Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems: Proceedings of the 8th Changeable, Agile, Reconfigurable and ... (Lecture Notes in Mechanical Engineering) 303090699X, 9783030906993

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Lecture Notes in Mechanical Engineering

Ann-Louise Andersen · Rasmus Andersen · Thomas Ditlev Brunoe · Maria Stoettrup Schioenning Larsen · Kjeld Nielsen · Alessia Napoleone · Stefan Kjeldgaard  Editors

Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2021) and the 10th World Mass Customization & Personalization Conference (MCPC2021), Aalborg, Denmark, October/November 2021

Lecture Notes in Mechanical Engineering Series Editors Francisco Cavas-Martínez, Departamento de Estructuras, Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain Fakher Chaari, National School of Engineers, University of Sfax, Sfax, Tunisia Francesca di Mare, Institute of Energy Technology, Ruhr-Universität Bochum, Bochum, Nordrhein-Westfalen, Germany Francesco Gherardini , Dipartimento di Ingegneria, Università di Modena e Reggio Emilia, Modena, Italy Mohamed Haddar, National School of Engineers of Sfax (ENIS), Sfax, Tunisia Vitalii Ivanov, Department of Manufacturing Engineering, Machines and Tools, Sumy State University, Sumy, Ukraine Young W. Kwon, Department of Manufacturing Engineering and Aerospace Engineering, Graduate School of Engineering and Applied Science, Monterey, CA, USA Justyna Trojanowska, Poznan University of Technology, Poznan, Poland

Lecture Notes in Mechanical Engineering (LNME) publishes the latest developments in Mechanical Engineering—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNME. Volumes published in LNME embrace all aspects, subfields and new challenges of mechanical engineering. Topics in the series include: • • • • • • • • • • • • • • • • •

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Ann-Louise Andersen Rasmus Andersen Thomas Ditlev Brunoe Maria Stoettrup Schioenning Larsen Kjeld Nielsen Alessia Napoleone Stefan Kjeldgaard •

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Editors

Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2021) and the 10th World Mass Customization & Personalization Conference (MCPC2021), Aalborg, Denmark, October/November 2021

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Editors Ann-Louise Andersen Department of Materials and Production Aalborg University Aalborg East, Denmark

Rasmus Andersen Department of Materials and Production Aalborg University Aalborg East, Denmark

Thomas Ditlev Brunoe Department of Materials and Production Aalborg University Aalborg East, Denmark

Maria Stoettrup Schioenning Larsen Department of Materials and Production Aalborg University Aalborg East, Denmark

Kjeld Nielsen Department of Materials and Production Aalborg University Aalborg East, Denmark

Alessia Napoleone Department of Materials and Production Aalborg University Aalborg East, Denmark

Stefan Kjeldgaard Department of Materials and Production Aalborg University Aalborg East, Denmark

ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-3-030-90699-3 ISBN 978-3-030-90700-6 (eBook) https://doi.org/10.1007/978-3-030-90700-6 © Springer Nature Switzerland AG 2022, corrected publication 2022 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 last two years have taught us to be changeable in extreme ways. While the pandemic caused disruptions to practically every aspect of our lives, manufacturing companies had to handle not only increased needs for workers’ safety and health but also major supply chain disruptions and changed demand patterns. Some products suddenly had skyrocketing demand, while demand for other products practically disappeared overnight. Failures and uncertainty in supply of raw materials, difficulty in ensuring workforce capacity, and logistical bottlenecks challenged manufacturing companies on a global level. The immediate response to this new reality is increased supply chain resilience, high-speed innovation in businesses, extreme levels of adaptability in manufacturing systems, and a continuous matching of product and process design with changing customer habits and preferences through high levels of customization at low cost. Thus, academia and industry are now more than ever challenged to find new ways of ensuring manufacturing competitiveness in highly volatile times and establish the basis for adapting supply chains, manufacturing systems, equipment, and products toward disruptive changes, while simultaneously ensuring environmental, economic, and social sustainability. We consider this as the challenge of achieving sustainable customization, meaning that the continued success of a company is highly dependent on the ability to detect and fulfill idiosyncratic and changing customer needs in a cost-efficient, environmentally friendly, and socially responsible way. The theme of the joint 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2021) and the 10th World Mass Customization and Personalization Conference (MCPC2021) is “Toward Sustainable Customization: Bridging Smart Products and Manufacturing Systems.” However, while both conferences have existed for almost two decades, we have been in need for changing the setting of CARV and MCPC to fit a new reality. First and foremost, we needed to add a “1” to our conference name, as the conferences were initially scheduled for 2020 but were postponed until October and November 2021 due to the global pandemic. Also, for the first time, CARV is hosted and organized by Aalborg University, while MCPC is actually returning to Aalborg. In 2014, the 7th World Conference on Mass Customization, Personalization, and Co-Creation v

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(MCPC2014) was hosted by Aalborg University. This time, and also for the first time, CARV2021 and MCPC2021 are organized jointly and as a hybrid online/physical event. We believe this offers a safe and unique setting for experts from academia, industry, and research institutes to discuss and exchange the newest scientific contributions regardless of global and local restrictions and conditions. This book of proceedings features all 119 papers presented at the CARV/MCPC2021 conference. All papers have been through a two-phased peer-reviewing process. Initially, all submitted manuscripts received at least two review reports evaluating quality, novelty, and relevance of the contribution. Secondly, revised papers were evaluated by the editors before final decisions were made. For MCPC, a total of 48 abstracts were initially submitted, while 33 full papers were eventually accepted for publication. For CARV, 117 abstracts were submitted, while 86 full papers were eventually accepted for publication. The final 119 accepted papers have been divided into 14 relevant topical parts featured in this book: • • • • • • • • • • • • • •

Changeable, Reconfigurable, and Flexible Manufacturing Smart Automation and Human–Machine Collaboration Additive Manufacturing Smart Factories and Cyber-Physical Production Systems Machine Learning for Smart Manufacturing Global Production and Supply Chain Networks Factory and Shop Floor Planning Data-driven Approaches for Manufacturing and Variety Management Digital Transformation and Maturity Assessment Smart Products, Services, and Product-Service Systems Configuration Management and Choice Navigation Learning Factories and Engineering Education Insights from Case Studies and Experiments Sustainable Manufacturing and Circular Economy

In addition, this book of proceedings also includes an opening paper with a bibliometric and sentiment study of papers published in CARV and MCPC conferences in the last ten years. This paper sets the stage for the entire book by outlining past, present, and future research trends in the research communities of CARV and MCPC. We hope that readers, both from academia and industry, will enjoy and find inspiration in reading these conference proceedings. August 2021

Ann-Louise Andersen Rasmus Andersen Thomas Ditlev Brunoe Maria Stoettrup Schioenning Larsen Kjeld Nielsen Alessia Napoleone Stefan Kjeldgaard

Acknowledgements

The 8th Changeable, Agile, Reconfigurable, and Virtual Production Conference (CARV2021) and the 10th World Mass Customization and Personalization Conference (MCPC2021) were jointly hosted by Aalborg University, Denmark. The conferences were organized by the Mass Customization Research Group in the Department of Materials and Production. The Department of Materials and Production, Faculty of Engineering and Science at Aalborg University supported the organization of CARV/MCPC2021. Moreover, the CARV2021 conference was sponsored by the International Production Engineering Academy (CIRP). The editors and local organizing committee would like to give thanks to all authors for their high-quality contributions and cooperation in preparing the manuscripts. We also express our gratitude to all reviewers and the international scientific committees that supported us in the manuscript evaluation process with expert knowledge and valuable inputs. Furthermore, we would like to thank the honorary co-chairs Professor Hoda ElMaraghy, Professor Waguih ElMaraghy, Professor Michael Zäh, Professor Frank Piller, and B. Joseph Pine II for their collaboration and for supporting the local organizing committee in preparing the conferences. We would also like to thank all members of the international program committees for support and assistance throughout the review process and in the conference planning.

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Organization

Conference Chair CARV2021 Ann-Louise Andersen

Aalborg University, Denmark

Conference Chair MCPC2021 Kjeld Nielsen

Aalborg University, Denmark

Industry Track Chair Thomas Ditlev Brunoe

Aalborg University, Denmark

Local Organizing Committee Rasmus Andersen Maria Stoettrup Schioenning Larsen Alessia Napoleone Stefan Kjeldgaard

Aalborg University, Denmark Aalborg University, Denmark Aalborg University, Denmark Aalborg University, Denmark

Honorary Co-chairs CARV2021 Hoda ElMaraghy Waguih ElMaraghy Michael Zäh

University of Windsor, Canada University of Windsor, Canada Technical University of Munich, Germany

Honorary Co-chairs MCPC2021 Frank Piller B. Joseph Pine II

RWTH Aachen University, Germany Strategic Horizons, USA

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International Program Committee CARV2021 Catherine Da Cunha Khaled Medini Carin Rösiö Mats Jackson Tehseen Aslam Arne Bilberg Ole Madsen Simon Bøgh Stefan A. Wiesner Charles Møller

Ecole Centrale de Nantes, France Ecole des Mines de Saint-Etienne, France Jönköping University, Sweden Jönköping University, Sweden Skövde University, Sweden University of Southern Denmark, Denmark Aalborg University, Denmark Aalborg University, Denmark University of Bremen, Germany Aalborg University, Denmark

International Program Committee MCPC2021 Stephan Hankammer Margherita Pero Astrid Heidemann Lassen Brian Vejrum Waehrens Stig Taps Peter Nielsen Kaj A. Joergensen Paul C. Gembarski Cheng Yang

Alanus University, Germany Politecnico di Milano, Italy Aalborg University, Denmark Aalborg University, Denmark Aalborg University, Denmark Aalborg University, Denmark Aalborg University, Denmark Leibniz University Hannover, Germany Aalborg University, Denmark

International Scientific Committee CARV2021 Alain Bernard Norbert Gronau Dominik Matt Stephen Newman Peter Nyhuis Hong-Seok Park Paul Schönsleben Wilfried Sihn Rikard Söderberg Kirsten Tracht Gisela Lanza Jozcef Vancza Lyes Benyoucef Marco Bortolini Dimitris Mourtzis Mukund Nilakantan Janardhanan

Ecole Centrale de Nantes, France University of Potsdam, Germany Free University of Bozen-Bolzano, Italy University of Bath, England Leibniz University Hannover, Germany University of Ulsan, South Korea ETH Zürich, Switzerland Vienna University of Technology, Austria Chalmers University of Technology, Sweden University of Bremen, Germany Karlsruhe Institute of Technology, Germany MTA Sztaki, Hungary Aix-Marseille University, France University of Bologna, Italy University of Patras, Greece University of Leicester, England

Organization

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International Scientific Committee MCPC2021 Jocelyn Bellemare Salvador Fabrizio Niels Henrik Mortensen Lars Hvam Zoran Anisic Lars Skjelstad Goran Stojanovic Thorsten Blecker Herwig Schreiner Ulrich Berger Kristina Säfsten Claudio Boer Frances Turner Volker Swarek Maria K. Kollberg Thomassen Mukund Nilakantan Janardhanan

The Universit du Québec à Montréal, Canada IE Business School, Spain Technical University of Denmark, Denmark Technical University of Denmark, Denmark University of Novi Sad, Serbia SINTEF, Norway University of Novi Sad, Serbia Hamburg University of Technology, Germany Siemens AG, Austria Brandenburg University of Technology, Germany Jönköping University, Sweden SUPSI, Switzerland Christian Brothers University, USA HAW Hamburg, Germany SINTEF, Norway University of Leicester, England

Reviewers Alberto Regattieri Alessandro Bruzzone Alessia Napoleone Ali Ahmad Malik Ali Bozkurt Amelie Beauville Dit Eynaud Amila Thibbotuwawa Anders Nilsson Andreas Hansen Ann-Louise Andersen Arne Bilberg Arnthor Gunnarsson Astrid Heidemann Lassen Audrey Cerqueus Benyoucef Lyes Bjørn Christensen Brendan Sullivan Brian Waehrens Carin Rösiö Carl Steinnagel Carsten Schaede

Casper Schou Catherine Da Cunha Charles Møller Chen Li Christian Brabrand Christian Petersson Nielsen Christopher Gustafsson Claudio Boer Dag Raudberget Dan Palade Daniel Hussmo Daniel Sørensen David Romero Dimitrios Chrysostomou Dimitris Mourtzis Dominik T. Matt Dora Strelkova Elias Arias Nava Elias Ribeiro da Silva Emil Blixt Hansen Emre Yildiz

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Esben Skov Laursen Fabrizio Salvador Federica Costa Ferdinando Chiacchio Finn E. Nordbjerg Frances Turner Francesco Gabriele Galizia Frank Gertsen Frank Piller Gary Linneusson Gisela Lanza Haider Al-Fedhly Hans-Henrik Hvolby Helge Glinvad Grøn Henrik Saabye Hewrig Schreiner Hichem Haddou Benderbal Hoda ElMaraghy Hong-Seok Park Ioan-Matei Sarivan Irene Campo Gay Isabela Maganha Iskra Dukovska Popovska Izabela Nielsen Jannik Schneider Jens Peder Meldgaard Jerome Uelpenich Jesper Kristensen Jessica Olivares Aguila Jocelyn Bellemare Jonas Nygaard Jozsef Vancza Julian Grimm Julian Hermann Kai Müller Kaj Jørgensen Kerstin Johansen Khaled Medini Kjeld Nielsen Kristina Säfsten Lars Skjelstad Lasse Christiansen Lorenzo Prataviera Marc Gebauer

Organization

Marco Bortolini Margherita Pero Maria Camila Rincon Maria Giuffrida Maria Stoettrup Schioenning Larsen Maria Thomassen Marina Mafia Michael Kick Michael Zäh Michele Colli Miguel Vidal Milad Ashour Pour Mohsin Raza Nadeem Iftikhar Nadia Hamani Nick Szirbik Niels Henrik Mortensen Norbert Gronau Ole Madsen Oliver Bruetzel Osman Altun Ottar Bakås Paul Blazek Paul Christoph Gembarski Peter Frohn-Sörensen Peter Nielsen Peter Nyhuis Poul Kyvsgaard Hansen Quirin Gärtner Rasmus Andersen Rikard Söderberg Robert Schmitt Sabri Baazouzi Sabrina Schreiner Saeedeh Shafiee Kristensen Sagar Rao Sara Shafiee Simon Boldt Simon Bøgh Simon Dürr Soujanya Mantravadi Stefan Kjeldgaard Stefan Plappert Stefan Wiesner

Organization

Steffen Foldager Jensen Stephan Hankammer Stig Taps Subrata Saha Tehseen Aslam Thomas Ditlev Brunoe Thorsten Blecker

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Thorsten Wuest Ulrich Berger Verena Stingl Waguih ElMaraghy Xavier Delorme Yang Cheng

Contents

Bridging CARV and MCPC A Bibliometric and Sentiment Analysis of CARV and MCPC Conferences in the 21st Century: Towards Sustainable Customization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ann-Louise Andersen, Thomas D. Brunoe, Maria Stoettrup Schioenning Larsen, Rasmus Andersen, Kjeld Nielsen, Alessia Napoleone, and Stefan Kjeldgaard

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Changeable, Reconfigurable and Flexible Manufacturing A Classification of Different Levels of Flexibility in an Automated Manufacturing System and Needed Competence . . . . . . . . . . . . . . . . . . Anders Nilsson, Fredrik Danielsson, Mattias Bennulf, and Bo Svensson Manufacturing Genome: A Foundation for Symbiotic, Highly Iterative Product and Production Adaptations . . . . . . . . . . . . . . . . . . . . Patrizia Gartner, Alexander Jacob, Haluk Akay, Johannes Löffler, Jack Gammack, Gisela Lanza, and Sang-Gook Kim

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Advanced Reconfigurable Machine Tools for a New Manufacturing Business Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alessandro Arturo Bruzzone

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Design and Fabrication of Novel Compliant Mechanisms and Origami Structures for Specialty Grippers . . . . . . . . . . . . . . . . . . . Dora Strelkova and R. Jill Urbanic

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Configuration Design of Delayed Reconfigurable Manufacturing System(D-RMS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shiqi Nie, Sihan Huang, Guoxin Wang, and Yan Yan

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Classification of Reconfigurability Characteristics of Supply Chain . . . . Slim Zidi, Nadia Hamani, and Lyes Kermad

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Reconfigurable Manufacturing: An Investigation of Diagnosability Requirements, Enabling Technologies and Applications in Industry . . . Alessia Napoleone, Brendan P. Sullivan, Elias Arias-Nava, and Ann-Louise Andersen A Classification of the Barriers in the Implementation Process of Reconfigurability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Isabela Maganha, Ann-Louise Andersen, Cristovao Silva, and Luis Miguel D. F. Ferreira Development of a Parallel Product-Production Co-design for an Agile Battery Cell Production System . . . . . . . . . . . . . . . . . . . . . . . . . . J. Ruhland, T. Storz, F. Kößler, A. Ebel, J. Sawodny, J. Hillenbrand, P. Gönnheimer, L. Overbeck, Gisela Lanza, M. Hagen, J. Tübke, J. Gandert, S. Paarmann, T. Wetzel, J. Mohacsi, A. Altvater, S. Spiegel, J. Klemens, P. Scharfer, W. Schabel, K. Nowoseltschenko, P. Müller-Welt, K. Bause, A. Albers, D. Schall, T. Grün, M. Hiller, A. Schmidt, A. Weber, L. de Biasi, H. Ehrenberg, and J. Fleischer

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Towards the Swarm Production Paradigm . . . . . . . . . . . . . . . . . . . . . . 105 Casper Schou, Akshay Avhad, Simon Bøgh, and Ole Madsen Challenges Towards Long-Term Production Development: An Industry Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Simon Boldt, Carin Rösiö, and Gary Linnéusson The Use of Principal Component Analysis for the Construction of a Reconfigurability Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Antonio Mousinho de Oliveira Fernandes, Isabela Maganha, and Jose L. F. Martinho A Real Options Approach for NPV Investment Evaluation of Changeable Manufacturing Systems . . . . . . . . . . . . . . . . . . . . . . . . . 130 Fredrik Olsson, Alexander Werthén, and Ann-Louise Andersen Methods and Models to Evaluate the Investment of Reconfigurable Manufacturing Systems: Literature Review and Research Directions . . . Stefan Kjeldgaard, Ann-Louise Andersen, Thomas D. Brunoe, and Kjeld Nielsen

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Smart Automation and Human Machine Collaboration Aiming for Knowledge-Transfer-Optimizing Intelligent Cyber-Physical Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Marcus Grum, Christof Thim, and Norbert Gronau Comparison of AI-based Task Planning Approaches for Simulating Human-Robot Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Tadele Belay Tuli and Martin Manns

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Towards Flexible PCB Assembly Using Simulation-Based Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Simon Mathiesen, Lars Carøe Sørensen, Thorbjørn Mosekjær Iversen, Frederik Hagelskjær, and Dirk Kraft Towards Automatic Welding-Robot Programming Based on Product Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Ioan-Matei Sarivan, Ole Madsen, and Brian Vejrum Waehrens Design of an Intelligent Robotic End Effector Based on Topology Optimization in the Concept of Industry 4.0 . . . . . . . . . . . . . . . . . . . . . 182 Dimitris Mourtzis, John Angelopoulos, and Nikos Panopoulos Towards a Structured Decision-Making Framework for Automating Cognitively Demanding Manufacturing Tasks . . . . . . . . . . . . . . . . . . . . 190 Robbert-Jan Torn, Peter Chemweno, Tom Vaneker, and Soheil Arastehfar Enabling Resilient Production Through Adaptive Human-Machine Task Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 Deepak Dhungana, Alois Haselböck, Christina Schmidbauer, Richard Taupe, and Stefan Wallner Feasibility of Augmented Reality in the Scope of Commission of Industrial Robot Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Lukas Antonio Wulff, Michael Brand, Jan Peter Schulz, and Thorsten Schüppstuhl Assembly Process Digitization Through Self-learning Assistance Systems in Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 Marlon Antonin Lehmann, Ronny Porsch, and Christopher Mai Detecting Faults During Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving . . . . . . . . . 224 Błażej Leporowski, Daniella Tola, Casper Hansen, and Alexandros Iosifidis Virtual Modeling as a Safety Assessment Tool for a Collaborative Robot (Cobot) Work Cell Based on ISO/TS 15066:2016 . . . . . . . . . . . . 233 Mohsin Raza, Ali Ahmad Malik, and Arne Bilberg A Case Study of Plug and Produce Robot Assistants for Hybrid Manufacturing Workstations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 Sebastian Hjorth, Casper Schou, Elias Ribeiro da Silva, Finn Tryggvason, Michael Sparre Sørensen, and Henning Forbech Integrated COBOT, Human, and Manufacturing Task Kinematic Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250 Yun Bi, Jeremy J. Rickli, and Ana Djuric

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Additive Manufacturing Assessment of Repairability and Process Chain Configuration for Additive Repair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Nicola Viktoria Ganter, Stefan Plappert, Paul Christoph Gembarski, and Roland Lachmayer Additive Manufacturing of TPU Pneu-Nets as Soft Robotic Actuators . . . Peter Frohn-Sörensen, Florian Schreiber, Martin Manns, Jonas Knoche, and Bernd Engel

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Applicability of Snap Joint Design Guidelines for Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Florian Schreiber, Thomas Lippok, Jan Uwe Bätzel, and Martin Manns A Reduced Gaussian Process Heat Emulator for Laser Powder Bed Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Xiaohan Li and Nick Polydorides Smart Factories and Cyber-Physical Production Systems Demonstrating and Evaluating the Digital Twin Based Virtual Factory for Virtual Prototyping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Emre Yildiz, Charles Møller, and Arne Bilberg Applying Robotics Centered Digital Twins in a Smart Factory for Facilitating Integration and Improved Process Monitoring . . . . . . . 305 Simon Mathiesen, Lars Carøe Sørensen, Alberto Sartori, Anders Prier Lindvig, Ralf Waspe, and Christian Schlette A Concept for a Distributed Interchangeable Knowledge Base in CPPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 Christof Thim, Marcus Grum, Arnulf Schüffler, Wiebke Roling, Annette Kluge, and Norbert Gronau Generating Customer Insights Using the Digital Shadow of the Customer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322 Kristof Briele, Marie Lindemann, Raphael Kiesel, and Robert H. Schmitt Development of a IIoT Platform for Industrial Imaging Sensors . . . . . . 330 Christian Borck, Randolf Schmitt, Ulrich Berger, and Christian Hentschel Digital Twin Design in Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Sarah Wagner, Michael Milde, Félicien Barhebwa-Mushamuka, and Gunther Reinhart Requirements Analysis for Digital Shadows of Production Plant Layouts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 Julian Hermann, Konrad von Leipzig, Vera Hummel, and Anton Basson

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Deconstructing Industry 4.0: Defining the Smart Factory . . . . . . . . . . . 356 Casper Schou, Michele Colli, Ulrich Berger, Astrid Heidemann Lassen, Ole Madsen, Charles Møller, and Brian Vejrum Wæhrens Application of Multi-Model Databases in Digital Twins Using the Example of a Quality Assurance Process . . . . . . . . . . . . . . . . . . . . . . . . 364 Julian Koch, Gerald Lotzing, Martin Gomse, and Thorsten Schüppstuhl Adaptive Manufacturing Based on Active Sampling for Multi-component Individual Assembly . . . . . . . . . . . . . . . . . . . . . . . 372 Alex Maximilian Frey and Gisela Lanza A Requirement Engineering Framework for Smart Cyber-Physical Production System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 Puviyarasu Subramaniam Anbuchezhian, Farouk Belkadi, Catherine da Cunha, and Abdelhamid Chriette Agile Machine Development from Virtual to Real . . . . . . . . . . . . . . . . . 389 Jesper Puggaard de Oliveira Hansen, Elias Ribeiro da Silva, Arne Bilberg, and Carsten Bro Framework for Adoption of Freeform Injection Molding in Discrete Manufacturing Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 Elham Sharifi, Atanu Chaudhuri, Brian Vejrum Waehrens, Lasse Guldborg Staal, and Saeed Davoudabadi Farahani Machine Learning for Smart Manufacturing Weld Seam Trajectory Planning Using Generative Adversarial Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 Michael K. Kick, Alexander Kuermeier, Christian Stadter, and Michael F. Zaeh A New Authentic Cloud Dataset from a Production Facility for Anomaly Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 Emil Blixt Hansen, Emil Robenhagen van der Bijl, Mette Busk Nielsen, Morten Svangren Bodilsen, Simon Vestergaard Berg, Jan Kristensen, Nadeem Iftikhar, and Simon Bøgh Framework for Potential Analysis by Approximating Line-Less Assembly Systems with AutoML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 Lea Grahn, Jonas Rachner, Amon Göppert, Sazvan Saeed, and Robert H. Schmitt Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431 Till Böttjer, Georg Ørnskov Rønsch, Cláudio Gomes, Devarajan Ramanujan, Alexandros Iosifidis, and Peter Gorm Larsen

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Clustered Problems and Machine Learning Methodologies: A New Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440 Díez Álvarez Daniel, Væhrens Lars, and Berger Ulrich Implementing Machine Learning in Small and Medium-Sized Manufacturing Enterprises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448 Nadeem Iftikhar and Finn Ebertsen Nordbjerg Global Production and Supply Chain Networks Automated Production Network Planning Under Uncertainty by Developing Representative Demand Scenarios . . . . . . . . . . . . . . . . . . 459 Oliver Bruetzel, Daniel Voelkle, Leonard Overbeck, Nicole Stricker, and Gisela Lanza Automated Model Development for the Simulation of Global Production Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 Michael Milde and Gunther Reinhart Exploring the Requirements and Challenges in Production Logistics for Different Sectors of the Manufacturing Industry . . . . . . . . . . . . . . . 475 Ali Bozkurt, Roman Weiner, Isabella Rusch, and Robert Schulz Industry 4.0: The Case-Study of a Global Supply Chain Company . . . . 483 Cezar Honorato and Francisco Cristovão Lourenço de Melo Fostering the Diffusion of Intelligent Transport Systems (ITS) in Intermodal Logistics in Italy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 Maria Giuffrida and Sara Perotti Concept for a Token-Based Blockchain Architecture for Mapping Manufacturing Processes of Products with Changeable Configurations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 Fabian Dietrich, Louis Louw, and Daniel Palm Blockchain as a Sustainable Service-Enabler: A Case of Wind Turbine Traceability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516 Kristoffer Holm Factory and Shop Floor Planning Understanding Shared Autonomy of Collaborative Humans Using Motion Capture System for Simulating Team Assembly . . . . . . . . . . . . 527 Tadele Belay Tuli, Martin Manns, and Michael Jonek Dynamic Task Allocation for Cooperating, Heterogeneous Assembly Resources in LMAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535 Aline Kluge-Wilkes and Robert H. Schmitt

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Risk Assessment in Factory Planning Projects – An Empirical Evaluation of Industrial Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Peter Burggräf, Tobias Adlon, Steffen Schupp, and Jan Salzwedel Approaches for Generating Synthetic Industrial Load Profiles in Greenfield Energy System Planning . . . . . . . . . . . . . . . . . . . . . . . . . . 551 Julian Joël Grimm, Max Weeber, and Alexander Sauer Incremental Manufacturing: Process Planning for a Scalable Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559 Ann-Kathrin Reichler, Benjamin Schumann, and Klaus Dröder Constraints for Motion Generation in Work Planning with Digital Human Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 567 Michael Jonek, Tadele Belay Tuli, and Martin Manns Identification and Categorization of Assembly Information for Collaborative Product Realization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575 Nathaly Rea Minango, Mariam Nafisi, Mikael Hedlind, and Antonio Maffei Balancing Workers in Divisional Serus . . . . . . . . . . . . . . . . . . . . . . . . . 584 Marco Bortolini and Francesco Gabriele Galizia Data-Driven Approaches for Manufacturing and Variety Management Machine Vision: Error Detection and Classification of Tailored Textiles Using Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595 Kai Mueller and Christoph Greb Similarity-Based Process and Set-Up Time Estimation . . . . . . . . . . . . . . 603 B. Denkena, M.-A. Dittrich, and S. J. Settnik A Holistic Methodology for Successive Bottleneck Analysis in Dynamic Value Streams of Manufacturing Companies . . . . . . . . . . . 612 Nikolai West, Marius Syberg, and Jochen Deuse A Data-Driven Approach for Option-Specific Order Freeze Points in Mass-Customized Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 620 Simon Dürr, Rainer Silbernagel, Hannah Bartsch, Gwen Louis Steier, Marco F. Huber, and Gisela Lanza Impact of Dough Property Characterization on Industrial Bread Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 628 Anne-Sophie Schou Jødal, Thomas D. Brunoe, and Kjeld Nielsen Complexity Management in Engineer-To-Order Industry: A Design-Time Estimation Model for Engineering Processes . . . . . . . . . 636 Christian Victor Brabrand, Sara Shafiee, and Lars Hvam

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Contents

Design Catalogues as Knowledge-Base for CAD-Based Design Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645 Paul Christoph Gembarski Implicit and Explicit Modeling of Uncertainty in Early Design Stages of Product Design: A Comparative Study . . . . . . . . . . . . . . . . . . 653 Stefan Plappert, Philipp Wolniak, Paul Christoph Gembarski, and Roland Lachmayer Applying Modular Function Deployment for Non-assembled Products in the Process Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661 Maja K. Mogensen, Rasmus Andersen, Thomas D. Brunoe, and Kjeld Nielsen Parametric Topology Synthesis of a Short-Shaft Hip Endoprosthesis Based on Patient-Specific Osteology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669 Patrik Müller, Paul Christoph Gembarski, and Roland Lachmayer Exploring a Data-Augmented Approach for Improved Module Driver Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 677 Rasmus Andersen, Thomas D. Brunoe, and Kjeld Nielsen Characteristic-Oriented Complexity Cost Analysis for Evaluating Individual Product Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 686 Juliane Kuhl, Christoph Rennpferdt, and Dieter Krause Product Architecture Mining: Identifying Current Architectural Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694 Morten Skogstad, Thomas D. Brunoe, Kjeld Nielsen, and Ann-Louise Andersen Digital Transformation and Maturity Assessment An ‘End to End’ Methodological Framework to Assist SMEs in the Industry 4.0 Journey from a Sectoral Perspective an Empirical Study in the Oil and Gas Sector . . . . . . . . . . . . . . . . . . . . 705 Lourdes Perea Muñoz, M. Laura Pan Nogueras, and Daniel Suarez Anzorena RAISE 4.0: A Readiness Assessment Instrument Aimed at Raising SMEs to Industry 4.0 Starting Levels – an Empirical Field Study . . . . . 713 M. Laura Pan Nogueras, Lourdes Perea Muñoz, Juan Pablo Cosentino, and Daniel Suarez Anzorena Industry 4.0 Holds a Great Potential for Manufacturers, So Why haven’t They Started? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721 Maria Stoettrup Schioenning Larsen, Mats Magnusson, and Astrid Heidemann Lassen

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Identifying Production Improvement Opportunities Enabled by Digital Innovation: The Digital Factory Mapping Approach . . . . . . . . . 730 Michele Colli, Morten Wagner, Søren Bronnée Sørensen, and Brian Vejrum Wæhrens Development of a Human-Centered Implementation Strategy for Industry 4.0 Exemplified by Digital Shopfloor Management . . . . . . . 738 Magnus Kandler, Marvin Carl May, Julian Kurtz, Andreas Kuhnle, and Gisela Lanza Teaching Old Dogs New Tricks - Towards a Digital Transformation Strategy at the Shop Floor Management Level: A Case Study from the Renewable Energy Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 746 Pernille Clausen and Benjamin Henriksen The Effect of Digital Maturity on Strategic Approaches to Digital Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 754 Caroline Christensen, Maya Kousholt Schmitt, Maria Stoettrup Schioenning Larsen, and Astrid Heidemann Lassen Implementing Virtual Prototyping for the Production of Customized Products: An SME Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 762 Lasse Christiansen, Thorbjørn Borregaard, Mikkel Graugaard Antonsen, Esben Skov Laursen, and Thomas D. Brunoe Smart Products, Services and Product-Service Systems Leveraging the Value of Data in the Continuum of Products and Services: Business Types in the Function-Oriented Offerings Model . . . 773 Friedemann Kammler, Paul Christoph Gembarski, and Henrik Kortum Framing Development Methodologies for Product-Service Systems . . . . 781 Paul Christoph Gembarski and Friedemann Kammler Going Above and Beyond eCommerce in the Future Highly Virtualized World and Increasingly Digital Ecosystem . . . . . . . . . . . . . 789 Jean-Philippe Harrisson-Boudreau and Jocelyn Bellemare Tools for the Variety-Oriented Product-Service System Design . . . . . . . 798 Christoph Rennpferdt, Juliane Kuhl, and Dieter Krause Coherent Next Best Experience How to Create Coherent Touchpoints Across Firm Boundaries . . . . . . . . . . . . . . . . . . . . . . . . . . 807 Erik Kayser, Andreas Trägårdh, Rikard Boije af Gennäs, and Linnea Fyrner

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Configuration Management and Choice Navigation Enabling Mass Customization Life Cycle Assessment in Product Configurators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819 Noemi Christensen and Robin Wiezorek Configuration Systems Applied to the Healthcare Sector for an Enhanced Prescription Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 827 Irene Campo Gay and Lars Hvam Creating Customizable Co-Innovation Spaces . . . . . . . . . . . . . . . . . . . . 835 Paul Blazek and Verena Aschenbrenner Measuring User Experience Related Data of Online Product Configurators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 843 Paul Blazek and Georg Strassmayr Looking for Patterns: A Comparative Analysis of Mass Customization Co-design Toolkits for Tangible Versus Intangible Offerings . . . . . . . . . 851 Frances Turner and Marie Watts An Integrated Method for Knowledge Management in Product Configuration Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 860 Marjolein Deryck and Joost Vennekens Learning Factories and Engineering Education Human Capital Transformation for Successful Smart Manufacturing . . . Jessica Olivares-Aguila, Waguih ElMaraghy, and Hoda ElMaraghy

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Benefits of Modularity Strategies - Implications of Decisions and Timing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 879 Poul Kyvsgaard Hansen, Magnus Persson, and Juliana Hsuan Analysis of Industry 4.0 Capabilities: A Perspective of Educational Institutions and Needs of Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 887 Kashif Mahmood, Tauno Otto, Jesper H. Kristensen, Astrid Heidemann Lassen, Thomas D. Brunoe, Casper Schou, Lasse Christiansen, and Esben Skov Laursen State of the Art of European Learning Factories for the Digital Transformation - A Survey on Technologies, Learning Concepts and Their Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 895 Grit Rehe and Marc Gebauer A Learning Factory for Teaching the Transition from Conventional to Industry 4.0 Based Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 903 Isabela Maganha, Tábata Fernandes Pereira, Luiz Felipe Pugliese, Ana Carolina Oliveira Santos, and Ann-Louise Andersen

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A Framework for Manufacturing Innovation Management and the Integration of Learning Factories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 911 Quirin Gärtner and Benedikt G. Mark Project and Engineering Management in the Era of Industry 4.0 – An Overview of Learning Requirements . . . . . . . . . . . . . . . . . . . . . . . . 919 Khaled Medini, Julien De Benedittis, and Stefan Wiesner Design Automation of a Motor Hoisting Crane – Results of Student Project on Knowledge-Based CAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 927 Paul Christoph Gembarski, Dörthe Behrens, Jan Feldkamp, Lorenz Kies, and Lukas Hoppe Considering Intelligent Tutoring Systems as Mass Customization of Digital Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 935 Paul Christoph Gembarski and Lukas Hoppe Insights from Case Studies and Experiments Knowledge Integration in Industrialized House Building – Current Practice and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 945 Daniel Hussmo, Kristina Säfsten, and Paraskeva Wlazlak Human-Centered Design and Co-design Methodologies for Mass Customization in Housing: A Case Study Using Cloud Computing Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 953 Miguel Vidal Calvet, Silvio Carta, and Juan Lago-Novás Developing a Two-Hour Design Thinking Workshop to Examine the Potentials of Age-Divers Co-creation: Why Product Design Teams Should Invite Users Aged 50+, When Designing for the Demographic Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 962 Sabrina Schreiner, Elvira Radaca, and Patrick Meller Improving the Patient Visit Process in the Pre-treatment Phase . . . . . . 970 Saeedeh Shafiee Kristensen and Sara Shafiee The Smart Suits Retailer A Case of Onward Personal Style Co, Ltd. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 978 Seiji Endo Sustainable Manufacturing and Circular Economy Sustainability of Factories in Urban Surroundings Enabled by a Space Efficiency Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 987 Peter Burggräf, Matthias Dannapfel, and Jérôme Uelpenich A Framework for Industry 4.0 Implementation in Circular Economy Manufacturing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 997 Saleh M. Bagalagel and Waguih ElMaraghy

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Exploring Simulation as a Tool for Evaluation of Automation Assisted Assembly of Customized Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1006 Sagar Rao, Kerstin Johansen, and Milad Ashourpour The Phenomenon of Local Manufacturing: An Attempt at a Differentiation of Distributed, Re-distributed and Urban Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1014 Pascal Krenz, Lisa Stoltenberg, Julia Markert, Dominik Saubke, and Tobias Redlich Sustainability Assessment of Manufacturing Systems – A Review-Based Systematisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1023 Daniel Schneider, Magdalena Paul, Susanne Vernim, and Michael F. Zaeh Reverse Logistics for Improved Circularity in Mass Customization Supply Chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1031 Ottar Bakås, Stine Sonen Tveit, and Maria Kollberg Thomassen Mass Customizing for Circular and Sharing Economies: A Resource-Based View on Outside of the Box Scenarios . . . . . . . . . . . 1039 Paul Christoph Gembarski and Friedemann Kammler Correction to: Manufacturing Genome: A Foundation for Symbiotic, Highly Iterative Product and Production Adaptations . . . . . . . . . . . . . . Patrizia Gartner, Alexander Jacob, Haluk Akay, Johannes Löffler, Jack Gammack, Gisela Lanza, and Sang-Gook Kim

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Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1047

Bridging CARV and MCPC

A Bibliometric and Sentiment Analysis of CARV and MCPC Conferences in the 21st Century: Towards Sustainable Customization Ann-Louise Andersen(B) , Thomas D. Brunoe , Maria Stoettrup Schioenning Larsen , Rasmus Andersen , Kjeld Nielsen , Alessia Napoleone , and Stefan Kjeldgaard Department of Materials and Production, Aalborg University, Fibigerstraede 16, 9220 Aalborg East, Denmark [email protected]

Abstract. This opening paper of the CARV/MCPC 2021 book of proceedings presents a study of papers published within the series of Changeable, Agile, Reconfigurable and Virtual Conferences (CARV) and Mass Customization & Personalization Conference (MCPC). In total, 398 papers are included from the three most recent MCPC conferences and the four most recent CARV conferences. In addition, 119 papers from the CARV/MCPC 2021 conference are included as well. Bibliometric analyses are presented, highlighting the most cited papers and authors, the most productive authors, and recurrence of authors across conference years. In addition, a sentiment analysis highlights trends in research, applying text mining techniques on paper titles, keywords, and abstracts. Finally, past trends are compared to trends found in papers published in the joint CARV/MCPC 2021 conference proceedings, which highlights future prominent research areas and new emerging topics relevant to the CARV and MCPC communities and future conferences. Keywords: Mass customization · Changeable manufacturing · Reconfigurable manufacturing · Bibliometric analysis · Sentiment analysis

1 Introduction In the last two years, the manufacturing industry has undergone significant change, as new innovations and technologies continue to disrupt the way manufacturing companies operate and compete, e.g. advanced industrial robotics, additive manufacturing, cloud computing, big data analytics, artificial intelligence, and internet of things [1, 2]. At the same time, consumers are more than ever demanding customized and personalized products/services and the rapidness of new product introductions is similarly growing [3]. Traditionally, mass-produced consumer goods such as clothing, shoes, cars, food items, health, and cleaning products are still increasingly moving towards customization and personalization, as e-commerce and social media are expanding markets, increasing © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 3–24, 2022. https://doi.org/10.1007/978-3-030-90700-6_1

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competition, and expanding product availability [3]. Other industries that have traditionally been characterized by high customization are seeking for more cost-effective ways of delivering, and producing high product variety and products optimized for specific customers, e.g. many types of industrial products and capital goods [4]. Thus, there is still a predominant need for companies to provide high levels of product and service differentiation, innovation, customization, and even personalization at a low cost. In addition, recent years have presented much higher focus on sustainability, not only in an economical sense, but also in a social and environmental sense, which impacts every aspect of businesses and manufacturing [5]. To summarize, sustainable customization can be identified as a key competitive priority and with recent disruptive technological developments, there is now much higher ability to actually achieve this. Enabling companies to match these aforementioned demands in a both responsive and efficient way has always been the core theme of the two conferences in focus of this paper. The Changeable, Agile, Reconfigurable, and Virtual Conferences (CARV) have since 2005 been a primary forum for researchers sharing leading edge research and best practice within design, operation, and control of manufacturing systems and supply chains to achieve a high degree of responsiveness and productivity. On the other hand, the Mass Customization and Personalization Conferences (MCPC) have since 2001 brought researchers and practitioners together with emphasis on real life applications of mass customization, including all related aspects such as open innovation, choice navigation, solution space development, robust process design, customer interactions, smart products, and smart services. In Table 1 and Table 2, summaries of all past CARV and MCPC conferences are provided. Table 1. CARV conference series from 2005–2021. Conference

Location and Host

Theme

CARV2005 (1st )

Munich, The Technical University of Munich, Germany

N/A

CARV2007 (2nd )

Toronto, IMS University of Windsor, Canada

N/A

CARV2009 (3rd )

Munich, The Technical University of Munich. Germany

N/A

CARV2011 (4th ) Montreal, IMS University of Windsor, Canada

Enabling Manufacturing Competitiveness and Economic Sustainability

CARV2013 (5th ) Munich, The Technical University of Munich, Germany

Enabling Manufacturing Competitiveness and Economic Sustainability

CARV2016 (6th ) Bath, University of Bath/University N/A of Bristol, United Kingdom (continued)

A Bibliometric and Sentiment Analysis of CARV and MCPC Conferences

5

Table 1. (continued) Conference

Location and Host

Theme

CARV2019 (7th )

Nantes, Ecole Centrale de Nantes, France

N/A

CARV2021 (8th ) Aalborg, Aalborg University, Denmark

Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems

Table 2. MCPC conference series from 2001–2021. Conference

Location and Host

Theme

MCPC2001 (1st )

Hong Kong, Hong Kong University of Science and Technology

N/A

MCPC2003 (2nd )

Munich, The Technical University of Munich, Germany

Leading Mass Customization and Personalization from an Emerging Stage to a Mainstream Business Model

MCPC2005 (3rd )

Hong Kong, Hong Kong University of Science and Technology

Converging Mass Customization and Mass Production

MCPC2007 (4th )

Boston, MIT & Montreal, HEC, United States and Canada

Extreme Customization

MCPC2009 (5th )

Helsinki, Aalto University, Finland

Mass Matching - Customization, Configuration & Creativity

MCPC2011 (6th )

Berkeley, University of California, United States

Bridging Mass Customization and Open Innovation

MCPC2014 (7th )

Aalborg, Aalborg University, Denmark

Twenty Years of Mass Customization – Towards New Frontiers

MCPC2015 (8th )

Montreal, ESG UQAM, Canada

Managing Complexity

MCPC2017 (9th )

Aachen, RWTH Aachen University, Germany

Customization 4.0

MCPC2021 (10th )

Aalborg, Aalborg University, Denmark

Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems

It is evident that bringing together CARV and MCPC communities is a natural extension of the core themes in each conference series and a viable step towards covering all necessary aspects of enabling sustainable customization. In this regard, all aspects are considered from the management and optimization of supply chains, manufacturing systems, and equipment to ensuring close interaction with customers and bringing

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customized and innovate products and services to market. Thus, building on the knowledge and impact of past CARV and MCPC conferences, this opening paper of the joint CARV/MCPC 2021 book of proceedings seeks to investigate trends in past CARV and MCPC conferences concerning research impact, authors, key topics, and new emerging topics relevant to the CARV and MCPC community and future conferences. In total, 398 papers published in past conferences are included in the analyses of this paper, covering both bibliometric and sentiment analyses. In addition, the 120 papers published in this year’s proceedings are also included in the sentiment analyses. This provides a solid basis for investigating past, present and future trends relevant to both the CARV and MCPC communities. The remainder of this paper is structured as follows: First, the data and methods are elaborated in Sect. 2. Section 3 presents insights from bibliometric analyses. Section 4 presents results from sentiment analyses performed on title, abstract, and keywords of past and present CARV and MCPC papers. Finally, Sect. 5 and 6 conclude the paper with a discussion of key findings and future trends and research directions relevant for the CARV and MCPC communities.

2 Data and Methods 2.1 Data on Conference Proceedings The first three CARV conferences (CARV2005–2019) and the first six MCPC conferences (MCPC2001-MCPC2011) featured only internal publication of proceedings, hence, no citation records are available from common databases such as Web of Science, Scopus and Google Scholar. Consequently, the focus of this paper is on the conference proceedings published within the last ten years, as outlined in Table 3. Likewise, the only common citation database that can be used for comparative bibliometric analysis of all conference proceedings is Google Scholar. Thus, citation and bibliometric data on all papers were retrieved in Google Scholar using Publish or Perish. However, as the Google Scholar citation records solely include number of citations per paper including biographical data on each paper, advanced bibliometric analyses taking outset in networked citation records are unattainable. Moreover, it should be noted that citations are only considered in total number to the point in time where data were extracted (June 2021). Also, for the bibliometric analyses presented in Sect. 3, only the 4 most recent CARV conferences and the 3 most recent MCPC conferences are covered, while the sentiment analyses presented in Sect. 4 also includes the CARV/MCPC 2021 papers. However, only 84 CARV papers and 33 MCPC papers are included while the conferences actually featured 85 and 34 papers respectively, as only accepted papers at the time of analyses (August 2021) could be included. 2.2 Data Analyses The analysis of data from CARV and MCPC proceedings as outlined in Table 3 is conducted in two main parts. The first part covers analysis of papers, authors and citations based on the citation records from Google Scholar. Specific analyses include total

A Bibliometric and Sentiment Analysis of CARV and MCPC Conferences

7

Table 3. Proceeding covered in analyses of this paper. Conference

Publisher of proceedings

Citation database(s)

Number of papers

Data

CARV Conferences CARV2011

Springer

Google Scholar

108

Citation records and full text

CARV2013

Springer

Google Scholar

80

Citation records and full text

CARV2016

Procedia CIRP

Web of Science, Scopus

52

Citation records and full text

CARV2019

Procedia Manufacturing

Web of Science, Scopus

33

Citation records and full text

CARV2021

Springer Lecture Notes in Mechanical Engineering

N/A

85 Full text (only 84 covered in sentiment studies)

MCPC Conferences MCPC2014

Springer Lecture Notes in Production Engineering

Google Scholar, Web of Science

45

Citation records and full text

MCPC2015

Springer Proceedings in Business and Economics

Google Scholar, Web of Science

37

Citation records and full text

MCPC2017

Springer Proceedings in Business and Economics

Google Scholar

43

Citation records and full text

MCPC2021

Springer Lecture Notes in Mechanical Engineering

N/A

34 (Only 33 covered in sentiment studies)

Full text

citations across conference years for both MCPC and CARV, most cited papers and authors, most productive authors, and recurrence of authors across both CARV and MCPC conference series. The second part of the analyses covers a sentiment study conducted with Voyant Tools [6], which is a web-based reading and analysis environment for digital texts. In Voyant Tools, a corpus is uploaded, which is a series of documents in text form that is to be analyzed. In this case, the corpus consisted of nine PDF documents; one for every year of conference proceedings, i.e. 5 for CARV (2011–2021) and 4 for MCPC (2014– 2021). Each PDF contained the title, keywords, and abstract of the papers published in

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the respective proceedings. The resulting corpus included 91,761 words in total. The performed sentiment analyses were made as comparisons either between CARV and MCPC proceedings in general or as comparison across each years’ proceedings of each conference, rather than as comparisons on paper-level. For instance, CARV conferences were analyzed in terms of most frequent and distinct words from and distinct words were compared across all CARV and all MCPC proceedings. Additional analyses conducted in Voyant include analysis of keywords and their frequencies. In all analyses, stop words were applied, consisting of e.g. regular function words that do not carry meaning, as well as regularly used words such as conference, chapter, author, etc. that neither represent meaning in the analyses.

3 Results of Bibliometric Analysis 3.1 Citations for Conferences and Papers For both the conference series of CARV and MCPC, the number of papers published in each years’ proceedings varies greatly. In Table 4, information on number of papers and citations are outlined. The citations are both depicted in terms of total citations to date, yearly average citations to date, citations per paper to date, and yearly average citations per paper to date. Obviously, for the current year, there are not yet citations. Table 4. Citations (to date) per conference. Year

Conference

# Papers

#Citations

#Citations per year

#Citations per paper

#Citations per paper per year

CARV Conferences 2011

CARV2011

108

798

79,8

7,4

0,7

2013

CARV2013

80

479

59,9

6,0

0,7

2016

CARV2016

52

3050

610,0

58,7

11,7

2019

CARV2019

33

379

189,5

11,5

5,7

2021

CARV2021

86

N/A

N/A

N/A

N/A

MCPC Conferences 2014

MCPC2014

45

194

27,7

4,3

0,6

2015

MCPC2015

37

213

35,5

5,8

1,0

2017

MCPC2017

43

138

34,5

3,2

0,8

2021

MCPC2021

34

N/A

N/A

N/A

N/A

For MCPC, the number of papers published has remained relatively stable, while for CARV, the conference in 2011 features the highest number of published papers. Regarding citations, CARV2016 is characterized by a significantly higher number of citations both per year and per paper, which is largely attributed to a number of highly cited papers, as evident in the following subsection.

A Bibliometric and Sentiment Analysis of CARV and MCPC Conferences

9

3.2 Most Cited Papers In Table 5 and Table 6, the ten most cited papers for both CARV and MCPC are listed. It is evident that CARV 2016 features a number of highly cited papers, in particular in the area of industry 4.0, digitalization, and related topics. For the most cited papers in MCPC, the topics are more widespread and range from modelling techniques to sustainability, mass customization, open innovation, additive and reconfigurable manufacturing. Table 5. Ten most cited papers for CARV. Conference

Paper title

Author(s)

#Citations

#Citations per year

CARV2016

A maturity model for Schumacher, A., assessing Industry 4.0 Erol, S., & Sihn, readiness and maturity of W manufacturing enterprises [7]

869

174

CARV2016

A categorical framework of manufacturing for industry 4.0 and beyond [8]

Qin, J., Liu, Y., & 846 Grosvenor, R

169

CARV2016

Software-defined cloud manufacturing for industry 4.0 [9]

Thames, L., & Schaefer, D

322

64

CARV2016

E-commerce logistics in supply chain management: Practice perspective [10]

Yu, Y., Wang, X., 156 Zhong, R.Y. et al

31

CARV2016

Procedure for defining the Albers, A., system of objectives in the Gladysz, B., initial phase of an industry Pinner, T. et al 4.0 project focusing on intelligent quality control systems [11]

100

20

CARV2016

Digital manufacturing and Jackson, K., flexible assembly Efthymiou, K., technologies for Borton, J reconfigurable aerospace production systems [12]

90

18

CARV2016

On Servitization of the Huxtable, J., Manufacturing Industry in Schaefer, D the UK [13]

76

15

(continued)

10

A.-L. Andersen et al. Table 5. (continued)

Conference

Paper title

Author(s)

CARV2011

Change in manufacturing–research and industrial challenges [14]

ElMaraghy, H., AlGeddawy, T., Azab, A. et al

CARV2019

CARV2016

#Citations

#Citations per year

72

8

Digital twin for adaptation Kousi, N., of robots’ behaviour in Gkournelos, C., flexible robotic assembly Aivaliotis, S. et al lines [15]

62

31

Requirements Lopes, I., Senra, specification of a P., et al computerized maintenance management system–a case study [16]

50

10

Table 6. Ten most cited papers for MCPC. Conference

Paper title

Author(s)

#Citations

#Citations per year

MCPC2015

Proximity marketing Levesque, N., & as an enabler of mass Boeck, H customization and personalization in a customer service experience [17]

31

8

MCPC2015

KBE-Modelling Gembarski, P.C., 26 Techniques in Li, H., Lachmayer, Standard R CAD-Systems: Case Study—Autodesk Inventor Professional [18]

7

MCPC2014

Product, Boer, H.E organizational, and performance effects of product modularity [19]

3

21

(continued)

A Bibliometric and Sentiment Analysis of CARV and MCPC Conferences Table 6. (continued) Conference

Paper title

Author(s)

#Citations

#Citations per year

MCPC2014

Is sustainable mass customization an oxymoron? An empirical study to analyse the environmental impacts of a MC business model [20]

Pourabdollahian, G., Taisch, M., Piller, F.T

20

3

MCPC2015

Reconfigurable manufacturing systems in small and medium enterprises [21]

Brunoe, T.D., Andersen, A., Nielsen, K

18

5

MCPC2014

Open innovation, co-creation and mass customization: What role for 3D printing platforms? [22]

Rayna, T., Striukova, L., Darlington, J

16

2

MCPC2015

A business typological framework for the management of product complexity [23]

Gembarski, P.C., & Lachmayer, R

15

4

MCPC2015

Mass customization in SMEs: literature review and research directions [24]

Taps, S.B., Ditlev, T., Nielsen, K

15

4

MCPC2015

The potential of product customization using technologies of additive manufacturing [25]

Lachmayer, R., Gembarski, P. C., Gottwald, P. et al

12

3

MCPC2014

Food customization: Kolb, M., Blazek, An analysis of P., Streichsbier, C product configurators in the food industry [26]

11

2

11

12

A.-L. Andersen et al.

3.3 Most Productive and Cited Authors In Table 7, Table 8, Table 9, and Table 10, the most productive and most cited authors for all CARV and MCPC are listed. Table 7. Ten most productive authors for CARV. Author

#Papers

#Citations

G Reinhart

26

286

H ElMaraghy

12

269

W ElMaraghy

7

50

A Nassehi

7

75

M Germani

6

40

K Tracht

6

25

J Urbanic

6

51

A Kampker

5

26

A Verl

5

24

DT Matt

5

28

Table 8. Ten most cited authors for CARV. Author

#Papers

#Citations

W Sihn

1

869

S Erol

1

869

A Schumacher

1

869

R Grosvenor

1

846

Y Liu

1

846

J Qin

1

846

D Schaefer

3

419

L Thames

1

322

G Reinhart

26

286

H ElMaraghy

12

269

3.4 Author Recurrence As a final analysis made based on bibliometric data from past CARV and MCPC conferences, the recurrence of authors across CARV and MCPC conference years were analyzed respectively. Based on the paper lists of each year’s conference, lists were

A Bibliometric and Sentiment Analysis of CARV and MCPC Conferences

13

Table 9. Ten most productive authors for MCPC. Author

#Papers

#Citations

TD Brunoe

24

108

K Nielsen

22

116

R Lachmayer

8

79

P Blazek

7

23

PC Gembarski

7

73

L Skjelstad

5

7

KA Joergensen

4

11

JK Larsen

4

17

AL Andersen

4

43

M Bejlegaard

4

23

Table 10. Ten most cited authors for MCPC. Author

#Papers

#Citations

K Nielsen

22

116

TD Brunoe

24

108

R Lachmayer

8

79

PC Gembarski

7

73

AL Andersen

4

43

N Levesque

1

31

H Boeck

1

31

HEE Boer

2

29

H Li

1

26

generated including all author entries for every paper. As an example, if two authors co-authored three papers, this would result in six entries. These are referred to as authorpaper entries and are indicated for the two conferences in the first row of Table 11. Following this, the number of unique authors in each conference across the years was determined, indicated in the second row of Table 11. The ratio between these two figures was then calculated, which was 0, 75 for CARV and 0, 73 for MCPC, i.e. almost the same. This indicates a relatively high number of authors compared to the number of author entries. This was confirmed by determining the number of authors, who have only authored a single paper on the conferences and calculating the ratio to the number of unique authors, which was 0, 81 and 0, 84 for

14

A.-L. Andersen et al.

CARV and MCPC respectively. This indicates that a relatively high number of nonrecurring researchers has published within the two conferences, while a much smaller number of researchers constitute the backbone of the conferences. Table 11. Unique authors for CARV and MCPC conferences. CARV

MCPC

Number of author entries

852

363

Unique authors

636

265

Ratio

0.75

0.73

One-time authors

516

223

Ratio to unique authors

0,81

0,84

4 Results of Sentiment Analysis 4.1 High-Frequency and Distinctive Words For the collective group of CARV proceedings, the most frequent words used are manufacturing (716), production (682), systems (405), process (364) and product (298). For the MCPC proceedings, the most frequently used words are mass (325), customization (306), product (300), and design (233). In Table 12 and Table 13, the most distinctive words for each document in the analyzed corpus, i.e. one year of proceedings, compared to the rest of the corpus, i.e. all proceedings, are listed for CARV and for MCPC. The distinctive words are measured by TF-IDF scores, where TF (term frequency) is the number of times a word occurs in a document compared to the total number of words in the document and IDF (inverse document frequency) is the inverse of the number of documents divided by the number of documents the word occurs in. The higher the TF-IDF score, the more distinctive a word would be to that particular document. In this context, this implies that the words identified with the highest TF-IDF score, are the words that occur most frequently at a specific conference and least frequently in the other conferences. This could indicate topics that have received particular attention at each conference. In the tables, the numbers indicate the number of times the specific term occurred in the specific document. Evidently, trends can be identified from beginning of CARV and MCPC proceedings to the present proceedings. For instance, the word “4.0” and “transformation” appears as a distinct word in the proceedings for CARV2021, while “CAM” and “RFID” were distinctive in CARV2011. Moreover, for MCPC2021, “blockchain” is a distinct word as well as “reverse”. 4.2 Keywords Frequency In order to analyze how research interest in different topics in CARV and MCPC has evolved over time, an analysis of keywords was performed, using a collocation analysis

A Bibliometric and Sentiment Analysis of CARV and MCPC Conferences

15

Table 12. Distinctive words for papers in CARV proceedings. Conference proceeding Distinctive words and count compared to the rest of the corpus CARV2011

CAM (9), warm (8), job (8), ancient (8), RFID (7)

CARV2013

press (15), electric (14), soil (7), eol (7), towers (6)

CARV2016

synchronization (9), hadoop (9), 4.0 (28), commerce (7), servitization (6)

CARV2019

ontologies (10), 4.0 (23), ontology (19), block (6), urban (17)

CARV2021

4.0 (70), ml (11), fim (11), dataset (10), transformation (29)

Table 13. Distinctive words for papers in MCPC proceedings. Conference proceeding

Distinctive words and count compared to the rest of the corpus

MCPC2014

cutting (14), laser (10), public (9), scheduling (8), mechatronic (8)

MCPC2015

ramp (8), retail (12), proximity (6), investment (6), risk (11)

MCPC2017

smart (26), 3d (21), 4.0 (19), textile (9), pss (18)

MCPC2021

logistics (14), blockchain (11), reverse (10), patient (9), touchpoints (8)

in Voyant Tools. In this analysis, the frequency of words occurring close to the term “keywords” is analyzed. The reason for conducting the analysis in this way, was in order to avoid excessive manual processing of full texts. This produced a list of the most frequent keywords shown in Table 14. The table shows the 30 most frequent keywords in both CARV and MCPC conferences combined, and in CARV and MCPC respectively. Table 14. Most frequent keywords for CARV and MCPC combined and respectively. CARV & MCPC

CARV

MCPC

Keyword

#

Ratio

Keyword

#

Ratio

Keyword

#

Ratio

Manufacturing

206

0,40

Manufacturing

188

0,53

Customization

112

0,71

Production

190

0,37

Production

168

0,47

Mass

108

0,68

Product

145

0,28

Systems

87

0,24

Product

84

0,53

Design

128

0,25

Planning

67

0,19

Design

63

0,40

Customization

125

0,24

Assembly

66

0,18

Customer

27

0,17

Mass

121

0,23

Design

65

0,18

Development

26

0,16

Systems

109

0,21

process

64

0,18

Innovation

25

0,16

(continued)

16

A.-L. Andersen et al. Table 14. (continued)

CARV & MCPC Keyword

CARV #

Ratio

Keyword

MCPC #

Ratio

Keyword

#

Ratio

Process

77

0,15

Product

61

0,17

Service

23

0,15

Management

76

0,15

Management

53

0,15

Management

23

0,15

Planning

73

0,14

Industry

47

0,13

Configuration

23

0,15

Development

69

0,13

Simulation

45

0,13

Systems

22

0,14

Industry

67

0,13

Reconfigurable

44

0,12

Production

22

0,14

Assembly

66

0,13

Development

43

0,12

Creation

22

0,14

Model

53

0,10

Digital

42

0,12

Industry

20

0,13

Digital

53

0,10

Factory

40

0,11

Open

18

0,11

Data

51

0,10

Data

39

0,11

Manufacturing

18

0,11

Reconfigurable

50

0,10

Model

37

0,10

User

18

0,11

Factory

49

0,10

Machine

34

0,10

Model

16

0,10

Simulation

47

0,09

4.0

34

0,10

Variety

16

0,10

4.0

47

0,09

Virtual

30

0,08

Smart

15

0,09

Smart

42

0,08

Learning

29

0,08

Study

15

0,09

engineering

40

0,08

Engineering

29

0,08

Engineering

14

0,09

Machine

40

0,08

Analysis

26

0,07

Configurator

14

0,09

Virtual

37

0,07

Smart

26

0,07

Case

14

0,09

Analysis

36

0,07

Control

25

0,07

Research

14

0,09

Configuration

36

0,07

Knowledge

25

0,07

Process

13

0,08

Customer

35

0,07

Human

22

0,06

Experience

13

0,08

Innovation

34

0,07

Time

22

0,06

Sustainability

13

0,08

Learning

33

0,06

Robot

21

0,06

Order

12

0,08

Case

32

0,06

Technology

21

0,06

Data

12

0,08

The keywords “manufacturing” and “production” are both frequently occurring at both conferences, however, relatively more frequent at CARV, which is expected since MCPC also has a focus on other topics such as innovation and product configuration, which can be seen from the keywords for MCPC. Furthermore, the frequent keywords for MCPC include “customer” and “service”, which are also indicating that the MCPC conferences cover a wider range of topics than pure manufacturing and production, since these or similar words do not occur frequently at the CARV conference. In relation to this, the keyword “product” also occurs frequently at both conferences, however, relatively more frequent in the MCPC domain. The terms “design”, “management”, “development”, and “engineering”, also occur frequently at both conferences and at almost the same relative frequency. This might be due to the fact that the conferences

A Bibliometric and Sentiment Analysis of CARV and MCPC Conferences

17

are predominantly engineering conferences, focusing on designing and developing solutions. One other interesting finding from this analysis is that both conferences have the keyword “Industry” occurring frequently, which may indicate that the research presented at the conferences has an applied focus, which matches well with the statement above related to engineering solutions. Though, it may also be a sign that Industry 4.0 is a topic being addressed in both CARV and MCPC conferences. In regard to the type of research, the keyword “data” also occurs frequently at both conferences, however, at a much higher relative frequency in the CARV conferences. This could indicate that the CARV research has a more quantitative focus compared to MCPC conferences, which is supported by the MCPC conference also having the keyword “case” occurring frequently indicating a stronger focus on case-based research which is traditionally more qualitative. This is further supported by the CARV conference having the keywords “planning”, “simulation”, and “control” occurring frequently which are inherently quantitative disciplines. Both conferences appear to address topics related to configuration. In MCPC conferences, the terms configuration and configurator are occurring frequently, which they do not in CARV. Likewise, in CARV, the keyword reconfigurable occurs frequently which is not the case for MCPC. This indicates that at the CARV conference, configuration is addressed from a manufacturing perspective, since reconfigurable is occurring in relation to “Reconfigurable manufacturing”, whereas configuration and configurators are more related to the product domain. However, there is a significant relation between the two conferences, since the configuration of products and their manufacturing systems are heavily interdependent. In Table 15 and Table 16, a similar analysis is performed on keywords for CARV and MCPC respectively across years. In the first column of both tables, the 30 most frequently occurring keywords at the conferences before 2015 are listed, while the second column in both tables show the 30 most frequently occurring keywords at conferences after 2016. A number of additional relevant observations can be made from this analysis with a yearly split. For CARV, it is first of all noteworthy how the use of the term manufacturing compared to the term production appears to be increasing in the most recent years. Despite various different perceived conceptual understandings of the terms “production” and “manufacturing”, it is common in English usage that the term “manufacturing” addresses a wider scope than the term “production”, which may be mostly used in connection with single workstations or specific processes. Moreover, for the CARV conferences in the early period, keywords such as “planning”, “management” and “assembly” have decreased in relative usage. Contrary, the keyword “digital” has increased significantly in usage from the first to the second period and the word “smart” is also only featured in the most recent period. Finally, it is evident that keywords related to reconfigurable manufacturing, i.e. “reconfigurable” has increased and the terms “reconfigurable” and “reconfigurability” now is more used than the terms “reconfigurable” and “reconfiguration”, which may indicate a move from focus on planning and performing reconfigurations to the development of reconfigurability as a capability in the manufacturing system. For MCPC, the keywords “customization” and “mass” appears universally important for both time periods, as well as “product” and “design”. A noteworthy difference is

18

A.-L. Andersen et al.

Table 15. Most frequent keywords for CARV split between the group “CARV2011, CARV2013 and CARV2015” and the group “CARV2019 and CARV2021”. CARV2011, CARV2013 and CARV2015

CARV2019 and CARV2021

Keyword

#

Ratio

Keyword

#

Ratio

Production

125

0,52

Manufacturing

71

0,61

Manufacturing

117

0,49

Production

43

0,37

Systems

66

0,28

Industry

35

0,30

Design

59

0,25

4.0

29

0,25

Process

53

0,22

Learning

24

0,21

Planning

52

0,22

Digital

24

0,21

Assembly

48

0,20

Smart

22

0,19

Product

47

0,20

Systems

21

0,18

Management

40

0,17

Machine

20

0,17

Development

31

0,13

Simulation

19

0,16

Reconfigurable

27

0,11

Data

18

0,15

Simulation

26

0,11

Assembly

18

0,15

Model

24

0,10

Reconfigurable

17

0,15

Factory

23

0,10

Factory

17

0,15

Control

23

0,10

Planning

15

0,13

Engineering

23

0,10

Product

14

0,12

Data

22

0,09

Human

14

0,12

Virtual

21

0,09

Model

13

0,11

Based

20

0,08

Management

13

0,11

Analysis

19

0,08

Robot

12

0,10

Technology

19

0,08

Reconfigurability

12

0,10

Digital

18

0,08

Development

12

0,10

Knowledge

18

0,08

Process

11

0,09

Time

17

0,07

Automation

11

0,09

Reconfiguration

16

0,07

Virtual

10

0,09

Tool

16

0,07

Ontology

10

0,09

Multi

15

0,06

Based

10

0,09

Integration

15

0,06

Physical

9

0,08

Enerssgy

15

0,06

Json

8

0,07

A Bibliometric and Sentiment Analysis of CARV and MCPC Conferences

19

Table 16. Most frequent keywords for MCPC split between the group “MCPC2014 and MCPC2015” and the group “MCPC2017 and MCPC2021”. MCPC2014 and MCPC2015

MCPC2017 and MCPC2021

Keyword

#

Ratio

Keyword

#

Ratio

Customization

56

0,68

Customization

56

0,74

Mass

53

0,65

Mass

55

0,72

Product

38

0,46

Product

46

0,61

Design

34

0,41

Design

29

0,38

Innovation

19

0,23

Service

17

0,22

Customer

18

0,22

Smart

15

0,20

Creation

16

0,20

Development

15

0,20

Production

14

0,17

Industry

14

0,18

Open

14

0,17

Systems

12

0,16

Management

12

0,15

Configuration

12

0,16

Model

11

0,13

User

11

0,14

Development

11

0,13

Management

11

0,14

Configuration

11

0,13

Sustainability

10

0,13

Systems

11

0,13

Variety

10

0,13

Performance

10

0,12

Experience

10

0,13

Manufacturing

10

0,12

Engineering

9

0,12

Construction

10

0,12

Customer

9

0,12

Case

10

0,12

Production

9

0,12

Study

10

0,12

Order

9

0,12

Challenges

9

0,11

Manufacturing

8

0,11

Process

9

0,11

Factory

8

0,11

Capabilities

8

0,10

Digital

8

0,11

Modelling

8

0,10

4.0

8

0,11

Configurator

7

0,09

Supply

8

0,11

Complexity

7

0,09

Research

8

0,11

Business

7

0,09

Demand

7

0,09

Analysis

7

0,09

Data

7

0,09

Variety

7

0,09

Configurator

7

0,09

Service

6

0,07

3d

7

0,09

20

A.-L. Andersen et al.

the high usage of the term “innovation” in the early period of MCPC, while being more absent in the recent period. Contrary, the term “smart” is frequent in the recent period, but absent in the earliest period. Moreover, the term “service” appears to have increased its relevance throughout the years, while sustainability has also entered the list for frequent keywords in the recent period.

5 Discussion 5.1 Research Impact and Relevancy of CARV and MCPC Based on the findings presented in this paper, it is evident that the CARV and MCPC conferences indeed can be recognized as both relevant and influential conferences in the domain of industrial and manufacturing engineering research. First and foremost, CARV and MCPC have both existed as conference series for almost two decades and are definitely global in both reach and authorships. While CARV conferences have been hosted only in North America and Europe, the MCPC conference was initially hosted in Hong Kong and has since then been hosted in both North America and Europe. Moreover, both conferences present an increase in citations per year looking across most recent years of conference proceedings. This might of course be attributed mainly to a general focus on publications in academia, however, at the same time confirming that the conferences have remained relevant in the last decade. Looking specifically at the CARV conferences, it is clear that some highly cited seminal works exist, primarily in the domain of maturity assessment for Industry 4.0 and related topics within that domain. Moreover, various widely recognized researchers in the field of manufacturing are evidently highly productive and cited in the CARV community, which cements the position of CARV as a conference with a strong position in manufacturing research. For MCPC it is clear that the most cited papers are more widely spread in terms of topics, which confirms the notion that mass customization is a business strategy that can only be achieved through the successful development of three interrelated capabilities, i.e. choice navigation, robust process design, and solution space development. Inherently, the topics of MCPC conferences are more widespread and the conference community covers various authors within both management and manufacturing. However, it appears that the topic of mass customization indeed still attracts research and remains relevant. For both CARV and MCPC, the ratio of recurring authors compared to unique authors appears relatively low (without available comparative numbers for other conferences). This may be attributed to the somewhat unstable occurrence of the conferences, compared to e.g. annual conferences that are known to researchers long time in advance. Thus, it is likely that both the CARV and MCPC conferences would gain an even stronger position in research, if their occurrence was more stable and foreseeable in the future. 5.2 Past and Present Research Trends in CARV and MCPC In this paper, various past research trends in the CARV and MCPC conferences were highlighted. For CARV, it is clear that emphasis has always been on enabling technologies

A Bibliometric and Sentiment Analysis of CARV and MCPC Conferences

21

in manufacturing, e.g. digitalization, smart factories, reconfigurability, and robots, applying various techniques in planning, simulation, design, development, and modelling. In MCPC, emphasis is also on the business and management aspects, e.g. innovation, customers, service, configuration, and capabilities, where also case-based research appears as a dominant research method applied. Some of the most noteworthy past research trends are summarized, based on the keyword analyses featured in this paper: • CARV conferences – appear to have had a strong focus on manufacturing and production systems, and on everything from control, planning, simulation, to design and development. – appear to have had a focus on industry 4.0, smart manufacturing, digitalization, and reconfigurable manufacturing from the beginning. – address topics from factory, system, process, machine and human aspects. – have always had some element of sustainability focus, e.g. featuring papers focused on EOL, urban manufacturing, and sustainability in general. • MCPC conferences – are focused on products, design, service, customers, innovation, configuration, modelling, and management. – appear to be strong in case-based research. – appear to be strong in the research domains of configuration systems, configuration management and modelling. – have papers focused on sustainability as most cited papers. Furthermore, several of emerging research themes were identified in this paper, which are summarized below: • For CARV conferences, – it appears that a recent strong emphasis on industry 4.0 and digital transformations is emerging. – learning is an emerging research focus, e.g. in connection to learning factories. – focus appears to increase and transition into more focus on achieving reconfigurability rather than performing reconfigurations. – human aspects are emerging, e.g. in terms of human-machine collaboration, as well as focus on (big) data. • For MCPC conferences, – mass customization still appears as a research area rather than topics of personalization or individualization. – sustainability is indeed an emerging research priority.

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– connecting the fields of digitalization and smart manufacturing with services, customer experience, configuration, and design appears as an emerging research direction. 5.3 Bridging CARV and MCPC Clearly, the CARV2021 conference and the MCPC2021 conference differ compared to their predecessors in being jointly organized and hosted. As stated in the analyses above, the conferences share the same overall objectives; supporting industry in becoming changeable and delivering customized products in an efficient way. Also, a number of publications could have easily been published at both conferences, emphasizing that a clear connection between the two. On the other hand, the keywords analyses also showed that the research topics addressed within the two conferences differ slightly, where CARV conferences tend to be more focused on pure manufacturing, technology, and quantitative methods from an engineering perspective, whereas MCPC conferences to a higher degree focuses also on customer perspectives, products, cases, and qualitative approaches. It is generally acknowledged that integrating and coordination between multiple disciplines in the value chain, e.g. marketing, product development, manufacturing, and logistics yields better results in practice taking a holistic perspective, rather than silo-thinking leading to sub-optimization. Therefore, it seems obvious that bringing together the themes from CARV and MCPC will provide the opportunity to foster research that integrates more different perspectives, leading to more societal impact and higher quality. The theme of the joint CARV2021 and MCPC2021 conferences i.e. “Sustainable Customization” also stresses the importance of bridging the conferences. In order to succeed in a sustainable transformation, industry and society cannot focus on single issues, but must address this from a host of different perspectives. Sustainability has implications on customer behavior, buying behavior, manufacturing processes, distribution, take-back systems, and manufacturing technologies, etc. Hence, bringing together the themes of the two conferences would provide the opportunity to share knowledge across more different research disciplines, possibly inspiring and creating new ideas for future research supporting this sustainable transformation. Hence, we believe that bringing together CARV and MCPC is a natural extension of the core themes in each conference series and a step towards covering all necessary aspects of enabling sustainable customization.

6 Conclusions This opening paper of the joint CARV/MCPC 2021 book of proceedings investigated trends in past and present CARV and MCPC conferences regarding research impact, authors, and key topics. The first part of the paper featured a bibliometric analysis, highlighting the most cited conferences, most cited papers, most cited authors, as well as most productive and recurring authors. The second part of the paper featured a sentiment analysis, highlighting the frequent and distinct words used in the conference proceedings of CARV and MCPC in comparison and across years, while an analysis

A Bibliometric and Sentiment Analysis of CARV and MCPC Conferences

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of keywords frequency indicated both the most frequent keywords to the CARV and MCPC communities respectively and also over time in regards to the most recent and earliest conferences. Thus, noteworthy insights building on the knowledge and impact of past CARV and MCPC conferences were set forward as being relevant to the CARV and MCPC communities and future conferences. Several limitations should be mentioned, primarily in regard to data availability. First and foremost, the presented bibliometric analyses were limited in scope due to Google Scholar being the only common citation database across conference proceedings. Consequently, network and research cluster analyses were unfortunately not possible to conduct. Moreover, only the four most recent CARV conferences and the three most recent MCPC conferences were analyzed, as the earliest conferences in each series feature only internal paper publications with no currently available citation records. Evidently, this limited the ability to analyze trends in the communities prior to the last decade.

References 1. Mc Cormack: ‘Made in europe’ – the future of european manufacturing?ssss Publications Office of the European Union, Luxembourg (2019) 2. Eurofond: The future of manufacturing in europe. Publications Office of the European Union, Luxembourg (2019) 3. Probst, L., Monfardini, E., Frideres, L., et al.: Advanced Manufacturing Mass Customisation. Publications Office of the European Union, Luxembourg (2020) 4. Christensen, B., Brunoe, T.D.: Product configuration in the ETO and capital goods industry: a literature review and challenges. In: Hankammer, S., Nielsen, K., Piller, F.T., Schuh, G., Wang, N. (eds.) Customization 4.0. SPBE, pp. 423–438. Springer, Cham (2018). https://doi. org/10.1007/978-3-319-77556-2_26 5. Machado, C.G., Winroth, M.P., da Silva, R., Dener, E.H.: Sustainable manufacturing in industry 4.0: an emerging research agenda. Int. J. Prod. Res. 58(5), 1462–1484 (2020) 6. Sinclair, S., Rockwell, G.: Voyant Tools (2021) 7. Schumacher, A., Erol, S., Sihn, W.: A Maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP 52, 161–166 (2016) 8. Qin, J., Liu, Y., Grosvenor, R.: A categorical framework of manufacturing for industry 4.0 and beyond. Procedia CIRP 52, 173–178 (2016) 9. Thames, L., Schaefer, D.: Software-defined cloud manufacturing for industry 4.0. Procedia CIRP 52, 12–17 (2016) 10. Yu, Y., Wang, X., Zhong, R.Y., et al.: E-commerce logistics in supply chain management: practice perspective. Procedia CIRP 52, 179–185 (2016) 11. Albers, A., Gladysz, B., Pinner, T., et al.: Procedure for defining the system of objectives in the initial phase of an industry 4.0 project focusing on intelligent quality control systems. Procedia CIRP 52, 262–267 (2016) 12. Jackson, K., Efthymiou, K., Borton, J.: Digital manufacturing and flexible assembly technologies for reconfigurable aerospace production systems. Procedia CIRP 52, 274–279 (2016) 13. Huxtable, J., Schaefer, D.: On servitization of the manufacturing industry in the UK. Procedia Cirp 52, 46–51 (2016) 14. ElMaraghy, H., AlGeddawy, T., Azab, A., et al.: Change in manufacturing–research and industrial challenges. In: Enabling Manufacturing Competitiveness and Economic Sustainability, pp. 2–9. Springer, Heidelberg (2012)

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15. Kousi, N., Gkournelos, C., Aivaliotis, S., et al.: Digital twin for adaptation of robots’ behavior in flexible robotic assembly lines. Procedia Manuf. 28, 121–126 (2019) 16. Lopes, I., Senra, P., Vilarinho, S., et al.: Requirements specification of a computerized maintenance management system–a case study. Procedia CIRP 52, 268–273 (2016) 17. Levesque, N., & Boeck, H.: Proximity marketing as an enabler of mass customization and personalization in a customer service experience. In: Managing complexity, pp. 405–420. Springer (2017) 18. Gembarski, P.C., Li, H., Lachmayer, R.: KBE-modeling techniques in standard CAD-systems: case study—autodesk inventor professional. In: Bellemare, J., Carrier, S., Nielsen, K., Piller, F.T. (eds.) Managing Complexity. SPBE, pp. 215–233. Springer, Cham (2017). https://doi. org/10.1007/978-3-319-29058-4_17 19. Boer, H.E.E.: Product, organizational, and performance effects of product modularity. In: Brunoe, T.D., Nielsen, K., Joergensen, K.A., Taps, S.B. (eds.) Proceedings of the 7th World Conference on Mass Customization, Personalization, and Co-Creation (MCPC 2014), Aalborg, Denmark, February 4th - 7th, 2014. LNPE, pp. 449–460. Springer, Cham (2014). https:// doi.org/10.1007/978-3-319-04271-8_38 20. Pourabdollahian, G., Taisch, M., Piller, F.: Is sustainable mass customization an oxymoron? an empirical study to analyze the environmental impacts of a MC business model. In: Brunoe, Thomas D., Nielsen, Kjeld, Joergensen, Kaj A., Taps, Stig B. (eds.) Proceedings of the 7th World Conference on Mass Customization, Personalization, and Co-Creation (MCPC 2014), Aalborg, Denmark, February 4th - 7th, 2014. LNPE, pp. 301–310. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-04271-8_26 21. Brunoe, T., Andersen, Ann-Louise., Nielsen, K.: Reconfigurable manufacturing systems in small and medium enterprises. In: Bellemare, Jocelyn, Carrier, Serge, Nielsen, Kjeld, Piller, Frank T. (eds.) Managing Complexity. SPBE, pp. 205–213. Springer, Cham (2017). https:// doi.org/10.1007/978-3-319-29058-4_16 22. Rayna, T., Striukova, L., Darlington, J.: Open innovation, co-creation and mass customisation: what role for 3D printing platforms? In: Brunoe, Thomas D., Nielsen, Kjeld, Joergensen, Kaj A., Taps, Stig B. (eds.) Proceedings of the 7th World Conference on Mass Customization, Personalization, and Co-Creation (MCPC 2014), Aalborg, Denmark, February 4th - 7th, 2014. LNPE, pp. 425–435. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-04271-8_36 23. Gembarski, P., Lachmayer, R.: A business typological framework for the management of product complexity. In: Bellemare, Jocelyn, Carrier, Serge, Nielsen, Kjeld, Piller, Frank T. (eds.) Managing Complexity. SPBE, pp. 235–247. Springer, Cham (2017). https://doi.org/10. 1007/978-3-319-29058-4_18 24. Taps, S.B., Ditlev, T., Nielsen, K.: Mass customization in SMEs: literature review and research directions. In: Bellemare, J., Carrier, S., Nielsen, K., Piller, F.T. (eds.) Managing Complexity. SPBE, pp. 195–203. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-29058-4_15 25. Lachmayer, R., Gembarski, P., Philipp Gottwald, R., Lippert, B.: The potential of product customization using technologies of additive manufacturing. In: Bellemare, Jocelyn, Carrier, Serge, Nielsen, Kjeld, Piller, Frank T. (eds.) Managing Complexity. SPBE, pp. 71–81. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-29058-4_6 26. Kolb, M., Blazek, P., Streichsbier, C.: Food customization: an analysis of product configurators in the food industry. In: Brunoe, T.D., Nielsen, K., Joergensen, K.A., Taps, S.B. (eds.) Proceedings of the 7th World Conference on Mass Customization, Personalization, and CoCreation (MCPC 2014), Aalborg, Denmark, February 4th - 7th, 2014. LNPE, pp. 229–239. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-04271-8_20

Changeable, Reconfigurable and Flexible Manufacturing

A Classification of Different Levels of Flexibility in an Automated Manufacturing System and Needed Competence Anders Nilsson(B)

, Fredrik Danielsson , Mattias Bennulf , and Bo Svensson University West, 461 86 Trollhättan, Sweden [email protected]

Abstract. Mass customization has become more attractive but requires a transformation towards more flexible solutions in contrast to dedicated manufacturing systems. Flexibility includes complex tasks such as the introduction of new products or new manufacturing processes as well as to efficiently handle daily balancing. The main challenge when it comes to flexibility in manufacturing is to be able to handle the technical aspects and still be competitive. In this article we consider the cost for flexibility to include two main things; (1) setup time, e.g., time for planning, design, programming and configuration, installation, ramp-up, scrapping of old equipment, preparation of facility, hardware installation, and (2) need of competence, inhouse knowledge, external competence, or external expert competence. This article presents an overview of available solutions and the level of flexibility and the level of competence that is needed for a reconfiguration one can expect out of a specific solution. Further, most of the existing solutions found do not consider or address the full problem of flexibility. However, we describe a possible future of industrial concept: Plug & Produce, which can address flexibility within manufacturing more completely and sustainably over time. Methods for configuration instead of programming are developed by University West. Keywords: Plug & Produce · Flexibility · Re-configurability · Automation · Competence

1 Introduction To be able to discuss flexibility concepts in detail it is too coarse to only consider a manufacturing system by itself. A manufacturing system must also be broken down into individual resources. Hence, flexibility on resource level is equally important as on system-level and product level. For example, the manufacturer of prefabricated wooden houses belongs to an industry that traditionally uses manual manufacturing methods that are performed by skilled craftsmen. These industries have relatively low production volumes and offer a high degree of customization. Humans are the most flexible resources in manufacturing making them impossible to replace by dedicated automation, traditionally used for mass production. Instead, new modular and flexible technology © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 27–34, 2022. https://doi.org/10.1007/978-3-030-90700-6_2

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are needed. Flexibility in automation tends to demand a high level of competence in computer modeling of reference system architectures and programming on a level that is hard to find in the industry. Thus, this paper proposes the use of methods for softwarebased configuration rather than time-consuming “bare bone” programming applied on a modular Plug & Produce concept. The flexibility of automation has drawn attention since the early 80’s both in research as well as in industry, starting from the Flexible Manufacturing System (FMS) approach. Flexibility can be discussed and analyzed in many ways depending on a chosen point of view, such as product flexibility, manufacturing flexibility, hardware flexibility, or enterprise flexibility. In this article, the focus is on flexibility from an automated manufacturing perspective limited by the borders of a manufacturing cell, knowledge, and competencies. A job is in parallel ongoing concerning networked manufacturing systems where diagnosability, controllability, cost-effectiveness, and performance measurement are analyzed [1].

Planning & Offline Engineering

Re-cycling Adding Scraping

Online Programming

New Requirement

Production & Maintenance

Ramp-up

Fig. 1. Six main activities during lifecycle of an automated manufacturing system

But what is flexibility when it comes to automation? Flexibility can be considered throughout the entire life cycle of a manufacturing system. To describe the lifecycle of an automated manufacturing system, six main activities have been identified that will affect flexibility (see Fig. 1). The level of flexibility can then be defined as; how efficient a full loop can be carried out in contrast to the following demands (flexibility aspects): • • • •

Time (total project time and downtime of production) Resources (money, manning, space, equipment) Knowledge (required internal knowledge) Competence (required external competence or external expert competence)

For those who are familiar with the concept of manufacturing system commissioning, minimized time and resources are nothing new or controversial and are usually discussed in the literature [2] [3]. However, knowledge and competencies are seldom discussed in the literature. In this article we consider knowledge to be internal and competence to be external from a manufacturer company perspective. Many solutions found in the literature expect limitless access to knowledge and competencies. However, from an industrial point of view, this is never the case, e.g., advanced flexible automation concepts might require extensive programming and modeling in new advanced languages and

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frameworks where the company is lacking knowledge or access to competencies. It is possible to argue that all automated manufacturing systems can handle a loopback (see Fig. 1). However, for most of the existing solutions, it is impossible to address all the flexibility aspects at the same time. The highest level of flexibility as identified in this article is to be able to handle the recycling of resources into new areas of manufacturing with minimal need of time and personal resources handled by using existing knowledge.

2 Automation Concepts in the Manufacturing Context Several concepts exist for automated discrete industrial manufacturing. This section presents some of the main concepts suitable for discrete automated manufacturing. Dedicated Manufacturing Systems (DMS) is dedicated to manufacturing one single product during the whole lifetime of the system. Multi operative machines that are customized and optimized for the product ensure high and energy-efficient production at a comparatively low cost per produced unit [4]. On the contrary, DMS cannot respond to any changes. Modifications will often cause losses of efficiency due to a disturbed balance of operations among the resources. If the request from the market exceeds the maximum production rate, a new investment of extra capacity is needed [5]. The software that is used to control a DMS is fixed and relatively easy to implement and maintain especially if a good fault detection system is implemented [6]. Flexible Manufacturing System (FMS) consisting of flexible and programmable devices as computer numerically controlled (CNC) machines, industrial robots, programmable logic controllers (PLC), and other programmable systems [5]. CNC and Industrial robots are single-action machines, generally designed, without knowledge of the end customer’s business. The machines need several tools, grippers, and pallets combined with tool-changing systems to handle different products [7]. The resources must fit the largest and most advanced product that possible will be produced during the calculated lifetime. The low production capacity is something that has engaged researchers since the introduction of FMS [8]. The programming language is often standardized and has user-friendly interfaces with good debugging properties. Even if this type of software is relatively easy to use, they need competent and educated staff to handle, a knowledge that most of the manufacturers have in-house. Education on these systems has been established for a long time. Reconfigurable Manufacturing System (RMS) is built upon modules that are replaceable and compatible in different combinations by using standardized interfaces, adaptable to different scenarios. RMS combines the high production rate of DMS and the flexibility of FMS [9]. RMS utilize customized multi-operating modules to ensure the production rate [5]. The application software must be reconfigurable according to the actual setup, normally achieved by selecting pre-programmed pieces of software. Controllers that have firmware that is build up on reconfigurable open architectures are proposed for introducing completely new hardware. High knowledge of programming and system architectures is needed. A knowledge that most of the manufacturers are lacking. Thus, special competence must normally be hired. Multi-Agent System (MAS) is a pure software concept and cannot directly be compared to DMS, FMS, and RMS that are complete concepts that include both hardware

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and software. MAS is a distributed system with separate agents that negotiates with each other and takes individual decisions and actions [10]. An agent is an independent piece of software that is running in parallel with other agents in a cloud environment. Agents can represent products, physical or logical resources as an automatic production planner. Agents can have goals to fulfill and skills to do actions, in the case of industrial applications often by utilizing physical resources [11]. MAS can solve complex tasks by dividing them into simpler tasks, distributed on several agents that interact [12]. Agents can react to changes and disruptions by continuously judging their surroundings [13]. Well-designed MAS can take proactive decisions and find paths around breakdowns or other disturbances [14]. Self-learning abilities are possible in MAS but hard to realize in a manufacturing context because of safety issues due to unpredictable behavior. The multiagent framework Java Agent Developing Framework (JADE) [15] is one of several multiagent platforms that will ease up the creation of MAS. MAS are a highly flexible concept but need a competence of high-level object-oriented and distributed programming on a high and specialized expert level of competence that is hard to find on the market [16]. Part-oriented Sequence of Operations (P-SOP) is a part routing description language for the sequence of operations for parts [17]. Parts are here pieces of a complete product. The description language will simplify and fastening the part routing programming when a new part or resource is introduced or changed [17]. The description language is easy to learn for a person without any programming skills. A graphical environment based on Microsoft Power-Point is used [18]. MAS code is automatically generated and is running on distributed PLCs. Configurable MAS (C-MAS) is a continuation of P-SOP and support configuration by digital tools instead of high-level object programming to hide the complexity of MAS, as the idea of P-SOP, but C-MAS controls the whole manufacturing cell not only part routing [11]. Only one agent code exists in C-MAS, the digital configuration describes the functionality and behavior of each agent. When a resource or product is added an agent will be instantiated and assigned to the corresponding agent digital configuration. A slight distinguish between agents has been done, product agents have goals, and resource agents have skills. Goals are supported by process plans that are recipes listing the skills required to fulfill the goal. The goals and process planes are intended to be managed by in-house knowledge available in the manufacturing company. No knowledge of the hardware is needed during this level of the digital configuration, only knowledge of the product is required. Development of graphical tools is ongoing.

3 The State of the Art Plug & Produce concerns in this article the optimum of flexibility and reconfigurability among the concepts of automated discrete manufacturing systems. Plug & Produce is an industrial derivation of plug-and-play that is common on personal computers [18]. However, Plug & Play is more passive, for example, a keyboard that requires human interaction for something to happen, while plug and produce are more active, and production starts automatically. Interchangeable process modules equipped with an interface for the mechanical, electrical, and other media make the process modules easy to plug in and out. Each process module contains a local controller for real-time functions and

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communication to its agent representative. Process modules are active and begin to act and make their own decisions as soon as they are plugged in [20]. Plug & Produce enables the rapid and cost-effective transformation of the factory for agile adaption to the actual production situation. The idea to have interchangeable product dedicated process modules will speed up the production rate like the principles of RMS. MAS has been proposed as a key enabler for Plug & Produce since MAS has proven to be a robust, agile, flexible, modular, and distributed control strategy [19], and C-MAS will shorten the engineering time and reduce the level of needed competence.

4 Classification of Automation Concepts

Plug & Produce flexibility

MAS

C-MAS

Reconfigurable flexibility

P-SOP

RMS Trend

Product flexibility

FMS

Static

DMS Inhouse knowledge

External competence

Ext. expert competence

Fig. 2. A classification of automation concepts in respect on flexibility (vertical axis) and need of competence to handle it (horizontal axis). Note that, the classification contrast with the intended use of the concept. I.e., DMS is classified with the assumption that it will not change over time while RMS is classified with the assumption that the hardware and software configuration will change during lifetime.

Flexibility is in this article classified as static, product, reconfigurable, and Plug & Produce flexibility depicted on the vertical axis (see Fig. 2): • Static, systems that are not changed during their lifetime. • Product flexibility, systems that can handle product variations within a family of similar products. • Reconfigurable flexibility, systems that are reformed by changing compatible dedicated units to adapt to the manufacturing of different products. • Plug & Produce flexibility, systems that are highly flexible reformed to fit new production situations on a minimum of time.

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Competence is classified as inhouse knowledge, external competence, and external expert competence depicted on the horizontal axis (see Fig. 2): • Inhouse knowledge, continuously available competencies at manufacturing companies. • External competence, competence that is needed periodically and hard for a manufacturing company to maintain in-house. • External expert competence is a competence that is challenging to find even globally, only a limited number of persons have that competence on the market. The trend is that modern flexibility concepts demand a higher level of expertise that is hard to find and that leads to hat companies tend to hold on to static or product flexible systems or avoid automated manufacturing in favor of manual production.

5 Discussion and Conclusion Manufacturing companies strive to be flexible enough to be able to deliver customized products at a low cost. A human worker is far more flexible than any machine. Manual manufacturing is, therefore, the most flexible approach of producing but is costly and often encumbered with quality issues and wear-out problems for the workers. Automated solutions that can meet this requirement of flexibility are crucial for the future of the manufacturing industry. Evolution on the development of flexible automated systems goes in the wrong direction in the sense of needed competence. DMS and FMS belong to the traditional concepts of automated discrete manufacturing. Educating programs for these concepts are well established and most of the automated manufacturing companies have the knowledge to handle these systems. It is convenient for companies to stick to that established technology even if they strive for higher flexibility and efficiency than are possible whit these concepts. Manual manufacturing tends to replace automated manufacturing when flexibility is needed. Producers of premanufactured wooden houses that normally have manual manufacturing have hard to find automated solutions that fit their highly customized and low volume production. The highest level of flexibility as identified in this article is to be able to handle the recycling of resources into new areas of manufacturing to minimize the cost of the investment, save environment due to less scrapping, to a minimum of setting times, and achieve mostly by in-house knowledge. Recyclable process modules in Plug & Produce systems can meet up to those requirements if the Plug & Produce is supported by a flexible control system that is easy to handle and reconfigure. MAS is proven to be a robust, agile, flexible, modular, and distributed control strategy. Properties that can be achieved in a control system if a major effort is addressed to a good system reference architecture and structured high-level object programming, done by a skilled programmer with expert competence. Competence is hard to find on the market and is hard to finance due to long and costly engineering time. P-SOP and C-MAS are concepts developed at University West to break the undesirable direction of the evolution in sense of needed competence related to increased flexibility by introducing digital configuration instead of programming.

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The contribution of this article is to highlight the fact that manufacturing companies need an automated system that is flexible enough to adapt to customized production and at the same time is easy enough to handle by in-house knowledge. Flexible systems tend to be more complex and harder to design that needs expert competence and is too costly to implement due to long engineering time.

6 Future Work The digital tool for configurations of C-MAS is mainly text-based. The goal for the near future is to have a seamless flow of data from the computer-aided design (CAD) that contains the 3D model of the product down to the C-MAS and Plug & Produce without re-entering any data. In the 3D model of the product imported to software there the user can point out locations for joining, gripping, mounting, and processing, then connect the data to goals and add requirements like speed, temperature, and color. Data that goes seamlessly direct to C-MAS.

References 1. Milisavljevic-Syed, J., Commuri, S., Allen, J.K., Mistree, F.: A method for the concurrent design and analysis of networked manufacturing systems. Eng. Optim. 51(4), 699–717 (2019) 2. Liu, Z., Suchold, N., Diedrich, C.: Economic application of virtual commissioning to mechatronic production systems. Prod. Eng. Res. Dev. 1, 371–379 (2007) 3. Ballard, G., Koskela, L., Howell, G., Zabelle, T.: Production system design in construction. In: 9th annual conference of the Int’l. Group for Lean Construction, Singapore (2001) 4. Zhang, G., Liu, R., Gong, L., Huang, Q.: An analytical comparison on cost and performance among DMS,AMS, FMS and RMS. In: Reconfigurable Manufacturing Systems and Transformable Factories, pp. 659–673. Springer, Heidelberg (2006) 5. Koren, Y.: Chapter 3 General RMS characteristics. comparison with dedicated and flexible systems. In: Reconfigurable Manufacturing Systems and Transformable Factories, pp. 27–45 (2006) 6. Cong, M., Zhang, J., Qian, W.: Fault diagnosis system for automated assembly line. In: IEEE International Conference on Intelligent Processing Systems, Beijing (1997) 7. ElMaraghy, H.A.: Flexible and reconfigurable manufacturing systems paradigm. Int. J. Flex. Manuf. Syst. 17(4), 261–276 (2006) 8. Suri, R.: New techniques for modelling and control of flexible automated manufacturing systems. IFAC Proc. Vol. 14(2), 20127–20133 (1981) 9. Bi, Z.M., Lang, S.Y., Shen, W., Wang, L.: Reconfigurable manufacturing systems: the state of the art. Int. J. Prod. Res. 46(4), 967–992 (2008) 10. Wooldrige, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995) 11. Bennulf, M., Fredrik, D., Bo, S., Bengt, L.: Goal-oriented process plan in a multi-agent system for Plug & Produce. IEEE Trans. Ind. Inf. (2020) 12. Leitão, P.: Agent-based distributed manufacturing control: a state-of-the-art survey. Eng. Appl. Artif. Intell. 22(7), 979–991 (2009) 13. Ribeiro, L., Hochwallner, M.: On the design complexity of cyberphysical production systems. Complexity 2018, 13 (2018)

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14. Monostori, L., Vancza, J., Kumara, S.R.T.: Agent-based systems for manufacturing. CIRP Ann. 55(2), 697–720 (2006) 15. Bellifemine, F., Poggi, A., Rimassa, G.: JADE a FIPA2000 compliant agent development environment. In: Proceedings of the Fifth International Conference on Autonomous Agents, vol. 01, pp. 216–217 (2001) 16. Farid, A., Ribeiro, L.: An Axiomatic design of a multiagent reconfigurable mechatronic system architecture. IEEE Trans. Ind. Inf. 11(5), 1142–1155 (2015) 17. Svensson, B., Danielsson, F.: P-SOP – a multi-agent based control approach for flexible and robust manufacturing. Rob. Comput.-Integr. Manuf. 36, 109–118 (2015) 18. Svensson, A.: Automatic generation of control codefor flexible automation. Diva, Linköping (2012) 19. Arai, T., Aiyama, Y., Maeda, Y., Sugi, M., Ota, J.: Agile assembly system by “plug and produce.” CIRP Ann. Manuf. Technol. 49(1), 1–4 (2000) 20. Antzoulatos, N., Castro, E., Scrimieri, D., Ratchev, S.: A multi-agent architecture for plug and produce on an industrial assembly platform. Prod. Eng. Res. Devel. 8(6), 773–781 (2014). https://doi.org/10.1007/s11740-014-0571-x 21. Bennulf, M., Danielsson, F., Svensson, B.: Identification of resources and parts in a Plug and Produce system. Procedia Manuf. 38, 858–865 (2019)

Manufacturing Genome: A Foundation for Symbiotic, Highly Iterative Product and Production Adaptations Patrizia Gartner2(B) , Alexander Jacob1 , Haluk Akay1 , Johannes Löffler1 , Jack Gammack2 , Gisela Lanza1 , and Sang-Gook Kim2 1 Institute of Production Science, Karlsruhe Institute of Technology (KIT),

Kaiserstr. 12, 76131 Karlsruhe, Germany 2 Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT),

77 Massachusetts Ave, Cambridge, MA 02139, USA [email protected] Abstract. Increasingly shortening product life cycles, regional market challenges and unforeseeable global events require highly iterative product and production adaptions. For faster adaptation, it is necessary to have a systematic understanding of the relationships between product design and production planning. A unified model and data structure are fundamental. Basic data must be extracted from both domains and integrated for consistent product-production co-design. For this purpose, we use a biological analogy, the genome-proteome phenomenon, to model the interdependencies of product (customer needs, functional requirements, design parameters) and production (technologies capabilities, machine information, process chain alternatives). From the genome, which represents the totality of available data of product and production, we contextualize the proteome, which represents an instance of a concrete product design and the corresponding production configuration. Thereby, one gene represents one incremental information set consisting of all above mentioned product and production information for a specific product function. For each of the mentioned information domains (e.g. product requirements) within a gene, a methodology exists (e.g. NLP) to model the interlinkage to the adjacent information domain (e.g. product function). Utilizing the interdependencies and heredity of product design and production planning enables quick analysis of adaptation-induced impact which will provide enhanced competitiveness in a volatile world. Keywords: Product-production co-evolution · Bioinspiration · Changeability

1 Introduction and Problem Statement The design of products and production systems is becoming increasingly complex due to frequently changing customer requirements and shorter product life cycles [30]. Additionally, knowledge about the development and production of products is largely based on experts’ experience. This makes it difficult to comprehend interrelationships and The original version of this chapter was revised: The author affiliation has been amended. The correction to this chapter is available at https://doi.org/10.1007/978-3-030-90700-6_120 © Springer Nature Switzerland AG 2022, corrected publication 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 35–46, 2022. https://doi.org/10.1007/978-3-030-90700-6_3

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dependencies. At the same time, digitalization provides the opportunity to use collected data to perpetuate past successes and avoid repeating past failures. Consideration and evaluation of the full evolutionary history of products including the symbiotic relationship between products and production systems need to be understood to extract a structure of successful products. Various data types and data interdependencies challenge a full and structural integration of all product-production domains. A multidirectional mapping of change impact along the product-development process has not been sufficiently implemented. Innovations in engineering have been widely impacted through a deeper look into nature. The term biologicalization is defined as “[…] the use and integration of biological and bioinspired principles, materials, functions, structures and resources for intelligent and sustainable manufacturing technologies and systems with the aim of achieving their full potential” [6]. A detailed overview of existing productand production-related bioinspired approaches is given in Sect. 2. Bioinspirated mechanisms enable adaptations in constantly changing environments. It is to be assessed how the development towards symbiotic co-evolution and changeability in production can be supported by analogy building with biological processes. This contribution presents a novel bioinspired framework establishing the missing link to a full integration of all product-production domains. The framework allows reacting in a more targeted and quicker manner to external influences and disturbances.

2 State of the Art: Bioinspired Integration of Product Design and Production Planning Recent advances can be assigned to three stages of the product development process depending on their bioinspiration: conceptual design, product design, and production planning. Approaches based on the idea of DNA are the most common ones in the conceptual design stage, focussing on gathering knowledge and build up databank-architectures with so called product-genes or chromosomes [7, 8]. Some approaches are extended with methods of formalized and structured language, to describe the function’s structure precisely [2] or early-stage decision making [32]. Hence, predictive applications in manufacturing in combination with support-vector-machines [7, 34] are applied. In the product domain, crossover-operators are used on design feature catalogues to develop novel design solutions iteratively [33]. Other approaches like the embryogenetic growth theory adapt the ability of embryonic cells to grow into many different shapes and functions [15]. In production planning, the variety of considered analogies deals with holonic manufacturing systems and ant-colony approaches utilized in manufacturing scheduling and layout optimization [13, 20, 21], as well as part routing [25] and attraction-based algorithms to improve a production system’s resilience [11]. Further analogies to the neural- or immune-system also capture the ability of task and resource planning [29], with additional memory and learning capabilities [1]. A general approach compares manufacturing systems with biological metabolisms [9]. Also gene-based concepts exist and are used with genetic algorithms for machine-part-incident-matrices [18]. Overarching concepts considering two of the three stages deal with a DNA-based manufacturing system utilizing cellular, adaptive structures to implement capabilities of self-growth [17],

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Fig. 1. Literature review for bioinspired frameworks and clustering into the three stages of conceptual design (CD), product design (PD) and production planning (PP) with their respective interfaces.

an automatic distribution and integration by joining information into products [31], combined with intelligent capabilities and heredity to pass on lifecycle-information [23] or planing production sequences using “gentelligent” components [10]. Further, the usage of evolutionary history and product families enables developing platforms for hybridmass-manufacturing [22] or predicting novel parts or machines [5]. As indicated in Fig. 1 none of the frameworks integrates all three phases and is therefore unable to consider the full potential. Nevertheless, the bioinspired concept considering DNA-related mechanics could be found in all three domains, making an integrated solution probable. The capabilities cover knowledge structuring and evolutionary mechanisms for crossgenerational knowledge transfer. Optimization algorithms, adaptive capabilities, and the potential to conduct the manufacturing processes of cells are also implemented. On that base an integrated genome-based framework is developed, that enables mapping the whole product-production process. It shall lead to a clear understanding of its interdependencies and shall facilitate optimization as well as the development of new product variants with associated next-generation production processes. The framework further supports the product development process by conceptualizing the development of databank architectures considering multiple domains with various datatypes and connecting all domains and their possible ramifications to the respective product function.

3 Manufacturing Genome Framework The integrated product-production framework is based on the analogy to genomes. In nature, the genome holds the entire information of a system. The same applies here: The so-called Manufacturing Genome represents the totality of product gene sequences in which a single gene sequence assimilates information of the six main domains of product

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design and production planning. These are customer needs, functional requirements, design parameters, technologies, possible technology chains, and the choice of machines (Fig. 2).

Fig. 2. Manufacturing Genome framework by which production planning domains may be modeled according to function. FRs = functional requirements, DPs = design parameters, NLP = natural language processing, ML = machine learning, UML = unified modeling language.

Customer needs are derived from the product’s end-users and are attributes which the design will need to satisfy [24]. From Axiomatic Design [28] we define the functional requirements as the minimum set of independent requirements that a design must fulfill (what). Design Parameters are chosen by the designer and define the physical domain (how). A production technology is based on a unique physical principle whereas technology chains represent a connected sequence of individual production technologies [27]. A machine is the individual realization of a technology. The sum of all product genes, or also called the Manufacturing Genome, constitutes the totality of information and is therefore a database for all valid variations. The lower derivation of the matrix contains explicit product variants that are instantiations of the overall Manufacturing Genome. These occur as a combination of the product genes whereas a product is modelled by a unique combination of product genes. The right derivation shows potentially new, optimized product variants, consisting of unaltered genes as well as new mutated genes. 3.1 Modelling the Horizontal Interlinkage (Gene Sequence) For modelling the horizontal axis and interconnections between the domains, the data type and storage must be presented in a way that allows easy read and write capabilities for the application of machine learning algorithms. The customer needs domain encompasses the needs of a number of stakeholders including the end-user of the product to be produced. Researchers may document these needs through interviews and field observations resulting in unstructured textual documentation. From such documentation,

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functional requirements (FRs) must be extracted. For cases with well-defined needs, a human subject-matter expert (SME) together with a designer may define FRs manually. However, for larger and complex systems, human production planners benefit from working together with AI-based models to utilize both human expertise, design thinking, and data to enhance early-stage design [19]. Natural language processing (NLP) models may be applied to decompose design documentation and automatically extract hierarchical structures of FRs [4] from text. When data includes 2D and 3D drawings, AI-based image processing may be applied to complement the extraction of FRs. After sets of FRs are defined, the functional domain may be mapped to the physical domain, and design parameters (DPs) which best fulfill the set of FRs are selected. As sets of FRs and DPs scale in size, evaluating and identifying the best set of DPs can be assisted with AI-based representations of designed systems to quantify metrics of good FR-DP systems [3]. The set of parameters within a design module is then compared to the set of parameters presented by technology-capabilities (Tech.). Restrictions and sequence requirements are structured in a technology-relations-matrix. A gap-analysis for valid assignments [16] is executed and parameters with potential for iterative optimization are identified. Technology-chain (Chain) generation considers the relation generated in the design-structure- and technology-relation-matrices as well as selected technologies. A technology sequence for the specific DPs and Technologies is generated. Consecutively, machine selection matches machine capabilities with technology requirements and singles out the ideal machine for each technology (Mach.). The advantage of the framework is that all possible product design and manufacturing options are evaluated. Moreover, the methodologies applied allow a multi-directional mapping for adaptions in each of the six domains. This allows the system to take into consideration a change in any domain for adjustments to be made in the remaining domains. 3.2 Modelling the Vertical Interlinkage (Interdependencies Between Genes) Even if a manufacturing “gene” is well-defined as the sequence of needs, FRs, DPs, technology, chain, and machines, which address and satisfy a functional requirement of the product, at each domain in the product/production flow it is crucial to select a set of design or production elements which are not coupled and together are the optimal path in terms of maximizing metrics of downstream production (cost, rate, quality, flexibility) [14]. Despite these elements not existing along the time-domain (e.g.

Fig. 3. Recurrent Neural Network-like approach to model sequence interdependencies at each domain of the design/production process.

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one DP does not occur “after” or “before” another DP), human experts tend to select such elements individually or in small groups [26] such that each domain is defined sequentially. The selection of each element is therefore dictated by the sequence of elements in the previous domain, and any elements which may have already been defined in the current domain. This work proposes a Recurrent Neural Network (RNN) model for computing the relationships between elements in the design/production process and passing pertinent product-genome information along to a subsequent domain. By using an RNN architecture to model production sequences, as depicted in Fig. 3, long-term interdependencies can be captured and the model may be trained to compute metrics of good design and production such as functional independence, cost, rate, quality, and flexibility. In the case of missing elements, such as a DP not identified or a machine not yet selected, the RNN model may be used to probabilistically suggest elements which maximize relevant metrics while satisfying the function dictated by their respective product-gene. One limitation of the RNN-based approach is the “vanishing gradient” problem where, for longer sequences such as those across all domains of design and production, the gradient descent method of training becomes infeasible to implement. In such cases, Long Short-Term Memory (LSTM) or Transformer architectures may be explored as appropriate modeling alternatives to capture long-term dependencies of functional requirements throughout the manufacturing genome. A critical resource needed to train such an RNN model is relevant data. This is especially challenging for this task given that not only the data sources but also the data format varies from step to step in the design and production cycle. For this reason, a specific data representation and source of ground truth is required for each domain. This information is detailed in Table 1. Table 1. Data sources across all production planning domains Data source

Data format

Data representation

Ground truth (Validation)

Customer Needs

User interview Audio, text transcripts, use case observation, research notes

Distributed language vector embeddings

Past stakeholder interview transcripts, crowd-sourced labeling

FRs

Design documentation: notes, slides, communications in design organization

Distributed language vector embeddings, fine-tuned pretrained domain-specific embeddings

Human Subject Matter Expert (SME), surveys of designers

Text, images

(continued)

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Table 1. (continued) Data source

Data format

Data representation

Ground truth (Validation)

DPs

Design Text, images, documentation, tabulated numbers process planning notes

Combination of language vectors and quantitative representation

Human Subject Matter Expert (SME), surveys of designers

Technology

Experiments, averages of machine data, expert knowledge, technology databases, CAM

Analytical or stochastic or ML-based models, parameter databases, hybrid text

Data sets (e.g. Excel, .csv)

Field data of several machines of the same technology

Chain

Expert knowledge, production system simulations

Object oriented models

Object instantiation, data sets (e.g. Excel, .csv)

Field data of the respective production systems

Machines

Data sheets and handbooks, experiments, machine databases, CAM

Analytical or stochastic or ML-based models, parameter database, hybrid text

Data sets (e.g. Excel, .csv)

Machine specific field data

3.3 Mutations/Instances vs. Global sequencing It is useful to find similarities in known designs in order to quickly attain potential new solutions to requirements that have been previously seen. In our framework, when two product-genes have an identical module in one domain, they overlap. This overlap allows the genes to crossover and exchange the genetic material. For example, when identical FRs are satisfied by unique DPs, as shown in Fig. 4, the genes may crossover to create a new mutated gene that has the information from one gene to the left of the FR, and information from the other gene to the right of the FR. Similarly, when identical DPs are produced by unique technologies, the genes may crossover. Performing this crossover for each gene and each domain, we can enumerate all of the possible permutations that arise from the overlapping of our existing product-production knowledge. As engineering documentation is not standardized, there may be instances where two sets of text or CAD files may be describing the same or very similar need, FR or DP, but the extracted module parameters may differ slightly. A semantic similarity metric to determine which needs, FRs and DPs, are sufficiently similar to other modules within the same domain may be used to discover very similar or identical modules that could serve as candidates for overlapping genes [12]. Similarly, for the production design

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Fig. 4. Candidate gene mutation permutations arising from overlapping of similar modules between product-genes.

domains, similarity metrics comparing the capabilities of technologies, machines, and chains may be used [16].

4 Brief Case Study By identifying the totality of relevant information and solutions for each domain, we are able to build up the Manufacturing Genome as a database for joint implants (Table 2). With the instantiation considering the individual functions this becomes product-specific. For an implant, the general consideration is between accuracy of fit and enabling new bone formation. This requires a very precise adaptation to the bone-bed and weight and size of the patient (needs). Therefore, individualization in medicine can provide superior solutions for the patient. The key functions can be divided into medical-related and mechanical ones. For a reduction of complexity in the following example, only the tibial component is considered. It can be further broken down into the modules tibial-stem and tibial-tray. To each of these two modules a set of general and module-specific parameters, e.g. stem length or pouch depth, can be assigned. The last two represent geometric parameters that should be individualized as they greatly impact the functionality via ingrowth rate and load transmission. Therefore, they are DPs of their respective product gene further assessed in optimization. The most feasible technology to satisfy all design modules and fulfill the needs of highly individualized, small batch production requirements are a mix of conventional manufacturing and additive manufacturing to enable the individualized aspect. A software technology tool then considers relations between applicable technologies (e.g., CNC, grinding, milling, LPBF), module relations (e.g., tray and stem) as well as a pairwise comparison of parameter values (e.g., yield strength and tensile strength) presented in the respective databases. Technology chains are iteratively generated for optimizing the parameter “yield strength” by incrementally adapting the respective module or technology. Machines are then selected depending on factors such as “cost per unit”, “time”, and “technology readiness level”. In the technology domain, additive manufacturing serves as a technology which may be implemented to fulfil the fabrication of all the

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Table 2. Case study: manufacturing genome framework for the knee joint Needs

FR

DP

FR1: implant must be securely connected to the tibia

DP1: stem to connect bone to tibial implant

Need 1: artificial knee must feel like healthy human knee

FR 2: implant must interface with other implant components

DP2: “pouches” to connect implant to another implant component?)

Need 2: artificial knee must withstand normal operating conditions

FR 3: implant’s compliance must mimic that of a biological joint

DP3: flexibility of implant (tensile strength)

FR 4: implant must withstand at least the same loads of a biological joint

DP 4: yield strength of implant

Tech

Chains

Mach

Tech. 1: hybrid Additive Manufacturing (satisfy DP1-DP4)

casting - LPBF - CNC vibratory grinding -washing blasting - HT

Mach.1 (LPBF) EOS M-290

forging - LPBF - CNC vibratory grinding -washing blasting - HT

Mach.2 (LPBF)EOS M-400

The second hybrid sequence is 0.73% cheaper, 0.21% quicker and therefore more suitable. Possible machines for this sequence are known with a project partner. Machines are selected depending on the predicted average part-volume and resulting machine hour-rates. The LPBF process with EOS M-290 is for the use-case 54% cheaper (referred to machine-hour rate), and 30% less time efficient than the competitor (referred to build time, depending on build parameters).

physical components, features, and parameters defined by the design parameters. The use of the same technology across each function represents the relationship between manufacturing genes. Enumerating the permutations of candidate new products, the genes may be evaluated based on product design and production metrics. The cost of producing the DPs through solely conventional or additive manufacturing while effectively fulfilling the FR of individualization proves to be much larger than using a hybrid manufacturing method due to the large expense of individualizing conventional manufacturing and the long lead time and expense of additive manufacturing large components. This detailed evaluation and visualization of interdependencies and optimization potentials is offered by the framework. In future work, new optimized product variants shall be generated on that base.

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5 Conclusion and Future Work A framework for holistically modelling function driven production planning from earlystage conceptual design to end manufacturing based on the concept of a Manufacturing Genome is presented. The framework is intended to be deployed to “sequence” the Manufacturing Genomes of a wide variety of product domains such that a library of product genes encoding every design/production stage of various commonly addressed functions is available to industry. Massive datasets of information, sampled at high frequencies, are available due to Industry 4.0 and smart manufacturing integration of existing production systems. By sequencing a Manufacturing Genome that structures this data according to function, a true digital twin may be created to model end-to-end production systems and serve for process optimization and new product variant experimental purposes. Such a digital twin can be constantly updated with real-time data to provide a highfidelity model. Furthermore, it may be used to actively train and fine-tune the neural networks implemented to model production sequences for maximizing accuracy and performance. Design parameters, technologies, chains, and machines may be grouped into cells based on function and “libraries” of optimized production sub-processes, avoiding “re-inventing the wheel” by perpetuating past successes in production engineering when manufacturing new products. Futher it holds the potential by “mutating genes” to explore new production architectures. With this unified framework, the effects of design and manufacturing decisions made at any stage of production planning may be modeled and evaluated. Acknowledgements. The authors gratefully acknowledge funding from “MeSATech” as part of the “ProMed” project: production in medical technology (funding reference number: 02P18C135) and supervised by the Project Management Agency Karlsruhe (PTKA).

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Advanced Reconfigurable Machine Tools for a New Manufacturing Business Model Alessandro Arturo Bruzzone(B) DIME Università di Genova, via Opera Pia 15, 16145 Genova, Italy [email protected]

Abstract. A new Reconfigurable Machine Tools (RMT) based on an innovative modular and scalable axis driver is presented. The elements and the characteristics of the RMT are analysed and discussed. A comparison between the conventional and the new RMT by using the Entity-Relationship model is reported. The features of the new RMT enable a new manufacturing organization based on manufacturing capacity sharing that increases environmental sustainability. Keywords: Reconfigurable Machine Tools · Reconfigurable Manufacturing Systems · Supply chain · Sustainability

1 Introduction Industrial revolutions have deep impact on social organization as well as on the growth and welfare of humankind. Nowadays manufacturing relies on global supply chains where the focus is on the efficient exploitation of the production factors [1]. The supply chain model, emerged from the optimisation of the manufacturing resources, include several elements: materials, supplier, manufacturer, production capacity, warehouse, shipping, end user. Although supply chains provide manufacturing cost reduction, they show several drawbacks when the market demand varies suddenly or when geoeconomics changes occur [2]. As a matter of fact, the supply chains transfer the products from a network of processing companies to the end user while the manufacturing capacity of the processing companies does not move. In this paper an innovative scalable Reconfigurable Machine Tool (RMT) that can be easily relocated within the same job shop, as well as within the same industrial district or, worldwide, is presented. The reconfigurability characteristics of the new machine tools system, developed by the startup Profacere®, a spinoff acknowledged by the Universities of Genoa and Naples, Federico II, are analysed and discussed. Specifically, the paper, starting from an original classification of reconfigurability levels, introduces the main peculiarities of the new RMT, its modular structure, and compares the new RMT to the conventional machine tools with the support of Entity Relationship (ER) models [3, 4]. The possibility to use the new RMT features for sharing manufacturing capacity and reshoring manufacture is finally considered.

© Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 47–54, 2022. https://doi.org/10.1007/978-3-030-90700-6_4

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2 Novel Classification of Reconfigurability in Manufacturing There are several levels of reconfigurability in the machining systems. Starting from low level to high level, reconfigurability can assume different features. At low level, the possibility to change functionality by installing devices such as dividing tables or boring heads on a conventional machine tool extends the processing capability of the machine tool. This kind of reconfigurability is nowadays considered normal; it is enabled thanks to the standardization of specific components such as the tool holder. At middle level, reconfiguration requires to change some fundamental elements of the machine tool by considering the structure at the basis of the machine tool design. In general, a machine tool contains the following elements [5]: • a device that supplies energy, by virtue of which a relative coupled motion is obtained between the tool used to provide the process and the workpiece; • a device for fixing the workpiece; • a device for conveniently fixing and orientating a tool; • a device for controlling the three above mentioned elements; • a device for operating the tool according to the used transformation process. All the devices in the machine tool are controlled according to kinematic schemes that are specific to the executed operation, e.g., turning, milling, drilling etc. Two principal kinematic chains, the tool, and the workpiece chains, are coordinated to follow the tool path necessary for the shape generation. Accordingly, several proposals have been made [6–8]. At higher level, the reconfiguration is performed assuming a system view. The reconfiguration starts from conventional machining centres that are considered as interacting elements thanks to a material handling subsystem [9, 10]. This approach, the Reconfigurable Manufacturing System, RMS, essentially scales down to a specific products family the Flexible Manufacturing System (FMS) introduced in the 1970 [11]. Indeed, the RMS incorporates the Group Technology and the cellular manufacturing concepts; it extends from a static to a dynamic time frame the controlling scheme to face the change of the market demand for products belonging to the same family. The reconfigurability level influences the time required to reconfigure the machining system; it can range from some minutes for low level reconfigurability to weeks for high level reconfigurable systems (RMS) [12, 13]. Nowadays Industry 4.0 shifts the focus on the manufacturing arena from the hardware process to the information related to the transformation process to achieve better performance in the manufacturing industry. Unfortunately, although information could be a valuable resource for a prompt and sound process control, information alone cannot change the physics of the processing technology unless the technology can accommodate the reconfiguration of the process at the hardware level. The link between information and physical processes requires a new machine tool design that extends the plug and produce approach [13, 14] to the hardware machining resources. The availability of efficient plug and produce reconfigurable machine tools could enable a quick and useful reconfiguration ranging from low level to high level reconfigurability.

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3 From Conventional to New Reconfigurable Machine Tools 3.1 Conventional Machine Tools The analysis of the machine tools characteristics can benefit by a formal description based on the Entity-Relationship (ER) model [3, 4]. Figure 1 shows a simplified ER scheme of conventional metal cutting machine tools processing a workpiece. Machining is carried out on a workpiece through a machine tool (Machine). The entities, in rectangular box, are things, physical or logical object, that have independent existence and are uniquely identified; a relationship captures how entities are related. The conventional machine tool is characterized by its kinematics that provides the action for cutting (rotational or linear) and feeding (continuous or intermittent) to actuate the coupled motion between the tool and the workpiece. The ER scheme is enriched by information concerning the cardinality relationships, e.g., the Manufacturing Process to obtain the Processed Item can require several Conventional Machine Tools. The tool is a cutting tool or in general a device, e.g., a boring head. The conventional machine tools permit only low level reconfigurability; for example, a dividing table changes the kinematics of the machine tool and extends the operations potential.

Fig. 1. ER diagram for conventional machine tools.

3.2 New Reconfigurable Machine Tool The new Reconfigurable Machine Tool [5] is based on the exchange of functions between the lead screw and nut kinematic mechanism generally used to drive linear axis. Instead of giving the nut the function of converting the lead screw rotation into a linear movement, this function is assigned to the screw (Fig. 2).

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Fig. 2. New linear driving mechanism: a) rack and screw, b) actual screw detail.

Fig. 3. CAD render of a RMT line with upper and lower cross-tables for turning.

The new linear driving mechanism overcomes the limit on the axis length typical of lead screw and nut mechanism; the racks, hosted on the bed modules, have a length multiple of the screw pitch. This feature provides the way to modularly assemble beds line [15, 16] where the cross-tables, holding the workpiece and the processing devices, can be driven thanks to the screw-racks geometry. Figure 3 shows a CAD render of a RMT line with three beds each one with 1000 mm length and racks-screw pitch 10 mm; the line hosts cross-tables on the upper and lower racks to carry out turning operations. The upper cross-table drives the cutting tool; the lower cross-tables provide the cutting movement by holding and rotating the workpiece; feed can be given by the upper and/or lower cross-tables. The single bed module conforms to the plant dimension of the EUR 2 pallet standard (1200 mm × 1000 mm); this feature eases shipping, by intermodal containers, and handling, by pallet trucks. Additionally, the bed modules integrate four pipelines to deliver processing fluids. Figure 4 shows the elements of the Profacere® RMT system: beds and cross-tables. The basic functions required to machine a workpiece identify the type of processing and permit to map each function into a physical device, i.e., a set of specific cross-tables whose coordination implements the required manufacturing operation. The ER diagram of the new RMT is shown in Fig. 5. The main differences between the RMT ER and the Conventional Machine Tool ER are the possibility of 1) building a line by assembling bed modules to change the working volume along the main axis and, 2) installing different cross-tables characterised by specific kinematics to carry out the required manufacturing operation through the coordination of two or more cross-tables.

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Fig. 4. Elements making the new RMT system.

Fig. 5. ER diagram for the generic new RMT.

The standardised dimensions of the bed modules and a new liquid-based positioning device, shown Fig. 6, and aligning procedure, claimed in [16], permit a rapid extension of the beds line providing working volume scalability. The reconfiguration to change the processing technologies relies on the standardized interface, the racks-screw mechanism, that decouples the structural function of the bed from the kinematics of the processing devices which in the conventional machine tools are physically integrated. Cross-tables have a standardised driving frame subsystem that permits to move along the bed module thanks to a couple of screws, coupled with the racks’ helicoidal grooves, driven by two motors (Fig. 7). The components necessary to implement specific operation functions are installed on the cross-table driving frame subsystem. Several cross-tables sets have already been designed for milling, turning, 3D FDM printing, welding and filament winding.

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Fig. 6. Device for positioning and installing modular beds [18].

Fig. 7. Cross-table basic driving frame subsystem.

4 Capacity Sharing by RMT for a New Manufacturing Model Today, products manufacturing relies on global supply chains; products are built thanks to a worldwide network of suppliers [1]. This approach although providing several advantages such as reduced overhead cost and higher cost efficiency, must rely on a logistic system whose resilience and environmental impact are often underrated [17]. The recent pandemic crisis pointed out the weakness of the supply chain systems based on globalization and the negative effect on the manufacturing companies [2]. Within the supply chain the manufacturer simultaneously is the supplier and user of products. The manufacturing capacity necessary to carry out the transformation is provided by the suppliers’ network; in this way industries reduce investments in manufacturing capacity and the related fixed asset. Capacity is indeed a fundamental factor for every manufacturing process since it permits to satisfy the market demand. The possibility to limit investment in manufacturing capacity is an advantage when the market demand decreases, and the company profitability could severely fall due to overcapacity [18]. These vantages together with a worldwide suppliers’ network, characterized by differential costs of the production factors, and an efficient logistic system determined the success of the supply chain business model. Usually, the evaluation of supply chain neglects the effects of the externalities affecting sustainability: the environmental and social costs. Environmental costs depend on activities that increase entropy [19]; human capital and the associated activities have

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a low direct effect on environment costs. Technological knowledge can drive sustainability by expanding the Production-Possibility Function (PPF) that sets the maximum quantities of outputs, with fixed resources, for a given technological progress [20]. However, since in supply chain the manufacturing capacity is globally distributed, the products must be transported along the supply network. Transport impacts seriously on the environment: globally, in 2015 according to the International Energy Agency 64.7% of the petroleum consumed worldwide was used by the transportation sector [21]. The balance between transportation and production capacity costs is therefore fundamental for sustainability. The modular structure of the new RMTs permits to configure a specific manufacturing function by combining cross-tables on the beds line. The utilization of the production capacity increases with reconfigurability: while conventional machine tools provide only a single typology of operations, reconfigurability supports a larger set of operations through the reuse of the same elements. Consequently, reconfigurability reduces the investments and the fixed costs necessary to get the manufacturing capacity. The transportation of the RMT elements is enabled by the Profacere® RMT positioning device [16] and the standardized pallet size; this feature consents to move beds and cross-tables easily and rapidly. The RMT elements can be shared within the same shop floor, or within two or more shop floors in the same plant, or within the shop floors of companies in the same industrial district, companies. The proposed RMT enables in this way a new manufacturing model based on capacity resource sharing; resource sharing, when implemented locally in the industrial districts, permits statistically to increase the utilization of the manufacturing capacity [22, 23].

5 Conclusion Mass customization, sustainability and social welfare demands a strong coordination effort to the manufacturing industry. The new RMT is a promising system, focused on hardware, to face sustainability issues and increase manufacturing capacity utilization. A global manufacturing system can amplify local phenomena to a global level, as experienced with COVID 19. Local events affecting a local manufacturing organization have mainly a local effect. Uncoupling geographic regions could hence increase resiliency and the overall reliability of the global manufacturing industry. These considerations suggest a reshoring policy for manufacturing. The Profacere® RMT could provide the hardware means to implement a new manufacturing organization and strategy for a sustainable mass customization.

References 1. Lanza, G., et al.: Global production networks: design and operation. CIRP Ann. 68(2), 823– 841 (2019) 2. Chowdhury, P., Paul, S.K., Kaisar, S., Moktadir, M.A.: COVID-19 pandemic related supply chain studies: a systematic review. Transp. Res. Part E Logist. Transp. Rev. 148, 102271 (2021) 3. Chen, P.: The entity-relationship model - toward a unified view of data. ACM Trans. Database Syst. 1(1), 9–36 (1976)

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4. Brown, A.P.G.: Modelling a real-world system and designing a schema to represent it. In: Douque and Nijssen (eds.) Data Base Description, North-Holland (1975) 5. Bruzzone, A.A., Monti, M., Godani, A.: Reconfigurable machining center, WIPO WO2017/137938 (2017) 6. Landers, R.G., Min, B.-K., Koren, Y.: Reconfigurable machine tools. CIRP Ann. 50(1), 269– 274 (2001) 7. Singh, A., Asjad, M., Gupta, P.: Reconfigurable machine tools: a perspective. Life Cycle Reliabil. Saf. Eng. 8(4), 365–376 (2019). https://doi.org/10.1007/s41872-019-00096-x 8. Dhupia, J.S., Galip Ulsoy, A., Koren, Y.: Arch-type reconfigurable machine tool. In: Wang L., Xi J. (eds.) Smart Devices and Machines for Advanced Manufacturing, pp. 219–238. Springer, London (2008). https://doi.org/10.1007/978-1-84800-147-3_9 9. Koren, Y., et al.: Reconfigurable manufacturing systems. CIRP Ann. 48(2), 527–540 (1999) 10. Mehrabi, M., Ulsoy, G., Koren, Y.: Reconfigurable manufacturing systems: key to future manufacturing. J. Intell. Manuf. 11(4), 403–419 (2000) 11. ElMaraghy, H.A.: Flexible and reconfigurable manufacturing systems paradigms. Int. J. Flex Manuf. Syst. 17, 261–276 (2005) 12. Koo, C.H., Vorderer, M., Junjer, S., Schrock, S., Verl, A.: Challenges and requirements for safety compliant operation of reconfigurable manufacturing systems. Procedia CIRP 72, 1100–1105 (2018) 13. Colledani, M., Angius, A.: Integrated production and reconfiguration planning in modular plug-and-produce production systems. CIRP Ann. 68(1), 435–438 (2019) 14. Arai, T., Aiyama, Y., Maaeda, Y., Sugi, M.: Agile assembly system by “plug and produce”. CIRP Ann. 49(1), 1–4 (2000) 15. Bruzzone, A.A., Monti, M., Rosciano, I.: Modular frame structure for machining center, WIPO, WO2019/106104 (2019) 16. Bruzzone, A.A., Monti, M., Rosciano, I.: Device and method for positioning a module of modular bed for machine tools, WIPO, WO2021/013837 (2021) 17. Takata, S., Yamanaka, M.: BOM based supply chain risk management. CIRP Ann. 62(1), 479–482 (2013) 18. Bruzzone, A.A., D’Addona, D.M.: Lego-like reconfigurable machining system; new perspectives to optimize production capacity. In: The 15th IEEE International Conference on Industrial Informatics, INDIN 2017, 24–26 July Emden, Germany (2017) 19. Bruzzone, A.A., D’Addona, D.M., Rosciano, I.: Thermoeconomic analysis of LEGO® -Like reconfigurable machine tools. Procedia CIRP 88, 375–380 (2020) 20. Sickles, R., Zelenyuk, V.: Measurement of Productivity and Efficiency: Theory and Practice. Cambridge University Press, Cambridge (2019) 21. IEA: Key world energy statistics – 2016, International Energy Agency, Paris, France (2016) 22. Becker, T., Stern, H.: Impact of resource sharing in manufacturing on logistical key figures. Procedia CIRP 41, 579–584 (2016) 23. Epureanu, B.I., Li, X., Nassehi, A., Koren, Y.: An agile production network enabled by reconfigurable manufacturing systems. CIRP Ann. 70(1), 403–406 (2021)

Design and Fabrication of Novel Compliant Mechanisms and Origami Structures for Specialty Grippers Dora Strelkova(B)

and R. Jill Urbanic

University of Windsor, Windsor, ON N9B 3P4, Canada {strelko,jurbanic}@uwindsor.ca

Abstract. New automation solutions need to be developed for flexible materials such as fabrics. Compliance in grippers should be incorporated to prevent product damage while firmly securing an item. The solution needs to be low cost, customizable, and sustainable. Using origami-inspired lamina emergent mechanisms and a material extrusion based, additive manufacturing (AM) process, two compliant clamping style gripper designs with living hinges are fabricated and tested. The AM process allows designs to be readily realized; however, the AM build strategies introduce process related sensitivities. Additional research will be conducted in both design and manufacturing aspects. This solution has potential for many domains, including agriculture and fabric handling applications. Keywords: Automation · Flexible components · Compliant mechanisms · Re-configurability · Rapid-prototyping · Origami robotics

1 Introduction Automation systems are ubiquitous in many environments. However, the components being manipulated are typically rigid. Currently in industry, flexible component picking and handling operations are performed manually. This includes several industrial domains, such as material handling of limp and air-permeable textile products, which are used for many manufacturing processes, or pick and place activities for various agricultural produce. With flexible components, there are variable shape characteristics to manage, and depending on the environment or product, it may be possible to readily mark or introduce damage to the material. Material handling of flexible components leads to high labour intensity in the work environment. There is the potential for workplace safety issues due to over exertion, uneven work movements, and repetitive lifting and twisting motions. Production bottlenecks or other system management related issues can result due to workload uncertainties. Innovative automation is required, and novel gripper mechanisms leveraging soft robotics or other compliant mechanisms can be offered as new solution approaches. Additive manufacturing can assist with realizing gripper design variants, which are dependent on the work environment, and the characteristics of the products being manipulated. This paper explores sustainable and customizable origami-inspired designs that can be made readily available through additive manufacturing (AM) processes. © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 55–62, 2022. https://doi.org/10.1007/978-3-030-90700-6_5

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1.1 Flexible Sheet Component and Material Handling Grippers Background Flexible fiber components are 2D shapes fabricated by creating an interlocking network of raw fiber materials. Leather, felt, thin sheets of plastic, etc. also exhibit similar behaviours; therefore, components consisting of these materials are also considered for this research as they are flexible and readily deformable. The fabric structures influence the drape, the porosity characteristics, the smoothness, and crimp. The shapes of the fabrics can be very complex, with intricate boundaries, internal slits, and cut-out pockets. Thin flexible sheets may be placed onto variable three-dimensional mold surfaces, such as composite fiber layups for resin transfer moulding. Specialty grippers solutions for flexible components have been designed and developed by many researchers and there exist some industrial solutions. The main categories are needle based, clamping, vacuum and adhesive, but others are being developed [1–7]. There are issues with damage ease and quality of release (i.e., introducing wrinkles and folds). Solutions that are energy efficient tend to have a high holding force, which has a high potential to damage the fabric. Adhesives remaining on the fabric may cause an undesired impact. The clamping method has potential (Table 1), but new solutions need to be developed, which is the goal of this research. Introducing more sophisticated mechanisms and compliance would reduce or eliminate the potential for damage and improve gripper dexterity. Table 1. Gripper characteristics summary where =

best,

= moderate, and

= worst.

Using origami/kirigami inspired designs, and compliant mechanisms, combined with advanced manufacturing solutions, introduces new possibilities that can be readily fabricated and tested. The amalgamation of paper art and robotics allows for unique designs, where traditional origami folds are replaced by living hinges within the gripper model (Fig. 1). Built-in crease regions of origami structures are comparable to actuated hinges or spring elements, allowing for the controlled movement of a final body. Contrastingly, uncreased regions provide structure and rigidity where it is needed in the model. Design for origami robotics often involves the integration of folds into thicker, more rigid, and non-traditional materials such as metals and 3d printed thermoplastics. The Miura origami (or Miura-Ori) fold [9] is leveraged in this paper. This fold uses a surface tessellation of parallelograms to collapse a large flat surface into a smaller one that is readily expandable.

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Fig. 1. Examples of 3D Printed Lamina Emergent Mechanisms (LEM) (a) Alpha prototype living hinges (b) Beta prototype living hinges

Kerf bending and the design for living hinges has been inspired by kirigami, an origami variant that uses strategic cuts rather than material folds to create 3D geometries. Kerf bending successfully generates curvature in solid materials (i.e., wood, plastic, or metal), through slits or cut-outs. This induced curvature is also referred to as a living hinge and offers flexibility in design and motion, alongside minimal friction. Compliant mechanisms are strong and flexible devices capable of transmitting precise forces through elastic body deformation and shape memory (Fig. 2). Any device that achieves motion by the bending of its flexible components can be classified as a compliant mechanism [10]. This introduces the potential to establish contact with high gripping forces without damaging the fibers or the weave pattern of a material. Prior researchers have developed cable, pneumatic, hydraulic, and combined actuation solutions for finger inspired gripper solutions.

Fig. 2. (a) Cable actuated elastomer finger system designed to grip fabric (b) a linkage skeleton embedded into elastomer fingers, (c) sample mold section for (a) [1, 7].

Although innovative solutions have been proposed, fabric material handling and gripper solution research is not a mature area of work. When picking and placing the fabric, it cannot be damaged. It should not move during transportation, and end-effector fabrication, process automation, and gripper recycling should be considered. Design applicability to a wide variety of environments is also a topic of consideration. Other design solutions need to be introduced. Lamina emergent mechanisms (LEM) combine origami, kirigami, and compliant mechanisms aspects. LEM are made from flat materials and exhibit motion that emerges from their fabrication planes [10]. These initially flat, monolithic devices can be derived from parametric designs and be fabricated using additive manufacturing.

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1.2 Additive Manufacturing Overview Additive manufacturing (AM) is defined as “the process of joining materials to make parts from 3D model data, usually, layer upon layer” [8]. For AM, the CAD model is divided into a series of planar 2D slice layers. These layers are fabricated and stacked to generate the final component. Intricate designs can be readily manufactured. ISO/ASTM 52900 divides the AM technologies into seven distinct categories. This research project employed a CREALITY Ender 3 material extrusion process [11]. The build material is fed through a heated element to place beads side by side for each layer. The materials used in this research are summarized in Table 2. Table 2. AM build materials [12] – note there are wide ranges of values for the strength characteristics, therefore a general comparison is used – H – high. M – medium, L - low. Material

General properties

Biodegradable

Shrinkage/Warpage

Print temperature

Polylactic acid (PLA)

Strength: H | Flexibility: L Durability: M

Yes

Minimal

180 °C – 230 °C

Acrylonitrile butadiene styrene (ABS)

Strength: H | Flexibility: M Durability: H

No

Considerable

210 °C – 250 °C

Polyethylene terephthalate glycol-modified (PETG)

Strength: H | Flexibility: M Durability: H

No

Minimal

220 °C – 250 °C

Thermoplastic polyurethane (TPU)

Strength: M | Flexibility: H + Durability: H+

No

Minimal

210 °C – 230 °C

2 Research Methodology The research process flow is shown in Fig. 3(a). This research focuses on two LEM grippers, the Miura-Material and Ori Grip (Fig. 3(b)). Preliminary research has been performed to classify the problem space (material weave, size, shape, strength, and related characteristics), but is not included here due to page limitations. Parametric models are developed for the grippers and living hinges. Design variants were built using a paper-based solution as well as with AM and different thermoplastics. Ease of manufacturing was considered as a sub-goal to minimize folding operations. Material selections were also based on recyclability considerations. The Miura fold makes it possible to collapse a flat surface/piece of paper into a smaller area that is readily expandable, through a tessellation of parallelograms. A pattern of

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mountains and valleys are created by the creases. A selection of Miura fold variants are built and tested. The parallelogram angles for two variants have internal angles 83° and 97°, and 45° and 135° (Fig. 4(a) and (b)). The initial prototypes were fabricated from paper and rubber tape. The paper thickness inhibited folding capabilities. To mitigate this, a composite fabrication approach was taken to manufacture the alpha prototypes. First prototypes used segments connected with tape. Following prototypes had the individual parallelogram segments printed onto a paper and fabric substrate. This substrate acted as the hinge for the origami crease pattern instead of the previous rubber tape. The Ori Grip, as shown in Fig. 3(b), has a more conventional gripper appearance when in its folded stated. Living hinges are incorporated at the bend/crease regions. To reduce stresses and leverage the advantages of AM processes, traditional hinges were developed, and an embedded living hinge assembly was fabricated and tested. From Alebooyeh et al. [7], it was found that silicone contact points provide high friction and additional compliance to allow for effective high force gripping (20 N) without damaging fibers, and no slippage at 100 mm/s travel velocity. Therefore, silicone tips are included as needed for both gripper design variants. Initial testing has been performed to compare the gripper and living hinge designs. The limitations of additive manufacturing were explored. COVID19 restrictions inhibited the validation activities performed.

(a)

(b) Miura pattern

Ori Grip

Fig. 3. (a) Research process flow, (b) Miura pattern, where α = 135°, and δ = 45°, and Ori Grip (with an assembly) variants.

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3 Results and Discussion 3.1 Miura Fold Grippers The Miura fold gripper prototypes are shown in Fig. 4. The parametric variables are the internal angles, segment spacing, and the edge length. The basic segment variables influence the contact areas, the height of the wrinkles, and the longitudinal compression of the fabric. Initial results indicated that a more ‘rhombical’ tessellation (Fig. 4(b)) was ideal for the pick and place of thicker, woven textiles such as tweed or canvas. A tessellation with more distinct obtuse and acute angles (Fig. 4 (a) and (c)) resulted in a gripper with tighter pinch points, ideal for thinner materials such as silk and cotton.

Fig. 4. (a) Miura pattern, where α = 135°, and δ = 45°, (b) Miura pattern, where α = 93°, and δ = 87°, (c) Miura gripper within a frame, and picking up fabric, (d) printed on substrate & cylindrical based compression. This gripper would be able to open and close similarly to esophageal muscles, with degrees of freedom in the X and Y axes.

An AM based Miura fold gripper with the rigid structural segments built on paper is shown in Fig. 4(d). This shows that novel patterns can be created in a CAD package, and easily manufactured without introducing the need for manual folding, or an intense assembly process. This fabrication strategy introduces the possibility of building circular origami grippers, or grippers that can conform to curved surfaces without introducing process complexities related to folding or managing gripper segments and their placements. The circular Miura fold pattern could be used for the handling of symmetrical or asymmetrical 3D bodies such as fruits/vegetables. More fabric-geometry testing needs

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to be performed to derive quantifiable design parameters for the segment sizes, angles, and spacing. Once baseline parameters are established, different folding patterns will be explored, as well as exploring multi-material AM printing. 3.2 Ori Grip with Living Hinges The Ori-Grip alpha prototype is shown in Fig. 5(a). It was found that a more controllable and durable method is required when inducing folds for this LEM. Consequently, living hinges are applied and realized using AM. Living hinge elements employ strategic hole patterns as featured in Fig. 5(d). The initial beta prototype consisted of living hinges for all bend locations. This strategy highlighted the limitations of the 3D printed materials and process. Therefore, four print-in-place bar hinges were integrated into a new parametric CAD model. As they were featured around the center of the gripper, a strong center flex zone was created (Fig. 5(d)). Beta prototypes included these print-in-place hinges and four concentrated areas of kerf inspired living hinge elements allowing the end effector to fully realize its flexible structure. A NEMA 17 stepper motor, A4988 motor controller, worm gear mechanism and linkages were utilized to replicate the original hand movements used to operate the Ori-Grip. This system was used to test the lifespan of the gripper and study points of stress and failure by automating the opening and closing of the end effector. Kerf patterns and spacing, contact tip geometry and gripper size variations will be explored to derive quantifiable design parameters.

Fig. 5. (a) Alpha prototype parametric model, (b) Testing mechanism for Ori-Grip, (c) Material testing prototype of Ori-Grip featuring 3D printed models in PLA, ABS and PETG (d) Location of conventional hinges (left) and location of living hinges (right).

PETG was the best material for the living hinges. PLA and ABS were brittle and quickly failed. Prototypes made in PETG were more pliable, exhibiting flexibility and excellent inter-layer adhesion, crucial for living hinge designs. One $160 USD printer with a $25, 1 kg spool of PETG filament can manufacture 25 grippers (19 × 11 cm) in 140 h. Although this is a low-cost, low-energy solution, this AM build process introduces voids potentially weakening the compliant mechanisms.

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4 Conclusion This research focuses on the development of origami-inspired grippers for use in automated pick and place applications. The proposed clamping-based solutions are efficient mechanisms for material handing in assembly lines and produce handling within the green house setting. Compliant mechanisms and parametric LEMs are strong and flexible devices capable of transmitting precise forces through elastic body deformation and shape memory. Gripper variants with these structures are more affordable in terms of replacement compared to traditionally actuated, rigid link systems. Desired characteristics of the grippers include high friction between the end-effector and the textile, compliance, mechanism compactness, and substantial gripping force. Material extrusion-based AM processes allow for rapid prototyping and testing but, build parameters and process related voids can introduce crack propagation sites. This living hinge manufacturing issue can be resolved by leveraging to other AM processes such as material jetting, as there will be no voids and multi-material grippers can be fabricated. The proposed LEM gripping strategies can be employed in many industries such as greenhouse, aerospace, automotive and medical tech, specifically for pick and place tasks. Future research includes continued exploration of origami design variants, additional material testing, implementing injection molding for mass customization, and designing a recycling process for the used monolithic grippers.

References 1. Wang, B.: Design and Development of a Soft Robotic Gripper for Fabric Material Handling. MASc Thesis, University of Windsor (2020) 2. Needle Grippers from ATS Automation Technology Schwope Inc., Germany. www.eoat.net, Accessed 15 May 2021 3. Doulgeri, Z., Fahantidis, N.: Picking up flexible pieces out of a bundle. IEEE Rob. Autom. Mag. 9(2), 9–19 (2002) 4. Walton Picker from PickRobotics Inc., USA. https://eximindex.com/pickrobotics-united-sta tes/, Accessed 15 May 2021 5. Fleischer, J., Ochs, A., Förster, F.: Gripping technology for carbon fibre material. In: CIRP International Conference on Competitive Manufacturing, pp. 65–71 (2013) 6. Ozcelik, B., Erzincanli, F., Findik, F.: Evaluation of handling results of various materials using a non-contact end-effector. Ind. Robot Int. J. 30(4), 363–369 (2003) 7. Alebooyeh, M., Wang, B., Urbanic, R.: Performance study of an innovative collaborative robot gripper design on different fabric pick and place scenarios. SAE Technical Paper 2020– 01–1304 (2020). https://doi.org/10.4271/2020-01-1304. 8. ISO / ASTM52900–15, Standard Terminology for Additive Manufacturing – General Principles – Terminology, ASTM International, West Conshohocken, PA (2015) 9. Monmonier, M.: Folding, unfolding. In: Patents and Cartographic Inventions, pp. 105–135. Palgrave Macmillian Cham, London Borough of Camden (2017) 10. Howell, L., Magleby, S., Olsen, B.: Handbook of Compliant Mechanisms, 1st edn. John Wiley & Sons Ltd., Hoboken (2013) 11. CREALITY Ender-3 3D Printer. https://www.creality.com/goods-detail/ender-3-3d-printer, Accessed 13 May 2021 12. MatWeb Material Property Data Homepage. http://www.matweb.com/index.aspx, Accessed 15 May 2021

Configuration Design of Delayed Reconfigurable Manufacturing System(D-RMS) Shiqi Nie, Sihan Huang, Guoxin Wang(B) , and Yan Yan School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China [email protected]

Abstract. Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), was proposed to solve the convertibility problems of traditional RMS. The core philosophy of D-RMS is to maintain partial production capability through postponing reconfiguration to latter stages of manufacturing system. Configuration design is necessary to implement D-RMS with the consideration of postponing reconfiguration. Therefore, a configuration design method of D-RMS is proposed in this paper. To cater for the demand of intelligent manufacturing, the industrial robot is considered during configuration design as well. Two illustrate examples are provided to show the effectiveness of the proposed configuration design method. Keywords: Intelligent manufacturing · Delayed reconfigurable manufacturing system · Configuration design · Industrial robot

1 Introduction In intelligent manufacturing era [1], the market demand presents the characteristics of individuation, diversification and increasing uncertainty. The concept of RMS [2] proposed by Koren is the most eligible paradigm to handle the requirements of intelligent manufacturing, which can response to the market changes rapidly through scalability and convertibility. However, RMS also a defect that convertibility is not outstanding enough. To promote the convertibility of existing RMS, a concept of delayed reconfigurable manufacturing system (D-RMS) is proposed by Huang et al. [3]. D-RMS, working by delaying part of the reconfiguration process, attempts to reduce the complexity of reconfiguration and decreases the production loss resulting from the manufacturing system shutdown during reconfiguration. Configuration design is core step of RMS/D-RMS implementation [4], which fundamentally determines the capacity of the machine tool in space and limits the degree of reconfiguration. There are many researchers concerned the configuration design issues. Kumar [5] proposed a heuristic algorithm for linear sequence layout of machine tools based on multiple product requirements, and it was proved in running examples that this method could greatly improve the response speed of the system. Hassan and Bright [6] proposed to construct a system configuration with the manufacturing capability of the computer integrated manufacturing unit and the characteristics of the reconfigurable © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 63–71, 2022. https://doi.org/10.1007/978-3-030-90700-6_6

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manufacturing system, so as to achieve the required effect of the reconfigurable manufacturing unit, and to reconstruct the system according to the system configuration results. Lateef-Ur-Rehman [7] established a simulation model and selected the configuration of the manufacturing system by using the multi-index decision-making tool, but he lacked the consideration of the formation and risk of each configuration and the stability analysis of each decision. Benderbal and Benyoucef [8] studied the problem of machine tool reconfiguration by considering customer requirements and the limitation of machine tool location, and determined the optimal machine layout of all selected machines in the product by combining meta-arteriology, archived multi-objective simulation degradation (AMOSA) and other methods. Huang et al. [9] proposed a conceptual model design method for the multi-scale configuration of reconfigurable manufacturing system based on the theory of life system. In the face of order fluctuation, rapid design of new configurations can be realized by adding, deleting and replacing existing configurations. The existing relevant work focuses on the transformation problem in intelligent manufacturing configuration design, and few studies have considered the delayed reconfiguration characteristics, except Huang et al. However, this paper is still based on the traditional RMS structure to do some research, has certain limitations. In addition, the industrial robot plays an important role in the intelligent manufacturing stage, and it is necessary to integrate it into the research of configuration design. Mario Cesar Reis Bonifacio et al. [10] believe that the application of collaborative robots has become a trend in the field of processing and manufacturing, which brings greater flexibility, shortening implementation time and improving system reconfigurable degree. Barbosa [11] implemented all modifications, adjustments and applications on the PUCRIO using different forms of robots such as milling machines, foam cutting machines, pick picks and placement machines. Therefore, a robot-assisted feeding D-RMS configuration is proposed in this paper. Based on the traditional configuration, industrial robots are introduced to further improve the flexibility. The remainder of this paper is structured as follows: Sect. 2 explains the definition of D-RMS. Section 3 presents the configuration design for D-RMS considering industrial robot. Section 4 provides a case study to implement the proposed configuration design method. Section 5 summarizes this paper.

2 D-RMS Definition D-RMS is proposed to improve the flexibility of reconfiguration and keep part of production activities in the process of reconfiguration the same as traditional RMS. D-RMS adopts the mixed production organization mode of Metro-Unit, which not only ensures the production efficiency, but also has enough space for reconfiguration (capacity expansion).The new system architecture will be the RMS is divided into two subsystems, front-end and back-end are connected by a buffer between the two subsystems, front end production of semi-finished products can enter the buffer parts warehouse, waiting for the terminal system after reconfiguration completed. As shown in Fig. 1. During the period of reconfiguration subsystem can maintain a certain production capacity, will not lead to idle resources. Subsystem 2 is built based on the production demand of the current

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part and has high flexibility. Since the system is divided into two parts, the reconfiguration will not affect the production efficiency of the front end of the system. Therefore, this D-RMS configuration reduces the complexity of reconfiguration to some extent.

Fig. 1. D-RMS configuration [12]

3 Configuration Design The existing configuration can realize the delayed reconfiguration of the system to some extent, but when it is faced with some parts families with more complex process routes, it is inevitable that there will be too many machine tools waiting for reconfiguration, it is difficult to return to the front-end process after failure and the material handling reliability is not high. In order to meet the market demand for a variety of small batch products and improve the flexibility of D-RMS, the system can be divided into any node in the unit of machine tools to achieve rapid reconfiguration within the interval. At the same time, combining with the research progress of industrial robot in the field of machine tool assisted machining, this paper proposes the robot-assisted feeding D-RMS configuration from the system level, as shown in Fig. 2. The configuration is made up of the ring main conveyor, cell conveyor, various types of machining machine tools, feeding robots and movable feeding robots. The main body of the configuration is designed as a circular conveyor belt rotating clockwise to facilitate the mobile robot to achieve full coverage of the operating range of the circular guide rail conveyor line can put each station as close as possible together, reduce the movement distance between the parts in the station, and reduce the space to accommodate more machine tools. The movable robot is located in the center of the annular belt. It realizes the material transportation in the whole area through the horizontal movement on the guide rail and the spatial freedom of the robot itself, which solves the problem of the insufficient flexibility of the conveyor belt in the working process. The elements around the conveyor line of the annular guide rail are composed of independent processing units, each of which includes the loading robot, the machine tool and the Cell Conveyor. Each square in the figure represents a working station. The robot beside the machine tool can cooperate with Cell Conveyor to send the products in the processing into and

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Fig. 2. D-RMS configuration with robot-assisted feeding

out of the warehouse at any node when it completes the material feeding work. The whole configuration has enough spatial extension, which can be expanded horizontally and vertically according to the actual production requirements and the space of various processing workshops. Compared to the traditional D - RMS configuration, the new configuration obviously has more flexibility. When dealing product processing tasks with complicated process, it is a good solution to the process adjustment problems in the manufacturing process. With the help of the industrial robot at the same time, the new configuration improves the degree of intelligent manufacturing system and is advantageous for the multi-agent optimization design.

4 Case Study In engineering practice, there are a variety of complex process routes of the part family which have the characteristics of coupling process and repeating process. In the DRMS configuration of existing implementation, reconfiguration cost is higher. This paper enumerates the two case, case 1 focus on description of artifacts and the topography map, case 2 focuses on the emphasis on the role of industrial robots in refactoring. The operation of the two cases in the configuration starts from the first process of the part family, ends after the finished product is stored in the warehouse, and goes through the process of robot loading machine machining conveyor belt transportation and delayed reconfiguration. 4.1 Case 1 Part family A includes three parts, that is, part a, part b and part c. The process route of part a is {1, 2, 3, 4, 5, 6}. The process route of part b is {1, 2, 3, 7, 8, 9}. The process route

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of part c is {1, 2, 3, 10, 11, 12}. These three parts share the same first three operations, that is, {1, 2, 3}. In other words, there is not decoupling point among these three parts in the first three operations, which shows the typical characteristics of D-RMS. It is an ideal part family for D-RMS, as shown in Fig. 3. It is necessary to implement the proposed configuration design of D-RMS based on the typical part family of D-RMS.

Fig. 3. Process route of part family A

To complete the processing of part family A on the basis of the existing configuration, a total of six stations are needed, with the starting point at the station I, the end point at the station VI, and the reconfiguration node at the station III. The system is divided into front-end and back-end parts. After the front-end processing, the parts enter the buffer warehouse system from the III reconfiguration point to the back end of the buffer warehouse system for reconfiguration according to the order requirements. After the completion of the machine tool with the corresponding station configuration in line with the process route, the part is transported to the warehouse from the last working procedure (station VI). The whole process is realized by the feeding robot to grab and clamp the part, and the front-end production does not interrupt in the reconfiguration process. The mapping process is shown in Fig. 4. According to the requirements of the process sequence, the blank enters the system from the station I, and is transported to the buffer warehouse from the station III after the front-end process 1, 2 and 3. At this time, the back end of the system enters the reconfiguration stage, and the machine tool is configured according to the production requirements. After completing the process 4, 5 and 6 of part a, it is transported to the warehouse from station VI. After going through the procedure 7, 8 and 9 of part b, it is transported to the warehouse from station VI. The part c is delivered to the warehouse from station VI after processing 10, 11 and 12. The whole system runs smoothly without using the movable feeding robot in the process. 4.2 Case 2 Part family B includes three parts as well, that is, part d, part e and part f, as show in Fig. 5. The process route of part d is {1, 2, 3, 4, 5, 6, 7}. The process route of part e is

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Fig. 4. Part family A machining path

{1, 2, 3, 4, 7, 5, 6}. The process route of part f is {1, 2, 3, 4, 8, 9, 10}. Similarly, these three parts share the same first forth operations, that is, {1, 2, 3, 4}. Moreover, there are additional three the same operation between part d and part e without considering the process order, that is, {5}, {6}, {7}. These three operations appear after decoupling point, which can be achieve through reconfiguration.

Fig. 5. Part family B process route

Same as above mentioned, part family B in accordance with the traditional configuration, that is, without the use of mobile feeding robot. The whole system has a total of 7 stations, which are divided into two parts: the front end and the back end. The station I is the starting point, the station IV is the reconfiguration point, and the station VII is the

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end point. Each station in the reconfiguration interval has 3 machine tools on standby. The mapping process is as Fig. 6.

Fig. 6. Part family B traditional machining path

According to the requirements of the process sequence, the blank enters the system from the station I, and is transported to the buffer warehouse from the station IV after the front-end process 1, 2, 3 and 4. At this time, the back end of the system enters the reconfiguration stage, and the machine tool is configured according to the production requirements. After the production of Part d goes through the process 5, 6 and 7, it is transported to the warehouse from station VII. After the production of Part e goes through the procedure 7, 5 and 6, it is transported to the warehouse from station VII. The production of Part f is delivered to the warehouse from station VII after working procedure 8, 9 and 10. The whole process requires a lot of machine tools and there is a lot of repetition and high cost. When the movable robot is used to participate in the part distribution process, the main change of the whole system is the reconfigurable part at the back end. Compared with the previous one, this configuration reduces 3 standby machine tools and greatly reduces the reconfigurable cost. The mapping process is shown in Fig. 7. According to the requirements of the process sequence, after finishing the front-end processing process, the blank returns back the system from the station IV of the back end of the buffer warehouse system. The production of the part D goes through procedures 5, 6 and 7 in turn, and then enters the warehouse from the station VII. The production of part d goes through working procedure 5, 6 and 7 in turn, and then enters the warehouse from station VII. The production of part e is carried by the mobile robot to the station VII to complete the procedure 7, and then transported by the mobile robot to the station

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Fig. 7. Part family B new machining path

V to complete the procedure 5, and then transported by the conveyor belt to the station VI to complete the procedure 6, and finally put into the warehouse from the station VI. The production of Part f goes through working procedure 8, 9 and 10 in turn, and then enters the warehouse from station VII. The whole process runs well with the help of the mobile robot. From the results, the configuration is flexible enough to meet the needs of various complex process routes, and the configuration of 3 machine tools is reduced, which is more economical and efficient.

5 Conclusion In this paper, a robot-assisted D-RMS structure is proposed to solve the problems of poor flexibility and high cost of reconfiguration in the existing RMS/D-RMS configuration. This configuration can be reconfigured with the assistance of robot, which increases the flexibility and intelligence of D-RMS. Two cases are presented to prove the effectiveness of the proposed configuration design of D-RMS. On the basis of the application of delayed reconfiguration, show the new configuration of the unitized configuration advantages and industrial robots play an important role. In the future, the further research will focus more on the cooperative relationship between the configuration, digital twin and multi-agent system to achieve more accurate efficiency optimization. Acknowledgement. The authors acknowledge the supporting fund, the China National Postdoctoral Program for Innovative Talents (BX20200053), the National Natural Science Foundation of China (51975056). This manuscript has been approved by all coauthors. The authors declare no competing interests.

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References 1. Zhou, J., Li, P., Zhou, Y., Wang, B., Zang, J., Meng, L.: Toward new-generation intelligent manufacturing. Engineering 4(1), 11–20 (2018) 2. Koren, Y., et al.: Reconfigurable manufacturing systems. CIRP Ann. 48(2), 527–540 (1999) 3. Huang, S., Wang, G., Yan, Y.: Delayed reconfigurable manufacturing system. Int. J. Prod. Res. 57(8), 2372–2391 (2019) 4. Koren, Y., Gu, X., Guo, W.: Choosing the system configuration for high-volume manufacturing. Int. J. Prod. Res. 56(1–2), 476–490 (2018) 5. Kumar, M.S., Islam, M.N., Lenin, N., Vignesh Kumar, D., Ravindran, D.: A simple heuristic for linear sequencing of machines in layout design. Int. J. Prod. Res. 49(22), 6749–6768 (2011) 6. Hassan, N., Bright, G.: A hybrid reconfigurable computer-integrated manufacturing cell for the production of mass customised parts. S. Afr. J. Ind. Eng. 23(1), 139–150 (2012) 7. Lateef-Ur-Rehman, A.U.R.: Manufacturing configuration selection using multicriteria decision tool. Int. J. Adv. Manuf. Technol. 65, 625–639 (2013) 8. Benderbal, H., Benyoucef, L.: A new hybrid approach for machine layout design under family product evolution for reconfigurable manufacturing systems. IFAC-PapersOnLine 52(13), 1379–1384 (2019) 9. Huang, S., Wang, G., Shang, X., Yan, Y.: Conceptual model design of multi-scale configuration for reconfigurable manufacturing systems. Comput. Integr. Manuf. Syst. 25(11), 2803–2812 (2019) 10. Bonifácio, M.: Assessment of potentially attractive tasks to collaborative robotics application: a case study based on electronics industry. J. Mech. Eng. Autom. 11(2), 37–45 (2021) 11. Barbosa, W.S.: Industry 4.0: examples of the use of the robotic arm for digital manufacturing processes. Int. J. Interact. Des. Manuf. (IJIDeM) 14(4), 1569–1575 (2020) 12. Huang, S.: Reconfigurable Scheduling Method for Variable Batch Production of Multiple Varieties. Beijing Institute of Technology, Beijing (2020)

Classification of Reconfigurability Characteristics of Supply Chain Slim Zidi1,2(B)

, Nadia Hamani2

, and Lyes Kermad1

1 University of Paris 8, 140 Rue de la Nouvelle France, 93100 Montreuil, France

[email protected] 2 University of Picardie Jules Verne, 48 Rue d’Ostende, 02100 Saint-Quentin, France

Abstract. Nowadays, supply chain disruptions caused by COVID-19 pandemic, demand variation, raw material shortage, etc., made the supply chains unable to deal with emerging market problems. Responding to the new requirements has underscored the need to ensure a reconfigurable supply chain in order to survive in this uncertain economic environment. Indeed, the objective of this study is to identify the quantitative factors of each reconfigurability characteristic representing the reconfigurability assessment indicators (modularity, integrability, convertibility, diagnosability, scalability and customization). Based on the literature review, quantitative factors used to assess the degree of reconfigurability in supply chain are determined. These factors allow classifying reconfigurability characteristics according to the degree of their influence on the supply chain structure or supply chain functions. Through this research work, we try to facilitate the assessment of reconfigurability based on its characteristics in order to determine the ability of the supply chain to cope with new emerging disruptions. Keywords: Reconfigurable supply chain · Reconfigurability characteristics · Quantitative factors of reconfigurability · Reconfigurability classification

1 Introduction Nowadays, the supply chain faces disruptions and environment changes that affect its performance. Managers became aware of the importance of adapting and implementing new strategies to deal with market uncertainty and to react quickly and cost-effectively to the changing customer needs [1]. Among these solutions, we can cite reconfiguration that allows changing the capability and functionality of the system [2, 3]. It consists in adding, removing or modifying the system components and functions in order to respond to new requirements. Reconfiguration can be applied at different levels [4, 5] (network, system, plant and workstation). Indeed, Reconfigurable Supply Chain (RSC) is defined as a network designed in a cost-efficient, responsive, sustainable and resilient manner and can change rapidly its structure [6]. In order to have an efficient reconfiguration process, it is necessary to evaluate the degree of reconfigurability which consists in measuring the ability of the supply chain to cope with disruptions and unexpected events. This paper is a specific literature review that aims to define the six reconfigurability characteristics by determining the quantitative factors allowing the assessment of their © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 72–79, 2022. https://doi.org/10.1007/978-3-030-90700-6_7

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degrees. The objective is to facilitate the understanding of the characteristics in order to facilitate the improvement of the degree of reconfigurability. The rest of the paper is organized as follows. In Sect. 2, we define the supply chain reconfigurability. Section 3 summarizes the identified quantitative factors of each reconfigurability characteristics. A classification of reconfigurability characteristics is presented in Sect. 4. Section 5 concludes the paper.

2 Supply Chain Reconfigurability Supply chain reconfigurability is the ability of supply chain to change its structure and its functions to cope with disruption and market changes [7]. The reconfiguration in supply chain combines both a positive side indicating innovation and a negative side indicating disruption risks. For this reason the innovation is important for disruption recovery [6]. Indeed, there is a significant dependency between reconfiguration and the supply chain flexibility. [8] defined the RSC as a flexible chain that is able to alter its configuration with the minimum of resources to respond to the changing customer demands. Reconfiguration can be applied at all the levels of supply chain. Reconfiguration is the addition, removal or change of supply chain components related to its structure or functions. The objective of implementing a reconfigurable supply chain is to make the latter able to easily and quickly change its structure or functions in response to market changes and new customer requirements. Reconfiguration can also be considered as a strategy to improve the performance of the supply chain or as a strategy to deal with an unexpected event. It can be achieved essentially through its six characteristics: modularity, integrability, convertibility, diagnosability, scalability and customization. These characteristics reduce reconfiguration efforts [5], which justifies the consideration of these characteristics as metrics used to measure reconfigurability. [9–11] pointed out the effective role of these characteristics in assessing reconfigurability in reconfigurable production systems and supply chains. Therefore, it is important to better understand the concept of reconfigurability by focusing on its six previously-mentioned characteristics.

3 Characteristics of the Supply Chain Reconfigurability 3.1 Supply Chain Modularity [12] defined the supply chain modularity as the degree to which all products, processes and resources are modular. It is characterized by the network structure design, the responsiveness and the total cycle time [13]. Modularity is related to the degree of non-proximity (geographical, organizational, cultural, electronic) of the supply chain components [14]. It consists in clustering the supply chain activities under a set of independent modules. In this clustering, the interactions between the activities of supply chain are considered. Indeed, the aim of the supply chain modularity is to reduce the lead time and provide independence between supply chain activities. These objectives are achieved by increasing the degree of “intra-module” interactions and decreasing the degree of “inter-module” interactions. These interactions include the physical and information

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flows linking all components of the supply chain. The factors proposed to evaluate the degree of modularity are [15]: • Number of modules: the number of modules/units obtained after the modular decomposition; • Intra- and inter- modules interactions: the number of links connecting the different modules and the elements of each module; • Lead time: corresponds to the time between the ordering of a supplier and the delivering of goods to the customer. 3.2 Supply Chain Integrability The integrability of the supply chain is its ability to introduce quickly and cost-effectively new resource, product, activity and entities [12] that cannot be integrated if the supply chain is complex. Complexity is highly dependent on the structure of the supply chain. The latter is considered as a complex system composed of a set of nodes connected by flow. Otherwise, the less complex the supply chain is, the easier the integration of a flow, a process, an information system or an actor will be. The factors used to measure the degree of integrability are the number of nodes: that refers to the number of companies coordinating the management of goods (purchase, stock, transport…) within the same supply chain and the number of connections: corresponding to the number of interactions between the nodes of the supply chain. 3.3 Supply Chain Convertibility The supply chain convertibility designates the ability of the product, process and resource entities to quickly changeover between the existing products and adapt the company to manufacture/deliver new products [12]. It refers to the ability of a supply chain to easily convert its components to meet the new customers’ needs by having redundant entities to quickly deal with disruption [16]. In other words, a supply chain with redundant entities has a greater degree of structural convertibility, related to the nodes and the connections between all the entities of the supply chain, and functional convertibility linked to resources. Therefore, the main factor to evaluate the supply chain convertibility is the redundancy that consists in providing additional capacity to avoid delivery delays or stops due to disruptions. 3.4 Supply Chain Diagnosability Diagnosability is the ability to quickly identify the sources of problems which hamper the supply network effectiveness and efficiency [12]. This characteristic is highly dependent on the information flows. Thus, the rapid detection of failures requires a good visibility of the supply chain related to its physical and information flows. Diagnosability consists of three enablers (flow visibility, data reliability and resilience) are directly linked to the flow of information circulating in the supply chain which must be shared just in time and in a reliable and accurate way. [17] considered that the supply chain visibility

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is measured based on the quantitative and qualitative measurement of the information flow. Indeed, the quantity of information refers to the accessibility of information, while the quality of information is related to its accuracy and freshness. Thus, supply chain visibility allows quickly detecting failures, which improves diagnosability. The latter can be evaluated according to the supply chain visibilitywhich designates the sharing of information in a just-in-time, reliable and accurate manner and the detection time which refers to the time measured from the moment when a company realizes that it will be affected by a supply chain disruption to the moment at which the incident really occurs. 3.5 Supply Chain Scalability Supply chain scalability refers to the ability of the supply chain to quickly change its throughput capacity and reduce the lead time to be more responsive and more flexible. It refers to the ability of the supply chains to meet service quality requirements when the number of customers and their demands are high [18]. This characteristic depends on the ability of the supply chain to increase its capacity with the minimum time. Indeed, it depends on the latency and throughput capacity. [18] considered the latency as the ability to achieve performance objectives in a dynamic and uncertain environment. Scalability affects the delay in the supply chain [19], hence it can be evaluated based on the latencywhich is the ratio between the delivery time and the throughput time and the throughput capacity which designates the number of the performed orders. 3.6 Supply Chain Customization In [12], customization is defined as the degree to which the capability and flexibility of the supporting infrastructure of supply network match the application (supply chain activities). It refers to the ability of the supply chain to customize its products and services in a very short response time and with a wide variety of customized functionalities to meet the customer’s requirements. An efficient customization means reduced order response time and just-in-time delivery [20]. Indeed, response time is a crucial factor in evaluating the supply chain customization. Besides, the customer’s implication in the manufacturing of the product is an essential factor that allows increasing the degree of customization, i.e. i. e. the number of customers requesting a customized product. In addition, the more the supply chain offers customization functionality related to its products and services, the more the customization degree rises. The proposed factors used to assess the supply chain customization are the response time which refers to the total amount of time spent to satisfy clients’ need and the number of customized functionswhich designates the number of functions related to the customization of the product/service.

4 Classification of Reconfigurability Characteristics Although [12] have extracted the characteristics of reconfigurable manufacturing systems and applied them to the level of logistics networks, it remains difficult to evaluate their degree of reconfigurability. The results of this paper provide a way to evaluate the ability of the supply chain to adapt its structure and functions with new changes

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in order to ensure the continuous improvement of its performance. The determination of the factors related to each reconfigurability characteristic can be classified into four groups of indicators: time, capacity, reactivity and flexibility (Fig. 1). In fact, Modularity, scalability and diagnosability are measured based partially on the time aspect related to lead-time, latency or response time whose reduction leads to the improvement of the degree of reconfigurability. The capacity of the reconfigurable supply chain is essentially related to its structural capacity in terms of number of modules, number of nodes and number of connections. Then, scalability, diagnosability and customization are rather linked to the functional aspect of the as they depend on those related to the structural aspect. Indeed, a high degree of modularity, integrability and convertibility ensures a high degree of customization, diagnosability and scalability as integrability is measured by the degree of complexity of the supply chain, which in turn affects the visibility of the chain and, therefore, the diagnosability. Thus, an increasingly modular design facilitates customization, especially in terms of response time, because with a modular structure and rapid changes of these modules, the response time will be considerably minimized. Flexibility includes the factors that contribute to making the RSC more flexible, particularly in terms of the redundancy of entities that facilitates conversion in the chain. Thus, flexibility is related to the degree of the supply chain complexity affected by the number of connections. The last indicator is responsiveness that depends on the response time related to customization as well as on their customized functions because a supply chain that has a large number of customization functions can quickly respond to the variations in customers’ requirements. Responsiveness depends also on the detection time. In other words, the quick detection of failures allows reacting rapidly to the detected hazards.

Fig. 1. Classification of the supply chain reconfigurability characteristics

The results of this paper have clarified and facilitated the assessment of supply chain reconfigurability objectively and quantitatively. Indeed, the defined factors present a tool that allows decision makers and managers to improve the degree of reconfigurability through its six characteristics. Thus, these factors are easy to use for measuring the degree of modularity, integrability, convertibility, diagnosability, scalability and customization.

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On the other hand, the results of the classification of these characteristics give an overview of the characteristics that ensure the reconfiguration of the supply chain structure and those that ensure the reconfiguration of its functions. From an industrial point of view, this classification facilitates the interpretation of the degrees of the six characteristics according to the metrics proposed by [7]. Indeed, managers will be able to see the advantage and the contribution of each characteristic on the improvement of the reconfigurability degree. 4.1 Structural Aspect The structure of the reconfigurable supply chain is represented using three characteristics (modularity, integrability and convertibility). This aspect allows identifying and characterizing the entities and connections composing the RSC. In fact, modularity includes the number of nodes, which affects the determination of the number of modules and the number of connections between them. It is important to mention that this characteristic is evaluated using two parameters: the number of modules and the number of connections that are inversely proportional to the degree of modularity. Indeed, with a reduced number of modules and connections, the degree of modularity increases. The ability to introduce new entities into the supply chain (resource, product, activity…) is correlated with the RSC structure that affects the integrability of the supply chain through its degree of complexity. This means that, if the RSC structure is more complex, the degree of integrability will be less important. Finally, convertibility is related to the structural aspect of the RSC. Although it is less influential on the RSC structure, convertibility requires the redundancy of the entities to be quick and less expensive. 4.2 Functional Aspect For the functional aspect, scalability, diagnosability and customization are related to the different functions of the RSC. This aspect contributes to analyze the impact of reconfigurability on time (lead time, latency and response time) and reactivity. Thus, these characteristics affect the ability of the supply chain to improve its functions performance after reconfiguring its structure through modularity, integrability and convertibility. Time is directly influenced by scalability and diagnosability through ramp-up time and lead time. Indeed, a high degree of scalability reduces the ramp-up time and makes the supply chain more reactive when it changes its capacity. Diagnosability is influenced by modularity, which is shown by the impact of the RSC modular design on the reduction of the detection time of failures and their causes. Obviously, this reduction is also influenced by modularity because a modular structure of the supply chain ensures more flows visibility and allows detecting the root cause of the defects that may affect it and correct them in a shorter time. It is important to orient the supply chain functionalities towards mass customization, i.e. making customized functions more reactive and flexible.

5 Conclusion In this paper, factors to measure and evaluate the degree of modularity, integrability, convertibility, diagnosability, scalability and customization were identified and analyzed

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according to their effect on the degree of reconfigurability of the supply chain. Indeed, the objective of this paper is to provide a tool for decision makers to evaluate the degree of reconfigurability of the supply chains by using six characteristics (modularity, integrability, convertibility, diagnosability, scalability and customization). The latter were classified according to their impact on the supply chain performance and reconfigurability improvement. Modularity, integrability and convertibility are related to the supply chain structure, while diagnosability, scalability and customization are related to the supply chain functions. By considering these characteristics, managers can reduce the effort of supply chain reconfigurability and improve its performance related to capacity, responsiveness, time and flexibility. As a perspective, we will perform an empirical study to investigate the experts’ opinion on factors that should be taken into account to evaluate and improve the supply chain degree of reconfigurability.

References 1. Zidi, H., Hamani, N., Laajili, C., Benaissa, M.: A reconfiguration approach for a supply chain tracking platform. Int. J. Ship. Transp. Logist. 13(6), 1 (2021). https://doi.org/10.1504/IJSTL. 2021.10037168 2. Koren, Y., et al.: Reconfigurable manufacturing systems. CIRP Ann. 48, 527–540 (1999). https://doi.org/10.1016/S0007-8506(07)63232-6 3. ElMaraghy, H.A.: Flexible and reconfigurable manufacturing systems paradigms. Int J Flex Manuf Syst. 17, 261–276 (2005) 4. Wiendahl, H.P., Heger, C.L.: Justifying changeability. a methodical approach to achieving cost effectiveness. J. Manuf. Sci. Prod. 6(1–2), 33–40 (2004). https://doi.org/10.1515/IJMSP. 2004.6.1-2.33 5. Napoleone, A., Pozzetti, A., Macchi, M.: A framework to manage reconfigurability in manufacturing. Int. J. Prod. Res. 56, 3815–3837 (2018). https://doi.org/10.1080/00207543.2018. 1437286 6. Dolgui, A., Ivanov, D., Sokolov, B.: Reconfigurable supply chain: the X-network. Int. J. Prod. Res. 58, 4138–4163 (2020) 7. Zidi, S., Hamani, N., Kermad, L.: New metrics for measuring supply chain reconfigurability. J. Intell. Manuf. (2021). https://doi.org/10.1007/s10845-021-01798-9 8. Chandra, C., Grabis, J.: Supply Chain Configuration: Concepts, Solutions, and Applications. Springer, New York (2016). https://doi.org/10.1007/978-1-4939-3557-4 9. Biswas, P., Kumar, S., Jain, V., Chandra, C.: Measuring supply chain reconfigurability using integrated and deterministic assessment models. J. Manuf. Syst. 52, 172–183 (2019) 10. Napoleone, A., Pozzetti, A., Macchi, M.: Core characteristics of reconfigurability and their influencing elements. IFAC-PapersOnLine 51, 116–121 (2018) 11. Rösiö, C., Aslam, T., Srikanth, K.B., Shetty, S.: Towards an assessment criterion of reconfigurable manufacturing systems within the automotive industry. Procedia Manuf. 28, 76–82 (2019). https://doi.org/10.1016/j.promfg.2018.12.013 12. Kelepouris, T., Wong, C.Y., Farid, A.M., Parlikad, A.K., McFarlane, D.C.: Towards a reconfigurable supply network model. In: Intelligent Production Machines and Systems, pp. 481–486. Elsevier (2006) 13. Biswas, P.: Modeling reconfigurability in supply chains using total interpretive structural modeling. J. Adv. Manag. Res. 14, 194–221 (2017) 14. Wolters, M.J.J.: The Business of Modularity and the Modularity of Buisiness. Selbstverl, Rotterdam (1999)

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15. Zidi, S., Hamani, N., Kermad, L.: Modularity metric in reconfigurable supply chain. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds.) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems: IFIP WG 5.7 International Conference, APMS 2021, Nantes, France, September 5– 9, 2021, Proceedings, Part V, pp. 455–464. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-85914-5_49 16. Sheffi, Y., Rice, J.: A supply chain view of the resilient enterprise. MIT Sloan Manag. Rev. 47, 41 (2005) 17. Caridi, M., Crippa, L., Perego, A., Sianesi, A., Tumino, A.: Do virtuality and complexity affect supply chain visibility? Int. J. Prod. Econ. 127, 372–383 (2010). https://doi.org/10. 1016/j.ijpe.2009.08.016 18. Ball, M.O., Ma, M., Raschid, L., Zhao, Z.: Supply chain infrastructures: system integration and information sharing. SIGMOD Rec. 31, 61–66 (2002) 19. Durowoju, O., Chan, H., Wang, X.: The impact of security and scalability of cloud service on supply chain performance. J. Electron. Commer. Res. 12, 243–256 (2011) 20. Chandra, C., Kamrani, A.: Mass Customization. Springer, Boston (2004). https://doi.org/10. 1007/978-1-4419-9015-0

Reconfigurable Manufacturing: An Investigation of Diagnosability Requirements, Enabling Technologies and Applications in Industry Alessia Napoleone1(B) , Brendan P. Sullivan2 , Elias Arias-Nava3 , and Ann-Louise Andersen1 1 Department of Materials and Production, Aalborg University, Aalborg, Denmark

[email protected]

2 Department of Management, Economics and Industrial Engineering, Politecnico Di Milano,

Milan, Italy 3 Department of Industrial and Operations Engineering, Instituto Tecnologico Autonomo de

Mexico, Mexico City, Mexico

Abstract. In the dynamic environment of today’s manufacturing industry, companies need to be changeable, i.e. capable of adapting to changes quickly and cost-effectively. In this context, the diagnosability characteristic, allowing fast and economic ramp-ups of new manufacturing settings, becomes particularly relevant. Depending on their diagnosability requirements, companies can exploit different technologies and applications. In this study, five diagnosability requirements have been identified. Through a literature review, the five requirements have been further investigated; thus, the extent to which these five requirements can be fulfilled, and their enabling technologies and applications has been specified. Finally, a case study has been conducted to show how diagnosability requirements are fulfilled differently in three manufacturing contexts. Keywords: Changeable manufacturing · Reconfigurable manufacturing system · Diagnosability · Industry 4.0

1 Introduction Manufacturing companies incessantly face challenges due to evolving market requirements, governmental regulations, dynamic shifts in technology and sustainability requirements. In response to these challenges, companies are required to change their processes and manufacturing systems. To this end, the term changeability has been used in literature as an umbrella concept to refer to the generic capability of a system to dynamically change as quickly, effectively and economically as possible [3, 20]. At the manufacturing system level, changeability can be fulfilled through Reconfigurable Manufacturing Systems (RMS) [3]. RMSs are capable to repeatedly change and/or rearrange their components in a cost-effective way in order to quickly adjust production © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 80–87, 2022. https://doi.org/10.1007/978-3-030-90700-6_8

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capacity and functionality to accommodate evolving requirements [15]. To this purpose, RMSs can be deconstructed according to six core characteristics: modularity, integrability, diagnosability, scalability, convertibility and customization [9]. Amongst these characteristics, diagnosability allows fast and eventually automatic fulfillment of one or more of the following requirements: • • • • •

Req. 1. Avoid quality and reliability problems [4, 12, 19]; Req. 2. Detect and localize quality and reliability problems [9, 18]; Req. 3. Identify causes of quality and reliability problems [9, 19]; Req. 4. Correct quality and reliability problems (local action) [4, 21]; Req. 5. Identify alternative solutions for error or failure recovery (systemic action) [11].

Two reasons make diagnosability particularly relevant. First, as reconfigurations of manufacturing systems should be quick and economic, diagnosability allows companies to implement more frequent reconfigurations by ensuring that the system is capable to quickly reach stable production after reconfigurations. Secondly, Industry 4.0, leading to the increasing availability and exploitation of high quantities of digital data, promises to support companies in developing diagnosability. Therefore, this paper focuses on the diagnosability characteristic of RMSs and addresses the following two research questions: “How can the five diagnosability requirements be fulfilled in different manufacturing contexts?” and “which technologies and applications enable the five diagnosability requirements?”. Through literature review, a framework for mapping the five diagnosability requirements and enabling technologies and applications is provided in this study. Subsequently, through a case study, the framework is applied to three cases to show how diagnosability requirements are fulfilled in these contexts.

2 Literature Review A literature search was conducted in Scopus, combining the key-words “diagnosability” and “manufacturing system”. Literature published after 2011 was considered. Through this search, 18 papers were selected and used for the construction of the proposed framework due to their relevancy, as detailed in the remainder. These 18 papers were selected and classified based on the following criteria: (i) the publication referenced diagnosability requirements, meaning any of the five requirements (from req. 1 to req. 5) listed in Sect. 1; and/or (ii) the publication suggested technologies and applications enabling the fulfillment of the stated requirements. Hereafter, the results of the literature review are summarized. Diagnosability Requirements Publications: three papers referenced req. 1 (avoid quality and reliability problems) [9, 15, 19]; 11 papers referenced req. 2 (detect and localize quality and reliability problems) [1, 4–9, 13, 14, 16, 18]; 14 papers referenced req. 3 (identify causes of quality and reliability problems) [2, 4–11, 13, 17–19, 21]; five papers referenced req. 4 (correct quality and reliability problems) [4, 6, 10, 13, 21]; and, one paper referenced req. 5 (identify alternative solutions for error or failure recovery) [11].

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Technologies and Applications Publications: from the 18 papers identified in the review, three papers referenced reconfigurable inspection resources [8, 18, 19]; one paper referenced units designed for replacement [17]; and one paper referenced built-in redundancy [11]. As a consequence of Industry 4.0, a number of not necessarily recent technologies coupled with new applications can be associated to diagnosability. This includes: sensors [11, 14, 19]; Internet of Things [14]; big data analytics [7, 17]; tracking/monitoring information systems [7–9]; digitally assisted operators [12]; digitallyenabled poka-yoke mechanisms [9, 15]; verification of correct position of products in machines’ feeding systems [6, 19]; smart devices [14]; actuators [14]. Figure 1 summarizes the results of the literature review: diagnosability requirements can be fulfilled to different extents, recurring to a number of technologies and applications. For example, req. 1 can be fulfilled through either manual, semi-automatic or automatic avoidance of problems, exploiting technologies and applications such as digital poka-yoke mechanisms, verification of the correct position of products in machines’ feeding systems and digital assistance systems.

Req.1

Extent to which requirements can be fulfilled (different options, depending on manufacturing contexts) Automatic vs semi-automatic vs manual

Enabling technologies and applications Poka-yoke mechanisms and similar

Req.5

Diagnosability Requirements Req.4 Req.3 Req.2

Automatic vs semi-automatic vs manual

Tracking/ monitoring information systems

Adjustable detection capability vs non adjustable detection Real-time vs event-based vs periodic detection Sample dimension (all products vs % of products)

Sensors

Automatic vs semi-automatic vs manual Required time (ranging from very fast to very slow)

Verification of correct position of products in machines’ feeding systems

Big data analytics

Smart devices and actuators

Digitally assisted operators

Internet of Things

(Reconfigurable) Inspection resources (e.g. machines)

Replaceable units

Digitally assisted operators

Accuracy of diagnostics (ranging from very high to very low) Automatic vs semi-automatic vs manual Required time (ranging from very fast to very slow) Automatic vs semi-automatic vs manual Required time (ranging from very fast to very slow)

Actuators

Built-in redundancy

Fig. 1. Diagnosability requirements, extent to which they can be fulfilled, and their enabling technologies and applications

3 Case Study Three companies, which over the past five years have been working to increase their diagnosability and the level of automation within their processes, were analyzed from October 2020 to May 2021. • Case #1 is involved in the manufacturing of outdoor furniture, producing roughly different 216 products with 800 employees. The facility is equipped with automated robotic stations, manual production stations and various finishing solutions.

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• Case #2 designs and manufactures 41 high-precision telecommunications and technological solutions, for commercial, space and defense. The facility with 1200 employees is equipped with automated and manual production processes. • Case #3 manufactures 11 specialized products for military aircraft, with international operations. Comprised of roughly 850 employees they design and manufacture highprecision products through various automated and manual processes. Based on the results of the literature review, a questionnaire comprised of both closed and open-ended questions was built and used for the analysis. Closed-ended questions aimed at the: (i) identification of diagnosability requirements (from req. 1 to req. 5), (ii) identification of technologies and applications supporting diagnosability requirements, and (iii) quantification of the existing level of diagnosability, based on requirements and technologies and applications. Open-ended questions aimed at catching further insights on enabling technologies and applications. In each case, the production manager (or delegate) was interviewed. To quantify the existing level of diagnosability, closed-ended questions were scored using a Likert scale (5 points scale; 1, 3, 5, 7, 9), so the respondent could choose their company’s practice level. The lowest levels scored with 1 corresponded to a poor practice, while the highest-level scored with 9 corresponded to a best practice. Finally, an overall measure of the extent to which the five classes of diagnosability requirements are fulfilled was obtained.

4 Results The results of the case study are summarized in the remainder, where the requirements and the technologies and applications within each case were analyzed and detailed. Table 1. A measure of the overall level of diagnosability, based on the fulfilment of the five requirements in the three cases (classes of diagnosability requirements are listed in Fig. 1)

Req. 2 - Detection

Req. 3 - Identification

Req. 4 - Recovery

Req. 5 -Alternatives

Case #1 – 80.0% Case #2 – 87.5% Case #3 – 89.1% Average Standard Deviation

Req. 1 - Avoidance

Classes of Diagnosability Requirements

78.5% 86.3% 85.2% 83.3% 0.042

88.6% 93.4% 94.6% 92.2% 0.032

83.8% 89.8% 93.2% 88.9% 0.048

73.3% 86.4% 92.4% 84.0% 0.098

63.6% 80.7% 82.3% 77.2% 0.104

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For each case, the overall level of diagnosability, based on the fulfillment of the five classes of requirements (from req.1 to req. 5), is synthesized in Table 1. As shown in Table 1, Case #3 has generally a higher level of fulfillment of the diagnosability requirements. Moreover, the highest variation between the three cases is observed in req. 5. An analysis of the extent to which requirements are fulfilled, allowing to better understand how these are fulfilled, is summarized in Table 2. For example, from Table 2 it can be deducted that the highest variation between cases observed in req. 5 can be attributed to differences in the level of automation and the responsiveness of the solutions adopted in the three cases. Table 2. Extent to which requirements are fulfilled in the three cases Case #1

Case #2

Case #3

Req.1

Semi-automatic and manual

Automatic and semi-automatic

Automatic and semi-automatic

Req.2

Semi-automatic and manual Adjustable detection Event-based detection Sample dimensions (% of products)

Automatic and semi-automatic Adjustable detection Event-based detection Sample dimensions (all products)

Automatic and semi-automatic Adjustable detection Event-based detection Sample dimensions (all products)

Req.3

Manual Required time (average/slow) Required accuracy of diagnostics (average)

Semi-automatic and manual Required time (fast) Required accuracy of diagnostics (high)

Semi-automatic and manual Required time (very fast) Required accuracy of diagnostics (very high)

Req.4

Manual Required time (average/slow)

Manual Required time (fast)

Semi-automatic and manual Required time (very fast)

Req.5

Manual Required time (slow)

Semi-automatic and manual Required time (fast)

Semi-automatic and manual Required time (fast)

Finally, different technologies and applications are exploited to fulfill the five diagnosability requirements, as summarized in Table 3. Five technologies and applications have been added to those identified through the literature review, these are: (i) augmented reality to fulfill req. 1 and req. 5; (ii) machineto-machine communication to fulfil req. 5; (iii) Manufacturing Execution System and control system to fulfil req. 1 and req. 3; (iv) Programmable Logic Controllers to fulfil req. 4; and (v) robotics, to fulfil req. 1. Moreover, Case #3 shows that the combination of sensors and IoT can be used to support req. 5 (thus, extending the results of the literature review which show – in Fig. 1 - that this technology supports the req. 2 and req. 3). Case #3 also shows that the tracking/monitoring information system can be used to support req. 1 and req. 3 (other than req. 2 as resulting from the literature review).

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Table 3. Enabling Technologies and Applications (the list is in alphabetic order) in the three cases (Cases #1, #2 and #3 are indicated in the table respectively with 1, 2 and 3) Enabling Technology/Application

Req. 1 Req. 2 Req. 3 Req. 4 Req. 5

Actuators Augmented reality

1,2,3 3

3

Big data analytics

3

Built in redundancy

1,2,3

Combination of sensors and IoT

1,2,3

Digitally assisted operators

1,2,3

Machine-to-machine communication

3

Manufacturing Execution System and control system 1,2,3 (Programmable Logic Controllers) Poka-yoke mechanism (or similar)

1,2,3

1,2,3

Reconfigurable inspection resources

1,2,3

1,2,3

Replaceable units

1,2,3

Robotics

1,2,3

Tracking/monitoring information systems

3

Sensors

3 1,2,3

Smart devices Verification of correct position of products in machine feeding systems

1,2,3

1,2,3

1,2,3

1,2,3

1,2,3

5 Conclusions Given the relevance of the diagnosability characteristic in the current manufacturing scenario, this study aimed to uncover and detail five diagnosability requirements (from req. 1 to req. 5) through a literature review and a case study. The literature review found that the five diagnosability requirements can be fulfilled to different extents, through several technologies and applications. From the literature review, it can be concluded that req. 2 and req. 3 are the currently most investigated, while req. 1, req. 4 and req. 5 deserve further research, especially in the light of new potentialities offered by Industry 4.0. The case study showed how diagnosability requirements are fulfilled in three different manufacturing companies in disparate manners. The managerial implications of this study lie in the possibility for a company to describe its diagnosability, and eventually identify requirements, and select technologies and applications to improve the diagnosability level. Given the relevance of diagnosability today, further research should aim at building a maturity assessment model based on the results of this investigation. Moreover, technologies and applications should be further investigated through additional empirical research.

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A limitation of this study is that the technologies and applications have been roughly treated together, future research should first specify which applications allow companies fulfilling their diagnosability requirements, and then specify the technologies that can be used in the applications. Moreover, the results of the three cases are based on qualitative information collected through interviews, further research should aim at providing unbiased measures of the fulfilment of diagnosability requirements, to then allow a proper investigation of the status of fulfillment of diagnosability requirements in industry.

References 1. Cabasino, M.P., Giua, A., Marcias, L., Seatzu, C.: A comparison among tools for the diagnosability of discrete event systems. In: 2012 IEEE International Conference on Automation Science and Engineering, pp. 218–223 (2012) 2. Djaker, A., Sekhri, L.: Structure theory of petri nets to deal with diagnosability of automated manufacturing systems. EEA – Electroteh. Electron. Autom. 64, 121–126 (2016) 3. ElMaraghy, H., Wiendahl, H.P.: Changeability – An Introduction. In: ElMaraghy, H. (eds,) Changeable and Reconfigurable Manufacturing Systems. Springer Series in Advanced Manufacturing, pp. 3–24. Springer, London (2009). https://doi.org/10.1007/978-1-84882067-8_1 4. Gumasta, K., Kumar Gupta, S., Benyoucef, L., Tiwari, M.K.: Developing a reconfigurability index using multi-attribute utility theory. Int. J. Prod. Res. 49, 1669–1683 (2011) 5. Huang, A., Badurdeen, F., Jawahir, I.S.: Towards developing sustainable reconfigurable manufacturing systems. Procedia Manuf. 17, 1136–1143 (2018) 6. Jiao, Y., Djurdjanovic, D.: Compensability of errors in product quality in multistage manufacturing processes. J. Manuf. Syst. 30, 204–213 (2011) 7. Jimenez, J.F., Zambrano-Rey, G., Aguirre, S., Trentesaux, D.: Using process-mining for understating the emergence of self-organizing manufacturing systems. IFAC-PapersOnLine 51, 1618–1623 (2018) 8. Koren, Y., Gu, X., Badurdeen, F., Jawahir, I.S.: Sustainable living factories for next generation manufacturing. Procedia Manuf. 21, 26–36 (2018) 9. Maganha, I., Silva, C., Ferreira, L.M.D.F.: The sequence of implementation of reconfigurability core characteristics in manufacturing systems. J. Manuf. Technol. Manag. 32(2), 356–375 (2020). https://doi.org/10.1108/JMTM-09-2019-0342 10. Marangé, P., Philippot, A., Pétin, J.F., Gellot, F.: Diagnosability evaluation by modelchecking. IFAC-PapersOnLine 28, 308–313 (2015) 11. Milisavljevic-Syed, J., Commuri, S., Allen, J.K., Mistree, F.: A method for the concurrent design and analysis of networked manufacturing systems. Eng. Optim. 51, 699–717 (2019) 12. Napoleone, A., Andersen, A.: Reconfigurable manufacturing: how shop floor digitalisation supports operators in enhancing diagnosability. Adv. Transdisc. Eng. 13, 525–536 (2020) 13. Napoleone, A., Pozzetti, A., Macchi, M.: Core characteristics of reconfigurability and their influencing elements. IFAC-PapersOnLine 51, 116–121 (2018) 14. Pal, D., Vain, J.: A systematic approach on modeling refinement and regression testing of real-time distributed systems. IFAC-PapersOnLine 52, 1091–1096 (2019) 15. Prasad, D., Jayswal, S.C.: Assessment of a reconfigurable manufacturing system. Benchmarking Int. J. 28(5), 1558–1575 (2019). https://doi.org/10.1108/BIJ-06-2018-0147 16. Rajaoarisoa, L., Sayed-Mouchaweh, M.: Adaptive online fault diagnosis of manufacturing systems based on DEVS formalism. IFAC-PapersOnLine 50, 6825–6830 (2017) 17. Schmidt, K.W.: Verification of modular diagnosability with local specifications for discreteevent systems. IEEE Trans. Syst Man. Cybern. Part A Syst. Humans 43, 1130–1140 (2013)

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18. Shang, X., Milisavljevic-Syed, J., Huang, S., Wang, G., Allen, J.K., Mistree, F.: A key featurebased method for the configuration design of a reconfigurable inspection system. Int. J. Prod. Res. 59, 2611–2623 (2020) 19. Singh, A., Asjad, M., Gupta, P., Khan, Z., Siddiquee, A.: Measuring the relative importance of reconfigurable manufacturing system (RMS) using best–worst method (BWM). In: Pandey, V.C., Pandey, P.M., Garg, S.K. (eds.) Advances in Electromechanical Technologies. LNME, pp. 253–275. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5463-6_24 20. Sullivan, B., Rossi, M., Terzi, S.: A review of changeability in complex engineering systems. IFAC-PapersOnLine 51(11), 1567–1572 (2018) 21. Wang, G.X., Huang, S.H., Yan, Y., Du, J.J.: Reconfiguration schemes evaluation based on preference ranking of key characteristics of reconfigurable manufacturing systems. Int. J. Adv. Manuf. Technol. 89(5–8), 2231–2249 (2016)

A Classification of the Barriers in the Implementation Process of Reconfigurability Isabela Maganha1,2(B) , Ann-Louise Andersen3 , Cristovao Silva2 , and Luis Miguel D. F. Ferreira2 1 Institute of Integrated Engineering, Federal University of Itajubá, Itabira, Minas Gerais, Brazil

[email protected] 2 Department of Mechanical Engineering, University of Coimbra, CEMMPRE, Coimbra, Portugal 3 Department of Materials and Production, Aalborg University, Aalborg, Denmark

Abstract. Implementing reconfigurability is essential to manufacturing companies that aim to make the transition from conventional to reconfigurable manufacturing systems (RMS). The novel technologies promoted by the industry 4.0 paradigm are key factors to make the implementation of reconfigurability possible. However, previous research highlights that there is a small number of studies that explore this idea of integrating reconfigurability and industry 4.0 technologies. In practice, companies recognize five core characteristics that enables reconfigurability: modularity, integrability, customisation, adaptability and diagnosability. This work conducts a literature review to identify the barriers in the process of implementing reconfigurability. After that, it classifies the barriers identified in three contexts: technology, organisation and environment. Finally, the paper discusses whether some of the industry 4.0 technologies can contribute to overcome these barriers. The findings show that technology and organisation barriers can be exceeded with the acquisition and use of some novel technologies promoted by the industry 4.0. Keywords: Reconfigurability · Barriers of implementation · Industry 4.0 technologies

1 Introduction In the late 1990’s, reconfigurable manufacturing systems (RMS) emerged as a novel engineering response to volatile global markets in which forecasting future product demand became a big challenge [1]. In such scenario, manufacturing companies must deal with increasing product variety, frequent introduction of new products and fluctuations in demand volumes, to extend the lifetime of the manufacturing system and keep competitiveness. For this reason, reconfigurability and its core characteristics: modularity, integrability, customisation, adaptability and diagnosability, became key abilities to implement in manufacturing systems [2]. © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 88–95, 2022. https://doi.org/10.1007/978-3-030-90700-6_9

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However, there is a gap between theory and practice in respect to the implementation of reconfigurability due to the lack of knowledge on how to achieve its core characteristics in real systems. Part of the existing research on reconfigurability address the assessment of its implementation level through its core characteristics, although some works did not consider all of them [3, 4]. Others concern the concept and how reconfigurability can be translated to actual manufacturing companies [2, 5–8]. Several works developed global indices to measure the reconfigurability present in manufacturing systems using multi-criteria decision-making [9–11]. Despite the relevance of these contributions, they assume that reconfigurability is already implemented in the manufacturing system, which is not consistent with the reality of many industries. Since the pre-requisites for RMS design differ from those for conventional manufacturing systems, the implementation of reconfigurability presents many challenges. This is why this paper take a step back, emphasising the need to identify main barriers faced by companies in the process of implementing reconfigurability. This is of the highest importance to implement reconfigurability successfully. Up to date, these barriers have been investigated mainly concerning the implementation and operation or in terms of the development and design of RMS [12, 13]. The technologies promoted by industry 4.0, e.g. internet of things (IoT), virtual reality (VR) and augmented reality (AR), are expected to change the overall manufacturing process, thus contributing to the implementation of reconfigurability. Among the aforementioned studies, there is a small number that relates the reconfigurability and the industry 4.0 concepts [2]. Thus, to support practitioners in identifying and overcoming the most common difficulties in the implementation process of reconfigurability, this paper is guided by the following research questions: What are the barriers to implement reconfigurability in manufacturing systems? Which industry 4.0 technologies can contribute to overcome these barriers? To address these research questions, this study conducts a literature review, identifying and classifying the most common barriers in the implementation process of reconfigurability. The classification criteria split the barriers in three contexts: technology, organisation and environment. Then, it discusses which industry 4.0 technologies might contribute to overcome the barriers identified. The remainder of this paper is organized as follows. Section 2 reports and classifies the barriers of the implementation of reconfigurability found in the literature. Section 3 discusses the relationship between the barriers and the Industry 4.0 technologies. Finally, Sect. 4 summarizes the main conclusions of this paper, highlights its limitations and suggests directions for further studies.

2 Barriers in the Implementation Process of Reconfigurability 2.1 The Identification of Barriers The barriers were identified through a non-exhaustive literature review. Only peerreviewed journal articles and international conference proceedings were considered. The language, keywords and content availability were considered as elimination criteria.

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There are only a few investigations on the barriers in the implementation process of reconfigurability. Malhotra et al. [12] described a list of barriers related to the implementation and operation of RMS that includes: the complexity to develop a design methodology for RMS; the hardness to create a standardised and reconfigurable control system for RMS; the difficulty to handle a huge variety of products; the use of rigid system interfaces; and expensive tooling. Later, Rösiö [14] identified many theoretical and practical challenges to achieve RMS. Based on them, Andersen et al. [13, 15] identified several barriers concerning the design of RMS, which were then used to establish the prerequisites to design manufacturing systems for reconfigurability. The barriers are related to: the reuse of equipment throughout the entire system’s life cycle, which arise from the separation between the responsibilities of production and product design, the division of responsibilities between development teams and operations, and the definition of manufacturing systems requirements during its lifetime; the correlation between the system design and product development; a long-term view on investments; the development of a structured design process of RMS; an holistic view of the manufacturing system; the knowledge and skills related to the reconfigurability. The last barrier is also supported by other research, that emphasise the need of training the employees (managers and operators), who might be resistant to changes [16]. Lastly, even though the analysis of the barriers of the implementation of reconfigurability was not the main focus of Maganha et al. [2], their empirical results highlights some difficulties faced by manufacturing companies in order to implement the core characteristics. The main difficulties reported are: the lack of modular equipment and reconfigurable inspection machines; the disability of automatically detect the sources of failures, quality problems or defective products, diagnose their root causes and reset its parameters to restore the initial situation; the lack of an integrated control system, an open-architecture environment and interfaces that permit an easy integration of new elements and/or technologies; and the complexity to add, remove and displace equipment in the shop floor area. 2.2 The Classification of the Barriers The barriers identified in the literature are classified according to three contexts: technology, organisation and environment. The technology context refers to the equipment and processes required, as well as their related internal and external technologies. The organisation context includes the resources and other characteristics of the company (e.g. size and structure) and employees’ skills. The environment context considers company’s partners and competitors, the macro-economic context and the regulatory environment [17]. A classification of the barriers is presented in Table 1.

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Table 1. Classification of the barriers in the implementation process of reconfigurability. Classification

Barrier

References

Technology

1. Lack of machine tool design methodology

[1, 2]

2. Lack of standardized design and control methodologies

[2, 12]

3. Lack of a reconfiguration of controller architecture and/or an open-architecture environment

[2, 12]

4. Lack of modular equipment

[2]

5. Lack of an integrated control methodology

[2, 12]

6. Lack of integration interfaces

[1, 2]

7. Lack of reconfigurable inspection machines

[2, 12]

8. Difficulty to select machine modules and operations

[12]

9. Difficulty to integrate new components and/or technologies

[2]

10. Difficulty to detect defective products automatically

[2]

11. Difficulty to add, remove and/or reorganize equipment in the [2] shop floor Organisation

12. Expensive tooling

[12]

13. Difficulty to handle product variety

[12]

14. Reuse of equipment throughout the entire system’s lifecycle [13–15]

Environment

15. Definition of manufacturing system’s requirements throughout its lifetime

[13–15]

16. Reluctance towards predicting products roadmaps

[13, 15]

17. Lack of knowledge of the manufacturing system and its components

[13–15]

18. Lack of knowledge and skills related to reconfigurability

[13–15]

19. Lack of employees’ training

[16]

20. Resistance to change (by employees)

[16]

21. Long-term view on investments

[13, 15]

22. High costs (in general)

[13, 15]

3 Discussion Reconfigurability is achieved by implementing its core characteristics [18]. Thus, first, the barriers are associated to the core characteristics and then, industry 4.0 technologies that might support overcoming the barriers are identified. The technology barriers can be exceeded by manufacturing companies with the acquisition and implementation of novel technologies, such as those promoted by the industry 4.0. These barriers address three core characteristics of reconfigurability: modularity, integrability and diagnosability.

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The challenges faced to implement modularity and integrability are closely related. Indeed, previous research support the idea that both characteristics impact on each other [18]. Reconfigurable machine tools (RMT) are essential to achieve modularity. RMT can perform several operations in their existing configurations and their functionality can be changed by changing their modules along with an open architecture software. Advanced solutions as mobile collaborative robots can benefit manufacturing companies in order to overcome the technological barriers related to modularity. Mobile collaborative robots perform in the same manner of modular systems. But, in this case, the manufacturing system might be fixed, while the resources can move around the system easily and be added, removed or reorganised rapidly in the manufacturing area [19]. In sum, RMT and mobile collaborative robots provide elements that can be reused throughout the entire system’s life cycle, promoting the modularity required to achieve reconfigurability. Wireless devices, such as integrated sensors and wireless information network can be useful to collect and synchronise real time data, helping to overcome the barriers of implementation of integrability. Also, in industry, practitioners are trying to use the IoT to integrate their manufacturing systems. Particularly in RMS, companies need to apply IoT to connect machines and logic, to solve, automate and control problems [20]. Thus, companies that acquire and implement the aforementioned technologies can benefit of an integrated manufacturing system in which new components and other technologies can be included anytime. The difficulty to detect failures, quality problems and defective products can be exceeded by using reconfigurable inspection machines (RIM), which permit the quality inspection at a separated stage in the manufacturing process. Coordinate measuring machines (CMM) and 3D scanning can be also used for this purpose. Commonly, CMM allow the quality inspection of processing materials and final products. In the industry 4.0 environment, CMM become faster in terms of quality assessment for individual products and to measure complex geometric parts of manufactured products. 3D scanning, on the other side, represent an advanced optical machine vision technology that can be applied to execute inspection tasks [21]. The organisation barriers can be related to the other two characteristics of reconfigurability: adaptability and customisation. Specifically, the difficulty to handle variety can be understood from a technical or a decision-making point of view. In the first, this problem is related to the complexity of adjusting the structure of the manufacturing system to new production requirements such as higher/low volumes or different product mixes. In this case, this barrier points to the core characteristic of adaptability. It can be overcome by using all the technologies referred in Table 2 that are intended to provide the ability to adapt manufacturing system’s physical structure. In the second, this difficulty addresses the complexity of the manufacturing system’s design for reconfigurability, in which managers should decide the investment requested for implementing reconfigurability; identify the requirements of each core characteristic, consequently, the level of reconfigurability required for the manufacturing system; identify and select product families; select the manufacturing system’s configurations; among others; therefore, referring to the characteristic of customisation. The remaining barriers follow the same analogy of the second point of view; thus, they are also related to the customisation. Furthermore,

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barriers 14 to 16 highlight the importance of the planning in RMS. In fact, since the reconfiguration process requires high expense, effort and time, the RMS planning must be systematic and organised to minimise costs and/or save time and energy [20]. Overall, to defeat the organisation barriers, technologies such as VR, AR and CPPS can be used to provide a holistic view of the manufacturing system. Based on that, companies will be capable of controlling and monitoring their manufacturing systems, in real-time. IoT, wireless devices, communication and information network can also contribute to this regard. Moreover, real-time information sharing and collaboration is vital to the decision-making process in RMS and industry 4.0 environments. Big data analytic and cloud computing can contribute to achieve that [21]. In respect to the organisation barriers referred to the employees (18 to 20), whether on one side the industry 4.0 brings automation and robotising, making it possible to avoid high costs; on the other side, companies should adapt their human resources activities to market and technological conditions that change more frequently. However, there is a current gap of knowledge and skills between labour market requirements and the work force available, that can be surpassed with training and education focused on industry 4.0 [22]. In this case, the learning factories can be used to link the theory and practice [16]. In addition, VR and AR might be interesting to deal with these barriers. Table 2 summarises the technology and organisation context barriers, their relationship with the core characteristics of reconfigurability and with industry 4.0 technologies. Table 2. Relationship among the barriers, the core characteristics of reconfigurability and Industry 4.0 technologies. Barriers

Type

Core characteristic

How to overcome

References

1, 2, 3, 4, 8, Technological 11

Modularity

RMT and mobile [19] collaborative robots

5, 6, 9

Technological

Integrability

IoT

[20]

7, 10

Technological

Diagnosability

RIM, CMM and 3D scanning

[21]

13

Organizational

Adaptability/customization

[20, 21]

14, 15, 16, 17, 18, 19, 20

Organizational

Customization

VR, AR, CPPS, big data analytics, cloud computing

Finally, barriers 12, 21 and 22 represent one of the biggest challenges in the process of implementing reconfigurability. Although the industry 4.0 technologies can improve the presence of reconfigurability in manufacturing systems significantly, companies struggle to find a cost-effective way to do it. This happens because implementing reconfigurability requires an additional investment, which must be analysed by companies in order to verify its feasibility. As a result, instead of contributing to overcome the environment context

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barriers, the industry 4.0 technologies impose a dilemma. Therefore, companies must evaluate the trade-offs between the objectives of flexibility and productivity; postponement and risks; scalable capacity and reconfiguration costs; functionalities changes/new products introduction and increased initial system investments in order to overcome these barriers [15].

4 Conclusion and Further Research This work identified 22 barriers in the process of implementing reconfigurability and classified them according to three contexts: technology, organisation and environment. The classification is based on theory, considering the existing works related to the concept of reconfigurability. In short, the outcomes indicate that the technology barriers are widely emphasised in previous research, while organisation and environment barriers are mentioned to a lesser extent. Such barriers cannot be ignored, as well as critical social and human aspects, that depends on companies’ culture, traditions and readiness to change. The technology and organisation context barriers can be related to the core characteristics of reconfigurability: modularity, integrability, diagnosability, adaptability and customisation. The findings highlight that novel technologies promoted by the industry 4.0 can contribute to overcome these two types of barriers. Organisation barriers related to the employees can be overcome with education and training. Environment barriers should be analysed in terms of trade-offs. This paper conducted a literature review. Despite that, the number of studies that address the barriers to implement reconfigurability in actual manufacturing systems is small and need further investigation. Future works should consider deepening the investigation on the organisation and environment context barriers, which have been less exploited. Interpretive structural modelling (ISM) can also be applied in future works in order to investigate the interrelationships between barriers in the implementation process of reconfigurability. Acknowledgements. This research is sponsored by national funds through FCT – Fundação para a Ciência e a Tecnologia –, under the project UIDB/00285/2020.

References 1. Koren, Y., et al.: Reconfigurable manufacturing systems. CIRP Ann. Manuf. Technol. 48, 527–540 (1999) 2. Maganha, I., Silva, C., Ferreira, L.M.D.F.: Understanding reconfigurability of manufacturing systems: an empirical analysis. J. Manuf. Syst. 48, 120–130 (2018) 3. Gumasta, K., Gupta, S.K., Benyoucef, L., Tiwari, M.K.: Developing a reconfigurability index using multi-attribute utility theory. Int. J. Prod. Res. 49, 1669–1683 (2011) 4. Farid, A.M.: Measures of reconfigurability and its key characteristics in intelligent manufacturing systems. J. Intell. Manuf. 28(2), 353–369 (2014). https://doi.org/10.1007/s10845-0140983-7

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5. Andersen, A.L., Larsen, J.K., Brunoe, T.D., Nielsen, K., Ketelsen, C.: Critical enablers of changeable and reconfigurable manufacturing and their industrial implementation. J. Manuf. Technol. Manag. 29, 983–1002 (2018) 6. Maganha, I., Silva, C., Ferreira, L.M.D.F.: The impact of reconfigurability on the operational performance of manufacturing systems. J. Manuf. Technol. Manag. 31(1), 145–168 (2019) 7. Spena, P.R., Holzner, P., Rauch, E., Vidoni, R., Matt, D.T.: Requirements for the design of flexible and changeable manufacturing and assembly systems: a SME-survey. Procedia CIRP 41, 207–212 (2016) 8. Maganha, I., Silva, C., Ferreira, L.M.D.F.: An analysis of reconfigurability in different business production strategies. IFAC-PapersOnLine 52, 1028–1033 (2019) 9. Goyal, K.K., Jain, P.K., Jain, M.: A novel methodology to measure the responsiveness of RMTs in reconfigurable manufacturing system. J. Manuf. Syst. 32, 724–730 (2013) 10. Benderbal, H.H., Dahane, M., Benyoucef, L.: A new robustness index for machines selection in Reconfigurable Manufacturing system. In: Proceedings 2015 International Conference Industrial Engineering System Management, IEEE IESM 2015, pp. 1019–1026 (2015) 11. Maganha, I., Silva, C., Ferreira, L.M.D.F., Thurer, M., Frazzon, E.M., Silvestri, M.: Proposal of a reconfigurability index using analytic network process. In: IEEE International Conference Industrial Engineering and Engineering Management, pp. 1310–1313 (2019). 12. Malhotra, V., Raj, T., Arora, A.: Evaluation of barriers affecting reconfigurable manufacturing systems with graph theory and matrix approach. Mater. Manuf. Process. 27, 88–94 (2012) 13. Andersen, A.L., Nielsen, K., Brunoe, T.D.: Prerequisites and barriers for the development of reconfigurable manufacturing systems for high speed ramp-up. Procedia CIRP 51, 7–12 (2016) 14. Rösiö, C.: Supporting the design of reconfigurable production systems, Mälardalen University (2012) 15. Andersen, Ann-Louise., Larsen, J., Nielsen, K., Brunoe, T., Ketelsen, C.: Exploring barriers toward the development of changeable and reconfigurable manufacturing systems for masscustomized products: an industrial survey. In: Hankammer, S., Nielsen, K., Piller, F.T., Schuh, G., Wang, N. (eds.) Customization 4.0. SPBE, pp. 125–140. Springer, Cham (2018). https:// doi.org/10.1007/978-3-319-77556-2_8 16. Bortolini, M., Galizia, F.G., Mora, C.: Reconfigurable manufacturing systems: literature review and research trend. J. Manuf. Syst. 49, 93–106 (2018) 17. Sun, S., Cegielski, C.G., Jia, L., Hall, D.J.: Understanding the factors affecting the organizational adoption of big data. J. Comput. Inf. Syst. 58, 193–203 (2018) 18. Maganha, I., Silva, C., Ferreira, L.M.D.F.: The sequence of implementation of reconfigurability core characteristics in manufacturing systems. J. Manuf. Technol. Manag. 32, 356–375 (2020) 19. Maganha, I., et al.: Hybrid optimisation approach for sequencing and assignment decisionmaking in reconfigurable assembly lines. IFAC-PapersOnLine 52, 1367–1372 (2019) 20. Kurniadi, K.A., Lee, S., Ryu, K.: Digital twin approach for solving reconfiguration planning problems in RMS. In: Moon, I., Lee, G.M., Park, J., Kiritsis, D., von Cieminski, G. (eds.) APMS 2018. IAICT, vol. 536, pp. 327–334. Springer, Cham (2018). https://doi.org/10.1007/ 978-3-319-99707-0_41 21. Zheng, P., et al.: Smart manufacturing systems for Industry 4.0: conceptual framework, scenarios, and future perspectives. Front. Mech. Eng. 13, 137–150 (2018) 22. Rajnai, Z., Kocsis, I.: Labor market risks of industry 4.0, digitization, robots and AI. In: SISY 2017 - IEEE 15th Int. Symposium Intelligent System Informatics, pp. 343–346 (2017)

Development of a Parallel Product-Production Co-design for an Agile Battery Cell Production System J. Ruhland1 , T. Storz1(B) , F. Kößler1 , A. Ebel1 , J. Sawodny1 , J. Hillenbrand1 , P. Gönnheimer1 , L. Overbeck1 , Gisela Lanza1 , M. Hagen8 , J. Tübke8 , J. Gandert2 , S. Paarmann2 , T. Wetzel2 , J. Mohacsi3 , A. Altvater3 , S. Spiegel3 , J. Klemens3 , P. Scharfer3 , W. Schabel3 , K. Nowoseltschenko4 , P. Müller-Welt4 , K. Bause4 , A. Albers4 , D. Schall5 , T. Grün5 , M. Hiller5 , A. Schmidt6 , A. Weber6 , L. de Biasi7 , H. Ehrenberg7 , and J. Fleischer1 1 Wbk Institute of Production Science, Karlsruhe Institute of Technology,

76131 Karlsruhe, Germany [email protected] 2 TVT Institute for Thermal Process Engineering, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany 3 TFT Thin Film Technology, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany 4 IPEK Institute of Product Engineering, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany 5 ETI Institute of Electrical Engineering, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany 6 IAM-ET Institute for Applied Materials - Electrochemical Technologies, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany 7 IAM-ESS Institute for Applied Materials – Energy Storage Systems, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany 8 Fraunhofer-Institut Für Chemische Technologie (ICT), 76327 Pfinztal, Germany Abstract. Since current battery cell production lines are not flexible regarding format and material, it is necessary to develop new production systems. It is also required to develop this production line as an agile system in order to be able to flexibly counteract unit-specific capacity fluctuations. In addition, only low scrap rates are allowed when integrating new material systems which requires a holistic in-process or in-line control and the associated quality assurance. Agile production systems open up new possibilities for developing the battery cell product. Therefore, this article will present a novel product-production co-design that can be specifically adapted to customer requirements. Keywords: Flexibility · Agile production · Battery cell manufacturing

1 Motivation – Agile Production Systems for Battery Cell Manufacturing Current production systems for battery cells are not capable of producing different formats, different materials or even in variable quantities on one production line. They © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 96–104, 2022. https://doi.org/10.1007/978-3-030-90700-6_10

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produce standardized cells which, although of high quality, are not specifically adapted to customer requirements – on the contrary, customers adapt their wishes to the product portfolio offered. Today’s automated production systems are very different from Henry Ford’s original rigid transfer lines. Each product produced is individually adapted to the customer’s requirements, with the series basing on platforms. In order to make maximum use of the available installation space, the battery in products like consumer electronics is subsequently integrated as an energy storage in the installation space that is still available (for example an L-shaped battery cell in the iPhone). In contrast, a lot of products like power tools have to be designed around the battery system due to the rigid cell formats provided by current battery cell manufacturers. A scalable production system for battery cell manufacturing that is flexible in terms of format, material and number of units would provide a new degree of freedom with regard to the product development of battery cells. Within this paper, a novel productproduction co-design that can be specifically adapted to customer requirements will be presented. For this purpose, the need for an agile production system is first derived in a questionnaire-based scenario analysis. Based on a selected extreme scenario, the automated production of 1 unit or 1 battery cell, a series of preliminary experiments is used to illustrate how the plant modules are designed for this scenario and how the link to the battery cell product is established under the aspect of the complete digitalization of the production system. Finally, the opportunities for product development resulting from the new production system show the high potential of such an agile production system, which we will call AgiloBat in the following.

2 Scenario Analysis for an Agile, Robust and Versatile Production System for Battery Cells A scenario analysis shall ensure that the flexible production system can cover a wide range of the possible future requirements coming from technological progress and market demands. Basis of the scenario analysis was a workshop to identify all possible change drivers for battery cells coming from different sides, as for example politics, technology, society or market. In this workshop 26 researchers from diverse disciplines as material science, electric, mechanical, industrial and process engineering participated. A total of 35 relevant drivers of change were identified and prioritized in this workshop. The second step of the scenario analysis was a survey which was sent to battery cell experts from industry. The survey contained two parts: first general trends in battery cell production, design and quality assurance were requested. Secondly, the identified change drivers were assessed concerning their influence on battery cell production in Germany and the probability of their occurrence. 13 surveys were answered and provided insights into the current status and possible future trends of battery cell development and production. In the next step, interviews with six of the industry experts were performed to clarify the given answers and validate the results. Based on this input from research and industry, seven possible scenarios for battery cell production in Germany were created (Table 1). They cover most possible future developments and serve as a guideline for understanding the requirements a production system for battery cells in Germany

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No Scenario name

Implications for flexible cell production

1

Soaring subsidies create cell-boom for everybody

Flexible production system as “ramp-up line” → flexible production quantities with fast changing cell materials and forms

2

Foreign competition wins price war/LiB as Supplying niche markets → flexible in commodity material and form by small quantities

3

LiB – eternal all-rounder

LiB can cover all market needs → low material flexibility required → focus on form and quantities

4

Goodbye LiB

Other materials will dominate market → focus on material flexibility

5

Individualization as order winner

Small lot sizes will become standard → focus on material and form flexibility

6

All-Solid-State breakthrough

Flexible cell production has to be able to also produce ASS-battery cells

7

LFP-Renaissance

Complete change of material type; form and quantity flexibility remains

may face. Given the broad range of potential requirements, an agile production system is indispensable, to ensure economic viability of the system in the long run. The needed production system should be able to respond to most of the seven scenarios. To achieve this, the most challenging scenarios were identified. For an extreme flexibility a lot size of 1 is required. This can be implemented only by a sheet-to-sheet production, a technology that is not yet industrialized.

3 Development of Production Modules for Sheet-To-Sheet Production The variant-rich production of lithium-ion battery cells in very small batch sizes is currently carried out manually in laboratories and, due to the high costs and low throughput, purely for product development. Commercial battery cells, on the other hand, are manufactured on rigid production lines especially with roll-to-roll processes in electrode production. In addition to high throughput, these production systems are also characterized by low unit costs, provided that line utilization is high and product changes are infrequent. In the following, an approach will now be presented as to how the sheet-bysheet material flow established on a laboratory scale can be transferred to commercial battery cell production. In essence, this approach concerns the process steps coating, drying and calendering. The sheet-by-sheet material flow requires the adaptation of the known process steps of the roll-to-roll processes as well as a new development of the associated production equipment (classified as resources). In addition, it must be noted that the two previously mentioned aspects of production (processes and resources) are closely related to the product to be manufactured.

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In order to enable a parallel development procedure, the so-called product-production co-design, basic principles must be created which unambiguously reflect the requirements. In this context, the product, process, resource model (PPR model) is used. These comprise a framework structure and represent all products, processes and resources occurring in production through their information and data flows. On the one hand, the hierarchical structure of the data space of the battery cell production is given by the frame structure. On the other hand, a holistic representation of the production is obtained by mutual referencing of the objects product, process and re-source via attributes. In the following, the basics of the process steps coating, drying and calendering are explained under consideration of the sheet-by-sheet material flow. Based on these findings, the structure of the PPR model is then presented using the example of the coating process. 3.1 Coating and Drying Slot-die coating is a pre-metered coating process and is particularly suitable for applications that require a high degree of precision with regard to the application of the fluid. Compared to the doctor blade coating process, a variation of geometries and thicknesses is designed by changing the width of the slot nozzle insert and the volume flow. Mounting the slot dies vertically pivotable enables additional variation of the electrode width and the production of different electrode geometries such as parallelograms or trapezoids. The optimized setting of the process ensures high product quality and a reduction of production waste by avoiding start, stop and side edges of the electrodes. In first trials, rectangular anode layers, which already show a good agreement with the desired geometry, were produced. In particular, the coating and drying module should ensure that the production of electrodes with different material systems with different flow properties and drying requirements is guaranteed. This enables optimized production of, for example, new material systems through very short changeover times. [1] To achieve this, the development of novel drying processes is required. Therefore, a stationary convective dryer with impinging jets called “Comb Nozzle” (Fig. 1(b)) is adapted [2, 3]. The entire scheme of the dryer is illustrated in Fig. 1ss. In order to optimize the drying process for the requirements of agile electrode production, the heat is not only supplied convectively by heating of the drying air, but in a heat conductive manner by positioning the sheets on a temperature-controlled plate (Fig. 1(a)). By controlling the heat flow of the plate, the drying temperature can be sensitively adjusted to the material. As a result, it is possible to react flexibly to changing drying requirements and, in addition, to achieve a multi-stage drying profile, as proposed by Jaiser et al., resulting in consistent product properties at increased average drying rates [4]. A particular challenge hereby is the thermal design of the heat supply system, which has been taken into account in a simulation model. The model gives advice about the reached drying temperature of the electrode for a defined heat flux. Temperature gradients of less than 1 K on the electrode surface can be observed, which is an important aspect concerning a homogeneous drying behavior and has a direct impact on the electrode properties.

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Fig. 1. (a) Schematic of the drying plant; (b) field of impinging jets of the Comb Nozzle Dryer.

3.2 Calendering The calendering of parallelogram shaped coated electrode sheets has not yet been the focus of research. An automated, single sheet material flow, as well as an adaptable calendering process, needs to be developed. In contrast to rectangular coated state-ofthe-art electrodes, it is assumed that trapezoidal electrodes need a higher variation in the calendering forces because the coating width y(t) in the gap of the calendering machine d(t) is time-related. To achieve a constant electrode thickness and active material density throughout the whole electrode sheet, the forces on the rollers F(t) need to be adapted to the current electrode width in the gap. The adaption of the single sheet process to a state of the art calendering process is investigated. The experiments were conducted on an industrial calendering machine (Saueressig GKL 500 MS). Graphite based anode material was chosen as test material. For simulating trapezoidal coating areas, triangles were cut out of an electrode, as can be seen in the lower part of Fig. 2, which causes changes in the electrode width in the calendering gap. Triangular specimens allow the most drastic variation in sample width while using less material. The experimental plan included a variation of the angles between coating edge and copper film (20°, 30°, 45°, 60°, 80° and 90° respectively) and of the roll temperature (room temperature, 50 °C and 60 °C). The process speed was kept constant (1 m/min), and the double-sided coated electrode was compressed from 150 µm to 120 µm in each experiment. During the experiments, the calendering forces were tracked, and the thickness of each electrode was measured by the use of a dial gauge. Figure 2 shows a course of the resulting force at a test sample at 50 °C roller temperature. A significant decrease of the force corresponding to a decrease in the coating width can be observed. It can be determined that there is an approx. proportional correlation between these parameters (r ≈ 0.98). Furthermore, the ratio of the line load was calculated. For the reference test samples, the line load (temperature 50 °C) shows a deviation of ± 45 N/mm, or 18% of the average line load. In the specimens, the line load scattered more strongly and, in extreme cases, reached twice the value of the average line load. This indicates a more inhomogeneous compaction due to the coating width changes. However, thickness measurements showed no significant thickness changes over the specimen length. Rather, the measured thickness scattered by ±2.5 µm.

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Fig. 2. Resulting force during calendering a test sample (50 °C).

The results show that state-of-the-art calendering machines appear to be suitable for calendering trapezoidal electrodes. Furthermore, the influence of the roll diameter on the coating thickness during compaction of changing coating widths can be investigated. To conclude, the discussed results can help to develop a single sheet calendering machine for trapezoidal electrodes and other shapes with non-constant coating widths to support the approach of an agile battery cell manufacturing. 3.3 PPR-Models as the Basis for Structured Mapping of Aspects of the Battery Cell Production and Derivation of the Database Structure In the following, the relationships between products, processes and resources within battery cell production, as captured by the information model, will be presented using the example of coating, as explained in Sect 3.1. A single process step in battery cell production usually consists of products that represent the inputs of a process. For the coating process step, the input is represented by the products current collector foil and slurry. Processes are performed on resources that require specific materials to produce intermediate or final products. The coating process is a production step interacting with upstream or downstream processes. Therefore, no end product is produced. As an intermediate product, coating provides a coated electrode on the current collector foil. These coated electrodes represent the output of the coating process. Waste products can also be modeled through the information model. In the coating process step, this product type relates to slurry residues that may adhere to the slot-die lip during production. These previously described relationships are each defined by attributes of the objects. Accordingly, a process step can be described as a vector consisting of all data and information flows of the products, processes and resources. Mapping the identified product, process, and resource descriptive parameters into an object-oriented representation ensures the further use of the information captured therein. This object-oriented representation of production further forms the basis for the efficient development of a quality management database. The direct derivation of the database structure from the previously defined information models ensures a consistent mapping of all dependencies of products, processes and resources.

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The quality management database built here is document-oriented and was realized as a mongoDB database. The transfer of the information models in a JSON file enables the direct integration of the digital data models into the database structure. The interconnection of products, processes and resources, which are represented by specific parameters in the information models, are also reflected by the database. Thus, interlinkages can be traced back through the entire process chain. The database is filled by dynamically reading in job data, plant parameters and inline sensor measurement values. One main target of the project is to correlate this data with electro-chemical measurements of the final assembled battery cells. Consequently, specific processes and parameter values and ranges having a high impact on the product’s quality could be identified. Ideally, such correlations could be discovered by an AI approach in the future. Such knowledge can then be reported to higher levels and used to optimize the whole production chain.

4 Outlook – Flexible Product Development for Battery Cell Compound As demonstrated in the previous chapters, the foreseen production plant allows the fabrication of different-scale cells between the high-energy and high-power segments. To fully exploit the potential of a flexible and agile production line, the battery should be designed in parallel, with its characteristics precisely tailored to the intended application. How a specific cell compound is designed, holistically optimized and how the cell parameters are chosen for the particular application is addressed in this section. In the following, it is explained how the multitude of cell and compound parameters with their multiple degrees of freedom are narrowed down to a manageable number of variables with the help of an analysis of requirements and the scenario analysis in Sect. 2. Thereby, it is firstly evaluated which kind of cells are in demand on the market. In this context, numerous battery-powered applications from consumer electronics through power tools to the automotive sector were investigated, and ranges of required electrical properties like C-rate and energy density and thermal conditions were defined. The derived data is visualized in the Ragone plot in Fig. 3. Furthermore, it is shown in which area the energy and power density shall be enhanced, starting with the characteristics of a cell manufactured at KIT in a former project. Besides the market demand, the cell configurations to be produced are limited by the technical constraints of the production line concerning materials, formats and microstructure parameters. The main parameters with which the energy and power density can be adjusted at the cell level are the layer thickness and porosity of the active material. These are determined during coating and calendering. A high-energy cell is characterized by thicker layers and lower porosity, but they are limited by the production processes. A thick layer may result in exfoliation of the active material from the current collector, and low porosity requires enormous forces during calendering. Moreover, the production range is not only constricted by technical aspects, but also by the profitability of the production process. According to the scenario analysis in Sect. 2, it is not economical for AgiloBat to include automotive applications into its spectrum due to the use of large standard format serial production cells in that sector. Instead, the project will focus on

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smaller, customized applications, which can reach a great improvement of performance by the use of format flexible cells. In order to achieve the best possible design, an integral approach is applied to develop a tool for a holistic design and optimization. Thereby all the different aspects, as electrical and thermal behavior from cell to pack level as well as safety-related concerns are considered in a set of coupled models, which are embedded in an automated design optimization process. Furthermore, a selection of suitable wiring topologies, cooling strategies and space-filling options is implemented on pack level. A major research topic here is finding the best ratio between the effort and time needed for computation and parameter determination and the inclusion of parameters and effects to reflect changes in production related parameters reliably. The final design tool is able to extract the parameters required by the process design and production line to manufacture the cells as designed. Thereby it provides all needed information for the configuration of the machinery and possible plant conversions. For example, the desired layer thickness and porosity are converted into the corresponding necessary width of the calendering gap and the force, and these are transmitted to the calendering unit.

Fig. 3. Ragone plot with applications according to the requirement analysis and the target ranges of the future AgiloBat production line, starting with the characteristics (x) of a cell manufactured in the SmartBatteryMaker Project.

Acknowledgements. This work contributes to the research performed at CELEST (Center for Electrochemical Energy Storage Ulm Karlsruhe) and KIT Battery Technology Center. It was funded by the Baden-Württemberg Ministry of Economics, Labor and Housing within the Project “SmartBatteryMaker”, by the Baden-Württemberg Ministry of Science, Research and the Arts within the project “AgiloBat” as part of the Innovation Campus Mobility of the Future and by the BMBF funded project “AgiloBat2” (03XP0369A) as part of the Cluster InZePro.

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References 1. Hofmann, J., Wurba, A.-K., Bold, B., Maliha, S., Schollmeyer, P., Fleischer, J., Klemens, J., Scharfer, P., Schabel, W.: Investigation of parameters influencing the producibility of anodes for sodium-ion battery cells. In: Behrens, B.-A., Brosius, A., Hintze, W., Ihlenfeldt, S., Wulfsberg, J.J. (eds.) WGP 2020. LNPE, pp. 171–181. Springer, Heidelberg (2020). https://doi.org/10. 1007/978-3-662-62138-7_18 2. Kumberg, J., Baunach, M., Eser, J.C., et al.: Investigation of drying curves of lithium-ion battery electrodes with a new gravimetrical double-side batch dryer concept including setup characterization and model simulations. Energy Technol. 9, 2000889 (2021). https://doi.org/ 10.1002/ente.202000889 3. Cavadini, P., Scharfer, P., Schabel, W.: Investigation of heat transfer with-in an array of impinging jets with local extraction of spent fluid (2017) 4. Jaiser, S., Friske, A., Baunach, M., et al.: Development of a three-stage drying profile based on characteristic drying stages for lithium-ion battery anodes. Drying Technol. 35, 1266–1275 (2017). https://doi.org/10.1080/07373937.2016.1248975

Towards the Swarm Production Paradigm Casper Schou(B) , Akshay Avhad, Simon Bøgh, and Ole Madsen Department of Materials and Production, Aalborg University, Fibigerstræde 16, 9220 Aalborg East, Denmark [email protected]

Abstract. In this paper, we propose a new production paradigm called Swarm Production. A swarm production system employs both dynamic part routing and movable workstations. This allows it to adjust its topology during operation, in contrast to existing flexible production paradigms such as matrix production and reconfigurable manufacturing systems where the topology is defined during setup. Run-time optimization of the topology yields an unprecedented ability to quickly adapt to fluctuating demands or new production constraints. We present a formal definition of the swarm production paradigm and outline a preliminary roadmap by highlighting key research challenges imposed by swarm production. Finally, we present an exemplifications of a swarm production system in terms of key technologies and systems, based on an existing customisable product. Keywords: Manufacturing · Swarm production paradigms · Production topology

1

· Production

Introduction

Over the past decades, manufacturers have continuously perfected the paradigm of line production, increasing efficiency and driving down unit cost. However, manufacturers are today met with ever-increasing demands for product innovation, resulting in shorter product lifespans, shorter time to market, increased product variety and increased global competition. This leads to an increased demand for innovative production equipment that can accommodate much greater variety and easily be reconfigured to new products and components. Hence, agility is needed alongside efficiency. To meet these simultaneously is not a trivial task. It requires innovation at all levels of a manufacturing system. In this paper, we focus on achieving flexibility and efficiency of the material flow through the use of a novel manufacturing concept we call Swarm Production. 1.1

Material Flow Flexibility

The material flow in discrete manufacturing is constrained by: 1) the spatial position of the workstations performing the processes, and 2) the layout of the c Springer Nature Switzerland AG 2022  A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 105–112, 2022. https://doi.org/10.1007/978-3-030-90700-6_11

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routes between the workstations. Routes can range from being fixed (as in traditional conveyor-belt-based system) to being free (e.g. by using mobile robots). Likewise, the position of the workstation can range from being fixed to being movable. This gives rise to the 2 × 2 matrix shown in Fig. 1, which presents a simplified view on the two constraints resulting in the four quadrants.

Fig. 1. Simple classification of manufacturing topology in four quadrants based on routing flexibility and workstation position changeability.

Quadrant 1 is characterised by static workstation positions and static routing between these workstations. Such systems perform well on mass production and an example is the traditional dedicated manufacturing system. Quadrant 2 entails fixed routing, but the workstations are movable. Systems of this quadrant perform well on batch production with variations between the batches. An example is the paradigm reconfigurable manufacturing systems (RMS) [1,2]. Quadrant 3 entails fixed workstations, but free product routing. Such system is exemplified by the recent paradigm of matrix production [3], and enables single piece order flow with high product variation. Quadrant 4 is characterised by both completely dynamic routing and movable workstation. To our best knowledge, no current product system paradigm address this topology. Consequently, in this paper we propose a new manufacturing system paradigm called Swarm Production, offering both completely dynamic part routing as well as movable, self-containing workstations. In Sect. 2 we present the state of the art within each of the four quadrants in Fig. 1. In Sect. 3 we present and argue the definition of swarm production, and we outline a roadmap in terms of open research challenges. In Sect. 4 we provide an exemplification of how a swarm production system can be composed. Finally, in Sect. 5 we discuss the proposed swarm production paradigm and its implications.

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State of the Art Fixed Routing and Fixed Workstations

Fixed routing and workstations are typically used in product and line layouts as found in dedicated manufacturing lines. This layout has been successfully applied for mass production [4]. The linkages between subsequent workstations are normally ridged, limiting the process flow in a unidirectional manner. Thus, the product moves between the workstations in a predetermined, fixed sequence. This limits the takt-time to the cycle-time of the slowest workstation [5,6]. 2.2

Flexible Routing and Fixed Workstation Position

Flexible routing and fixed workstations is the basic principle used in cellular and process layouts, where the traditional operation involves manual transportation between the workstations. The process layout has in particular been successful in batch production [4]. Flexible Manufacturing Systems (FMS) introduced generalized flexibility addressing routing, process, volume, machine and product aspects of a production system. The most adopted FMS included a diversified routing environment through overhead, in-floor conveyors and wire carts that can run through multiple workstations or machine systems [7,8]. Matrix production is a recent paradigm enabling one-piece-flow by decoupling workstations and connecting them with mobile robots. This creates a high degree of freedom in how products are routed between workstations, making the system less product-specific and more resilient to individual workstation breakdowns. The actors in this production paradigm demand a level of adaptive intelligence to align the decision-making within workstation and logistical components for complex routing and scheduling the production flow [9,10]. 2.3

Fixed Routing and Movable Workstations

Fixed routing and moveable workstations are often used in production systems based on modular concepts such as Reconfigurable Manufacturing System (RMS) [1] and Changeable Manufacturing Systems (CMS) [11], which allows quick adaptation to fluctuating expectations from manufacturing. RMS and CMS adopts a modular approach for both software and hardware, focusing on the structural and functional modularity of the machines and workstations in the system. Using the modular concepts, layouts can be changed by moving, adding and removing modular workstations so the layout fits the requirements of the production. Normally the workstations are moved manually and once moved the routing is fixed according to a predetermined flow. 2.4

Free Routing and Movable Workstations

Only very few paradigms have previously been proposed using both free routing and movable workstation. Holonic Manufacturing Systems (HMS) are manufacturing systems that are controlled according to the holonic system paradigm.

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A holon is an autonomous and co-operative building block for transforming, transporting, storing and/or validating information and physical objects [12]. This makes an HMS not just an automated system, but makes it capable of self-organization, allowing quick adaptation to changing manufacturing needs [13]. Agent-Based Manufacturing Systems (ABMS) constitute manufacturing systems based on an agent-based architecture. Similar to HMS, an ABMS applies a highly distributed control and self-organization architecture [14], allowing it to cope with the increasing demand for mass customization. Smart agents communicate and negotiate over the cloud, making dynamic reconfiguration among the agents possible. 2.5

Summary

Production paradigms from quadrants 1, 2 and 3 on Fig. 1 have traditionally provided systems fit for either line, batch or one-of-a-kind production. The combination of movable workstations and free routing has been partly explored in few paradigms (HMS and ABMS), but only with a few types of entities being mobile on their own; e.g. AGVs. The remaining agents have either been virtual (software) or statically fixed in the environment. Hence, to our knowledge no production system paradigm exists which fully exploits free routing as well as movable workstations. Thus, we propose exactly such a paradigm called Swarm production, offering both the highest degree of material flow flexibility, but also the ability to adapt its topology during operation.

3

Swarm Production Paradigm

In this section we present and argue our definition of swarm production, and we outline a road-map in terms of key research challenges. 3.1

Definition of Swarm Production

Definition The swarm production paradigm entails a production system extensively composed of multiple, different purpose autonomous mobile entities, dynamically adapting the system topology the current constraints and requirements for the production outcome. “a production system” - Swarm production is first and foremost a production system [4]. “extensively composed of multiple, different types of autonomous mobile entities” - The key characteristic of the swarm paradigm is that it is solely composed of a pool (multiple) of autonomous mobile entities. Similar to many swarms found in nature, it must consist of entities with different purposes and thus capabilities; e.g., the use of mobile part-transporting entities and

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mobile processing entities. Opposed to the matrix production paradigm, the processing units are also autonomously moving in the swarm production paradigm. “dynamically adapting the system, control and communication topology” - The use of mobile processing units allows the system to change its topology without human interference. Coupled with free routing of parts, the system can change its topology during operation without any downtime. “to the current constraints and requirements for the production outcome.” - The autonomous mobility of both processes and parts gives the swarm paradigm unparalleled flexibility. The ability to move workstations during operation allows the system to seamlessly adapt its topology and part routing to both the general production goals, but also to requirements of individual orders. By changing its topology, the swarm production system can also mimic other paradigms, e.g. by structuring itself as a line or a matrix. Thus, a swarm production system offers not only to be a swarm topology, but also to be both a line production system, a matrix production system on demand. 3.2

Research Challenges

The swarm production paradigm imposes several open research challenges which must be addressed in order to realise the paradigm. Design. An exploration of industry and process types must determine the constraints and limitations of the swarm production paradigm in terms of tasks. Like other production paradigms, a design methodology is required for swarm production systems which determines a suitable system design based on business demands and process constraints. For the swarm paradigm, the obvious design parameters is the types of mobile entities, the number of each entity, and the spatial layout of the swarm area. Contrary to traditional manufacturing paradigms, the topology is not determined during design time, but during operation. To evaluate the suitability of a swarm design, research must address how traditional performance KPIs of manufacturing (e.g. cycle-time, capacity, OEE and lead-time) transfer to the swarm production paradigm. Implementation. The implementation of a swarm production system could be done following any custom architecture. However, we propose to develop a reference architecture, identifying necessary system functionalities on control and planning layers, and interfaces towards the business layers (MOM and ERP). Faster adaptation of the paradigm by industry could be facilitated by mapping the identified functionalities to existing methods, standards and frameworks widely adopted in industry. The origin of the mobile entities is not constrained by the definition. However, the nature of the swarm paradigm motivates a design using cheaper, simple and redundant entities over complex expensive entities.

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Operation. Efficient operation of a swarm production system requires coordinated control and holistic planning. The control should be orchestrated so a collective swarm intelligence emerges. This points at centralised control, however, some functions could be handled with distributed control. Research must address a control scheme for efficient, collective control of the swarm, including strategic route planning for each asset. Determining a near-optimum plan for a matrix production has proven a complex task due to the large solution space for routes [3,9]. This complexity will increase even further with the added mobility of the workstations. Research should investigate approaches and algorithms providing a near-optimum plan with a short planning time. Artificial intelligence techniques have proven suitable in swarm robotics [15] and should be explored.

4

Exemplification of Swarm Production

To illustrate the swarm production paradigm, this section provides an exemplification based on the AAU Smart Factory [16] which assembles a dummy smartphone. The production system is a modular, reconfigurable manufacturing line built on the FESTO CP Factory concept. The product is fully customisable with currently 54 variants possible. Figure 2 illustrates the production of the dummy smartphone using swarm production paradigm.

Fig. 2. Exemplification of a conceptual system architecture for a swarm production system implemented using mobile robots. The figure shows how different typologies can be formed on the shop floor. ERP = Enterprise resource planning, MES = Manufacturing execution system.

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The part transport mobile robots, known as product robots, constitute the majority of the swarm. Equipped with simple sensors and a low powered controller to lower the cost, they rely on external positioning and computational systems and attain a top speed of up to 2 m/s. The process units also deployed as mobile robots are designed as gantry systems, allowing the product robots to pass under the centre of it to undergo processing. The process robots have more onboard processing power and feature a lower top speed than the product robots. The central edge-cloud platform hosts a swarm manager, topology manager, and MOM systems that handles order data from MES and demand forecasts from the ERP system. The swarm manager intelligently emulates a fleet controller for robots for routing and individual task control on macro and lower levels. The topology manager addresses the enumeration of product-specific topologies in Fig. 2 with subgraphs like assembly line and matrix production layouts from a static, undirected graph [17]. A heuristic strategy is required to identify more configurations suited to high variant production and enumerate connected subgraphs from the static full-scaled swarm topology. Robust low-latency communication is required due to the close interaction between the swarm manager and the individual robots. This includes offboarding certain parts of the low-level control of each robot to the swarm manager which thus draws on the collective knowledge from all robots. Robust lowlatency communication is achieved using a 5G system, and in-door localization is done using ultra wide band positioning.

5

Discussion

In this paper, we introduced the paradigm of swarm production as a new material flow strategy allowing a manufacturing system to change its topology during run-time. This removes the need to ultimately choose the topology and routing strategy during design-time, and enables a very high degree of routing flexibility. Other paradigms introduce flexibility on either process or machine level, and to what extend the swarm paradigm contradicts or compliments these becomes an evaluation of the individual paradigms. The application of swarm production is not confined to any particular industry or type of task as long as appropriate mobile entities are available given the product properties. However, the nature of the system makes is best suited for discrete operations such as assembly. Also, certain processes do not apply well to mobile entities, either due to spatial, energy or safety constraints; e.g., metal casting or laser welding would often need to remain stationary for safety and energy reasons. A swarm production system could incorporate these as elements in a matrix layout. It would constrain the ability to freely change topology, but it would extend the paradigm’s applicability

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and ease the adoption by industry. In Sect. 3 we outlined key research challenges. Some are not unique to the swarm paradigm, and research could be expedited by transferring results from other paradigms. Likewise, research on challenges in swarm production could be transferred to existing paradigms and thus accelerate the transfer of results to industry.

References 1. Koren, Y., et al.: Reconfigurable manufacturing systems. CIRP Ann. Manuf. Technol. 48(2), 527–540 (1999) 2. Koren, Y., Shpitalni, M.: Design of reconfigurable manufacturing systems. J. Manuf. Syst. 29(4), 130–141 (2010) 3. Greschke, P., Sch¨ onemann, M., Thiede, S., Herrmann, C.: Matrix structures for high volumes and flexibility in production systems. Procedia CIRP 17, 160–165 (2014) 4. Groover, M.P.: Automation, Production Systems, and Computer-Integrated Manufacturing. Pearson Education Limited, Boston (2015) 5. Becker, C., Scholl, A.: A survey on problems and methods in generalized assembly line balancing. Eur. J. Oper. Res. 168(3), 694–715 (2006) 6. Dolgui, A., Eremeev, A., Kolokolov, A., Sigaev, V.: A genetic algorithm for the allocation of buffer storage capacities in a production line with unreliable machines. J. Math. Model. Algor. 1(2), 89–104 (2002) 7. Browne, J., Dubois, D., Rathmill, K., Sethi, S., Stecke, K.: Classification of flexible manufacturing systems. FMS Mag. 2(2), 114–117 (1984) 8. Buzacott, J.A.: Fundamental principles of flexibility in manufacturing systems. In: Proceedings of the 1st International Conference on Flexible Manufacturing Systems, England, pp. 13–22 (1982) 9. Schmidtke, N., Rettmann, A., Behrendt, F.: Matrix production systems - requirements and influences on logistics planning for decentralized production structures. In: Proceedings of the 54th Hawaii International Conference on System Sciences, pp. 1665–1674 (2021) 10. Trierweiler, M., Foith-F¨ orster, P., Bauernhansl, T.: Changeability of matrix assembly systems. Procedia CIRP 93, 1127–1132 (2020) 11. Wiendahl, H.P., et al.: Changeable manufacturing-classification, design and operation. CIRP Ann. 56(2), 783–809 (2007) 12. Giret, A., Botti, V.: Engineering holonic manufacturing systems. Comput. Ind. 60, 428–440 (2009) 13. Papp, J., Tokody, D., Flammini, F.: From traditional manufacturing and automation systems to holonic intelligent systems. Procedia Manuf. 22, 931–935 (2018) 14. Tang, H., Li, D., Wang, S., Dong, Z.: Casoa: an architecture for agent-based manufacturing system in the context of industry 4.0. IEEE Access 6, 12746–12754 (2017) 15. Hamann, H.: Swarm Robotics: A Formal Approach, 1st edn. Springer, Heidelnerg (2018). https://doi.org/10.1007/978-3-319-74528-2 16. Madsen, O., Møller, C.: The AAU smart production laboratory for teaching and research in emerging digital manufacturing technologies. Procedia Manuf. 9, 106– 112 (2017) 17. Kamada, T.: An algorithm for drawing general undirected graphs. Vis. Abstract Objects Relat. 31, 69–104 (1989)

Challenges Towards Long-Term Production Development: An Industry Perspective Simon Boldt(B)

, Carin Rösiö , and Gary Linnéusson

School of Engineering, Jönköping University, Jönköping, Sweden [email protected]

Abstract. A well-performing product realisation process in order to introduce new products with high frequency to a low cost, is becoming more of a prerequisite for manufacturing companies. In a multiple case study, this paper investigates applied industrial practices in production development to support the production realisation process and reports on the current ways of working and challenges therein. The areas of current production development practices, production platforms, standardised work, and knowledge development are explored. Identified challenges towards long-term production development based on the explored areas are presented. The inclusion of future need of production system adaptions from future products is argued for to increase its efficiency. Through including future need of the production system, the notion of considering one product at the time during industrialisation is challenged and a more proactive perspective can be taken. The production platform approach is considered as one enabler for such an improved production development. Keywords: Production development · Production platform · State-of-practice · Long-term perspective

1 Introduction To manage both short-term and long-term changes within production development are becoming increasingly important for manufacturing companies. Especially, manufacturing companies with their own product portfolios do not afford to develop production systems only for the current product family, but also need to develop capabilities to face future possible requirements [1], and thus be more long-term oriented. A long-term perspective entails that the stream of new products to be industrialized are simultaneously considered when developing the production capabilities. This is challenging the notion of industrializing one product at the time and required subsequent production system solutions, to expand it into consider possible future product requirements consequently from encompassing the stream of new products as well. Applying such a proactive approach in the development of production capabilities is believed a future avenue for competitiveness. A promising approach towards a long-term perspective in production development is the production platform approach. The principles of commonality and reuse of modules, © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 113–121, 2022. https://doi.org/10.1007/978-3-030-90700-6_12

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found in the product and production platform literature [2, 3] can be a path forward to consider both existing and future requirements. These principles has been argued to achieve more variants while keeping the benefits of economy of scale [4]. The platform approach, frequently applied in product development, can be defined as a ‘collection of assets shared by a set of products’ [2]. Within the production domain the platform approach has been introduced more recently [5], and have until now mainly paid attention to production system architectures, see e.g., [6, 7]. A modular production platform architecture can be reused and enables upgrades and changes of the production system over time as the product requirements change [7]. This research identifies potentials to support long-term production development by production platforms. However, limited research exists on the application of production platforms and how it can support long-term production development. Thus, this paper investigates the industrial challenges to support how the application of platform thinking can improve the long-term production development. Consequently, the purpose of the study is to present the industrial practices in production development and identify challenges to apply long-term production development.

2 Method To unveil the current practices of long-term production development, a descriptive study including five industrial companies was conducted. The industrial companies have their own product- and production development inhouse and consider the challenge of integrating these to be prominent; see Table 1 for size, type of product, and type of customers. Data was collected within a multiple case study and consisted of semi-structured interviews and documents [8]. The interview guide was developed by the project team to cover the product realisation process (PRP). Reported in this study are the aspects related to production development in the context of the PRP of the cases. Hence, the respondents were working within multiple roles in the PRP from the product domain and production domain; covering roles such as project managers and lead engineers in both domains, and specifically in the product domain it was method and process developers, materials specialists, product platform engineers, CAD engineers, simulation managers, technology managers, engineering design managers, product lab manager, product owners and business area managers, and in the production domain it was different roles of managers for production, industrialisation, production engineering, tooling, sourcing, quality process, as well as specialists in production engineering. In total 51 interviews were conducted with 9–11 interviews per case and interviews lasted for 63–107 min with one outlier of only 29 min. Each interview was conducted by two researchers and all the interviews were recorded and transcribed. The studied documents included formal descriptions of the company PRP. Analysing the data followed Miles et al. [9] three steps (1) data condensation, (2) data display, and (3) drawing and verifying conclusions. To condense the data each transcribed interview was analysed, and content was coded based on its relation to production development, ranging from future challenges the production system needs to manage, to specific questions on production platforms and long-term plans to cooperate with these challenges. Parts not directly linked to this scope was omitted. Each case’s data

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was condensed and analysed first separately, where the result was reported in workshops to validate the findings with the case. Subsequently, further data condensation resulted in the displayed themes herein, i.e. production development, production platforms, and standardised work and knowledge development. Regarding the research quality of the study measures of construct validity, internal validity, external validity, and reliability [8] was considered. Table 1. Case description Case

Size

Products

Customers

1

Large

Forestry and gardening

Professional customers

2

Large

Outdoors and automotive accessories

Consumers

3

Large

Industrial house builder

Consumers

4

Large

Lighting solutions

Professional customers

5

Medium

Industrial house builder

Professional and public customers

3 Theory 3.1 The Product Realisation Process and Production Development PRP includes both the development of new products and corresponding production systems [10] and can be fulfilled by a stage gate model [11]. The stage gate model is supposed to act as a blueprint for product development while having gates along the way to ensure the right maturity of the product concept before progressing [11], the model can take many forms. Commonly does the model consists of the stages planning, conceptual development, system-level design, detail design, testing and refinement, and production ramp-up with gates in between each stage [12]. In practice, the focus has often been on designing products over developing production solutions. This has come to be known as “over-the-wall engineering” where the work is conducted in functional silos within the company [10]. It has resulted in a call for increased integration and earlier involvement of production in the product development, to understand the constraints of the product design and the production capabilities [13]. Normally, the production capability is developed during the PRP in projects, as the new production solutions are required to meet new demands of new products. To bridge functional silos, Bruch and Bellgran [1], proposed a mindset of production generations and portfolios, similar to product generations and product portfolios. This mindset would enable development of production capability through introducing systematically one generation at the time [1] thus, enable development of production capabilities over time with a long-term perspective.

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3.2 Production Platforms Platforms provide a foundation to understand what assets that are used and required to realise the market demand. More specifically, it offers a way of determining what should remain stable and what should be allowed to vary in the system in order to fulfil the market demand [14]. By determining what should or should not be standardised, the platform offer an opportunity to operationalize the company strategy, ensuring that the right assets are readily available when the need arises. There is no consensus in platform definitions and terminology within the production domain. Related terms that have been used in literature e.g. production platform [3], process platform [15], manufacturing platform [6], and manufacturing system platform [16], see Table 2. To summarise, the usage of platforms in the production domain is scattered and multiple descriptions exist. Table 2. Platforms in the production domain Term

Ref

Description

Production platform

[3]

“A production platform is a platform, which is about sharing of production components or architectures for a product family.”

Process Platform

[15] “A process platform involves de facto three aspects: (1) a common process structure shared by all process variants; (2) derivation of specific process variants from the common structure; and (3) correspondence between product and process variety, which resembles the correlation between the generic product and routing structures.”

Manufacturing platform [6]

Manufacturing system platform

“Manufacturing platforms are interpreted as a structural description of a subset of a manufacturing architecture including only the reusable or widely-used standard designs. This interpretation includes both existing and future standard designs, this due to the low volume of specific manufacturing systems and hereby the related use as a design platform for future manufacturing systems.”

[16] “Manufacturing system “platform” represents the core machines capable of performing all the processes required to fabricate the core product features. The term machines are used to represent all processes and tools that allow it to perform many operations and processes to produce certain product features.”

4 Empirical Findings Within all cases, stage-gate models were applied to support product realisation projects. The models were specifically adapted to their organisations and included stages and

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activities related to specification, conceptual design, detailed design, tooling and production preparation, and product release and start of production. For case 3 and 5 there was an extra stage including adaptions to meet the local needs of the building site for the houses. The stage-gate models were mainly support for the product development and seldom for the development of the production system. Production development activities were triggered by product realisation projects, even though there were few cases of production development occurring outside such projects. The following sections will present the findings for each case regarding current ways of long-term production development, production platforms, standardization, and knowledge reuse. 4.1 Case 1 There was a clear production development focus present, 5-year future plant visions for digitalization and automation were established, yet the vision was not connected to the product portfolio plans. Several respondents could not relate to the production platforms considered it to be equal to the production system. One respondent argued that it was more the description of how the production system was established. A view also existed that the production platform was similar to plug-and-play concepts. The concept would be based on previous evolved knowledge and testing from earlier evolutions of production equipment. As well as, that production had defined the description of the production platform through which future product platforms could be produced. High level of standardization, including work descriptions in operations, utilized requirement specifications, standard types for production equipment, continuous evaluation process to standardize solutions, and teams for advance and standardize automation concepts were present. Even so, learning between projects was reported to be lacking, it was experience-based, instead, it was strived for reusing staff with suitable experience in new projects to bridge this aspect. Lessons learned were collected, yet the documented knowledge was challenging to access. 4.2 Case 2 Their long-term objective was to increase the level of automation, however, a concrete strategic plan for it was lacking. Also, increased reuse of production solutions between product variants and generations was deemed as important for long-term success in the production. Increased alignment with research and development (R&D) was pinpointed as a way to identify possibly better production solutions. Even though one of its production sites abroad applied a long-term plan of 6–7 years there was no formal global strategy was applied. Case 2 had a highly developed product platform, however, the respondents had diverse views on production platforms, such as: “the equipment such as presses or assembly lines which can manage several variants”, “assembly modules with standard measures and function which could be equipped with different specific units such as

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robots and fixtures”, “the described capabilities of the processes”, “[the production platform] is applied where we know about the limits of the production system within which future products need to be contained”. Continuously were new off-the-shelf production solutions strived to later be adapted to meet specific product requirements. This had contributed to standard automation assembly modules, developed in collaboration with the machine supplier. This work procedure was not formalized in the production development and was largely experienced based. Standard requirement specifications in the acquisition of production equipment were used to get a homogeneous machine park. Moreover, best-practice between production sites was strived for. Reusing production solutions between product realisation projects occurred from time to time but no formal process existed for this. 4.3 Case 3 In case 3 their long-term objective was to increase the level of automation, while also lacking a concrete plan for reaching the objective. Their new five-year vision was to lead the development of the industrial housebuilding industry, while one open answer from a respondent revealed reactiveness in preparing current production for future products. The need for an increased shared view on how long-term production development should be conducted, as well as improving the acquisition process of new production equipment was identified. The production platform concept was not a well-established concept at the company. In case 3, standardized work descriptions existed. Knowledge was regarded as hard to reuse as it was mostly experienced-based, and the existing documents were not searchable due to lacking classification. However, as one respondent described “we have an integrated culture between product development and production which extracts much of the tacit person-based knowledge” which enables problem-solving. 4.4 Case 4 According to the respondents in case 4, the production development followed a one-year plan aligned with the general budget process for the factory but was lacking a long-term strategic plan. The respondents were not familiar with production platforms and described it through descriptions such as “it constitutes some kind of common base and something modularized which can be reused, a foundation to stand on”. Production solutions were reused through experience-based problem-solving. To some extent standard production equipment existed, but these standards were not fully formalized. Lessons learned were documented, yet mostly related to product development. Mistakes in production development were repeated and much of the reusing of production solutions was dependent on the people and their non-formalized experiences. 4.5 Case 5 Production development recently became a prioritized and conscious activity for case 5. Until recently, there had been no dedicated production development staff. Thus, the

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production engineer applied a strategy of “trial and error” and had started to implement KPIs to monitor productivity, quality, and the work environment, as well as introducing different lean tools. Few respondents in the case were familiar with production platforms but referred to it as “how one works in production” or “[company name] production system”. Standardizations for how to assemble the products through blueprints existed. However, currently, these were mostly used by new workers as the more experienced assembled by habit, even though each project was regarded as unique. The uniqueness was argued to be a hinder to the ability to standardize the production solutions between projects. There were no formalized procedures for capturing challenges and lessons learned between projects. Instead, these experiences were re-captured at the beginning of a new project.

5 Discussion In the five cases production development was mainly carried out as a part of the PRP but not clearly guided by production strategies and visions. In the cases which had established long-term production strategies were these not connected to the product portfolio plans. Thus, a long-term production capability perspective as described by Bruch and Bellgran [1] could not be considered applied. Instead, the focus was mainly on increasing certain capabilities in general terms, such as increasing the level of automation or increasing digitalisation to reduce the manufacturing costs. Within all cases a standardised approach was applied for assuring high quality of products in operation. However, the reuse of production solutions between projects was not conducted in any formalised procedure. By determining what should be standardised or not [14] could the platform offer an opportunity to operationalize the company strategy, enabling that the right assets are readily available when required. None of the cases used production platforms and the term was neither well-known among the respondents. The descriptions of production platforms from Table 2, captures the expressed need from the respondents, i.e., to reuse production solutions and knowledge, through determining what should be standardised and what should be left to remain specific. Both the respondents and previous research put attention to physical aspects such as the modularity of the production architecture, production equipment, or constraints of the production system. Although, an expansion of the production platform concept to a holistic platform could be fruitful. The holistic platform could include other types of assets compared to Robertson and Ulrich [2] four assets of a product platform, i.e. components, process, knowledge, and people and relationships. How these assets could be interpreted in a production domain remains to be explored. The identification and utilisation of such assets could be a promising avenue to explore as a potential strategy towards long-term production development. To summarise, several challenges exist regarding long-term production development. Even if the companies strived for acting long-term and develop solutions that could be applied for multiple products, a reactive approach was often applied where solutions were developed for the immediate product requirements. Another challenge was that lessons learnt was lacking and resulting in frequently “reinventing-the-wheel”. Which

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also connects to the challenges of reusing standard solutions, where the standardisation differs between product realisation projects. A final challenge was that there was diverse definitions and conceptions of production platforms within the cases, it was not a mature concept in the five cases. 5.1 Conclusions The purpose of this study was to present the industrial practices in production development and identify challenges to apply long-term production development. Production development was found to be mainly conducted reactively and that reusing knowledge and production solutions is of importance but was reactively conducted and based on experience and were largely dependent on the project members. Further, the production platforms concept had diverse definition focusing on physical aspects of the production system and were not applied in the cases. Though, production platforms were not applied, were its fundamental aspects i.e., commonality and reusing modules, sought after. The production platform concept has potential to provide structure to increasing the reuse of solutions and knowledge in production development.

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13. Vandevelde, A., Van Dierdonck, R.: Managing the design-manufacturing interface. Int. J. Oper. Prod. Manag. 23(11–12), 1326–1348 (2003) 14. Sørensen, D.G.H.: Developing Manufacturing System Platforms. Ph.D. -thesis, Dept. of Materials and Production, Aalborg Univ., Aalborg, Denmark (2019) 15. Zhang, L. L., Jiao, J. R., Pokharel, S.: Process platform-based production configuration. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 1–12 (2005) 16. ElMaraghy, H.A., Abbas, M.: Products-manufacturing systems co-platforming. CIRP Ann. Manuf. Technol. 64(1), 407–410 (2015)

The Use of Principal Component Analysis for the Construction of a Reconfigurability Index Antonio Mousinho de Oliveira Fernandes1 , Isabela Maganha2(B) , and Jose L. F. Martinho1 1 Coimbra Polytechnic - ISEC, Coimbra, Portugal 2 Integrated Engineering Institute, Federal University of Itajubá, Itabira, Minas Gerais, Brazil

[email protected]

Abstract. Reconfigurable manufacturing systems (RMSs) emerged as a strategy to achieve more responsive systems, capable of adjust the functionality and capacity when required. This topic is a current issue to manufacturing companies because the feasibility of RMSs was achieved recently due to the novel technologies promoted by the Industry 4.0. In RMSs, the reconfigurability is the ability that allows changes from one product to another, the addition or removal of resources, with minimal effort and without delay. For this reason, the level assessment of the reconfigurability is of utmost importance for the industry. The objective of this paper is to describe the development a reconfigurability index that can be used by companies to define how reconfigurable their manufacturing systems are. To build the index, a questionnaire survey was used to select the variables and a principal component analysis (PCA) was applied to the survey results to determine the contributions of the core characteristics. The findings show that each core characteristic contributes with a different amount to the composition of reconfigurability. Adaptability and diagnosability contribute the most, with 25% each. Keywords: Reconfigurable manufacturing system · Reconfigurability index · Principal component analysis

1 Introduction Reconfigurable manufacturing systems (RMSs) emerged aiming at achieving more responsive production systems, capable of manufacture high quality products at low costs. Such systems are designed to adjust the production capacity and functions, through reconfigurability, to respond to unpredictable changes in the production requirements quickly. To enable the reconfigurability, manufacturing systems must have some core characteristics such as modularity, integrability, customization, convertibility, scalability and diagnosability [1]. Even though RMSs were introduced 20 years ago, the implementation potential was achieved recently, due to the novel technologies promoted by the industry 4.0 paradigm [2]. In general, the studies on RMSs can be divided in five main research lines: reconfigurability level assessment, analysis of RMSs features, analysis of RMSs performances, applied research and field applications, and reconfigurability toward industry 4.0 goals [3]. This study aims to contribute to the first research line. © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 122–129, 2022. https://doi.org/10.1007/978-3-030-90700-6_13

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To evaluate the level of reconfigurability present in industries, the existing works refer, mainly, to empirical studies or construction of indices. Despite the significant contributions, the majority of studies adopts multi-criteria decision techniques, in which the choice of weights for each criterion is subjective. Therefore, accurate and quantitative indices are needed [3]. For this reason, this work is intended to describe the construction of a reconfigurability index, using principal component analysis (PCA) with orthogonal rotation (varimax) to determine the contribution, i.e., the weights, of the core characteristics to the composition of reconfigurability. These weights are the basis to calculate the index. The index can be used by industries to assess the level of reconfigurability in their manufacturing systems. The remainder of this paper is structured as follows. Section 2 provides related works on the development of reconfigurability metrics. Section 3 presents the questionnaire survey and the PCA. The results are shown in Sect. 4. Finally, Sect. 5 presents the conclusions, the limitations of this research and suggestions for future studies.

2 Related Works Reconfigurability is a vital feature of RMSs. Many authors consider that reconfigurability is a combination of six characteristics: modularity, integrability, customization, scalability, convertibility and diagnosability [1]. Empirical studies have investigated the reconfigurability level present in industries [1, 4–6]. One of these studies showed that industries recognize five characteristics of reconfigurability instead of six; scalability and convertibility are interpreted as a single characteristic, called adaptability [5]. Reconfigurability metrics have also been developed to determine the readiness of a production system to change its configuration. Such metrics consider the core characteristics of reconfigurability or other criteria, at the machine or system level [7]. The research on reconfigurability metrics can be divided in two main groups: 1) RMSs assessment through the definition of global reconfigurability indices, and 2) mapping the manufacturing system capabilities, providing a set of metrics composed by the core characteristics of reconfigurability [3]. Global indices are those that consider the smoothness of the reconfiguration [8]; responsiveness, operational capacity, machine reconfigurability and costs; reconfigurability efforts [9–11]; sustainability [12]; technology, people, management and production strategy [13]. This group contains the largest number of studies, but these indices do not include the core characteristics of reconfigurability. Considering them is essential to measure reconfigurability properly, as the effectiveness of RMSs depends on the implementation of the core characteristics [5]. Therefore, despite these contributions, there is space for the development of reconfigurability indices based on the core characteristics. Among the studies that consider the core characteristics of reconfigurability, Gumasta et al. [14] mapped the characteristics of modularity, scalability, convertibility and diagnosability, using the multi-attribute theory to develop a reconfigurability index. Wang et al. [15] proposed quantitative models for each of the six core characteristics of reconfigurability, adopted by the majority of authors. These models were considered to determine a reconfigurability index using the analytical hierarchy process (AHP), to assign the weights to each of them. Farid [16] considered the characteristics that drove qualitative and intuitive design of technological advances: integrability, convertibility and

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customization, discussing how these characteristics fit the requirements for reconfigurability measures. Mittal et al. [17] used the weighted sum theory to map the characteristics of modularity, convertibility and diagnosability to develop a reconfigurability index. The aforementioned works do not have well-defined or standardized aspects. This is because they consider three, four or six core characteristics. Most importantly, these studies seem to ignore the dependence that exists between the core characteristics and the impacts that they may have on each other. Such relationships must be considered in the development of a reconfigurability index [18]. All of these studies adopt multi-criteria decision analysis (MCDA) to develop models and methods for evaluating reconfigurability. The MCDA are able to assess conflicting criteria, supporting managers in decision making. These techniques include the steps of criteria selection, criteria weighting, evaluation and final aggregation. However, the choice of weights to be assigned to the criteria is subjective [3]. On the other hand, PCA is an exploratory multivariate analysis that assesses the correlation between variables through statistical procedures. It has been used to construct indices in several domains [19, 20]. In this case, the PCA is applied to weight each core characteristic according to its contribution to the general variance of the data. This work, therefore, aims to contribute to the development of a reconfigurability index, considering the existing relationships between the core characteristics. In addition, despite the fact that most authors identify six central characteristics, this study adopts the five core characteristics that were recognized by manufacturing companies: modularity, integrability, customization, adaptability and diagnosability [5].

3 Research Methodology Two aspects must be considered to build an index: the selection of variables and the weight derivation of each variable [19]. In this work, the first aspect was based on a questionnaire survey and the second was carried out through a PCA. The PCA is used to handle the subjectivity involved in the index construction, which would not have been addressed through multi-criteria decision analyses [20]. 3.1 Questionnaire Survey The questionnaire survey and data collection were developed by Maganha et al. [5]. The instrument is composed of 21 variables that are measured using a 7-point Likert scale, with responses varying from 1 (strongly disagree) to 7 (strongly agree). The questionnaire was applied to Portuguese manufacturing companies to identify the implementation level of each core characteristic of reconfigurability and establish their relationship with operational performance measures. A full version of the questionnaire, the data validation, a full description of the variables and the characterization of the sample can be found in Maganha et al. [21]. From the distribution, there were 112 viable answers. 3.2 Principal Component Analysis The PCA is a multivariate exploratory analysis that aims to transform a set of correlated variables into a smaller set of independent variables, with the least possible loss of

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information. The factor loadings of rotated principal components are used to determine the variables’ weights. In this way, it is possible to preserve the proportion of the variance of the original data set [22]. In this work, the software R was used to conduct the PCA. The weighting process consists of three steps, as follows. Check the Correlation Matrix. The calculation of the principal components consists of maximizing the variance explained. Thus, the principal components are represented by the eigenvectors associated with the eigenvalues of the covariance matrix. The correlation matrix is used when the data are standardized. This matrix is adequate to evaluate the linear relationship between two variables in relation to their variance. The variance values can vary in a range from −1 to 1. Values close to zero indicate that there is no linear relationship between the original variables. Some tests must be conducted to assure the adequacy of the original variables to the PCA: Bartlett’s sphericity, Kaiser-Meyer-Olkin (KMO) and multicolinearity. The Bartlett’s sphericity test returned a p-value of 2.67 × 10–122 , i.e., approximately zero. The KMO test returned a value of 0.73, indicating good results. This test was also performed for each variable. The results ranged from 0.53 to 0.83, all above the threshold value of 0.50. The result obtained at the multicolinearity test was 2.12 ×10–5 . Values greater than 1 ×10–5 are acceptable [23]. Identify the Principal Components. The principal components are obtained by a spectral decomposition of the correlation matrix. The results are expressed by the factor loading, which indicate how much each variable is related to each factor, and by the eigenvalues of each principal component. The first component is the linear combination of the most representative variables in terms of variance. The second component represents the second most representative variance and it is not related to the first component. The following components explain lower values of the total variance progressively and are not related to each other. The outcomes indicate five principal components that explain 66.05% of the total variance explained. Rotate the Principal Components Matrix. The interpretation of the principal components may be difficult due to the similar numerical magnitude of coefficients of different variables. In such case, the purpose of the rotation is to obtain a simpler structure to better understand the contribution of each variable to each principal component. This work uses the orthogonal rotation (varimax). Besides, the communality should be verified. The results range from 0 to 1; it is zero when the common factors do no explain any variance and it is 1 when they explain all the variance. When the value is lower than 0.50, it should be considered to increase the size of the sample or eliminate variables [23]. For samples with 100 to 200 observations, the communality that vary from 0.40 to 0.70 should have at least three factor loadings greater than 0.40 [24]. Here, all the principal components have at least three variables with factor loadings greater than 0.40.

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4 Results 4.1 Weighting the Variables The weights are defined to correct the overlapping information between correlated variables. This means that the weights are assigned in accordance to their statistical importance in the index construction process. To build the reconfigurability index, the approach developed by Nicoletti [20] was used to determine the weights of variables and principal components. The principal components represent the core characteristics of reconfigurability. After the rotation, each variable is weighted according to the proportion of its variance explained (λ). The weights are obtained by squaring and normalizing the estimated factor loadings that represent the proportion of the total unit variance of the indicator, which is explained by the factor [20]. This calculation is represented in Eq. 1, where wi is the weight of each variable and an is the factor loading of the rotated matrix. a2 wi =  n 2 an

(1)

The principal components are weighted according to its contribution to the variance explained in the data set. The Eq. 2 is used to determine the weights of the principal components (yn ), from the variables [20]. In Eq. 2, zn represents the answers of the questionnaire survey, that range from 1 to 7.  yn = wni zni (2) The principal components were calculated based on the weights of variables and the answers of the questionnaire. For instance, for the first respondent company, the modularity implementation level, ymodl, was calculated multiplying the weight of the variable modl01 by the value of its answer plus the weight of the variable modl02 by the value of its answer plus the weight of the variable modl03 by the value of its answer, as follows in Eq. 3. This procedure was replicated for the 112 respondent companies and the other principal components. ymodl = 0.35zmodl 01 + 0.37zmodl 02 + 0.28zmodl 03

(3)

4.2 Reconfigurability Index The reconfigurability index is calculated as described in Eq. 4. Since the index adopts the same scale of the measurement instrument, the results can vary from 1 to 7.  RI = yn λn (4) In sum, the λ values of adaptability and diagnosability are 0.25 each, integrability is 0.19, modularity is 0.16 and customization is 14%. This means that adaptability and diagnosability contribute to 25% each, integrability contributes to 19%, modularity to 16% and customization to 14% of the reconfigurability index.

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The index results are classified in accordance to the following scale: none (1.00 ≤ RI ≤ 2.00), very low (2.00 < RI ≤ 3.00), low (3.00 < RI ≤ 4.00), moderate (4.00 < RI ≤ 5.00), high (5.00 < RI ≤ 6.00) and very high (6.00 < RI ≤ 7.00). From the total of 112 respondent companies, the results ranged from 2.08 to 5.66. 10 companies present the reconfigurability index between 2.00 and 3.00; 39 companies present the index between 3.00 and 4.00; 49 companies present the index between 4.00 and 5.00; and 14 companies present the index between 5.00 and 6.00. A sample of two results is shown in Table 2. Table 2. A sample of the results. Company

ymodl

yintg

ycust

yadap

ydiag

RI

89

5.63

5.47

5.19

5.92

5.81

5.66

110

3.00

1.47

2.61

2.53

1.21

2.08

The company that shows the highest reconfigurability index (89) belongs to the industrial sector of leather and related products and adopts the make to order (MTO) business production strategy. It has complex products, bill of materials (BOM) and processes. This company faces fluctuations on volume, product mix, supply requirements, technical changes of products and modifications of parts by suppliers weekly, but does not face demand variations frequently. Adaptability is the core characteristic with the highest level of implementation in this company. This means that the company is able to change between products easily and adjust system’s capacity and throughput in a short time to match the market demand. This can only happen if modularity and integrability are implemented in the system as well [18]. As can be observed in Table 2, both characteristics show high levels of implementation. Customization, which is the characteristic that synthesizes the reconfigurability, on the other side, presents the lowest level of implementation. This indicates that there is room to improve the reconfigurability in this manufacturing system [25]. The company that shows the lowest reconfigurability index (110) belongs to the industrial sector of basic metals and adopts the engineering to order (ETO) business production strategy. The complexity of company’s products, BOM and processes is low. The company faces variations in demand, volume and product mix from week to week. The suppliers need to carry out modifications to the parts frequently, even though the supply requirements do not vary drastically and the products do not suffer a lot of technical modifications. In this case, modularity has the highest level of implementation. The results seem to indicate that the company is starting the process of implementation of reconfigurability, which begins with the implementation of modularity [18]. On the other hand, diagnosability shows the lowest level of implementation. This may be related to the type of product manufactured. In contrast to the company with the highest reconfigurability index, which manufactures complex products, such as luggage, handbags and footwear, this company manufactures fewer complex products, such as tubes and pipes.

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5 Conclusion and Further Research This study is intended to contribute to the reconfigurability level assessment in manufacturing companies. This work describes the construction of a reconfigurability index, which is based on the core characteristics of modularity, integrability, customization, adaptability and diagnosability. To build the index, the variables were selected from the measurement instrument proposed by Maganha et al. [5]. The index can be used by manufacturing companies to assess their current level of reconfigurability. Then, managers can use the results to improve the current level of reconfigurability, focusing on weak links and addressing barriers in attaining them. A PCA with orthogonal rotation (varimax) was used to derive weights and determine the contribution of each core characteristic to the reconfigurability. The results show that adaptability and diagnosability contribute with 25% each, integrability with 19%, modularity with 16% and customization with 15%. This research can be further extended by investigating the implementation level of reconfigurability among different industrial sectors.

References 1. Andersen, A.L., Larsen, J.K., Brunoe, T.D., Nielsen, K., Ketelsen, C.: Critical enablers of changeable and reconfigurable manufacturing and their industrial implementation. J. Manuf. Technol. Manag. 29(6), 983–1002 (2018) 2. Napoleone, A., et al.: towards an industry-applicable design methodology for developing reconfigurable manufacturing. In: Lalic, B., Majstorovic, V., Marjanovic, U., von Cieminski, G., Romero, D. (eds.) APMS 2020. IAICT, vol. 591, pp. 449–456. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57993-7_51 3. Bortolini, M., Galizia, F.G., Mora, C.: Reconfigurable manufacturing systems: literature review and research trend. J. Manuf. Syst. 49, 93–106 (2018) 4. Hollstein, P., Lasi, H., Kemper, H.-G.: A survey on changeability of machine tools. In: Zaeh, M.F. (ed.), Enabling Manufacturing Competitiveness and Economic Sustainability, pp. 93–98. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-23860-4_15 5. Maganha, I., Silva, C., Ferreira, L.M.D.F.: Understanding reconfigurability of manufacturing systems: an empirical analysis. J. Manuf. Syst. 48, 120–130 (2018) 6. Spena, P.R., Holzner, P., Rauch, E., Vidoni, R., Matt, D.T.: Requirements for the design of flexible and changeable manufacturing and assembly systems: a SME-survey. Procedia CIRP 41, 207–212 (2016) 7. Khanna, K., Kumar, R.: Reconfigurable manufacturing system: a state-of-the-art review. Benchmarking Int. J. 26(8), 2608–2635 (2019) 8. Youssef, A.M.A., ElMaraghy, H.A.: Assessment of manufacturing systems reconfiguration smoothness. Int. J. Adv. Manuf. Technol. 30(1–2), 174–193 (2006) 9. Hasan, F., Jain, P.K., Kumar, D.: Performance issues in reconfigurable manufacturing system. In: Katalinic, B. (ed.), DAAAM International Scientific Book 2014, pp. 295–310. Viena: DAAAM International (2014) 10. Mittal, K.K., Jain, P.K.: An overview of performance measures in reconfigurable manufacturing system. Procedia Eng. 69, 1125–1129 (2014) 11. Prasad, D., Jayswal, S.C.: Scheduling of products for reconfiguration effort in reconfigurable manufacturing system. Mater. Today Proc. 5(2), 4167–4174 (2018)

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12. Garbie, I.H.: DFSME: Design for sustainable manufacturing enterprises (an economic viewpoint). Int. J. Prod. Res. 51(2), 479–503 (2013) 13. Garbie, I.H.: Performance analysis and measurement of reconfigurable manufacturing systems. J. Manuf. Technol. Manag. 25(7), 934–957 (2014) 14. Gumasta, K., Gupta, S.K., Benyoucef, L., Tiwari, M.K.: Developing a reconfigurability index using multi-attribute utility theory. Int. J. Prod. Res. 49(6), 1669–1683 (2011) 15. Wang, G.X., Huang, S.H., Yan, Y., Du, J.J.: Reconfiguration schemes evaluation based on preference ranking of key characteristics of reconfigurable manufacturing systems. Int. J. Adv. Manuf. Technol. 89(5–8), 2231–2249 (2016). https://doi.org/10.1007/s00170-016-9243-7 16. Farid, A.M.: Measures of reconfigurability and its key characteristics in intelligent manufacturing systems. J. Intell. Manuf. 28(2), 353–369 (2014). https://doi.org/10.1007/s10845-0140983-7 17. Mittal, K.K., Jain, P.K., Kumar, D.: Configuration selection in reconfigurable manufacturing system based on reconfigurability. Int. J. Logistics Syst. Manage. 27(3), 363–379 (2017) 18. Maganha, I., Silva, C., Ferreira, L.M.D.F.: The sequence of implementation of reconfigurability core characteristics in manufacturing systems. J. Manuf. Technol. Manag. 32(2), 356–375 (2020) 19. Giri, A.K., Bansod, D.: Establishing finance-growth linkage for India: a financial condition index (FCI) approach. Int. J. Emerg. Mark. 14(5), 1032–1059 (2019) 20. Nicoletti, G., Scarpetta, S., Boylaud, O.: Summary indicators of product market regulation with an extension to employment protection legislation. SSRN Electron. J. 1–86 (2005) 21. Maganha, I., Silva, C., Ferreira, L.M.D.F.: The impact of reconfigurability on the operational performance of manufacturing systems. J. Manuf. Technol. Manag. 31(1), 145–168 (2019) 22. Greco, S., Ishizaka, A., Tasiou, M., Torrisi, G.: On the methodological framework of composite indices: a review of the issues of weighting, aggregation, and robustness. Soc. Indic. Res. 141(1), 61–94 (2019) 23. Jolliffe, I.T.: Principal components in regression analysis. In: Principal Component Analysis. Springer Series in Statistics. Springer, New York (2002). https://doi.org/10.1007/0-387-224 40-8_8 24. Pituch, K.A., Stevens, J.P.: Applied Multivariate Statistics for the Social Sciences: Analyses with SAS and IBM’s SPSS. Routledge, Milton Park (2015) 25. Goyal, K.K., Jain, P.K., Jain, M.: A novel methodology to measure the responsiveness of RMTs in reconfigurable manufacturing system. J. Manuf. Syst. 32(4), 724–730 (2013)

A Real Options Approach for NPV Investment Evaluation of Changeable Manufacturing Systems Fredrik Olsson1(B) , Alexander Werthén1 , and Ann-Louise Andersen1,2 1 Industrial Product Development, Production and Design, School of Engineering, Jönköping

University, Gjuterigatan 5, 551 11 Jönköping, Sweden 2 Department of Materials and Production, Aalborg University, Fibigerstraede 16,

9220 Aalborg East, Denmark

Abstract. Dealing with uncertain market conditions is a growing challenge for manufacturers. Changeable manufacturing systems have been suggested as a means to overcome the challenges resulting from uncertain markets. However, for changeability to acquire wide acknowledgement and implementation within industry, a clear economic justification considering the higher initial investment of such a system is needed. This paper attempts to develop an investment model comprehending the benefits of changeability, while focusing on applicability in industry. The proposed model complements a traditional Net Present Value method by evaluating the added value for changeability at either system or machine level, using a combination of Real Options approach. The model is applied in an industrial case and demonstrates an added value for changeability in both a flexible and reconfigurable manufacturing system. Keywords: Changeability · Reconfigurable manufacturing system · Investment model · Real options approach · Changeable manufacturing

1 Introduction Flexibility in manufacturing systems is used as a counterbalance for external uncertainties [1, 2]. To surmount this challenge, flexible and reconfigurable manufacturing allow for cost-effective changes and rapid response to uncertain conditions [3–5]. Flexibility is defined as the possibility to change the nature without needing to change the configuration of the system [5]. Reconfigurability is defined as the ability to change the system structure i.e. the capability and capacity by altering configuration [6]. In combination, flexibility and reconfigurability indicate the ability of a manufacturing system to accommodate early and foresighted adjustments in an economically feasible way, defined as system changeability [5]. Realizing a wide implementation of Changeable Manufacturing System (CMS) within industry have been hampered by the challenge of economically justifying such system, when the associated initial costs are larger than for traditional systems [7]. Decision makers in industry often make use of Net Present Value © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 130–137, 2022. https://doi.org/10.1007/978-3-030-90700-6_14

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(NPV) in order to economically evaluate the benefit of an investment [8]. This approach uses discounted cash flows prognosed over the lifetime of the investment provided by a single scenario. Consequently, this will generate an evaluation based on certainty, while in reality, the outcome is far more uncertain [9]. The benefit with CMS is its incremental nature, where the system can adapt to different market conditions creating a sense of “flexibility on demand” [7]. With NPV lacking the possibility to present this changeability, a model is required that can take uncertainty into account [9]. One approach for investment evaluation that has received increased traction in academia is Real Options, as it demonstrates the value of multiple outcomes [9–11]. This approach can evaluate the options embedded within a CMS and has been applied in multiple investment models [12]. However, while previous research indicates that the real options approach can be used for evaluating changeability, the method still lacks wide implementation in industry [7, 13]. Based on this, the following research question is addressed in this paper: How can a real options approach to NPV be developed and used for evaluating investments in DMS, FMS, and RMS in a manufacturing company? The remainder of the paper is structured as follows: Sect. 2 presents a review of related literature and Sect. 3 present the proposed model for investment evaluation. Section 4 discusses results and Sect. 5 summarizes the contribution and outlines future research directions.

2 Literature Review The term “real options” was first phrased by Myers [14] and has since changed the research field of capital budgeting [15]. Similar to a financial options, a real options is about the right, without obligation, to sell or buy an asset with the difference of the asset being non-financial [11, 12]. As the value of an option increases with uncertainties [16], the ability to, in an economic manner, swiftly adapt to changes can define the prospects of flexibility and increase in value as uncertainty increases [11]. Hence, flexibility can act as a remedy against market uncertainty by creating options, however, with more complex pay-back structures than the market-based options of the financial world [17]. Having an options mindset applied in a manufacturing system investment evaluation could demonstrate the investments made to change the system configuration over time, at the point of the initial investment decision [10]. Provided that the market outcome is extremely uncertain, the option can either be to expand the system at such time when the capacity or capability need occur [18]. The option to postpone or defer an investment decision can be exercised, taking advantage of information available in the future [17]. Both these options can reduce the capital exposure for a firm, as the configuration of the system can incrementally be invested into when a certain demand arise [11]. This gives decision makers the possibility of mitigating the challenges present with volatile production demand, which is the main source of uncertainty in a manufacturing environment according to Jiao et al. [19]. Multiple researchers have applied real options into models for manufacturing equipment investment decisions. Amico et al. [18] focused on product demand as a stochastic variable and used a Monte Carlo approach to estimate demand of the planning horizon. Building on this, later works of Amico et al. [10] combined real options theory with NPV to create an Extended NPV (ENPV) model, which is an NPV and the additional options embedded in an investment. This combination of real options and NPV

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is also done by Abele et al. [9], suggesting to use either the Black Scholes formula or binominal lattice to properly evaluate an investment in changeability. In the paper by Karsak [11], the proposed framework considers the benefits of keeping an expansion option, as well as the consequence of missing an opportunity to a competitor. The suggested framework ultimately gives a value for strategic decisions, committing to the opportunity presented by delaying an expansion [11]. Although Real options have been proved to efficiently present the value of changeability, it has yet to be acknowledged by industry as a result of its perceived complexity [7, 13]. To facilitate an adoption of real options within industry investment practices, explicit consideration must be made in regards to the user friendliness and understandability of the model [19]. This notion is further motivated by Horn et al. [13], stating that industry prefer simpler methods rather than heavy computation such as in most real options evaluations. Furthermore, Cheng et al. [20] elaborate on practitioners’ desire for convenient and understandable methods, supporting the formulation, interpretation, and computation for stakeholders with varying financial proficiency. Presenting a model giving the added monetary value of changeability in a system, using a recognizable investment approach, will provide the economic justification needed during the design phase to motivate the greater initial investment of a changeable system. This follows the conclusion by Aravindan and Punniyamoorthy [21], that higher initial investment and managements aspiration for justifying a new acquisition in financial terms is a barrier for introducing advanced manufacturing systems. Therefore, developing an investment model proving the economic benefit of changeability, while focusing on the usability for industry is desirable.

3 Research Method In order to develop an investment model for changeable manufacturing, based on industry needs, it was imperative to have close collaboration with industry in order to contextualize the use and ensure applicability of the model. Thus, the framework for quantitative modelling suggested by Mitroff et al. [22] was used, as it has an outset in an industry problem and a final goal of implementation. Thus, based on this model, the following phases were followed in this research: (1) conceptualization based on reality and the industrial problem, i.e. the need for a readily applicable investment model that captures the value of changeability, (2) modelling based on the model concept, (3) model solving, and finally (4) implementation in the industrial context (Fig. 1). Evidently, this model development process allows for revisiting phases and applying a largely iterative approach, where validation is preformed through iterations between phases.

Fig. 1. Model development process adapted from Bertrand and Fransoo [23]

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An industrial case was used for the above-mentioned phases and served as both a basis for formulating the needs in regard to evaluation of financial value of changeability, testing the model, and eventually implementing the model. The case company is operating on a global scale within a technology intensive market and is transitioning into a more reconfigurable manufacturing setting. The transition is a response to disruptive technology, which is increasing the uncertainties within their market. The case company has initiated a reconfigurable development project, where one of the identified challenges was to motivate the higher initial investment with RMS and FMS. Currently, NPV is used to evaluate investment projects in the case company. However, an obvious challenge in the company is to create an approach that represents the benefit of changeability, while still being understandable and ready for implementation. For this research, the case company was used for interviews, document studies, and a focus group in order to get insight into investment procedures at the focal company, as well as validating the appropriateness of the model in their context. In Table 1, the data collection methods are outlined. Table 1. Data collection approach in case study. Data collection method Object studied

Occasions

Interviews

Production Engineer site one Biweekly, five months Production Engineer site two One interview Business controller One interview

Document studies

Investment NPV model Investment case

Focus group

Production Engineer site one One focus group with four participants Production Engineer site two Business controller Global manager

Continuously Continuously

4 Proposed Model 4.1 Conceptual Model From the interviews and document studies it was established that the focal company currently make use of an NPV approach for investment evaluations. This approach was then verified in literature to be the most conventional investment method in industry [8, 13]. The focal company also stressed the need for monetary motivation value as a result from the model, in order to have a unanimous point of analysis in between production engineers, business controllers and management. Moreover, during the focus group discussions it became clear that the inclusion of new investment evaluation KPIs would be strenuous for the organization to absorb. Thus, the model should introduce new concepts, yet strive to use already known KPIs. When developing an investment model for practitioners, consideration taken to already existing practices will ease smooth transitions.

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Since NPV was the model mostly used in industry, the barrier for practitioners would be smaller if a new model were built on the same constructs. Thus, when developing a model to economically evaluating an investment of CMS, the model must be complex enough to capture the benefits of changeability. While at the same time be simple enough for practitioners to use, independent on former economic knowledge. In Fig. 2, the resulting model concept is provided, based on requirements from the case company.

Fig. 2. Model overview

4.2 Scientific Model One weakness with the traditional application of NPV is that it only considers the initial investment. Consequently, it does not consider the present value of investments made after the initial investment. To be able to do this in Eq. 1, the incremental investments have been added. These investments are discounted back with a risk-free rate, which is the rate of return for an investment with zero risk. n Investment t n Cash flowt − Initial investment − (1) NPV = t=1 (1 + r)t t=1 (1 + rr)t With the addition of scenarios into the model an expected NPV is calculated. Taking the NPV for one scenario and weighing it with its probability. These are then summarized to gain the expected NPV (Eq. 2). eNPV = NPV1 ∗ P1 + NPV2 ∗ P2 + NPV3 ∗ P3

(2)

The value of the flexibility is then calculated using Eq. 3. The expected NPV for the least flexible system is subtracted from the more flexible system. VF = eNPVCMS − eNPVDMS

(3)

For the volume scenarios there is also a calculation for the postponing of the investment. It is like Eq. 1 with the addition of discounting the initial investment with the risk-free rate (Eq. 4). NPVX =

n t=1

CFt Initial investment n Investment t − − t=1 (1 + rr)t (1 + r)t (1 + rr)ti

(4)

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The NPVs are then calculated into an expected NPV for the postponement. If an investment when not postponing is negative this “check” is something that should be done. If scenario three had a negative NPV, the project in the later stage will be cancelled leading to it not generating any cash flow (Eq. 5). eNPV = NPV1 ∗ P1 + NPV2 ∗ P2 + NPV3 ∗ P3

(5)

4.3 Model Application The following section presents an illustrative case, based on the context of the industrial case company. Due to confidentially reasons, data is only illustrative. The different input variables are depicted in Table 2. The demand scenarios, depicted in Table 3, was formulated in collaboration with the focal company. New product introduction happens in year four and eight, which the manufacturing system must adjust to. The added flexibilities of FMS and RMS are depicted in Table 4. Table 2. Input data for model application Variable

DMS

FMS

RMS

Initial capacity

135 000 pc

125 000 pc

125 000 pc

Initial investment

50 MSEK

60 MSEK

70 MSEK

Volume flexibility Incremental investment

50 MSEK

17 MSEK

10 MSEK

Incremental capacity steps

135 000 pc

5 000 pc

5 000 pc

Operational cost

3 MSEK

1,5 MSEK

0,5 MSEK

Operational profit

14 MSEK

3 MSEK

3 MSEK

Volume change implication

11 MSEK

1,5 MSEK

2,5 MSEK

Incremental investment

50 MSEK

5 MSEK

5 MSEK

Operational cost

1 MSEK

1,5 MSEK

1 MSEK

Product flexibility

Operational profit

3 MSEK

3 MSEK

3 MSEK

Introduction implication

2 MSEK

1,5 MSEK

2 MSEK

Table 3. Demand scenarios (1000 pc.) Y1

Y2

Y3

Y4

Y5

Y6

Y7

Y8

Y9

Y10

1

123

125

130

132

133

135

135

135

135

135

2

116

122

123

125

126

128

128

128

128

128

3

135

141

143

145

146

148

148

148

148

148

136

F. Olsson et al. Table 4. Summarization of flexibilities (MSEK) DMS

FMS

RMS

Value of volume flexibility

0,00

−17,5

Value of product flexibility

0,00

26,5

17,6

Sum of flexibilities

0,00

9,06

19,0

Value of postponement (Volume)

4,11

10,4

−5,6

1,4

5 Conclusion and Future Work For the transition into CMS, investment models developed for justifying the higher initial investment of such a system is needed taking uncertainty into account. Thus, in this paper, a simplistic model is developed to act as the initial step for both real options, but foremost for justifying changeability. The developed model manages to comprehend and present a value for changeability by applying a real options approach. Moreover, the model can still be recognized and accepted by industry, as it builds upon the most common investment approach NPV. Thereby the contribution of this paper is twofold; a simplistic investment model for both using real options and evaluating changeability. The paper addresses the research question through an iterative modellings approach using a case company as empirical context. In the paper, the model is demonstrated in terms of the value for changeability in an illustrative case. The model will be further applied to more investment projects in the case company and will be vital when evaluating investment alternatives. Suggested further research is to incorporate more aspects of changeability into the model e.g. product mix flexibility and test the model in further industrial settings. Acknowledgements. The research presented in this paper is funded by the Danish Industry Foundation in connection to the project “Development of Reconfigurable Manufacturing” (REKON).

References 1. Kampker, A., Burggräf, P., Wesch-Potente, C., Petersohn, G., Krunke, M.: Life cycle oriented evaluation of flexibility in investment decisions for automated assembly systems. CIRP J. Manuf. Sci. Technol. 6(4), 274–280 (2013) 2. Rocky Newman, W., Hanna, M., Jo Maffei, M.: Dealing with the uncertainties of manufacturing: flexibility, buffers and integration. Int. J. Oper. Prod. Manag. 13(1), 19–34 (1993) 3. ElMaraghy, H.: Flexible and reconfigurable manufacturing systems paradigms. Int. J. Flex. Manuf. Syst. 17(4), 261–276 (2006) 4. Koren, Y.: The rapid responsiveness of RMS. Int. J. Prod. Res. 51(23–24), 6817–6827 (2013) 5. Wiendahl, H.P., et al.: Changeable manufacturing - classification, design and operation. CIRP Ann. Manuf. Technol. 56(2), 783–809 (2007) 6. Kuzgunkaya, O., ElMaraghy, H.A.: Economic and strategic perspectives on investing in RMS and FMS. Int. J. Flex. Manuf. Syst. 19(3), 217–246 (2008)

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7. Milberg, J., Möller, N.: Valuation of changeable production systems. Prod. Eng. 2(4), 417–424 (2008) 8. Siziba, S., Hall, J.: The evolution of the application of capital budgeting techniques in enterprises. Glob. Finan. J. 47, 100504 (2021) 9. Abele, E., Liebeck, T., Wörn, A.: Measuring flexibility in investment decisions for manufacturing systems. CIRP Ann. Manuf. Technol. 55(1), 433–436 (2006) 10. Amico, M., Asl, F., Pasek, Z., Perrone, G.: Real options: an application to rms investment evaluation. In: Dashchenko, A.I. (ed.) Reconfigurable Manufacturing Systems and Transformable Factories, pp. 675–693. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-293973_34 11. Karsak, E.E., Özogul, C.O.: Valuation of expansion flexibility in flexible manufacturing system investments using sequential exchange options. Int. J. Syst. Sci. 36(5), 243–253 (2005) 12. Nembhard, H.B., Shi, L., Park, C.S.: Real option models for managing manufacturing system changes in the new economy. Eng. Econ. 45(3), 232–258 (2000) 13. Horn, A., Kjærland, F., Molnár, P., Steen, B.W.: The use of real option theory in Scandinavia’s largest companies. Int. Rev. Financ. Anal. 41, 74–81 (2015) 14. Myers, S.C.: Determinants of corporate borrowing. J. Financ. Econ. 5(2), 147–175 (1977) 15. Triantis, A.: Realizing the potential of real options: does theory meet practice? J. Appl. Corp. Financ. 17(2), 8–16 (2005) 16. Du, J., Jiao, Y.-Y., Jiao, J.: A real-option approach to flexibility planning in reconfigurable manufacturing systems. Int. J. Adv. Manuf. Technol. 28(11–12), 1202–1210 (2006) 17. Bengtsson, J.: Manufacturing flexibility and real options: a review. Int. J. Prod. Econ. 74(1–3), 213–224 (2001) 18. Amico, M., Pasek, Z.J., Asl, F.M., Perrone, G.: A new methodology to evaluate the real options of an investment using binomial trees and Monte Carlo simulation. Winter Simul. Conf. Proc. 1, 351–359 (2003) 19. Jiao, Y.Y., Du, J., Jiao, J.: A financial model of flexible manufacturing systems planning under uncertainty: identification, valuation and applications of real options. Int. J. Prod. Res. 45(6), 1389–1404 (2007) 20. Agnes Cheng, C.S., Kite, D., Radtke, R.: The applicability and usage of NPV AND IRR capital budgeting techniques. Manag. Finan. 20(7), 10–36 (1994) 21. Aravindan, P., Punniyamoorthy, M.: Justification of advanced manufacturing technologies (AMT). Int. J. Adv. Manuf. Technol. 19(2), 151–156 (2002) 22. Mitroff, I.I., Betz, F., Pondy, L.R., Sagasti, F.: On managing science in the systems age: two schemas for the study of science as a whole systems phenomenon. Interfaces (Providence) 4(3), 46–58 (1974) 23. Bertrand, J.W.M., Fransoo, J.C.: Operations management research methodologies using quantitative modeling. Int. J. Oper. Prod. Manag. 22(2), 241–264 (2002)

Methods and Models to Evaluate the Investment of Reconfigurable Manufacturing Systems: Literature Review and Research Directions Stefan Kjeldgaard(B)

, Ann-Louise Andersen , Thomas D. Brunoe , and Kjeld Nielsen

Department of Materials and Production, Aalborg University, Aalborg, Denmark [email protected]

Abstract. Reconfigurable Manufacturing Systems (RMS) have been proposed as a means to accommodate today’s dynamic requirements in a rapid and costefficient way. However, the industrial transition towards RMS is limited as it is perceived to be uneconomic due to an increased initial investment, yet desirable for the ability to respond to uncertainty and co-evolve with life-time requirements. This ability makes economic evaluation of RMS concepts inherently complex, which is not supported by traditional approaches. Nevertheless, concept evaluation remains critical during development as the majority of the life-time cost is determined by initial design decisions. Therefore, the objective of this paper is to review state-of-the-art literature to provide an overview of methods and models to evaluate the investment of RMS. Papers were retrieved from a structured subject search and screening, then classified according to seven characteristics, and mapped in a decision tree to aid practitioners in the selection of suitable evaluation approaches. Based on the review, there is a lack of quantitative models for comparative investment evaluation which is validated in industry and consider network implications, uncertainty and life-time requirements. To mitigate this gap, aforementioned is proposed as viable directions for further research. Keywords: Financial justification · Investment analysis · Concept evaluation · Reconfigurability · Changeability

1 Introduction Today’s manufacturing environment is characterized by dynamic and volatile markets with increased fragmentation and uncertainty of demand [1]. To remain competitive, manufacturers are required to (i) provide a wide variety of products at a faster rate, and (ii) respond to changes in functionality and capacity requirements in a rapid and cost-efficient way [2–4]. In this context, traditional dedicated manufacturing systems (DMS) capable of producing a single product at a high rate, risk being under-utilized and misaligned with product life cycles [1, 2, 5]. In contrast, flexible manufacturing systems (FMS) capable of producing a high variety of products at a low rate, requires © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 138–146, 2022. https://doi.org/10.1007/978-3-030-90700-6_15

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large capital investments as they are designed with a wide range of apriori generalpurpose functionality which often remains under-utilized [1, 5]. As an intermediate, the reconfigurable manufacturing system (RMS) was conceptualized in the late 90s with the aim to provide the capacity of DMS and the functionality of FMS [6]. While DMS and FMS are static systems, the functionality and capacity of RMS can be converted and scaled to what is needed, when needed [1, 6]. This dynamic ability can be implemented across several levels of the manufacturing hierarchy e.g. systems and machines [1, 5]. Evaluation of design concepts is critical in the development of manufacturing systems [7, 8]. Generally, 80% of the development and life-time cost of the systems is committed by decisions made during the conceptual design phase [9]. Thus, a wrong choice of concept can rarely, if ever, be recouped in the subsequent embodiment and detailed design phases [10] as it is difficult, if not impossible, to mitigate fundamental flaws of the concept at later stages [11]. For RMS concepts, the evaluation is inherently complex due to the ability to respond to uncertainty and co-evolve with life-time functionality and capacity requirements [12], which can be enabled in various ways, to various extents [13]. In practice however, concept evaluation is prone to be unstructured and based on subjective opinions which is insufficient to capture the benefits of reconfigurability [14]. Moreover, the traditional objective economic measures are neither suitable to capture the benefits of reconfigurability as the scope is usually limited to requirements of a single product. These benefits of reconfigurability are usually felt in the long term, which makes it difficult to economically justify the increased initial investment of RMS over dedicated counterparts [4]. This has led to the perception that RMS is uneconomic, which is a main barrier towards the industrial implementation [15]. In order to aid practitioners in selecting suitable evaluation approaches to advance the industrial implementation, the research question of this paper is: which methods and models are provided by state-of-the-art literature to evaluate the investment of reconfigurable manufacturing systems? The remainder of the paper is structured as follows: Sect. 2 presents the methodology for the search, screening and classification of literature, Sect. 3 presents findings of the classification and the applied characteristics, Sect. 4 presents discussions of the findings, and Sect. 5 presents conclusions and future research directions.

2 Methodology The method for the search, screening and classification of literature for the review is based on the frameworks provided by Pare et al. [16] and Hart [17]. The protocol for the structured subject search and screening of literature is presented in Table 1. The protocol covers (i) the search string, (ii) the exclusion criteria applied at each phase of the screening, (iii) the topics of excluded papers at each phase, and (iv) the number of included papers at each phase. The keywords and structure of the search string are based on (i) a preliminary search and scoping exercise with repeated iterations (ii) advice from researchers on the topic. The sources for the search are: peer reviewed journals, conference proceedings and book chapters in the Scopus database. The language of papers included in the search is limited to English and the coverage is limited to between 1998 and 2021. The characteristics applied for the classification are presented in Sect. 3.

140

S. Kjeldgaard et al. Table 1. Protocol for the search and screening of literature

Phase

Description

Search

(method* OR tool* OR model*) AND (evaluat* OR justif* OR 107 papers asses*) AND (investment OR performance OR cost OR financial) AND “reconfigurable manufacturing” in title, abstract or keywords

First exclusion

Exclusion based on screening of titles. Exclusion applies to 29 papers papers which do not meet the criteria of being relevant in relation to evaluation of RMS. Excluded papers treat (i) problems post-evaluation e.g. planning and control of reconfigurations, production and/or logistics including routing and scheduling, (ii) problems pre-evaluation e.g. methods for design of families, platforms and modularity in the product domain and changeable, reconfigurable or flexible systems in the manufacturing domain (iii) Industry 4.0 problems e.g. digital twins, control systems, sensors, middleware etc.

Second exclusion

Exclusion based on screening of abstracts. The same exclusion 15 papers criteria applied as in the first exclusion. Excluded papers treat e.g. surveying the impact of RMS implementation, evaluating performance of FMS, evaluating configurations of RMS, evaluating performance of RMS if breakdown occurs, selecting machines for RMS, generating process plans for RMS, designing concepts of RMS, optimizing the layout of RMS, and assessing the extent of reconfigurability

Third exclusion

Exclusion based on screening of full papers. Exclusion applies to 6 papers papers which do not meet the criteria of providing a method or method to evaluate the investment or performance of RMS. Excluded papers treat (i) identification and consolidation: objectives, metrics and parameters to consider in evaluation (ii) evaluating schemes of reconfigurations or configurations of RMS

Snowball

Search for additional papers by a front/backwards snowball procedure based on references and authors of papers included post the third exclusion. Similar criteria applied as in the third exclusion

Consolidation Consolidation of papers from (i) the third exclusion phase of the structured subject search (ii) the snowball phase

Results

7 papers

13 papers

3 Classification The literature search and screening process resulted in thirteen papers which were classified according to seven characteristics, where the results are presented in Table 2. The first characteristic covers the evaluation approach e.g. Discounted Total Cost (DTC), Mixed Integer Programming (MIP), Linear Programming (LP), Analytic Hierarchy Process (AHP) or Break-Even Analysis (BEA). Approaches applying (i) a single measure e.g. responsiveness index is denoted as IDX (ii) fuzzy logic is denoted by an F in the

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abbreviation. The second characteristic concerns the type i.e. whether quantitative (QN) or qualitative (QL) and whether a model (ML) or method (MD) is provided. This characteristic is selected as it reflects the extent of information required to apply the approach, where e.g. quantitative models are more feasible to apply during or post conceptual design due to the availability and accuracy of information increasing concurrent with the state of development. The third characteristic covers the means applied to account for uncertainty i.e. simulation (SI), scenario analysis (SC) and/or sensitivity analysis (SE). This characteristic is selected as (i) manufacturing system concepts differs in their ability to cope with changes of requirements in a rapid and cost-efficient way, which is important to account for by e.g. evaluating across what-if scenarios or with stochastic demand (ii) the assumptions related to the performance of concepts is often uncertain, where sensitivity analysis can provide relevant insights e.g. minimum reconfiguration time and cost required for the concept to be economically viable. The fourth characteristic concerns the focus i.e. whether focused on an overall evaluation of the investment or whether focused on evaluating the performance on a subset of relevant metrics. This characteristic is selected as investment evaluation is in some cases required to provide a business case to decision makers to pass through the gate to detailed design, whereas performance evaluation can provide relevant insights to enrich the conceptual design. The fifth characteristic covers whether the approach is validated in an industrial or theoretical case, and is selected to reflect the maturity of the approaches to be applied in practical contexts. The sixth characteristic covers the object which is evaluated i.e. manufacturing system and/or machine/equipment. The seventh characteristic reflects the schemes of the object that is evaluated i.e. reconfigurable (R), flexible (F) or dedicated (D). If an approach evaluates multiple schemes belonging to the same paradigm, it is denoted by an uppercase x. This characteristic is selected to aid practitioners conducting brownfield development in selecting an approach which can support comparative evaluation against their current system e.g. flexible or dedicated. Four papers provide a qualitative method for evaluation, covering AHP [22, 23, 25, 29] where the majority are focused on evaluating the performance on a system level without comparative evaluation, consideration of uncertainty and industrial validation. An exception, is the method provided by Singh et al. [25] which is applied for comparative evaluation in the automotive industry, where the method incorporates fuzzy cash flow analysis to generate objective criteria, where some uncertainty is considered. Four papers provide quantitative methods where the majority evaluates performance and none considers uncertainty. Two methods are applied to evaluate reconfigurable machines where (i) Puik et al. [27] evaluates the resources and lead-time required across reconfigurations for multiple schemes in an industrial case (ii) Goyal et al. [21] evaluate the responsiveness of a machine in a theoretical case. Two methods are applied to comparatively evaluate systems where (i) Wiendahl [30] evaluates the investment of material supply systems in the automotive industry through break-even analysis (ii) Zhang et al. [26] evaluates paradigms on a satisfaction degree index in a theoretical case. Five papers provide a quantitative model, where (i) the majority focus on investment evaluation (ii) all are applied for comparative evaluation (iii) all consider uncertainty, although by a variety of means for various purposes. Of these, the DTC model proposed by Andersen et al. [18] incorporating Monte-Carlo simulation on demand, is the only

142

S. Kjeldgaard et al. Table 2. Results of the literature classification

Ref

Approach

Type

Uncertainty

Focus

Validation

Object

Scheme

[18]

DTC

QN ML

SI

Investment

Industrial

System Fixture

R, F R, D, F

[19]

MIP

QN ML

SC

Investment

Theoretical

System

R, D, F

[20]

LP

QN ML

SC

Performance

Theoretical

Machine

R, D

[21]

IDX

QN MD

N/A

Performance

Theoretical

Machine

R

[22]

AHP

QL MD

N/A

Performance

N/A

System

R

[23]

FAHP

QL MD

SE

Performance

N/A

System

R

[24]

FMIP

QN ML

SE

Investment

Theoretical

Machine

R, F

[25]

FAHP

QL MD

N/A

Investment

Industrial

System

R, D

[26]

IDX

QN MD

N/A

Performance

Theoretical

System

R, D, F

[27]

IDX

QN MD

N/A

Performance

Industrial

Machine

Rx

[28]

MIP

QN ML

SC, SE, SI

Investment

Theoretical

Machine

Rx , Dx

[29]

AHP

QL MD

N/A

Performance

Theoretical

System

Rx

[30]

BEA

QN MD

N/A

Investment

Industrial

System

R, D

which is (i) validated in an industrial case (ii) applied to evaluate objects on multiple levels i.e. schemes of assembly systems in the mechatronic industry and fixtures in the capital goods industry. The LP model proposed by Bortolini et al. [20] is applied in a theoretical case to evaluate the performance of machine schemes in a cellular layout. The model seeks to minimize the combined time of reconfiguration time and intercellular travel time, and considers uncertainty by means of scenario analysis where the schemes are evaluated across multiple assignments of machines and cells. The MIP model proposed by Niroomand et al. [19] is applied in a theoretical case and differs from aforementioned, as it decides the optimal mix of capacity investments among schemes, thereby supporting decisions regarding investment in a mix of paradigms. Moreover, the model accounts for uncertainty by sensitivity analysis on 54 scenarios based on the combination of values for four parameters (i) cost ratio between excess capacity and product shortage (ii) reconfiguration time (iii) cost ratio between base and auxiliary modules (iv) demand patterns. The MIP model proposed by Niroomand et al. [28] is applied in a theoretical case, to evaluate the investment of machine schemes in three types of manufacturing layout configurations, applying similar scenarios as their previous work to account for uncertainty. Moreover, the MIP results are validated in a discrete-event-simulation to account for stochastic demand patterns and ramp-up capacity. The FMIP model proposed by Kuzgunkaya et al. [24] is applied in a theoretical case to evaluate machine schemes, where sensitivity analysis is conducted on the results. The model diverges from the rest, as it is the only which applies fuzzy logic, reflecting its applicability in contexts where the preference of decision makers among objectives

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are uncertain. additionally, the model includes the possibility for outsourcing, which is the only network related component of all the approaches. In order to support industrial practitioners in selecting a suitable tool for evaluation of reconfigurable manufacturing system and constituents, a decision tree which compliments the classification in Table 2, have been constructed and provided in Fig. 1.

Industrial (1)

Covered (4)

Investment (5)

Quantitative (9)

Start

Approach

Qualitative (4)

Focus

Performance (4)

Uncertainty

Uncovered (1)

DCT

Validation

Theoretical (3)

MIP

BEA

IDX / IP

AHP

Fig. 1. Decision tree for the selection of an evaluation approach

4 Discussion A main challenge in the industrial implementation of RMS is a lack of research on the manufacturing network [31], only covered by 5% of publications on RMS [32]. This neglect also prevails across the evaluation approaches classified in this paper, where neither considers the impact of RMS throughout the manufacturing network. Therefore, the application of the approaches carries a risk of insufficient evaluation in industrial contexts where the reconfigurable object implicates (i) the suppliers e.g. being required to transition from the supply of integrated to modular objects, affecting the design and operations of their manufacturing and supply-chain systems (ii) the footprint e.g. affecting the cost-efficiency and rapidness of changing the footprint to create a competitive advantage [32, 33]. To capture these aspects, an evaluation approach which extends the scope from the focal factory to the manufacturing network is needed. Another aspect to consider in evaluation are the life-time capacity and functionality requirements [12]. However, only six of the classified approaches explicitly considers such in numerical terms. These six comprise: BEA [30] with a 20-year horizon, DTC [18] with a 10-year horizon, LP [20] with a ~3-year horizon and MIP [19, 24, 28] with a 9-, 8- and 10-year horizon. The fallacy of disregarding the life-time requirements, is an introduction of bounded rationality for decision makers in choosing the appropriate concept where the economic viability of reconfigurability increase in the long-term.

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5 Conclusion and Further Research To remain competitive in today’s dynamic environment, manufacturers are required to respond to changes in a rapid and cost-efficient way. RMS has been proposed to achieve this ability, which can be enabled in various ways to various extents, - increasing the complexity of the critical concept evaluation. Moreover, as traditional evaluation means are not suitable to capture the long-term benefits of RMS, it is difficult to justify the increased initial investment, which is a main barrier in the industrial implementation. Therefore, this paper provides a review of methods and models to evaluate the investment of RMS. Thirteen approaches were identified from a structured search and screening of literature, which were classified according to seven characteristics and mapped in a decision tree. Based on the review, there is a lack of quantitative models for comparative investment evaluation which is validated in industry, applicable to objects across levels, considers uncertainty, network implications and life-time requirements. To mitigate this gap, aforementioned is proposed for further research. Acknowledgements. The research presented in this paper is supported by the MADE research program and the REKON research project. MADE is funded by the Innovation Fund Denmark and REKON is funded by the Danish Industry Foundation.

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13. Benkamoun, N.: Systemic design methodology for changeable manufacturing systems (Doctoral Dissertation) (2016) 14. Bellgran, M., Säfsten, K.: Production Development. Springer, London (2010). https://doi.org/ 10.1007/978-1-84882-495-9 15. Heisel, U., Meitzner, M.: Progress in reconfigurable manufacturing systems. In: Dashcenko, A.I. (ed.) Reconfigurable Manufacturing Systems and Transformable Factories, pp. 47–62. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-29397-3_4 16. Paré, G., Tate, M., Johnstone, D., et al.: Contextualizing the twin concepts of systematicity and transparency in information systems literature reviews. Eur. J. Inf. Syst. 25(6), 493–508 (2016) 17. Hart, C.: Doing a Literature Review: Releasing the Social Science Research Imagination. Sage Publications (1998) 18. Andersen, A.-L., Brunoe, T.D., Nielsen, K., Bejlegaard, M.: Evaluating the investment feasibility and industrial implementation of changeable and reconfigurable manufacturing concepts. J. Manuf. Technol. Manage. 29(3), 449–477 (2018). https://doi.org/10.1108/JMTM03-2017-0039 19. Niroomand, I., Kuzgunkaya, O., Bulgak, A.A.: Impact of reconfiguration characteristics for capacity investment strategies in manufacturing systems. Int. J. Prod. Econ. 139(1), 288–301 (2012) 20. Bortolini, M., Galizia, F.G., Mora, C., et al.: Reconfigurability in cellular manufacturing systems: a design model and multi-scenario analysis. Int. J. Adv. Manuf. Technol. 104(9–12), 4387–4397 (2019) 21. Goyal, K.K., Jain, P.K., Jain, M.: A novel methodology to measure the responsiveness of RMTs in reconfigurable manufacturing system. J. Manuf. Syst. 32(4), 724–730 (2013) 22. Abdi, M.R., Labib, A.W.: Performance evaluation of reconfigurable manufacturing systems Via Holonic architecture and the analytic network process. Int. J. Prod. Res. 49(5), 1319–1335 (2011) 23. Abdi, M.R., Labib, A.W.: Feasibility study of the tactical design justification for reconfigurable manufacturing systems using the fuzzy analytical hierarchical process. Int. J. Prod. Res. 42(15), 3055–3076 (2004) 24. Kuzgunkaya, O., ElMaraghy, H.A.: Economic and strategic perspectives on investing in RMS and FMS. Int. J. Flex. Manuf. Syst. 19(3), 217–246 (2007) 25. Singh, R.K., Khilwani, N., Tiwari, M.K.: Justification for the selection of a reconfigurable manufacturing system: a fuzzy analytical hierarchy based approach. Int. J. Prod. Res. 45(14), 3165–3190 (2007) 26. Zhang, G., Liu, R., Gong, L., et al.: An analytical comparison on cost and performance among DMS, AMS, FMS and RMS. In: Dashcenko, A.I. (ed.) Reconfigurable Manufacturing Systems and Transformable Factories, pp. 659–673. Springer, Heidelberg (2006). https://doi. org/10.1007/3-540-29397-3_33 27. Puik, E., Telgen, D., van Moergestel, L., et al.: Assessment of reconfiguration schemes for reconfigurable manufacturing systems based on resources and lead time. Robt. Comput. Integr. Manuf. 43, 30–38 (2017) 28. Niroomand, I., Kuzgunkaya, O., Bulgak, A.A.: The effect of system configuration and rampup time on manufacturing system acquisition under uncertain demand. Comput. Ind. Eng. 73, 61–74 (2014) 29. Wang, G., Wang, G., Huang, S., et al.: Reconfiguration schemes evaluation based on preference ranking of key characteristics of reconfigurable manufacturing systems. Int. J. Adv. Manuf. Technol. 89(5), 2231–2249 (2017) 30. Wiendahl, H.P., Heger, C.L.: Justifying changeability. a methodical approach to achieving cost effectiveness. J. Manuf. Sci. Prod. 6(1–2), 33–40 (2004). https://doi.org/10.1515/IJMSP. 2004.6.1-2.33

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Smart Automation and Human Machine Collaboration

Aiming for Knowledge-TransferOptimizing Intelligent Cyber-Physical Systems Marcus Grum(B) , Christof Thim, and Norbert Gronau Potsdam University, 14482 Potsdam, Germany [email protected]

Abstract. Since more and more production tasks are enabled by Industry 4.0 techniques, the number of knowledge-intensive production tasks increases as trivial tasks can be automated and only non-trivial tasks demand human-machine interactions. With this, challenges regarding the competence of production workers, the complexity of tasks and stickiness of required knowledge occur [1]. Furthermore, workers experience time pressure which can lead to a decrease in output quality. Cyber-Physical Systems (CPS) have the potential to assist workers in knowledge-intensive work grounded on quantitative insights about knowledge transfer activities [2]. By providing contextual and situational awareness as well as complex classification and selection algorithms, CPS are able to ease knowledge transfer in a way that production time and quality is improved significantly. CPS have only been used for direct production and process optimization, knowledge transfers have only been regarded in assistance systems with little contextual awareness. Embedding production and knowledge transfer optimization thus show potential for further improvements. This contribution outlines the requirements and a framework to design these systems. It accounts for the relevant factors.

Keywords: Smart automation Human-machine-interaction

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· Smart production ·

Introduction

The capabilities of cyber-physical systems (CPS) are increasing, with regard to data gathering and analysis. They tap numerous data sources provided in a production environment and increasingly use AI-based techniques to analyze this data and learn from it [3]. The interplay of humans and machines becomes essential, as all CPS are based on either knowledge representations from AIbased systems, externalized human experiences or a combination of both. Thus, knowledge transfer between machines and operators as well as among operators is key for an optimal production environment. c Springer Nature Switzerland AG 2022  A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 149–157, 2022. https://doi.org/10.1007/978-3-030-90700-6_16

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Faced with the hunt of reducing costs, one can argue knowledge in production is the only meaningful resource today. Yet, the potential of the adaptability and information processing power of CPS has not be considered or assessed so far: For the situational awareness and the selection of interventions, influencing factors need to be known and incorporated into CPS. Therefore, the following paper outlines the systematical examination of context factors and the design of CPS for the optimization of knowledge transfers. The following research will focus on the optimization of knowledge transfers by answering the following main research question: “How can CPS enable the production context by knowledge transfer optimization?” This paper intends not to present an ready-to go description of concrete, technical realizations of those novel process optimization machines. It intends to structure the research and identify artifacts required for CPS implementation. Section two presents a theoretical foundation. It ties the research on CPS to previous studies on knowledge transfer. Knowledge-intensive artifacts are introduced and defined as the pivotal element for the conjunction of CPS and knowledge transfers. The third section elaborates the design requirements for a knowledge transfer optimizing CPS by detailing the knowledge transfer process and hypothesized influencing factors. The fourth section outlines a framework, which allows to specify the learning task of AI-based CPS and will guide the CPS and intervention design as well as their experimental validation. Finally, a conclusion points to the concrete next steps for an implementation and appraises the progress critically.

2 2.1

Theoretical Foundation Smart Production Systems

Smart production systems or rather Cyber-Physical Systems (CPS) refer to any kind of production resource, which is accompanied by IT systems so that it autonomously can realize an individual production strategy. Hence, they provide components of the schematic structure of sensors, processors, communicators, actuators and processors [4]. Sensors obtain data from the its environment, so that the CPS is able to individually decide with the aid of its processors. Actuators carry out decisions and enable interactions with the physical environment. At least one communicator realizes the information exchange with other CPS. So, their joint interplay within a Cyber-Physical Production System (CPPS) leads to the flexible and efficient production outcome generation [5]. Mostly, because of their Artificial Intelligence (AI), they foster opportunities to create value within the business and the community it operates [3]. In our context, CPS will serve as an adaptive assistance system in the following three aspects: first, to classify and evaluate the current knowledge transfer situation using sensors and AI techniques (situational awareness), second, to select and recommend interventions that optimize ongoing knowledge transfers using AI techniques (contextual recommender system), third, to either carry it out or accompany its implementation, and fourth, gathering data about its

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appropriateness and effectiveness, so that it can learn from its experience (adaptive system). 2.2

Knowledge Transfers

Knowledge transfers are considered as the process of the identification of knowledge, its transmission from knowledge carrier to knowledge receiver and its application by the knowledge receiver [2]. Particularly, knowledge application is essential, so that the result or manifestation of knowledge transferred becomes observable. From hereon, the knowledge transfer optimization (KTO) is referred to as the improvement of the entire knowledge transfer process. It can ease knowledge identification, aid its low-loss transmission and support the receiver in re-contextualizing and applying the knowledge. The extent of improvement can be assessed by comparing appropriate performance indicators, so that the process is optimized successively. Example indicators refer to a reduced time consumption, lower failure rates or lowering the loss of knowledge transferred. In order to specify the experimentation of knowledge, particularly the process-oriented knowledge management has been proven to function efficiently, because of the objectification of knowledge, which means its provision as impartial form as modeling object. By this, the fluid state of knowledge can be specified over the course of time. So, knowledge changes and conditions of knowledge transfers can be identified and by whom they are caused. This regards the behavioral perspective of knowledge [6]. Hence, beside the sequential description of a knowledge-intensive process (process perspective), at this research, the dealing with different knowledge forms and their conversion [7] will be separated, so that the activities of knowledge creation, transfer and application can be specified individually (knowledge perspective). As these will be embedded in the experiment process, the fluidity of the transfer can be captured and visually represented. So, it becomes controllable and an object to interventions [8]. Knowledge-intensive artifacts are the observable signposts in these fluid knowledge transfer processes. 2.3

Knowledge-Intensive Artifacts

The objectification of knowledge being part of a knowledge transfer process are referred to as Knowledge-Intensive Artifacts (KIA). As these either can be considered at the knowledge transfer input or at its output, the former are regarded as influence factors on knowledge transfers and the latter can be surveyed by performance measurement. According to the conversion, which refers to the interplay of different forms of knowledge, KIA consist of explicit and tacit forms of knowledge [7] as well as embodied knowledge. While the first refers to a well documentable form of knowledge, that can be handed among any kind of process participant, the second form of knowledge is hard to document as it is knowledge-bearer-bound, and the third form of knowledge refers to their physical manifestation.

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In order to identify or rather examine the factors and interventions, which influence knowledge transfers, a framework is required, that incorporates knowledgeform-specific operationalizations and artifact-specific performance measures. Considering this framework, a laboratory study will serve to preselect identified factors and designed interventions because of the following: First, interventions need to be set up creatively by production experts. Contemporary AI is not ready for generating interventions but for their selection as recommendation. Second, hardware and implementation costs for CPS are reduced since sensors can be limited to the measurement of relevant factors. Based on these insights, CPS can be designed that adapt to the specific situation by sensing relevant indicators and suggesting relevant interventions.

3

Methodology of KT Optimizing CPS

Knowledge Transfer Optimization. Based on the intervention-focusing proceeding for KTO, which has been verified in experiments for product development [2], manual assemblies [9] and knowledge-driven IS [10], this research structures research activities for the creation of knowledge-transfer-optimizing CPS. Figure 1 shows relevant proceeding steps and their outcomes as follows. Proceeding steps

Outcomes

1. Project initiation

Environmental conditions

2. Agreement on objectives 3. Knowledge transfer situation analysis 4. Intervention selection

Scope of systems Situation awareness Set of attractive interventions

5. Iterative intervention implementation

Historic path of interventions

6. Evaluation of intervention e ects

Performance indicators

7. Continual improvement and maintenance

CPS optimization

Fig. 1. Proceeding of knowledge transfer optimization (KTO).

As the first two steps refer to one-time steps required for the organizational setup for CPS use, and the seventh step refers to a continuous optimization in the sense of Kaizen of CPS designed, the following focuses on the proceeding steps in between. Steps three to six are iterations, in which the CPS is put in a CPPS production network. It continuously analyses the current knowledge transfer situation (3rd proceeding step), and thus constructs a situational awareness. The identified situation can be recognized as first outcome of an iteration. After that, the CPS selects an intervention or a combination of interventions from a set of predefined interventions in order to optimize the knowledge transfers in the current situation (4th proceeding step). This initial set of interventions needs to be created prior to CPS implementation and has to be verified experimentally.

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The intervention is carried out either by the CPS, or by its supervisor (5th proceeding step). This includes the recurrent consideration of prior interventions as well as its compatibility, which is labeled as historic path of interventions. On base of this, e.g. the repetitive recommendation of the same intervention can be avoided. With regard to artifacts to be designed, this demands for a generic CPS design being embeddable in production settings. Finally, with the aid of key performance indicators, the success of CPS interventions is evaluated and fed back into the initial intervention set. This connects a situation to a successful intervention (6th proceeding step). This demands for a performance measurement system and is the foundation for an AI-based training and learning. The seventh step of the KTO-process refers to a continuous optimization of CPS with new diverging algorithms and intervention sets. Requirements. In a two year research project, a situation analysis will be designed that enables CPS. Interventions will be derived that improve knowledge transfer situations. Furthermore, experiments will be conducted, that observe relevant effects, gather data material and the training and testing of AI-based CPS can be realized. In Table 1, requirements for the knowledge-transfer optimizing intelligent CPS are summarized, that will guide the CPS construction process.

4

Design of a Framework for the Knowledge-TransferOptimizing CPS

Faced with the proceedings and requirements specified, the following issues the conceptual design, so that it becomes clear, which kinds of factors need to be addressed either by experiments or by CPS implementation. Individual influence factors or rather dependent variables as well as target factors or rather independent variables have been identified by systematic literature research studies [2] and systematized, here. Figure 2 presents the systematization of factors, which must be considered at the operationalization of knowledge transfers. Considering them, the following sets of factors become obvious. First, factors that need to be considered at the experiment-based examination of quantitative effects on knowledge transfers. Second, factors that need to be addressed at the design of interventions because their variation leads to an optimization of the current knowledge transfer. Third, factors with whom the CPS needs to deal with in order to analyze to the current knowledge transfer situation, for which interventions will be evaluated. Fourth, factors with whom the CPS needs to deal with in order to recommend adequate interventions. Fifth, factors that need to be considered by the CPS in order to learn from intervention implementation. Following the definition of knowledge transfers, factors need to address three kinds of measure points - the identification, the transmitting and the applying. The different types of influence factors will be addressed individually in the following.

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Description

AI-system

1) The CPS is designed to be person-facing, so that it is able to suggest changes the current production knowledge worker shall carry out in order to enhance knowledge transfers for the joint setting. Hence, the system considers the current process participant context and includes relevant factors, such as its competences, mother tongue and experience levels 2) The CPS is designed to be a collaborative learning system, that observes the joint setting in its production situation, so that it is able to suggest changes that support the knowledge transfers for individuals. Hence, the system considers the context-specific production tasks and includes factors addressing the knowledge transfer itself and factors addressing the knowledge transfer output 3) The CPS targets the optimization of both, quality and time, so that an ideal compromise is supported and economic conditions are met 4) The CPS is designed to be based on an empirical examination, so that practical suggestions can be derived. Hence, the system is able to operate on a stream of data, which is tracked right within the live situation or can refer to the simulation-based testing either

Intervention 5) Interventions are designed to be based on influence factors, which have an empirical relevance on knowledge transfers among both, individuals and groups, so that the SECI conversions are considered [11] 6) Interventions are chosen by the AI system in regard with the production situation analysis and follow the “framework for the analysis of knowledge transfer situations”. 7) Interventions are designed to optimize knowledge transfers by the enhancement of the production outcome quality as well as the time required. Experiment

8) The performance is measured via various feedback channels. These include tests for the successful knowledge transfer in form of product quality tests, surveys and KPI-based production quality criteria tracked during the course of production time 9) The environment is measured via various IT system sensors, such as video material of process participants, all being tracked during the course of production time. Here, the same sensors are focused that can be found at the AI system to be designed 10) Influence factor focused information will be surveyed, so that meta information is available, that cannot be tracked via the CPS itself. In real production settings, these are intended to be gathered by the integration with IT, such as ERP systems, MIS, etc 11) The environmental characterization shall follow a typical production setting, which can be found in the Industry-4.0-Simulation-Laboratory called RACI4.0 12) The design of intervention experiments considers test groups for the use of AI-based systems to be designed and control groups being not faced with any intervention

A first set of influence factors refers to the identification of knowledge artifacts. These need to be considered as input objects. Beside factors concerning the choice of the instrument for the measuring of input artifacts, and experimentrequired factors, which need to be eliminated by a well-defined experimentation setting, preconditions of the knowledge bearers need to be addressed. Examples

Knowledge-Transfer-Optimizing Intelligent CPS Identifying Knowledge Preconditions of the knowledge bearer participation Instruments for measuring the knowledge input artifacts Experiment process

Transmitting Knowledge

Applying Knowledge

Content-related knowledge characterization Form-related knowledge characterization

Content-related knowledge manifestation Form-related knowledge manifestation

Knowledge transmitter characterization

Knowledge transmitter manifestation

Knowledge receiver characterization Environmental characterization of knowledge transfers Instruments for measuring the knowledge transfer characterization

Knowledge receiver manifestation

Information Object

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Quality of knowledge artifacts

Quality of knowledge transfers Time of knowledge transfers Instruments for measuring the knowledge output artifacts Information Object

Knowledge Transfer

Knowledge Object

Knowledge Object

Legend: abc abc abc abc

-

knowledge transfer to be optimized to be sensed to be eliminated

- conversion - relevance to be veri ed - statistical instrument, such as regression analysis

- activity - explicit knowledge - tacit knowledge

Fig. 2. Different kinds of factors at knowledge transfers.

refer to the willingness to share knowledge from both sides, the knowledge sender and the knowledge receiver, as well as their production task specific competences. When knowledge has been identified, a characterization of knowledge input artifacts is present, which functions as influence on the transmitting of knowledge. Here, one can find a characterization of knowledge to be transmitted, that differentiates in form and content [2]. Further, the knowledge transmitter characterization is issued (e.g. by the person’s capability for articulation) and the knowledge receiver is characterized (e.g. by its capability for interpretation) [12]. As the environmental conditions of a knowledge transfer have been identified to be relevant [11], one e.g. must consider the time available for the knowledge transfer (time pressure). If the time is cut, one can assume to receive bad quality outcomes. Lastly, if knowledge has been transmitted, the manifestation of influence factors can be derived, that reveal the involvement of the specific characterization at the transfer process. For example at the content-related knowledge manifestation, one issues inhowfar the complete knowledge to be transmitted has been issued at the knowledge transfer. The form-related knowledge manifestation addresses inhowfar the codification of knowledge sender and receiver have been compatible. Examples for the knowledge bearer manifestations refer to the time pressure perception of knowledge sender and receiver, the complexity of a task in the case of an explicit KIA or the stickiness of a competence in the case of a tacit KIA. The activity jointly is characterized by the quality and time for a certain knowledge transfer. Particularly the last is to be optimized by interventions. Further, we assume to have higher quality outcomes when the conversion quality is increased. All these factors function as influences on the applying of knowledge and do finally result in KIA outcomes. As target dimension, the time of a knowledge transfer and the quality of KIA are considered.

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Conclusion

In this paper, a framework for influence factors on knowledge transfers has been drawn. This structures factors along the process of knowledge transfers, so that the dealing with KIA as input and output objects can be operationalized. By following the framework presented, factors can be controlled systematically. Hence, the research question can be answered as follows: For the construction of CPS that enable the production context by knowledge transfer optimization, with the aid of this framework, the evaluation of concrete factors can be prepared by the conductance of experiments in laboratory studies. Based on them, quantitative models of knowledge transfers can be derived, which enable the situation analysis of CPS. Further, the framework prepares the intervention design: As interventions build on the very same influence factors examined, the effect on KTO can be evaluated. If sensors of the CPS correspond to them, CPS are enabled to recommend and carry out interventions autonomously and instantly. Furthermore, if CPS sense the very same factors, intervention success can be recognized. So, the continuing training and testing of AI-based CPS is enabled. By basing the training error on the deviation of resulting and expected KIA as well as desirable factors, in the long run, best interventions are learned as preferable. As designs presented so far have not been realized yet, they will stand as a structuring for individual artifacts. Next steps will focus on the experimentation having a baseline in time consumption and quality achieved and it will be observed in how far interventions optimize this baseline. Finally, it will be demonstrated how CPS can bring all together and lead to quantitative effects.

References 1. Grum, M., Rapp, S., Gronau, N., Albers, A.: Accelerating knowledge - the speed optimization of knowledge transfers. In: Shishkov, B. (ed.) Business Modeling and Software Design. BMSD 2019, vol. 356, pp. 95–113. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24854-3 7 2. Gronau, N., Grum, M.: Chap. Towards a prediction of time consumption during knowledge transfer. In: Knowledge Transfer Speed Optimizations in Product Development Contexts, pp. 25–69. Empirical Studies of Business Informatics, GITO (2019) 3. Waibel, M., Steenkamp, L., Moloko, N., Oosthuizen, G.: Investigating the effects of smart production systems on sustainability elements. Procedia Manuf. 8, 731–737 (2017) 4. Veigt, M., Lappe, D., Hribernik, K.: Development of a cyber-physical logistic system. Industrie Manag. 1(2013), 15–18 (2013). (in German) 5. Gronau, N., Grum, M., Bender, B.: Determining the optimal level of autonomy in cyber-physical production systems. In: 2016 IEEE 14th International Conference on Industrial Informatics (INDIN), pp. 1293–1299 (2016) 6. Grum, M., Gronau, N.: Process modeling within the augmented reality - the bidirectional interplay of two worlds. In: Proceedings of the Eighth BMSD, pp. 206–214 (2018)

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7. Nonaka, I., Takeuchi, H.: The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, Oxford (1995) 8. Grum, M., Gronau, N.: A visionary way to novel process optimizations - the marriage of the process domain and deep neuronal networks. In: Shishkov, B. (ed.) Business Modeling and Software Design. BMSD 2017, vol. 309, pp. 1–24. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78428-1 1 9. Grum, M., Gronau, N.: Chap. Acceleration of knowledge transfers with AR technologies. In: Knowledge Transfer Speed Optimizations in Product Development Contexts, pp. 105–124. Empirical Studies of Business Informatics, GITO (2019) 10. Grum, M., Gronau, N.: Adaptable knowledge-driven information systems optimizing the speed of knowledge transfers. In: Shishkov, B. (ed.) Business Modeling and Software Design. Springer, Cham (2020). https://doi.org/10.1007/978-3-03052306-0 13 11. Albers, A., et al.: Influencing factors and methods for knowledge transfer situations in product generation engineering based on the SECI model. In: NordDesign (2018) 12. Gronau, N.: Knowledge Modeling and Description Language 3.0. GITO mbH Verlag Berlin (2020)

Comparison of AI-based Task Planning Approaches for Simulating Human-Robot Collaboration Tadele Belay Tuli(B) and Martin Manns FAMS - Chair for Production Automation and Assembly, PROTECH - Institute of Production Technology, University of Siegen, 57076 Siegen, Germany [email protected] https://protech.mb.uni-siegen.de/fams

Abstract. Today, increased demands for personalized products are making human-robot collaborative tasks a focus of research mainly for improving production cycle time, precision, and accuracy. It is also required to simplify how human-robot tasks and motions are generated. A graphical flow control-based programming can be one of such methods. This work investigates whether the graphical approaches (e.g., using RAFCON) yield a better real-time simulation or not compared to agent approaches (e.g., using MOSIM-AJAN). This work may support the agility of the digital manufacturing process by enhancing the efficiency of human-robot collaboration. Keywords: Task planning · Graphical human-robot programming Virtual manufacturing · Shared autonomy

1

·

Introduction

Today, increased demands for personalized products are making human-robot collaborative tasks a focus of research. Predictable human behavior would help robots to adapt tasks for assisting human workers dynamically. In particular, time-dependent process flow requires agile and intuitive high-level planning that may include graphical representation. Analysis of time dependency, e.g., in the assembly process, is crucial for reducing cost and improving efficiency. Moreover, harmonizing how humans and robots interact in collaborative planning plays a crucial role for efficiency of smart workplaces. Whether it is a sequential operation or simultaneous activities, the reasoning and semantic interpretations of motions and actions may cause time delay. Robots may assist the worker with tedious tasks such as inserting pins for assembling handle rods into the car body if an assembly of a pedal car in a hybrid working place is considered. This could improve the production time by saving some time for the worker to execute the next operation. In this regard, semantic or agent-based task planning approaches have been considered for distributing tasks between humans and robots. Existing c Springer Nature Switzerland AG 2022  A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 158–165, 2022. https://doi.org/10.1007/978-3-030-90700-6_17

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and open source frameworks such as AJAN (Accessible Java Agent Nucleus) and RAFCON (RMC advanced flow control) can be mentioned as artificial intelligence (AI)-based and graphically supported task planners. AJAN applies a webbased behavior modeling, whereas RAFCON generates a state machine-based sequence of operations. Comparing the two approaches or investigating ways to combine both approaches could help exploit the combination’s best performance. However, it is not easy to select a better fitting approach for simulating collaborative tasks.

2

Related Works

The approaches presented in state of the art are analyzed as simplified programming approaches for task planning and control for abstracted high-level actions. The existing approaches are discussed in the category of task planning for human-robot collaboration (HRC) in Sect. 2.1, and methods for action representation in Sect. 2.2. 2.1

Automated Planning Approaches for HRC

Pre-allocation of roles has been defined using humans’ and robots’ cognitive capabilities and reasoning skills in simulation environments. The process of defining problems for generating a sequence of actions that aims to achieve the desired goal is called planning (cf. [11]). If we consider difficult tasks, it is hard to find a generalized solution. Instead, other representations by decomposing into sublevel tasks should be considered using AND/OR graphs, heuristic visualization of behavior trees, or state machines. An AND/OR graphs help to quickly implement transition states with fewer nodes [9]. AI-based planners such as fast forward/downward planning system [5,7,8] are implemented using Planning Domain Definition Language (PDDL) semantic descriptions for progressive planning using hierarchical decomposition, including robots and human tasks. PDDL is among the multiple expressive syntax’s that define an AI Planning by decomposing tasks into sub-levels to find a solution. There are various versions of PDDL, e.g., PDDL 2.1, PDDL 2.2, and PDDL 3. Problems in PDDL comprise a domain and problem file. [11] created a web planner that solves the planning problems and visualizes the heuristic graphs. Similarly, it has been implemented in the task planning plugin of RAFCON. RAFCON1 is a graphical planning and programming approach developed to improve the usability and acceptability of robot programming. It has an intuitive graphical interface that can be interfaced into a robot operating system (ROS). This is advantageous for planning collaborative tasks and sharing autonomy between human and robot operator [2,3,14]. Web-supported agent-based behavior planner so-called AJAN (Accessible Java Agent Nucleus) has been presented in [1]. According to [1], AJAN2 is an 1 2

https://github.com/DLR-RM/RAFCON. https://github.com/aantakli/AJAN-editor.

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agent system that comprises graphical behavior modeling for interacting with linked data domains. Behavior tree (BT) and state machines-based task plannings can be distinguished based on modularity, state transition, and transparency. In this regard, the state can be described with high-level tasks, and internal sub-tasks represent basic actions that define robots or humans’ behavior in hybrid production environments. BTs are increasingly gaining more acceptance e.g., for designing games for non-player characters [6,13]. Their scalability and ease of implementation give them a priority over hierarchical state machines. Notably, it is advantageous for certain tasks that provide success or fail output. Moreover, a hybrid approach that combines both state machine and behavior helps achieve better performance than either of the two methods. In this case, humans and robots comprise multi behavior trees and state machines in which the currently running behavior tree can be represented as state machines for detecting events and creating smooth transitions. Furthermore, methods of distributing tasks in hybrid environments may include spatial and multi-criterion analysis. In [12], multi-criteria-based tasksharing assuming a shared working space with multiple resources has been presented. In such methods, humans and robots are considered active resources that are sharing tasks. Further, role adaptation during interaction has been presented, e.g., [10] in which a robot adapts its role based on the interaction forces and yields better performance. 2.2

Action Representation for Simulation

High-level tasks accomplish the desired job by executing actions that are abstracted by semantic representations. For assembly operations, basic motions such as reach, pick, move, place, insert ad others have been frequently presented. Such motions comprise a set of trajectories and constraints that can be defined by method time measurement (MTM) and robot time measurement (RTM) (for humans and robots, respectively cf. [9]). Data-driven and physics model-based approaches have been used for generating and simulating motions. In data-driven approaches, the representation of motion actions and interface instructions is crucial for simulating in hybrid environments. According to [15], data-driven motion clips have been used to simulate humans and robots. A framework MOSIM has been proposed in [4], in which the motion model interface (MMI) is being used for controlling actions concurrently or hierarchically with task description. Furthermore, combining human and robot actions in hybrid environments has been achieved using ROS frameworks, (see e.g., [1]). In general, the current work investigates if the semantic and graphical-based human-robot programming is expressive and easily understandable by engineers who have no prior knowledge about RAFCON and AJAN. Various basic human motion simulations such as walk, reach, move, gaze, and release are semantically described for comparison. Furthermore, the current investigation remarks the need for a hybrid approach in future works.

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Graphical Planning and Role Distribution for Collaborative Assembly

As it is highlighted in Sect. 2, this work aims to investigate planning approaches by comparing graphical approaches for automated task planning using simulation tools for improving the process of reasoning and planning a sequence of actions in the context of HRC. Approaches considered in the current investigation include open-source tools that are using state machines and behavior tree models. In this regard, a semantic-based problem description such as Planning Domain Definition Language (PDDL) and SPARQL query are used for defining process plans. The execution of motions that apply models and constraints takes place using a robot operating system (ROS) and MOSIM’s MMI. ROS establishes an interface between the high-level task plans and motion model units (MMU). 3.1

Comparison of RAFCON and AJAN Features

Parameters for comparing RAFCON’s and AJAN’s features include expressiveness, productivity, and HRC applicability. Expressiveness shows how syntax is engaging to understand the described actions. In this context, a questionnaire consisting a syntax of an action (e.g., Listing 1.1 and 1.2) is used for survey. Participants have to choose the right action from lists of ten actions. Productivity measures how accurately the user can apply the concept from a demonstrative example to define missing parameters. Applicability is the suitability of the respective lists of actions for humans and robots. These parameters are evaluated using survey questionnaires. Five basic actions such as move, gaze, reach, insert and release are used in the evaluation. The semantic definition of these motions are; – – – –

Gaze: Focusing eyesight and movement of the head Insert: Physics-based translation/rotation by applying pressure Reach: Hand movement towards an object Move: Move from current location to destination (without need for locomotion) – Release: Detach object from hand(s), palm animation. 3.2

Use Case Description and Evaluation of RAFCON and AJAN

Assuming a hybrid assembly environment for pedal car assembly, we are interested in distributing tasks between robots and human workers using semantic reasoning and automated planners. A virtual assembly environment has been implemented to simulate the scenario using existing motion interfaces that are mentioned in the earlier sections. Initially, the robot is at the assembly workplace, and the human worker is at an arbitrary position inside the room. Then, the worker is supposed to pick a tool from a resource center, carry and move it to the assembly workplace, insert it at the desired pose by applying force. Finally, the robot is proposed to insert a pin to locate the part into position. This problem is described in the RAFCON task planner and AJAN behavior tree.

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Problem Definition Using PDDL in RAFCON - Graphical process flow control provides ways to model complicated robotic tasks. The task is often executed in small steps (blocks), and each block has predefined sets of objectives to accomplish. It provides a graphical user interface (GUI) to perform a hierarchical state machine’s visual programming using the Python programming language. The visualization of a hierarchical state machine with various debugging tools provides easy and fast development of robotics tasks without deep programming skills. A PDDL based task domain and a problem description are defined in RAFCON. The task descriptions are the semantic representation for the basic motions (e.g., see Listing 1.1). Listing 1.1. PDDL Assembly operation (e.g. Insert MMU).

( define (domain hrc−domain ) ( :predicates ( hand ?h ) ( t a r g e t ? t ) ( a t ?h ? t ) ( f o r c e ? f ) ( t o r q u e ? t r ) ( on ? f ? t ) ) ( :action applyPressure :parameters ( ? t a r g e t ? hand ? f o r c e ? t o r q u e ) :precondition (and ( hand ? hand ) ( torque ? torque ) ( force ? force ) ( target ? target )) : e f f e c t ( hand ? t a r g e t ) ) Problem Definition Using SPARQL-BT in AJAN - In the MOSIM project 3 , pedal car assembly use case has been demonstrated with AJAN behavior control. An example of Insert MMU is given in Listing 1.2. Listing 1.2. SPRQL-QUERY Assembly operation (e.g. Insert MMU). PREFIX mosim : < http :// mosim . eu / vocabs / mosim - ns # > PREFIX rdf : < http :// www . w3 . org /1999/02/22 - rdf - syntax - ns # > SELECT ? hand ? target ? force ? torque WHERE { ? task rdf : type mosim : Task . ? task mosim : part ? target . ? task mosim : tool ? hand . ? task mosim : operation ? applyPressure . } LIMIT 1

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Result and Discussion

The overall task description in the current work is that the worker looks at the part (e.g., frame). Then tries to reach the part, and moves the part to the 3

https://mosim.eu/.

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hybrid assembly station, and applies pressure on the target object. Finally, the worker releases the part after insertion. Five basic motions such as gaze, reach, move, insert, and release are selected (see Fig. 1). The actions are described using AJAN and RAFCON tools for comparison. ROS framework offers possibilities for interfacing various packages and libraries that can be re-used for developing complicated human and robotics tasks. Also, it has a function that bridges communication between different tools, either on the same or remote network (cf. [1]). In the current work, ROS is used as a bridge for communication between graphical process flow control software (RAFCON) and Unity3D (i.e., MOSIM) scene environment. In this regard, human-robot models’ action and their ability to complete a given task can be evaluated and tested in simulation without the risk of hurting human operators.

Fig. 1. Pedal car hybrid assembly virtual environment

To analyze if such tools are expressive, productive and easily applicable, survey questionnaires are given to twelve master’s degree students in the Mechatronics Engineering field. All participants have responded as they have no prior experience in RAFCON, AJAN, SPARQL, and PDDL tools or languages. Based on the task and motion description applied in MOSIM, they are requested to choose from the lists of AJAN and RAFCON programming for the five basic motions described in Fig. 1. According to the result analysis, more than half of the participants able to correctly respond to PDDL based problem descriptions. In the same manner, quarter of the participants correctly answered the action lists described using SPARQL. To be more specific, more than half of the participants vote for PDDL based task descriptions for expressiveness, productivity and applicability than SPARQL based action descriptions. This may reflect that PPDL based action descriptions are more expressive and easily understandable for engineers with basic programming knowledge. In smart and hybrid manufac-

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turing, semantically represented action descriptions may help to improve engineers’ involvement in automated reasoning and task planning. However, both of the investigated approaches have shown pose applicability issues for engineers with mechanical and industrial engineering background.

5

Conclusion

To conclude, a graphical approach has been used to create intelligent and automated task/action planning in virtual HRC environments. Graphical approaches may be considered easy and reliable for HRC tasks focusing on step-wise objective completion. Moreover, it may allow planners in the context of HRC to quickly implement AI-based reasoning and planning models. Ultimately, it may also simplify how humans and robots dynamically share tasks and roles with little knowledge of programming. This may make planners to concentrate more on a solution to a problem rather than programming hurdles. In the future, it is necessary to develop a hybrid approach that enables people with no experience to perform better. Acknowledgment. The authors would like to acknowledge the financial support by the Federal Ministry of Education and Research of Germany within the ITEA3 project MOSIM (grant number: 01IS18060AH), and by the European Regional Development Fund (EFRE) within the project SMAPS (grant number: 0200545).

References 1. Antakli, A., et al.: Agent-based web supported simulation of human-robot collaboration. In: Proceedings of the 15th International Conference on Web Information Systems and Technologies, pp. 88–99. SCITEPRESS - Science and Technology Publications, Vienna, Austria (2019). http://www.scitepress.org/DigitalLibrary/ Link.aspx?doi=10.5220/0008163000880099 2. Brunner, S.G., D¨ omel, A., Lehner, P., Beetz, M., Stulp, F.: Autonomous parallelization of resource-aware robotic task nodes. IEEE Robot. Autom. Lett. 4(3), 2599–2606 (2019). Conference Name: IEEE Robotics and Automation Letters 3. Brunner, S.G., Steinmetz, F., Belder, R., Domel, A.: RAFCON: a graphical tool for engineering complex, robotic tasks. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, pp. 3283–3290. IEEE (October 2016). http://ieeexplore.ieee.org/document/7759506/ 4. Gaisbauer, F., Agethen, P., Otto, M., B¨ ar, T., Sues, J., Rukzio, E.: Presenting a modular framework for a holistic simulation of manual assembly tasks. Procedia CIRP 72, 768–773 (2018). http://www.sciencedirect.com/science/article/pii/ S2212827118304578 5. Helmert, M.: The fast downward planning system. J. Artif. Intell. Res. 26, 191–246 (2006). https://jair.org/index.php/jair/article/view/10457 6. Hilburn, D.: Simulating behavior trees a behavior tree/planner hybrid approach. In: Game AI Pro: Collected Wisdom of Game AI Professionals. CRC Press (September 2013)

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7. Hoffmann, J.: FF: the fast-forward planning system. AI Mag. 22(3), 57–57 (September 2001). https://ojs.aaai.org/index.php/aimagazine/article/view/1572 8. Jiang, Y., Zhang, S., Khandelwal, P., Stone, P.: Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems. Front. Inf. Technol. Electron. Eng. 20(3), 363–373 (2019). http://link.springer.com/10.1631/FITEE.1800514 9. Lamon, E., Franco, A.D., Peternel, L., Ajoudani, A.: A capability-aware role allocation approach to industrial assembly tasks. IEEE Robot. Autom. Lett. 4(4), 3378–3385 (2019). Conference Name: IEEE Robotics and Automation Letters 10. Li, Y., Tee, K.P., Chan, W.L., Yan, R., Chua, Y., Limbu, D.K.: Role adaptation of human and robot in collaborative tasks. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 5602–5607 (May 2015). ISSN 1050-4729 11. Magnaguagno, M.C., Pereira, R.F., M´ ore, M.D., Meneguzzi, F.: Web planner: a tool to develop classical planning domains and visualize heuristic state-space search. In: Proceedings of the Workshop on User Interfaces and Scheduling and Planning, UISP, pp. 32–38 (2017) 12. Michalos, G., Spiliotopoulos, J., Makris, S., Chryssolouris, G.: A method for planning human robot shared tasks. CIRP J. Manuf. Sci. Technol. 22, 76–90 (August 2018). http://www.sciencedirect.com/science/article/pii/S1755581718300300 13. de Pontes Pereira, R., Engel, P.M.: A framework for constrained and adaptive behavior-based agents. arXiv:1506.02312 [cs] (June 2015) 14. Steinmetz, F., Wollschlager, A., Weitschat, R.: RAZER - a human-robot interface for visual task-level programming and intuitive skill parametrization. IEEE Robot. Autom. Lett. 3(3), 1362–1369 (2018) 15. Tuli, T.B., Manns, M.: Real-time motion tracking for humans and robots in a collaborative assembly task. Proceedings 42(1), 48 (2019). https://www.mdpi.com/ 2504-3900/42/1/48

Towards Flexible PCB Assembly Using Simulation-Based Optimization Simon Mathiesen(B) , Lars Carøe Sørensen, Thorbjørn Mosekjær Iversen, Frederik Hagelskjær, and Dirk Kraft SDU Robotics, Maersk McKinney Moller Institute, University of Southern Denmark, Odense, Denmark {simat,lcs,thmi,frhag,kraft}@mmmi.sdu.dk

Abstract. Populating printed circuit boards (PCB) with through-holetechnology (THT) components for small-sized batch productions remains a manual task. Placement of delicate components is inherently difficult, and complexity is added when components are delivered to the system in bulk. Part feeding remains an obstacle in automation, and economically feasible flexible part feeding for electronics components even more so. This paper presents an assembly platform for placement of THT components. The platform handles feeding of components from bulk, tubes, and trays, and features two robotic manipulators for feeding and placement. To simplify the setup task and increase flexibility each component of the system is programmed and designed from digital models and simulation and uncertainties are corrected for during execution. Keywords: Flexible robotics automation · Flexible part feeding Simulation-based optimization · PCB assembly

1

·

Introduction

The design of printed circuit boards (PCB) is biased heavily towards the use of surface-mounted technology (SMT). However, the traditional use of through-hole technology (THT), where leaded electronic components are inserted into holes and subsequently soldered to the board is still used for specific applications throughout the industry. Placement of THT components can be done with automated solutions, typically referred to as “Odd-form” or “Odd-shape” placement machines. However, this is only done automatically for high-volume production as the machines are costly. Although the odd-form machines are effective for standard components they are unable to handle unstructured bulk fed components without the costly and time-consuming development of dedicated feeding solutions. This work presents a flexible robotic component placement cell with a novel solution for handling components fed to the system unstructured from bulk. The complexity introduced with these technologies is handled through simulation-based setup and configuration of the cell. This approach provides c Springer Nature Switzerland AG 2022  A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 166–173, 2022. https://doi.org/10.1007/978-3-030-90700-6_18

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long term benefits from the possibility of using the simulation for continuous process improvement and facilitating further system design [1]. Section 2 reviews the most relevant literature. Section 3 describes the system, while system performance is subsequently evaluated in Sect. 4. The results are discussed and concluded upon in Sect. 5 and Sect. 6, respectively.

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State of the Art

Odd-form machines are proven technology that typically uses high-precision cartesian coordinate robots with multiple work heads to efficiently populate PCBs. However, academic work on odd-form machines is not focused on the placement, but instead on planning the optimal placement sequence for the components and determining the optimal configuration of multi-machine systems. This topic of research is well described the literature [2–4]. For high volume PCB productions manufacturers often ensure that components are stored structured in tubes, trays, or on tape, however, this is rarely the case for SMEs whose supply of components is often dictated from availability. When components are only available unstructured from bulk, a dedicated feeding system must currently be designed specifically to that component. Vibratory bowl feeders (VBF) as described in [5] is an example of such a system, but due to the complexity of designing these feeders they are infeasible for low volume production. In this work, we propose a low-cost flexible part feeding solution based on VBFs and inspired by [6]. When a component has been singulated on the feeder, the 6D pose is estimated using an overhead camera such that the component can be picked by a robot. The computer vision literature on 6D object pose estimation in cluttered scenes is dominated by methods that rely on deep neural networks [7]. However, for relatively simple pose estimation problems, such as pose estimating singulated parts resting on a flat surface, template-based methods can provide accurate pose estimates if physical pose constraints are considered [8]. To the best of our knowledge, the scientific work closest to our proposed system can be found in [9], which presents an odd-form insertion system using two SCARA robots for dual insertion. Their system does not solve the feeding problem as it is limited to only handle pre-kitted components in trays.

3

System Description

Our system for flexible assembly of PCBs with THT-components is shown in Fig. 1. The setup features two 6-DOF serial robot manipulators: 1) A Universal Robots UR3e (A) which grasps the components from different storage location and inserts them into the PCB (B) and 2) a UR5 (C) which services the flexible feeding system and kits parts up so they are ready for the insertion robot. Both robots use the electronic parallel gripper Robotiq Hand-E with slim flat fingers for a nimble but strong grasp on the components. The Hand-E also enables checking whether a part was grasped successfully.

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Fig. 1. The system components marked A-J and one of each of the components in their area on the board. They are: round capacitor (blue), sub-d connector (grey), square capacitor (black), LED (purple), and header pin (yellow). The flowchart shows the tasks carried out by each robot.

This system addresses feeding parts in bulk using a modified vibratory bowl feeder (D) and a novel reorientation concept called Chutes (E). This feeding system is elaborated in Sect. 3.1. As input to this feeding system, the robot can take a box (F) from a rack and empty its content onto the bowl. The boxes contain one component type and the rack system serves as a simple interface for an operator to introduce parts to the system. Unused parts are automatically emptied into their box and returned to the rack. The feeder then circulates the components around until a component can be grasped. This is identified by an overhead vision system (G). The kitting robot picks up the component and drops it into a chute, which is both a storage unit and a reorientation device ensuring that the component can be picked with its leads pointing downward. The UR3e can also pick components stored in tubes (H) and from manually kitted trays (I). It is also common to feed electronic components from tape in high-volume production, but it has been considered beyond the scope of this work. Uncertainties from the system affecting the insertion process are corrected using both a secondary inspection vision system (J) for grasp correction and by a robust insertion strategy. Both are elaborated in Sect. 3.4.

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The holistic design of the system, which relies on simulation and digital models, ensures that the system can combine flexible hardware devices while being easy to configure to new components.

Fig. 2. The five components used for experiments (left) and one of the parameterized chute designs (right). Red parameters are automatically optimized, green directly measured from the part, and blue is chosen manually.

3.1

Flexible Feeder and Part Reorientation

The presented flexible part feeder is inspired by the work of Quaid [6]. The feeder consists of a ring-shaped surface on which the parts are moved around (see Fig. 1). The ring-shaped surface consists of an incline, followed by a flat surface that ends in a drop, also called a step. This produces a natural spread of the parts when they start traveling faster on the flat section, and also allows for reorientation when they go over the edge of the step. Additionally, several small bumps are encountered as the parts travel across the initial flat section which serves to disperse clusters and rotate individual parts so un-graspable parts eventually assume new poses. The control loop of the feeding system is based on input from the vision system: The feeder vibrates the parts for a specified time, stops, and checks for successfully singulated and pose estimated objects. This is done until a part is found, and is continued when the part has been picked up. When the feeder runs its emptying routine, a servo motor opens a section of the feeder wall and the feeder vibrates until the vision system detects the bowl is empty. Insertion of the components into the PCB naturally requires the pins to be aligned facing down towards it. This specific alignment is rarely encountered among the natural stable poses of the components. Therefore, our system reorients the components by introducing a novel mechanism called chutes which works similarly to vibratory linear feeders. A component is dropped into the chute and the geometric shape of the chute will slide the component into a pose with the leads vertically downwards. The important geometric features are the enclosing shape and the dimensions of a groove in which the leads end. Vibration is produced by a motor rotating with a displaced mass, which makes the components convey to the front of the chute from where they can be picked.

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Fast Setup and Design from Simulation

The robot programming, the feeder, and the chutes are designed using simulation to reach a working system with significantly less effort than physical prototyping. For this the robotics framework RobWork and its dynamic simulation extension RobWorkSim [10] were used. To design the flexible feeder, models of the components (and the physical properties such as mass, inertia, and friction) were simulated being vibrated on the bowl surface. It was then possible to effortlessly vary the inclination angle, drop height, bumps position, and size until a suitable spread of components was achieved. The resulting model was 3D-printed and used in the system. While the feeder is flexible and can accommodate most components, each chute needs to be designed for a single component type. For this, an automated optimization approach was set up based on previous work [11]. In short, a model of a chute is first automatically generated from its parameterized design (see Fig. 2). Then a component is spawned in the drop position above the chute and vibration is simulated. If the components reach the pickup position of the chute in the desired orientation it results in a success and otherwise a failure. A statistical learning algorithm is used to optimize the parameters and find the optimal design which can subsequently be 3D-printed. In addition, maintaining a virtual model of the complete workcell allows for evaluation prior to physical execution and together with the simulation-based design makes it possible to easily extract key reference frames and adapt the robot programming to new products. RobWork provides path-planning for the robots between frames and both the drop and pick-up positions of the chutes are directly generated from the automated design. Thus, by simple labeling of the digital model of the PCB, programming an insertion can become as simple as calling a function with the correct component and hole identifier. 3.3

Vision-Based 6D Pose Estimation

The pose estimation methods used in the industry tend to lag behind the state of the art in academia since many companies are reluctant to rely on experimental methods. Therefore, our vision system for feeding is based on a commercial general-purpose template-based pose estimation algorithm [12,13]. The algorithm renders templates of an object from different angles. As both the render and run time of the algorithm depends on the number of sampled poses it is crucial to configure the algorithm such that only poses, which occur at run-time, are considered. These are found using the digital model of the workcell in an intermediate step where the stable poses of a component are computed. Object clearance is performed using classical machine vision methods using mixture-of-gaussian background subtraction to find foreground objects and bounding rectangles to determine if objects have the necessary clearance. Pose estimation is performed once for each stable pose and the estimate with the highest estimation score among those inside a bounding rectangle is used as the final estimate. To further increase the accuracy of the final pose estimate, the pose is

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adjusted to conform with the physical setup: The pose is matched to the closest stable pose and aligned with the feeder surface. Template-based pose estimation is also used for grasp correction, where the pose of a component is re-estimated after it has been grasped from a storage location but before it is inserted into the PCB. This is done to reduce the uncertainty to get a more reliable insertion. The automated configuration process and the pose adjustments allow for fast (re)configuration with a high reliability. This is facilitated through the reliance on CAD models of the parts and the calibrated digital model of the workcell. 3.4

Insertion

Large grasp misalignments are handled by the grasp correction in the vision system, but small uncertainties and imprecisions can still occur during insertion which highly impacts the success rate. To account for this the system uses the inbuilt force/torque sensor in the UR3e to first find the contact point between component and board and then search for the holes in a spiral pattern [14] until the leads go in. However, the standard approach of keeping constant contact proved too damaging to the components, which are very sensitive to bending from side-way forces. Thus, an adaption where the robot probes for the holes in a spiking motion of discrete steps was implemented. The system remembers the previous successful insertion positions for each component-hole-pair and averages over these to calibrate itself during run time.

4

Experiment and Results

The performance of the automation solution presented in this paper is evaluated by how fast the system can equip a PCB when already configured for this task. A dedicated test PCB will populated with five different component types (see Fig. 1 and 2), which represents a broad range components. A total of 15 components needs to be placed to populated one test PCB, since three individual spots for each component type is utilized1 . For the experiment a total of 14 test PCBs has been populated which results in a total of 210 individual component insertions. In advance, a calibration run has been performed by populating one test PCB. Moreover, two of the five components (the square capacitor and the sub-d) are fed to the insertion robot by the kitting robot using the flexible feeder, vision system, and chutes whereas the last three components (the LED, the round capacitor, and the header pin) are fed through a tray and tube. The experiment shows that the average insertion time of a component is 16.4 s with an improvement from 21.1 s converging towards 14.2 s during the process due to the self-calibration. No insertions fail in the experiments. Moreover, the 1

The test PCB is made for seven different component types, and each type has four individual spots - one with slightly undersized holes (not used), one with slightly oversized holes, and two normal-sized holes where one is turned 90◦ . However, only five component types are considered in the conducted experiment.

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fastest insertion is performed in 8.5 s (including the picking of the component for approximately 2 s and the grasp correction for 1.5 s). The kitting robot on average picks a black capacitor or a sub-d from the bowl and place it into the chute in 11.3 s and 15.4 s, respectively. This does not include the changeover between components which takes approximately 15.4 s.

5

Discussion

For the current selection of components2 the flexible feeder is able to keep up with the placement robot given that component changeover does not happen too often. However, from an investigation into the industry, it is estimated that the average placement time must be below 10 s to be feasible and in this case, the part feeding solution performs insufficient. Thus, some work is yet to be done to optimize overall system performance, but as shown in the results this cycle time is achievable for the insertion with the current setup. The core problem is hitting the holes as early as possible. Although not directly visible in the granularity of the results, the hardest components to insert are the square and round capacitor. Common for these components is that they have been pre-cut to get the right lead length and generally suffer from high uncertainty on the straightness of the leads. As long as the leads are parallel they can be inserted, but the offset from the ideal position leads to long probing sequences. For the system to perform better, this issue must be remedied in the future either by greater quality control in the cutting process or by swift rejection of bad components. The latter by preferably detecting bent leads prior to insertion. However, such conservative component rejection will put further strain on the speed of the feeding system. Another issue that impacts the precision of the insertion is the unavailability of precise CAD models of the components. With our CAD-based template matching methods, discrepancies between the physical part and its model lead to imprecise pose estimations adding further uncertainty to the insertion process. From our investigation, this has shown to be a general problem in the industry as demand for accurate models have been low, but if the industry is ever to transition to truly digital production methods these must be made available by the component manufacturers or feasible model reconstruction methods must be implemented.

6

Conclusion

A flexible system for the placement of THT components has been presented. The system is able to equip the test PCBs without failures with 210 component placements across five different types of components. A flexible feeding system efficiently feeds part from bulk to the insertion robot through reorientation and storage devices called chutes. The system is made easy to set up, where core components are designed and configured directly from simulation. Regarding future work, the implementation of a full Digital Twin for the system will allow for a 2

Two from the flexible feeder, two from trays, and one from tubes.

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closer integration with the simulation capabilities of the system. Furthermore, optimizing and streamlining performance remains a crucial task, however, even the system in its current state is a significant step towards achieving economically feasible and flexible PCB assembly for low volume production. Acknowledgments. This work was supported by Innovation Fund Denmark as a part of the project “MADE Digital”.

References 1. 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) 2. Ball, M.O., Magazine, M.J.: Sequencing of insertions in printed circuit board assembly. Oper. Res. 36(2), 192–201 (1988) 3. Crama, Y., Kolen, A.W., Oerlemans, A.G., Spieksma, F.C.: Throughput rate optimization in the automated assembly of printed circuit boards. Ann. Oper. Res. 26(1), 455–480 (1990) 4. Castellani, M., Otri, S., Pham, D.T.: Printed circuit board assembly time minimisation using a novel bees algorithm. Comput. Ind. Eng. 133, 186–194 (2019) 5. Boothroyd, G.: Assembly Automation and Product Design, 2nd edn. CRC Press (2005) 6. Quaid, A.E.: A miniature mobile parts feeder: operating principles and simulation results. In: Proceedings 1999 IEEE International Conference on Robotics and Automation, vol. 3, pp. 2221–2226. IEEE (1999) 7. Hodaˇ n, T., et al.: BOP challenge 2020 on 6D object localization. In: European Conference on Computer Vision Workshops (ECCVW) (2020) 8. Hagelskjær, F., Savarimuthu, T.R., Kr¨ uger, N., Buch, A.G.: Using spatial constraints for fast set-up of precise pose estimation in an industrial setting. In: 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), pp. 1308–1314. IEEE (2019) 9. Lin, Y., Li, Y., Huang, Y., Zhang, K., Zhu, H., Liu, Y., Guan, Y.: An odd-form electronic component insertion system based on dual SCARA. In: 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1514–1520. IEEE (2018) 10. Joergensen, J.A., Ellekilde, L.P., Petersen, H.G.: RobWorkSim - an open simulator for sensor based grasping. In: 2010 41st International Symposium on and 2010 6th German Conference on Robotics (ROBOTIK), Robotics (ISR), pp. 1–8. VDE (2010) 11. Mathiesen, S., Sørensen, L.C., Kraft, D., Ellekilde, L.-P.: Optimisation of trap design for vibratory bowl feeders. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 3467–3474. IEEE (2018) 12. Ulrich, M., Wiedemann, C., Steger, C.: Combining scale-space and similarity-based aspect graphs for fast 3d object recognition. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 34(10), 1902–1914 (2012) 13. Halcon. find shape model 3d. https://www.mvtec.com/doc/halcon/13/en/find shape model 3d.html 14. Van Wyk, K., Culleton, M., Falco, J., Kelly, K.: Comparative peg-in-hole testing of a force-based manipulation controlled robotic hand. IEEE Trans. Rob. 34(2), 542–549 (2018)

Towards Automatic Welding-Robot Programming Based on Product Model Ioan-Matei Sarivan(B)

, Ole Madsen , and Brian Vejrum Waehrens

Department of Materials and Production, Aalborg University, Fibigerstræde 16, 9220 Aalborg, Denmark [email protected]

Abstract. In the past decades, the programming of welding-robots has been given significant attention in the literature and commercially. However, in an engineer-toorder context, it still involves hard-coded parameters and requires a high amount of time to complete in an error-free manner. Welding-robot programming is a time-consuming process that requires critical human input to plan and execute the welding task. A direct consequence is low utilisation of welding-robots which renders the ownership of such devices unprofitable. This paper presents a novel approach for automatic programming of welding-robots based on product models. Instead of relying on hard-coding, the proposed system draws the necessary parameters from a product model, which contain most of the data needed to obtain a robot welding program. The aim is to integrate welding-robot programming in a seamless one-piece information flow across the production chain. The conceptual approach is demonstrated in practice and validated by experimenting with an elementary product. Results show that human input can be eliminated when it comes to the programming of robots, as long as specific criteria are met. Keywords: Digitalization · Smart production system · Digital integration · Welding-robot programming · Product model

1 Introduction The digital integration of industrial operations opens the way towards the high economic potential for small-medium enterprises (SME). The motivation of increasing competitiveness through digital integration [1] is underlined by the need for companies in high-cost environments to withstand international competition from low-cost environments by improving the production flexibility without compromising the production performance [2–4]. Lack of digital integration is a cause of processes being executed manually, in a time consuming, inefficient, and prone to error manner [5]. Furthermore, as the company seeks business growth and expansion, the chain of operations needs to be adapted accordingly in order to lower production costs [4]. This paper focuses on developing a new approach towards organizing the data for the automatic programming of welding-robots which are using the MIG welding process (gas metal arc welding). It is targeted towards engineering-to-order (ETO), smallmedium-enterprises (SME) leaning towards one-of-a-kind-production. The project takes © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 174–181, 2022. https://doi.org/10.1007/978-3-030-90700-6_19

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outset in previous initiatives focusing on the digital integration of sales and engineering tasks within ETO SMEs [2]. The next step is the digital integration of pre-production operations like robot welding programming. Welding-robot programming (WRP) is a complex operation generally carried by an expert robot programmer using software tools, product data, explicit knowledge, and tacit knowledge for creating a robot program. In this paper, a conceptual solution is proposed for integrating product data and knowledge under a digital product model [3]. By using product models, it is desired to automate the WRP process. It is intended through the automation of the WRP operation to obtain a continuous information flow between engineering and production and radically reduce the time needed for pre-production. The following sections are structured as follows: Sect. 2 provides information about related work relating to digital integration, which has the purpose of automating WRP. Section 3 provides an overview of the status-quo at a case ETO SME related to how WRP is handled manually. A detailed description of the proposed product model for digital integration of data needed for WRP is given in Sect. 4. Experiments are presented in Sect. 5. The paper is concluded in Sect. 6, and a discussion is made on how the proposed solution can benefit an ETO SME company.

2 Related Work The most common methods for robot programming make use of the robot’s teach pendant while the programmer sits right next to the robot, also known as online programming. However, this method is unfit for highly flexible manufacturing environments, where the robot must be reprogrammed often, as the production needs to be paused. Therefore, today commercial systems are available to assist the robot programmers for offline WRP quickly and precisely by using digital simulation environments without the need to pause production. However, automated WRP, without the need of an expert programmer, has had the attention of both research and industry for decades. These methods for automated robot programming make use of the geometry defined by the CAD model to extract the relevant data from which the movement of a robot can be programmed to perform assembly and surfacing tasks, where the entire geometry of the product is needed. Thomas and Wahl [4] present a set of algorithms that use CAD models from different items for the automated creation of a plan for assembly and robot task execution. The algorithms decompose the objects defined in the CAD models in triangle meshes, which are then used to identify the topological elements needed for the assembly plan generation. Subsequently, using manipulation primitives, the robot program is generated. The method of generating robot tasks by triangle tessellation of the CAD model is also used in surface coating tasks, as presented by Chen and Sheng [5]. By tessellating the model of the product, algorithms modify already programmed robot trajectories to obtain the new desired robot tasks and reduce robot programming time. When it comes to the programming of welding-robots, for generating the trajectory of the welding torch, it is essential to consider the orientation of the torch to not collide with surrounding objects. Shi et al. [6] developed algorithms that overlay the desired weaving patterns and orientation for the torch for circular intersecting pipes based on an already

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generated trajectory. The overall trajectory was generated by taking into consideration various obstacles like the attached welding cables. A method for programming welding methods targeted towards SMEs is proposed by Larkin et al. [7], which needs under one minute of human interaction to generate the collision-free motion of the robot and its program based on CAD models. The feature extraction from CAD models has been made possible by various commercial CAD software developers, thus making it possible to use the graphic descriptions of robot paths created by the designer. Neto and Mendes make use of the AutoDesk Inventor’s application programming interface (API) to extract positions and orientations directly from the CAD model designed in this software to generate trajectories for an industrial manipulator. The main advantage of this approach is that the processing time of the CAD model is reduced to a few minutes [8]. To match the virtual product design with the real-world components that need to be welded, Ferreira et al. propose a computer vision-based system. The system automatically identifies the position of the items to be welded and adjusts the welding trajectories accordingly. The system comes with the possibility to program the robot off-line, based on the CAD model. It has a graphical user interface (GUI) available, making the programming process accessible for inexperienced workers [9]. The literature study in automatic WRP shows that the digital data structure that facilitates this operation is the CAD model of the product. The necessary geometric data necessary to plan the movement of the welding-robot is obtained using various methods for processing the CAD model or for extracting data from the CAD model. However, automatic methods for fully automating the WRP process, are only researched to a limited extent. In this paper, a method is proposed to facilitate the automated WRP process by extracting the necessary parameters from a product model without the need for these parameters to be hard coded by a human worker when a new robot program is needed.

3 Case: Robot Programming at ETO SMEs To better understand how WRP is handled at ETO SMEs, interviews were conducted at a Danish ETO SME involved in the manufacturing of welded steel structures and a Danish software company that provides digital integration solutions for ETO SMEs around the world. The purpose of the interviews was to determine and outline the chain of operations conducted at ETO SMEs for obtaining a robot program that can be used to manufacture at least one item. An overview of these operations described by the partner company’s robotics engineers can be observed in (Fig. 1). The partner software company confirmed the findings from the partner ETO SME to be valid for most of their ETO and MTO (manufacturing to order) SME customers. The companies rely on expert robot programmers to operate the offline robot programming software, which generates the robot program based on the CAD models received from CAD engineers. As observed in the representation of the offline programming process in (Fig. 1), the offline programming software assists the engineer in identifying the weldment seams by having the CAD model as input and using the product’s documentation. Consequently, the weldment geometry is obtained, and via points

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(points which the welding torch is going through) are automatically generated by the programming tool. The via points are then manually configured by the programmer with the required welding parameters, which can take anywhere from seven to thirty hours. The welding parameters are selected by making use of the relevant standard welding procedures and tacit knowledge. The illustration of the process in (Fig. 1) is limited to only showcasing the initial programming of the robot to weld a specific product. After a robot program is generated and uploaded on the robot, several further steps are needed to validate the program with the actual workpiece.

Fig. 1. Representation of the operations executed to obtain the robot program for welding starting from the product configuration received from the customer.

It was determined during the interviews that to configure the welding parameters; the robot programming expert uses both explicit knowledge under the form of documented procedures and tacit knowledge, e.g., personal experience. The use of tacit knowledge makes the company heavily reliant on their robot programming expert. The product documentation contains data that helps the programmer to make an appropriate parameter selection. In contrast, the product configuration is a set of modifiers brought to the product captured in the CAD model.

4 Automatic Robot Programming Based on Product Model As determined in Sects. 2 and 3, the primary purpose of offline robot programming tools is to assist the robot programmer in planning the motion of the robots and configuring the necessary welding parameters once the motion plan is obtained. Manual input is

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most of the time required, without which the robot program cannot be successfully generated. This section presents a new automatic programming approach that uses the product model to generate the welding-robot program, as illustrated in (Fig. 2). The input required from the programming expert is removed, and the whole programming of the welding-robot is carried on with the product model as input without any further human input.

Fig. 2. Representation of the envisioned system for offline WRP without human input

The product model represents an abstract collection of the model’s design, engineering, and manufacturing rules to integrate the related data and knowledge digitally [3]. The product model serves as a control mechanism for the CAD engineers to generate the product’s CAD models while using the product configuration as input coming either from a dedicated configuration system or directly from the customer. The robot programmer is tasked with digitalizing their explicit and tacit knowledge to build the product model. Through consultation with the partner ETO SME, the following data and knowledge related to welding were found relevant to be contained within the product model: • • • •

Weld geometry data: weld bead location, weld bead start/end points, weld length Material data: alloy type, plate thickness Weld geometry knowledge: weld thickness, weld multi-pass number, weld offset Welding process knowledge: optimal power input, speed, weaving parameters, torch angle

By digitalizing the engineer’s knowledge within the product model, the offline programming tool is expected to automatically configure the welding parameters based on the product’s model.

5 Experiment An experimental setup was put together to test the concept proposed in Sect. 5, that a welding-robot program can be obtained based on the product’s model without further input from an engineer to configure the welding parameters. The setup is formed of

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1. Product model containing the necessary data and knowledge to set the welding parameters of fillet weldments required for a box (set as entries within an Excel spreadsheet) 2. CAD model of a box with variable width, length, plate thickness and alloy type, designed in SolidWorks, can be observed in (Fig. 3) 3. C# application making use of the SolidWorks API for extracting welding geometry from the CAD model 4. Python application for trajectory planning, configuration of the welding parameters based on product model and robot program generation 5. UR5 robot equipped with a Migatronic Sigma ROBO welding machine By configuring the simple box with several values for width, length and plate thickness, the system is tested to determine if it can successfully weld without any human input. Once the box is configured and the CAD model is generated, the C# application extracts the geometric data directly from the weld bead features inside the CAD model as observed in (Fig. 3). SolidWorks facilitates the storage of material data like the steel alloy type. The part thickness is determined from the CAD geometry, which, together with the knowledge stored in the product model, are used to configure the welding parameters. The geometry of the weldments is passed further to the Python program together with the material data. The Python program plans the trajectory and configures the welding parameters accordingly in under a minute. The generated motion plan can be observed in (Fig. 4). With the welding parameters set and the motion plan generated, the robot program is finally uploaded on the UR5 robot. The task is executed without further human input.

Fig. 3. Screenshot of the SolidWorks CAD model of a metal box containing eight weldments. The weldments’ definitions can be observed in the “Feature Tree” of the CAD model on the left-hand side and the graphical representation of the weldment beads can be observed on the right-hand side.

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Fig. 4. Plot of the weldment trajectories that the robot needs to follow in order to perform the weldments of the box in (Fig. 2). The weaving pattern and the via points of the robot movement can be observed in the magnifier glass view taken from one of the weldments to be performed.

6 Discussion and Conclusion The solution presented in the previous section serves as proof of concept for the system presented in Sect. 4. The solution was successfully tested by designing and welding steel boxes with variable width and length. The manual programming of the welding-robot was successfully digitalised and automized by using the product’s model. The need for hard-coded parameters is eliminated by using a product model based WRP approach. Thus, the needed parameters are extracted from the product model instead. The WRP task generally requires several human workforce hours; the time needed for WRP is reduced to under one minute using the proposed solution, The presented work is aligned with the ETO SME initiatives of increasing production flexibility without compromising production performance in the context of one-of-akind production. Moreover, through the digitalisation of the WRP, the gap between preproduction operations (programming of machines) and production operations (actual program execution) is closed, thus obtaining a one-piece chain of operations. By having a one-piece digitalised chain of operations, time is saved, and information doubling is eliminated [4]. The presented solution is built upon previous initiatives for end-toend digitalisation of supply chains [6]. It draws its novelty from the implemented data structure for digitalising welding process data. The experiments were only limited to generating welding trajectories for the steel box presented in (Fig. 3) only. Therefore, further development is needed to overcome the challenge of complex product geometries. Also, a validation system is required, which allows the system to correct deviations caused by heat deformations in the steel. Further development will seek digitalisation methods of the tacit knowledge held by human welders by investigating the possibility of using self-learning systems powered by artificial intelligence.

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Acknowledgments. The authors thank the MADE FAST (Manufacturing Academy of Denmark, Flexibility-Agility-Sustainability) association for providing financial support. The authors thank the MADE FAST associated companies for providing data that is used as fundamental underlay for the information presented in this paper.

References 1. Björkdahl, J.: Stategies for digitalisation in manufacturing firms. Calif. Manage. Rev. 62(4), 17–36 (2020) 2. Sansone, C., Hilletofth, P., Eriksson, D.: Critical operations capabilities in a high cost environment: a multiple case study. In: IOP Conference Series: Materials Science and Engineering (2018) 3. Waehrens, B.V., Slepniov, D., Johansen, J.: Offshoring practices of Danish and Swedish SMEs: effects on operations configuration. Prod. Plann. Control 26(9), 693–705 (2015) 4. Jünge, G., Alfnes, E., Nujen, B., Emblemsvag, J., Kjersem, K.: Understanding and eliminating waste in Engineering-To-Order (ETO) projects: a multiple case study. Prod. Planning Control (2021) 5. Slack, N.: Product-process matrix. In: Wiley Enciclopedia of Management (2015) 6. Bejlegaard, M., Sarivan, I.-M., Waehrens, B.V.: The influence of digital technologies on supply chain coordination strategies. In: Journal of Global Operations and Strategic Sourcing, no. Smart Production and Industry 4.0: Current and Future Design-Science Research Supporting the (Danish) Manufacturing Industry (2021) 7. Hvam, L., Mortensen, N.H., Riis, J.: Product Customization. Springer, Heidelberg (2008) 8. Thomas, U., Wahl, F.M.: Assembly planning and task planning − prerequisites for automated robot programming. In: Daniel, S., Wahl, F.M. (eds.) Robotic Systems for Handling and Assembly, pp. 333−354. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-64216785-0_19 9. Chen, H., Sheng, W.: Transformative CAD based industrial robot program generation. Robot. Comput. Integr. Manuf. 27(5), 942−948 (2011) 10. Shi, L., Tian, X.: Automation of main pipe-rotating welding scheme for intersecting pipes. Int. J. Adv. Manuf. Technol. 77(5–8), 955–964 (2014). https://doi.org/10.1007/s00170-0146526-8 11. Larkin, N.P., Short, A., Pan, Z.S., van Duin, S.: Automatic program genration for welding robots from CAD. In: International Conference on Advanced Intelligent Mechatronics (2016) 12. Neto, P., Mendes, N.: Direct off-line robot programming via a common CAD package. Rob. Autom. Syst. 61(8), 896−910 (2013) 13. Ferreira, L.A., Figueira, Y.L., Iglesias, I.F., Souto, M.Á.: Offline CAD-based robot programming and welding parametrization of a flexible and adaptive robotic cell using enriched CAD/CAM system for shipbuilding. In: International Conference on Flexible Automation and Intelligent Manufacturing, Modena (2017)

Design of an Intelligent Robotic End Effector Based on Topology Optimization in the Concept of Industry 4.0 Dimitris Mourtzis(B)

, John Angelopoulos , and Nikos Panopoulos

Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Rio Patras, Greece [email protected]

Abstract. Over the past decades, automation industry has seen a major shift from traditional, hard-tooled lines to reconfigurable and reprogrammable robotic cells. Robots have added increasing value to the industry with special focus on robotic arms which enable many repetitive tasks to be carried out with high repeatability, reliability, flexibility, and speed. Grasping, carrying and placement of objects are the basic capabilities of robotic arms. However, with the integration of new technologies the capabilities, of robotic arms can be extended. As such, grippers being an essential component of robots play an important role in many handling tasks since they serve as end-of-arm tools. The aim of this paper is to propose a new end effector design, which is integrated with a sensing system, for improving the adaptivity and flexibility of a robotic cell in comparison with the State-of-theMarket end effector solutions. The proposed design is used to extend this research work and to develop an intelligent end effector based on the implementation of a Machine Vision algorithm, for the recognition of the part, the gripper, and 3D scanning the produced part. The recognition of the part is essential in order for the robot to grasp the object appropriately and facilitate the machining process. The 3D scanning of the part geometry will be utilized for CAD comparison versus the original drawings. Finally, based on the Finite Element Analysis (FEA) and the topology optimization, a reduction of the material used for the 3D printing of the gripper has been reduced by 19.57%. Keywords: Finite element analysis · Topology optimization · Robotic end effector · Simulation

1 Introduction During the last decade, under the framework of Industry 4.0, manufacturing is undergoing a digitalization phase, thus requiring the design, development, and integration of more sophisticated and connected machine tools, and production resources [1]. Further to that, due to the implementation of global production networks, and the mass customization of products, the current market landscape is characterized by increased volatility and shorter product lifecycles [2–4]. Consequently, manufacturing companies, in order to © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 182–189, 2022. https://doi.org/10.1007/978-3-030-90700-6_20

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cope successfully follow the volatile market demand, have to integrate manufacturing systems with intelligence. Moreover, during the Industry 4.0, robotic manipulators have become a necessity, as they offer increased repeatability, fast execution of actions, and can collaborate with human operators, by undertaking heavy jobs, thus relieving human operators from the physical stress [5]. Therefore, robotic manipulators grippers and tools are playing a key role in the industrial value chain [6]. Past implementations of robotic arms in production focused in the automation of individual processes. However, latest implementations of intelligent robotic systems can improve the added value of manufacturing systems [7]. Under the scope of this paper, the concept is designed towards a pick and place task for small additively manufactured objects of various geometries with maximum dimensions of 100 mm * 100 mm * 100 mm. In addition to that, the maximum weight of the handled parts, should not be greater than 5,000 g. The gripper should also be capable of working within a flexible manufacturing cell comprising a 3D printer and a CNC milling machine. The sequence of actions performed by the robotic arm can be summarized to i) approaching the 3D printed part, ii) picking the part from the printing bed, iii) positioning and place the part in a particular place on the working bench, iv) 3D scanning the geometry of the produced part, v) holding the part in the CNC milling machine for post-processing, and vi) placing the finished part on the buffer. The key aspects to be taken under consideration are mechanical, electrical, and material properties of the end-effector. As such, the paper proposes a new end effector design, which is integrated with a sensing system, in an attempt to further improve the adaptivity and flexibility of a flexible robotic cell that has already been developed under the concept of Learning Factories [8] in comparison with commercial end effector solutions. The rest of the paper is structured as follows. In Sect. 2, the most pertinent and relevant publications in the fields of CAD optimization, topology optimization algorithms, and robotic end effector design are presented. Then in Sect. 3, the proposed framework is analyzed. Afterwards, in Sect. 4, conclusions are drawn based on the derived design and future research directions are discussed as well.

2 State of the Art For many years, mechanical and civil engineers have used topology optimization to reduce the amount of material used and the strain energy of structures while maintaining their mechanical strength [9]. Topology optimization is a mathematical method for spatially optimizing the distribution of material within a defined domain by minimizing a predefined cost function while satisfying previously established constraints [10]. There are numerous techniques for performing topology optimization in the scientific literature. The two most popular methods are the Solid Isotropic Material with Penalization (SIMP) and the Evolutionary Structural Optimization (ESO). Xie and Steven [11] developed the ESO method, which has been used in a variety of optimization research areas. The ESO method is based on the straightforward principle of gradually removing inefficient material from a structure. A rejection criterion (RC) that identifies ineffective material is used to remove the unnecessary material. This method has the advantage of being simple to comprehend and is also simple to program and to link with existing computer-aided engineering software e.g. ANSYS. Next, a comprehensive study

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of all basic calculations including a taxonomy of the types of gripping, load cases and the forces involved while operating conventional grippers is presented in reference [12] which provided useful insight for the design and optimization of the proposed robotic gripper. Moving on, the paper in [13] introduces a new composite objective function that can consider both the output force (with contact) and the output displacement (without contact) of the synthesized compliant mechanism in order to further control the desired output displacement for the compliant constant-force mechanism before contacting the object. The proposed topology optimization method is used to create a prototype of an innovative constant-force compliant finger, which is 3D printed using a flexible thermoplastic elastomer. AM technologies allow designers to manufacture complex robot parts and transfer bionic design elements to them. To that, the goal of the paper in [14] was to design a three-finger gripping device for a fruit sorting robot while keeping in mind the desired anthropomorphic geometry. To improve the gripper’s design, topology optimization methods and ANSYS software’s special finite-element (FE) modules were used leaded to weight minimization that reduced the required power of compact drives located on moving parts of the structure. Similarly, the authors in [15] created a multi-input, multi-output cable-driven soft robotic gripper with geometric nonlinearity topology optimization, which not only performs adaptive grasping but also allows for finer manipulations such as rotating or panning the target and the FEA was performed in ANSYS. Finally, interesting research works in the field of methodology for topology and shape optimization in the design process is presented in [16]. As it regards the sensor integration, a sensor can be integrated as an insert into the component with the help of a spring clip for example. In addition, the sensor can be inserted into a grove to ensure proper fit. In another case, as described in [17], the sensor is integrated into a carbon fiber reinforced plastic plate that uses piezoelectric transducer sensors to measure normal forces. The insertion of a metal plate with an embedded sensor into casting parts [18] is a similar case study. Based on the literature, it is concluded that one of the most important components of a robotic manipulator is the gripper. According to the current state of the art and existing case studies, sensor integration implies that there are challenges in the integration of various fields. The commercially available grippers in the field are more likely to evolve into a human hand-like application. Although this is an obvious direction, integrating IoT sensors into existing grippers (i.e. retrofitting existing equipment) for production is important by the time parallel grippers with simple kinematic movements handle a large portion of today’s production. Therefore, the contribution of this research paper is that since the end effector will be utilized for both scanning the part and holding the part for machining, there should be taken into consideration the bending forces, and the elastic and plastic deformation of the gripper. Consequently, this could affect the performance of the gripper on the one hand during the 3D scanning operations, which by extension could lead to erroneous 3D scans of the manufactured parts, and thus conclude to a poorquality inspection [19]. On the other hand, bending forces during the machining of the part, i.e. part post-processing, could lead to poor-quality of the process even destruction of the part. Further to that, the elastic and plastic deformation of the gripper could be proven dangerous for the processing of the part, as poor handling of the part from the gripper is considered as a hazard [20].

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3 End-Effector Design Workflow The implementation steps of the proposed framework will be discussed. In Fig. 1, the workflow for the design of the gripper for the robotic arm is presented. The first step is the elicitation of the requirements. During this phase it was decided what would be the main functionalities of the gripper, as well as what type of sensors should be installed, in order to perform the 3D scanning of the part. More specifically, the main functionality of the end-effector is to manipulate 3D-printed objects, including picking them from the 3D-printer bed, position them on a specific location for 3D-scanning, and if required to hold them in position for machining, in a CNC milling machine. As regards, the 3D scanning. Then, a conceptual 3D design was created implementing the basic geometric features for the integration of the sensors. Afterwards, the 3D design was imported to the FEA tool, and the simulation environment was set up. It is stressed out that in the FEA model, only the main component of the gripper has been imported, thus auxiliary parts/components were omitted, in order to simplify the mesh of the model.

Fig. 1. Workflow for the design and topology optimization of the proposed gripper

In order to ensure that the numerical solution is not affected in a significant percentage (greater than 10%), a grid independence study has been done as presented in Table 1. As regards the mesh of the model, the size of the elements was set 0.5 mm, and the number of elements for the geometry, as presented in Fig. 2 is 252,154. Further to that,the quality of the produced mesh is adequate, taking into consideration that the average skewness is approximately 0.44. Based on the results of the FEA analysis, according to the step in Fig. 1, the topology of the gripper has been automatically optimized. The criterion for the topology optimization, was set to reduction of the volume. Then, in continuation, a new FEA analysis was

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Equivalent (von-Mises) Stress at Point A (MPa)

169,436

18,50

252,154

19,67

378,231

19,69

567,346

19,71

FEA Analysis Monitoring of Point A

run, in order to validate that the optimized geometry is suitable for use as per the functional requirements set in the initial phase of the design process. However, it became evident, that the optimized geometry could be further, manually optimized, in order to improve the manufacturability of the gripper (Fig. 3(ii)). Thus, the derived geometry for the gripper is the one presented in Fig. 3(iii). Consequently, with the utilization of the FEA results and the topology optimization algorithm, it became evident that less material could be utilized for the production of the gripper, with equivalent structural properties.

Fig. 2. Generated mesh of the 3D model

In order to develop the proposed gripper, the Dassault Catia CAD suite was used for the design of the components as well as for the creation of the gripper assembly. For the FEA analysis and the topology optimization, the ANSYS 2021 R1 was used. From a hardware point of view, a desktop PC has been utilized, equipped with an Intel core i7 processor, 16 Gb of RAM memory, and an Nvidia 1060 GPU with 8 Gb of dedicated RAM memory.

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Fig. 3. i) Equivalent (von Mises) stress for initial geometry; ii) topology optimization result; iii) equivalent stress for optimized geometry.

4 Conclusions and Outlook The objective of this paper is to propose a new end effector design, which is integrated with a sensing system, in an attempt to further improve the adaptivity and flexibility of the robotic cell versus existing, commercially available solutions. More specifically, the proposed end-effector design has been integrated with an adjustable gripper for handling a variety of different part geometries, as well as with a depth camera, which is utilized for 3D scanning the additively manufactured parts. The latter is aimed to

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facilitate the quality inspection phase, by producing a 3D geometry to be compared with the original CAD file of the part. Therefore, it becomes evident, that with the integration of the proposed end effector, the robotic cell becomes more intelligent and requires less human intervention. Based on the FEA analysis and the workflow followed as presented in the previous paragraphs, a reduction of the material used for the 3D printing of the gripper has been achieved. More specifically, the volume of the initial model was 218.789e−03 cm3 , whereas the volume of the optimized model has been reduced by 19.57%, thus the new volume is equal to 175.973e−03 cm3 . Even though the volume reduction is lower in comparison with the literature (range from 25 to 35%) [21], we decided not to remove further material because of the need for the sensor wiring, and the cavities required for the installation of the wiring. However, in order to avoid any negative impact by cavities to the structural integrity of the gripper, it is recommended to mount the wiring externally, without creating any cavities to the geometry of the gripper. In a future version of the gripper design, the wiring harness will be fed internally in the main body of the gripper. Additionally, in terms of industrial implication it will also serve as the foundation for smarter, more efficient manufacturing processes by retrofitting the commercial grippers on the market. Future research could be directed towards the investigation of additional AM methods, such as Laser Powder Bed Fusion (LPBF), in which the functional requirements of the robotic gripper, include the withstanding of the gripper in high temperature environments. Further to that, further investigation is required in that direction, due to the high thermal profile of the additively manufactured part, which could affect the behavior of the gripper itself. Besides that, since the proposed gripper design is integrated with sensors, attention should be drawn on the thermal shielding of the sensing system and the associated wiring.

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8. Mourtzis, D., Angelopoulos, J., Dimitrakopoulos, G.: Design and development of a flexible manufacturing cell in the concept of learning factory paradigm for the education of generation 4.0 engineers. Procedia Manuf. 45, 361–366 (2020). https://doi.org/10.1016/j.promfg.2020. 04.035 9. 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 (2015). https://doi.org/10.1007/s00170-015-7576-2 10. Rosinha, I.P., Gernaey, K.V., Woodley, J.M., Krühne, U.: Topology optimization for biocatalytic microreactor configurations. Comput. Aided Chem. Eng. 37, 1463–1468 (2015) 11. Xie, Y.M., Steven, G.P.: Evolutionary structural optimization for dynamic problems. Comput. Struct. 58(6), 1067–1073 (1996) 12. Birglen, L., Schlicht, T.: A statistical review of industrial robotic grippers. Rob. Comput.Integr. Manuf. 49, 88–97 (2018). https://doi.org/10.1016/j.rcim.2017.05.007 13. Liu, C.-H., Chung, F.-M., Ho, Y.-P.: Topology optimization for design of a 3D-printed constant-force compliant finger. IEEE/ASME Trans. Mech. (2021). https://doi.org/10.1109/ TMECH.2021.3077947 14. Stupin, S.A., Ogorodnikova, O.M.: Topology optimization in designing of anthropomorphic gripper for a robot. In: AIP Conference Proceedings, vol. 2313, no. 1, p. 040011. AIP Publishing LLC (2020 15. Wang, R., Zhang, X., Zhu, B., Zhang, H., Chen, B., Wang, H.: Topology optimization of a cable-driven soft robotic gripper. Struct. Multidiscip. Optim. 62(5), 2749–2763 (2020). https://doi.org/10.1007/s00158-020-02619-y 16. Patel, A.A., Palazotto, A.N.: Design methodology for topology optimization of dynamically loaded structure. J. Dyn. Behav. Mater. 5(1), 59–64 (2019). https://doi.org/10.1007/s40870019-00184-0 17. Kaufmann, J., et al.: Smart carbon fiber bicycle seat post with light and sensor integration. Procedia Eng. 147, 562–567 (2016) 18. Tiedemann, R., et al.: Integrating sensors in castings made of aluminum – new approaches for direct sensor integration in gravity die casting. Procedia Manuf. 24, 179–184 (2018) 19. Luz Castro Pena, M., Carballal, A., Rodríguez-Fernández, N., Santos, I., Romero., J.: Artificial intelligence applied to conceptual design: a review of its use in architecture. Autom. Constr. 124 (2021). https://doi.org/10.1016/j.autcon.2021.103550 20. Li, F., Jiang, Y., Li, T., Feng, Y., Chen, S.: Design of a robot end effector with measurement system for precise pick-and-place of square objects. Procedia Manufacturing 48, 172–180 (2020). https://doi.org/10.1016/j.promfg.2020.05.035 21. Zhu, J., Zhou, H., Wang, C., Zhou, L., Yuan, S., Zhang, W.: A review of topology optimization for additive manufacturing: status and challenges. Chin. J. Aeron. (2020)

Towards a Structured Decision-Making Framework for Automating Cognitively Demanding Manufacturing Tasks Robbert-Jan Torn(B) , Peter Chemweno, Tom Vaneker, and Soheil Arastehfar Faculty of Engineering Technology, University of Twente, 7522 NB Enschede, The Netherlands [email protected]

Abstract. Due to commercial pressure and changing consumer needs, organizations continuously strive to automate their manufacturing processes. Robotisation options, such as collaborative robots, are attractive to maximize the benefits of higher throughput for highly cognitive tasks. Nevertheless, automation and robotization efforts continue to be limited by highly cognitive processes that are either complex to automate or do not make sense from a business perspective. This challenge is further compounded by an absence of clear guidelines and structured frameworks for guiding automation decisions. This paper aims to bridge this gap by reviewing automation decision-making, including task design and psycho-social influences such as cognitive decisions. This includes consideration of cognitive decisions made by operators, further influenced by the task design, workplace design, and task safety. The proposed approach is demonstrated for real-world use cases, of which feasible automation solutions are proposed following structured decision-making steps. Keywords: Automation · Manufacturing process · Cognitive tasks · Decision support · Taxonomy

1 Introduction In recent decades, automation has evolved into smart manufacturing systems where workers supervise operations. A subset of manufacturing and assembly tasks remains for which the level of automation is still low. These so-called ‘difficult to automate’ tasks require flexibility, agility and high cognition, attributes best suited for human operators. New trends in automation have emerged that partly address these issues. Collaborative robots are used for joined manufacturing tasks. In contrast, ‘smart’ robotic actuators for gripping parts with complex geometry, or ‘soft actuators’ that respond to biomimetic stimuli [1–3], mimic some of the humans’ abilities. In addition, automation is influenced by sensory devices (machine vision), for instance, to recognize part geometry, and controllers to ease manipulation of challenging tasks. Grube, Malik [4] notes that manufacturing flexibility and automation are historically contradictory because of the limited ‘smartness’ of the automation technologies. Morel, Pereira [5] mention the additional need for operator knowledge and skills, implying © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 190–197, 2022. https://doi.org/10.1007/978-3-030-90700-6_21

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interdisciplinarity involvement in automation decisions, focusing on task complexity or complexity of actuation, making automation challenging. Mourtzis, et al. [6] argues the importance of centralized mobile and remote decision-making, supported by information technology when focusing on mass customized or personalized products. Karanasiou et al. [7] discuss automated decision making from a legal perspective towards artificial intelligence, while highlighting the role of AI algorithms, data analytics and deep learning. Their paper expresses concerns related to automation since AI plays a vital role in embedding intelligence and automating cognitively demanding tasks. This argument is extended in Zerilli, et al. [8], where they urge the importance of structuring algorithmic decision making for human-in-the-loop control challenges. At the work-cell level, task complexity depends on factors like part geometry, physical features of the product, and manufacturing steps [6, 7]. Part features, in turn, influences the choice of actuation, including the gripper design, sensors for detection of orientation of the part, and controllers for enhancing the accuracy of manipulation. These complexities have traditionally motivated a drive towards flexible manufacturing cells integrating collaborative robots [9]. Moreover, task allocation is a significant challenge to feasibly align manufacturing task elements, to capabilities on the robot or human agent. Task allocation strategies discussed in the literature, include task decomposition methods such as Hierarchical Task Analysis (HTA), and complexity-based task allocation (focusing on a skill-based task distribution) [9]. In other studies, Mourtzis, et al. [10] discuss the importance of defining key performance indicators, as a basis for evaluating product-service systems and, consequently, identifying robust (and feasible) design of production systems. Nevertheless, a robust structured framework is missing that can assist automation decisions, considering varying automation criteria and task complexities. This paper proposes a framework aiming at more optimally matching challenging tasks with capabilities of automation solutions. In particular, this paper addresses an important research question faced by decision-makers: 1) How can automation solutions be identified for new or existing production processes that involve cognitively demanding tasks?

2 State-of-the-Art 2.1 Automation Perspectives As automation evolves within the industry 4.0 era, developing manufacturing technologies compatible with humans is gaining more traction. According to Lee [1], defining automation perspectives ‘correctly’ is crucial as it influences decision processes needed to decide a go or no-go for automation. They define a ‘levels-of-automation’ (LOA) framework, consisting of five levels, with ‘no automation’ at the lowest level and ‘full automation at the highest level. The lowest LOA level is characterized by complete human intervention, while the highest LOA is characterized by complete autonomy of the equipment/robotic agent. Kaber [2] and Lee [1] highlight essential criteria to consider when making automation decisions, including repetitiveness of the task per cycle. This criterion characterizes lowlevel automation tasks, that are highly repetitive, and hence, automation solutions are

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usually mature [3]. High cognitive tasks do not fall within the scope of low level-ofautomation; instead, the high level of automation spectrum is more interesting. This is due to the difficulty of defining in greater precision human behavioural constructs that relate to the task (i.e., involving many cognitive decision-making points) as argued by Kaber and Making [2]. Examples of manufacturing tasks potentially falling within this scope include quality inspection tasks that are not straightforward to substitute with an automation solution, such as machine vision, or developing such a machine vision solution is challenging and costly. Oh, Suppé [11] further defines high-level cognition tasks as involving highlevel reasoning, (likewise, multiple cognitive decision-making points, combining pick and place, manipulation and inspection). Similarly, such tasks include interpreting a significant number of information cues. Choe, et al. [12] mention cognitive demand on operators, dealing with multiple decision-making aspects, owing to many information cues. They suggest cognitive automation as a solution to this challenge, and show that it can enhance decision making for flexible material handling systems. Looking further, Zhu, Wei [16] characterizes high cognitive tasks, as involving nonrepetitive motor actions. The author notes that the demand for high cognition, increases with product variety where they cite examples of customized products. From a manufacturing perspective, Thorvald, Lindblom [17] similarly cites collaborative tasks combining manufacturing speed, high cognition, rapid change-overs, and requiring high levels of coordination as falling with the spectrum of high-level automation. Examples include intricate assembly tasks associated with detailed process information, and requiring a high degree of flexibility and customization. 2.2 Quantifying Task Complexity (and Criteria) To understand task complexity, it is crucial to characterize tasks and for this, defining tasks characterization criteria and associated quantification metrics is rather important. Predominant criteria mentioned in the literature include cognitive load [6], for which Zaeh, Wiesbeck [9] mention associated characteristics including task coordination as task execution time (i.e., tasks that require multi-tasking). Goh et al. [18] attribute task complexity to human factors. They argue the capability of a human worker to adapt to uncertainty or variability in tasks enables the accomplishment of tasks that are challenging to automate. Mourtzis, et al. [13] proposes an approach quantifying manufacturing complexity. They argue using metrics based on the information theory, such as Shannon’s theory, which maps manufacturing complexity based on information exchange between actors involved during product development. This includes the number of information cues and content. Goh et al. [18] further associate task complexity to part attributes, including geometry, surface properties, and quality specifications. They also highlight the number of information cues (including the ‘number of decision points’ considered by the operator when performing intricate tasks), product variety (influenced by the level of customization needed), a need for multi-tasking by the operator, and time pressure (which increases mental and cognitive strain on workers). Other authors link task complexity with the accuracy required for handling the part characteristics, for example, Malik,

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Bilberg and application [19]. They link accuracy to part grasp-ability, further influenced by the shape (geometrical symmetry), and stability (the ability of a part to remain stable when grasped). More recently, Mourtzis [14] review studies highlighting the use of simulation as a decision support approach for designing and automating production systems, including collaborative production cells. They emphasize attractiveness of simulation for gaining insights into production cells, considering factors such ergonomics and human factors, cell design, and integration of supervisory control and data acquisition. Though out of the scope of this paper, simulation approaches, including agent-based simulation forms an essential basis for integrating and assessing the feasibility of automation solutions. Aziz et al. [15] discuss the importance of integrating operation costs for single and multi-robot task allocation. They define this problem as a multi-objective mathematical problem, considering the number of task allocation instances, given budgetary constraints (including implementation costs for the automation solution). In this paper, though a detailed cost analysis is not included, the price is inferred when comparing feasible solutions in Sect. 4. In summary, the state-of-the-art mentions seven criteria, attributes and examples characterizing task complexity. Based on experiences in the field, we added the criterium of physical difficulty for humans, and named the list ‘automation feasibility criteria’. For continuity, the criteria are further used in this paper to judge the automation potential for an industrial use case.

3 Method The main contribution of this paper is to propose a structured decision-making framework for defining feasible automating solutions for cognitively challenging manufacturing processes. Such a framework is seemingly missing in the literature and consequently, decision-makers resort to ad-hoc solutions that often are not suitable from a business perspective. The proposed framework defines criteria for supporting automation decisions. Next, the criteria are used to characterize cognitively challenging manufacturing tasks, whereof the tasks are decomposed to basic task elements using a structured HTA. Based on the task characterization, automation solutions are defined considering the capabilities of the solutions vis a vis the characterized tasks. The proposed framework is demonstrated using an industrial use case. Figure 1 summarises the proposed framework in three steps: 1) Assessment of clustered tasks for automation based on criteria discussed in Sect. 2; 2) Task analysis of a cognitively demanding manufacturing process using HTA; 3) Exploration of alternative automation solutions to achieve the desired outcome of the manufacturing process.

4 Case Study 4.1 Hierarchical Task Analysis for a Press Brake Tooling Manufacturer To demonstrate the purpose of the framework, a case study was conducted at a manufacturer of press brakes, tools used for bending sheet metal. Hereby, the operation of

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Fig. 1. A visual summary of the proposed framework.

cleaning and finishing of the press brake machine tool is decomposed for analysis. In subsequent steps, the high-level task of cleaning is further decomposed into lower-level task elements to discretize the tasks prior to allocating tasks to either the robot or the human operator. The task of cleaning and finishing the press brake tool was decomposed using the HTA method. HTA utilizes information about high-level manufacturing tasks and implements a step-by-step procedure starting from super-ordinate tasks to decomposed tasks to their basic task elements. Table 1 shows the decomposition of the high-level task resulted in 12 sub-ordinate tasks, whereby a distinction is made between the role of each task, denoted by ‘main’ or ‘supportive’. We define main sub-ordinate tasks as tasks to achieve the goal of the high-level task. In contrast, supportive tasks are defined as additional tasks required to complete the main tasks successfully (e.g., manipulating, or fixating objects). 4.2 Comparison of Sub-ordinate Tasks Against Automation Feasibility Criteria Based on the decomposition and considering the criteria described in Sect. 2, we explored feasible options for automation as a proof-of-concept. The comparison results for the sub-ordinate tasks against automation feasibility criteria are summarised in Table 2. The cleaning tasks provides a typical example of a task that is easily performed by human operators but ill-suited to automation solutions. The key attribute that contributes to the complexity of this task is the use of cloth since manipulating deformable objects is regarded as one of the ongoing challenges in robotic automation [11]. However, reaching the desired outcome, a clean tool does not persist in using a piece of cloth, as shown in the comparison in the previous section. Regarding the task in this way (i.e., the manufacturing task is successfully accomplished if the result of the task is achieved) leaves room for redesigning manufacturing tasks. As such, the intended outcomes of

Towards a Structured Decision-Making Framework Table 1. Decomposed task steps cleaning and finishing the press brake machine tool

Table 2. Comparison of sub-ordinate task 1 against automation feasibility criteria

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a cognitively demanding task can be used as input for a redesigned task that is better adapted to the requirements of automation solutions.

5 Conclusion In this paper, we propose a methodology for guiding decision making for manufacturing tasks that are challenging to automate. As a first step, we derived from literature criteria that influence the feasibility of automation, focusing on broad aspects such as the characteristics of the manufacturing task, the cognitive demand of the task, task characteristics, among other aspects. Next, we define guidelines for analyzing tasks in which task decomposition is suggested as a task discretization step. To structure this step, a hierarchical task analysis is performed. The overall manufacturing task (or goal) is decomposed into task elements and sub-ordinate tasks compared against criteria derived from literature at an earlier phase. Feasible solutions are, after that, defined and evaluated against broad considerations, including technical feasibility and automation effort needed. As a proof-of-concept, a case study is used to demonstrate the applicability of our proposed approach. We view the step-by-step approach as innovative and bridge a gap towards developing a robust framework for automating challenging manufacturing tasks. A more in-depth definition of selection criteria for matching automation solutions for task analysis will be further developed for future research. Moreover, we propose to extend the framework to a wide range of use cases to refine the decision-making framework further.

References 1. Lee, J.D.: Perspectives on automotive automation and autonomy. J. Cogn. Eng. Decis. Mak. 12, 53–57 (2018) 2. Kaber, D.B.: Issues in human–automation interaction modelling: presumptive aspects of frameworks of types and levels of automation. J. Cogn. Eng. Decis. Mak. 12, 7–24 (2018) 3. Dimeas, F., Fotiadis, F., Papageorgiou, D., Sidiropoulos, A., Doulgeri, Z.: Towards progressive automation of repetitive tasks through physical human-robot interaction. In: Ficuciello, F., Ruggiero, F., Finzi, A. (eds.) Human Friendly Robotics. SPAR, vol. 7, pp. 151–163. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-89327-3_12 4. Grube, D., Malik, A.A., Bilberg, A.: Generic challenges and automation solutions in manufacturing SMEs. In: Annals of DAAAM & Proceedings, vol. 28 (2017) 5. Morel, G., Pereira, C.E., Nof, S.Y.: Historical survey and emerging challenges of manufacturing automation modeling and control: a systems architecting perspective. Annu. Rev. Control 47, 21–34 (2019) 6. Mourtzis, D., Doukas, M., Vandera, C.: Smart mobile apps for supporting product design and decision-making in the era of mass customization. Int. J. Comput. Integr. Manuf. 30(7), 690–707 (2017) 7. Karanasiou, A.P., Pinotsis, D.A.: A study into the layers of automated decision-making: emergent normative and legal aspects of deep learning. Int. Rev. Law Comput. Technol. 31(2), 170–187 (2017) 8. Zerilli, J., Knott, A., Maclaurin, J., Gavaghan, C.: Algorithmic decision-making and the control problem. Mind. Mach. 29(4), 555–578 (2019)

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9. Zaeh, M.F., Wiesbeck, M., Stork, S., Schubö, A.: A multi-dimensional measure for determining the complexity of manual assembly operations. Prod. Eng. Res. Dev. 3, 489 (2009) 10. Mourtzis, D., Papatheodorou, A.M., Fotia, S.: Development of a key performance indicator assessment methodology and software tool for product-service system evaluation and decision-making support. J. Comput. Inf. Sci. Eng. 18(4), 041005 (2018) 11. Oh, J., et al.: Toward mobile robots reasoning like humans. In: Proceedings of the AAAI Conference on Artificial Intelligence (2015) 12. Choe, P., Tew, J.D., Tong, S.: Effect of cognitive automation in a material handling system on manufacturing flexibility. Int. J. Prod. Econ. 170, 891–899 (2015) 13. Mourtzis, D., Fotia, S., Boli, N., Vlachou, E.: Modelling and quantification of industry 4.0 manufacturing complexity based on information theory: a robotics case study. Int. J. Prod. Res. 57(22), 6908–6921 (2019) 14. 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) 15. Aziz, H., Chan, H., Cseh, Á., Li, B., Ramezani, F., Wang, C.: Multi-robot task allocation-complexity and approximation (2021) 16. Zhu, Q., Wei, P., Shi, Y., Du, J.: Cognitive benefits of human-robot collaboration in complex industrial operations: a virtual reality experiment. In: Construction Research Congress 2020: Infrastructure Systems and Sustainability, pp. 129–138. American Society of Civil Engineers, Reston (2020) 17. Thorvald, P., Lindblom, J., Andreasson, R.: On the development of a method for cognitive load assessment in manufacturing. Robot. Comput.-Integr. Manuf. 59, 252–266 (2019) 18. Goh, Y.M., Micheler, S., Sanchez-Salas, A., Case, K., Bumblauskas, D., Monfared, R.: A variability taxonomy to support automation decision-making for manufacturing processes. Prod. Plan. Control 31(5), 383–399 (2020) 19. Malik, A.A., Bilberg, A.: Complexity-based task allocation in human-robot collaborative assembly. Ind. Robot Int. J. Robot. Res. Appl. 46, 471–480 (2019)

Enabling Resilient Production Through Adaptive Human-Machine Task Sharing Deepak Dhungana1(B) , Alois Haselb¨ ock2 , Christina Schmidbauer3 , 2 Richard Taupe , and Stefan Wallner2 1

IMC University of Applied Sciences, Krems, Austria [email protected] 2 ¨ Siemens AG Osterreich, Vienna, Austria {alois.haselboeck,richard.taupe,stefan.wallner}@siemens.com 3 Institute of Management Science, TU Wien, Vienna, Austria [email protected]

Abstract. Human capabilities to interact, interfere, support, supervise and take over different tasks in a production environment are often not considered in the context of automated and self-organizing factories (smart factories). Adaptive Task Sharing (ATS) is a method to combine the strengths of automation and human skills to provide flexible and resilient factories. ATS offers huge potential for improving and enhancing ergonomics and human factors in production. The assembly worker plays a vital role in such hybrid manufacturing as a partner and coordinator of production services. In recent years, techniques have been developed to efficiently compute production execution graphs optimized for various technical criteria, like minimum makespan or energy consumption. This paper shows how to use these techniques for a flexible production planning that incorporates human workers and investigates different scenarios of task allocation between humans and machines and their impact on production workflows. Such socio-technical interactions between humans and machines need to be at the core of resilient production systems to deal with unforeseen circumstances, provide the flexibility required in high-variability, low-volume production scenarios, and increase productivity and workplace quality of human workers. Keywords: Scheduling · Production planning Resilience · Human-machine task sharing

1

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Introduction and Motivation

In dynamic production process management, we see a transition towards a more flexible production planning by separating product models from production models and production operations from production resources. Separating product models from production models allows for late decisions on which factory will produce the product while separating production operations from production resources allows for late decisions on which machine or worker in the factory will c Springer Nature Switzerland AG 2022  A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 198–206, 2022. https://doi.org/10.1007/978-3-030-90700-6_22

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execute the next production step. It has been demonstrated that such flexibility can be achieved by a skill-based representation of production processes and production resources [7,10,17]. Our research in the area of flexible production systems [2–5] has provided promising results regarding the computation of production plans (production execution graphs) by considering the skills/capabilities of machines in a factory. Recently, we have been investigating Adaptive Task Sharing (ATS) methods [20] to combine the strengths of automation and human skills. This paper summarizes our research on how the vital role of assembly workers can be systematically considered when generating production plans and how such plans impact the resilience of factories. Such socio-technical interactions between humans and machines improve the efficiency of production and increase productivity and workplace quality of human workers. Especially in high-mix and lowvolume production environments, where flexibility and resilience are particularly important, systematic integration of human factors in the production planning can provide additional leeway in the workflows.

2 2.1

Related Research and Background Adaptive Task Sharing

Optimal allocation of tasks between humans and machines has been intensively discussed in the literature. Various algorithms for such assignments have been reported e.g., multiple-objective optimization [6], minimization of operation time [23], capability-based, so-called compensatory approaches [8,13,14], heuristic approaches such as A* [12], or optimization of cognitive workload based on Markov Decision Processes [19]. The objective of task allocation problems is often the minimization of operation time or costs. Some authors incorporate the human capacities and need: they should be neither too stressed (over-utilized) nor too bored (under-utilized) [18]. However, all these approaches prescribe the allocation of tasks to humans and leave the decision to the algorithm. If a new situation arises, such as special customer requests for a small lot size, these calculations may not be appropriate. The algorithms are not flexible enough for such cases, which can also be caused by, e.g., a machine breakdown. Also, a rigid allocation of tasks makes it difficult for human workers to maintain an overview and to intervene in problems [1]. Therefore, Adaptive Task Sharing between humans and robots in assembly has been proposed as a complementary task allocation approach [20]. This method foresees the human to share tasks not only in the engineering phase of a process but also after the implementation. Tasks that a machine and a human can execute are called “shareables” and may be performed by humans or machines, depending on decision criteria such as the order size, ergonomics, or minimal costs. If human workers wish to take over tasks, they have the higher priority over machines. From a manufacturing system point of view, the main difference is that the allocation is not finalized in the engineering phase, but, similar to holonic manufacturing systems [24], task sharing is done decentralized at e.g., the workplace

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and remains flexible during the production process. In accordance with “ad hoc” task allocation by Tausch et al. [22], the final decision is undertaken by the human worker at the workplace who is provided with different task allocation options. 2.2

Skill-Based Modeling

Production processes are typically based on a BoM (bill of materials) and a BoP (bill of process). The BoP describes all production steps that are necessary to manufacture the product. In a skill-based modeling approach, the BoP consists of a generic and standardized definition of all machine capabilities necessary to execute the steps (aka machine skills). Often semantic/knowledge graph technologies are used to represent skill requirements and skills offered by factories. Thus semantic reasoning techniques can be used for the skill matching task: to find out which machines can execute which BoP operation [5,7,11]. Factory Model A

Product Model

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os: {s1,s5}

product

t1

material a

t3

material b

os: {s6,s7}

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material d ...

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material n

os: {s2,s4}

Fig. 1. Skill-based model of factory and product.

The result of skill matching is used by a service that generates (one/some/all) possible production execution graph(s). Such a graph contains an assigned resource (corresponding to a matching skill offer) for each BoP operation and determines the sequence of production operations. The generation of such graphs is a combinatorial problem [4,5]. The scope of an execution graph is that of one product (type). Beyond that, and building on that, a production scheduler must make a production plan for an entire order stack containing different product types with different lot sizes. The execution graphs for the different products are the primary input for its decisions. Figure 2 shows the layered architecture of the services skill matching, production execution graph generation, and production scheduling. The result is a production plan as input for the MES (manufacturing execution system). Constraint reasoning and algorithms from Operations Research are strong in optimizing solutions, providing that production execution graphs and schedules

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Product Model

Skill Matching Execuon Graph Generaon Order Stack

Producon Scheduling

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Fig. 2. Layered architecture of production planning services.

are assessed and preferred according to different key performance indicators (KPI). Standard KPIs are minimum makespan or minimum production costs. Energy consumption or CO2 footprint can also be used as KPIs [3]. Multiperspective optimization can also integrate criteria that focus on human workers’ health and safety. A production plan that promotes worker well-being, even with a makespan slightly worse than the optimum, may be beneficial in the long run. 2.3

Human Resilience Factors

Resilience is associated with the recovery from failures [25] or, more generally, from changes [9]. In the context of ATS, resilience is about strengthening the role of human beings so that the overall costs of production can be minimized. Examples of such typical costs are energy consumption, CO2 equivalent, and makespan. Responsible for minimizing such technical KPIs is the scheduler, that computes an optimized sequence of production steps for a given planning period. However, when human workers are involved in the process, we need to consider additional cost categories such as cognitive and physical ergonomics. Physical ergonomics can be evaluated using RULA (rapid upper limb assessment) [15], which is often deployed for assembly workplaces. The assessment can be performed via the “RULA Employee Assessment Worksheet” or calculated via a simulation and results in a final score ranging from 1–7, whereby 1 indicates an acceptable workplace and 7 indicates that it should be changed immediately. Cognitive ergonomics evaluation is often undertaken by using the NASA Task Load Index (TLX) [21]. The assessment consists of six variables, including the perceived mental and physical demand, stress, success, effort, and frustration rated on a 5-point Likert scale whereby 1 indicates a very low and 5 a very high level.

3

Considering Human Resources in Production Planning

In our modeling approach, a factory is defined as a set of interconnected production resources (humans and machines), their capabilities being described as skills [4]. A particular product to be manufactured is defined as a combination of BoM and BoP. In Fig. 1, we sketch a simple, abstract example of such a factory and product model.

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Table 1. Exemplary list of production operation KPIs. The last 3 KPIs are especially relevant for human workers. KPI

Unit

1 Time/makespan

s

2 Costs 3 Carbon dioxide equivalent kg 4 Energy consumption

Wh

5 Cognitive ergonomics

Score range [1. . . 5] (from low to high load)

6 Physical ergonomics

Score range [1. . . 7] (from good to bad)

7 Worker preferences

{dislike, ok, like}

Every production resource has defined multiple possible transport routes (t) in the factory, determined by physical and organizational constraints, as well as offered skills (os), which are represented by a set of capabilities and their properties (e.g., assembling, drilling). In the product model, each material in the BoM is related to the list of processes required to produce sub-parts of a product. Each of these production processes defines the set of required skills (rs), including significant production constraints (see Sect. 2.2) required to be met by a production resource’s offered skills. Based on a defined skill-based factory and product model, it is possible to calculate multiple possible production execution graphs optimized according to different KPIs. An exemplary list of such KPIs is shown in Table 1. Such an execution graph determines the complete sequence of needed production operations and the assigned resources to produce a concrete product. To support needed short-term planning flexibility, we provide a service function called NextOps, which calculates all next possible production operations leading to an entirely manufactured product, given the factory’s current resource utilization. The NextOps function is implemented by looking up all possible continuations of the current production state in all valid execution graphs. Applying both of these functionalities (execution graph generation and NextOps) allows providing essential decision support for human workers to make flexible decisions

Order Stack

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Required KPIs

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Human

operaon a: KPIs operaon b: KPIs operaon c: KPIs …

Producon State NextOps()

Product & Factory Model

Fig. 3. Decision support system for human workers.

operaon b

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for their production tasks. Figure 3 outlines a decision support system based on these ideas. The main inputs are the skill-based factory and product model and the current production state (e.g., the production tasks currently executed on each machine). Additionally, the required KPIs for the production process (e.g., expected makespan for a particular product, cognitive load to produce a product) are essential. High-level input like the current order stack may influence the task decision as well. The system proposes several possible next working tasks for an individual worker. Each task is part of a calculated execution graph, including values for all significant KPIs. The tasks are ranked considering the current production requirements. The topmost task (operation a) is regarded as the best task; however, the worker can always favor a different task (operation b) from the list. Thereby the workers are guided by the calculated KPIs attached to each task allowing them to estimate the consequences of their decisions. The decision support system could even provide a filter function that enables the worker to define their currently desired human-based KPIs (e.g., cognitive load) and thus influence the system’s task suggestion.

4 4.1

Example Scenarios Scenario 1: Considering Ergonomics and Human Preference

While computing a production plan, KPIs (such as the ones described in Table 1) provide the basis for optimization. Because all production resources must provide values for all KPIs, we must decide which values machines use for the humancentric KPIs (like cognitive or physical ergonomics). There are two different strategies: Using the “best” value for robots (i.e., low physical and cognitive load) will yield production plans in which robots execute all shareables. Using medium values for robots will yield production plans with mixed assignments of humans and robots. In any case, a production plan assigning shareables to humans will be computed if the KPI values of skills offered by humans “dominate” those of skills offered by robots. This may be due either to human preference or to the human’s superiority in technical aspects such as processing time. For example, the processing time for assembling tasks may be shorter when executed by humans because conducting these tasks by machines requires a complex handover. Thus, the assembling task will be assigned to a human worker if the production plan is optimized for time, and it will be assigned to a machine if the production plan is optimized for ergonomics. In more complex scenarios, human workers may be assigned only those tasks with good ergonomic values, and production planning may react to changing human preferences each time the NextOps function is called. Suppose there is currently a high load in the factory and all robots are busy. NextOps may provide the human worker with a selection of tasks to execute, from which the human worker (or their supervisor) can select by individual preference. Since each human worker offers their own set of skills, KPI values may differ from person to person.

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Scenario 2: Improving Resilience by Sharing Tasks

The suitability of machines to execute specific tasks may decline abruptly due to changes in the production plan or environment. For example, a robot may suffer from planned or unplanned downtime and not offer its skills for some time. As another example, a change in the supply chain may necessitate timeconsuming changes in the set-up or programming of a robot (e.g., the exact physical dimensions of a part may vary from supplier to supplier). In such cases, the production plan can seamlessly switch to a human workforce if human skills are modeled for shareable tasks. For example, when a machine skill is not offered, human skills will be the only choice for production planning; and if set-up times for machine skills are excessively high, production planning will prefer human skills if time is one of the KPIs the production plan is optimized for.

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Conclusions and Future Work

In flexible cyber-physical production processes, with high-mix or low-volume production scenarios, it is essential to consider the human presence and behaviour as a key inclusive part of the system [16]. Moreover, the production process needs to be designed to support humans in utilizing their core capabilities like flexibility, self-organization, and strong cooperative skills. In this paper, we presented the Adaptive Task Sharing approach for considering human and machine skills in an integrated manner when computing production plans. In order to achieve the required adaptability, short planning cycles are required. Planning the production of an entire day or shift is most probably not flexible enough to deal with changing order priorities or human conditions. To support short-term planning, as implemented in receding-horizon scheduling, we implemented a service function called NextOps (short for “get possible next operations”). Given the state of all products currently in production, the NextOps function returns a list of next possible production steps that could be started, ensuring that all these steps are valid in the sense that they will finally lead to a complete product. Step-wise planning with a short planning horizon provides perfect flexibility to take the current needs of human workers into account. Determining real data values for KPIs such as worker preferences, cognitive and physical ergonomics using questionnaires or simulation, belongs to future work.

References 1. Bainbridge, L.: Ironies of automation. Automatica 19(6), 775–779 (1983) 2. Dhungana, D., Falkner, A.A., Haselb¨ ock, A., Schreiner, H.: Smart factory product lines: a configuration perspective on smart production ecosystems. In: Proceedings of the 19th International Conference on Software Product Line, USA, pp. 201–210. ACM (2015)

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3. Dhungana, D., Falkner, A.A., Haselb¨ ock, A., Taupe, R.: Enabling integrated product and factory configuration in smart production ecosystems. In: 43rd Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2017, Vienna, Austria, 30 August–1 September 2017, pp. 266–273. IEEE Computer Society (2017) 4. Dhungana, D., Haselb¨ ock, A., Taupe, R.: A marketplace for smart production ecosystems. In: Hankammer, S., Nielsen, K., Piller, F.T., Schuh, G., Wang, N. (eds.) Customization 4.0, pp. 103–123. Springer, Cham (2018). https://doi.org/10. 1007/978-3-319-77556-2 7 5. Dhungana, D., Haselb¨ ock, A., Wallner, S.: Generation of multi-factory production plans: enabling collaborative lot-size-one production. In: 46th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2020, pp. 529– 536. IEEE (2020) 6. Fei, C., Kosuke, S., Jian, H., Baiqing, S., Hironobu, S., Toshio, F.: An assembly strategy scheduling method for human and robot coordinated cell manufacturing. Int. J. Intell. Comput. Cybern. 4, 487–510 (2011) 7. Gocev, I., Grimm, S., Runkler, T.A.: Supporting skill-based flexible manufacturing with symbolic AI methods. In: The 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020, Singapore, 18–21 October 2020, pp. 769–774. IEEE (2020) 8. Gualtieri, L., Rojas, R.A., Ruiz Garcia, M.A., Rauch, E., Vidoni, R.: Implementation of a laboratory case study for intuitive collaboration between man and machine in SME assembly. In: Matt, D., Modr´ ak, V., Zsifkovits, H. (eds.) Industry 4.0 for SMEs, pp. 335–382. Springer, Cham (2020). https://doi.org/10.1007/978-3-03025425-4 12 9. Heinicke, M.: Implementation of resilient production systems by production control. Procedia CIRP 19, 105–110 (2014). 2nd CIRP Robust Manufacturing Conference (RoMac 2014) 10. J¨ arvenp¨ aa ¨, E., Luostarinen, P., Lanz, M., Tuokko, R.: Presenting capabilities of resources and resource combinations to support production system adaptation. In: 2011 IEEE International Symposium on Assembly and Manufacturing (ISAM), pp. 1–6. IEEE (2011) 11. J¨ arvenp¨ aa ¨, E., Siltala, N., Hylli, O., Lanz, M.: Capability matchmaking procedure to support rapid configuration and re-configuration of production systems. Procedia Manuf. 11, 1053–1060 (2017) 12. Johannsmeier, L., Haddadin, S.: A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes. IEEE Robot. Autom. Lett. 2(1), 41–48 (2017) 13. Malik, A., Bilberg, A.: Complexity-based task allocation in human-robot collaborative assembly. Ind. Robot. 46(4), 471–480 (2019) 14. Mateus, J.C., Claeys, D., Lim`ere, V., Cottyn, J., Aghezzaf, E.H.: A structured methodology for the design of a human-robot collaborative assembly workplace. Int. J. Adv. Manuf. Technol. 102, 2663–2671 (2019) 15. McAtamney, L., Corlett, E.N.: RULA: a survey method for the investigation of work-related upper limb disorders. Appl. Ergon. 24(2), 91–9 (1993) 16. Nunes, D.S.S., Zhang, P., Silva, J.S.: A survey on human-in-the-loop applications towards an internet of all. IEEE Commun. Surv. Tutor. 17(2), 944–965 (2015) 17. Pfrommer, J., Schleipen, M., Beyerer, J.: PPRS: production skills and their relation to product, process, and resource. In: Proceedings of 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation, pp. 1–4. IEEE (2013)

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Feasibility of Augmented Reality in the Scope of Commission of Industrial Robot Plants Lukas Antonio Wulff1,2(B) , Michael Brand2 , Jan Peter Schulz1 and Thorsten Schüppstuhl2

,

1 ICARUS Consulting GmbH, Friedrich-Penseler-Straße 10, 21337 Lüneburg, Germany

{lukas.wulff,jan-peter.schulz}@icarus-consult.de

2 Institute of Aircraft Production Technology, Hamburg University of Technology,

Denickestraße 17, 21073 Hamburg, Germany {michael.brand,schueppstuhl}@tuhh.de

Abstract. This paper analyses the synergetic potential of Augmented Reality (AR) and offline programming (OLP) in the commission of industrial robots. We discuss that AR could utilise existing programming paradigms present in the OLP to simplify the online commission in manufacturing plants with its ability to freely combine digital and real content. Focussing on the complementary use of AR and OLP a theoretically promising use case is derived. The use case is implemented in a simplified mock-up and a user study is conducted to practicably evaluate the feasibility. Ten participants commissioned an offline created program using the developed AR application. As nine out of ten participants could achieve a valid commission by using the developed AR application, the study indicates the feasibility of the presented use case. Keywords: Augmented Reality · Digital factory · Reconfigurable manufacturing systems · Robot programming

1 Introduction Creation and commission of robot programmes is a crucial reoccurring task in the lifecycle of an industrial robot plant. Directly affecting parameters like quality and cycle time, efficient robot programming methods are highly sought. In the scope of high tier mass manufacturing, the offline programming (OLP) is a widely established programming method [1]. OLP offers the ability to create and optimise a programme offside the physical machine in a fully digitalised environment by utilising CAD based simulation systems as well as realistic robot controllers. In contrast to online programming, where programming is done by directly controlling the motion of the robot, OLP offers flexibility and plannability, as programme creation and optimisation can be executed independently from the physical robot. However, as OLP is based on a digital model of the actual factory environment, the quality of the created programme depends on the quality of the digital model. Offline created programmes usually need to be adjusted during an online commission because deviations between the factory and the digital model are likely. © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 207–215, 2022. https://doi.org/10.1007/978-3-030-90700-6_23

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During commission, the offline created programme is evaluated and optimised by a worker in the production plant. After calibrating the machine and all tied components the programme is optimised manually until a satisfactory result is achieved. Caution is necessary as robots move with high velocities and potentially hazardous materials like outgassing paints are processed. Depending on the variability of the processed material – such as viscosity of a sealant – adjustments to commissioned programmes are made repeatedly during the lifecycle of the factory.

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Fig. 1. Teach-Pendant (a), robot motion simulation (b), collision detection (c)

A typical textual programming interface or teach-pendant utilised during online programming is depicted in Fig. 1(a). In contrary to the variety of visually appealing features of OLP like path visualisation Fig. 1(b) or automated collision detection Fig. 1(c), a teach-pendant offers a less intuitive programming environment. A technology with the potential ability to harness similarly intuitive features and to simplify the programming of industrial robots inside a factory is Augmented Reality (AR). AR can selectively enhance the cognition of a user by displaying three-dimensional elements overlayed on real physical objects offering unique methods of data visualisation and interaction. As it combines the physical and digital world, AR has the potential to not only show deviations of the digital model and factory, but also make use of the abilities of OLP directly in the productive environment. In this paper we aim to evaluate these potential synergies of AR and OLP. After briefly recapping the general properties and requirements for employing AR, we present literature where the abilities of AR are utilised in the scope of robot programming. Focussing on the area of industrial robot commission, we then derive and discuss a feasible use case that utilises AR and OLP complementary. We develop and implement an AR application in a simplified mock-up to gain a practical impression on the viability. Finally, we conduct a short user study to compare the feasibility of AR assisted programming to the traditional Teach-In method.

2 Background and Related Work AR is a novel medium that can enhance a worker’s visual perception with additional digital elements offering unique methods of data visualisation and interaction. As a cyberphysical system, it enables workers to interact with non-physical entities like a digital

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robot in a factory environment. Hence, AR can provide a peculiar type of non-hazardous human-machine-interface (HMI). Technology-wise, AR is not limited to a specific implementation. A common definition describes three core properties: Overlap of virtual and real space, interaction in real-time, and three-dimensional registration [2]. Thus, the term AR includes any implementation that presents interactive time and position variant content to one or more users in a three-dimensional space. In accordance with this flexible definition, the concrete properties like accuracy, field of view, and mobility as well as the available interaction methods depend on the deployed AR display.

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Fig. 2. AR display types: head mounted display (a), handheld display (b), spatial display (c)

Figure 2 shows three different types of AR display. Figure 2(a) depicts a headmounted display (HMD). It presents AR content to a single user without limiting his mobility while keeping his hands free. Figure 2(b) shows a handheld tablet. This AR display has similar capabilities as the HMD in terms of mobility. Additionally, more than one user can work with this device at a time. Yet, the user’s hands are occupied. Lastly, Fig. 2(c) shows a spatial AR display. A static laser projector illuminates a measured plane to show AR content. Although it is locked to a static position, it can be used by more than one user at a time. The crucial joint task of each AR display is the identification of the users’ position in a three-dimensional space and the appropriate augmentation of their cognition. While this task is still essential the recent availability of hardware, software engines [3, 4] and SDKs [5, 6] as well as innovative tracking methods like SLAM [7] simplify the development of AR applications. This development is also indicated by the increase of SCOPUS listed papers listing the keyword “augmented reality”. The number has doubled from 4,000 papers in 2015 to 8,000 in 2019 [8]. Similarly, the popularity of AR assisted robot programming has also increased. Fang et al. used an AR setup to facilitate the trajectory planning of a robot [9]. Users were able to evaluate and optimise the programme without moving the physical robot by displaying the trajectory of the tool-centre-point (TCP), path points, and robotic motion on an augmented camera display. Additionally, like adjacent works from Rückert et al. [10] and Ong et al. [11] the limitations of the applicability caused by accuracy and stability of the system and the displayed content are discussed. Focussing on the intuitiveness of AR, Lambrecht presented a robot programming method which makes use of gestures [12]. Different movement instructions for a tied robot were available by capturing hand gestures with the inbuilt camera of a tablet.

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He showed that the usage of AR results in a significant reduction of programming time comparing the programming duration and user-related errors of his spatial programming to the classic Teach-In and OLP in two example scenarios. Furthermore, the user-related errors occur more often than in OLP but less often as in Teach-In. The presented research shows that, while limited by achieved accuracy and available hardware, AR can be used in the field of robot programming. While these works discuss the applicability of AR assisted programming and utilise features already present in the OLP the complementary application of AR and OLP is not fully explored yet.

3 AR in the Commission of Offline Created Robot Programmes Briefly summarised, AR can visually map digital data to elements and processes in the real world. This can be harnessed to either enhance the abilities of a user with interactive abilities or to simplify a task by displaying additional information.

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Fig. 3. Optimal, simulated sealing bead (a); uneven real sealing bead (b)

Figure 3 shows an example where such a mapping could be beneficial. Figure 3(a) depicts a simulated bead application. Figure 3(b) shows the process result executed by a robot. In the depicted process, seams of joined work pieces are covered with a sealant bead. The sealant can serve different purposes: as a water repellent, corrosion inhibition, and optical improvement. Sealant beads are analysed in quality control processes and

Fig. 4. Overlay of digital process data on a workpiece

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requirements like surface contour, brittleness, thickness, and height are evaluated to ensure the optimal behaviour. Hence, errors like the uneven thickness of the bead at the upper edge of the longitudinal profile will be recognised. However, as the manufacturing process involves different areas, e.g. robot motion and sealant application, a worker needs a high level of expertise in both fields or assistance to deduct a solution. In Fig. 4, a conceptual assisting AR view is sketched. In addition to the real elements, the coloured TCP trajectory as well as the name and position of the involved path points is shown in AR. The colour scheme follows a simple traffic light system to make irregular TCP speed easily recognisable. Green: Target speed has been reached. Yellow: Target speed has fallen up to 5% below target speed. Orange: Target speed has fallen up to 10% below target speed. Red: Target speed has fallen more than 10% below target speed. As usual applicators have a fixed material flow, the uneven sealant thickness is caused by the irregular speed of the machine. This benefit of mapping digital data to real process results is unique to AR. Contrary to AR - while OLP offers similar analytical abilities - the mapping between faulty process and the causal parameters has to be made by the working engineer in the digital environment. Therefore, while possible, the division between real process result and original digital database e.g., OLP system complicates the analysis and optimisation. The presented AR-assisted analysis could be achieved without an OLP system as only a connection between AR display, programme database, and the analytical algorithms are necessary. But, in addition to the ability of recycling existing features of OLP systems instead of redeveloping them from scratch, the industry 4.0 paradigm offers additional benefits for the complementary use of AR and OLP. Industry 4.0 motivates the implementation of a network enabling communication between each entity in a smart factory. As each machine, service, and database is available, a quality controller could harness the abilities of a connected OLP service to analyse a processed part in AR, modify faulty parameters, and update both the manufacturing robots and original digital programme database. Optimised execution is possible without halting the plant as the next component is processed with the updated parameters. Also, as each optimisation is documented, the exact processing parameters of each part is available in future tasks like repair, maintenance, and possible failure evaluation.

4 Mock-Up Implementation The complementary use of AR and OLP is theoretically viable. However, the properties of currently available AR displays limit the applicability in industrial environments. We constructed a mock-up, created an AR application, and conducted a user study to get practical insight into the feasibility. Figure 5(a) shows the physical mock-up. It consists of a phased out A320 shell element and a network accessible Kuka KR 6 sixx industrial robot. A laser pointer is mounted as a tool to mimic the application of sealant. Figure 5(b) depicts the digital model, created in the OLP system Siemens Process Simulate (PS). The use case is defined as the commission of an automated application of sealant on window frames. A

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Fig. 5. Physical mock-up (a), digital mock-up (b)

Microsoft HoloLens 2 is used as the AR display, an Xbox controller is used as the input device. The developed application is centred around a network accessible server. The server hosts and manages a single instance of PS, has access to the database of the present robot, and can bidirectionally communicate with the AR display. A user can work with the server to access the programme database of the robot, select a programme, display a trajectory in AR, simulate and visualise the motion, optimise the programme by adding, removing, or modifying path points, and finally deploy the modified programme to the robot through interaction with the AR display. While the user interacts with the UI presented by the AR display to initiate modifications, the calculations are done in the employed OLP system. This is possible as the server application utilises the API of PS. Among other features, the server can import and export programme data as well as shape information, calculate a motion simulation, and update the original OLP database. Theoretically, any available feature could be utilised in AR since the developed application has full access to the API of the encapsulated OLP system.

5 Experiment The presented mock-up enables a variety of different experiments. However, in contrast to the originally derived scenario, no sealant is applied. Due to the toxicity of sealant,

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Fig. 6. Displaced path in AR (a), allegedly corrected path in (b), allegedly correct path from different viewpoint (c)

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additional measures to ensure the safety of probands would be necessary. Consequently, we simplified the use case and used a laser pointer as a tool. During motion the position of the laser pointer on the window frame indicates the position of a potentially applicated sealant bead. We displaced the robot in the mock-up to create a correctable deviation in each path point. Figure 6(a) shows the resulting visible deviation of the path points (blue) to the planned path (grey) as well as the TCP trajectory (white) in AR. Each path point needs to be corrected manually during commission of a sealant application. To compare the feasibility of AR assisted commission to the traditional Teach-In, ten participants used both methods to correct the deviation. We split the participants into two groups. One group handled the optimisation with AR first and Teach-In second. The other group acted vice versa. Each participant had a time slot of 30 min to optimise the programme and ensure a collision free motion. In case of the Teach-In, the motion of the machine itself was used to measure the success. As the AR commission was completely based on the digital motion of the virtual robot controller, after finishing the AR-assisted modifications the programme was deployed to and evaluated with the physical robot. Each participant achieved a valid collision free motion utilising the Teach-In programming method. Of the ten participants working with AR, nine achieved a valid motion.

6 Discussion The experiment shows that the complementary application of AR and OLP is generally feasbile. While none of the participants were skilled in the field of commission prior to the experiment most could achieve the goal of a collision free optimisation. The failing of one participant while utilising AR is due to the depth perception of the employed HoloLens 2. Even though the HoloLens 2 is binocular, the fixed focal plane limits the natural mechanisms a human utilises to perceive depth. As visible in Fig. 6, the perceived positions of the path points in Fig. 6(b) and Fig. 6(c) differ. To counter this limitation, we advised the participants to only modify the position of path points parallel to the coronal plane. They quickly adapted to this limitation and worked from different orthogonal perspectives, thus minimising the effect of the limited depth perception as the AR device enabled the users to move around freely. The failing participant continued to work from a fixed position even after our aid. For that reason, while he perceived the position of the path points to be on the surface of the window, they were inside, which when run on the real machine - would result in several collisions.

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7 Conclusion and Outlook In the scope of this paper, we presented the theoretical synergetic potential of OLP and AR in the commission of industrial robots. We then derived a promising use case, implemented an application, and conducted a user study in a simplified mock-up. The application utilised a Microsoft HoloLens 2 to manage the simulative abilities of the OLP system PS to visualise and modify a programme of an industrial robot. The study indicates the feasibility of AR in the scope of industrial robot commission as most study participants were able to modify the programme until a collision free motion was achieved. The complementary use of AR and OLP simplified the application development as existing features could be reused. Furthermore, a more accurate digital model was achieved as each modification of the programme, or the cell-layout was reintroduced to the original OLP database. Additionally, in the scope of the industry 4.0 paradigm, the complementary use of AR and OLP could simplify the creation and maintenance of digital twins. The study further indicates the negative impact of poor depth perception on the quality of the achieved commissioned programme. Hence, we will utilise the constructed mockup to evaluate the effect of additional depth cues like occlusion on the user perception in future works. Finally, in this study we solely focused on the achievement of a collision free motion. Thus, another task will be a proper assessment of the perceived and absolute accuracy of AR content and applied modifications. Acknowledgement. This work was created in the scope of the research project MiReP – Mixed Reality Programming and is supported by the Federal Ministry for Economic Affairs and Energy as part of the Federal Aeronautical Research Programme LuFo V-3.

References 1. Hägele, M., Nilsson, K., Pires, J.N.: Industrial robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 963–986. Springer, Heidelberg (2008). https://doi.org/ 10.1007/978-3-540-30301-5_43 2. Azuma, R.T.: A survey of augmented reality. Presence Teleoperators Virtual Environ. 6(4), 355–385 (1997). https://doi.org/10.1162/pres.1997.6.4.355 3. Unity Engine documentation. https://docs.unity3d.com/Manual/index.html/. Accessed 25 July 2021 4. Unreal Engine 4 documentation. https://docs.unrealengine.com/4.26/en-US/. Accessed 25 July 2021 5. Vuforia documentation. https://library.vuforia.com/. Accessed 25 July 2021 6. visionLib documentation. https://docs.visionlib.com/v2.1.0/. Accessed 25 July 2021 7. Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robot. Autom. Mag. 13, 99–110 (2006) 8. Makhataeva, Z., Varol, H.: Augmented reality for robotics: a review. Robotics 9(2), 21 (2020). https://doi.org/10.3390/robotics9020021

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9. Fang, H.C., Ong, S.K., Nee, A.Y.C.: Interactive robot trajectory planning and simulation using augmented reality. Robot. Comput.-Integr. Manuf. 28(2), 227–237 (2012). https://doi.org/10. 1016/j.rcim.2011.09.003 10. Rückert, P., Meiners, F., Tracht, K.: Augmented reality for teaching collaborative robots based on a physical simulation. In: Schüppstuhl, T., Tracht, K., Franke, J. (eds.) Tagungsband des 3. Kongresses Montage Handhabung Industrieroboter, pp. 41–48. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-56714-2_5 11. Ong, S.K., Yew, A.W.W., Thanigaivel, N.K., Nee, A.Y.C.: Augmented reality-assisted robot programming system for industrial applications. Robot. Comput.-Integr. Manuf. 61(1), 101820 (2020) 12. Lambrecht, J., Krüger, J.: Spatial programming for industrial robots: efficient, effective and user-optimised through natural communication and augmented reality. AMR 1018, 39–46 (2014). https://doi.org/10.4028/www.scientific.net/AMR.1018.39

Assembly Process Digitization Through Self-learning Assistance Systems in Production Marlon Antonin Lehmann(B) , Ronny Porsch, and Christopher Mai Brandenburg University of Technology Cottbus-Senftenberg, 03046 Cottbus, Germany [email protected]

Abstract. As product specifications change, manufacturing processes have to adapt. In manual production tasks, the human worker is forced to adapt at the same pace. Fast-changing work tasks lead to high stress and therefore increase failures. Digital assistance systems aim to support the human workforce by providing assembly instructions at the right time and in the right place to reduce the cognitive load. The latest digital assistance systems provide multimodal humanmachine interfaces, such as augmented reality, haptic feedback, and voice control to provide information or react to the user’s input. However, those digital assistance systems require the manufacturing information themselves, which are mostly provided through text-based or graphical programming. Both manufacturing experts and programmers are needed to create a digital assistance system workflow or adapt it to changes. This process is costly, time-consuming, and inflexible. This work presents a gesture recognition based approach for a self-learning digital assistance system. Therefore, assembly gestures are classified based on anatomical grip descriptions. Assembly sequences are recognized and learned by the digital assistance system using machine learning techniques. The learned procedures are used to automatically generate work instructions and guide the worker through the assembly task. Keywords: Digital assistance system · Human-machine interaction · Digitization

1 Introduction Customer requirements are constantly changing. Companies are tackling the gap by individualizing their products. Therefore, the product portfolio increases and production systems need to be adapted. A broader product range leads to increasing production complexity in terms of larger amount of parts, tools and assembly operations in production. To reduce the operators cognitive load cognitive assistance systems can provide missing information in the right moment. Several cognitive assistance systems have already been introduced in the scientific community and successfully applied in production scenarios e.g. [1–3]. The assistance systems provide work instructions such as assembly information, maintenance guidance, order details or machine parameters. All those information have to be digitally stored © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 216–223, 2022. https://doi.org/10.1007/978-3-030-90700-6_24

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and be accessible to the digital assistance system in advance. However, to create work instructions, expert knowledge and software tools are required e.g. assisting in an assembly process requires the assembly steps to be documented by a supervisor. A software expert then implements the information. The workflow of adding assembly jobs is time consuming and error-prone. Domain experts often leave out relevant information for beginners. This paper presents a concept of using the context-awareness of the assistant systems not only to assist but also to learn from an expert by observing the expert performing the assembly job. This approach bypasses the need of the explicit assembly sequence description. The assistance system automatically generates the assembly information while an expert performs the assembly process. A smart glove setup is applied to enable the context-awareness by recognizing gestures. The smart glove system has some major advantages over vision-based systems, which usually record gesture data in such applications [3, 4]. This paper presents a glove based way of automated assembly process learning by formalizing the assembly processes.

2 Terminology and Definition Production assistance systems are classified as either being cognitive or physical [5]. The first one assists by providing information whereas the second supports physically e.g. trough force augmentation. Cognitive assistance systems are also known as digital assistance systems [6]. An assistance system is an computer based system which realizes worker support [7]. It has to have a human machine interface for interaction. The selflearning assistance system learns through machine learning based on the human interaction data. In this paper, the term assistance system is used for self-learning, cognitive, digital assistance systems in production. Assistance systems support the worker and reduce failures. They recognize assembly states using human input data and output multimodal assistance information. To provide such functionality, the assistance system has to have the necessary human machine interface to track the assembly operations and have access to the formalized, digital process model. Latest assistance systems such as the SmartFactorKL [4] use depth-image cameras for hand, tool and object recognition [8] or 2D-cameras as in the cognitive assistance for rework in assembly [9]. The same assistance systems use monitors and projectors as a visual output technology. Vision systems have been proven great success in real world applications from autonomous driving, to the robotic bin-picking problem in industrial robotics [10]. Nevertheless, vision systems require a stable line of sight from the object to the camera. During assembly tasks, hands will often cover the actual work piece. The camera systems have to wait until hands are removed from the field of vision to compare the static image of the assembled product using machine learning object recognition. In this approach smart gloves are used as the human machine interface to track hand movements and detect assembly gestures. Mario Aehnelt, Enrico Gutzeit, Bodo Urban [11] discuss the activity recognition based process modelling in assembly processes considering VDI 2860 and DIN 8580. Therefore, the activities are classified as either handling, adjusting or joining, whereas joining consist of manufacturing subtasks e.g. assembling, filling, press fitting [11]. The use of the VDI 2860 allows for standardized

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assembly description, as a well-defined basis for the automatic assembly digitization. In addition, the parameters seen in Fig. 1 are used to ease the assistance systems situation awareness.

Fig. 1. Contextual assembly information for assembly activity recognition [11]

To merge the assembly parameters and estimate the assembly sequence, Aehnelt, Bader [12] applied the approach in a finite state machine and extended it through a Hidden-Markov Model (HMM). Bertram, Kränzler, Rübel, Ruskowski [4] used a similar approach combining petri nets with HMM and implemented it in the SmartFactorKL. The probabilistic approach allows training models with real shop floor production data. The machine learning approach requires training with labeled data and inference. Using shop floor assembly data means that both implicit knowledge and the workers experience are learned by the assistance system. In this case, a data glove is used for gesture recognition. As there are no data sets available for this specific set up, the data have to be recorded. Labeling data for a data set is a time consuming process. Basic machine learning techniques such as naïve bayes need little data and therefore are preferable for concept feasibility. The required data set consists of assembly grips. Kapandji, Tubiana [13] proposed a taxonomy, shown in Fig. 2. Grips are specific to the weight and the geometry of an object and therefore reveal information about the handled object. The grips are clustered in digital grips, representing grips were a certain number of fingers is involved and palmer grips, when the full hand is used. Symmetrical grips are those, where the grip axis aligns to the forearm. Therefore, symmetrical grips aren’t taken into account in the setup since they don’t provide any information about the finger positions.

Fig. 2. Grip taxonomy for data labeling [13]

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The latest assembly assistance systems were discussed, together with approaches of assembly data modelling in a standardized way and a grip taxonomy.

3 Methodology The proposed assistance system estimates assembly steps by only processing grip data. Therefore, acceleration and orientation data are classified with the naïve bayes algorithm. The naïve bayes approach was chosen because it needs little processing power and a small amount of training data to achieve acceptable classification results. The advantage of the proposed assistance system is, that it automatically generates new assembly sequences, while they are performed. This enables reconfiguration of the assistance system on the shop floor. The overall system setup is shown in Fig. 3. A pair of smart gloves with a hub is connected to a WLAN-Hub via cable. It measures acceleration and orientation with seven inertial measurement units (IMU) on each hand at a framerate of 30 fps. The hub is wirelessly connected to an Android handheld device with a self-developed application. The application sends the glove data to a PostgreSQL database, where all the data is stored. The classification runs on the handheld device.

Fig. 3. Assistance system architecture

The assembly process digitization through a self-learning assistance system in production is implemented in the three steps (1) assembly grip library creation, (2) assembly sequence recording and automated digitization and (3) assistance through work instructions and monitoring. First, gesture data are recorded accordingly to the assembly tasks. Most assembly processes involve similar tasks, e.g. screwing. Therefore, a basic set of grips is recorded and classified. The grips are then assigned to specific tools and assembly operations. The assistance system provides recording and learning functions, which can be selected by the user. The model parameters are stored in the database. Tasks can only be recognized as long as the pattern already exists in the database. Once there are sufficient assembly grips in the database, those are used to classify and automatically generate digitized assembly sequences. The second step contains no

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assistance function, but the worker or supervisor teaches the assistance system himself by performing the assembly process as it should be performed in future. The assistance system classifies the atomic assembly operation and the used tool by comparing it to the stored machine learning parameters. The output is a digitized assembly sequence and the system is now aware of the whole assembly job. In the third step, the assistance system provides assistance functions to workers, which are new to the assembly station by displaying work instructions based on the current assembly state recognized by the real-time hand grip data. The data model, which is applied, is based on the previously introduced assembly modelling approaches. The Fig. 4 shows the data structure. The overall assembly sequence description requires information about the place, people, things, VDI assembly operations and assembly grips. The place is the manual assembly station. This study only includes one place. Therefore, the assistance system knows the relevant tools and parts. People assigned to the manual assembly station are the supervisor and the worker, who has to perform the assembly process. Things contain information about the tools and the parts applied to that specific assembly process.

Fig. 4. Assistance system data structure

The proposed concept for automated assembly sequence digitization is applied to a turner’s cube assembly, which will be explained in the following section. The proposed data structure is utilized.

4 Results Parts of the application were implemented in the context of the Kuka Innovation Award 2021 by the team Advanced Robot Assistance Solution (ARAS) from the Brandenburg University of Technology Cottbus-Senftenberg. The assistance system automatically

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generated a sequence of robot assembly operations. Within this publication, the recognition is tested on a manual assembly station including three tools and four different parts. The concept is applied to a turner’s cube assembly process to identify grips of the specific parts and tools and estimate the assembly sequence. The result is a digitized assembly operation sequence, which can be used for automatic work instruction generation. The testing setup includes the parts: turner’s cube body, cover, 4x hex screw (three for hex key, one for spanner) and tools: hex key, metric spanner, cordless drill (see Fig. 5). The assistance system derived the assembly sequence in (see Table 1) by observing the workers grip gestures using the data gloves parameters. The contribution should contain no more than four levels of headings. The following Table 1 gives a summary of all heading levels. Table 1. List of the recognized assembly sequence based on grips Nr.

Estimated sequence

Estimated assembling

Classified assembly grip

1

Insert cube into fixture

Handling

Pentadigital grip

2

Put cover on cube

Handling

Tridigital grip

3

Insert screw

Handling

Prehension by terminal opposition

4

Insert screw

Handling

Prehension by terminal opposition

5

Insert screw

Handling

Prehension by terminal opposition

6

Insert screw

Handling

Prehension by terminal opposition

7

Screwing with cordless drill

Screwing

Full palmar prehension

8

Screwing with cordless drill

Screwing

Full palmar prehension

9

Screwing with hex key

Screwing

Palmar grip with using the thumb

10

Screwing with spanner

Screwing

Palmar grip with the thumb

11

Remove cube from fixture

Handling

Pentadigital grip

To classify the grips the naïve Bayes algorithm was applied to the 98 parameters. One model was trained for each grip. The worker performed the training and classification on a tablet device without any programming or data analysis. In Fig. 5 some of the recognized grips are shown. Some misclassification and failures occurred while testing the assistance system. If grips are too similar, the recognition is not reliable. The palmar grip with using the thumb came out twice in the assembly sequence. Once for the spanner and secondly for the hex key. In case of the hex key, the thumb is placed from the side, which differs from the

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Fig. 5. Recognized assembly gesture using the data glove

spanner grip. Therefore, the assistance system was able to recognize it. Nevertheless, the similarity causes misclassifications. Furthermore, a wrong grip recognition is possible in case the hand is hold similar to a grip but without any part or tool.

5 Discussion The feasibility of the assembly activity recognition approach was proven, but still causing errors. Each grip was trained separately. That resulted in overlapping recognition spaces, leading to false-positive classified frames. Therefore, further research on the features is needed. In addition, the presented approach requires tool and task information in advance, because the data glove can only recognize grips and not the parts itself. It is possible to recognize known parts by specific grasp differences but the reliability seems not high enough. The chosen anatomical descriptions are not sufficient to model the detailed finger position on tools or parts. Further parameter such as tightening torque cannot be recorded. Pictures are missing for the assistance information display. However, those could easily be added by integrating a tablet or smart phone camera. To tackle the discussed drawbacks, movement data could be included to recognize assembly specific changes in velocity and position. Adding time and frequency as features could increase the model accuracy as well. The machine learning algorithms are capable of modelling the small differences. Nevertheless, the approach shows the high potential using automated learning in assistance systems, which results in increased flexibility. At the same time development cost because of product changes are reduced.

6 Conclusion In this paper, an assembly data structure was derived from existing assembly modelling approaches and implemented in an assistance system to automatically generate assembly sequences. The assembly sequence was automatically digitized without any need of manual or graphical programming. The shop floor worker or supervisor is enabled to easily document assembly processes. Additionally, the assistance system is able to use those data to instruct and monitor assembly operations. This adds flexibility to assistance systems for easy reconfiguration in case of product changes. Because of misclassifications, the recognition requires further development. Next steps include dynamic grip

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recognition and movement classification to include part and tool positions, characteristic assembly movements and to achieve higher recognition rates. In a future iteration, the self-learning assistance system should increase flexibility and reduce development costs by automatically learning assembly sequences.

References 1. Bächler, A., et al.: Systeme zur Assistenz und Effizienzsteigerung in manuellen Produktionsprozessen der Industrie auf Basis von Projektion und Tiefendatenerkennung. In: Wischmann, S., Hartmann, E.A. (eds.) Zukunft der Arbeit – Eine praxisnahe Betrachtung, pp. 33–49. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-49266-6_3 2. Bertram, P., Birtel, M., Quint, F., et al.: Intelligent manual working station through assistive systems. IFAC-PapersOnLine 51, 170–175 (2018). https://doi.org/10.1016/j.ifacol.2018. 08.253 3. Gräßler, I., Roesmann, D., Pottebaum, J.: Traceable learning effects by use of digital adaptive assistance in production. Procedia Manuf. 45, 479–484 (2020). https://doi.org/10.1016/j.pro mfg.2020.04.058 4. Bertram, P., Kränzler, C., Rübel, P., et al.: Development of a context-aware assistive system for manual repair processes - a combination of probabilistic and deterministic approaches. Procedia Manuf. 51, 598–604 (2020). https://doi.org/10.1016/j.promfg.2020.10.084 5. Samtleben, S., Rose, D.: Die kleinen Helfer in der Produktion – ein Assistenzsystem wird konzipiert. In: von Garrel, J. (ed.) DIGITALISIERUNG DER PRODUKTIONSARBEIT: Arbeitsfähig sein und bleiben, pp. 197–214. Springer GABLER, Wiesbaden (2019). https:// doi.org/10.1007/978-3-658-27703-1_12 6. Apt, W., Schubert, M., Wischmann, S.: Digitale Assistenzsysteme: Perspektiven und Herausforderungen für den Einsatz in Industrie und Dienstleistungen (2018) 7. Steil, J., Wrede, S.: Maschinelles Lernen und lernende Assistenzsysteme. In: Digitalisierung und künstliche Intelligenz, pp. 14–18 (2019) 8. Anisimov, Y., Wasenmüller, O., Stricker, D.: A compact light field camera for real-time depth estimation. In: Vento, M., Percannella, G. (eds.) CAIP 2019. LNCS, vol. 11678, pp. 52–63. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29888-3_5 9. Müller, R., Horauf, L., Bashir, A.: Cognitive assistance systems for dynamic environments. In: 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 649–656. IEEE (2019) 10. Kuo, H.-Y., Su, H.-R., Lai, S.-H., et al.: 3D object detection and pose estimation from depth image for robotic bin picking. In: 2014 IEEE International Conference on Automation Science and Engineering (CASE), pp. 1264–1269. IEEE (2014) 11. Aehnelt, M., Gutzeit, E., Urban, B.: Using activity recognition for the tracking of assembly processes: challenges and requirements. In: Proceedings of the Workshop on Sensor-Based Activity Recognition 2014 (2014) 12. Aehnelt, M., Bader, S.: Tracking assembly processes and providing assistance in smart factories. In: Duval, B. (ed.) Proceedings of the 6th International Conference on Agents and Artificial Intelligence, pp. 161–168. SCITEPRESS, Angers (2014) 13. Kapandji, I.A., Tubiana, R.: The Physiology of the Joints, 7th edn. Handspring Publishing, Pencaitland, East Lothian (2019)

Detecting Faults During Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving Bla˙zej Leporowski1(B) , Daniella Tola1 , Casper Hansen2 , and Alexandros Iosifidis1 1

Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark {bl,dt,ai}@ece.au.dk 2 Technicon ApS, Hobro, Denmark [email protected]

Abstract. Detecting faults in manufacturing applications can be difficult, especially if each fault model is to be engineered by hand. Datadriven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest, where a ML model can be trained on a set of data from a manufacturing process. In this paper, we present a use case of using ML models for detecting faults during automated screwdriving operations, and introduce a new dataset containing fully monitored and registered data from a Universal Robot and OnRobot screwdriver during both normal and anomalous operations. We illustrate, with the use of two time-series ML models, how to detect faults in an automated screwdriving application. Keywords: Time-series dataset · Automated screwdriving Robots · Fault detection · Anomaly detection

1

· Universal

Introduction

Screwdriving is performed at 58% of U.S. assembly plants, placing it at the forefront of the automation drive in the industrial manufacturing sector [9]. Automation of such a process promises significant gains in efficiency without compromising the assembly quality [10]. Events such as cross-thread, sufficient torque, and correct pickup of the screws, create challenges that may affect the quality of screwdriving applications [7]. To maintain the process quality, effective means of monitoring and detecting faults need to be developed. Creating such an automatic system or models to detect all the potential faults is challenging, due to the various failure types and different disturbances that can occur [2]. Machine Learning (ML) has the potential to provide automated solutions for such problems, potentially eliminating or limiting the need for human experts. c Springer Nature Switzerland AG 2022  A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 224–232, 2022. https://doi.org/10.1007/978-3-030-90700-6_25

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By collecting more data from faulty operations a better understanding of the process critical parameters can be obtained. The insufficient knowledge of these processes and limitations in early fault detection are a major barrier preventing deployment of more robots at lower cost in the industry. With improved understanding and applying the ML models into robotics modules the robot operators will be able to benefit more from robotic automation in general. The main contribution of this paper is an introduction of a novel AURSAD dataset, that we have made publicly available on Zenodo1 , and presentation of a fault detection use case in an automated screwdriving applications using two Deep Learning (DL) models. By performing these experiments we illustrate the feasibility of fault detection in automated screwdriving applications. To our knowledge this is a unique dataset that provides comprehensive information related to all available robot sensors continuously recorded throughout the whole process. The dataset contains 2,045 samples of labeled normal and faulty data, which can be used to train DL or ML models for fault or anomaly detection. Using state-of-the-art DL models we perform fault detection, where we demonstrate the suitability of the dataset for further analysis and experimentation. The description of the AURSAD dataset is accompanied by a technical report [8], and a Python library2 with functionalities that can be used for dataset preprocessing. Additionally, code that could be used for replicating our experiments is available on GitHub3 .

2

Background and Related Work

A critical analysis of public anomaly datasets presented in [4], concluded that the most relevant attributes are: 1) point difficulty, 2) relative frequency, 3) semantic variation, and 4) feature relevance. The characteristics of the AURSAD dataset makes it possible to select variable amounts of anomalies, satisfying attribute 2, as well as varying cluster density, thus satisfying attribute 3. AURSAD allows for easy feature selection and analysis which satisfies attribute 4. Moreover, the data collection process was designed to avoid patterns that can reduce the point difficulty of the resulting time-series data, thus considering the attribute 1. Section 3 describes this in more detail. To our knowledge, the only other available dataset for anomaly detection on screwdriving is The Manipulation Lab Screwdriving Dataset (TMLSD) [1,3]. Table 1 shows a comparison of the characteristics of our AURSAD dataset and the TMLSD dataset. A notable difference between the two datasets is that TMLSD is more focused on each substage of the screwing process, i.e. hole finding, mating etc. while AURSAD focuses on the complete screwdriving process. Moreover, the annotations for the different categories included in the AURSAD 1 2 3

https://doi.org/10.5281/zenodo.4487073. https://pypi.org/project/aursad/. https://github.com/CptPirx/AURSAD-source.

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dataset, as well as the publicly available Python library make it easy to employ in mainstream deep learning frameworks.

3

The AURSAD Dataset

The AURSAD dataset contains time-series sampled 100 Hz across 2,045 samples. The data comes from two sources: the UR3e robot and the OnRobot screwdriver attachment. Technical details about the hardware and software setup can be found in our technical report [8].

Table 1. Dataset characteristics comparison Dataset

TMLSD [1, 3] AURSAD

Number of samples

1862

2045

Number of anomaly samples

291 (15.6%)

625 (30.5%)

Number of anomalies

6

4

Number of features

17

Labels for supervised classification –

125 

Publicly available





Publicly available source code

–



Off-the shelf components

–



The UR3e robot provides sensor data on multiple aspects of its operation. Among them are: – target and actual joint positions, velocities, accelerations, currents and torques, – target and actual Cartesian coordinates and speed of the tool, and – 3-dimensional tool accelerometer values. The OnRobot screwdriver main sensor measurements are the target and current torque and torque gradient. In total, the screwdriver has 7 sensor features. 3.1

Dataset Structure

The dataset setup can be described using 3 main parts: plate A, plate P and the robot with the attached screwdriving tool. All samples in the dataset follow the same procedure shown in Fig. 1. Data was collected by recording a series of tightening and loosening movements. On each of these recordings, one of the plates, in the example of Fig. 1 plate A, is initially filled with screws in all its thread-holes. The second plate, in this example plate P, initially has no screws. Using the auxiliary python library it is possible to label the data in different

Detecting Faults During Automatic Screwdriving: A Dataset and Use Case

Plate P

Plate P 1

Plate A

Plate A

a) Home position

Plate P

b) Plate A

Plate P

Plate A c) Home position

Plate P 4

3

2

227

Plate A d) Plate P

Plate A e) Home position

Fig. 1. An example of a screw sequence: home → plate A (loosen) → home → plate P (tighten) → home

ways. Each sample in the dataset can be chosen to consist of either the complete sequence shown in Fig. 1, or only subsequent parts of it. Each time-series i in the dataset has a size of ti ×134 elements, where ti is the number of events of the operation sampled at 100 Hz and 134 is the number of dimensions for each event. The duration of each time-series may vary depending on the location of the hole on the plate and the specific operation performed. The further away the hole used for the sample is, the longer it takes the robot to reach the destination. Out of the 134 dimensions, 125 are measurements coming from the robot and screwdriver sensors, 8 dimensions (highlighted in bold text in Table 8 in [8]) correspond to control variables hard-coded during data collection to facilitate data annotation, and one dimension indicating the label of each event. The 8 control variable dimensions should be removed from the dataset when the data is used for ML, however, they can be useful for conducting a more in depth data analysis. 3.2

Anomalies

The dataset contains 5 main types of operations plus 1 supplementary category. The main types are: – Normal operation: the screwdriving process is completed successfully and according to the expectations. – Damaged screw anomaly: the screw that the screwdriver picks up and then tightens has a damaged thread. – Missing screw anomaly: the screwdriver fails to pick up the screw and proceeds to the tightening stage without a screw. – Extra assembly component anomaly: during the tightening there is an additional, unexpected element (a washer) present. – Damaged plate thread anomaly: the threaded hole of the plate has been damaged. The damaged plate thread class is underrepresented, but can be useful for experiments with rare occurrence anomalies. Otherwise, this class can be easily discarded from the dataset. The supplementary type is the loosening label which describes the loosening motion. Table 2 presents the distribution of the classes in the dataset. Figure 3 shows the mean time-series of the current torque sensor readings for all classes.

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Dataset Applicability

The main target of the dataset is anomaly and fault detection using ML. The sequence of each operation was chosen specifically to start and end in the home position, as indicated in Fig. 1, to allow users of the dataset to mix the data in a different order and experiment with different numbers of anomalies when training, validating and testing. This gives the users flexibility, while at the same time fulfilling the attributes defined by [4] which state that an anomaly detection dataset must have a relative frequency, and semantic variation. Figure 2 gives examples of how the time-series data can be shuffled into different datasets, depending on the user’s needs. The dataset also contains boolean register flags, indicating when the robot moves to home position, performs a tightening operation or a loosening operation. These flags provide the users with data to determine the trajectory of the robot, its joint angles etc., meaning that this data can also be used during the creation of models of the UR3e robot or the Onrobot screwdriver.

Table 2. Dataset statistics Type

Label Samples Percentage

Normal operation 0 Damaged screw

1

1420

69.44%

221

10.81%

Extra assembly

2

183

8.95%

Missing screw

3

218

10.65%

Damaged plate

4

Total

3 2045

0.15% 100%

Fig. 2. Example of how the time-series data can be shuffled to create new datasets

4

Experiments

In this Section, we provide a benchmark of the use of AURSAD dataset for multi-class classification and binary anomaly detection problems. The experiments performed in this paper are based solely on the tightening and movement operation, consisting of sub-sequence c, d and e illustrated in Fig. 1. The loosening and damaged plate thread classes have been excluded from the experiments because of their irrelevance and small sample count, respectively. We use all 125 features of the dataset. To effectively perform the fault classification experiments with different deep learning model architectures, the time-series data have been zero padded to achieve identical lengths. We used the following deep learning models in our experiment:

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Residual Neural Network: According to the review of deep learning methods for TSC in [6], ResNet [5] architecture is consistently among the highest scoring ones across different datasets, and is therefore used in our experiments. The implementation of the network we used in our experiments is based on the publicly available repository of the TSC review. It consists of 3 blocks employing 1-dimensional convolution with Rectified Linear Unit activation and batch normalization. Temporal Attention-Augmented Bilinear Network (TABL): The TABL [11] network is based on bilinear layers (BL) with introduced attention mechanism along the time dimension. The method was designed for use with time-series, and the BLs are able to learn two separate dependencies for the two modes of a multivariate data. We have performed a small hyperparameter search, and the variant of TABL network used in our experiments contains 3 BL with shapes [240, 5], [120, 2] and [60, 1] and a TABL layer of shape [4, 1].

Fig. 3. Mean time-series of current torque measurements for all classes in the AURSAD dataset

In all our experiments, we used a split of 70/30% of data for training and testing the neural networks, respectively. Training was conducted for 100 epochs with learning rate reduction on plateau, drop rate varying from 10% to 30% and using the Adam optimizer. We used the averaged F1 score to measure the performance of each method, as well as measuring per class F1 score. For each experiment the networks have been trained and evaluated 3 times and the averaged performance is reported. The results for the TABL and ResNet classifiers are shown in Table 3 and Table 4, respectively.

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Table 4. ResNet performance

Table 3. TABL performance Label

Precision Recall F1

Label

Precision Recall F1

0 1 2 3

0.839 0.183 0.179 0.881

0.488 0.455 0.436 0.894

0.617 0.261 0.254 0.887

0 1 2 3

0.913 0.48 0.786 0.927

Average 0.520

0.568

0.505

Average 0.776

0.887 0.546 0.8 0.955

0.9 0.511 0.793 0.94

0.797 0.789

Figure 3 shows the missing screw anomaly is distinct compared to other types of operations. It could therefore be expected that the models distinguish it from the other classes with good confidence. The experiment results reaffirm this observation, as both tested models achieved good performance on this anomaly. The normal operation class was also classified well by both of the tested models, which can probably be attributed to its large representation in the dataset. Differences between the extra assembly component class and the damaged screw class are more subtle, and the smaller, less complicated TABL model struggled to recognise those classes. To check if the robot features provide meaningful data to distinguish the classes, a comparison between the full dataset and a limited amount of dimensions containing only the 7 features directly sampled from the screwdriver itself has been performed. The performance achieved by each deep learning model is shown in Table 5. Comparing the results in Table 5 with those in Tables 4 and 3 it can be seen that the models performed worse when using the sensor measurements directly sampled from the screwdriver itself. The TABL performs better than ResNet, which can probably be attributed to the more complicated ResNet model overfitting the now reduced dataset. The smaller amount of features alleviates the underfitting problem of the TABL model, hence it performs better on the reduced dataset.

Table 5. Screwdriver subset F1 results

Table 6. Binary classification F1 results

Label

Classifier ResNet TABL

Label

0 1 2 3

0.773 0.34 0.424 0.927

0.85 0.413 0.417 0.931

Normal 0.91 Anomaly 0.8

0.86 0.532

Average 0.855

0.696

Average 0.616

0.653

Classifier ResNet TABL

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Finally, we tested the case of binary anomaly detection, where all classes corresponding to an anomaly are merged to form one anomaly class that needs to be distinguished from the normal class. The performance of the models is provided in Table 6. The ResNet network has achieved a good averaged F1 score of 0.855 on the binary classification problem. The 0.8 F1 score for the anomaly conglomerate class shows that the anomalies in the AURSAD, without distinction between their types, can be distinguished from normal operation with high accuracy. The difference most likely stems from the binary classes being more balanced in terms of number of samples.

5

Concluding Remarks

This paper introduced a time-series dataset focused on anomaly detection in automated screwdriving based on machine learning. The AURSAD dataset has been created with the critique of previous time-series datasets [4] in mind and meets the criteria set out to rectify issues commonly appearing in time-series datasets. The dataset contains the full range of robot and screwdriver sensor data for the whole operation of picking up/loosening the screws, movement to position and tightening, which potentially makes it also useful for tasks included in the modelling of such procedures. We also provided benchmarks based on well established deep learning models for time-series classification and showed that the AURSAD dataset has good potential to facilitate research for fault and anomaly detection in time-series. The current limitations of the AURSAD dataset and our use case approach are the limited amount of samples in the damaged plate thread class and arbitrary choice of the supposedly most important features. In the future it may be worthwhile to consider other approaches for determining the most relevant features, such as explainable AI methods, which could help to identify the most important features as well as highlight potential issues. Acknowledgment. This work is supported by the Smart Industry project (Grant No. RFM-17-0020) granted by the EU Regional Development Fund.

References 1. Aronson, R., et al.: Data-driven classification of screwdriving operations. In: Kuli´c, D., Nakamura, Y., Khatib, O., Venture, G. (eds.) ISER 2016. SPAR, vol. 1, pp. 244–253. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-50115-4 22 2. Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M.: Diagnosis and FaultTolerant Control, 3rd edn. Springer, Heidelberg (2015). https://doi.org/10.1007/ 978-3-662-47943-8 3. Cheng, X., Jia, Z., Bhatia, A., Aronson, R.M., Mason, M.T.: Sensor selection and stage result classifications for automated miniature screwdriving. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6078–6085 (2018)

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Virtual Modeling as a Safety Assessment Tool for a Collaborative Robot (Cobot) Work Cell Based on ISO/TS 15066:2016 Mohsin Raza1(B) , Ali Ahmad Malik2 , and Arne Bilberg1 1 University of Southern Denmark, 6400 Sønderborg, Denmark

[email protected] 2 Siemens Gamesa Renewable Energy, 6430 Brande, Denmark

Abstract. This paper describes a framework for using 3D simulation as a safety assessment tool based on ISO/TS 15066:2016 guidelines for a cobot work cell. A human-robot collaboration-based work cell has been developed. The digital counterpart of this work cell is developed beforehand to perform several safety assessments based on ISO/TS 15066:2016. It is observed that the 3D simulation model can be used as a safety assessment tool to ensure the safety of a cobot work cell even before its creation. The study also signifies the simulation software for safety and human factors in the design of a cobot cell. . Keywords: Collaborative robot · Safety · Ergonomics · Risk assessment

1 Introduction Human-robot collaboration (HRC) is gaining interest in manufacturing for flexibility and productivity [1]. Therefore, the importance of safety in designing a cobot work cell is also important as highlighted by Carole Franklin, Secretary of ISO/TC 299/WG 3 [2]: “when robots work alongside humans, we have to be very careful that the application does not put a human in danger. Up until now, robot system suppliers and integrators only had general information about requirements for collaborative systems. ISO/TS 15066 is, therefore, a game-changer for the industry. It gives specific, data-driven safety guidance needed to evaluate and control risks.” Safety is one of the biggest challenges in the application of human-robot collaboration [3]. Therefore, human safety is important to be considered in the design of a collaborative robot cell [1]. Also, it is found that in the available literature [4–6], there is more focus on safety aspects and human factors, or ergonomics are often ignored. This study shows the use of simulation to design a collaborative robot work cell in accordance with the requirements given in ISO/TS 15066:2016. Tecnomatix Process Simulate (TPS) software is used for safety and human-factors assessment in a cobot work cell design.

© Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 233–241, 2022. https://doi.org/10.1007/978-3-030-90700-6_26

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2 Method A framework based on the work of [1] is used for the safety assessment of the collaborative robot work cell. This framework presents a specific focus of designing a safe collaborative robot work cell in accordance with the requirements of ISO/TS 15066:2016. The idea is to look at the built simulation model with an ISO/TS 15066:2016 auditor‘s eye to make sure that the designed cobot work cell meets all the requirements given in the standard. The proposed framework is shown in Fig. 1.

Fig. 1. Framework for virtual safety assessment of a cobot work cell based on ISO/TS 15066:2016 [1]

3 Literature Review 3.1 Simulations and Collaborative Robots Safety is one of the biggest challenges in human-robot collaboration [3]. Simulation and modeling are emerging technologies of the 21st century [7]. Simulations-based methods in the design of cobot work cells have emerged in recent years [3]. Previous studies on simulation-based cobot work cells are more focused on evaluation and design as compare to safety [1]. However, safety is one of the important prerequisites in the design of cobot work cell [1]. In the initial design phase of a cobot work cell virtual workstation can be very helpful to do several safety assessments in a simulation setup upfronts [1]. The work of [1] has presented an evaluation method of designing a collaborative robot work cell to carry out risk assessments based on safe human–industrial robot collision behavior. This study specifically focused on the simulation-based design of a cobot work cell that will meet the requirements of ISO/TS 15066:2016. It is argued that the simulation model of a planned cobot work cell can be audited in accordance with the requirements of ISO/TS 15066:2016.

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3.2 Collaborative Robot Safety Standard and ISO/TS 15066:2016 ISO 10218 Robots and robotic devices—Safety requirements for industrial robots are insufficient to ensure both maximum safety and an effective human-robot collaboration[8]. Hence, technical specifications ISO/TS 15066:2016 are introduced to provide additional guidelines for the safe design of collaborative application [9]. ISO/TS 15066:2016 are becoming the globally accepted guidelines for the safety of cobot work cell [10]. ISO/TS 15066:2016 provides the guidelines for collaborative robot operations where humans share the workspace with the robot system. ISO/TS 15066:2016 supplements and supports the industrial robot safety standards ISO 102181 and ISO 10218-2 [11]. A list of the standards used as a normative reference in ISO/TS 15066:2016 is as follow [11]: 1. ISO 10218-1:2011, Robots and robotic devices—Safety requirements for industrial robots—Part 1: Robots 2. ISO 10218-2:2011, Robots and robotic devices—Safety requirements for industrial robots—Part 2: Robot systems and integration 3. ISO 12100, Safety of machinery—General principles for design—Risk assessment and risk reduction 4. ISO 13850, Safety of machinery—Emergency stop function—Principles for design 5. ISO 13855, Safety of machinery—Positioning of safeguards with respect to the approach speeds of parts of the human body 6. IEC 60204-1, Safety of machinery—Electrical equipment of machines—Part 1: General requirements

4 Case Study An exemplary collaborative robot work cell is being developed in the lab environment. The digital counterpart part of this work cell is built-in simulation software. This study explains that a dynamic 3d simulation model can be used to verifying a cobot work cell safety beforehand. It also shows that all the stakeholders can see how future robot solutions will operate. Several safety risks can be seen in advance through simulation and modifications can be made to avoid or reduce those risks. Siemens TPS software is used to make the 3D simulation models. TPS is a useful tool for the verification and validation of manufacturing processes in a dynamic 3D environment upfront [12]. 4.1 Design Specification of the Cobot Work Cell The work cell will be consisting of a UR 5 robot and human performing operations together. RG 6 gripper is selected to perform pick and place operations by the UR 5 robot. Humans will complete the assembly of a box and the robot will transfer the assembled box to another location. A worktable of dimensions (width 1000 mm. length 1500 mm and height 860 mm) is designed to place robot, fixture, and assembly components. Two more tables of dimensions (width 395 mm, length, 395 mm, and height 860 mm) each will be used in the work cell to place screws, screwdriver and finished assembly parts, etc.

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4.2 Building of 3D Simulation Model To start building the 3D model of the desired cobot work cell, CAD files of all the components are exported into the TPS and place at their assumed position as shown in Fig. 2. After placing all the desired components in their assumed position, human model is selected from the inbuilt database of the TPS to perform the human operations in the simulation. In the final step, a simulation with human and robot operations is created. The human operation consisted of placing the box base and top in the fixture and perform a screwing process to assemble the box. The assembled box was then shifted to Table 2 by the UR 5 robot pick and place operation. The complete simulation model is shown in Fig. 3.

Fig. 2. Cobot work cell layout

Fig. 3. Complete simulation model in TPS

4.3 Safety Analysis Once the simulation model of the work cell with required human and robot operations is built, it is ready to perform the safety analysis. Safety analysis in this study is focused on the safety requirements given in ISO/TS 15066:2016. The safety analysis is based on performing risk assessment related to the identification and mitigation of three main hazards given ISO/TS 15066:2016 [11]. 1. Robot related hazards 2. Hazards related to the robot system 3. Application related hazards Robot Related Hazards. ISO/TS 15066:2016 standard is required to identify and mitigate the hazards related to the robot characteristics (load, speed, force, and torque, etc.), quasi-static contact condition, and operator location with respect to the proximity of the robot. The speed of the robot with respect to each joint is monitored in the simulation. The results are shown in Fig. 4. It is identified that in this application the chances of quasi-static contact with the robot are minimal. also, from the simulation model, the operator location with respect to the proximity of the robot can be determined with an exact value. Hazards Related to the Robot System. The designed work cell is relatively simple, and hazards related to the fixture design, workpiece, and gripper are not significant to

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Fig. 4. Robot joints speed in mm/sec

consider. There is no intentional contact between the human and robot in the work cell. It is identified that an unintentional transient contact situation between the gripper and human hand may occur during operation as shown in Fig. 5. The impact of this contact can be calculated. But in this particular application, it is decided that a poka-yoke method will be applied to eliminate or avoid human error [13]. This will eliminate the chance of human and robot contact to make this operation even safer for the human. The details of this method will be shown in the modified simulation model.

Fig. 5. Potential human-robot contact area

Application Related Hazards. A detailed ergonomic analysis has been performed to ensure that the work cell is ergonomically designed. TPS gives the option of performing several ergonomic analysis: • • • • • • •

Fatigue analysis: NIOSH lifting analysis Rapid upper limb assessment (RULA) Static strength prediction (SSP) Ovako working posture analysis (OWAS) Lower back analysis ForceSolver

All the relevant ergonomic analyses are performed. The results show that the current work cell has no issues related to ergonomics. Some of the analysis are discussed below in detail:

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OWAS Analysis. Ovako Work Posture Analysis System (OWAS) is a method to verify the safety level related to the work posture and evaluate the risk level [14]. OWAS analysis of the designed work sell is performed in a virtual environment and it has shown that no corrective measures are needed for the current setup of the collaborative robot work cell. The detailed analysis report is presented in Fig. 6. Static Strength Prediction Analysis. Static Strength Prediction (SSP) analysis in TPS is based on the University of Michigan’s 3D Static Strength Prediction Program. It uses force as input and calculates the percentage of the male or female population capable of doing a given task [15]. The analysis results show that static strength is ok in the current design. The detailed results are shown in Fig. 7.

Fig. 6. OWAS analysis report generated in TPS

Fig. 7. Static Strength Prediction (SSP) analysis generated from TPS

4.4 Modified Simulation Model In the first simulation run, it was found that there is a chance that humans may get in contact with the robot while performing assembly tasks. To avoid this situation, a push-button is introduced in the new work cell design, that human will press to call the robot after performing the assembly task. The teach pendant is also placed within the easy reach of humans to make sure that in case of any emergency humans should be able the stop the robot immediately from the teach pendant emergency stop button.

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All the potential hazards found in the first simulation run are eliminated in the new simulation model in Fig. 8. Once again, the new simulation model is verified against the requirements of ISO/TS 15066:2016, and the results are found satisfactory.

Fig. 8. A modified simulation model

5 Discussion This study shows the use of a simulation model to perform safety assessments of a cobot work cell to meet the requirements of ISO/TS 15066:2016. But the case presented is a very simple work cell being constructed in the lab environment for research purposes. It is proposed that this type of virtual safety assessment should be tested and verified with a real industrial case. In the research of collaborative robots work cell design safety aspects are focused more compare to the ergonomics or human factors [16]. The work of [16] has highlighted the importance of aligning the collaborative robot research work regarding safety and ergonomics. It is observed that the simulation software used in this study offers a very good solution to make the balance between safety and ergonomics-related aspects. In the simulation different potential contact events can be identified, for example, the Operator body region exposed in the contact event, the frequency of the contact, and the type of contacts (quasi-static or transient). And hence, contact areas, speed, forces, pressure, and momentum, etc. can be measured base on the guidelines given in ISO/TS 15066:2016. This may lead to taking several proactive actions to mitigate or avoid risks in the new cobot work cell. The simulations do not only use to perform safety assessments of collaborative robot work cell but can also be used for the operator training through virtual reality [3,17]. It is argued that operator training through virtual reality upfront may not only enhance operator understandings about the working environment of the new cobot cell but also increase the operator confidence. Hence, it will lead to less human error in the new cobot cell.

6 Conclusion This study shows the importance of virtual safety assessments using a 3D simulation model to design a cobot work cell that will meet the requirements of ISO/TS 15066:2016.

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It is also observed that the simulation software TPS used in this study offers comprehensive ergonomic analysis options. TPS offers a balanced approach to do safety assessments and ergonomic analysis of cobot work cell. The in-built virtual reality feature of TPS also presents an opportunity to train humans beforehand.

References 1. Ore, F., Vemula, B., Hanson, L., Wiktorsson, M., Fagerström, B.: Simulation methodology for performance and safety evaluation of human–industrial robot collaboration workstation design. Int. J. Intell. Robot. Appl. 3(3), 269–282 (2019). https://doi.org/10.1007/s41315-01900097-0 2. ISO - Robots and humans can work together with new ISO guidance. https://www.iso.org/ news/2016/03/Ref2057.html. Accessed 11 May 2021 3. Land, N., Syberfeldt, A., Almgren, T., Vallhagen, J.: A framework for realizing industrial human-robot collaboration through virtual simulation. Procedia CIRP 93, 1194–1199 (2020). https://doi.org/10.1016/j.procir.2020.03.019 4. Heydaryan, S., Suaza Bedolla, J., Belingardi, G.: Safety design and development of a humanrobot collaboration assembly process in the automotive industry. Appl. Sci. 8(3), 344 (2018). https://doi.org/10.3390/app8030344 5. Vemula, B., Matthias, B., Ahmad, A.: A design metric for safety assessment of industrial robot design suitable for power- and force-limited collaborative operation. Int. J. Intell. Robot. Appl. 2(2), 226–234 (2018). https://doi.org/10.1007/s41315-018-0055-9 6. Gopinath, V., Johansen, K., Derelöv, M., Gustafsson, Å., Axelsson, S.: Safe collaborative assembly on a continuously moving line with large industrial robots. Robot. Comput. Integr. Manuf. 67, 102048 (2021). https://doi.org/10.1016/j.rcim.2020.102048 7. Hosseinpour, F., Hajihosseini, H.: Importance of simulation in manufacturing. World Acad. Sci. Eng. Technol. 51(3), 292–295 (2009) 8. Rosenstrauch, M.J., Krüger, J.: Safe human-robot-collaboration-introduction and experiment using ISO/TS 15066. In: 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), pp. 740–744 (2017). https://doi.org/10.1109/ICCAR.2017.7942795. 9. Matthias, B., Reisinger, T.: Example application of ISO/TS 15066 to a collaborative assembly scenario. In: Proceedings of ISR 2016: 47st International Symposium on Robotics, pp. 1–5 (2016) 10. Ferraguti, F., Bertuletti, M., Landi, C.T., Bonfe, M., Fantuzzi, C., Secchi, C.: A control barrier function approach for maximizing performance while fulfilling to ISO/TS 15066 regulations. IEEE Robot. Autom. Lett. 5(4), 5921–5928 (2020). https://doi.org/10.1109/LRA.2020.301 0494 11. ISO: ISO/TS 15066:2016 - Robots and robotic devices — Collaborative robots. ISO, Geneva (2016) 12. Siemens Tecnomatix Process Simulate | Engineering USA. https://www.engusa.com/en/pro duct/siemens-tecnomatix-process-simulate. Accessed 07 May 2021 13. Dudek-Burlikowska, M., Szewieczek, D.: The Poka-Yoke method as an improving quality tool of operations in the process. J. Achiev. Mater. Manuf. Eng. 36(1), 95–102 (2009) 14. Caputo, F., Di Gironimo, G., Marzano, A.: Ergonomic optimization of a manufacturing system work cell in a virtual environment. Acta Polytech. 46(5) (2006). https://doi.org/10.14311/872

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15. Chiang, J., Stephens, A., Potvin, J.: Retooling Jack’s static strength prediction tool. SAE Technical Paper (2006). https://doi.org/10.4271/2006-01-2350 16. Gualtieri, L., Rauch, E., Vidoni, R.: Emerging research fields in safety and ergonomics in industrial collaborative robotics: a systematic literature review. Robot. Comput. Integr. Manuf. 67, 101998 (2021). https://doi.org/10.1016/j.rcim.2020.101998 17. Matsas, E., Vosniakos, G.-C.: Design of a virtual reality training system for human–robot collaboration in manufacturing tasks. Int. J. Interact. Des. Manuf. (IJIDeM) 11(2), 139–153 (2015). https://doi.org/10.1007/s12008-015-0259-2

A Case Study of Plug and Produce Robot Assistants for Hybrid Manufacturing Workstations Sebastian Hjorth1(B) , Casper Schou1 , Elias Ribeiro da Silva2 , Finn Tryggvason2 , Michael Sparre Sørensen3 , and Henning Forbech4 1 Aalborg University, Fibigerstræde 16, 9220 Aalborg East, Denmark

[email protected]

2 University of Southern Denmark, Alsion 2, 6400 Sønderborg, Denmark 3 Integrate A/S, Gammel Gugvej 17C, 9000 Aalborg, Denmark 4 4TECH Robotics ApS, Bülowsgade 36, 8000 Aarhus, Denmark

Abstract. In the paradigm of smart factories, flexible and scalable manufacturing resources are essential. The human worker offers great flexibility; however, the operators are often a sparse resource in high-wage countries. Consequently, they are often responsible for several tasks at once and must prioritise the most critical ones. Consequently, the productivity on less critical tasks will suffer in the absence of the operator. In this paper, we present a case study on the effect on productivity when deploying a collaborative robot assistant in a plug and produce fashion to substitute a human worker at manual workstations on a production line. Realistic cycle and changeover times are derived from physical experiments and used in discrete event simulation to analyse two scenarios. The results show that if an operator must abandon his/her workstation, deploying a robot assistant as a substitute reduces the loss of productivity. Keywords: Plug and produce · Collaborative robot · Robot assistant · Smart manufacturing · Simulation

1 Introduction The smart factories of the future will depend on flexible and reconfigurable manufacturing equipment to cope with the increasing consumer need for product innovation and customisation. This allows the smart factory to introduce new products quickly, produce at batch sizes down to a single product and dynamically scale the capacity as demand varies. For scaling the capacity, the need to scale the automation level is also a preferred capability. In this way, new product variants with low or unknown demand can be initially introduced using manual processes. As demand increases, the manual processes are step-wise replaced with semi or fully automated solutions. Plug and produce robot assistants (from here on referred to as robot assistants) offer the flexibility to handle a multitude of tasks and have become a natural element in the smart factory paradigm. If workstations are prepared for both humans and robots © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 242–249, 2022. https://doi.org/10.1007/978-3-030-90700-6_27

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attending them, scaling back and forth between manual labour and automated labour can be done quickly and frequently. Moreover, the robot assistants could also be used as substitutes for the human operators, who often have to prioritise between several tasks for which they are responsible. A likely scenario is that an operator is attending a manual workstation close to an assembly line, whilst the operator also has to keep the entire production line running. This entails resolving stops on the line and breakdowns in the automated equipment. In case of a stop, the operator must often prioritise it over the manual work at the station. This means the operator leaves the manual station unattended until the stop is resolved. In case the stop requires a prolonged intervention, deploying a robot assistant to substitute the operator at the workstation would retain some of the otherwise lost productivity. As highlighted in our review of related research presented in Sect. 2, realising robot assistants comprises several technical challenges, which have attracted most of the research attention until now. However, in recent years we have seen real-world demonstrators and implementations of robot assistants; thus, indicating that the technology has reached a Technology Readiness Level (TRL) of seven or above on the EU TRL scale [1]. With the maturation of the technology, research into the implications on production operations when applying such robot assistants becomes relevant. Considering this, we investigate the operational potential of robot assistants. We base our work on the following hypothesis: “In a production environment with high task variety, deploying plug and produce robot assistants to aid the human operators will increase the productivity.” Our investigation into this hypothesis is based on a case study that mixes the use of human operators and robot assistants. We investigate the scenario of deploying a robot assistant as a substitute for a human operator for short periods in an industrial-like lab setting containing multiple workstations and tasks. We determine how long an operator should leave the production line before deploying a robot assistant is favorable in terms of productivity.

2 Related Research The plug and produce paradigm represents the idea of a quick and seamless connection of production equipment with minimal or no setup needed. Derived from the term plug and play from the IT domain, plug and produce was first mentioned by Arai et al. [2]. Current research in plug and produce primarily addresses the technical challenge of adding and removing components instead of the implications and benefits from an operations perspective [3–11]. Schleipen et al. [3] present a comprehensive overview of the requirements and technical challenges in implementing plug and produce. In particular, the challenge of modelling and sharing the necessary equipment and product information is pointed out. The use of AutomationML and OPC/UA is proposed as a solution. Focusing on plug and produce for robotics, Schou and Madsen [4] propose a roadmap to enable shop floor operators to reconfigure industrial collaborative robots easily and quickly. The roadmap highlights the need for modularity in both hardware and control systems, and the need for intuitive tools supporting the configuration task for the operator. Maeda et al. [5] developed and conducted a feasibility test on a multi-robot setup. Three fixed manipulators were amended with a plug and produce movable robot

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for assembly tasks. Both Antzoulatos et al. [6] and Michalos et al. [7] propose the use of an agent-based architecture for configuring plug and produce assembly systems. Michalos et al. [7] combine the agent-based architecture with ontologies for knowledge-capturing in managing the production resources. Schou and Madsen [8] propose an architecture and control framework allowing commercial robotic components to be adapted into plug and produce components for building industrial robot setups. The framework introduces a generic function layer, abstracting away from specific vendor syntax and implementations. Wojtynek et al. [9] present a scheme promoting robot autonomy for the robot to self-adapt to a given task context in a modular production system. Hence, the task of the human operator only includes plugging the robot in and omits any complicated setup and installation. Zimmer et al. [10] see plug and produce enabled resources as a key to decreasing the ramp-up time of assembly systems. Looking at the operation of plug and produce resources, Colledani and Angius [11] propose a method for combined planning of both operation and reconfiguration tasks for modular plug and produce systems. The method optimises batch completion time and sequence for maximising the system utilisation. In a final reflection, Schleipen et al. [3] highlight the need for research on how production plants can benefit from plug and produce solutions. Despite a significant body of research within the paradigm of plug and produce, we have not been able to find research explicitly evaluating the operational benefits of using plug and produce collaborative robots in a dynamic production setting.

3 Methodology As aforementioned, this paper investigates the effect on productivity when using a robot assistant to cover for an absent operator. For that, the following approach was used. In order to obtain realistic data on the productivity when the robot assistant and the human operator are attending the same tasks, physical experiments were conducted. The physical setup included a modular production line in a lab setting and a robot assistant. The latter was composed of a movable platform equipped with a collaborative robot and a tool changing mechanism, enabling the manipulator to adapt to different tasks autonomously. A plug and produce interface between the robot assistant and the production line was implemented. The data obtained from the physical experiments were used as input to a discrete event simulation, simulating several different staffing compositions. Based on these results, a breakeven time was computed. The breakeven time reflects the duration an operator must be absent before it is favourable to deploy the robot assistant.

4 Case Study In the case study, we use a modular, industrial-like production line called AAU Smart Production Line (AAU SPL) situated in the AAU Smart Production Laboratory at Aalborg University [12]. We use a movable collaborative robot assistant equipped with a tool changer and a variety of tools.

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4.1 AAU Smart Production Line The AAU SPL is a modular manufacturing line based on the FESTO Didactic CyberPhysical Factory concept. It consists of eight standardised transportation modules, each with two slots on top for processing equipment. The product produced is a customisable dummy smartphone consisting of a back cover, a PCB, fuses and a front cover. In total, 1632 different variants of the product are currently possible. The products are transported on pallets incorporating RFID chips which store the individual product’s recipe and specification. The process equipment on AAU SPL is a feeding unit for the back cover, a caged robot assembly unit for mounting PCB and fuses, a quality control unit for checking the PCB and fuses, an automatic assembly unit for mounting black front covers, and two manual stations; one station for mounting blue and white front covers and packaging, and one station for adding fuses and dispensing glue to waterproof variants of the product. The manual stations are an integral part of the process flow, and thus the production flow will halt if they are not attended. For an illustration of the robot tending the two manual stations, see Fig. 1.

Fig. 1. The robot assistant tending the two manual workstations at AAU Smart Production Line. The robot carries generic tools on its platform, see right-hand picture. The robot also makes use of station specific tools available at the stations, see left-hand picture.

4.2 Plug and Produce Robot Assistant The robot assistant hardware components and plug and produce interface are described in this section. Robot Hardware The robotic platform used for the physical implementation was derived from the Little Helper family of robot assistants [13] (see Fig. 1). It consists of: a movable platform on wheels, a Universal Robots UR5, a Kelvin Tool Changer, and four task-specific tools. The

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movable platform was designed in such a way that it could hold all the necessary hardware as well as being able to be maneuvered easily between stations. The UR5 is mounted on top of the movable platform, equipped with the Kelvin Tool changer at its end-effector. The Kelvin Tool Changer enables the robot to change its tools autonomously, consisting of a mounting plate at the robot’s end-effector and a counter plate that holds the tools. The mounting plates are connected with a mechanical locking mechanism and are equipped with a pass-through for pneumatic states and electronic signals to the tool. Lastly, the four tools used are: a calibration-block, an OnRobot RG2 gripper, a 4TECH pneumatic gripper and an AIM Robotics glue dispenser tool. Plug and Produce Procedure A plug and produce concept entails that the robot is deployed for different predefined tasks at various stations on a production line. The main challenge regarding implementing such a system is to reduce the time needed to set up the platform at a given station. By introducing a specific calibration point at each station at which the robot can be accurately calibrated once deployed, the manual positioning of the movable cart only requires centimeter accuracy and can thus be done rather quickly. The individual steps of the deployment procedure are the following: – Step 1: Place the robotic platform within a marked area at one of the plug and produce enabled stations. – Step 2: Connect the robot to stations dedicated power and Ethernet circuit. – Step 3: Equipped the UR5 with the Calibration-block. – Step 4: Hand-guide the UR5 to the station’s dedicated calibration point. – Step 5: Confirm the station and the UR5s calibration position. After these steps are completed, the robot will autonomously exchange the calibration tool with a task-specific tool depending on the Task-ID. These Task-IDs are stored on the RFID chip and hold all the information relevant for the product assembly. Whenever a new product arrives at the station, the RFID data is read by the station PLC, negotiated with the line’s MES and then send to the robot via a MODBUS connection. This flow enables the robot to carry out different task variations depending on product variant. Whenever the robot receives a new task, it acknowledges the received information before either starting immediately with the assigned task or changing its tool before proceeding with the task. The latter happens when the robot has executed a task with a different operation. 4.3 Production Scenarios Following the motivation for this paper, we address the setting of a highly dynamic production environment where the human operators are responsible for multiple tasks at once. Consequently, an operator might need to leave one task to solve another with higher priority, e.g., resolving a stop on an automated line nearby. Specifically, to our case, we consider a scenario where initially two operators are tending the two manual stations shown in Fig. 1. Due to a critical event elsewhere, one operator must leave his/her station for a period, resulting in two scenarios: #1 The remaining operator must

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tend both tasks, or #2 the remaining operator deploys a robot assistant as a substitute. The left-hand image in Fig. 2 illustrates the two scenarios. In scenario #1, the changeover is immediate, leaving no production gap. In scenario #2, the remaining operator must spend time plugging in the robot, leaving a period with no production. In scenario #2, once the leaving operator returns, he/she must spend time unplugging the robot before he/she can resume work. The scenario has a duration of three hours, with one operator leaving after one hour. The duration of the away time is the variable of interest.

5 Results To simulate the productivity in scenarios #1 and #2, we first need to determine the cycle times for both the robot and the operator, and the plug-in and plug-out time for the robot. To do so, multiple physical experiments using the AAU SPL and the robot assistant were carried out. The experiments included running the production at each manual station for both a human operator and the robot. Furthermore, the experiments include the plug-in and plug-out procedure at each of the two stations for the robot assistant. The time for each task is the average resulting of 25 trials to avoid bias, see Table 1. The operator is in all tasks quicker than the robot assistant. The reasons for this are (1) the robot’s velocity is limited due to safety, and (2) the task was originally designed for human labour. For the plug-in and plug-out procedure (see Sect. 4.2), the robot was idle and located 15 m from the AAU SPL. Table 1. Average cycle times of physical experiment. Task

Operator [s]

Robot [s]

Dispense glue

30

42

Add fuse

12

37

Mount cover and package

8

45

Plug-in

–

187

Plug-out

–

62

To study the outcome of scenario #1 and #2 described in Sect. 4.3, we first need to determine the productivity when the line is 1) operated by two operators, 2) a single operator that must travel between the stations, and 3) one operator and one robot. In this effort, a discrete event simulation has been made using the Enterprise Dynamics software. The simulation model is based on a previous study, where a digital twin of AAU SPL was designed. The robot assistant is added to the digital twin of the AAU SPL, and a production scenario of three hours is simulated. For the results of the simulation, see Table 2. From these results and the results of the physical experiments, see Table 1, we can now compute the productivity of scenario #1 and #2 as a function of the time the

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Table 2. Results of the discrete event simulations. Op = operator; Rob = robot assistant. Station 1

Station 2

Total productivity [s]

Products per hour [s]

Op1

Op2

134.2

44.7

Op1

Op1

109.6

36.5

Op1

Rob1

132.4

44.1

Rob1

Op1

133.0

44.3

Fig. 2. The left-hand graphical illustration shows scenarios #1 and #2. The robot plug-in is done by the remaining operator, and the plug-out is done by the returning operator. Op = operator, R = robot. The right-hand image plots scenarios #1 and #2 as a function of the time the operator is away. If the operator needs to leave for longer than the breakeven time, a productivity increase will be gained from investing time in deploying the robot assistant.

operator is away. The graph on the right-hand side of Fig. 2 shows the productivity of both scenarios and highlights the breakeven time. The breakeven time is found to be approximately 19 min. Thus, if the operator needs to be absent for longer than 19 min, the highest productivity is obtained by scenario #2; hence, spending the time on deploying the robot assistant. If the operator is away for less than 19 min, the highest productivity is obtained by continuing with just a single operator.

6 Conclusion We have in this paper investigated the operational potential of deploying plug and produce robot assistants in an ad-hoc manner to substitute for human operators. With offset in the hypothesis that: “In a production environment with high task variety, deploying plug and produce robot assistants to aid the human operators will increase the productivity”, we have studied an industrial-like scenario of an operator leaving a manual station for a short period of time and deploying a robot assistant as a substitute. We have used the realworld timing of cycle- and changeover times combined with discrete event simulation to estimate its productivity.

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Our study found that if the operator is away for more than 19 min, the robot assistant should be deployed. Although the breakeven time is unique for this specific scenario, the approach used to determine it will be applicable to other scenarios. In future research, we are investigating the applicability of the above-described approach in a real industrial setting. Additionally, we explore how combining the approach used in this study with key production-related features could be used to evaluate the applicability of plug and produce robot assistants for a given scenario. Lastly, it is relevant to investigate what effect maturation of collaborative enabled manipulators and their control strategies have on the field of such robot assistants.

References 1. European Commission: Technology readiness levels (TRL) - Part 19 - Commission Decision C(2014)4995 (2014) 2. Arai, T., Aiyama, Y., Maeda, Y., Sugi, M., Ota, J.: Agile assembly system by “plug and produce.” CIRP Ann. 49, 1–4 (2000) 3. Schleipen, M., Lüder, A., Sauer, O., Flatt, H., Jasperneite, J.: Requirements and concept for plug-and-work. Autom. 63 (2015) 4. Schou, C., Madsen, O.: Towards shop floor hardware reconfiguration for industrial collaborative robots. In: Advances in Cooperative Robotics: Proceedings of the 19th International Conference on Clawar, pp. 158–168 (2016) 5. Maeda, Y., Kikuchi, H., Izawa, H., Sugi, M., Arai, T.: Plug & produce functions for an easily reconfigurable robotic assembly cell. Assem. Autom. 27, 253–260 (2007) 6. Antzoulatos, N., Castro, E., Scrimieri, D., Ratchev, S.: A multi-agent architecture for plug and produce on an industrial assembly platform. Prod. Eng. Res. Dev. 8(6), 773–781 (2014). https://doi.org/10.1007/s11740-014-0571-x 7. Michalos, G., Makris, S., Spiliotopoulos, J., Tsarouchi, P., Chryssolouris, G.: ROBOPARTNER: seamless human-robot cooperation for intelligent, flexible and safe operations in the assembly factories of the future. Procedia CIRP. 23, 71–76 (2014) 8. Schou, C., Madsen, O.: A plug and produce framework for industrial collaborative robots. Int. J. Adv. Robot. Syst. 14 (2017) 9. Wojtynek, M., Steil, J.J., Wrede, S.: Plug, plan and produce as enabler for easy workcell setup and collaborative robot programming in smart factories. KI - Künstliche Intelligenz 33(2), 151–161 (2019). https://doi.org/10.1007/s13218-019-00595-0 10. Zimmer, M., Ferreira, P., Danny, P., Gentile, V.: Towards a decision-support framework for reducing ramp-up effort in plug-and-produce systems. In: 2019 IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), pp. 478–483. IEEE (2019) 11. Colledani, M., Angius, A.: Integrated production and reconfiguration planning in modular plug-and-produce production systems. CIRP Ann. 68, 435–438 (2019) 12. Madsen, O., Møller, C.: The AAU smart production laboratory for teaching and research in emerging digital technologies. Procedia Manuf. 9, 106–112 (2017) 13. Andersen, R.A., et al.: Integration of a skill-based collaborative mobile robot in a smart cyber-physical environment. Procedia Manuf. 11, 114–123 (2017)

Integrated COBOT, Human, and Manufacturing Task Kinematic Chain Yun Bi(B) , Jeremy J. Rickli, and Ana Djuric Wayne State University, Detroit, MI 48201, USA [email protected]

Abstract. This paper develops and describes a model for the forward kinematic chain between a Collaborative Robot (COBOT), human worker, and a manufacturing task. This paper aims to solve the forward kinematic equations of the COBOT, human, manufacturing task kinematic chain in order to open new possibilities in COBOT manufacturing work cell design and optimization. Results compare the simulated and predicted position and orientation matrices for the COBOT, human and task object. The simulated results were consistent with the predicted values with small differences in a small number of instances. Keywords: COBOT · Interaction · Kinematic chain · Cell design · Visual Components

1 Introduction and Literature Review Collaborative robots, often abbreviated as “COBOTs”, are the robotic devices that are capable of aiding human workers by manipulating task objects cooperatively, which are typically done within the motion constraining and guiding virtual surfaces set up [1]. Their appearances in tasks with danger, repetition and boredom in nature greatly enhances human potentials, and are able to work with and even compensate humans in commonly seen scenarios [2]. In the last 20 years, artificial intelligence, autonomy, and intelligence for robots has been a high priority for academic and industrial research [3]. Based on related research, it is suggested that because of their passivity, COBOTs are considered highly effective in performing tasks where safety is emphasized or those where significant forces are involved [1, 2]. It was found in previous studies that, observing motion features enables the possibilities on prediction of human motion which is one of the key approaches to certain designing and planning difficulties [1]. The said observations do not necessarily require a complex task model for specific cases [2, 4, 5]; though a generalized model would greatly help in understanding the mechanisms behind the cases by providing graphical and mathematical presentations [3]. Those studies [2–4] show the kinematic models of the cobot, and human workers each in their separated forms with kinematics of the task objects missing due to their nature of ambiguity; however, the complete operator-COBOT-task interaction kinematic chain has not yet been properly investigated [2–4]. © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 250–258, 2022. https://doi.org/10.1007/978-3-030-90700-6_28

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Human knowledge of a task is defined as a “mental model and includes the human’s knowledge of a task which, if known, helps the COBOT assist only where and when needed” [5]. The goal of understanding the COBOT, human worker, and task from the industry’s standpoint is to pursue ergonomic improvements for the human operator and productivity enhancements [5–8]. Furthermore, the benefit in industrial uses go beyond accommodating the human operator, including maximizing task efficiency by improving product quality and minimizing production time [5, 6]. A kinematic chain can be used to generate higher levels of trust between COBOT and the human worker in the way of interacting during cooperating in task, which were shown to increase the safety level when performing the assigned tasks by reducing the stress load of the human worker [5, 9]. Even though the scale of manufacturing varies from a single cell, single COBOT, single human worker, and a single task, to multiple COBOT, with multiple human workers, performing multiple tasks; the basic principle of the kinematic chain remains the same and can be applied to enhance manufacturing operations [7, 10]. Achieving integration of the whole assembly system into other more complicated system can be realized, both graphically and mathematically, in the way of schematics and matrix.

2 Statement of Purpose and Scope One of the current restrictions that delay the implementation of COBOTs into work cell design are the absence in critical functions such as tracking and mapping human movements, predicting their intended future position and then adjust accordingly, because true collaboration does face challenges in understanding human workers’ movement and intentions [8, 11]. In order for COBOTs to reliably track, predict, and adjust accordingly, modelling the human dynamics are necessary, which analyzing and understanding in elements such as body postures, forces, moments and their effects upon contacting, are critical [8, 11]. In this paper, the focus is on how to utilize current software on the market to correctly map human workers’ movements based on the COBOT-Task-Human Worker interaction chain. It was proven that modelling an unstructured workspace can be a vital issue on interaction between COBOT and human via tasks [7, 9, 12]; and among which, the accuracy of COBOT kinematic models is critical. Furthermore, this paper aims to contribute in conceptualize and generate a fundamental, complete and scalable solution from planning aspect of the manufacturing cell design, by exploiting graphically modelled physical COBOT-Task-Human Interaction. The said kinematic chain was intended to be the mentioned solution and was formulated to model the operator-COBOT interaction during their shared task assignment.

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3 Methodology Figure 1 is a diagram representing the relationship between the human worker and the COBOT interact around a task, which can be used as a generalized interaction model in most cases. The diagram can be divided into three elements: the COBOT, the human worker, and the task whilst the elements between them are the jointing part. In general, if the steps of identifying and representing each part of the kinematic and then combining them into a kinematic chain can be done, representation of the position and movement of each joint and parameter of the human-COBOT-kinematic chain can be performed.

Fig. 1. General kinematic model for COBOTs, human worker and tasks [11]

The general kinematic chain under this specific topic has the general equation of: [Ttotal ] = [TCOBOT ][Ttask ][Tworker ]

(1)

Where TXXX is the respective kinematic for each element and the general forward kinematic equation is: [T]= [Z 1 ][X1 ][Z2 ][X2 ] · · · [Zn ][Xn ]

(2)

For forward kinematics modeling, values such as rotational angles, translational values, joint positions, movement trajectory, singularity points and so on, can be used for calculating the values of specific joints if others are known. In this case, one can determine in details of the exact status the specific end effector is at, its position, movement, how each variable along the matrix affects the joint and based on those information, forces and torques applied to human’s joints can be analyzed and explained within the model [4, 9]. Figure 2 is one way of interpreting the graphical model of the human worker kinematic, with seventeen DOF from seventeen different joints [13, 14]. For the paper, the inverse kinematic is, to the best of our knowledge, ineffective because currently there is no effective ways to completely control the movement of each body joints.

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Fig. 2. The human skeleton kinematic model [13]

Figure 3 shows the complete relationship among all three elements in the kinematic chain in a graphical presentation. In this diagram, all twelve degrees of freedom are represented in two types: translational, X, Y, Z, and rotational: yaw (ϕ), pitch (θ ) and roll (∅). Each of the item has independent values to each other and determines the kinematic chain independently.

Fig. 3. Graphic structure of the kinematic chain

Both of the D-H parameters between COBOT to task, and the one between task and human worker are six DOF for each of them, with three rotational parameters and three translational parameters, and one DOF for the task alone given by its ambiguity.

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The chosen FANUC CR-4iA is a typical single-armed COBOT commonly seen in industry. Kinematic models and the associated Denavit-Hartenberg (D-H) parameters were required to capture the physics of a COBOT. Due to concerns in balancing complexity and performance, seven DOF arms are often suitable for their redundancy [15]. Data were collected from the series of designed motion during the period when the human worker was completing the task within the kinematic system. The kinematic chain was built using kinematic theory, including inverse kinematics, the kinematic chain configuration, and information available from manufacturers [16, 17]. It is possible to expand this model by replacing the corresponding part of the kinematic chain with kinematic model of desired COBOT models. The model was simplified in a way that it includes 3 DOF for right hand, which is typically used as gripper. The tool that was used to verify the results was the simulation software, Visual Components. The interaction between COBOT, task and the human worker were modelled, then different scenarios were tested by altering all six parameters of translational and rotational joints to fine tune the desired motion [18]. Specifically, task objects, bottles in this case, were generated at the start of an assembly belt, and were then transported to a location where the COBOT picked up the bottle. The COBOT then rotated and held the bottle at the second platform where the worker proceeded to inspect the build quality of the bottle to see if the bottle passed a quality check. After the inspection, the worker labels the bottle, then the bottle is manually placed at the third location where the bottle is transported by the belt to the next location. To validate the structure of the kinematic chain conducted, a series of four tests were completed. Each set varied from one to another in how each translational and rotational joints were controlled in the ways that they would show how differently each setting would impact the joint values as result. The comparison was done between the theoretical values of the joints and the actual obtained values. The detailed design will be explained in the results and discussion section.

4 Results and Discussion The case study contains two parts of parameters, part A and part B. Part A contains controlled parameters on how the interactions were occurring, values of preset translational and rotational parameters, noted as Yaw, Pitch, Roll, L1, L2 and L3, in Table 1. Part B contains the hypothetical and actual joint value, marked as Hyp. and Act in Table 2. Hypothetical joint values were obtained theoretically using the kinematic chain formulas. The process was done separately from the software. Actual values of joints were collected from the software simulation. Theoretically, actual values should be exactly matching if software runs perfectly or fairly close to the calculated values if not. Differences of each set of the values were then calculated and compared to their original value, to determine their percentage error. The units for rotational joint values, yaw, pitch, and roll, are degree(s) and millimeters(mm) for the translational joint values.

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There are four sets of designed situations where for each set the joints are in different predetermined values (Table 1). For set 1, all rotational angles being zero degree and all translational values being fifty mm, it is to show if the model will work without any rotation to reduce unpredictability. Set 2 indicates if the rotational values of each joint can be obtained correctly with all rotational angles remaining constant at fifteen degrees. Also, the translational joints are slightly increasing, at fifty, sixty and seventy mm respectively, to see if the method is valid with different translational values. Test set 3 and 4 were designed to represent if, all translational and rotational joints were to be different in values, the combination of parameters would still present the similar results. Small and constant increments in positive and negative values were made to avoid potential singularity points where there can be infinite solutions, or no solutions, known as singularity points. The rotational joints for both COBOT-task and task-human are regulated at fifteen, twenty and twenty-five degrees in set 3, while the translational parameters are seventy, sixty, and fifty mm for COBOT-task and the other way around for task-human. Set 4 shares the same principle with set 3, where rotational angles remaining thirty, twenty-five and twenty degrees, and L1, L2 and L3 are the same at set 3. Rotational order is yaw, pitch and roll around z, y, x axes respectively. In Table 2, X, Y and Z, coordination of joints right hand of human worker, are abbreviated as RHX, RHY and RHZ respectively, and same principle applies for gripper and task object, abbreviated as GripX, GripY, GripZ, ObjX, ObjY and ObjZ. Table 1. Sets of pre-determined joints values Yaw (◦ )

Pitch (◦ )

Roll (◦ )

Set

Link

Set 1

Cobot-task Task-human

0

0

0

50

50

50

Set 2

Cobot-task

15

15

15

50

60

70

Task-human

15

15

15

50

60

70

Set 3

Cobot-task

15

20

25

70

60

50

Task-human

15

20

25

50

60

70

Cobot-task

30

25

20

70

60

50

Task-human

30

25

20

50

60

70

Set 4

0

0

0

L1 (mm)

L2 (mm)

L3 (mm)

50

50

50

As can be seen in Table 2, set 1 had 0% error for all hypothetical and actual joint values, except for GripZ, which had an error of 4.80%. Set 2 had 0% error for all joint values except for GripX, which had error of 9.43%. For set 3, four out of nine pairs of data showed disagreement at RHX, RHY, GripX and GripY, with respectively 2.42%, 4.76%, 2.86% and an abnormally high 129% percent error, which will be addressed later. The rest five data pairs had 0% error. For set 4, two in nine pairs of data showed disagreement at 27.0% for RHX and GripZ, and the rest seven pairs showed 0% error. Overall, none except two pairs of data were with more than 10% difference.

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Y. Bi et al. Table 2. Results of the hypothetical and actual joint values

Set

Unit: mm

RHX

RHY

RHZ

GripX

GripY

GripZ

ObjX

ObjY

ObjZ

Set1

Hyp.

50

50

250

−50

50

250

0

0

200

Act.

50

50

250

−50

50

262

0

0

200

Err%

0

0

0

0

0

4.80

0

0

0

Hyp.

106

81

84

−106

−81

16

0

0

200

Act.

106

81

84

−116

−81

16

0

0

200

Err%

0

0

0

9.43

0

0

0

0

0

Hyp.

124

63

242

−105

−76

150

0

0

200

Act.

127

66

242

Err%

2.42

4.76

Hyp.

115

Act. Err%

Set2

Set3

Set4

−108

−174

150

0

0

200

0

2.86

129

0

0

0

0

82

92

−98

−91

1

0

0

200

146

82

92

−98

−91

4

0

0

200

27.0

0

0

0

0

300

0

0

0

After inspecting the data collection process, some potential reasons that may lead to this result are: First off, the timing alignment on the software cannot be precisely controlled, thus leading to the slight difference in predicted results and actual values obtained, further validating the point that timing is a significant factor in data collection. Secondly, there were situations where the model of the kinematic chain could not cover, in which, they may appear as singularity points where rational values cannot be concluded. Moreover, there could be rounding errors in calculations of hypothetical value and collecting process which might be showing as marginal difference in data pairs. Lastly, computational errors might occur which most likely can be shown as opposing symbols or non-marginal errors. In general, thirty-three out of thirty-six pairs of data showed either exact match or trivial percentage difference (less than 5%). In the cases where disagreement did happen, there were tendencies to be observed: RHY showed the least differences among the different values, at 4.76%; this might be due to the time alignment issue mentioned. Gripper X values show one smaller difference and one greater difference, this might be due to the combination of built-in software timing control issue and calculation errors, which also applies to RHX. The worst offender was Gripper Y, for set 3, it might be due to the combination of all the previous reasons and especially for the reason of singularity in solutions, shown as absurdly high error percentage rate at 129%. Though Gripper Z did show one high error, the magnitude was rather small. Due to current state on software design and coding, there is no software on the market where kinematics of human, COBOT and tasks all present. Thus, software simulation in Visual Components is all that can be done at this stage.

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5 Summary and Conclusions One of the current restrictions that delay the implementation of COBOTs into work cell design are the absence in critical functions such as tracking and mapping human movements, predicting their intended future position and then adjust accordingly [8, 11]. Data were collected from the series of designed motion during the period when the human worker was completing the task within the kinematic system using calculations for hypothetical values of joints and software simulation for actual values. Results showing that the data pairs were mostly identical or within 10% difference to the predicted values, thus lead to the conclusion that the model is at least partially correct, those pairs that were not were analyzed in results and conclusion part. By forming the complete kinematic chain, the parameters and motion of specific joints, can now be predicted, for the joints alone or to achieve the prediction on the intended movement of the human worker. Given current software limitation, to better utilize the framework provided in the research, a better software solution is required.

References 1. Colgate, J.E., Wannasuphoprasit, W., Peshkin, M.A.: Cobots: robots for collaboration with human operators. In: Proceedings of the ASME Dynamics System and Control Division, vol. DSC-58, pp. 433–440 (1996) 2. De Santis, A.: Modelling and control for human–robot interaction. Research doctorate thesis, Università degli Studi di Napoli Federico II, Italy (2007) 3. Evrard, P., Gribovskaya, E., Calinon, S., Billard, A., Kheddar, A.: Teaching physical collaborative tasks: object-lifting case study with a humanoid. In: IEEE-RAS International Conference on Humanoid Robots, pp. 399–404 (2009) 4. Book, W., Charles, R., Davis, H., Gomes, M.: The concept and implementation of a passive trajectory enhancing robot. In: Proceedings of the ASME Dynamics System and Control Division, vol. DSC-58 (1996) 5. El Zaatari, S., Marei, M., Li, W., Usman, Z.: Cobot programming for collaborative industrial tasks: an overview. Robot. Auton. Syst. 116, 162–180 (2019). ISSN 0921-8890. https://doi. org/10.1016/j.robot.2019.03.003 6. Sadrfaridpour, B., Saeidi, H., Wang, Y.: An integrated framework for human-robot collaborative assembly in hybrid manufacturing cells. In: IEEE International Conference on Automation Science and Engineering (CASE), pp. 462–467 (2016) 7. Cavuoto, L.A., Bisantz, A.M.: Distributed cognition and human-cobot manufacturing teams: issues in design and implementation. In: Fields of Practice and Applied Solutions within Distributed Team Cognition. CRC Press (2020). ISBN: 9780429459542 8. Spong, M., Hutchinson, S., Vidyasagar, M.: Robot Modeling and Control. Wiley, New York (2006) 9. Gillespie, R.B., Colgate, J.E., Peshkin, M.A.: A general framework for cobot control. IEEE Trans. Robot. Autom. 17, 391–401 (2001) 10. Soerdalen, J.J., Nakamura, Y., Chung, W.J.: Design of a nonholonomic manipulator. In: Proceedings of the IEEE International Conference on Robotics and Automation (1994) 11. Peternel, L., Kim, W., Babic, J., Ajoudani, A.: Towards ergonomic control of human-robot comanipulation and handover. In: 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids). IEEE (2017)

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12. Burgkart, R., Bicchi, A., Albu-Schäffer, A.: On making robots understand safety: embedding injury knowledge into control. Int. J. Robot. Res. 31, 1578–1602 (2012) 13. Addi, K., Rodic, A.: Impact dynamics in biped locomotion analysis: two modelling and implementation approaches. Math. Biosci. Eng. 7(3), 479–504 (2010). https://doi.org/10. 3934/mbe.2010.7.479 14. Akella, P., et al.: Cobots for the automobile assembly line. In IEEE Intemational Conference on Robotics anddutomation, pp. 728–733 (1999) 15. Djuric, A., Rickli, J.L., Jovanovic, V.M., et al.: Hands-on learning environment and educational curriculum on collaborative robotics. (2017) 16. Pan, P., Lynch, K., Peshkin, M., Colgate, E.: Human interaction with passive assistive robots. In: Proceedings of the 9th International Conference on Rehabilitation Robotics (2005) 17. Nakamura, Y., Chung, W., Sordalen, O.J.: Design and control of the nonholonomic manipulator. IEEE Trans. Robot. Autom. 17(1), 48–59 (2001) 18. Milliez, G., Lallement, R., Fiore, M., Alami, R.: Using human knowledge awareness to adapt collaborative plan generation, explanation and monitoring. In: 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 43–50 (2016)

Additive Manufacturing

Assessment of Repairability and Process Chain Configuration for Additive Repair Nicola Viktoria Ganter(B) , Stefan Plappert(B) , Paul Christoph Gembarski(B) , and Roland Lachmayer(B) Institute of Product Development, Leibniz University Hannover, An der Universit¨ at 1, 30823 Garbsen, Germany {ganter,plappert,gembarski,lachmayer}@ipeg.uni-hannover.de

Abstract. Repairing defective parts offers the potential to provide spare parts more cost-effectively, faster and with less use of resources. High process reliability and reproducibility in the repair of metallic parts can be achieved by using additive manufacturing. However, additive repair has only been used in a few cases for the maintenance of parts. For a broader use, users lack concrete guidance regarding the technical feasibility of additive repair and the design of the repair process. For this reason, the paper presents a decision support tool for the evaluation of a part’s repairability by additive processes. Therefore, a knowledge-based assistance system was developed containing manufacturing restrictions and application examples of additive repair. The system additionally configures a suitable repair process chain if additive repair can be used. The applicability of the system is evaluated using a specific part as an example. Keywords: Additive repair · Repair planning · Decision support system · Design catalogue · Case-based reasoning

1

Introduction

The repair of parts using additive processes, so-called additive repair, offers promising potential for after-sales service and spare parts procurement of companies. For example, parts can be repaired that were previously considered unrepairable, and in a more energy- and material-efficient way than the production of a spare part [1]. To implement additive repair as a strategy in spare parts management or procurement, it is necessary to assess for which components additive repair is technically feasible and which process steps are required. The decision to Repairor-Replace, i.e. whether additive repair is an advantageous strategy, involves subsequently estimating the time and costs of the repair process and checking the availability of the necessary resources. The decision on technical feasibility can be based on the limitations of additive repair processes [2] and on the experience of whether comparable components have already been successfully repaired by additive repair. The challenges c Springer Nature Switzerland AG 2022  A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 261–268, 2022. https://doi.org/10.1007/978-3-030-90700-6_29

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of this include the need to consider numerous part characteristics, including material, size and geometry. Additionally, these characteristics must be compared to the limitations of the individual additive processes and in some cases even production equipment, as these differ considerably in terms of the possible repair applications, as shown for example in [3]. Therefore, a significant hurdle exists for the use of additive repair if the according expert knowledge is unavailable. The objective of this paper is to address this hurdle by providing decision-makers a recommendation for a specific damaged metallic part. The recommendation will indicate whether the technical feasibility of additive repair is given and which steps, technologies and equipment are suitable and necessary. Our approach is to extract and formalize the domain knowledge of the individual additive repair processes, equipment and application examples from literature and self-performed repairs. For fast and demand-oriented access to that knowledge, it is provided by a knowledge-based decision support system. The system-based decision support and the detail of the stored technical information, which includes machine data, differentiates this contribution from existing approaches in literature for assessing the repairability of components.

2

Theoretical Background

For additive repair of metallic components, certain additive processes are identified as suitable and applied in literature [4,5]. These are processes from the groups Direct Energy Deposition (DED) and Powder Bed Fusion (PBF) as well as the Cold Spray (CS) process. In addition to the material deposition, the process chain for repairing a part includes further steps, e.g. preparation of the joining zone by milling to produce a flat surface or post-processing to achieve required tolerances. Existing approaches, methods and tools with different focus for the assessment of a product remanufacturing and for the planning of a remanufacturing process are presented in literature. In Bras and Hammoud [6] as well as Amezquita et al. [7] a series of metrics for evaluating the remanufacturability of products are proposed and in Sundin and Bras [8] supplemented by design guidelines. The studies have in common that the remanufacturability of assemblies, but not single parts, is considered. Lahrour and Brissaud [2] address the repairability of parts using additive processes. For the assessment of whether a damaged part can be repaired with DED or PBF, a decision table is given. The table contains relevant process limitations, e.g. whether metal can be processed, and values for the maximum manufacturing volume. Furthermore, favorable characteristics for the repair process are described, e.g. that a suitable form and material for the additive technology of the region to be remanufactured is necessary. A systematic approach to select additive process chains for a remanufacturing process with regard to its economic efficiency and environmental impact is proposed by Jiang [9]. Existing metrics and guidelines, which have often a directional and qualitative nature, require considerable know-how to apply. In addition, generalized

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specifications, as listed in [2], have the disadvantage that they do not apply to a number of materials and additive systems. To support decision makers without additive repair expertise, the necessary information should be provided with the required level of detail and on this basis conclusions should be drawn. A knowledge-based decision support system (DSS) is helpful for this purpose, as it can make inferences or recommendations through stored knowledge [10].

3

Assistance System for Additive Repair

A DSS should overcome the hurdle of unavailable expert knowledge for decision makers by assessing the technical feasibility of additive repair and recommending a suitable process chain. For this purpose, the DSS must meet specifications that were derived from an analysis of performed repair processes. An excerpt is shown in Fig. 1.

Fig. 1. Excerpt from the use case diagram of the assistance system for additive repair.

The assistance system contains manufacturing restrictions, design guidelines and machine data for the additive repair processes DED, PBF and CS. These were obtained through a systematic literature search in the databases Google Scholar, Perinorm and Springer Link as well as performed additive repairs. The knowledge for assessing the technical feasibility is formalized into exclusion criteria for the individual processes with regard to possible component characteristics. The limitations of the additive repair processes are fixed restrictions, e.g. when a part cannot be disassembled PBF is not suitable, since a repair by PBF can only be carried out in the enclosed installation space. Therefore, many limitations can be formulated as production rules and executed procedurally in the program (Fig. 2). To raise the decision quality, additional parameters like part size, material and element thickness need to be related to the available production equipment, e.g. the size of its process chamber. For this reason, a catalogue is stored in the assistance system that contains the specifications of common additive systems

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regarding installation space, minimum structure size, processable materials and accessibility to different geometries. The part characteristics are compared with the limitations of the individual systems to assess whether a system is suitable. If no system of a process can be used, this process is identified as unsuitable. If all additive systems or processes are identified as unsuitable, it is concluded that additive repair is unlikely technically feasible.

Fig. 2. Program flow of the assistance system.

In contrast, the recommendation of a suitable process chain requires the consideration of a significantly larger number of variables, which also interact with one another. Therefore, it is appropriate to code empirical knowledge in a case-based reasoning system. In case-based reasoning (CBR), a new case or new problem is compared to a case base or knowledge base and evaluated for its suitability to solve the new case [11]. After the successful application of the new case, it is added back to the case base, so that the case base grows larger as the application increases. For an application in the engineering domain, a design catalogue can be used as a case base [12,13]. A design catalogue contains either objects, operations (rules) or principle solutions for designs and has a defined structure that divides the catalogue into classifying criteria, a main part and selection characteristics [14]. To recommend a suitable additive repair process chain a knowledge base of 35 additive repair application cases is stored in a design catalogue. These

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were obtained in the systematic literature research and self-conducted repair studies. In order to identify repair cases in the knowledge base comparable to the examined part, similarity characteristics had to be defined. These include the characteristics identified by Lahrour and Brissaud [2] for a part’s repairability: type of damage, material, size and properties essential for post-processing steps, e.g. surface tolerances in the damaged part area. In addition, the part’s name as well as further characteristics relevant for the design of the repair process, e.g. internal structures in the damaged part area, are used. To facilitate targeted and fast access to the repair cases, the knowledge base is systematized as a design catalogue (Fig. 3). The repair cases are classified according to a damage classification which was developed for the catalogue based on the guideline VDI 3822 [15]. The first level of classification refers to the damaged area: surface, cross-section, volume or external. In the second level, the types of damage are categorized as corrosion damage, wear damage, damage due to diffusion processes, fracture, crack, deformation and malfunction due to deposits. In the third level, these types of damage are further specified according to the triggering stresses and boundary conditions, e.g. sliding wear. The main part contains the part designation, the machine in which it is installed and the source of the application example. In the following columns, the repair process is specified by performed operations, used technologies, systems, materials and process parameters, if known. The similarity analysis for case-based reasoning is based on the classifying criteria and the selection characteristics of the design catalogue. When a repair is successfully carried out for the assessed part, the design catalogue is extended to include this new repair case.

Fig. 3. Extract from the design catalogue containing successfully repaired parts using additive processes.

4

Application Example

In the following, the use of the assistance system is demonstrated exemplarily for a broken angle lever from a winding system. The part is used to adjust rollers so that a defined contact or gap is set between them. It is assumed that the part failed due to a local manufacturing defect in the critically stressed part area.

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The recommendations of the assistance system were verified by carrying out the additive repair process. Input into the user interface of the assistance system is the information of the damaged angle lever. Besides the name and the machine, the dimensions L = 120 mm, W = 159 mm, H = 20 mm, the material 1.0420 and that the part is demountable are entered. Regarding the damage, it is stated that a fracture is present and specified as a forced fracture with an area of L = 45 mm, W = 15 mm and a height of the broken-off section of H = 65 mm. The damaged section is further specified as having no internal structures, a minimum structure size of 15 mm, a polycrystalline microstructure, surfaces of Rz 100 and a drilling tolerance of H13. According to these inputs, the system recommends the repair as technically feasible with the processes DED and PBF, various equipment of these processes and the corresponding materials, including the EOS M280 and the material MS1. The recommended repair process is output as shown in Fig. 4a. It is based on repair case no. 28 in the catalogue, in which a wheel carrier that also failed due to a forced fracture was repaired using laser powder bed fusion (LPBF) [16].

Fig. 4. a) Assistance system for additive repair, b) Additive repair of the angle lever.

The proposed repair process was carried out as the necessary resources like production equipment, material, parameter-sets and knowledge of execution were present. In the preparation, a plane was created by milling which is necessary for a later material application in LPBF. In order to select the position and orientation of the plane, it was first visually assessed which areas affected by the damage had to be removed. In addition, the plane was selected so that support structures could be avoided in the later LPBF process and the part could be fixed in the installation space with little effort. Next, the volume model was prepared for the material application. According to the selected plane, the CAD model of the undamaged component was separated and an oversize was provided for finishing the drilling. Furthermore, a fixture was built to fix the angle lever in the EOS M280, with the plane aligned parallel to the building platform. In order to align the print job correctly on the part, its position relative to the build platform was measured beforehand. Since the LPBF process requires the setting

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of numerous parameters, parameter-sets are usually used, e.g. tailored to the EOS M280, MS1 and the production of structures with good mechanical properties. Figure 4b shows the angle lever in the building chamber after the remaining powder had been removed. In the post-processing, the drilling tolerance H13 was produced and the surfaces sandblasted. Since the angle lever, like the reference case from the catalogue, is an exemplary use case for the application of additive repair, the part can be tested destructively which allows metallographic examinations of the interface between the old part and the applied material or experimental examinations of mechanical properties. The recommended process chain of the tool was found to be suitable for a successful repair of the angle lever. After the repair process, the part met its requirements which concern its shape and mechanical properties.

5

Conclusion and Discussion

This study set out to develop a DSS regarding the repairability of a metallic part by additive processes and the design of a suitable process chain. Within the system, the domain knowledge of additive repair processes is formalized from literature and self-performed repair studies. A rule-based comparison of the additive repair limitations with the part characteristics is used to assess the technical feasibility and select suitable additive processes and equipment. A suitable repair process chain is recommended by case-based reasoning based on a catalogue of successful additive repair cases. The developed system was proven to be suitable for the support of technical questions regarding the use of additive repair by means of an application example. By considering the repairability of single parts with a detailed view of technical aspects, this contribution provides an important extension of existing approaches to remanufacturability. Compared to existing generalized technical specifications and directional guidelines, this contribution presents a more comprehensive repair decision support through a knowledge-based system based on technical domain knowledge with a high level of detail. However, to enhance the benefits of this system with regard to the overarching goal of expanding the use of additive repair in the spare parts sector, its current limitations must be addressed. It is challenging for the application of the tool that users might not know or be able to check all required inputs. An automatic extraction of information, e.g. from a part scan or CAD model, could facilitate the use of the tool. In addition, the repair decision could be more extensively supported by checking the availability of needed resources for the proposed repair process. Acknowledgment. This research was conducted within the research project RePARE- Regeneration of product and production systems through additive repair and refurbishment (funding reference number 033R229) funded by Federal Ministry of Education and Research (BMBF) within the funding measure “Resource-efficient Circular Economy - Innovative Product Cycles” (ReziProK).

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References 1. Wilson, J.M., Piya, C., Shin, Y.C., Zhao, F., Ramani, K.: Remanufacturing of turbine blades by laser direct deposition with its energy and environmental impact analysis. J. Clean. Prod. 80, 170–178 (2014) 2. Lahrour, Y., Brissaud, D.: A technical assessment of product/component remanufacturability for additive remanufacturing. Procedia CIRP 69, 142–147 (2018) 3. Saboori, A., Aversa, A., Marchese, G., Biamino, S., Lombardi, M., Fino, P.: Application of directed energy deposition-based additive manufacturing in repair. Appl. Sci. 9(16), 3316 (2019) 4. Rahito, Wahab, D.A., Azman, A.H.: Additive manufacturing for repair and restoration in remanufacturing: An overview from object design and systems perspectives. Processes 7(11) (2019). https://doi.org/10.3390/pr7110802 5. Ganter, N., Gembarski, P.C., Lachmayer, R.: Einsatz additiver Fertigungsver¨ fahren f¨ ur die Bauteilreparatur: Ein literaturbasierter Uberblick. In: Lachmayer, R., Rettschlag, K., Kaierle, S. (eds.) Konstruktion f¨ ur die Additive Fertigung 2020, pp. 283–300. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3662-63030-3 15 6. Bras, B., Hammond, R.: Towards design for remanufacturing–metrics for assessing remanufacturability. In: Proceedings of the 1st International Workshop on Reuse, Eindhoven, The Netherlands, pp. 5–22 (1996) 7. Amezquita, T., Hammond, R., Salazar, M., Bras, B., et al.: Characterizing the remanufacturability of engineering systems. In: ASME Advances in Design Automation Conference, vol. 82, pp. 271–278. Citeseer (1995) 8. Sundin, E., Bras, B.: Making functional sales environmentally and economically beneficial through product remanufacturing. J. Clean. Prod. 13(9), 913–925 (2005) 9. Jiang, Z., Zhang, H., Sutherland, J.W.: Development of multi-criteria decision making model for remanufacturing technology portfolio selection. J. Clean. Prod. 19(17–18), 1939–1945 (2011) 10. Plappert, S., Gembarski, P.C., Lachmayer, R.: Product configuration with Bayesian network. In: Proceedings of the 9thInternational Conference on Mass Customization and Personalization –Community of Europe (MCP-CE 2020), pp. 184–190 (2020) 11. Agnar, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994) 12. Gembarski, P.C., Bibani, M., Lachmayer, R.: Design catalogues: knowledge repositories for knowledge-based-engineering applications. In: Proceedings of International Design Conference, DESIGN DS, vol. 84, pp. 2007–2016 (2016) 13. Bibani, M., Gembarski, P.C., Lachmayer, R.: Ein wissensbasiertes System zur Konstruktion von Staubabscheidern. In: DFX 2017: Proceedings of the 28th Symposium Design for X, Bamburg, Germany, 4–5 October 2017, pp. 165–176 (2017) 14. Roth, K.: Konstruieren mit Konstruktionskatalogen. Springer, Heidelberg (2001). https://doi.org/10.1007/978-3-642-17467-4 15. VDI Society Materials Engineering: Vdi 3822:2011-11 failure analysis - fundamentals and performance of failure analysis 16. Zghair, Y.: Rapid Repair hochwertiger Investitionsg¨ uter. In: Lachmayer, R., Lippert, R.B., Fahlbusch, T. (eds.) 3D-Druck beleuchtet, pp. 57–69. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49056-3 6

Additive Manufacturing of TPU Pneu-Nets as Soft Robotic Actuators Peter Frohn-Sörensen1(B) , Florian Schreiber2 , Martin Manns2 Jonas Knoche1 , and Bernd Engel1

,

1 Chair of Forming Technology, University of Siegen, 57076 Siegen, Germany

[email protected] 2 Chair of Production Automation and Assembly, University of Siegen, 57076 Siegen, Germany

Abstract. Soft robots provide the opportunity to handle a diverse range of products, contributing to mass customization in modern production environments. Both, their manufacturing and behavioral modelling are crucial challenges, due to their unique, bio-inspired design, as well as with respect to the elastic materials, which are applied. Commonly, the actuators and grippers of these robots are manufactured in a traditional casting approach, which is both elaborate and requires molding clearances. In this paper, the additive manufacture (AM) of thermoplastic polyurethane (TPU) is investigated in the context of its application as soft robotic components. Compared to other elastic AM materials, TPU reveals superior mechanical properties with regard to strength and strain. By selective laser sintering, pneumatic bending actuators (pneu-nets) are 3D printed as soft robotic case study and experimentally evaluated with respect to deflection over internal pressure. Leakage due to air tightness is observed as a function of minimum wall thickness of the actuators. In an automated production environment, soft robotics can complement the transformation of rigid production systems towards agile and smart manufacturing. Keywords: Additive manufacturing · Soft robotics · SLS · Thermoplastic polyurethane

1 Introduction Robots are part of many modern production facilities. While robot arms can be considered as standardized flexible production equipment, robot grippers are usually task specifically built components. Therefore, grippers may vary in design, function and cost with respect to their designated tasks [1]. When the product family is extended or changed, grippers need to be replaced, exchanged or reconfigured causing undesired investments in exchange systems or new grippers. Such changes are expected to be more and more frequent because of an increasing demand in mass customization leading to an expansion of product families [2]. One approach to address this problem is the introduction of soft grippers and actuators, which provide inherent variant flexibility by their compliant behavior [3, 4]. © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 269–276, 2022. https://doi.org/10.1007/978-3-030-90700-6_30

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During recent years the soft robotic community has presented various soft gripper and actuator designs [5]. Most soft grippers and actuators consist of flexible, elastic materials such as elastomers. Due to their intrinsic flexibility, soft grippers and their actuators are able to adapt to different geometries and can handle products with unknown geometry. Soft actuators can be operated using different energy sources such as pneumatics [6], cables [7], shape memory alloys [8]. Usually, soft robotic actuators and grippers are cast using molds. In order to adapt the soft robotics regarding parameters such as wall thickness, length or chambers, the molds need to be redesigned. Moreover, the process of molding requires curing time for the material components to crosslink as well as the subsequent unmolding and assembly/joining steps. Additive manufacturing methods can circumvent these aspects by their intrinsic ability to create shapes, which are unfeasible by common methods such as undercuts, in a self-contained continuous fabrication process. For different additive manufacturing processes, flexible materials have been recently developed. For a soft robotic application, the bending actuator design by Polygerinos et al. [9] has been reproduced in [10] by additive manufacturing using a silicon-based material. These actuators have been printed using photopolymer phase change ink jet technology. It has been found that the deformation potential is considerably lower than that of cast actuators and the removal of the necessary support material was prone for damaging the structure as well as too elaborate for any practical application. The findings have led to the question if other additive manufacturing technologies are better suited for soft robotic actuators. This study investigates using an additive manufacturing method based on sintering thermoplastic powder particles, thereby avoiding any need of support structures. The general manufacturing procedure as well as the resulting structural integrity of the soft robotic bending actuator and its deformation potential are key aspects of the paper. As this paper investigates general manufacturing procedure and the influence of the wall thickness on the actuators’ performance, only one specimen per design is tested.

2 Pneumatic Networks Soft, elastic actuators that comprise small air-channels and chambers which expand when pressurized are called pneumatic networks (pneu-nets). Pneu-nets are known to be lightweight and inexpensive, but they provide a nonlinear actuation [11]. When pressurized, the chambers, which are most compliant or have the lowest stiffness, expand. Considering a homogeneous elastomer, the expansion areas can be influenced by adapting wall thickness [12]. One common type of pneu-net is the so called fast pneu-net, which is able to swiftly react to quick changes in chamber pressure, see Fig. 1. This kind of pneu-net consists of multiple air-chambers which are linked by a central air-channel and an inextensible bottom layer [9]. With an increasing air pressure inside the pneu-net, the chambers slowly expand, while the bottom layer limits strain at the base. As a result, the pneu-net expands at the upper part, which caused a bending motion. Following this concept, a range of pneu-net variations with additional features such as reinforcement by fiber glass to influence the actuators performance have been described [13, 14].

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flange pressure chambers

air inlet central tunnel base

Fig. 1. Longitudinal cut view of the herein used pneu-nets. Square shaped hollow chambers are linked by a central tunnel and inflated by pressure to inflate and thereby promote mutual repulsion and bending of the actuator.

3 Additive Manufacturing Method This work considers the pneu-net design of Polygerinos et al. [9]. Selective laser sintering (SLS) is employed instead of conventional silicone casting. In order to investigate feasibility of manufacturing pneumatic actuators from thermoplastic polyurethane (TPU) by SLS, the pneu-nets are tested regarding their displacement when pressurized. Different wall thicknesses are tested in order to improve the actuators bending behavior. For SLS, an EOS P-series 3D printer for polymer materials is employed [15]. A focused laser spot is utilized as power source to selectively melt a layer of polymer particles within a bed, which represents the sliced cross section of the AM method. The advantages of the SLS process compared to concurring AM processes such as fused deposition modelling (FDM) or a stereolithography apparatus (SLA) are the abundance of support structures and therefore a high achievable part complexity. In addition, no binders or agents are required besides the material particle powder. On the downside, a coarser, rough surface quality is obtained from SLS as well as possible formation of pores in the material [16].

4 Material Granular TPU powder with the brand name Luvosint is introduced to the SLS process to manufacture material testing specimens and pneumatic actuators as application. The elastic mechanical parameters of the material are given in Table 1. As pneu-nets locally need to perform large deformations, the TPU material shows promising properties for the soft robotic application. Photopolymer phase change ink jetting has revealed problems when removing support material from the pneu-nets’ cavities in an elaborate procedure. When laser sintering, no additional support is required as the powder self supports the sintered structures.

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Table 1. Mechanical parameters of the SLS TPU material Luvosint as provided by the supplier. Mechanical parameter

Symbol

Luvosint

Unit

Compressive elastic modulus

Ec

20

MPa

Tensile elastic modulus

Et

27

MPa

Ultimate tensile elongation

ef

5.2

–

Ultimate tensile stress

Rm

20

MPa

Poisson ratio

μ

0.45

–

Density

ρ

1200

kg/m3

5 Actuator Geometry Pneu-Net Design. A pneumatic network with a total length of 160 mm according to the design of Polygerinos et. al [9] is adopted for the present study. 14 interlinked pressure chambers with a square section of 20 × 20 mm are arranged on a base layer in a parametrical CAD model illustrated in Fig. 1. The CAD model is parameterized, in particular with respect to wall thickness t 0 . For the present investigations, a number of actuator specimens feature a wall thickness variation, as derived from the model. Each variant, i.e. t 0 = {1.0, 1.2, 1.4, 1.6} mm, is tessellated in order to obtain the input files for additive manufacture and, thereby, practical experiments. Experimental Setup and Evaluation. For the practical experiments, the additively manufactured actuators are adapted to a pneumatic hub (Festo Motion Terminal), cf. Fig. 2.

Fig. 2. Webcam image of the laboratory test assembly used for digital image recognition. Circular recognition marks at the base and tip of the actuator are detected to determine deflection as a function of internal pressure and wall thickness (here p = 400 kPa, t 0 = 1.0 mm).

Air pressure is applied to the pneu-nets via the central air inlet. In each cycle, pressure is increased successively by 20 kPa steps and held for five seconds in order to allow for time dependent relaxation effects of the material. After the actuators motion has

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converged, an image is captured before raising pressure by the following step. The procedure is raised until one of the following criteria is achieved: i) the actuator entangles itself fully, ii) leakage becomes considerably large so that no more deflection is achieved by pressure raise, iii) maximum pressure of 500 kPa. In addition, the actuators are tested for leakage in a water basin under constant pressurization of 30 kPa.

6 Results This paper investigates the feasibly to additively manufacture pneumatic actuators from TPU by the SLS technology. Resulting from the coordinates of the tips of the pneu-nets set in relation to its origin, a characteristic curve of the curling actuator is obtained, which resembles the actuators from Polygerinos et al. [9]. Figure 3 illustrates the deflection curves of the pneu-nets. x - coordinate [mm] -50

0

50

100

150

z - coordinate [mm]

0 TPU 1.4 mm -50

TPU 1.6 mm TPU 1.2 mm TPU 1.0 mm

-100

Manns et al. Polygerinos et al.

-150

Fig. 3. Comparison of the characteristic displacement curves of the actuators obtained from the experiments. The tip of an actuator is traced in the x-z plane over internal pressurization of the pneu-nets. Despite changing thickness, the positions show very similar curves.

Despite the different wall-thicknesses, the actuators show similar curves starting at the top-right corner of the chart. The pneu-net with a wall-thickness of 1.0 mm achieves the smallest deflection. Due to considerable leakage, the test of this actuator is aborted at 300 kPa. The test of the 1.2 mm pneu-net is also aborted due to leakage at a pressure of 400 kPa. Both, the 1.4 mm and 1.6 mm pneu-net, are tested to a maximum pressure of 500 kPa. As seen in Fig. 3, the deflection of the 1.4 mm actuator is considerable larger than that of the 1.6 mm pneu-net due to stiffness resulting from wall thickness. In order to compare the results with previous works, the deflection curves obtained by Polygerinos et al. [9] and Manns et al. [10] are added to Fig. 3. In order to illustrate the influence of pressure, the angle of the curling actuator is determined from its tip position related to the origin at the edge of its mount (see Fig. 2). The results are illustrated in Fig. 4. The pneu-nets show a slightly different influence of gravity, which can be derived from the curves’ starting points. When pressurized, the different slopes of the obtained graphs reciprocally indicate the stiffness of the actuators.

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curling angle [deg]

180 TPU 1.4 mm

150

TPU 1.6 mm

120

TPU 1.2 mm

90 TPU 1.0 mm 60 Manns et al. 30

Polygerinos et al. 0 0

100

200

300

400

500

pressure [kPa] Fig. 4. Curling angle of the tested pneu-nets depending on pressurization.

Due to the very high leakage losses, the 1.0 and 1.2 mm TPU actuators exhibit only few deflections over pressure despite their delicate structure, which is why the tests are aborted before reaching full pressure of 500 kPa. Ultimately, the 1.0 mm actuator achieved a maximal deflection of 32° at 300 kPa, while the 1.2 mm reached 54° at 400 kPa. The 1.4 mm TPU actuator allows a higher deflection over pressure (at maximum pressure of 500 kPa a curling angle of 93°), than the 1.6 mm where the influence of structural stiffness becomes more relevant, than leakage losses. Ultimately, the 1.6 mm pneu-net achieved a deflection of 68° at 500 kPa. Additional tests under 30 kPa internal pressurization are conducted with each actuator in a water basin because leakage is identified as a major issue during the deflection tests. The pneu-nets are subjectively examined by the amount of bubbles evacuating from the actuators, see Fig. 5. A large leaking rate is evidently discernible from the thin-walled actuators. The actuator with t 0 = 1.6 mm indicates the transition towards air tight structures from TPU by SLS.

a)

t0 = 1.0 mm

b) 1.2 mm

c)

1.4 mm

d) 1.6 mm

Fig. 5. Leakage tests at 30 kPa of the additively manufactured, pneumatic actuators in a water bath. With increasing wall thickness up to 1.6 mm, a better gas tightness is observed, i.e. less bubbles evacuate from the structure.

7 Discussion Compared to results from literature on conventionally cast or additively manufactured silicone components, the TPU actuators were inflated by considerably larger internal

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pressure, which still ranges within common industrial supplies. The manufacturing challenges associated to a multistep assembly (conventionally cast actuators) or the removal of support structures (AM polyjetted structures) are avoided by SLS. The materials in literature are roughly twenty times softer regarding their elastic moduli, which leads to a larger influence of gravity and, consequently, to higher curling angles in the beginning compared to TPU. Therefore, TPU soft robotic components might be suitable for terrestrial use cases at ambient air conditions. However, long term tests must still be conducted to allow judging industrial applicability. A large number of actuating cycles may lead to the initiation of material failure while the fabrication of identical components should be considered to judge on the repeatability of the presented deformation properties. Below a wall thickness of 1.6 mm, increasing leakages are observed from the SLS TPU pneunets. If leakage is a problem for control or long-term integrity of the actuators stays an open research question. In addition, manufacturing of the herein objected structures was contracted externally. The precise influence of raw material properties and manufacturing parameters on the elastic behavior and integrity of the pneu-nets may be related to these aspects.

8 Conclusion and Outlook In the present paper, SLS additive manufacture of pneumatic actuators (pneu-nets) from thermoplastic polyurethane (TPU) is investigated. Because preceding research on fabrication of pneu-nets has revealed disadvantages, i.e. manual joining of component parts (casting, e.g. [9]) or in relation to support material and limited deformation potential (photopolymer phase change ink jetting [10]), new manufacturing approaches are in need. The results of this work indicate suitability of SLS in combination with TPU for pneumatically excited hollow structures, however leakages are found for wall thickness below 1.6 mm. The drawbacks of the referenced fabrication methods are avoided, as SLS requires no additional support, the TPU material allows considerably large deformation – even if additively manufactured – and assembly steps are avoided. While leaking pneu-nets can be used in practice, motion controllability in an industrial environment is an open research question. Likewise, if leakage can be reduced by adjusting the process parameters or post treating of the actuators after manufacture is planned for future research on SLS. The evaluation of long-term reliability is a crucial aspect for the introduction of soft robotics in production environments with respect to a large number of actuation cycles [17], which needs to be addressed in the case of SLS TPU for reliable actuators and grippers of soft robots.

References 1. Wolniakowski, A., et al.: Task and context sensitive gripper design learning using dynamic grasp simulation. J. Intell. Rob. Syst. 87(1), 15–42 (2017). https://doi.org/10.1007/s10846017-0492-y

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2. Publications Office of the European Union: Europe 2020 flagship initiative Innovation Union: SEC(2010) 1161, communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. http://op.europa.eu/en/publication-detail/-/publication/440f4722-e9ad-43b2892a-aba42909c54a/language-en. Accessed 17 May 2021 3. Onal, C.D., Rus, D.: A modular approach to soft robots. In: 2012 4th IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 1038– 1045 (2012). https://doi.org/10.1109/BioRob.2012.6290290 4. Tawk, C., Alici, G.: A review of 3D-printable soft pneumatic actuators and sensors: research challenges and opportunities. Adv. Intell. Syst. 3, 2000223 (2021) 5. Long, Z., Jiang, Q., Shuai, T., Wen, F., Liang, C.: A systematic review and meta-analysis of robotic gripper. IOP Conf. Ser. Mater. Sci. Eng. 782, 042055 (2020). https://doi.org/10.1088/ 1757-899X/782/4/042055 6. Schreiber, F., Manns, M., Morales, J.: Design of an additively manufactured soft ring-gripper. Procedia Manuf. 28, 142–147 (2019). https://doi.org/10.1016/j.promfg.2018.12.023 7. Wang, H., Chen, W., Yu, X., Deng, T., Wang, X., Pfeifer, R.: Visual servo control of cabledriven soft robotic manipulator. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 57–62 (2013). https://doi.org/10.1109/IROS.2013.6696332 8. Cianchetti, M.: Fundamentals on the use of shape memory alloys in soft robotics. In: Interdisciplinary Mechatronics, pp. 227–254. Wiley (2013). https://doi.org/10.1002/978111857 7516.ch10 9. Polygerinos, P., et al.: Towards a soft pneumatic glove for hand rehabilitation. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1512–1517 (2013). https://doi.org/10.1109/IROS.2013.6696549 10. Manns, M., Morales, J., Frohn, P.: Additive manufacturing of silicon based PneuNets as soft robotic actuators. Procedia CIRP 7, 328–333 (2018) 11. Mosadegh, B., et al.: Pneumatic networks for soft robotics that actuate rapidly. Adv. Func. Mater. 24, 2163–2170 (2014) 12. Ilievski, F., Mazzeo, A.D., Shepherd, R.F., Chen, X., Whitesides, G.M.: Soft robotics for chemists. Angew. Chem. 123, 1930–1935 (2011). https://doi.org/10.1002/ange.201006464 13. Deimel, R., Brock, O.: A compliant hand based on a novel pneumatic actuator. In: 2013 IEEE International Conference on Robotics and Automation, pp. 2047–2053 (2013). https://doi. org/10.1109/ICRA.2013.6630851 14. Galloway, K.C., Polygerinos, P., Walsh, C.J., Wood, R.J.: Mechanically programmable bend radius for fiber-reinforced soft actuators. In: 2013 16th International Conference on Advanced Robotics (ICAR), pp. 1–6 (2013). https://doi.org/10.1109/ICAR.2013.6766586 15. SLS 3D Printers for various Materials | EOS GmbH. https://www.eos.info/en/additive-man ufacturing/3d-printing-plastic/eos-polymer-systems 16. Schmid, M., Amado, A., Wegener, K.: Materials perspective of polymers for additive manufacturing with selective laser sintering. J. Mater. Res. 29(17), 1824–1832 (2014). https://doi. org/10.1557/jmr.2014.138 17. Miriyev, A.: A focus on soft actuation. Actuators 8, 74 (2019). https://doi.org/10.3390/act804 0074

Applicability of Snap Joint Design Guidelines for Additive Manufacturing Florian Schreiber1(B)

, Thomas Lippok1 , Jan Uwe Bätzel2 , and Martin Manns1

1 FAMS – Chair for Production Automation and Assembly, PROTECH – Institute of Production

Technology, University of Siegen, 57076 Siegen, Germany [email protected] 2 57076 Siegen, Germany https://protech.mb.uni-siegen.de/fams

Abstract. Snap joints provide the opportunity of joining two components in a very simple, economical and rapid way. Therefore, snap joints are a feasible option for assembly of prototypes. Snap joint design guidelines currently focus on injection-molded parts, which may not be suitable for rapid prototyping. In contrast to injection molding, additive manufacturing provides a higher degree of design freedom. Applicability of design guidelines for injection-molded snap joints to additive manufacturing technologies has not been comprehensively investigated yet. In this work, we present a study comparing mechanical properties of snap joint specimen that are manufactured from three different materials with the two manufacturing processes FDM and SLS. Results show significant impact of both material and manufacturing technology. The presented results may lead to improved design guidelines for additively manufactured snap joints. Keywords: Additive manufacturing · Snap joints · Snap fit · Rapid prototyping

1 Introduction In recent years, product lifecycles have shortened, e.g. in automotive industry [1]. This has introduced increased demand for rapid manufacturing of prototype parts, which today are ubiquitous in early planning phases [2]. One established way for rapid prototyping is additive manufacturing. Additive manufacturing can be used for both, concept modeling and functional prototypes [3]. While geometric accuracy of additively manufactured parts has significantly increased, snap joints in these parts are most often not functional, i.e. they break when snapping. If the prototype consists of two or more parts, a reliable assembly method is desirable. As the assembly of snap joints is safe, fast and economical, snap joints are a good solution for the assembly of prototypes [4].

2 Snap Joints Snap joints can be found in a large variety of applications. The basic concept of snap joints is the displacement of a flexible feature during the assembly or disassembly. During © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 277–284, 2022. https://doi.org/10.1007/978-3-030-90700-6_31

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the joining process, the two snap joint parts are pushed together, and the flexible feature locks their position once fully inserted. They can be designed for permanent or separable joints depending on the product. There are several kinds of snap joints such as torsion or annular snap joints, however, the most common form is a cantilever snap joint [5]. Currently, snap joint design guidelines, e.g. [6], focus on injection-molded parts. For these snap joints, the joining force can be calculated by Eq. 1 retrieved from [7]. F =f ∗

μ + tan(α) E ∗ b ∗ h3 ∗ 4 ∗ l3 1 − μ ∗ tan(α)

(1)

While friction coefficient μ and secant modulus E are depending on the used material, the geometrical parameters deflection f, width b, height h, length l and cantilever angle α can be influenced by the design of the cantilevers. Considering the tool costs, the production costs of injection molded parts for batchsize 1 are relatively high and the design freedom is limited [8]. In contrast, additive manufacturing is suitable for batch-size 1 production and provides a high degree of design freedom. The applicability of design guidelines for injection-molded snap joints to additive manufacturing technologies has not been comprehensively investigated yet. Klahn et al. applied general additive manufacturing guidelines to the design process of 3d-printed snap joints [9]. In a use case, they show how the design freedom in additive manufacturing can be used to integrate snap joints in new ways. A methodology for the design process of additively manufactured snap joints has been provided in [10]. The design process suggested by [10] has been investigated in [11]. They found, that the calculated forces did not correlate with the forces derived from the simulation. In [12] the joining forces registered during an experiment are compared to an FEM simulation. The results show that the experimental tests correlate with the simulations.

3 Objectives In this work, applicability of design guidelines for injection molded snap joints to additive manufacturing is investigated. Therefore, sets of specimens are additively manufactured from common materials using different printing technologies. The design of the additively manufactured specimens is based on the guidelines for injection molded snap joints [6, 7]. In order to investigate the assembly of the snap joints produced by different printers, the joining force is investigated. Additionally, the measured forces are compared to theoretically calculated joining force. In order to verify the results, an FEM simulation for each snap joint is conducted.

4 Methodology 4.1 Snap Joint Geometry The snap joints consist of a T-shaped male part and a female part with elastic cantilevers. In order to compensate tolerances, which may lead to errors during the assembly, this symmetric design with two cantilevers is chosen. The basic parameters of the snap joints

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are presented in Fig. 1. The present snap-hook joints were designed in accordance with Bonenberger’s design guidelines [6]. Thus, the ratio of hook length l to hook height h is greater than 5 to ensure sufficient flexibility of the cantilevers. In order to avoid stress peaks, the connection between the main body of the female part and the cantilevers has been reinforced by radii. The radius R is half the cantilever height h. The width b of the cantilevers is half the length l in order to ensure that the cantilevers behaves like a bending beam and not like a plate. The cantilever angle α was kept small at 15° to favor the frictional behavior of the joining partners. The maximal deflection of the cantilevers f can be calculated by the Eq. 2: f = H − amin

Parameter

(2)

Height_female

Symbol Value l 20.0 h 1.52

mm

Width

b

10.0

mm

Radius

R

0,76

mm

Cantilever angle

α

15

°

Distance

amin

12

mm

Height_male

H

16.4

mm

Length

Unit mm

Fig. 1. Design of the snap joints with the applied set of parameters.

4.2 Material For the tests, snap joints from 3 different materials have been manufactured using 2 different printing technologies. Two sets of specimens consisting of PLA and PETG have been manufactured by fused deposition modeling (FDM) using a FLSUN Cube 3D MK7 with a 0.4 mm nozzle. A third set of specimens consisting of FLGPGR02 has been produced by a FormLabs Form3 printer using stereolithography. The parts have been printed in a horizontal position. Thus, the cantilevers are perpendicular to the printing direction as suggested by [9]. For each of the three sets, nine specimens have been manufactured. 4.3 Test Setup In the experiment, each snap joint is assembled ten times successively. While the female part is exchanged after every test, the male part remains the same. The test setup is

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illustrated in Fig. 2. In order to provide a comparable joining process, a Universal Robot UR5 is used. For joining the snap joints, the male part is mounted on the robot while the female part is fixed to the ground. The robot moves the male part with a velocity of 40 mm/s from the starting point vertically down towards the joined position. After that, the robot moves horizontal to disconnect the male part. After disconnecting the two parts, the cycle starts again and is repeated ten times. The force-torque sensor Gamma from ATI-industrial automation with a resolution of 0.025 N is mounted at the robot’s tool center point to measure the forces applied to the male part during the joining process. The force-torque sensor registers force and torque along x-, y- and z-axes. For this experiment only the force along the z-axis, i.e. in joining direction, is analyzed.

Fig. 2. Test setup with double cantilever snap joints.

4.4 Joining Force Calculation The measured joining forces are compared with the theoretically calculated forces using Eq. 1. For each snap joint, the geometric parameters h, l, b, H, amin and α are measured by a digital caliper from Orthland (± 0.01 mm) and a digital protactor from Fixkit (± 0.3°). Parameter f is calculated using Eq. 2. Table 1 presents the parameters used for the calculation of the joining force. As the material data sheets do not provided friction coefficients, an experiment suggested by [13] to determine the friction coefficient is conducted. For this purpose, the female part is placed with one cantilever on an angle-prism (±0.14°). On top of the other cantilever, the male part is positioned. The angle of the angle-prism is slowly increased until the male part slides off the cantilever. The friction coefficient can be calculated by the tangent of the measured angle. For the secant modulus, a literature analysis is conducted. As found by [12], mechanical properties for additively manufactured materials such as secant moduli are rarely investigated. For the materials tested in this work, secant moduli have not been determined yet. Alternatively, the secant moduli are estimated from tensile tests. Therefore, the elongation at yield εm (elongation value at tensile strength) are determined. 80% of

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this value is determined as the allowable strain εzul for one-time joining according to [7]. The stress value σzul at εzul was derived from the σ-ε diagram. The ratio σzul /εzul corresponds to the secant modulus. For FLGPGR02, the secant modulus is derived from tensile tests presented in [14]. For PETG and PLA, tensile tests according to ISO 527–1 with specimens shaped as type 1A in ISO 527–2 are conducted. Table 1. Parameters for the calculation of joining forces. Symbol

Unit

PETG

PLA

FLGPGR02

l

mm

19.79

19.90

20.02

h

mm

1.83

1.95

1.54

b

mm

10.14

10.38

9.77

f

mm

4.30

4.46

4.34

α

°

15.4

15.7

15.3

μ

−

0.32

0.34

0.37

E

MPa

1741

2877

1495

4.5 Joining Force Simulation An FEM using the parameters presented in Table 1 is conducted with SolidWorks 2020 to verify the secant moduli. First, the given deflection of the snap hooks is simulated. With a new simulation, the force acting perpendicular to the joining surface is increased iteratively, so that the same strain occurred in both simulations. The bearing reaction acting in the joining direction at the female part is determined as the joining force.

5 Results To investigate the joining force during each assembly processes, a violin plot for each material and assembly iteration is created which can be seen in Figs. 3 and 4. As each violin represents one joining iteration of one material, every violin contains nine values. The snap joints manufactured by FDM show a constant performance regarding the joining force. Forces from 7 N to 9 N have been measured for PLA, while 3.5 N to 5 N have been registered for PETG. FLGPGR02 achieved joining forces between 3.5 N and 5.9 N and showed a slight decrease of the deviation during the assembly iterations. To examine the applicability of design guidelines for injection molded snap joints, the test results are compared with theoretically calculated joining forces. For all tested materials, the median remains constant throughout the assembly iterations. Therefore, the median of all iterations for each material is compared with the theoretical joining force. The theoretical joining forces are calculated by using Eq. 1 and the parameters presented in Table 1. The joining forces calculated for PETG (9.8 N) and PLA (21.5 N) are more than two times higher than the measured forces during the tests (PETG 4.0 N,

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PLA 8.1 N). In contrast, the forces derived from the simulations are 20% lower (PETG 3.2 N, PLA 6.3 N). For FLGPGR02 the simulated force of 1.5 N is more than 3 times lower than the measured (4.6 N) and calculated (5.2 N) forces.

Fig. 3. Joining force of snap joints consisting of PLA (orange) and PETG (green) depending on the assembly iteration (Color figure online).

Joining force [N]

Fig. 4. Joining force of snap joints consisting of FLGPGR02 depending on the assembly iteration.

22 20 18 16 14 12 10 8 6 4 2 0

F measured F calculated CAD PETG

PLA

FLGPGR02

Fig. 5. Comparison of measured (red), calculated (blue) and simulated (green) joining forces (Color figure online)

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6 Discussion The comparison of measured, calculated and simulated joining forces for the materials manufactured by FDM shows a similar behavior. The simulated forces are 20% lower than forces received from the tests as can be seen in Fig. 5. One reason for the difference is friction. As friction forces have been neglected in the simulation, the actual forces are expected to be higher than in the simulation. To estimate the impact of the error caused by the neglection, approximate friction forces can be derived from the recorded test data. After the cantilevers reach their maximum deflection, the male part slides along the deflected cantilevers. The registered forces during this phase are caused by friction. For PETG average friction forces of 1 N are registered, while the friction forces for PLA are 2.1 N. Hence, the measured and simulated forces with consideration of the neglected friction forces can be considered as almost the equal (less than 5% difference). This shows that the secant moduli derived from the tensile tests correspond to the behavior of the materials. However, the calculated forces are higher than the measured forces. As the secant moduli are verified by the conducted FE simulations, the differences might have been caused by wrong friction coefficients. In this experiment, the calculated joining force is more than two times higher than the actual forces. Therefore, the differences can not only be caused by wrong friction coefficients. Thus, we conclude Eq. 1 is not applicable for calculating the joining force for the tested PLA and PETG. For FLGPGR02, the actual joining force is three times higher than the force derived from the simulation. This difference can not only be explained by the neglection of friction force but most likely indicates a wrong secant modulus. The simulation shows that the actual strain is 5.5 times lower than εzul . Therefore, the calculated secant modulus is lower than the actual one, which leads to lower joining force in the simulation. As the secant modulus is not appropriate for the tested FLGPGR02 snap joints, the calculated joining force is considered to be wrong. Hence, results regarding applicability of the design guidelines for additively manufactured FLGPGR02 are unconclusive.

7 Conclusions and Outlook In this work, the applicability of design guidelines for injection molded snap joints to additive manufacturing has been investigated. Three sets of snap joints additively manufactured from different materials using two printing technologies have been tested regarding their joining force during assembly. For the snap joints consisting of PLA and PETG, almost equal joining forces are registered in the test and the simulation. As the calculated joining force according to the design guidelines for injection molded snap joints is more than two times higher, the considered guidelines are not applicable for snap joints consisting of additively manufactured PLA and PETG. Future work should focus on the introduction for additively manufactured snap joints to shorten prototyping processes. Moreover, further research on this topic could help to bridge the gap between additive manufacturing (prototyping) and injection molding (large scale manufacturing) to accelerate the ramp up phase. For FLGPGR02 the secant modulus is not representing the snap joints’ behavior. In future research, different analytic approaches for the calculation of different secant moduli to describe the performance FLGPGR02 should be investigated.

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Acknowledgment. The authors would like to acknowledge the financial support by the European Regional Development Fund (EFRE) within the project SMAPS (grant number: 0200545). Additionally, the authors would like to thank the Chair of Forming Technology UTS of the university of Siegen and the FabLab Siegen for their contribution to this work.

References 1. Moreno-Moya, M., Munuera-Aleman, J.-L.: The differential effect of development speed and launching speed on new product performance: an analysis in SMEs. J. Small Bus. Manage. 54(2), 750–770 (2016) 2. Sharma, V., Singh, S.: Rapid prototyping: process advantage, comparison and application. Int. J. Comput. Intell. Res. 12(1), 55–61 (2016) 3. Wong, K.V., Hernandez, A.: A review of additive manufacturing. Int. Sch. Res. Not. 12 (2012) 4. Meitinger, T., Pfeiffer, F.: Modeling and Simulation if the assembly of snap joints. In: IEEE International Symosium on Assembly and Task Planning 1995. Proceedings of the IEEE International Symosium on Assembly and Task Planning, pp. 15–20 (1995) 5. Messler, R.W., Genc, S., Gabriele, G.A.: Integral attachment using snap-fit features: a key to assembly automation. Part 1 - introduction to integral attachment using snap-fit features. Assembly Autom. 17(2), 143–155 (1997) 6. Boneberger, P.R.: The First Snap-Fit HandBook: Creating Attachment for Plastic Parts. Carl Hanser Verlag, Munich (2000) 7. Eyerer, P., Hirt, T., Elsner, P.: Polymer Engineering. Springer, Heidelberg (2008) 8. Franchetti, M., Kress, C.: A economic analysis comparing the cost feasibility of replacing injection molding processes with emerging additive manufacturing techniques. Int. J. Adv. Manuf. Technol. 88, 2573–2579 (2017) 9. Klahn, C., Singer, D., Meboldt, M.: Design Guidelines for Additive Manufactured Snap-Fit Joints. 26th CIRP Design 2016. Procedia CIRP 50, 264–269 (2016) 10. Ramirez, E.A., Caicedo, F., Hurel, J., Helguero, C.G., Amaya, J.L.: Methodology for design process of a snap-fit joint made by additive manufacturing. In: 12th Conference in Intelligent Computation in Manufacturing Engineering 2018, Procedia CIRP vol. 79, pp. 113–118 (2019) 11. Amaya, J.L., Ramirez, E.A., Galarza F.M., Hurel, J.: Detailed design process and assembly considerations for snap-fit joints using additive manufacturing. 29th CIRP design 2019, Procedia CIRP 84, 680–687 (2019) 12. Rizescu, C.I., Rizescu, D.: Mechanical behaviour analysis of snap joints for haptic evaluation. In: Gheorghe, G.I. (ed.) ICOMECYME 2020. LNNS, vol. 143, pp. 12–19. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-53973-3_2 13. Roth, S., Stahl, A.: Mechanik und Wärmelehre. Springer, Heidelberg (2016). https://doi.org/ 10.1007/978-3-662-45304-9_18 14. Cosmi, F., Maso, A.: A mechanical characterization of SLA 3D-printed specimens for lowbudget applications. Mater. Today: Proc. 32(2), 194–201 (2020)

A Reduced Gaussian Process Heat Emulator for Laser Powder Bed Fusion Xiaohan Li(B) and Nick Polydorides School of Engineering, University of Edinburgh, Edinburgh, UK [email protected]

Abstract. Laser Powder Bed Fusion (LPBF) is a promising additive manufacturing technique used for realizing complex and bespoke designed metal parts. Despite its good performance, its quality assurance is still hampered by the absence of in-process optimization and control. In this sense, real-time thermal analysis can facilitate fault predictions and rectifications. High-fidelity three-dimensional thermal modelling with the Finite Element Method (FEM) is generally time-consuming since the heat transfer equation is nonlinear and high-dimensional. The challenge is thus to compute fast, reliable and accurate thermal predictions that capture the nonlinearity triggered by the phase changes of the part during printing. Gaussian Process (GP) with Isomap dimension reduction is investigated to find and predict the low-dimensional representations of the high-dimensional thermal profiles in FEM without intricate processing. Based on these representations, the high-dimensional predictions are then approximated using localized radial basis functions. To validate the performance of this reduced GP heat emulator, a heat simulation during fabricating an Aluminum object is performed to compare FEM-based temperature calculations against reduced GP emulations. Retaining 0.06% of the original model dimension the execution time per temperature profile is 0.70s on average achieving a 95.07% reduction, while maintaining at least 85% accuracy (with respect to the FEM simulation) for 96.80% of the thermal profile queries and at least 80% for 89.38% of the thermal history queries. With this encouraging performance, the reduced GP heat emulator can be a step forward in online defect prediction, process optimization and closed-loop control in LPBF. Keywords: Finite element method · Gaussian process · Laser powder bed fusion · Nonlinear dimension reduction · Transient thermal model

1

Introduction

Additive manufacturing (AM) has driven a revolution of the manufacturing industries. Though it has promising future, the quality of printed parts is not assured and online process dynamic analysis is imperative. Real-time thermal history simulation is an essential part of process dynamics analysis for a thermaldriven AM like LPBF, since it describes the transient temperature gradient in c Springer Nature Switzerland AG 2022  A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 285–293, 2022. https://doi.org/10.1007/978-3-030-90700-6_32

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localised melting and cooling [1] and is used in the predictions of residual stress, microstructure, defect and mechanical property [2,3]. There are an extensive amount of literatures on thermal models of AM validated by experiments. A nonlinear temperature simulation of Selective Laser Melting (SLM) utilizing FEM was validated in [1] by an experiment with AlSi10Mg powder and in [4] by the AM benchmark experimental set AMB201802 with anisotropic thermal conductivity. Though FEM produces high-fidelity thermal profiles, the nonlinearity, fine spatial and temporal resolution entail a high time cost constraining in-situ operations. To reduce the degrees of freedom, adaptive meshing [5] and decomposed domain [6] were applied, both of which only slightly reduce the time cost. Alternatively, a statistical Surrogate Model (SM) is considered as a reasonable trade-off between accuracy and time cost. Trained by data generated from a high-fidelity physics-based model, it makes fast predictions for given design points [7]. Lening et al. designed a Gaussianprocess-constrained general path model in [8] describing heterogeneous discrepancies between low-fidelity and high-fidelity thermal modelling in AM. Mriganka et al. [9] predicted thermal histories almost in real-time using deep learning with different part sizes and same printing parameters based on a unique design of heat influence zone. In this paper, the reduced-dimensional GP with Isomap scheme is developed to emulate the thermal modelling of LPBF with Al materials. With a given pair of laser power and scan speed, the high-dimensional thermal profiles are constructed by their low-dimensional representations predicted by GP emulators. One prediction takes around 0.7026 s reduced from 14.2617 s on average regardless of the degrees of freedom. 95.07% of execution time is saved while maintaining acceptable accuracy (around 96.8% of predictions having relative error less than 15%). This SM provides real-time thermal histories needed for in-situ optimizations and controls.

2 2.1

Thermal Simulator and Emulator Numerical Solver with FEM

The temperature u := u(x, t) within a computational domain Ω ⊂ R3 is governed by the time-dependent, nonlinear heat transfer equation ρc

∂fp ∂u + ρL −∇·κ ¯ ∇u = 0, ∂t ∂t

in Ω × [0, tf ],

(1)

where ρ, c and κ ¯ are respectively the temperature dependent density, heat capacity and conductivity of the materials, L is the latent heat of the materials, while x := (x1 , x2 , x3 ) and t are the spatial and temporal coordinates. The thermal conductivity is modelled as a symmetric, positive definite tensor  field, and thus 3 ∂u ∂ κij (x) ∂x . The the elliptic term in (1) has the form ∇ · κ ¯ (x)∇u := i,j=1 ∂x i j 1   with the material-dependent phase change function is fp (u) = 1+exp −β(u−um )

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melting temperature um and β > 0 controlling the smoothness. The boundary of Ω is split into three disjoint regions as Γ = Γt ∪ Γs ∪ Γb spanning the top, side and bottom surfaces respectively, while n ˆ denotes the outward unit normal on Γ . We apply a time-varying sintering heat source q(x; t) moving on a fixed trajectory embedded on Γt . In effect, the imparted heat flux is expressed by the Neumann boundary condition [1]  2 3 x − xq (t)2  i . 2aP i i=1 x on Γt , exp − κ ¯ ∇u·ˆ n = q(x; t) where q(x; t) = 2 πrq rq2 (2) where a is the laser energy absorptivity, P is laser power, xq (t) is the position of beam center, and rq is the effective laser beam radius. The bottom surface of the powder bed is on a temperature controlled platform hence a Dirichlet condition u(x, ·) = ub ,

x on Γb ,

(3)

where ub is the temperature of the building platform. On the top and side surfaces, the heat loss due to convection qc and radiation qr leads to −¯ κ∇u·ˆ n = qc +qr ,

qr (x, t) = σs ε(u4 −u4a ),

x on Γs ∪Γt , (4) where h > 0 is the coefficient of heat convection, σs is the Stefan-Boltzmann constant, ε is the emissivity and ua is the ambient temperature [11,12]. The dynamical equations (1)–(4) together with the initial condition u(·, 0) = u0 and the parameter values in Table 1 admit a unique solution u(x, t) ∈ Ω × [0, tf ]. qc (x, t) = h(u−ua ),

Table 1. Model parameters used in the FEM simulation as in [1]. Symbol Definition

Value

Unit

a

Absorptivity

0.09

–

ε

Emissivity

0.04

–

rq

Laser spot radius

35

μm

ua

Ambient temperature

20

◦

C

◦

C

ub

Building platform temperature 200

h

Heat convection coefficient

10

W/(m2 K)

σs

Stefan-Boltzmann constant

5.67 × 10−8

W/(m2 K4 )

-

Powder layer dimension

1.54 × 0.7 × 0.1 mm

Following a Galerkin approach the continuous temperature field is projected on a finite dimensional space of piecewise linear functions in space and time [4]. On a discretised domain with d nodes, we arrive at the nonlinear system for the FEM coefficients ut ∈ Rd of the temperature at time steps t as A(ut )ut+1 = b(ut ),

for t = 0, 1, . . . , tf ,

(5)

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where A ∈ Rd×d and b ∈ Rd both depend non-linearly on the temperature. The nonlinear matrix equation for ut+1 can be solved iteratively via Picard iterations until the residual r(u) = A(u)u − b(u) below a very small constant [13]. 2.2

Reduced Gaussian Processes

Gaussian Process Emulators (GPEs) with isomap dimension reduction are proposed to make almost instantaneously temperature predictions irrespectively of the dimension of FEM. Before outlining our method we fix our notation. For a matrix X, Xi denotes the i-th column, XiT the corresponding row and Xij the (i, j)-th element. For a vector y, yi denotes the i-th element. In our case the triplet of beam speed v, power P , and time t are input parameters controlling the printing process. We thus consider an input matrix X whose i-th column is (vi , Pi , ti )T . Repeating the FEM simulation in (5) for n  3 sampling points yields matrix X ∈ R3×n and a respective FEM snapshots matrix F ∈ Rd×n whose i-th column is u(Xi ). We then center the elements of the rows of F to a zero mean output matrix Y ∈ Rd×n by YiT = FiT − n1 FiT 1. We assume that the elements in the vector YiT ∈ Rn satisfy a discrete Gaussian process with zero mean and positive definite covariance matrix Σ = C + σ 2 I ∈ Rn×n as YiT ≈ GP(X),

. Cˆij ≈ k(Xi , Xj ; θ) = θ0 exp(−Xi − Xj 2 /2θ1 ).

(6)

where k(x, x ; θ) is the squared exponential function (kernel) and hyperparameters θ = [θ0 , θ1 , σ 2 ]T are strictly positive obtained via maximum like˜ ∈ R3 with variance lihood estimation, we can then predict yˆi for a test input x Var(ˆ yi ) yˆi = kxT Σ −1 (YiT )T ,

Var(ˆ yi ) = θ0 − kxT Σ −1 kx ,

for i = 1, . . . , d,

(7)



ˆ . . . , k(Xn , x ˆ T [14]. where kx := k(X1 , x ˜; θ), ˜; θ) It is easy to see that applied to a high-dimensional discrete model, evaluating (7) d times becomes cumbersome. To alleviate this computational burden, we seek to construct a low-dimensional representation Z ∈ Rr×n with r d of the data matrix Y ∈ Rd×n with Isomap approach so that in predicting u(˜ x) requires r instead of d GPE evaluations. Isomap dimension reduction captures the nonlinear dependence of the data in Y on the parameters in X based on the symmetric dissimilarity matrix D ∈ Rn×n . Dij is the shortest path distance between Yi and Yj points computed via the Floyd–Warshall algorithm [15] in a graph where edges are built between neighbour points with weights equal to Euclidean distance. The points are assumed to belong in the same neighbourhood if Yi − Yj  is less than or equal to a chosen constant. Forming the n × n matrix 1 Q = − H(D ◦ D)H, 2

where

H=I−

1 T 11 n

(8)

and ◦ denotes the Hadamard product. From the eigendecomposition Q = EΛE T 1 we can compute a r-rank approximation basis with columns Zi = Λii2 Ei for i = 1, . . . , r. Effectively, for a test input x ˜, we solve

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zˆi = kxT Σ −1 (ZiT )T ,

(9)

for i = 1, . . . , r,

where kx and Σ are trained by the reduced data {(Xi , Zi )}ni=1 . To extrapolate for the high-dimensional temperature solution we compute the weights w = [w1 , . . . , wn ]T via localized radial basis functions  ˆ z − Zi 2  , wi ∝ exp − ξ2

such that

n 

wi = 1,

(10)

i=1

for a constant parameter ξ > 0, and thereafter the prediction yˆ(˜ x) = Y w.

3

Results

To demonstrate the performance of our method we consider a small scale numerical study on the thermal modelling involving a cuboid Aluminium structure consisted of four adaptive layers. Three straight laser trajectories are simulated on the top surface as shown in Fig. 1. The computational times for each layer are provided in Table 2 running Matlab R2020b on an Intel Core i7 CPU at 2.6 GHz, 16 GB RAM computer. The high-fidelity FEM-based heat simulation with fine spatio-temporal discretisation has been utilised to generate training data for the reduceddimensional GP emulation. The accuracy of this surrogate model depends on finding representative training data so as to guarantee an accurate response surface. In the absence of the information on the printing parameter distribution, we applied a uniform grid search in the admissible range of laser power: 200–400 W, scan speed: 200–400 mm/s and time: 0–6 ms to obtain 1844 pairs of uniformly (i) (i) (i) (i) sampled Xj = [Pj , vj , tj ]T and the corresponding FEM temperature snap(i)

(i)

(i)

shots Yj forming the training dataset {Xj , Yj }1844 j=1 in i-layer domain for i = 1, · · · , 4.

Fig. 1. The laser scanning pattern on the i-th layer surface, indicating the location of the reference points si1 , si2 , and si3 .

Fig. 2. The relative reduction error Q for different reduced dimensions r.

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In order to trace the lower-dimensional representation by Isomap method, we require the neighbourhood size and the reduced dimension r. While the neighbourhood size is chosen to ensure the connectivity of the constructed graph, the reduced dimension r is heuristically chosen to keep low relative reduction error T Z . The decreased trends of Q with r for all four domains are shown Q = Q−Z Q in Fig. 2 making r = 8 a reasonable choice for our setup. A set of predictions are computed for 219 testing inputs, aimed to assess the prediction speed and accuracy. Collectively, these results suggest that about 96.80%, 87.44%, 61.87% and 19.18% of predictions have respective relative errors that are no bigger than 15%, 10%, 5% and 1%. A representative subset of these result at P = 255 W and v = 215 mm/s are illustrated in Fig. 3. It compares FEM

Fig. 3. The thermal histories from FEM and SM with P = 255 W and v = 215 mm/s

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simulation and SM prediction at the 12 reference points {si1 , si2 , si3 } for i = 1, · · · , 4 as in Fig. 1 throughout the entire printing, where each layer takes about 6 ms to print. Besides these 12 reference positions, thermal history predictions at 744 other locations were tested indicating that 47.45%, 77.82% and 89.38% of those have relative errors bounded below 10%, 15% and 20% respectively. The computational times in our SM are listed in Table 2. Note that only the prediction is required online. The prediction time alone costs, on average, 95% less than the more accurate FEM simulation. Table 2. The time cost of SM and the reduction of execution time. Number of Layers

Degrees of Freedom

Data Generation(hr)

Training(min) Average Execution Time

1

14219

3.0857

34.5171

6.0242

2

25201

7.2799

30.7677

14.2124 0.6868

3

26633

9.3825

28.7027

18.3472 0.7093

4

26841

9.3135

38.1348

18.4629 0.6936

FEM(s) Prediction(s) 0.7206

The proposed approach couples data-driven machine learning and nonlinear model order reduction to expedite thermal simulation in LPBF. In principle, the proposed reduced GPEs and the FEM simulator are also applicable to other types of thermal-driven AM, since we can modify the heat source and its trajectory or indeed consider materials with different thermal properties that also undergo phase transitions without changing the main structure of our computational models and algorithms. Our numerical tests showed that 96.8% of temperature profile predictions at a given time have relative error less than 15% and 89.38% of thermal history predictions at the control points have relative errors less than 20%. Predictions at arbitrary times after the start of the printing process are computed fast at around 0.7 s. Further, as the domain is augmented layer by layer the complexity of our computations is controlled since the Isomap scheme compresses the amount of Gaussian processes required to a fairly small number (r = 8). Also, the process of pre-processing is fixed for different object geometries. With different scanning patterns, a related representative training dataset is required but no additional trajectory-based design is needed. Less cumbersome design and computation means more swift applications in thermal analysis, planning and decision making.

4

Conclusions

A time-efficient SM based on the reduced GP with Isomap method is developed to predict the thermal history of LPBF from laser power and scan speed, the

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pre-processing process of which is less geometry-dependent in dealing the redundancy and cyclic heating and cooling. The showcase of fabricating a cuboid with Al material is made to assess the performance. The SM shows encouraging performance in predicting temperature profiles by reducing 95.07% of time cost (from 14.3 s to 0.7 s) with high prediction accuracy (>85% for 96.80% of testings). Thermal history predictions for 89.38% of tested positions have relative error less than 20%. Since a 3D printer can alter laser power and scan speed during the printing process, the SM takes one step further in quality assurance by possible in-situ optimizations and controls. While this GP surrogate model allows us to bypass FEM’s long time-marching process to compute the temperature, the accuracy of SM highly depends on finding a representative training dataset, and only test inputs within the window of interest can be predicted.

References 1. Li, Y., Gu, D.: Parametric analysis of thermal behavior during selective laser melting additive manufacturing of aluminum alloy powder. Mater. Des. 63, 856–867 (2014) 2. Tong, Z., et al.: Laser additive manufacturing of FeCrCoMnNi high-entropy alloy: Effect of heat treatment on microstructure, residual stress and mechanical property. J. Alloy. Compd. 785, 1144–1159 (2019) 3. Moran, T., Warner, D., Phan, N.: Scan-by-scan part-scale thermal modelling for defect prediction in metal additive manufacturing. Add. Manuf. 37, 101667 (2020) 4. Kollmannsberger, S., Carraturo, M., Reali, A., Auricchio, F.: Accurate prediction of melt pool shapes in laser powder bed fusion by the non-linear temperature equation including phase changes. Integrating Mater. Manuf. Innovation 8(2), 167– 177 (2019). https://doi.org/10.1007/s40192-019-00132-9 5. Patil, N., Pal, D., Stucker, B., et al.: A new finite element solver using numerical eigen modes for fast simulation of additive manufacturing processes. In: Proceedings of the Solid Freeform Fabrication Symposium, pp. 12–14 (2013) 6. Moran, T., et al.: Utility of superposition-based finite element approach for partscale thermal simulation in additive manufacturing. Add. Manuf. 21, 215–219 (2018) 7. N. V. Queipo et al. “Surrogate-based analysis and optimization”. In: Progress in aerospace sciences 41.1 (2005), pp. 1–28 8. Wang, L., et al.: Meta-modeling of high-fidelity FEA simulation for efficient product and process design in additive manufacturing. Add. Manuf. 35, 101211 (2020) 9. Roy, M., Wodo, O.: Data-driven modeling of thermal history in additive manufacturing. Add. Manuf. 32, 101017 (2020) 10. Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000) 11. Sheikhi, M., Ghaini, F.M., Assadi, H.: Prediction of solidification cracking in pulsed laser welding of 2024 aluminum alloy. Acta Mater. 82, 491–502 (2015) 12. Ramanathan, K., Yen, S.: High-temperature emissivities of copper, aluminum, and silver. JOSA 67(1), 32–38 (1977) 13. Larson, M.G., Bengzon, F.: The finite element method: theory, implementation, and practice. Texts Comput. Sci. Eng. 10, 23–24 (2010)

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14. Xing, W., Shah, A.A., Nair, P.B.: Reduced dimensional Gaussian process emulators of parametrized partial differential equations based on Isomap. In: Proceedings of the Royal Society A: Mathematical, Physicaland Engineering Sciences, vol. 471 no. 2174, p. 20140697 (2015) 15. Wu, Y., Chan, K.L.: An extended Isomap algorithm for learning multi-class manifold. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 04EX826), vol. 6, pp. 3429–3433. IEEE (2004)

Smart Factories and Cyber-Physical Production Systems

Demonstrating and Evaluating the Digital Twin Based Virtual Factory for Virtual Prototyping Emre Yildiz1(B) , Charles Møller1 , and Arne Bilberg2 1 Center for Industrial Production, Aalborg University, Fibigerstraede 16,

9220 Aalborg, Denmark [email protected] 2 Mads Clausen Institute, University of Southern Denmark, 6400 Sønderborg, Denmark

Abstract. Virtual prototyping (VP) technologies promise a viable solution to handle challenges in shorter product and production lifecycles and higher complexity. In this paper, we present the demonstration, and preliminary evaluation of the previously introduced digital twin (DT) based virtual factory (VF) concept for VP in the context of new product introduction (NPI) processes. The concept is demonstrated in two cases: blade manufacturing and nacelle assembly operations Vestas Wind Systems A/S. The preliminary evaluation results show that DT based integrated VF simulations provide immersive virtual environments, which allow users to manage complex product and production systems and significant cost savings. Finally, we present and discuss the evaluation of the concept demonstration by industry experts for the proposed solution. Keywords: Virtual factory · Digital twin · Virtual prototyping · Virtual reality · Simulation and modelling · Industry 4.0

1 Introduction Forces like innovation and technology, competition, changing demands, and regulations are among the main dynamics shaping industries’ evolution [1]. The specific rhythm of the evolution in each manufacturing industry occurs in three domains: products, processes, systems [2]. Therefore, companies need to handle concurrent evolution (coevolution) of product, process, and system models to achieve the capability to adapt to their respective industrial environments [3]. However, increasing the frequency of chances results in shorter product and production lifecycles and shifting decision-making from companies to customers resulting in higher complexity. Thus, achieving architectural isomorphism across product, process and organisation (system) architecture required to maintain effective alignment of an organisation with its evolving environment [4] becomes a significant challenge for manufacturing organisations. Physical prototype building activities during the introduction of a new product can be considered among the most critical activities to achieve and ensure architectural isomorphism. However, physical prototype builds are often highly time-consuming, costly, and complex due to models and operations’ uncertain and genuine nature. When © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 297–304, 2022. https://doi.org/10.1007/978-3-030-90700-6_33

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it comes to the wind industry, turbine manufacturing covers a wide variety of production and manufacturing operations, such as heavy metal manufacturing (towers), large-size fibreglass composite material production (blades), complex and heavy parts assembly (gearbox, nacelle, generator, etcetera), and electrical & electronic systems manufacturing (control and grid infeed systems). Therefore, physical prototype builds during the NPI becomes more challenging for companies like Vestas Wind System A/S (later Vestas). In this regard, DT based VF concept [5] becomes highly relevant solutions by enabling integration, interoperability, and interaction capabilities across product and production lifecycle processes in virtual worlds [6]. In recent studies, DT based VF was considered a promising solution to deal with co-evolution problem with its potential to achieve dynamic, open, holistic and cognitive system capabilities [6, 7]. Moreover, industry experts considered VP among the highest value promising industrial use cases for DT based VF concept [6]. Although a virtual prototype as a computer simulation of a physical product covers all product lifecycle aspects, including service and maintenance [8], building and testing the virtual prototype of NPI processes is particularly challenging due to the need for concurrent engineering complex and ambiguous models and operations. However, due to the same challenges, VP maintains the significant potential for high value for including but not limited to 1) early testing, 2) fewer physical builds, 3) reduced cost, 4) complexity handling, and 5) reduced time to market [9]. Thus, the need to evaluate the DT-based VF concept in more particular VP use cases was raised by [6]. Therefore, in this study, we respond to the need addressed by [6] and present the DT based VF evaluation introduced by [5], in VP during NPI processes by industry experts. The concept is demonstrated in wind blade manufacturing and nacelle assembly cases of Vestas. The study draws upon previous researches, including concept design and development of VF [10] and its extension with DT [5, 6] and collaborative VR [11] capabilities. Therefore, we spared the reader from prolonged discussions on the concept and its design and development methodologies. However, we would strongly recommend the reader refer to the subject studies for in-depth discussions on practical and theoretical aspects of the work. Following the next section, which summarises related works on VP and VF, Sect. 3 presents and discusses the methodology of demonstration and evaluation. Section 4 introduces the summary of preliminary results and discusses the evaluation of the concept before concluding in Sect. 5.

2 Related Works 2.1 Virtual Prototyping Since the advances in computer-aided design (CAD), computer-aided manufacturing (CAM) and visualisation and interaction capabilities prevailed, the development of virtual environments and realistic virtual representations of product models gain more attention from scholars and industry experts. Thus, development and interaction with such virtual models become viable solutions promising significant advantages for the industrial processes in terms of reducing time, decreasing costs, and increasing quality [12]. As a result, VP, a key aspect from the application point of view, starts getting

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attention both in the application and knowledge domains. There were, however, many different interpretations of VP techniques which cause some confusions. To prevent such confusions, Wang defined a virtual prototype as “a computer simulation of a physical product that can be presented, analysed, and tested from concerned product lifecycle aspects such as design/engineering, manufacturing, service, and recycling as if on a real physical model. The construction and testing of a virtual prototype is called virtual prototyping (VP)” [8]. Wang also addressed the need for concurrent design, analysis, optimisation, and integration of simulations tools. Some studies consider VP to alleviate the shortcomings of rapid prototyping (RP) [13], while others stress the difference between RP and VP [14]. Alongside early adoptions in the aerospace and automotive industry [9], VP technologies are also promising significant value in different industries such as construction [15], maritime industry [16] and heavy machinery industries [17]. Studies about VP techniques focus on product lifecycle, including product design, analysis, testing and assembly process design [18–21]. However, pretty limited studies focus on the VP of products from manufacturing aspects [22]. Although scholars concentrate on various VP simulations such as structural material and structural behaviour simulations [21, 23], recent studies show more attention to immersive VR integrated simulation tools [24]. Recent review studies show that advancements in simulation technologies, including real-time data integration, realistic visual representations and embedded VR and AR capabilities, can make simulations a proven enabler for digital integration and access to data across the product and production life cycles [25]. In this respect, the VF concept as high-fidelity integrated factory simulations representing factories as a whole can provide viable virtual environments for constructing and testing virtual prototypes since they enable the experimentation and validation of the various product, process and system models concurrently [5]. Therefore, we will shortly present some studies focusing on VF in the next section. 2.2 Virtual Factory Since Onosato and Iwata [26] introduced the integration of product and factory models as an integral aspect of VF and virtual manufacturing, various definitions were made for VF, including emulation facility, integrated simulation and virtual organisations [27]. Jain et al. defined VF “as an integrated simulation model of major subsystems in a factory that considers the factory as a whole and provides an advanced decision support capability.” [27]. An integrated VF framework concept which can synchronise real factory and VF was introduced by Sacco, Pedrazzoli, and Terkaj [28]. The utilisation of collaborative VR training simulations in the VF concept, together with DT capabilities, was also studied by Yildiz et al. [5, 11]. Yildiz and Møller [10] presented the VF concept as a more dynamic and open system by integrating VF to actual manufacturing execution and product lifecycle systems. They also considered VF as “an immersive virtual environment wherein digital twins of all factory entities can be created, related, simulated, manipulated and communicate with each other in an intelligent way” [10]. A comprehensive demonstration of the DT based VF concept in industrial cases [6] showed significant potential for handling co-evolution and called for more particular evaluation in VP cases.

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In this regard, we discussed the methodology for the demonstration of the DT based VF concept in wind blade manufacturing and nacelle assembly cases, as well as its evaluation in the context of VP in NPI processes.

3 Methodology This article covers only the demonstration and evaluation activities of six Design Science Research Methodology (DSRM) activities [29]. Four demonstration and evaluation sessions, each of which with five participants, were performed. Each session covered 1) presentation of the artefacts, tools, and capabilities, 2) live demonstration of DT based VF, including VR interaction, and 3) semi-structured group interviews. Therefore, the demonstration and evaluation methods were shortly articulated further on. 3.1 Demonstration DT based VF demonstrated in two diverse cases, including large composite manufacturing (blade) and complex and heavy parts assembly (nacelle) of Vestas. Although such cases are unique to the wind turbine manufacturing industry, there are significant common aspects with vari- Fig. 1. Scan ous industries such as shipbuilding, automotive, and aviation. Thus, the the QR code knowledge can provide guidelines for experienced professionals in the for demonindustry and enable the evaluation of deviations in the form of “if-then” stration specific to each context. Some parts of the data about the demonstration are subject to the intellectual and financial interest of the Vestas. Therefore, a significant part of the data is covered by the provisions given to the Vestas by the research collaboration agreement. However, we strongly advise the readers to access the part of the demonstration video, which is publicly available, via [30] or scanning the QR code in Fig. 1. 3.2 Group Interviews The group interviews aim to collect data to evaluate the DT based VF concept in the context of VP during the NPI processes with well-grounded pieces of evidence and arguments by exploring interpretations and perspectives of industry experts. During the group discussions, the interpretations of experts were critically examined by instigating a process of reflection to gain more specific, accurate and grounded pieces of evidence. The number of interviewees was limited, with five for each session to avoid uneven participation in discussions and to acquire more valuable knowledge with more intensive interviews and penetrating interpretations. Participants were intentionally mixed for each session based on their background and departments to increase the diversity of expertise. Please refer to [31] for the list of interviewees as a public online appendix.

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4 Evaluation and Discussion As anticipated, expert discussions on the evaluation of DT based VF roamed around four types of prototyping activities, representing the main exercises of NPI. These are 1) mock-ups, 2) design prototypes, 3) process prototypes, and 4) 0-series production. Therefore, the evaluation summary is grouped and presented in Table 1 based on the respective headlines. Table 1. Summary of DT based VF evaluation Prototyping activity Summary of evaluation Mock-up builds

• Most of the experts agree that the DT based VF concept cannot fully eliminate physical mock-up builds due to mandatory physical tests for legal certification • A minimum of 4 mock-up builds for the wind turbine blade is inevitable • 3D simulation is beneficial for higher confidence in the models • Time and error reduction during mock-up build can be promising value cases for DT based VF • Extending the simulation tools with detailed material behaviours and resin injection is considered highly valuable for blade manufacturing

Design prototypes

• The majority of experts consider the DT-based VF concept more beneficial for design prototypes than mock-up builds since it is less focused on specific/critical materials behaviours • The majority of the failures, corrections, improvements faced during the late blade introduction processes were design-related • Some stated that eliminating the physical design prototype for the near or medium future is optimistic • Utilising DT based VF during the design prototype processes is considered useful for decreasing time to market

Process prototypes

• DT based VF is considered highly useful for process prototypes to simulate production execution • Most experts considered the results of physical prototype activities highly useful for 0-series and serial production optimisation • Some underlined the significance of integrating the high- and low-resolution simulations to achieve a holistic view within the single platform

0-Series production • The 0-series production is considered the most effective use case for DT based VF since the design and processes are more mature in this phase • DT-based VF promises high value by enabling the right sequence, the right staffing, the right factory layout, and having a shorter time to market • Some stated that VF could accelerate the learning curve and achieve actual takt time by reducing 25% time of 0-series which may take three to six months at present • Discovering the limiting factors and bottlenecks such as lack of crane capacity is considered very valuable

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4.1 Discussions DT based VF is also considered for various capabilities besides simulating various prototype building activities during NPI. VR capabilities of the DT based VF was considered valuable for pre-training for big scale production roll-outs. Moreover, collaborative VR shows a sign of higher value for communication with suppliers and is considered very useful to have VF during technology transfer between factories. However, some argued that implementing VR requires a significant focus on change management and competence build. Some also addressed the risk of a rapidly increasing size of data that needs to be input into the models for achieving close to reality models and stressed the importance of DT technology.

5 Conclusion This paper addresses the need for more particular case evaluations of the DT based VF concept in the context of the VP of a product during NPI activities. The concept was demonstrated in the blade and nacelle production facilities of Vestas and evaluated by industry experts during semi-structured group interviews. The preliminary results show that the DT based VF concept is more value promising in the later phases of product introduction to its intensity on the integrated representation of product, process, and system (factory) models. Thus, experts address the need for extending the demo with higher resolution simulations like material design and behaviour simulations. Funding. The Manufacturing Academy of Denmark (MADE) and Vestas Wind Systems A/S funded the research presented in this article, including equipment support (Grant: 6151-00006B). The authors of this article also would like to thank FlexSim for supporting our research by providing the licence free of charge. Declarations. Consent to participate:. Informed consent was obtained from all individual participants included in the study. Consent to publish:. The participants provided informed consent for the publication of their statements. Conflict of interest:. No potential conflict of interest was reported by the authors and the stakeholders.

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3. Tolio, T., Ceglarek, D., Elmaraghy, H.A., Fischer, A., Hu, S.J., Laperrière, L., et al.: SPECIESCo-evolution of products, processes and production systems. CIRP Ann. – Manuf. Technol. 59, 672–693 (2010) 4. Sanchez, R.: Architecting organizations: a dynamic strategic contingency perspective. Res. Competence-Based Manag. 6, 7–48 (2012) 5. Yildiz, E., Møller, C., Bilberg, A.: Virtual factory: digital twin based integrated factory simulations. In: Procedia CIRP, 53rd CIRP Conference on Manufacturing Systems, pp. 216–221. Elsevier BV (2020) 6. Yildiz, E., Møller, C., Bilberg, A.: Demonstration and evaluation of a digital twin-based virtual factory. Int. J. Adv. Manuf. Technol. 114, 185–203 (2021) 7. Yildiz, E., Møller, C., Bilberg, A.: Virtual factory: competence-based adaptive modelling and simulation approach for manufacturing enterprise. In: Grabis, J., Bork, D. (eds.) PoEM 2020. LNBIP, vol. 400, pp. 197–207. Springer, Cham (2020). https://doi.org/10.1007/978-3-03063479-7_14 8. Wang, G.G.: Definition and review of virtual prototyping. J. Comput. Inf. Sci. Eng. 2, 232 (2003) 9. Ahmad, A., Al-Ahmari, A.M., Aslam, M.U., Abidi, M.H., Darmoul, S.: Virtual Assembly of an Airplane Turbine Engine. In: 15th IFAC Symposium on Information Control in Problem Manufacturing, pp. 1726–1731 (2015) 10. Yildiz, E., Møller, C.: Building a virtual factory: an integrated design approach to building smart factories. J. Glob. Oper. Strateg. Sourc. (2021, ahead-of-print) 11. Yildiz, E., Møller, C., Melo, M., Bessa, M.: Designing collaborative and coordinated virtual reality training integrated with virtual and physical factories. In: International Conference on Graphics and Interactions 2019, pp. 48–55. IEEE Press (2019) 12. Rix, J., Haas, S., Teixeira, J.C., (eds.): Virtual Prototyping - Virtual Environments and Product Design. Springer, Heidelberg (1995). https://doi.org/10.1007/978-0-387-34904-6 13. Choi, S.H., Chan, A.M.M.: A virtual prototyping system for rapid product development. Comput. Aided Des. 36, 401–412 (2004) 14. Park, S., Jun, Y., Lee, C., Yang, M.: Rapid Prototyping versus virtual prototyping in product design and manufacturing. Int. J. Adv. Manuf. 70, 61–70 (1993) 15. Huang, T., Guo, H.L., Kong, C.W., Li, H., Baldwin, A.: A virtual prototyping system for simulating construction processes. Autom. Constr. 16, 576–585 (2006) 16. Skjong, S., Rindarøy, M., Kyllingstad, L.T., Æsøy, V., Pedersen, E.: Virtual prototyping of maritime systems and operations: applications of distributed co-simulations. J. Mar. Sci. Technol. 23(4), 835–853 (2017). https://doi.org/10.1007/s00773-017-0514-2 17. Karkee, M., Steward, B.L., Kelkar, A.G., Kemp, Z.T.: Modeling and real-time simulation architectures for virtual prototyping of off-road vehicles. Virtual Real. 15, 83–96 (2011) 18. Jayaram, S., Connacher, H.I., Lyons, K.W.: Virtual assembly using virtual reality techniques. Comput Aided Des. 29, 575–584 (1997) 19. Shyamsundar, N., Gadh, R.: Collaborative virtual prototyping of product assemblies over the Internet. CAD Comput. Aided Des. 34, 755–768 (2002) 20. Ramakrishnan, R., Gaur, L., (eds.): Innovation in Product Design from CAD to Virtual Prototyping. Springer, London (2017). https://doi.org/10.1007/978-0-85729-775-4 21. Łukaszewicz, K.: Testing virtual prototype of a new product in two simulation environments. Manag. Prod. Eng Rev. 10, 124–135 (2019) 22. da Silva Bartolo, P.J., de Lemos, A.C.S., Pereira, A.M.H., Mateus, A.J.D.S., Ramos, C., Dos Santos, C. (eds.) High Value Manufacturing: Advanced Research in Virtual and Rapid Prototyping: Proceedings of the 6th International Conference on Advanced Research in Virtual and Rapid Prototyping, Leiria, Portugal, 1-5 October 2013, CRC Press (2013)

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Applying Robotics Centered Digital Twins in a Smart Factory for Facilitating Integration and Improved Process Monitoring Simon Mathiesen(B) , Lars Carøe Sørensen, Alberto Sartori, Anders Prier Lindvig, Ralf Waspe, and Christian Schlette SDU Robotics, Maersk McKinney Moller Institute, University of Southern Denmark, Odense, Denmark {simat,lcs,asar,anpl,raw,chsch}@mmmi.sdu.dk

Abstract. This work presents a Digital Twin (DT) architecture for smart production cells tested on a case of automated drone assembly. In this Industry 4.0 setting each step of the robotic assembly sequence is carefully monitored through feedback from each hardware component being relayed to the overall smart factory, where it is located through an IoT messaging protocol. Through this case study, we illustrate how robotics-centered DTs are programmed through simple visual programming blocks, and assisted by detailed simulation, can be a powerful tool for facilitating production in smart factories. Keywords: Digital twins · Process monitoring Assembly automation · Smart factory

1

· Collaborative robots ·

Introduction

Successful operation of a smart factory requires support from software on all levels of the manufacturing hierarchy. This ranges from applying top-level decisionmaking on central production parameters down to controlling and receiving performance feedback on the manufacturing cell level. On the cell level, the software complexity increases further as control of advanced hardware components such as industrial robots is added. However, enabling performance monitoring of these components and feeding this back to a Digital Twin (DT) allows relevant data to systematically connect to a so-called information backbone of the smart factory, in which central control decisions for the production are made. Here the input from the DTs can close the information loop and makes it possible to directly regulate production based on feedback from individual cells. In this work we present a DT architecture for robotic workcells that allows for rapid development of robot solutions through simulation. Results from the simulation can directly be used for controlling the hardware components of the robotic workcell across different interfaces, and data from these components can be fed back to the c Springer Nature Switzerland AG 2022  A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 305–313, 2022. https://doi.org/10.1007/978-3-030-90700-6_34

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information backbone through standard IoT messaging protocols. This is applied and tested in a case of assembling custom drones by means of flexible robotic assembly cells. We show how the assembly can be programmed with visual programming blocks encapsulating both the necessary assembly operations as well as the communication between the low level cells and the surrounding production system. The goal of this research is to demonstrate how concrete DTs with 3D-simulation capabilities can provide intuitive means of programming while architecturally supporting the necessary flow of data to efficiently monitor and control production in smart factories.

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State of the Art

Digital Twins are well suited to facilitate the operation of complex systems such as smart factories, and across the literature, DTs are applied to utilize analyses, predictions, planning, and operations and digitally mirroring the lifecycle of physical assets to assist in decision-making [1]. Although the definitions of DTs are many, it is common to distinguish between the concept of i) the Digital Model that can be used for simulation, and ii) the Digital Shadow that is the virtual representation of the state of a physical asset [2]. In the work of Kritzinger et al. it is stated that the DT exists only when a bidirectional data flow between the virtual and the physical system is present so that a change in the physical system directly invokes a change in the virtual representation and vice versa. Our DT complies with this and expands this definition further as we keep an additional digital model next to the DT to be used for experimental scenarios (e.g. robot simulation and path-planning). To initialize experiemental scenarios, the digital model can be synchronized to the current state of the physical asset on demand. From our perspective, DTs can thus be seen as comprehensive virtual replicas of physical equipment - both in terms of the exposed functionality of the equipment as well as its relevant parameters and inherent behaviors. This view on DTs is similar to that of Schluse et al. [3] whose Experimentable Digital Twins promote the use of advanced simulation capabilities to utilize DTs by enabling simulation and development of capabilities beyond its initial intent. For example, to use DTs to experiment with the development of new robotic tasks based on existing physical setups, as it is also possible with our DT implementation. The focus of this work is on DTs for assembly systems in which robots perform complex automated tasks such as those found in the manufacturing industry. In Bilberg and Malik [4] it is illustrated how a DT can be used to dynamically redistribute workload between a human worker and a collaborative robot. However, their DT is limited to discrete event simulation and consequently, the data from the physical asset is only able to identify whether specific predefined states of the assembly have been reached. Another use of DTs for collaborative robot tasks is found in Kousi et al. [5] with the purpose of online obstacle avoidance and maintaining a map of a shop floor. In their work, Kousi et al. use ROS1 and 1

The Robot Operating System (ROS) [Online] https://www.ros.org/.

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Gazebo2 to link a simulation of the robots to mimic the feedback to the DT. However, to the best of our knowledge, they have yet to validate their approach by integrating their DT with an actual physical productions setup.

3

Task Programming and Monitoring with Digital Twins

The presented Digital Twin platform is based in Verosim [6], a versatile software system for 3D modelling and simulation of automated systems. Due to its modular software architecture, it is possible to extend DTs and their functionality with relative ease, including interfaces linking to physical assets and intuitive means of simulation-based robot programming. To facilitate the robot programming here, we make use of what is in the robotics community referred to as Skills for encapsulating specific functionality. However, skills as a concept are not strictly defined. Pedersen et al. [7] describe them as “object-centered robot abilities, which can easily be parameterized by a non-expert” and configured through kinesthetic teaching and gesture-based inputs. Instead, our simulation-based approach allows configuration of system operations through intuitive use of digital models. This enables users to modify and optimize the existing system behavior directly from the top-level feedback to the DT, but also creates a sandbox for experimentation and expansion without the need for intrusive physical alterations of the production setup. This idea was first introduced in Sørensen et al. [8] - however, for clarification, the visual programming (VP) blocks are in this paper referred to as ServiceBlocks. This is to emphasize our deliberate distancing from a strict definition of simple and highabstraction skills often referred to as Skill Primitives and Skills in robotics [7,9]. In contrast, our service-oriented VP blocks can cover functionality of varying function complexity due to their dynamic implementation. The strength of the VP method presented in this work comes from the concreteness of each of the blocks’ functionality, the directness of modifying their inputs, the explicitness of the program structure, and the immediate visual feedback provided by the simulation [8,10]. Furthermore, ServiceBlocks are distinguished from previous work on VP, such as [11], by the use of a highly modular software architecture that is facilitating the required feedback loop to form the DT with exchangeable execution layers and seamless switching between simulated and physical execution of the programmed tasks. The ServiceBlocks are further described in Sec. 3.1. Moreover, as a further novelty in this work, relevant feedback from the production cell can now be further relayed beyond the cell itself. This is implemented through IoT messaging and ensures that information is propagated to the information backbone of the smart factory at a suitable granularity, i.e. by pre-processing in the DT. The benefit is for the cell to directly contribute with performance metrics and flagging of critical situations to provide an overview, while also allowing for the detailed inspection down to device-specific operational parameters. These system monitoring capabilities are further elaborated on in Sec. 3.2. 2

Gazebo [Online] https://www.gazebosim.org/.

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The ServiceBlock Framework

Verosim and the ServiceBlocks are used to program, simulate, and inspect the assembly sequence as well as for controlling the devices (e.g. robots) in the physical cell. Figure 1 shows the 3D simulation of the DT in Verosim with a visual representation of the workcell with robots, grippers, and a screwdriver mounted on the left robot as well as various fixtures and parts. The opaque robots are used for programming the sequence in simulation whereas blue silhouettes represents the connected physical robots. The information that these Digital Shadows provide is a key component of the DT concept. For example, this state information can be used to fetch the position of the real robot and to use it for offline path-planning to support programming, of the state information can be fed to the information backbone of the smart factory for monitoring purposes. The programming is done by ServiceBlocks, which each represent a dedicated functionality such as robot motions, control of devices, or communication to the information backbone. It is possible to seamlessly switch between executing ServiceBlocks in simulation and controlling the physical assets. Moreover, ServiceBlocks can be clustered into so-called Macros to group a certain functionality for reuse and to simplify the visualization. Macros can be re-opened to review their internal structure and to select which inputs and outputs should be exposed at a macro level. 3.2

System Monitoring

A simple Graphical User Interface called Cell Manager has been designed to emulate the information backbone of the smart factory in our experiments (see Fig. 1). The Cell Manager shows the current status of the workcell. The graphical interface is updated with messages to describe the current assembly step. The Cell Manager includes a log of the errors that occurred in the assembly that consist of a timestamp, an error code, a message, and an identifier of the component that caused the error. For this demonstration, it also shows the current joint configurations of the robots. The ServiceBlocks (and macros of ServiceBlocks) that are used to program the assembly sequence are shown in Fig. 1 as a flow diagram. The actual visual representation of programming with the ServiceBlocks is similar to a flow diagram with connecting wires between blocks dictating the order of execution. An example of the ServiceBlocks is given later in Fig. 3. The functionality of the Cell Manager is linked to different ServiceBlocks through the MQTT protocol. The assembly sequence can be started with the button in the Cell Manager which directly connects to the Start ServiceBlock. The Complete ServiceBlock is in the end activated upon a successful full assembly and informs the Cell Manager that the operation was concluded. This block also communicates with the Restart ServiceBlock to reset all the blocks in the sequence and thereby making the cell available for the next call. A stream of data for all the factory components is constantly updated by the Cell Manager from the Status ServiceBlocks, which in this case is the robot joint configurations and safety information. By using the

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Fig. 1. The Verosim interface with the 3D simulated environment of the workcell is shown together The Cell Manager, which in this project emulates a visual interface to the information backbone. Simulated robots are represented as opaque models whereas the physical robots are shown as blue silhouettes. Furthermore, a flow diagram of the assembly sequence is shown. MQTT ServiceBlocks are green, macros are blue, Robot blocks are orange, and utility blocks white.

Run in Simulation utility block it is possible to select the physical or simulated execution of the assembly represented in the diagram as a sequence of macros.

4

Experiment and Result

This section shows how the proposed system can be used in a smart factory setting for the assembly of drones by giving an example of how the ServiceBlocks are used to program an assembly sequence. The robotic workcell used for this demonstration is responsible for the installation of a propeller on a drone motor. The task requires the insertion of a spring in the motor shaft, followed by the insertion of the propeller and concluded by the fixation using a flange nut. Initially, all the components involved in the assembly are placed in fixtures on the workcell table, as shown in Fig. 2. Two UR5e robot manipulators are mounted on the table and equipped with the required tools. On Robot A a Weiss CRG 200 gripper with custom 3D printed fingers is mounted which is used to grasp the drone motor. On Robot B both a Desoutter ECSF screwdriver and a Robotiq Hand-E Adaptive Gripper with a pneumatic finger exchange

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Fig. 2. The robotic workcell with all the involved components.

system [12] is mounted. The Robotiq gripper can automatically change between two different types of 3D printed fingers used for grasping the spring and the propeller. The screwdriver is used to pick up and fasten the flange nut. The two UR5e robots are controlled via the UR RTDE interface. We have implemented ServiceBlocks in Verosim making use of the ur rtde Cpp library3 to access the RTDE Control, Receive, and IO interfaces. Through those interfaces, it is also possible to control the connected tools including grippers, screwdriver, and fingertip exchanger. 4.1

Programming the Assembly Sequence

The Digital Twin of the workcell is represented in Verosim, where the sequence is programmed using ServiceBlocks as shown in Fig. 1. All ServiceBlocks are added to a visual programming editor and their parameters are initialized. The inside of the Place Flange Nut Macro is shown as an example in Fig. 3. The Macro starts with an Update ServiceBlocks that signals with a message to the Cell Manager that this particular step in the assembly sequence has been reached. The second block retrieves a robot joint configuration from a database which is used to perform a PTP joint motion in the third block. To approach the place pose for the nut, a Cartesian pose is retrieved from the database in the fourth block which is used to perform a linear motion in the fifth block. On the sixth block, the screwdriver is activated. The force mode of the robot is started as the seventh block as soon as the screwing has started. This way the screwdriver follows the screw while tightening. Both error outputs along with the 3

Cpp library for interfacing UR RTDE [Online] https://sdurobotics.gitlab.io/ ur rtde/.

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error message and code are connected to their individual Error ServiceBlocks used to communicate possible errors to the Cell Manager. On the eighth block is the screwdriver is deactivated after which the ninth block gets the Cartesian pose for the linear motion performed by the last block that retracts the screwdriver. The assembly sequence can seen in a video made available in [13]. The video shows the execution of the physical setup and the Digital Shadows in the simulated environment as well as the Cell Manager.

Fig. 3. The Place Flange Nut macro in the situation where the screwdriver block finishes with an error.

5

Discussion

The system and proposed programming method work well on the given case. The robots carry out the programmed task and by introducing faulty conditions a meaningful error is relayed from the robots and up to the overview on the Cell Manager. However, there is still work to be done to make programming with the system easier. The simulation environment makes it quick and intuitive to program each individual step of a process. This is exemplified by giving a user direct access to play with advanced tasks such as robot motion planning without committing to moving the robot physically. However, the programmed sequence consists of 113 individual ServiceBlocks with the majority making up the 10 macros (see Fig. 1), and a challenge remains in addressing the complexity from the considerable amount of ServiceBlocks to configure. Furthermore, the MQTT protocol is found to be a flexible choice for communication across different layers of system granularity, as it allows for a customized message structure. This was adapted to the current needs of the project. However, in order to make it valuable for industry, the messages relayed in the system should adhere to an industrial relevant standard for smart factory production.

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Conclusion and Future Work

This work presented a Digital Twin architecture for binding a low-level robotic production cell to the smart factory in which the cell is located. This was done by relaying relevant data from its devices through the MQTT protocol. This both at the higher level to indicate whether the cell is running as expected, but also

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by providing access to very specific information to enable detailed error tracking and process optimization. Robot programming was made intuitive through visual programming with ServicesBlocks. Future work will be focused on minimising complexity with automatic generation ServiceBlock networks and research into developing assistance tools on how to best guide a user in the configuration and initialization of the networks. With this challenge addressed and the system matured, the presented robotics centered Digital Twin will become a powerful tool to facilitate integration and monitoring of advanced automation processes in smart factories. Acknowledgement. This work was supported by Innovation Fund Denmark through the project MADE FAST.

References 1. Cimino, C., Negri, E., Fumagalli, L.: Review of digital twin applications in manufacturing. Comput. Ind. 113, 103130 (2019) 2. Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W.: Digital twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 51(11), 1016–1022 (2018) 3. Schluse, M., Priggemeyer, M., Atorf, L., Rossmann, J.: Experimentable digital twins—streamlining simulation-based systems engineering for industry 4.0. IEEE Trans. Ind. Inform. 14(4), 1722–1731 (2018) 4. Bilberg, A., Malik, A.A.: Digital twin driven human-robot collaborative assembly. CIRP Ann. 68(1), 499–502 (2019) 5. Kousi, N., Gkournelos, C., Aivaliotis, S., Giannoulis, C., Michalos, G., Makris, S.: Digital twin for adaptation of robots’ behavior in flexible robotic assembly lines. Procedia Manuf. 28, 121–126 (2019) 6. Rossmann, J., Schluse, M., Schlette, C., Waspe, R., Van Impe, J., Logist, F.: A new approach to 3d simulation technology as enabling technology for erobotics. In: 1st International Simulation Tools Conference & EXPO, pp. 39–46 (2013) 7. Pedersen, M.R., et al.: Robot skills for manufacturing: from concept to industrial deployment. Robot. Comput. Integr. Manuf. 37, 282–291 (2016) 8. Sørensen, L.C., Mathiesen, S., Waspe, R., Schlette, C.: Towards digital twins for industrial assembly-improving robot solutions by intuitive user guidance and robot programming. In: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), vol. 1, pp. 1480–1484. IEEE (2020) 9. Huckaby, J.O., Christensen, H.I.: A taxonomic framework for task modeling and knowledge transfer in manufacturing robotics. In: Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence, (2012) 10. Burnett, M.: Software engineering for visual programming languages. In: Handbook of Software Engineering and Knowledge Engineering. World Scientific Publishing Company, Singapore, vol. 2, (2001) 11. Schlette, C., Losch, D., Rossmann, J.: A visual programming framework for complex robotic systems in micro-optical assembly. In: ISR, Robotik,: 41st International Symposium on Robotics, VDE, vol. 2014, pp. 1–6 (2014)

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12. Kramberger, A., Wolniakowski, A., Rasmussen, M.H., Munih, M., Ude, A., Schlette, C.: Automatic fingertip exchange system for robotic grasping in flexible production processes. In: 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), pp. 1664–1669. IEEE (2019) 13. Mathiesen, S.F., Sørensen, L.C., Sartori, A., Lindvig, A.P., Waspe, R., Schlette, C.: Video - Applying Robotics Centered Digital Twins in a Smart Factory for Facilitating Integration and Improved Process Monitoring (May 2021). https:// doi.org/10.5281/zenodo.4742115

A Concept for a Distributed Interchangeable Knowledge Base in CPPS Christof Thim1(B)

, Marcus Grum1 , Arnulf Schüffler2 , Wiebke Roling2 Annette Kluge2 , and Norbert Gronau1

,

1 University of Potsdam, August-Bebel-Str. 89, 14482 Potsdam, Germany

[email protected] 2 Ruhr University Bochum, Universitätsstraße 7, Bochum, Germany

Abstract. As AI technology is increasingly used in production systems, different approaches have emerged from highly decentralized small-scale AI at the edge level to centralized, cloud-based services used for higher-order optimizations. Each direction has disadvantages ranging from the lack of computational power at the edge level to the reliance on stable network connections with the centralized approach. Thus, a hybrid approach with centralized and decentralized components that possess specific abilities and interact is preferred. However, the distribution of AI capabilities leads to problems in self-adapting learning systems, as knowledgebases can diverge when no central coordination is present. Edge components will specialize in distinctive patterns (overlearn), which hampers their adaptability for different cases. Therefore, this paper aims to present a concept for a distributed interchangeable knowledge base in CPPS. The approach is based on various AI components and concepts for each participating node. A service-oriented infrastructure allows a decentralized, loosely coupled architecture of the CPPS. By exchanging knowledge bases between nodes, the overall system should become more adaptive, as each node can “forget” their present specialization. Keywords: Learning · Distributed knowledge base · Artificial intelligence · CPPS

1 Introduction Artificial intelligence (AI) diffuses into different areas of factory operation, from image recognition and fault detection to advanced process control and autonomous decision making [1–5]. The distribution and connection of sensors within a factory allow databased decisions and largely unsupervised operations in cyber-physical production systems (CPPS). AI capabilities can be found on different computational levels. Most architectures rely on the processual strength and flexibility of cloud-based solutions [6]. Recently a shift towards edge-based analytics has become more prominent in distributed environments [7]. Shifting analytical capabilities to edge components brings data processing closer to the machinery and sensors and thereby avoids latency and high-volume data transfers into the cloud. However, there are constraints to AI edge computing. Most © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 314–321, 2022. https://doi.org/10.1007/978-3-030-90700-6_35

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devices do not possess the computational capabilities to generate, train and maintain an artificial knowledge base (KB) such as a neural network. Furthermore, energy consumption is an issue for battery-powered edge components. Using edge devices is fruitful if data needs to be processed in proximity to physical entities, static decision models are used, and computationally intensive learning is avoided. Thus, there is a trade-off between computing power in a centralized entity and autonomy and reaction time of decentralized components. As relatively stable, repeating tasks characterize manufacturing, machine learning does not have to occur on the general level regularly. However, local specificity, e.g., machine configuration, specialized production lines and tools, requires optimization. Hence, the need to learn is compartmentalized for different production objects and leads to local specialization. The parallel, incremental learning and optimization in production objects leads to diverging KBs and reduces flexibility. This can be problematic in the long run as production programs and utilization fluctuates. Our research question is, therefore: How can decentralized AI specialization be kept flexible and interoperable within an overall production system? This paper first discusses different AI concepts and their use in CPPS. Accounting for distributed knowledge within the CPPS, architectures for AI in computer science are presented as a related concept (Sect. 2). A conceptual model is designed (Sect. 3), which combines different elements and describes the interaction processes thereof.

2 Related Literature and Concepts In the last decade, the topic of AI in production processes has received increasing attention, fueled by the discussion about smart manufacturing, Industry 4.0, the industrial internet of things, and cyber-physical production systems. Data collection and analysis for autonomous decision making is the key aspect of AI in production systems. Multiple facets and domains can be found where AI is applied successfully, e.g., fault recognition, predictive maintenance, and adaptive process control. To understand the different technologies used in AI, this section introduces the most common AI variants, their application in the production domain. Different approaches from computer science that deal with distributed systems are presented to address the aspect of distribution. 2.1 AI in CPPS AI in production systems encompasses a broad area with different technological approaches: Artificial Cognitive Systems, Classifiers and Neural Networks. Layered or component-based architectures [8] and task-based distributions [9] are used to couple AI approaches for complex problem-solving. Each approach has a different notion of what decision making is based on respectively what its KB is. Artificial Cognitive Systems [10, 11] (ACS) are based on a formal, semantic representation (ontology) of the environment. It distinguishes procedural (rules) and declarative knowledge (facts about the environment). ACS mimic the human perceptual and decision-making system’s working through different cognitive modules in architectures

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like ACT-R [12] and Soar [13]. Intelligence contextualizes perceptions as facts in the KB and selects appropriate rules to initiate actions. New aspects can be deduced for their KB and environmental observation (learning). In the industrial setting, ACS combine and contextualize different sensory inputs and relate these to past experiences, e.g., in robotics [14], for describing smart interaction of parts of manufacturing systems [15] and in product-based production planning and control [1]. As the symbolic systems of ACS are representations of the real world, they are understandable for humans, and less data is necessary for reasoning. However, this is also a downside of artificial cognitive systems as their vocabulary and rule sets need to be modelled by humans. Case-based reasoning (CBR) is another approach, which draws knowledge from experience collected in a case base. Learning takes place through the refinement and retaining of a classified case base (CB). Each existing case is associated with a successful solution and its context. Existing solutions from similar cases can be retrieved from a repository, reused, and refined. The CB is constantly evolving with each case. In digitized manufacturing, CBR is used for complexity management of volume, sequence and variants in production processes [4, 16] and process selection [3]. While the previously presented AI techniques use formalized human knowledge in addition to data, classifiers [17] are purely data-driven, relying on sufficient training data to calibrate the classifier. Methods like decision trees, regressions, clustering (k-means), naïve Bayes-Classification, random forest or support vector machines rely on statistical methods to classify data objects within a population. Classifiers are used for anomaly detection [18], quality assurance [19] and predictive maintenance [5]. Closely related to classifier systems are artificial neural networks (ANN). They mimic human neural systems by formalizing neuron activities. By balancing and optimizing the reaction of the neurons to an input signal, the connections between neurons are reinforced or weakened. There are different types of networks (convolutional, recurrent, with tensors or long-term memory) and learning approaches (supervised, unsupervised, reinforced). A network of connected neurons is established through large sets of training data. Cloud services and specialized hardware is used as training is time- and resource consuming. Once trained, ANN is precise and fast to identify and classify different aspects. ANN are unflexible, as new situations require retraining. Industrial applications are image detection, optimization [2] and analytics on IIoT-devices [6]. 2.2 Distributed Functions in CPPS One of the critical advantages of CPPS is decentralization and distributed decision making [20]. Regarding the use of AI, the cloud computing paradigm is most widely used as it provides scalability and computation power. However, with growing data streams and response-time requirements, the latency of cloud-based services becomes an obstacle in CPPS. Task allocation at build-time leads to an optimized architecture. Existing approaches propose different strategies, balancing effort, divisibility, priority and workload [21]. Also, fog computing approaches, like the in-situ AI, rely on autonomous and incremental deep learning in IoT (edge) systems [22]. The KB is split into different facets executed at the local CPS level and refined using unsupervised learning. The centralized, computationally powerful component aggregates these facets using supervised learning and distributes them back to the CPS.

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While service-oriented approaches still have some degree of centrality, e.g., through a coordinative entity, distributed ledger approaches (Blockchain, IOTA) are entirely decentralized. They distribute intelligence between devices, edge components, a fog level, and the cloud level [23, 24]. However, the high level of decentralization also increases the coordination effort in the network. The presented approaches coordinate fixed architectures and do not dynamically assign tasks. To balance the degrees of freedom of decentralized components and the coordination effort, a service-oriented approach like BaSys-approach [25] is a promising and mature distribution concept.

3 Architecture for Distributed CPPS with Interchangeable Knowledge Bases From the related work in Sect. 2, three different problems can be deduced when designing a distributed CPPS: 1) The upcoming analytical task of the machine needs to be assessed regarding the current local capabilities; 2) Adequate solutions in the CPPS network need to be assessed; 3) The analytical task needs to be solved. As different AI approaches and specialized KBs can be appropriate to solve problem 3. Problems 1 and 2, on the other hand, rely on prior experiences of the overall CPPS. Following the CBR approach proposed by Schott et al. [4], tasks are assigned based on previous cases. Thus, the best fit to solve problem 3 is identified for local, distributed or cloud components. Therefore, the hybrid analytics pattern [26] is used for CPPS with interchangeable KBs. Different components interact to solve the problems described above: edge devices, centralized cloud services, and a central KB repository. 3.1 Components Edge components are coupled to machinery on the shop floor. They can be described by their purpose and their types of sensors and actuators, e.g., a grinding machine is programmed with different impact and rotation speeds; pressure, acoustic, and position sensors monitor its operation. A specialized local KB at the edge level optimizes how machinery is best operated given the input and target parameters and can predict errors and reconfigure the program accordingly. Similar edge components, therefore, possess similar KBs. A homogeneous production program, e.g., in mass production, will produce similar cases (narrow CB). This will lead to a parallel, non-diverging development in each KB. However, customization requirements and small batch sizes lead to a mixed production portfolio. The number of different observed cases increases with the number of product variants (broad CB). If certain product variants are only produced in a specific line, the local KB can become more specialized due to reinforcement learning based on sensor and operator input [27, 28]. The local KBs will significantly diverge between the lines. This specialization can be captured by maintaining a case base to specify the context in which it is used. Specialization tightens the association between the edge component and specific production cases, leading to less flexibility between the lines. To loosen this association, components must be able to intentionally forget the specialization [29]. Flexibility can thus be regained by a case-specific selection of the

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appropriate KB. The appropriateness can be established by case similarity using product and machine specifications. Problems that are locally unknown or yield inefficient results are identified, and better solutions might be identified elsewhere in the CPPS network. Searching across the edge components of the production network can identify better suiting KBs. Substituting KBs between edge components will lead to forgetting and higher performance. Also, central cloud entities could compensate for the limited processing power in edge components for specific analytical problems. Here communication latency between edge and central component is tolerated for faster results [30]. The central cloud entities possess a KB themselves, which is more general compared to the local level. Instead of planning the task distribution beforehand, our approach is deciding based on the CB whether the analytical task is better executed locally or remotely. Components are dynamically orchestrated, which requires an entity to coordinate task requests. A service-based architecture allows event-based identification of entities that possess the specific KB for the case identified. The KB and the analytics task can both be described as services offered either by an edge device or by a cloud component. The constant evolution of the CB dynamically identifies the appropriate KB. A KB repository using the OPC UA Discovery Service and a service registry [31] allows registering and allocating components that are apt to perform the required analytical task or provide an appropriate KB. 3.2 Interactions Suppose an edge component is confronted with a new task. In that case, it matches the parameters provided by sensors and from the production information system with the local CB to identify a fitting solution. If a similar request had been executed before, the existing KB is used. If an insufficient fit between the parameters and existing solutions is yielded, the centralized repository is queried with the case parameters. A larger remote CB encompassing all analytical tasks in the CPPS is checked, and an adequate KB carrier is identified. The edge component is supplied with the service specification and address to establish a connection to the other component and request the fitting KB (from the edge component) or the computational service (from the cloud component). If the communication peer is another edge component, its local KB is transferred to the requesting component. Suppose the cloud service provides a superior solution to the identified case. In that case, an interface is established, which transmits the data relevant for the analysis to the cloud component and receives the results. The original component can now operate and infer or reason about the problem at hand, either with the substituted KB or external service. Once the operation is completed, a feedback loop is initiated. The results of the operation are assessed by sensors or through human inspection. The operation’s success is fed into the reward function for the reinforcement of the local KB and the local CB. To retain the solution, the updated local CB needs to be added to the repository as well. Figure 1 depicts the interaction process.

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Fig. 1. Concept of distributed interchangeable knowledge bases

4 Conclusions The usage of AI technologies in decentralized CPPS requires a dynamic allocation of analytics tasks. Different AI approaches are currently used in CPPS. It was shown how edge specialization leads to less flexibility. Forgetting this specialization on the edge level makes the overall system more agile. An architecture of interchangeable knowledge bases was developed. Conceptualizing KBs as services which each component of the production system provides and identifying similar cases in which KBs are interchangeable enhances the flexibility. Using case-based reasoning to allocate KBs and analytics tasks within a CPPS creates a dynamic production system, which learns and adapts to the production portfolio and machine layout. The generality of the proposed architecture is one of the limitations of this approach. Understanding the structure of the different types of knowledge bases and comparing them over time can shed light on the specific application. Moreover, the performance of the service-oriented approach needs to be assessed. Further steps are thus the implementation and test of the proposed architecture. Furthermore, the effect of edge device isolation on the development of the KB needs to be investigated. Acknowledgements. The research was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) with grant number KL2207/6–2 and GR 1846/21–2.

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Generating Customer Insights Using the Digital Shadow of the Customer Kristof Briele1(B) , Marie Lindemann1,2 , Raphael Kiesel1,2 , and Robert H. Schmitt1,2 1 Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen

University, Campus-Boulevard 30, 52074 Aachen, Germany [email protected] 2 Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, 52074 Aachen, Germany

Abstract. Smart products, Social Media and innovative market research lead to an abundance of customer data, yet due to their heterogeneous sources and structures, they are scattered throughout the company. Joining these different types of data can lead to a large gain in customer insights that would not have been possible by analyzing the data individually. It is a necessary step for the transition of the current mostly hypothesis-based product design process towards a data-driven one and enables accelerated product development with truly innovative products tailored to the customer. This paper explains the holistic approach to identifying customer needs and requirements: the digital shadow of the customer. It is a concept transferred from the Internet of Production and its digital shadows of products and processes. The paper first gives an overview of customer data that form the customer data lake and reviews current data analysis methods using an explorative literature review. We then explain the concepts of the digital shadow and data lake, their main principles and benefits of using digital shadows for product development. Keywords: Product development · Customer data · Internet of Production

1 Motivation The widely accepted understanding of Industry 4.0 and the increasing digitalization is that it will lead to better, faster and/or more robust decisions. This development can be traced back to early statistical analysis in quality management but is currently represented in real-time models of highly complex production processes like fine blanking or diecasting [1]. The Internet of Production is a framework for Industry 4.0 to aggregate data, derive digital representations such as Digital Shadows, and support the decision-making process [2]. It includes product development as one of its three phases with the goal of transforming hypothesis-based product design into a customer-centered and data-driven one. However, customer data are more subjective than production data and the decisions have a higher impact on the overall product quality [3, 4]. This research starts with a comprehensive overview of customer data and the corresponding analytical methods through an explorative literature review and subsequent © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 322–329, 2022. https://doi.org/10.1007/978-3-030-90700-6_36

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derivation of their properties. The analysis of both data and analysis methods leads to a discussion of if and how the concepts of the Internet of Production can be applied to customer data and product development.

2 Analysis of Customer Data for Product Development In product development, companies can use a variety of customer data to derive customer insights and improve the product. However, research focuses on the use of individual data types. The goal of this chapter is to achieve a basic understanding of the relevant data and their properties. It is a necessary step for automating the data analysis in the future. 2.1 Data for Product Development Lindemann et al. proposed a first overview of customer data types relevant for product development including social media data, complaint data, user opinion survey, lead user workshops, expert interviews, internal audits, and descriptive studies [5]. The first part of our research aims to create a more holistic overview by both extending and validating the aforementioned data types. This is achieved by conducting an explorative literature review that focuses on deriving a broad overview of the data types and not a structured, quantitative analysis of the data. The literature review is combined with a brainstorming workshop with five experts who are experienced in both research and industry in the field of customer insights and product development of consumer goods; their input extended the list of relevant data types. In the literature review, redundant data types do not affect the importance and benefits of each individual data type and are, therefore, not registered. Keywords of this review are: customer data types, customer data sources, product development data, product design data, customer profile, customer data categories, customer data properties, and voice of the customer. The publications include journals and conference proceedings published from 2015–2021, but some older publications are also accepted. From a total of 53 papers, depending on the manner of counting, approx. 100 different customer data examples are extracted. To achieve a comprehensive overview, the expert workshop identified eight main customer data types from the full list used in product development (see Table 1). Data types and data sources are strongly connected and the focus of this paper is not to show a definite separation, thus, we use the term data type going forward. Twenty-seven exemplary literature references are listed in the table that represent the spectrum of the customer data examples. Table 1. Relevant customer data types for product development Data types

Examples

Literature

Social Media Data

Customer reviews, discussion forums

[6–11]

Complaint Data

Formal complaints

[12]

Customer Study Data

Interviews, questionnaires, behaviour analysis, lab experiments, workshops

[12–19] (continued)

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Data types

Examples

Literature

Usage Data

Visits to websites, click stream, customer location, banking logs

[13, 18, 20–22]

User Data

Customer type, demographics, 3D anthropometry

[23–27]

Sales Data

Purchase history, behavioural analysis

[20, 23]

CRM Data

Relationship details, call history, interaction data

[23, 28]

Market Data

Neuromarketing, crowdsourcing, market surveys

[25, 29–32]

Although all examples fit into these main data types, a clear assignment to one data type and a distinction between data type and data source may be difficult. E.g., the difference between social media data and complaint data is blurry on the content level since social media data contain complains; yet these data types differ strongly in their data structure as social media data are more unstructured texts rather than complaints though a standardized form. Standardized descriptions of data using properties such as “structure” are helpful to make use of this heterogeneous pool of customer data. With these descriptions, companies can more easily decide which data types to use to reach a certain decision. Furthermore, the description is a necessary step for automated data analysis. Lindemann et al. proposed a first set of properties of customer data, which we extended to include subjectivity, degree of structure, degree of specificity, number of data points, update frequency, and cost [5]. In product development, not only customer information is relevant but also information about the product and process capabilities; thus, the customer data are extended by product data. Since the focus of this research is on customer data, the list of product data types are not derived by a literature analysis but in a second workshop with the same five experts to get a first understanding of product data. The experts were given the previously accumulated list of data types and assessed both, product and customer centered data types, using the accumulated knowledge of the literature review and their experience (see Table 2). It shows that product and customer data differ – as expected – in subjectivity. Design parameters, e.g., are precise values while measurements only vary inside the accuracy of the measurement system. Customer data, on the other hand, include the subjective opinion of different customer groups due to factors such as age or cultural background. In general, this high subjectivity is accompanied by a lower specificity. The main reason is that a high specificity requires a defined research question, which limits the expression of subjective opinions. The structure of the data is subject to similar effects. A high degree of structure limits the customer’s freedom of expression but also limits the subjectivity. An equally high degree of structure, specificity and subjectivity – as represented by customer study data – is expensive to buy, because it requires a dedicated team investigating a limited number of research questions.

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An interesting development in customer data is the integration of “newer” data such as social media and usage data from automated sensors in cyber physical systems (e.g. smart fridges). Both offer a vast amount of data that provide insights into unfiltered customer opinions and real-life usage behavior. The necessary amount of data requires automated data crawling and analysis to be financially valuable. Table 2. Structured data types for product design assessed in 6 properties Subjectivity

Degree of Degree of Number of Update Structure Specificity Data Points Frequency

Cost

Customer Centered

Social Media Data Complaint Data Customer Study Data Usage Data User Data Sales Data

Product Centered

CRM Data Market Data Quality Control Data Measurement Study Data Design Parameters Manufacturing Processes Data Laws and Norms low

high

2.2 Analysis Methods and Goals Companies use different methods to acquire and gain insights from customer data (customer insights). These methods differ in terms of the research question, the scope of the method and the output. Simplified, they are divided into two steps: First, the company addresses the question of which features determine the customer experience, and consequently, how these features should be specified to enhance the product experience. The first step toward obtaining an overview of product acceptance on the market is opinion mining on the basis of customer reviews [33]. The output includes terms that describe or evaluate the product and their frequency. This can help determine the customers’ impressions of the product, their overall satisfaction, and establish the connection between the emotions and sentiments about certain product features. An alternative approach is eye tracking. Heat map analyses of the recorded eye movement data when

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observing a product can be used to determine focus points and gaze time of the customer when participating in a customer study [34]. These results can be used to draw conclusions about the perceived quality of the product. Other methods for determining what drives the customer experience can be the Kano model [35] or Failure Mode and Effects Analysis (FMEA). In the second step, individual product features are analyzed. Kansei engineering aims to develop or improve products by translating the customer’s psychological feelings and needs into parameters [36]. The output includes quantitative links made between so called Kansei words and product features. As a result, products can be designed to evoke the intended feeling. Another method to learn more about the customer’s desired and undesired characteristics of a product is Conjoint Analysis. This method estimates the structure of consumers’ preferences by using their overall judgements about a set of alternatives, specified by expressions of different features [37]. Other methods are VR Testing, Total Quality Management or User Centered Design [38]. The aim of this multi-step approach is to combine the results of the individual methods to obtain a larger picture of customer requirements and how these can be fulfilled. The gained insights must then be integrated into the product development process and the company’s knowledge management.

3 Enabling Customer-Centered Data-Driven Product Development The Internet of Production as a reference infrastructure for Industry 4.0 offers two concepts of particular interest for the storage, analysis and application of data: the data lake and the digital shadow. Both concepts are researched in depth, especially machine data and the subsequent production planning. [2]. We discuss both concepts for the application in product development. The addition of customer data is an especially novel but important step for data-driven decisions in every step of production. After a brief general explanation, we lay out potentials and requirements based on the previous chapter about customer data. This is the second contribution of this paper since the discussed concepts do not include customer data so far. 3.1 The Data Lake The data lake is the foundation of the Internet of Production to realize a fully interconnected production landscape. In contrast to other data warehouses, it allows for storing raw data in an unstructured “storage first, query later” manner. This approach offers real-time control of tightly integrated production processes, storing and processing heterogeneous production data, and secure privacy-aware collaboration [1]. Customer data in product development is an even more heterogeneous pool of data that ranges from unstructured opinions of a single customer up to broad market analyses. This makes data and knowledge management a key challenge in product development [39]. The data lake is not a rigid construct and is explicitly suitable for unstructured data. It can serve as a database for both customer wishes and production capabilities.

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Extending the data lake to include customer data may improve the data availability and accessibility across the company. The decisions in product development are typically made in weeks rather than milliseconds as they serve to control production machines. Different departments, however, require access to the data at different phases of product development. Since the data lake stores the entire data history with high accessibility, the requirements of customer data are met. It offers the possibility to access historical customer information and decisions, which do not change as often as, e.g., innovations, and make decisions based on transparent data more robust against budget cuts. The data lake is designed for large data input streams from sensors of production machinery in the magnitude of 6.2 Gbit/s [1]. For customer data, especially for usage and social media data, high data streams are also important. E.g., a smart fridge with a small uplink of only 1 kB/s accumulates up to 1 GB/s in total for a company that has sold one million fridges. Though the data do not need real-time analysis per se, the constant data stream of highly distributed units must be considered. 3.2 The Digital Shadow The digital shadow is a “sufficiently accurate mapping of the processes in production, development and adjacent areas with the purpose of creating a real-time capable evaluation of all relevant data” [40]. Since most digital shadows describe production machines, Gussen et al. created the first definition of the digital shadow of the customer as all “data that encompass aspects of the direct interaction between the customer and the product” [41]. Using the overview of data and data analysis methods in Sect. 2, we can further refine the understanding of the digital shadow. In product development, the digital shadow of the customer is used together with information about product and process capabilities to create a holistic base of information. The key challenge is to combine different data types to derive customer insights that are feasible in the production landscape. Meyer et al. propose the idea of redefining the digital shadow during the product development process [42]. The sequential methods regarding the questions: which product features have an impact on the customer and how these features have to be specified, fit into this understanding of refinement. Not only data types and data analysis methods are heterogeneous, but the resulting information is also used by various departments with a specific purpose at different phases of the development process. The digital shadow must be task specific. In product development, objectives vary from increasing the Perceived Quality, reducing complaints, generating innovations or tailoring the product experience. The digital shadow must allow for this range of tasks by providing relevant information for specific questions. In summary, both concepts, data lake and digital shadow, can be used for customer data and product development. However, the decisions in product development are more diverse and are based on multiple data types. An automation of the data processing and analysis – the vision of the Internet of Production – requires a deep understanding of the data’s structure and application possibilities.

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4 Conclusion The research shows the heterogeneity of data used for product development and their application in a company. An explorative literature review and two expert workshops identified eight customer data and five product data types relevant for product development, which we assessed based on five main properties. The analysis of data types and analysis methods in the context of the Industry 4.0 framework, Internet of Production, showed that its main principles, the data lake and digital shadow, are applicable to customer data and have the potential to increase automation of analysis and decision quality. Further research is necessary on integrating the data lake into a company’s knowledge management and further describing data types as machine-readable input using, e.g., UML. Acknowledgement. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2023 Internet of Production – 390621612.

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Development of a IIoT Platform for Industrial Imaging Sensors Christian Borck1(B) , Randolf Schmitt2 , Ulrich Berger1 , and Christian Hentschel2 1

2

Automation Technology, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany [email protected] Media Technology, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany

Abstract. In the industry, connecting machines and tools - also known as the industrial Internet of things (IIoT) - is an essential part of the digital transformation of a company. The aim is to increase the efficiency and predictability of complex processes. In manual and semi-automatic processes, imaging sensors can help to monitor conditions, gives automated feedbacks to a central system, and e.g. provide current information for a digital twin. However, when imaging sensors are integrated into established IIoT platforms, they quickly reach their system limits due to the multidimensionality and high update and data rates. This paper presents a software platform that enables decoupled automated image processing through the abstraction and contextualization of the sensor technology and its data as well as a plugin architecture. Analogous to edge computing, partial processing can already be performed close to the sensor node to condensate data and reduce network loads and latencies. Thereby, all these approaches increase the longevity, flexibility and scalability of multi-sensor systems and associated processing algorithms. Based on the generic structure of the sensor network, the user is provided with an intuitive user interface that is based on IIoT platforms and enables the integration of their processing pipelines even for non-experts, despite the high complexity of the data. Keywords: Multi camera system · Hardware abstraction fusion · IoT · Rapid prototyping · Big data

1

· Sensor

Introduction

The digitalization and automation of processes are currently one of the major challenges for the industry and sensors are one of the major for automatically acquire data point and transform it from the real world into a digital image [16]. For the integration, networking and evaluation of low-dimensional sensors (e.g. pressure sensors), the Internet of Things (IoT) has become established in recent c Springer Nature Switzerland AG 2022  A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 330–338, 2022. https://doi.org/10.1007/978-3-030-90700-6_37

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years [18]. With it, machines, tools and other crucial components communicate with each other to enable higher-level intelligence. For this purpose, the sensor information is usually exchanged via a so-called IoT platform. Algorithms can perform faster and sometimes more complex evaluations based on this centralized and homogenized sensor information compared to classic methods and thus, under certain circumstances, make more valid decisions. In manufacturing with fewer machines and sensor, camera systems can be used for automation like stock taking or quality control of assemblies. In contrast, multidimensional sensors such as imaging sensors and depth cameras are still underrepresented in IoT systems, as they place different demands on the systems in terms of algorithms, performance and data transfer rates. Established image processing systems (vision systems) already demonstrate the added value for data acquisition in production through imaging sensors and, like IoT, are among the enabling technologies for Industry 4.0 [4]. The platform shown in this paper demonstrates the advantages of combining both technologies in one framework.

2

Related Work

In the context of the fourth industrial revolution, the networking of devices (IoT) and the evaluation of images (computer vision) are becoming increasingly important. Dedicated solutions for both areas are already established in the industry, such as Mindsphere [11] and openCV [15] to name a representative of each. The first systems that could be interpreted as mixed solutions, such as DEEVA [1] and CVToolkit [7], have also been presented in the research. These use sensors networked via IoT systems to implement application-based image processing on the collected data. Due to the focus on two-dimensional image data, the data volumes to be transmitted there are higher than with usual onedimensional data from IoT systems, but the transmission was not optimized for higher data volumes as with point clouds. In addition, the solutions are very application-oriented and do not represent a user-oriented platform with corresponding tools for processing in a wide range of application areas. Merging image processing systems into the world of IoT requires the implementation of a well-designed architecture covering fundamentals to integrate different types of camera systems, image data and algorithms in a popular fourlayer IoT structure: Perception, Network, Edge/Fog (optional) and Application - PNA for short [2]. Designing such an architecture can be challenging, has no best practices and is based on personal experiences and various techniques out of software engineering. This paper describes the core elements of our system with a focus on the machine to machine communication. To prove our concepts in this work we develop an open-source software solution called the IVI-Framework developed in .Net Core and .Net Framework.

3

M2M Communication

Communication between things is one of the major tasks in IoT to get everything connected and achieve a higher intelligence based on complex algorithms of a

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data collective. IoT systems and their PNA layers are loosely connected to the hierarchical heterogeneous network structure of the Internet which is split into the different ranges GAN, WAN, LAN and PAN - Global, Wide, Local and Personal Area Networks accordingly [3]. This hierarchical network is built of many technologies and standards such as 4G for WAN, Ethernet (IEEE 802.3) and Wi-Fi (IEEE 802.11) for LAN, Bluetooth (IEEE 802.15.1) for PAN [17] and 5G as spanning technology. Most of the technologies especially 4G, Wi-Fi and Bluetooth are bad in scalability, which can be an issue when connecting 50 active devices and more. The massive scalability with the mMTC profile of 5G revision 17 is scheduled for mid-2022 and there were no real competitors, standards like ZigBee (IEEE 802.15.4) and Z-Wave were established to connect up to 6000 active devices and more inside one network [8]. The connection to digital camera systems is mostly established in one of two ways: closed circuits like IP (Internet protocol) cameras or host-driven like USB cameras. Both are directly or indirectly able (using a host system) to build IP camera networks to unicast, multicast or broadcast their video signals using streaming protocols like Secure Real-Time Transport Protocol (SRTP). Typically, both categories are externally powered, so there is no need for low power network standards. When it comes to distributed camera-based images processing, the load on the network is one of the bottlenecks of such systems because of the multidimensional bulky characteristic of its data. Not each network can handle this. Compressing a typically RGB and an unreduced (double precision) point cloud stream of a Microsoft KinectTM V2 with our system results in a symbol rate of 9.1 Mb/s and 23.3 Mb/s respectively. These rates are over the limits of many IoT network standards like Bluetooth or Z-Wave but can be handled by many current IP-based networks like Wi-Fi, 4G, Ethernet and especially by 5G-eMBB depending on the topology and traffic of the distributed camera system. Beside Lumi United Technology sells a ZigBee camera named Camera Hub G2H F93C with a resolution of 1080p 24 Hz [20]. Doing a hypothetical encoding with an HEVC encoder under ideal conditions results in a symbol rate of 200 kb/s or a theoretical maximum framerate of 1.35 Hz using the current standard of ZigBee. The specifications of the camera indicate that the video signal must be transferred over its Wi-Fi interface. In summary, our solution is split into Perception Server, Network Server and the Client Application according to the PNA structure of the IoT architecture communicating of TCP/IP and some interprocess standards described in the next section. The Perception Server integrates the different camera systems into the abstract Node Tree structure (Sect. 3.3) of our system. The Network Server connects to the Perception Servers to acquire the camera systems of the different subnetworks, acquires and processes the data from the sensors and provides the results to the client applications. Additionally, multiple Network Servers are instantiable in the same network and can connect to the same Perception Server to allow cluster processing. The integration of camera systems is currently focussed on USB cameras with available API (application programming interface) for .Net and IP-based cameras because of its population. Other types

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of camera interface and systems also are integrated by extending our Node Tree and Data Objects (Sects. 3.3 and 3.2). 3.1

Interprocess Communication

Communication is also a task between instantiated programs (processes) called interprocess communication (IPC). Processes a usually isolated by the underlying operating system (OS) to prevent data conflicts and to implement security aspects. Especially in complex applications, it is an advantage to split it into multiple processes communicating with each other to improve modularity, computing speed, stability and scalability. Sharing data between those processes is possible in several ways. Many modern OS provides functionalities for shared memories and synchronization in their APIs to allocate a memory address room for sharing data between process and keeping in consistent respectively. This allows the splitting of monolithic processes into smaller more responsive processes. Message passing (MP) and remote procedure call (RPC) are two other common methods of IPC to send and receive an addressed data package (message) with parameters for a pro- Fig. 1. Mean sum of output size and cedure call on the receiving side. Both (un-)marshalling time of .Net serializers methods differ in the way the destined based on 25k objects [13]. procedure is called. While in MP the call is implemented on the receiving side following depending on the information in the message, RPC allows calling the procedure directly on the sender side using proxy implementation with hidden network capabilities. Interfaces are defined using an IDL (interface definition language). MP and RPC are popular in local and remote IPC because of availability, flexibility and interoperability [6,19]. Sending data structures over networks requires any kind of marshaling to bring those structures in serial order in this way, that they can be reconstructed on the receiver side. Benchmarking different .Net implementations of marshaling can help to choose a suitable solution for MP or RPC for our project. We have to keep in mind, that benchmarks are only snapshots and can differ depending on the different implementations of the same standard, the version, on the benchmark host. Nagy and Kovari [13] has evaluated the serialization and deserialization times as well as its binary size for six .Net protocols as shown in Fig. 1. He concludes that binary serializers are performing significantly better in serialization time and size compared to textual serializers. He shows, that Avro - closely followed by Protobuf and Thrift - scales the best on small and large numbers of objects. Also other benchmarks are getting similar results [10,12,14]. Comparing

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protobuf-net with Google.ProtoBuf shows 1.7 to 2.7 longer marshaling (depending on the data objects) and 1.3 longer unmarshalling times [9]. Investigating ProtoBuf (Google.ProtoBuf and protobuf-net), Avro and Thrift shows, that all are use some kind of data schematic and furthermore implementing RPC with these schematics as IDL. Additionally, protobuf-net supports class annotations to define those schematics directly from code, which prevents inconsistencies between code and schematics. In our system, implementing the communicating with MP via TCP/IP using protobuf-net has led to practicable results. The messages are containing processing or synchronization data for the node tree explained in Sect. 3.2 and 3.3 respectively. We decided to use message passing over RPC so we have more flexibility in applying compressions on messages. Furthermore, not every RPC standard can use UDP for message transportation, which may have lower latency compared to TCP, but can be an aspect in future works [5]. 3.2

Data Objects

In general, there are different types of cameras directly or indirectly producing different kinds of multidimensional data objects: color image, depth image, point clouds, light field, voxel grids, octrees, skeletons and others. Integrating such data into an IoT platform according to data structures in the IoT middleware supporting marshaling technologies. The fact that the data is in a discrete and finite space basically allows its marshaling, which is for example a common strategy to serialize digital images. Most of the IoT systems only support data up to dimension one such as temperature, states, pressure or strings. Since multidimensional data objects are not natively supported, it is difficult to work with and can often bring two big side effects. The size grows with nd depending on the dimension d, so multidimensional data can become bulky. A second major issue is that images and point clouds have high update rates in common scenarios like video camera streams or 3D factory scans. Combined they create huge data traffic, which must be handled by all IoT layers. Another problem is that multidimensional data may require metadata to be well defined, such as resolution or bounding boxes, so the data object is not just serial and need to be marshaled. Since we focus on camera-based systems, we started to implement different image and 3D types which are inspired by different application programming interface (API) types of the RGB-D (a combination of color and depth) cameras Microsoft KinectTM V2 and the Intel RealSenseTM D400 series. The currently available types are Color bitmaps covering sRGB, YUY2 and 16-bit grayscale as well as 16-bit Depth images with extra attributes such as the depth range. It also supports colorized PointCloud and Voxel structures (Voxel grid and octree) for flexible or compact 3D representations respectively. Also, Body objects can be used to represent humanoid skeleton structures with named joints and states for gesture recognition.

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Addressing and Accessing

Deterministically accessing huge amounts of local and remote device requires a addressable hierarchy referenced by identifier paths like real-world addresses (country, city, street, etc.), telephone numbers (country, city, etc.), IPv4/v6 addresses (country, substructures, etc.) or in case of ZigBee from a frequency spectrum over network, node and service IDs down to single attributes of an object inside a service. One of our core structures is the addressable 4-layer node tree with up and downlinks as shown in Fig. 2a, which gives access to the available camera systems without the need for knowledge about network topologies and technical details of devices. Each node covers its UID and state events (both explained later in this section) as well as its layer (root, host, device or channel), parent-children links and extensible properties. Each node layer is implemented in a separate class deriving from the base class node containing node-common parts listed before and is implementing INode, IHost, IDevice or IChannel respectively for abstraction and capsulation. The root node is the central access point to every node in the tree and is a singleton.

Fig. 2. Our node system split into the tree structure (a) and the state machine of each node (b).

From there, every node of the tree can be accessed indirectly by traversing over the tree or directly by using our address system which we explain later in this section. A host is directly referenced by the root and represents the local host HL [0] or a joined remote host HR [i], i ∈ N. A remote host instance encapsulates its message-passing implementation as described in Sect. 3.1 and imitates a local host and its subtree to the outside. The counterpart of the remote host is a class simply called sender, which awaits incoming connections, listens to the event on the local host and sends it to the remote host instance. A device is a generalized abstraction of any kind of IoT device which owns channels and can provide its metadata such as the serial number. The current node concept only supports sensors but is extensible to actuators. Each channel of a device is represented

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by a typed channel sub-node which provides the typed data object using event messages for observers. The tree structure simplifies the process of accessing different nodes such as different camera models and unifies the access to local and remote devices using the interfaces INode, IHost, IDevice and IChannel. Simplifying the access and control of states of local and remote nodes, we decided to implement a network synchronized state machine as shown in Fig. 2 (b). A NEW enabled component passes through the processes of INITIALIZING the node instance and CONNECTING to the hardware until it is connected and RUNNING. The state machine also maps a DISABLED by user state and non-deterministic errors by an OFFLINE state. Both states are followed by a CONNECTING attempt to get to RUNNING. In case of unused node or invalid behavior, a node must be DISABLED and DISPOSED. The state machine has also up and down dependencies in the node tree. Enabling or disabling a node also recursively enables its parent nodes or disables its child nodes respectively. Disposing of a node also disposes of its subtree. Additionally, there are some corner cases such as the root which cannot be disabled. In this case, we added an EnablingConstraint which can be annotated to such a class to deny disabling such a node. Since sensors don’t have a constant way of addressing we developed a combination of two different unified address systems to simplify the access to each node. Both identification systems are implemented as a path with dot-separation to access a tree node by routing through the tree. The InternalID is an automatically 16 bit index vector as shown in Fig. 2 (a). It is used to provide a compact structure for network routing. The highest-level index also indicated if a node is local (zero) or remote (none zero). A remote host can only receive messages from local nodes (InternalID is starting with “0.”) of the remote system. The identifiers are automatically remapped according to the remote host’s internal ID. Besides, the UID (Unique Identifier) is a user-readable identifier, which is unique over network structures and robust against reinitialization. Its path is built of a dot-separated chain of different hardware identifiers like names N (e.g. host, product, channel names) and serial numbers S in the form N #S or if no serial number is available just N . The tree structure also allows wildcards search so e.g. similar channels in the sensor network can be grabbed easily to simplify building sensor fusion systems.

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The paper shows that it is possible and useful to implement a multi-camera system based on the architecture of an IIoT system, thus combining the advantages of classical sensor/image processing platforms and common IIoT platforms. The developed software can process multidimensional data and provide the data across the three IoT layers by decentralized processing in the sensor node and the choice of suitable IoT network protocols. Abstraction to the different channels provided by a sensor enables the decoupling of hardware and software, improving the longevity of developed algorithms and applications.

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In future work, our system can pass many ways to extend and improve. Our work has shown that simplifying the process of integrating sensors and building algorithms is very important especially for non-experts. We also determined high load networking and garbage collection, so further work is required. In addition, security aspects have to be revisited to build secure application especially with camera systems.

References 1. Awalgaonkar, N.M., Zheng, H., Gurciullo, C.S.: Deeva: A deep learning and iot based computer vision system to address safety and security of production sites in energy industry (2020) 2. Bilal, M.: A review of internet of things architecture, technologies and analysis smartphone-based attacks against 3d printers (2017) 3. Brown, A.: LAN WAN PAN MAN: Staying Connected While Working Remotely. Techopedia 2013 (2013). https://www.techopedia.com/2/29090/ networks/lanwanman-an-overview-of-network-types 4. Cohen, Y., Faccio, M., Pilati, F., Yao, X.: Design and management of digital manufacturing and assembly systems in the industry 4.0 era. Int. J. Adv. Manuf. Technol. 105(9), 3565–3577 (2019). https://doi.org/10.1007/s00170-019-04595-0 5. Coonjah, I., Catherine, P.C., Soyjaudah, K.M.S.: Experimental performance comparison between TCP vs UDP tunnel using OpenVPN. In: 2015 International Conference on Computing, Communication and Security (ICCCS), pp. 1–5. IEEE (2015) https://doi.org/10.1109/CCCS.2015.7374133 6. Czaja, L.: Interprocess communication. In: Introduction to Distributed Computer Systems. LNNS, vol. 27, pp. 119–139. Springer, Cham (2018). https://doi.org/10. 1007/978-3-319-72023-4 5 7. Deshpande, A.M., Telikicherla, A.K., Jakkali, V., Wickelhaus, D.A., Kumar, M., Anand, S.: Computer vision toolkit for non-invasive monitoring of factory floor artifacts. Procedia Manuf. 48, 1020–1028 (2020). https://doi.org/10.1016/j.promfg. 2020.05.141 8. Flynn, K.: 5g in release 17 – strong radio evolution (2019). https://www.3gpp.org/ news-events/2098-5g-in-release-17-%E2%80%93-strong-radio-evolution 9. Geeknoid, Serialization Benchmark (2021). https://github.com/geeknoid/ SerializationBenchmark 10. Gravell, M.: (18.04.2021) c# - Performance Tests of Serializations used by WCF Bindings - Stack Overflow. https://stackoverflow.com/questions/3790728/ performance-tests-of-serializations-used-by-wcf-bindings/3793091#3793091 11. MindSphere, Homepage (2021). https://siemens.mindsphere.io/en 12. mythz (18.04.2021) c# - Fast and compact object serialization in .NET - Stack Overflow. https://stackoverflow.com/questions/549128/fast-and-compact-objectserialization-in-net/3508940 13. Nagy, A., Kovari, B.: Analyzing .NET serialization components. In: Szak´ al A (ed) SACI 2016, Piscataway, NJ, pp. 425–430. IEEE (2016). https://doi.org/10.1109/ SACI.2016.7507414

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14. Novak, M.: Serialization Performance comparison (C#/.NET) - Formats & Frameworks(XML-DataContractSerializer & XmlSerializer, BinaryFormatter, JSON- Newtonsoft & ServiceStack.Text, Protobuf, MsgPack) (2014). https://maxondev.com/serialization-performance-comparison-c-net-formatsframeworks-xmldatacontractserializer-xmlserializer-binaryformatter-jsonnewtonsoft-servicestack-text/ 15. OpenCV, Home - opencv (2021). https://opencv.org/ 16. Sehrawat, D., Gill, N.S.: Smart sensors: Analysis of different types of IoT sensors. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp 523–528. IEEE (2019). https://doi.org/10.1109/ICOEI.2019.8862778 17. Silva, B.N., Khan, M., Han, K.: Internet of things: a comprehensive review of enabling technologies, architecture, and challenges. IETE Tech. Rev. 35(2), 205– 220 (2018). https://doi.org/10.1080/02564602.2016.1276416 18. Stankovic, J.A.: Research directions for the internet of things. IEEE Internet Things J. 1(1), 3–9 (2014). https://doi.org/10.1109/JIOT.2014.2312291 19. Stevens, W.R.: Interprocess communications, UNIX network programming, vol / by W. Richard Stevens ; Vol. 2, 2nd edn. Prentice Hall, Upper Saddle River, NJ (2009) 20. Alliance, Z.: Camera Hub G2H (2021). https://zigbeealliance.org/de/zigbee products/camera-hub-g2h/

Digital Twin Design in Production Sarah Wagner1(B) , Michael Milde1 , Félicien Barhebwa-Mushamuka2 , and Gunther Reinhart1 1 Institute for Machine Tools and Industrial Management, Technical University of Munich,

Boltzmannstraße 15, 85748 Munich, Germany [email protected] 2 Institut Mines-Télécom Atlantique Bretagne, Rue Alfred Kastler 4, 44307 Nantes, France

Abstract. Current trends, such as globalization, demographic change, and individualization are increasing the complexity of production. In many production applications, holistic decision-making presents a challenge due to the lack of transparency, inadequate databases, and the unknown effects of decision alternatives [1]. Digital twins as high potential decision support tools are widely recognized as addressing these challenges in production. The design process, however, still requires research and methodologies. This paper presents a requirements-oriented procedure for designing digital twins in production. The successive specification of requirements results in validated digital twin concepts for various applications. Keywords: Requirements · Methodology · Procedure · Development · Concept

1 Introduction Production companies face an increasingly dynamic environment including globalization, demographic change, and individualization. These factors complicate holistic decisions in production due to the lack of transparency, inadequate databases, and the unknown effects of decision alternatives. Information systems used for decision support are not sufficient due to missing holistic representations of reference objects, poor simulation capabilities, and a lack of interconnection. The limited data integration and interoperability of existing systems increase costs, times, and customer dissatisfaction. Companies need a tool to support their complex production applications effectively. Due to its character as an enabler and new technologies supporting its implementation, a digital twin (DT) can solve the mentioned challenges and empower the production of the future. Current research, however, lacks methodologies for designing DTs in various production applications. Existing publications address specific DT solutions without describing a generally valid procedure for designing DTs in various production applications. This paper, therefore, presents a requirements-oriented procedure to answer the research question of how to design a DT for numerous production applications. To achieve this goal, Sect. 2 introduces the fundamentals and the state of research of DTs in production. To address the research question, Sect. 3 describes the procedure for DT design. Finally, the conclusion and outlook of the paper follow in Sect. 4. © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 339–346, 2022. https://doi.org/10.1007/978-3-030-90700-6_38

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2 Fundamentals and State of Research Fundamentals. The DT concept, with its origins in the aerospace industry, entered the production sector for different reference objects [2]: DTs for production objects (resources or products), people, processes, or production networks as a linkage of the previous levels. Due to the lack of a unique definition, we conducted a literature analysis and expert interviews to develop a unique DT definition: A DT is the realistic and realtime representation of a physical space along its lifecycle through correct and data and information of varying granularity. It enables simulations and predictions about the physical space and has an application- and task-oriented architecture. We define DT design and implementation as processes of DT development. A technology-independent DT concept results from the design and is realized during implementation. State of Research. By conducting a literature review, we identified five categories of approaches to design DTs in production. First, structure-based publications (1) do not provide a procedure for designing DTs but present the DT structure [2–4]. They define different DT modules including the physical space, its representation in the virtual space, and the communication interface between the two. Dimension and layer-based procedures (2) provide DT dimensions or layers representing the essential characteristics of a DT [5–7]. This decomposition allows the development of layer after layer to alleviate the complexity of DT design. The dimensions range from defining the level of the granularity to specifying the hardware and software aspects and the level of autonomy. Functionality and service-based procedures (3) guide the DT design using the various features and services a DT can provide [8–10]. Different DT components are defined based on the functionalities accomplished, such as data collection and processing, or on the integration of independent and autonomous control decision-making functionalities into the existing system. The DT objective determines its design process. Sequentialbased methodologies (4) propose step-by-step guidelines for DT design [11–13]. In general, these procedures start with the definition/analysis of requirements and go through the modeling of the components up until the interaction between the physical system and the virtual system. Design procedures adapted from other disciplines (5) form the last category and are based either on certain existing methodologies in IT design or on observations of nature such as biomimicry [14, 15]. Research Objectives. As several definitions of DTs exist, various procedures support DT design in literature dedicated to specific problems. Only two publications provide a complete generic procedure to design DTs for predefined areas [4, 5]. In all but one publication [4], the sequential-based DT design procedures enumerate steps to design a dedicated DT without showing how to apply these steps application-specific. Only a few contributions [9, 12] discuss the validation and verification of the DT concepts and their design procedures. Although the lack of a unique DT definition infers specifying DT requirements as a prerequisite, only two publications [11, 16] present DT requirements without providing a procedure for their application-specific determination. Indeed, no article proposes a generic requirements-oriented DT design procedure in production as is the aim of this paper.

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3 Digital Twin Design Although the state of research does not provide a procedure for DT design, there are several requirements-oriented procedures for software design. They recommend identifying requirements as a prerequisite for software design to ensure the development of the desired system. This paper tailors the identified design procedure from software design to DT design in production. The underlying procedure developed from an extensive literature analysis consists of five partially iterative phases shown in Fig. 1. The phases requirements determination, concept modeling, and requirements specification are almost sequential for one requirement, but the respective refinement of subrequirements and concept modules is iterative and incremental supported by design thinking. Although requirements management is part of several software design procedures, it is not yet required in the conceptual DT design. The following sub-sections explain all five phases and refine their application to design DTs in production.

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3.1 System Context Setting The system context is the part of the DT environment that significantly influences the DT concept and its requirements. To set the system context, designers separate the DT system from its environment and differentiate the system context from the irrelevant environment. Relevant research and industry stakeholders support the procedure. These include publications or experts that deal with the specific DT application and/or DTs in general. With their help, designers identify which company-specific constraints, like existing systems, are part of the DT system. This does not include constraints resulting from the objective to develop a DT considered during requirements determination. A morphological box and definitions of the underlying reference object support the definition of the system. For example, application areas, time horizons, and decision areas are possible categories. The system context consists of technical systems, DT stakeholders, and business processes with their artifacts and events. Often, technical systems like a database or other DT elements already exist within the company. Designers do not need to develop them as part of the DT but need suitable interfaces to those technical systems. DT stakeholders are mainly the users. Depending on the area, these are, for example, process engineers, production planners, and operators, each with their possibly different characteristics and interests. Preprocesses or postprocesses can influence the DT even though they are not part of the reference object. This can include processes like logistics to be ignored within the system. Existing artifacts like three-dimensional representations may need an interface inside the DT. Lastly, various events can influence the DT like the planned introduction of a new information system.

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3.2 Requirements Determination Once the system context is known, requirements determination develops DT use cases and preliminary DT requirements. Through brainstorming, review meetings, and document analysis, the stakeholders help to define the core DT functionality within use case diagrams. The users of those diagrams are a subset of the DT stakeholders. Although the use cases depend on the application functionality, predefined DT services like fault analysis [17] can support the use case definition. Together with the system context, the resulting use cases are the basis for determining several DT requirements. To identify and master all DT requirements, a requirements hierarchy is necessary, which provides layered super- and sub-requirements structured in a manageable way. To ensure the development of a DT, we recommend using the six most important general DT requirements as super-requirements. These resulted from an analysis of papers written by the central DT authors from industry and research [1]. Figure 2 provides an overview of the possible ways to application-specifically derive so-called building and detailed (sub-) requirements from the base (super-) requirements. Designers work on all requirements (R) iteratively and incremental due to their correlation. Varying Granularity (R1). A DT must consider different granular levels of abstraction and has to map the reference object from a micro-atomic level to a macro-geometric level. Due to the complexity of reference objects, the DT as a representation requires a transparent structure with an abstract and detailed mapping of the physical object. Existing abstraction level definitions help to specify and detail the granularity requirement depending on the object of consideration. For example, Wiendahl [18] defines various extendable abstraction levels for production networks and products as shown in Fig. 2. When introducing DTs for processes, higher-level procedures and lower-level operations enable a holistic representation. Application-specific fundamental research helps define super-(procedures) and sub-processes (operations). Several employees grouped, for example, by shift calendars as well as individual employee components like the physics of a hand represent different levels of granularity. Besides production employees, customers also have specific abstraction levels. Finally, the production network combines the previous components and levels of abstraction. A network at a high abstraction level usually implies an application-specific mix of abstract consideration of the respective objects, people, and processes. The abstraction levels enable structural requirements and detailed requirements result from their application. Realistic Representation (R2). The DT must realistically map the reference object in the defined granularity without any gaps, also referred to as high fidelity. Representation refers primarily to data and information, which then allow visualization. Which data and information the DT provides at each abstraction level highly depends on the DT functionality. Depending on the DT use cases, a different representation is relevant for the gap-free reflection. Definitions and concretizations of the reference object and the use cases help to determine which data and information are necessary. Depending on the category of the reference object, Fig. 2 shows the possible data and information for production objects, people, processes, and networks.

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Simulation and Prognosis Capability (R3). Using simulations, the reference object can be monitored, analyzed, optimized, and verified. This requirement is specified by what should be simulated with which constant input and how. Depending on the system context, DTs can simulate everything from logistics to specific processes to support planning, realization, and operation. Figure 2 shows possible simulation categories (output) [19]. Depending on the output, we need to define constant parameters to extract the predefined simulations (input). Lastly, requirement engineers have to specify the steps of how simulations generate output from input. Depending on the desired degree of automation, the simulation capability of the DT must support or completely automate the procedure to generate simulations [20]. Detailed requirements must describe how the DT covers each phase. The forecasting capability can be equivalent to the simulation capability if simulations achieve the forecasts. DT prognosis modules can also predict the future by using artificial intelligence, for example. Similarly, DT designers must then define what to predict with which input and which solution approach. Life Cycle Orientation (R4). The virtual DT aims at accompanying the physical reference object throughout its entire life cycle. Depending on the application, the DT must support different life cycles or phases. Designers can take literature-defined life cycles as a reference and refine them application-specifically. The phases of the life span of a person can be considered, omitting childhood and retirement for workers. If one considers an operative time horizon, performance curves may fit. Product life cycles or bathtub curves represent the life cycles of production objects. Similarly, the factory life cycle includes dimensions relevant to the production network. For processes and as more and more DTs focus specifically on defined life phases, the lifetime view within the phase/process becomes important by considering changes (see Fig. 2). Task and Application-oriented Architecture (R5). The requirement for varying granularity and the high system complexity of the DT result in the need for a suitable architecture combining all DT elements. Although the publications described in Sect. 2 explain which elements the DT architecture can cover, a task and application-oriented DT architecture is necessary. The main DT goal to make well-founded decisions concretizes the necessary architecture requirements. Numerous publications explain the data information knowledge wisdom hierarchy to support founded decisions. This pyramid describes how data can be transformed into information, knowledge, and decisions (wisdom). Defining requirements at each level helps to find all architectural requirements: How should the DT store, manage, and generate data? Which algorithms are required to process data into information? What meaning and semantics (and thus knowledge) should be connected to the data? Which requirements ensure the interactions of all DT elements to apply the DT and make founded decisions? Quality (R6). Qualitative requirements include requirements that do not focus on the DT functionality. Real-time capability and correctness are obligatory DT quality requirements. Quality aspects for software can also be important. The trustworthy guidelines of artificial intelligence and ISO 9126 [21] give guidance on possible qualitative requirements, which must be detailed regarding the application.

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Portfolio, variant, individual, part group, and single part Customer: customer portfolio, customer area, sales areas, individual customer, order, and order item Employee: employee portfolio, skill variant, individual, element group, and human element Procedure portfolio, procedure, process group, process, operation group, and single operation Network, location, area, system, cell, and station

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R1 The DT for order processing must consider different granular levels of abstraction. base requirement R1.1 The DT must map production planning and scheduling with varying granularity . building requirement R1.1.1 The DT should take network planning into account. detailed requirement

Fig. 2. DT requirements determination

3.3 DT Concept Modeling An application and task-oriented DT concept can be derived from the requirements. A concept refers to a rough architecture without technology selection, which is also referred to as a framework. The DT concept, therefore, is understood to describe individual DT components and their interactions with each other and the real system. The essential components for storing data, generating new information, and capturing knowledge within the DT can be derived from R5 (referring to all sub-requirements as well). The former can be implemented as a central database (1) with the ability to store all relevant data in a structured form. Application-specific modules (2) process the data to generate specific functionality/information. The latter corresponds to a knowledge model (3), which stores data in a semantic form that can be read by humans and machines. Knowledge models can be realized, for example, via ontologies, which in turn also support the automation of the required simulation/prognosis (4) based on R3 [22]. To fulfill R4, it is also necessary to integrate functions for versioning all modules in the DT concept. The exact functionality of these elements is defined by the specific R1 to R5. In addition to those basic components, their interfaces (5) to each other and to the real system are also part of the DT concept. Although only sporadically described in the literature, they are crucial for R4, real-time capability, and other quality aspects defined in R6. These include (a) the interaction between the database and the simulation module and (b) the interface between the database and the knowledge model. The first interface provides the logic

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for the automated updating of the simulation model and its initialization with the current system state. This interface is bidirectional in order to store the results of simulation runs in the database. The second interaction is responsible for updating the knowledge model. Furthermore, suitable interfaces must integrate all application-specific modules (c). Lastly, the interaction of the DT with its real counterpart is part of the concept (d). Again, this interaction does not represent a pure transfer of data to the DT but implements processes for data transformations, cleaning, quality assurance, and merging. R1 to R4 and R5, in particular, show how a virtual DT of the physical system is conceptualized while R6 reinforced by R4 and R5 provides elements that allow a good synchronization of the DT with its physical equivalent. 3.4 Requirements Specification Formulation rules and blueprints help to document the requirements. For each requirement, designers have to use a subject, object, predicate, and a modal verb like ‘must’, ‘shall’, ‘will’, or ‘may’ (descending priority) as exemplified in Fig. 2. Numerous formulation rules like the use of short sentences are provided [23]. Bidirectional traceability has to be specified by indicating (1) the derivation of requirements from sources and (2) the relation of the DT elements to its requirements [23]. The requirements derivation is included in each requirement explanation and a traceability matrix ensures the traceability of all DT elements to illustrate which part applies to which requirement. 3.5 Validation and Verification Requirements validation checks if the requirements are formulated as desired by evaluating criteria like the use of short sentences [23]. Specifying the degree of fulfillment of all detailed requirements verifies the concept. The fulfillment of structural/basic requirements in concept verification results from the average of its sub-requirements. The degree of fulfillment should be consistently high with only a few justifiable exceptions.

4 Conclusion and Outlook The paper presents a requirements-based procedure for DT design in production. We tailored existing software procedures for designing DTs. The main body is the derivation of application-specific DT requirements that guide the user towards developing the desired and validated DT concept. A DT for order processing, other DTs within the ongoing research project, e.g. for process planning (ASSISTANT project), and DTs for battery production validate the procedure. The limitations are the choice of technology and the lack of implementation. Further research, therefore, will focus on implementation guidelines for various applications to support the whole DT development. Acknowledgment. The European Commission, H2020 – ICT-38-2020, artificial intelligence for manufacturing funded this work under grant agreement No. 101000165 (Project ASSISTANT). The authors thank all academic and industrial participants.

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References 1. Wagner, S., Milde, M., Reinhart, G.: The digital twin in order processing. In: CIRP Conference on Manufacturing Systems p. 54 (2021, in press) 2. Lu, Y., Liu, C., Wang, K., et al.: Digital twin-driven smart manufacturing: connotation, reference model, applications and research issues. Robot. Comput. Integr. Manuf. 61, 101837 (2020) 3. Fang, Y., Peng, C., Lou, P., et al.: Digital-twin-based job shop scheduling toward smart manufacturing. IEEE Trans. Industr. Inf. 15, 6425–6435 (2019) 4. Tao, F., Qi, Q., Wang, L., et al.: Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering 5, 653–661 (2019) 5. Stark, R., Fresemann, C., Lindow, K.: Development and operation of Digital Twins for technical systems and services. CIRP Ann. 68, 129–132 (2019) 6. Bazaz, S.M., Lohtander, M., Varis, J.: 5-dimensional definition for a manufacturing digital twin. Procedia Manuf. 38, 1705–1712 (2019) 7. Zhang, C., Xu, W., Liu, J., et al.: A reconfigurable modeling approach for digital twin-based manufacturing system. Procedia CIRP 83, 118–125 (2019) 8. Tao, F., Zhang, H., Liu, A., et al.: Digital twin in industry: state-of-the-art. IEEE Trans. Industr. Inf. 15, 2405–2415 (2018) 9. Min, Q., Lu, Y., Liu, Z., et al.: Machine learning based digital twin framework for production optimization in petrochemical industry. IJIM 49, 502–519 (2019) 10. Negri, E., Berardi, S., Fumagalli, L., et al.: MES-integrated digital twin frameworks. J. Manuf. Syst. 56, 58–71 (2020) 11. Biesinger, F., Meike, D., Kraß, B., et al.: A digital twin for production planning based on cyber-physical systems. Procedia CIRP 79, 355–360 (2019) 12. Schützer, K., Bertazzi, J., Sallati, C., et al.: Contribution to the development of a digital tin based on product lifecycle to support the manufacturing process. CIRP 84, 82–87 (2019) 13. Kortabarria, I.A.: Discrete event simulation procedure to build the production digital twin of highly automated and complex production systems. DYNA 95(5), 478–481 (2020) 14. Schroeder, G.N., Steinmetz, C., Rodrigues, R.N., et al.: A methodology for digital twin modeling and deployment for Industry 4.0. In: Proceedings of the IEEE (2020) 15. Liu, S., Bao, J., Lu, Y., et al.: Digital twin modeling method based on biomimicry for machining aerospace components. J. Manuf. Syst. 58, 180–195 (2021) 16. Durão, L.F.C., Haag, S., Anderl, R., et al.: Digital twin requirements in the context of industry 4.0. In: International Conference on Product Lifecycle Management, pp. 204–214 (2018) 17. Cimino, C., Negri, E., Fumagalli, L.: Review of digital twin applications in manufacturing. Comput. Ind. 113, 103130 (2019) 18. Wiendahl, H.: Veränderungsfähigkeit von Produktionsunternehmen. ZWF 104, 32–37 (2009) 19. Jahangirian, M., Eldabi, T., Naseer, A., et al.: Simulation in manufacturing and business: a review. Eur. J. Oper. Res. 203, 1–13 (2010) 20. Wenzel, S.: Simulation logistischer Systeme. In: Tempelmeier, H. (ed.) Modellierung logistischer Systeme. FL, pp. 1–34. Springer, Heidelberg (2018) 21. DIN ISO 9126: Qualitätsmerkmale von Software. Beuth, Berlin (2020) 22. Jurasky, W., Modder, P., Milde, M., et al.: Transformation of semantic knowledge into simulation-based descision support. RCIM (in press) 23. Grande, M.: 100 Minuten für Anforderungsmanagement. Vieweg, Wiesbaden (2011)

Requirements Analysis for Digital Shadows of Production Plant Layouts Julian Hermann1,2(B) , Konrad von Leipzig1 , Vera Hummel1,2 , and Anton Basson1 1

University of Stellenbosch, 145 Banghoek Rd., Stellenbosch 7600, South Africa 2 Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany [email protected]

Abstract. Global, competitive markets which are characterised by mass customisation and rapidly changing customer requirements force major changes in production styles and the configuration of manufacturing systems. As a result, factories may need to be regularly adapted and optimised to meet short-term requirements. One way to optimise the production process is the adaptation of the plant layout to the current or expected order situation. To determine whether a layout change is reasonable, a model of the current layout is needed. It is used to perform simulations and in the case of a layout change it serves as a basis for the reconfiguration process. To aid the selection of possible measurement systems, a requirements analysis was done to identify the important parameters for the creation of a digital shadow of a plant layout. Based on these parameters, a method is proposed for defining limit values and specifying exclusion criteria. The paper thus contributes to the development and application of systems that enable an automatic synchronisation of the real layout with the digital layout.

Keywords: Requirements analysis Facility layout

1

· Digital shadow · Plant layout ·

Introduction

The recording of the actual plant layout is used as a basis for the restructuring process regarding layout optimisations. At this point the first difficulties already present themselves. In most cases, the available plans of the existing factories are outdated and modifications are rarely included in plans [1]. Smart factories need the ability to make flexible changes to the shopfloor layout [2] which requires a record of the current system landscape [3]. The question therefore arises which parameters must be recorded and what accuracy is required in order to represent the actual plant layout in adequate quality. This representation describes the digital shadow of the plant layout, which is defined by the automated data flow of the captured physical parameters to the digital object [4]. c Springer Nature Switzerland AG 2022  A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 347–355, 2022. https://doi.org/10.1007/978-3-030-90700-6_39

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Plant Layout and Its Properties

Plant layout is also known under other names such as facility layout, facility planning, production layout or facility design. The definition of plant layout is described by Sansonetti and Mallick [5] in the following words: ”It is placing the right equipment, coupled with right place, to permit the processing of a product unit in the most effective manner, through the shortest possible distance and in the shortest possible time”. As shown in Fig. 1, a plant layout can be represented by a coordinate system containing a collection of facilities. The position and orientation can be grouped under the term pose, which describes the position and orientation of an object in three-dimensional space, as defined in ISO 8373 [6]. Since the facilities in a factory are located on the floor, the simplified pose definition can be used, which is also common for mobile robots [7]. The pose of an object/facility in relation to a world coordinate system W can therefore be clearly described by the parameters xi , yi and θi , with the indication W ξOi = (xi , yi , θi ). Besides the pose in the plant coordinate system, each of the facilities has its own properties such as length, width, height, weight, colour, etc. and can be classified into different classes (machine type, production type, manufacturer, ...) depending on their function in the production process.

Fig. 1. Exemplary arrangement of facilities within a plant coordinate system.

3

Requirements Analysis

In order to derive the requirements for the layout recording, the first step is to analyse the purpose of the data. The data is mainly used for layout planning which is part of the material flow planning, with the main objective of minimising the transport distances [8]. The preparations for a layout change can be divided into the following steps: Layout recording, data collection and processing, presentation and evaluation of the actual state. The actual layout thus serves as the basis for subsequent optimisations. The optimised layout is then compared with the actual state to find out whether the changeover is profitable

Requirements Analysis for Digital Shadows of Production Plant Layouts

(a)

(b)

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(c)

Fig. 2. Layout optimisation and evaluation procedures: (a) Material flow matrix adapted from [9] (b) Sankey diagram adapted from [12] (c) Circle diagram adapted from [12]

[11]. Figure 2 shows typical procedures used for layout optimisation. The procedures shown have in common that they basically consider the order of the facilities. Thus, the measurement accuracy of the facility poses must be at least good enough to reconstruct the order of the facilities. Therefore it must be possible to determine the neighbourhood connections of the facilities. There are two basic concepts to describe the neighbourhood, the Moore neighbourhood and the von Neumann neighbourhood. These concepts are illustrated in Fig. 3 and show that there are eight possible neighbours for the Moore neighbourhood and four possible neighbours for the von Neumann neighbourhood. Applying these neighbourhood concepts to, for example, the Sankey diagram (Fig. 2b) or the circle diagram (Fig. 2c), it becomes clear that diagonal relationships are also needed. Therefore it is useful to consider the relationships between factory facilities as Moore neighbourhoods, because it allows the indication of e.g. northeast or southwest.

NW

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Fig. 3. Illustration of the (a) Moore- and (b) von Neumann neighbourhood.

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Position Accuracy

To determine the maximum permissible position error, the worst case scenario is examined below. This scenario consists of the smallest facilities located directly next to each other, as shown in Fig. 4. The neighbourhood areas of facility 1 are represented by the dotted lines in 45◦ division. Facility 2 is located in the eastern part of facility 1. A wrong assignment of the directions would occur as the centre of facility 2 (C2 ) would be shifted over the border g into the north-eastern area due to the measurement error. This leads to the fact that |e2 | must be smaller than the distance between g and C1 . Since the orientation of C1 also contributes to the measurement error and in the worst case is shifted against the direction of C2 , this must also be taken into account, which reduces the maximum possible measurement error to e < |e22 | . For the overall worst-case scenario, it is assumed that both facility are the smallest in the factory with an edge length of |a|. The shortest distance between C2 and g is e2 . Thus for the maximum error of the position measurement applies: e8)

Med (7–4)

Low (18)

Med (17–9)

Low (21)

Med (21–12)

Low (1.5). Hypothesis 3: The rated market potential did not differ significantly between the cocreation and the designer groups. In both jury votes the medians show higher ratings for the co-creation groups (Mdnexp = 1.95, Mdnnon-exp = 1.72) than for the designer groups (Mdnexp = 1.70, Mdnnon-exp = 1.59).

5 Discussion. 5.1 Age-Divers Co-creation with Users as a Benefit for Design Teams The statistics supported our first hypothesis: The outcomes of the design thinking workshops were more creative and had more impact on life-long autonomy (H1). The differences in the U statistics for the expert and non-expert juries are plausible. We expected that the term “overall creativity” would depend on market expertise to identify new ideas and that the expert jury would vote more critically. The descriptive data shows that in both juries the medians of the creativity rates were higher for the co-creation groups. We expected the category “Impact on life-long autonomy” to be not intuitive for laypersons and psychological knowledge is recommended. Thus, we assume the experts’ votes to be more reliable. The rated market potential of the ideas differed not significantly between both groups (H3). We conclude that co-creation does not impede commercially applicable product innovations, but does not foster them either. We can also report that age diversity in the co-creation teams had positive influence on the workshop dynamics: All co-creation workshops were described as more joyful and communicative by the facilitators. The sharing of information and personal stories were described as exciting, fun and valuable insides. Time-boxing and the “persona empathy card game” removed contact barriers, so that everyone was engaged fast and without prejudices. Discussed in the context of “value for company” [26], professional designers should also benefit from integrating elderly users into their ideation process, because it contributes to “economic value” and “functional value”. 5.2 Design Thinking Facilitates Social Innovations No significant difference was found between the groups concerning contribution to social sustainability (H2). Within the pool of ideas, we found a high share of social products or service systems, which were e.g. community-based, fostered sharing or helping. Our findings show that social innovations emerged in both groups, co-creation and solely-designers. We conclude that the design thinking workshop itself brought up social coherence into the solution space with a higher probability. One explanation could be that our first working sheet “map of living 75+” worked as a priming effect, as one major issue for elderly people presented there was “Lack of social contacts”.

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5.3 Limitations Further design thinking workshops should be conducted with the help of our standardized workshop tools and materials to analyze the reasons for the stimulation of social innovations and the role of the participants’ context (e.g. level of education). Due to the project’s resources, we limited this study to 12 workshops. From a qualitative point of view, we saw no saturation within the pool of 75 ideas. As the project was solely located in North-Rhine-Westphalia in Germany, it would be of great interest, if other local settings would influence the outcomes.

References 1. Nations, U.: Department of economic and social affairs, population division. world population prospects: the 2017 revision, key findings and advance tables. In: Working Paper No. ESA/P/WP/248 (2017) 2. von Hippel, E.: The dominant role of users in the scientific instrument innovation process. Res. Policy 5(3), 212–239 (1976). https://doi.org/10.1016/0048-7333(76)90028-7 3. Weber, M.E.A., Van der Laan, D.H.: Does Customer Co-creation Really Pay Off? An Investigation into the Firm’s Benefits from Customer Involvement in New Product and Service Development. In: Brunoe, T.D., Nielsen, K., Joergensen, K.A., Taps, S.B. (eds.) Proceedings of the 7th World Conference on Mass Customization, Personalization, and Co-Creation (MCPC 2014), Aalborg, Denmark, February 4th - 7th, 2014. LNPE, pp. 145–157. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-04271-8_14 4. von Hippel, E.: Democratizing innovation, 1st edn. MIT Press, Cambridge, Mass (2006) 5. Sanders, E.B.-N., Stappers, P.J.: Co-creation and the new landscapes of design. CoDesign 4(1), 5–18 (2008). https://doi.org/10.1080/15710880701875068 6. Beckman, S.L., Barry, M.: Innovation as a learning process: embedding design thinking. Calif. Manage. Rev. 50(1), 25–56 (2007). https://doi.org/10.2307/41166415 7. Gardiner, P., Rothwell, R.: Tough customers: good designs. Des. Stud. 6(1), 7–17 (1985). https://doi.org/10.1016/0142-694X(85)90036-5 8. Kuhn, M., Prettner, K.: Population age structure and consumption growth: evidence from national transfer accounts. J. Popul. Econ. 31(1), 135–153 (2017). https://doi.org/10.1007/ s00148-017-0654-z 9. Halskov, K., Hansen, N.B.: The diversity of participatory design research practice at PDC 2002–2012. Int. J. Hum Comput Stud. 74, 81–92 (2015). https://doi.org/10.1016/j.ijhcs.2014. 09.003 10. Kohtala, C., Hyysalo, S., Whalen, J.: A taxonomy of users’ active design engagement in the 21st century. Des. Stud. 67, 27–54 (2020). https://doi.org/10.1016/j.destud.2019.11.008 11. Großklaus, R.H.G.: Techniken zur Ideengenerierung aus Kundensicht. In: Neue Produkte einführen. Gabler. (2008). https://doi.org/10.1007/978-3-8349-9589-6_10 12. Steen, M., Menno M., De Koning, N.: Benefits of co-design in service design projects 5(2), 53–6 (2011) 13. Minder, B., Lassen, A.H.: The designer as jester: design practice in innovation contexts through the lens of the jester model. She Ji J. Design Econ. Innovat. 4(2), 171–185 (2018). https://doi.org/10.1016/j.sheji.2018.05.003 14. Manzini, E.: Design, when everybody designs. an introduction to design for social innovation. design thinking, design theory. The MIT Press, Cambridge, Massachusetts (2015) 15. Buchanan, R.: Wicked problems in design thinking. Des. Issues 8(2), 5 (1992). https://doi. org/10.2307/1511637

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16. Sarwar, A., Fraser, P.T.: Explanations in design thinking: new directions for an obfuscated field. She Ji J. Design Econ. Innov. 5(4), 343–355 (2019). https://doi.org/10.1016/j.sheji.2019. 11.002 17. Kelley, T.: The art of innovation. lessons in creativity from IDEO, America’s Leading Design Firm. Profile Books. London (2016) 18. Liedtka, J., Ogilvie, T.: Designing for Growth. A design thinking tool kit for managers. Columbia Business School Publishing, Columbia Business School Publications New York (2011) 19. Lewrick, M., Patrick, L., Leifer, L.: The design thinking playbook. Mindful digital transformation of teams, products, services, businesses and ecosystems. John Wiley and Sons, Hoboken, New Jersey (2018) 20. Burkhardt, N., et al.: Das große Handbuch Innovation. 555 Methoden und Instrumente für mehr Kreativität und Innovation im Unternehmen, 1. Aufl. Franz Vahlen, München (2018) 21. Mannix, E., Neale, M.A.: What differences make a difference? the promise and reality of diverse teams in organizations. Psychol. Sci. Public Interest 6(2), 31–55 (2005). https://doi. org/10.1111/j.1529-1006.2005.00022.x 22. O’Reilly, C.A.III, Williams, K.Y., Barsade, S.: Group demography and innovation: does diversity help?. In: Gruenfeld, D.H. (Hrsg) Research on Managing Groups and Teams, Bd 1. Jai Press, Stamford Conn. u.a., pp. 183–207 (1998) 23. Tajfel, H.: Human groups and social categories. Studies in social psychology. Cambridge University Press, Cambridge, London, New York (2010) 24. Hogg, M.A., Turner, J.C.: Intergroup behaviour, self-stereotyping and the salience of social categories. Br. J. Soc. Psychol. 26(4), 325–340 (1987). https://doi.org/10.1111/j.2044-8309. 1987.tb00795.x 25. Kim, W.C., Mauborgne, R.A.: Blue Ocean Strategy with Harvard Business Review Classic Articles “Blue Ocean Leadership” and “Red Ocean Traps” (3 Books). Harvard Business Review Press, La Vergne (2017) 26. Piligrimien˙e, Ž., Dovalien˙e, A., Virvilait˙e, R.: Consumer engagement in value co-creation: what kind of value it creates for company? EE 26(4) (2015). https://doi.org/10.5755/j01.ee. 26.4.12502

Improving the Patient Visit Process in the Pre-treatment Phase Saeedeh Shafiee Kristensen1(B)

and Sara Shafiee2

1 Department of Management, Aarhus BSS, Aarhus University, Aarhus, Denmark

[email protected] 2 Department of Mechanical Engineering, Technical University of Denmark, Lyngby, Denmark

Abstract. During the last decade, more healthcare organizations have been turning to Lean and data driven principles to improve the efficiency and generate a positive impact on throughout, patient satisfaction and quality care. Given the high competition in private healthcare market, the need for achieving higher quality medical care to attract new patients and retain existing patients is getting even more critical for private healthcare organizations such as infertility treatment clinics. This paper builds, tests, and reports the results from the application of value stream mapping during the pre-treatment stage in an infertility treatment clinic. In the case study presented here, we demonstrate how analyzing healthcare processes early in the patient journey and mapping the pre-treatment stage enables the organization to identify the bottlenecks, eliminate waste and deliver more efficient customized patient-focused care. After running a multitude of simulations, this study results in recommending two scenarios as the optimal future states that increase the capacity of the clinic, lower waiting times for patients, improve their experience, and lower the stress on the staff. Keywords: Healthcare · Lean · Patient satisfaction · Value stream mapping · Customized health care

1 Introduction In the last decade, medically assisted reproduction has significantly been improving in terms of outcome indicators. However, process indicators, such as staff and patients’ satisfaction or delivery time, turn to be a vital component in assessing the quality of fertility care specifically due to the physical and emotional burdens of medical interventions on women and men [1, 2]. Research shows that along with medical treatment, a patient-centered quality of fertility care also matters to the patients which in turn has highlighted the adoption of lean principles to affect quality, cost, delivery time, and satisfaction of both service providers and customers [1–3]. Lean methodology can be used as an integrated template to improve efficiency through removal of activities that do not create value for the customer [4]. For health care stakeholders, however, the effective application of lean can be difficult, given the industry’s service-oriented nature and the complexity of its patient care processes. The © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 970–977, 2022. https://doi.org/10.1007/978-3-030-90700-6_111

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focus of lean healthcare is not only on eliminating waste that costs money and evokes patient dissatisfaction but also on any improvement opportunities that leads to iterative improvement and sustained benefit. Besides, it must be embraced as a fundamental shift in thinking that will allow a health organization to more efficiently and effectively provide patient-centered care [4, 5]. The aim of this study is to evaluate the implementation of lean practices in an infertility treatment clinic (hereafter the Clinic) in Iran. First, the paper reports the application of value stream mapping (VSM) in the pre-treatment stage of the fertility care at the Clinic to map and analyze the current state. Second, we will develop the desired future states focusing on performance improvement actions.

2 Case Study: Infertility Treatment Clinic The Clinic mapped in this study was established in 1991 as nongovernmental, non-profit center for the treatment of infertility and In-Vitro Fertilization. The Clinic supports innovation, excellence and the highest ethical standards to increase the success rate of infertility treatment and acts as the leader of stem cell research in the Middle East. In 2019, time of data collection, more than 100,000 couples were under treatment at the clinic, 8% of this number were medical travelers from 23 countries. The Clinic competes with fertility care providers in Iran, Turkey and Dubai who strive to go beyond simple efficiency gains and enhance processes that improve patient satisfaction and quality care. While the Clinic also seeks to maintain a commitment to patient-centered service, there are non-value-added processes that deprive their clients of timely care because their services have not been designed to make the patient care process streamlined. The main deficiencies are attributed to increasing number of patients, inefficient patient flow, lack of standardized processes, overwhelmed physicians and stressful staff, and long patient wait times. The Clinic has recognized that the decision of most couples to choose any specific solutions significantly depends on their experience in the pretreatment phase. Therefore, they need to enhance operations within this phase and redesign processes early in the patient journey. 2.1 Data Collection: Mapping the Current State The Importance–Performance Matrix (IPM). We used IPM to examine areas where the clinic was underperforming or over-performing towards efficient and effective resources allocation as well as to gain insight into which attributes require firm’s managerial attention to achieve customer satisfaction [6, 7]. The IPM was developed based on the results of a patient satisfaction survey that analyzed two dimensions of service attributes: performance level (satisfaction) and importance to customers. At the end of the pre-treatment phase, eighty couples were asked to fill in the questionnaire measured by the five-point Likert Scale and ranged from “strongly agree” to “strongly disagree”. The functional and emotional aspects of patient experience attributes were selected from the relevant literature on patient satisfaction in the healthcare [3, 8–10].

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Analyses of these attributes were then integrated into the matrix to identify primary drivers of customer satisfaction and, based on these findings, set improvement priorities, and identify areas of ‘possible overkill’ and areas of ‘acceptable’ disadvantage (see Fig. 1). Hence, the Clinic could make rational decisions about how to best deploy scarce resources to attain the highest degree of customer satisfaction.

Fig. 1. The performance-importance matrix of pretreatment phase at the Clinic

The matrix shows that attitudes such as quality medical care fall within the appropriate zone. Some emotional support and staff attitude seem to be in need of improvement. However, three competitive factors, communications and responsiveness, hosting amenities and delivery speed, are clearly in need of immediate improvement. These three factors should be assigned the most urgent priority for improvement. The Value Stream Mapping Tool. At the Clinic, the couple should navigate through various stages in their visit. The steps that constitute the couples visit are interrelated, yet they are often performed discretely and without consideration of their impact on care delivery as a whole. In order to think analytically about patients’ visit and from a systems perspective, VSM as a Lean tool was used to understand the flow of patients, supplies or information through the journey of a patient, and map all processes required to deliver a health care service during the pretreatment phase [11, 12]. The daily staff at the Clinic in the pretreatment phase includes eleven people. One clerk schedules appointments at the call center. One nurse registers patient at the reception. Two specialists and two experienced nurses serve patients in the clinical examination stage (CE Stage) in a day and diagnosis and solution stage (DS Stage). There are around 10 specialists, each two serves couples one day per week. Three scanning and laboratory specialists and two nurses work in the Scanning Stage (Sc Stage). All of the staff had their own ideas of ‘what’s wrong with this place’, but in general expressed a sincere desire to serve its customer base better.

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The mapping project concentrated on the woman flow through the pre-treatment phase. Talking with two doctors indicated that they felt that the staff in the scheduling and reception department is inadequate and incompetent. Early in the day the workload was often slow. Then late in the day, they often felt as if they were constantly backed-up and they worked long hours. Having a slightly different take on the situation, the operator focused on as the high number of no-shows and the consequent scheduling plan designed by the specialists, and complexity of non-Iranian patient cases. When a couple called in, the staff would process their request asking a few questions and they would then input the appointment into the computer system. The appointment cannot be earlier than one week. The same appointment time is given to four patients, 35 patients a day from 8 a.m. to 5 p.m. Due to high rate of no-shows (13%), the management who are all specialists asked the scheduling staff to create appointment for five patients at the same time and date to avoid inefficient use of clinic resources. Form around 28 couples who make the visit, 70% choose the Clinic’s solution. At the scheduled appointment time, the couple would arrive at the office and wait in line to check in with the receptionist. While the man is in the Andrology Department for Spermiogram, the woman would wait until the nurse called her to the examination room. Although this would typically occur in order of scheduled appointment time, a long queue was formed to the end of the day. After the CE stage which typically took between 12 to 20 min, the scanning department would take the patient in a first-in, firstout (FIFO) method. The Sc Stage takes in average 30 min with some waiting time. The couple would get into the DS Stage, which can take 45 min to two hours. The waiting time in the CE Stage and consequently in the DS stage increased late in the day but the staff in the Sc Stage are underworked. We simulated the patient flow in the Excel and then mapped the state (see Fig. 2).

Fig. 2. The Clinic’s current state map

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2.2 Summary of Observations on the Current State After mapping the current state, we could highlight main process flow problems: 1. The patients are pushed through the system by scheduling department far upstream in the process several days in advance. Office process occurs with no immediate feedback to scheduling or reception. The patients just keep being pushed into the process according to a schedule set, days in advance. Taking a push strategy where estimated demand defines the inputs of the process decreases desired flexibility for adapting to consistently fluctuating demands. 2. We calculated the process cycle efficiency [13] by dividing the value adding time for the patient after being present in the clinic (94´) by the total time spent in the clinic (94´ + 210´). In the current state, it is 0.31 which means only 31% of the process above is considered value-adding to the customer. The result of low efficiency is hidden costs in overhead, rework, invested capital, overwhelmed staff and unhappy customers. 3. With the exit rate of 140 patients discharged per week, the average working hours per staff would be 50 h week (3000 min). Therefore, the required staff is more than 14 people after dividing exit rate × lead time by average working hours per staff. 4. The main time trap is the wait times for CE and DS Stages that insert the largest amount of time delay into a process. The current state is naturally going to have dead time and backlogs. When the backlogs hit in the mornings, sometimes a slow afternoon would allow them to catch up before evening. But if a backlog hit in the afternoon, the possible slow time from that morning could not be retrieved.

3 Discussions: Suggested Future States 3.1 Proposal I Proposal I includes changes in scheduling, adding an appointment confirmation stage to the workflow of clinic, substituting the two specialists with a General Physician and a midwife in CE stage and allocating the specialist to DS Stage per se (see Fig. 3). Based on the interviews, the service offered in the CE Stage can be provided by a General Physician with the assistance of an experienced midwife (less expensive than a specialist). Because of a significant reduction in the wait time in the CE Stage, the cycle efficiency increases to 36%. One of the key aspects of the proposed system is the proper use of a scheduling plan to keep a steady flow of patients. With a 15-min average time for the CE Stage, two appointments could be scheduled every 30 min evenly. This new scheduling system will benefit the process because waiting time for half of the patients would be zero and for the other half around 15´. When the patient gets back from Sc stage, they should report to the reception again to enter DS Stage. No-shows at the Clinic resulted in decreased efficiency and clients’ dissatisfaction in not receiving earlier appointments. To decrease the no-show rate, the proposal includes telephone reminders by the staff. The clerk sits in the call center 6 days a week from 8 am to 4 pm to schedule appointment for outpatients. The average number of received calls is 65. With the average call duration of 3.5, the time needed to receive all calls is

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Fig. 3. Future state map I for the Clinic

3.7 h. In the proposed system, the booking time will change from 8–16 to 8–12. The same resource can call back the patients two days before the scheduled appointment for confirming or canceling the appointment. Given each confirmation takes 3 min, more than sixty calls can be made per day. 3.2 Proposal II While in Proposal I, the waiting time for CE Stage significantly decrease (80%), the waiting time for DS Stage increases for 27%. We consider some of the principles of waiting psychology [14, 15] to develop this proposal. Accordingly, preprocess waits are perceived as longer than in-process waits. Hence, the waiting time for CE Stage needs to be shorter because after the first contact with the physician is performed, the patients feel they had been ‘entered into’ the system. Accordingly, solo waits feel longer than group waits. As during the time waiting for specialist consultation and diagnosis the man joins woman, there is more comfort than waiting alone. However, based on the same principles, anxiety makes waits feel longer. The DS Stage to realize if there is a successful solution for them or not creates a lot of anxiety and it needs to be shorter. In this proposal, we added one more specialist to the diagnosis stage to work parttime from 12 p.m. to 7 p.m. (see Fig. 4). Not only does the waiting time decreases to 25%, but the number of patients served also increases from 28 to 34. With the exit rate of 170 patients discharged per week, the average working hours per staff would be 45 h per week (2700 min). Therefore, the required staff calculated by dividing exit rate × lead time by average working hours per staff is more than 12 people. In Proposal II, the system will add capacity because the ability to use a part-time specialist will eliminate the bottleneck after 12 p.m. when the two other specialists go for a lunch break. This proposal can balance employee workloads.

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Fig. 4. Future state map II for the Clinic

4 Conclusion The adoption of lean practices, which have been traditionally applied in manufacturing sector, may characterize an important contribution to the area in healthcare organizations. This research enables the healthcare practitioners to properly translate and adapt the terminology conceived in a manufacturing environment to healthcare organizations. This case study is an example of applying lean principles to health services to achieve patient satisfaction within higher quality and lower cost aspects. The application of VSM enables healthcare organizations to present system parameters such as operations’ cycle times and resources capacity and availability. Moreover, the mapping develops better and leaner healthcare processes and discloses how a current process works. The analysis also highlights the significance of moving from a push system where the health care organizations anticipate the needs of the customer in advance and prepare the solution ahead of time to a pull system where the services are given based on actual demand. A build-to-order system enables health sector to design customized care around a deeper understanding of what happens along the patient pathway [16]. Implementing lean management approaches with a patient-centric philosophy reinforce the need for re-orientation of healthcare services towards more flexible and responsive processes for individualized care. Lean can help the health care organizations to take a major step toward customized care by focusing on the experience of the patients [16]. Existing practices focuses on the elimination of non-value-added-processes and disregards the elimination of waste processes by dropping of the unwanted and unneeded practices. Thus, further studies are needed to see how healthcare organizations can meet specific and unique needs of each patients with the help of a proper integration of healthcare mass personalization and lean healthcare.

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References 1. Gonen, L.D.: Satisfaction with in vitro fertilization treatment: patients’ experiences and professionals’ perceptions. Fertil. Res. Pract. 2, 1–11 (2016). https://doi.org/10.1186/s40738016-0019-4 2. van Empel, I.W.H., et al.: Measuring patient-centredness, the neglected outcome in fertility care: a random multicentre validation study. Hum. Reprod. 25, 2516–2526 (2010). https:// doi.org/10.1093/humrep/deq219 3. Mourad, S.M., et al.: Determinants of patients’ experiences and satisfaction with fertility care. Fertil. Steril. 94, 1254–1260 (2010) 4. Ballé, M., Régnier, A.: Lean as a learning system in a hospital ward. Leadersh. Heal. Serv. 20, 33–41 (2007). https://doi.org/10.1108/17511870710721471 5. Purdy, L., Lee, Y., Lockyer, S.: Lean in health care: taking a patient-centred approach (2014). https://www2.deloitte.com/ca/en/pages/life-sciences-and-healthcare/articles/lean-inhealth-care.html. Accessed 20 November 2020 6. Minta, N.K., Stephen, O.: Importance-performance matrix analysis (IPMA ) of service quality and customer satisfaction in the ghanaian banking industry (2017) 7. Slack, N.: The importance-performance matrix as a determinant of improvement priority. Int. J. Oper. Prod. Manage. 14, 59–75 (1994) 8. Hailu, H.A., et al.: Patients’ satisfaction with clinical laboratory services in public hospitals in Ethiopia. BMC Health Serv. Res. 20, 13 (2020). https://doi.org/10.1186/s12913-019-4880-9 9. Senitan, M., Alhaiti, A.H., Gillespie, J.: Patient satisfaction and experience of primary care in Saudi Arabia: a systematic review. Int. J. Qual. Heal. Care. 30, 751–759 (2018) 10. Press, D.: Patients’ preferences for attributes related to health care services at hospitals in Amhara Region, northern Ethiopia: a discrete choice experiment. Patient Prefer. Adherence 9, 1293–1301 (2015) 11. Ramaswamy, R., Rothschild, C., Alabi, F., Wachira, E., Muigai, F., Pearson, N.: Using value stream mapping to improve quality of care in low-resource facility settings. Int. J. Qual. Heal. Care. 29, 961–965 (2017). https://doi.org/10.1093/intqhc/mzx142 12. Tortorella, G.L., Fogliatto, F.S., Anzanello, M., Marodin, G.A., Garcia, M., Reis Esteves, R.: Making the value flow: application of value stream mapping in a Brazilian public healthcare organisation. Total Qual. Manage. Bus. Excell. 28, 1544–1558 (2017) 13. George, M.L.: Lean six sigma for service : how to use lean speed and six sigma quality to improve services and transactions (2003) 14. Maister, D.H.: The social psychology of waiting lines. In: Czepiel, J.A., Solomon, M.P., Surprenant, C.F. (eds.) The Service encounter: managing employee/customer interaction in service businesses, pp. 113–124. Lexington Books, Lexington, MA (1985) 15. Peter, P.O., Sivasamy, R.: Queueing theory techniques and its real applications to health care systems–outpatient visits. Int. J. Healthc. Manage. 14, 114–122 (2021). https://doi.org/10. 1080/20479700.2019.1616890 16. Minvielle, E., Waelli, M., Sicotte, C., Kimberly, J.R.: Managing customization in health care: a framework derived from the services sector literature. Health Policy (N. Y.) 117, 216–227 (2014). https://doi.org/10.1016/j.healthpol.2014.04.005

The Smart Suits Retailer A Case of Onward Personal Style Co, Ltd. Seiji Endo(B) Tokai University, Takanawa Tokyo 1088619, Japan [email protected]

Abstract. Today, retailers are looking for new ways to build relationships with their customers. In this context, retailers are shifting to a multichannel retailing strategy, but how exactly will they do so? In this study, we interviewed Onward Personal Style Co, Ltd. (OPS) to analyze how customization is being used in their multichannel retailing strategy. Specifically, OPS strategically integrates both real stores and online operations to build long-term relationships with customers by providing customized suits within a week at a price comparable to ready-to-wear and high-quality suits that fit each customer’s size. In particular, the company has developed a directly to Consumer (D2C) strategy using a smartphone with QR codes to create a system that delivers high-quality customized products to customers. Keywords: Customization · Smart retailing · Multichannel retailing strategy

1 Introduction Today, the retail industry is entering a new era (Briedis et al. 2021). In this context, as many retailers are shifting to multichannel retailing strategies, a new type of customization is emerging through co-creation with customers. Customization is a research field that has been attracting attention again in recent years after almost two decades of twists and turns (e.g., Tseng et al. 1996, Fogliatto et al. 2012, Madhavaram and Hunt 2017). In particular, the evolution of various technologies (e.g., communication technologies) has led to a marked evolution of service customization as well as product customization (Gandhi et al. 2013). As an example, the proliferation of smartphones and social networking systems has enabled companies to interact directly with individual customers in real time, enabling them to analyze specific data and provide customized products that meet the exact needs of customers in real time. (cf., Chintagunta et al. 2016). Customized products such as Panasonic Ordering System (POS) and Nike by You (formally called NIKEiD) were mainly offered as customized products without coordinating with standardized products (Kotha 1996, Endo 2017). Later, customized products were provided with standardized products for customers. Furthermore, customization is evolving through multichannel retailing strategies (cf., Tseng et al. 2010). Therefore, this study analyzes how customization is being used in multichannel retailing strategies through interviews with Onward Personal Style Co, Ltd. (OPS). © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 978–983, 2022. https://doi.org/10.1007/978-3-030-90700-6_112

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2 Methods Onward Personal Style Co, Ltd. (OPS) was selected for this study because the selection is that the company has established a new customization system through its multichannel retailing strategy. It is experimenting with the possibility of developing new products and building relationships with customers through this system. In this study, we used the case study method to examine the mechanism of customization through the activities of OPS (Yin 2013, Gummesson 2017, Endo 2017). The analysis was based on interviews with Mr. Takeshi Sekiguchi, President and CEO of OPS, store observations, websites, and literature. The main questions of the interview were developed based on those used in previous studies (e.g., Endo and Kincade 2008, Endo 2020). Examples of specific questions include those regarding the activities of OPS, the status of customization, and the current market trends, and challenges of the new retailing market.

3 Case: Onward Personal Style (OPS) Inc. 3.1 Onward Kashiyama Inc. Onward Kashiyama Co., Ltd. is one of Japan’s leading apparel companies, founded in Osaka in October 1927 by Junzo Kashiyama. Since then, Mr. Kashiyama had established various innovative manufacturing systems, products, and sales methods. After World War II, Kashiyama believed that “the era of ready-to-wear clothes would come to Japan” and developed a new market for ready-to-wear clothes (Kashiyama 1998). He established himself as one of the major apparel manufacturers in Japan with his original brand strategy and licensed brand strategy. As for licensed brands, the company has partnered with world-famous companies such as Sonia Rykiel, Paul Smith, and Calvin Klein. In addition, the company has partnered with Jean Paul Gaultier and Yves Saint-Laurent. Mr. Kashiyama’s contribution regarding customization is the Easy Order System, which was developed in 1954. The Easy Order System is a streamlined and simplified manufacturing process that can provide a suit that fits the customer’s size in a short period of time (about two weeks) at a low price, whereas the conventional made-toorder suits were expensive and time-consuming. This system is also known as “pattern making”. The advantage of this system is that it not only allows customers to purchase a suit that fits their size in a short period of time at a low price, but also allows companies to respond flexibly to customer needs by having only the fabric in stock, thus greatly reducing their financial burden (Kashiyama 1998). Since the company’s inception, Mr. Kashiyama has promoted the sale of consumer-oriented products and the development of the latest manufacturing and sales systems while closely observing consumer behaviors. Among them, he developed the “Easy Order System”, which can be said to be the origin of the customization system. In other words, Onward Kashiyama is a marketing-oriented company (Kashiyama 1998). 3.2 Onward Personal Style (OPS) Co. Inheriting Mr. Kashiyama’s marketing-oriented spirit, OPS was born in 2017 as offering customized suits that evolved from the Easy Order System. As a sign of its inherited

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tradition, the URL of OPS’s website (https://kashiyama1927.jp) secretly uses “1927”, the year Onward Kashiyama Co., Ltd was founded. It is truly a challenge for Onward Kashiyama to return to its roots. The customization system developed by OPS strategically integrates not only the manufacturing process, but also store operations, new customized product development, long-term customer relationship building, and D2C (Directly to Consumer). 3.3 Store Operations Launched in 2017, OPS stores are currently located throughout Japan in a format consisting of four main types of stores. The first is the core stores located in Ginza, which is the center of Tokyo Metropolitan area. These stores display fabrics and samples of suits to be measured and have a section for customized suits with high-end fabrics. The stores are staffed with style guides, who measure sizes and provide various kinds of support to customers, building relationships with them. In addition, the company has smaller stores around Japan that are not as large as the Ginza store. These stores are staffed by one or two clerks (called style guides) and have the same array of sample fabrics and sizing jackets as the core stores. Temporary stores are a noteworthy retailing format. For example, they are placed in We Work, but there are no style guides stationed there. When an appointment is made, a style guide visits the temporary store to measure the customer’s size and provide advice. This operation contributes to significant savings in labor costs by not having salesclerks stationed at the store, as well as to the productivity of the style guides by saving them from wasting time waiting to serve customers. These temporary stores are very mobile and flexible. Finally, there are online stores. The point of this operation is that once customers have been measured their size in the physical store, they can purchase a customized suit using their smartphone the next time. Customers can easily choose the fabric, design, and detail parts online according to their size and preference. The whole process is a very straightforward system that is in the vein of POS (Panasonic Order System) and Nike By You. The OPS system strategically integrates both real and virtual environments and builds long-term relationships with customers by developing a system that allows them to purchase customized products easily. This is exactly the kind of system that drives OPS’s “democratization of the custom-made”.

4 Findings This section discusses the findings from the interviews and other sources. First, technological points and customization of OPS are discussed, and finally, multichannel retailing strategy through customization is argued. 4.1 Technology First, technology is an important role of OPS’s customization. Today, the new customization is a strategic integration of online and offline environments, reintegrating the real

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and virtual worlds to create a new business model (Briedis et al. 2021). OPS uses smartphones and other mobile devices, as well as QR codes and other technologies (e.g., body scanning systems) to read data instantly. As a result, the company has been able to reduce the number of stores and contact personnel (mainly, style guides). In addition, the company applied this system to expand its customized products to women’s shoes, and it is expected that OPS will expand its “democratization of order-made” to various products in the future. In other words, the customization will become a standard in the field of ready-to-wear clothing, and it will be possible to offer a variety of customized products that meet the needs of individual customers. 4.2 Customization OPS’s customized suits are of high quality and are offered at prices comparable to ready-made suits. The Easy Order System developed by Onward Kashiyama provided customized suits in about two weeks, but OPS has halved the delivery time to one week for customer-oriented reasons that focus on the lifestyle of businesspeople. The story goes that you can order a customized suit on a weekend, pick it up the next weekend, and come back to work the following Monday with a fresh new look. Therefore, the period is not eight days, nor is it two weeks. Moreover, the lead time would be even shorter near future. The main products include men’s and women’s suits, set-ups, and women’s shoes. Men’s suits are available in three styles: Standard, Comfort, and High Grade, each with a price range of 30,000 to 70,000 yen. Each has a basic suit color such as navy, black, and gray. For jackets, you can choose the number of buttons, sides, lining, and other details, and for pants, you can choose single or double. For men’s suits, formal suits are also offered, and tuxedos can be purchased starting at around 70,000 yen. OPS’s system is a combination of a system like Nike by you, where the price remains the same no matter how many items are selected (Nike 2020), and a system like TREK’s Project One, where the price varies depending on the items selected. (Project One 2019). The price range for women’s shoes is between ¥10,000 and ¥20,000, and like suits, this system can be provided to customers in as little as one week. As with suits, the system is very straightforward, allowing customers to choose the design, color, heel height, and shoe tip design. 4.3 Multichannel Retailing Strategy Near future, customization will allow customers to participate freely and actively in the supply chain, which is a two-way, open chain of information flow. As technology advances, more interactive and flexible relationship chains are being explored. At the same time, the role of style guides at the touch points is important. In other words, in the supply chain of the new customization, companies were looking for long-term relationships by building more co-creative and direct relationships with customers through a flexible combination of store and supply chain networks (cf. Endo 2020). In this sense, OPS’s challenge is a very noteworthy activity in the future of customization and presents the possibility of a multichannel retailing strategy in the future.

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5 Conclusions The study analyzed, through an interview with OPS, how customization is used in a multichannel retailing strategy, in both physical and online stores to build long-term relationships with customers by providing them with customized suits and other apparel products. Especially OPS develops the touch points (stores) that directly interact with customers, and the QR codes that directly connect manufacturing sites with customers, reducing unnecessary human contact as much as possible while focusing on the important contact between customers and employees (mainly style guides) to simplify the shopping process, and building a system to deliver customized products quickly and directly from the factory to customers. In terms of products, the company expanded the range of customized products to include casual apparel, shoes, and accessories. The customization would become the standard in the field of ready-to-wear clothing and make it possible to offer a variety of customized products that meet the needs of individual customers. OPS aims to build long-term relationships with its customers through co-creation through a multichannel retailing strategy.

References Briedis, H., Gregg, B., Heidenreich, K., Liu, W.W.: Omnichannel/the path to value. https:// www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-survival-guideto-omnichannel-and-the-path-to-value. Accessed 21 May 2021 Chintagunta, P., Hanssens, D.M., Hauser, J.R.: Marketing science and big data. Mark. Sci. 35(3), 341–342 (2016) Endo, S.: The mechanism of customization through co-creation: focusing on the ecosystem of Hirosaki industrial research institute. Tokai Univ. Soc. Sci. Res. Inst. 3, 35–42 (2020) Endo, S., Kincade, D.H.: Mass customization for long-term relationship development: Why consumers purchase mass customized products again”. J. Cetacean Res. Manage. 11(3), 275–294 (2008) Fogliatto, S.F., da Silveira, G.J.C., Borenstein, D.: The mass customization decade: an updated review of the literature. Int. J. Prod. Econ. 138(1), 14–25 (2012) Gandhi, A., Magar, C., Roberts, R.: How technology can drive the next wave of mass customization. McKinsey Bus. Technol. 32(Winter), 2–9 (2013) Grönroos, C.: From marketing mix to relationship marketing - towards a paradigm shift in marketing. Manage. Decis. 35(4), 322–339 (1997) Gummesson, E.: Case Theory in Business and Management. SAGE Publications, Thousand Oaks (2017) Kashiyama, J.: Run Onward: Fifty Years of Business and Racing. Japan Library Center, Tokyo (1998) Kincade, D.H., Cassil, N., Williamson, N.: The quick response management system: structure and components for the apparel industry. J. Text. Inst. 84(2), 147–155 (1993) Komoto, M.: Experience: Management Reconstruction at Matsushita. Dobunkan Publishing Co., Tokyo (2006) Kotha, H.: From mass production to mass customization: the case of the national industrial bicycle company of Japan. Eur. Manage. J. 14(5), 442–450 (1996) Madhavaram, S., Hunt, S.D.: Customizing business-to-business professional services: the role of intellectual capital and internal social capital. J. Bus. Res. 74(1), 38–46 (2017) Nike Homepage. https://www.nike.com/men. Accessed 2 Sept 2020

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OPS homepage. https://kashiyama1927.jp. Accessed 16 Aug 2019 O’Flaherty, C.M.:Meeting Mr. Miyake. https://mds.isseymiyake.com/wp-content/uploads/2014/ 02/FINANCIAL-TIMES2.pdf. Accessed 22 Jan 2019 Project One Homepage. https://projectone.trekbikes.com/us/en/#model/madoneslr9etap. Accessed 22 Sept 2019 Tseng, M.M., Jiao, R.J., Wang, C.: Design for mass personalization. CIRP Ann. Manuf. Technol. 59, 175–178 (2010) Tseng, M.M., Jiao, R.J., Merchant, E.: Design for mass customization. CIRP Ann. Manuf. Technol. 45(1), 153–156 (1996) Yin, R.K.: Case Study Research: Design and Methods, 3rd edn. Sage Publications, Thousand Oaks (2013)

Sustainable Manufacturing and Circular Economy

Sustainability of Factories in Urban Surroundings Enabled by a Space Efficiency Approach Peter Burggräf1,2

, Matthias Dannapfel1 , and Jérôme Uelpenich1(B)

1 Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen

University, Campus-Boulevard 30, 52074 Aachen, Germany [email protected] 2 Chair Of International Production Management and Engineering, University of Siegen, Paul-Bonatz-Str. 9-11, 57076 Siegen, Germany

Abstract. The sustainability of factories is becoming more and more important for manufacturing companies. Resource efficiency is one of the most important fields of action when implementing sustainable production systems. Additionally, industry increasingly focuses on the revitalisation of urban areas as innovative and important places for value-creation. Thus, the resource space is getting more important as a typical characteristic of sustainable factories and the increase in space efficiency is one of the most urgent tasks. Hence, a measurement approach for space efficiency of production systems is necessary. However, in existing approaches there is no method for identifying and measuring space efficiency and its drivers within manufacturing companies. The goal of this paper is to describe and evaluate space efficiency indicators by investigating the value-creation of manufacturing companies in combination with the limited space availability at urban factories. Thereof, this paper presents a descriptive and evaluative model for space efficiency. It is applicable regardless of type and size of the production system and summarizes space efficiency as one key metric. This key metric can be used to compare different production systems with each other. Both theoretical and practical implications of the model as well as limitations and future research directions are discussed. Keywords: Urban production · Urban factory · Sustainability · Space efficiency · Value-creation

1 Introduction Current megatrends such as urbanization, digitalization, individualization or the demographic change require a new way of thinking and acting in the industrial sector [1]. Future challenges have to be addressed proactively and the sustainability of corporate business models is becoming increasingly important for manufacturing companies [2]. Additionally, the manufacturing industry focuses increasingly on urban areas as an innovative and important place for value-creation of manufacturing companies [3]. © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 987–996, 2022. https://doi.org/10.1007/978-3-030-90700-6_113

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Juraschek et al. [4] state that urban production offers the greatest potential for cities in the future on a direct-economic and indirect-ecological and social level. There is a large variety of opportunities and advantages for value-creation in urban areas, which result from the connection between city and production [5]. Richter et al. [6] underlines the importance of urban space for current decisions made during the location planning process of a company. Firstly, Piller et al. [7] and Rauch et al. [8] assume that successful and interactive value-creation is not possible without production in an urban environment. Secondly, the approach of Matt and Rauch [9] describes that urban production is essential for the implementation of the 4th industrial revolution and industry 4.0. The manufacturing industry in German-speaking countries also recognizes the necessity of production in urban areas [10]. As space in urban areas is scarce, an efficient land-use is essential for a sustainable production in urban areas and must be considered already in factory and layout planning processes. The rising consumption of resources resulting from the increasing degree of urbanization requires a structural change and a reorientation of production with regard to the sustainability of cities [11]. In factory and layout planning, the focus is primarily set on economic efficiency [12]. As a result, only a few models exist that make space efficiency for production systems measurable. Therefore, it is necessary to introduce a measurement approach for space efficiency of production systems and it is important to identify drivers for space efficiency. The goal of this paper is to measure the use of space and to describe the efficiency of a production layout. Within the structure of the paper, Sect. 2 explains the theoretical background of the sustainability of urban areas and industrial value-creation in cities. Section 3 describes the selected concept of the model derived from its main requirements. The main findings for the descriptive and evaluative model for space efficiency are presented in Sect. 4. Finally, Sect. 5 concludes the paper and discusses both theoretical and practical implications.

2 Sustainability and Industrial Value-Creation in Urban Areas This section presents the theoretical background of the paper. In addition, essential terms are defined in order to explain the conceptual understanding. The concept of urban production received increasing attention in research and industry in the past few years (e.g. [5, 13–17]). However, no consistent definition for urban production exists in literature. Depending on country and organization for instance, different boundaries for an urban area and urban space are defined according to criteria like minimum population, population density or the presence of certain infrastructure facilities such as roads, electricity or water supply [18]. This paper follows the definition of urban production according to the understanding of Herrmann et al. [19], whereas urban space is a densely developed and inhabited area with high potential for “multifunctional utilization structure that offers functionalities for production sites and manufacturing companies”. In addition to the new thinking and acting from an industrial point of view, increasing land consumption is one of the most pressing environmental challenges of these days from an ecological point of view. For this reason, political and governmental sustainability strategies limit the increase in urban and commercial areas - in order to reduce

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new land consumption. For achieving this goal, measures such as greater inner urban development, the clearance and use of brownfields and the containment of the expansion of existing settlement areas are being promoted [20]. One focus lies on the revitalisation and more efficient space use of already sealed areas [21]. It is important to enhance the sustainability strategy of manufacturing companies with using already sealed areas and existing infrastructure. The increasing demand for space exceeds the limited availability of urban space. Urban production faces a particular dilemma. Although the (re)integration of production in urban areas is seen by many as necessary and beneficial, a broad and sustainable redevelopment of urban production sites is not forthcoming. This ‘space dilemma’ of urban production causes new challenges for manufacturing companies (see Fig. 1).

Fig. 1. The ‘space dilemma’ of urban production causes new challenges for companies

In the field of industrial value creation, the paper is based on the findings of Juraschek et al. [5] who examined a life-cycle oriented approach. It became apparent that the vast majority of the surveyed urban factories make a direct value creation contribution in the sectors of prefabrication and manufacturing/ processing [5]. Therefore, this paper focuses on areas with direct value creation within manufacturing companies. Urban production sites are, however, associated with challenges. For example, cost disadvantages (remediation of contaminated sites, preparation of construction sites, exploding land prices, etc.), space disadvantages (unfavorable shape or size of available plots, limited expansion possibilities, etc.) as well as restrictions imposed by the urban environment (logistics, noise, emissions, etc.) are among the factors that cause hesitation. The reasons for this hesitant action can be attributed, among other things, to the limitation of available construction space in cities [10]. The growing demand and the inefficient use of already poorly available space limit the opportunities for industrial value creation and development in urban areas. Existing approaches regarding spatial land-use efficiency indicators can be found in the construction sector. Most of the information on space efficiency, such as the ground floor index (GSI), the floor space index (FSI), the open space ratio (OSR) or others, are used in the construction sector [22–25]. These are partly two-dimensional approaches. However, they are related to neither the actually usable area nor the production area. The basic approach for the evaluation of the used area as well as the calculation of a

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comparable key figure is available here. They are useful for making different buildings or construction objects comparable with each other and for evaluating subsequent densification measures. Nevertheless, they are not related to production. Therefore, they cannot be considered for describing space efficiency in the context of this paper. The most relevant numerical approach for production areas is presented by Tomanek et al. [26] with the added-value concentration. Within their approach, the authors try to analyze the value of space in relation to personnel, machines and plants, material stocks, information transfer as well as the intralogistics traffic volume in manufacturing or service companies. To evaluate how added-value in an area is used, the authors use the added-value concentration. The added-value concentration is a key metric that indicates the usage in the corresponding area [12]. Existing approaches do not focus holistically on the description and evaluation of space efficiency in manufacturing companies. Therefore, the central question of this paper is how space efficiency can be described and evaluated with a focus on the addedvalue of a manufacturing company.

3 Requirements for the Descriptive and Evaluative Model This section describes the concept of the descriptive and evaluative model for space efficiency. In order to cope with the limited space need, an effective use of the available space is indispensable. This is especially important for urban production, as production facilities tend to require a larger amount of space over time. Space cannot be consumed like a production tool based on its use, but it is occupied by space utilization. Space utilization is the occupation of an area by an object located on it. For example, a drilling machine occupies 10 m2 of the usable area. As long as this machine is located in this spot, the space cannot be used for any other purpose and is occupied for the duration of the machine’s presence. In urban production sites, efficient use of space is particularly important, since space in urban areas is associated with higher costs and is very limited. Therefore, it is necessary to enable a higher degree of added-value by manufacturing in urban areas. For the purpose of developing the concept of the descriptive model, the requirements and the framework are developed. In the first step, the model should make space need of a production system describable. In the second step, it should make the space need measurable and comparable with other types of use. The following sections describes the three main requirements of the model. It is important to create a non-distorted key figure for achieving comparability of the individual types of use. In order to represent space efficiency of a production system and added-value activities by the model successfully, a holistic view of the production space in connection with a reference area is necessary. This holistic view as well as the implementation should be complete, transparent and easy to handle. A transparent and comprehensible model can ensure the general validity and comparability of the results, because the application of the model for different production layouts must be ensured. Space design features also aim to make the model controllable and comprehensible for the external user. In order to ensure comparability of many possible uses among each

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other, it is necessary to describe space efficiency through the usage of space of addedvalue activities in one key figure. Therefore, the chosen approach defines as the first requirement a one-dimensional, numerical key figure for this purpose. For instance, all possibilities for increasing the efficiency of space, such as vertical expansion, cannot be considered in the existing approach of the added-value concentration according to Tomanek et al. [26], as this approach only uses a two-dimensional grid as the basis for the evaluation. Especially in the context of urban production, it is often unavoidable to build higher building structures for increasing the added-value in the existing usable area. In order to be able to guarantee a complete description of space efficiency, the second requirement is a three-dimensional approach to be used. In order to develop a key figure for space efficiency, it is also necessary to establish a relation between the used scales to describe production-related added-value in factories and a normalized space or area the model refers to. The relation to a normalized area prevents a distortion of the results and identifies potential for space efficiency improvements within the existing areas. Existing approaches select the total area as reference area randomly [26], which leads to no consistent basis for calculation.

4 Details of the Model for Space Efficiency in Production Areas The following section highlights the details of the model for space efficiency in production areas. It is necessary to identify calculation elements for determining the space efficiency of manufacturing activities. In order to ensure a description of different activities and to transfer these activities into the model, an assignment based on a one-dimensional scale is necessary. This scale expresses the added-value of the use of space as evaluation criterion for manufacturing. By elaborating general calculation rules and one key indicator, the quantification of the fulfilment of objectives for increasing the efficiency per area is possible, e.g. an annual increase of 5%. The concept for calculating the space efficiency for production systems is based on the idea of the Overall Equipment Effectiveness (OEE) according to Nakajima [27]. The OEE is a key metric for measuring the efficiency and process safety of machines, plants and assembly stations and is widely applied as one of the most important process-oriented key performance indicators. Equation 1 shows the elements of the calculation of the OEE. OEE = Availability ∗ Performance ∗ Quality

(1)

The OEE is an indicator that measures the degree of waste, independent of its causes. It can also be used to compare different processes or machines in order to derive production targets. For example, goals such as increasing OEE by 10% are very common and can be easily measured due to the simple handling of the calculation. Finally, the OEE summarizes all types of efficiency loss in one key metric based on the theoretically achievable maximum utilization time [28, 29]. By analogy, the description and valuation of space efficiency of a production system will be characterized by a similar key metric. Focusing on the application for space efficiency and its model, the distinctive area must be classified. For this purpose, a three-dimensional area grid according to the requirements mentioned above describes

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the accessibility of space. Besides and in order to ensure an objective description of space efficiency, it is necessary to quantify the individual production activity of each area grid element according to its degree of added-value. The reference to the normalized space or area is implemented by a so-called space correction factor. The space correction factor is used to adjust the specific value of space efficiency so that the used area, to which no specific degree of added-value can be assigned, is corrected. It ensures that the production site will not be changed in terms of maximizing the added-value. Concluding, the model consists of three calculation elements according to Eq. 2. Space_Efficiency = Accessibility ∗ Degree_added _value ∗ Correction_F

(2)

A description of the scale and detailed requirements for the explanation of the degree of added-value can be found in Table 1. Table 1. Scale for value-adding activities in specific manufacturing areas. Degree of added-value Description

Dimension of the space occupancy

0

Non-value-adding

Unused area (not accessible and not-used), e.g. area for empties, waste and blocked/ defective parts

1

Limited value-adding

Unused area (accessible and not-used)

2

Limited value-adding

Area with finished/ semi-finished products and material storages (more than 5 m distance to workstation)

3

Limited value-adding

Area with finished/ semi-finished products and material storages (from 1 to 5 m distance to workstation)

4

Limited value-adding

Area with finished/ semi-finished products and material storages (max. 1 m distance to workstation)

5

Maximum value-adding Operational workstation on the shop floor, e.g. actual production line or machines

Accessibility describes the usable space respectively volume of the production site. This term is essential in order to be able to divide the facility into different areas and assign it to the individual machines or work areas. For this classification, it is important to choose a three-dimensional approach. In order to be able to determine the space efficiency easily, the accessibility of a production area is divided into cubic blocks (CBi ) with a total volume of one cubic meter. The accessibility is defined in Eq. 3. n Accessibility = CBi (3) i=1

In order to create a basis for the determination of space consumption and, subsequently, space efficiency, a scale to describe the production-related added-value is

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necessary. This classification helps to set the usable production space in relation to other usable areas. The degree of added-value describes on a numerical scale of one to five the value creation of each cubic block (avi ). In order to be able to compare the space efficiency factor, the degree of added-value achieved by a cubic block must be set in relation to the maximum achievable degree of added-value (avmax ). Degree_added _value =

avi avmax

(4)

A non-value-adding area is the area that does not contribute to the added-value of the products. These are mostly material storage areas for empties, waste, defective parts and inaccessible, unused areas. These areas do not have any significance for the value creation of the production facility itself and correspond with the degree of added-value of zero. Due to the existing equipment and building structures, these areas cannot be allocated to value-adding activities without additional effort. Examples of such areas are support equipment and technical building equipment within the manufacturing area. With a degree of added-value of one, accessible but not-used areas are described. Although, like the inaccessible and not-used areas, they do not offer a direct value contribution to the products, these volumes can be transformed to areas with value creation. Value-adding activities can be assigned here quickly and with less effort. For this purpose, no further expenses are required, apart from the tools required for the value-adding activity. A degree of added-value between two and four describes limited value-adding activities of the material storage e.g. for finished or semi-finished products. They are categorized according to their distance from the workstation. Storage areas that are more than five meters away from the workstation are assigned to a degree of added-value of two. Storage areas that are between one and five meters away from the workstation correspond to a degree of added-value of three and storage areas that are less than one meter away from the workstation correspond to degree of added-value of four. These storage areas contain all materials for inputs and outputs of production and include all intermediate storage facilities such as transport routes or people movement. With the maximum degree of added-value of five, all operative workstations on the production line and the production line itself are described, such as machines, lathes or machining centers. Here, no evaluation is made based on the output quantity of individual machines. Instead, each production step as well as the corresponding workstation have the highest degree of added-value of five. The third part of the formula for calculating space efficiency is the area correction factor. Correction is necessary because various areas cannot be directly assigned to any degree of added-value or because they are important for a safe working environment. In addition to safety, recreational and sanitary areas, buffer areas below the area correction factor are also taken into account. In order to avoid falsifying the space efficiency metric by areas with no degree of added-value, the so-called neutral operating area (NA) sums up these non-value-adding areas. The neutral operating area is subtracted from the production area, which is explained by the floor space as part of the net building area according to DIN 277 [30] and multiplied by the height of the production area (h). During area correction, the dimension of the space efficiency formula is adjusted by

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dividing the factor according to Eq. 5. Correction_F =

1 (FS − NA)∗h

(5)

With the help of the analogy to the OEE, the manufacturing activities are evaluated based on a three-dimensional indicator system and depending on the degree of addedvalue. In addition, the correction factor with regards to a normalized area enables the model to compare different production systems with each other. Hence, Eq. 6 merges the formula for space efficiency of production systems into one model. n avi 1 (6) CBi ∗ ∗ Space_Efficiency = i=1 avmax (FS − NA) ∗ h Space efficiency is determined by the space correction in the third part of the formula independently of the production area. This implies that the calculated measure of space efficiency thus becomes unaffected by the considered size of the production area. Thus, production areas of different sizes and locations can be compared with each other.

5 Conclusion Urban production offers opportunities to exploit new forms of added-value of manufacturing companies in cities. Nevertheless, one of the major challenges is dealing with the limited space in urban areas. This paper explained and developed a model that makes space efficiency describable. For calculating space efficiency of a manufacturing company, the production system is divided into a three-dimensional grid. Each volume element of the grid is assigned to the corresponding degree of added-value according to the developed scale. In order to ensure comparability of different production layouts, the formula for calculating the space efficiency is scaled to a single, one-dimensional key figure. Independent of the type and size of the production system, the developed model allows measuring space efficiency and making it comparable by means of a single key figure. Driver for space efficiency in this model is the value creation of a manufacturing company. A way to increase space efficiency is to increase the degree added-value, e.g. via the rearrangement of the production layout, vertical expansion of value creation or outsourcing of less added-value activities. By considering the third dimension, it is possible to record the use of the available production space with the usable area. High roofs in production facilities increase the demand for a three-dimensional description of value-adding activities, because several value-adding activities can be located on top of each other. In addition, a computer-aided evaluation is possible within the assessment of many different possibilities of space utilization with regard to their space efficiency. This allows for an automatic calculation of space efficiency already during the planning phase of a production layout. Further research is needed to examine the value of individual production steps more in detail. Further differentiation into different stages of the value chain would usefully extend the model. In order to achieve a more precise calculation of space efficiency, the grid elements can be reduced in size. Additionally, the widespread application of the model in different production systems of manufacturing companies will increase its accuracy and validate the calculation concept.

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A Framework for Industry 4.0 Implementation in Circular Economy Manufacturing Systems Saleh M. Bagalagel(B) and Waguih ElMaraghy Intelligent Manufacturing Systems (IMS) Centre, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada [email protected]

Abstract. The modern manufacturing challenge of meeting increasing demands while reducing environmental impact requires a shift to a circular manufacturing business model. Industry 4.0 technologies can be used to streamline the transition from linear economy to circular economy manufacturing systems through the collection, analysis and coordination of product and process data at various stages in the system. A framework for Industry 4.0 implementation in manufacturingremanufacturing closed loop system is proposed. The framework assists decision makers in identifying Industry 4.0 technologies and their interactions across the entire supply chain. It also provides a basis for system-wide idea generation and evaluation as opposed to isolated and uncoordinated case-by-case implementation approach. Furthermore, the implementation plans can be coordinated in the long term to maximize the efficiency and eliminate conflicts. Keywords: Remanufacturing · Industry 4.0 · Circular economy · Closed-loop supply chains · Product recovery

1 Introduction Industry 4.0 is a major enabler of circular economy manufacturing systems that rely heavily on data and materials flow from the forward and reverse logistics streams in the closed loop system. Literature on the implementation of Industry 4.0 in product recovery and remanufacturing is scarce. Industry 4.0 can be used to streamline many of the challenging tasks and bottlenecks in remanufacturing such as used product condition monitoring, quality assessment, disassembly, processing and production planning. For example, product data can be collected using embedded sensors to determine the optimal return time and autonomously plan inspection, disassembly and remanufacturing processes. An Industry 4.0 implementation framework for manufacturing-remanufacturing closed-loop systems is proposed to explore the potential applications of Industry 4.0 at various stages in these systems. The paper is organized as follows. A literature review is presented in Sect. 2 to introduce relevant concepts and highlight previous discussion of the topic. Section 3 addresses the scope of Industry 4.0 implementation. The framework is presented in Sect. 4 with an example to illustrate its implementation. The conclusions are discussed in Sect. 5. © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 997–1005, 2022. https://doi.org/10.1007/978-3-030-90700-6_114

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2 Industry 4.0 and Remanufacturing Industry 4.0, a term referring to the fourth industrial revolution, was launched in Germany in 2011 [1]. It represents a megatrend in the manufacturing industry today. Unlike the previous industrial revolutions that were triggered by a specific technological invention, Industry 4.0 encompasses a number of technological advancements that allowed autonomous management and operation of complex systems. The main technologies of Industry 4.0 include Cyber-Physical Systems (CPS), Internet of Things (IoT), Augmented Reality, Big Data, and Radio Frequency Identification (RFID) [2]. Other technologies such as advanced robotics, data mining, horizontal and vertical integration can also be parts of Industry 4.0 as mentioned in the literature [2]. The different ways of listing the technologies behind Industry 4.0 originate from the expanding foundations of Industry 4.0 and the way the elements of this foundation are defined and grouped. For example, virtualization technologies can be described as virtual reality (VR) and augmented reality (AR) components [3]. As a general remark, Industry 4.0 technologies are rapidly evolving with a sense of urgency in the communication and networking sectors. Some details of basic technologies are provided to serve as a foundation for the proposed Industry 4.0 process framework for product recovery and remanufacturing. The cyber-physical systems are also referred to as embedded systems [3]. The physical assets are embedded with sensors to create cyber twins. Cyber-physical systems (CPS) are defined as technologies that manage interconnected systems of physical assets and computational capabilities [4]. The cyber-physical systems and their interconnections for circular economy manufacturing systems are illustrated in Fig. 1. Some of Industry 4.0 technologies that can be implemented are also shown.

Fig. 1. Cyber-physical systems (CPS) in circular economy manufacturing systems.

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The Internet of Things (IoT) is a network of machines or things that are “capable of communicating autonomously with each other along the value chain activities by creating huge amounts of data available for further analysis” [5]. Each machine or thing in the IoT has its cyber-physical system [6]. The IoT structure can be divided into three layers: the frontend layer, the connectivity layer and the backend layer [5]. The frontend layer includes the physical assets and embedded hardware and software. The connectivity layer transmits the data from the frontend layer to the backend layer and transmits commands from the backend layer to the frontend layer. The backend layer contains the storage and computing hardware and software to perform big data analytics [5]. Because of the massive transformations that take place between the physical assets in the frontend layer and the digital format in the backend layer, Industry 4.0 is characterized as the “digitalization” revolution in industry.

3 Industry 4.0 Scope of Implementation The wide range of technologies that can be adopted in an Industry 4.0-based manufacturing system implies that Industry 4.0 offers highly customized and modular solutions that suit different organization needs and circumstances. In order to maximize the outcomes of Industry 4.0 implementation, decision makers in manufacturing organizations need to start with a long-term plan that allows progressive implementation levels over time. A study published by Deloitte’s Research Center for Energy and Industrials Group [7] have identified three levels of smart factory adoption among manufacturing companies: trailblazers, explorers and followers. High level adoption includes the transformation of complete facilities and supply chains. Medium level adoption of Industry 4.0 is the transformation of selected physical assets. Low level adoption is limited to proven technologies. The study did not include product recovery and remanufacturing systems. The implementation levels in these systems can be defined as shown in Table 1. Table 1. Industry 4.0 implementation levels in product recovery and remanufacturing. Level

Description

High

Adoption of Industry 4.0 in the integrated manufacturing-remanufacturing plant and all other facilities in the closed loop system

Medium Adoption of Industry 4.0 in the integrated manufacturing-remanufacturing plant and selected facilities in the closed loop system Low

Adoption of Industry 4.0 in the integrated manufacturing-remanufacturing plant only

Decision-making for Industry 4.0 implementation in remanufacturing is widely seen as a business process that takes into account the relevant financial, environmental and social circumstances surrounding different industries and organizations. For example, a framework of Industry 4.0 utilization in waste collection and conversion was proposed for manufacturing in Singapore [8]. The framework is focused on government efforts to minimize waste generated from multiple industries through waste measurement, collection,

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and conversion technologies. However, manufacturer involvement in waste reduction is a more effective approach as it leads to higher value recovery from end-of-life products. Another framework focused on data-driven remanufacturing of rechargeable energy storage systems [9]. The framework does not consider the novel approach of integrated manufacturing-remanufacturing operations. Moreover, the framework scope does not include the use of Industry 4.0 technologies to streamline the decision-making process. A remanufacturing system with reverse logistics was studied using simulation modeling for a refrigerator manufacturer in India [10]. The model considers a partial Industry 4.0 implementation, and it doesn’t provide a framework for a wide implementation scope which can result in greater benefits. The proposed framework in this paper provides a generic approach to explore Industry 4.0 potentials in a closed-loop manufacturing-remanufacturing system, and it serves as the first step to other stages of qualitative and quantitative methods to address the specific needs of different organizations. The framework promotes better transition to remanufacturing practice because it • Emphasizes manufacturer involvement in the entire product life cycle, • Maximizes value recovery by redirecting used products into the original manufacturing system, and • Considers a comprehensive application of Industry 4.0 technologies. The outcome of the framework can be used as an input to guide subsequent stages of multi-criteria decision-making and detailed quantitative analysis. The COVID-19 crisis highlights the need for a faster transition to circular economy as the global supply chains of new raw materials become more unreliable.

4 Industry 4.0 Implementation Framework for Circular Economy Manufacturing Systems Unlike conventional linear manufacturing system models, the circular economy manufacturing system model relies on data and materials flow from the forward and backward ends of the supply chain. Among the important parameters to be communicated are the quality, quantity and timing of returns. In the linear forward flow, the uncertainty in data is far less because the manufacturing operations use new materials and parts with specified quality, quantity and delivery schedules from suppliers. The framework is illustrated in Fig. 2 and 3 for linear manufacturing systems and circular economy manufacturing systems for comparison. The framework shows the flow of materials and data and the connections across the closed-loop supply chain. The first step in the framework is to identify the network parties and their roles. Next, the processes, information and materials flows are outlined. Industry 4.0 technologies are then used to integrate and streamline the manufacturing-remanufacturing closed loop system. Washing machines can be used to illustrate the framework use. Washing machines and other household appliances are of particular interest from the circular economy perspective. Given their large size and production volume, remanufacturing can make

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a significant reduction in energy and raw materials consumed in the industry. Washing machine remanufacturing requires the use of Industry 4.0 technologies due to the significant level of product variety and complexity involved. Examples of washing machine variants and components are shown in Fig. 4 [11].

Fig. 2. Industry 4.0 implementation framework in linear economy manufacturing systems.

Using washing machine industry as an example, the scope of implementation at each stage in the frontend layer (as shown in Fig. 3) can be further elaborated as follows. Customers • The washing machine use patterns and service data is transmitted to the manufacturer’s data center and cloud condition monitoring software. • Based on data analysis, the product is collected, and a remanufactured product is delivered to the customer with the same warranty and service terms as new products. • The customer receives alerts about the condition of the product and instructions to take appropriate actions to prevent premature aging of the washing machine. Collection • The product is received from the customers and scanned to retrieve the condition reports. An estimate of remaining life and value is generated. • A financial discount is issued towards the purchase of a remanufactured product. • Data are shared with the inspection and disassembly facility, part suppliers, remanufacturing facility for process planning. • Continuous feedback is sent to machine learning algorithms to forecast the quantity and quality of returns in the short, medium and long range. Inspection and Disassembly • The quantity, timing and condition of the returned products are transmitted, and process planning is autonomously created. • The returned product is inspected using non-destructive testing methods following instructions based on the big data analytics from the cloud computing.

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Fig. 3. Industry 4.0 implementation framework in circular economy manufacturing systems.

Fig. 4. Washing machine product variants and their components [11].

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• Robotic disassembly is used with the aid of feedback from the pre-disassembly inspection results. • Continuous feedback during inspection and disassembly is used to guide the operation and improve future planning (AI, Big Data, Cognitive Robotics). • Robotic sorting, disassembly and storage of returned products and modules. • Feedback is communicated to the product development team to improve the design for ease of inspection and disassembly Remanufacturing of modules • The module is assessed to determine whether it can be sent for remanufacturing. • The factors examined include the age of the module, use history, repair and maintenance history and results from tests specified for the components. • Continuous feedback of the assessment results is communicated to the appropriate module remanufacturing units for process planning. • Continuous feedback is also shared with the manufacturing-remanufacturing facility to plan the production of new and remanufacture products. • Feedback from the remanufacturing process is communicated to the product development team to improve the module design for ease of remanufacturing. Refurbishing • Modules that do not meet the remanufacturing criteria are sent for refurbishing. • Continuous feedback is sent to the refurbishing units for process planning. • Feedback from the refurbishing process is communicated to the product development team to improve the module design for ease of refurbishing Recycling • If the module is found inappropriate for remanufacturing or refurbishing, it is sent to recycling for materials recovery. • Potential uses of the recovered materials are identified. Disposal • Machine learning is used to identify parts disposal patterns. • The design factors contributing to disposal of parts are investigated to reduce disposal rate in future product design. Suppliers • The module inspection data are communicated to the suppliers to plan the production of new modules. • Supplier instructions to remanufacture and refurbish the modules are updated based on feedback received from the remanufacturing and refurbishing processes.

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Manufacturing-remanufacturing plant • The optimal product mix is determined based on real-time data feedback from the inspection and disassembly facilities • The schedule of delivery of new and remanufactured modules is produced and a production plan is generated. • Based on the production plan, robotic operations, testing, materials handling systems, and other production resources are deployed and managed. Distribution Dealers • Delivery schedule of new and remanufactured products is sent to the dealers. • Data from distribution dealers is used to forecast future market demands using machine learning algorithms. • Post-sale customer relationship is established and managed to ensure customer retention and higher collection rates.

5 Conclusions Industry 4.0 technologies have the potential to fast-track remanufacturing by increasing its economic benefits and accelerating the shift from linear to circular economy manufacturing systems. However, the coverage of Industry 4.0 implementation in remanufacturing is still in the early stage. The overall success of individual Industry 4.0 applications depends on the level of coordination across the entire closed-loop system. In this paper, a system-wide implementation framework is proposed and illustrated using the case of washing machine manufacturing. The framework provides a basis for idea generation and coordinated implementation plans. The framework does not include selection and prioritization of technologies. As a further extension, level of implementation and technology selection can be determined using other methods such as multi-criteria decision making and mathematical optimization models to evaluate plans and identify significant product and system parameters. The obstacles to Industry 4.0 implementation include lack of historical data, capital investment issues and a large and expanding number of emerging technologies. The proposed framework provides a basis for long-term implementation that can be coordinated and developed as more funds and information become available.

References 1. Xu, L.D., Duan, L.: Big data for cyber physical systems in industry 4.0: a survey. Enterprise Inform. Syst. 13, 148–169 (2019) 2. Butzer, S., Kemp, D., Steinhilper, R., Schötz, S.: Identification of approaches for remanufacturing 4.0. In: 2016 IEEE European Technology and Engineering Management Summit (E-TEMS), pp. 1–6. IEEE, Frankfurt am Main, Germany (2016) 3. Salkin, C., Oner, M., Ustundag, A., Cevikcan, E.: A conceptual framework for industry 4.0. In: Industry 4.0: Managing the Digital Transformation, pp. 3–23. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-57870-5_1

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4. Lee, J., Bagheri, B., Kao, H.-A.: A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015) 5. Karacay, G., Aydın, B.: Internet of things and new value proposition. In: Ustundag, A., Cevikcan, E. (eds.) Industry 4.0: Managing the Digital Transformation, pp. 173–185. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-57870-5_10 6. Bagheri, B., Lee, J.: Big future for cyber-physical manufacturing systems (2015). https:// www.designworldonline.com/big-future-for-cyber-physical-manufacturing-systems 7. Wellener, P., Shepley, S., Dollar, B., Laaper, S., Manolian, H.: 2019 Deloitte and MAPI Smart Factory Study (2019). https://deloitte.com/content/dam/insights/us/articles/6276_2019-Del oitte-and-MAPI-Smart-Factory-Study/DI_2019-Deloitte-and-MAPI-Smart-Factory-Study. pdf 8. Kerdlap, P., Low, J., Ramakrishna, S.: Zero waste manufacturing: a framework and review of technology, research, and implementation barriers for enabling a circular economy transition in Singapore. Resour. Conserv. Recycl. 151, 1–19 (2019) 9. Okorie, O., et al.: A decision-making framework for the implementation of remanufacturing in rechargeable energy storage system in hybrid and electric vehicles. Procedia Manuf. 25, 142–153 (2018) 10. Dev, N.K., Shankar, R., Qaiser, F.H.: Industry 4.0 and circular economy: operational excellence for sustainable reverse supply chain performance. Resour. Conservation Recycl. 153, 1–15 (2020) 11. ElMaraghy, H., AlGeddawy, T.: New dependency model and biological analogy for integrating product design for variety with market requirements. J. Eng. Des. 23, 722–745 (2012)

Exploring Simulation as a Tool for Evaluation of Automation Assisted Assembly of Customized Products Sagar Rao(B) , Kerstin Johansen, and Milad Ashourpour Jönköping University, Tekniska Högskolan i Jönköping, 551 11 Jönköping, Sweden [email protected]

Abstract. Due to increased customization of products, manufacturing companies are subjected to assembly challenges arising from unique product configurations and higher number of variants to be able to produce in low volumes. This translates to an increased production flexibility requirement, while maintaining a high production efficiency. A higher variation among products would potentially disrupt the assembly line and create discrepancies in sequence, tools, and flow of operations. This paper explores how discrete event simulation (DES) can be used as a tool for assembly cell planning by applying key performance indicators (KPIs). A literature review is conducted to identify relevant KPIs that can be applied to compare different assembly scenarios. The KPIs are grouped under the three pillars of sustainability and analyzed as to how DES can be applied to assess them, thereby helping in evaluation and planning of an automation-assisted assembly cell. Finally, an interplay between Automation, KPI and DES, is presented. Keywords: Discrete event simulation · Key performance indicators · Mixed model assembly · Flexible automation · Sustainability

1 Introduction As companies are aiming towards achieving better efficiencies through cost reduction and waste elimination in the production processes, there is a conspicuous trend towards sustainability. This trend is in fulfilment with EU’s vision of a sustainable production sector by the year 2030, which will require addressing the United Nations sustainability development goals (SDG) (EU 2019). Along with this trend, there is an ongoing shift in production systems from mass customization of products to personalized production as mentioned by Hu et al. (2011). They furthermore state that the products being produced indicate a scenario which will involve low volume while having a higher mix of variants. In an assembly process, this constitutes as a mixed model assembly line (MMAL), where the assembly operations are performed at different arbitrary sequences (Rabbani et al. 2020). Due to an imbalance created in the assembly lines by a high mix of product variants, there will be hindrances such as time delays and wastages in the production process (Huang et al. 2018). One way of dealing with the imbalance created during high mix low volume (HMLV) production, can be designing a layout where operators and © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 1006–1013, 2022. https://doi.org/10.1007/978-3-030-90700-6_115

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robots are performing assembly tasks in collaboration (Colim et al. 2021). However, to first understand the implications of an automated collaborative assembly processes, there needs to be a method of analyzing the performance implications of automation as well as comparing different assembly scenarios such as manual, collaborative, and fully automated assembly, which can aid in decision making about how to design efficient assembly operations. While introducing complexities arising from automation in a production system, the integration of aspects such as performance assessment, capacity planning, is quite crucial and discrete event simulation is one way of evaluating these at the design phase of a production system (Barosz et al. 2020). To address this, the purpose of this paper is to understand what the interplay between automation implementation in assembly and KPI is, through the aid of discrete event simulation (DES). The role of DES in creating knowledge about KPIs for automation assisted assembly, are explored. This will be further analyzed as to whether these KPIs can help a company position itself within sustainable assembly practices. 1.1 Methodology A study was conducted with limited, unstructured virtual interviews with case companies. The case companies, Company A and Company B, produce products in the automotive and pump industry. The interviewees worked as production managers, supervisors, occupational health, and safety experts. A common challenge identified among the case companies is high product variants and low volume production due to customization, operator safety due to electrical hazards and heavy equipment handling. To address these challenges, we identified a need for evaluating and comparing different assembly scenarios with the help of KPIs, if automation is to be implemented in the current assembly lines of the case companies, and if so, to what extent and for which type of assembly tasks. This indicates a need for simulation software that supports these evaluations and comparisons. In this case we choose to use Discrete Event Simulation as a simulation software to evaluate the possibilities. The literature search thus used the keywords ‘key performance indicator’, ‘discrete event simulation’, ‘automation’,’flexible automation’, ‘manufacturing’, ‘production’, ‘assembly’. The literature search made use of databases such as Scopus and Google scholar. The articles were carefully reviewed before selection, for relevance to the challenges identified during the unstructured interviews. The criteria for selection were that the literature should provide comprehensive information in three areas i.e., barriers and enablers in implementing flexible automation in assembly operations, KPIs related to production systems performance, and finally, explore the application of DES for performance of assembly operations.

2 Literature Review 2.1 Flexible Automation in Assembly According to a case study conducted by Löfving et al. (2020), increased productivity, reduction of costs, improved ergonomics and flexibility are some of the drivers for implementing automation. However, they mention that there is lack of knowledge around

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implementation, specifications, and investments, at an organizational level for high mix low volume (HMLV) production. Some of the key enabling technologies that drive innovation in safe and efficient assembly processes are human robot collaboration (HRC) for hand guiding, speed and separation monitoring, power and force limiting applications (Hanna et al. 2020), integration of sensors and standardized interfaces (Bortolini et al. 2021), sensor technologies for separation and speed monitoring during human robot collaboration (Kim et al. 2021). Despite collaborative robots being more flexible, there are limitations in terms of task allocation, economic viability, safety hazards to the human operators according to a task allocation method proposed by Gualtieri et al. (2021). Furthermore, they state in their study that the limitations need to be tested with a process that can demonstrate how the dynamics of an assembly workstation need to be designed for a safe and economically viable human robot collaborative assembly process. For example, to address operator safety, risk assessment methods need to be adopted (Wadekar et al. 2018), which can help in addressing risks related to loss of situational awareness and mode-awareness in the design process of collaborative assembly layout (Gopinath and Johansen 2019). 2.2 Key Performance Indicators Key performance indicators (KPIs) are typically set as targets by an organization, and it is an iterative process (Rakar et al. 2004) where based on the targets that are set, the implemented KPIs are used to monitor and communicate results at various levels of an organization. According to Bishop (2018), these targets must be set based on the strategic goals and visions of a company, as they are critical in decision making process. This helps in reviewing the targets and setting objectives based on operational parameters in production. ISO 22400 has defined set of KPIs that are used in operations management, for automation systems and integration (ISO 22400-2:2014). The standard states that KPIs will help in determining the critical success factors. Jochem et al. (2010) visualize KPIs in a company in the form of a pyramid which is divided into the strategic KPIs at the top, followed by the departmental level which includes R&D, Assembly, purchase, production, and operational level at the bottom. Furthermore, they state that a top-down approach in the form of the pyramid helps in addressing the KPIs address the strategic visions of a company, while the bottom-up approach from an operational level helps in addressing targets set by different departments (Jochem et al. 2010). 2.3 Discrete Event Simulation Discrete event simulation (DES) is a simulation tool that has been widely used in supporting the management in a company as it helps in decision making (Johansson et al. 2003). Some of the advantages of DES mentioned by Montevechi et al. (2010) are that it helps in simulating already existing systems as well as upcoming ones, further helps in identifying bottlenecks, material, and product flows, allows to get more insights about variables and performance characteristics of a system and finally gives the freedom to experiment and analyze different scenarios. However, they state that performing simulation requires special training and the results can be quite complex to interpret. DES has been applied in statistically analyzing different parts of a production system by using

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operational parameters to compute KPIs (Barosz et al. 2020). The applications of DES in arriving at KPIs are presented in the upcoming Sect. 2.4. 2.4 Application of DES and KPIs for Performance Evaluation The approaches for categorizing KPIs under the three pillars of sustainability presented by Amrina and Yusof (2011) and Helleno et al. (2017) are integrated into a framework that supports a structure for exploring the results of this literature review (see Table 1). Amrina and Yusof (2011) summarize KPIs such as carbon footprint and energy utilization under environmental; product reliability, material cost, setup cost, cycle time, and OEE under economic; and occupational health, safety, under social. Similarly, Helleno et al. (2017) categorize KPIs that are time bound and cost related are under economic, KPIs involving direct interaction with employees, health and safety under social, and finally, KPIs related to resource consumption and environmental impact, under environmental. Using an integration of both types of categorizations, Table 1 presents the literature findings related to application of DES to calculate KPIs. Table 1. KPIs utilized in the application of DES, from literature Authors Paju et al. (2010)

Categorization KPIs

Type

Energy (kWh), emissions

Environmental

Materials (kg, cost), production time and Economic quantity, logistics (load rate, capacity), unit and investment costs (robots, machines, facility) Number of staff work hours

Social

Zhu et al. (2018)

MTTF, MTBF

Economic

Malik and Bilberg (2019)

Cycle time, Productivity

Economic

Barosz et al. (2020)

Throughput, Lead time of manufacturing, Work Economic in progress, Quality of products, utilization of equipment, OEE

Alkan and Bullock (2020)

Average product flow time, Average queue length of buffer

Economic

Chinnathai et al. (2021)

Scale up cost, production throughput

Economic

Prajapat et al. (2020)

Average assembly time

Economic

Abubakar and Wang (2021) Throughput Total assembly times

Economic

As it can be seen in Table 1, there are applications of DES in analyzing KPIs which are mostly economic parameters, and very few related to social and environmental aspects. To address mapping of sustainable manufacturing indicators, Paju et al. (2010) use an integrated approach of life cycle analysis (LCA), value stream mapping (VSM) and DES.

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In their study, they map the parameters for sustainable manufacturing by augmenting the economic parameters from DES, and environmental aspects such as emissions, energy consumption, and social aspects related to staff work hours, from LCA into a VSM model (Paju et al. 2010). For automation systems integration, an example of applying the ISO 22400 standard is provided by Zhu et al. (2018) in an automotive industrial case to improve productivity by analyzing the KPIs related to breakdown and maintenance of robots such as mean time to failure (MTTF) and mean time before failure (MTBF). Further to address robotic assembly, Malik and Bilberg (2019) calculate the cycle time and productivity to compare manual and collaborative assembly operation, by utilizing a DES model. Another time related KPI is used by Prajapat et al. (2020), where they apply DES for a turbine assembly case study to optimize the average assembly time. As it is crucial to understand the implications of introducing robots, Barosz et al. (2020) apply DES for comparing a manual and automatic workstation, with the help of KPIs such as production throughput, lead time of manufacturing, work in progress, quality of products, utilization of equipment, which are results that can be obtained from a DES model. They further arrive at overall equipment effectiveness (OEE), to compare different assembly scenarios. Alkan and Bullock (2020) apply DES model to analyze the complexities arising from different product variant mix in MMAL. Automated workstations involve configurations that will affect the performance. To address this, Chinnathai et al. (2021) use an integrated approach of DES with kinematic modelling. The kinematic model is applied to select suitable workstation configurations and DES is used to analyze the impact of the workstation configurations on throughput and scale up cost of the system. To analyze human factors in workstations, Abubakar and Wang (2021), considered human functional abilities based on experience and age, and used a DES model to analyze the effects of such human factors on throughput and assembly times.

3 Discussion and Conclusion: Interplay Between KPI and DES Some of the barriers identified from Sect. 2.1, for increasing flexibility in HMLV assembly lines include high costs of production, technological complexity, safety hazards, lack of organizational support in companies (Löfving et al. 2020). The enablers from a technological point of view include HRC, sensor-based technologies for safety (Hanna et al. 2020; Bortolini et al. 2021; Kim et al. 2021). However, as Gualtieri et al. (2021) suggested, the dynamics of assembly process needs to be evaluated before implementing any of the enabling technological solutions. To bridge the gap between the barriers and enablers, DES can be applied to arrive at KPIs that can aid in decision making process at various levels of a company (Johansson et al. 2003; Rakar et al. 2004; Bishop 2018). An example is decision making for implementation of automation to perform certain assembly operations. A categorization of KPIs such as in Table 1, can help in setting targets based on sustainability development goals (SDGs). Following the linkage between KPIs, strategic goals and vision of a company as stated by Bishop (2018), we visualize the interplay between automation, DES and KPIs in a pyramid model (see Fig. 1) which is adapted from Jochem et al. (2010). The strategic level represents the overall vision and challenges of a company, operational level involves process characteristics and tasks involved, departmental level sets KPIs based on the operational level performance (Jochem et al. 2010).

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Fig. 1. As suggested, an interplay between Automation, DES and KPI to support SDGs. Adapted from Jochem et al. (2010)

In Fig. 1, we see that the SDG goals trickles down from the top management in an organization as it is addressing a higher global vision. To adhere to this vision, the next level in the pyramid is implementation phase of automation, which is further dependent on how the decision makers can evaluate and compare KPIs. In the level below decision making, the economic, social, and environmental KPIs (see Table 1) need to be considered by decision makers in the assembly department, to determine the critical success factors, based on ISO 22400 and vision of the company. DES has been represented at the operational level because, DES helps in analyzing operational parameters of both already existing systems and in the design of new systems (Montevechi et al. 2010). DES can assist the assembly department in setting KPIs, based on performance evaluation. Referring to Table 1, one of the ways of achieving better production efficiency is to lower the cycle time of assembly processes (Malik and Bilberg 2019), which is interlinked with improving throughput and efficiency of the assembly process. DES can be applied in this context to statistically analyze the effects of varying cycle times, MTBF, MTTF, on the efficiency of assembly process. Using these statistical results, it would be possible to determine quantified effects of automation on flexibility and reliability of processes. For instance, if the demand for a product increase, the lead time, process, and volume flexibility required to account for that increased demand, can be identified in the form of varying throughput, work in progress, or bottlenecks in the DES results. Referring to the environmental KPIs of Table 1, energy and carbon footprint have not seen direct application in DES, unless an integrated approach is followed with other methods such as LCA (Paju et al. 2010). While there is an indication that DES can support assembly layout planning and understand the effects of automation on performance characteristics indicated mostly by economic KPIs (see Table 1), to the authors knowledge, there are certain limitations especially concerning safety of operators. Hence, to address the challenges in our study, DES alone cannot be applied to evaluate the full potential of automation. To understand the overall implication of automation implementation, the DES model needs to be augmented with methods such as risk assessment for operator safety (Wadekar et al. 2018; Gopinath and Johansen 2019), and explore integrated approaches as seen in the literature review (Paju et al. 2010; Chinnathai et al. 2021). Although, it might be a challenge to have an integrated DES model, since the safety aspects and risk assessment are adding new dimensions related to collaborative operations between human and robots. Another limitation we saw in the literature was the lack of understanding in the assembly system level about sustainability. Although some literature categorizes KPIs based on the pillars of sustainability, we also found that there

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is a lack of clear definition of sustainability at the lower levels of the pyramid. Due to some of the limitations identified, we regard DES as a piece of the puzzle in evaluating the overall effects of automation implementation on economic, safety and environmental factors. For the future scope of this study, the authors suggest adopting a use case method to develop a DES model and compute the KPIs. Acknowledgement. The authors acknowledge the SPARK research environment at Jönköping University funded by the Knowledge Foundation in Sweden (Agreement 20200215 – SABACE).

References Alkan, B., Bullock, S.: Assessing operational complexity of manufacturing systems based on algorithmic complexity of key performance indicator time-series. J. Oper. Res. Soc. 1–15 (2020) Amrina, E., Yusof, S.M.: Key performance indicators for sustainable manufacturing evaluation in automotive companies. In: IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1093–1097 (2011) Barosz, P., Gołda, G., Kampa, A.: Efficiency analysis of manufacturing line with industrial robots and human operators. Appl. Sci. 10(8), 2862 (2020). https://doi.org/10.3390/app10082862 Bortolini, M., Faccio, M., Galizia, F.G., Gamberi, M., Pilati, F.: Adaptive automation assembly systems in the Industry 4.0 era: a reference framework and full-scale prototype. Appl. Sci. 11(3), 1256 (2021) Colim, A., et al.: Lean manufacturing and ergonomics integration: defining productivity and wellbeing indicators in a human-robot workstation. Sustainability 13(4), 1931 (2021) Bishop, D.A.: Key performance indicators: ideation to creation. IEEE Eng. Manag. Rev. 46(1), 13–15 (2018) Gopinath, V., Johansen, K.: Understanding situational and mode awareness for safe human-robot collaboration: case studies on assembly applications. Prod. Eng. Res. Devel. 13(1), 1–9 (2018). https://doi.org/10.1007/s11740-018-0868-2 Gualtieri, L., Rauch, E., Vidoni, R.: Methodology for the definition of the optimal assembly cycle and calculation of the optimized assembly cycle time in human-robot collaborative assembly. Int. J. Adv. Manuf. Technol. 113(7–8), 2369–2384 (2021). https://doi.org/10.1007/s00170-02106653-y Hanna, A., Bengtsson, K., Götvall, P.L., Ekström, M.: Towards safe human robot collaboration - Risk assessment of intelligent automation. In: IEEE International Conference on Emerging Technologies and Factory Automation, pp. 424–431 (2020) Helleno, A.L., de Moraes, A.J.I., Simon, A.T., Helleno, A.L.: Integrating sustainability indicators and Lean Manufacturing to assess manufacturing processes: application case studies in Brazilian industry. J. Clean. Prod. 153, 405–416 (2017) Huang, M., Guo, Q., Liu, J., Huang, X.: Mixed model assembly line scheduling approach to order picking problem in online supermarkets. Sustainability 10(11), 3931 (2018) ISO 22400–2:2014(en). Automation systems and integration — Key performance indicators (KPIs) for manufacturing operations management — Part2: Definitions and descriptions. https:// www.iso.org/obp/ui/#iso:std:iso:22400:-2:ed-1:v1:en. Accessed 28 Apr 2021 Jochem, R., Menrath, M., Landgraf, K.: Implementing a quality-based performance measurement system a case study approach. TQM J. 22(4), 1754–2731 (2010) Johansson, B., Johnsson, J., Kinnander, A.: Information structure to support discrete event simulation in manufacturing systems. In: Proceedings of Winter Simulation Conference, vol. 2, pp. 1290–1295 (2003)

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Chinnathai, M.K., Alkan, B., Harrison, R.: A novel data-driven approach to support decisionmaking during production scale-up of assembly systems. J. Manuf. Syst. 59, 577–595 (2021) Kim, E., Yamada, Y., Okamoto, S., Sennin, M., Kito, H.: Considerations of potential runaway motion and physical interaction for speed and separation monitoring. Robot. Comput. Integrat. Manuf. 67, 102034 (2021) Löfving, M., Almström, P., Jarebrant, C.: Guide for automation of low volume production. In: Proceedings of the Swedish Production Symposium, vol. 13, pp. 13–23 (2020) Malik, A.A., Bilberg, A.: Human centered lean automation in assembly. In: Procedia CIRP, vol. 81, pp. 659–664 (2019) Montevechi, J.A.B., de Almeida Filho, R.G., Paiva, A.P., Costa, R.F.S., Medeiros, A.L.: Sensitivity analysis in discrete-event simulation using fractional factorial designs. J. Simulat. 4(2), 128–142 (2010) Paju, M., et al.: Framework and indicators for a sustainable manufacturing mapping methodology. In: Proceedings of the Winter Simulation Conference, pp. 3411–3422 (2010) Prajapat, N., Turner, C., Tiwari, A., Tiwari, D., Hutabarat, W.: Real-time discrete event simulation: a framework for an intelligent expert system approach utilising decision trees. Int. J. Adv. Manuf. Technol. 110(11–12), 2893–2911 (2020). https://doi.org/10.1007/s00170-020-06048-5 Rabbani, M., Behbahan, S.Z.B., Farrokhi-Asl, H.: The collaboration of human-robot in mixedmodel four-sided assembly line balancing problem. J. Intell. Rob. Syst. 100(1), 71–81 (2020). https://doi.org/10.1007/s10846-020-01177-1 Rakar, A., Zorzut, S., Jovan, V.: Assesment of production performance by means of KPI. In: Proceedings of the Control, pp. 6–9 (2004) Wadekar, P., Gopinath, V., Johansen, K.: Safe layout design and evaluation of a human-robot collaborative application cell through risk assessment – a computer aided approach. Procedia Manuf. 25, 602–611 (2018) Zhu, L., et al.: Key performance indicators in manufacturing operations management: a case study of the IS022400-standard applied at Volvo cars. In: IEEE International Conference on Emerging Technologies and Factory Automation, pp. 1149–1152 (2018)

The Phenomenon of Local Manufacturing: An Attempt at a Differentiation of Distributed, Re-distributed and Urban Manufacturing Pascal Krenz, Lisa Stoltenberg(B) , Julia Markert, Dominik Saubke, and Tobias Redlich Helmut-Schmidt-University, 22043 Hamburg, Germany [email protected]

Abstract. The unpredictable occurrences of a pandemic and trade conflicts have recently demonstrated the fragility of global, industrial value chains. Local value creation structures have the potential to mitigate these issues by increasing resilience and meeting present ecological, economic and social challenges. However, the idea of localizing manufacturing encompasses various concepts of value creation that are often used without clearly differentiating them. This paper presents a meta-synthesis which evaluates study results on the topic of local manufacturing aiming to outline Distributed Manufacturing (DM), Re-Distributed Manufacturing (RDM) and Urban Manufacturing (UM). Key attributes are identified and used to characterize the concepts, also highlighting overlaps and differences between them. This allows for a better understanding of local manufacturing and consolidates multiple descriptions of these concepts, thus enabling a more universal and unambiguous communication when referring to DM, RDM or UM. Keywords: Local Manufacturing · Distributed Manufacturing · Re-Distributed Manufacturing · Urban Manufacturing · Urban production · Sustainability

1 Introduction 1.1 The Paradigm Shift from Global to Local Manufacturing Value creation today often relies on global, linear supply chains [1, 2]. However, in recent years global concerns, such as climate change and the increasing scarcity of resources, have become more pressing than ever. With the growing awareness of finite resources and other environmental issues, sustainability has become imperative for value creation and is increasingly demanded by consumers [2–4]. Many concepts in the field of production science focus on solving these new problems by increasing energy and resource efficiency of established systems [5]. However that approach will not suffice [6], instead a fundamental change in the way products are manufactured is required and there is a “need to move towards an increasing share of local production” [2]. Enabled by digitalization the paradigm shift in production from a centralized to a decentralized model is further accelerated by modern technologies [3, 7–9]. © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 1014–1022, 2022. https://doi.org/10.1007/978-3-030-90700-6_116

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Due to these developments several new concepts of local manufacturing exist today. Prior to the Industrial Revolution production was characterized by the local manufacture of products on a small scale by regionally based craftsmen [10] as an original form of Distributed Manufacturing (DM), also known as Distributed Production [1, 11]. With globalization in the early 90s, distributed product manufacturing made a comeback [3, 7, 8, 10]. It is derived from an idea in the field of computing that a network with distributed nodes is more resilient than a centralized system [1]. The term Re-Distributed Manufacturing (RDM) emerged in 2013, when new patterns of local manufacturing could no longer be attributed to the prevailing understanding of DM [12]. RDM refers to a manufacturing concept that meets these challenges through inclusive and resource-efficient value creation with purely local and small-scale production [13]. Since then, the term has been much discussed and increasingly associated with the maker movement [14]. In the era of industrialization the proximity between housing and factories increasingly became a health risk for the urban population, so that in the 20th century the functional, spatial separation of cities was decided and continues to have an impact today [15]. However, due to new digital and low-emission production, there has been an increasing interest in Urban Manufacturing (UM), leading to a shift of production back to (inner) cities, and therefore reconnecting living and working [16]. 1.2 Objective and Motivation The necessary shift in value creation will not happen suddenly, but stepwise [2]. Local manufacturing plays an important role in this process [2]. During our research we noticed that current concepts dealing with the field of urban, local and distributed manufacturing have been rather vague and inconsistent in their definitions of DM, RDM and UM, the differentiation between these concepts as well as the use of terminology. Because of this and the lack of a holistic view, the systematics of local manufacturing and different design-approaches are difficult to fully understand. This paper therefore aims to identify the main attributes of DM, RDM and UM to differentiate the concepts and to place them within the phenomenon of local manufacturing. This will enable future discourse to be more precise and unambiguous in its terminology, which we believe to be important as research interest in different forms of local manufacturing is rising.

2 Methodical Approach: Meta-synthesis A meta-synthesis (also known as qualitative meta-analysis [17]) is an evaluation of existing analyses aiming to bring together previous research in a particular field and thereby gaining new, more generally valid insights, that emerge by extracting the contents of a text from its individual context [18, 19]. In this way a meta synthesis “enables the nuances, taken-for-granted assumptions, and textured milieu of varying accounts to be exposed”, allowing a deeper understanding of the entire field of research [20]. We conducted a meta-synthesis following the procedure by Cooper [21] to reach a better and more clear-cut understanding of the presented concepts (DM, RDM and UM). With this we also aimed for a holistic perspective, considering technical, economic and

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social factors. In order to achieve a demarcation of the concepts about 100 texts were found (using the keywords DM, RDM, UM and local production) and sighted. The complete list of texts can be found under the following DOI link (https://www.doi.org/ 10.17632/h74xrvc9z3.2). Subsequently, about 30 texts were selected for a further content analysis, that included definitions, descriptions or case studies. In selecting these texts, the idea of theoretical saturation was applied [22]. The content analysis was performed following the Grounded Theory by Glaser and Strauss, in which text material is dissected in a comparative, abstracting way [22, 23], also referred to as coding. Through the coding process, the statements of the texts are abstracted and condensed into categories to identify descriptive and delimitating attributes. The attributes’ relevance was assigned by their overall significance in the texts, taking into consideration the frequency of the occurrences in its tendency (not in absolute quantities). With the help of these single attributes an overall characteristic and description of the individual manufacturing concepts was achieved (see Table 1).

3 Results 3.1 Differentiation of the Concepts DM, RDM and UM After the initial coding process (according to Grounded Theory) the codes were condensed until relevant attributes could be identified. Table 1 classifies these attributes into key attribute (++), attribute (+) and neutral (0) according to how relevant they were for each of the three concepts. This represents the substantive focus of the concepts and allows for the identification of overlaps and differences between them. Key Attributes of Distributed Manufacturing (DM) Since DM portrays a large scope of possible configurations [1, 3], this paper identifies common denominators capable of outlining and defining the concept. In all configurations production must be located geographically close to the customer. Unlike the production itself, the stakeholders of the value chain do not necessarily have to be local. While in early configurations all sites belong to one enterprise [7, 10], DM also occurs in the form of Distributed Manufacturing Systems (DMS) as a production network [7]. Table 1. Attributes of a local manufacturing [++] Key Attribute [+] Attribute [0] Neutral Stakeholder

Attribute

DM

RDM

UM

Value creation (VC) through local stakeholders

0

++

++

VC through various and diverse stakeholders

0

++

+

Spatial proximity of producers

+

++

++

Integration of stakeholders into a local network

+

++

++

Need for new skills/qualification of the workforce

0

++

+ (continued)

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Table 1. (continued) [++] Key Attribute [+] Attribute [0] Neutral Process

Structure

Product

Benefit

Attribute

DM

RDM

UM

Participation of the customer/end user in the VC

+

++

0

Participation of social actors in system design

+

+

++

Co-creation / collaboration in the VC

+

++

0

Value chain: Coordination/control/reconciliation

++

0

0

Use of local materials and resources

+

+

0

On-demand

+

++

0

Reduction of the steps of the value chain

0

++

0

Production on a small scale

+

++

+

Low-emission production (city-compatible)

0

+

++

On-site production (spatial bundling)

++

++

++

Global product development

+

+

0

Universal & flexible manufacturing technologies

+

++

++

High degree of digitalization of the value chain

++

++

0

VC structures capable of change

+

0

+

Shared use of resources (resources, land, tools…)

0

+

++

Mix of uses

0

0

++

Space-saving production

0

++

++

Individualized / adapted to local needs

+

++

+

Adapted design due to new forms of production

+

++

0

Limited product range

+

++

+

Open-source rights of disposal

+

++

0

Satisfying local demand through production

++

++

+

Fulfilment of individual user preferences

+

++

0

Sustainable production

+

++

+

Empowerment of the people/ local stakeholders

0

++

0

Resilience of the VC-System

+

0

+

Cost-efficient / monetary orientated VC

+

0

0

Innovativeness of the value creation system

+

+

++

Promotion of local structures / economy

+

++

++

Promotion of local quality of life

0

0

++

Promotion of understanding between stakeholders

0

0

++

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Participation of the customer is another key attribute [10], albeit that the degree of involvement in the processes of design, product development and production varies [1, 3, 8, 10]. Simple configurations and prosumption are included as well as Personal Fabrication [1, 11, 24]. The required communication and coordination along the value chain is facilitated through digitalization and modern ICT [8, 10]. These technologies enable sending information and product data to any production site without having to move physical goods [3, 8, 24, 25]. The difficulty is the vitally important management and control of the quality standards at all distributed sites [8, 24]. Personalization is a key attribute of DM [10], because with its proximity of production and consumption, its often highly digitalized value chains and flexible production technologies it has the ideal prerequisites to meet individual customer needs [3, 8, 24]. The higher degree of personalization and each site producing for a smaller, local market [8, 11, 24] also automatically lead to smaller batch sizes and a small(er) scale [3]. In this context, new digital production technologies (i.e. Additive Manufacturing, laser cutting) are important, because they enable more flexibility in product design and choice of production site (i.e. micro-production) [3, 25]. As a result of the localization of manufacturing, transportation distances, costs and effects on the environment are reduced [1, 3, 8, 25]. Production on demand avoids the manufacturing and storage of unneeded products, reducing waste [8]. Therefore, while sustainability seems to originally not have been a main goal but rather a beneficial side effect of DM, it has become an important motivation for the implementation of DM. Key Attributes of Re-Distributed Manufacturing (RDM) RDM pushes local manufacturing and sets itself apart from the prevailing understanding of globally controlled distributed manufacturing [12] and follows the principle ‘local for local’ [26]. Local Networks play a central role in RDM. The well-known barriers between the companies are softening and allowing cross-company operations [12]. The diversity of the stakeholders is pushed by merging roles [11, 27]. Their physical proximity plays a key role; technology, producers and consumers are bundled in one location. Through new value creation patterns (e.g. makespaces, in-store-fabrication), the end consumer is integrated into the process and the degree of participation is increasingly high [11, 14, 27]. The process is shaped by the collaborative aspect of co-creation [12, 27] and the use of local resources. The value creation process strongly follows the on-demand philosophy and targets a reduction of steps along the value chain [26, 28]. The RDM concept requires a high degree of digitalization along the entire value chain, therefore Digital Production, the IoT and Big Data in general enable manufacturers to reach new levels of automation and efficiency [11]. RDM is characterized by using universal, space-saving and flexible manufacturing technologies [26]. RDM represents the production of individualized products adapted to local needs [12]. This leads to a change in product design, not least empowered by the high degree of digitalization, networking and the dominant new flexible forms of production [13]. This initially limits the product range, with a focus on consumer products and the fulfilment of individual preferences [28]. RDM favors local fabrication and resilience of local structures, resulting in the empowerment of local stakeholders [14, 26].

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One potential is a significant increase in the degree of sustainability. Unnecessary transport routes and overproduction are avoided, the supply chain is shortened. Through a consistent ‘on-demand’ strategy, only needed goods are produced exactly where they are needed. The high degree of individualization and strong involvement of people increases identification with the product, which leads to longer product lifetimes. [13, 28]. Key Attributes of Urban Manufacturing (UM) UM is located in the city center, in areas close to the city center or in the proximity of residential areas [16, 29]. The various actors of urban manufacturing are usually small manufacturers or enterprises cooperating in local networks [4, 16, 30–34]. They are in close proximity to each other, which facilitates communication and cooperation between them [29, 35], sharing of resources (e.g. space, employees, equipment) and the exchange of knowledge [13, 15, 26]. As this proximity between production and living areas has a potential for conflicts [16, 31, 36, 37], the participation of the residents in the process of establishing UM is important to gain their acceptance by being low-emission, saving resources and through social engagement of companies [31]. Space-saving production concepts (such as vertical production) or the use of flexible and digital production systems are essential due to limited space in the city [16, 33]. Politics must organize the division of space and create framework conditions for industrial utilization in the city [16, 29, 34, 36]. Then UM enables a fruitful mix of uses between working, living, shopping, research, etc. [29, 33, 34, 37]. Due to the small size of the companies, the scarcity of space and the requirement to produce in a way that is compatible with the city, UM is usually suitable for small scale and thus individualized products [29, 35]. UM aims to strengthen the local or urban economy and make it more robust and resistant to crises (e.g. by creating new jobs) [4, 34, 36]. The proximity between residential space and workplace is considered an advantage to attract skilled workers [33]. Furthermore, the networks between all stakeholders (companies, science, research, educational institutions) increase the innovative capacity of the region [16, 29, 31, 34, 36, 38]. Ultimately, UM strives to increase prosperity and the quality of life in the city by encouraging a greater understanding between the local stakeholders to create symbiotic relationships that promote urban development as a whole [33, 36, 37]. 3.2 Positioning of the Concepts Within the Phenomenon of Local Manufacturing The implementation of local manufacturing depends on the following dimensions: • Utilisation of local resources (stakeholders, materials) • Local production of goods • Addressing of local demands The prior analysis shows that DM, RDM and UM describe different value patterns which can be placed in different areas of these dimensions, as visualized in Fig. 1.

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Fig. 1. Positioning of DM, RDM and UM within the three dimensions of local manufacturing.

DM, RDM and UM all conduct their manufacturing locally, and spatially bound (see Fig. 1: overlapping of RDM, DM and UM in the square in the lower left corner). Starting from this common attribute, the concepts extend to different areas within local manufacturing. Since DM does not focus on local resources, globally acting stakeholders with a central organization and control are not excluded. In contrast, UM does focus on regional stakeholders in order to strengthen the local economy. On the flipside, UM may service global markets, whereas DM produces primarily for local consumption. RDM in turn combines the goal of sustainable, local manufacturing by locally producing goods, strengthening the local structures and empowering local stakeholders.

4 Outlook: Design of Local3 Manufacturing Local manufacturing must provide various benefits in order to gain relevance compared to global manufacturing and to have an impact on the sustainable transformation of the production economy. The highest degree of implementation of local manufacturing is achieved by operating within the following three dimensions: location of production in close proximity to the consumption, utilization of local resources and fulfilment of local demand. To describe this, we introduce the term Local3 Manufacturing (L3M). It is a promising approach to enable the transition to a sustainable, circular economy by reducing transport, empowering local stakeholders, (re)using local resources, and avoiding overproduction. DM, RDM and UM have different perspectives and priorities for designing local manufacturing. However, they all include value patterns that shape L3M. To obtain a broader understanding of the systematics of these patterns, it is necessary to further examine the identified similarities and distinctions between the concepts. Based on this, it is finally possible to derive design principles for L3 M systems.

References 1. Kohtala, C.: Addressing sustainability in research on distributed production: an integrated literature review. J. Cleaner Prod. 106, 654–668 (2015). https://doi.org/10.1016/j.jclepro.2014. 09.039 2. Larsson, M.: Circular Business Models: Developing a Sustainable Future. Springer International Publishing Chamber (2018). https://doi.org/10.1007/978-3-319-71791-3

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3. Matt, D.T., Rauch, E., Dallasega, P.: Trends towards distributed manufacturing systems and modern forms for their design. Procedia CIRP 33, 185–190 (2015) 4. Läpple, D.: Produktion zurück in die Stadt? In: Kronauer, M., Siebel, W. (eds.) Polarisierte Städte: Soziale Ungleichheit als Herausforderung für die Stadtpolitik, pp. 130–150. CampusVerl, Frankfurt am Main (2013) 5. Weber, T., Stuchtey, M.: Deutschland auf dem Weg zur Circular Economy. Vorstudie (2019) 6. Allwood, J.M., Gutowski, T.G., Serrenho, A.C., et al.: Industry 1.61803: the transition to an industry with reduced material demand fit for a low carbon future. Phil. Trans. R. Soc. 375 (2017) 7. Krenz, P.: Formen der Wissensarbeit in einer vernetzten Wertschöpfung (Diss.) (2020) 8. Petrulaityte, A., Ceschin, F., Pei, E., et al.: Supporting sustainable product-service system implementation through distributed manufacturing. Procedia CIRP 64, 375–380 (2017) 9. Rauch, E., Dallinger, M., Dallasega, P., et al.: Sustainability in manufacturing through distributed manufacturing systems (DMS). Procedia CIRP 29, 544–549 (2015) 10. Srai, J.S., Kumar, M., Graham, G., et al.: Distributed manufacturing: scope, challenges and opportunities. Int. J. Prod. Res. 54, 6917–6935 (2016) 11. Zaki, M., Theodoulidis, B., Shapira, P., et al.: The role of big data to facilitate redistributed manufacturing using a co-creation lens: patterns from consumer goods. Procedia CIRP 63, 680–685 (2017). https://doi.org/10.1016/j.procir.2017.03.350 12. Pearson, H., Noble, G., Hawkins, J.: Re-distributed manufacturing workshop report (2013) 13. Moreno, M., Charnley, F., Tiwari, A.: Network on Consumer Goods, Big Data and Re-Distributed Manufacturing - RECODE Network. Figshare (2018) 14. Freeman, R., McMahon, C., Godfrey, P.: Design of an integrated assessment of re-distributed manufacturing for the sustainable, resilient city. In: Setchi, R., Howlett, R.J., Liu, Y., Theobald, P. (eds.) Sustainable Design and Manufacturing 2016. SIST, vol. 52, pp. 601–612. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32098-4_51 15. Gold, J.R.: Athens Charter (CIAM ), 1933 (2019) 16. Erbstößer, A-C.: Produktion in der Stadt: Berliner Mischung 2.0 (2016) 17. Döring, N., Bortz, J.: Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften. Springer Berlin Heidelberg, Berlin, Heidelberg (2016). https://doi.org/10.1007/ 978-3-642-41089-5 18. Kornmeier, M.: Wissenschaftstheorie und wissenschaftliches Arbeiten: Eine Einführung für Wirtschaftswissenschaftler. Physica-Verlag, Heidelberg, BA KOMPAKT (2007) 19. Hoon, C.: Meta-synthesis of qualitative case studies. Organ. Res. Methods 16, 522–556 (2013). https://doi.org/10.1177/1094428113484969 20. Walsh, D., Downe, S.: Meta-synthesis method for qualitative research: a literature review. J. Adv. Nurs. 50, 204–211 (2005) 21. Cooper, H.M.: Scientific guidelines for conducting integrative research reviews. Rev. Educ. Res. 52, 291 (1982). https://doi.org/10.2307/1170314 22. Strübing, J.: Grounded Theory: Zur sozialtheoretischen und epistemologischen Fundierung eines pragmatistischen Forschungsstils. Springer VS, Wiesbaden (2014). https://doi.org/10. 1007/978-3-531-19897-2 23. Böhm, A.: Theoretisches kodieren. Textanalyse in der grounded theory. In: Flick, U, von Karrdorf, E, Steinke, I, Qualitative Forschung, ein Handbuch. Vol, 12, pp, 475–485. Aufl (2017) 24. Lowe, A.S.: Distributed manufacturing: make things where you need them. In: Redlich, T., Moritz, M., Wulfsberg, J.P. (eds.) Co-Creation. MP, pp. 37–50. Springer, Cham (2019). https:// doi.org/10.1007/978-3-319-97788-1_4 25. Yakovleva, N., Frei, R., Rama Murthy, S.: Sustainable development goals and sustainable supply chains in the post-global economy, vol 7. Springer International Publishing Chamber (2019). https://doi.org/10.1007/978-3-030-15066-2

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Sustainability Assessment of Manufacturing Systems – A Review-Based Systematisation Daniel Schneider(B) , Magdalena Paul, Susanne Vernim, and Michael F. Zaeh Institute for Machine Tools and Industrial Management, Technical University of Munich (TUM), Boltzmannstr. 15, 85748 Garching, Germany [email protected]

Abstract. Socio-political and environmental factors are increasingly pushing companies worldwide towards more sustainable production. When considering whether to invest in new production systems, the question arises as to the expected increase in sustainability, i.e. the ecological, economic, and social properties of the system. It is, therefore, necessary to have appropriate methods for assessing the sustainability of manufacturing systems. Based on a comprehensive literature review, this publication presents and categorises current approaches for production-related sustainability assessment. The authors systematised existing approaches in three steps: First, the underlying methodological concept was analysed, differentiating material flow-based methods, indicator-based methods, and multicriteria-based methods. Second, the normative scope was assessed, i.e. the sustainability dimensions considered and the objective of the respective approaches. Lastly, the operational setting was evaluated, i.e. the company level at which the approach can be applied. Analysing the systematisation, an unequal distribution of assessment methods across the categories became noticeable. Few methods for system and cell level assessment were identified; in particular, a lack of social aspects is evident. Concluding, the authors propose future research in the field of manufacturing-related sustainability assessment. Keywords: Sustainability · Assessment methods · Manufacturing · Systematisation · Literature review

1 Introduction Sustainable development, initially defined as the “development which meets the needs of current generations without compromising the ability of future generations to meet their own needs” [1], is becoming an increasingly important driver for companies, especially in the manufacturing industry. It is usually operationalised through the triple bottom line, a concept pioneered by Elkington [2] that simultaneously considers economic, environmental, and social issues from a microeconomic perspective. Companies encounter difficulties in assessing their production’s sustainability because they regularly do not have an overview of the multitude of existing assessment systems and often have no experience with production-related sustainability assessment so far. The objective of © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 1023–1030, 2022. https://doi.org/10.1007/978-3-030-90700-6_117

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this paper is therefore to provide an overview of existing approaches to productionrelated sustainability assessment and to systematise them according to the methodology used, normative scope, and operational context for ease of use and selection in future applications.

2 Methodology The search terms and aspects used for the literature research originate from the areas of sustainability, production, and assessment and were supplemented by the company levels according to [3]. The terms were ordered according to their thematic affiliation as shown in Table 1 and combined into search groups. Table 1. Subject areas (bold) and aspects (italic) for the search (search terms not shown) Company levels

Sustainability

Production

Methodology & Assessment

Network

ecology

remanufacturing

method

Site

economy

production

approach

Segment

social issues

manufacturing

tool

System

assembly

model

Cell

disassembly

assessment

Station

analysis

Process

Thereupon, search term combinations were formed using Boolean operators. The search was conducted using Scopus in December 2020 filtering for publications since the year 2005. The subsequently obtained search results (N 1 > 1,000) were then checked for relevance. For this purpose, the abstracts related to relevant titles, i.e. titles that contained search terms of at least three subject areas, were examined. Publications with pertinent abstracts (N 2 ≈ 300) were then further checked for content and, if confirmed to be relevant, were recorded as initially relevant publications (N 3 = 189). Relevance of paper abstracts and contents was assessed following [4]. From this set of publications, those that represent systems for assessing sustainability (N 4 = 68) were then identified and recorded separately. Assessment methods that also have a clear reference to production represent the sought-after state of research and were recorded for the subsequent classification and systematisation (N 5 = 26). The description, classification, and systematisation of the identified sustainability assessment systems with reference to production were carried out as follows: After the overview presentation of analysed publications, a methodological classification of the sustainability assessment systems was undertaken. Four basic types can be distinguished: monetary or value-based methods, material flow-based methods, multicriteria methods, and indicator-based methods (cf. Sect. 4). Then the normative framework conditions of sustainability assessment were examined and, in parallel, the individual operational

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implementation of sustainability assessment was analysed. The normative classification, on the one hand, is based on the sustainability dimensions considered, e.g. the extent to which ecological, economic, and social criteria are taken into account according to the triple bottom line. The operational classification, on the other hand, was carried out according to the operating level, at which the respective assessment methods are used, and is based on the company level scheme according to [3]. With the choice and combination of these three systematisation approaches, this paper sets itself apart from existing systematisation approaches, which e.g. sort by indicators used and product references [5] or by assessment methodologies and sustainability aspects [6]. However, the choice of this approach also implies limitations as publications from further back in time and from adjacent disciplines were not analysed.

3 Current Sustainability Assessment Methods

Number of publications

The methodology outlined above leads to 26 sustainability assessment systems found. Of those 26 systems, a majority was published in the year 2013 or later, as can be seen in Fig. 1. 21 of these works were published in journals, the rest were contributions to conferences. Most of the analysed publications originate from the USA (five), Germany (four), Brazil (four), India (four), and Italy (three). 5

6 4 2 0

1

2 0

1

1

0

1

1

2

2

4

3 1

2

1

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Year of publication

Fig. 1. Number of publications analysed by year of publication

Of the analysed sustainability assessment systems, eleven publications [7–17] understand the topics “sustainable production” or “sustainability” in general as entrepreneurial development processes that need to be supported scientifically. This includes in particular the support of the conceptualisation, introduction or use of sustainability assessment systems. Other considered sustainability assessment systems [18–24] also serve to support decision-making processes and seek to provide concrete operational support in the process of dealing with production-related sustainability aspects. What these systems thereby have in common is an increasingly quantitative assessment approach through the choice of objectively quantifiable indicators and the goal of being able to carry out an assessment not only in an abstract form at the company level, but also in an isolated way, for example at the level of a company segment or a production cell. Further studied sustainability assessment systems have a dedicated process reference [25–32]. The process orientation aims at determining the environmental impact of technological processes and to carry out comparative analyses of alternative technologies.

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4 Systematisation of Analysed Methods 4.1 Systematisation by Methodology In the following, the assessment approaches just presented will be classified in terms of the methodology used. The result of the methodological systematisation is summarised in Table 2. Monetary or Value-Based. Monetary or value-based sustainability assessment methods are usually extensions of existing cost accounting and valuation systems used to include so-called environmental costs. Heinemann et al. [19], for example, use the socalled total cost of ownership method in an environmentally-related variant as an accounting method for determining all environmental costs incurred by production systems and processes. As an alternative, Germani et al. [20] use a combination of a life-cycle costing (LCC) and a life-cycle assessment (LCA) approach to consider all (environmental) costs that occur over the life cycle of a production line. Comparable approaches are presented in [24] and [28]. Material Flow-Based Methods. Material flow-based methods are based on the fundamental idea of an LCA. This form of accounting is increasingly establishing itself in engineering application research and industry as an instrument of eco-controlling. The basic assumption, corresponding to a balance sheet, is that all output of a (production) system must be generated by input and changes in inventory, being the starting point for the ecological assessment of the (environmental) impact of all input and output flows [33]. Of the systems reviewed, Jiang et al. [27], Egilmez et al. [9], and Germani et al. [20] use or build on LCA procedures. Fijał [25] and Jeswiet & Nava [18] use the carbon footprint approach to measure production sustainability. Multicriteria Methods. Multicriteria methods consider a multitude of different criteria referring to decision-making situations with several potentially conflicting objectives [34]. Thus, the central intention of multicriteria methods is usually to select from a range of alternatives the one that best satisfies the preferences of the decision-makers. It is noticeable that most of the analysed systems follow a multicriteria approach, for example, to determine an aggregate sustainability index [7–11, 25, 26, 30], or to carry out parallel assessments for each of the sustainability dimensions [13, 14, 16, 21, 28]. Regularly, analytical hierarchy processes [19, 27] or methods of production planning and control [12, 17, 31, 32] are integrated into the assessment. Indicator-Based Methods. Indicators are defined by Gallopín [35] as an extended variable for the operational description of one or more properties of a system enabling the operationalisation of theoretical constructs. Indicator-based approaches to operationalise sustainability assessment are presented in [7, 8, 10–12, 23, 30]. These highly aggregated indicators are used to facilitate the decision-making process by reducing the number of indicators or used as inputs for system dynamics modelling to measure the sustainability of production processes.

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Table 2. Systematisation regarding the methodology used Methodology used

Publications

Monetary or value-based methods

[19, 20, 24, 28]

Material flow-based methods

[9, 18, 20, 25, 27]

Multicriteria methods

[7–17, 19, 21, 25–32]

Indicator-based methods

[8, 10–12, 14, 23, 24, 30]

4.2 Systematisation by Normative and Operational Scope For the normative classification, the assessment systems described above were systematised with regard to the sustainability dimensions considered according to the triple bottom line. Consequently, the classification scheme as shown in Table 3 consists of the categories ecology, economy, and social aspects. The operational classification, on the other hand, is based on the company level, resulting in the categories industrial sector, site, segment/system, cell, station, and process. It is noticeable that the different approaches follow certain patterns: For instance, almost all assessment systems that are to be used at site level cover all three sustainability dimensions. Examples of this are [7, 8, 10–14, 16, 17]. Another big group of definable assessment systems is the group of systems for assessing the sustainability of processes. Here, too, some approaches take all sustainability dimensions into account, such as [26, 29, 30]. However, there are significantly more outliers here that only cover one or two dimensions [18, 25, 27, 28, 31]. Between the site level and the process level are the levels segment/system, cell, and station, which cannot always be clearly distinguished from each other. It becomes clear that current approaches exist in this area, primarily for the segment/system level. As with [21, 23], either all sustainability dimensions are considered, or, as with [19, 20, 29], only ecological and/or economic aspects are evaluated specifically. Table 3. Normative and operational systematisation Sustainability dimension

Company level

Ecology

Economy

Social Issues

Site

[7–9, 11–14, 16, 17, 20–22]

[7–9, 11–17, 20–22]

[7, 8, 11–17, 21, 24]

Segment/System

[19, 20, 22, 23]

[19, 20, 22, 23]

[23]

Cell

[19, 21]

[19, 21]

[21]

Station

[18, 19]

[19]

Process

[18, 25–27, 29–31]

[26, 27, 29, 30]

[26, 29–31]

However, only a few of the identified approaches can be assigned to the cell and station levels. In addition, it stands out that only one publication, namely [21], covers all

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three sustainability dimensions, while [19] exclude social aspects and [18] concentrate their method only on ecological aspects.

5 Discussion and Need for Action As described above, it is evident that many published approaches to sustainability assessment exist for the operational levels site and process, and that these approaches largely cover all three dimensions of the triple bottom line in their assessment systems. In contrast, fewer approaches exist for the segment/system, cell, and station levels, and these few approaches hardly consider all three dimensions of sustainability, especially at the cell and station level, but also at the segment/system level. Usually, only ecological and economic, or even only ecological, aspects are assessed here, while social aspects play no or only a subordinate role. Based on this, two first needs for action (NA) concerning production-related sustainability assessment can be identified: NA1 In general, more sustainability assessment systems should be developed for application at segment/system, cell, and/or station level. NA2 Particularly, more systems for application at segment/system, cell, and/or station level should be developed that also take social aspects into account. Besides, most of the systems considered are only applied at one level of operation. Considering the increasing demands to analyse value creation processes holistically and across all levels with regard to sustainability [36], the following is also advisable: NA3 The development of sustainability assessment systems across several or even all company levels should be promoted to enable a comprehensive assessment of manufacturing companies and their value creation processes. Lastly, according to Table 2, most of the approaches considered use multicriteria methods for the assessment. Scientifically interesting would therefore be the following: NA4 The suitability of other methodological approaches for assessing productionrelated sustainability, such as financial management methods, should be investigated to show the importance of sustainable production also in monetary terms.

6 Conclusion Founded on a broad literature review, current approaches to a production-related sustainability assessment were presented and systematised. The methodological concepts, the normative scopes as well as the operational setting were assessed. Analysing the systematisation revealed an unequal distribution of assessment methods across the categories. Few methods for system and cell level, as well as cross-level assessment, were identified; notably, a lack of social aspects became evident. Building on this, the authors identified four research gaps in manufacturing-related sustainability assessment.

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Acknowledgement. This research and development project is funded by the German Federal Ministry of Education and Research within the “SME – Innovative: Research for Production” Funding Action (02K20K103) and implemented by the Project Management Agency Karlsruhe (PTKA). The authors are responsible for the content of this publication.

References 1. Brundtland Commission (1987) Our Common Future 2. Elkington, J., John, E.: Cannibals with Forks: The Triple Bottom Line of 21st Century Business. Capstone, Oxford, United Kingdom (1999) 3. Wiendahl, H.-P., ElMaraghy, H., Nyhuis, P., et al.: Changeable manufacturing – classification, design and operation. CIRP Ann. 56, 783–809 (2007) 4. Vernim, S., Krauel, M., Reinhart, G.: Identification of digitization trends and use cases in assembly. Procedia CIRP 97, 136–141 (2021) 5. Ness, B., Urbel-Piirsalu, E., Anderberg, S., et al.: Categorising tools for sustainability assessment. Ecol. Econ. 60, 498–508 (2007) 6. Singh, R.K., Murty, H.R., Gupta, S.K., et al.: An overview of sustainability assessment methodologies. Ecol. Ind. 15, 281–299 (2012) 7. Krajnc, D., Glavic, P.: A model for integrated assessment of sustainable development. Resour. Conserv. Recycl. 43, 189–208 (2005) 8. Singh, R.K., Murty, H.R., Gupta, S.K., et al.: Development of composite sustainability performance index for steel industry. Ecol. Indic. 7, 565–588 (2007) 9. Egilmez, G., Kucukvar, M., Tatari, O.: Sustainability assessment of U.S. manufacturing sectors: an economic input output-based frontier approach. J. Clean. Prod. 53, 91–102 (2013) 10. Salvado, M., Azevedo, S., Matias, J., et al.: Proposal of a sustainability index for the automotive industry. Sustainability 7, 2113–2144 (2015) 11. Doˇcekalová, M.P., Kocmanová, A.: Composite indicator for measuring corporate sustainability. Ecol. Indic. 61, 612–623 (2016) 12. Moldavska, A., Welo, T.: Testing and verification of a new corporate sustainability assessment method for manufacturing: a multiple case research study. Sustainability 10, 4121–4160 (2018) 13. Bhakar, V., Digalwar, A.K., Sangwan, K.S.: Sustainability assessment framework for manufacturing sector. Procedia CIRP 69, 248–253 (2018) 14. Pinto, L.F.R., Venturini, G., Digiesi, S., et al.: Sustainability assessment in manufacturing under a strong sustainability perspective – an ecological neutrality initiative. Sustainability 12, 9232–9271 (2020) 15. Delai, I., Takahashi, S.: Sustainability measurement system: a reference model proposal. Soc. Responsib. J. 7, 438–471 (2011) 16. Golinska, P., Kübler, F.: The method for assessment of the sustainability maturity in remanufacturing companies. Procedia CIRP 15, 201–206 (2014) 17. Singh, S., Olugu, E.U., Fallahpour, A.: Fuzzy-based sustainable manufacturing assessment model for SMEs. Clean Technol. Environ. Policy 16(5), 847–860 (2013). https://doi.org/10. 1007/s10098-013-0676-5 18. Jeswiet, J., Nava, P.: Applying CES to assembly and comparing carbon footprints. Int. J. Sustain. Eng. 2, 232–240 (2009) 19. Heinemann, T., Schraml, P., Thiede, S., et al.: Hierarchical evaluation of environmental impacts from manufacturing system and machine perspective. Procedia CIRP 15, 141–146 (2014)

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Reverse Logistics for Improved Circularity in Mass Customization Supply Chains Ottar Bakås1,2(B)

, Stine Sonen Tveit2

, and Maria Kollberg Thomassen2

1 NTNU, Trondheim, Norway

[email protected]

2 SINTEF, Trondheim, Norway

{stine.tveit,maria.thomassen}@sintef.no

Abstract. Manufacturing companies that seek to improve circularity performance across the supply chain, face many challenges in the transition of traditional linear approaches into more circular supply chain models. Reverse logistics is a key area for reuse, recycling and refurbishment of products and materials, where collection and material handling are often critical barriers. This research identifies strategic aspects of reverse logistics in circular supply chains, with focus on mass customization. A literature review on reverse logistics and reverse supply chain management is carried out and used as a basis for a case study of a mass customization furniture manufacturer. Key aspects of a reverse logistics strategy in mass customization settings are discussed, considering supply chain, product and customer-related factors. The large variety of products often complicates collection, material handling and recovery processes after end-of-life. This study presents further insights to strategic reverse logistics aspects for improved circularity performance of mass customization manufacturers, for instance how modular product architectures across the product portfolio may be beneficial for increasing circularity. Keywords: Reverse logistics · Supply chain management · Closed-loop supply chains · Furniture manufacturing · Circularity strategies

1 Introduction Reverse logistics (RL) is a vital part of a circular economy and has the potential to decrease material usage of manufacturers by enabling reuse, recycling of refurbishment of products [1]. Making a transition to a circular economy requires a fundamental change, influencing design, production, distribution, consumption. RL can be defined as the process of planning, implementing and controlling backward flows of raw materials, in-process inventory, packaging and finished goods, from a manufacturing, distribution or use point, to a point of recovery or proper disposal [2, 3]. Due to a combination of economic, environmental, and social factors, reverse logistics is becoming a key competence in modern supply chains [4, 5]. From start-ups to big brands, businesses are offering more personalized product options to extend their product lines. Mass customization (MC) as a business strategy © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 1031–1038, 2022. https://doi.org/10.1007/978-3-030-90700-6_118

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profit on the fact that customers have individualized needs, and their solution space will continue to develop due to the customers changing needs [6]. The products often consist of modules with common interfaces making the solution space immense in terms of number of variants. MC companies thrive to deliver according to the customers’ expectations while keeping a mass production efficiency [7]. It has enabled many companies to penetrate new markets by capturing customers whose personal needs were not met by standard products and services. This implies a large variety within the product portfolio which complicates the establishment of efficient reverse logistics systems for further handling of end-of-life products. The purpose of this paper is to identify key strategic aspects of reverse logistics and present challenges and opportunities in achieving reuse of materials in MC products compared to mass production settings. The aim is to gain greater insight on how MC both enables and inhibits the transition of traditional linear approaches into circular supply chains, with focus on reverse logistics. The remainder of the paper is structured as follows. First, a literature review is presented, followed by a description of the research approach applied, including an introduction to the case company. Thereafter, the empirical data is presented, followed by a discussion of findings. In the conclusion, main findings are highlighted and directions for further research are proposed.

2 Literature Review The transition to a circular economy may have major consequences for MC companies. While current research is mainly concentrated on specific areas within reverse logistics and closed-loop supply chains [8, 9], and only few studies address issues specifically related to MC products. Thus, literature addressing MC issues related to RL is still scarce. To increase the level of circularity, mass customizers can adopt a range of end-oflife (EOL) strategies for their products, including refuse, rethink, reduce, reuse, repair, refurbish, remanufacture, repurpose, recycle and recover [10]. Reverse logistics plays a vital role for succeeding with these EOL strategies. RL is the process of planning, implementing and controlling backward flows of raw materials, in-process inventory, packaging and finished goods, from a manufacturing, distribution or use point, to a point of recovery or proper disposal [2, 3]. In a supply chain perspective, the RL process mainly involves the backward flows, such as collecting and sorting products from end-users and managing these products to be used in new value streams. In general, product customization is considered a major barrier for increased circularity since custom products are produced to satisfy the individual needs of a specific customer [11, 12]. High product variety and high degree of customization typically requires a high number of potential spare parts, complicates demand planning, and hinders efficient optimization and automation of disassembly processes [13]. Different EOL strategies may have different implications for MC products, depending on aspects such as reconfigurability, customer demand fit, modularity, parts variety, product variety degree and custom components [13]. While the reuse strategy can be especially challenging, due to that MC products are tailored to individual customer needs, MC products that are reconfigurable and can be adapted after purchase may allow for further customization

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of products to the needs of new users and thus increase the chance of reuse [11, 13]. For customized products return policies may be more difficult to establish, since these are produced to satisfy the individual needs of a specific customer [11]. Modular product designs and parts commonality, which are typical MC product features, are recognized as an enabler for managing returned products, especially related to remanufacturing and recycling [13]. Modular product platforms supports the use of shared components and modules for product families, and facilitate component replacement, disassembly and re-assembly [13]. Customized products tends to lead to a higher complexity when products are disassembled for remanufacturing [12]. However, due to the modular architecture of MC products, re-designing and remanufacturing are considered as especially applicable EOL strategies [11]. High uncertainty in return flows regarding quantity, mix, quality, time and place reduces the chance of achieving economies of scale in reverse logistics activities [14]. Companies may develop partnerships with actors who have access to intellectual property, know-how and necessary information [12]. A major challenge is to find appropriate supply chain partners and to ensure that partners have specific skills and competences for handling the reverse logistics processes, especially when when handling products with high complexity and customization [12]. The customer is a key actor in a RL system [2]. Without returned products there will be no reverse flows. Successful performance of a RL system depends on customers willingness to return their obsolete products to a complete cycle [15]. The profitability of a reversed network increases when number of returned products increases [16]. Thus, specific measures can further encourage customers to initiate the return flow, such as offering discounts, charity donations or vouchers for bringing back end-of-life products [2, 17]. Also, customers willingness to return EOL products have been found to increase after governmental incentives [17]. To summarize, the reverse logistics systems of a mass customization company is dependent on supply chain factors (e.g. network design, partnerships, and competence), product-related factors (e.g. product variety, inflow volume, and product modularity), and customer-related factors (e.g. relationships, awareness, and incentives). These three domains will be used in the further analysis of the case.

3 Methodology This research is based on a literature review to identify key aspects for RL strategies for MC companies, combined with empirical case data collected from a large European manufacturer. The literature review aimed to identify strategic aspects of a reverse logistics system. Search engines combined with snowballing method were used to identify relevant literature. The empirical data were collected from a manufacturing company within the furniture industry. The furniture industry contributes large waste volumes, has low product recovery rates and have a major improvement potential. Today, approximately 10 million tons of furniture are discarded every year in the EU, and the overall recycling rate for furniture is estimated at only 10 percent [18]. The case company was selected due to its match with three criteria: use of mass customization, experience with reverse logistics and sufficient company size (sales and production in multiple markets).

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Various methods for data collection were used throughout a two-year period, such as site visits, workshops, observations, and interviews. Respondents were selected based on relevant competence and represented top management, operations and supply chain, research and development, sales and marketing, product, category and branding, country, and commercial, environmental, and business development. Interviews were semistructured with a set of guiding topics. To add further insight to current practices and potential development for RL system for furniture, four other leading companies were also part of the investigation. The data collection was carried out between 2018 and 2019. In total, primary data sources consisted of 10 interviews, 5 site visits and 3 workshops. The study is part of three-year research project funded by the Norwegian Research Council, addressing circularity strategies for the furniture industry.

4 Case Study Results The case company is a European manufacturer, headquartered in Norway, that offers customized products within the office furniture industry. MC is enabled through modular product architectures, from which a wide variety of products can be configurated and assembled. The case company has dedicated over forty years to sustainability and design and has become a leading supplier of furniture with low environmental impact. The supply chain of the company has grown to include international operations with several factories in Europe, the USA and Asia. Their products are sold globally, with an export share of 80%. A main challenge for the case company is to ensure a stable volume of returned products. The case company operates within the business-to-business (B2B) segment of office furniture. Many retailers manage the relation to the end-customer; hence the retailer has a major role within the RL strategy through communicating and providing customers with necessary knowledge. Due to varying environmental awareness across markets and fluctuating volume of inflow, it is challenging to identify partners and establish a set of roles and responsibilities in the reverse supply chain. In one of their international markets with a high demand of recovered products, the case company has established collaboration with a third-party supplier. The service provider specializes in RL activities and offer recovery services for four product brands of the case company. It is challenging making the reverse supply chain financially feasible due to unpredictable inflow of used products as well as uncertain demand of refurbished products, but they achieve a high level of social and environmental profit. By dismantling and rebuilding products according to certified procedures, components are reused or replaced. The service provider conducts four main decision processes for the reverse logistics process: preliminary sorting based on products brands, transport considerations (recovery centers vs. manufacturing facilities), component quality (level of defects) and separate sorting for recycling. A main product characteristic for the case company is a modular architecture across the product portfolio. Standard locking mechanisms enhances the disassembly process and facilitate separation of components. Even with a large product variety, the common interfaces enable dedicated disassembly lines to improve efficiency. Modular components, product structure and standard locking mechanisms is part of design for disassembly and helps to improve efficiency across product brands and makes it easier for

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RL partners to perform the disassembly. However, the furniture’s long lifespan and uncertainty in product inflow makes the financial risk high. Through monitoring used products in recovery, the case company and their RL partner can identify usual wear and tear and give feedback to internal operations that helps improve product development, manufacturing, and re-manufacturing so they continue to manage to reuse and recover a larger part of the products. When the case company or the RL service provider experience components that often breaks when transporting or dismantling the products they change the manufacturing process to fit the RL activities better. The company is planning to at expanding their product range with smart furniture features with self-monitoring and communication capabilities. This can become a vital tool for aiding partners in sorting and quality control. Customer requirements in selected markets places high demands for documentation. Circular tenders require proper product data regarding circular performance, such as tracking of materials and emissions within transportation. The product information required includes a wide range of material information from multiple tiers of suppliers and partners in the supply chain. Information and communication technology (ICT) systems that support sharing information on products and suppliers across the supply chain are critical. As information is rapidly outdated and new suppliers enter the supply chain, more efficient data sharing is needed to further improve RL systems. Through the strategic collaboration with the service provider, they offer a buy-back solution that constitutes an incentive for return in selected markets. Customers contact the service supplier when they need to upgrade their furniture. Customers rarely have furniture only from one brand and supplier, so the service provider must resell furniture they lack competence to recover efficiently. The screening process is a critical aspect of an efficient RL system. It demands a high level of product competence of the service provider to assess the quality of the parts and whether they can recover the components or not. The case company experiences that most customers are seldom aware of how to maintain the products, and a lack of spare parts of older models makes it challenging to repair some products.

5 Discussion The literature review and the following case study show that MC presents both challenges and opportunities for achieving a circular business model with reverse logistics, and will be discussed considering supply chain, product and customer-related factors. 5.1 Supply Chain Related Factors A major concern for reverse supply chains is the predictability of products returned from the customer. High uncertainty in volume, quality and product mix of return flows make it harder to plan for different recovery option (re-use, repair, refurbish, re-manufacture or recycle). The network design of the supply chain plays a critical role, as the number of actors involved can confound the return flow substantially. Issues of return policies and responsibilities for used products gets more complex as the number of actors increases. In our case company, the manufacturer is reliant on sales through a high number of

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retailers, who are then becoming the contact point for the end customer in case of a product return. The management of partners for collection, sorting and disassembly therefore becomes crucial for succeeding with reverse supply chains. The competence and skills of the partners are also vital in ensuring that the correct decisions are made when assessing returned products. Detailed product knowledge is vital for assessing residual value of products and proper actions. As identified in our case, the capability of sharing product data and coordinating service and repair-efforts are therefore vital in building efficient reverse logistics activities. 5.2 Product Related Factors The product variety offered by a company, combined with components variety for each product, is a clear contributor to high product complexity. The high complexity of MC companies could complicate spare parts planning and distribution and be impeding efficient disassembly and re-manufacturing [13]. As learned from the case, the adherence to modular product structures with shared product platforms is a key element for MC companies to overcome the inherit complexity of MC product structures. The modular product architecture provided the case company to create the desired amount of variety to customers, but it also supported key steps in the reverse logistics process, related to sorting, disassembly, and refurbishment of their furniture. The modular architecture of MC products makes them apt for recovery. In this regard, remanufacturing of an MC product would be more cost-efficient compared to a product with integrated product architecture. For modular products to be easily disassembled, companies can take important steps in the initial product design phase. For customized products, a “reuse” strategy might seem challenging since MC products are tailored to the individual needs of the respective customers. Hence, it seems unlikely that the product and its attributes fit an entirely different consumer and can thus be re-used. From this perspective, an MC product initially seems quite non-reusable. However, it can be argued that if a product contains the possibility to adapt or reconfigure after purchase, the likelihood of re-use increases. Capabilities of reconfigurable products can countermeasure the decreased flexibility of individualized products. With the growth of sensor technologies and smart product features, the ability for soft customization, achieved by software, can contribute to prolonging product life. Further, adding self-monitoring capabilities to products can be vital in initiating return loops (when a component is failing) and assessing the quality of the product needed for efficient sorting and disassembly. 5.3 Customer Related Factors Individually adapted products are assumed to have a greater use value for the customer, and thus a longer service life. In this sense, the use of customized products can help reduce the “use-and-throw” mentality that mass-produced goods can promote. From a circularity standpoint, MC is contributing to extending the product life span. As each product is manufactured or assembled only after a customer order is obtained, it reduces the stock of finished goods and risk of obsolescent products. Governmental incentives and requirements can support increased circularity [17], but can also propose challenges for MC companies. As we saw in our case example, there were very high requirements

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for documentation in public tenders in certain markets that involved a share of circular products in the procurement deal. Fulfilling these requirements proved tedious and challenging. Improved ICT systems for sharing product information were seen as a measure to counteract this labor-intensive process. As we have seen in this paper, MC companies needs to overcome challenges related to product structure, supply network design and customer relationships to fulfil this vision and increase the reverse flows of customized products.

6 Conclusion The main contribution of this paper is to show how MC models offers specific challenges and opportunities related to reverse logistics, including supply chain, product, and customer factors. It provides a contemporary case, that both confirms previous findings, i.e. the role of product modularity in mass customization companies, and its importance for circular economy models, but also extends understanding of customer- and supply chain-related factors. The supply chain actors are vital for establishing efficient reverse logistics, and relies heavily on network design, information sharing, partner skills and competencies. The customer is the key initiator of reverse product flows. The customer role can be strengthened by accessible knowledge, close customer relationships and clear return policies. Higher product complexity from high parts variation, custom components and individualized design can complicate reverse logistics flows related to key EOL strategies, such as repair (access to spare parts), reuse (customization) and re-manufacturing (disassembly efficiency). However, the modularization strategy of MC with shared product platforms and interfaces can facilitate both reverse logistics flows and recovery processes. Making reconfigurable products with smart self-monitoring capabilities can further aid efficient product return flows and product recovery. Guidelines for design for disassembly and design for reuse serves as the basis for achieving efficient reverse logistics for circular business models. Further research is called for to study how MC can further contribute to expanding the life span of products. Current MC principles and frameworks need to be further adjusted to circular business models for the customization option also being the sustainable option.

References 1. Rubio, S., Jiménez-Parra, B.: Reverse logistics: overview and challenges for supply chain management. Int. J. Eng. Bus. Manage. 6(12), 7 (2014) 2. De Brito, M.P., Dekker, R.: A framework for reverse logistics. In: Reverse logistics, pp. 3–27. Springer (2004) 3. Rubio, S., Chamorro, A., Miranda, F.J.: Characteristics of the research on reverse logistics (1995–2005). Int. J. Prod. Res. 46(4), 1099–1120 (2008) 4. Agrawal, S., Singh, R.K., Murtaza, Q.: A literature review and perspectives in reverse logistics. Resour. Conserv. Recycl. 97, 76–92 (2015)

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5. Morgan, T.R., Richey, R.G., Autry, C.W.: Developing a reverse logistics competency the influence of collaboration and information technology. Int. J. Phys. Distrib. Logist. Manag. 46(3), 293–315 (2016) 6. Salvador, F., De Holan, P.M., Piller, F.: Cracking the code of mass customization. MIT Sloan Manag. Rev. 50(3), 71–78 (2009) 7. McCarthy, I.P.: Special issue editorial: the what, why and how of mass customization. Prod. Plan. Control 15(4), 347–351 (2004) 8. Kazemi, N., Modak, N.M., Govindan, K.: A review of reverse logistics and closed loop supply chain management studies published in IJPR: a bibliometric and content analysis. Int. J. Prod. Res. 57(15–16), 4937–4960 (2019) 9. Govindan, K., Soleimani, H.: A review of reverse logistics and closed-loop supply chains: a journal of cleaner production focus. J. Clean. Prod. 142, 371–384 (2017) 10. Potting, J., et al., Circular Economy: Measuring Innovation in the Product Chain. PBL Publishers (2017) 11. Pourabdollahian, G., et al.: A contribution toward a research agenda: identifying impact factors of mass customization on environmental sustainability. Int. J. Ind. Eng. Manage. 5(4), 169–178 (2014) 12. Bressanelli, G., Perona, M., Saccani, N.: Challenges in supply chain redesign for the circular economy: a literature review and a multiple case study. Int. J. Prod. Res. 57(23), 7395–7422 (2019) 13. Nielsen, K., Brunø, T.D.: Closed loop supply chains for sustainable mass customization. In: Prabhu, V., Taisch, M., Kiritsis, D. (eds.) Advances in Production Management Systems. Sustainable Production and Service Supply Chains, pp. 425–432. Springer Berlin Heidelberg, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41266-0_51 14. Kumar, S., Putnam, V.: Cradle to cradle: reverse logistics strategies and opportunities across three industry sectors. Int. J. Prod. Econ. 115(2), 305–315 (2008) 15. Shaharudin, M.R., et al.: Managing product returns to achieve supply chain sustainability: an exploratory study and research propositions. J. Clean. Prod. 101, 1–15 (2015) 16. Ene, S., Öztürk, N.: Open loop reverse supply chain network design. Procedia Soc. Behav. Sci. 109, 1110–1115 (2014) 17. Heydari, J., Govindan, K., Jafari, A.: Reverse and closed loop supply chain coordination by considering government role. Transp. Res. Part D: Transp. Environ. 52, 379–398 (2017) 18. European Commission: Sustainable products in a circular economy - towards an EU product policy framework contributing to the circular economy. In: Commission Staff Working Document, p. 75 Brussels (2019)

Mass Customizing for Circular and Sharing Economies: A Resource-Based View on Outside of the Box Scenarios Paul Christoph Gembarski1(B)

and Friedemann Kammler2

1 Institute of Product Development, Leibniz Universität Hannover, An der Universität 1,

30823 Garbsen, Germany [email protected] 2 Smart Enterprise Engineering, German Research Center for Artificial Intelligence (DFKI), Parkstraße 40, 49080 Osnabrueck, Germany [email protected]

Abstract. To link the two research fields of Sustainable Development and Mass Customization (MC), areas like enablers and impact factors, business models for sustainable MC and analyzing or nudging consumer purchase decisions are often themed. Robust process design as MC key competence allows a different view on this, putting process-oriented and resource-based approaches to the foreground. Since a resource-based view is also followed partly in the discussion about circular economy and the sharing economy, we would like to motivate new research at this intersection. We thus discuss the scenarios of MC for circular maintenance and a waste bin manufacturer who turns from MC supplier to a sharing economy supplier taking part in urban freight cycles. Following this we develop further related research questions. Keywords: Sustainable mass customization · Sharing economy · Circular economy · Research scenario

1 Introduction While Mass Customization (MC) as a competitive strategy was introduced more than 25 years ago, the integration of MC and sustainability still offers many research opportunities [1]. One of the main hypotheses is that MC, by addressing individual customer needs, is able to reduce overproduction and resource consumption, as well as extend the product use phase of the life cycle [2]. Existing research streams cover areas like impact factors and enablers of MC on sustainability, business models for sustainable MC and analyzing or even influencing consumer purchasing decisions [3–5]. The definition for Robust Process Design as MC key competence leads to a more competence-oriented view on this topic: It focuses on the ability to quickly connect organizational units and resources in order to configure a customer order-specific value network and thus efficient manufacturing and distribution of mass customized products and services [6, 7]. But until now, efficient return, refurbishment and disposal have not © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 1039–1046, 2022. https://doi.org/10.1007/978-3-030-90700-6_119

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been included to the discussion, neither for single mass customizers, nor, more likely, for value creation networks. The mechanics of actually forming and maintaining value creation networks have been investigated from viewpoint of strategic management, e.g., under the umbrella of resource-based theory [e.g. 8, 9]. A core aspect in resource-based theory is the differentiation between resources (what a company owns) and capabilities (what a company is able to do). With this contribution we would like to incorporate this view and motivate new research by highlighting business model scenarios that integrate MC for circular economies as well as the sharing economy. Therefore, we carried out a multi case-study on actual research projects in our institutions concerning circular economies and the sharing economy. From these, we consolidated two scenarios, a platform-based MC re-manufacturing service (Sect. 3) and a waste bin manufacturer who turns from MC supplier to a sharing economy supplier (Sect. 4). For the scenarios we propose research questions at the intersection of MC and resource-based theory.

2 Theoretical Background Circular Economies: In contrast to linear economies, where natural resources are more or less consumed and usually considered as infinite, circular economies target on closing the loop of somehow reusing once delivered goods and thus minimize the utilization of limited natural resources [10]. Many governmental programs offer incentives for implementing circularity in business models, like the “European Green Deal” that was promoted end of 2019 [11]. Nonetheless, long promoted strategies like recycling or recovery of resources from waste do not necessarily promote circular economies but offer business opportunities for isolated market participants [12]. Beside research in eco-friendly design of products and services there is continuous need for rethinking businesses and closing production loops [13]. It remain the questions, what to circulate (whole products, components or even separated materials), in which condition (functional, damaged or dismantled as waste) and for which use case (same or different). Nonetheless, the decision whether a circular approach is beneficial is highly case sensitive. Additionally, many metrics like e.g. the material circularity indicator are limited in their informative value [14]. In this context, parts of the actual discussion shift from business models to more process-oriented disciplines like supply chain management and the configuration and coordination of organizational units and value creation networks [15]. Here, processoriented does also mean that additional resources are being used in order to run closed production loops. Sharing Economy: The change in consumer behavior from traditional ownership to temporary use of goods for reasons of cost, sustainability and convenience supports the development to a sharing economy [16]. From an economic perspective, the central goal of SE is maximization of utilization of an asset. In addition to this, different goals result from the motivation to participate such as sustainable consumption behavior or enjoyment in actively taking part in sharing [17]. The spread of digital technologies

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makes it hereby easier to share goods. Companies such as AirBnB as a housing agency or BlaBlaCar as a platform for ridesharing offers have therefore developed according business models [18]. At the same time, also due to the “self-definition” of individual companies and the public discourse, there is a critical discussion: As a provider on AirBnB can advertise his temporarily unused apartment in order to share the living space for the period of his absence (whether for a fee or not) in the sense of sharing economy, investors can also buy living space and rent it out permanently via AirBnB in the sense of a hotel [19]. In the same context of resource utilization, companies from mechanical and plant engineering outsource non-core competence business units as engineering or production services. After emancipating from the mother company, these acquire orders from other manufacturers and increase capacity utilization as well [20]. Such controversies are also reflected in the various concepts such as “collaborative consumption” or “access economy” [17]. We thus refer following Görög [21] to the strict consideration of the SE as a “re-use of underutilized assets”.

3 Scenario 1: MC for Circular Maintenance 3.1 Case Description The first scenario is in the domain of after-sales services. Such are usually provided with products that require maintenance strategies due to high acquisition costs or life span expectations. Typical strategies pursue the goal of avoiding downtimes by stockpiling known wear parts as a precaution and carrying them out in the event of damage (reactive maintenance), on the basis of lifecycle-related safety intervals (preventive maintenance), or by collecting operating data and forecasting events (predictive maintenance). The latter “data-based” strategies in particular help to further improve the logistical challenge of spare parts supply. In addition, there are already strategies that minimize warehousing (just-in-time) or establish spare part rotations. However, the latter have so far been intended less for the effective reuse of the individual components than for active control over the availability of know-how parts on the open market. In contrast, repair and refurbishment strategies that utilize additive technologies are currently under research to narrow logistic efforts and establish virtual spare part circulation. Offerings of this kind aim to enable customers to remove worn components independently, identify faults and - after the automated re-application of material in a 3D-printer - carry out the installation. In this process, the manufacturer evolves from a supply chain specialist to a closed-loop provider who supplies individualized repair strategies based on virtual diagnoses [22]. The offering includes the engineer-to-order repair geometry (complement between design data and component scan) and process information for manufacturing. In order to be able to offer such integrated services, manufacturers must employ modular strategies that adapt to the individual customers’ resources. This encompasses, on the one hand, the variability at the spare part level that must allow for additive repair. On the other hand, additive repair providers require information on the available competence network of the respective customers and to be able to offer tailored service.

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If these steps are mastered, additive refurbishment can be implemented as a further configuration stage. The aim here is no longer to restore components to their original condition, but to produce customized variants that meet specific requirements (operational load patterns or special material characteristics). Each spare part’s cycle can be used for iterative improvement of the component, which makes the adaptation de facto a generation development. The manufacturer adopts an engineer-to-order perspective, in which the automation of component optimization strategies is paramount. 3.2 Related Research Questions Without question, additive repair strategies cannot be implemented overnight for all components nor are they inherently advantageous. However, they give momentum to the discussion on the recycling of critical components by demonstrating that reuse strategies can be implemented with short response times and small quantities of exchange parts. We see some important contributions that Mass Customization can make on the basis of its own preliminary work: Variant Design Guidelines for Additive Repair:

– What guidelines must a design framework contain to re-engineer traditional components and describe a solution space for additive repair-enabled variants? (e.g. material selection, geometry requirements (e.g. predetermined breaking points), repairability and serviceability requirements). – How can product generation development be used to optimize components on a customer-specific basis through iterative refurbishment, also in the sense of customer co-design in case of updated requirements?

Resource-Based Configuration of Services:

– How do customer competencies and available resources apply to modular service strategies as a means for configuration and what is a conceivable portfolio of capabilities in this context? – How do manufacturers preserve competitive advantage while externalizing key activities to customers or other participants in a value network? – What are meaningful hybrid offerings that combine traditional spare part exchange and additive repair-services?

Resource-Based Configuration of Closed Production Loops:

– How can robust process design and resource-based configuration support in designing and assessing closed production loops?

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– How can individualized goods efficiently be lead back into closed production loops and in which way can these goods be accessed as resource themselves?

4 Scenario 2: The Journey of a Waste Bin Manufacturer 4.1 Case Description Subject of the second scenario is a MC supplier of waste bins, waste separation bin systems and waste bin storages for the public and B2B sectors (Fig. 1).

Fig. 1. Illustration of the strategic decisions on becoming a sharing economy supplier

The solution space mirrors the capabilities of the in-house job shop processes (sheet metal construction, polymer and wood processing, paint shop). It incorporates models of standard product lines that can be configured in their dimensions and, in the case of the waste bin storages, also in the outer appearance (stage 1). The transformation to stage 2 is triggered by a repeatedly formulated demand from city infrastructure managers to extend other suitable urban furniture, such as benches, planter and storage boxes or lighting/wireless stations. From a robust process design point of view, all necessary manufacturing processes are available in the company and the expansion of the product range is possible, including new supply chain partners. At stage 3, more and more customers couple their order on a waste collection and disposal service in the sense of outsourcing a “total” waste management. In this case, the portfolio of capabilities has to be extended by a logistics network and corresponding resources, i.e. mainly the garbage trucks in order to service the individualized bins. But as the goal is to further maximize the utilization of underused assets, the task is to maximize the use of all operational resources that occur in the waste management system. In order to track the utilization, the filling level is an interesting parameter which was not considered a variable until now. Thus the company introduces two boundary conditions: (1) only a filled or nearly filled waste bin may be emptied and (2) only fully filled garbage trucks may return to the disposal site. To realize the first condition the customers can either call for emptying the bins which has to be achieved in a guaranteed

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time frame or, the filling level can be monitored by sensors in a smart bin. Together with a forecasting model, data about the filling level can be used to dynamically plan the route of the collection run and thus design the service of waste collection more flexible and demand-oriented (stage 4). To further increase the workload of each truck, the question must be raised how empty runs can be turned into benefit, e.g. by offering it as resource for other stakeholders. At this point, the garbage truck becomes a shared good. The truck runs empty to the customer and returns filled, so a supply network partner needs to be found and integrated where this is opposite, e.g. parcel delivery. Additionally, a platform needs to be created that mediates between the operational needs of waste collection and other service providers (stage 5). A next stage may involve the decentralization of such an urban freight cycle in order to be more responsive to changing demands (stage 6). Here, the solution space of urban furniture is an obvious starting point since storage boxes had been already implemented in stage 2. The supplier will introduce such storage for waste and parcels that are another shared good in the Sharing Economy ecosystem. The definition of the storage capacity is a result of supplier demand management as the demand from both parcel and waste management service need to be taken into account. A last step now is to mediate the demands of customers and multiple suppliers in platforms for several decentrally organized urban freight cycles (stage 7). 4.2 Related Research Questions This scenario is only hypothetical in parts. While the demand-oriented expansion of Mass Customization offerings like at stage 2 and 3 is already reported in literature, the mentioned urban freight cycles can be observed e.g. in Stockholm [23]. In order to effectively maximize the utilization of idle capacities, there should be a balance between supply and demand for a shared good. However, the domain is not limited to the coordination of existing “static” goods, as the ability to personalize a shared good to a distinct customer using digital technologies is also researched in the field of information systems [24]. Mass Customization can contribute to this by answering the following questions: Resource-Based Configuration and Orchestration of Sharing Economy Systems:

– How to model shared goods on a functional and configurable basis so that different participants of value creation networks can co-design operational resources? – How can data and information be integrated as marketable good in such value creation systems and possibly lead to data-driven value chain configuration? – How to coordinate and to mediate the interests of multiple value creation partners? – Which compensation mechanisms apply for value creation systems in sharing economies, when the customer access is provided by a single participant of the network (face to the customer)? – What can choice navigation and recommender systems contribute to the mechanics of sharing economies in this context?

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Customization and Personalization of Shared Goods:

– In which way do degrees of freedom and a solution space apply for the design and creation of shared goods? What is the potential of modular shared goods? – What is the opportunity to extend this solution space and portfolio of capabilities with digital capacities, thus allowing for the virtual ex-post configuration of the shared good along changing customer requirements?

5 Conclusion Taking a process-oriented and resource-based view on circular economies and the sharing economy promise the potential for operationalization of current discussions regarding a sustainable development. Mass Customization can contribute to this in multiple ways as its three key competences incorporate diverse mechanisms which are applicable like configuration, value chain orchestration or modeling the portfolio of capabilities. The research questions we stated above are possible starting points for further exploring this. We intended not to weight or prioritize the questions or assign them to disciplines like Information Systems Research or Operations Research in order to motivate a holistic view. Once the first step towards this has been taken, external resources in particular, such as customers’ skills and assets, promise further development potential that can contribute to the realization of innovative circular and sharing economy applications. Beside the theoretic foundation, reporting about and thinking further applications like Stockholm’s urban freight cycles in the sense of best practices is still necessary. To promote the concepts of circular economies and sharing economies, the formulation of design guidelines is an obvious prerequisite and interesting avenue for research.

References 1. Gembarski, P.C., Schoormann, T., Schreiber, D., Knackstedt, R., Lachmayer, R.: Effects of mass customization on sustainability: a literature-based analysis. In: Hankammer, S., Nielsen, K., Piller, F.T., Schuh, G., Wang, N. (eds.) Customization 4.0. SPBE, pp. 285–300. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77556-2_18 2. Boër, C.R., Pedrazzoli, P., Bettoni, A., Sorlini, M.: Mass Customization and Sustainability: An Assessment Framework and Industrial Implementation. Springer, Heidelberg (2013). https:// doi.org/10.1007/978-1-4471-5116-6 3. Pourabdollahian, G., Steiner, F., Rasmussen, O.H., Hankammer, S.: Impact factors of mass customization on sustainability. In: Presented at the 6th International Conference on Mass Customization and Personalization in Central Europe (MCP-CE), Novi Sad, Serbia (2014) 4. Hora, M., Hankammer, S., Canetta, L., Sel, S.K., Gomez, S., Gahrens, S.: Designing business models for sustainable mass customization: a framework proposal. Int. J. Ind. Eng. Manag. 7(4), 143–152 (2016) 5. Hankammer, S., Kleer, R., Piller, F.T.: Sustainability nudges in the context of customer codesign for consumer electronics. J. Bus. Econ. 91(6), 897–933 (2020). https://doi.org/10. 1007/s11573-020-01020-x

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6. Pine, B.J.: Mass Customization. Harvard Business School Press, Boston (1993) 7. Gembarski, P.C., Brinker, J., Thomas, O.: An extended key competence framework to mass customize after-sales services. In: Proceedings of the 9th International Conference on Mass Customization and Personalization - Community of Europe (MCP - CE 2020), pp. 67–76, Novi Sad, Serbia (2020) 8. Barney, J.B.: The resource-based theory of the firm. Organ. Sci. 7(5), 469 (1996). https://doi. org/10.1287/orsc.7.5.469 9. Das, T.K., Teng, B.S.: A resource-based theory of strategic alliances. J. Manag. 26(1), 31–61 (2000). https://doi.org/10.1016/S0149-2063(99)00037-9 10. Corona, B., Shen, L., Reike, D., Carreón, J.R., Worrell, E.: Towards sustainable development through the circular economy—a review and critical assessment on current circularity metrics. Resour. Conserv. Recycl. 151, 104498 (2019). https://doi.org/10.1016/j.resconrec.2019. 104498 11. Eckert, E., Kovalevska, O.: Sustainability in the European Union: analyzing the discourse of the European green deal. J. Risk Finan. Manag. 14(2), 80–102 (2021). https://doi.org/10. 3390/jrfm14020080 12. Morseletto, P.: Targets for a circular economy. Resour. Conserv. Recycl. 153, 104553 (2020). https://doi.org/10.1016/j.resconrec.2019.104553 13. Heyes, G., Sharmina, M., Mendoza, J.M.F., Gallego-Schmid, A., Azapagic, A.: Developing and implementing circular economy business models in service-oriented technology companies. J. Clean. Prod. 177, 621–632 (2018). https://doi.org/10.1016/j.jclepro.2017. 12.168 14. Lonca, G., Muggéo, R., Imbeault-Tétreault, H., Bernard, S., Margni, M.: Does material circularity rhyme with environmental efficiency? Case studies on used tires. J. Clean. Prod. 183, 424–435 (2018). https://doi.org/10.1016/j.jclepro.2018.02.108 15. Geissdoerfer, M., Morioka, S.N., de Carvalho, M.M., Evans, S.: Business models and supply chains for the circular economy. J. Clean. Prod. 190, 712–721 (2018). https://doi.org/10.1016/ j.jclepro.2018.04.159 16. Puschmann, T., Alt, R.: Sharing economy. Bus. Inf. Syst. Eng. 58(1), 93–99 (2015). https:// doi.org/10.1007/s12599-015-0420-2 17. Hamari, J., Sjöklint, M., Ukkonen, A.: The sharing economy: why people participate in collaborative consumption. J. Am. Soc. Inf. Sci. 67(9), 2047–2059 (2016). https://doi.org/10. 1002/asi.23552 18. Frey, A., Trenz, M., Veit, D.: Three differentiation strategies for competing in the sharing economy. MIS Q. Exec. 18(2), 143–156 (2019). https://doi.org/10.17705/2msqe.00013 19. Meelen, T., Frenken, K.: Stop saying Uber is part of the sharing economy. Fast Company, 14 January 2015 20. Kremic, T., Tukel, O.I., Rom, W.O.: Outsourcing decision support: a survey of benefits, risks, and decision factors. Supply Chain Manag. 11(6), 467–482 (2006). https://doi.org/10.1108/ 13598540610703864 21. Görög, G.: The definitions of sharing economy: a systematic literature review. Management 13(2), 175–189 (2018). https://doi.org/10.26493/1854-4231.13.175-189 22. Kammler, F., Hagen, S., Brinker, J., Thomas, O.: Leveraging the value of data-driven service systems in manufacturing: a graph-based approach. In: European Conference on Information Systems (ECIS), Stockholm-Uppsala, Sweden, June 2019 23. Browne, M., McLeod, S.: The sustainability of last-mile freight in cities. In: Handbook of Sustainable Transport. Edward Elgar Publishing, Cheltenham (2020) 24. Tiihonen, J., Felfernig, A.: An introduction to personalization and mass customization. J. Intell. Inf. Syst. 49(1), 1–7 (2017). https://doi.org/10.1007/s10844-017-0465-4

Correction to: Manufacturing Genome: A Foundation for Symbiotic, Highly Iterative Product and Production Adaptations Patrizia Gartner, Alexander Jacob, Haluk Akay, Johannes Löffler, Jack Gammack, Gisela Lanza, and Sang-Gook Kim

Correction to: Chapter “Manufacturing Genome: A Foundation for Symbiotic, Highly Iterative Product and Production Adaptations” in: A.-L. Andersen et al. (Eds.): Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems, LNME, https://doi.org/10.1007/978-3-030-90700-6_3

In the original version of the book, the following belated correction has been incorporated: In the chapter “Manufacturing Genome: A Foundation for Symbiotic, Highly Iterative Product and Production Adaptations”, the affiliation of Patrizia Gartner has been changed from “Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany” to “Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT), 77 Massachusetts Avenue, Cambridge, MA 02139, USA”.

The updated version of this chapter can be found at https://doi.org/10.1007/978-3-030-90700-6_3 © Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, p. C1, 2022. https://doi.org/10.1007/978-3-030-90700-6_120

Author Index

A Adlon, Tobias, 543 Akay, Haluk, 35 Albers, A., 96 Altvater, A., 96 Andersen, Ann-Louise, 3, 80, 88, 130, 138, 694, 903 Andersen, Rasmus, 3, 661, 677 Angelopoulos, John, 182 Antonsen, Mikkel Graugaard, 762 Arastehfar, Soheil, 190 Arias-Nava, Elias, 80 Aschenbrenner, Verena, 835 Ashourpour, Milad, 1006 Avhad, Akshay, 105 B Bagalagel, Saleh M., 997 Bakås, Ottar, 1031 Barhebwa-Mushamuka, Félicien, 339 Bartsch, Hannah, 620 Basson, Anton, 347 Bätzel, Jan Uwe, 277 Bause, K., 96 Behrens, Dörthe, 927 Belkadi, Farouk, 381 Bellemare, Jocelyn, 789 Bennulf, Mattias, 27 Berg, Simon Vestergaard, 415 Berger, Ulrich, 330, 356 Bi, Yun, 250 Bilberg, Arne, 233, 297, 389

Blazek, Paul, 835, 843 Bøgh, Simon, 105, 415 Boije af Gennäs, Rikard, 807 Boldt, Simon, 113 Borck, Christian, 330 Borregaard, Thorbjørn, 762 Bortolini, Marco, 584 Böttjer, Till, 431 Bozkurt, Ali, 475 Brabrand, Christian Victor, 636 Brand, Michael, 207 Briele, Kristof, 322 Bro, Carsten, 389 Bruetzel, Oliver, 459 Brunoe, Thomas D., 3, 138, 628, 661, 677, 694, 762, 887 Bruzzone, Alessandro Arturo, 47 Burggräf, Peter, 543, 987 C Campo Gay, Irene, 827 Carta, Silvio, 953 Chaudhuri, Atanu, 396 Chemweno, Peter, 190 Chriette, Abdelhamid, 381 Christensen, Caroline, 754 Christensen, Noemi, 819 Christiansen, Lasse, 762, 887 Clausen, Pernille, 746 Colli, Michele, 356, 730 Cosentino, Juan Pablo, 713

© Springer Nature Switzerland AG 2022 A.-L. Andersen et al. (Eds.): CARV 2021/MCPC 2021, LNME, pp. 1047–1051, 2022. https://doi.org/10.1007/978-3-030-90700-6

1048 D da Cunha, Catherine, 381 Daniel, Díez Álvarez, 440 Danielsson, Fredrik, 27 Dannapfel, Matthias, 987 Davoudabadi Farahani, Saeed, 396 De Benedittis, Julien, 919 de Biasi, L., 96 de Melo, Francisco Cristovão Lourenço, 483 Denkena, B., 603 Deryck, Marjolein, 860 Deuse, Jochen, 612 Dhungana, Deepak, 198 Dietrich, Fabian, 508 Dittrich, M.-A., 603 Djuric, Ana, 250 Dröder, Klaus, 559 Dürr, Simon, 620 E Ebel, A., 96 Ehrenberg, H., 96 ElMaraghy, Hoda, 871 ElMaraghy, Waguih, 871, 997 Endo, Seiji, 978 Engel, Bernd, 269 F Feldkamp, Jan, 927 Fernandes, Antonio Mousinho de Oliveira, 122 Ferreira, Luis Miguel D. F., 88 Fleischer, J., 96 Forbech, Henning, 242 Frey, Alex Maximilian, 372 Frohn-Sörensen, Peter, 269 Fyrner, Linnea, 807 G Galizia, Francesco Gabriele, 584 Gammack, Jack, 35 Gandert, J., 96 Ganter, Nicola Viktoria, 261 Gartner, Patrizia, 35 Gärtner, Quirin, 911 Gebauer, Marc, 895 Gembarski, Paul Christoph, 261, 645, 653, 669, 773, 781, 927, 935, 1039 Giuffrida, Maria, 499 Gomes, Cláudio, 431 Gomse, Martin, 364 Gönnheimer, P., 96

Author Index Göppert, Amon, 423 Gorm Larsen, Peter, 431 Grahn, Lea, 423 Greb, Christoph, 595 Grimm, Julian Joël, 551 Gronau, Norbert, 149, 314 Grum, Marcus, 149, 314 Grün, T., 96 Guldborg Staal, Lasse, 396 H Hagelskjær, Frederik, 166 Hagen, M., 96 Hamani, Nadia, 72 Hansen, Casper, 224 Hansen, Emil Blixt, 415 Hansen, Jesper Puggaard de Oliveira, 389 Hansen, Poul Kyvsgaard, 879 Harrisson-Boudreau, Jean-Philippe, 789 Haselböck, Alois, 198 Hedlind, Mikael, 575 Heidemann Lassen, Astrid, 754, 887 Henriksen, Benjamin, 746 Hentschel, Christian, 330 Hermann, Julian, 347 Hillenbrand, J., 96 Hiller, M., 96 Hjorth, Sebastian, 242 Holm, Kristoffer, 516 Honorato, Cezar, 483 Hoppe, Lukas, 927, 935 Hsuan, Juliana, 879 Huang, Sihan, 63 Huber, Marco F., 620 Hummel, Vera, 347 Hussmo, Daniel, 945 Hvam, Lars, 636, 827 I Iftikhar, Nadeem, 415, 448 Iosifidis, Alexandros, 224, 431 Iversen, Thorbjørn Mosekjær, 166 J Jacob, Alexander, 35 Jødal, Anne-Sophie Schou, 628 Johansen, Kerstin, 1006 Jonek, Michael, 527, 567 K Kammler, Friedemann, 773, 781, 1039 Kandler, Magnus, 738

Author Index Kayser, Erik, 807 Kermad, Lyes, 72 Kick, Michael K., 407 Kies, Lorenz, 927 Kiesel, Raphael, 322 Kim, Sang-Gook, 35 Kjeldgaard, Stefan, 3, 138 Klemens, J., 96 Kluge, Annette, 314 Kluge-Wilkes, Aline, 535 Knoche, Jonas, 269 Koch, Julian, 364 Kortum, Henrik, 773 Kößler, F., 96 Kraft, Dirk, 166 Krause, Dieter, 686, 798 Krenz, Pascal, 1014 Kristensen, Jan, 415 Kristensen, Jesper H., 887 Kristensen, Saeedeh Shafiee, 970 Kuermeier, Alexander, 407 Kuhl, Juliane, 686, 798 Kuhnle, Andreas, 738 Kurtz, Julian, 738 L Lachmayer, Roland, 261, 653, 669 Lago-Novás, Juan, 953 Lanza, Gisela, 35, 96, 372, 459, 620, 738 Lars, Væhrens, 440 Larsen, Maria Stoettrup Schioenning, 3, 721, 754 Lassen, Astrid Heidemann, 356, 721 Laursen, Esben Skov, 762, 887 Lehmann, Marlon Antonin, 216 Leporowski, Bła˙zej, 224 Li, Xiaohan, 285 Lindemann, Marie, 322 Lindvig, Anders Prier, 305 Linnéusson, Gary, 113 Lippok, Thomas, 277 Löffler, Johannes, 35 Lotzing, Gerald, 364 Louw, Louis, 508 M Madsen, Ole, 105, 174, 356 Maffei, Antonio, 575 Maganha, Isabela, 88, 122, 903 Magnusson, Mats, 721 Mahmood, Kashif, 887 Mai, Christopher, 216 Malik, Ali Ahmad, 233 Manns, Martin, 158, 269, 277, 527, 567 Mark, Benedikt G., 911 Markert, Julia, 1014

1049 Martinho, Jose L. F., 122 Mathiesen, Simon, 166, 305 May, Marvin Carl, 738 Medini, Khaled, 919 Meller, Patrick, 962 Milde, Michael, 339, 467 Mogensen, Maja K., 661 Mohacsi, J., 96 Møller, Charles, 297, 356 Mourtzis, Dimitris, 182 Mueller, Kai, 595 Müller, Patrik, 669 Müller-Welt, P., 96 N Nafisi, Mariam, 575 Napoleone, Alessia, 3, 80 Nie, Shiqi, 63 Nielsen, Kjeld, 3, 138, 628, 661, 677, 694 Nielsen, Mette Busk, 415 Nilsson, Anders, 27 Nordbjerg, Finn Ebertsen, 448 Nowoseltschenko, K., 96 O Olivares-Aguila, Jessica, 871 Olsson, Fredrik, 130 Ørnskov Rønsch, Georg, 431 Otto, Tauno, 887 Overbeck, L., 96 Overbeck, Leonard, 459 P Paarmann, S., 96 Palm, Daniel, 508 Pan Nogueras, M. Laura, 705, 713 Panopoulos, Nikos, 182 Paul, Magdalena, 1023 Perea Muñoz, Lourdes, 705, 713 Pereira, Tábata Fernandes, 903 Perotti, Sara, 499 Persson, Magnus, 879 Plappert, Stefan, 261, 653 Polydorides, Nick, 285 Porsch, Ronny, 216 Pugliese, Luiz Felipe, 903 R Rachner, Jonas, 423 Radaca, Elvira, 962 Ramanujan, Devarajan, 431

1050 Rao, Sagar, 1006 Raza, Mohsin, 233 Rea Minango, Nathaly, 575 Redlich, Tobias, 1014 Rehe, Grit, 895 Reichler, Ann-Kathrin, 559 Reinhart, Gunther, 339, 467 Rennpferdt, Christoph, 686, 798 Ribeiro da Silva, Elias, 242, 389 Rickli, Jeremy J., 250 Roling, Wiebke, 314 Rösiö, Carin, 113 Ruhland, J., 96 Rusch, Isabella, 475 S Saeed, Sazvan, 423 Säfsten, Kristina, 945 Salzwedel, Jan, 543 Santos, Ana Carolina Oliveira, 903 Sarivan, Ioan-Matei, 174 Sartori, Alberto, 305 Saubke, Dominik, 1014 Sauer, Alexander, 551 Sawodny, J., 96 Schabel, W., 96 Schall, D., 96 Scharfer, P., 96 Schlette, Christian, 305 Schmidbauer, Christina, 198 Schmidt, A., 96 Schmitt, Maya Kousholt, 754 Schmitt, Randolf, 330 Schmitt, Robert H., 322, 423, 535 Schneider, Daniel, 1023 Schou, Casper, 105, 242, 356, 887 Schreiber, Florian, 269, 277 Schreiner, Sabrina, 962 Schüffler, Arnulf, 314 Schulz, Jan Peter, 207 Schulz, Robert, 475 Schumann, Benjamin, 559 Schupp, Steffen, 543 Schüppstuhl, Thorsten, 207, 364 Settnik, S. J., 603 Shafiee, Sara, 636, 970 Sharifi, Elham, 396 Silbernagel, Rainer, 620 Silva, Cristovao, 88 Skogstad, Morten, 694 Sørensen, Lars Carøe, 166, 305 Sørensen, Søren Bronnée, 730 Sparre Sørensen, Michael, 242 Spiegel, S., 96 Stadter, Christian, 407

Author Index Steier, Gwen Louis, 620 Stoltenberg, Lisa, 1014 Storz, T., 96 Strassmayr, Georg, 843 Strelkova, Dora, 55 Stricker, Nicole, 459 Suarez Anzorena, Daniel, 705, 713 Subramaniam Anbuchezhian, Puviyarasu, 381 Sullivan, Brendan P., 80 Svangren Bodilsen, Morten, 415 Svensson, Bo, 27 Syberg, Marius, 612 T Taupe, Richard, 198 Thim, Christof, 149, 314 Thomassen, Maria Kollberg, 1031 Tola, Daniella, 224 Torn, Robbert-Jan, 190 Trägårdh, Andreas, 807 Tryggvason, Finn, 242 Tübke, J., 96 Tuli, Tadele Belay, 158, 527, 567 Turner, Frances, 851 Tveit, Stine Sonen, 1031 U Uelpenich, Jérôme, 987 Ulrich, Berger, 440 Urbanic, R. Jill, 55 V van der Bijl, Emil Robenhagen, 415 Vaneker, Tom, 190 Vejrum Waehrens, Brian, 396 Vennekens, Joost, 860 Vernim, Susanne, 1023 Vidal Calvet, Miguel, 953 Voelkle, Daniel, 459 von Leipzig, Konrad, 347 W Wæhrens, Brian Vejrum, 174, 356, 730 Wagner, Morten, 730 Wagner, Sarah, 339 Wallner, Stefan, 198 Wang, Guoxin, 63 Waspe, Ralf, 305 Watts, Marie, 851 Weber, A., 96 Weeber, Max, 551

Author Index Weiner, Roman, 475 Werthén, Alexander, 130 West, Nikolai, 612 Wetzel, T., 96 Wiesner, Stefan, 919 Wiezorek, Robin, 819 Wlazlak, Paraskeva, 945 Wolniak, Philipp, 653 Wulff, Lukas Antonio, 207

1051 Y Yan, Yan, 63 Yildiz, Emre, 297

Z Zaeh, Michael F., 407, 1023 Zidi, Slim, 72