by conventional manufacturing. The awareness of the potentials of AM has not yet been pushed in the minds of designers and the adoption of AM in design process often fails due to a status quo in design or limited knowledge of the employees. Against this background, this paper proposes a framework to change Designer’s mindset towards AM. By means of indepth interviews with designers and design engineers from different industries, the common challenges and implemented solutions were investigated. From these expert interviews, the following key challenges were identified: AMadjusted design methodology, standards implementation and software support. Based on those, a wide literature review of possible solutions was carried out and its result was combined with the already implemented solutions in industry. The proposed framework not only takes advantage of currently available human capital in the organization but also paves a sustainable way to train new personnel and create momentum towards AM adoption. By means of a structured learning path and a knowledge management platform integrated into design software, the proposed framework effectively extracts tangible and part-specific design rules and assures optimal knowledge transfer among employees. This framework was subsequently validated in a workshop with industry experts. Keywords: Additive Manufacturing development  Change management

 Design methods  DfAM  Product

© Springer Nature Switzerland AG 2021 M. Meboldt and C. Klahn (Eds.): AMPA 2020, Industrializing Additive Manufacturing, pp. 17–25, 2021. https://doi.org/10.1007/978-3-030-54334-1_2

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1 Introduction AM has been emerging strongly in recent years. Growth in machine sales and increased numbers of equipment manufacturers show how the AM market has been expanding [1]. AM enables the fabrication of products with high complex design with various functionalities [2–4]. However, design engineers often think in the restrictions imposed by conventional manufacturing or link AM to unrealistic expectations [2]. The awareness of the potentials and restrictions of AM has not been effectively pushed in the minds of design engineers. Moreover, the adoption of AM in design process often fails due to a status quo in design or limited knowledge of the employees. A sustainable adoption of AM is only possible by means of complete mindset shift of designers and design engineers from conventional manufacturing towards AM [3]. In other words, today’s professionals need to change the way they approach design problems. Against this background, some companies have already started their journey to train their employees in AM. Academic literature has dealt with education in the field of AM for almost a decade. Since AM has been of growing interest, Geraedts et al. [4] investigated the role of AM in the light of design engineering in three domains: business, research, and education. At the same time Williams and Seepersad [5] developed a concept combining projectbased and problem-based learning for a university course. In both papers, the dominant topic was education for future designers in AM. Then, Ford and Dean [6] discussed the general necessity of teaching conventional manufacturing in comparison to AM. They conclude that designer should not ignore conventional design and AM should be added to the curriculum. A sole focus on AM could result in diminishing conventional technologies. Loy [7] puts this conclusion into a different perspective, by stating that design educators face a number of different challenges in terms of AM in design education. Minetola et al. [8] use a survey to investigate the impacts of early exposure with AM in engineering education and find that a “think-additive” approach early on leads to a full facilitation of the benefits of AM. Simpson et al. [9] and Prabhu et al. [10] conclude in a similar way. Yet, only Watschke et al. [11] propose a methodical approach for design education, however they focus on the ideation process. In the light of previous and current research, the prevailing need of companies for designer with an AM mindset has not yet been addressed. As researcher focused on the secondary and tertiary education to train future talent, the education for professionals, also referred as continuing education, has been neglected. Of course, educating professionals in AM is core to a number of certificate courses and workshops, but literature does not provide a systematic approach that addresses the needs of companies. Therefore, this paper aims to develop a systematic framework to educate design engineer professionals and provide insight into the development of an AM mindset in industrial companies. Against this background, this paper presents a survey among industry participants for a deeper understanding of challenges, goals and current implemented solutions in companies. Subsequently, a broad literature study is carried out in order to collect further best practices among the academia and industry beyond our focus group. Finally, based on the two steps before mentioned, a systematic framework is developed and validated through a workshop with the interview participants.

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2 Method The presented research utilizes qualitative research. AM mindset cannot be described by a defined set of variables, it rather emerges from a dynamic model based on qualitative data: In order to generate such data, we used two types of methods. We base on the concept for grounded theory [12] as we obtain data by interviewing a group of representatives of companies that facilitate AM in their organization. In addition to the interviews, we conducted a systematic review of existing literature based on codes from the interviews. Table 1 gives an overview of the addressed industries. The participants of each company are in charge of the AM activities and are ranked in middle management.

Table 1. Overview of interview participants. Industry Machine manufacturing Automotive Materials & process

Number of companies Number of participants 3 3 3 3 4 4

The interviews were semi-structured, conducted by one of the authors and recorded for documentation purpose. Before the interview, the participants received a guideline containing seven open questions to prepare the interview. During the interview, the interviewer could alter the question, if needed, to enlarge on topics of interest for the study. In order to keep track of such changes to the guidelines, interview reviews were conducted and if necessary the guidelines were adjusted. However, every participant was only interviewed once. After all interviews were conducted, we coded the transcripts and categorized findings. Our three main categories were “common challenges”, “implemented solutions” and “shared goals”. As our participant group was small compared to other qualitative studies, we ensured iteration between initial coding and categorizing for an objective analysis of the interview data. Subsequently to the interviews, a systematic literature review based on the procedure by Kitchenham [13] was applied. This systematic is divided into three phases: planning, conducting and reporting. Within the planning phase the objectives of the literature review was defined. From the interviews (“common challenges” and “shared goals”), three key areas were identified as vital for mindset shift towards AM: • AM-adjusted design methodology, which raises awareness and increase know-how • Implementation of standards, which uses a structured and accessible approach • Provision of better software, which supports expert knowledge exchange During the conductive phase, the literature was collected and analysed. For this purpose, the literature was divided in two categories according to their scientific value: primary source (e.g. paper, standards and technical books) and second source (e.g. magazine, internal knowledge and online guidelines). By means of the systematic

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proposed by Kitchenham, numerous literature studies were screened, with a focus on the last decade. The identified literature was subsequently presented to in-house AM design experts who selected the most relevant and comprehensive ones. In the reporting phase, the literature is summarized and reported for further investigation.

3 Result The goal of the interviews in our study group was obtaining a description of the status quo of continuing education for AM at the respective companies. During the coding of the interviews, we identified emerging themes via our in-vivo codes, as exemplified in Table 2. We avoided any early categorization for an objective analysis of the data [14]. At the end of our interview transcription and coding, we found that three categories were fitting our themes and codes best: “common challenges”, “implemented solutions”, and “shared goals”. Table 2. Examples from in-vivo codes, themes, and categories. In-vivo code “However, we are still in the learning phase. You get the standard design rules, but often it’s still try and fail and then redesign.” “The very first fruitful way was that we sent designers to user training.” “Certification of components: This is a K.O. criterion for us. If there is not something clearer there, I do not know whether it [AM] goes on here.”

Theme Design methodology

Category Common challenges

Design training Certification

Implemented solutions Shared goals

Among the challenges, we identified in our study, AM know-how and methods for continuing education in particular were the most relevant topics for the majority of the partners. While software and norms were the second and third largest concerns. “Implemented solutions” summarize indication for approaches to solve the challenges along the implementation of an AM mindset. Unsuccessful solutions have led to remaining issues in the category of “Common challenges”. Across our interviewee group we found a number of different approaches. Very common was to establish an in-house expert team responsible to develop and hold workshops in different aspects of AM. Those teams also managed internal databases for design guidelines and bestpractice projects. In summary, three categories of implemented solutions were identified: Collecting information through learning by doing, creation of own guidelines, and discussion with experts in user groups. However, there is agreement among the participant that the challenges still remain and the shared goals have not yet been reached. A new type of design methodology has to be established. This methodology must be adapted to AM. Furthermore, the new methodology needs support by international standards and procedure for certification. Lastly, due to the complexity, software tool must improve and be able to support decisions faced by designers.

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Throughout the literature review study, it is elaborated that an AM-adjusted design methodology requires not only AM design workflow, but also the key design guidelines and how effectively learn DfAM. Among the most important workflows are the one from ASTM 52910 (Additive manufacturing—Design—Requirements, guidelines and recommendations) [15] and workflow of approaches typically enabled by AM (e.g.: topology optimization [16], cellular materials – lattice structures [17], monolithic design – part consolidation [2], and function integration [18]). In the topic of design guidelines, besides the major reference ASTM 52910, online available guidelines were suggested along recent and updated AM design books (e.g.: A Practical Guide to Design for Additive Manufacturing) [19]. Lastly, the most suitable learning approaches for DfAM were presented, from lecture, through problem until project-based learning [5, 20]. Regarding the implementation of standards, key norms, handbooks and guidelines were covered. Among those are the already mentioned ASTM 52910 and the VDI 3405 – part 3 [21]. The most valuable contribution is the method which describes how to implement those guidelines. In general, the AM industry currently lacks fundamental principles for establishing derivative rules based on guidelines and best practices. To be useful to designers, design guidance needs to consist of rules with numeric values capturing the limitations of AM technologies, processes, and machines. The Guide-toPrinciple-to-Rule Approach offers a structured implementation framework from the abstractness of design guidelines, through design principles, until the concreteness of design rules [22]. Concerning the provision of better software, three phases throughout the product design process were delimited: before, during and after design. Before design, some solutions based on Artificial Intelligence (AI)/Augmented Intelligence were presented as promising, e.g.: AI sketch-based design tool [23]. During design, the most established solution was generative design, along with new coming approaches as real-time generative-design (e.g. Autodesk and Desktop Metal Live Parts). Lastly after design, printability checker and build simulation analysis tools are the most common used ones (e.g. ANSYS).

4 Development and Validation of Framework The broad literature review demonstrated that continuing education is key actor to raise awareness, increase know-how and knowledge exchange. Moreover, a structured approach was presented to extract, from high level guidelines presented in standards, consistent and tangible rules for their wider adoption. Lastly, software works as a right support to enhance human capabilities, acting as soon as possible in the design process. Based on those findings, a holistic solution was proposed. The solution takes into account two main important aspects: the product and the individual. The first, represented here by the product design process, ranges from conceptual to final design. The second, represented here by the design knowledge, ranges from novice to expert. On one hand, the final design requires an expert level of knowledge, on the other, novices perform better and more innovative than experts

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during the conceptual phase of product design due to lack of fixation [24, 25]. The solution is presented through the framework in Fig. 1.

Fig. 1. Framework with structured learning path, objectives and final output used in assistant for design software.

This path from conceptual to final design optimally linking novice and expert knowledge is assured by means of different learning methods, from lecture, via problem and lastly project-based. Firstly, lecture-based learning explores what is possible through AM, presenting some approaches and features to be used in this sense. Problem-based learning aims the correlation design-material-process and their trade-off to meet requirements (from a client, for example). Lastly, project-based learning addresses how to quantify restrictions, being therefore able to optimize regarding quality, cost and time. Each learning phase covers, in different time frames – respectively days, weeks and months – specific topics in order to guarantee a transition from potential of additive manufacturing until its restrictions. The covered topics are design heuristics, guides, principles and rules. Design Heuristics are cognitive shortcuts that help designers explore variations in designs. Design Guides offer feature-based best practices when using AM in product design to take advantage of AM capabilities. Design Principles are basic, logical correlations capturing process parameter and control parameters. And Design Rules are explicit value-based constraints that provide needed insight into manufacturability. Those rules are subsequently stored in a knowledge repository which works as database for a software support application. In conclusion, this integrated solution uses continuing education to raise awareness and improve knowledge exchange among professionals and uses a structured and optimal path to get concrete design rules from the abstractness of norm and standards. This proposed framework works therefore as a bridge between theory and practice in how to design for AM. Lastly, a software support application makes the knowledge developed during this learning process available for new design engineers and coming products.

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By means of a workshop, the above mentioned solution was presented to representatives from interviewed companies in order to collect their industrial and business perspectives. At the end of the workshop, a round table discussion took place and the perspective of all participants was individually expressed and clarified. The proposed approach was positively evaluated by all participants of the workshop. Numerous valuable points came up from the discussion which are summarized below: • Design for AM should cover not only the relations between material, properties and 3D printing process, but also post-processing; • The proposed solution suits the niche of businesses with low product diversity and mid-series production due to the highly needed internal efforts and costs; • In order to be scalable, as shareable approach should be merged into the solution for cost and risk sharing; • OEMs play an important role in order to make this scalable solution possible. IP of design is the main point of attention; • The main objective is to identify the right moment, financially speaking, to quit the learning track and use the shared knowledge base for desired design rules.

5 Conclusion and Outlook The paper revealed that the interviewees have similar experiences regarding the continuing education of designers and other employees in AM. The design methodology, norms & standards and design software have been identified as key areas to improve AM adoption. The proposed solution consists of a structured framework to optimally take advantage of company’s human capital and to extract tangible rules for 3D printing an optimal part, fostering alongside knowledge transfer and creating awareness. Lastly, a software application makes expert know-how more easily available with fewer resources. This integrated solution successfully tackle the three key areas identified as vital for mindset shift towards AM: AM-adjusted design methodology, implementation of standards and provision of better software. However, our theory is grounded on a qualitative approach, thus cannot provide any statistical evidence. Nonetheless, the researched phenomena of continuing education for AM is no topic for quantitative research only. Therefore, we conclude that our model contributes to the understanding of professional continuing education for AM design, but can be extended by evaluating a larger group of employees on different hierarchical levels and professional tenure. As outlook, the future work comprises the development of an open innovation platform for sharing of design rules. A first solution proposed is based on the automatically extraction of design rules via on-premise software, subsequently encryption and lastly upload to the cloud only after prior authorization of the respective IP owner. The main issue to be investigated is how refractory will be the industry to share their development in exchange of others.

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References 1. Campbell, I., Diegel, O., Huff, R., Kowen, J., Wohlers, T.: Wohlers Report 2019: 3D Printing and Additive Manufacturing State of the Industry. Wohlers Associates, Fort Collins (2019) 2. Schmelzle, J., Kline, E.V., Dickman, C.J., Reutzel, E.W., Jones, G., Simpson, T.W.: (Re) designing for part consolidation: understanding the challenges of metal additive manufacturing. J. Mech. Des. 137(11), 111404 (2015) 3. Ahuja, B., Karg, M., Schmidt, M.: Additive manufacturing in production: challenges and opportunities. In: Laser 3D Manufacturing II, California, vol. 9353. International Society for Optics and Photonics (2015) 4. Geraedts, J., Doubrovski, E., Verlinden, J., Stellingwerff, M.: Three views on additive manufacturing: business, research and education. In: Horváth, I., Albers, A., Behrendt, M., Rusák, Z. (eds.) Ninth International Symposium on Tools and Methods of Competitive Engineering, Karlsruhe, pp. 1–15 (2012). 5. Williams, C.B., Seepersad, C.C.: Design for additive manufacturing curriculum: a problemand project-based approach. In: International Solid Freeform Fabrication Symposium, Virginia, pp. 81–92 (2012) 6. Ford, P., Dean, L.: Additive manufacturing in product design education: out with the old and in with the new? In: DS 76: Proceedings of E&PDE 2013, the 15th International Conference on Engineering and Product Design Education, Dublin, pp. 611–616 (2013). 7. Loy, J.: The future for design education: preparing the design workforce for additive manufacturing. Int. J. Rapid Manuf. 5, 199–212 (2015) 8. Pei, E., Minetola, P., Iuliano, L., Bassoli, E., Gatto, A.: Impact of additive manufacturing on engineering education–evidence from Italy. Rapid Prototyp. J. 21(5), 535–555 (2015) 9. Simpson, T.W., Williams, C.B., Hripko, M.: Preparing industry for additive manufacturing and its applications: summary & recommendations from a national science foundation workshop. Addit. Manuf. 13, 166–178 (2017) 10. Prabhu, R., Miller, S.R., Simpson, T.W., Meisel, N.: The earlier the better? Investigating the importance of timing on effectiveness of design for additive manufacturing education. In: ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Quebec City, Canada (2018) 11. Watschke, H., Bavendiek, A.K., Giannakos, A., Vietor, T.: A methodical approach to support ideation for additive manufacturing in design education. In: DS 87-5 Proceedings of the 21st International Conference on Engineering Design (ICED 17): Vol 5: Design for X, Design to X, Vancouver, Canada, pp. 41–50 (2017). 12. Tweed, A., Charmaz, K.: Grounded theory methods for mental health practitioners. In: Harper, D., Thompson, A.R. (eds.) Qualitative research methods in mental health and psychotherapy, pp. 131–146. Wiley-Blackwell, Oxford (2012) 13. Kitchenham, B.: Procedures for performing systematic reviews, Keele, UK (2004) 14. Gummesson, E.: All research is interpretive! J. Bus. Ind. Mark. 18, 482–492 (2003) 15. ISO/ASTM52910-18, Additive manufacturing—Design—Requirements, guidelines and recommendations. ASTM International, West Conshohocken, PA (2018) 16. Gebisa, A.W., Lemu, H.G.: A case study on topology optimized design for additive manufacturing. Mater. Sci. Eng. Conf. Ser. 276(1), 012026 (2017) 17. Merkt, S., Klocke, F.: Qualifizierung von generativ gefertigten Gitterstrukturen für maßgeschneiderte Bauteilfunktionen, Aachen, Germany (2015) 18. Klaiber, D., Fröhlich, T., Vietor, T.: Strategies for function integration in engineering design: from differential design to function adoption. Procedia CIRP 84, 599–604 (2019)

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19. Diegel, O., Nordin, A., Motte, D.: A Practical Guide to Design for Additive Manufacturing. Springer, Singapore (2020) 20. Yang, L.: Introducing the state-of-the-art additive manufacturing research in education. In: Pei, E., Monzón, M., Bernard, A. (eds.) Additive Manufacturing: Developments in Training and Education, pp. 53–65. Springer, Cham (2019) 21. VDI 3405 Blatt 3:2015-12: Additive Fertigungsverfahren - Konstruktionsempfehlungen für die Bauteilfertigung mit Laser-Sintern und Laser-Strahlschmelzen (2015) 22. Mani, M., Witherell, P., Jee, H.: Design rules for additive manufacturing: a categorization. In: ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, Digital Collection, Ohio (2017) 23. Wang, F., Kang, L., Li, Y.: Sketch-based 3D shape retrieval using Convolutional Neural Networks. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, pp. 1875–1883 (2015) 24. Kim, J., Ryu, H.: A design thinking rationality framework: framing and solving design problems in early concept generation. Hum. Comput. Interact. 29, 516–553 (2014) 25. Crilly, N.: Fixation and creativity in concept development: the attitudes and practices of expert designers. Des. Stud. 38, 54–91 (2015)

An Interactive, Fully Digital Design Workflow for a Custom 3D Printed Facial Protection Orthosis (Face Mask) Neha Sharma1,2 , Dennis Welker2,3, Shuaishuai Cao1,2, Barbara von Netzer4, Philipp Honigmann5 , and Florian Thieringer1,2(&) 1

Department of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, Spitalstrasse 21, 4031 Basel, Switzerland [email protected] 2 Medical Additive Manufacturing Research Group, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 16, 4123 Allschwil, Switzerland 3 Department of Biomechanics, University of Applied Sciences, Badstrasse 24, 77652 Offenburg, Germany 4 University Clinic of Oral and Maxillofacial Surgery, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria 5 Hand Surgery, Cantonal Hospital Basel-Land, Rheinstrasse 26, 4410 Liestal, Switzerland

Abstract. Sport-related injuries have an increased prevalence of maxillofacial fractures among professional (soccer) players. From professional players’ perspective, these injuries can have career-detrimental effects when followed with prolonged recovery periods. Therefore, to facilitate an earlier training and competition return, and reduce the chances of re-injury, the use of faceprotective orthosis, commonly known as a face mask, in rehabilitative management is of paramount importance. To date, the fabrication of a customized face mask has been an entirely manual and time-consuming process. To mitigate the issues with conventional customized face masks, the authors have presented a fully digital “contactless design and production” workflow for the fabrication of a patient-specific face mask. This work aimed to integrate the existing tools of medical image processing software, computer-aided design (CAD), three-dimensional (3D) digitization, and additive manufacturing (AM) to provide a cost-effective, practitioner/patient-friendly solution for the design and manufacturing of patient-specific face-protective orthosis or face masks. Considering the functional and clinical aspects at the fractured site, a virtually designed face mask was fabricated in-house with carbonreinforced polylactic acid composite material via material extrusion or Fused Deposition Modeling (FDM) technology. The face mask had a comfortable fit, required no alterations, was lightweight, and shortened the convalescence period for the patient. The results from the selected design case accurately represent the clinical scenario and shows the potential of the proposed workflow in similar facial fracture situations. With greater ease of fabrication and production validity, this study highlights an alternative approach applicable in clinical practice. © Springer Nature Switzerland AG 2021 M. Meboldt and C. Klahn (Eds.): AMPA 2020, Industrializing Additive Manufacturing, pp. 26–36, 2021. https://doi.org/10.1007/978-3-030-54334-1_3

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Keywords: Face-protective orthosis  Additive manufacturing  Face scanning  Fused Deposition Modeling  Composite thermoplastics

1 Introduction Sport-related injuries result from a variety of different mechanisms and often vary in location and pattern. These injuries account for 6 to 10% of maxillofacial traumas, with the most common association to the fractures of nose followed by zygomatic bones [1, 2]. In general, sport-related injuries have an increased prevalence of facial fractures in soccer and basketball players [3, 4]. From professional players’ perspectives, especially in high contact sports, these injuries can have career-detrimental effects when followed with prolonged convalescence periods. Face-protective orthoses, commonly known as face masks, are patient-specific splints, that primarily protect the face and redistribute the impact forces during sport activities [5]. Therefore, to facilitate an earlier training and competition return, and reduce the chances of re-injury, the role of face-protective orthosis in rehabilitative management is of paramount importance [6]. Face masks can be either prefabricated or customized (also known as patientspecific). Over-the-counter or prefabricated face masks are commonly available in the market and fit a range of athletes during sports activities. However, the use of prefabricated face mask in the clinical treatment of athletes sustaining maxillofacial trauma is limited [7, 8]. These devices do not provide an individualized fit to the patient and, therefore, are considered inferior to customized face masks during the rehabilitation phase. On the other hand, customized or patient-specific face masks are solely designed for the individualized rehabilitative management of athletes with maxillofacial injuries [9]. Until now, the fabrication of customized face masks has been an arduous task. This fabrication method is an entirely manual, labor-intensive process comprising face impression or moulage (negative), plaster mold (positive) fabrication, adaptation of thermoplastic sheet onto the mold, and last, fine-tuning of the mask with cutting tools. This conventional approach, although widely used among clinicians, is time-consuming and unpleasant for the patient. Besides, this approach often requires frequent adjustments compromising both the comfort and function of the face mask [5, 9–11]. To address the challenges mentioned above and increase patient compliance, the integration of less invasive technologies into the clinical workflow is imperative. Medical image processing software and additive manufacturing (AM) technology have been applied in several medical applications, including the design and manufacturing of medical splints for ankle-foot or wrist [12–14]. Furthermore, advancements in digitization technologies such as three-dimensional (3D) face-scanning have made it conceivable to generate digital models of surface topography of human face [15]. The assimilation of these technologies contributes to patient-specific digital data in a contactless manner, and has the potential to change the conventional workflow for customized face mask. This work aimed to integrate the existing tools of medical image processing software, computer-aided design (CAD), 3D digitization, and AM to provide a cost-effective solution for the design and manufacturing of patient-specific faceprotective orthosis or face masks. More specifically, a practitioner/patient-friendly

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“contactless design and production” approach was devised that enabled the clinicians with point-of-care manufacturing to fabricate a customized face mask for a patient operated at a distant hospital.

2 Materials and Methods In this section, an interactive digital design workflow for customized face-protective orthosis or face mask is introduced for athletes with sports-related injuries of the maxillofacial region. The entire workflow has been established using a procedural methodology, including four phases, each involving several steps. An overview of the schematic representation of the digital workflow is displayed in Fig. 1.

Fig. 1. An overview of the schematic representation of the digital workflow.

2.1

Clinical Case Fracture Treatment

The workflow started with an appropriate clinical case selection, referred to the Department of Cranio-Maxillofacial Surgery, University Hospital Basel, to discuss the treatment option for the fabrication of a patient-specific face mask. The patient was a professional soccer player of the Austrian First League who suffered an injury to the face during sport activity. He experienced a fracture of the right zygomaticomaxillary complex (ZMC) region, which was treated by immediate open reduction and internal rigid fixation. The digital imaging and communications in medicine (DICOM) dataset from immediate postoperative cone-beam computed tomography (CBCT) provided by the operating hospital were imported into Materialise Interactive Medical Image Control System (MIMICS) medical software (MIMICS Innovation Suite v. 20.0, Materialise, Leuven, Belgium). Following this, Hounsfield unit (HU), which expresses the grayscale, was adjusted accordingly using the thresholding method. Subsequently, a semiautomatic segmentation of the region of interest was performed, and the respective

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bony and soft tissues 3D volumetric reconstructions were generated, which were consequently exported and saved in a standard tessellation language (STL) file format. The 3D volumetric reconstructions confirmed adequate reduction with three-point fixation of the ZMC region with titanium miniplates at the right frontozygomatic suture, right infraorbital, and right zygomatic buttress regions (Fig. 2).

Fig. 2. Postoperative CBCT 3D volumetric reconstructions A: Bony 3D volumetric reconstruction showing titanium miniplates fixation at right frontozygomatic suture, infraorbital region, and zygomatic buttress regions. B: 3D volumetric reconstructions of soft and bony components with noticeable soft tissue swelling (right side).

2.2

Computational Image Data Acquisition and Virtual Model Registration

An accurate visualization of anatomical bone and soft tissue components is an essential step for the design of a patient-specific face mask. As the immediate postoperative CBCT dataset does not corroborate with the soft tissue component because of postoperative swelling, a 3D optical face scan (Vectra M3 3D Imaging system, Canfield Scientific, New Jersey, USA) was scheduled after the swelling had subsided. Prior to data acquisition, the optical scanner was calibrated following the manufacturer’s instructions. After the scanning procedure was completed, the digitized surface geometry of the face was transferred as polygonal STL (triangular mesh) containing over 200,000 points and 474,180 triangle elements. The mesh generated was of high quality, and no further post-processing procedures were required. To register the face’s surface topography generated from a 3D optical scan dataset to the native anatomical bony structures generated from the postoperative CBCT dataset, superimposition via a best-fit alignment method was executed (3-matic medical v. 13.0, Materialise, Leuven, Belgium). Using an iterative closest point (ICP) algorithm, surface registration protocol (n-point and global registration) was accomplished between the 3D volumetric reconstruction of CBCT soft tissue component and 3D optical face model (Fig. 3A, 3B). The registration protocol was based on the selection of similar anatomical points on the healthy (non-operated) side of the face, unaffected by postoperative swelling. Integrating 3D volumetric reconstructions of bony anatomical structures with a 3D

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optical face scan allowed an accurate representation of the patient’s maxillofacial region, which served as a reference for the digital designing of face mask (Fig. 3C).

Fig. 3. Illustration of the subsequent steps of the computational medical image registration protocol. A: Selection of points on the 3D volumetric reconstruction of soft tissue component from postoperative CBCT. B: Selection of points on the 3D optical face scan model. C: Profile view of the patient after registration of a 3D optical face model on the 3D volumetric reconstruction of the bony component from postoperative CBCT.

2.3

3D Modeling of Patient-Specific Face Mask in CAD Software

Following the computational medical image registration protocol, modeling of the face mask was accomplished in an open-source CAD software (Meshmixer v.3.5.474, Autodesk Inc.). Anatomical and functional requirements were taken into consideration during this design phase. The maxillofacial skeleton has areas of strength (maxillofacial buttresses or pillars) and areas of weakness (in-between walls). The components of the buttress system in the exemplary case consist of vertical (zygomaticomaxillary) and horizontal (frontal bar, infraorbital rim) buttresses. These regions have increased bone thickness and act as a supporting base for the design of the face mask. The STL (triangular mesh) file from the 3D optical face model was used for the digital modeling of the face mask. Using Meshmixer’s select tool feature, the region of interest (ROI) was highlighted using unwarp brush tool. A specific selection mode, limited to symmetry, was turned on during this phase, to allow equivalent boundary extensions of the face mask (Fig. 4A). The smooth boundary of the mask was then defined using the expand mode filter for geodesic distance (Fig. 4B). Keeping the select tool on, the edit functionality was used, and the mask surface was extracted. Next, using the offset tool, a clearance of 2 mm was created. (Fig. 4C). This was necessary to have an adaptation space between the face and inner surface of the face mask to prevent over-compression of the mask on the patient’s face and also to allow sufficient space for the padded lining. The extracted surface of the mask was separated from the 3D optical face model STL. Next, the thickness of the mask was defined. Using the edit mode feature and keeping the surface connected functionality on, the offset tool was used to extrude the mask surface and modeled to a thickness of 3 mm (Fig. 4D). To refine the boundaries of extruded mask, smooth boundary tool was used, keeping the shape-preserving functionality on (Fig. 4E). Further refinement of the mask was accomplished using the

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sculpt tool, limited to surface only functionality. This fine-tuning of the mask overlaying the fractured regions was limited to the outer surface with a selective increase in the thickness. Lastly, using Boolean subtraction tool, four rectangular retentive grooves (dimensions: 5 mm  2 mm) were bilaterally designed onto the frontal and zygoma region of the mask to secure the fastening band (Fig. 4F). These grooves are for assembling and disassembling the face mask, making it adjustable during the rehabilitation phase of the patient. The modeled mask file was at last exported in STL file format.

Fig. 4. Illustration of the subsequent steps of 3D modeling workflow. A: Selection of the region of interest (ROI), B: Defining smoothed boundaries, C: Creation of gap between mask and face, D: Extraction of the mask’s surface to add thickness, E: Shape-preserving symmetrical boundary refinement of extruded mask, F: Creation of rectangular pattern retention grooves.

2.4

Material Extrusion Additive Manufacturing and Post-processing of Patient-Specific Face Mask

The STL file of the virtually designed face mask was imported into the slicing software (MakerBot Print v. 4.5.0.1729, MakerBot Industries, USA) of a 3D printer. The face mask was fabricated in PLA filament reinforced with short carbon fibers (in a weight fraction of 30%) (Patona 1.75 mm Black Carbon Fiber PLA filament, Patona International S.L.U, Germany) using a desktop Fused Deposition Modeling (FDM) 3D printer (MakerBot Replicator+, MakerBot Industries, USA), with the following settings: infill: 40%; layer height: 0.2 mm; shells: 3; nozzle diameter: 0.6 mm, extrusion temperature: 220 °C. For optimal printing, the generation of both raft and support structures were selected in the slicing software. After manual removal of support structures, the surface of the face mask was smoothed using 1000 grit sandpaper, and two layers of carbon-fiber fabric 200 g/m2 (HP-T200/120C, HP-Textiles, Schapen, Germany) were adhered on the inner side of the mask for additional reinforcement.

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In succession, to achieve a glossy smooth surface finish, the mask was coated with a layer of an epoxy resin material (Epoxy Resin 4305, DD composite, Germany), and left to dry. Custom graphics were later added for aesthetic purposes. Finally, a padded foam lining was adhered to the undersurface of the face mask.

3 Results Figure 5 shows the result of the described interactive fully digital workflow – from computational 3D planning/designing, additive manufacturing to the realization of patient-specific face mask in a professional soccer player. Due to the nature of the maxillofacial fracture pattern, in this case, the customized face mask was fabricated as a one-piece structure, which provided optimal protection to the operated site. Results from subjective assessment by the patient were entirely satisfactory. According to these results, the face mask had a comfortable snug-fit requiring no alterations, was impactresistant, and provided a significant earlier return to his athletic practice sessions. The digitally contoured ocular apertures gave an unobstructed view during sport activity. Overall, this in-house fabricated face mask was sturdy, lightweight (50 gm), and aesthetically pleasing.

Fig. 5. Interactive digital workflow. A. 3D computer-aided design and planning B. FDM printed carbon-reinforced PLA face mask C. A professional soccer player with a customized face mask during his sport’s practice session.

4 Discussion Recently, the use of face-protective orthosis or face masks by professional athletes has significantly increased. While several over-the-counter options for face masks already exist, these masks are not as efficient as custom-made masks due to diverse patterns of maxillofacial injuries [7]. Patient-specific face masks are more comfortable and provide a better fit. However, the traditional fabrication method for custom-made masks come with some disadvantages, the most significant being the unpleasant fabrication process. This conventional method is an entirely manual process, starting with a negative impression (sometimes called moulage) of the face taken with materials such as plaster,

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alginate, or silicon rubber. Next, a positive replica of the face is made from the impression using plaster or dental stone. Subsequently, a thermoplastic sheet material is heated, and together with the positive replica is placed in a vacuum former. Once set, the sheet is removed from the mold and adjusted to the dimensions of the mask using a heated knife or a grinding/cutting tool. Finally, holes are cut for the eyes, nostrils, mouth as required, and retention grooves are made for straps. Although this conventional process is widely used among clinicians, the process requires frequent iterations, resulting in a time-consuming process where much material and prosthetic clinic time is invested [8, 9]. However, such procedures can be outsourced to an external company, but this fabrication process is expensive and requires long lead times. Therefore, to increase efficiency from conventional methods and outsourcing, a novel in-house, fully digital workflow for the design and manufacture of a patient-specific face mask was devised. Digitization technologies such as 3D face scanning and AM contributed to the contactless production of patient-specific digital data, which correlates to anatomical features [16, 17]. A device created from a patient’s data makes a bespoke orthosis, which provides the best fit geometry. For a clinical fit patient-specific face mask, each clinical case should be assessed individually. One of the essential aspects that need consideration is adequate fixation of the fracture. A face mask designed over improperly reduced fracture site can cause more damage with fracture-dislocation when in contact with external forces. Several design criteria are also relevant to determine, which anatomical structures such as fractured bones and soft tissue lacerations need protection and which anatomical structures contribute to maximal support for the face mask. In the present clinical case, bilateral supraorbital rims, glabella, and contralateral non-injured zygoma region acted as supporting anatomical structures for the face mask. An elevated surface that helps in the distribution of forces away from the operated ZMC region protected the fractured region. Lateral and anterior field of view was maintained by contouring and widening the ocular region. As an ill-fitting face mask can cause discomfort to the patient, smooth boundary edges and padded lining were added to prevent abrasion of the underlying skin from mild movement. This imagebased design workflow is minimally invasive, less stressful for the patient, and facilitates quick rehabilitation during the convalescence period by protecting the traumatized anatomic region. In this study, we chose the option of using a biodegradable composite thermoplastic material, carbon-reinforced PLA, for the fabrication of a lightweight face mask. The addition of reinforced materials (such as carbon fibers) to PLA to form a thermoplastic composite, helps in matrix binding and transfers the load to the reinforcing fibers. This results in carbon-reinforced composites with high strength-to-weight ratio, excellent corrosion and wear resistance, and high dimensional stability [18]. In terms of utility and performance of the face mask, it can be ascertained that the AM manufactured carbon-reinforced PLA mask achieved a good balance between strength and weight reduction. The subjective assessment by the patient validates the high durability and friendly wear of the face mask. The complete workflow from planning to fabrication of

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mask was accomplished in less than one day. This shows that using an in-house AM setup not only results in a shorter overall turnaround time, but is also a relatively costeffective production solution. The cost of the proposed in-house digital workflow is represented in a general scenario, for instance, the clinician already has the required equipment for the scanning and printing process, and the overall cost is lower due to less time and material consumption. Nevertheless, these cost-effective benefits should be further evaluated where clinicians verify the proposed workflow with conventional fabrication methods. The medical application of AM is increasing with the potential integration of an automatic design process. Some novel concepts for substitutes in medical splints for orthopedic aids are already reported [13]. These digital solution platforms automatically generate a design, which is digitally validated through finite element simulations. Once a feasible structure is obtained, the design is manufactured. Such digital platforms can be exploited and expanded in the field of face masks by establishing a digital process chain. Although the proposed workflow is based on a patient with a fracture of the zygoma region, this sequential methodology, with slight design alterations (for example: the selection of specific ROI), can apply to other fractures of the maxillofacial region that need protection during the convalescence periods. This digitized workflow enables design freedom in a virtual environment, and various modifications can be rapidly integrated before proceeding with manufacturing [13, 19]. The digitized approach provides an easier means of reproducibility in case of lost/damaged mask. Finally, incorporating digital technologies in a clinical environment allows the clinician to customize the patient’s mask with minimal effort, increasing patient satisfaction, and an improvement in treatment efficacy.

5 Conclusion In this article, we presented a fully digital workflow that combined state-of-the-art digitization technology with medical imaging and additive manufacturing to rapidly fabricate a custom-made face-protective orthosis for rehabilitative management in patients with sports-related maxillofacial injuries. To sum up, the exemplary case demonstrates how the unique properties of point-of-care manufacturing can be exploited in the field of orthoses and prostheses through the establishment of a digital process chain. This workflow has allowed an improvement of some characteristics of conventional custom-made masks as follows: • • • •

Minimally invasive, contactless production method Decrease in lead times Easier reproducibility Cost-effectiveness

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Opportunities of 3D Machine Learning for Manufacturability Analysis and Component Recognition in the Additive Manufacturing Process Chain Tobias Nickchen1(B) , Gregor Engels1 , and Johannes Lohn2 1

Paderborn University, 33098 Paderborn, Germany [email protected] 2 Protiq GmbH, 32825 Blomberg, Germany [email protected], https://www.uni-paderborn.de/en/home/, https://www.protiq.com/en/

Abstract. Additive Manufacturing (AM) is one of the manufacturing processes with the highest potentials in the current transformation of the industry. To make use of this potential and to achieve consistent product quality at decreasing costs not only the 3D printers themselves but also the whole process chain has to be automated. Due to the high degree of digitalization and the use of 3D Computer Aided Design (CAD) models within the entire process chain, it is possible to use these information for automation via intelligent data analysis. In this paper, the potential of using 3D Machine Learning (ML) approaches for automation and optimization of sub-processes of the process chain is analyzed. Therefore, we consider the information flow of the 3D models in the process chain of an AM service provider. The potential of using state-of-the-art algorithms from the field of 3D ML for automation of sub-processes like manufacturability analysis, production cost calculation or 3D-component recognition is analyzed and feasibility is examined. For the sub-processes of manufacturability analysis and 3D-component recognition prototype solutions have been implemented and evaluated. For the production cost calculation, only preliminary analyses were carried out, on the basis of which the possible applications of 3D ML algorithms can be estimated. With our analyses, we demonstrate that it is possible to further automate the process chain of AM service providers through the use of 3D ML algorithms.

Keywords: Additive Manufacturing Networks · Process chain automation

· 3D Machine Learning · Neural

c Springer Nature Switzerland AG 2021  M. Meboldt and C. Klahn (Eds.): AMPA 2020, Industrializing Additive Manufacturing, pp. 37–51, 2021. https://doi.org/10.1007/978-3-030-54334-1_4

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Introduction

Additive Manufacturing (AM) offers enormous potentials for the use of optimized components in many highly technical industries. However, since it is still a relatively young process on an industrial scale, many steps of the process chain that go beyond actual production are characterized by manual work. Since the beginning of the fourth technological revolution, industrial processes have been iteratively optimized. The use of interconnected sensors and cyber-physicalsystems leads to intelligent self-adaptive production chains. This enables a more efficient production and a reduction of costs while achieving higher quality [1,2]. Currently, conventional manufacturing processes cover the majority of industrial production. Only 0.04% of global goods production is additively manufactured [3]. According to the same study, however, 5% is quite within the realm of what is possible in the future, if the AM industry takes advantage of its development opportunities. In order to keep pace with common manufacturing processes such as casting, forging or milling and to enable series production, production costs must be further reduced, consistent quality ensured and the advantage of short time to market further expanded. To achieve this, the potential of process chain automation must be further exploited. For the analysis of the process chain we worked together with an AM service provider. As the service provider works with 3D printers from the Powder Bed Fusion (PBF) field [4], we also focus on the process chain of this production method. PBF processes for processing polymers and metals are widely used in the AM industry. The actual AM process of the PBF 3D printers themselves is already completely automated.

Fig. 1. Structure of PBF 3D printers [5].

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Nevertheless, large parts of the upstream and downstream processes are characterized by manual work steps. In the context of our work, we focus on the process steps which are directly linked to the information flow of the 3D models and identify the potentials of using 3D Machine Learning (ML) algorithms for process automation. 3D ML is one of the evolving fields of ML and offers enormous potentials for the analysis of 3D-data. Our main contribution is the proof of concept that parts of the AM process chain can be automated by using 3D ML techniques. For the steps manufacturability analysis and 3D-component recognition, we already implemented evaluation systems and proved the feasibility of these systems. For the process step of production cost calculation, only preliminary analyses were carried out which underline the potentials for this sub-process. In Sect. 2, we describe the major parts of the process chain and analyze which sub-processes are suitable for further automation. Afterwards we give an overview of state-of-the-art 3D ML algorithms which can be used for our purposes. In the following Sect. 4, the results of our evaluation studies are presented, followed by a conclusion and the future perspectives.

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AM process Chain Analysis

In this chapter, we take a deeper look on the AM process chain with its upstream and downstream processes. For getting a detailed insight of all processes, we worked together with an industrial AM service provider and focused on the PBF process chain. All analyses in this paper refer to these processes. We first describe the basic sub-steps of the process chain in Sect. 2.1 and subsequently analyze the automation potentials of the different steps in Sect. 2.2. The basic structure of PBF 3D printers can be seen in Fig. 1. The main characteristic of PBF is the layer-wise application of powder and subsequent selective melting of the powder using a laser with up to 200 W for polymers and around 400 W for metals [6]. This specific approach leads to the necessity of various additional steps, such as the manufacturability analysis, production cost calculation or 3D-component recognition. Since we deal with the analysis of the information flow in the process chain, we only cover sub-processes that are directly related to the digital 3D models. Of course, the process chain includes further steps besides those shown in Fig. 2. However, these have been deliberately removed in our graphic, as they have no direct relation to the digital information flow. Subsequently, we work out sub-processes with automation potentials in Sect. 2.2. 2.1

The Process Chain of an AM Service Provider

The process chain of an AM service provider includes many up- and downstream processes besides the actual production. We deal with all steps that take place in the environment of the service provider and therefore start with the data upload. The construction of the Computer Aided Design (CAD) models is on the side

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Automated on server Data conversion

Data upload

3D model repair

Manual analysis

Build job preparation

Manufacturability analysis Production cost calculation

Additive Manufacturing

Shipping

Quality assurance

Postprocessing

3D-component recognition

Fig. 2. Process steps in the AM process chain which are directly linked to 3D models.

of the customer and is therefore not included. An overview of the process steps related to the 3D models is given in Fig. 2. Data Upload. As mentioned before, the process chain begins with an upload of 3D models by a customer. The data can be uploaded in nearly all common 3D data types. Regardless of the uploaded data type, the 3D models are automatically converted to the Standard Tessellation Language (STL) [7] file format. The STL file format is the standard data format used for AM. All following steps are based on the STL models. Model Repair. The three subsequent steps 3D model repair, manufacturability analysis and production cost calculation are already performed automatically on a server. It is necessary to perform these steps automatically in near real time

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so that potential customers can receive a quote for their uploaded 3D models without delay. 3D model repair involves the checking and correction of errors that may occur during the CAD construction or the conversion to STL. For that task, software with ready-to-use functions can be used to repair STL models completely automatically [8]. Manufacturability Analysis. The manufacturability analysis is necessary to verify if an STL model with a given geometry can be manufactured with a specific manufacturing technology. The manufacturability of an object is defined by geometric features like overhangs, bores or channels and process parameters like layer thickness or build orientation [9]. Only parts of these features can currently be checked automatically with software tools. Problematic here is above all that the existing definitions of manufacturability can only be applied to existing models to a limited extent. Possible guidelines for the manufacturability of 3D models were considered by various researchers in the last years [10,11]. Most of these design guidelines are based on standard geometries like cylinders or cuboids. To use these guidelines, 3D models must be approximated by combinations of the standard geometries. The guidelines can then be applied to the approximated 3D models. A major problem is that complex 3D models can often only be approximated very imprecisely by standard geometries. Therefore, only the guideline of minimum wall thickness is currently automatically tested by a software tool since this feature is not based on standard geometries. At the AM service provider, a more detailed check of the 3D models is therefore performed manually by AM experts in the following step. Production Cost Calculation. The production cost calculation is the last step which has to be performed automatically after the upload to enable realtime pricing. Based on the 3D models geometry and operational parameters, the production cost of 3D models has to be calculated. The costs of a component are influenced by all processes in the process chain. From the preliminary analyses to the manual build job preparation, the actual 3D printing process and the various post-processing steps right through to shipping. Due to the great variety of individual geometries, however, it is hard to exactly predict the costs for some of these steps, e.g. the time necessary for the post-processing steps like sandblasting or support removal. Owing to the complexity, the relationship between the geometry of the 3D model and the amount of work required for the post-processing steps cannot easily be described mathematically. Therefore, the costs of these procedures can only be approximated with current software solutions. Build Job Preparation. The build job preparation is split into manual work steps carried out by AM engineers work steps executed by software tools. Single CAD models must be combined to build jobs. Depending on the requirements of

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the final product, e.g. different build orientations of a 3D model in the construction space are necessary. Automated functions are in principle available for calculation of a good orientation and position of the 3D models in the build chamber [8,12]. Nevertheless, especially the orientation has a strong influence on component properties such as surface quality or mechanical characteristics. Therefore, objects produced in automatically calculated orientations do not always meet the specifications a customer expects. On the basis of interviews with the experts of the AM service provider, it has become clear that especially the orientation of the 3D models is currently still chosen manually by AM engineers in order to guarantee the optimal quality of the components to be produced. Additive Manufacturing. After the build job preparation, the physical process steps begin. As mentioned in Sect. 1, the AM process itself is already completely automated. Depending on process and material, production is followed by various technical finishing steps such as powder removal or sandblasting. These processes run apart from the digital information flow. The physical production and the virtual information flow converge again in the 3D-component recognition step. 3D-Component Recognition. In PBF processes, different components are manufactured together in one batch. Especially with the PBF 3D printers for polymer processing, up to 100 different components are often produced in one build job. After production, they must be assigned to the appropriate digital 3D model again, in order to be able to continue with the subsequent post-processing steps. This is still a manual process which is time consuming and expensive. Post Processing. After the individual components have been separated again, all digital information is available in order to be able to carry out post-processing steps such as surface treatment and to subsequently check whether all customer criteria have been met in the quality control process step. If this is the case, the process chain can be completed with the dispatch of the components to the respective customer. 2.2

Automation Potentials in the AM Process Chain

In this section, we analyze the optimization potential of the process steps described in Sect. 2.1 to decide which process steps are suitable for automation using 3D ML algorithms. The process steps manufacturability analysis, build job preparation, 3D-component recognition and post-processing are potentially of interest for optimization because they are characterized by manual work. Furthermore, the step production cost calculation can be considered. Although this step is automated, it still offers further potential for optimization to improve the current approximation of the calculation of real production costs. In our work, we do not consider build job preparation any further because we believe that the

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expertise required in this step is difficult to replace with intelligent algorithms. Additionally, the step post-processing will neither be considered in the context of this paper, since intelligent data analysis is not relevant for that process. Manufacturability Analysis. In Sect. 2.1, we explained that current manufacturability criteria are represented by design guidelines which are based on standard geometries. With increasing complexity of components, the guidelines are no longer sufficient to represent the real limitations of processability. Since it is difficult to describe the restrictions by concrete mathematical representations, we believe it is more goal-oriented to investigate the possibility of generating conclusions about the manufacturability directly from existing production data. Intelligent systems are capable of independently learning the features that are decisive for manufacturability. Therefore, we see the potential of automation with 3D ML algorithms for this process step. According to AM experts of the AM service provider, errors occurring in the production can be triggered by many different causes. Partly it is filigree details in the 3D models, partly larger connected features that lead to faulty production. Therefore, a solution for this application has to be able to capture and process both fine details and global correlations within the 3D models. Production Cost Calculation. As described in Sect. 2.1, it is difficult to define the mathematical relationships between the geometry of a 3D model and the actual costs for 3D printing itself and the pre- and post-processing steps. The cost of a component depends on many different factors, such as the objects volume and geometric complexity or the orientation in the build chamber. These factors in turn influence direct cost factors such as construction time, material consumption or the amount of work required for the various finishing steps such as sand blasting and surface preparation or in metal processing the removal of support structures. Our research and discussions with the AM service provider have shown that in particular sand blasting, surface preparation and support removal in metal processing are a major cost factor as they are mostly performed manually. Due to the great geometric individuality of the 3D models, it is difficult to calculate the exact amount of work required for the manual postprocessing steps. The amount of work and thus the costs are strongly dependent on the geometric complexity. Complex geometries can contain many different features like free-form surfaces, small openings or complex internal structures. It is practically impossible to create a mathematical formula with hand-generated features to calculate the correlation between these features and the costs for post-processing. At this point we see clear opportunities for optimization. Data-driven systems are able to learn to extract the relevant features. The big advantage of Deep Learning (DL) models is that they are able to generate so called Deep Features from raw inputs on their own. This enables them to learn the complex mathematical relationships that exist here on the basis of their own automatically generated features. By collecting data such as the manual processing time

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for post-processing of produced objects during real production, a 3D ML system can then learn the relationship between the geometry and the processing time and thus the processing costs. By determining the costs for individual post-processing steps more precisely, the total costs for an object can be significantly improved compared to the estimation currently in use. 3D-Component Recognition. For manually assigning physical components to digital 3D models, an employee visually compares the properties of the objects with the 3D models. Due to the increasing number of produced objects, manual sorting is today already connected with an extremely high effort. Therefore, we see a high potential for cost savings through an automated solution. Since in AM completely new components have to be detected in real time every day, conventional computer vision approaches reach their limits. In contrast, 3D ML applications can adapt to the daily changing components. During our work on this topic a first commercial approach recently emerged for this task (AM Flow [13]). According to their data, they achieve a detection rate of 95%. To the best of our knowledge, their system has never been evaluated on a standardized, publicly available data set. In our opinion it is necessary to achieve a detection rate of almost 100% for complete automation. Therefore, a further optimization with 3D ML is possible for the step of component recognition.

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3D Machine Learning

For the process steps of manufacturability analysis, production cost calculation and 3D-component recognition explained in Sect. 2.2, we have examined the possibility of using 3D ML approaches to automate that steps. To find the most suitable solution for our task, we compared several state-of-the-art 3D ML approaches. Existing 3D ML algorithms for popular tasks like 3D object recognition, segmentation or tracking are using either directly 3D data or 2D projections like images. Both variants contain different positive and negative aspects. By sensors like 3D scanners, 3D data in the form of point clouds, meshes or Red, Green, Blue, Depth (RGB-D) data can be generated. 3D representations offer the advantage of displaying the scanned data in great detail but have the disadvantage of being very memory intensive and therefore place high demands on the processing hard- and software. On the other hand, 2D projections in the form of images offer the advantage of being less memory intensive but have the disadvantage of information loss. In order to find the best solution, we figured out the positive and negative aspects of the different approaches and decided which approach is most useful for our purposes.

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Image-Based

Image-based approaches like RotationNet [14] or Group-view Convolutional Neural Network (GVCNN) [15] have the advantage of less memory consumption, faster training times and lower hardware requirements compared to approaches using 3D data. For frequently used benchmark tasks such as the classification of the Princeton ModelNet40 dataset [16], they achieve classification rates of up to 97% which is state of the art and exceeds 3D-data-based approaches.

Fig. 3. Data representation of RotationNet [14]

In most state-of-the-art image-based approaches the basic drawback of losing information about the 3D objects by using 2D projections is counteracted by using multi-view representations of the 3D models. For each 3D object of the data set, multiple images are generated by using different view points around the 3D model (see Fig. 3). These groups of images are then used as collective input data for the model, giving more information to the model what leads to the state-of-the-art results in classification of ModelNet40 [14,15]. However, differentiating 3D models from ModelNet40 differs from our issue because filigree details are relatively irrelevant. Therefore, it must be proven that the imagebased approaches are also capable of differentiating very similar objects. 3.2

3D-Data-Based

Approaches based on 3D data can be assigned to the two sub-classes point-cloudbased and voxel-based. Voxels are the three-dimensional equivalent of pixels in two-dimensional space. Point-Cloud-Based. Point-cloud-based approaches like PointNet++, Relationshape Convolutional Neural Network (CNN) [17,18] or Linked Dynamic Graph CNN [19] directly work on 3D point clouds which can be generated by different types of sensors (Fig. 4).

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Fig. 4. Data representation of relation-shape CNN [18]

Since point clouds are unstructured data sets with possibly varying point densities in different parts of the cloud, convolutional filters which are often used for image processing can not be used. For being capable of handling the varying resolutions, point-cloud-based approaches learn features in multiple hierarchical scales. However, the possibility to capture filigree and global features at the same time can only be realized in theory because it is limited by the size of Graphics Processing Units (GPUs). In practice, a maximum of 5000 points is usually used which is not sufficient to display all details of complex objects. For the analysis of fine details of 3D models, point-cloud-based approaches reach their limits. Voxel-Based. Similar to point clouds, so called voxels capture three dimensional information. The difference to point clouds is that the data is stored structured in a three-dimensional grid. Therefore, common mathematical operators like convolution used in Convolutional Neural Networks (CNNs) can be applied on the 3D data. This enables the same behaviour of voxel-based systems in the three-dimensional as the conventional CNNs in the two-dimensional space. The main drawback similar to point-cloud-based approaches is the high usage of computational memory with growing resolution of the voxel grid. Developers of the approaches VoxNet, OctNet [20,21] or Spatial-hashing-based Convolutional Neural Network (HCNN) [22] have thus tried to optimize the data structure to enable a high resolution. Wang et al. [22] represent the state of the art and reach a resolution of 5123 voxel using high end GPUs. Therefore, voxel-based approaches are best suited to capture filigree details in 3D models (Fig. 5). 3.3

Approach Selection

Based on the requirements of the process steps manufacturability analysis, production cost calculation and 3D-component recognition and the characteristics of the different types of approaches, the following potentials for process automation arise. Since both local and global features must be considered in the subprocesses of manufacturability analysis and production cost calculation, the use

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Fig. 5. Data representation of HCNN [22]

of a voxel-based approach is reasonable for these sub-processes. The longer training time is negligible, since no recurring training process is necessary. For 3Dcomponent recognition all described types of algorithms offer positive arguments which argue for a use. Therefore, we evaluate all described algorithms for that task.

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Evaluation

In order to prove that the described algorithms are suitable for solving the tasks of manufacturability analysis, production cost calculation and 3D-component recognition, an evaluation must be carried out. To the best of our knowledge there are currently no benchmark data sets available for the problems we are investigating. Therefore, we generated our own data sets based on the Thingi10K data set [23] which contains 10000 3D models from the AM domain and trained the algorithms with subsets of that data. The subsets were adapted to the respective problem definition and are oriented towards real production data. 4.1

Manufacturability Analysis

To verify the basic usability of the HCNN approach for manufacturability analysis, we had to generate a labeled data set. Since manual labeling of thousands of 3D models is extremely time-consuming, we decided to use the wall thickness tool which is currently used at the AM service provider. With the help of the tool and the Thingi10K data set, a labeled data set with about 5000 3D models has been created where the 3D models are divided into the categories “manufacturable” and “non-manufacturable” with respect to the feature “minimum wall thickness”. Labeling this data set, enables us to verify the ability to recognize filigree details in 3D models. Some example 3D models can be seen in Fig. 6. This data set has been used to train the HCNN model which is presented in Sect. 3.2 with a resolution of 5123 voxels. The HCNN model is able to achieve an accuracy of 94% correctly classified 3D models in first studies. By applying

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Fig. 6. Example 3D models with colorization of thin areas. Red areas are beneath a threshold value of 0.5 mm.

parameter tuning, this value can be increased even further. The studies have shown that a voxel-based model has the ability to learn high-resolution features from 3D models. Especially, it has shown that it is possible to recognize the wall thickness feature despite strong variance in the exact expression of the feature. Therefore, the next step of our work will be to back up the generated results with further data sets. We want to use the data generated during the production of the AM service provider to generate additional data sets. With that information we can create labeled data sets regarding other geometric features like overhangs, bore holes or channels described by Bikas et al. [9]. 4.2

3D-Component Recognition

For 3D-component recognition six different evaluation data sets have been generated based on the Thingi10K data set in order to evaluate the performance of the described algorithms for the recognition of physical components. These data sets contain between 10 and 100 different 3D models and were composed in a way that they represent typical build jobs of the currently common industrial PBF 3D printers for polymer processing. Three data sets contain random compositions of 3D models from the AM domain, the other three explicitly very similar 3D models (Fig. 7). The data sets are available for public download in order to offer other researchers the possibility of comparison1 . To prove that the approaches we are using are applicable for a real application, we need to adapt it to a possible recognition station setup. After printing, the objects will be separated on a conveyor belt and passed into a scanning area consisting of multiple 2D- or 3D-sensors installed in elevated view-points like 1

https://gpstd.s3.eu-central-1.amazonaws.com/Raw datasets.zip.

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shown inf Fig. 3. The biggest unknown in this system is the orientation in which a component is sensed by the sensors. Therefore, the creation of training data for the 3D ML algorithms is a deciding factor. Depending on the algorithm, virtual images, point clouds or voxel representations have been generated from the 3D models, which have then been used as training data for the systems. For the creation of these input representations we implemented an algorithm which generates physically sound training data. This means that only virtual sensor views are used, which can also occur in the described setup with real sensors. Our evaluation has shown that this method for training data generation has a major influence compared to randomly generated training data.

Fig. 7. Example of 3D models with high similarity.

The data sets have been used to train the approaches [14,15,17–19,22] presented in Sect. 3.1. For the data sets with up to 30 different randomly chosen 3D models all approaches reached an accuracy of more than 99%. For higher numbers of different 3D models the accuracy of the image-based approaches decreases to values around 93%. The accuracy of the point-cloud-based approaches can still reach 96% and the voxel-based approach is close to 99%. This confirms the expectations that image-based approaches in particular reach their limits, especially when there are strong similarities between individual objects. The evaluation experiments have shown that Deep Neural Network (DNN)based approaches are able to recognize physical objects after production with high accuracy. Especially the generation of physically sound training data brings us one step closer to the goal of a 100% recognition rate and thus to the possibility of complete automation. In the next step, we will evaluate the approaches in the production process of the service provider to move from artificially generated data sets to real production data.

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Conclusion

In the context of our work we have shown that the process chain of AM offers various aspects that can be optimized with the use of 3D ML algorithms. The studies have shown that state-of-the-art 3D ML algorithms are capable of analyzing 3D models in high detail with regard to various issues. For the sub-processes of manufacturability analysis and 3D-component recognition we implemented prototypes which have shown that these two tasks can be optimized using the proposed 3D ML algorithms. For the production cost calculation we have carried out preliminary studies. These have shown that using 3D ML algorithms for a more precise calculation of the production costs is promising. In our future work we are going to verify this assumption by using the algorithms with real production data. Our results show that there are different potentials for further automating the process chain of AM. In our research we will continue to build on the studies described in this paper and apply the applications in real production. Acknowledgements. We gratefully acknowledge the funding of this project by computing time provided by the Paderborn Center for Parallel Computing (PC2). A special thanks belongs to the Phoenix Contact Foundation for supporting this research project.

References 1. R¨ ußmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., Harnisch, M.: Industry 4.0: the future of productivity and growth in manufacturing industries. Boston Consult. Group 9(1), 54–89 (2015) 2. Liao, Y., Deschamps, F., Loures, E.D.F.R., Ramos, L.F.P.: Past, present and future of industry 4.0-a systematic literature review and research agenda proposal. Int. J. Prod. Res. 55(12), 3609–3629 (2017) 3. Caffrey, T., Wohlers, T., Campbell, R.: Executive summary of the Wohlers report (2016) 4. King, W.E., Anderson, A.T., Ferencz, R.M., Hodge, N.E., Kamath, C., Khairallah, S.A., Rubenchik, A.M.: Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges. Appl. Phys. Rev. 2(4), 041304 (2015) 5. Woesz, A.: Rapid prototyping to produce porous scaffolds with controlled architecture for possible use in bone tissue engineering. In: Virtual Prototyping & Bio Manufacturing in Medical Applications, pp. 171–206. Springer (2008) 6. Bikas, H., Stavropoulos, P., Chryssolouris, G.: Additive manufacturing methods and modelling approaches: a critical review. Int. J. Adv. Manuf. Technol. 83(1–4), 389–405 (2016) 7. Ciobota, N.D.: Standard tessellation language in rapid prototyping technology. National Institute of Research and Development for Mechatronics and Measurement Technique, Bucuresti, The Scientific Bulletin of Valahia University–Materials and Mechanics, vol. 7 (2012) 8. Materialise Magics Sinter Module. https://www.materialise.com/en/software/ magics/modules. Accessed 22 Apr 2020

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9. Bikas, H., Lianos, A., Stavropoulos, P.: A design framework for additive manufacturing. Int. J. Adv. Manuf. Technol. 103(9–12), 3769–3783 (2019) 10. Adam, G.A., Zimmer, D.: Design for additive manufacturing–element transitions and aggregated structures. CIRP J. Manuf. Sci. Technol. 7(1), 20–28 (2014) 11. Rudolph, J.P., Emmelmann, C.: Analysis of design guidelines for automated order acceptance in additive manufacturing. Procedia CIRP 60, 187–192 (2017) 12. Zwier, M.P., Wits, W.W.: Design for additive manufacturing: automated build orientation selection and optimization. Procedia CIRP 55, 128–133 (2016) 13. Am-Vision: 3D-Part Recognition. https://am-flow.com/vision/. Accessed 19 Feb 2020 14. Kanezaki, A., Matsushita, Y., Nishida, Y.: RotationNet: joint object categorization and pose estimation using multiviews from unsupervised viewpoints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5010–5019 (2018) 15. Feng, Y., Zhang, Z., Zhao, X., Ji, R., Gao, Y.: GVCNN: group-view convolutional neural networks for 3D shape recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 264–272 (2018) 16. Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3D shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912–1920 (2015) 17. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems, pp. 5099–5108 (2017) 18. Liu, Y., Fan, B., Xiang, S., Pan, C.: Relation-shape convolutional neural network for point cloud analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8895–8904 (2019) 19. Zhang, K., Hao, M., Wang, J., de Silva, C.W., Fu, C.: Linked dynamic graph CNN: learning on point cloud via linking hierarchical features. arXiv preprint arXiv:1904.10014 (2019) 20. Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network for realtime object recognition. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 922–928. IEEE (2015) 21. Riegler, G., Osman Ulusoy, A., Geiger, A.: OctNet: learning deep 3D representations at high resolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3577–3586 (2017) 22. Shao, T., Yang, Y., Weng, Y., Hou, Q., Zhou, K.: H-CNN: spatial hashing based CNN for 3D shape analysis. IEEE Trans. Vis. Comput. Graph. 26, 2403–2416 (2018) 23. Zhou, Q., Jacobson, A.: Thingi10K: a dataset of 10,000 3D-printing models. arXiv preprint arXiv:1605.04797 (2016)

Review on the Design Approaches of Cellular Architectures Produced by Additive Manufacturing Marco Pelanconi(&)

and Alberto Ortona

Mechanical Engineering and Materials Technology Institute (MEMTi), University of Applied Sciences (SUPSI), Via Cantonale 2C, 6928 Manno, Switzerland [email protected]

Abstract. The advent of additive manufacturing (AM) has allowed conceiving components by their function and no longer by their manufacture. This benefit allows improving the components’ performances and the fabrication of geometrically complex parts such as cellular structures. The design method is fundamental for the layout of these components. In this work, we present the design approaches of regular and irregular strut-based and triply periodic minimal surface-based structures. We propose a novel design method of multifunctional cellular architectures enabling to generate structures with morphological variations that provide a component with different features and functionalities in its own volume, depending on its requirements. The final chapter present the study of a bio inspired cellular material, from the design to experimental testing through manufacture and simulation. The structure consists of an ultra-lightweight body made up of gyroid cells and reinforced with carbon fiber bars. This concept was used to design an innovative helmet, which aims at combining the lightness and impact resistance. Keywords: Additive manufacturing

 Lattice design  Helmet

1 Introduction Porous cellular materials are used in many different engineering industrial fields, such as high temperature application, catalyst, protection systems, weight saving applications, thermal storage, composites [1–5]. They are employed exploiting their properties basically related to the material (polymeric, metallic or ceramic) and to the morphology. Additive Manufacturing (AM) made it possible to produce these structures that previously could not be fabricated with conventional manufacturing methods. AM allows to fabricate metal, plastic and ceramic objects starting from a 3D computeraided drafting (CAD) file [6]. It has opened the doors to the generation of more and more complex CAD models, leading to the need for more advanced generation tools. Cellular porous architectures are very complex in their morphology and they need special design tools. A proper design of the morphology can result in structures with optimized properties for specific applications [7–9]. Such structures contain large numbers of geometrical details, which are impossible to generate with standard CAD © Springer Nature Switzerland AG 2021 M. Meboldt and C. Klahn (Eds.): AMPA 2020, Industrializing Additive Manufacturing, pp. 52–64, 2021. https://doi.org/10.1007/978-3-030-54334-1_5

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packages [10]. In general, “classic” CAD tools are not able to perform quick and efficient design of lattice structures. Ashby and Gibson [11] introduced a simplified model of foams. Each cell was assumed as “cube like”. Their idea of using cubic lattice structures was introduced to explain the behavior of foams, with analytical models, in terms of pressure drop, heat and mass transfer, and stiffness [12–14]. Compared to random foams, lattice structures are regular and reproducible. They offer more design freedom, which results in structures with enhanced properties and novel functionalities [15]. In this paper, we report computational design approaches in different dedicated software environments, using a combination of purpose-built algorithms and scripts. The study has focused on regular and irregular strut-based lattices and triply periodic minimal surface-based structures. A novel design method of multifunctional cellular architectures is presented (Fig. 1).

Fig. 1. Design solutions for cellular architectures produced by additive manufacturing: a) irregular strut-based structures; b) regular strut-based structures; c) multifunctional cellular architectures; d) triply periodic minimal surface based-structures.

2 Irregular Strut-Based Structures An irregular strut-based structure consists of a random arrangement of cells composed by struts, which are connected one to another by nodes. This means that the architecture is not periodic and the cells are geometrically different one from another.

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Random Foam Design

Foams exhibit scattered properties due to their random and non-periodic structure: defining their behavior is very difficult. One way is to study experimentally their properties [16] and the other is to simulate computationally [17], but this requires a CAD model of the foam. A Matlab (MathWorks. Natick, Massachusetts, USA) code was developed for the generation of random foams made up of struts elements. The script uses two purpose-built .TXT files, one with points and the other with connection, which generate a large random foam domain. Setting the size of the sample as input, the algorithm randomly chose a region inside this domain, cutting a random foam with the specified dimensions. The result is an array of point and lines [18] that can be scaled in order to reach a certain dimension of the pores. The array is subsequently converted into cylinders and spheres of a specified diameter. The code does not allow any other type of control on the geometry. Ceramic foams are widely employed in high temperature applications thanks to their outstanding properties. We designed and produced Si-SiC open cell foams as active zone in porous burners for heat radiation application [19], catalyst carriers and solar radiation absorber [2] (Fig. 2).

Fig. 2. Random foam design

2.2

Voronoi-Based Design

There are only few parameters that can be varied in order to engineer the properties of a foam. The need to represent a random foam, instead of periodic arrangement of the regular lattices, with a random engineerable structure has led to the development of a new algorithm based on the Voronoi 3D tessellation [20, 21]. This method consists in the decomposition of the space determined by the distances from a given discrete set of random points. For each point there is a corresponding region consisting of all points closer to that point than to any other. These regions are called Voronoi cells [22].

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The edges of each cell are subsequently converted into solid struts. The code, realized into Rhinoceros (McNeel, Seattle, Washington, USA) using the Grasshopper plug-in, allows the generation of Voronoi-based lattices of any shape (Fig. 3) with a parametrization of the following geometrical quantities: mean cells size, struts diameter, gradient for struts diameter, sample size. The code was further developed allowing the implementation of a cells size gradient along one or more axis and more directions: the cells size changes starting from one value and arriving at another, even with more variations. In this way, it is possible to generate a lattice with different porosities in different regions of its volume.

Fig. 3. Voronoi-based design

3 Regular Strut-Based Structures A regular strut-based structure consists of a periodic arrangement of cells composed by struts, which are connected one to another by nodes. The architecture construction takes place from a unit cell that is replicated in the three-dimensional space until forming a sample of the desired overall dimensions. 3.1

Structured Lattice Design

A parametric algorithm code [23, 24] was developed using Matlab, which generates structured lattices made up of struts elements. The script allows varying the following parameters: cells type, cell size, struts diameter, elongation angle [25] and XYZ cell replication (sample overall size). The code allows generating several types of cells [4, 5, 15]: octet, modified octet, Weaire-Phelan, Tetrakaidecahedron, hexagon, modified hexagon, diamond, straight cube and rotated cube. The selected cell is replicated in the space until forming the desired sample dimension. With Boolean tools in then possible to crop the lattice into the desired shape. The struts, consisting of cylinders and spheres, are then exported in .STEP file into the commercial computer-aided design software Siemens NX (Siemens. Munich, Germany) and joined in a single solid body. This code was implemented for the fabrication of a: (i) Si-SiC periodic architecture for catalytic supports (based on rotated cube cells) within the FP7 BioRobur European research project [26, 27]; (ii) Tubular Si-infiltrated SiCf/SiCm composites with a lattice

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structure inside were fabricated for concentrated solar receiver applications [28, 29]; (iii) SiSiC porous burners [19]; (iv) ceramic automotive catalyst substrates [30]; (v) Al2O3 tubular high temperature heat exchanger with a rotated cube lattice inside [31] (Fig. 4).

Fig. 4. Structured lattice design

The numerical tool was further developed by using the visual programming language Grasshopper that runs within the Rhinoceros 3D CAD software. This new algorithm allows the generation of lattices structures of any shape, with a quickly parametrization of the geometrical quantities: cells size, struts diameter, gradient for struts diameter, sample size. The following types of cell can be generated: x, star, cross, tesseract, vintiles, diamond, honeycomb, Tetrakaidecahedron, Weaire-Phelan, octet, modified octet, hexagon, modified hexagon, straight cube and rotated cube. This code can be further developed for any type of cell. Figure 5 shows a structured lattice design with a gradient for the strut’s diameter, created using Grasshopper. The advantage of this tool is the possibility to convert the body into a 3D triangular mesh using Cocoon add-on: the output is a STL file that can be processed immediately for 3D printing. 3.2

Unstructured Lattice Design

Unstructured lattices are preferred with respect to structured ones. This is due to the possibility to have components with heterogeneities such as variable cell size and orientation. A 3D Matlab tools was developed in order to produce unstructured lattices. This method starts with a 3D CAD model of the bulk volume to fill with lattice cells. This code needs a cad-mesh with hexahedron elements as input and converts all the elements in any type of unit cell, which must fit into the hexahedron. The script allows varying the cells size and the struts diameter. Moreover, it is possible to perform a

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Fig. 5. Structured lattice design with Grasshopper

distortion of the cells that allows filling at best the complex volumes. The code was implemented to produce an Innovative Thermal Management Concepts for Thermal Protection of Future Space Vehicles (THOR project) [32] with a particular shape. Figure 6 shows a simple example of an unstructured lattice design.

Fig. 6. Unstructured lattice design: a) CAD model of a bulk volume; b) hexahedral mesh with a linear degrowth rate of 0.9; c) CAD model with tetrakaidecahedron unit-cells.

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4 Multifunctional Cellular Architectures Design Additive manufacturing allows producing very complex geometry. This means that different components, which perform different functions, can be fabricated together in one piece having the same features of them. In this field, we developed a novel design method of multifunctional cellular architectures enabling to generate structures with morphological variations that provide a component with different features and functionalities in its own volume, depending on its requirements. The purpose-built Grasshopper algorithm allows generating structures with different cell types in the same volume, therefore there is a gradient between a cell type and another, made up of Voronoi 3D tessellation. The flexibility of Voronoi tessellation can be employed to join different structures. Figure 7 shows a 2D representation of a multi-lattice produced with this approach.

Fig. 7. Multifunctional cellular architectures design: a) quad lattice; b) hexagonal lattice; c) rotated quad lattice; d) multi-lattice structure consisting of quad, hexagonal and rotated cube cells.

5 Triply Periodic Minimal Surface Design Additive Cellular structures made up of struts elements are mainly employed for their high porosity, flow [33] and thermal properties [34, 35]. In applications where the objective is to maximize the surface area of a component (for example catalytic substrates), these structures are not the best solution. A design approach, based on triply

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periodic minimal surface (TPMS) [36], was performed in order to produce high-surface area structures. A TPMS consists of a single, continuous, smooth and periodic arrangement of surfaces. The architecture construction takes place from a single surface that is replicated in the three-dimensional space until forming a sample of the desired overall dimensions. The surface divides space into two interwoven domains. The TPMS is then converted into a lattice using two different approaches [37]: (i) Strutbased structure: one domain is filled with solid material and the other is left empty (void domain). The resulting structure is a lattice made up of struts, i.e. as the previous presented structures but with a TPMS unit cell; (ii) Sheet-based structure: the surface is thickened of a desired value, forming two separate empty domains, which are infinite and intertwined, but not interconnected. The resulting structure is a warped sheet with the constant thickness everywhere. A 3D numerical tool was developed in Grasshopper. It allows the generation of TPMS-based structures of any shape, with a parametrization of the following geometrical quantities: cells size, struts diameter or surface thickness, sample size. The algorithm was employed for the realization of novel cylindrical catalytic substrates [38] produced by directly 3D printing of alumina powders [39]. Five different structures were evaluated in order understand their mechanical and fluid-dynamic behavior (Fig. 8).

Fig. 8. TPMS design

6 Innovative Bio Inspired Helmet Biological structures such as butterfly wings are natural hybrid materials that are made up of multiple components that are combined in specific geometries and scales. Butterfly wings have widely inspired researchers due to their particular design and multifunction such as attracting their mates (optical) or escaping predators (aero-mechanical). Indeed, from a mechanical perspective, their wing can be considered a structure that is optimized for bending loads. In a cross section of a wing scale, the highly porous central region

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separates two outer regions which are supported by a frame in whereby load-bearing bars are connected to the porous core by perpendicular smaller bars. The topology of the inner porous region maximizes the structure’s rigidity while simultaneously minimizing its weight. The architecture of the porous structure can be described as the TPMS of the gyroid (Sect. 5). The structure was further evolved by reinforcing the external elements with carbon fiber-reinforced plastic (CFRP) rods [40]. The model was designed and simulated with finite element analysis (FEA) to optimize its hybrid structure and parameters: CFRP rods diameter and gyroid surface thickness. The structures were AM manufactured through the stereolithography (SLA) technique. SLA allows for the fabrication of three-dimensional polymeric parts with UV radiation that induces the photopolymerization of a reactive monomer. In this process, the STL model is sliced into two-dimensional cross-sections, allowing for their projection in sequence and building the part layer by layer. The CFRP rods were inserted in their casings and bonded to them through a thin layer of a two-component epoxy glue that was applied to the rods before their insertion. In order to evaluate the mechanical behavior of the structures, 3-point bending, quasi-static experimental tests were performed with a universal material electromechanical testing machine at standard conditions. A 3D FEM-based approach was followed to simulate the experimental 3point bending tests on the structures. Ultimately, experimental results were compared with the FEA output in the linear-elastic regime. The validation of the simulation was performed comparing the bending deformation of the structure with the experimental one under the same condition. The structure with CFRP rods had more than twice the stiffness of the non-reinforced structure (46 N/mm and 20 N/mm, respectively) and withstood a maximum load of about 280 N (100 N without CFRP). The results of the FE model confirm these values. An advantage of this solution over the standard sandwich structures is that it directly connects the solid part of the porous core to the mating reinforcing element and further minimizes its mass. The proposed topological approach can be applied to many materials as long as there is a difference in the elastic modulus between the core and the ribs. The design can become more demanding from a computational point of view if you want to generate complex objects. To demonstrate the feasibility of this concept, thanks to nTop Platform (New York, NY 10013, USA), an innovative helmet was designed (Fig. 9). The structure consists of an ultra-lightweight helmet made up of gyroid cells and reinforced with carbon fiber bars. This concept design aims at combining the lightness and transpiration of gyroid architectures with the unmatched mechanical properties of carbon fiber reinforced plastics (CFRP), which are placed (like in the butterfly wing) to enhance the impact protection right where it is needed.

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Fig. 9. An innovative helmet designed with nTopology

7 Conclusions The strategy of designing a component has always been influenced by its manufacturing. The advent of AM has allowed conceiving components by their function and no longer by their manufacture. This benefit allows improving the components’ performances and the fabrication of geometrically complex parts such as cellular structures. The computational design is performed through the simulation of the component behavior (mechanical, thermal, fluid dynamic, etc.) based on a trial & error approach. Therefore, the possibility of quickly modify the geometric parameters of the component becomes fundamental. This study reviews alternative design methods to create cellular structures with regular and irregular configurations. The design tools presented in this work were employed to design components for several engineering applications: active zone in porous burners, heat radiation application, catalyst carriers, solar radiation absorber, high performance protectors for space vehicles, complex templates for heat exchangers, water treatment, lightweight materials, composites. With the irregular strut-based lattice approach, it is possible to generate random foams and Voronoi lattice. These structures exhibit scattered properties and they are used when randomness is needed. The regular strut-based lattice approach is based on the creation structured and unstructured lattice structure. The 3D numerical tools are based on the replication of a desired unit-cell in the space followed by Boolean tool to cut the desired final shape. This is the most used approach to design engineering components due to the control and parametrization of the numerical model. The multilattice approach, based on the Voronoi tessellation, allows to generate structure with different morphologies in a single volume. It can be used to join different lattices and generate very complex components. The TPMS design approach consists in the creation of surface-based components with high mechanical properties. This numerical

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tool was used to design an innovative helmet inspired by the butterfly wings scales. The structure consists of an ultra-lightweight body made up of gyroid cells and reinforced with carbon fiber bars. This innovative concept aims at combining the lightness and impact resistance.

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35. Ferrari, L., Barbato, M., Esser, B., Petkov, I., Kuhn, M., Gianella, S., Barcenad, J., Jimenez, C., Francesconi, D., Liedtke, V., Ortona, A., Ortona, A.: Sandwich structured ceramic matrix composites with periodic cellular ceramic cores: an active cooled thermal protection for space vehicles. Compos. Struct. 154, 61–68 (2016) 36. Schoen, A.H.: Infinite periodic minimal surfaces without self-intersections (1970) 37. Kapfer, S.C., Hyde, S.T., Mecke, K., Arns, C.H., Schröder-Turk, G.E.: Minimal surface scaffold designs for tissue engineering. Biomaterials 32(29), 6875–6882 (2011) 38. Al-Ketan, O., Pelanconi, M., Ortona, A., Abu Al-Rub, R.K.: Additive manufacturing of architected catalytic ceramic substrates based on triply periodic minimal surfaces. J. Am. Ceram. Soc. 102(10), 6176–6193 (2019) 39. Santoliquido, O., Colombo, P., Ortona, A.: Additive manufacturing of ceramic components by digital light processing: a comparison between the “bottom-up” and the “top-down” approaches. J. Eur. Ceram. Soc. 39(6), 21 (2019) 40. Pelanconi, M., Ortona, A.: Nature-inspired, ultra-lightweight structures with gyroid cores produced by additive manufacturing and reinforced by unidirectional carbon fiber ribs. Materials 12(24), 4134 (2019)

Process Chain

Multi-material 3D Printing of Thermoplastic Elastomers for Development of Soft Robotic Structures with Integrated Sensor Elements Antonia Georgopoulou1,2(&), Bram Vanderborght2, and Frank Clemens1 1 Department of Functional Materials, Empa – Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland [email protected] 2 Department of Mechanical Engineering (MECH), Vrije Universiteit Brussel (VUB), and Flanders Make, Pleinlaan 2, 1050 Brussels, Belgium

Abstract. Embedded sensing can benefit soft robots with the ability to interact with their environment but producing embedded soft sensors can be challenging. Multi-material Fused Deposition Modeling (FDM) additive manufacturing allows producing complex structures, by combining more than one kind of polymeric material. For multi-material FDM, conductive thermoplastic elastomer filaments have been developed. This allows the printing of flexible functional structures, based on thermoplastic elastomer structures with conductive paths that are of great interest for stretchable electronics and soft robotic applications. In this study, stretchable piezoresistive elastomer strain sensor composites were successfully produced by using multi-material FDM. A piezoresistive thermoplastic elastomer was printed on the top of a nonconductive, flexible thermoplastic elastomer strip using FDM multi-material 3D printer. FDM elastomer filaments with different shore hardness as substrate materials for the gripper structure were used. The hardness of the elastomer affected the printability and the adhesion to the conductive elastomer material, which was used as a strain sensor material. The hardness affected the strain sensor properties too. The piezoresistive response, dynamic behavior, drift, relaxation and sensitivity of the printed multi-material strips were investigated by tensile tests. Soft robotic grippers with integrated sensing elements to detect deformation while touching the objective were selected as a case study. The soft grippers with the integrated sensors exhibited intelligent response by recognizing when they were griping a small or big object and when an obstacle was inhibiting their function. Keywords: Piezoresistive elastomer sensor Soft robotic gripper

 FDM additive manufacturing 

© Springer Nature Switzerland AG 2021 M. Meboldt and C. Klahn (Eds.): AMPA 2020, Industrializing Additive Manufacturing, pp. 67–81, 2021. https://doi.org/10.1007/978-3-030-54334-1_6

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1 Introduction Important applications for additive manufacturing (AM) are the development and fabrication of products for consumer goods and electronics [1]. 3D printing in combination with functional materials can be used for the production of sensors integrated in substrates and robotic devices [2–4]. The FDM additive manufacturing is one of several material extrusion methods, which can be used to develop robotic devices with integrated sensors. One of its main advantages in comparison to direct energy deposition, material jetting or powder bed fusion AM techniques is the fact that multimaterial printing is easy to implement. In addition, FDM is cost-efficient, has good resolution and compatibility with many materials and composites [5, 6]. Multimateiral FDM includes a combination of different materials and can be a useful production method for robotic systems [7]. In the case of sensing in robotics, multimaterial 3D printing can be used for producing the robotic body with integrated sensors, based on functional materials, in one-step [8]. FDM additive manufacturing is compatible with thermoplastic elastomer materials like thermoplastic polyurethane (TPU). By using TPU filaments, it is possible to print soft robotic structures like pneumatic actuators and grippers. Soft robotic grippers with integrated sensors are intelligent systems that can alter the posture, gripping force, and gripping geometry according to the object they are gripping [9, 10]. These intelligent grippers can find application where a careful manipulation of objects is needed but also for the handling of complex geometries, where compliance with the object surface is important [11]. Therefore, soft grippers have been developed for the food industry to handle sensitive food and perform complex tasks that involve food like packaging [12, 13]. Furthermore, the soft robotic gripper can be used for the exploration of unknown environments like space or underwater and can be particularly useful for acquiring sensitive samples from these environments [14, 15]. Soft grippers based on pneumatic actuators can be controlled by pressure [16]. It is also possible to develop soft grippers using servomotors. However, they must be controlled optically or by piezoresistive sensors to be able to use them for sensitive objectives. Under harsh conditions, optically monitoring of gripper movements is difficult. Multi-material FDM to develop soft robotic grippers with integrated sensors has been used before [17]. However, the effect of the stiffness of the soft gripper structure on the sensing behavior has never been investigated. In this attempt, soft robotic grippers with integrated strain sensors have been investigated using a commercial FDM multi-material printer. TPU filament with carbon black filler was used for printing of the sensing paths on the surface of the gripper structure. The body of the soft gripper was printed with TPU filaments of two different shore hardness to investigate the effect of the shore hardness on the sensing behavior of the conductive sensing structure on the surface of the gripper. Furthermore, the effect of the thickness of the gripper on the sensing behavior was investigated and the potential of the sensor to be used for monitoring the function of the robotic gripper was explored.

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2 Materials and Methods 2.1

Sensor Printing and Robotic Gripper

Filaments based on TPU were supplied from Recreus Industries (Elda, Spain) in two different shore harnesses (FilaFlex 82A and FilaFlex 95A). The conductive TPU filament Eel based on thermoplastic polyurethane and carbon black was supplied by Fenner Drives (Ninjatek Eel, Manheim, USA). For 3D printing, the FDM 3D printer Pro2 Dual Extruder 3D Printer (Raise 3D, Irvine, USA) was used. The printing of the multi-material strips and soft grippers was done at a temperature of 230 °C with a printing speed of 15 mm/s for the perimeters and 20 mm/s for the infill. A layer height with 0.2 mm was printed with a nozzle size of 0.6 mm. To achieve a dense structure, infill was set to 100%; the extrusion multiplier was set to 120%. The setup of the multimaterial FDM additive manufacturing process can be seen in Fig. 1.

Fig. 1. Setup of the process of multi-material FDM additive manufacturing with a conductive and a non-conductive filament.

For the assessment of the mechanical and electrical behavior of the system, the Eel conductive TPU was printed on the surface of TPU strips with dimensions130  10  0.3 mm. For the multi-material soft gripper structure, first, the sensor structure was produced and on top of it, the gripper body was printed. After the printing of the gripper, silver wires were inserted at the gripper to act as tendons. These tendons were necessary for the actuation and motion of the robotic gripper. Additional to the wires, a Tower Pro MG90S micro servo (Adafruit Industries. New York, USA) was used. The control of the motor was performed with an Arduino microcontroller. Both structures are shown in Fig. 2.

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Fig. 2. a) Sensors integrated on the surface of an elastomer strip from FilaFlex 82A (yellow) and FilaFlex 95A (blue) b) robotic gripper with integrated sensing elements made with FilaFlex 82 and c) with FilaFlex 95A for the robotic body using multi-material 3D printing.

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For minimizing the slipping during the tensile testing, pneumatic clamps were used. 4 bar pressure had to be applied to avoid slipping of the samples during the testing. For measuring the resistivity during the tensile test, a Keithley 2450 source meter (Keithley, Solon, USA) was used with the KickStart software from the same company. For the measurements, a two-terminal sensing mode was used and the change in current was measured while a constant voltage of 1 V was applied. Two different types of tensile tests were performed. First, the strips were tested up to the breaking point, and later strips were tested dynamically with consecutive cycles of loading and releasing, separated by a dwell time of 30 s at the maximum and minimum strain levels. From the measurements of the resistance, the relative resistance (Rrel) was calculated according to the formula where R is the electrical resistance of the sensor and Ro the electrical resistance of the sensor when no strain is applied to it: Rrel ¼

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3 Results and Discussion 3.1

Tensile Test to the Breakpoint

Tensile tests were performed on multi-material printed strips (Fig. 2a). From the results of the tensile test, the stress-strain was constructed (Fig. 3).

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From the stress-strain plot, it can be seen, that the TPU with lower shore hardness could endure much larger elongation (up to 500%) before the break. The strip with higher shore hardness broke at 260% strain. As expected, the stiffness was higher for the strip, which was printed from TPU filament with higher shore hardness. The elasticity modulus was calculated at 13 MPa for the 82A strip sample and 23 MPa for the 95A sample. No delamination between the sensor part and the substrate strip was observed during the tensile testing. The sensor broke before the substrate strip did. 82A

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Looking at the sensor response (Fig. 4), up to 250% strain, the strips of the two different TPU strips show similar behavior. The electrical resistance changed with the applied deformation, showing that the system exhibited a piezoresistive response. 3.2

Dynamic Tensile Testing

Looking at the dynamic testing (Fig. 5), it was seen that after five cycles of loading and releasing, the mechanical properties of the strips with integrated sensor elements responded with good repeatability.

Fig. 5. Mechanical response during dynamic tensile testing between the strain 0 and 30% for strips of shore hardness a) 82A and b) 95A. Stress relaxation was observed for both sensor systems but was larger for the strip of lower shore hardness.

At the dwell time, when the strain was held constant for 30 s the mechanical relaxation was investigated as described by Melnykowycz et al. [18]. Stress relaxation often occurs in elastomers and can affect the sensor response during the dynamic test. As proposed by Melnykowycz et al., the drift of the mechanical stress was investigated between different cycles. In this case, the drift between the second and fifth cycle at 30% strain was evaluated (Table 1).

Table 1. Mechanical relaxation and drift during dynamic tensile testing for strips made with multi-material 3D printing calculated at 30% strain. Substrate material Mechanical relaxation Mechanical drift FilaFlex 82A 24% 20% FilaFlex 95A 20% 5%

It was observed that at 0% strain, the strips were bucking and therefore the mechanical relaxation and drift could not be calculated. Based on the measurements shown in Fig. 4, buckling occurred at strains below 16%. The stress relaxation was higher in the case of the material with the lower shore hardness, FilaFlex 82A. As for

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the mechanical drift, it was relatively low for both systems, except for the FilaFlex 95A system with a drift of 89%. The presence of drift especially at low strains can be attributed to the presence of buckling during the tensile testing. Looking at the piezoresistive behavior of the 3D printed sensor elements on top of the TPU strips during the dynamic testing it was seen, that the relative resistance could follow the change in strain for both the loading and the releasing phase of the tensile test (Fig. 6). Rel.Resistance

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The reverse piezoresistivity that was seen during the tensile test to the breakpoint for strains lower than 30%, was also seen at the dynamic testing. At low strain higher conductivity and at high strain lower conductivity can be observed. It is worthwhile to mention that due to the buckling behavior, the electrical relaxation and drift could not be determined at 0% strain. However, it can be seen in Fig. 6, that during buckling of the strips at lower than 16% strain the electrical signal of the 82A show an unexpected drift. The response of the sensor was linear but the relative resistance decreased with an increase in strain and increased when the strain decreased. The relaxation and drift that was observed for the mechanical dynamic behavior of the sensor, was also seen in the response of the sensor signal (Table 2). Table 2. Electrical relaxation and relaxation during dynamic tensile testing for the sensors integrated into an elastomer substrate produced with multi-material 3D printing calculated at 30% strain. Substrate material Electrical relaxation Electrical drift FilaFlex 82A 28% 9% FilaFlex 95A 25% 8%

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In the case of the electrical signal, the relaxation was slightly higher for the system with a strip of lower shore hardness. This finding agreed with what was also observed for stress relaxation. However, in the case of the drift of the electrical signal, there was almost no difference seen when comparing the two systems. 3.3

Application: Robotic Gripper with Integrated Piezoresistive Gauge Sensor

The aim of this study was the printing of soft robotic grippers with integrated strain sensing elements. The grippers consisted of a flexible belt and elements called phalanges (Fig. 7).

Fig. 7. Sketch of a tentacle, showing the belt, the phalanges and the wires (tendons) which are connected to the servomotor.

Inside the phalanges structure, a wire (tendon) is connected with a servomotor. If the servomotor will coil the tendon, the belt will bended until the phalanges will touch each other. With the assistance of flexible tendons, the belt could bend because of the reduction in tendon length that is connected to the servomotor. The black lines in Fig. 7 are the piezoresistive sensor parts, which were printed on top of the belt structure. Varying thickness of the belt from 2 to 6 mm was used to investigate the effect of the geometrical stiffness, and therefore the bending stress and deformation, on the sensor behavior. Grippers with three different belt thicknesses (2 mm, 4 mm and 6 mm) were printed. The grippers were assessed for their sensor performance during consecutive cycles of opening and closing (Fig. 8).

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Fig. 8. Gripper with embedded sensors produced with multi-material FDM additive manufacturing operates between positions a) open and b) closed and close up for the gripper of shore hardness c) 82A and d) 95A.

It is worthwhile to mention that by adding the sensor structure on top of the printed gripper substrate no significant stiffening effect could be observed and the servomotor had no problem opening and closing the gripper. Based on mechanics for bending, the sensor will see larger stress and deformation if the thickness of the gripper will increase. As it was seen from the sensor response, by increasing the belt thickness, the change in the electrical resistance of the strain sensor decreased (Fig. 9).

Fig. 9. Signal response of the strain sensor on the surface of the belt structure in a robotic gripper with three different belt thicknesses during a cyclical test. The gripper moved five times between positions open and close a) relative resistance during testing and b) resistance during the testing. The gripper with a belt of the smallest thickness had the largest change in relative resistance but the sensor response was accompanied by significant noise.

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Independent on the belt thickness, all the grippers showed the reverse piezoresistivity that was seen during the tensile testing (Fig. 6). In general, for all belt thicknesses it could be observed that while the deformation increased, the relative resistance decreased. For the gripper with the smallest thickness of the belt, it was seen that the change in the resistance and relative resistance was the highest. However, when looking at the gripper with the 2 mm belt thickness, observed higher noise at a closed position and a lager relaxation at an open position can be observed. The appearance of this noise could be considered undesirable for many applications like soft robotics and might be an artifact of the servomotor because of small voltage fluctuations. This noise was only observed in the gripper with the smaller belt thickness and is not noticeable for the other systems. The relative resistance change changes for the three different grippers. To verify these differences, some deformations calculations where done (Table 3). Table 3. The deformation of the gripper and change in relative resistance when the gripper moved between positions open and close for grippers with different belt sizes. Belt thickness (mm) 2 4 6

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The wires were used to open and close the tentacles of the soft grippers by a servomotor and the change in their length, when the gripper moved between positions open and closed, was used to calculate the relative deformation. The wires were coiled up and thus the length of the wires gets shorter when the gripper is closed. Looking at the values in Table 3 it was observed, that the relative deformation decreased by increasing the belt thickness. Smaller deformation caused a smaller change in resistance, an effect that is expected from a deformation sensor. The results in Table 3 are in good agreement with optical observations. Due to the design change of the gripper (thicker band) it was observed, that the phalanges would touch each other at lower strain, which will block further deformation of the tentacle of the gripper. The initial value Ro for the resistance was also different for the grippers with different band thicknesses (Fig. 9b). The Ro was higher for the gripper with the lowest belt thickness and decreased with the increasing belt thickness. In order to verify if this difference was caused by the printing procedure, the initial resistance Ro was measured for all the four tentacles of each gripper (Table 4).

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Table 4. The deformation of the gripper and change in relative resistance when the gripper moved between positions open and close for grippers with different belt sizes. Gripper material FilaFlex 82A FilaFlex 82A FilaFlex 95A FilaFlex 82A

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From comparing the values of the resistance in the different tentacles, it was seen that the values can vary a lot for all the grippers made with the material FilaFlex 82A. The change in resistance was the same for every tentacle, independent of the initial value in the resistance as it is a parameter that depends on the deformation, which was the same for all the tentacles. As a result, there can't be made a conclusion about the effect of the thickness of the belt on the values of the resistance. However, this was not the case for the material FilaFlex 95A. In this system, the values of the resistance had consistency between the different tentacles of the grippe. This large deviation in the values of the resistance can be traced back to the printing procedure. When observing optically the produced grippers, for the case of FilaFlex 82A material that there could be seen traces of carbon black powder all over the first layer. This effect was distinguished as the original color of the filament appeared darkened by black particles at parts of the first layer. This was not the case for the FilaFlex 95A gripper, that the coloration of the first layer was unaltered. A possible interpretation is that during the printing, in the case of the FilaFlex 82A, the nozzle that printed the robotic body carried away some of the particles of the carbon black of the conductive TPU that was print first. However, this change was not consistent during the printing, the discoloration appeared more intense in some tentacles compared to others, and this could be an explanation for the deviation of the values of the resistance. Based on those results of the gripper design study, grippers with a belt thickness of 4 mm were made with the FilaFlex 82A the FilaFlex 95A. The grippers were compared for the electrical signal and stability of the sensor response. Both structures showed a relative deformation of around 16% for a fully closed position. In order to assess the sensor signal for the grippers, the soft structures were opened and closed with a dell time during each position. In addition, an objective (orange) was grabbed by the soft grippers (Fig. 10 a)). In addition, the electrical signal of the sensor was investigated when the movement of the tentacles was blocked by an obstacle (Fig. 10 b)).

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Fig. 10. a) The robotic gripper with the integrated sensors and a band thickness of 2 mm is gripping an orange to test object recognition and b) the hand is preventing the gripper from opening to test obstacle recognition by the integrated sensor.

The sensor response for the grippers made with the materials of different shore hardness performing different tasks can be seen in Fig. 11

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Fig. 11. Sensor response for grabbing objective and obstacle during obstacle recognition test for grippers with a robotic made out of elastomer with shore hardness a) 82A and b) 95A and sensor response during obstacle recognition test for the robotic body made out of elastomer with shore hardness c) 82A and d) 95A. Both sensors could indicate when the gripper was gripping an object, when not and when an obstacle was impairing the function of the system

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Looking at Fig. 11 a) and b) it was calculated, that the difference in resistance was 52% for the 82A and 59% for the 95A. The relative change in resistance between the closed positions and holding an objective was 7.4% and 11.5% for the 82A and 95A, respectively. Therefore, in both cases, the relative change of the sensor signal was slightly higher for the TPU gripper with higher shore hardness. An important parameter for deformation sensors that are targeted for robotic applications is the ability to distinguish from the sensor signal the different positions of the robot system. The initial resistance at 6.1 kX was higher for the system with higher shore hardness compared to the lower shore hardness, with initial resistance of 4.2 kX. As already mentioned, the maximal deformation from open to the closed position (touching of the phalanges) was 16.2%. For grabbing the orange, the deformation was calculated to 6.5%. According to the electrical resistance values, the piezoresistive sensors integrated into the soft gripper structures can identify if the gripper is open, closed or if an objective is grabbed. In soft robotics, grippers with integrated piezoresistive sensor elements that can distinguish between when the gripper is grabbing an object, open and closed position lead to the creation of intelligent robotic systems. Additionally to the gripping test, an obstacle test was performed. During the obstacle test, the gripper movement (open-closed-open-closed) was blocked manually after two cycles. In the first two, the closing and opening of the soft gripper could be easily detected by the change in resistance. However, at the time point when the obstacle was imposed, the value for the resistance did not return to the value of the resistance for the open position. As already explained, wires were used to open and close the tentacles of the soft grippers by a servomotor. The wires are coiled up and thus the length of the wires gets shorter when the gripper is closed. The resistance stayed close to the value for the closed position for the entire time the obstacle was imposed. After the obstacle was removed, the resistance returned to the previous values for the position open. With the ability to distinguish between positions open and close, but also recognizing obstacles and when the gripper is gripping an object, these intelligent soft robots can be used for applications where effective monitoring of the gripper function is needed. As a result, these grippers with integrated sensing elements could be used for more efficient production in many sectors of the industry that requires soft robots, as it is, for example, the food industry. In this attempt, soft robotic grippers with integrated sensing elements were produced in the one-step process using multi-material FDM. These grippers consisted of two materials, one conductive TPU that can be used to sense the deformation of the gripper structure and one non-conductive TPU to fabricate the structure of the gripper. In order to investigate, the behavior of the printed composite systems and the effect of the shore hardness of the strips, tensile testing was performed. The TPU with lower shore hardness can be used for applications with larger elongation. However, cycling experiments showed that the sensor behavior was similar for both types of TPU.

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4 Conclusions For the functional soft gripper application, first, the optimal thickness of the sensor part of the soft gripper was adjusted to 4 mm thickness. Later, two TPU filaments with different shore hardness (82A and 95A) were investigated as candidate materials for the gripper structure. The gripper with the TPU 95A showed a slightly higher difference in electrical resistance value between open and closed positions. This resulted in a higher sensitivity. The grippers with the integrated sensing elements exhibited intelligent function. The deformation of the sensor enriched their function with the ability to distinguish when the gripper is gripping an object, when not and when an obstacle is preventing the gripper from functioning properly. This intelligent soft robotic gripper with the integrated sensors that was produced in one-stem with a simple and low cost could be potentially found each place in production lines for sensitive objects and lead to more efficiency and accuracy in production. Acknowledgement. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 828818 (SHERO project).

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13. Naghdy, F., Esmaili, M.: Soft fruit grading using a robotics gripper. Int. J. Robot. Autom. 11, 93–101 (1996) 14. Hao, Y., Wang, T., Ren, Z., Gong, Z., Wang, H., Yang, X., Guan, S., Wen, L.: Modeling and experiments of a soft robotic gripper in amphibious environments. Int. J. Adv. Robot. Syst. 14, 1729881417707148 (2017) 15. Licht, S., Collins, E., Mendes, M.L., Baxter, C.: Stronger at depth: jamming grippers as deep sea sampling tools. Soft Robot. 4, 305–316 (2017) 16. Yang, Y., Chen, Y.: Innovative design of embedded pressure and position sensors for soft actuators. IEEE Robot. Autom. Lett. 3, 656–663 (2018) 17. Schmitt, F., Piccin, O., Barbe, L., Bayle, B.: Soft robots manufacturing: a review. Front. Robot. AI. 5, 84 (2018) 18. Melnykowycz, M., Koll, B., Scharf, D., Clemens, F.: Comparison of piezoresistive monofilament polymer sensors. Sensors 14, 1278–1294 (2014)

Solution Approaches and Process Concepts for Powder Bed-Based Melting of Glass Susanne Kasch1(&), Thomas Schmidt1, Fabian Eichler2, Laura Katharina Thurn2, Simon Jahn1, and Sebastian Bremen2 1

ifw Jena– Günter-Köhler-Institut für Fügetechnik und Werkstoffprüfung GmbH, Jena, Germany [email protected] 2 Faculty of Mechanical Engineering and Mechatronics, University of Applied Sciences Aachen, Aachen, Germany

Abstract. In the study, the process chain of additive manufacturing by means of powder bed fusion will be presented based on the material glass. In order to reliably process components additively, new concepts with different solutions were developed and investigated. Compared to established metallic materials, the properties of glass materials differ significantly. Therefore, the process control was adapted to the material glass in the investigations. With extensive parameter studies based on various glass powders such as borosilicate glass and quartz glass, scientifically proven results on powder bed fusion of glass are presented. Based on the determination of the particle properties with different methods, extensive investigations are made regarding the melting behavior of glass by means of laser beams. Furthermore, the experimental setup was steadily expanded. In addition to the integration of coaxial temperature measurement and regulation, preheating of the building platform is of major importance. This offers the possibility to perform 3D printing at the transformation temperatures of the glass materials. To improve the component’s properties, the influence of a subsequent heat treatment was also investigated. The experience gained was incorporated into a new experimental system, which allows a much better exploration of the 3D printing of glass. Currently, studies are being conducted to improve surface texture, building accuracy, and geometrical capabilities using three-dimensional specimen. The contribution shows the development of research in the field of 3D printing of glass, gives an insight into the machine and process engineering as well as an outlook on the possibilities and applications. Keywords: Glass powder  Laser processing Melting  L-PBF  Fused silica  Borosilicate

 Additive manufacturing 

1 Introduction The laser-based powder bed fusion (L-PBF) process offers an alternative to already established manufacturing processes due to the comparable component properties, the freedom in geometric design and the production costs in small and medium series [1]. © Springer Nature Switzerland AG 2021 M. Meboldt and C. Klahn (Eds.): AMPA 2020, Industrializing Additive Manufacturing, pp. 82–95, 2021. https://doi.org/10.1007/978-3-030-54334-1_7

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Laser-based powder-bed fusion is already an industrially established process for the manufacturing of three-dimensional components from polymers and metallic materials. Sintering of ceramics and glass is increasingly being investigated [2]. Laser sintering of glass powders is an effective technology for manufacturing products for microelectromechanical systems or objects for medical applications [3]. Glass is a material with a wide variety of compositions. The characteristics of glasses can be adjusted very variably, which enables many applications of this material group [4]. The conventional production of glass powders involves the steps of melting the glass composition from a batch, glass fritting, grinding and sieving the glass powders into the required particle size and particle size distribution. This produces non-spherical, irregularly shaped particles. In addition to the geometric properties (particle size, particle shape) of the glass powders, the mechanical-physical properties (e.g. flowability, absorption/ transmission versus the laser wavelength) are essential for processing by means of powder bed fusion with laser beams. Furthermore, the process control has to be adapted to the thermophysical properties of the glasses such as poor heat conduction or low thermal shock resistance depending on the thermal expansion coefficients. The present article examines different glass powders along the L-PBF under these aspects in order to provide potential users with a systematic approach as well as material- and application-specific processing concepts.

2 Fundamentals The process of laser-based powder bed fusion (L-PBF) is characterized by a multitude of influencing factors. The influencing parameters are differentiated according to [5] with regard to the material properties and the laser and process conditions. The material properties are described in • Optical properties • Thermal properties • Technological properties of the powders and the laser and process conditions in • • • •

Laser parameters Exposure parameters Process environment and Machine control

The process effectiveness depends on the main influencing factors mentioned, whereby a significant influence is determined by the properties of the used powders and the absorption or transmission of the used laser radiation. A targeted optimization of the overall process must therefore always be considered dependent on the material and laser wave. The absorption of the laser radiation in the powder provides the energy required to melt the powder by means of L-PBF. According to [6], the energy absorption on or in a powder layer is in general significantly higher compared to absorption on a compact solid of the same material. For different one-component powders, N. Tolochko carried

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out investigations on absorption with two different wavelengths [7]. In the investigations laser beams from a Nd:YAG and a CO2 laser source were coupled into an experimental plant. For SiO2 glass powder an absorption for k = 1.06 µm of A = 0.04 and for k = 10.6 µm of A = 0.96 was determined. For an SiO2 glass component, comparable absorption and transmission values (Fig. 1) are shown for the CO2 laser wavelength of k = 10.6 µm and the wavelength of a solid-state laser with approximately 1 µm. On the other hand compact soda-lime or borosilicate glasses, have a higher absorption rate for wavelengths smaller than 2 µm, due to their material composition, which probably also applies to the absorption for these glass powders.

Fig. 1. Transmission spectrum of different compact glasses

Compared to crystalline solids (e.g. metals), solid glass components are amorphous. The glassy state is classically described as the frozen state of a supercooled liquid, whose property is characterized by the temperature-dependent viscosity behavior. Depending on the glass composition, characteristic fix-points [8] must be observed throughout the entire temperature and viscosity curve during glass production and glass processing. Methods to analyze this temperature-dependent behavior are differential scanning calometry (DSC), differential thermal analysis (DTA) or high temperature microscopy (HTM) of glass powder samples or compacts. The values thus determined provide information for processing the L-PBF process. Defined coating of the powders by means of a blade (e.g. rubber lip,) metal blade on a building platform and their defined lowering after laser irradiation (exposure) perform the building process in L-PBF. The processability of powders during coating depends essentially on particle shape, particle size and particle distribution, which can be determined experimentally by technological parameters bulk density and flow behavior.

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Bulk density directly relates to the compaction of a powder, i. e. how strongly it is compressed (solidification stress). As the solidification stress increases, the bulk density increases and the void volume between the particles decreases. With fine-grained bulk solids, the bulk density is usually more strongly influenced by the solidification stress. The flow properties of powder particles also depend on the particle shape, particle size distribution, the chemical composition of the powder particles, but also on the temperature as well as on the humidity. The particle shape has a decisive influence on the flow behavior of a powder. In theory, smooth, round particles larger than 0.5 mm flow more easily than rough, spherical particles. The adhesive forces between the particles are also responsible for the flow behavior of powders [9]. Due to their larger specific surface area, finer powders exhibit higher adhesive forces and change the flowability to lower values [10]. The particle size distribution, in addition to the compaction and flow of a powder, also has a significant influence on the sintering or melting behaviour in the L-PBF process. A homogeneous powder mass distribution ensures a homogeneous energy input during laser exposure and thus a homogeneous melting of the material. For this reason, a constant particle size should be achieved. In practice, an average diameter with a certain deviation is usually achievable [11].

3 Experimental Investigations and Results 3.1

Characterization of the Glass Powders

Various glass powders with different compositions and geometric, thermo- and mechanical-physical properties were available for the investigations. First, an extensive analysis of the initial state of the glass powders was carried out to select glass powders with good processing properties for the experimental investigations in the L-PBF process. The following methods were used to determine the properties: • Geometric properties – Powder geometry (Grain shape microscopically using a scanning electron microscope (REM) JSM-6300 with energy dispersive X-ray spectroscopy (EDX) (JEOL, Japan; EDX: NORAN Instruments, USA) – Particle distribution by means of laser diffraction LS230 (Beckman Coulter) • Mechanical and physical properties – Flowability (according to DIN EN ISO 4490) with Hall flowmeter – Tab density (according to DIN EN ISO 3953) – Coating quality manually by means of defined doctor blade and – Coating system SLM 50 by ReaLizer • Thermo-physical properties – Temperature-dependent viscosity behavior using a high-temperature microscope HTM, differential scanning calometry (DSC) and differential thermal analysis (DTA) • Optical properties, – Absorption measurement

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Based on these investigations, two SiO2 glass powders and one borosilicate glass powder were selected for their technological suitability. These are SiO2 powders from the company QSIL with the designation “NC4A” (GP4) as well as a powder from the company Heraeus with the designation “Zandosil” (GP6). Both powders consist of more than 99.999% silicon dioxide and have a low coefficient of expansion of a = 0,5 * 10–6 K−1. The manufacturer specifies further thermal properties for GP4 as follows: softening limit 1730 °C, transformation range from 1075 °C to 1210 °C, processing range from 1700 °C to 2100 °C. The thermal conductivity of this material is 1.38 W/m * K and is significantly lower than that of classic metallic materials. For powder materials, the thermal conductivities are correspondingly lower up to a factor of 100 [12]. A borosilicate glass powder (GP10) is used from the company Schott AG, which was specially ground from borosilicate flat glass “BOROFLOAT® 33”. The manufacturer specifies the characteristic viscosity dependent fix points as follows: Working Point (104 dPa * s) with 1270 °C, Softening Point (107.6 dPa * s) with 820 °C, transformation temperature (Tg) with 525 °C. The specific thermal conductivity of the material is 1.2 W/m * K and the thermal expansion coefficient of a = 3.25 * 10–6 K−1 is higher compared to SiO2 and thermal stresses can be reduced less easily. Glass powders are usually present as broken grains due to their production by crushing and grinding (Fig. 2). Spherical glass powders can be produced by using complex technologies and are therefore more expensive and not available for all glass compositions and grain sizes. Powders GP4 and GP10 are characterized by an irregular, angular broken grain. Powder GP10 also has a high proportion of fines, sticking on a larger surface, which can be seen in the SEM image by the light particles on the powder grains. Within the scope of powder characterization, a powder size distribution for GP4 of 137 µm to 340 µm and for GP10 of 115 lm to 250 lm was determined. The SiO2 glass powder GP6 was produced by a special process is spherical and amorphous with a grain size of 141 µm to 434 µm. The particle distribution of the powders mentioned corresponds to a Gaussian-like distribution, which is also typical for metallic powders in the L-PBF process. Based on these results, the minimum layer thickness to be applied was determined experimentally (see Table 1). It is assumed that the better the flowability and tapping density of the powder, the smaller the layer thickness that can be set.

GP 4 – SiO2 glass (QSIL), broken grain, angular and angular, no fines

GP6 – SiO2 glass (Heraeus), round grain

GP 10 – Borosilicate glass (SCHOTT), broken grain, various angular geometries, high fine grain content

Fig. 2. REM images of various glass powders

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For the technological processing of the glass powders in the powder bed, the spreading and the layer thickness is of great importance. Therefore, within the scope of the experimental investigations, the glass powders were first examined with a hand operated blade and adjustable gap size, homogeneity of the coating application and the coating thickness. The SLM 50 coating system by ReaLizer was then used to mechanically process relevant powders and to evaluate the quality of the result, depending on the layer thickness by means of visual assessment. Figure 3 left shows an example of the homogeneous coating application for the glass powder GP10 with a layer thickness of 200 µm, which can be rated as good for this material. In opposite Fig. 3 right shows exemplarily an inhomogeneous coating result of a borosilicate glass mainly due to particle size distribution between d10 = 1 µm and d90 = 25 µm and irregular broken shape. A homogeneous coating thickness is achieved for all three powders with 1 to 1.5 times the average particle size (see Table 1).

Table 1. Geometrical and technological properties of the used powders Sample number

Particle size MW D 10 [µm] [µm]

D 50 [µm]

D 90 [µm]

Tap density [g/ml]

Applied coating thickness [µm]

340

Flowability Funnel Funnel Ø Ø 5 mm 2.5 mm [s] [s] 95.4 18.42

SiO2 (GP4) SiO2 (GP6) B3.3 (GP10)

232

137

228

1.39

300

277

141

264

434

194.4

24.90

0.81

400

176

115

175

250

148.2

21.30

1.15

200

Fig. 3. Result of single coating: left: Borosilicate glass powder GP10 with SLM 50 by ReaLizer (substrate platform diameter: 70 mm); right: exemplarily borosilicate glass with smaller particle size distribution.

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Furthermore, the respective processing temperatures are relevant for the thermal processing. Compared to pure quartz glass, these temperatures are significantly lower for borosilicate glass. Thermal behavior of pressed borosilicate glass powders is shown in Fig. 4 at typical processing temperatures. They show the compression or the start of melting of the glass powder. It can be seen that above the softening point of 820 °C at 900 °C there is a clear shrinkage of the powder compact and above 975 °C melting can be observed. These results were confirmed by DSC/DTA. In the L-PBF process these temperatures determine the adjusted quality of the glass component (sintered or melting) and its later application.

Fig. 4. HTM images – borosilicate glass powder GP10

The optical properties of the glass powders GP4, GP6 and GP10 were determined with respect to absorption versus the wavelength k = 1.064 µm (Table 2). The determined values show a good agree with the values in the literature [7]. For SiO2 powders with broken grains, a slightly higher absorption is determined than for round grains, which is due to the shape of the grain and the associated scattering and reflection behavior. Borosilicate glass powder shows an even better absorption than pure SiO2 glass powders, which is probably due to the glass composition. Depending on the manufacturer, borosilicate glass consists of up to 80% silicon dioxide (SiO2), 13% boron trioxide (B2O3), 8% alkali oxides (sodium oxide Na2O; potassium oxide K2O), 7% alumina (Al2O3) and 5% alkaline earth oxides (CaO, MgO, …) [8]. Table 2. Absorption characteristics for k = 1,064 µm Sample number Material GP4 SiO2, broken grain GP6 SiO2, round grain GP10 Borosilicate glass, broken grain

3.2

Absorption 0.0957 0.0825 0.137

Experimental Investigation of the L-PBF Process

3.2.1 Systems Engineering The experimental investigations were carried out on two different systems, a commercial SLM 50 by ReaLizer with a Nd:YAG laser (manufacturer: IPG Laser GmbH) of the wavelength k = 1.064 µm and an experimental CO2 laser plant with different sources (SYNRAD 57-1 series Pmax = 100 W, FEHA Pmax = 1200 W) of the wavelength k = 10.6 µm.

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The SLM 50 is an encapsulated system with a heatable copper platform, which can be preheated up to 190 °C during the process, and a coater, which distributes the powder on the platform. The experimental CO2 laser plant was further developed during the investigations to meet the requirements of powder processing of glass. Figure 5 shows the schematic principle. To ensure a constant glass temperature during processing, a pyrometric control system was integrated into the plant. For this purpose, the measuring beam of the pyrometer is coaxially superimposed on the laser beam so the glass temperature is recorded directly in the laser action zone. This prevents partial evaporation of the glass and ensures a constant temperature during the entire construction period. The powder is applied via a pneumatically movable hopper, through whose slotted opening the powder can flow to the building platform. This slot is sealed with a temperature-resistant lip all around.

Fig. 5. Schematic setup of the experimental CO2 laser plant, coater and inductive heating

A challenge with L-PBF processing of glass powders is the required heating of the building platform to the range of the transformation temperature of the glass. For GP4 approx. 1000 °C, for GP10 approx. 500 °C are required. In the experimental plant heating is carried out indirectly by induction. A conductive ceramic under the building platform provides the necessary temperature transfer. This allows high temperatures to be quickly achieved without contact but leads to an extremely high load on the entire plant. Therefore, the construction of the plant and the selection of suitable materials is a great challenge.

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3.2.2 Investigations and Results for Melting Glass Powder The aim of the investigations was to melt the glass powders GP4, GP6 and GP10 using the laser wavelengths 1.064 µm and 10.6 µm in the L-PBF process and to produce compact glass components. Laser and process parameters such as laser power, beam diameter (focus position), scan speed, track pitch and scan strategy were varied (Table 3). The temperature of the installation space was kept constant according to the equipment used. In the investigations with the experimental station, the temperature of the platform was adjusted depending on the transformation temperature of the glass powders. The temperature-dependent laser power control was adjusted to the melting temperature of the glass powders that was determined in the HTM investigations. Based on the results of the coating tests for powders GP4, GP6 and GP10, the layer thickness was also adjusted to a constant value. Table 3. Overview of essential laser and process parameters of the test facilities Heading level

SLM 50, ReaLizer Experimental station SYNRAD 57-1 FEHA 1200 Laser wavelength k [µm] 1.064 10.6 10.6 Laser power Pmax [W] 100 100 1200 Laser power PL [W] 80–100 10–60 200–800 Scanning speed vs [mm/s] 2–50 1–10 800–1200 Temperature Building platform T [°C] 190 500–1000 500–1000

The investigations with SLM 50 by ReaLizer show that both SiO2 powders (GP4, GP6) cannot be processed regardless of the powder geometry. By increasing volume energy densities of up to 1 kJ/mm3 and multiple exposures of identical layers, an attempt was made to get the necessary heat input into the powders. This should allow the necessary processing temperatures of about 1585 °C to be reached. Due to the solid-state laser (Nd:YAG) with a wavelength of 1.064 µm, which is only poorly absorbed in the SiO2 powder, and the low preheating temperature of the substrate plate (190 °C), it was not possible to produce any solids. Although the borosilicate glass powder (GP10) also has low absorption at the fixed laser wavelength used in this system, the powder with its significantly lower softening temperature (820 °C) could be processed with the SLM 50 [13]. Significant fusions of the powder are achievable by a volume energy density of about 200 J/mm3 to 250 J/mm3. Evaluations of these experiments showed that there is a conflict of objectives between achieving a melt line and maintaining geometric accuracy. With the aid of higher volume energy densities, better fusions can be achieved. However, this increases the geometric deviations as a result of higher amount of particles sticking on the surface powder. This is due to the low thermal conductivity of borosilicate glass powder and the resulting heat accumulation in the powder bed. In addition, tests with different scanning strategies showed that the best results can be achieved by scanning the outer contour and the hatch. In the tests, parameters could be determined to produce

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test specimens for materials testing and improvement of quality (porosity, surface, tightness) by a subsequent heat treatment (Fig. 6). Before

contour Hatch

Fig. 6. Test specimen of borosilicate glass (GP10), diameter 10 mm, height 10 mm, wall thickness 0,5 mm

Investigations with different CO2 laser systems at the experimental plant showed that the two quartz glass powders GP4 and GP6 can be processed due to their almost 100% absorption for the wavelength of a CO2 laser. Compared to metal powders, glass powders have a lower thermal conductivity by a factor of 100. This makes it more difficult to bond the upper powder layers to the layers below. For this reason, the glass powder must be converted to its molten state with high energy densities in order to achieve fusing. Depending on the exposure strategy, beam diameters of 2 to 3 mm were used to achieve the necessary processing temperature of 1700 °C to 2100 °C and to establish the fusing. Two exposure strategies were investigated: • Slow, progressive exposure due to small beam movement: – Speed: 1 to 10 mm/s – Laser power: 10 to 60 W – Volume energy density: 5 to 7 kJ/mm3 • Number of scans: 1quasi-simultaneous exposure through high beam movement: – Speed: 800 to 1200 mm/s – Laser power: 250 to 800 W – Volume energy density: >30 kJ/mm3 – Number of scans: approx. 1000 The construction rate for circular geometries could be increased considerably by the quasi-simultaneous exposure. However, this requires an energy density that is 5 times higher. Furthermore, the experiments showed that SiO2 solids (Fig. 7, Ø 12 mm, height

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Fig. 7. Test specimen from SiO2 glass (left: GP6, right: GP4) diameter 12 mm, height 30 mm

30 mm) can be produced, which have a density of approximately 2.2 g/cm3 and are comparable with literature values. The component itself has a milky appearance, because during the construction process, in addition to the melting trace, other particles adhere and do not melt completely. The interior of the wall (approx. 1.5 mm) is fused compactly (Fig. 8).

Fig. 8. Test specimen from SiO2 glass (GP4), wall thickness 1,5 mm

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Fig. 9. Test specimen from SiO2 glass (GP4), left: macroscopic view, right: CT-scanning

Furthermore, the influence of inductive platform/building space heating was investigated (Fig. 9). At a temperature of 600 °C a homogeneous, pore-free glass body (10  10 10 mm3) made of SiO2 (GP4) was produced. In comparison to the conventionally produced substrate plate, no differences are optically detectable. The specimen appears as transparent as the substrate plate. Only in the edge area up to approximately 1.5 mm no complete fusion of the glass particles on the test specimen took place.

4 Summary and Outlook This paper presents investigations for laser beam powder bed fusion of borosilicate and quartz glass powders. Different glass powder classes were characterized with respect to their geometric, thermo- and mechanical-physical properties and the technological processability in the L-PBF process using different laser wavelengths was investigated. The plant technology was adapted to the different requirements of glass processing, especially for quartz glass powders with CO2 laser radiation. In contrast to metallic materials, the mentioned properties of glass materials, which make glass the relevant and technically usable material that it is, present challenges to processability by L-PBF. The poor absorption rate, especially for wavelength of 1.064 µm, in combination with the high temperature resistance and poor heat conduction are the technical barriers to be overcome by this basic research.

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In order to achieve the goal of a complete fusion of the materials and to provide potential users with a systematic approach as well as material- and application-specific processing concepts test specimens were produced and evaluated. For the successful production of test specimens from the different glass powders, process parameters were determined, and initial quality evaluations were carried out. In the tests the basic processability of the glass powders was proven. Glass specimen could be manufactured successfully from borosilicate and quartz glass powder by laser radiation. As a result of the experiments, parameter windows could also be diverted, within which processing is possible. It was found that the parameters strongly depend on the geometric shape of the specimen due to heat agglomeration. The qualification of process and machine parameters is therefore much more difficult than with metallic materials. In further investigations, however, the process and experimental setup must be further optimized. To improve the component quality, further investigations on post heat treatment (e.g. stress relief) are to be carried out. Furthermore, the analysis of the material component quality with regard to porosity, roughness and tightness in relation to the developed L-PBF parameters is to be continued and possible further fields of application are to be shown. Acknowledgements. The investigations were carried out in the project “Application limits in melting of glass materials” (Aif-IGF 19673 BG), funded by the Federal Ministry of Economics and Energy on the basis of a resolution of the German Bundestag.

References 1. Küsters, Y., et al.: Robuster Strahlschmelzprozess durch methodische Parameterfindung. RTejournal 8 (2011) 2. Luo, J., Pan, H., Kinzel, E.: Additive Manufacturing of Glass. J. Manuf. Sci. Eng. Am. Soc. Mech. Eng. 136, 061024 (2014) 3. Tolochko, N.K., Arshinov, M.K., Arshinov, K.I., Ragulya, A.V.: Laser sintering of SiO 2 powder compacts. Powder Metall. Met. Ceram. 43(1/2), 10–16 (2004) 4. Scholze, H.: Glas - Natur, Struktur und Eigenschaften. Springer, Heidelberg (1988) 5. Bliedtner, J., Müller, H., Barz, A.: Lasermaterialbearbeitung: Grundlagen - Verfahren Anwendungen – Beispiele. Hanser, München (2013) 6. Hügel, H.: Laser in der Fertigung - Strahlquellen, Systeme, Fertigungsverfahren, 2. Auflage. Vieweg+Teubner Verlag, Wiesbaden (2009) 7. Tolochko, N.: Absorptance of powder materials suitable for laser sintering. Rapid Prototyp. J. 6, 155–160 (2000) 8. Vogel, W.: Glaschemie, 3. Auflage. Springer, Heidelberg (1992) 9. Schulze, D.: Pulver und Schüttgüter: Fließeigenschaften und Handhabung, VDI-Buch, 2., bearb. Aufl. Springer, Heidelberg (2009) 10. Schatt, W.: Pulvermetallurgie - Technologien und Werkstoffe, 2. Auflage. Springer, Heidelberg (2007) 11. Fateri, M., Gebhardt, A., Thuemmler, S., Thurn, L.: Experimental investigation on selective laser melting of glass. Phys. Procedia 56, 357–364 (2014)

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12. Gusarov, A.V., Smurov, I.: Modeling of interaction of laser radiation with powder bed at selective laser melting. Phys. Procedia 5, 381–394 (2010) 13. Eichler, F., Skupin, M., Thurn, K., Kasch, S., Schmidt, T.: Operating limits for beam melting of glass materials. In: Vortrag: The 14th International conference on modern technologies in manufacturing. Department of Manufacturing Engineering (DME) – TU Cluj-Napoca and DiCoMI, Cluj-Napoca (2019)

Additive Manufacturing of Ti-Nb Dissimilar Metals by Laser Metal Deposition Di Cui1(&), Briac Lanfant1, Marc Leparoux1, and Sébastian Favre2 1

Laboratory for Advanced Materials Processing, Empa-Swiss Federal Laboratories for Materials Science and Technology, 3602 Thun, Switzerland [email protected] 2 Medtronic Europe Sàrl, 1131 Tolochenaz, Switzerland

Abstract. Conventional technologies for joining dissimilar metals have become insufficient, as the need for designing and fabricating products with complex shape and integrated composition variation have arisen. Laser metal deposition (LMD), a powder injecting additive manufacturing (AM) technology, is capable to build complex geometries and tailor material composition locally within one single workpiece. In this work, thin walls transitioning from titanium to niobium were made by LMD with controlled injection of Ti and Nb powders. The morphologies and microstructures were observed with optical microscopy (OM) and scanning electron microscopy (SEM). The cross-sections showed fully dense deposition, without cracks in any of the transition area. Deposition of Nb powder resulted in partially melted Nb particles embedded in the transition area, which was a result of the significantly higher melting point of Nb. Energy-dispersive spectroscopy (EDS) confirmed metallurgical bonding at the transition areas and showed variation of composition along the build direction. In the transition area, microhardness was 204 ± 5 HV at the Ti-rich side, 155 ± 6 HV in the solid solution, with an atomic composition of Ti70Nb30, and 120 ± 16 HV at the Nbrich side. Electron backscattered diffraction (EBSD) results revealed hcp structure in the pure Ti region and bcc structure in the transition and pure Nb regions. Columnar growth was revealed in the pure metal regions and equiaxed growth in the transition region. X-ray computed tomography (X-CT) showed 3D element distribution and revealed very small number of pores in the transition area, which were not observed by previous microscopy on cross-sections. Keywords: Dissimilar metals  Laser metal deposition bonding  Microhardness  EBSD

 Metallurgical

1 Introduction Laser additive manufacturing (AM) is a solid freeform manufacturing technology. It enables direct fabrication of detailed work pieces by accurately depositing desired material at set positions within a pre-determined domain [1]. Laser metal deposition (LMD) is an advanced powder-injecting laser AM technology capable of directly producing dense metal parts with complex geometries, and, of special interest, variation

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of composition through the control of the powder type injection. A variety of materials can be manufactured by LMD for numbers of applications in the fields of medical device, automobiles and aerospace. Despite the lower flexibility in geometry complexity compared to powder bedfusion technologies, LMD has the advantage of mixing at least two different powders with the desired composition to synthesize alloys in-situ [2], metal matrix nanocomposites [3] and functionally graded materials (FGM) [4]. Many binary systems have been studied to produce FGM with LMD. Ti + Ta deposition by LMD was carried out on Ti6Al4V substrate [5]. An increase of Ta concentration resulted in an increase of minimum laser power for successful deposition and resulted in good biocompatibility, Young’s modulus and 0.2% offset yield strength. Ti6Al4V-Inconel 625 FGM was fabricated but cracks appeared due to formation of brittle intermetallic phases such as in the Ti-Ni and Ti-Fe material systems [6]. Schneider-Maunoury et al. reported a study on LMD of Ti-Nb samples with several incremental increases of Nb content [7]. Many unmelted Nb particles were observed in the produced bimetal part. Microhardness of the samples decreased with the increase of Nb content. The lowest elastic modulus of 58 ± 8 GPa was found at the composition of Ti40Nb wt%. The adaptation of the elastic modulus of Ti-Nb parts to the human bone (10–30 GPa) is of prime interest for orthopedic parts and additive manufacturing offers additionally a customization to the individual patient. This paper summarizes the preliminary results on the fabrication of Ti-Nb assemblies using LMD process with separate injection of Ti and Nb powders. The focus of this study is the investigation of the interfacial zones between these two materials and the occurrence of cracks depending of main process parameters.

2 Materials and Methods 2.1

Materials

The powders utilized were Cp-Ti grade 1 powder (oxygen < 0.08 wt%, Hall flow 0.1 in. 23 s) with a diameter around 45–106 lm supplied by AP&C Advanced Powders & Coatings Inc., Canada, and AMPERTEC MAP Nb powder (oxygen = 373 ppm, Hall flow 0.1 in. 13 s) with a diameter of 63–100 lm, supplied by H.C. Starck Tantalum and Niobium GmbH, Germany. Powder particles of both titanium and niobium were primarily spherical, with minor satellites (Fig. 1), exhibiting desirable flowability for powder transportation during the LMD process. Pure niobium and titanium bulk foils with various thicknesses have been used as substrates.

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Fig. 1. SEM images of titanium (left) and niobium (right) powder particles.

2.2

Fabrication of Thin Walls

A commercial LMD machine (Mobile 1.0, BeAM, France) has been used to build the 3D structures. This LMD system uses a continuous wave (CW) fiber laser with a maximum power of 500 W operating at a wavelength of 1068 nm (YLR-Series, IPG Photonics, USA) as the heating source. The laser has a focal spot diameter of 800 µm and a Rayleigh range of 18 mm. The powders are feed through a volumetric powder feeder (Medicoat, Switzerland) with two powder containers. The conventional microscale powders are then transported by a carrier gas (argon) through two tubes which are joined by a Y junction into one single line connected to the processing head. A specific conical nozzle focuses the powder jet at 3.5 mm below its exit at the same position as the laser focus. The processing head is mounted on a 3-axis system (x, y, z) and the substrate holder has 2 rotative axes. The printing process takes place in an airtight chamber that offers possibility to work under low level of oxygen, which is crucial for processing materials with high affinity of oxygen like titanium. Images of the system and schematic of the nozzle are presented in Fig. 2.

Fig. 2. Laser metal deposition (LMD) machine: (a) dual-container powder feeder; (b) interior of the printing chamber; (c) schematic of the coaxial nozzle.

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4 Groups of samples have been fabricated as illustrated in Fig. 3: G1 G2 G3 G4

-

deposition of a Ti wall on a Nb foil (200 lm); deposition of a Nb wall on a Ti foil (300 lm); deposition of Ti followed by Nb, using a Ti grade 23 plate (4 mm) as substrate; deposition of Ti and then a mixing area Ti+Nb followed by a pure Nb structure using a Ti grade 23 plate (4 mm) as substrate.

All of the samples have been produced under argon (O2 < 30 ppm, H2O < 170 ppm, argon filling overpressure  3 mbar).

Fig. 3. Schematics of the printed structures.

The targeted dimensions of the samples were lengths of 6 mm, and a height of 4 mm for the pure Ti and Nb walls of samples G1, G2 and G3. The pure material structures were targeted to be 2 mm high with an intermediate mixing zone of 4 mm for the G4 sample. The z step-increment of the nozzle, i.e. designed layer thickness, has been fixed at 0.2 mm for all samples. Beside the laser power, the linear axis moving speed and the powder feedrates were varied. For comparing the used conditions, a linear energy is defined as power divided by speed, reflecting input laser energy per unit length. Accordingly, a linear feedrate is defined as feedrate divided by speed, which reflects input powder mass per unit length. Finally, energy per feed is defined as power divided by feed rate, reflecting input laser energy per input powder mass. The printing parameters were selected according to a previous study performed with Titanium powder [3] and are summarized in Table 1 (samples G1 to G3) and Table 2 (sample G4). For all the G3 samples, a power of 325 W, speed of 2000 mm/min and feed rate of 3.6 g/min have been employed to deposit the first 4 mm height of Ti on the substrate. A delay of 10 s was then necessary to switch the powder feeder from pure Ti to Nb and to stabilize the powder flow before depositing the next 4 mm of Nb on top of the as-deposited Ti using the parameters presented in Table 1.

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Group

Sample

Power (W)

Speed (mm/min)

Feedrate (g/min)

G1

1 2 1 2 3 4 5 6 7 8 9 1 2 3 4

325 360 150 188 213 248 94 281 375 281 375 375 425 475 475

2000 2000 1000 1000 1000 1000 500 1500 2000 1500 2000 1200 1200 1200 1400

3.9 3.9 6.0 6.0 6.0 6.0 6.0 6.0 6.0 7.0 7.0 6.0 6.0 6.0 7.0

G2

G3

Linear energy (J/mm) 9.8 10.8 9.0 11.3 12.8 14.9 11.3 11.3 11.3 11.3 11.3 18.8 21.3 23.8 20.4

Linear feedrate (mg/mm) 2.0 2.0 6.0 6.0 6.0 6.0 12.0 4.0 3.0 4.6 3.5 5.0 5.0 5.0 5.0

Energy per feed (J/mg) 4.9 5.4 1.5 1.9 2.1 2.5 0.9 2.8 3.8 2.3 2.8 3.8 4.3 4.8 4.1

Table 2. Printing parameters for G4 Sample Powder Height (mm)

Power (W)

Speed Feedrate (mm/min) (g/min)

G4_1

2 4

325 375

2000 1040

2

375

1500

Ti Ti+ Nb Nb

3.9 1.9+ 3.5 4.8

Linear energy (J/mm) 9.8 21.6 15.0

Linear federate (mg/mm) 2.0 1.8+ 3.4 3.2

Energy per feed (J/mg) 4.9 4.2 4.7

It should be noted when analyzing the results that the input laser energy is distributed in four fractions: (1) absorbed by substrate or previously deposited material; (2) absorbed by powder particles during flight; (3) scattered by powder particles and directed away; (4) reflected by substrate or previously deposited material. 2.3

Characterization Methods

The produced samples have been cut perpendicular to the laser scanning directions. The cross-sections have then been embedded in Demotec 10 resin, ground up to grit 2500 SiC grinding paper, and polished with 6 lm and 3 lm diamond pastes and finally with an OPS solution (0.04 lm SiO2 with H2O2). Cross-section morphology and microstructure have been examined with an optical microscope (OM) (ZEISS Axioplan, Germany) and a scanning electron microscope

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(SEM–Hitachi S-4800, Japan). An energy-dispersive spectrometer (EDS–Ametek Edax Octane plus, USA) has been used to investigate the elemental spatial distribution. Microhardness HV0.2 has been measured with a load of 200 g and a dwell time of 10 s. An electron backscattered diffraction camera (EBSD–Ametek, USA) has been used to investigate the grain morphology and the crystalline structure. X-ray computed tomography (X-CT-RX Solutions EasyTom XL Ultra, France) has been used on one sample to visualize the elemental distribution in three dimensions and examine for potential pores.

3 Results and Discussion 3.1

Ti Walls Deposited on Nb Foil

Side views and cross-sections of G1 by OM are presented in Fig. 4. Samples G1_1 and G1_2 exhibited good adherence to the Nb foil. They both have dimensions close to the programmed ones (L  H = 6 mm  4 mm) with a length of 6.5 mm and height of 3.8 mm. Widths of G1_1 and G1_2 are 800 lm and 940 lm, respectively, similar to

Fig. 4. OM images of G1 samples. (a) and (b) are side view and cross-section of G1_1; (c) and (d) are side view and cross-section of G1_2. Arrows in the lower left caption indicate direction of laser movement in the horizontal plane and building direction. Purple broken frames indicate designed dimension of L  H ¼ 6 mm  4 mm. White broken lines indicate positions of the cross-sections in the as-built thin wall.

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the provided laser beam diameter of around 800 lm. The 18% wall width difference between the samples is the result of an increase of 11% in laser power, and therefore energy at the sample level. At the interface with the Nb foil, both Ti walls are slightly thinner, as a result of enhanced heat dissipation through the metallic substrate. Both of the walls appear crack-free and dense in the cross-sections, but thermal deformation of the substrate can be observed with thin foils. Several SEM images of the interface between Ti wall and Nb foil are presented in Fig. 5. SEM imaging of the Ti/Nb interface in G1_1 reveals a thin band of intermediate contrast between Ti wall and Nb foil (Fig. 5(a–c)). Thickness of this band is usually around 0.5 lm. Occasionally, the band protrudes into the pure Ti part, reaching a thickness approximately 30 lm (Fig. 5(c)). EDS line analysis shows variation of relative concentrations of Ti and Nb across the band, confirming metallurgical bonding with mixing of the two elements in the band.

Fig. 5. (a) SEM image of the interface between Ti wall and Nb foil. (b) The thin band with a thickness of 0.5 lm. (c) thin band protruding into pure Ti part, reaching a thickness of 30 lm. (d) EDS line analysis along the yellow broken line across the protrusion; red and green curves show variation of intensities of NbL and TiK signals respectively, demonstrating their correspondence with SEM image contrast.

3.2

Nb Walls Deposited on Ti Foil

Samples G2_1 to G2_9 exhibited good adherence to the Ti foil. Influence of Power. Samples G2_1 to G2_4 have been fabricated with increasing power while keeping constant the speed and feed rate. Figure 6 shows bottom parts of cross-sections of the samples. All Ti foils appear melted through at the bottom of the Nb wall deposition. A higher energy density is indeed required for depositing Nb compared to Ti due to the higher melting temperature of Nb (2742 K instead of 1941 K for Ti). Additionally, many spherical features are present in this transition area. These features are unmelted or

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Fig. 6. OM of cross-sections of G2_1 to G2_4 with processing power indicated. Distances between bold white lines indicate laser beam diameter. Red arrows show spherical features in the transition area. The inserted image shows grain boundaries in the wall.

partially melted Nb inlet particles as confirmed by EDS analyses. Grain boundaries in the main body of the pure Nb walls were revealed by polishing. In the studied parameters range, a power increase induces an increase of the width of melted part in the substrate, the volume of the transition area and the width of the wall as previously observed with Ti. Influence of Proportionally Changing Power and Speed. Samples G2_5, G2_2, G2_6 and G2_7 have been produced by proportionally increasing power and speed, while keeping the feedrate constant. The samples have been fabricated with the same linear energy, but with a decreasing linear feedrate. Figure 7 shows bottom parts of cross-sections of the samples. Similar to previous samples, all Ti foils are melted and partially melted Nb particles are observed.

Fig. 7. OM of cross-sections of G2_5, G2_2, G2_6 and G2_7 with processing power and speed indicated. They all have an identical linear energy of 11.3 J/mm. Distances between bold white lines indicate laser beam diameter.

In sample G2_5, the Ti foil has been melted and severely deformed, and traces of individual deposited layers are observed in the wall body, exhibiting poor homogeneity of the wall. As the linear energy was sufficient to create a melt pool within the used parameter range, the lower power for G2_5 could have created a cooler melt pool. The slower speed then allowed this cooler melt pool to exist for longer time thus increasing the probability of melted metals to flow sideways before solidifying, resulting in the protrusions on both sides of G2_5. Compared to other samples where power and speed

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increased proportionally, the width of melted substrate and the volume of transition area decreased. The width of wall body decreased dramatically, corresponding to the greatly decreased linear feedrate. Influence of Feedrate. Samples G2_6 and G2_8 as well as G2_7 and G2_9 have been produced respectively with the same power and speed, but different feedrates.

Fig. 8. OM of cross-sections of (a) G2_6, (b) G2_8, (c) G2_7 and (d) G2_9. Distances between bold white lines indicate laser beam diameter.

Figure 8 shows bottom parts of cross-sections of the samples. Full penetration of the Ti substrate was observed for the lower feedrate 6 g/min while increasing the feedrate to 7 g/min prevents the full penetration (white arrow). Large Nb particles were observed in the mixing zone between Nb and Ti, and their quantity appears to be larger for lower power and speed. The wall thickness and homogeneity is smaller for higher power and speed but no significant changes were observed depending on the feedrate in the investigated range. Unmelted or Partially Melted Nb Particles. For all samples in G2, meaning where Nb was injected above Ti, Nb particles have been observed in the transition area, but not in the main body of the walls. This indicates that conditions with the lowest energy input in any aspect (power down to 94 W, linear energy down to 9.0 J/mm or energy per feed down to 0.9 J/mg) have been sufficient to melt Nb inlet particles completely. To explain the presence of Nb particles only in the transition area and not in the wall body, different effects could be considered:

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1) The heat dissipation at the bottom is higher than the upper in the wall body because of larger heat transfer through the bulk substrate. This induces the necking observed at the bottom of the walls built on bulk substrates as seen for instance in Fig. 4. 2) The heat accumulated layer by layer induces a higher melt pool temperature in layers upper in the wall. 3) The boiling point of Nb (5015 K) is significantly higher than that of Ti (3560 K). Potential boiling or overheating of a melt pool of Ti+Nb mixture could dissipate more heat, compared to a melt pool of pure Nb which is hot, but not boiling. Therefore, melt pool of pure Nb can reach potentially much higher temperature than that of Ti+Nb mixture. Additionally Ti vapors may absorb the infrared laser wavelength leading to less energy in the melt pool [8]. These above mentioned effects are all related to heat accumulation and dissipation due to the presence of the substrate. Obviously, these physical phenomena occur not only individually but are combined promoting the non-fully melting of the niobium particles. Higher energy input in the system could however overheat the titanium substrate leading to perforation, extensive evaporation and enhanced instability of the melt pool. Smaller Nb particle sizes that would need less energy for melting could be considered for the transition area. 3.3

Deposition Directly Changing from Ti to Nb

A pure Ti wall is first deposited on a Ti grade 23 substrate, and then Nb is deposited onto it (scheme G3 – Fig. 3). All samples have similar dimensions close to the target programmed. Figure 9 presents optical micrographs of the cross-sections of the transition areas between the deposited Ti and Nb walls for the different processing conditions in Table 1. Negligible cracks nor delamination at the interface between the substrate and the printed structure have been observed. Futhermore, negligible cracking nor porosity have been observed within the printed structures as well. In all samples, there is a transition area between pure Ti part and pure Nb part, with a sharp interface at the pure Ti side but no well-defined interface at the pure Nb side. Several Nb particles are observed in this transition area, but not in the pure Nb wall side located on the top of the manufactured structures.

Fig. 9. OM of cross-sections of samples G3_1 to G3_4 (from left to right). The color and morphology difference is due to different polarization when taking the images. Black dots on G3_2 are indents for microhardness measurements.

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X-CT has been done on a part of the G3_3 sample to visualize the 3D elemental distribution and check for potential pores. This sample and its reconstructed image are shown on the left in Fig. 10. Nb with a higher atomic number has a brighter contrast in the reconstructed image, while Ti appears darker. Six horizontal slices of the reconstructed image (indicated by the dashed lines) are presented on the right side of Fig. 10. Slice 1 shows fully dense Ti part with a few Nb particles sticking on the side. An area with intermediate contrast appears in slice 2, indicating emergence of Ti/Nb solid solution. Slices 3 to 5 are characteristic of the transition from Ti to Nb. Several bright spheres in these slices correspond to the Nb particles observed in OM images. A few pores with sizes of tens of microns are observed in slice 4, which were not observed in cross-section OM images. The stepped contrast in slice 5 shows traces of a few different scanning tracks, indicating stepped composition gradient in the transition area. Slice 6 shows the dense pure Nb part.

Fig. 10. Photograph of the G3_3 sample part and its reconstructed image by X-CT. 6 slices of the reconstructed image are shown on the right. There positions are indicated by the white dashed lines.

Coupled EDS and EBSD have been performed on sample G3_4. The results are shown in Fig. 11. It can be noticed that, hcp structure (a-Ti) is only present where no Nb element is detected. However as soon as Nb appears in the area, a bcc (Nb or b-Ti) phase is observed, regardless of the presence of Ti. This is in agreement with Nb being a b-stabilizer for Ti. A sharp interface is observed between the transition area and pure Ti part, as shown in samples presented in Fig. 9. These interfaces could then be explained by a mismatch of both crystal structures. This interface would be an interesting subject of testing in future mechanical tests. The inverse pole figure (IPF) maps in Fig. 11 show a tendency of columnar growth in pure Ti and pure Nb parts, and equiaxed growth in the transition area.

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Fig. 11. Coupled EDS and EBSD mapping of Ti and Nb in transition area of G3_4.

EDS line scan, mapping and microhardness measurements have been done on sample G3_2. The results are shown in Fig. 12. The line scan (Fig. 12 middle) reveals a basically 3-step transition in the transition area. Immediately above pure Ti part with an average microhardness of 148 ± 9 HV is a band with a width of 40–100 lm, where the atomic composition is Ti90Nb10. Microhardness in this area is highest, with an average of 204 ± 5 HV. Then in the main part of the transition area, An atomic composition is Ti70Nb30 was observed in the primary part of the transition area. The atomic ratio converted to mass ratio is Ti:Nb = 55:45, essentially the same as a Ti grade 36 alloy composition. This area exhibits an average microhardness of 155 ± 6 HV. The embedded Nb particle has an average microhardness of 81 ± 1 HV which could indicate weak points in future mechanical tests. Further up in the structure, traces of a Nb-rich track (Nb > 95 at.%) wrapped in the Ti grade 36 were observed, with an average microhardness of 120 ± 16 HV. Finally, the pure Nb part has an average

Fig. 12. OM image of G3_2 with microindents corresponding HV0.2 numbers. The EDS line scan was done along the green arrowed line, and atomic composition of Nb and Ti (yellow and teal curves, respectively) are shown in the chart. Key composition points are directed by red arrows to corresponding areas in the OM image, indicating a composition-contrast correlation. EDS mapping of Nb and Ti on the right shows similar element distribution to that of G3_4, with slightly more Nb particles.

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microhardness of 100 ± 12 HV. The highest microhardness in the Ti90Nb10 band could result from finer grains due to crystal structure mismatch, as it is transitioning from hcp a-Ti to bcc Ti70Nb30. Unmelted or Partially Melted Nb Particles. For all G3 samples, Nb has been deposited on as-deposited Ti walls. Therefore, enhanced heat dissipation through the bulk substrate should not play a role anymore as compared to the deposition of Nb directly on a Ti substrate (samples type G2). In this configuration, the heat dissipated in the Ti-Nb mixing zone due to overheating of Titanium could explain the presence of non-fully melted Nb particles. Above this transition zone, in the pure Nb wall, no nonfully melted niobium particles could be observed as for G2 samples. 3.4

Deposition Changing from Ti to Nb with a Mixing Area in Between

In sample G4_1, a pure Ti wall has been first deposited on a Ti grade 23 substrate. Nb powder feeder has then been activated and a waiting time of 10 s has been imposed before beginning deposition in the mixing area, in order to guarantee a stable flow of sufficiently mixed powder in the feeding line. After finishing the mixing area, the Ti feeder was turned off and Nb feeder was adjusted to the desired feedrate, but no waiting time has then been imposed at this transition. The parameters listed in Table 2 have been employed to immediately deposit the Nb part of the structure. Figure 13(a) shows cross-section of the sample G4_1.

Fig. 13. (a) OM image of cross-section of G4_1. (b) Microhardness profile along the building direction from Ti through the Ti+Nb mixing zone to Nb. Yellow boxes show transitions between the pure metal and the mixing zone. (c) EDS line scan across the mixing area.

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The pure Ti part has dimensions close to the programmed ones. The mixing area is longer and pure Nb part is shorter than programmed, which is a result of an unstable powder flow caused by absence of the 10 s waiting time. Many Nb particles were observed in the mixing area as seen by the sparks of Nb signal in the EDS mapping and in Fig. 14, but neither crack nor porosity were present. Microhardness measurements in Fig. 13(b) and EDS line scan along the sample in Fig. 13(c) show results similar to those from G3_2: a Ti90Nb10 transition band follows immediately after the pure Ti, exhibiting an average microhardness of approximately 182 ± 1 HV. The primary part of the mixing area consists of a Ti70Nb30 solid solution as the matrix, with Nb particles embedded (the abrupt changes on the Nb curve in Fig. 13(c)). The average microhardness of the solid solution matrix and the embedded particles were measured to be 154 ± 10 HV and 85 ± 1 HV, respectively. The EDS map of Ti close to the Ti-to-Ti+Nb transition in Fig. 14 shows a slightly higher concentration of Ti between the first and second deposited mixing layers, which should be the same case for the layers above. Microhardness of these features ranged from 192 to 259 HV.

Fig. 14. OM image of the mixing area next to the pure Ti part (left) and EDS map of Ti of the same area (right). White arrows point to the features where Ti is slightly richer.

Unmelted or Partially Melted Nb Particles. As for G2 (Nb on Ti bulk) and G3 (Nb on Ti wall) samples, Nb particles have been observed throughout the mixing area in G4_1 where Ti and Nb powders were fed together. Here also the overheating of the mixture Ti+Nb and the resulting heat dissipation could explain the presence of Nb particles that seem well distributed within one layer and not confined at the interface between two subsequent layers as reported by Schneider-Maunoury et al. [7] From this study and the different configurations investigated, it appears that melting together materials with different melting points is challenging even for a simple material system leading to a solid solution. It would be interesting to measure then the temperature of the melting pool in this mixing zone depending on the Nb content. As proposed previously, a possibility to solve the issue of having unmelted particles would be to use finer particles for the higher melting point material, here Nb. However, working with fine powders induced flowability and safety issues. Preheating Niobium

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starting particles either in the container, the transporting line or in-flight after exiting the nozzle could be envisaged but would bring in potential fire hazard and add technical complexity to the system.

4 Conclusions This preliminary study demonstrates the ability to deposit Titanium on Niobium and inversely Niobium on Titanium for building 3D structures using a direct energy deposition process. Additionally, a mixing zone obtained by the separate feeding of both powders could also be deposited with the additive manufacturing facility, offering new opportunities for building functionally graded materials. The microscopic observations of the interfaces between these two metals as well as of the mixing zone reveal no cracks and a dense structure. The composition of the built walls varies from one pure metal to the other one through a solid solution that may present various compositions. The two metals grow with columnar grains whereas the solid solution shows an equiaxed structure. The hcp phase is only observed in pure Ti parts and as soon as Nb is present, the crystal structure becomes bcc. Fine grains due to the sharp transition between these two crystalline phases induces a high hardness. Even if the energy input was always sufficient to melt completely the niobium particles, some unmelted Nb particles have been found at the interfaces with titanium in the mixing zone also far away from the bulk substrate. Further analyses are required to explain this phenomenon that could be induced by the evaporation of titanium that cools down the melt pool and absorbs the laser wavelength. These large particles could be an issue for the mechanical response of the material. Future activities will then focus on the correlation between the process parameters and the Ti-Nb microstructures. This would be necessary for tailoring the performances such as tensile strength, fatigue strength, corrosion resistance and biocompatibility of the parts for industrial applications. Acknowledgements. The authors are thankful to Bernhard von Gunten and Peter Ramseier (Laboratory for Advanced Materials Processing–EMPA Thun) for EBSD sample preparation EBSD. The authors would also like to thank Xavier Maeder (Laboratory for Mechanics of Materials & Nanostructures–EMPA Thun) for the assistance with EBSD analysis and Kai Zweiacker for assistance with X-CT analysis.

References 1. Loh, G.H., Pei, E., Harrison, D., Monzon, M.D.: An overview of functionally graded additive manufacturing. Addit. Manuf. 23, 34–44 (2018) 2. Yan, L., Chen, X., Li, W., Newkirk, J., Liou, F.: Direct laser deposition of Ti-6Al-4V from elemental powder blends. Rapid Prototyp. J. 22(5), 810–816 (2016) 3. Lanfant, B., Bär, F., Mohanta, A., Leparoux, M.: Fabrication of metal matrix composites by laser metal deposition-a new process approach by direct dry injection of nanopowders. Materials 12, 3584 (2019)

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4. Yan, L., Chen, Y., Liou, F.: Additive manufacturing of functionally graded metallic materials using laser metal deposition. Addit. Manuf. 31, 100901 (2020) 5. Feurst, J., et al.: LASER powder deposition of titanium-tantalum alloy structured interfaces for use in orthopedic devices. In: Medical Device Materials VI: Proceedings from the Materials and Processes for Medical Devices Conference 2011, pp. 159–164. ASM International, Minneapolis (2013) 6. Pulugurtha, S.R.: Functionally graded Ti6Sl4V and inconel 625 by laser metal deposition. Ph. D. dissertation, Missouri University of Science and Technology, Rolla (2014) 7. Schneider-Maunoury, C., et al.: An application of differential injection to fabricate functionally graded Ti-Nb alloys using DED-CLAD® process. J. Mater. Process. Technol. 268, 171–180 (2019) 8. Mohanta, A., et al.: Influence of temporal and spectral profiles of lasers on weld quality of titanium. Opt. Lasers Eng. 134, 106173 (2020)

Investigation of Plastic Freeformed, Open-Pored Structures with Regard to Producibility, Reproducibility and Liquid Permeability Andre Hirsch1(&), Christian Dalmer2, and Elmar Moritzer1 1

Kunststofftechnik Paderborn (KTP), Direct Manufacturing Research Center (DMRC), Paderborn University, 33098 Paderborn, Germany [email protected] 2 Paderborn University, Warburger Str. 100, 33098 Paderborn, Germany

Abstract. The Arburg Plastic Freeforming (APF) is an additive manufacturing process which allows the production of three-dimensional thermoplastic components in layers. The components are produced by depositing fine, molten plastic droplets. The main advantage of the APF is the open-parameter control of the associated machine system. Thus, the process parameters can be optimized for individual applications. A special and new application of the APF is the production of interconnecting porous structures. As this is a novel approach with this manufacturing process, the general producibility and reproducibility must first be proven. Therefore, the relevant process parameters with an influence on the open-pored structures are identified. The volume of the individual plastic droplets, the distance between the droplets and the layer thickness are the three decisive influencing factors. With the use of analysis methods, the free spaces created in the structure are described by a uniformly constructed, interconnected pore structure. This means that the pores are interconnected in three dimensions. Reproducibility is evaluated by repeated production and thru the changed conditions during the manufacturing process. In addition, the multiplication and a change of geometry are evaluated in such a way that there is no influence on the pore size. Irregularities when depositing the first layer are caused by unevenness of the building platform. A suitable test arrangement is set up to determine the liquid permeability. A characteristic value is determined to describe the permeability to liquids. Keywords: Arburg Plastic Freeforming Producibility  Liquid permeability

 Porous plastic structures 

1 Introduction The Arburg Plastic Freeforming (APF) is an additive manufacturing process that allows three-dimensional, thermoplastic components to be produced layer by layer. The components are generated by depositing fine, molten plastic droplets. One of the main advantages of the APF process is the open machine control. Thus, the process parameters can be adapted and optimized for individual applications. In addition, due to © Springer Nature Switzerland AG 2021 M. Meboldt and C. Klahn (Eds.): AMPA 2020, Industrializing Additive Manufacturing, pp. 112–129, 2021. https://doi.org/10.1007/978-3-030-54334-1_9

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the open-parameter control, it is possible to process own materials on the corresponding machine system Freeformer. The objective of this paper is to investigate the general producibility of open-pored structures using the APF process. Furthermore, influences on the reproducibility of the manufacturing process are to be identified and their effects are to be shown. With regard to possible fields of application, a correlation between the manufactured structures and their fluid permeability shall be investigated.

2 State of the Art 2.1

Arburg Plastic Freeforming (APF)

The Arburg Plastic Freeforming is characterized in particular by the processing of standard plastic granules as well as by the production of components out of very fine molten thermoplastic droplets. The associated machine system for this technology is the Freeformer from Arburg GmbH & Co KG. Its most important machine components are shown in Fig. 1. The raw material, a qualified standard thermoplastic granule, is fed via a hopper. In the material preparation unit, the granulate is molten with a screw as in the injection molding process. The molten material is then pressed into the material reservoir. Here, a piezo actuator performs a pulsed nozzle closure. The nozzle moves up and down, producing almost 250 droplets per second. The movement of the building platform, for the precise positioning of the discharged droplets in the x- and y-direction, is realized by two linear motors. After the completion of a layer the platform is lowered by one-layer thickness in z-direction, using a spindle drive [1–3]. Basis: Qualified standard granule

Piezo actuator performs pulsed nozzle closure

Material preparation with screw as in injection molding process

Nozzle closure

Material reservoir between screw and nozzle tip is under pressure

y

Discharge of tiny droplets from the nozzle tip

z x

© ARBURG

Part carrier moves the part along the x- and y-axes and downwards layer by layer along the z-axis

Fig. 1. Schematic setup of the Freeformer [1]

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In the literature there is a large number of publications by the Arburg company with process descriptions, the advantages of the technology, the available materials and application examples [2–5]. In [6–8] the resulting mechanical properties of APF components were investigated and optimized. Furthermore, new approaches for the production of two-component (hard-soft) parts with interlocking interfaces as well as for the suitability of the APF process for the processing of Metal-Injection-Molding granules are found in the literature [9, 10]. 2.2

Porosity

Porosity is a structural characteristic of a component and describes the existence of pores. Pores are classified into open and closed structures. Closed pores are located within a component. They are completely enclosed within the component so that there is no connection to the environment. Open pores are further divided into blind and continuous pores (see Fig. 2). Blind pores have one connection to the environment, whereas continuous pores have at least two connections to the environment. When a medium (liquid or gas) flows through a component, the number and geometry of the pores contributes to the degree of permeability [11, 12].

Continuous Pore

Closed Pore Blind Pore

Fig. 2. Division of the pore types into open, closed and blind pores

A further classification is based on the size of the pore size. The following size ranges are differentiated by diameter: • Micropores: 50 nm (0.05 µm) The porosity  of a material is determined using Eq. (1) and describes the percentage of free volume: ¼1 qSpecimen : qReference :

qSpecimen qReference

Density of the porous specimen Density of the non-porous material (Reference)

ð1Þ

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Applications of Open-Pored Structures

An exemplary application of open-pored structures is filtration, where a filter is able to serve different purposes. If two media are separated from each other, this is called filtration or separation. The filter retains particles that are larger than the minimum pore diameter. Furthermore, it is possible to mix different gases with each other (dispersion) by using branches and a multi-layer structure within the filter element [11]. 2.4

Liquid Permeability

The permeability is a material constant and describes the flow-through capability of a porous material. Permeability provides information about the volume of a permeant (gas or liquid) that penetrates a barrier of a given thickness and area per unit of time, provided that there is a partial pressure difference at the interfaces of the barrier [13]. The following three basic equations are used for mathematical description: • Continuity equation (conservation of mass) • Law of Darcy (conservation of momentum) • Thermodynamic equation of state The continuity equation implies that the sum of all masses flowing in and out is equal to the mass change of the sample. Darcy’s law specifies the relationship between the flow velocity within the pores and the potential gradient, considering the height and acceleration due to gravity. With the help of the thermodynamic equation of state, the dependence of the density of the permeant on the pressure, at constant temperature, is included [14]. By combining the basic equations, Eq. (2) defines the permeability (K) with the unit m2: K¼

_ V: g: L: q: g: A: Dh: kf :

V_  g  L kf  g ¼ q  g  A  Dh q  g

ð2Þ

Volume flow Dynamic viscosity of the permeant Length of the barrier Density of the permeant Acceleration of gravity Cross-sectional area of the barrier through which flow occurs Difference in height between inlet and outlet Permeability coefficient

As can be seen from Eq. (2), permeability can be expressed by a second definition, the coefficient of permeability (kf), with the unit m/s. The formulaic relationship is described in DIN 18130-1 and is given in Eq. (3) below:

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kf ¼ Dp:

V_  L  q  g A  Dp

ð3Þ

Pressure difference between inlet and outlet

Once the permeability coefficient has been calculated, the permeability of water can be classified according to DIN 18130-1 (see Table 1).

Table 1. Classification of permeability according to DIN 18130-1 Description Very highly permeable Highly permeable Permeable Low permeability Very low permeability Almost completely impermeable to water

Permeability coefficient kf [m/s] >10−2 10−2 bis 10−4 10−4 bis 10−6 10−6 bis 10−8 10−8 bis 10−9 300 1950 Closing angle [°] +18 0 (Parallel gripper) ±90 Additional inclination [°] ±7.5 N.A. N.A. Degrees of freedom 2 1 2 Multifunctionality High Low Very high Price range [€] 1.450 >1.000 >45.000

5 Economic Evaluation Furthermore, an economic evaluation was carried out. To determine the material consumption and the related costs, all components had to be evaluated using the printer software of both the Stratasys J750 and the HP Designjet Color 3D. The material costs could then be determined using the cost rates, which were calculated on the basis of the manufacturer’s price information. The gripper fingers are made of the materials RGD515, RGD531 and TangoBlackPlus. The two RGD components result in digital ABS when mixed. All other materials with the exception of the SUP706 support material are not required for the gripper fingers. With material extrusion (FDM), care is taken to cause as little support material as possible and thus rework. This is achieved by the fact that the support material can be removed manually by breaking it away. After adding up all material costs for printing with both 3D printers, the gripping system incurred material costs of € 366.17. In order to determine the printing time and the corresponding costs, all components first had to be evaluated using software programs. Furthermore, it was necessary to determine the hourly machine rates of the devices used. Most of the costs are due to the high hourly rate for printing with the Stratasys J750. Despite the triple print time of the

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HP Designjet, the cost is less than 50% compared to the J750. The auxiliary components for post-processing the components are very small compared to the pressure. All control components, with the exception of the printed actuators and the electrical connection line from the main valve to the robot, were purchased from a leading supplier. The connecting line was used by manufacturer of the robot. Small parts such as cable ties, solder, screws, etc. were not considered in the calculation. At the end of the cost calculation, the total costs of the gripping system were determined (see Table 2). The biggest cost driver is the printing and cleaning costs. This is due to the high machine hourly rate of the Stratasys J750. For this reason, it is advisable to only use it to print components that have high quality requirements.

Table 2. Comparison of cost types and cost shares. Costs Absolute costs [€] Relative costs [%] Material costs 366.17 25.3 Printing and post-processing costs 748.41 51.6 Additional parts 335.04 23.1 Total costs 1,449.62 100.0

When planning the gripping system, the costs were estimated at around € 1,500. With total costs of € 1,449.62, this value was even undercut by 3.36%. That represents a satisfactory result. It should be added that no personnel costs have been factored into the developed gripping system. For this reason, the comparison with other gripping systems should be treated with caution, since other components such as research and development costs, overhead costs, personnel costs and a profit have been included.

6 Conclusion With the help of this contribution it was possible to develop and to implement a multifunctional and customer-specific human-robot collaboration gripping system using additive manufacturing. A satisfactory result was achieved at a cost of € 1,449.62 and a weight of 998 g. In particular, the use of lightweight design made it possible to realize the low weight of the gripping system, which only uses a small portion of approx. 13% of the load capacity of the robot used. By using a multi-material printer, complex components in different materials and functions (e.g. rigid and solid structure and flexible bellows) could be combined in one component. This approach has also contributed significantly to weight loss. It has been shown that in addition to the material costs, the printing costs also have a high impact on the overall costs. It was demonstrated that a reasonable combination of demanding and therefore expensive printing processes (e.g. for the manufacturing of the fingers) and simple and inexpensive processes (e.g. for the manufacturing of the housing) enables compliance with the specified costs. The budget was met with a saving of 3.36% compared to the calculated costs. It should be noted that no personnel costs

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and small parts with minimal and therefore negligible costs have been included. It could be shown that with the approach presented here, a practical example in which a gripper is used to hold a pen when writing and painting, can be successfully implemented.

References 1. Wendt, T.M., Himmelsbach, U.B., Lai, M., Waßmer, M.: Time-of-flight cameras enabling collaborative robots for improved safety in medical applications. Int. J. Interdiscipl. Telecommun. Networking 9(4), 10–17 (2017) 2. Oberc, H., Prinz, C., Glogowski, P., Lemmerz, K., Kuhlenkötter, B.: Human robot interaction – learning how to integrate collaborative robots into manual assembly lines. Procedia Manuf. 31, 26–31 (2019) 3. Vogel, C., Elkmann, N.: Novel safety concept for safeguarding and supporting humans in human-robot shared workplaces with high-payload robots in industrial applications. In: Mutlu, B., Tscheligi, M., Weiss, A., Young, J.E. (eds.) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on HRI, pp. 315–316. ACM Press, New York, USA (2017) 4. Ranz, F., Komenda, T., Reisinger, G., Hold, P., Hummel, V., Sihn, W.: A morphology of human robot collaboration systems for industrial assembly. Procedia CIRP 72, 99–104 (2018) 5. Mourtzis, D.: Simulation in the design and operation of manufacturing systems: state of the art and new trends. Int. J. Prod. Res. 58(7), 1927–1949 (2020) 6. Lachmayer, R., Lippert, R.B., Fahlbusch, T. (eds.): 3D-Druck Beleuchtet. Additive Manufacturing auf dem Weg in die Anwendung. Springer, Berlin (2016) 7. Knapp, M.: Roboter bauen mit Arduino, 1st edn. Galileo Press, Bonn (2015) 8. Schreiber, F., Manns, M., Morales, J.: Design of an additively manufactured soft ringgripper. Procedia Manuf. 28, 142–147 (2019) 9. Sanchez-Tamayo, N., Wachs, J.P.: Collaborative robots in surgical research. In: Kanda, T., Ŝabanović, S., Hoffman, G., Tapus, A. (eds.) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 231–232. ACM Press, New York (2018) 10. Hatscher, B., Luz, M., Nacke, L.E., Elkmann, N., Müller, V., Hansen, C.: GazeTap: towards hands-free interaction in the operating room. In: Lank, E., Vinciarelli, A., Hoggan, E., Subramanian, S., Brewster, S.A. (eds.) Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 243–251. ACM Press, New York (2017) 11. Safeea, M., Neto, P.: Minimum distance calculation using laser scanner and IMUs for safe human-robot interaction. Robot. Comput.-Integr. Manuf. 58, 33–42 (2019) 12. Strobel, M., Döttling, D.: High dynamic range CMOS (HDRC) imagers for safety systems. Adv. Optical Technol. 2(2), 147–157 (2013) 13. Zanella, A., Cisi, A., Costantino, M., Di Pardo, M., Pasquettaz, G., Vivo, G.: Criteria definition for the identification of HRC use cases in automotive manufacturing. Procedia Manuf. 11, 372–379 (2017) 14. Haase, T. Wörn, H., Nahrstaedt, H.: Shared memory in RTAI Simulink for kernel and userspace communication at the example of the SDH-2 - ICINCO 2010. In: Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics, vol. 3, pp. 160–165 (2010) 15. Spiliotopoulos, J., Michalos, G., Makris, S.: A Reconfigurable gripper for dexterous manipulation in flexible assembly. Inventions 3(1), 1–4 (2018)

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16. Ngo, T.D., Kashani, A., Imbalzano, G., Nguyen, K.T.Q., Hui, D.: Additive manufacturing (3D printing): a review of materials, methods, applications and challenges. Compos. Part B Eng. (2018) 17. Kadkhoda-Ahmadi, S., Hassan, A., Asadollahi-Yazdi, E.: Process and resource selection methodology in design for additive manufacturing. Int. J. Adv. Manuf. Technol. (2019) 18. Bourell, D., et al.: Materials for additive manufacturing. CIRP Ann. 66(2), 659–681 (2017)

Enhanced Cooling Design in Wire Drawing Tooling Using Additive Manufacturing Joakim Larsson(&), Patrik Karlsson, Jens Ekengren, and Lars Pejryd School of Science and Technology, Örebro University, Fakultetsgatan 1, 701 82 Örebro, Sweden [email protected]

Abstract. Wire drawing is a manufacturing process in which metal rods or wires are drawn through a single or a series of dies, reducing the wire crosssection and enhancing the mechanical properties of the wire. The tribological conditions in wire drawing are quite extreme and high friction between the wire and the die results in an increased die temperature. Previous studies have shown that by reducing the die temperature the lifetime of the die increases and thus efficient cooling of the die is of high importance. Additive manufacturing enables fabrication of tools with advanced conformal cooling channels with high cooling efficiency. This technique may, therefore, be of high importance in the design of the cooling system of drawing dies. In the present study, the effect of conformal cooling design of die holder on the die temperature, and thus die performance, was investigated. A die holder was manufactured by means of laser powder bed fusion (LPBF) in an EOS M290 machine using atomized corrosion resistant steel (Corrax). The cooling efficiency of the manufactured tool holder was evaluated in an industrial wire drawing process and further analysed using FEM modelling. This study shows promising results on improved cooling efficiency for die holder designed and manufactured by additive manufacturing. Keywords: Wire drawing

 Cooling  Additive manufacturing

1 Introduction Wire drawing is a manufacturing process in which metal rods or wires are drawn through a single or a series of dies, reducing the wire cross-section and enhancing the mechanical properties of the wire. Even though the process is defined as a cold working method, heat is generated in the wire and the tool as the wire is deformed. Studies have shown that up to 15% of the total power used for the reduction stays in the drawing die, Enghag [1]. The part of the drawing tool that is in contact with the wire (drawing die) is usually made of either cemented carbide or diamond and can either be integrated in a steel/carbide die or used as nibs in an interchangeable die core system. The cemented carbide used for the dies is sensitive to high temperatures. At 800 °C already 50% of the hardness is lost, according to a producer’s data sheet [2]. Finite element studies of an industrial wire drawing situation have previously been reported by Larsson and Jarl [3]. The simulations were verified against the actual © Springer Nature Switzerland AG 2021 M. Meboldt and C. Klahn (Eds.): AMPA 2020, Industrializing Additive Manufacturing, pp. 426–436, 2021. https://doi.org/10.1007/978-3-030-54334-1_30

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industrial process and the result showed that temperatures can exceed 800 °C in the drawing die. Improving the cooling of dies is, therefore, of highest importance in order to ensure long life time of the drawing die. In another study, Larsson and Jarl [4] showed that more efficient cooling may indeed extend the life of the die. However, the tool holder used in that particular experiment had a short life time due to the hardness of the used material, making it unsuitable for industrial use. In order to increase the understanding of the factors influencing the performance and for further development of the process to extend the life of the tools, a revisiting of the cooling of drawing dies and nibs was therefore deemed interesting. Another limiting factor is the lubricants used. The most commonly used in dry drawing wire drawing processes are calcium or sodium soaps that oxidize when exposed to high temperatures. If the lubricant oxidizes, it loses its lubricating abilities [5]. If the cooling of the inlet cone of the die could be improved, increased productivity may be achieved. The idea to use additive manufacturing (AM) as a production method for metallic tools in order to allow for more freedom in the design of cooling systems is not new. The AM methods have advanced in maturity lately and examples of applications of conformal cooling channels through the use of AM can be found in literature, e.g. Hölker et al. [6] and Jahan and El-Mounayri [7]. Hölker et al. [6] investigated a hot extrusion die for aluminium using AM to produce the tool with conformal cooling channels, claiming a 300% productivity increase. Jahan and El-Mounayri [7] reported on a design process for optimizing the cooling of a die for plastic injection moulding. Based on the potential seen in the increased life of the drawing tools by improved cooling and the identified possibilities in using AM to accomplish this, the current project was directed towards investigating the cooling capabilities of a tool holder for wire drawing nibs with conformal cooling channels. This was done both by experimental investigations of the cooling process in a controlled laboratory situation and experiments in an industrial wire drawing environment. The data from the experiments was used to verify the finite element model that was developed. The verified model was then used for simulation to give further understanding of the cooling in the wire drawing process.

2 Materials and Methods 2.1

Wire Drawing Theory

The force required to pull the wire through the drawing die can be calculated theoretically. This is commonly done using the formula derived by Siebel and Kobitzsch [8]   A0 2a l A0 F ¼ A1 Rem ln þ ln þ ; 3 a A1 A1

ð1Þ

where F is the total drawing force, A0 and A1 are the area of the wires cross section before and after the reduction, Rem is the mean flow tension for the material before and after the reduction, 2a is the semi-die angle of the die and l the coefficient of friction

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between the wire and the die. According to literature, the friction coefficient between the wire and the die lies between 0.01 and 0.07 for a well functional dry lubricated wire drawing process using soap powder as lubrication [9]. The power needed for a pass in a wire drawing process can be calculated as P ¼ VF;

ð2Þ

where P is the needed power and V is the drawing speed. When the wire is drawn through the die, heat is generated due to the plastic deformation of the wire and friction between the wire and the die. The temperature increase in the drawn wire during one reduction step can be estimated using the following equation [1], DT ¼ k

F=A1 ; q Cp

ð3Þ

where DT is the temperature increase, q is the density of the wire, Cp is the specific heat capacity of the drawn material and k is a correction factor. The loss of energy from the wire that the constant k represents is mostly due to the cooling of the wire in the drawing die, which is depending on die design, drawing speed, die cooling system, drawing tools material, the thermal conductivity of the wire and die material and other parameters. Literature states that up to 15% of the energy produced in the wire during the reduction process is removed by energy transport to the drawing die and subsequent cooling [1]. Many parameters influence the exact number, but the drawing speed is one of the most important factors. Although the heat generated by the deformation of the wire is independent of the drawing speed, an increase in drawing speed increases the energy released per time unit. This results in an increased die temperature due to insufficient removal of heat from the drawing die. As the die temperature increases this leads to a reduction of the fraction of the total energy that goes to the drawing die. In a previously study, it was shown that as a carbon steel wire was drawn at 0.33 m/s, 7.5% of the total energy went to the drawing die [10]. 2.2

Die Holder

There are many different designs of cooling systems for the drawing tool in a conventional wire drawing setup. The most efficient setup is a so called direct cooled die holder. The drawing tool is placed in a die holder and the holder is placed in a cooling body. As the process runs, the cooling body is filled with coolant, meaning that the die holder is surrounded by coolant. Figure 1a and b shows a conventional wire drawing die holder that is made for a directly cooled system. The die is positioned in the conical surface that can be seen inside of the cooling flanges illustrated in Fig. 1 b).

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Fig. 1. CAD models of the two tool holders a) the conventional direct cooled b) a schematic cut view of the conventional tool holder c) the new printed design with conformal cooling channels.

As the die holder itself, already in the conventional setup, is submerged in turbulently flowing coolant, the idea to be able to increase the cooling rate of the die is to move the coolant closer to the die. Thus, minimizing the distance between the die and the coolant should result in enhanced cooling of the die. In this study, the die was designed with a cooling channel placed as close to the drawing tool as possible. The cooling channel was designed to revolve around the drawing tool in a helical geometry. The coolant channel cross-section has the geometry of a teardrop to avoid problems when the roof of the channel was being printed [11]. The top of the teardrop was oriented in the building direction, along the positive Z-axis direction during printing. The cross-sectional area of the channel was designed to be 8.9 mm2. A more detailed description of the design process for the cooling body can be found in [12]. The resulting cooling body used in this study is shown in Fig. 1c. 2.3

Laboratory Experiments

A previous study reports an experimental trial performed in order to evaluate and compare the cooling capacity between the conventional die holder and the additive manufactured holder in a laboratory setup. The results in form of temperature differences between the coolant and the cooled object placed at the position were the die would sit in an industrial setup is shown in Fig. 2. The graph shows that the die holder with conformal cooling gives roughly 25% lower temperature difference between the coolant and the cooled object, thus increasing the cooling efficiency. During the laboratory experiments, other parameters such as temperature of coolant and flow of coolant was also measured to ensure that the experiments with the different die holders were performed under the same conditions. The results were also used to obtain suitable boundary conditions for finite element simulations (FEM) [12].

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ΔTemperature measuring probe/coolant ∆T (°C)

20 15 10

Conventional

5

AM

0 0

50

100

150

Time (s) Fig. 2. Results from laboratory experiments using conventional die holder and AM die holder with conformal cooling channels. The graphs show temperature differences between the cooled object and the coolant as a function of running time.

2.4

Finite Element Study

FEM analyses were made using Ansys 2019 R3 [13], using a standard implicit transient heat solver. The models consisted of approximately 19000 elements. Boundary conditions, convection and heat flow, were taken from the laboratory experimental conditions [12]. The convection boundary condition was used to simulate the heat removal, which is done by the coolant. A cut of the mesh (without the die) used for the analysis of the die holder with conformal cooling channels is shown in Fig. 3.

Fig. 3. Sectioned image of the FEM model used in this study

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The heat generated by the wire drawing process was added to the model as a heat flow on the inside surface of the drawing die. The heat flow was set to represent the measured results from the industrial experiments. 2.5

Industrial Experiments

In order to verify that the results found in the laboratory experiments are valid for the real application, industrial experiments were made. The die holder was used in the last draw of an industrial drawing machine with 9 steps. In the process, a high carbon steel wire was produced and at the finishing block the wire had a diameter of 4.24 mm and a maximum speed of 7.5 m/s (multiple drawing speeds were tested during the experiments). To be able to compare the conventional die holder with the additive manufactured tool holder, data for the conventional tool holder first needed to be acquired. The same experimental procedure was used for the two die holders. To study the differences in cooling efficiency between the two die holders, the drawing dies used in the experiments were equipped with type k thermocouples. The thermocouples were spot welded to the exit side of the dies as shown in Fig. 4.

Fig. 4. Drawing die equipped with a thermocouple on the exit side.

The thermocouples were sampled during the experiments using a Testo 176T4. The sampling frequency was 1 Hz and the reported measuring error is ±0.5% of measured value.

3 Results and Discussion 3.1

Wire Drawing Experiments

In the industrial wire drawing tests, the additively manufactured die holder with conformal cooling channels was mounted in the same manner as a conventional die holder. The coolant was feed to the die holder through an 8 mm (28 mm2) hose. The coolant

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outlet of the die holder was equipped with a short hose in order to steer the coolant into the standard outlet of the coolant container. An image of the die holder mounted in the cooling body can be seen in Fig. 5.

Fig. 5. Additively manufactured die holder mounted in a conventional wire drawing machine.

Tool temperature (°C)

During the experiment, five different drawing speeds were used in order to study if any differences could be found depending on the drawing speed. The highest drawing speed used in the experiments, 7.5 m/s, is the normally used speed for the specific product. To ensure a good lubrication in the start, the highest speed was used in the beginning, as the lubricant used was chosen for that speed. The highest drawing speed

400 350 300 250 200 150 100 50 0

3d-printed Conventional 0

1000

2000

3000 Time (s)

4000

5000

6000

Fig. 6. Measured temperatures from the two different experiments. Five different drawing speeds were used (one specific was used twice, resulting in six distinct levels).

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was also used for an extended time at the end of the experiments. Results from the experiments, in form of temperature measured at the exit side of the drawing die, is shown in Fig. 6. The temperature curves have been analysed and mean temperature values for the different drawing speeds have been extracted, as presented in Table 1. Table 1. Mean temperatures from the performed industrial experiments. Drawing speed (m/s) 3 4 5 6 7,5

Conventional (°C) 253 265 285 305 329

Additive manufactured (°C) 206 216 231 250 269

Difference (K) 47 49 54 55 60

As shown, both the temperature itself and the temperature difference between the conventional die holder and the additive manufactured die holder increase with increased drawing speed. This is explained by the higher power added to the system at a higher drawing speed. However, if the percentage difference is studied for the different drawing speed almost no difference can be seen, the result show that the die holder with conformal cooling channels gives roughly 18% lower temperatures than the conventional die holder for all drawing speeds. 3.2

Finite Element Analysis

To be able to evaluate the cooling process in the industrial wire drawing process by FEM, the amount of energy that goes into the die needs to be evaluated. This was done by iterating the value of the boundary condition (heat flow) for the ingoing heat until the surface where the thermocouple was mounted during the experiments showed the same value as from the experiments. This calibration was performed for all different drawing speeds for the conventional die holder case. The heat flows used in the simulations of the additively manufactured die holder by FEM were those obtained by calibrating the simulations of the conventional die holder to the measured temperatures, for each drawing speed. The rest of the boundary conditions used were the same as in the FEM study reported earlier [12]. For the normal production speed (7.5 m/s), it was estimated that around 485 W is going into the drawing die. Using Eq. 1 and 2, an approximate power of 33.25 kW was needed for the specific case, meaning that around 1.5% of the total power that was used in the forming process went to the drawing die as heat. These parameters were put into the FEM models for the conventional case and for the case with the additive manufactured die holder. The resulting temperature plots from the end (steady state) of two of the FEM simulations can be seen in Fig. 7.

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Fig. 7. Temperature result plot from FEM simulations. Left: conventional die holder. Right: AM die holder with conformal cooling channels.

As can be seen, the estimated maximum temperature of the die is much lower when using the additively manufactured die holder. In the real process, there will be much higher temperature in local spots where the highest pressures occur during the forming process. To be able to catch these high temperature peaks, a different type of simulation needs to be performed. However, to reach a steady state situation regarding temperature, with the cooling body modelled, would with that type of simulation take immense time and computer power. To reach steady state approximately 15 min of process needs to be simulated, each second 7.5 m of wire is drawn. This means that around 6 750 m of wire drawing needs to be simulated to reach a steady state using a transient mode simulation. Researchers at Örebro University have simulated wire drawing processes including wire deformation. These simulations only handled a few centimetres of wire being drawn, and still the simulations took hours. The results from the simulations with the other drawing speeds from the experiments are presented in Table 2. PowerT is the total power needed for the reduction pass calculated using Eq. 1 and 2, PowerD is the power that was found going to the drawing die, AM represents the new die holder with enchanted cooling, C represents the conventional die holder, Tm is the maximum temperature from the simulation, Tmp is the temperature from the simulation in the point where the temperature was measured during the industrial experiments and Δ%EvFEM is the percentage temperature difference between the simulations and the experiments in the measuring point. Although a larger power is needed to perform the drawing at higher drawing speeds, a lower fraction of that power is transferred as heat to the die holder. Figure 8 graphically show the results regarding the percentage of the energy that goes to the drawing die.

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Table 2. Results from the FEM studies PowerT (W)

PowerD (W)

PowerD (%)

3 4 5 6 7,5

13000 17300 21700 26000 32500

370 390 420 450 485

2,8 2,3 1,9 1,7 1,5

Percentage of total energy that goes to drawing die (%)

Speed (m/s)

AM TM (°C) 264 278 297 317 340

AM Tmp (°C) 212 223 238 254 272

C TM (°C) 307 323 346 369 396

C Tmp C Δ% (°C) EvFEM (%) 254 0,5 267 0,7 286 0,5 304 0,3 327 0,6

AM Δ% EvFEM (%) 2,8 3,2 3,1 1,7 1,0

5 4.5 y = 5.9867x-0.693

4 3.5 3 2.5 2 1.5 1 0.5 0 2

3

4

5

6

7

8

Drawing speed (m/s)

Fig. 8. Percentage of the total energy needed for the drawing pass that ends up in the drawing die.

4 Conclusions and Future Work In the present study, a conventional tool holder for the wire drawing process was compared to a tool holder with conformal cooling channels manufactured in an anticorrosive tool steel using additive manufacturing. The additive manufactured tool holder had cooling channels placed as close as possible to the drawing die. The goal of the project was to increase the cooling of the drawing die, and thereby increase the lifetime of the drawing die. The cooling efficiency was evaluated in an industrial wire drawing process. Multiple production speeds were used to study the influence of the drawing speed. To be able to compare the two die holders, the dies used in the experiments were equipped with thermocouples. The results from the experiments show that the die cooled using the additive manufactured tool holder had roughly 18% lower temperature for all drawing speeds.

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Finite element calculations were performed for the different experimental situations and the result from the simulations were in good agreement with experimental results. The models can be used to further understand and develop cooling systems for the wire drawing process. The present study has shown the potential of additively manufactured tool holder for wire drawing processes. New design of the holder resulted in enhanced cooling of the die, which may enable longer tool life or increased production speed. At a set speed, there will be a lower temperature in the die and thus also in the lubricant. The cooling may be used to extend the life of the tool, and may also be used to increase drawing speed while still working in a temperature range where the lubricant is working properly. Wire drawing lubricants are designed for optimal properties in different temperature regions and increased cooling would extend the useable production speed window for the specific lubricant. However, to be able to verify these benefits, further studies need to be conducted. To study the effect of the increased cooling capacity on the lifetime of the drawing dies, longer industrial experiments need to be performed, where the wear is measured over time.

References 1. Enghag, P.: Steel Wire Technology. Materialteknik HB, Örebro (2009) 2. Sandvik: Understanding Cemented Carbide The material with staying power, p. 20 (2011) 3. Larsson, J., Jarl, M.: Högre draghastigheter/temperaturens inverkan. NTTF, pp. 51–58 (2011) 4. Larsson, J., Jarl, M.: Högre draghastighet. NTTF Årsb., pp. 63–70 (2012) 5. Haglund, B.O., Enghag, P.: Characterization of lubricants used in the metalworking industry by thermoanalytical methods. Thermochim. Acta. 282–283(SPEC. ISS.), 493–499 (1996) 6. Hölker, R., Haase, M., Ben Khalifa, N., Tekkaya, A.E.: Hot extrusion dies with conformal cooling channels produced by additive manufacturing. Mater. Today Proc. 2(10), 4838–4846 (2015) 7. Jahan, S.A., El-Mounayri, H.: Optimal conformal cooling channels in 3D printed dies for plastic injection molding. Procedia Manuf. 5, 888–900 (2016) 8. Siebel, E., Kobitzsch, R.: Die Erwärmung des Ziehgutes biem Drahtzienhen. Stahl Eisen 63 (6), 110–114 (1942) 9. Shemenski, R.: Ferrous Wire Handbook. Wire Association International, Guildford (2008) 10. Larsson, J., Jansson, A., Karlsson, P.: Monitoring and evaluation of the wire drawing process using thermal imaging. Int. J. Adv. Manuf. Technol. 101(5), 2121–2134 (2019) 11. Adam, G.A.O., Zimmer, D.: On design for additive manufacturing: evaluating geometrical limitations. Rapid Prototyp. J. 21(6), 662–670 (2015) 12. Pejryd, L., Larsson, J.: Additively manufactured tool holder for wire drawing processes. In: EURO PM2018 Congress Proceedings (2018) 13. Ansys: 2019 version 19.0 R3

Aortic Model in a Neurointerventional Training Model – Modular Design and Additive Manufacturing Nadine Wortmann1(&) , Andreas M. Frölich2 , Anna A. Kyselyova2 , Helena I. De Sousa Guerreiro2 Jens Fiehler2 , and Dieter Krause1 1

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Institute of Product Development and Mechanical Engineering Design, Hamburg University of Technology, 21073 Hamburg, Germany [email protected] 2 Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf (UKE), 20246 Hamburg, Germany

Abstract. For training physicians in endovascular techniques such as mechanical thrombectomy in acute stroke, synthetic in-vitro models may replace animal models. A neurointerventional training model was developed in previous works using additive manufacturing (AM) for the reproduction of patient specific anatomy. Different patient anatomies, such as curvatures, can complicate the pathway of treatment. For this reason, realistic training requires a simulation of the entire access path from the femoral artery to the affected vessel in the brain, which includes the simulation of the aorta. The training model currently uses a commercially available silicone aorta, which has several disadvantages, including high cost and unrealistic surface friction. Furthermore, the aortic model is not modular and therefore does not allow changes in configuration of the aortic arch, which is a strong factor influencing procedural difficulty and therefore an important variable for training. In this study, a modular aortic model is designed and manufactured according to the requirements for training endovascular stroke treatment. AM offers many advantages in the production of anatomical models. Therefore, different manufacturing alternatives are tested based on a modular concept, using both direct and indirect manufacturing. Criteria for an evaluation of the production processes and the resulting models are defined and the test set-up is described. In this study, the procedures are first evaluated under cost and time aspect and a first assessment of the qualitative criteria is given. Keywords: Synthetic aortic model  Modular design  Additive manufacturing

1 Introduction 1.1

Motivation

The training of catheter-based interventions for endovascular treatment of vascular diseases (e.g. thrombectomy for treatment of acute stroke) is mainly performed on © Springer Nature Switzerland AG 2021 M. Meboldt and C. Klahn (Eds.): AMPA 2020, Industrializing Additive Manufacturing, pp. 437–454, 2021. https://doi.org/10.1007/978-3-030-54334-1_31

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animal models [1]. For example, an anesthetized pig is injected with previously collected and clotted blood and the mechanical removal of the blood clot via the femoral artery is trained [2]. However, training on animal models has many disadvantages. In addition to the general ethical aspects of animal tests, the vascular anatomy of pigs does not correspond to that of humans [1], which reduce the training effect. In particular, vascular curves that occur in elderly patients and challenge the intervention cannot be trained in the animal model [3]. In order to avoid animal models and achieve better training possibilities, various endovascular training models were developed and partly marketed. The training model of the company Vascular Simulations, Inc. (New York, USA) allows the training endovascular techniques, such as aneurysm or stroke treatment. The company produces patient-specific vascular models [4]. The model EVE (EndoVascular Evaluator) from FAIN-Biomedical Inc. (Nagoya, Japan) is also a holistic model for training endovascular diseases. It allows the exchange of different modules to allow training on different vascular diseases [5]. Spallek et al. pointed out the advantages and disadvantages of these models and justified the need for a new neurointerventional training model by stating that a simple and cost-efficient exchange of patient-specific models is not possible even during training with the commercial models [6]. In previous works the training model HANNES (Hamburg ANatomical NEurointerventional Simulator) was developed for training of aneurysm treatment [6]. HANNES is characterized by its high modularity, which allows for easy change of vessel models to represent a wide range of anatomies. Additive Manufacturing (AM) is used for the production of the vessel replicas because it offers a high degree of geometric freedom and enables fast production in small quantities [7]. Essential adaptations to HANNES for use in the training of stroke treatments were shown in Wortmann et al. [8]. This includes the possibility to replace the aortic arch with different models to achieve different levels of training difficulty. Currently, HANNES has a commercial silicon aorta (United Biologics, Inc., Santa Ana, USA), which is not modular and therefore does not allow the replacement of the aortic arch. The aim of this study is to design a modular aortic model, utilizing AM to replicate patient-specific anatomy. Three different manufacturing processes are compared. Both direct and indirect AM is taken into account. A comprehensive evaluation is being prepared to assess cost, time and quality aspects. Criteria will be defined for this purpose. The different processes are evaluated in this study under the focus of cost and time aspects. 1.2

Medical Background

The common femoral artery often serves as the access point for endovascular treatment. The catheters and treatment devices are advanced via the aorta, the cervical arteries to the cerebral arteries where the treatment takes place. The aorta is the central artery of the human body and transports the blood from the heart into the large blood circulation. The aorta is an elastic artery which, like the other arteries of the body, is made up of three layers of walls [9]. Anatomically, the aorta can be divided into the five segments aortic root, ascending aorta, aortic arch, descending thoracic aorta and abdominal aorta (Fig. 1).

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Fig. 1. Division of the aorta into segments (based on [10, 11])

The aortic root, shown on the left in the figure, connects to the aortic valves and, together with the ascending aorta, forms the transition to the aortic arch up to the outlet of the brachiocephalic trunc (the first large branching vessel). The supraaortic vessels brachiocephalic trunc dexter, carotis communis sinistra and subclavia sinistra arise from the aortic arch, which in turn ensure blood flow to the arm and the cervical and cerebral arteries [9]. Anatomically, three types of aortic arches can be classified [9, 12, 13]. These differ mainly in the position of their outlets to the cervical arteries (see Fig. 2) and thus represent different curves for the treatment path, resulting in different levels of difficulty in treatments [9, 12].

Fig. 2. Classification of the aortic arch by the location of the brachiocephalic trunc into three types (based on [9])

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Type I aortic arch is characterized by the brachiocephalic trunc lying on the same horizontal plane that describes the curvature of the outer aortic arch contour (see Fig. 2, (a)). In type II aortic arch, the vessel outlet lies deeper between the outer and inner aortic arch curvature (see Fig. 2, (b)). An aortic arch is categorized as type III if the outlet of the brachiocephalic trunc is below the inner aortic arch curvature (see Fig. 2, (c)) [9]. Thus, a type III arch results in more severe curvature to overcome during catheter delivery, making the intervention more difficult [3]. The aortic arch is further bordered by the descending thoracic aorta, which extends to the diaphragm, and then the abdominal aorta, which extends to the aortic bifurcation [9]. In this study the focus is on the reconstruction and manufacturing of the aortic arch and the possibility of exchangeability of different aortic arch types in the training model. 1.3

Hamburg ANatomical NEurointerventional Simulator (HANNES)

The study is based on HANNES (Hamburg ANatomical NEurointerventional Simulator). HANNES is an endovascular training model for aneurysm treatment and has completely replaced animal-based training in the rabbit model at the University Medical Center Hamburg-Eppendorf (UKE) since 2016. HANNES was developed in a collaborative project between the Hamburg University of Technology (TUHH) and the Department of Diagnostic and Interventional Neuroradiology at UKE. HANNES consists of a base frame, electronic and control unit, fluid system, the purchased aorta, a head module with skull base and interchangeable cerebral and cervical vessel models. In-house developed adapters allow an easy change of vessel models even during training without creating inner edges [6]. Figure 3 shows HANNES in the angio suite environment.

Fig. 3. HANNES in the experimental angio suite at the Medical Center Hamburg-Eppendorf (UKE)

Wortmann et al. show the extensions of the HANNES platform for stroke treatment. Besides the integration of synthetic blood clots and stenosis models, the different types of aortic arch will be integrated into the training [8].

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HANNES’ current aorta is a commercially available model (United Biologics, Inc., U.S.A.). As shown in the Fig. 4, the aorta is not modular and an exchange of different aortic arches is not possible. To enable connection to the HANNES model, the supraaortic connections were replaced by the adapters typical for HANNES.

Fig. 4. Silicone aorta of the company United Biologics (a) [14] and the aortic model integrated into HANNES (b)

2 Modular Design and Manufacturing Process Selection First, the requirements for the aortic model were determined together with the neuroradiologists of the UKE. The model should be transparent so that the catheter guide is visible even without fluoroscopy. Furthermore, the aorta should be elastic so that it behaves similar to reality and it should provide realistic friction between catheter and vessel material. An interchangeability of the aortic arch should be given so that training on the different arch types is possible. At the same time, compatibility with the adapters previously used in the model should be ensured. The variety required by the customer (UKE) in relation to the aortic model was included in the form of a variety tree. The variety driving properties are especially the anatomy of the aortic arch. Based on anonymized CT imaging data of a type II aortic arch, a model was designed with Meshmixer (Autodesk, U.S.A.) and reconstructed in CAD with CATIA V5 (Dassault Systemes SA, France), resulting in an hollow vessel model (Fig. 5). The wall thickness was set to 2 mm based on experience with the cerebral vessel models.

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Fig. 5. CT-scan of an aorta of aortic arch type II (a) and the generated CAD model in CATIA V5 (b)

HANNES adapters were added to the CAD model at the supraaortic outlets. Due to the larger diameters at the transition between the aortic arch and the descending thoracic aorta, a new adapter was developed, which also allows an edge-free connection of the models (see Fig. 6).

Fig. 6. Reconstruction of the aortic arch in CATIA V5 (a) and subsequent generation of the STL-file (b)

Spallek et al. compared different AM procedures and materials for the direct manufacturing of cerebral vessel models. It was shown that the procedures Material Jetting (MJ) and Stereolithography (SLA) are well suited for the fabrication of cerebral vessel models with aneurysms. For the MJ, the materials TangoPlus FLX930 and HeartPrint Flex (Materialise GmbH, Munich) on the Objet printer proved to be promising [7]. With the HeartPrint Flex material, Materialise is able to produce models such as vessels with elasticity similar to the real vessel. [15]. No elastic material was available on the Form 1+ from Formlabs (U.S.A.) at the time of the study, making MJ the preferred procedure.

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Since 2019 Formlabs has been offering the material Elastic Resin. The properties can be taken from the material data sheet [16]. Due to its promising properties and to the fact, that this printer is available to the research partners at the university as well as at the university medical center, this material is included in this study. Also the direct manufacturing out of HeartPrint Flex (Materialise) is to be compared. Indirect manufacturing is chosen as another manufacturing alternative. In her work, Heidemanns produced a silicone model of the aorta [17]. First, she used the CT data to create native segments from a modelling compound in order to make an impression using wax, silicone and gypsum. The wax models were then poured into the prepared mould and served as a positive model to apply the silicone in several layers with a brush [17]. Heidemanns did not use the possibilities of AM in her approach. Macroni et al. produce a parameterized aortic model based on literature data. The model was produced by casting the silicone in a 3D-printed mould. Inner and outer shells were used and the model was cast under vacuum [18]. In both described studies the aortic arches are not interchangeable. In this work, a mould printed by means of Stereolithography is to be produced, which is then used to create a wax model. This in turn forms the core for the layered application of silicone.

3 Manufacturing 3.1

Stereolithography (SLA) with Formlabs Form 3

Method and Material: Stereolithography (SLA) with the Form 3 from Formlabs, Elastic Resin (209.25 ml), Form Wash (IPA (90%)), Form Cure, in-house production. Production-Specific Preparation of the Model: The interfaces on the aortic arch model had to be modified in the CAD model so that the model fits into the permissible installation space of Form 3 145  145  185 mm. In the PreForm software by Formlabs, the model is virtually orientated on the building platform and support structures can be generated (see Fig. 7, (a)). The model almost fills the permissible installation space. From PreForm the model can be transferred directly to Form 3 and is ready for printing. The process of preparation is calculated at about 1.5 h. Production of the Model: The Elastic Resin material is inserted at the printer and printing is started. The printing time for the model is 30 h. Figure 7, (b) shows the model after printing on the building platform and in (c) the finished model. The postprocessing time is about 1 h.

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Fig. 7. Aortic arch model in the PreForm software (Formlabs) for preparing the print (a), model after printing on the building platform of the printer Form 3 (b), model after post processing (c)

3.2

Material Jetting (MJ) with Materialise

Method and Material: Material Jetting (MJ), HeartPrint Flex, order production. Production-Specific Preparation of the Model: In a telephone conversation the requirements for the model were clarified and a decision was made to print it. The finished STL file is sent to Materialise for printability testing. With this printing method it is possible to have the model printed in places with different Shore hardnesses. For this model a uniform Shore hardness is chosen first. The material properties of the aortic arch model have a Shore hardness of 30 A and correspond to a tensile strength of 1.04 ± 0.04 MPa with the wall thickness of 2 mm used [19]. Production of the Model: The model is produced by Materialise after the STL file has been sent. From receipt of order the delivery time is 14 days. The total price including tax and shipping is just above the three-figure range. The model is shown in Fig. 8.

Fig. 8. Heartprint aortic arch model ordered from Materialise

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Silicone Cast with a Wax Model

Method and Material: Paraffin pastilles (idee. Creativmarkt, Germany) (200 g), cooker, melting pot, silicone shore hardness 33 (250 g), thickener (Thixotropic additives) (silikonfabrik.de, Germany) (2 ml), mixing bowl, mixing paddle, brushes, Formlabs Form 3, Clear Resin (547,53 ml), Elastic (Formlabs, USA) (103,9 ml). Production-Specific Preparation of the Model: To produce the wax model, a casting mould needs to be created from the CAD file first. For this purpose, a block is created around the model in CATIA and the model is removed using Boolean operations. The mould created in CAD then has to be further divided to allow casting and wax removal. Holes are provided for fixing the mould parts (see Fig. 9 (a)).

Fig. 9. Casting mould generated in CAD for the wax model, failed attempt with one casting mould (a) and several moulds for the individual vessel models (b)

The first attempt to create the wax model failed because the relatively thin branches of the aortic arch broke off when the mold was removed. The arch itself could be produced well by the mould. It was therefore decided to produce separate casting moulds for the individual branches (see Fig. 9, (b)). The construction of the mould in CATIA and the production is calculated with about 75 h (65 h printing time). The adapters were printed separately in Elastic. An exemplary form is shown in Fig. 10, (a). The individual wax patterns were casted into the pre-warmed casting moulds and cooled down completely. Afterwards the individual wax models were melted at the interfaces and connected to form a uniform model (see Fig. 10, (b)). The process of creating the wax model is calculated at about 11 h.

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Fig. 10. Casting mould (Clear Resin) for the creation of the wax model (a), the assembled wax model with attached adapters/Elastic Resin) (b), application of the silicone layers (c)

Production of the Model: The silicone was mixed in a ratio of 1:1 base: catalyst and thickened with one percent by weight thixotropic additives. The model was coated with two additional layers at intervals of 2 h, with the third layer dispensing with the thickener in order to produce a smoother surface (see Fig. 10, (c)). The process of silicone application is calculated with 2 h. The model is then melted out in a water bath (Fig. 11). The process step is calculated with 1.5 h.

Fig. 11. Melting of the wax in a water bath (a), silicone model after loss of wax with detached adapters and wax layer outside and inside (b)

3.4

Production of the Other Aortic Model Parts

The remaining aortic model sections are divided into thoracic aortic section, abdominal aortic section and femoral arteries. These models are made with Formlabs 2/3 and connected with adapters with outer shells (Tough Resin, Formlabs). The complete aorta is shown in Fig. 12. It was decided to use Elastic Resin for the rest of the aorta because the catheter-vessel wall contact is not as high as in the aortic arch and the models can be produced at low cost by the authors themselves.

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Fig. 12. Total aortic model consisting of (a) femoral arteries, (b) abdominal aorta, (c) thoracic aorta, (d) aortic arch

4 Evaluation of the Production Processes and Materials 4.1

Evaluation Criteria

The evaluation criteria are stored based on the production requirements and defined as follows: The production process must not restrict the accurate reproduction of the inner contour of the aortic arch model. The model should be made of an elastic material. The model should have a certain degree of transparency to facilitate catheter positioning. The production must guarantee a tightness of the model wall. The aim is to produce the model as quickly as possible so that it can be put back into use as soon as possible after any damage (Wish). The material should be robust so that it can be used for several training sessions (Wish). On the first level, this results in the criteria of cost, time and quality for the evaluation of the models from the various production processes. Figure 13 shows the sub-criteria for evaluating the finished models in terms of time, cost and quality.

Fig. 13. Criteria for the evaluation of manufacturing processes and the resulting models

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A quantitative testing of criteria would go beyond the stress limit of the materials (e.g. testing of elasticity). Due to the requirement to keep the models non-destructive, which results from the high costs and the unit of 1, the criteria are mainly tested qualitatively. The existing aortic model from United Biologics serves as a reference for the evaluation. The sub-criteria are described in more detail below. The production time is considered from the point of production initiation to the finished and usable vessel model and includes the necessary preparation and follow-up procedures. The basis for the production of the aortic arch is the completed CAD construction, which was exported as an STL file. For the aortic arch from contract manufacturing, the delivery time and costs are included in the evaluation. The production costs are the sum of the manufacturing and material costs. The manufacturing costs include the labor costs for production. Production overheads are calculated under assumptions. Statements on reproducibility can be made qualitatively on the basis of production. The elasticity is assessed by the physician on the basis of experience with real aortic vessels and is put into practice with the existing aortic model. An evaluation of the geometrical correctness of the models by overlaying the scanned models with the CAD model is not carried out for two reasons. Firstly, the geometric correctness of the aortic model is only partially relevant for the intended intracanial treatment simulation and the CAD model was reconstructed on a patientbased level (no patient-specific model). On the other hand, the elasticity of the model allows a certain deviation. The tightness of the aortic arch is tested in itself and at the junctions to the cervical vessels and brachial vessels in the existing neurointerventional training model. The transparency of the model is assessed during operation with the blood surrogate (water and soap). For this purpose, it is assessed whether the catheter is adequately visible. The surface quality is qualitatively evaluated after production and in tests with the physicians. Therefore the behavior of the catheter on the vessel wall is evaluated qualitatively. For the reasons mentioned above, a stress test is not carried out. The robustness of the model is qualitatively assessed in the application at HANNES. For this purpose, it is assessed to what extent the model shows material stress in the application, e.g. during assembly at the interface and during pressurization in the system. The quality criteria will be tested by means of qualitative testing in HANNES together with experienced neuroradiologists. The behavior in angiography and in interaction with the treatment devices will be tested. The focus in this study is on the evaluation of the different production processes in terms of cost and time and a first assessment of the qualitative criteria resulting from it. The test setup in HANNES is planned.

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Evaluation of the Production Processes

The evaluation of production time and costs is based on certain assumptions: The costs are calculated excluding all taxes. The labor cost rate for one hour is estimated at 40 €. The printing time on the AM printers is not included in the working time, as they can run unattended. The starting point for the calculation is the finished STL file of the aortic arch. Production overheads are assigned to the manufacturing costs using the machine hour rate [20]: machine hour rate ¼

machine dependent costs running hours

ð1Þ

Machine-dependent costs represent cost-accounting depreciation, accounting interest, costs for maintenance & repair, space costs and energy costs [20]. The cost-accounting depreciation of the machine is calculated as follows [21]: cost accounting depreciation ¼

replacement value  residual value useful life

ð2Þ

The replacement value is calculated as follows [21]: replacement value ¼ acquisition cost ð1 þ inflationÞn

ð3Þ

The acquisition cost of the Form 3 was 3299 € without taxes and shipping costs. The average of the inflation rates for the years 2015 to 2019 was used as the calculated inflation rate [22], resulting in a value of 1.14%. The residual value and the useful life of the printer were estimated. It is assumed that the printer has a useful life of 5 years and a residual value of 500 €. This results in the replacement value of Form 3 at 3491.38 € and the cost-accounting depreciation at 598.276 € per year. The accounting interest and space costs are not included in the calculation. The repair costs are estimated at 100 € per year. The energy costs are calculated on the basis of the energy requirement of 220 W of the Form 3 [16], the machine running time and an electricity price of 0.29 € per kWh. The machine running time results from the assumption that the printer is used two days a week with an average printing time of 6 h. With 230 working days per calendar year, the machine running time is 552 h/year. These assumptions result in an annual electricity price of 40 € for the Form 3 [23]. The calculation of the machine hour rate is shown in Table 1. Based on the assumptions made, this results in a machine hour rate of 1.52 h/€.

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Table 1. Calculation of the machine hour rate of the Formlab Form 3 (based on assumptions) Machine hours per year: Cost-accounting depreciation Accounting interest Maintenance and repair cost Space cost Energy cost Summe Machine hour rate

552 h 698.28 € −€ 100 € – 40 € 838.28 € 1.52 €/h

Evaluation of the Current Aorta Model of the Company United Biologics. The purchase price of the entire Aorta model was in the four-figure € range at that point in time. The model was characterized by its good and constant transparency and high robustness. The elasticity appears good. The connections to the HANNES model cannot be made immediately. The friction of the catheter on the model vessel wall is also considered by the physicians to be too high. It is not possible to change the aortic arch type. Evaluation of the SLA Print in Elastic Resin. Production time: The process of preparing the model for the printer consisted of the creation of the PreForm file with 1 h and the machine preparation with about 0.5 h. The printing time of the aortic arch was 30 h. For the post-processing of the model 1 h was needed. Production Costs: The material costs are calculated on the basis of the material consumption calculated in PreForm and the cost of a tank of Elastic Resin (1 l). This results in material costs of 39.76 € for the aortic arch. The labor costs for a total working time of 2.5 h at an hourly rate of 40 € results in 100 €. With a machine hour rate of 1.52 €/h, the production overheads for printing the aortic arch are 1.52 €/h  30 h = 45.6 €. The production costs are calculated in total at about 186 € (exclusive taxes). The production of the model is subject to high reproducibility due to the settings on the printer. The first impression of the model in terms of elasticity, geometrical correctness and tightness appears good. At first the model shows a high transparency, which however decreases over time and appears rather milky. The inner surfaces of the models are initially sticky after post-treatment, but this also subsides over time. In terms of robustness, the models appear to be relatively sensitive. Evaluation of the Heartprint Flex Model by Materialise. Production and delivery time: With the aortic arch model manufactured by Materialise, it is not possible to divide production and delivery time. The total time is the period from order confirmation by Materialise until delivery of the aortic arch model. This results in duration of 14 days. Purchase Price: The purchase price includes the total cost price of the product, which cannot be further broken down. The purchase price without taxes and transport is in the upper three-figure € range.

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The production of the model is subject to high reproducibility due to the settings on the printer. The first impression of the model in terms of elasticity, geometrical correctness and tightness appears good. The model initially shows good transparency and seems to maintain this over time. The inner surfaces seem to be smooth and the whole model robust. Evaluation of Silicon Casting. Production time: Since moulds have to be developed, the design time for the initial production is taken into account (95.5 h). Production Costs: For the calculation of the labor costs, the construction of the casting moulds, the production of the wax pattern, the application of the silicone and the melting of the wax are considered. This results in labor costs of 980 € at a calculated 40 €/h. The material costs include the proportionate costs for Clear Resin (mould), paraffin wax, 2-component silicone, thickener and Elastic Resin (adapter). This results in material costs of about 105 €. The production costs are calculated on the basis of the machine hour rate of Form 3. With a printing time of 65 h for the mould and 4 h for the adapters, the manufacturing overheads are approximately 105 €. In total the production costs amount to approximate 1190 €. The reproducibility of the model is estimated to be low, as no reproducibility can be guaranteed, especially by manual application of the silicone. The first impression of the model in terms of elasticity and geometrical correctness appears good. The tightness of the model is not ensured due to many defects caused by an irregular wall thickness. There is no transparency of the model due to wax residues inside the model. The inner surface of the model has adopted the structure of the wax core and is therefore rough. In terms of robustness, the model appears to be very sensitive, especially due to the insufficient wall thickness. 4.3

Test Set-Up for the Evaluation of Quality Criteria

For a further evaluation of the qualitative criteria, the aortic arch models must be connected in HANNES and tested with the experienced physicians at the UKE. For this purpose, the connections must be designed for the new aortic arch model. Figure 14 shows a first test for the geometric requirements of the aortic arch model. Questionnaires were created to evaluate the criteria, which should first give an assessment of the current aorta model. Based on this, the criteria are to be evaluated in real terms in relation to the current aortic model. The focus of the evaluation is on the aortic arch model.

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Fig. 14. Geometric comparison of the aorta model from United Biologics (above) with the aorta model made of Elastic Resin, manufactured on Formlabs Form 3 (below). (a) Comparison of the aortic arch with outlets, (b) comparison of the remaining aorta

5 Discussion of the Results and Outlook This study described the design of an interchangeable aortic arch and its fabrication using the SLA fabrication procedure with Formlabs Form 3, the contract fabrication by Materialise with HeartPrint Flex and the fabrication of a silicone model by applying it to a cast wax model. The evaluation of the different manufacturing processes was quantitatively based on production costs and time. An initial assessment was given with regard to a qualitative evaluation of the models resulting from the processes. Some disadvantages have occurred when fabricating the silicone model using a wax model. The production of the casting mould based on the positive model is very time-consuming. The wax model must cure for several hours. To create the smoothest possible surface inside the silicone model, the wax model must be finished. Between the applications of the different layers, the silicone must be cross-linked for at least 2 h, which prolongs the whole process. When applying the silicone to the wax model it is difficult to create a constant wall thickness. In addition, the wax could not be completely removed from the silicone model when melting it in a water bath, the surfaces were covered with wax and no good surface properties or transparency was produced. The process is far more expensive than direct printing and the reproducibility is low. Direct production with Formlabs in Elastic Resin has several advantages. It is particularly convincing due to its short production time and low production costs in comparison to the procedures compared. The good availability of the printing process at both project partners (TUHH and UKE) plays an important role. In addition to these criteria, the model scores well in terms of the qualitative criteria in the first estimation. The HeartPrint model seems to be the best in terms of quality, although it is much more expensive than the manufacturing process in Elastic Resin. A comparison of the two models in HANNES with the medical professionals is necessary to make a final selection. Further work consists in the integration of other production processes and materials in the evaluation. Based on the currently purchased aorta, it could be observed that silicone is very well suited for long-term use, while e.g. the Elastic Resin shows material changes in the long run. Due to the complex manufacturing process of the silicon casting, a silicone print is to be included in order to be able to evaluate the materials silicone, Elastic and HeartPrint in comparison and in realization to the current

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aorta model of the company United Biologics. The models are to be tested qualitatively regarding their suitability in the training model HANNES with the experienced physicians of the UKE. Acknowledgment. The authors would like to thank the German Federal Ministry of Education and Research for founding this work within the project COSY-SMILE (031L0154A).

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14. United Biologics. https://unitedbiologics.com/product/neuro-testing-model/. Accessed 29 Jan 2020 15. Baeck, K., Lopes, P., Verschueren, P.: Material Characterization of HeartPrint® Models and Comparison with Arterial Tissue Properties. Materialise, Leuven (2020) 16. Formlabs. https://formlabs.com/de. Accessed 17 Feb 2020 17. Heidemanns, S.: Konzeption, entwicklung und evalution eines kostengünstigen reproduzierbaren Gefäßmodells für die simulation und das training endovaskulärer interventioneller Prozeduren an der Aorta anhand anatomischer Vorlagen eines realen Patienten. Dissertation, Ludwig-Maximilians-University München, Faculty of Medicine, München (2015) 18. Marconi, S., Lanzarone, E., van Bogerijen, G.H.W., Conti, M., Secchi, F., Trimarchi, S., Auricchio, F.: A compliant aortic model for in vitro simulations: design and manufacturing process. Med. Eng. Phys. 59, 21–29 (2018) 19. Schickel, M., Townsend, K., Farotto, D.: Material Characterization of 3D Printed HeartPrint Flex Plus Models and Comparison with Arterial Tissue Properties. Materialise NV, Leuven (2019) 20. Kaesler, C.: Kosten- und Leistungsrechnung der Bilanzbuchhalter IHK. Mit Übungsklausuren für die Abschlussprüfung, 6th edn., Springer Fachmedien Wiesbaden GmbH (2018). https://doi.org/10.1007/978-3-658-18305-9 21. reimus.NET GmbH. https://www.controllingportal.de/Fachinfo/Kostenrechnung/kalkulatorische-Abschreibungen.html. Accessed 20 May 2020 22. finanz-tools.de. https://www.finanz-tools.de/inflation/inflationsraten-deutschland. Accessed 20 May 2020 23. qmedia GmbH. https://www.rechner.pro/stromkostenrechner/. Accessed 20 May 2020

Integration of Additive Manufacturing into Process Chain of Porcelain Preservation Bingjian Liu1(&), Fangjin Zhang2, Xu Sun1, and Adam Rushworth1 1

2

University of Nottingham, Ningbo, China [email protected] Design School, Loughborough University, Loughborough, UK

Abstract. Relic restoration and preservation is a huge market. Antique Chinese Porcelain, which is regarded as a symbolic type of artefacts representing Chinese art, craft and culture, has attracted significant study into its preservation, crossing the fields of policy-making, science and emerging technologies. In recent years, Additive Manufacturing (AM) has demonstrated its advantages in the restoration of relics made of a variety of materials. However, the study of its implementation with antique porcelain, which not only has different shapes, but also glossy surface and rich colours, still remains in its infancy. This paper presents the case studies into the application of AM of antique porcelain preservations and creation. The studies include one practice converting the image of a vase on a Chinese painting to a 3D object with AM and another practice that applied AM to replicate an antique Famille-rose porcelain piece featuring rich colour in order to restore the missing pieces on a window in the Palace Museum, Beijing. The aim is to use AM polymer to simulate the visual features of antique porcelain. It was found that with the proper set-up of parameters and integration of other technologies and skills, AM with polymer materials also can support the replication of the features of antique porcelain to a significant extent. However, the glossy surface of porcelain made it difficult to acquire the surface details. In addition, the rich colours of Famille-rose porcelain were not only presented challenges with regard to directly obtain its colour features through 3D scanning, but also limited the application of AM owing to the limited colour series of 3D printing materials. In this paper, integrated methods were proposed and tested to address the above challenges which could impact on the application of AM in imitating porcelain features while attempting to contribute to its preservation and creation. Keywords: Additive manufacturing Archaeological preservation

 Antique porcelain  Integrated 

1 Introduction As the origin of the English name of the country, porcelain reserves an irreplaceable position in Chinese cultural heritage and played top roles in connecting China and Europe through trade [1]. However, due to the natural calamities and man-made misfortunes, numerous pieces of antique porcelain work were missing or damaged. On the

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other hand, there is a big shortage of the number of craftsmen who are able to restore or duplicate the porcelain with the conventional manual craftsmanship. Additive Manufacturing (AM) is a well-acclaimed emerging and disruptive technology [2, 3]. Thanks to its advantages in producing intricate and customized parts, the application of AM in heritage preservation has attracted continuous research attentions. This can be proved by the ample number of publications in the application of AM to the restoration/reproduction of different types of historical artefacts [4–6]. In these studies, the application of digital fabrication has been investigated for the restoration of a variety of types of antiques such as wood furniture, bronze and stone sculpture, enamel, and so on. In summary, AM has shown its benefits in these applications in several key aspects, such as: 1. AM is able to produce objects with intricate geometry [7]. This is an important feature since most antiques were made by crafting and thus have diverse shapes that are usually difficult to be reproduced directly with traditional manufacturing processes. 2. As a type of digital fabrication, AM has the strength in its precision compared to manual work. In addition, the digital model of the AM is more editable than the manual work handling the real materials in the physical world, which can further save cost and time. Although AM has already shown its advantages in the aforementioned applications, it is found that the related work on one important relic material, porcelain ware, is still in its infancy. Within the limited publications, it is realized that some desired requirements are difficult to meet if AM is used alone, which indicates that the integration/hybrid way that involves AM and other emerging or traditional technologies could be a valuable approach to problem-solving in such practices. This paper will review the related work that could influence the application of AM in these practices, then introduce and analyze two relevant projects completed by the research team and conclude with discussions and suggestions for the future research.

2 Related Work In this section, the Chinese porcelain is briefly reviewed to understand its key features that should be reproduced through additive manufacturing; the relevant technologies including colour 3D printing and hybrid technologies are also investigated to clarify the advantages and disadvantages of AM. 2.1

Chinese Antique Porcelain

Porcelain is a type of translucent ceramic material made by heating, generally including kaolin (a fine white clay), in a kiln to high temperatures between 1,200 and 1,400 °C. The materials and the heating process give porcelain the glossy surface. Chinese porcelain has a long history of around 2,000 years and became well known in Europe through trade from the Ming Dynasty (AD 1368-1644) onwards [8]. By then

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there were two types of porcelain-ware popular in the market: 1, the well-known white and blue porcelain; 2, single colour porcelain ware, such as, white, black, or celadon. From Qing Dynasty, another type of Chinese antique porcelain, Famille-rose, became popular as well. Different from other types of porcelains, Famille-rose is distinctive for its great range of colours. In addition, different from underglaze blueand-white porcelain, which has smooth surface, Famille-rose porcelain’s colours are overglazed and the colour pigments cause textures on the surface after heating. The application of AM to porcelain can be for preservation/restoration, historical and educational purposes. Currently, the study of this type of application still remains limited. One possible reason is: as a type of historical relic, porcelain-ware are not as popular in the rest of the world as in China, and hence have not attracted global attention. For instance, within the limited relevant publications, the project completed by Miller 3D in the United States was able to create the replica of a Chinese artefact, a blue and white antique Chinese porcelain vessel from Qing Dynasty [9]. 2.2

Colour Additive Manufacturing

Currently, Additive Manufacturing is still in the research era of monochrome with a focus on materials [10–13], printing precision [14, 15] and speed [16]. As an emerging and advanced technology of prototyping and manufacturing, AM has been applied in multiple fields [2, 17], particularly with its strength introducing complex components [7]. However, in some applications, other important attributes have challenged the development of this technology. For instance, to simulate the rich colours as well as the glossy and translucent surface of Chinese antique porcelain wares, it is necessary to investigate how to add colour and required surface effect to the 3D printed objects. Basically, there are two ways to have colours on 3D printed objects, one is direct 3D printing with colour materials and another is to add colours to the surface of the print through post-processing. Direct colour 3D printing here refers to that the printing substrates have colour property so the printed objects can have two or more colours [3, 16]. Compared to monochrome, colour 3D printing has its advantages in some applications, such as customized decorative accessories and teaching models [18], which make it attract many investments to explore the methods for colour 3D printing [2, 14]. Among these explorations, the researchers try to produce colour printing through different perspectives, for instances, colour 3D printing based on UV ink technology [19]; single and multiple nozzle colour 3D printing [2] (Yang et al. 2018); new algorithms [20] or even the integration of Artificial Intelligence (AI) into colour 3d printing technology [21]. Although the research into the direct colour 3D printing has become quite popular, there are still lots of limitations to apply this technology to practice. Firstly, the variety of colours that be 3D printed are very limited and hard to print more than two colours [2] and usually have poor colour feature quality [22, 6]. Secondly, when compared to 2D printing, there is a lack of standards related to the evaluation of the colour quality of 3D printing [3]. The last but might be the most important, despite vast academic research into different colour 3D printing methods, only a few are commercialized to market and can be used in practice, such as Connex 3 from Stratasys [23], Zprinter850 from 3Dsystem [24] and Mcor IRIS from Mcor Company [25]. Therefore, in many

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practices, it is hard to use direct colour 3D printing to obtain the required colour properties in terms of colour variety and quality. 2.3

Ceramic Additive Manufacturing

The study of AM on the material of ceramics has started since 1990’s [26, 27]. Attempts to apply AM of ceramic have been made in various areas, such as the construction industry [28], prothesis [29] and fine art [30]. However, compared to stateof-the-art polymer and increasingly developed metal, the application of AM ceramics is much more limited [31]. Although ceramics are an ideal material for use in the production of porcelain, the high cost of the technology and the brittleness (which is the same weakness as antique porcelain) of materials [31, 32] limit its application. In addition, porcelain is the combination of ceramic and glaze. Therefore, the AM of ceramic cannot solely create the features of porcelains and conventional post processing is still needed and aforementioned problems still remain. Therefore, given the advantages in accessibility, cost performance and mechanical toughness (not as brittle as ceramics) of polymer AM [33], it is worthwhile to investigate the application of polymer in simulating the features of porcelain. 2.4

Hybrid Technology with Additive Manufacturing

Like most technologies, Additive Manufacturing is not all-powerful but unavoidably has both strengths and limitations. To obtain optimal results in applications, attempts have been made to create hybrid technologies that combine the advantages of AM and other technologies to solve practical problems, e.g. CNC with AM or 2D with 3D printing [34–38]. These studies acknowledged the disadvantageous side of the Additive Manufacturing and showed the necessity of the hybrid approach in solving practical problems. Although these findings from these studies cannot be directly deployed in this study, it provides an inspiring methodology that in the application of AM, integration with other technologies can be proactively considered to explore a better solution than using AM alone.

3 Practices To investigate the integration method on Chinese porcelain, two projects collaborated with museums were conducted. In both projects, AM was integrated into process chain that include AM, 3D scanning, 2D printing and Manual work, in order to complete the tasks in a proper manner of time, cost and quality.

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3D Creation from 2D Chinese Painting

The first project is 2D-to-3D creation to produce a physical model for exhibition purpose, based on the Emperor Qianlong’s painting: ‘Shi Yi Shi Er Image’ in Qing Dynasty (Fig. 1). To understand the size, colour and material of the objects on the painting, especially the porcelain vase, comprehensive literature reviews were conducted and experts were consulted. It is believed that, the vase on the painting is a type of ‘Ru Yao’ porcelain which is from the Song Dynasty with blue colour. Regarding the technologies for the project, although direct coloured ceramic or plaster 3D printing is possible, several constraints were discussed with stakeholders, such as the fact that texture base on powdered printing is not good enough, the choice of colour 3D printers is limited and needs unexpected experiments to adjust, technical difficulties of finishing and the overall cost and time scale. Given these limitations, the integrative method with other technologies was decided as the best compromised solution for this project at this current stage. The Integrative process was adopted using forward engineering - digital modelling to produce a virtual vase, reverse engineering - 3D scanning data of real lotus followed by data manipulation for the lotus, after the virtual testing, high resolution AM photopolymer physical model with traditional finishing techniques, such as manual assembly and refining with modelling knife, to create the physical model of the vase, as shown in Fig. 2. After that, the colour and gloss were added with hand painting.

Fig. 1. Chinese painting from the Qing Dynasty

Fig. 2. The porcelain and lotus produced through AM

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Fig. 3. The finished creation of the 3D prints for exhibition

The final model (Fig. 3) was accepted in the ‘Painting and Calligraphy Exhibition of Emperor Qianlong’s Meeting with Ministers’ after it passed the experts group evaluation, displayed as the first exhibits at the entrance to welcome up to 80,000 visitors daily views for three months. 3.2

Replication of the Antique Famille-Rose Porcelain Piece (More Detailed Process)

Palace Museum is one of the biggest and most famous imperial palaces around the world and also has the biggest collection of Chinese historical relics. The second project is to replicate a missing Famille-rose porcelain piece, which used to be a decorative part on the window of the museum. As aforementioned, the features of Famille-rose porcelain cause several challenges to the application of AM, which can be summarized as follows: 1. The glossy glaze layer of the porcelain makes it difficult to conduct 3D scanning to acquire surface geometry. In addition, the use of contact 3D scanning and contrast intensifying agent is prohibited on the precious royal porcelain relic. 2. The painting on the Famille-rose has very rich colour including gradient change colours. In addition, different from the modern artificial paint, the painting on the antique piece was made with natural mineral dye, which gives Famille-rose 3D embossment-like texture. With similar considerations to the first project, the project team decided to use the integration approach that, adopt AM to build the monochrome shape and 2D printing to create the colour painting. The 3D data of the piece was collected through a non-contact 3D scanner and then printed with Stratasys objet24 with rigid white opaque material (VeroWhitePlus) as printing materials. The colour of the material is suitable since the white is suitable as

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base to add other colours later on. To get the best texture quality, the model was placed in four orientations to compare the results, as shown in Fig. 4. It was found that the prints from No. 1 and No. 4 can show the texture more clearly than No. 2 and No. 3, mainly due to the staircase effect.

Fig. 4. 3D printing set-ups and printed samples with VeroWhitePlus

Figure 5 shows the acquisition process of the porcelain piece 2D image. From left to right, first of all, the photograph needs to be taken in a professional environment with proper set-up on lighting condition and DSLR camera to get high-resolution picture. After further editing, the picture was printed on the AM piece surface with printer UJF3042HG which can provide over-coating on transparent and coloured materials as well as glossy finish, which is exactly needed in replicating Famille-rose porcelain. UJF3042HG is a type of 2D printer produced by the company Mimaki. There are no official specifications from the product booklet stating that it can print on surface with uneven textures and depth. However, through experiments in this study and later expert evaluation, it has acceptable performance in printing pictures on uneven surfaces and matches the texture pattern. Further study is needed to investigate the maximum depth of texture the printer can work on. Figure 6 shows the results of the project at the moment.

Fig. 5. The acquisition of high-resolution image of the porcelain piece

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Fig. 6. Final result after the combination of 2D and 3D printing process

The results were then presented to the staff the from Palace Museum to evaluate and give feedbacks on the quality. There were five people invited including three experts from Architecture, Technology, and Exhibition Department respectively and two non-specialists, from Sales Department and Administration. The evaluation is presented in Table 1. The numbers refer to the number of participants in that category. The data showed that in general the reviewers gave positive feedbacks to the results. Overall speaking, non-experts gave higher scores than experts who have higher expectations on some specific parameters, such as the colour and texture quality. However, both experts and non-experts agreed that the results can be beneficial for temporary exhibition, tourism and product design, with comments, such as: ‘Overall, the range of experiments and efficiency are impressive, digital technologies are very useful for relic preservation’

Table 1. The feedbacks from the participants on the quality of the prints (the numbers refer to the number of participants in that category) Quality parameter

Excellent

Good

Acceptable

Participants (E: expert; N: Non-expert) E N E N E Measurements Visual effects Resolution Colour Texture Surface roughness

3

N

Poor E

2 2

1 2

1

2 2

3 1 2 1 2

1 2

1 2

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4 Discussions From the application view of AM, the case studies explored integration approaches to create Chinese antique porcelains. These approaches set additive manufacturing as a core technology and the materials, machines and printing orientation should be properly chosen and tested. In addition, a process chain that flexibly used pre- or postprocessing methods, such as 2D and 3D image acquisition, manual work, 2D and 3D printing, will be employed to complete the task and meet the customer’s requirement. The feedback from experts and non-specialists showed the advantages of the integration in terms of flexibility, quality, cost and time. However, although exploratory in nature, the studies still possess many limitations. Due to the constraints on budget and time, the projects did not systematically test the quality of direct colour 3D printing to reproduce the porcelains and make comprehensive comparison with the proposed hybrid methods. Therefore, these studies only proved the effectiveness of the methods on the two projects, but cannot indicate they have advantages over direct 3D printing on all dimensions. Chinese antique porcelain has a great diversity in terms of age, shape, colour, texture and surface features. The presented practices indicate that more work needs to be done to simultaneously solve various visual and geometrical attributes when using Additive Manufacturing. In the future, it is expected that, through more practices in this area, some guidelines can be generated which will provide a positive impact on the development of Additive Manufacturing.

5 Conclusions The exploration of application is essential to the sustainable development of Additive Manufacturing. Although the research on aspects such as materials, precision and speed are fundamentally important to the technology, critical thinking is also needed when considering the research investment. For instance, in some applications, the current precision and printing speed have well met the requirement; behind the vast researches on colour 3D printing, only a few color 3D printers are successfully commercialized. Continuous exploration of the application of AM will open more markets to this technology and could inspire research directions for the above fundamental aspects. For example, the industry can develop AM materials with particular glossy effect to meet customer’s requirement on the surface. In recent years, apart from industrial manufacturing, Additive Manufacturing has been applied into different fields, such as medical science, education, fine art and culture heritage preservation. The practices in these areas showed that, to complete the tasks more efficiently and effectively, more in-depth studies into the integration approach are needed to combine the advantages of Additive Manufacturing and other methods or technologies. The study presented in the paper indicated that Additive manufacturing is an effective means to bridge the ‘old’ and ‘new’, to work on one of the oldest objects with the most modern technology. In addition, it also proved that polymer, which is the most popular and state of the art material of AM, can be used to simulate other material, such as porcelain.

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The study presented in the paper also indicates that some applications may not have global significance but under specific cultural background, Additive Manufacturing still can play an important role, e.g. in the cultural heritage of Chinese porcelain in this study. In the field of cultural heritage preservation, different countries could have completely different historical relics to be preserved that have different materials and features, therefore, the integration of local resources, such as craftsmanship and other technologies, into Additive Manufacturing could contribute to the problem-solving. In addition, the successful application of AM in this area will not just benefit the archiving and repairing, but also can develop a new medium for the creativity in product design, for instance, the derivative souvenir products, which will create economic benefits to financially support heritage preservation and the development of AM in turn.

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Business Cases

Decision Support System for a Metal Additive Manufacturing Process Chain Design for the Automotive Industry Markus Johannes Kratzer1,2(&), Julian Mayer1,3, Florian Höfler1, and Nikolaus Urban3 1

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BMW Group, 80788 Munich, Germany [email protected] Institute of Industrial Manufacturing and Management, University of Stuttgart, 70569 Stuttgart, Germany 3 Institute of Factory Automation and Production Systems, FAU ErlangenNuremberg, 90429 Nuremberg, Germany

Abstract. Additive Manufacturing (AM) is becoming increasingly important in various industries, particularly due to its freedom of design, functional integration and faster product development cycles. In many applications, additively manufactured components cannot be used directly after the printing process, but require subsequent process steps such as heat and surface treatment. A large number of process alternatives are available both for the execution of the printing process and for the subsequent steps. Hence, there are numerous possible combinations for the design of the entire production process chain. In order to simplify process selection in the area of AM, approaches and systems for decision support have already been developed in research. However, they do not consider the entire process chain including post-processing. This extended perspective is necessary to make full use of the technical potential of AM components and to optimize production with regard to economic criteria. Furthermore, automotive specifics regarding selection criteria and an underlying database of materials and processes are not taken into account in most cases. Thus, the following article presents a decision support system for the design of the entire production process chain in the conceptual planning phase. It will be useful for storage and retrieval of knowledge about process alternatives. The work focuses on the use of powder-bed-based metal AM in automotive applications. For this purpose, the process chains of Binder Jetting and Laser Powder Bed Fusion including their alternatives in post-processing are considered. Evaluation criteria and general conditions for automotive production are identified. Subsequently, the individual process steps and their properties are logically linked based on the defined criteria to support the selection of the optimal process chain. Finally, the methodology is demonstrated with an automotive component. Keywords: Additive Manufacturing selection  Automotive industry

 Decision support  Process chain

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1 Introduction Additive Manufacturing (AM) is increasingly applied in industrial production [1]. It is defined as a layer by layer building-up process from formless substances, without the need for any tools [2, 3]. By triangulation, a CAD model is transferred into an STLformat, which is then sliced into single layers, ready for the build-up process [4]. The production technology AM has enormous potential due to its flexibility in design [2]. Even though the roots of AM in the 1990 s are found in polymer-based processes [5], nowadays ceramic-, foundry sand- and especially metal-based processes gain importance [6]. Today, metal AM represents around 16,2% of the whole market volume [3]. Beside the medical and aerospace industries, metal AM technologies are increasingly used in automotive applications [3, 7]. There is enormous potential, considering that modern cars mostly consist of metal components. With a ratio of 60%, steel is the dominant material in car production [7, 8]. The automotive industry utilizes metal AM mostly for tooling, motorsport applications, prototyping and also series production [1]. In 2018, the German car company BMW Group released its first metal additively manufactured component in series production [9] To unfold the potential of industrialized AM and widespread the technology in the automotive field, academia and industry recognizes the need to consider not only the AM process, but the entire process chain from CAD to the final part [1, 10]. In order to derive the respective production system for a newly designed part, process planning builds the link between R&D and production [11]. Here, it is specified, which materials, process steps and operating resources are required to produce the part. On this basis, processing times and part costs can be estimated [11]. To the author’s knowledge until today, there is no applicable methodology for metal AM to link the component’s technical requirements to the selection process of appropriate materials, combined with suitable printing processes and respective post processing steps. The goal of this paper is to close this research gap by representing an approach for deriving feasible technical combinations.

2 State of the Art in AM Process Selection 2.1

Metal AM Process Steps

This paper focuses on Laser Powder Bed Fusion (LPBF) and Binder Jetting (BJT), two powder bed based additive manufacturing processes. Due to the good mechanical properties, accuracy and material variety, LPBF has the highest industrial relevance today [10]. However, BJT might have future potential in series production, due to its advantages in productivity and surface quality [12, 13]. As opposed to LPBF, the powder particles are not melted by laser energy. Instead, the powder cohesion is first generated by a binder and then in a second step, by sintering [14] In order to produce a final part, not only the fully automated printing process is required. There is also a need for pre-processing, which consists of build job preparation (digital), powder supply (physical) and preparation of the build platform (physical) [10]. More crucial for achieving technical requirements are post-processing steps after the AM process and

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therefore considered as more important in the present paper [10, 15] Based on a literature review of existing post-processing steps for LPBF and BJT, a classification for the main groups is gathered (Fig. 1) providing the basis for the developed method introduced in Sect. 3. There are obligatory process steps for each printed part and optional process steps to improve properties. In addition, there must be a distinction between “process specific steps”, which cannot be transferred to the process chain of the other printing technology, and “synergetic steps”, which can be used in the same manner for post-processing of LPBF and BJT parts. LPBF Depowdering

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Directly after the LPBF printing process follows the depowdering of the build job with vacuum cleaners. In areas that are difficult to reach it can also be done with ultrasonic vibrations, air pressure, brushes and alternatively by vibration and rotation in automated depowdering systems [1, 3]. To reduce thermal stress and control mechanical properties, heat treatment processes, depending on the material and the expected mechanical properties, are applied [3, 10]. Part separation from the build plate is typically realized with wire-cut EDM machines, band saws or by manual separation using hammer and chisel [3, 10]. Also the required support structure for the LPBF process is either removed manually, or by using chemical or electrochemical removal processes [10, 16]. Still in research stage is an automated support removal with multi axis flexible cutting tools [17]. Output of the BJT process are green parts embedded in a build box, which are then transported to an oven for the curing process. The goal is to raise the hardness and consequently the manageability of the parts [14]. Depowdering is carried out by air pressure and brushes, since the parts are still fragile. The next step is thermal, thermalcatalytic or fluid based debindering, followed by a sintering process [2, 18]. Depending on geometrical conditions of the part and sinter profile, material shrinkage rates are already considered in the printing process [18]. For complex parts, support structures generated by the printing process to control shrinkage during sintering have to be

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removed manually afterwards. Refractory barriers between part and support structure simplify the removal process [14]. Subsequent bronze infiltration is optional and not common in automotive applications [2] To improve mechanical properties, heat treatment is used similarly to the LPBF process chain. For both LPBF and BJT parts further improvements of mechanical properties are possible. In order to generate a positive impact on fatigue strength and breaking elongation, hot isostatic pressing (HIP) is used in aerospace applications [3, 10, 19]. Laser peening and shot peening are also employed to optimize fatigue behavior and mechanical strength of AM components. Furthermore, mechanical surface treatment methods like abrasive sandblasting, vibratory grinding and machining are carried out [6]. Alternatively, there are physical methods like plasma cleaning, ion beam cleaning and laser polishing, as well as different chemical and electrochemical methods [3, 20]. The collection of post-processing alternatives in this paper should not be seen as complete. On principle all production technologies listed in DIN 8580 can be considered to fulfill part specific requirements [21]. 2.2

Process Selection in AM

The number of AM processes as well as the variety of manufacturing techniques described in the previous section offer a large solution space for producing a part. In order to support the determination of the optimal process chain, numerous methodical approaches for process selection in the field of AM have been developed [22, 23]. In principle, all methodologies have the following essential elements: In the beginning, selection criteria are defined, which have an influence on the decision-making process [23]. The extensive literature review by Wang et al. [23] on different methodologies of AM process selection shows that the criteria of costs, surface quality, time, mechanical properties and dimensional accuracy are considered most frequently. The second element of a decision support system (DSS) is the creation of a database [22]. Here, various AM processes are included and evaluated with regards to the defined selection criteria [22, 24, 25]. While some methods consider the material to be used as predefined [26, 27], other methods also build up a database with respect to different material alternatives [22, 24, 25, 28]. This supports the user of the system not only in the selection of the AM technology but also in material selection. In addition, some methodologies also provide data of market available machines in order to suggest a suitable machine for the user’s application [22, 24, 27]. Finally, an evaluation method is established in each of the cited works in order to select the most appropriate process, material or machine based on the user’s requirements and by using the established database. In most cases, methods such as Simple Additive Weighting (SAW) or Analytical Hierarchy Processes (AHP) are used. With SAW, a result is calculated for each possible process, based on the stored data and the weighting of the defined criteria [25] An integer scale is typically used for the weighting. With AHP the weighting is done by comparing all criteria in pairs [29]. For each pair, the user indicates which criterion is preferred over the other and by what extend, using a quantitative scale. Both evaluation methods result in a ranking of

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processes, materials or machines and thus provide the user a foundation for making decisions on the best possible design of the production process.

3 Methodology for AM Process Chain Selection Even though numerous methodologies for the selection of the optimal AM process are already available, they are not sufficiently suitable for industrial application in automotive series production. Therefore, a methodology with the following requirements was developed to support the decision process in conceptual production planning: A. B. C. D. E.

Focus on automotive applications and its requirements Selection of specific material alternatives Selection of specific post-processing steps for AM technologies Use of an appropriate evaluation system Consideration of the entire process chain in the decision making process

In order to select the optimal production process chain, the two-stage approach shown in Fig. 2 is proposed, taking into account the criteria listed above. In the first stage, all process chains are examined with regard to the manufacturability of a specific part. Based on this, in the second stage the optimal process chain can be selected using additional criteria such as time, costs or sustainability. This work concentrates exclusively on the first stage. A. When defining the relevant selection criteria and building up a solution space, specifics of automotive series production are not taken into account in any of the literature investigated. Therefore, relevant criteria have to be identified, depending on the company and the different applications. First, the company’s conventional product portfolio for suitable AM parts should be screened, based on simple and practical criteria like part size and lot size. After having enough example parts, the characteristics and requirements of the parts (which are manufactured conventionally today), as well as the further processing steps of the parts in the automotive value chain should be examined by interviewing product developers, reading specification sheets and collecting the requirements mentioned. Thus it becomes obvious what kinds of requirements an AM part has to fulfill in the company and area of application. B. In order to select appropriate AM materials for the selection process, investigating the company’s product portfolio for commonly used materials is proposed. This can be done by screening the parts lists for different materials and their respective properties. After that, an investigation of market-available AM materials and their attainable properties should be made. Matching AM materials and materials used in the company, based on its properties leads to a narrowed solution space, applicable for the method. C. Post-processing steps – as described in Sect. 2.1 – can have an enormous influence on part properties, as well as time and costs. Therefore, they must be included in the selection process of the AM technology in order to be able to make comprehensive process chain statements with regard to technical and economic criteria. To

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reduce the amount of data to collect, only the two or three most promising postprocessing technologies for each process step should be included. Criteria for whether or not considering a specific technology can be the amount of expert knowledge or the technological maturity. D. Almost all open literature approaches contain a structured evaluation system which, based on defined criteria, leads to the selection of the best of various alternative solutions. With the widespread methods SAW and AHP, a qualitative assessment of the user is transformed into a quantitative assessment in the form of a weighting. This procedure is particularly critical in the area of vehicle manufacturing, since for certain selection criteria, such as mechanical properties, fixed specifications in the form of quantitative values already exist. If, for example, a part is required to have a tensile strength of 250 MPa and a mean roughness value of 10 µm, this is a must-meet criterion. For this reason, weighting the mechanical properties and surface quality is not appropriate in the present case. Rather, the decisive factor for the user is whether or not the required values are achieved by certain materials and process alternatives. Therefore, threshold conditions should be used to identify technologically suitable process chains in order to examine whether a technical requirement is fulfilled or not. E. By including materials, AM processes and post-processing steps, an integrated consideration of the entire process chain can be made. The necessary consideration of dependencies between the individual processes is missing in previous research approaches. In addition, the evaluation in regard to the selection criteria is also carried out for individual process steps instead of combining several processes. The solution space of potential process chains is already narrowed down to LPBF and BJT, since interviews of automotive AM experts lead to the conclusion, that these two technologies are currently most promising for Metal AM series production. For the realization of the first stage of the methodology, it is essential to build up the database shown on the left of Fig. 2, which contains all conceivable production process chains, including materials. To do so, first, all unfeasible combinations of materials, AM- and post processes should be excluded. This is done with a Design Structure Matrix (DSM) [30, 31]. With this method, all elements are compared and marked manually as compatible or not. This can be used to demonstrate, for example, that aluminum alloys currently cannot be used in the binder jetting process [18]. Second, individual process steps along the sequence must be linked to “fragments of process chains”, enriched with information, such as the achievable tensile strength, surface quality or dimensional deviation. For each combination of process-steps, achievable properties have to be determined by investigating data sheets, speaking with technologists or by performing tests. To reduce the amount of work and generalize the findings, ranges should be specified instead of single thresholds. For example, the combination of the LPBF process, the material AlSi10Mg and abrasive blasting creates an average roughness of 10 to 30 µm. The user’s part specific requirements and constraints, entered in the system (Fig. 2 middle), must be logically linked to the ranges specified for the different “fragments of process chains”. It comes naturally, that different “fragments of process chains” fulfill different requirements. As an example, there are “fragments of process chains” needed to fulfill mechanical requirements and others to fulfill

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surface requirements. Logically linking the value enriched “fragments” to entire process chains allows the user of the method/ system to get all technical feasible “entire process chains” to fulfill his requirements (Fig. 2 right). The result is that process steps are never evaluated in isolation with regards to one criterion. Instead, all corresponding elements of the process chain that influence the respective value are taken into account. The methodology is intended to structure the selection of suitable manufacturing steps and provides transparency about the process sequence. The structure of the system also offers the possibility to store and retrieve knowledge. By using the system, planning times can be reduced due to the rapid generation of possible solutions. optimal selection

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4 Implementation of a Method for AM Process Chain Selection For the implementation of the methodology in Microsoft Excel, specific selection criteria were identified and based on further requirements the developed methodology was transferred into a computer-aided decision support system. 4.1

Selection Criteria for Identifying Technical Feasible Process Chains

As described in the previous section, tangible technical requirements and general conditions for the selection procedure must be defined for the use and implementation of the methodology. For this purpose, literature on technical selection criteria for production process chains and AM processes as well as automotive-specific part and

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manufacturing characteristics were considered. The identified selection criteria are shown in Fig. 3. The four superior selection criteria are mechanical requirements, requirements on further processing and utilization of the part, geometrical requirements, and requirements on part quality. In particular regarding further processing and utilizations, industry specifics play a role in defining requirements (e.g. automotive might differ from medical applications). Among the mechanical requirements, the yield strength, tensile strength, elongation at break as well as the Youngs’s Module E and Vickers hardness are identified as the most important criteria. The consideration of subsequent processing and utilization of a part allows further implications with regards to mechanical and quality requirements. As an example, for punch riveting a certain elongation at break or for cathodic electrophoretic deposition (EPD) a certain surface quality is required. This has to be taken into account when selecting the process chain. Further, geometrical requirements, like information on component dimensions, edges, cylindrical elements, bores, cavities and auxiliary geometries should be used to test the suitability of additive manufacturing technologies and post-processing steps for AM part manufacturing. Other conditions like the score of support structures request technological knowledge of the user. Finally, process chains should also be assessed on quality requirements like surface quality and dimensional accuracy. Figure 3 does not claim to fully represent all relevant criteria for the selection of process chains. Some of the relevant criteria might also be applicable in other industries, whereas others might matter less. Obviously, in specific cases, numerous other criteria for the selection of materials and processes must be taken into account, such as requirements for acoustics, fatigue strength as well as thermal and electrical conductivity. It should also be noted that each additional criterion requires a corresponding

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acquisition of data and therefore increases the effort required for the development and use of the system. The developed structure nevertheless covers the most important criteria, which are particularly relevant for a large number of automotive AM applications. 4.2

System Requirements

Key features of a decision support system are the easy handling, the simple execution of alternative calculations and the consideration of different variants. Typically, the results are presented in graphics or tables [32]. In combination with the goal of applying the system within an industrial context on the one hand and the continuous improvement of AM-performance on the other hand, following requirements for the decision support system are formulated: • • • •

Intuitive usability without specific software knowledge Updateability and expandability Barrier-free application within companies Simple subsequent use of the generated results

The system should be intuitively usable without specific software knowledge. Even without the complex provision of a GUI, the user should be able to easily interact with the system by entering data and obtaining results. Furthermore, the updatability and expandability of the system must be ensured. Values stored in the database, which may change over time, should be easily adjustable by a system administrator. An example for the required update of the database is the availability of a new machine or optimized parameter sets, which allow lower mean roughness values on the surface of a component. Furthermore, the database should be easily expandable with new materials and process step alternatives. In addition, a barrier-free application within the company without the need for new installations must be ensured. Finally, the generated data should be easy to use and further processing of data should be possible without new interfaces to be programmed. Moreover, the overall results should be usable by other stakeholders – for example for detailed cost calculations. 4.3

Overview, Structure and Description of the Implemented System

To meet the requirements defined in Sect. 4.2, the spreadsheet program Microsoft Excel in combination with the programming language Visual Basic Applications (VBA) was chosen to implement the decision support system. The structure of the data basis, the input of data by the user and the presentation of results is done in the form of rows and columns. Since Microsoft Excel is a standard software widely used in industrial companies, intuitive usability can be ensured, if the system was designed accordingly. Updatability and expandability is also given, if the system can be used to modify or add new cells without having to adapt the program code. Due to access to Microsoft Excel within many companies, the developed system can be quickly made accessible to potential users. Figure 4 provides an overview and illustrates the structure of the system.

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In total, there are nine linked sheets, whereby the user’s interaction is limited to the yellow marked input (6) and output sheet (8). In the input sheet the user can enter the “part-specific requirements and constraints” shown in Fig. 3. In case one criterion does not have any requirements, it is not considered in the decision making process. In addition, the user can specify which part properties must be quality checked during the production process. Finally, the user can indicate which process steps are already available in the existing production system, so the system is supporting by making transparent, whether or not the part can already be manufactured by the existing operating equipment. After completing the data input, the user can trigger the generation of the output sheet by clicking a button. In the output sheet (8), the user receives all the technologically feasible process chains based on the input. Each process chain is represented in the form of a row with each process step being represented by a cell. In addition to the recommended material and printing processes, specific post-processing procedures and methods for quality assurance are suggested to the user. Furthermore, process steps that already exist in the production system are highlighted. To increase knowledge transfer from the system to the user, by clicking on a specific process step, the user can obtain further information on the selected process step in a text box. This knowledge is retrieved via the linked knowledge sheet (9). Lastly, the user receives information about the values that the different process chains achieve with regard to the selection criteria. This approach creates transparency, which enables a simple comparison between different alternatives. 1

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C2 (99.5% (Meiners 1999). After the melting step the substrate plate moves down by an incremental distance corresponding to the nominal powder layer thickness in a typical range from 20–50 µm. Thereafter the sequence is finished with the deposition of a new powder layer. This cycle repeats, often several thousand times until the last layer of powder has been fused, see Fig. 1. More details can be found in Schurb et al. (2016).

Fig. 1. Principle of Selective laser melting (SLM)

One of the key advantages of additive manufacturing is the fact that manufacturing cost is decoupled from part complexity, and that is a differentiator from standard casting methods where cost scales typically with complexity. That is illustrated in Fig. 2. The cost of a cast typically scales linearly with the weight of the part, and the gradient increases for more complex components (curves (i) & (ii)). An identical part with additive manufacturing has in most cases a penalty on cost due to the high equipment cost etc., however the gradient is at first order independent from the complexity of the part, curve (iii). This implies that an identical part of low complexity is typically more expensive in additive (A), but this can often be mitigated by introducing weight reduction measures in the additive part (C). Also, for more complex components, the additive part actually becomes cheaper due to elimination for sophisticated casting cores, or elimination of additional manufacturing steps like brazing or machining (B).

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Fig. 2. Schematic illustration for one the key features of additive– “complexity for free”.

With Additive Manufacturing, GE transformed how to design and manufacture components and how to manage the supply chain. GE started exploring 3D printing more than a decade ago, when its Aviation division tasked a group of engineers with developing a highly efficient fuel nozzle for a new jet engine. Currently, GE has already shipped more than 30.000 3D-printed parts for machines like the LEAP jet engine, which uses the nozzle that started it all. Referring to the schematic above, for the LEAP fuel nozzle 20 parts could be combined into one, five times the durability and 25% less part weight. In the Advanced Turboprop (ATP) engine for Cessna’s new aircraft, GE’s design condensed 855 conventionally manufactured parts down to 12 additive parts. By now, the engineers are also 3D printing turbine blades for the GE9X, the world’s largest jet engine. GE Power is applying successfully the additive technology in several power generation turbines of its product portfolio. For example, the world-record efficiency of its HA technology is driven by its advanced combustion system with additive technology, see Goldmeer (2018). This paper discusses the application of additive manufacturing to a unique performance upgrade for the GT13E2 gas turbine model. Based on an existing, retrofittable solution for the turbine hot section, two components have been completely reimagined, in order to fully exploit the potential of additive technology.

2 Additive Components in the GT13E2 Gas Turbine The GT13E2 is one of the world’s most reliable gas turbines and also offers classleading efficiency for an E-class engine. Most recently, GE engineers were able to increase the efficiency and performance of the engine by applying additive technologies. This upgrade is called “AMP” for Additive Manufactured Performance.

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Fig. 3. GT13E2 MXL2 gas turbine

The baseline for this new performance improvement package is the GT13E2 MXL2 rating (see Fig. 3), designed and introduced in 2012 as a retrofit upgrade to the GT13E2 (Magni et al. 2014). Currently 40 MXL2 GTs are in operation and have accumulated >430.000 operating hours, >3.800 starts. The new MXL2 with AMP includes two components produced by additive manufacturing: the first-stage turbine vanes and the heat shields (see Fig. 4) at the outer diameter of the first stage turbine blades. These parts are among the turbine’s hottestrunning parts, and the significant amount of cooling air they traditionally require impacts the engine’s performance.

Fig. 4. (a) GT13E2 MXL2 turbine section; the arrow indicates the location of the additive parts in the engine. (b) stator heat shield. (c) first stage vane with integrated additive coupon

Integrating advanced cooling technologies by means of additive manufacturing results in an improved cooling effectiveness. This enables the engine operation at higher firing temperature as well as a reduction of the required amount of cooling air to the additive component. The result is an improved turbine performance which offers our customers remarkable benefits.

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Stator Heat Shields

GE manufactured heat shields with near wall cooling scheme for the GT13E2 AMP gas turbine. Near wall cooling as well as cooling improvement features were introduced with the SLM printed design. The cooling effectiveness of the parts is increased and at the same time the required cooling air mass flow reduced. An additional advantage of the additive based design is that typically machined features such as seal slots, pockets and cooling holes can now be directly integrated in the printing process. The design is light weight and can achieve cost reduction via material savings. The parts are produced in the GE additive factory in Greenville, USA. An example of one set of prints is shown in Fig. 5; the optimization of the printing process, i.e. making maximal use of the printing volume of the printer, is evident from the image. The lead time for producing these components is reduced significantly compared to conventional casting process, thereby supporting the customer with the part available in a shorter time.

Fig. 5. GT13E2 gas turbine heat shield

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First Stage Vane

For the first vane configuration the so called printed “coupon” concept was used. In other words, the front part of the cast vane is removed and replaced by an additive “coupon” with integrated cooling features. Comparable to the heat shield the near wall cooling effectiveness will allow higher firing temperatures and therefore an improved turbine performance. This technology can be applied to both, new components and as a repair solution to increase the performance of the used component. These cooling schemes could only be realized using additive manufacturing technology. Figure 6 gives an overview of the cast vane body, printing coupon & the final engine ready part. It is worth mentioning that this concept has been possible by mastering not only the additive manufacturing itself, but also using a highly accurate bonding of the coupon to the cast.

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Fig. 6. GT13E2 MXL2 with AMP, first stage vane with coupon

3 Examples of Specific Design Challenges The important step towards additive enabled designs is the substitution of today’s conventionally manufactured parts and assemblies. Reaching this more advanced level requires good knowledge of the conventional production process chain and operational engine experience. Prior to replacing conventional manufacturing methods such as investment casting by additive technology, an in-depth benchmarking and investigation of relevant material properties is indispensable. GE created design rules based on the experience of usage of additive manufacturing in aviation industry and lessons learned during the development phase of additive manufacturing of stator parts for the turbine. These rules determine the limits, constraints and opportunities of the new manufacturing approach. The part functionality and interfaces with other components of the assembly defines the possible build-up directions due to the following main aspects: Overhangs. Printing the component using additive manufacturing, overhanging regions should be avoided during the design phase for a specific build direction. However, sometimes overhangs cannot be completely avoided due to functional surfaces or interfaces with other components. In these cases, supports need to be used. These can either be included as part of the structure or are removed after the process. Due to the geometrical complexity of many hot gas parts, it is usually not straight forward to determine a perfect solution to avoid overhangs. GE has therefore developed tailored CAD tools, which help designers to find an optimum orientation. Despite these helpful SW tools, a good interaction between the design teams and the SLM processing specialists remains essential to minimize process iterations until a complex part can be built with adequate quality. Morphing. Deformation can typically occur during the selective laser melting and subsequent heat treatment process, having an impact on intricate design features and potentially driving the need for additional machining stock. Due to high material cost of nickel based super alloys and the requirement to minimize any additional machining steps, the printed part is required to stay as close to the design intent as possible.

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In order to morph a part, a comparison between a 3D scan of printed hardware with the CAD design model will reveal which sections need to be corrected. Based on the deltas measured from the comparison, the design engineer will adjust the component model for the DMLM process to morph in the opposite direction. Subsequent components printed with the morphed model will deform during the process in such a way that final part shape is towards the design intent. For complex parts the trials showed, that the deformations are not necessarily linear and also linked with each other so that morphing of one section alters the deformation of another section. Due to this, multiple morphing iterations are deemed necessary until the part fulfills the design specification and the process can be frozen. Figure 7 illustrates the schematic morphing process.

Fig. 7. Illustration of Morphing

In general, it is recommended to consult the additive manufacturing expert immediately if uncertainties exist regarding a design to avoid working on unfeasible designs. In the specific case of the heat shield, the vertical build orientation of the component has been varied with angles ranging from 0 to 15°. In fact, the left picture in Fig. 5 shows an example of a trial build with different build angles. From these variations the best orientation has been identified, first by establishing a build without material cracking which may occur immediately during the material deposition or after heat treatment, and secondly by verifying geometrical accuracy with minimal need for morphing corrections. This has been an iterative process, and in practice, a substantial experience of the additive process engineers is needed to make these iterations effective and efficient. Figure 8 shows the outcome of the carried-out iteration to achieve the desired dimensional results.

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Fig. 8. Printed heat shield fulfilling the dimensional tolerances after several iterations

4 Engine Validation Results The MXL2 AMP upgrade does not only rely on GE’s long-term experience in additive manufacturing but in fact also on a substantial operation experience of additive parts, specifically in the GT13E2 engine. A few additive parts were installed already in 2015 in the GT13E2 with the primary focus to validate the additive manufacturing technology. The components were produced with identical geometries as produced with the precision cast process and the validation has been very successful (Hoebel 2018). The validation of the new printed first stage components has been carried out in several steps during the commissioning of the upgrade. Selected parts were equipped with temperature and pressure measurement probes, to verify the temperature distribution on the part surfaces to measure actual temperatures at the most critical locations. Other parts have been prepared with thermal paint on the hot gas surface allowing a detailed analysis of the upgrade performance through qualitative temperature maps, in order to confirm that the distribution pattern corresponds to expectation. Figure 9 shows examples of the instrumented part.

Fig. 9. GT13E2 MXL2 with AMP, instrumented parts

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With the thermal paint tests the material temperature distribution of the new printed parts have been confirmed. The cooling effectiveness of the brazed area also matches with the models. Figure 10 shows the representative thermal paint results. The pressure and temperatures measurements of the cooling air and material are used for the confirmation of the proprietary layout calculations and tools.

Fig. 10. GT13E2 MXL2 with AMP, thermal paint observed after operation

The validation campaign was successfully completed. The accumulated data and results confirmed the validation of the additive components. The expected impact of the additive components could be demonstrated with broad operation concept and lower emissions. The performance targets of the upgraded gas turbine were achieved.

5 Summary The GT13E2 AMP upgrade is a showcase example how additive manufacturing can bring a new step-change in performance to a proven engine platform with large operating experience. The improvements in cooling technologies could be successfully implemented in two components of the turbine engine, to produce a substantial performance increase for the gas turbine customer operational requirements in the new complex power offering space. The additive technology was an enabler for this technology step and shows the way for smart solutions in the competitive environment that our customers face today. Acknowledgements. GE additive manufacturing team at GVL/Birr & Design Team at Baden. Dietmar Kodim & Julian Ahlhoff, Vattenfall Wärme Berlin AG. Matthias Hoebel, Professor at University of Applied Sciences and Arts Northwestern Switzerland, for providing the technical expertise in the field of material properties and its sensitivity on the DMLM process. Andrew Passmore, Senior Product Manager, for supporting the project.

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References Meiners, W.: Direktes Selektives Laser-Sintern einkomponentiger metallischer Werkstoffe (Doctoral thesis). Shaker Verlag GmbH, RWTH Aachen, Aachen (1999) Schurb, J., Hoebel, M., Haehnle, H., Kissel, H., Bogdanic, L., Etter, T.: Additive manufacturing of hot gas path component and engine validation in a heavy duty GT. In: Proceedings of ASME Turbo Expo 2016, GT2016-57262, Seoul, South Korea (2016) Magni, F., Zoli, R., Marx, P.: Betriebserfahrungen mit dem neuesten Upgrade der GT13E2 Gasturbinenfamilie; 9. VDI-Fachtagung – Stationäre Gasturbinen (2014) Hoebel, M.: Direct Metal Laser Melting (DMLM) for Gas Turbine Applications; Stuttgart Laser Technology Forum. University of Stuttgart, Stuttgart (2018) Goldmeer, J.: Fuel flexible gas turbines as enablers for a low or reduced carbon energy ecosystem. Presentation at Electrify Europe 2018 and GE white paper GEA33861 (2018)

Author Index

A Abdelwahab, Moustafa Mahmoud, 374 Arvieu, Corinne, 177 B Bajaj, Pankaj, 500 Balicki, Peter, 391 Baumgartner, Harry, 391 Bliedtner, Jens, 223 Bochsler, Janine, 399 Bos, Philip, 130 Braschkat, Andrés, 321 Bremen, Sebastian, 82 Buhk, Jan-Hendrik, 321 C Cao, Shuaishuai, 26 Clemens, Frank, 67, 293 Cloots, Michael, 192 Comminal, Raphaël, 241, 251 Cui, Di, 96 D Dalmer, Christian, 112 De Sousa Guerreiro, Helena I., 437 de Souza Melo, Gustavo Menezes, 17 Dennig, Hans-Jörg, 304, 337 Ding, Andreas, 321 Dinner, Hanspeter, 337 Duda, Tom, 399

E Eckhardt, Lukas, 223 Eichler, Fabian, 82 Eisenbarth, Daniel, 160 Ekengren, Jens, 426 Elspass, Wilfried J., 130 Engels, Gregor, 37 Eßig, Michael, 483 F Favre, Sébastian, 96 Fiehler, Jens, 321, 437 Flohr, Peter, 500 Fontana, Filippo, 391 Frauchiger, Alex, 192 Frölich, Andreas M., 437 G Gardner, Leroy, 357 Georgopoulou, Antonia, 67 Ghanbari, P. Gh., 268 Glas, Andreas H., 483 Golab, Mark, 279 Gorjan, Lovro, 293 Gutknecht, Kai, 192 H Hangst, Nikolai, 415 Hirsch, Andre, 112 Höfler, Florian, 469

© Springer Nature Switzerland AG 2021 M. Meboldt and C. Klahn (Eds.): AMPA 2020, Industrializing Additive Manufacturing, pp. 511–513, 2021. https://doi.org/10.1007/978-3-030-54334-1

512 Holdener, Simon, 130 Höller, Anton, 337 Honigmann, Philipp, 26 Hopf, Andreas, 223 Hosseini, Ehsan, 268 Huber, Frank, 337 Huber, Marc, 399 J Jafarzadeh, Sina, 241 Jahn, Simon, 82 Junk, Stefan, 415 K Karlsson, Patrik, 3, 426 Kasch, Susanne, 82 Keller, F., 268 Kirchheim, Andreas, 304, 337 Kraenzler, Thomas, 146 Kratzer, Markus Johannes, 469 Krause, Dieter, 321, 437 Kuhl, Juliane, 321 Kyselyova, Anna A., 437 L Lacoste, Eric, 177 Lanfant, Briac, 96 Larsson, Joakim, 426 Layher, Michel, 223 Le Guen, Emilie, 177 Leparoux, Marc, 96 Li, Zhengyao, 357 Liersch, Antje, 293 Liu, Bingjian, 455 Löffel, Kaspar, 399 Lohn, Johannes, 37 Lukas, Gerret, 17 Lüscher, Patrick, 399 M Magni, Fulvio, 500 Marelli, S., 268 Masinelli, Giulio, 205 Massey, Sam, 279 Mayer, Julian, 469 Mazza, Edoardo, 268 Meboldt, Mirko, 391 Menichelli, Alessandro, 160 Meyer, Matthias M., 483

Author Index Moritzer, Elmar, 112 Moultrie, James, 279 N Ngo, Ngoc Tuan, 321 Nickchen, Tobias, 37 O Omidvarkarjan, Daniel, 391 Ortona, Alberto, 52 P Pandiyan, Vigneashwara, 205 Pedersen, David Bue, 251 Pejryd, Lars, 3, 426 Pelanconi, Marco, 52 Q Quang-Le, Tri, 205 R Rettberg, Robin, 146 Rigo, Olivier, 177 Rosenbauer, Ralph, 391 Rushworth, Adam, 455 S Sarraf, Fateme, 293 Schleifenbaum, Johannes Henrich, 17 Schmidt, Thomas, 82 Schuh, Günther, 17 Schulz, Josef, 293 Sebastian, Tutu, 293 Serdeczny, Marcin, 241, 251 Sharma, Neha, 26 Shevchik, Sergey A., 205 Soffel, Fabian, 160 Spangenberg, Jon, 241, 251 Strömberg, Niclas, 3 Sun, Xu, 455 T Thieringer, Florian, 26 Thurn, Laura Katharina, 82 Tsavdaridis, Konstantinos Daniel, 357, 374 U Umbricht, Michael, 399 Urban, Nikolaus, 469

Author Index

513

V Vanderborght, Bram, 67 von Netzer, Barbara, 26

Wirth, Florian, 192 Wortmann, Nadine, 437 Wüthrich, Michael, 130

W Wasmer, Kilian, 205 Wegener, Konrad, 160 Weiss, Daniel, 399 Welker, Dennis, 26 Wendt, Thomas, 415 Willkomm, Johannes, 17

Y Yadav, Pinku, 177 Z Zhang, Fangjin, 455 Ziegler, Stephan, 17 Zumofen, Livia, 304, 337