Advances in Design, Simulation and Manufacturing VI: Proceedings of the 6th International Conference on Design, Simulation, Manufacturing: The ... (Lecture Notes in Mechanical Engineering) 9783031327667, 3031327667

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Lecture Notes in Mechanical Engineering

Vitalii Ivanov · Justyna Trojanowska · Ivan Pavlenko · Erwin Rauch · Ján Piteľ   Editors

Advances in Design, Simulation and Manufacturing VI Proceedings of the 6th International Conference on Design, Simulation, Manufacturing: The Innovation Exchange, DSMIE-2023, June 6–9, 2023, High Tatras, Slovak Republic - Volume 1: Manufacturing Engineering

Lecture Notes in Mechanical Engineering Series Editors Fakher Chaari, National School of Engineers, University of Sfax, Sfax, Tunisia Francesco Gherardini , Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy Vitalii Ivanov, Department of Manufacturing Engineering, Machines and Tools, Sumy State University, Sumy, Ukraine Mohamed Haddar, National School of Engineers of Sfax (ENIS), Sfax, Tunisia

Editorial Board Members Francisco Cavas-Martínez , Departamento de Estructuras, Construcción y Expresión Gráfica Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain Francesca di Mare, Institute of Energy Technology, Ruhr-Universität Bochum, Bochum, Nordrhein-Westfalen, Germany 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 Jinyang Xu, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

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. To submit a proposal or request further information, please contact the Springer Editor of your location: Europe, USA, Africa: Leontina Di Cecco at [email protected] China: Ella Zhang at [email protected] India: Priya Vyas at [email protected] Rest of Asia, Australia, New Zealand: Swati Meherishi at swati.meherishi@springer. com Topics in the series include: • • • • • • • • • • • • • • • • •

Engineering Design Machinery and Machine Elements Mechanical Structures and Stress Analysis Automotive Engineering Engine Technology Aerospace Technology and Astronautics Nanotechnology and Microengineering Control, Robotics, Mechatronics MEMS Theoretical and Applied Mechanics Dynamical Systems, Control Fluid Mechanics Engineering Thermodynamics, Heat and Mass Transfer Manufacturing Precision Engineering, Instrumentation, Measurement Materials Engineering Tribology and Surface Technology

Indexed by SCOPUS and EI Compendex. All books published in the series are submitted for consideration in Web of Science. To submit a proposal for a monograph, please check our Springer Tracts in Mechanical Engineering at https://link.springer.com/bookseries/11693

Vitalii Ivanov · Justyna Trojanowska · Ivan Pavlenko · Erwin Rauch · Ján Pitel’ Editors

Advances in Design, Simulation and Manufacturing VI Proceedings of the 6th International Conference on Design, Simulation, Manufacturing: The Innovation Exchange, DSMIE-2023, June 6–9, 2023, High Tatras, Slovak Republic Volume 1: Manufacturing Engineering

Editors Vitalii Ivanov Sumy State University Sumy, Ukraine

Justyna Trojanowska Pozna´n University of Technology Pozna´n, Poland

Ivan Pavlenko Sumy State University Sumy, Ukraine

Erwin Rauch Free University of Bozen-Bolzano Bolzano, Italy

Ján Pitel’ Technical University of Košice Prešov, Slovakia

ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-3-031-32766-7 ISBN 978-3-031-32767-4 (eBook) https://doi.org/10.1007/978-3-031-32767-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed 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

This volume of Lecture Notes in Mechanical Engineering contains selected papers presented at the 6th International Conference on Design, Simulation, Manufacturing: The Innovation Exchange (DSMIE-2023), held in High Tatras, Slovak Republic, on June 6–9, 2023. The conference was organized by the Sumy State University, Technical University of Košice, and International Association for Technological Development and Innovations, in partnership with Poznan University of Technology (Poland), Kielce University of Technology (Poland), University of West Bohemia (Czech Republic), Association for Promoting Innovative Technologies—Innovative FET (Croatia), and Society for Robotics of Bosnia and Herzegovina (Bosnia and Herzegovina). DSMIE Conference Series is the international forum for fundamental and applied research and industrial applications in engineering. The conference focuses on research challenges in the fields of Manufacturing Engineering, Materials Engineering, and Mechanical Engineering, addressing current and future trends in design approaches, simulation techniques, and manufacturing technologies, highlighting the growing role of smart manufacturing systems, artificial intelligence, standards-based integration, and innovations implementation to the transition to sustainable, human-centric, and resilient engineering solutions. DSMIE brings together researchers from academic institutions, leading industrial companies, and government laboratories worldwide to promote and popularize the scientific fundamentals of engineering. DSMIE-2023 received 135 contributions from 14 countries around the world. After a thorough peer-review process, the Program Committee accepted 66 papers written by 259 authors from 12 countries. Thank you very much to the authors for their contribution. These papers are published in the present book, achieving an acceptance rate of about 49%. Extended versions of selected best papers will be published in scientific journals: Management and Production Engineering Review (Poland), Journal of Engineering Sciences (Ukraine), Advances in Thermal Processes and Energy Transformation (Slovak Republic), Assembly Techniques and Technology (Poland), and a special issue of Machines (Switzerland) “Innovations in the Design, Simulation, and Manufacturing of Production Systems” and Materials (Switzerland) “Novel Approaches in the Design, Simulation, and Manufacturing for Processes and Systems”. We would like to thank members of the Program Committee and invited external reviewers for their efforts and expertise in contributing to reviewing, without which it would be impossible to maintain the high standards of peer-reviewed papers. Eightythree Program Committee members and ten invited external reviewers devoted their time and energy to peer-reviewing manuscripts. Our reviewers come from around the world, representing 17 countries, and are affiliated with 39 institutions. Thank you very much to the keynote speakers: Prof. Fazel Ansari (Vienna University of Technology, Austria), Prof. Michał Wieczorowski (Poznan University of Technology, Poland), and Prof. Alexander Hošovský (Technical University of Košice, Slovak Republic).

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Preface

The book “Advances in Design, Simulation and Manufacturing VI” was organized into two volumes according to the main conference topics: Volume 1—Manufacturing Engineering and Volume 2—Mechanical and Materials Engineering. Each volume is devoted to research in design, simulation, and manufacturing in the main conference areas. The first volume consists of four parts. The first part includes recent developments in smart manufacturing. It presents intelligent solutions for sensor selection in plant retrofitting. This part also includes improving machine vision systems for collaborative assembly applications. Recent developments in digital twins for industrial robotics, robotic arms in a virtual environment, and designing a workplace in virtual and mixed realities are presented in this part. The evolution of competence management in manufacturing industries and digital business models is also discussed. Finally, the first part includes studies on recent developments in digital and information technologies according to the Industry 4.0 strategy. The second part includes studies in information management systems, particularly ensuring quality management at manufacturing enterprises, improving information management systems, and perspectives of lean management. Studies in increasing the production process efficiency in new production facilities, implementing intelligent transport systems, and improving logistics analysis are additionally presented. Moreover, this part also analyzes the impact of standardized reusable packaging on supply chain design and environmental efficiency. The third part of manufacturing technology is devoted to modeling and surface modification of steel by laser radiation, fabrication and post-processing of metal products, and verifying additively manufactured and bulk metals. It presents studies in implementing computer-aided joint design systems, applying information technologies to ensure quality characteristics of the working surfaces during mechanical processing, and improving the quality of cutting tools based on convolutional models. This part also includes recent developments in contact processes during drilling, microcutting of titanium alloys and steel during grinding, and substantiation of chip removal models during milling. Moreover, issues regarding the stress-deformed state of the cylinder liner’s working surface, turning threads for drill pipes, surface relief formation in peripheral end milling, and just-in-time gear grinding wheel dressing are presented. Studies in material removal for aluminum alloys and surface roughness assessment during the milling of pure and carbon black-reinforced polypropylene materials are discussed in this part. Finally, the third part proposes ways to improve drives for a multi-operational milling machine, automatic workpiece clamping mechanisms of lathes to expand technological capabilities, and an impact of solid lubrication on the diamond grinding characteristics for difficult-to-machine materials. The fourth part regarding engineering education is based on studies in a dual educational system for the professional training of workers and the entrepreneurial mindset development of engineering students. A digital twin for remote learning is also proposed in this part. Finally, this part proposes a professional and creative competencies formation model for engineers based on information and digital technologies. We appreciate the partnership with Springer Nature, iThenticate, EasyChair, and our sponsors for their essential support during the preparation of DSMIE-2023.

Preface

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Thank you very much to DSMIE Team. Their involvement and hard work were crucial to the success of the conference. DSMIE’s motto is “Together we can do more for science, technology, engineering, and education”. June 2023

Vitalii Ivanov Justyna Trojanowska Ivan Pavlenko Erwin Rauch Ján Pitel’

Organization

Steering Committee General Chair Vitalii Ivanov

Sumy State University, Ukraine

Co-chair Jozef Zajac

Technical University of Košice, Slovak Republic

Co-chairs, Program Committee Chairs Michal Hatala Oleksandr Liaposhchenko

Technical University of Košice, Slovak Republic Sumy State University, Ukraine

Co-chairs, Publication Chairs Ivan Pavlenko Justyna Trojanowska

Sumy State University, Ukraine Poznan University of Technology, Poland

Co-chairs, Organizational Chairs Svetlana Radchenko Khrystyna Berladir Oleksandr Gusak

Technical University of Košice, Slovak Republic Sumy State University, Ukraine Sumy State University, Ukraine

Program Committee Katarzyna Antosz Michal Balog Yevheniia Basova Khrystyna Berladir Frantisek Botko Ricardo Branco

Rzeszow University of Technology, Poland Technical University of Košice, Slovak Republic National Technical University “Kharkiv Polytechnic Institute”, Ukraine Sumy State University, Ukraine Technical University of Košice, Slovak Republic University of Coimbra, Portugal

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Organization

Dagmar Caganova Emilia Campean Yelizaveta Chernysh Olivian Chiver Vasile G. Cioata Olaf Ciszak Radu Cotetiu Ivan Cvitic Predrag Dasic Yuliia Denysenko Oleksandr Derevianchenko Sergey Dobrotvorskiy Tygran Dzuguryan Milan Edl Sulaymon Eshkabilov Mathieu Gautier Jakub Grabski Marek Gucwa Oleksandr Gusak Michal Hatala Ihor Hurey Jozef Husar Vitalii Ivanov Maryna Ivanova Lydmila Kalafatova Gennadii Khavin Lucia Knapcikova Kateryna Kostyk Agnieszka Kujawinska Yaroslav Kusyi Maria Lazar Dmytro Levchenko Oleksandr Liaposhchenko Slawomir Luscinski Jose Mendes Machado Ole Madsen Angelos Markopoulos

Comenius University Bratislava, Slovak Republic Technical University of Cluj-Napoca, Romania Sumy State University, Ukraine Technical University of Cluj-Napoca, Romania Polytechnic University of Timisoara, Romania Poznan University of Technology, Poland Technical University of Cluj-Napoca, Romania University of Zagreb, Croatia High Technical Mechanical School of Professional Studies, Trstenik, Serbia Sumy State University, Ukraine Odessa Polytechnic National University, Ukraine National Technical University “Kharkiv Polytechnic Institute”, Ukraine Maritime University of Szczecin, Poland University of West Bohemia, Czech Republic North Dakota State University, USA University Lyon, France Poznan University of Technology, Poland Czestochowa University of Technology, Poland Sumy State University, Ukraine Technical University of Košice, Slovak Republic Lviv Polytechnic National University, Ukraine Technical University of Košice, Slovak Republic Sumy State University, Ukraine National Technical University “Kharkiv Polytechnic Institute”, Ukraine Donetsk National Technical University, Ukraine National Technical University “Kharkiv Polytechnic Institute”, Ukraine Technical University of Košice, Slovak Republic National Technical University “Kharkiv Polytechnic Institute”, Ukraine Poznan University of Technology, Poland Lviv Polytechnic National University, Ukraine University of Petrosani, Romania Lodz University of Technology, Poland Sumy State University, Ukraine Kielce University of Technology, Poland University of Minho, Portugal Aalborg University, Denmark National Technical University of Athens, Greece

Organization

Dariusz Mazurkiewicz Mykola Melnychuk Ronald L. Mersky Viktor Molnar Roland Iosif Moraru Dmitriy Muzylyov Marek Ochowiak Oleh Onysko Vitalii Panchuk Ivan Pavlenko Ján Pitel’ Grigore Marian Pop Oleksandr Povstyanoy Erwin Rauch Andrii Rogovyi Joanna Rosak-Szyrocka Alessandro Ruggiero Vira Shendryk Dusan Simsik Vadym Stupnytskyy Antoni Swic Anastasiia Symonova Marek Szostak Valentin Tikhenko Volodymyr Tonkonogyi Justyna Trojanowska Piotr Trojanowski Musii Tseitlin Alper Uysal Leonilde Rocha Varela George-Christopher Vosniakos Christain Willberg Jerzy Winczek Szymon Wojciechowski Jinyang Xu Oleg Zabolotnyi

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Lublin University of Technology, Poland Lutsk National Technical University, Ukraine Widener University, USA University of Miskolc, Hungary University of Petrosani, Romania State Biotechnological University, Ukraine Poznan University of Technology, Poland Ivano-Frankivsk National Technical University of Oil and Gas, Ukraine Ivano-Frankivsk National Technical University of Oil and Gas, Ukraine Sumy State University, Ukraine Technical University of Košice, Slovak Republic Technical University of Cluj-Napoca, Romania Lutsk National Technical University, Ukraine Free University of Bolzano, Italy National Technical University “Kharkiv Polytechnic Institute”, Ukraine Czestochowa University of Technology, Poland University of Salerno, Italy Sumy State University, Ukraine Technical University of Košice, Slovak Republic Lviv Polytechnic National University, Ukraine Lublin University of Technology, Poland Kremenchuk Mykhailo Ostrohradskyi National University, Ukraine Poznan University of Technology, Poland Odessa Polytechnic National University, Ukraine Odessa Polytechnic National University, Ukraine Poznan University of Technology, Poland West Pomeranian University of Technology, Poland National Technical University “Kharkiv Polytechnic Institute”, Ukraine Yildiz Technical University, Turkey University of Minho, Portugal National Technical University of Athens, Greece German Aerospace Center, Germany Czestochowa University of Technology, Poland Poznan University of Technology, Poznan Shanghai Jiao Tong University, China Lutsk National Technical University, Ukraine

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Organization

Jozef Zajac Volodymyr Zavialov Przemyslaw Zawadzki Predrag Zivkovic

Technical University of Košice, Slovak Republic National University of Food Technologies, Ukraine Poznan University of Technology, Poland University of Nis, Serbia

Invited External Reviewers Pawel Bun Bartosz Gapinski Nikolaos Karkalos Jakub Kascak Panagiotis Karmiris Obratanski Emmanouil Lazaros Papazoglou Sugar Ioan Radu Dariusz Sedziak Natalya Shramenko Robert Sika

Poznan University of Technology, Poland Poznan University of Technology, Poland National Technical University of Athens, Greece Technical University of Košice, Slovak Republic National Technical University of Athens, Greece National Technical University of Athens, Greece Technical University of Cluj-Napoca, Romania Poznan University of Technology, Poland Lviv Polytechnic National University, Ukraine Poznan University of Technology, Poland

DSMIE Team Khrystyna Berladir Oleksandr Gusak Michal Hatala Vitalii Ivanov Oleksandr Liaposhchenko Ivan Pavlenko Ján Pitel’ Svetalna Radchenko Justyna Trojanowska Jozef Zajac

Sumy State University, Ukraine Sumy State University, Ukraine Technical University of Košice, Slovak Republic Sumy State University, Ukraine Sumy State University, Ukraine Sumy State University, Ukraine Technical University of Košice, Slovak Republic Technical University of Košice, Slovak Republic Poznan University of Technology, Poland Technical University of Košice, Slovak Republic

Contents

Smart Manufacturing Sensor Selection for Smart Retrofitting of Existing Plants . . . . . . . . . . . . . . . . . . . Dmytro Adamenko

3

Machine Vision Systems for Collaborative Assembly Applications . . . . . . . . . . . Vladyslav Andrusyshyn, Vitalii Ivanov, Ján Pitel’, Kamil Židek, and Peter Lazorik

13

Digital Twins for Industrial Robotics: A Comparative Study . . . . . . . . . . . . . . . . . David Fait and Václav Mašek

26

A CANVAS Based Assessment Model to Evaluate SMEs Readiness for Digital Business Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manuel Holzner, Erwin Rauch, Guido Orzes, and Dominik T. Matt

36

PLC Control of a 2-Axis Robotic Arm in a Virtual Simulation Environment . . . . Martin Pollák, Marek Koˇciško, and Karol Goryl

50

Evolution of Competence Management in Manufacturing Industries . . . . . . . . . . Markus Steinlechner, Fazel Ansari, and Sebastian Schlund

60

Designing a Workplace in Virtual and Mixed Reality Using the Meta Quest VR Headset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adrián Vodilka, Marek Koˇciško, Simona Koneˇcná, and Martin Pollák Digital and Information Technologies in Metrology 4.0 . . . . . . . . . . . . . . . . . . . . . Michał Wieczorowski, Justyna Trojanowska, and Oleksandr Sokolov

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Information Management Systems Quality Management at the Manufacturing Enterprise: Repair Processes Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuliia Denysenko, Filip Górski, Olaf Ciszak, Khrystyna Berladir, and Pavlo Kushnirov

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Impact of Standardized Reusable Packaging on a Supply Chain Design and Environmental Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Damian Dubisz, Paulina Golinska-Dawson, and Adam Kolinski

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Contents

Agile Framework as a Key to Information Management Systems Delivery . . . . . 113 Bohdan Haidabrus, Janis Grabis, Oleksandr Psarov, and Evgeniy Druzhinin Perspectives of Lean Management Using the Poka Yoke Method . . . . . . . . . . . . . 121 Jozef Husár, Stella Hrehova, Piotr Trojanowski, Szymon Wojciechowski, and Vitalii Kolos Agile in the Context of Manufacturing SMEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Angelina Iakovets, Michal Balog, and Kamil Židek Efficiency of the Production Process in the New Production Facility . . . . . . . . . . 142 Matúš Matiscsák, Peter Trebuˇna, Marek Kliment, Marek Mizerák, and Jozef Trojan Implementation of Intelligent Transport Systems in an Urban Agglomeration: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Joanna S˛ek, Piotr Trojanowski, Łukasz Gilewicz, Bartosz Gapinski, and Artem Evtuhov Tracking of Trucks Using the GPS System for the Purpose of Logistics Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 Peter Trebuˇna, Marek Mizerák, Miriam Pekarˇcíková, Marek Kliment, and Matúš Matiscsák Manufacturing Technology Preliminary Study of Mass Material Removal for Aluminum Alloy by Low Pressure Abrasive Water Jet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Frantisek Botko, Jozef Zajac, Svetlana Radchenko, Dominika Botkova, and Dagmar Klichova Analysis of Microcutting of VT8 Titanium Alloy and 12Cr2Ni4A Steel During Grinding with Cubic Boron Nitride . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Tatiana Chumachenko, Oleksandr Derevianchenko, Tatiana Nikolaieva, Yevhen Omelchenko, and Alla Bespalova An Impact of the Cutting Fluid Supply on Contact Processes During Drilling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Eshreb Dzhemilov, Eskender Bekirov, Alper Uysal, and Ruslan Dzhemalyadinov

Contents

xv

Modeling and Surface Modification of AISI 321 Stainless Steel by Nanosecond Laser Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 Sergey Dobrotvorskiy, Borys A. Aleksenko, Mikołaj Ko´sci´nski, Yevheniia Basova, and Vadym Prykhodko Improvement of the Quality of Wear Zones for Cutting Tools Textures Classes Recognition Based on Convolutional Models . . . . . . . . . . . . . . . . . . . . . . . 216 Oleksandr Fomin, Oleksandr Derevianchenko, Natalya Volkova, and Natalia Skrypnyk Conductivity Measurement Verification of Additively Manufactured and Bulk Metals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 Matúš Gel’atko, Michal Hatala, Radoslav Vandžura, Martin Kasenˇcák, and Dušan Mandul’ák Substantiation of Chip Removal Models During Milling of Closed Grooves . . . . 238 Oleksandr Gnytko and Anna Kuznetsova Surface Roughness Assessment After Milling of Pure and Carbon Black Reinforced Polypropylene Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Ahmet Hakan Akin, Yusuf Furkan Yapan, Yaroslav Kusyj, Vitalii Ivanov, and Alper Uysal Improvement of a Two-Stage Drive for Multioperational Milling Machine . . . . . 264 Oleg Krol, Volodymyr Sokolov, and Petko Tsankov Information Technologies of the Analysis for Models to Ensure Quality Characteristics of the Working Surfaces During Mechanical Processing . . . . . . . 274 Maksym Kunitsyn, Anatoly Usov, and Yuriy Zaychyk CAD, Laser Powder Bed Fusion Fabrication and Post-processing of Customized Metal Dental Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286 Dmytro Lesyk, Oleksandr Lymar, Vitaliy Dzhemelinskyi, Dariusz Grzesiak, and Bartosz Powalka Just in Time Gear Grinding Wheel Dressing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Natalia Lishchenko, Garret O’Donnell, Vasily Larshin, Artem Mochuliak, and Sergey Uminsky Generalized Method for Rational Selection of Parameters for Interference Fits Using Computer-Aided Joint Design Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Vladimir Nechiporenko, Valentin Salo, Petro Litovchenko, Vladislav Yemanov, and Stanislav Horielyshev

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Contents

Surface Relief Formation in Peripheral End Milling . . . . . . . . . . . . . . . . . . . . . . . . 316 Yuri Petrakov, Yuri Danylchenko, Serhii Sapon, and Maksim Sikailo Improvement of the Automatic Workpiece Clamping Mechanism of Lathes to Expand Technological Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Borys Prydalnyi An Impact of Solid Lubrication on the Diamond Grinding Characteristics of Difficult-to-Machine Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Elena Sevidova, Aleksandr Rudnev, Magomediemin Gasanov, Alexey Kotliar, and Oksana Titarenko The Stress-Deformed State of the Cylinder Liner’s Working Surface . . . . . . . . . . 347 Ihor Shepelenko, Yakiv Nemyrovskyi, Oleksandr Lizunkov, Ivan Vasylenko, and Ruslan Osin Turning of NC10 Threads for Drill Pipes: Theoretical Study of the Designed Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356 Oleh Onysko, Volodymyr Kopei, Yaroslav Kusyj, Olena Kornuta, and Iryna Schuliar Engineering Education Dual Educational System of Professional Training of Future Skilled Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Andrii Kalenskyi, Tetiana Gerliand, Svitlana Kravets, Dmytro Homeniuk, and Viktor Nagayev A Digital Twin for Remote Learning: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . 379 Kateryna Kovbasiuk, Jakub Demˇcák, Jozef Husár, Alexander Hošovsky, and Vratislav Hladký A Model of Formation of Professional and Creative Competences for Engineers Based on Information and Digital Technologies . . . . . . . . . . . . . . . . 390 Viktor Nagayev, Viktoriia Vertegel, Galyna Nagayeva, Oksana Kovalenko, and Olena Smihunova Entrepreneurial Mindset Development in Engineering Students Through a Business Canvas Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 Olena Titova, Tetiana Ishchenko, Liudmyla Yershova, Ljudmila Bazyl, and Viktoria Kruchek Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411

Smart Manufacturing

Sensor Selection for Smart Retrofitting of Existing Plants Dmytro Adamenko(B) University of Duisburg-Essen, 1, Lothar Street, 47057 Duisburg, Germany [email protected]

Abstract. The chemical, process, and energy generation industries are essential production industries worldwide. However, these traditionally powerful industrial sectors have been under ever-increasing cost pressure in recent years, so the companies are continuously trying to improve wage- and energy-intensive products as well as services through the use of digitalization of production processes, e.g., to use operational data to optimize processes by tailoring them to customer needs and, at the same time, cutting operation and production costs. The machines and equipment in these industries are typically operated for several decades and cannot be directly integrated into the newly developed digitized processes. One of the possibilities to perform a so-called smartification of the machines and equipment is to retrofit them with sensors and data processing hardware. The present paper presents a methodology for the systematic selection of sensors for retrofitting existing and operating plants. For this purpose, the requirements for the selection process have been established. The advantages and disadvantages of existing methods for sensor selection are presented and analyzed. Subsequently, a new methodology for sensor selection is presented and successfully validated in the retrofit process of an existing and still operating for 20 years 900 kW wind turbine. Keywords: Sensors · Plant Retrofit · Predictive Maintenance · Digitalization · Industrial Growth

1 Introduction Asset-intensive industries such as chemicals, oil and gas, mining, metals, pulp and paper, and power production have been turning to new technologies to increase the reliability and availability of their equipment while keeping maintenance costs under control [1]. Using digital tools and advanced analytics capabilities alongside traditional lean techniques, they aim to predict and prevent equipment failures, increase labor productivity, and streamline the management of external contractors [2]. Recent studies show that maintenance expenses account for a large portion of the vulnerable operating costs of manufacturing companies and can be as high as 25% of operating costs [3]. So, 82% of the companies have faced unplanned machine breakdowns in recent years. For 40% of plant operators, one hour of downtime cost more than e1 million [4]. Thus, maintenance is a large part of the cost-intensive activities that can © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 3–12, 2023. https://doi.org/10.1007/978-3-031-32767-4_1

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D. Adamenko

be optimized, and maintenance costs are the only type of cost that can be influenced after a plant has been put into operation [5]. Maintenance is often planned at specific intervals based on manufacturer recommendation or operator experience. Industry 4.0 approaches and any process monitoring generally require a meaningful process variable that allows conclusions to be drawn about the current status of the production process or a machine [6]. Condition-based maintenance ensures full utilization of the service lifecycle of systems and components and saves additional maintenance work due to more frequent service downtimes. The prerequisite for the realization of the condition-based maintenance approach is the availability of actual process data, which must be measured and transferred for evaluation with the help of sensors and supporting hardware to the virtual models [7] that can describe the behavior and simulate the remaining useful life of the system so that predictive maintenance can be realized [8]. The transition from preventive to condition-based maintenance strategies becomes necessary to meet the described requirements [9]. In factories, assets are kept in operation as long as they continue producing highquality products [10]. It is not unusual to see machines working for several decades still in use on the shop floor [11]. Older machinery is usually in good condition but do not have the necessary infrastructure to collect the measurement data and derive from it the condition of the components. Investments in older equipment allow users to maximize functionality with new sensors and software features. Due to the extremely high cost of new machines, retrofit is being considered. Investments in modernizing equipment result [12] in a higher utilization [13] of existing assets [14] have also been considered. Sensor selection is often based on machine operator experience and sensor manufacturer expertise. However, it is not always clear which measurements must be measured and how they must be performed. Since machines and equipment with a variety of components can be exposed to different mechanical, electrical and other loads, selecting the correct sensor becomes a great challenge, as the resistance and compatibility of the measurement principle of sensors with the environmental conditions must be ensured. Another challenge [15] is to find a method suitable for multiple machines [16] within a single shopfloor [17].

2 Literature Review Scientific works and industrial practice have established that the digitization of industry and the transformation of production processes are necessary to secure the competitiveness of companies [18]. The long service lives and significant investments in machinery and equipment have led to the formation of a market for their upgrade through additional or replacement components. This market is constantly growing, and its turnover in 2021 is estimated at 45 billion dollars [19]. One of the main challenges of the retrofit process is the strong heterogeneity of machines within a single shopfloor, so each machine requires its retrofit methodology. Some of the global industrial companies, such as ABB [20], Festo [21], or TROX [22], offer specialized retrofit kits that adapt the older systems of these manufacturers and provide the corresponding functional extensions. Still, no holistic method for retrofitting existing machines and systems can be transferred to several machines presented. From the practical examples in the literature and

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presented retrofit concepts [13, 23], a generally applicable scheme for the process of a retrofit was extracted. The general retrofit process roughly consists of three steps [24]. First, the decision to implement the retrofit is to be made based on an analysis of operating, investment, and operating costs with predictive maintenance [25]. Subsequently, the selection of the components that are relevant for securing the plant operation and characteristic measured variables that can indicate a possible failure of defined components takes place [26]. At the end of the process, the actual state of the plant or machine is acquired, and sensors, hardware, and suitable mounts for attaching selected additional components are selected [27]. Selection of sensors for the producing machines and plants is challenging because many factors and requirements must be considered. A series of conflicting objectives arise when instrumenting a plant with a wide variety of transducers. Sensor systems with high sensitivity are desired so that a change in a physical process variable results in the most significant change in the sensor’s output variable. Often, this requires a certain proximity to the active point. The closer measurements are made to active components, the less favorable the environmental conditions for sensors often become. Depending on the distance from the active point, sensors are stressed by high temperatures, vibrations, or the effects of process fluids [6]. The selection is complicated by a multitude of sensors on the market with different operating principles, measurement characteristics, and specific necessary environmental conditions to ensure the reliable measurement and transmission of the signal. For the realization of an optimal sensor selection, the requirements for the selection process were established. These were derived from the available sensor selection methods of Löpelt [28], VDMA [29], Hesse [30], Meß [31], and Classe [32], but also from the context of the entire retrofit process. The most important of these has been summarized in Table 1. Subsequently, the requirements were mapped to the considered methods. The sensor selection approaches presented in the literature differ to some extent in the intended use for which they were designed and cover a wide range of sensor selection tasks. So, the approaches of Löpelt and VDMA focus on the retrofit processes. The methods of Hesse, Meß, and Classe focus on sensor selection for new designs. The known approaches require very application-dependent, specific information that is dependent on the product requirements and, apart from abstract recommendations for activities such as those offered in the well-known approaches to retrofitting and sensor selection, can hardly be presented in a generally valid way in their diversity. The situation is similar to reformulating the sensor task into a measurand to be recorded, for which a high level of metrological experience may be necessary. Table 1. Fulfillment of requirements for the retrofit process by known methods. Requirements + fulfilled ◯ partially fulfilled - not fulfilled 1 The methodology is continuous and holistic, so a link path between categories and sensors is given

Löpelt VDMA Hesse Meß Classe +

+

◯

+

◯ (continued)

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D. Adamenko Table 1. (continued) Requirements + fulfilled ◯ partially fulfilled - not fulfilled

Löpelt VDMA Hesse Meß Classe

2 The methodology considers the disturbances of the measurement process and provides suitable physical measurement principles for the defined task

-

-

-

-

3 The method considers key characteristics of sensors ◯ and measurand, i.e., measurement type, measurement range, environmental and installation conditions, signal and safety requirements

+

◯

◯

-

4 The method has been validated for a retrofit process ◯

+

-

-

-

As the level of knowledge progresses, the requirements evolve differently from the initial decision point. According to Löpelt, a mutually supportive process between requirements creation and sensor selection is encouraged by broadening the field of view to include different solution approaches. A similar importance is attributed to this concept in the approaches of Meß and Classe. However, the difficulty in successfully implementing it is a working system that alerts the user that the requirements set cannot be met, so there must be requirements correction because no sensor meets the requirements. At this point, a computer-based method can be helpful, which makes the deeper expertise of the user not indispensable to performing filtering tasks. Also, the analysis of the different treatments of the sensor selection process by different approaches can be helpful for the most suitable sensor selection process. Installation conditions, such as available installation space, possible mounting techniques, or existing interfaces, play an essential role in sensor selection. For this reason, each of the approaches presented here deals with this topic separately. Especially with the background of retrofitting existing technical systems, the design restrictions are often clearly defined [29]. Thus, in some cases, different focal points and associated strengths and weaknesses emerge, which, as mentioned earlier, is also partly due to the design situation being addressed. Any method could not fulfill the requirement to define the suitable measurement principles for the current task. The approaches offer the potential to complement each other and to extend them by further thoughts to define requirements for a new method.

3 Research Methodology The present paper aims to derive the methodology for sensor selection while considering the conditions of the existing machinery and production processes. To achieve this, the requirements for the sensor selection process are first established, the known methods [28–32] for sensor selection are analyzed concerning the requirements, and an optimized procedure for sensor selection is derived. The better suitable process is to be judged based on more requirements being fulfilled compared to the known methods. Variables to be

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measured and the operating and plant conditions are assumed to be known from previous steps of the retrofit process. For the realization of the sensor selection, a 5-step methodology was proposed. The central aspect of the developed methodology should be the interaction between sensor selection and coordination with the functional structure or the requirements and, thus, the integration of loops. Therefore, following the argumentation in the previous chapter, the arrangement of the steps is chosen as far as possible. So, the list of requirements for the sensor can be completed meaningfully. Using the integration of a permanent result display, and the thus guaranteed traceability of the exclusion steps in the configurator operation, this can be facilitated by the computer-aided procedure. An adjustment should be made if no result remains according to the display before the requirements list is processed. To facilitate operation, user access to selection steps that have already been run through should it be possible to make specific corrections, after which the results are updated without restarting the configurator. The selection process is illustrated in Fig. 1.

Fig. 1. Sensor selection process.

At the beginning of the process, the value to be measured, the installation space, and the ambient condition should be known. The sequence starts with the selection of the measurand. For this purpose, the user is presented with a list of common, industry-typical physical values in which the one required for the measurement task can be searched. This is important because, after each step, an exclusion of sensors takes place, in this case those that are not suitable for measuring the measurand. If required, the database can be extended to include other measured variables and other characteristics. In the same step, the user enters the required measuring range by selecting a minimum and maximum value in the appropriate unit. The configurator compares this input with the entered measuring ranges of the sensor database to reject sensors that do not fit. Thus, all sensors with minimum values that are above the entered minimum value and maximum values that are below the entered maximum value will not be considered further. In the requirements for the measurement process, the signal properties required to fulfill the measurement task are queried on a measurement-quantity-specific basis. Most cases should include linearity, resolution, accuracy, or hysteresis. However, it is not advisable to generalize this selection for all measurement quantities since the technical

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requirement criteria also change due to differences in the measurement strategy. The sensor’s bandwidth determines whether the measurement task can be fulfilled concerning technical requirements. Instead of the bandwidth, however, response times are an essential feature for temperature sensors. In this step, the input is made via the required value in the respective unit. In the same step, the conditions at the measurement location are described. The user is offered to describe the possible influences on the measurement location. In order to select a sensor according to the available space and the electrical connection requirements, mounting conditions are also requested in this step. This includes the size of the sensor, the type of connection, the type of mounting, and the supply requirement. This information can usually be obtained from the data sheets provided by the manufacturers. At this step of the developed methodology, the material properties (magnetic, conductive, and insulating) and environmental conditions (temperature, pressure, visibility and light conditions) are also recorded and queried. In the next step, the results are filtered for environmental disturbance variables. According to VDMA, relevant environmental variables that can influence the quality of the measurement are temperature, vibration and shock loads, ambient pressure, light radiation, contact with media, and electromagnetic fields. Classe, Hesse, and Hering supplement this list with dust and dirt particles, explosive environments, and radioactive radiation. Furthermore, the behavior with respect to electromagnetic interference and the effect of external interference fields on the radiation of a sensor are classified. The information on vibration and shock resistance refers to the ability of the sensor to withstand these loads mechanically without suffering damage.

Fig. 2. Selected environmental disturbances for defined sensing principles.

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A total of 45 disturbance variables, summoned from sensor principles handbooks were assigned to 19 different measurement principles. These were categorized by environmental, geometry, material and measurement dynamic disturbances. An excerpt from the table for the environmental disturbances is shown in Fig. 2.

4 Results and Discussion Within the framework of the developed methodology, all disturbance variables that usually lead to impaired measurement results on the sensor product ready for use have been recorded. This means that disturbances inherent in the active principle but compensated for standards in the sensor design are not included in the list. These are less interesting for sensor selection than for sensor manufacturing. Some of the listed items can also be assigned to the measurand-dependent characteristic conditions, such as the division of the ambient temperature into the corresponding ranges of low (lower than −30 °C), high (above 50 °C), and normal temperatures. The same categorization has also been performed for ambient pressure. After filtering the sensors resistant to the disturbance variables, the information in the last step concerns the preference of the output signal or desired communication interfaces. As a result of the safety analysis of the system, ideally performed by the user, information about the effect severity of a sensor system failure is available. In order to support the redundant arrangement of the sensor system that may be deemed necessary by this analysis result, the configurator solution can offer the advantage of suggesting several sensors, even of different operating principles, after the selection process has been run through. This can be the case if several sensors are available after using the configurator. Suppose no sensor is available as a result of the selection according to the strict exclusion principle. In that case, it is possible to carry out a utility value and weak point analysis of the sensors that are not one hundred percent suitable using user access to all steps in retrospect to weigh up the extent to which non-optimal circumstances can be accepted. Naturally, knock-out criteria should be defined for this purpose, which immediately excludes sensors in case of non-compliance. In order to design how cataloging could look like considering the mentioned disturbance variables, advantages, and influencing factors of manufacturer sensors, a scheme according to Roth [33] is used in the following. Accordingly, a division of the information into main, outline, and access parts takes place to ensure clarity and quick information access. A difference to the definition of a construction catalog after Roth is that the approximate completeness is fulfilled only conditionally with the cataloging of manufacturer sensors. Although the compilation aims to be prepared for many different applications, complete coverage of all products on the market is not intended in this framework. The one-dimensional structure is intended to serve as a uniform structure. First and foremost, the outline section contains the measured variable to which the following contents are related. As central, immediately to be considered further outline criteria apply against the background of the installation of the sensor into an existing product, the present or constructively still to be realized kind of the event change. The latter is assigned to the material of the measuring object and the permissible switching

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distance. The permissible materials are partially one-sided and not mutually exclusive. Like the sensing distances, whose structured arrangement would be arbitrary due to the different specifications of either a fixed value or a range, they do not assume a clear structuring function. Nevertheless, they occur in the outline part because of the close connection to the event change and their characteristic as a measurand-specific condition. Thus, a different outline part would be required for each measurand. The main part consists of the sensor model and the associated operating principle. The access part contains the other properties queried in the configurator mode, via the selection of which the most favorable solution for prevailing environmental conditions and interfaces can be found. The access part is subdivided into requirements for the equipment to fulfill the measuring task. In this case, only the frequency range, disturbance variables, environmental influences, installation conditions, which include the electrical connection and the supply voltage, and the type of output signal. Validation of the developed methodology has been realized with the retrofit of an operating wind turbine. The variables to be measured were defined as rotational speed, wind speed, wind acceleration, and elongation of the tower. The environmental conditions were recorded and evaluated with a questionnaire. The validation showed that suitable sensors could be found with the developed methodology because corresponding models are available in the library. In the use cases for the four defined measurands, only sensors were identified that could meet the technical requirements for measurement or were similar or even identical to the design of the sensors already in use. The sensors issued in these four cases and not considered suitable for the task were too oversized regarding functional properties and, therefore, not worth the potential cost and installation effort to the system operator. In the future, this problem could be counteracted by an additional display of the cost boundary conditions during the sensor output by the configurator for a convenient overview or the query of the intended service life. The selection of the search criteria thus seems to provide a reliable function in most of the present examples, apart from the few points mentioned. It was left to the user to collect the necessary data to work through the individual steps to find suitable manufacturer products without additional search effort and information requirements. In some cases, the decision already made by the responsible team of the company was confirmed, or, especially in the case of acceleration and wind speed measurement, the field of vision was extended to equally possible alternatives. No reason was given as to why the sensor types that were not used had not been applied so far. The compilation of the questions needed for the configurator arose from the orientation on the steps of the developed method, which suggests that even without using a configurator, the procedure can lead to the successful finding of a suitable sensor.

5 Conclusions The great importance of sensors emerges from the strategies discussed for the approach to retrofit measures and the requirements for systems to be retrofitted. Examining the strategies showed that integrating a detailed methodology for sensor selection in this context is still outstanding. Such a methodology, intended to support the planner of a

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retrofit, was developed and presented. It is based on the conditions and structures of a plant and, with the help of task-related requirement queries, limits the possible sensor selection according to the exclusion method. In particular, the intensive treatment of disturbance variables dependent on the active principle represents an essential supplement to existing approaches in the developed method. The detailed treatment of measurand-specific trade-offs in sensor selection, which is not addressed in every approach or is touched upon superficially, was a crucial filtering process during the validation. The validation results demonstrate that the developed method can be suitable for gathering information about the plant structure and requirements needed for a sensor selection for industrial applications, given a previously defined metric. By integrating sensors into existing plants, it is possible to gather essential knowledge about the process and advance the digitization of operations. On the way to a configurator that can cover a wide range of possible use cases, collecting comprehensive information about various measurement situations beyond the examples covered in the validation is still pending. Acknowledgment. This work was supported by the German Federal Ministry for Economic Affairs and Energy within the Central Innovation Program for SMEs.

References 1. Maciel, R., Safarik, P.: Power Plant 4.0: Embracing Next-Generation Technologies for Power Plant Digitization. McKinsey & Company (2020) 2. VDMA – Fit for Service (2017). https://www.vdma.org/viewer/-/v2article/render/1117285. last accessed 2021/09/21 3. ConMoto: Value-oriented maintenance: Strategic dimension of the wrench. ConMoto Consulting Group (2011) 4. Vanson Bourne: After the Fall: The Costs, Causes & Consequences of Unplanned Downtime. GE Digital (2017) 5. Leidinger, B.: Value-Oriented Maintenance: Reduce Costs, Maintain Availability, 2nd edn. Gabler Verlag, Wiesbaden (2017) 6. Bonitz, J., Hamm, L., Müller, P., Hermann, S.: Improved process supervision by highly sensitive strength receivers CNT. ZWF 115(7–8), 488–491 (2020). https://doi.org/10.3139/ 104.112373. (in German) 7. Blum, A., al Diban, R., Rudolph, M., Weickhardt, C.: Optical multi-sensor network for maintenance: implementation in a machine tool application. ZWF 115(5), 299–302 (2020). https:// doi.org/10.3139/104.112283. (in German) 8. Zhai, S., Reinhart, G.: Predictive maintenance as a pioneer for maintenance-oriented production control. ZWF 113(5), 298–301 (2018) 9. Paessler. IT Explained: Retrofitting. https://www.paessler.com/it-explained/retrofitting. last accessed 2021/09/21 10. BearingPoint: Predictive Maintenance Study 2021. BearingPoint, Frankfurt/Main (2021) 11. Schuh, G., Anderl, R., Gausemeier, J., ten Hompel, M., Wahlster, W.: Industrie 4.0 Maturity Index. Managing the Digital Transformation of Companies (Acatech STUDY). Herbert Utz Verlag, Munich (2017) 12. Zwick/Roell: Service Reliability and Upgradeability for Older Testing Machines. Zwick/Roell, Ulm (2016)

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13. Günther, B.: Funktionale Erweiterung von Investitionsgütern im Nachrüstmarkt. Shaker, Aachen (2015) 14. ITALTEL: Industry 4.0: How to improve quality with sensors, IoT & Data Analysis, Whitepaper. ItalTel, Milan (2020) 15. Liebertau, M., Shalashinski, A., Suciu, M.: A tech company’s view on IoT-based condition monitoring – utilizing web-based data assessment platform and connected devices to improve machine utilization and productivity. Mining Report Glückauf 155(2), 194–203 (2019) 16. Manufacturing’s digital transformation: eBook. Parker Hannifin Corporation, Cleveland, OH (2020) 17. Plattform Industrie 4.0: Integrity of Data, Systems and Processes as the Core Element of Networking and Digitalization – Discussion paper. MKL Druck GmbH & Co. KG, Ostbevern (2018) 18. Reichel, J., Müller, G., Haeffs, J. (eds.): V, Springer, Heidelberg (2018). https://doi.org/10. 1007/978-3-662-53135-8 19. VDMA: retrofit-market grows rapidly. VDMA-Magazine 6, 16–17 (2019) 20. Accenture: ABB: Making machinery speak volumes. https://www.accenture.com/be-en/casestudies/industrial/abb-machinery-speak-volumes. last accessed 2021/09/01 21. Middleton, P.: Industry 4.0 and the retrofit opportunity. MechChem Africa 4, 1–2 (2017) 22. TROX: Retrofit Easyset. https://www.trox.de/en/control-components/type-retrofit-e870ae dd40bde85c. last accessed 2021/09/01 23. Krüger, J., Verl, A.: RetroNet - Retrofitting of Machines and Systems for Networking with Industry 4.0 Technology. VDI Verlag, Düsseldorf (2019) 24. Wöstmann, R., Barthelmey, A., West, N., Deuse, J.: A retrofit approach for predictive maintenance. In: Schüppstuhl, T., Tracht, K., Roßmann, J. (eds.) Tagungsband des 4. Kongresses Montage Handhabung Industrieroboter, pp. 94–106. Springer Berlin Heidelberg, Berlin, Heidelberg (2019). https://doi.org/10.1007/978-3-662-59317-2_10 25. Ramakers, R., Anderson, F., Grossman, T., Fitzmaurice, G.: RetroFab: a design tool for retrofitting physical interfaces using actuators, sensors and 3D printing. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 409–419. Association for Computing Machinery, New York (2016) 26. Burggräf, P., Wagner, J., Koke, B., Manoharan, K.: Sensor retrofit for a coffee machine as condition monitoring and predictive maintenance use case. In: 14th International Conference on Wirtschaftsinformatik. Siegen (2019) 27. Krikke, M.: RETROFIT: RETROFITing ships with new technology for improved environmental footprint. Project final report. Stichting Netherlands Maritime Technology Foundation, Netherlands (2015) 28. Löpelt, M., Wilsky, P., Ruffert, J., Göhlert, N., Prielipp, R., Riedel, R.: Sensor selection for existing plants. ZWF 114(5), 273–276 (2019) 29. VDMA: Guide Sensor Technology for Industry 4.0. Ways to cost effective sensor systems. VDMA Verlag, Frankfurt (2018) 30. Hesse, S., Schnell, G.: Sensors for Process and Factory Automation. Function – Execution Apolication. Springer Vieweg, Wiesbaden (2014). (in German) 31. Mess, M.: Method Modules for Sensor Selection and Integration in Mechanical Engineering. Der Andere Verlag, Tönning (2007) 32. Classe, D.: Contribution to increasing the flexibility of automated assembly systems through sensor integration and extended control concepts. Erlangen (1988). (in German) 33. Roth, K.: Designing with design catalogues. Band 1: Konstruktion. Springer, BerlinHeidelberg (2000). https://doi.org/10.1007/978-3-642-17466-7. (in German)

Machine Vision Systems for Collaborative Assembly Applications Vladyslav Andrusyshyn1,2(B)

, Vitalii Ivanov1,2 , Ján Pitel’1 and Peter Lazorik1

, Kamil Židek1

,

1 Technical University of Košice, 1, Bayerova Street, Prešov 080 01, Slovak Republic

[email protected] 2 Sumy State University, 2, Rymskogo-Korsakova Street, Sumy 40007, Ukraine

Abstract. The collaboration of robots with workers in production is one of the most discussed topics within the framework of the Industry 4.0 concept. Collaborative production cells have increased flexibility and adaptability to production conditions because of combining the advantages of a human and a robot and expand the list of tasks that can be automated. However, the widespread adoption of collaborative robots in manufacturing is hampered by open questions about the safety of workers and ease of use. Machine vision systems allow collaborative robots to work more closely with the environment and endow them with basic cognitive functions. That is why this article is devoted to analyzing the use of machine vision systems in modern collaborative assembly cells. The authors analyzed scientific works in machine vision systems, described practical examples of their application, and catalogs of popular manufacturers to evaluate current propositions in machine vision systems. As a result, the main areas of application of machine vision systems were formed, their classification was presented, and recommendations for choosing an optimal machine vision system were proposed. In addition, the obtained recommendations were used in the practical case when analyzing the equipment of the collaborative assembly line of the SmartTechLab laboratory of the Faculty of Manufacturing Technologies of the Technical University in Kosice. Keywords: Industry 4.0 · Smart Assembly Line · Human-Robot Collaboration · Collaboration Safety · Visual Inspection System · Process Innovation · Sustainable Manufacturing

1 Introduction Collaborative robots and their implementation in production are an essential part of the Industry 4.0 concept [1]. Collaborative robots offer increased flexibility and mobility than classical industrial robots [2] and can also simplify or increase the productivity of applications where classic industrial robots were previously used. According to IFR statistics [3], as of 2021, 39,000 collaborative robots were installed. Compared to 2020, sales increased one and a half times; compared to 2017, more than three and a half times. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 13–25, 2023. https://doi.org/10.1007/978-3-031-32767-4_2

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At the same time, the percentage of collaborative robots compared to the total number of industrial robots sold in 2017 is 2.75%, and this ratio for 2021 is 7.54%. Despite the increased popularity of collaborative robots in production, the relative share of installations of collaborative robots is currently low. The authors noted that one of the reasons for the slow introduction of collaborative robots is the need to ensure a high level of worker safety [4]. In addition to physical safety, the mental safety of the worker must also be ensured to reduce worker anxiety and stress during the human-machine interaction by making the interaction more accessible and intuitive [5]. Moreover, it is necessary to simplify the work with modern technologies due to the lack of qualified workers. According to the survey, both enterprises that have implemented the Industry 4.0 technologies and enterprises that are just planning to modernize production feel a lack of qualified workers [6], negatively affecting the speed of spreading the Industry 4.0 concept. Due to the presence of open questions in collaborative robotics, this area is still open for new research, methodology development, and definition of basic requirements. The authors chose machine vision systems as a field of study. Machine vision systems have become one of the fundamental robotics technologies at the adaptation stage of information collection technologies (Robotics 2.0) and environment perception technologies (Robotics 3.0). The use of sensors (including cameras) and the correct analysis of their readings increase the adaptability and autonomy of industrial robots. Moreover, without information from sensors, it is impossible to realize the concept of Industry 4.0 [7] fully. This work is devoted to studying machine vision systems that can solve safety problems and simplify the work with collaborative robots, endowing them with basic cognitive functions, like parts recognition. The theoretical part of the article will be devoted to a brief overview of machine vision systems, their role in modern collaborative cells, and practical examples of their application will be considered. As a practical part, a case study of using a machine vision system in a collaborative cell to increase its flexibility will be considered.

2 Literature Review Machine vision systems in collaborative applications perform many tasks, such as object detection, sorting, robot control, visual inspection, barcode identification, control of employee movement, supervision identifying defects, tracking, reporting, etc. [8]. Speaking of using machine vision systems to increase the flexibility of production cells, the authors [9] proposed a deep reinforcement learning algorithm for grasping using machine vision. The perspective of studies in this field is that such algorithms will allow capturing various objects with high efficiency and minimal human involvement, including objects that did not participate in the training of the neural network. Moreover, the scientific developments in data exchange between robots will allow the exchange of experience in grasping various objects. In the future, work in this area can significantly increase robotic systems’ flexibility and performance [7]. The authors [10] proposed and verified a complex for grasping objects in a cluttered environment, which consists of a novel gripper with controlled rigidity, a depth camera, and a computer that can identify a suitable position for capturing correctly. Moreover, big companies are

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interested in conducting scientific research in grasping objects with undefined shapes or locations. So, for example, the Amazon Picking Challenge is dedicated to grasping objects with an unknown location in a limited time. The participants’ experience who won this challenge is described in [11]. The authors [12] suggested using several 3D cameras to detect the positions of randomly placed objects in a basket. Using multiple cameras will help to get a better view of the environment, remove background noise and avoid occlusions, which is especially critical for small objects. The study [13] also notes that the presence of occlusions is a challenge for machine vision systems. The paper proposed an approach based on a convolutional neural network that can detect objects with partial occlusion. Also, the complexity of programming prevents the wide spread of robotics, especially for small and medium-sized enterprises. The authors [14], as part of their participation in the KUKA Innovation Award 2016, have developed a system for developing robot task plans that is simple, flexible, powerful, and reliable. The research was verified using an RGB-D camera and collaborative robots Universal Robotic UR5 and KUKA LBR iiwa 14 R820. Also, machine vision systems can help to perform complex technical challenging tasks. The authors [15] proposed a solution for tracking and grasping falling objects. The solution uses a stereo vision system for image acquisition and a high-speed dual-mode visual tracking algorithm that can work with a partially observed trajectory and makes it possible to determine the capture point. The use of machine vision systems for visual inspection has been well-researched, and many applications have already been introduced into production. However, the demand for scientific research in visual inspection remains due to the increasing requirements for flexibility and adaptability of production. The authors [16] proposed using a machine vision system and an unsupervised machine learning method to study the morphology of melt pools in the laser powder bed fusion process. The proposed solution will confirm the presence or absence of defects in the melt pool, such as balling, undermelting, and keyholing porosity. The authors [17] developed a method for detecting loosened bolts in an assembly based on machine vision and machine learning systems, which can work in real-time. In addition, the proposed method is not demanding significant computing power. The authors [18] developed an image-processing algorithm using deep learning to check wine bottles in real time. The authors use a Gaussian filter to limit random noise and remove unrelated backgrounds to achieve real-time performance. Moreover, machine vision systems are an essential element of collaborative cells. Machine vision systems in collaborative cells can train the robot by demonstration, communicate with the operator through gestures, and ensure safety [19]. Moreover, machine vision systems allow these tasks to be performed using a single device, which is an advantage. Thus, the authors [20] proposed a gesture detection module with a library of human-robot physical interaction to provide safety and intuitive interaction with the robot. Concerning safety research, authors [21] proposed and tested in practice an optimization-based robot control algorithm to avoid obstacles without slowing down. The features of this work are that the algorithm considers the entire human body and the entire surface of the robot, including the end effector. This algorithm can increase efficiency if there is enough space for maneuvering. Further work will focus on predicting human behavior to obtain a smoother robot trajectory. The authors [22] proposed a collision detection system, among the advantages of which are scalability and accounting

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for the robot’s end effector. Also, the system can consider potential changes in the workspace [23], such as the movement of other people and machines, if there is data for training the neural network. As a future work, the authors consider the algorithm fitting for the work of several people with a robot and a person with several robots. As a result, scientific research in machine vision systems aims to improve existing image processing algorithms, develop new ones to optimize task performance or expand the technological capabilities of machine vision systems.

3 Research Methodology 3.1 Introduction to Classification of Machine Vision Systems The optimal choice of a machine vision system is based on a comprehensive analysis of the application, after which it will be possible to obtain a list of requirements for the machine vision system. During the analysis of the application, it is necessary to identify the type of application (providing safety, increasing flexibility, or providing quality control), size of controlled space, production conditions (lighting, air and surface dirtiness, optical parameters of the environment, etc.), parameters of the controlled object (optical properties, surface condition, geometric parameters, the speed of its movement relative to the machine vision system, etc.). After that, reducing the list of available solutions is necessary, leaving only suitable solutions for efficiently executing the application. To perform this task, it is required to analyze the properties and features of machine vision systems and identify their relationship with the requirements obtained earlier during the application analysis. The final step is to choose a machine vision system based on the acquired properties of the machine vision system, its local price, and availability. The authors have identified a list of machine vision systems’ main properties and features related to the requirements of modern assembly collaborative sites. The machine vision system consists of the following elements: an image sensor, a vision processing device, a lens, lighting, and communications components [24]. Only image sensors and vision processing devices will be considered in this paper since their optimal choice is essential to ensure the application’s quality and performance. 3.2 Parameters of Image Sensors There are 1D, 2D, and 3D machine vision systems available on the market. 1D machine vision systems analyze a linear region instead of an area analysis since these systems have one row of photodiodes. Based on their properties, they are most often used to detect objects and control them continuously (e.g., while moving on a conveyor). Information processing algorithms (which provide cropping, de-distorting, and joining images in the correct order) make it possible to form a 2D image, and the image width can be unlimited. In addition, providing sufficient and more uniform light will be necessary due to the need to reduce the exposure time when shooting in dynamic conditions, which is much easier when using 1D machine vision systems [25]. Also, 1D machine vision systems are used for high-resolution visual inspection. These systems can scan moving objects at high frequency, while the weight resolution of 1D machine vision systems can be from 512 to

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12888 pixels [26]. Among the industrial applications of these systems is the continuous control and analysis of sheet materials on the conveyor, such as paper, plastic [27], polymers [28, 29], and steel sheets. 2D machine vision systems are the most common because of their universality. They have a photosensitive matrix consisting of many rows of photodiodes. Therefore, they are optimal for working with static objects and without needing to work with height information. Also, they are the easiest to use. Among the limitations are sensitivity to light intensity and poor contrast, which in turn is solved by installing additional filters or using image processing algorithms, as well as the presence of parallax. Typical examples of using 2D machine vision systems are determining objects’ coordinates for further capture by an industrial robot, quality and size control, reading characters from packages, monitoring, ensuring worker safety, etc. [30]. Matrices of 2D machine vision systems can be designed to produce monochrome and color images. The main difference between monochrome and color matrices is that a monochrome matrix captures the entire incident light beam on each photodiode, regardless of the length of the wave. In contrast, in color matrices, light is captured by groups of pixels with additional color filters that transmit only a specific color (green, blue, or red). Then the missing color components are calculated by interpolation algorithms based on data from surrounding photodiodes [31]. Based on this, monochrome matrices work faster, have a more significant resolution and greater detail level, and work better in poor lighting conditions [32]. As a result, monochrome cameras remain the preferred choice, while color cameras are recommended when information about the color of an object is needed. In addition, matrices of 2D machine vision systems can be with global or rolling shutters. In a rolling shutter matrix, individual photodiodes or groups of photodiodes begin and end the absorption of a light beam. In contrast, in the global shutter matrix, all photodiodes begin to receive light simultaneously but end sequentially. Rolling shutter sensors are cheaper but do not work well in dynamic environments [33, 34], distorting moving objects’ images. As a result, global shutter systems are recommended, especially in dynamic scenes [35]. 3D machine vision systems provide a 3D digital model of the area or object being scanned and can optionally overlay a 2D color image for a complete object representation. Also, this type of camera usually is less sensitive to environmental conditions. But due to the complexity of the design and the high cost of components, this is the most expensive type of machine vision system. Typical practical applications of 3D machine vision systems include object size control with height inclusive, object scanning and digitization (reverse engineering), defect detection, and safety control of collaborative cells. The importance of choosing the proper resolution of the machine vision system should be noted. A study [36] during the practical test of the proposed safety solution revealed a relationship between the quality of the application execution and the camera resolution. But cameras that offer higher resolution come at a higher cost. As a result, paying attention to the application responsibility, the difference between the minimum and maximum controlled element that needs to be controlled simultaneously, and the budget is recommended. Based on this, choose the machine vision system. Also, as

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mentioned above, the maximum resolution of a camera sensor depends on its type. So, in some cases, it will be necessary to change the machine vision system with another type of camera or use several machine vision systems to control different parameters. 3.3 Parameters of Vision Processing Devices The machine vision processing device is responsible for pre-processing and postprocessing. The availability of image processing tools separates vision sensors from machine vision systems [37]. Pre-processing is necessary for the optimal preparation of input images, while post-processing is used to refine and improve the output results. From this, it turns out that the more pre- and post-processing tools the controller can perform, the more flexible the machine vision system is. On the other hand, controller performance requirements are growing, affecting its price. Therefore, for the optimal choice of a machine vision system, it is necessary to choose the set of necessary tools to complete the task successfully with a price in mind. Since 2D and 3D cameras use different sets of pre-processing and post-processing tools, they will be considered separately. Among the common and widely used pre-processing tools for 2D cameras are the binarize tool, edge detection tool, pixel correction tool, and stretch tool. Tools for postprocessing according to defined parameters are divided into tools for detecting the position of an object, comparing object properties, measuring object geometry, counting the number of objects, and identifying objects. Tools for detecting the position of an object can be based on the detection of a contour, edges, a cylindrical surface, and text. Tools for detecting the position of an object can be used to determine the position of the object’s local coordinate system relative to the global coordinate system of the machine vision system. As tools for comparing object properties, the comparison of brightness, contrast, color, contour, and pattern, as well as determining the area and counting pixels by color, can be used. These tools help to determine the presence or absence of an object and to check its properties, for example, checking for correct packaging and labeling or the presence of damages). Tools for measuring the geometry of an object can determine linear, angular, and diametrical dimensions, determine the number of faces and their properties, and are designed to check the correct dimensions, orientation, and current state of a part or assembly. Object counting tools work on edge recognition and help determine the number of occurrences of a given object in an image. Object identification tools use barcodes, matrix codes, and text recognition. As a pre-processing for 3D cameras, the stretch tool or distortions removal tool [38] can be used. For 3D cameras with defined parameters, object position detection, object properties comparison, object geometry measurement, and object counting are available. Tools for detecting the position of an object, comparing object properties, and counting the number of objects can be based on the detection of a specified 3D model or the 3d model with simple geometric shapes (cube, cylinder, etc.). Tools for measuring the geometry of an object can determine linear, angular, and diametrical dimensions, as well as dimensions between planes and 3D models in three axes, which is not available for 2D cameras. In addition, the vision processing device is responsible for automatically adjusting shooting parameters, such as autofocus, auto white balance, auto gain control, auto color adjusting, auto exposure, etc. These tools are especially useful in case of frequent

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changeovers (in case of single-piece flow) or in the case of a dynamically changing environment in which events cannot be predicted in advance (for example, to ensure the safety of a collaborative cell). Also, there are solutions in the machine vision market with NVIDIA Jetson modules or similar platforms which make it possible to run applications using artificial intelligence and neural networks (Allied Vision solutions, Basler solutions, Baumer AX series). Examples of these solutions are providing the operator’s safety in a collaborative assembly, visual quality control based on an operator’s subjective assessment, and object recognition.

4 Results and Discussion An example of choosing machine vision systems for the SmartTechLab laboratory will be considered as a practical verification of the obtained methodology. SmartTechLab laboratory is located at the Faculty of Manufacturing Technology with a seat in Prešov of the Technical University of Košice, Slovak Republic. A more detailed description of the laboratory is available in previous work [39]. The laboratory is dedicated to teaching students the key technologies of the Industry 4.0 concept, research, and development work. The key equipment of the laboratory is the collaborative assembly line (Fig. 1). In this example, it is planned to configure a line for the assembly of switches (Fig. 2a). Since the switch consists of many parts (Fig. 2b), there are problems with tool accessibility, small part sizes, and complex assembly steps (for example, installing a spring). Collaborative assembly is preferable to full automation.

Fig. 1. The collaborative assembly line.

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Fig. 2. Switch: a – assembled; b – exploded view.

The assembly of the switcher consists of the following stages: incoming inspection, assembly, and final inspection. During the incoming inspection, it is necessary to ensure that the base part of the switcher is correctly installed on the holder and that there are no external defects. The holder enters the field of view of the camera on the conveyor at low speed. In addition, it is allowed to stop the conveyor to perform the inspection operation since the bottleneck of the system is the assembly operation. Based on this, the system can be considered static. In addition, when choosing equipment for the laboratory, the focus should be more on flexibility and versatility than on performance. The fact that the base part is monotonous and dimpled makes the verification process more complicated. The 3D machine vision system is the optimal choice for these production conditions. Because the system is static, 1D machine vision systems are not the optimal choice. In

Fig. 3. Incoming inspection: a – camera vision system; b – application.

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addition, for a 3D machine vision system, the monotony of a part will not be an issue, unlike 2D machine vision systems. In addition, information about the height of the part will help determine the correct installation of the part on the holder. A machine vision system with a structured light sensor was chosen, as it can create a high-resolution model, works well with small parts, is relatively cheap, and works well with monotonous parts. Based on the analysis of available solutions, it was decided to choose the Keyence CAH048CX machine vision system (Fig. 3a). A 3D model comparison tool and a profile measurement tool were used to check the geometric parameters and the correct height of the profile (Fig. 3b). During collaborative assembly, the robot must be able to determine the exact coordinates of parts and pre-assemblies since a person can accidentally or deliberately change their position. Because there are small parts that are involved in the assembly, it can be critical to capture them. Since the system is static, and there is no need to determine the colors of the workpieces, a monochrome 2D machine vision system is the optimal choice. Since ABB YuMi is used as the collaborative robot, ABB offers a force-controlled collaborative gripper with an integrated Cognex AE3 machine vision system. Compared to an installation in a fixed place, the machine vision system built into the gripper has more flexibility since it is possible to change the inspection location freely, and there are no issues with dead zones. The process of controlling the position of the base part is shown in Fig. 4a. The circle finder is used as a tool for the controller (Fig. 4b).

Fig. 4. Machine vision system of the collaborative robot: a – general view; b –application.

After the assembly, it is necessary to perform a visual inspection of the final product. All remarks about the statics of the system during the incoming inspection are applicable to the final inspection control. However, unlike the input control, the appearance of the assembly will be checked, so there is no need for measurements in the three-dimensional plane. Moreover, since the assembly elements are only black and white, there will be high contrast for even monochrome cameras. Based on the description of the inspection conditions, it is optimal to use a monochrome 2D machine vision system. Keyence CAH048MX was chosen for the final inspection (Fig. 5a). The process of carrying out the final inspection of the assembly is shown in Fig. 5b, which compares the original pattern with the current one.

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Fig. 5. Final inspection: a – camera vision system; b – application.

Regarding research in this direction, the authors [8] presented an overview of the application of machine vision systems in smart factories, including considerations of collaborative applications. Still, they did not consider the choice of hardware. The authors [40] provided information about the features of the choice of agricultural machine vision systems. As a result, this study has a scientific novelty and describes the features of the choice of machine vision systems for smart factories with collaborative production cells.

5 Conclusions This article examined the main areas of application of machine vision systems for collaborative assembly cells and their place in the Industry 4.0 concept. The experience of using machine vision systems in collaborative cells was also systematized, and the market of machine vision systems was analyzed. The scientific novelty of this work lies in the fact that recommendations on the optimal choice of machine vision systems for collaborative areas were received. These recommendations were tested in a case study when choosing machine vision systems for the robotic collaborative assembly cell of the SmartTechLab laboratory (Faculty of Manufacturing Technologies with a seat in Presov, Technical University in Kosice). The practical value of this study lies in the fact that the received recommendations will allow researchers and engineers to simplify and speed up the decision-making process on the optimal choice of machine vision systems for laboratories and production lines, which may include collaborative robots. The limitations of this article include focusing only on the main elements of machine vision systems (image sensors and vision processing devices), as well as on the main parameters of the elements (for example, parameters such as aperture, image storage method, etc. were not considered). In future work, the authors consider a more detailed analysis of the application of machine vision systems in security and an analysis of other elements of machine vision systems, such as lighting and lenses.

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Acknowledgment. This work was supported by the Slovak Research and Development Agency under contract No. APVV-19-0590, and also by the projects VEGA 1/0700/20 and KEGA 022TUKE-4/2023 granted by the Ministry of Education, Science, Research and Sport of the Slovak Republic, and The NAWA Ulam Programme under the grant number BPN/ULM/2022/1/00045. The results were partially obtained within the Joint Ukrainian-Slovak R&D Project for the period of 2022–2023 granted by the Slovak Research and Development Agency (SK-UA-21-0060) and Ministry of Education and Science of Ukraine (State Reg. No. 0122U002657). The scientific results were partially obtained within the research project “Fulfillment of tasks of the perspective plan of development of a scientific direction “Technical sciences” Sumy State University” ordered by the Ministry of Education and Science of Ukraine (State Reg. No. 0121U112684). This research was partially supported by the International Association for Technological Development and Innovations.

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Digital Twins for Industrial Robotics: A Comparative Study David Fait

and Václav Mašek(B)

University of West Bohemia, Univerzitní 8, 301 00 Pilsen, Czech Republic [email protected]

Abstract. The paper aims to compare NX Mechatronic Concept Designer (MCD) and Tecnomatix Process Simulate (TPS) and their suitability for simulating mechatronic machines in digital twinning. A Digital Twin (DT) is a virtual model that serves as a real-time digital counterpart of a physical object or process. Compared to a conventional simulation, it differs in its ability to model the behavior of the actual counterpart from many points of view and with much greater complexity. Digital Twins have a crucial role in the Fourth Industrial Revolution but are also used in different areas like meteorology, traffic simulation in smart cities, and civil engineering. Digital Twins in industrial engineering help maintain the optimal pace of assembly lines, flexibly adapt production, increase manufacturing process efficiency, and generate cost-effective products. Increasing usage of IoT solutions drives the Digital Twin market, which is growing exponentially. The paper discusses the two software packages’ possibilities, advantages, and disadvantages and proposes the methodology and philosophy of making DT. The contribution of this work is to compare the possibilities of making mechatronic devices for educational and research purposes from scratch through to their final implementation. Keywords: Digital Twin · Mechatronic Concept Designer · Process Simulate · Virtual Commissioning · Process Innovation

1 Introduction The development of Industry 4.0 ideas supports the growth of Digital Twin technology, particularly in the manufacturing sector. It can be characterized as a two-way data integration between a physical and a virtual machine [1]. Intelligent manufacturing solutions use information from production processes to enhance individual and overall industrial production. Most of the time, the most expensive part of production is the human workforce. A Digital Twin is a virtual model of a physical object. It models the object’s whole lifecycle and uses real-time data sent from sensors on the object to simulate the behavior and monitor operations [2]. This method combines traditional mechanical engineering, mechatronics, computer science, and artificial intelligence. Engineers can evaluate the functionality of industrial concepts without creating a prototype due to the advanced simulation of the machine. Early correction of mistakes greatly minimizes the costs needed © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 26–35, 2023. https://doi.org/10.1007/978-3-031-32767-4_3

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for any modifications during a machine’s operational time. Manufacturing processes are complicated and involve significant data generation, conversion, storage, and processing. In these circumstances, simulations have become crucial and extremely useful. Digital Twin technology can be utilized to create cost-effective products for customers, maintain optimal production processes, and increase manufacturing efficiency. Efficient and correct Product Lifecycle Management (PLM) plays a significant manufacturing role. It allows the use of data from all life stages of the product supply chain, from design and manufacturing, sales, and usage to decommissioning, to improve the next product iteration [3, 4] (Fig. 1).

Fig. 1. Influence on Design and Cost in the Product Design and Process Planning [4].

Digital Twins and PLM software can also predict the product’s lifecycle before manufacturing. Siemens is one of the producers of PLM software and offers a whole PLM solution. However, smaller companies typically cannot afford the entire portfolio and are limited to only essential SW (software) [5]. This paper compares SW, which could be used for simulations of the main part of the manufacturing process. The study compares the simulation of educational and research models of assembly lines that can serve as a benchmark because they are in the fuzzy area between design and commissioning.

2 Literature Review During the design of mechatronic devices, it is necessary to consider the increasing complexity of the manufacturing machines. Since a mechatronic device comprises mechanical, electrical, and software parts, the number of potential failures is increased [6]. One of the most widely used development models is the “V-Model” (Fig. 2). It shows a product’s lifecycle from the requirements analysis to the final commissioning. Figure 2 shows the full version of the V-Model for designing mechatronic devices. For purely mechanical devices, the V-Model is simplified [7].

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Fig. 2. V-Model for Mechatronic Devices [8, translated from 9].

The left part of the diagram shows the problem specification, analyses, and design phase. The bottom part shows prototype testing and problem-solving (DTs are most beneficial in this part). The right part shows integration tests, Hardware in the Loop (HIL) testing, debugging, and preparation for commissioning [7]. One of the main advantages is avoiding errors in the control code. Testing a code on a physical mechatronic system is complicated because every mistake can lead to a crash. This is significant, especially for manufacturing machines, because they must move various components simultaneously, making crashes sometimes hard to predict [10]. However, the DT is not just that. Another essential part is physics simulation and line layout optimization by a real-time two-way flow of the data between the physical and digital models of the studied system. Correction of errors [11] during the operational phase of the lifetime [12] is also costly [1, 4, 13].

3 Research Methodology The educational assembly lines were chosen as practical examples because all the features of the programs could be easily demonstrated. The machine was designed in sections. Simulation SW (Process Simulate or Mechatronic Concept Designer) that allows users to define 3D models and all their movements, sensors, physical attributes, and input/output signals was used for the mechatronics design [4]. This paper compares DT creation of the assembly lines for education and research purposes, described in the following sections. Digital Twins are especially suitable for

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education not only to understand the concept and the methodology of making DTs but also for co-simulation purposes in Totally Integrated Automation (TIA) Portal programming. So each student has access to the device. Even though it is just a digital model, it suits the purpose well enough. Then we establish a connection with the TIA Portal, where DT can be used as a surrogate model of the real Hardware to create robotic manipulators. The work aims to make a functional DT and compare the possibilities of different software not just from a functionality point of view but also with the stress on educational purposes, user-friendliness, and the possibility of simulating real-life machine parts, similar to the solution presented in [13]. Another study goal is to discuss areas in the V-Model covered by combining the MCD and TPS with TIA Portal. It is also necessary to define which areas are covered by mentioned SW and deserve increased attention. A further aim is also to compare both programs and provide recommendations for the further development of the system engineering process. 3.1 Mechatronic Concept Designer An assembly line MecLab (Fig. 3) prepared by the company FESTO was used for NX Mechatronic Concept Designer. It consists of three separate stations that can be combined and/or used separately. The first one consists of a pneumatic gripper that can manipulate the part from one position to the second. The second part is the conveyor with a separating actuator that can push the part from the conveyor to the slide. The third part is the press that connects two manipulated parts. MecLab’s goal is to teach students automation technology through realistic, practical examples that are also simple to understand. Students can grasp new concepts or enhance their knowledge by comparing the digital simulation and the actual machine and understand the fundamentals of electrical and control design [14].

Fig. 3. Model of the assembly line.

3.2 Process Simulate A manipulation line shown in Fig. 4 was used for the Tecnomatix Process Simulate. It was designed at the Department of Machine Design for education in automation and fluid

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mechanism control using PLC. The device contains all the basic automation elements: a conveyor, linear and angular pneumatic drive, both bistatic and monostatic valves, two infrared position sensors and end position sensors, and two gravity conveyors. When the part is at the end of the conveyor, it is pushed by the pneumatic drive and slides to the position gripped by a magnetic actuator attached to the linear drive. Then it is manipulated above the second slide and slides back on the conveyor.

Fig. 4. Model of the manipulation line [contains parts from 15].

3.3 TIA Portal Defining the physical and mechanical properties of the various machine parts is the first step in creating a Digital Twin of a machine. It models the dynamic behavior and interactions of several components. TIA Portal enables a control program to be programmed. DT can be combined with the PLC SimAdvanced for the code function assessment. Finally, it can be connected to a PLC and used in a program control [16].

4 Results and Discussion In this part, the main areas of the SW comparison are covered. They can be divided into the following categories: • • • •

Physics and reality reflection Kinematics settings Sensors Others

Digital Twins for Industrial Robotics: A Comparative Study

31

The thing not discussed in the results chapter is the connection of the Digital Twin to the TIA Portal. This issue is beyond the scope of this paper because it relies a lot on the quality of the Digital Twin and will be discussed in the following papers in the future. Generally speaking, both SW packages allow a TIA Portal connection. 4.1 Model and Reality Reflection The objective of the Digital Twin is to reflect reality closely. However, because of the problematic physics involved, it is evident that some aspects are challenging to mimic. Co-Simulation using an actual physical model of pneumatic pistons is partly present in MCD but not in TPS. A similar issue is gripping. It usually combines two challenging steps – exact positioning and friction estimation during contact. The type of collision body selected has a significant influence on MCD. In TPS’s case, the gripping tool cannot be limited, e.g., by a maximum payload. It only considers the contact with the manipulated part. Also, the gripper can “pierce” the part, and nothing happens in the simulation. On the other hand, this can be used when simulating magnetic gripping. Magnetic grippers cannot be specified, but the magnetic force can be partially mimicked using an elongated “core” of the coil that “pierces” the manipulated object. Another necessary thing is the definition of forces. MCD allows gravity to be used and interaction between the objects for force transitions. TPS does not allow it. It can only change the position of the objects based on their kinematics or if they are “gripped” by another kinematic entity. In our version of TPS (16.0.2), there is a beta version of the physical module that allows parts to be “distributed” into the bin for “pick and place” simulations and bin picking. Although this function should allow gravitational conveyors, how it works is unclear. 4.2 Kinematics Analysis and Collisions MCD is suitable for simulating the general movements of machines. However, it is not made for precise prediction of gripping, collisions, sliding, or connecting of different parts. In MCD, when a rigid body is squeezed between two others, MCD often responds inadequately. Gripping is generally a problematic aspect to simulate. Due to the simplification of the parts, incorrect meshing, and assumption of perfect rigidity, MCD is not the best software for it. The selection of an appropriate collision shape significantly influences the “result” of a simulation. Choosing the correct mesh type is necessary because it affects how the bodies collide. For example, during a simulation involving the pressing of a puck, if the meshes of collided parts are not perfect, parts are not gripped correctly. Furthermore, stiffness and friction cannot be simulated. Therefore, the DT cannot validate this technological aspect of the line. TPS allows calculating the trajectory volume of the moving parts and collision states, even though physics is not generally allowed in the TPS. The user can define that

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the simulation stops when the moving parts interfere so they can investigate the problem and choose better operation timing. However, as with MCD, it cannot co-simulate deformations. Therefore, it cannot validate pressing and forging operations. 4.3 Sensor Simulation MCD does not simulate sensor functions. Their functionality is just mimicked. As mentioned, they are typically simulated as a collision of two parts. This, however, goes against the DT principle. Real sensors have flaws in particular environments and situations (due to magnetism, fast movements, etc.) All of this cannot be included in the simulation. Process Simulate allows the use of various sensors that are used in industrial robotics (like lidar or photoelectric sensors). Sensors in TPS cannot simulate advanced physics (e.g., electromagnetic compatibility), but they are customizable and behave relatively close to real ones. Although a camera tool is present, image processing is not available. This function can be mimicked by the “property sensor”, which can be “taught” to recognize defined properties like the color of an object. 4.4 Other Functionalities and Additional Notes Mechatronic Concept Designer is part of the Siemens NX CAD SW. Therefore, it is an excellent tool for making fast changes in a model or modeling new parts. Tecnomatix Process Simulate has limited modeling capability. An MCD user can also rapidly transition to other computational environments and make proper design evaluations using, e.g., the FEM solver. Process Simulate has a lot of additional tools for observing material flows and making Gant diagrams, etc. Human – Machine Interaction and ergonomics can be simulated with limitations as well. Another good thing is the ability to design a control panel directly inside Process Simulate. 4.5 Contribution The work compares SW for DT creation from the system engineering point of view. Features in design and simulation are compared and shown in Table 1. Attributes were scored as “Good” (allows to perform given task almost perfectly or on the professional level), average (allows only limited feature usage), or poor (doesn’t allow specified feature or is very limited). Based on this information, we can determine which areas of the V-Model are covered by the software tools (shown in Fig. 5). Siemens NX and its extension Mechatronics Concept Designer is more suitable for hardware development and testing and can be used for most parts during the product’s lifecycle.

5 Conclusions The paper aimed to compare MCD and TPS and their suitability for digital twinning. Both software were found to be helpful tools for research and industrial practice. However,

Digital Twins for Industrial Robotics: A Comparative Study

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Table 1. Feature comparison of selected SWs. Feature

TPS

MCD

Modeling Tool

Average

Good

Parametric Modelling

Poor

Good

Physics (Forces, Gravity, etc.)

Poor

Good

Kinematics Definition

Good

Good

Collision Detection

Good

Good

Reach Testing

Good

Average

Grippers

Good

Average

Logistic Functions

Good

Poor

Multiple Sensor Types

Good

Poor

Advanced Sensor Modelling (EMC, etc.)

Poor

Poor

Fig. 5. V-Model coverage by MCD (blue), TPS (green), and TIA Portal (black) [updated from 8, translated from 9].

each one is focused on a Digital Twin from a different point of view. MCD is better in general modeling and physics simulation. In those areas, TPS performs relatively

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poorly. SW is equally good in kinematics definition and collision detection. TPS is a little bit better prepared for gripping operations and has a significant advantage in logistic functions and simulation of different types of sensors. SW is performing poorly in advanced sensor modeling. Tecnomatix Process Simulate can be mainly used for testing from the signal and operational point of view. Necessary operation times and sensor placement evaluation can be done using this SW. Process Simulate has a lot of additional tools for observing material flows and making Gant diagrams, etc. Human – Machine Interaction and ergonomics can be simulated with limitations as well. Another good thing is the ability to design a control panel directly inside Process Simulate. Siemens NX and its extension Mechatronics Concept Designer is more suitable for developing and testing a new product. MCD is part of the Siemens NX CAD SW. Therefore, it is a great tool for making fast changes in a model or modeling new parts. TIA Portal is used mainly as a PLC programming tool but can be used with Digital Twin and co-simulation for software testing and evaluation. Even though this is not the DT’s primary purpose, it is a helpful feature that can decrease the possibility of damaging the machine. The machine’s error states can also be tested without an actual error state. Further work in this project is to design the optimal portfolio of SWs that can fulfill the whole V-Model and be presented to our students as guidelines and tools for the complete product development portfolio, which is essential knowledge of the engineer in the 21st century. Acknowledgment. This contribution is supported by project SGS-2022–009.

References 1. Karabegovi´c, I., Turmanidze, R., Daši´c, P.: Robotics and automation as a foundation of the fourth industrial revolution - industry 4.0. In: Tonkonogyi, V., et al. (eds.) InterPartner 2019. LNME, pp. 128–136. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40724-7_13 2. Aws: What is Digital Twin Technology. Amazon https://aws.amazon.com/what-is/digitaltwin/. last accessed 2022/12/06 3. Ashaolu, B.A.: What is Product Lifecycle Management (PLM)? (2020). Converged https:// converged.propelsoftware.com/blogs/what-is-product-lifecycle-management. last accessed 2023/01/06 4. Lenord, M.: Mechatronics Concept Designer, Quickstart and User Manual (2010). http:// www2.me.rochester.edu/courses/ME204/nx_help/en_US/graphics/fileLibrary/nx/mechtr onics/. last accessed 2023/01/06 5. Siemens software. Siemens Digital Industries Software (2022). https://www.plm.automation. siemens.com/global/en/#top. last accessed 2023/01/09 6. Ulf, S., Törngren, M., Malvius, D., Biehl, M.: PLM for mechatronics integration. In: Proceedings of the International Conference on Product Lifecycle Management. Inderscience Publishers (2009) 7. System lifecycle process models: VEE. Guide to the System Engineering Body of Knowledge (SEBoK) (2022). https://www.sebokwiki.org/wiki/System_Lifecycle_Process_Models: _Vee. last accessed 2023/01/06

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8. Salehi, V.: Development of an agile concept for MBSE for future digital products through the entire life cycle management called munich agile MBSE concept (MAGIC). Comput. Aided Des. Appl. 17, 147–166 (2019). https://doi.org/10.14733/cadaps.2020.147-166 9. Bender, K.: Embedded Systems - qualitätsorientierte Entwicklung. Springer-Verlag, Berlin, Heidelberg (2005). https://doi.org/10.1007/b138984. (in Germany) ˇ 10. Fait, D., Mašek, V., Cermák, R.: Using digital twins in mechatronics and manufacturing. In: 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 434−438 (2022). https://doi.org/10.1109/ISMSIT56059.2022.9932840 11. MarketsandMarkets. Opportunities in the digital twin market. News details,. https://www.ind ustrysourcing.com/mobile/article/details.html?id=320133. last accessed 2023/01/06 ˇ 12. Siemens Ceská republika, Digitální dvojˇce Siemens a jeho výhody v praxi (2022). https:// www.vseoprumyslu.cz/inspirace/nazory-a-komentare/siemens-digitalni-dvojce-a-jeho-vyh ody-v-praxi.html. last accessed 2023/01/06 13. Husar, J., Knapcikova, L., Balog, M.: Implementation of material flow simulation as a learning tool. In: Ivanov, V., et al. (eds.) DSMIE 2018. LNME, pp. 33–41. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93587-4_4 14. Hüttner, A., et al.: Teaching with MecLab. Festo Didactic GmbH & Co. KG, Denkendorf, Germany (2008) 15. 2D/3D CAD Library. SMC (2022). https://www.smcworld.com/cadlib/en-jp/. last accessed 2023/01/06 16. Tia Portal. siemens.com Global Website. https://new.siemens.com/global/en/products/automa tion/industry-software/automation-software/tia-portal.html. last accessed 2023/01/06

A CANVAS Based Assessment Model to Evaluate SMEs Readiness for Digital Business Models Manuel Holzner1 , Erwin Rauch1(B)

, Guido Orzes1

, and Dominik T. Matt1,2

1 Industrial Engineering and Automation (IEA), Free University of Bolzano, 1,

Universitätsplatz, 39100 Bolzano, Italy [email protected] 2 Fraunhofer Italia Research s.c.a.r.l., Innovation Engineering Center (IEC), 13B, Via A. Volta, 39100 Bolzano, Italy

Abstract. This work focuses on developing and implementing an assessment model to evaluate the readiness of small and medium-sized enterprises for digital business models. By virtue of an underlying systematic literature review, a total of 90 publications regarding existing assessment models to evaluate the digital readiness of business models were examined. However, none of these publications presents a suitable and holistic assessment model. This work presents an assessment tool to close this gap in the scientific literature. The assessment model includes the evaluation based on two parameters the representatives need to evaluate: the current score and the need for action. The user is guided through 45 assessment criteria organized according to the business model CANVAS in 9 categories or blocks during the assessment. Besides the numerical representation of the assessment results, the model reveals a visual representation of the results and a list of business model aspects that should be prioritized in reorganizing the currently existing business model. The assessment tool has been tested and validated in an SME machine-producing company. Based on the positive result and feedback, the tool is included in a web-based Industry 4.0 Toolbox to support SMEs and family business in introducing digital business models. Keywords: Assessment · Digital Transformation · Business Model · Industry 4.0 · SME · Sustainable Manufacturing

1 Introduction Beginning with the first industrial revolution at the end of the 18th century, companies had and still must face radical changes in industry, business, and society. Klaus Schwab describes in his book “The Fourth Industrial Revolution” that an industrial revolution is triggered by new technologies and novel ways of perceiving the world that cause a profound change in economic systems and social structure [1]. After Lean [2], the effects of Industry 4.0 are reflected in the digital transformation and lead to new business models with which companies create value throughout the customer lifecycle. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 36–49, 2023. https://doi.org/10.1007/978-3-031-32767-4_4

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37

In each revolution, young and dynamic companies emerge, which, by applying new technologies, try to challenge traditional companies and gain their market share. Today’s world, characterized by applying digital technologies to create customer value, business model analysis, and development, is becoming increasingly important. The risk of being disrupted by so-called born-digital companies is high for a company that does not respond to digital transformation. Born digitals are companies whose business models are characterized by exploiting data and digital technologies to create value for their customers and disrupt established enterprises [3]. Therefore, every company’s maxim should be “disrupt yourself or be disrupted”. It raises the question for most companies how to approach in a systematic and structured manner to minimize the risk of being disrupted by an existing competitor or digital-born and to exploit DT’s full potential to increase customer satisfaction and expand market share. Each company operates according to a specific business model. The business model describes the rationale of how an organization creates, delivers, and captures value in economic, social, cultural, or other contexts [4]. Through the development and use of new technologies, new and digital business models are constantly emerging, which can completely turn an industry and its markets upside down. An enormous challenge for existing companies, especially smaller companies like small and medium-sized enterprises (SMEs), is continuously analyzing their business models and adapting them to the new operating environment and emerging customer needs [5]. For this reason, every company, regardless of the sector in which it operates or the size, should evaluate its readiness for digitalization of its business model to decrease the probability of being disrupted by a competitor or digital-born and exploit the potential of those digital technologies.

2 Literature Review According to Drucker, successful business models must answer the following questions: “Who is the customer?”, “What does the customer value?”, “How do we make money in this business?”, and “How can we deliver value to customers at an appropriate cost?” [6]. In terms of the value chain, Joan Magretta defines the business model as consisting of two parts. While part one of a business model includes all activities required to produce a product (development, purchasing, raw materials, manufacturing, etc.), part two focuses on all activities required to sell the product (finding and reaching customers, transacting a sale, distributing the product, delivering the service, etc.) [7]. The following definitions can be grouped into three main categories to gain an overview of the various definitions of the term business model [8]. While the definitions of Baden-Fuller and Morgan, 2010 [9], as well as Knyphausen-Aufsess and Meinhardt, 2002 [10], describe a business model as a model of an organizational unit, Osterwalder and Pigneur, 2010 [4] and Teece, 2010 [11], among others, define a business model as an abstract characteristic of an organizational unit. The third category refers to the definitions with a reduced scope based on the individual scientific focus and the central role of customer value. While Richardson, 2008 [12] definition includes the value proposition, value creation, and value capture. Authors such as Zott and Amit, 2010 [13] also integrate the value network in their definitions.

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To better describe business models, Osterwalder developed in his Ph.D. work the concept of business model CANVAS utilized as a framework for the analysis, whereby nine canvas units are defined as illustrated in Fig. 1. The model is based on a visual chart describing nine business model areas. This chart includes information about key partners, activities, resources, value proposition, customer relations, sales channels, customer segments, cost structure, and revenue streams. The management typically fills it with data retrieved from internal strategic workshops.

Fig. 1. Nine CANVAS blocks (own illustration based on [4]).

3 Research Methodology The research methodology is based on the following four steps: • Step 1: First authors have reviewed existing assessment models to identify the research gap and ideas for improvement by creating a new tool. • Step 2: In the second step, the concept of the proposed assessment tool is defined on a theoretical and conceptual level. During this task, huge attention was given to making the assessment accessible for smaller companies using already-known concepts like the previously introduced CANVAS blocks. • Step 3: After defining the logic of the assessment model, the research team used the results from the literature review to define a set of assessment criteria for each single CANVAS block and to create an MS Excel-based prototype. • Step 4: This prototype tool will be applied at an Italian SME for testing and validation purposes before being further refined and disseminated as a web-based and freely accessible tool.

A CANVAS Based Assessment Model to Evaluate SMEs Readiness

39

4 Results and Discussion 4.1 Review of Existing Models By reviewing scientific literature regarding existing assessment models for the digital readiness of business models, the following five works were most relevant for this study (Table 1). Table 1. Resulting top 5 assessment models related to the objective of the study. Pos

Authors

Title

Reference

1

Stari & Neubauer, 2016

Industrial Challenges

[14]

2

Rübel et al., 2018

A maturity model for business model management in Industry 4.0

[15]

3

Galimova et al., 2018

Selecting the path of the digital transformation of business models for industrial enterprises

[16]

4

Wang et al., 2018

Toolbox approach for the development of new business models in Industry 4.0

[17]

5

Wagire et al., 2020

Development of maturity model for assessing the implementation of Industry 4.0: learning from theory and practice

[18]

Table 2 summarizes the advantages and disadvantages of each of the five found assessment models. Regarding the assessment models for the digital readiness and maturity of business models, the literature review revealed only a few publications presenting a specific model, which has also been implemented in practice. Based on the review, it can be stated that there seems still to be a lack of holistic assessment models for evaluating the readiness of companies to introduce digital business models. The models identified by the literature review and listed above only focus on a certain area of the business model or concentrate on digital technologies and thus on the era of industry 4.0 in general. In addition, the models are usually not tested and examined in practice based on case studies. Even though some assessment tools provide a detailed list of assessment criteria, there is often no specific guideline on evaluating them and assigning a readiness level to the company or business model [15]. However, if the digital readiness level can be identified, there is a lack of general guidance on interpreting the assessment results and on the digital transformation of the business model.

40

M. Holzner et al. Table 2. Analysis of the 5 assessment models found in the literature review.

Pos

Advantages

Disadvantages

1

- Evaluation of a company as a socio-technical system using the elements of human, organizational structures, and technology

- the proposed model is not a specific maturity model - the focus of the model is on a company as a socio-technical system and not specifically on the business model - the model describes the assessment criteria only in general terms

2

- based on the business model canvas - well-defined assessment elements - contains besides business model elements also, Industry 4.0 elements

- does not give guidance on how the assessment elements are evaluated - the assessment elements are not weighted according to their importance - does not give recommendations for action based on the maturity level

3

- contains a detailed description of the drivers for digital business models the maturity model gives recommendations for the transformation of the business model

- does not provide clear guidelines on how points are awarded for the maturity of the digital business model - lack of weighting of the drivers concerning their importance

4

- the application levels contain business model-related elements as well as digital technologies - the representation in a matrix gives a good overview of the digital maturity of the enterprise

- lack of weighting of the individual application levels concerning their importance - the specific assessment of the application levels is not described - does not give recommendations for action

5

- detailed list and description of the assessment criteria - the weighting of the assessment criteria considered

- focus on maturity of Industry 4.0, however, also includes elements of business models - the model does not provide standardized recommendations for action for each maturity level

4.2 Concept of the CANVAS Based Assessment Tool The developed tool is based on a questionnaire of 45 assessment criteria/questions grouped in the nine CANVAS blocks (see also Sect. 4.3). The quantitative evaluation of the questionnaire is based on two input data per assessment criteria/question encoded by the respondent. For each assessment question, the respondent must assign a value for the (i) Current Score and (ii) Need of Action applying a Likert scale ranging from 1 to 5. The introduction of a Likert Scale with exactly five readiness levels can be attributed to the fact that such a scale is most frequently adopted in the literature associated with digital maturity and readiness models [19]. The application of the Likert Scale is intended to provide a framework for the respondents’ subjective evaluation and to enable a quantitative analysis of digital readiness. The higher the level assigned to the questions, the more digitally mature the respective dimension (Table 3).

A CANVAS Based Assessment Model to Evaluate SMEs Readiness

41

Table 3. Digital readiness levels. Scale

Readiness level

1

No digital initiatives have been planned or introduced

2

First and simple digital initiatives have been implemented

3

Digital initiatives have been implemented, and others are planned

4

Few digital initiatives are missing to achieve high readiness

5

High level of digital readiness has already been achieved

The Current Score is a subjective value provided by the respondent of the analyzed enterprise. The respondent seeks to estimate the current degree of digital readiness respective to each evaluation criteria questioned. As described in the previous subchapter, the Likert scale has been adopted to guarantee uniformity and evaluability of the score. The Likert scale ranges from 1, considered the lowest level, to 5 representing the highest reachable account. For the (graphical) representation of the current score in relation to the digital readiness of the business model, the value between 1 and 5 was normalized to represent it on a scale ranging from 1 to 100. The Need of Action is, as well as the current score, a subjective value provided by the respondent of the assessed company. Also, for this evaluation element, the score assigned is based on the Likert scale ranging from 1 to 5. It epitomizes the degree of relevance of the respective assessment criteria since, due to the individual characteristics of the companies, not every criterion in the questionnaire has to be of the same importance. Therefore, the responder is given the freedom to weigh the criteria. Based on the subjective values for the current score and the need for action, which the respondent determines for each assessment criterion, the firms gap to maximum and the weighted gap can be calculated. The value for the Firms Gap to Max. Refers to the digital deficiency of the business model to achieve the highest form of digital readiness. This gap is calculated for each assessment criteria, the nine canvas building blocks, and the entire business model. The formula for calculating the Gap to Max. is a straightforward arithmetic operation and can be ascertained by subtraction between the current and maximum scores. The maximum score is represented by the highest value of the Likert scale, which is a value of 5. The formula for the calculation is presented as follows: Firms Gap to Max = 5−−Firms current score

(1)

The formula for the Weighted Gap also includes the specific need for action assigned to the assessment criteria. This weighted value allows all criteria from the new building blocks to be directly compared with each other, thus serving as a transparent prioritization methodology. In the end, the analyzed company receives. As a result, a list of the assessment criteria in the order of their ranking, ranging from the question with the highest weighted gap to the smallest weighted gap. The calculation of the weighted gap can be described by a division and is demonstrated as follows: Weighted Gap = (Firms Gap to Max. ∗ Need for Action)/5

(2)

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M. Holzner et al.

4.3 Detailed Overview of CANVAS Based Assessment Criteria Table 4 gives an overview of the assessment criteria retrieved from a deeper analysis of the literature and based on criteria/questions proposed by the authors. Assessment users must define the degree of fulfillment on a scale from 1–5. Table 4. List of 45 assessment criteria in the form of questions. Nr Canvas block Digital readiness evaluation criteria/questions 1 2

Value Proposition (5 criteria)

Does your company recognize the customer needs and the digital trends pertaining to their customers? Does your company offer each customer segment digital products and/or services?

3

Does your company understand new digital product features’ quantitative and qualitative impact on their customer experience and value?

4

Does your company provide suitable digital solutions to quickly and effectively address customers’ problems

5

Does your company understand what value and unique selling proposition their offering brings to their customer?

6 7

Customer Segments (5 criteria)

Does your company understand customers’ characteristics to segment them properly? Does your company collect data through digital tools to segment their customers properly?

8

Does your company understand which customer segment is the most important when offering new digital products and services?

9

Is your company willing to accept the definition of a new, most important customer if the digital transformation demands for?

10

Is your company aware of the changes your customer is exposed to due to digitalization?

11 Distribution Channel 12 (4 criteria)

Does your company use digital tools to interact with customers (e.g., mobile apps, social media, e-commerce platforms)? Does your company use digital tools to increase the cost efficiency of distribution channels?

13

Does your company have a good understanding of all the channels that are available to your business to sell and service products to their customers?

14

Has your company already activated new digital channels through digitalization?

15 Customer Does your company actively monitor, store, analyze, and respond to Relationships customer interactions at different touchpoints? (5 criteria) 16

Is your company willing to change the underlying customer partnerships through digitalization efforts? (continued)

A CANVAS Based Assessment Model to Evaluate SMEs Readiness

43

Table 4. (continued) Nr Canvas block Digital readiness evaluation criteria/questions 17

Does your company meet the customer segments’ expectations regarding digital relationships with them?

18

Are your company’s customers actively encouraged and incorporated into your innovation process of new digital products?

19

Has digitalization already brought an advantageous change in customer relationships to your company?

20 Key Resources 21 (6 criteria)

Are your employees fully on board with implementing and using new digital technologies? Are your company’s IT systems and capabilities a competitive advantage, giving them speed, flexibility, agility, and confidence?

22

Does your company have the required set of digital capabilities, competencies, and skills across the organization?

23

Does your company have the leadership and top management commitment to successfully implement a digital transformation strategy?

24

Does your company focus on the selection criteria digital capability during the recruiting process?

25

Does your company provide the financial resources needed for a digital transformation of the business model?

26 Key 27 Activities (4 criteria) 28

Does your company use digital technologies to transform its key activities? Do your employees collaborate and share information on digital platforms? Has your company digitalized and automated some of the core business processes, resulting in improved efficiency and effectiveness?

29

Does a changed value proposition already have had an impact on the key activities in your company?

30 Key Partners (6 criteria)

Does your company interact with partners or suppliers through digital platforms?

31

Does your company use digitalization to gain greater visibility across the entire supply chain?

32

Does your company have a digital transformation strategy that is shared with partners and revised based on their feedback?

33

Is your company seen as a digital pacesetter by external partners?

34

Can the existing partners provide support in the digital transformation of your company? (continued)

44

M. Holzner et al. Table 4. (continued)

Nr Canvas block Digital readiness evaluation criteria/questions 35

Is your company willing to change the key partners and suppliers if the digital transformation demands for?

36 Revenue 37 Streams (5 criteria)

Does your company’s digital strategy focus on how to turn data into value? Does your company’s leadership recognize the digital revolution as a chance to identify new revenue streams?

38

Does your company have an accurate picture of its digital spending and whether it realizes value from the investments in digital?

39

Are your company’s existing customers willing to accept a higher price for the offerings if they are equipped with digital features?

40

Has your company already analyzed or implemented new digital revenue models (e.g., subscription model)?

41 Cost Structure 42 (5 criteria)

Does your company invest in key resources to ensure they are ready to utilize new technologies? Does your company document the costs of digitalization initiatives in a transparent and structured manner?

43

Does your company’s governance ask for linking the impact of the digital initiative’s investments to financial results and business performance?

44

Is your company’s leadership willing to invest in digital technologies with high initial costs?

45

Is your company aware of the costs a digital transformation entails?

4.4 Implementation and Visual Representation The tool has been implemented in the first step as an MS Excel prototype to allow first tests for validation and subsequent refinement. Figure 1 illustrates how companies can encode their answer to the 45 assessment questions. Based on the values encoded by the company and a hidden calculation sheet, the tool automatically generates the results as a “result sheet” that can be printed or sent by email in.pdf format. This result sheet provides the top-related information on the overall readiness of the company toward digital business model implementation. As illustrated in Fig. 2, the tool creates a radar chart for visualization of the gap between the Current Score and the maximum score for each CANVAS block (see on the top left side of Fig. 2). In addition, a bar chart visualizes those assessment criteria with the highest need of action (see on top right side of Fig. 2).

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Digital Readiness of Business Models: Input Data Canvas Building Block: Value Proposition Nr. Q1 Q2 Q3 Q4 Q5

Digital readiness evaluation question Does your company recognize the digital trends pertaining their customers? Can your company easily change and adapt their existing products and services to a new digital business model? Does your company understand the quantitative and qualitative impact of new digital product features on their customer experience? Does your company provide suitable digital solutions to quickly and effectively address customers problems? Does your company understand what influence and unique selling proposition their offering brings to the customer?

Nr. Q6 Q7 Q8 Q9 Q10

Digital readiness evaluation question Does your company understand customer’s needs, problems, behaviors and expectations to segment them? Does your company collect data on your customers or website visitors? Does your company use digital information to define customer segments? Is your company willing to accept the definition of a new most important customer if the digital transformation demands for? Is your company's most important customer segment exposed to changes due to digitalization?

Nr. Q11 Q12 Q13 Q14

Digital readiness evaluation question Does your company use digital tools to interact with customers (e.g. mobile apps, social media, e-commerce platforms)? Does your company make use of digital marketing tools to understand which channels the customer prefers? Does your company have a good understanding of all the channels that are available to your business to sell and service products to their customers? Has your company already activated new digital channels through digitalization in the past?

Current Score Need for action 4 5 2 4 2 1 4 4 1 5

Canvas Building Block: Customer Segments Current Score Need for action 2 3 4 2 1 4 4 1 2 5

Canvas Building Block: Distribution Channel Current Score Need for action 2 3 1 3 3 2 2 1

Fig. 2. Excerpt of the input data sheet.

In the top middle of the result sheet, the encoded data calculates the normalized overall readiness score for introducing digital business models, ranging from 1 to 100. According to the value achieved, the tool classifies the company into one of the four following categories: • • • •

Digital Low Performer (0–25) Digital Newcomer (26–50) Digital Experienced (51–75) Digital Top Performer (76–100).

As the Likert scale, which ranges from 1 to 5, was applied in evaluating the current score, the results must be normalized to be assigned to an interval from 1 to 100. The factor’s current score and need for action are combined to calculate the digital readiness score. Based on the need for action value, the weighting is calculated as a percentage and multiplied by the current score. The sum of the values then results in the digital readiness score (Fig. 3). The exact visualization of results applies afterward for each single CANVAS block depicting the radar chart, the gap, and the bar chart for the need of action for each single assessment question. Similar to the overall result, a normalization takes place to identify the level of readiness on the CANVAS block stage. 4.5 Testing and Validation in an Industrial Case Study The proposed assessment tool was tested and validated by an Italian machine manufacturing company. The company employs 27 people, of which 13 are working in production, 7 in administration, and 7 in the service department. The company generates a revenue of about 4 million euros from the sale and service of harvesting machines for agriculture. According to the combination of the number of employees and turnover, the company can be categorized as a small-sized enterprise and enters the EU definition of SME. For

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Fig. 3. Excerpt of the results sheet provides visualization on Canvas and overall levels.

the testing of the assessment tool, a five-step approach has been followed (see Fig. 4): 1) introduction, 2) explanation, 3) assessment, 4) report, and 5) conclusion. The first step of the structured implementation approach is the Introduction, and as the name suggests, this phase is used to familiarize the engaged team from the company with the assessment tool. It means that the digital readiness moderator used this phase to show the participants of the analysis the theory of the business model canvas, the tool’s structure, and the analysis’s goal. The time frame for this first step of the implementation was 10 min. The Explanation phase, the second step in the implementation approach, is a crucial part of the analysis as it deals with the assessment questions and ensures that the respondents correctly interpret the questions to deliver meaningful answers. After the exhaustive explanation of the questionnaire, the participants can ask further questions and discuss the content of the assessment model. This explanation phase aims to ensure a complete understanding of the assessment criteria, directly affecting the assessment result’s quality. This step was scheduled to take 20 min. The step Assessment represents the core part of the digital readiness analysis and includes the assessment of each of the 45 assessment criteria. To obtain a meaningful result from the analysis, it is essential that the moderator takes a neutral position and does not influence the results. A time frame of 120 min was used for this phase. The penultimate phase of the digital readiness assessment is the Summary phase. This phase serves to conclude the workshop with the participants. Before a detailed analysis report is presented in the consecutive step, a short outlook on the analysis results is given

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in this phase. Furthermore, the participants were asked for feedback to incorporate their opinion into further refining the tool. This step took about 15 min. The conclusive step in the implementation approach is the Report phase, in which the readiness assessment results are presented in detail to the participants. The numerical results are presented visually using radar charts and bar charts. In addition, the final report contains the visual results and a prioritization ranking of the assessment criteria that should be addressed to improve the digital maturity of the business model. A time frame of 45 min was reserved for presenting the results and recommendations for action and the subsequent discussion round.

Fig. 4. Five-step implementation approach.

4.6 Discussion The holistic approach distinguishes the assessment tool developed in this thesis from the existing models as none contained a list of assessment criteria, guidelines for evaluating the criteria, a digital readiness indicator, and top priorities regarding the need for action. A feature that revealed the significant difference between the proposed and the existing assessment models is evaluating the need for action per assessment criteria. It helps in the transition phase from the assessment results into concrete measures for implementation. Another feature that distinguishes this model from other models is that the digital readiness of the business model is assessed concerning all nine canvas building blocks. Especially as the tool should provide a simple but effective instrument for SMEs, the ease of applying it in practice is crucial. In this regard, the feedback from the test case company was very promising as participants have found that the tool is easy to use and, at the same time, contributes to staff qualification as it aligns the participants’ level of knowledge regarding future business models and techniques. However, some limitations need to be considered when applying the tool. Although the assessment tool has been developed and structured so that a company can carry out the assessment without the support of a digital expert, it is recommended that the company has the support of a skilled moderator able to give the initial training and explanation. Another aspect that should be kept in mind is that it is recommended to apply the assessment tool only in groups (of around 5 people) and not alone to reduce subjectivity in the results. The test case study showed that such an assessment process

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provides an ideal opportunity for discussing together and finding a consensus along the management team when encoding the assessment criteria.

5 Conclusions This work aims to provide practitioners from SME companies an instrument to assess the readiness for digitalization not from a technical perspective [20] but for introducing digital business models. The proposed assessment tool promises to be highly suitable as it provides not only information about the own level of maturity in digital business modeling and needed requirements. Still, it is also helpful for deriving improvement measures and roadmaps. This tool has been developed within the research project MASTERMIL and will be integrated in the future in a web-based Industry 4.0 Toolkit. This toolkit includes an Industry 4.0 Assessment of technologies [21], a Technology Radar [22], and the here-discussed Readiness Model for digital business models. With free access of the tool on the MASTERMIL website (https://mastermil.projects.unibz.it/) the results will be disseminated on a large scale. Acknowledgments. This project received funding from the research project “MASTERMIL – Mastering the digital transformation in the family business: Getting ready for the Millennial generation” (WW201N), financed by the Free University of Bolzano.

References 1. Schwab, K.: The Fourth Industrial Revolution. Currency, Rosewood (2017) 2. Jiang, W., Sousa, P.S., Moreira, M.R., Amaro, G.M.: Lean direction in literature: a bibliometric approach. Prod. Manuf. Res. 9(1), 241–263 (2021). https://doi.org/10.1080/21693277.2021. 1978008 3. Tumbas, S., Berente, N., vom Brocke, J.: Born digital: growth trajectories of entrepreneurial organizations spanning institutional fields. In: 38th International Conference on Information Systems, South Korea (2017) 4. Osterwalder, A., Pigneur, Y.: Business Model Generation: a Handbook for Visionaries, Game Changers, and Challengers. John Wiley & Sons, New Jersey (2010) 5. da Silva, N.A., Abreu, J.L., Orsolin Klingenberg, C., Antunes Junior, J.A.V., Lacerda, D.P.: Industry 4.0 and micro and small enterprises: systematic literature review and analysis. Prod. Manuf. Res. 10(1), 696−726 (2022). https://doi.org/10.1080/21693277.2022.2124466 6. Drucker, P.: The Theory of the Business. Harvard Business Review (1994) 7. Magretta, J.: Why Business Models Matter. Harvard Business Review (2002) 8. Geissdörfer, M., Vladimirova, D., Evans, S.: Sustainable business model innovation: a review. J. Clean. Prod. 198, 401–416 (2018). https://doi.org/10.1016/j.jclepro.2018.06.240 9. Baden-Fuller, C., Morgan, M.: Business models as models. Long Range Plan. 43, 151–171 (2010). https://doi.org/10.1016/j.lrp.2010.02.005 10. Knyphausen-Aufsess, D., Meinhardt, Y.: Revisiting strategy: an approach to the systematization of business models. In: Bieger, T., Bickhoff, N., Caspers, R., zu Knyphausen-Aufseß, D., Reding, K. (eds.) Future Business Models, pp. 63–89. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/978-3-642-56048-4_4. (in Germany)

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11. Teece, D.: Business models, business strategy and innovation. Long Range Plan. 43, 172–194 (2010). https://doi.org/10.1016/j.lrp.2009.07.003 12. Richardson, J.: The business model: an integrative framework for strategy execution. Strategic Chance: Briefings in Entrepreneurial Finance 17, 133–144 (2008). https://doi.org/10.1002/ jsc.821 13. Zott, C., Amit, R.: Business model design: an activity system perspective. Long Range Plan. 43, 216–226 (2010). https://doi.org/10.1016/j.lrp.2009.07.004 14. Stary, C., Neubauer, M.: Industrial challenges. In: Neubauer, M., Stary, C., (eds.) S-BPM in the Production Industry, pp. 7−25. Springer, Cham (2016). https://doi.org/10.1007/978-3319-48466-2_2 15. Rübel, S., Emrich, A., Klein, S., Loos, P.: A maturity model for business model management in industry 4.0. In: MKWI 2018-Multi-Conference Business Informatics, pp. 2031–2042. Lüneburg, Germany (2018). (in Germany) 16. Galimova, M., Gileva, T., Mukhanova, N., Krasnuk, L.: Selecting the path of the digital transformation of business-models for industrial enterprises. IOP Conf. Ser. Mater Sci. Eng. 497, 012071 (2018). https://doi.org/10.1088/1757-899X/497/1/012071 17. Wang, Y., Tran, T., Anderl, R.: Toolbox approach for the development of new business models in industrie 4.0. In: Proceedings of the World Congress on Engineering and Computer Science 2018 WCECS 2018, vol. II, pp. 1−9. San Francisco, USA (2018) 18. Wagire, A.A., Joshi, R., Rathore, A.P.S., Jain, R.: Development of maturity model for assessing the implementation of industry 4.0: learning from theory and practice. Prod. Plann. Control 32(8), 603–622 (2020). https://doi.org/10.1080/09537287.2020.1744763 19. García-Muiña, F., Medina-Salgado, M., Ferrari, A., Cucchi, M.: Sustainability transition in industry 4.0 and smart manufacturing with the triple-layered business model canvas. Sustainability 12(6), 2364 (2020). https://doi.org/10.3390/su12062364 20. Sumrit, D.: Evaluating readiness degree for industrial internet of things adoption in manufacturing enterprises under interval-valued pythagorean fuzzy approach. Prod. Manuf. Res. 10(1), 226–256 (2022). https://doi.org/10.1080/21693277.2022.2064931 21. Rauch, E., Unterhofer, M., Rojas, R.A., Gualtieri, L., Woschank, M., Matt, D.T.: A maturity level-based assessment tool to enhance the implementation of industry 4.0 in small and medium-sized enterprises. Sustainability 12(9), 3559 (2020). https://doi.org/10.3390/su1209 3559 22. Rauch, E., Vinante, E.: Three dimensional technology radar model to evaluate emerging industry 4.0 technologies. In: Ivanov, V., Trojanowska, J., Pavlenko, I., Zajac, J., Perakovi´c, D. (eds.) DSMIE 2021. LNME, pp. 233–242. Springer, Cham (2021). https://doi.org/10.1007/ 978-3-030-77719-7_24

PLC Control of a 2-Axis Robotic Arm in a Virtual Simulation Environment Martin Pollák(B)

, Marek Koˇciško , and Karol Goryl

Technical University of Košice, 1, Bayerova Street, 08001 Prešov, Slovakia [email protected]

Abstract. The trend of automation can be observed in all areas of industrial production. Automation is an increasingly topical subject in today’s continuous production process optimization era. The article aims to bring closer the field of computer-aided programming and simulation of the movement of the selected 2-axis robotic arm using PLC automation in a specific work environment. The article describes programming procedures for robotic arms used in industry, PLC automation technology programming, and simulation programs commonly used to verify the functionality of proposed robotic workplace solutions in practice. The article deals with the specific design of the PLC automatic control application in a selected Factory I/O simulation program controlling robotic arms and production equipment in a simulated workplace. The conclusion of the contribution is a reference to experimental validation of the application proposal by simulation and an assessment of its suitability for future use in practice. Keywords: PLC Control · Virtual Simulation · Factory I/O · Robot Programming · Manufacturing Innovation

1 Introduction An ideal modern production process only contains a human component marginally, mainly for repairs and supervision of the predominant robotic component that performs the production. Currently, primary PLC machines are responsible for controlling the robotic component, although their activity often depends on cooperation with the operator. Authors K. S. Kiangala et al. were involved in monitoring the process of filling bottles in a plant to produce beverages with an orientation to the concept of Industry 4.0. They used a Siemens S7-1200 PLC communicating via Ethernet TCP/IP with the ZENON SCADA HMI interface to monitor the process. The result of the study was the optimization of production time by reducing the number of workers’ interventions in the production process [1]. The current industrial revolution aims to relieve the operator from routine and monotonous work, which production machines and robotic devices can perform through automation technology. Robot programming is the process of creating, debugging, simulating, and testing a set of instructions for the robot’s control system. Authors Z. Papulova et al. contribution focused on automated production in the automotive industry. They described specific technologies associated with Industry 4.0, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 50–59, 2023. https://doi.org/10.1007/978-3-031-32767-4_5

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such as advanced robotics, 3D printing, the Internet of Things (IoT), and production automation. The authors analyzed the level of implementation of these technologies into other Industry 4.0 technologies. The result was findings and pointing out the need for the application of sensors, programming devices such as PLC/HMI, and industrial robots in the automation sector of production [2]. Authors CH. Brecher et al. presented the development of production with an orientation to the concept of Industry 4.0 using automation approaches and standardized automation technologies [3]. The book by K. M. Lynch et al. deals more closely with modern robotics, describing the view on mechanics, planning, and control of robots. The list of all instructions is more extensive. The programming is therefore aimed at creating the most efficient path that the robot arm and the effector have to perform at the correct timing of the individual instructions and movements and at setting the robot’s interaction with the environment. The robot program is specified as commands and instructions in the robot programming language stored in its memory [4]. Another exciting book describing the use of automation, PLC control, DSC, and SCADA is the book written by D. Chanchal et al. This book deals in detail with the industrial sector of automation and its applications, IoT, and OPC data exchange between software applications of several manufacturers [5]. The article deals with the simulation design of a robotic workplace created in the Factory I/O simulator software environment. The article presents a workplace design consisting of a 2-axis robotic arm, a panel for PLC control, and two conveyor belts for moving material in a robotic cell. The complete solution of the simulation and control design is presented through a control diagram and individual programmed control blocks. The control program obtained from the created and verified simulation can then be applied to the robot controller in the real operation of the robotic workplace.

2 Literature Review Simulation tools are widely used in the automation of industrial processes. The simulation facilitates the implementation of engineering activities related to installing and optimizing measurement and control systems of real plants. With increasing use, simulation has become a vital support technology in decision-making, engineering, and operations, covering the entire scope of the manufacturing system. More detailed application use of PLC control is described in the book by author D. Patel [6]. PLC simulation allows writing, running, testing, and debugging PLC programs before commissioning without expensive PLC hardware. PLC simulation software allows you to create a virtual PLC on your computer, which will allow you to design and evaluate the behavior of the control system without any risk to the production process. PLC simulation software can write control programs before the hardware exists and test critical scenarios. PLC simulation software as a training tool helps to understand and learn PLC control issues. Programmers and other workers can use PLC simulation to familiarize themselves with programs and to test changes in a risk-free programming environment. An article by H. Eass et al. presents an architecture based on the RISC-V implementation of a PLC programmable logic controller. The presented architecture uses the flexibility and performance of FPGA to implement PLC. The proposed solution

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was written based on the created commands and functional blocks on the Altera S5G FPGA development accessory [7]. The book by U. Kumar describes the 4th industrial revolution from its beginning to the present, focusing on increasing the performance, production, and flexibility of the intelligent systems used in production [8]. For PLC simulations in the program, we have several well-known solutions on the market from leading manufacturers, such as Roboguide, Robot Studio, Siemens NX, Factory I/O, and others. 2.1 Roboguide In 2018, Fanuc launched its offline robotic software platform Roboguide as an implementation tool for its industrial robots. Roboguide combines programming and simulation software to thoroughly plan the distribution and operation of a robot work cell without the presence of the physical work cell itself. Using 3D technology, users can create a virtual representation of the robotic system, program it, and test it by simulating scenarios in a real production environment. Using Roboguide, users can create a replica of their production environment to allow complete application optimization before starting the program. It allows users to identify the most suitable robot for their production process and address any programming errors or inefficiencies in the work cell layout. Once the program is complete, it is possible to upload the created program to a real robotic arm and continue testing it. Roboguide eliminates the need to invest in expensive prototyping. For those with existing systems, Roboguide makes it possible not to shut down operations while a new program is created, as all programming is done outside of the production environment, reducing downtime in the robotic workplace [9]. 2.2 ABB Robot Studio The simulation and offline programming software ABB Robot Studio enables programming robots on a PC to program robots, optimizing the production process and ensuring the training of new workers. For programming, it uses the high-level programming language RAPID, which ABB introduced in 1994. This tool is built on the ABB Virtual Controller, an exact copy of the real software that controls the robots in the factory. It makes it possible to perform very realistic simulations. ABB Robot Studio does not support the use of the program as simulation software for another program, and it is possible to operate the program through an API interface or shared memory [10]. 2.3 Siemens NX MCD Siemens NX MCD (Mechatronics Concept Designer) allows us to model and simulate the mechanical and electrical components of the machine. MCD extends the classic CAD design functions of NX with a realistic simulation environment in which it is possible to map the effect of physical forces on moving objects. With a functional model of the machine (digital twin), it is possible to virtually test the machine’s behavior using a real control program. NX libraries can be used to speed up and standardize the development

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of NX MCD machine models. With the Kinematics Toolbox, you get an NX library that contains parametric NX MCD models for kinematics. You simulate your original SIMATIC S7-1500 T-CPU control project on a virtual CPU with PLC SIM Advanced [11].

3 Research Methodology Factory I/O is a 3D simulation software designed to simulate automation technologies developed by Real Games. Using the internal library of industrial components and equipment, it is possible to create a virtual layout of the entire factory. Factory I/O features many scenes inspired by typical industrial applications, suitable for beginners to advanced users as determined by the difficulty level. The most common use of Factory I/O is as a training platform for PLC machines since PLCs are the most common control units in industrial applications. Factory I/O can also be used with physical microcontrollers, e.g., SoftPLC, Modbus, and many other technologies. The implemented design of the 2-axis robotic arm consisted of several steps related to its integration into the simulation scene, the programming of its movements, and the development of steering instructions according to the designed diagram shown in Fig. 1.

Fig. 1. Development diagram 2-axis arm.

The selected 2-axis robotic arm contains three inputs - a movement sensor on the X-axis, a movement sensor on the Y-axis, and an object detection sensor near the manipulator. It also consists of three outputs: the motor providing movement in the X-axis, the motor providing movement in the Y-axis, and gripping with a suction cup. The created digital inputs and outputs of the PLC are presented in Fig. 2.

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Fig. 2. 2-axis robotic arm inputs and outputs positioned on the PLC.

Conveyor models were first placed in the simulation workplace (see Fig. 3). The settings of the logical operator Emitter, which generates products for the conveyor, and the logical operator Remover, which removes them from the workplace, have been added to conveyors. Subsequently, a 3D model of a 2-axis robotic arm was inserted into the workplace, to which three sensors were connected. The first of the sensors were placed at the entrance of the conveyor near the robotic arm. The remaining two sensors were placed on the output conveyor, the first on the output conveyor near the robotic arm and the second at the end of the output conveyor.

Fig. 3. Creating a robotic workplace in the Factory I/O simulation software.

In the final step, a control panel with Start, Stop, Reset, and Emergency Stop buttons and a display with a counter were inserted into the workplace. Inputs and outputs were then created for the robotic workplace developed in this way on the virtual PLC in the Controllers section. Based on the elaborated flow diagram describing the program’s logic, the robotic workplace simulation model will be further programmed and controlled in the individual elaborated control blocks. Control blocks are created in the software environment to control the simulation model’s instructions. The functions of individual devices in the workplace and their settings for performing object manipulation are described in more detail on the individual blocks.

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The first block describes saving the state value of the Emergency Stop button and storing it in memory. It also describes the button lights. The Stop button does not light up if the lighting is switched on with the Start button. At the same time, the process’s start is defined and saved in memory. Control I/O block 1 is presented in Fig. 4.

Fig. 4. Control I/O block 1.

The second block ensures the movement of the robotic arm on the Z-axis. The entire process of clamping, moving, and releasing is divided into six steps. All these steps are automatically stored in the Count memory. There is also an End_Cycle_Reset memory unit inserted into the block, which ensures the reset of the cycle, i.e., if we reach step six, the cycle is reset and repeated from the first step. Control I/O block 2 is presented in Fig. 5.

Fig. 5. Control I/O block 2.

The third control block ensures the movement of the robotic arm on the X-axis. While the fifth block specifies when the output conveyor is to be stopped. Control I/O block 3 is presented in Fig. 6. The fourth block describes when the suction cup at the end of the arm is to be activated while at the same time storing its state value in memory. It then describes when to turn on Remover, which is intended to send a value to the counter about the passage of the product across the conveyor. Control I/O block 4 is presented in Fig. 7.

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Fig. 6. Control I/O block 3.

Fig. 7. Control I/O block 4.

The fifth block contains the internal counter dilution, which we reset the whole cycle and, after resetting, has to count again from the beginning. Control I/O block 5 is presented in Fig. 8.

Fig. 8. Control I/O block 5.

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4 Results and Discussion The robotic arm and conveyor controls have been programmed in several development environments. Other control software used included Factory I/O, PLC SIM Advanced V4.0, and TIA PORTAL v17, while the article further presented a proposal in the Factory I/O software environment (Fig. 9).

Fig. 9. Simulation of controlling a 2-axis robotic arm in the software environment.

PLCSIM Advanced 4.0 is a software tool for simulating a wide range of PLC functions, enabling complex simulations during configuration and design in the TIA Portal program without a physical connection to the hardware. Using the software, it is possible to simulate several virtual PLCs, either on one PC or several PCs in one network. Tia PORTAL (with acronyms Totally Integrated Automation) is the development tool for designing, programming, and commissioning all automation instruments and drives from the TIA portal by Siemens. An example of integrated software is SIMATIC Step 7 and SIMATIC WinCC. Tia PORTAL works as a software environment and combines the functions needed for various automation applications. The applications used in the TIA-portal environment are SIMATIC STEP 7, with which the S7 logic from Siemens can be programmed, and SIMATIC WinCC visualization, which can be used to create remote user interfaces for automation systems. In addition, applications can be connected to the TIA portal, with which it is possible to modify the parameters of the frequency converters and perform automation checks. Currently, version V17 offers an entirely new graphics editor. The programs in these development environments are primarily written in Ladder Logic. To compare usage and work in different software, it can be concluded that while the programmer’s work is similar, the development environment itself is different, which is the main difference and, consequently, the programmer’s choice for which the instrument is decided. The book “Automating with SIMATIC S7-1200” describes in more detail the application use, configuration, and parameterization of the hardware components of the S7-1200 automation system. At the same time, it focuses on a detailed description of the introduction to work with STEP 7 Basic V11, which illustrates the basics of programming and solutions to real-life problems in production operations with PLC control [12].

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5 Conclusions Smart factories can have a layout of lines, automation machines, products, and other equipment. For a smart factory to be successful, no exact configuration ensures this. Features such as connectivity, proactivity, agility, optimization, and transparency play a key role in the smart factory. Each characteristic contributes to better decisions and better organization of the production process by individual undertakings. The power of an intelligent factory is directly proportional to the capacity to adapt to growth or changes in the factory. It may include functions such as changing demand for products, penetration of new markets, research and product development, maintenance, or real-time production changes. The use of PLC programming and simulation is currently very wide. This article describes one example of how the robotic arm process can be controlled and simulated by PLC automatons. Available simulation programs support the implementation of an external emulator in a physical way, such as Factory I/O and Siemens PLCSIM emulator, or additional implementation through the API is required. Here, the difficulty in programming is greater, given the system’s complexity. The user can also use additional middleware software to communicate between the PLC software and the simulator. Since the current market has a wide range of PLC simulation machines working in a virtual environment, the potential for quick software selection is high. The article described the design of a simulation model in the Factory I/O simulation software environment. The individual control blocks of the devices used in the simulation were described with an additional explanation of the individual functionalities of the simulation model devices in the manipulation process. As a result of the simulation created, a control program that can be applied to a real robotic operation for handling a 2-axis manipulator and a conveyor controlled by a PLC controller. In the case of the presented proposal, adding additional ways of controlling the robotic arm to the virtual model developed in Factory I/O would be possible. At the same time, the software supports 15 different ways of control. Another way would be to connect a real physical automaton to Factory I/O, which would not be emulated only virtually. An interesting extension would be a comparison with programming using an alternative simulation program from a robot manufacturer, e.g., ABB Robot Studio or Roboguide from Fanuc. Acknowledgment. The authors thank the Ministry of education of the Slovak Republic for supporting this research with the grant KEGA no. 038TUKE-4/2021 and project APVV-18-0316.

References 1. Kiangala, K.S., Wang, Z.H.: An industry 4.0 approach to develop auto parameter configuration of a bottling process in a small to medium scale industry using PLC and SCADA. In: 2nd International Conference on Sustainable Materials Processing and Manufacturing 2019, vol. 35, pp. 725−730. Elsevier Procedia (2019). https://doi.org/10.1016/j.promfg.2019.06.015 ˇ Implementation of automation technologies of indus2. Papulová, Z., Gažová, A., Šufliarský, L: try 4.0 in automotive manufacturing companies. Procedia Comput. Sci. 200, 1488–1497 (2022). https://doi.org/10.1016/j.procs.2022.01.350

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3. Brecher, C., Müller, A., Dassen, Y., Storms, S.: Automation technology as a key component of the industry 4.0 production development path. Int. J. Adv. Manufact. Technol. 117(7–8), 2287–2295 (2021). https://doi.org/10.1007/s00170-021-07246-5 4. Lynch, K.M., Park, F.C.: Modern Robotics: Mechanics, Planning and Control. Cambridge University Press, England (2017) 5. Chanchal, D., Sen, S.: Industrial Automation Technologies. CRC Press, United States (2020). https://doi.org/10.1201/9780429299346 6. Patel, D.: Introduction Practical PLC (Programmable Logic Controller) Programming. Grin Verlag, Germany (2017) 7. Eassa, H., Adly, I., Issa, H.H.: RISC-V based implementation of programmable logic controller on FPGA for industry 4.0. In: 31st International Conference on Microelectronics, pp. 98−102. IEEE, New Jersey (2019). https://doi.org/10.1109/ICM48031.2019.9021939 8. Pascual, D.G., Daponte, P., Kumar, U.: Handbook of Industry 4.0 and Smart Systems. CRC Press, United States (2019). https://doi.org/10.1201/9780429455759 9. Robot Simulation Software | FANUC ROBOGUIDE Simulation Software. https://www. fanucamerica.com/products/robots/robot-simulation-software-FANUC-ROBOGUIDE. last accessed 2023/01/06 10. RobotStudio® Suite. https://new.abb.com/products/robotics/robotstudio. last accessed 2023/01/06 11. Mechatronic Concept Design. https://www.plm.automation.siemens.com/global/en/pro ducts/mechanical-design/mechatronic-concept-design.html. last accessed 2023/01/06 12. Berger, H.: Automating with SIMATIC S7-1200. Publicis MCD Verlag, Germany (2018)

Evolution of Competence Management in Manufacturing Industries Markus Steinlechner1 , Fazel Ansari1,2(B) , and Sebastian Schlund1,2 1 Fraunhofer Austria Research GmbH, 7, Theresianumgasse, 1040 Vienna, Austria

[email protected] 2 TU Wien, Institute of Management Science, 27, Theresianumgasse, 1040 Vienna, Austria

Abstract. Manufacturing enterprises can identify organizational and human resource competence fields and individual competencies, which are documented. They mostly rely on technology management approaches to predict and supply future demanding competencies. Technology trends are examined and evaluated, and relevant competencies are derived. Others use data-driven trend analysis gaining benefits from internal and/or external data sources and expert opinions. However, a few manufacturing industries consider systematic selection and extensive use of competence-relevant sources and tools. Therefore, the planning and prognosis of future competencies remain inaccurate and incomplete. Although data-driven approaches majorly contribute to this area, the correct selection of data sources, objective assessment of causalities, and, thus, correct interpretation of findings are mostly unresolved challenges. A structured analysis of competence sources is carried out to contribute to the body of knowledge in competence management and provide a practice-oriented solution. External and internal competence sources are identified and combined in a targeted manner to compensate for the weaknesses of state-of-the-art approaches. In this paper, the initial results of the framework model are presented. A systematic literature analysis is conducted to identify possible competence sources. Target competence sources are described, and an initial classification is provided. As the main result, the classification of competence sources is transferred into a decision-support framework model for identifying future competence sources of workers in manufacturing companies. The developed framework model enables manufacturing enterprises to plan additional competence sources and relate them to job profiles on the shop floor level. Keywords: Competence Management · Competence Profiling · Competence-Sources · Future Competencies · Framework Model

1 Introduction In the era of digitalization and Industry 4.0, manufacturing enterprises have already recognized that employees, especially their competencies, are the most critical factor

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 60–70, 2023. https://doi.org/10.1007/978-3-031-32767-4_6

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in successfully implementing digital transformation strategies [1]. Introducing digitalization and automation solutions cannot create sustainable benefits without considering employees’ competencies. In other words, digital competencies can become the so-called “bottleneck” of digital transformation, and thus companies need to invest in innovative personal development and competence management strategies [2]. The scientific perspective supports these findings from practice. Research on the impact of digitalization and automation on business performance reveals the non-readiness of employees in dealing with digitalization solutions [3–5]. Industry pioneers start with identifying requirements created by emerging technology trends or digitized working steps and automated working environments. They try to link these requirements to the current competencies of their blue-collar workers, thus identifying gaps and developing training programs as counteract measures. Competence planning is centralized and is considered a strategic function. However, employees, especially blue-collar workers, are passive consumers of pre-defined qualification measures. Their qualifications, preferences, career development goals, and expectations are not individually concerned [6]. It is difficult to define which competencies will be needed for digital work in the future due to many influencing factors such as technology trends, guidelines issued by the European Union, corporate strategy, or the employees’ perspective. Tangible and understood influencing factors are used to define the needed competence level of employees. Influencing factors recognized but not considered due to lack of opportunities or unrecognized influencing factors lead to single approaches defining the future competence level. For example, a single approach arises when companies ask employees about their future competencies and exclude upcoming technology trends because it is unclear how to derive competencies from them. The focus is on the present (questionnaire) and future (technology trends). Influencing factors from the past, like documented maintenance activities, are not considered. Questionnaires and interviews [7, 8] defined future competence levels and focused on workers directly on the shopfloor or managers at various levels. Data-driven approaches [9, 10] only consider internal data sources like documented reports or work orders. External data stored in clouds and shared with customers and suppliers are unused. Part of the approaches to define the target competence level are based on a management perspective. HR, digitization, or process planning departments define competence levels. The remaining focus is on the shopfloor by asking workers or analyzing processes. The question arises whether any combined approaches integrate the process and technology view. Furthermore, one-time determinations, e.g., by text mining on LinkedIn [11, 12] or through expert surveys [13], become less accurate over time. Mechanisms that keep the competence level up to date should be integrated. Focusing on single approaches and not considering other relevant influencing factors leads to an incomplete definition of future competencies, wrong competence development, rising costs, employee fluctuation, low resilience, and increased training and qualification time. The roll out of training measures across the entire target group, without considering if identified influencing factors vary between individual job profiles, amplifies these effects. Based on these problems, the following hypothesis is derived: The more sources of competencies a company uses to define target competencies, the more accurate the

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target competency level will be, and fewer uncertainties will occur. This hypothesis leads to the following questions: Which competence sources can be used for which job profile? How can the competence sources be structured and depicted practically to enable companies to understand, select and evaluate relevant future competence sources for a job profile? How to evaluate the degree of improvement to which the incorporation of multiple sources of competency leads? The questions and hypotheses that arose were summarized under the following main research objective: Development of a procedure to define a digital future competence level for technical specialists in manufacturing companies, considering all relevant competence sources to achieve a higher degree of accuracy. This paper presents the first results of the desired solution. It is structured as follows: The first chapter presents the systematic literature analysis conducted to identify the sources of competencies and gives an overview of existing frameworks and approaches defining a future competence level. Chapter 3 contains the research approach and the procedure followed during development. In chapter 4, the competence sources are classified, and the classification is transferred into a decision-supporting framework model. The last chapter contains a critical reflection, conclusions, and future research steps.

2 Literature Review The current literature is divided into three basic levels: First, the literature focuses on competence sources outside an enterprise, which serve as a basis for defining future competence levels. Second, literature considers the strategic level within an enterprise and uses relevant competence sources as the base. Third, literature that identifies competence sources at the operational level within the enterprise and uses them for the definition of employee’s future competencies. In addition, there is a distinction between data-driven approaches, such as data-based competence planning using text mining, and non-data-driven approaches. Both span the three basic levels. To examine the current literature status even more objectively, a systematic literature review has been conducted with rigor. The following scientific databases were used for literature research: Scopus, Web of Science, Springer Link, Taylor and Francis, and Emerald Insight. Literature research was also conducted in non-scientific databases, Google and Google Scholar. The period searched for literature was set from 2010–2022, focusing on literature from 2017 onwards. Initial search strings: competencies AND target state AND workforce. After screening potentially relevant publications online, 89 papers were downloaded. After further review and categorization into Gap Analysis, Special Method, and Competency Survey, 33 papers remained for detailed analysis. In the detailed analysis, the 33 papers were evaluated according to 14 criteria oriented to scientific, development, and application quality paradigms. Previous literature reviews [14] were used as a basis. All papers with a rating >10 are left for final analysis. Table 1 depicts examples of the ratings of the highest-ranked papers. Gábor et al.’s (2018) work show that scientists’ findings are mostly obtained through expert interviews and trends in past data, but no systematic approaches are used. With the Leontief model, which is applied to labor market data from the internet and thereby

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reveals the effects of technologies on the labor market and competence sets for workers in the automotive industry, Gábor et al. attempt to provide a systematic approach. It is intended to stimulate the redesign of future competence sets. The high rating that indicates to be closest to the research gap includes several perspectives, complexity, a high degree of self-development, transferable application, and the consideration of actuality. Weaknesses lie in the fact that it is only used in tests and that applications are not made available. The systematic approach has not derived competency sets from different sources on various levels and areas of levels. The approach moves only on one level and in one area of the level (outside the company-labor market data from the internet). Table 1. Systematic literature review-evaluation. Author and Year

Orientation

Perspective

Complexity

Sources

0

Past

Employee

Questionnaire and Interviews

External

1

Present

Enterprise

Data-driven approach

Internal

2

Future

Industry

Gábor et al. (2018) [15]

0

2

1

1

Deciusa et al. (2017) [16]

2

1

1

1

Vladova et al. (2019 [17]

2

1

0

0

Szabó et al. (2020) [18]

1

1

0

0

Li et al. (2020) [19]

0

0

1

0

Kusmin et al. (2018) [20]

1

1

1

0

Silva et al. (2017) [21]

2

2

1

1

Deciusa et al. (2017) have developed a management tool that supports small companies in conducting needs analyses of relevant personnel competencies. The basis is a guideline for personnel planning under technical innovations. The tool uses competence definitions from a strategic perspective and defines so-called competence anchors derived directly from the work activities. The assessment results from the high selfdevelopment and the connection of future and current elements. Weaknesses include the fact that only internal data sources are used, and no data-based approaches are included. The developments did not include different approaches either. There was also no sufficient discussion of the different sources of competencies, their classification, and the comparison of their effects. Across all other publications in Table 1, isolated attempts are being made to link approaches to achieve better definitions of future competence levels. To the best of the authors’ knowledge, clear descriptions and evaluations of the used competence sources and their associated methods are unavailable. The topic of the improved definition of the

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competence level is in an experimental framework where a clear, over-arching approach is missing.

3 Research Methodology First, well-known research methodologies in design science, such as Hevner [22] and Pfeffer [23], were analyzed to develop artifacts and models. Peffers et al. implicitly build on the framework of Hevner et al. and develop a six-step sequential process. This process is used as the basis for the defined research methodology in this paper. The research methodology is based on design science and aims to combine practical and scientific elements see Fig. 1.

Fig. 1. Three-stage research methodology.

Step 1 – Start of the research process: This initial step is divided into two sub-steps. In the first sub-step, Problem description, relevance, and goals, the first rough descriptions of possible research gaps and problems were made based on state-of-the-art and projectspecific databases. The descriptions led to the objective of taking a multi-perspective look at the sources of the target competence levels of employees in manufacturing companies and investigating them more closely. Based on the initial descriptions, a state-of-the-art analysis was carried out. With the result of this analysis, findings from implemented projects, and semi-structured interviews with experts from the projects, a systematic literature research, step 1.2, was launched. It serves as a deepening and focuses on specific methods of target competence definition and their application areas. In an interplay between new findings from the systematic literature analysis and the step-by-step refinement of the problem description, the research gap and the goals of the intended project were finally identified and set. Step 2 – Design and Development: The second step contains the main steps for developing the framework model and is divided into three sub-steps. 2.1 Description of future competence sources: Identified future competence sources were listed. Data such as author, title, year, a short content description, the methodological approach, and used tools were added.

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2.2 Classification of competence sources: The listed future competence sources were analyzed and classified according to their primary method. Using an iterative approach and concept mapping, all competence sources of each classification were analyzed in parallel, and cross-sectional functions were developed. The cross-sectional functions enable an in-depth analysis, support the comparison, and the finding of similarities. 2.3 Transfer to a framework model: The last step was to transfer the developments into a decision-support framework model. The applicability and feasibility of different representation models understood and used practically by responsible persons in the enterprise were evaluated. The final framework model was developed after discussions with experts with a high level of consulting and project experience in the industry. Step 3 – Evaluation through expert review: The resulting framework model will be presented to experts from science and companies in an interview and evaluated based on predefined criteria. The aim is to increase the content’s comprehensibility and the framework model’s applicability.

4 Results and Discussion The examined papers identified the following methods for defining future competencies (Table 2). For the initial structuring, all papers using the same methods to define the target competencies were considered together. Cross-cutting functions were defined after several adaption cycles. On these cross-sectional functions, elements in the form of white boxes were placed, which included the headings of the papers’ contents. The connections between the elements on the cross-cutting functions were depicted as a hierarchic structure. Figure 2 depicts the cross-cutting functions added for the method Technology (Method, data source, adaptation, model, results), the elements, and the connections between the elements.

Fig. 2. Extract of structuring according to the method technology.

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M. Steinlechner et al. Table 2. Identified methods for defining future competencies.

Method

Description

Tool

Technology trend

Evaluation of upcoming technological trends and developments by employees and managers of the enterprise concerning relevance. Derivation of competencies from relevant trends

Questionnaire Workshops Interview

Industry trend

Examining industry trends and implementing interviews with industry experts to establish a set of future competencies for job profiles

Research Interview

Benchmark

Identify market leaders in a sector Research and define future competencies for employees by analyzing the activities and the use of technology

Competence database

Research in available databases Research, Excel-based evaluation such as the ESCO or O-Net templates database for future competencies. Usage of the results for the future competence level

Job profiles advertised Examination of job Data-processing Data-Mining advertisements on various job platforms regarding required competencies. Creation of a set of future competencies based on findings Corporate strategy

Definition of the importance of Excel-tools upcoming technological Questionnaire Workshops developments for the enterprise Interview by managers with insight into the strategic development. Derivation of future competencies from relevant technological developments

Training measures

Analysis of existing training Excel-based tools measures regarding future Workshops competencies. Depiction of future competencies for job profiles based on the analysis (continued)

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Table 2. (continued) Method

Description

Tool

Employees knowledge

Definition of the future Questionnaire competence level by interviewing Interview employees

Internal job profiles

Specify future competencies by analyzing job descriptions available in the enterprise or job descriptions from public databases

Activities analysis

Recording of activities carried out MTM, Observation, process and abstraction of necessary descriptions competencies. Defining future competencies out of abstracted competencies

Digital tracks

Tracking of digital traces left by a Special soft-ware user while using software (e.g., Text-Mining Word, MS Teams, ERP) Usage of the results for the future competence level

Process-activity analysis Text-Mining

With the support of cross-cutting functions, concept mapping, and the hierarchic structure, it was possible to represent interrelationships between the papers. Furthermore, a basis for a generic use case for each method, which enables further developments in subsequent considerations, was created. However, considerations were made about how an applicable and comprehensible decision-support framework for identifying future competence sources might look. The result is a value stream representation supplemented with personas in a hierarchical structure and parts of organizational departments, cf. Figure 3. Organizational departments such as IT, administration, controlling, research, and development have been omitted because no identified method can be directly assigned. The identified methods are depictured on the value stream in the correct area. For example, the activity analysis method is placed right to the process modules. The corporate strategy method is placed by the management board. The created framework model provides a good overview of the methods currently used to define future competencies in manufacturing companies. The following aspects are important for a correct interpretation: Not all necessary organizational departments are mapped in the framework model. Enterprises should insert their own organizational structure to see which areas are not yet used to define target competence sources. With this extension, maybe new future competence sources can be identified. The same applies to the value stream representation [24]. It represents only one possible structure of an enterprise. The process blocks and connections should be adapted to the actual conditions.

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Fig. 3. Framework model for future competencies.

Theoretically, a method can be assigned to other areas within the framework. This cross-application to other domains may lead to challenges in the application, but it could provide further opportunities for defining future competencies. Through an adaptation to the current organizational structure and the value stream, additional job profiles or process blocks will be added to the representation. Specific job profiles or groups of job profiles and process blocks could be competence sources that have not been considered yet. Some methods have only been applied at a high level. A subdivision into smaller portions on different levels within an area is possible and may lead to different findings than in a high-level generic perspective. For example, the method of employee knowledge could be differentiated from a general survey that is the same for all employees to different ones for different groups of employees or departments. In the company’s business area, methods are available which consider content outside the enterprise and derive content for the future competence level inside the enterprise. There may be more methods, but they are not yet known.

5 Conclusions The framework model has been developed specifically for manufacturing companies and serves as a decision-support framework for identifying future competence sources. With the developed model, it is possible to get an overview of currently used future competence sources and their methods for defining future competencies. A company can quickly place itself in the context of the model and analyze which future competence sources are available, which ones have already been used by the enterprise, and which ones have not. Short descriptions of the methods and tools in Table 2 can be compared

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to the methods and tools used by the enterprise for defining future competencies. It is up to the enterprises i) whether they use the framework model as an overview and use some of the mentioned methods in initial or further projects or ii) whether they use the framework model to uncover further target competence sources. The next step is finalizing the evaluation for applicability and comprehensibility through structured expert interviews. This step has been considered for future work. The scientific contribution of this work is the first in-depth analysis of currently used future competence sources, their comparison and structuring, and presentation in a comprehensible depiction. Limitations occur in the following manner: The model can only be used at a generic level. A detailed breakdown would make the assignment of the competence sources inaccurate, and enterprises would have difficulties placing them in the context of the model. Objectives for future research from a practical perspective are examining unused areas in the framework model to identify further competence sources. In areas such as supplier and customer, additional usable future competency sources may be found to improve the competency levels’ accuracy. In addition, further value stream representations and organizational structures are examined to uncover further future competence sources. To verify the initially formulated hypothesis, key figures will be defined to evaluate the contribution of the individual future competence sources to the overall contribution. Once the key figures have been compiled, and initial findings have been obtained, it is planned to develop a practically applicable procedure model which enables companies to select and combine future competency sources based on key figures and define future competency levels with high accuracy.

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Designing a Workplace in Virtual and Mixed Reality Using the Meta Quest VR Headset Adrián Vodilka(B)

, Marek Koˇciško , Simona Koneˇcná , and Martin Pollák

Technical University of Košice, 1, Bayerova Street, 08001 Prešov, Slovakia [email protected]

Abstract. The research examined the possibility of using the Meta Quest VR headset and the advanced Passthrough Sky 1:1 Mode in virtual reality VR and mixed reality MR to design the workspace. The development of Passthrough Sky technology enables the implementation of MR in the industrial environment to an extent greater than has been possible before. The article focuses on the research gap in this field. A room setup workspace was created in the GUI of the Meta Quest device, defining laboratory and references to objects in the real world. Later a Guardian Boundary was created, approximating the walls of the room. Subsequently, outline walls, desks, and a door were defined. The Passthrough Sky 1:1 Mode was selected with the alignment of the Room setup. Interactions were made possible between the virtual and real world in the mixed reality of MR. As part of the laboratory design, equipment models were designed and put in place. These objects were used to check the ergonomics and collisions and to verify the overall impression of the workplace designed based on the intuition of an experienced worker. The aim of the research is considered to be accomplished. The workplace design in this work focused on the description of the technology. The Meta Quest technology and the ShapesXR application are suggested for designing a specific workplace in the selected production plant within the proposed future work. Keywords: Virtual Reality · Augmented Reality · Mixed Reality · Extended Reality · Oculus Quest · Meta Quest · Passthrough Sky · Shapesxr · Workplace Design · Manufacturing Investment · Process Innovation

1 Introduction Virtual (VR), Augmented (AR), and Mixed Reality (MR) technologies are rapidly evolving. Their development is influenced by the development and quality of imaging technologies, increased equipment performance and mobility, or reduced technology costs and sales prices. They are used in the educational process, entertainment and experience industry, engineering and manufacturing, healthcare, and others. These technologies have a high potential to move society forward rapidly, and there are demands for research and implementation of these technologies in the real world. Such requirements include educating employees in the training process and supporting workplace design and technologies using these technologies. As the availability of these devices increases, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 71–80, 2023. https://doi.org/10.1007/978-3-031-32767-4_7

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it is possible to implement them in the industrial environment to an extent greater than has ever been possible. A significant advance is the Oculus Quest technology from 2019, using which virtual reality was available to the public at a relatively low cost. As part of ShapesXR, Passthrough Sky was released within the application update in August 2022. It is a relatively new technology available only for a short time, and further research is needed. The research aims to examine the possibility of using the Meta Quest VR headset and the advanced Passthrough Sky 1:1 Mode technology in virtual reality VR and mixed reality MR to be used in designing the workspace and on the description of technologies used. The contributions of this paper are a description of actual VR, AR, and MR technologies and a proposed workplace design using a novel Passthrough Sky 1:1 Mode for industrial applications. A scientific novelty of this research is the use of Passthrough Sky 1:1 Mode, which was not researched in terms of workplace design so far.

2 Literature Review Michalos et al. (2018) [1] propose a method to analyze and enhance industrial workplaces using immersive virtual reality. The work absented interaction with the real world within MR. An interesting paper dealing with workplace design in VR was presented by Ahmed S. (2019) [2]. They compared PC and VR prototyping regarding workplace design and ergonomic study. They placed CAD models of a cockpit in VR space within the VR approach. The work absented the interaction with the real world in MR, which this paper proposes. Doolani et al. (2020) [3] reviewed the current state-of-the-art use of XR technologies in training personnel in manufacturing. Malik AA (2020) [4] proposed the integration of human-robot simulation to design a workplace with VR. Designed workspace absented interaction with real-world within MR. Chaurasia G. et al. (2020) [5] from Facebook presented Passthrough + for Oculus Quest, real-time environment capture allowing users to see their surroundings while interacting in VR. Van Campenhout et al. (2020) [6] mapped the VR model to a physical prototype within workplace design. The work absented the implementation of MR. Tao W et al. (2021) [7] presented a case study that provided a practical guide to implementing an AR assembly simulation application. The available tools were presented within the paper, absenting Passthrough Sky 1:1 Mode, which was unavailable then. Liu et al. (2021) [8] investigated the potential applications of virtual, augmented, and extended reality and the potential drawbacks that should be considered when integrating these technologies into the workplace. They focused on CAD modeling and rendering methods, display technologies, and interaction modalities within applications in the industry. Stanney et al. (2021) [9] described modeling technologies, design principles, and applications for XR. Greenfield et al. (2022) [10] described the process of rapid prototyping in VR and proposed VRception, a concept to simulate different realities in VR. Reaver (2022) [11] discussed multiuser collaboration in mixed reality MR, placed point clouds as the background for the MR model, scanned artwork models, and placed them in mixed reality to conduct a design review.

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3 Research Methodology In this research, a workplace is designed using MR and Meta Quest device. The first part of the work focuses on describing VR – virtual reality, AR – augmented reality, and MR – mixed reality. The second part describes the All-in-One VR headset product line by Meta Quest. The third part aims at designing a workplace at the KPPVT FVT TUKE Virtual Reality Laboratory. The aim is to describe the process of using Passthrough Sky 1:1 Mode to design virtual objects as references to the real world. 3.1 Virtual Reality Virtual reality (abbr. VR) can be understood as a virtual simulation of a 3D scene using advanced technical devices to create an impression of the user’s interaction with the virtual space [12]. VR is an interactive, multi-sensory, three-dimensional computergenerated environment with the combination of technologies required to build such an environment. Perception of the virtual world is ensured through stereoscopic displays and complemented by haptic interaction and spatial sound. VR technologies usually use 3D tracking technologies that calculate the user’s movement and orientation in space [13]. The tracking process aims to locate the user to the established VR coordination system of the technology for continuous interaction. Tracking can be based on acoustic tracking, magnetic field based tracking, inertial tracking using accelerometers, laser tracking, outdoor position tracking, camera-based tracking, marker-based tracking, geometrybased tracking, Visual SLAM, finger tracking, eye tracking, mouth tracking, and others. When the user moves in space, the view from another perspective in the space is displayed automatically. The goal of using VR technologies is to ensure a more intuitive humancomputer interaction [14]. 3.2 Classification of VR Technologies VR technologies can be divided according to how the virtual world is projected to the user into stationary type projection systems and head-mounted displays (HMD). HDMs are devices with imaging in front of the user’s eyes. HMDs typically use binocular optics for stereoscopic VR perception. HMD devices are usually closed and isolated from the real world from the user’s perspective [15]. VR technology can be divided into desktop VR, standalone VR, and mobile VR devices in terms of technology ensuring computing power. Desktop VR devices use a remote machine, usually a computer, as a source of computing power, physically separated from the HMD or headset. In contrast, the headsets are connected as peripheral devices. The headset can be connected to a computer through cable or wireless. Standalone VR is a portable device and does not require an external power source, with computations performed within the VR headset. The advantage is that there is no need for a wired or wireless connection to an external computer, and a relatively lower price. A disadvantage lies in the lower computing power of the device and the need to use a battery or power supply. An example is Meta Quest, Meta Quest 2, and Meta Quest Pro [12]. Mobile VR is a simple device designed as a mobile phone holder used as a headset. The fidelity of the virtual simulation of mobile VR is limited [12]. VR has become popular, especially

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concerning its use in the gaming industry. It is possible to train movement and sports in VR. Examples of the use of VR technologies are several. Furthermore, virtual tours are popular with the public as a form and promotion of tourism. Another important use of VR is industrial design and support for engineering activities, where VR carries out control and rendering of product design, simulation, and visualization. With VR, verifying the designed product as a 3D model in space without producing physical models is possible, thus saving companies significant costs. At the same time, VR is used in the education and training of workers [12, 16, 17]. 3.3 Mixed and Augmented Reality AR - augmented reality is a variation of VR with virtual objects displayed in the real world. Mixed reality MR technology uses the insertion of virtual objects in combination with the real world. AR must meet three requirements: a combination of the virtual and real world, interactive foundation, and real-time operation. It allows the user to overlay the physical environment with virtual elements, thus simplifying the understanding of individual elements and processes. Augmented reality can be achieved using images, text, or other forms. The user environment is supplemented with the amount of information generated by the computer from data collected from the real world, improving the perception of reality at a given moment. The main goal of augmented reality is an accurate and effective display of the real world enriched with virtual elements [18, 19]. In the Meta Quest device, the Passthrough mode allows perspective and stereoscopic perception and viewing of the real world displayed in the headset in infrared imaging. At the same time, the user gets the impression of working in black-and-white imaging of the real world with added virtual models and elements, e.g., workspace boundaries, user’s hands, controls, and others. Passthrough is used to create a Guardian boundary of a safe space. Later, experimental functions that work only in selected applications were designed, e.g., added reference to real objects and virtual interaction with them [5]. 3.4 Meta Quest Devices The Quest product line from Meta, originally from Oculus, is an All-in-One standalone VR device designed as a VR headset with its own Meta Store and relatively high-quality stereoscopic imaging. The first device of the product line was the Oculus Quest of 2019, and the device was designed as a standalone VR redesign of the Desktop VR Oculus Rift. Quest uses the controls in the hands and fingers of the user. The current range of Quest devices is Quest, Quest 2, and the latest Quest Pro. Quest devices can be used as standalone and Desktop VR when plugged into a computer. Quest uses 1440 × 1600 OLED 72 Hz per eye, Qualcomm Snapdragon 835 chipset, 4 GB LPDDR4X RAM, and Adreno 540 (545 - 567 GFLOPS) graphics. Quest 2 uses 1832 × 1920 LCD 90 Hz per eye (52% improvement), 6GB LPDDR4X RAM, Snapdragon XR2 Gen 1 (11x performance improvement), and Adreno 650 (1.1 TFLOPS, 2x performance improvement). Quest Pro uses 1800 × 1920 QD-LCD 90 Hz per eye, Snapdragon XR2 + Gen 1 chipset (30% performance improvement over Quest 2), 12GB LPDDR5 RAM (50% RAM GB and 50% increase in performance over Quest 2). Quest Pro is focused on the widespread use of Passthrough while providing a new feature – color passthrough for mixed reality. Quest

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Pro includes Eye Tracking and Face Tracking for real-time facial expression tracking and mapping it to an avatar, optional for privacy reasons.

4 Results and Discussion 4.1 Creating a Room Setup Workspace in the Meta Quest GUI When defining the workplace for use within ShapesXR, it is necessary to define the Guardian Boundary in the main Graphical User Interface (abbr. GUI) within which the workspace boundary for movement and interaction in virtual reality is created. Within the Guardian Boundary, you can choose either the local stationary or spatial boundary within the Roomscale. As part of the experiment, Roomscale was chosen, and a freehand border was created on the floor of the room following the workspace and walls of the room. After creating the VR Roomscale workspace, selecting the Experimental Features within which the Room setup was selected was necessary. Within this function, virtual walls and objects can be defined as references to physical walls in the real world. These virtual references are used to blend real and virtual environments. These functions were experimental at the time of the experiment because the use of the defined Room setup was only available to a limited number of Meta Quest applications over time.

Fig. 1. Defining Outline walls, a) defining a point on the floor, b) defining the height of the wall, c) dragging the wall, d) dragging the next wall, e) closing the shape of the walls, f) displaying the closed Outline walls.

As seen in Fig. 1, the first step was to define Outline walls. Defining walls is intuitive, but it is not highly accurate, as virtual walls follow the actual walls only approximately and are created by clicking in the user’s space. The first point is created on the floor, then a vertical edge of the wall to the ceiling is created from this point, and after defining the height of the wall, it is possible to drag and define the wall manually. Subsequent clicks define additional walls until the shape of the walls closes. In addition to the user’s

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influence, the accuracy of the defined walls is also limited by local inaccuracies in the shape of the walls within the room. After confirming the Outline walls, it is possible to add Furniture and objects. The important thing is to add desks and doors. The first ones have been added to the Desk. As seen in Fig. 2, desks are added similarly to the walls. First, the point on the floor from which the vertical edge is dragged is selected, then the length of the desk. After marking the width of the desk, the desk definition is complete. In this way, all the desks in the room were added separately. By marking the objects, the door in the room was added. The process of defining the Room setup was completed.

Fig. 2. Defining a Desk within a Room setup.

4.2 ShapesXR ShapesXR is an application for Quest devices used for design and prototype models or immersive apps, a design and collaboration platform for remote teams. Within ShapesXR, it is possible to use the 1:1 Mode, which allows users to create MR content for real spaces. Passthrough Sky 1:1 Mode evokes the users‘ immersive sense of working with physical objects and allows them to work in the real and virtual world intuitively merged together, which has not been so easily accessible to the public before. The virtual world is aligned with the physical world by setting the scene’s center using a teleport tool, offset feature, and floor leveling for more precise alignment. Initially, the Meta Quest streaming application was launched on a personal computer, thanks to which other users could watch the user’s work in VR on the monitor. To avoid the user being constrained by a cable connected to a computer, a Wireless stream was selected using a Wi-Fi connection. A new Space workspace was created after launching ShapesXR with the Meta Quest device. In the application tab, it is possible to change the display option and select the Passthrough Sky. After selecting the Passthrough Sky view, a 1:1 view was selected, within which the user works in a 1:1 scale to the real world, as seen in Fig. 3. Subsequently, Real World Layer was imported, which loads and uses a Room

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setup created in advance. Within this view, it is possible to work with a headset, while the user can interact with the real world with a low risk of unwanted collisions or injury. Thus, adding virtual objects as references to the real world is possible. During the user’s work in the VR, other workers in the room can work without the interference of tracking, moving physical objects, or otherwise interacting with the workplace.

Fig. 3. ShapesXR GUI in 1:1 Mode, a) Real World Layer view, b) Passthrough Sky room view.

The next part of designing the workspace was designing and importing virtual models of objects. To achieve the insertion of objects, Auto snapping was selected, making it possible to use reference points in the space displayed at the user’s hand when inserting objects. Gradually, objects were designed and placed within Passthrough imaging to reference the real world. The designed desk was placed next to the actual desk at the same desk height. As seen in Fig. 4, a laptop, mouse, phone, VR headset, and monitor were placed on the desk.

Fig. 4. Visual display of the objects‘ insertion in the ShapesXR application within Passthrough Sky and 1:1 mode, a) user’s view, b) photo of the user working in VR.

After the object insertion, as seen in Fig. 5, the design of the virtual workplace was finished. Work within Passthrough Sky and 1:1 Mode can be used in workplace design within manufacturing enterprises and industries. A virtual workplace can be designed, and the user can use such a virtual workplace to verify ergonomics during work and

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the workspace in terms of collisions of objects. The user can create or import objects within multiple formats of the desktop modeling files. At the same time, ShapesXR allows you to intuitively export created models, while ShapesXR allows you to export models to Unity. The workplace design in this work was not very comprehensive and was largely focused on the description of the technology, not on a specific design. It corresponds to the fidelity of the models and the workplace. Future work is proposed in which the Meta Quest technology and the ShapesXR application would be used to design a specific workplace in the selected production plant. Virtual desktop models of real objects, such as production machines, stands, and fixtures, will be used. These models will be designed in advance using selected CAD systems and will be placed in the workplace in the ShapesXR Passthrough 1:1 Mode. The ergonomic side, dimensions, collisions of objects, and the overall impression of the selected workplace will be verified by the intuition of an engineer experienced in design.

Fig. 5. The resulting view of the virtual workplace designed in the ShapesXR GUI application.

5 Conclusions The work focused on designing a workplace using the Meta Quest VR headset within the virtual and mixed reality framework and on describing the principle of design and the technologies used. Within the KPPVT FVT TUKE Virtual Reality Laboratory, a Room setup workspace was created in the GUI of the Meta Quest device, and references to the laboratory and the objects in the real world were defined. As part of the Passthrough view, a Guardian Boundary was created, following approximately the room’s walls. Subsequently, outline walls, desks, and a door were defined. After running the ShapesXR application, the Passthrough Sky mode and 1:1 Mode were selected with the alignment of the Room setup, prepared in advance, with the current scene. As part of the work, it was possible to interact with both the virtual and real worlds in the mixed reality of MR.

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The desk, laptop, mouse, mobile phone, and monitor models were designed and put in place for laboratory design. Subsequently, they were used to check the ergonomics and collisions and to verify the overall impression based on the intuition of an experienced worker with the designed workplace. The major research findings in virtual and mixed reality are their rising availability to the public and increased capabilities. However, further research needs to be done for Passthrough Sky 1:1 Mode in particular. Acknowledgment. The authors thank the Ministry of education of the Slovak Republic for supporting this research by the grant VEGA no. 1/0026/22, KEGA no. 038TUKE-4/2021, KEGA no. 004TUKE-4/2022 and project APVV-18–0316.

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12. Linowes, J.: Unity Virtual Reality Projects: Learn Virtual Reality by Developing more than 10 Engaging Projects with Unity 2018, 2nd edn. Packt Publishing, Birmingham, England (2018) 13. Doerner, R., Broll, W., Grimm, P., Jung, B. (eds.): Springer, Cham (2022). https://doi.org/10. 1007/978-3-030-79062-2 14. Grimm, P., Broll, W., Herold, R., Hummel, J., Kruse, R.: VR/AR input devices and tracking. In: Doerner, R., Broll, W., Grimm, P., Jung, B. (eds.) Virtual and Augmented Reality (VR/AR), pp. 107–148. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-79062-2_4 15. Broll, W., Grimm, P., Herold, R., Reiners, D., Cruz-Neira, C.: VR/AR output devices. In: Doerner, R., Broll, W., Grimm, P., Jung, B. (eds.) Virtual and Augmented Reality (VR/AR), pp. 149–200. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-79062-2_5 16. Marr, B.: Extended reality in practice - 100+ amazing ways virtual, augmented and mixed reality are changing business and society: 100+ Amazing ways virtual, augmented and mixed reality are changing business and society. John Wiley & Sons, Nashville, TN (2021) 17. Greengard, S.: Virtual Reality. MIT Press, London, England (2019) 18. Doerner, R., et al.: VR/AR case studies. In: Doerner, R., Broll, W., Grimm, P., Jung, B. (eds.) Virtual and Augmented Reality (VR/AR), pp. 331–369. Springer, Cham (2022). https://doi. org/10.1007/978-3-030-79062-2_9 19. de Souza Cardoso, L.F., Mariano, F.C.M.Q., Zorzal, E.R.: A survey of industrial augmented reality. Comput. Ind. Eng. 139, 106159 (2020). https://doi.org/10.1016/j.cie.2019.106159

Digital and Information Technologies in Metrology 4.0 Michał Wieczorowski1

, Justyna Trojanowska1(B)

, and Oleksandr Sokolov2,3

1 Poznan University of Technology, 3, Piotrowo Street, 60-965 Poznan, Poland

[email protected]

2 Sumy State University, 2, Rymskogo-Korsakova Street, Sumy 40007, Ukraine 3 Technical University of Košice, 1, Bayerova Street, 08001 Prešov, Slovak Republic

Abstract. Industry 4.0, on which many different studies have already been written, brings with it a completely different outlook, especially in IT. Cyber-physical systems, the Internet of Things, networks and cloud-based big data, artificial intelligence, and robotics are essential elements that have also found their way into Metrology 4.0, sometimes called Metrology of the Future. In the paper, measurement technologies for Metrology 4.0 were presented. They belong to coordinate measurement techniques in different scales: macro, micro, and meso. Optical scanning and computed tomography were discussed, focusing on achievable big data. From an IT point of view, the use of artificial intelligence was shown. The possibility of using augmented and virtual reality was also depicted with a short note on cybersecurity. Finally, some ideas regarding Metrology for the Future were briefly presented. It will probably be more oriented to man and use the planet’s natural resources more efficiently. Systems will be based on objects working together, with the definition of connections using the idea of blockchain. And as the data overgrows, data management systems and applications will appear, allowing the user to select the most relevant data. Keywords: Metrology · Dimensional Measurements · Artificial Intelligence · Augmented Reality · Scale · Sustainable Manufacturing

1 Introduction During the first industrial revolution, the first vernier caliper was developed, and the meter was invented as a unit of length. The second revolution resulted in the start of regular production of the micrometer for the industry. The first designs of measuring microscopes and interferometers were also developed, as well as gauge blocks. Profile irregularities began to be measured. The metric convention was also signed, and the definition of a meter based on the distance of two dashes was adopted. With the advent of the automotive industry, mass production became more important. The third revolution brought further elements in metrology that are still known today. The industry developed automated machine tools producing large quantities of parts, generating the need for rapid and accurate measurement of spatial elements. The first coordinate measuring machines © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 81–90, 2023. https://doi.org/10.1007/978-3-031-32767-4_8

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were constructed to realize this, allowing measurements first in two (Fig. 1) and then in three axes [1]. The next step was to equip these machines with drives and controls to automate the measurement process and quickly process large amounts of measurement data. The first ideas and designs appeared, allowing the analysis of irregularities in three dimensions, i.e., surface topography measurements.

Fig. 1. Image of one of the first coordinate measuring machines [2].

Industry 4.0, on which many different studies have already been written, brings with it a completely different outlook, especially in IT. Cyber-physical systems, the Internet of Things, networks and cloud-based big data, artificial intelligence, and robotics are very important elements that have also found their way into Metrology 4.0, sometimes called Metrology of the Future. Industry 4.0 itself appears to be a strategy using various technologies to create autonomous and intelligent manufacturing systems that can selfconfigure, self-monitor, and even repair themselves. For this, it is essential to develop sensors capable of detecting a wide variety of factors to acquire information about the state of processes and their components. It, in turn, will enable multi-criteria optimization of processes and more efficient use of resources. Machines and technological equipment must function in readiness for rapid reconfiguration [3]. The primary goal of this activity is to create a so-called smart factory, with a high degree of automation and robotization, with processes implemented optimally (multi-criteria), fast and flexible product changes, and, very importantly, customer orientation. Each new technology, in the assumption, will give rise to a newer and even more efficient one, which is the only effective answer to the problems of modern industry, among which is the lack of hands to work (low population growth in developed countries), shortage of skilled labor and growing ecological requirements are at the forefront. It makes the fourth revolution quite different from the previous three: it changes what we do, how we do it, and who we are. It changes the engineering approach, and business, economic, and social dependencies, thus affecting not only machines and systems but all aspects of human functioning in the 21st century. To cope with this, a metrology engineer must have multidisciplinary knowledge that includes - in addition to metrology - mechatronics, automation, and computer science.

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For this reason, it is already less of a problem for many growing companies to invest in equipment than investing in a human to take care of it. Measuring devices and their new technologies require knowledge and understanding of what is happening during the measurement process for the measurement result to be a reliable reflection of reality.

2 Literature Review The Metrology 4.0 strategy is an approach to measurement and measurement systems based on various principles derived from physics. It can even be said that these systems derived from coordinate measurement technology have allowed at least some of the elements of Metrology 4.0 to exist, allowing, for example, the creation and work on large data sets (big data). One such technique is optical scanning in 3D. Its history is about 20 years, but despite such a short period, it has developed significantly and gained considerable popularity in the market. Current scanner designs are, therefore, very advanced. Laser scanning technology [4] is steadily displacing the structured light used in the previous generation, giving – in addition to very good accuracy parameters – mainly solutions that do not require tripods and negligible sensitivity to changes in reflectivity and external illumination [5]. More recently, blue light has been used. A schematic picture of exemplary laser scanners is presented in Fig. 2.

Fig. 2. Schematic picture of sample laser scanners.

Referring to the spirit of Metrology 4.0, the scanners also work in a robotic version, as shown in Fig. 3. The concept presented here is based on a scanner mounted on a robotic arm and a tracker that tracks its position. The object is mounted on a rotary table, providing an additional axis in the measurement system. In addition, the solution has a set of cameras and software that allows a remote user to participate in the measurement process remotely. The idea has proven to be a successful solution to the pandemic. In addition to scanning with a set of laser lines, laser rulers are becoming more common. Their operation is particularly justified when providing rapid inspection of a limited number of geometric features is necessary. It is often the case in the production

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Fig. 3. Robotized scanning workstation (courtesy of ITA).

line, where such systems enable in-line verification, i.e., where the detection of possible defects is most desirable [6]. An example station configuration based on laser rulers is presented in Fig. 4.

Fig. 4. Configuration of a station based on laser rulers (courtesy of ITA).

The youngest part of coordinate metrology from the point of view of physics is technical computed tomography. Devices that function with this technology are based on X-rays [7, 8]. Multisensory solutions are also crucial to Metrology 4.0 [9, 10]. The simplest version of such a solution is using a contact head (with different tips) and microscope optics, resulting in an optical-tactile CMM. On the micro and meso scale, it is also worth mentioning optical surface analysis techniques, which are slowly becoming present not only in the scientific but also in the industrial world. The most promising optical techniques are focus variation microscopy (FVM) and coherent scanning interferometry (CSI).

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3 Research Methodology The young generation entering the market perceives the world as decidedly digital, preferably in 3D, in real-time, and with an immersion effect, i.e., immersion in what’s happening, whether it’s social media or professional work. In Metrology 4.0, challenges also arise in the IT area. The technologies that are changing Industry 4.0 are also beginning to play an important role here. Starting with big data, i.e., datasets containing large amounts of information, the first element of this in length and angle metrology was the measurement of surface topography at the micro-scale [11]. The expansion of singleprofile measurement to a collection of profiles, and later also images, resulted in data sets that were difficult to process by the time’s computer systems. Only their development led to a progression of measurements towards increasing the number of profiles and representing surfaces (a prominent role is still played by graphics cards today). Another element was the scanning processes described above, where the ability to collect millions of points quickly created giant data sets. Here, appropriate software makes it possible to extract the points necessary to represent the nature of the surface through a grid of triangles with different sizes of basic elements [12]. The image of the surface, depending on the size of the grid, is presented in Fig. 5 [13].

Fig. 5. Mapping the surface with tiles of different sizes – scale: A – area, B – number of tiles, C – area versus flat surface.

A further use of big data is computed tomography. In it, the pixel turned into a voxel, which further increased the size of data files. Techniques for extracting surfaces from spatial data have emerged, which is essential for length and angle metrology. As with scanning, there is a need here not only to store these datasets but also to match them to

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CAD datasets, which further necessitates the development of hardware on the one hand and thoughtful software procedures on the other. In the coming reality, communication, which plays a significant role between people, will also develop between systems and their components and the individual sensors belonging to them. The IoT, or the Internet of Things, and artificial intelligence (AI) will play a momentous role. According to the IoT concept, all components and even objects are uniquely identifiable. Thus, collections of various sensors will be installed in measuring devices, monitoring their parameters and transmitting the collected information to further analysis and decision-making systems. Among this information will naturally be those related to the post-measurement process carried out (the measured object, the selected probe, and stylus, the measurement conditions, etc.) and environmental conditions (temperature, pressure, humidity), but also those related to the condition of individual components of the device. Based on these, it will be possible to decide whether to stop the measurement due to the possibility of an increase in measurement errors.

4 Results and Discussion An element of communication is the possibility of using augmented or virtual reality. AR technology is becoming more and more popular nowadays. The demand for these technologies results in the development of new specialized software and hardware solutions, and the use and availability are diametrically different compared to the past. It mainly benefits areas where the integration of these technologies is problematic or financially demanding [14]. Augmented reality involves, among other things, the ability to obtain more information using simple communication tools, such as a smartphone. On its screen, the view of the machine can show information about the temperature in selected areas, the operator, the measurement time, the task, etc. An example image is shown in Fig. 6. Virtual reality further extends measurement capabilities, especially in simulating measurement runs. The ability to realize a measurement on a virtual object means earlier detection of errors in the measurement program on a single device and simulation of measurements at multiple scales using different measurement systems. However, some risks are also considered in this regard. Namely, the mentality of people already includes permanent multitasking, only cursory reading of information, and dependence on mobile technologies and networks, which are not always seen as positive elements. We are also observing physiological changes because traditional work that causes strain on the muscles of the arms, forearms, and hands is being replaced by activities that strain the fingers, eyes, and head muscles. Besides, deeper immersion in virtual reality is a risk of changes in the psyche related to isolation and a sense of isolation. For this reason, virtual reality applications are now being introduced more slowly than might have been expected just a few years ago. Using artificial intelligence or AI in metrology is a topic that is receiving increasing attention [15]. It is predicted that it will simply become a necessity soon, as the cost of hiring an employee and fully preparing him or her for modern measurement workstations is time-consuming. Without this, on the other hand, a formal or interpretive error with

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Fig. 6. Sample image from an augmented reality application.

significant consequences may be made due to a lack of skills. Besides, although the increasing number of options in measurement software increases its capabilities, from a psychological point of view, too many options are a source of stress, a sense of paralysis, and dissatisfaction for humans [16]. A hint of this choice, or at least the existence of default options for a given application, will make work much easier. Based on a growing number of publications, the importance of AI’s impact on I&M (Instrumentation and Measurement) is very high [17]. For measurement instruments, AI can be used to select measurement conditions and strategies, and filtering techniques, among other things. In addition, each measurement technique still has specific elements that can be supported for the operator’s benefit. This support is particularly important for the less skilled and for new items to be measured when existing experience cannot be used directly. Macro-scale artificial intelligence can automatically load a CAD file for the object to be measured, select - depending on the instrument - the probe and pins, scanning density and illumination parameters, X-ray-related parameters, etc., and propose a measurement procedure (strategy) according to the feature, including a suitable data filtering method. Once the measurement procedure is completed, the feature and uncertainty calculations are based on the ambient data. For the micro scale, for example, this will be the loading of surface data on how it is made, functionality, material, etc., based on which AI will propose (or exclude) a specific measurement technique. It will also suggest the measurement conditions related to tips or lenses and illumination, depending on the measurement technique, the measurement field, and the nesting index, depending on the expected parameter values and the stitching procedure. Once the data is obtained, the system will select its filtering method, which, with the number of options currently

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available, including Gaussian (linear and robust) [18], morphological [19], spline [20], wavelet [21, 22] or multiscale [23], is a challenging task from the user’s point of view. Still, an important one to show the surface features [24] and calculate the parameters correctly [25]. Such a system based on artificial intelligence will significantly support the user, allowing efficient and fast decision-making, using data and premises from the knowledge base created and developed based on daily tasks. Cybersecurity is the last of the issues worth mentioning when discussing IT in metrology. Security of measurement data and more is an essential issue in the context of the operation of companies using Industry 4.0 strategies. Unwanted interference with design and technology data leads, of course, to erroneous assumptions and performance parameters. An attack on measurement data can lead to the belief that feature values are out of tolerance when nothing of the sort has happened. It can also lead to the opposite situation - the transmission of information that the item is correct when the actual results reach beyond the tolerance limits. To avoid this, increasingly sophisticated IT security is being used. Companies are also beginning to look more readily at building internal networks for such purposes, with no access from outside the corporation, using only wired connections. This problem will intensify with the development of computer technology, especially when quantum computing becomes more common.

5 Conclusions In summary, Metrology 4.0 is becoming an essential element in the functioning of industry, changing the philosophy and organization of measurements carried out based on new measurement techniques. It often entails more demanding conditions, as optical measurements, for example, require near-perfect surface cleanliness and sensitivity to the way and configuration of surface illumination with external light. They also often require better isolation from the environment from the point of view of vibrations. Coordinating measuring machines with contact heads - although slower and collecting fewer measurement points in a given time – still, allow much smaller measurement uncertainties than optical scanning or computed tomography. The world is slowly discussing Industry 5.0, so what can we expect from Metrology 5.0? This, of course, is not yet fully defined. Nonetheless, it is expected that the industry - in addition to innovation efficiency and productivity - will drive environmental and social change, putting the worker’s well-being at the center of the production process. It is in response to workers’ changing skills and training needs, ultimately enhancing competitiveness to ensure the well-being of life beyond the workplace while using the planet’s natural resources more efficiently. It should also contribute to better resilience to the global shocks that have been affecting societies in recent years. This change in metrology will be ushered in by the even more widespread use of information technology, including artificial intelligence and related learning systems. Systems will be based on objects working together, with the definition of connections using the idea of blockchain. But it will also be important to manage data properly, as the data will grow rapidly in the coming years, thanks to 5G technology and sensor developments. Thus, event-managed applications will appear, allowing the selection of the most relevant ones so that the measurement result reflects reality and its interpretation is not

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overly complicated for the user. Their functioning will reflect the mechanism: event information – the adoption of information. There will be enormous scope for startups, making it easier to adapt to changes than large organizations. Meeting these challenges will therefore be significant for the industry so that the industrial plant will be prepared for young engineers at least as well as they will be prepared for their work. Acknowledgment. The research was partially supported by the Polish National Agency for Academic Exchange within the project “Strengthening the scientific cooperation of the Poznan University of Technology and Sumy State University in the field of mechanical engineering” (agreement no. BPI/UE/2022/8–00). Also, this research was partially supported by the Slovak Research and Development Agency within the project SK-UA-21–0060 and the Ministry of Education, Science, Research and Sport of the Slovak Republic within the project VEGA 1/0268/22.

References 1. Sladek, J.: Coordinate Metrology Accuracy of Systems and Measurements. Springer Tracts in Mechanical Engineering, Berlin (2016) 2. Hocken, R.J., Pereira, P.H.: Coordinate Measuring Machines and Systems. CRC Press, Boca Raton (2012) 3. Pavlenko, I., Ivanov, V., Gusak, O., Liaposhchenko, O., Sklabinskyi, V.: Parameter identification of technological equipment for ensuring the reliability of the vibration separation process. In: Knapcikova, L., Balog, M., Perakovic, D., Perisa, M. (eds.) 4th EAI International Conference on Management of Manufacturing Systems. EICC, pp. 261–272. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-34272-2_24 4. Isa, M.A., Lazoglu, I.: Design and analysis of a 3D laser scanner. Measurement 111, 122–133 (2017). https://doi.org/10.1016/j.measurement.2017.07.028 5. Rekas, A., et al.: Analysis of tool geometry for the stamping process of large-size car body components using a 3D optical measurement system. Materials 14, 7608 (2021). https://doi. org/10.3390/ma14247608 6. Swojak, N., Wieczorowski, M., Jakubowicz, M.: Assessment of selected metrological properties of laser triangulation sensors. Measurement 176, 109190 (2021). https://doi.org/10.1016/ j.measurement 7. Carmignato, S., Dewulf, W., Leach, R. (eds.): Springer, Cham (2018). https://doi.org/10.1007/ 978-3-319-59573-3 8. Gapinski, B., Wieczorowski, M., Mietli´nski, P., Mathia, T.G.: Verification of computed tomograph for dimensional measurements. In: Diering, M., Wieczorowski, M., Harugade, M., Pereira, A. (eds.) Advances in Manufacturing III. Manufacturing 2022. Lecture Notes in Mechanical Engineering, pp. 142−155. Springer, Cham (2022). https://doi.org/10.1007/9783-031-03925-6_13 9. Sadaoui, S.E., Mehdi-Souzani, C., Lartigue, C.: Multisensor data processing in dimensional metrology for collaborative measurement of a laser plane sensor combined to a touch probe. Measurement 188, 110395 (2022). https://doi.org/10.1016/j.measurement.2021.110395 10. Zaloga, V., Yashyna, T., Dynnyk, O.: Analysis of the theories for assessment of the quality management product efficiency. J. Eng. Sci. 5(2), B1–B6 (2018). https://doi.org/10.21272/ jes.2018.5(2).b1 11. Sayles, R.A., Thomas, T.R.: Mapping a small area of a surface. J. Phys. E 9(10), 855–861 (1976)

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12. Herraez, J., Martinez, J.C., Coll, E., Martin, M.T., Rodriguez, J.: 3D modeling by means of videogrammetry and laser scanners for reverse engineering. Measurement 87, 216–227 (2016). https://doi.org/10.1016/j.measurement.2016.03.005 13. Brown, C.A., Charles, P.D.: Fractal analysis of topographic data by the patchwork method. Wear 161, 61–67 (1993) 14. Kašˇcak, J., Husár, J., Knapˇcíková, L., Trojanowska, J., Ivanov, V.: Conceptual use of augmented reality in the maintenance of manufacturing facilities. In: Trojanowska, J., Kujawi´nska, A., Machado, J., Pavlenko, I. (eds.) MANUFACTURING 2022. LNME, pp. 241–252. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99310-8_19 15. Wieczorowski, M., Kucharski, D., Sniatala, P., Krolczyk, G., Pawlus, P., Gapinski, B.: Theoretical considerations on application of artificial intelligence in coordinate metrology. In: 6th International Conference on Nanotechnology for Instrumentation and Measurement, NanofIM 2021 (2021). https://doi.org/10.1109/NanofIM54124.2021.9737344 16. Schwartz, B.: The Paradox of Choice. Harper Collins Publishers (2016) 17. Khanafer, M., Shirmohammadi, S.: Applied AI in instrumentation and measurement: the deep learning revolution. IEEE Instrum. Meas. Mag. 23(6), 10–17 (2020). https://doi.org/10.1109/ MIM.2020.9200875 18. Seewig, J.: Linear and robust Gaussian regression filters. J. Phys. Conf. Ser. 13(1), 254–257 (2005) 19. Pawlus, P., Reizer, R., Letocha, A., Wieczorowski, M.: Morphological filtration of two-process profiles. Bull. Pol. Acad. Sci. Tech. Sci. 67(1), 107–113 (2019). https://doi.org/10.24425/bpas. 2019.127339 20. Zeng, W., Jiang, X., Scott, P.J.: A generalised linear and nonlinear spline filter. Wear 271, 544–547 (2011). https://doi.org/10.1016/j.wear.2010.04.010 21. Jiang, X.Q., Blunt, L., Stout, K.J.: Development of a lifting wavelet representation for surface characterization. Proc. R. Soc. Lond. A. 456, 2283–2313 (2000) 22. Monkova, K., et al.: Condition monitoring of Kaplan turbine bearings using vibro-diagnostics. Int. J. Mech. Eng. Robot. Res. 9(8), 1182–1188 (2020). https://doi.org/10.18178/ijmerr.9.8. 1182-1188 23. Brown, C.A., et al.: Multiscale analyses and characterizations of surface topographies. CIRP Ann. 67(2), 839–862 (2018) 24. Berladir, K.V., Hovorun, T.P., Sviderskiy, V.A., Rudenko, P.V., Vyshehorodtseva, M.E.: Nanostructural modification of polytetrafluoroethylene and its composition by energy influence. J. Nano- Electron. Phys. 8(1), 01033–1–01033–5 (2016). https://doi.org/10.21272/jnep. 8(1).01033 25. Pawlus, P., Reizer, R., Wieczorowski, M.: Functional importance of surface texture parameters. Materials 14(18), 5326 (2021). https://doi.org/10.3390/ma14185326

Information Management Systems

Quality Management at the Manufacturing Enterprise: Repair Processes Case Study Yuliia Denysenko1,2(B)

, Filip Górski2 , Olaf Ciszak2 and Pavlo Kushnirov1

, Khrystyna Berladir1,3

,

1 Sumy State University, 2, Rymskogo-Korsakova St, Sumy 40007, Ukraine

[email protected]

2 Poznan University of Technology, 3, Piotrowo, 60-965 Pozna´n, Poland 3 Technical University of Košice, 1, Bayerova St, Prešov 08001, Slovak Republic

Abstract. Finding optimal mechanisms for effective management of production preparation processes is the focus when improving management systems. Emergency repairs accelerate the planning stages, and a significant part of the costs of the repair process is probabilistic. Therefore, to predict the occurrence of random factors in the repair process, a mathematical model is proposed based on forecasting the costs of the tool repair process and equipping and using a decision tree. The cost model includes fixed process costs, total salary costs, and machine tool depreciation; costs in the event of additional factors; probability of occurrence of factors. The tool repair costs for 6 months were taken as incoming data of the analyzed manufacturing enterprise. The predicted value of the costs for the tool and technological equipment repair process is equal to 60,219.94 UAH. Applying the proposed algorithm makes it possible to protect the repair management of the machine-building enterprise from inefficient costs or investments under limited resources. Keywords: Prediction · Repair · Process Innovation · Quality · Sustainable Manufacturing

1 Introduction One factor influencing an enterprise’s sustainable development is the speed of auxiliary processes for producing new products [1]. Therefore, the problems of finding optimal mechanisms for the effective management of production preparation processes become the focus of attention when improving control systems [2]. Considering the requirements for efficiency, quality, and continuity of production preparation, the processes of repairing equipment and technological equipment acquire particular importance. Modern information technologies represent a complex set of pre-production processes in logically linked subsystems with a flexible planning and control structure. It is known that technological equipment, tools, and equipment are essentially material resources for primary production at almost any enterprise [3]. At present, CAD/CAM systems and services [4], as well as modules of ERP systems (Enterprise Resource Planning) [2], © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 93–101, 2023. https://doi.org/10.1007/978-3-031-32767-4_9

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are already quite effectively used to manage material resources in the vast majority of enterprises. The basic advantage of such technologies is the ability to adapt the ERP process model to an already functioning system, taking into account the specifics and characteristics of the production process. At the same time, using the ERP system, the problem of natural integration into the quality management system is built according to the requirements of the standard DSTU EN ISO 9001:2018 (DSTU EN ISO 9001:2018 Quality management systems. Requirements) and is solved. However, when implementing such systems, it is still challenging to consider the peculiarities of the organization of production and operation of the tool (equipment), which are caused by a considerable nomenclature [5]. Although the enterprises have a well-established system of planned and preventive repairs, emergency repairs complicate the planning stages, and a significant part of the costs of the repair process is probabilistic. Therefore, an important requirement is the use of simple and, at the same time, reliable methods for predicting the occurrence of additional factors in repair processes. This work aims to increase the repair process quality by building a mathematical model for predicting the quality of the repair process based on the application of the decision tree.

2 Literature Review As a result of the analysis of the regulatory support of the processes of a manufacturing enterprise [6], it was found that the pre-production activities are regulated by outdated regulatory documents, for example, the guidelines outlined in [7]. The organizational structure of the repair economy is established depending on the conditional category of the enterprise, determined by the annual volume of consumption of technological equipment, including tools. The purpose of the functional activity of the tool production of the enterprise is the manufacture, overhaul, and restoration of technological equipment that is not provided by centralized supplies. The organizational and production structure of the repair services depends on the type and volume of production, its technological characteristics, the development of cooperation in the performance of repair work, etc. The task of the repair service of the enterprise is to ensure the constant operability of the equipment and its modernization, the manufacture of spare parts necessary for repairs, improve the culture of operation operating equipment and the quality of repairs, and reduce the cost of its implementation, increasing the efficiency [8, 9]. The main functions of the repair service of the enterprise [6]: – certification and certification of equipment; – development of standards for the care, supervision, maintenance, and repair of equipment; – planning preventive maintenance; – planning the need for spare parts; – operational planning and scheduling of complex repair work; – organization of work on installation, dismantling, and disposal of equipment; – development of design and technological documentation for repair work and modernization of equipment;

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– development of technological processes for repair and their equipment; – quality control of repairs. The system of repair and maintenance, depending on the nature and conditions of operation of the equipment, can function in various organizational forms: in the form of a post-inspection system, a system of periodic repairs, or a system of standard repairs. In the system of post-inspection repairs, equipment inspections are performed according to a predetermined schedule, during which its condition is established, and a list of defects is compiled [6, 7]. The timing and content of the upcoming repairs are determined based on the inspection data. An enterprise’s improvement process is accompanied by the continuous development of management decisions and their application in practice. In the production process, enterprise managers very often have to face critical problems, and the final result of the enterprise’s activities will depend on how optimally the decision is made. Since each decision is a projection of the future, which contains an element of uncertainty, it is crucial to correctly predetermine the degree of risks associated with implementing the decisions made. For this, forecasting methods are widely used. Many works have been devoted to their application. Armstrong J.S. [10] and Mare D. [11] describe the main principles in detail. The classification of forecasting methods was considered in detail in [12]. Lin C. and Yan F. introduce the classification and prediction of the similarities and differences by classification method [13]. Rodriguez J.J. et al. proposed using decision trees to predict roughness regarding tool wear. They noted two reasons: these trees show higher accuracy for the prediction, and tool-type decision trees have been selected as the most suitable technique for decisionmaking [14]. Authors of the research study [13] also use a decision tree to partition the data classification refinement steps. Gordon E.R. et al. presented a decision tree as a framework for the requirementsbased selection process [15]. A decision tree is a graph-analytical method that allows you to visually evaluate the various actions of various factors to choose from. Decision trees are diagrams that allow you to study various alternatives and the probabilities of their implementation visually. This method is used to develop solutions under conditions of uncertainty [16, 17]. Each option or event is represented by a separate branch, which leads to subsequent branches, reflecting further actions or possible events. The decision tree is constructed in such a way as to show the full range of alternatives and events that can occur under all analyzed conditions [18]. The value of a decision tree lies in its ability to conduct a logical analysis, which allows you to choose a complete strategy that considers all possible options before the company chooses one of them. At the same time, the method is integrated into the existing ERP system of the enterprise. The decision tree method is one of the most popular methods for solving classification and forecasting problems. Sometimes the Data Mining method is called decision trees, classification, and regression trees [19, 20].

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3 Research Methodology Thus, the quality management assessment of repair processes can be considered from the point of view of comparing the costs of quality assurance and the effect of its assurance. The concept of “process quality costs” itself is an integral part of the costs of production processes. It suggests that the costing-related quality costs should be directly related to quality parameters. Knowing the nature of this relationship, it is possible to model and predict quality costs and improve production processes’ quality. Therefore, the proposed mathematical model of quality management of the process “Repair of tools and technological equipment” is presented in the form: C = Cz + Cf 1 ·1 +Cf 2 ·2 + . . . + Cf · P,

(1)

where C – fixed costs of the “Repair” process; Cz – total costs for wages and depreciation deductions for machines; C f1 , C f2 , C fn – costs in the event of factors; P1 , P2 , Pn – the probability of occurrence of factors. Fixed costs include wages for workers (sharpeners, engineers, technical supervision department) and depreciation deductions for machine tools. The predicted value of the costs for the “Tool and equipment repair” process is determined by the mathematical expectation, which is calculated by the formula: n xi pi , (2) M (ξ ) = i=1

wherex – random value of process costs that can take the values x1, x2,…, xn with probabilities p1, p2,…, pn. The decision tree construction algorithm does not require the user to select input attributes (independent variables). All existing attributes can be given as input to the algorithm, the algorithm itself will choose the most significant among them, and only they will be used to build the tree. In comparison, for example, with neural networks, this greatly facilitates the user’s work since, in neural networks, the choice of the number of input attributes significantly affects the training time [16]. The authors clarified five main components of the Decision Tree Construction Algorithm proposed by J. Patalas-Maliszewska [21]: (1) determination of process indicators, (2) determination of the priority of the criteria, (3) collecting data to determine the values of the key objectives, (4) using a decision tree to forecast indicators, (5) indicating recommended actions for continuous monitoring of progress towards reaching objectives. At the analyzed enterprise, in addition to fixed costs, additional costs were determined: sharpening the cutter, refining stamps, repairing the grinder, sharpening the drill, repairing the busbar, straightening the grinding wheel, and sharpening the cutter. For the proposed model, it is necessary to determine the probabilities of the occurrence of these factors. The probability of the event A, or the probability of a “successful” outcome of a single operation, is the average value of the frequency, i.e., the average value of the ratio of the number of “successful” outcomes to the number of all performed single operations (tests) [19]: m (3) (A) = · n

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It goes without saying that if the probability of an event is m/n, then in n trials, the event A can occur both more than m times and less than n times; it only occurs m times on average, and in most series of n trials the number of occurrences of event A will be close to m, especially if n is a large number. Thus, the probability P(A) is some constant number between zero and one: 0  P(A)  1.

4 Results and Discussion According to the proposed model, the forecasting of the “Tool and technological equipment repair” process total cost is being tested. The decision tree method uses the Decision Tree add-in in Microsoft Excel. The costs of the tool shop for 6 months were taken as initial data. Spending for two months is given in Table 1. Based on formula (3), the probabilities of the occurrence of additional cost factors were calculated and summarized in Table 2. Table 1. Costs for the process “Tool and technological equipment repair”. Repairing Repairing Refining Sharpening Straightening Sharpening the the stamps the cutter the grinding the drill busbar grinder wheel August

Volume 0

1

1

1

1

0

Cost, UAH

179.12

118.51

49.56

251.47

0

1

1

0

1

0

179.12

118.51

0

251.47

0

1

1

0

1

0

179.12

118.51

0

251.47

0

0

1

0

0

0

0

0

118.51

0

0

0

Volume 0

1

0

1

1

0

Cost, UAH

179.12

0

49.56

251.47

0

Volume 1

1

1

0

1

0

Cost, UAH

179.12

118.51

0

251.47

0

0

September Volume 0 Cost, UAH

0

November Volume 0 Cost, UAH

0

December Volume 0 Cost, UAH January

February

0

251.25

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Fig. 1. Decision tree on the example of the process “Repair of tools and technological equipment”. Source: own elaboration.

Using the data from Table 1 and Table 2, we build the decision tree shown in Fig. 1. At this figure, the cost is determined according to the priority. Red text indicates the node of the tree (items of expenses), in which the condition of the probability of occurrence of the event (occurrence of these expenses) is checked, and their numerical value is indicated. According to formula (1), all possible costs of the tool and technological equipment repair process were calculated in the decision tree. Table 2. Summary table of probabilities of occurrence of additional cost factors. Factors

Occurrence probability

Repairing the busbar

0.16

Repairing the grinder

0.83

Refining stamps

0.83

Sharpening the cutter

0.16

Straightening the grinding wheel

0.83

Sharpening the drill

0.16

To find the predicted value of costs, you need to build a histogram and find the mathematical expectation. The resulting histogram is shown in Fig. 2. The number of

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√ intervals depends on the number of values k = N , where N is the number of cost values. In this case, N = 32, then k = 5. The length of the interval is 89.07 UAH. The range is 503.88 UAH.

Fig. 2. Histogram of costs for the process “Tool and technological equipment repair”.Source: own elaboration.

The predicted value of the costs for the tool and technological equipment repair process is determined by mathematical expectation and is equal to the: M(x)= 60, 219.94 (UAH). Having calculated the values, we got that for the seventh month. There will be costs of 60,219.94 UAH. Applying the proposed algorithm makes it possible to protect the repairing process of a manufacturing enterprise from inefficient costs or additional investments under limited resources. The advantages of using the proposed method are ease of use. It does not require long-term staff training. But using this tool will allow you to quickly determine the projected costs for the quality of the process. After obtaining excessive or unsatisfactory values, it is recommended to carry out preventive measures. Its use is justified for other production processes as well. For example, Han Y., Jia G. [22], and Deradjat D. [23] applied to the example of 3D manufacturing.

5 Conclusions Implementing ERP systems in modern enterprises increases the quality of resource management. However, difficulties arise in the organization of production and operation of tools (equipment), which are caused by a considerable nomenclature. Although the enterprises have a well-established system of planned and preventive repairs, emergency

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repairs complicate the planning stages, and a significant part of the costs of the repair process is probabilistic. Therefore, to predict the occurrence of additional factors in repair processes, a mathematical model of cost determination and their forecasting is proposed based on a simple and, at the same time, reliable method – a decision tree. The article clearly shows that this method is simple and does not require long-term staff training. This tool will allow you to quickly determine the projected costs for the quality of the process. Thus, the value of costs was predicted using the example of the process of “repair of tools and industrial equipment”. Having calculated the values, we got that for the seventh month. There will be costs of 60,219.94 UAH. Applying the proposed algorithm makes it possible to protect the repairing process of a manufacturing enterprise from inefficient costs or additional investments under limited resources. After obtaining excessive or unsatisfactory values, it is recommended to carry out preventive measures. The conceptual directions for developing the proposed method are its application on the example of other production processes and developing an interface that integrates the proposed method into the enterprise information system. Acknowledgment. The research was partially supported by the Polish National Agency for Academic Exchange within the project “Strengthening the scientific cooperation of the Poznan University of Technology and Sumy State University in the field of mechanical engineering” (agreement no. BPI/UE/2022/8–00).

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9. Pavlenko, I., Ivanov, V., Gusak, O., Liaposhchenko, O., Sklabinskyi, V.: Parameter identification of technological equipment for ensuring the reliability of the vibration separation process. In: Knapcikova, L., Balog, M., Perakovic, D., Perisa, M. (eds.) 4th EAI International Conference on Management of Manufacturing Systems. EICC, pp. 261–272. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-34272-2_24 10. Armstrong, J.S.: Selecting forecasting methods. In: Armstrong, J.S. (eds.) Principles of Forecasting. Kluwer Academic Publishers, Norwell, MA (2001) 11. Mare, D.: The oxford handbook of economic forecasting. J. Oper. Res. Soc. 66, 2102 (2015). https://doi.org/10.1057/jors.2015.29 12. Pogorzelska, Y., Zaloha, V., Ivchenko, O., Chiarm, V.: Recommendations for choosing a method for predicting the quality of the processes of instrumental production preparation. Modern Technol. Mach.-Build. 7, 208–216 (2012). [in Ukrainian] 13. Lin, C., Yan, F.: The study on classification and prediction for data mining. In: Seventh International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2015), pp. 1305−1309 (2015). https://doi.org/10.1109/ICMTMA.2015.318 14. Rodriguez, J.J., Quintana, G., Bustillo, A., Ciurana, J.: A decision-making tool based on decision trees for roughness prediction in face milling. Int. J. Comput. Integr. Manuf. 30(9), 943–957 (2017). https://doi.org/10.1080/0951192X.2016.1247991 15. Gordon, E.R., Shokrani, A., Flynn, J.M., Goguelin, S., Barclay, J., Dhokia, V.: A surface modification decision tree to influence design in additive manufacturing. In: Setchi, R., Howlett, R.J., Liu, Y., Theobald, P. (eds.) Sustainable Design and Manufacturing 2016. SIST, vol. 52, pp. 423–434. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32098-4_36 16. Azad, C., Chikalov, I.: Decision Trees with Hypotheses. Synthesis Lectures on Intelligent Technologies. Springer, Cham (2022) 17. Sherif, S., Albert, Y.: Zomaya Encyclopedia of Big Data Technologies. Springer, Cham (2019) 18. Shekhar, S., Xiong, H., Zhou, X.: Spatial decision tree. In: Encyclopedia of GIS. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-17885-1_101252 19. Hsu, C.H., Wang, M.J.: Using decision tree-based data mining to establish a sizing system for the manufacture of garments. Int. J. Adv. Manufact. Technol. 26, 669–674 (2005). https:// doi.org/10.1007/s00170-003-2032-0 20. Xingrong, S.: Research on time series data mining algorithm based on Bayesian node incremental decision tree. Clust. Comput. 22(4), 10361–10370 (2017). https://doi.org/10.1007/s10 586-017-1358-6 21. Patalas-Maliszewska, J., Łosyk, H., Rehm, M.: Decision-tree based methodology aid in assessing the sustainable development of a manufacturing company. Sustainability 14(10), 6362 (2022). https://doi.org/10.3390/su14106362 22. Han, Y., Jia, G.: Optimizing product manufacturability in 3D printing. Front. Comp. Sci. 11(2), 347–357 (2016). https://doi.org/10.1007/s11704-016-6154-6 23. Deradjat, D., Minshall, T.: Decision trees for implementing rapid manufacturing for mass customization. CIRP J. Manuf. Sci. Technol. 23, 156–171 (2018). https://doi.org/10.1016/j. cirpj.2017.12.003

Impact of Standardized Reusable Packaging on a Supply Chain Design and Environmental Efficiency Damian Dubisz1(B)

, Paulina Golinska-Dawson1

, and Adam Kolinski2

1 Poznan University of Technology, 2, J. Rychlewski St, 60-965 Poznan, Poland

[email protected] 2 Poznan School of Logistics in Poznan, 6, Estkowskiego St, 61-755 Poznan, Poland

Abstract. Reusable packaging is more and more commonly used in business practice. Numerous researchers attempt to identify the correlation between the parameters of a supply chain based on the standard logistic units. This paper aims to analyze the carbon dioxide equivalent emissions of transport processes in a supply chain, considering various reusable packaging variants. The novelty of the approach is based on the use of environmental assessment when evaluating the efficiency of the modeled solution. Based on the conducted literature and exploratory research, it can be stated that the implementation of standardized reusable packaging affects the design of the supply chain and its parameters. The presented case study outlines the direct impact of changes on environmental efficiency within the sustainable supply chain based on the application of standardized logistics units. The proposed changes influence is focused on reducing the carbon footprint of the entire supply chain. Keywords: Standardized Packaging · Sustainable Development · Environmental Performance · Sustainable Supply Chain

1 Introduction The trend to optimize a supply chain performance relates to processing changes, data management, and care for the entire product life cycle (LCA). The LCA method is also used to mitigate the carbon footprint emitted during manufacturing and the entire product life cycle and to make decisions when selecting a product for manufacturing with the lowest environmental impact [1]. Therefore, product design has a noticeable impact on its overall carbon footprint, which requires an adequate methodology to assess environmental products [2]. In addition, there is an increasing emphasis on designing sustainable product packaging [3]. This trend is also supported by the legislative activities of authorities [4]. The European Commission has established the European Green Deal [5], which precisely defines the principles of the sustainable development strategy that also applies to the use of reusable plastic packaging [5]. By 2030, product packaging sold in the European Union must be reusable, biodegradable, and based on bio-based plastics solutions [6]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 102–112, 2023. https://doi.org/10.1007/978-3-031-32767-4_10

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Reusable packaging is a solution that manufacturers and distributors increasingly use. Therefore, it is necessary to properly manage types of packaging regarding their reusability and their reverse flows between all participants in a supply chain [7]. This approach is highly recommended for organizations willing to minimize the energy consumption of their supply chain [5]. The annual increase in the amount of packaging in Europe is estimated at 1.9% [1]. There is a noticeable increase in the amount of reusable packaging. However, according to Coelho et al. [8], research 50% of paper production covers the manufacturer’s packaging materials demand. Given this trend, selecting the right, standardized, reusable packaging with the lowest environmental impact [9]. The impact of the product and its packaging on the environment at various stages of its life is a very extensive topic and undoubtedly requires in-depth research. This paper aims to analyze the carbon dioxide equivalent emissions of transport processes, taking into account various variants and standards of packaging.

2 Literature Review Numerous studies on product packaging design show growing interest in reusable packaging management. Researches aim to improve the efficiency of reusable package processes [10], waste minimization [2], and the mitigation of products’ carbon footprint related to distribution channels [11]. The verification of the mutual dependencies between the cost of maintaining and purchasing reusable packaging and their impact on the cost and process efficiency was also analyzed in terms of introducing this type of packaging. The emphasis in the previous research was put on the verification of control tools for logistics processes [12]. A furniture industry example has been pointed out as one where proper packaging can be essential to create a fully sustainable and green supply chain (GrSC) [13]. In the case of internal logistics tasks, the type of transport packaging does not affect the volume of flows [14, 15]. The problem of standardization of packaging and management of its flow in the military area has also been indicated in other studies [16]. The impact of the design on the size and shape of the packaging unit has been analyzed in the context of handling the flows of military goods. A modular approach has been proposed to indicate the most advantageous shape of the packaging. The George and Robinson algorithm was used to determine the correct packaging shape and optimize the quantity necessary to support the flow of goods. Thanks to the applied approach, an increase in economic parameters and efficiency of the supply chain was observed [16]. Typical supply chain models were analyzed primarily with data from companies producing reusable plastic pallets. Strengths and weaknesses resulting from applying the new standard of reusable plastic pallets to reverse the logistics supply chain were indicated. At the same time, based on the analysis carried out, the areas of the sustainable supply chain were outlined in which the implementation of a standardized logistic unit is justified and the most advantageous from the point of view of the efficiency of logistic operations [17]. The impact of plastic packaging on increasing the supply chain’s carbon footprint was verified. Supply chains operating on the German market were used in the analysis. It has been shown that the type of material used affects the sustainable development of the supply chain and its emission level [18].

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Based on the conducted review of reusable packaging within supply chains, three main approaches for the management of reusable packaging have been identified [19], as follows: • Environmental and economic impacts [20]; • Logistics system design [21]; • Operations management [22]. In this study, the emphasis is placed on verifying the interdependence between the standardization of logistics units and changes in the supply chain’s design that may reduce its overall carbon footprint.

3 Research Methodology The study combines the findings from the literature review and a two-staged case study (Fig. 1).

Fig. 1. Research method used in this study. Source: own elaboration.

The literature review aimed at verifying the current state of the art regarding packaging management and its relation to a supply chain design. In the case study, a logistics

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model corresponding to handling reusable packaging in the automotive industry was verified. In the first stage of the case study, the current carbon footprint in the supply chain was calculated with various reusable packing. In the second stage, changes were made within the existing model regarding using standardized reusable logistics units. Once all changes were implemented within SC, the corresponding carbon footprint was calculated and compared with the previous scenario’s outcome.

4 Results and Discussion The supply chain of the analyzed production company includes two distribution centers (DC) and 6 local branches from which last-mile distribution is carried out. The internal supply chain consists of multiple local branches, between which transportation is done by linehaul trucks. From the perspective of the development of sustainable supply chains, such a model seems to be ineffective in terms of carbon footprint. The focal company must ensure the availability of parts for production processes, which requires maintaining the current supply chain model. For transportation, standardized Euro-class pallet units are used. However, the individual production parts shipped from the two distribution centers are packed in different reusable multipacks. It makes it difficult to return the packages to the correct distribution center. The schematic flow of goods between production plants, distribution centers, and local branches is presented below in Fig. 2.

Fig. 2. Current logistics model based on various reusable packaging solutions. Source: own elaboration based on research data.

Currently, 12 types of reusable packages have been identified within the supply chain. Unfortunately, due to the lack of standardization across both distribution centers, each local branch has to return the packages to the DC of origin. That results in increased energy consumption, emissions, and costs during the return of empty packaging. A

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complete list of currently used types of the packaging within the internal supply chain is presented in Table 1. In the current logistics model determined by demand and production cycles, a certain seasonality is noticeable, as presented in Fig. 3. The largest distribution flows were recorded from February to March regarding cargo weight and in September and November regarding cargo volume. Table 1. Loading units used currently within the internal distribution chain. Source: analysis of the company’s basic data. Logistics unit type identification code

Total payload volume packed in [m3]

Amount of packages used [pcs]

Distribution Center of origin

NF

0.22

5

Distribution Center 1

NG

1.31

55

Distribution Center 1

NH

0.28

74

Distribution Center 1

NM

4.34

183

Distribution Center 1

NN

352.80

283

Distribution Center 1

NP

1.33

645

Distribution Center 1

NR

0.20

28

Distribution Center 2

NS

0.97

41

Distribution Center 2

NU

0.05

7

Distribution Center 2

NX

3.37

1421

Distribution Center 2

NZ

0.23

8

Distribution Center 2

SA

6845.37

6339

Distribution Center 2

Such characteristics of the goods flow are reflected in demand for means of transport estimated in Table 2. Due to the need to return packaging to the distribution center they come from, it is necessary to allocate dedicated vehicles for this purpose. Distribution center 1 is responsible for 4.997% of the flow of goods, while distribution center 2 handles 95.003% of the flow of goods to local branches. This results in non-effective usage of means of transport returning to DC1 in terms of their filling grade. Unfortunately, due to technological reasons for the production processes for which reusable packaging is needed, sending vehicles with the packages for reuse to DC less than once a month is impossible. The number of units used for maintaining primarily distribution tasks shown in Table 1 is based on primary company data analysis. The gathered information was used to determine the number of returning transports from local branches to distribution centers. Those estimations are indicated in Table 2 and Table 3. Demand for each packaging type has been outlined and presented in Table 1. Data show the total payload volume packed in each logistic unit type. The highest quantitative demand is characteristic of SA, NX, and NP packages. At the same time, the demand for the remaining types of packaging is significantly lower.

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Fig. 3. Seasonality of goods flow within internal supply chain processes. Source: own elaboration based on company’s data analysis.

The flows of goods with their characteristics between the distribution center and local branches are presented in Table 2 and Table 3. The values were expressed as the volume [m3 ] of the goods shipped. To improve accuracy while estimating the amount of necessary trucks to support the goods flow, the factors of the maximum weight and volume of a vehicle GVM up to 40 tons were also considered. Table 2. Goods flow between distribution center 1 and local branches. Reverse flow and packaging types included. Source: own elaboration. Sent goods volume per packaging type [m3] Month

NF

NG

NH

January

0.20 1.31 0.07

NM

NN

NP

Estimated amount of Returning truck trucks (once a month minimum)

5.92 0.07 1

1

February

0.10 4.11 149.35 0.55 2

1

March

0.10 0.23

41.13 0.61 2

1

0.01 1

1

April October December 0.02

0.01

1

1

156.40 0.08 2

1

Due to the minimal share of managing the goods flow by DC1, the number of return shipments should be limited. According to analyzed data, the number of returned vehicles does not exceed one per month. The volume of goods and the number of packages transported do not justify the number of returning trucks to DC1. From the point of view of sustainable development, it is reasonable to look for opportunities to consolidate the return loads. Regarding returning shipments to DC 2, data is shown in Table 3. Analyzing the DC1 goods flow share and returning shipments estimations shows that the number of returning vehicles does not increase during the year as per DC2’s returning shipments.

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On this basis, it can be concluded that the number of returning shipments to the DC1 can be reduced. Table 3. Goods flow between Distribution Center 2 and local branches. Reverse flow and packaging type included. Source: own elaboration. Sent goods volume per packaging type [m3] Month

January February March

NR

NS

NU

NX

NZ

0.03 0.05 0.18

SA

8

2

0.20 0.57

0.91 0.05

415.59 26

3

0.11

2.24 0.15

165.02 26

3

0.00

April

254.21

Estimated amount Returning truck of trucks (once a month minimum)

117.97

4

1

May

91.02

2

1

June

191.90

3

1

July

501.24

7

2

August

320.75

5

1

2160.10 29

3

September October

533.05

November December

0.27

0.04 0,03

9

1

1542.95 21

3

551.56 12

2

Following the adopted IPCC[23] and UK DEFRA[24] standards, the carbon footprint of the current logistics model has been calculated and compared with the proposed optimized model in the following section of this research paper. In the case of the IPCC 2014 Mitigation of Climate Change recommendations referring to the measurement of the carbon footprint level, reference was made to GHG decomposition factors such as System Infrastructure Modal Choice, Fuel Carbon Intensity, Energy Intensity, and Activity [23]. Related UK DEFRA emission factors were used for further GHG assessment in correlation with IPCC recommendations. 4.1 Implementation of Standardized Universal Logistic Unit Across the Supply Chain In the to-be logistics model of the supply chain, standardized reusable packaging was proposed. This approach will eliminate arranging extra linehauls for managing the return of packages to the warehouse of origin. As a result, it was possible to minimize the number of shipments in the entire chain by 3.16%. All returnable packaging can only be returned to the closest distribution center 2, and the volume of returned packaging

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can be handled by the vehicles already involved in the reverse logistic chain for DC2 specified in Table 3. This approach benefits other SC key process indicators (KPIs), such as the filling grade of linehauls within the reverse flow. The proposed to-be supply chain design has been presented in Fig. 4.

Fig. 4. Proposed return supply chain design based on standardized types of packaging. Source: own elaboration based on research.

4.2 Carbon Footprint Comparison of Both Supply Chain Models To assess the impact of changes between the two analyzed models, an environmental comparison has been conducted and expressed as kg CO2 e. The calculations were made using emission factors published by the UK DEFRA organization for a 40-ton vehicle for each kilometer driven, assuming the average filling of the vehicle. The UK DEFRA factors used for the calculations were published on June 22, 2022 [24]. For 1 km driven by this specified vehicle, it’s 0.92391 kg CO2 e, 0.90772 kg CO2 e of CO2 , 0.00013 kg CO2 e of CH4 , and 0.01606 kg CO2 e of N2 O. The logic and outcome of calculations are presented in below . Table 4. The environmental performance assessment relates to the number of kilometers traveled by 40 tons of GVM lorries within the supply chain model. The seasonality of flows was considered to determine the level of emissions of the supply chain in the case of the before and after design model. In the proposed model, it was possible to minimize the supply chain carbon footprint by 2355.97 kg CO2e. By increasing the quality of management and process control, reusable packaging can help further reduce the emissivity of the supply chain.

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Table 4. Environmental assessment of as is and to-be supply chain model. Source: own elaboration. Phase 1. Non-standardized reusable packaging within the supply chain DC 1

DC 2

Phase 2. Implementation of standardized reusable packaging within the supply chain DC 1

DC 2

Month

Estimated amount of trucks

Returning truck (once a month minimum)

Estimated amount of trucks

Returning truck (once a month minimum)

Estimated amount of trucks

Returning truck (once a month minimum)

Estimated amount of trucks

Returning truck (once a month minimum)

January

1

1

8

2

1

0

8

2

February

2

1

26

3

2

0

26

3

March

2

1

26

3

2

0

26

3

April

1

1

4

1

1

0

4

1

May

0

0

2

1

0

0

2

1

June

0

0

3

1

0

0

3

1

July

0

0

7

2

0

0

7

2

August

0

0

5

1

0

0

5

1

September

0

0

29

3

0

0

29

3

October

1

1

9

1

1

0

9

1

November

0

0

21

3

0

0

21

3

December

2

1

12

2

2

0

12

2

Total routes

9

6

152

23

9

0

152

23

Average trip distance [km]

425

320

425

320

Total kg CO2e per DC

5889.92625

51738.96

3533.95575

51738.96

Total kg CO2e in SC

57628.88625

55272.91575

5 Conclusions Based on the research, it has been shown that changing the type of packaging affects the design of logistics processes. Implementing the appropriate standard of reusable packaging helps optimize internal reverse logistics and reduce emissions. Understanding the direct impact of types of packaging units supports the minimization of CO2 equivalent emissions. The change in the standard of reusable packaging used to perform transport tasks between internal participants of the distribution process increases efficiency and reduces the total number of kilometers traveled by trucks. Further research should be carried out to reduce the carbon footprint related to the production of loading units and their lifetime using the LCA methodology. It is crucial to increase efficiency, considering the tasks dedicated to the reverse supply chain and recycling and reuse of packing.

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Acknowledgment. Damian Dubisz would like to thank the Ministry of Education and Science for funding his research within the framework of the Applied Doctorate Program of the Ministry of Education and Science of Poland (Program Doktorat Wdrozeniowy Ministerstwa Edukacji i Nauki) implemented in years 2021–2025 (Contract No. DWD/5/0015/2021 dated 23/12/2021).

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Agile Framework as a Key to Information Management Systems Delivery Bohdan Haidabrus1,2(B) , Janis Grabis2 , Oleksandr Psarov3 and Evgeniy Druzhinin4

,

1 University of Applied Science, 3, Meza Street, Riga 1048, Latvia

[email protected]

2 Riga Technical University, 6A, Kipsalas Street, Riga 1048, Latvia 3 Sumy State University, 2, Rymskogo-Korsakova Street, Sumy 40007, Ukraine 4 National Aerospace University, 17, Chkalova Street, Kharkiv 61070, Ukraine

Abstract. The article examines the nature of the agile framework, and explores the peculiarities of its management. Project oversight and controls are key for delivering expected project and service outcomes. Building consistency and learning from each other helps to improve every project. The objective of our research, based on the agile framework, is to provide a set of practices for project teams to deliver success and promote information management systems. It leads to reliable delivery based on project management competencies, agile working methods, scaled agile framework techniques, and project-based service provision. The proposed framework defines what work needs to be done and how it can be accomplished at the engagement. The framework is comprehensive, covering critical areas that impact solution delivery, including strategy, planning, mobilization, management, development, and operations which is essential to consider information management systems specific. The research is based on the following engagements: to promote consistency across engagements worldwide with a common language and standard approach; to support the industrialization approach with a disciplined process for delivering high-quality solutions across multiple locations, multiple workforces, and multiple cultures; to enhance reliable, high quality and on-time delivery of engagements by providing a field-tested approach gleaned from current subject matter experts’ experiences. Keywords: Agile Framework · Delivery Management · Increasing Efficiency · Sustainable Manufacturing

1 Introduction Agile Delivery Framework is a key to effectively delivering information systems in the modern consultancy industry for successful project and portfolio management. Client Portfolio Management is leveraging the correct information to make client portfolio decisions, categorizing clients based on their growth potential, and factoring client prioritization information into ongoing sales decisions. Regular client portfolio management is essential to ensure they focus their investments on relationships with the greatest potential return. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 113–120, 2023. https://doi.org/10.1007/978-3-031-32767-4_11

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In our research output, the clients are classified into several categories to identify and target the right consumers as key stakeholders. These classifications are used to influence and/or drive actions around Sales Planning and Targeting, Account Planning, Business Development investments, New Business Management (NBM) Approvals, and Access Protocols (see Fig. 1).

Fig. 1. The workflow of targeting new clients.

2 Literature Review Account Planning is a key vehicle for driving organic growth and a means to understand issues/shortfalls in achieving our long-term strategic growth agenda [1]. The proposed Agile Framework delivers information management systems [2] based on the research from at least 1500 client persons to connect with daily about how to deliver the projects and what can be done more for the client accounts. Therefore, in the Agile ways of working, we have introduced the role of Delivery Lead [3]. So, we could bring one of the delivery frameworks to support the client and overall project and portfolio management team. It is essential to understand the business environment, which helps to run pre-sales and staffing of the human resources. The information technology consulting industry faces unprecedented challenges, including weak markets, the global economic downturn, mergers and consolidations, stringent regulatory requirements, natural disasters, and the need to operate worldwide. Some examples of industries were collected in the research: Digital, Azure industry X, DevOps Data engineering, Interface Design, Data Engineering, etc. These challenges are further compounded by the emergence of a host of operational challenges [4]: • • • • • •

time to market for new products and new channels; the need for alternate sales channels; replacement of basic legacy systems with following generation insurance systems; concentrating exposures and creating a single view of the customer; price, service, and time pressures for new entrants to the market; difficulty entering new global markets – lack of understanding of the unique market conditions of each country.

In our Agile Delivery Framework, Compliance management is assessing and monitoring compliance with engagement policies, such as international standards, customer

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data protection and records management, and identifying and monitoring corrective actions to avoid or reduce engagement risks [5]. A Client Data Protection program (CDP) is a fundamental component of information security defenses. CDP provides engagement teams with a standardized approach to managing risk through management processes, controls, and metrics (see Fig. 2) [6, 7]. • Implement Client Data protection controls – for every required control, identify a control point owner, conduct a gap analysis of each control assigned, develop an action plan to close identified control gaps, and monitor overall compliance to controls throughout the contract’s life (s) [8]. • Monitor adherence to engagement policies related to travel, time and expense, procurement, mandatory training attendance, and methodology use [9]. • Identify compliance issues and implement corrective or preventive actions. • Should there be policy changes, communicate the changes to all team members.

Fig. 2. Lifecycle of Compliance activities as a part of Information Management Systems Agile Delivery.

3 Research Methodology In our proposed framework, there are a few statements for business quality: – Quality means meeting business requirements, not adding extras. – Business quality should be checked before an activity or scope works item is completed. – Quality should be considered whenever there is a change to any business constraints. – In the proposed framework, a quality department may perform some quality activities. Based on these statements business continuity plan (BCP) is an action plan to ensure the continuity of project-critical business processes and services in the event of disruptions caused by internal and external factors. The BCP is a set of documents that defines a recovery strategy during a crisis. As an essential part of the Agile Delivery framework addresses the following aspects: • people - safety and security of the people; • communication - communications control to avoid rumors and chaos;

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• infrastructure - the ability of the office to support and ensure service delivery during power outages; • technology - the impact of technology assets on service delivery. • There is an established business continuity management framework that includes: – – – – –

business continuity management policy; senior management responsible for business continuity management; documented project business continuity plans; an incident management capability; program of testing and maintenance of business continuity plans and recovery arrangements.

Dedicated persons from the Agile specialty take care of people working in small resource supply projects and people on the bench to inform them about business continuity actions and needed training. The next step is to determine the metrics used to measure and review quality. Quality is an essential means of protecting internal reputation and external Brand. This includes informal guidance and systematic reviews. We expect quality assurance to be applied to all areas of work/services we provide and at all stages of the project lifecycle – continuous monitoring and mitigation of risks, starting with appropriate risk assessment at the opportunity stage, followed by quality control and implementation of best practices (see Fig. 3) [10, 11]. There are several actions needed from the Delivery Lead, who is a driver of the proposed framework [12–15]: – define quality management processes for the project and have a plan for continuous improvement; – recommend improvements to organizations standards, policies, and processes, which are expected and welcome by management; – ensure that it is well understood the customer’s definition of quality.

Fig. 3. Applied Quality assurance to all areas of Agile Delivery work across all stages of the project lifecycle.

Project Process Maturity Assessments are performed to ensure the project’s compliance with: • policies;

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• Delivery Methods and Specialty track Process & Automation best practice usage Industry best practices based on Capability Maturity Model Integration (CMMI) (see Fig. 4, [16]); • projects defined processes.

Fig. 4. 5-levels Capability Maturity Model Integration.

Maturity Level (ML) is a degree of process improvement across the predefined set of process areas in which all goals in the set are attained: 1. Builds on the practices at lower levels. 2. Represents an increase in functionality and capability. 3. May add new functionality. The Delivery Quality Assurance review are independent assessments conducted by the Quality Assurance Directors (QADs). These QAs are designed to review and assess the overall risk based on the Risk Assessment framework. This is accomplished by determining the level of risk associated with the following four Blocks: • • • •

Block 1: Client Expectations and Context. Block 2: Contract and Deal Structure. Block 3: Solution Plan and Cost. Block 4: Underlying Capability.

For areas considered High or Above Normal risk, the assessment requires documented mitigating actions and unambiguous ownership by named individuals for these actions. On a monthly basis, the Project QA data team receives the data extracted from Quality Assurance & Risk Tool. The project QA data includes: • Project QA Plan status (Active, Waived). • Delivery QA Status by QAD (Green, Red, Yellow).

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• Last Conduct Date. • Next Delinquency Date. • QA Director Name. The project contract number identifies the project data from the global report. If the project does not appear in the worldwide extract, the team contacts the QA support team and clarifies the reason.

4 Results and Discussion A result of the proposer agile delivery framework is a continuous improvement phase0 as a part of the value proposition for customers. To deliver more value to customers and businesses, they must strive to provide it more effectively over time. Continuous improvement processes are ongoing efforts to improve products, services, or processes.

Fig. 5. Lean Delivery as a part of continuous improvement.

Lean is a principled approach that focuses on reducing lead times, accelerating speed, and reducing operating costs by continuously identifying and eliminating waste (non-value-added activities) in processes (value streams) (see Fig. 5): Client Team Satisfaction Survey (CTSS) allows us to understand the client team’s assessment of the services and value we have provided, our contribution to overall client expectations, and the overall strength of the relationship. The surveys are launched twice per year or at project closure by the Delivery Excellence team in collaboration with Portfolio and project Leads to clarify the survey scope and audience. The received project CTSS results, at least for one question, are below the fiscal year projects that should identify improvement actions and implement them. Delivery and project and delivery managers are involved in this process by setting up a discussion on improvements, helping to specify tasks, and monitoring implementation progress. All created CTSS improvement actions are shown as metrics (% Implemented CTSS actions). Each project should go through the following cycle: 1. Analyze received feedback. 2. Discuss results with the feedback provider. 3. Agree on Improvement activities and implement them in the agreed timeline.

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5 Conclusions The ultimate goal of the agile framework for information management systems delivery is to test and verify that each described stage (phase or deliverable) meets the metrics and requirements as stated in a framework, including the customers’ acceptance criteria checklist, and that it is ready to move to validate framework process. Before that, in conclusion, it is important to mention that closing a project or program needs to be planned while the project is still running. It involves thinking through how the final deliverables will be reviewed and approved by the client and the operational activities required to close a contract/project in project systems. The data and processes from closure help us build case studies and trending data to evolve and improve the processes permanently. Based on provided research outcomes, we can see that Agile Delivery is critical to empowering people to be in charge of the company’s future. Everyone can create more value for clients, especially Information Management Systems Delivery as a project, daily operations, and strategic changes.

References 1. Hoelbeche, L.: Designing sustainably agile and resilient organizations. Syst. Res. Behav. Sci. 36(5), 668–677 (2019). https://doi.org/10.1002/sres.2624 2. Nguyen, D.S.: Success factors that influence agile software development project success. Am. Sci. Res. J. Eng. Technol. Sci. 17(1), 172–222 (2016). https://doi.org/10.1016/j.jss.2007. 08.020 3. Haidabrus, B., Grabis, J., Protsenko, S.: Agile project management based on data analysis for information management systems. In: Ivanov, V., Trojanowska, J., Pavlenko, I., Zajac, J., Perakovi´c, D. (eds.) DSMIE 2021. LNME, pp. 174–182. Springer, Cham (2021). https://doi. org/10.1007/978-3-030-77719-7_18 4. URL. https://www.scaledagileframework.com/pi-planning. Accessed 15 Aug 2022 5. Hofman, M., Grela, G.: Project portfolio risk identification – application of Delphi method. J. Bus. Econ. 6(11), 1857–1867 (2015). https://doi.org/10.15341/jbe(2155-7950)/11.06.201 5/004 6. Kischelewski, B., Richter, J.: Implementing large-scale agile – an analysis of challenges and success factors. In: Proceedings of the 28th European Conference on Information Systems (ECIS), An Online AIS Conference, Marrakech, Morocco (2020) 7. Emovon, I., Nwaoha, T.C.: Application of rough TOPSIS technique for the analysis of engineering system failure causes. J. Eng. Sci. 5(2), E1–E6 (2018). https://doi.org/10.21272/jes. 2018.5(2).e1 8. Kotliar, A., et al.: Ensuring the economic efficiency of enterprises by multi-criteria selection of the optimal manufacturing process. Manag. Prod. Eng. Rev. 11(1), 52–61 (2020). https:// doi.org/10.24425/mper.2020.132943 9. Ivanov, V., Liaposhchenko, O., Denysenko, Y., Pavlenko, I.: Ensuring economic efficiency of flexible fixtures in multiproduct manufacturing. Eng. Manag. Prod. Serv. 13(1), 53–62 (2021). https://doi.org/10.2478/emj-2021-0004 10. Ivanov, V.: Process-oriented approach to fixture design. In: Ivanov, V., et al. (eds.) DSMIE 2018. LNME, pp. 42–50. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-935 87-4_5

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11. Ivanov, V., Pavlenko, I., Kuric, I., Kosov, M.: Mathematical modeling and numerical simulation of fixtures for fork-type parts manufacturing. In: Knapˇcíková, L., Balog, M. (eds.) Industry 4.0: Trends in Management of Intelligent Manufacturing Systems. EICC, pp. 133–142. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14011-3_12 12. Muzylyov, D., Shramenko, N., Ivanov, V.: Management decision-making for logistics systems using a fuzzy-neural simulation. In: Cagáˇnová, D., Horˇnáková, N., Pusca, A., Cunha, P.F. (eds.) Advances in Industrial Internet of Things, Engineering and Management. EICC, pp. 175–192. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69705-1_11 13. Medvediev, Ie., Muzylyov, D., Shramenko, N., Nosko, P., Eliseyev, P., Ivanov, V.: Design logical linguistic models to calculate necessity in trucks during agricultural cargoes logistics using fuzzy logic. acta logistica. Int. Sci. J. About Logist. 7(3), 155−166 (2020). https://doi. org/10.22306/al.v7i3.165 14. Pavlenko, O., Velykodnyi, D., Lavrentieva, O., Filatov, S.: The procedures of logistic transport systems simulation into the petri nets environment. In: CEUR Workshop Proceedings, vol. 2732, pp. 854−868 (2020) 15. Muzylyov, D., Shramenko, N.: Blockchain technology in transportation as a part of the efficiency in industry 4.0 strategy. In: Tonkonogyi, V., et al. (eds.) InterPartner 2019. LNME, pp. 216–225. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40724-7_22 16. ISACA: CMMI Performance Solutions. https://cmmiinstitute.com/. Accessed 01 Oct 2022

Perspectives of Lean Management Using the Poka Yoke Method Jozef Husár1(B) , Stella Hrehova1 , Piotr Trojanowski2 Szymon Wojciechowski3 , and Vitalii Kolos4

,

1 Technical University of Košice, 1, Bayerova Street, Prešov 080 01, Slovak Republic

[email protected]

2 West Pomeranian University of Technology in Szczecin, 17, Piastów Avenue,

70-310 Szczecin, Poland 3 Poznan University of Technology, 3, Piotrowo Street, 61-138 Poznan, Poland 4 Sumy State University, 2, Rymskogo-Korsakova Street, Sumy 40007, Ukraine

Abstract. The paper provides a theoretical perspective on Lean management, which is very often mentioned in connection with manufacturing companies. In the article, we present and offer an overview of Lean management used in the industry. Through this paper, we want to present the potential and advantages of the Poka-Yoke method for continuous improvement and modernization of preestablished and often outdated procedures in production processes. As an example, we describe the lean methodology and its elements. In conclusion, we focus on one method: the Poka-Yoke. It has advantages and disadvantages, but this paper points to selected parameters and possibilities for implementing this method in production as quickly and efficiently as possible. In conclusion, the advantages of implementing this method are highlighted. The research shows it is obvious how it is possible to contribute to increasing product quality, reducing costs associated with repairs and complaints, increasing productivity, and reducing loss of time caused by errors. By implementing lean management, we can also contribute to increasing the safety of the production process and reducing the risk of equipment failures. The research shows how lean production tools and the Poka-Yoke method can effectively develop intelligent manufacturing enterprises. Keywords: Lean Management · Poka-Yoke · Process Innovation · Sustainable Manufacturing

1 Introduction During World War II, Taichi Ohno, a production manager at a small Toyota plant, observed his employees and concluded that the workers at the waist had more knowledge than their foremen. He, therefore, chose a few of them and replaced them at the head of the workgroups instead of the aforementioned foremen. He assigned a part of the production line to individual groups and entrusted them with the task of jointly devising the best way to carry out the work entrusted to them. The Toyota company achieved unprecedented success with this system and enriched the world with a new term - Lean © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 121–131, 2023. https://doi.org/10.1007/978-3-031-32767-4_12

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management [1]. The priority of enterprises in a market economy is to ensure their commercial and economic success by producing competitive products and services. The identification, evaluation, and gradual improvement of the processes taking place in the company have become a common approach to the management of business agility oriented towards increasing performance in recent years. Insight into the organization’s internal activities, reactions to the surrounding external environment, and concentration on process flows have become justified managerial disciplines to manage the company’s internal activities. And that both in production processes and services and in the sphere of state administration [2]. In the past, attention was constantly paid to improving individual work activities or short-term process sequences. At the turn of the 90s of the 20th century, the first discussions about the possibility of process insight into the activities taking place in the company began. William Edwards Deming can be mentioned as an exception confirming the rule, who applied his process management methods in Japan, which remained unnoticed by the rest of the world for a long time [3]. At the beginning of the 20th century, several experts in managerial practice devoted their attention exclusively to production process improvement. As in the area of improving the quality of every single work operation, coordination of process operations, etc. Managers are currently oriented toward understanding business processes as complex process flows [4]. After the analysis, we try to point out the perspectives on applying poka-yoke as a management method in this paper.

2 Literature Review The nineties marked a revolution in improving business processes by applying a new concept - reengineering, which seemed like a savior solution at the time. The authors of this term were American consultants James Champy and Mike Hammer. These promoters claimed that by identification, visibility, subsequent understanding, and reinvention, complex business processes could be almost redesigned and thus radically improved [5]. Applying the newly proposed measures was supposed to find the sources of inefficiency and eliminate unjustified or erroneous steps and everyday, harmful habits that were the legacy of labor division. From measuring the quality of individual acts, the focus shifted to complex values, such as efforts to acquire new customers, compliance with set deadlines, or providing high-quality services. The decisive factor in successfully applying this method was the innovative use of information technology, the correction of sequences of work actions, the introduction of business rules, and many other factors liberating processes [6]. Until the mid-1990s, it was a trend to discuss reengineering in connection with any managerial-business activity. This term appeared in most private, corporate, and public claims regarding minor changes. This sharp increase in the new way of managing business processes, often accompanied by great enthusiasm but without sufficient knowledge, was the cause of hysterical efforts to change everything in the company from the ground up [7]. Businesses have been forced to move to combinations of workflow systems, information software applications, and the Internet. It led to the creation of new business process management systems denoted by the abbreviation BPMS, the task of which was to coordinate the acts carried out inside the system with human work.

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Creating business process architectures, determining the essence of success, and concentrating the forces of managers on achieving set goals were the first steps of large companies [8]. The trend in this period was using balanced indicators systems, denoted by the abbreviation BSC. It is a means of strategic management used to measure the company’s performance [9]. The business process modeling of the nineties resurrected the practices of the Six Sigma method, which expanded its scope from mass production to practically all areas of industry [10]. Its popularity became radical, especially when it implemented the basics of the Lean method, which stabilized into the currently used approach called Lean Six Sigma [11]. The term “Lean Management” includes several management methods aimed at identifying and eliminating activities that do not represent any benefit in creating products and services. However, it can be most briefly presented as a philosophy that the company cannot apply to a specific process but must implement in its entire operation. We can discover the roots of Lean in the relatively early stages of modern management. Already in 1910, Henry Ford promoted the groundbreaking theories of Frederick Taylor, Frank Gilbreth, or the founder of the Gantt chart, Henry Gantt. The common feature of these giants was the effort to maximize the production process in the shortest possible time. Other representatives of the Lean philosophy include James Womack, the author of the term lean production, or Taiichi Ohno, the author of the rapid rebuilding method, also known as the SMED method [12]. The industrial world gradually adopted Lean as a universal tool designed to improve processes in the company, which also found its further application in the service sector, healthcare, and banking [13]. The methodology is based on a cyclical approach aimed at improving business processes - work teams concentrate their attention on smaller improvement measures, and the resulting improvement is achieved in successive iterations. At the same time, they help to eliminate possible adverse consequences of the experimental solutions implementation. Lean management assumes the standardization of the processes themselves. It means documenting them and verifying that they work in harmony with the processed description before proceeding with their improvement [14].

3 Research Methodology The methodology of Lean management represents a summary of principles and methods aimed at identifying and eliminating activities that do not represent any benefit in creating products and services intended to serve the customers of the process. Based on this claim, these activities represent unnecessary waste in production. Originally, this “production lean” technique was intended to improve business processes in industrial production. Over time, however, it found its application in other fields, especially in services and administration. The essence of thinking in the Lean management style is to think simply, directly, or simply put, to use “peasant common sense”, and all this in a systematic arrangement and methodical application to specific aspects of the process [6]. The basic principles of Lean management are [2]: • Determining the customer-friendly process value. Value is the intended product or service satisfying the customer’s needs, provided at a time and a price corresponding to his ideas;

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• Identification of activities involved in the subsequent value creation. The process is defined by the sequence of steps that occur during the creation of value, from the creation of the product design to its presentation to the customer, from the order receipt to its dispatch, and from the used material to the finished product; • Setting processes in motion. Ongoing processes destroy the idea of dividing the company into separate departments. They pass through the company without respecting the rules of previous hierarchical structures. Many times, they even cross the company’s boundaries with a serious connection to selecting subcontractors or customers. Thus, they enable each participant in the process to contribute to the creation of value with their activity; • Management of customer needs. Processes are conditioned by the need to deliver a specific product or service. In other words, what the customer wants and when suits him is produced. This approach replaces the classic make-to-stock method, continuing to try to sell what is currently in stock; • Striving for perfection. It represents an endless effort to reduce time, cost, effort, space, errors, and failures, all under the same conditions under which the goods are produced, or the service provided simultaneously. As mentioned in the previous section, Lean management is based on a cyclical approach to process improvement. Individual company members will focus on smaller improvement tasks. The organization’s overall improvement will be achieved in successive iterations, which also help eliminate possible negative consequences caused by applying experimental solutions. To achieve the desired process improvement, it is necessary that the idea of Lean management grows deep into the thinking of employees and thus becomes an integral part of the corporate culture. The concept of Lean management methodology is [15, 16]: • Long-term philosophical approach promoted by management through long-term strategic plans. • Orientation to the process as the bearer of the produced object quality or the service provided, as well as the mediator of the performance capacity of key business functions, which thus transforms considerations closer to the specific characteristics and conditions of the process with subsequent assumptions: – If the process is properly designed, then the products will achieve the required quality. – If the process is balanced and smooth, then the costs of covering peaks or holding stock will be eliminated. – If the company produces what the customer requires, and when he needs it, he will not have to fight for every piece sold, and the stock level will tell, with its volume and structure, what the market is interested in. – If the process focuses on the quality of each operation, it will be relieved the costs resulting from repairs and complaints.

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• Intentional search for individuals through whom it is possible to implement the company’s intentions in the quality improvement area or cost reduction and to support their personal development. • Long-term support of self-educational processes and the prosperity of the company without exception: – About process control and a thorough understanding of addressing situations. – About a precise discussion with consideration of all eventualities before a solution is chosen and then their rapid implementation. – About constant efforts for organizational self-reflection and a systematic improvement program. Lean management methodology finds its application mainly where attention is paid to increasing process performance while simultaneously reducing operating costs. It manifests itself when reducing the amount of inventory, reducing the size of the production space, or saving the work spent on a specific performance. It is popular where it is necessary to simplify the processes and shorten the time of entering the material into the process and handing it over as an output to the next stage of the process or directly to the customer. Another important feature of this methodology is the division of activities taking place in the process into those that add value to the product through their activity and those that are not directly related to the added value, or do not contribute to its creation or, on the contrary, burden it [17]. If we are considering the use of the Lean management methodology, the following assumptions should be used in our analysis [18]: • • • •

Waste appears in many different forms in processes; The speed of making a change in an active process is critical; Processes are not kept in motion; Process changes must be systemic in nature, committed, and adapted to a balanced complex of small changes affecting all related areas, such as personnel, technology, and process systems. In practice, the Lean management methodology is applied primarily where [18]:

• If favorable market conditions require an increase in the performance of processes or a reduction in the time of order cycles; • The competition is characterized by its aggressive policy in the area of price and quality of services; • Customers demand lower prices; • Businesses try to minimize inventory; • Owners demand a higher return on capital; • The company sees the possibility of improving its position in the market by improving the quality of its products. Lean management methodology can be applied in two ways. The first method is Kaizen-style team meetings, which Japanese improvers successfully used, especially in

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earlier periods. The principles of Kaizen are mainly based on the assumption that the implementation of any small changes carried out at regular intervals and with long-term active maintenance can ultimately bring fruit in the form of a significant improvement in the performance of the process. Kaizen activities often referred to as lightning or accelerated process improvement, aim to eliminate waste in specific areas of the process, increasing performance and maintaining it. Most often, they gather people into a shortterm cooperative group in the range of 2 to 5 days. While value chain mapping projects typically focus on conceptual analysis and future prospects, Kaizen focuses on identifying unnecessary activities and sources of waste and planning turnarounds to correct identified problems [4]. The second method of application is classic project incentives using the traditional Deming PDAC cycle – i.e., Plan – Do – Check – Act. This method is successfully used in more extensive improvement programs or the planning and managing of changes occurring in more complicated processes and where the project scope and necessary preparations exceed the implementation possibilities within a few days [7]. A fundamental contribution of the lean manufacturing methodology to the corporate philosophy is the constant pursuit of perfection and the periodic improvement application. Lean management is based on the condition that there is no level of perfection with which the company should be satisfied and which cannot be further developed. After the values of one improvement cycle are designed, applied, and verified, it is necessary to reassess additional, new needs and customer requirements [19]. It is also necessary to pay attention to further improvement, whether it is changing in the area of increasing the processes’ capacity or removing everything undesirable and unnecessarily burdening processes [6]. For the correct implementation of Lean management, several principles, methods, and approaches are applicable in improvement projects. From a large number of methods, it is good to mention at least a few of them [9]: • • • • • •

Value stream mapping; Process flow analysis; Principles of push and pull; 5S Method; Accelerated transformation of activities; Poka-Yoke Method.

4 Results and Discussion A typical error prevention example is a warning that pops up on the screen of every personal computer. A box will appear asking if you want to save the open file before shutting the computer down completely. Other examples are safety caps on medicines that prevent children from opening them independently, safety barriers on slopes, or protective barriers in parking areas or areas with restricted movement. This technique is also used in industry. For example, using color coding, barcodes for recognizing specific products in production lines, different types of sensors or color-marked areas to ensure transport, or ordinary circuit breakers protecting the electrical network. For more examples, see Fig. 1 [5].

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Fig. 1. Typical examples of the Poka-Yoke method application [5].

The main mission of the Poka-Yoke method is to identify and prevent errors. If they do occur, the defective product mustn’t reach the customer. 4.1 The Most Common Ways of Applying the Poka-Yoke Method By correctly implementing the Poka-Yoke method, it is possible to detect deviations in the installed product from the basic caliber or the characters programmed in the machine. We know [19]: • Guide pins of different sizes – The pins located in the lower part of the mold exactly fit the holes in the upper part of the mold. – Pins placed in the foundation preparations’ storage surfaces ensure the desired object’s precise and correct foundation. • End sensors – They detect the exact part position and then start the work process. – They detect the movement of the tool and its displacement. The tool returns to its initial position when the boundary surface is reached. • Optical sensors – They detect the presence and position of the part after the assembly operation. If the sensor registers a missing part of part, it sends a signal to the machine’s control system, which blocks the object in the fixture or alerts the operator with light and sound signals.

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• Counters – Counters record the exact number of operations or the number of assembled pieces. A light and sound signal is triggered in the event of a difference between the actual number and the reference number. 4.2 Procedure for Implementing the Poka-Yoke Method Among the generally recognized guidelines applied when implementing the Poka- Yoke method in a company, we recommend the following activities [20]: 1. Identification of input parts based on their parameters: – Part weight - determination of weight standards, use scales to identify individual parts. – Part dimensions – determination of the standard for length, width, diameter, etc., identification of deviations utilizing mechanical stops, limit switches, etc. – Part shape – determination of standards for specific shape features, such as angles, contours, bends, hole positions, etc. 2. Detection of deviations from previous processes or detection of operation skipping: – Method of monitoring operations - the next cycle cannot be carried out if the employee or the machine does not perform the sequence of actions required by the standard during the work cycle. – The method of passing from process to process - we cannot carry out the cycle if one of the steps is skipped; therefore, the standard procedure was not followed. 3. Detection of deviations from fixed values: – Control via counter – Redundancy method – a specific number of parts is stored in a batch. If it happens that the batch contains a larger number of units, an error is signaled. 4. Measuring critical indicators–detecting critical production parameters, such as pressure, current, time, and temperature. The cycle is suspended until the monitored value returns to the prescribed tolerances. For the Poka-Yoke method to be successful in a business, it needs to have these qualities [21]: 1. Timeliness - the success of the method depends mainly on the time when it was implemented in the process. The sooner it is applied, the sooner its impact will be felt – help detect and remove unwanted errors.

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2. Accuracy – it should be a precise solution that easily diagnoses and identifies what caused the given problem. 3. Simplicity – The Poka-Yoke solution should be as simple and straightforward as possible. It is an important characteristic, as the employee does not have enough time and effort to take special care of a specific solution. The more complex the given solution is, the greater the chance of an error occurring. 4. Transparency – the Poka-Yoke solution must be unobtrusive and direct. If it becomes a burden on the process, the employee will start looking for ways to bypass such a solution.

5 Conclusions In conclusion, we want to evaluate that Poka-Yoke is a technique often used in lean management. This management approach aims to maximize value and minimize waste in all aspects of an organization. Lean management aims to create a continuous flow of value to customers while eliminating waste, defects, and delays. Poka-Yoke techniques can support this goal by helping eliminate errors and defects, streamline processes, and improve efficiency and productivity. One of the main potential benefits of using Poka-Yoke in a lean management context is that it can help to reduce or eliminate waste in the form of defects, rework, and other issues that result from errors. By detecting and correcting errors as they occur, PokaYoke can help minimize the need for rework and other corrective actions, saving time and resources. In addition, Poka-Yoke can help to improve the flow of value to customers by reducing delays and increasing efficiency, which can lead to increased customer satisfaction and loyalty. Overall, using Poka-Yoke as a mistake-proofing technique can be a powerful tool for achieving the goals of lean management and maximizing customer value. Some of the potential benefits of Poka-Yoke include: • Improving quality: By reducing or eliminating errors, Poka-Yoke can help improve the overall quality of products or services. It can lead to fewer complaints and returns from customers, as well as fewer defects and repairs. • Increase efficiency: By streamlining processes and eliminating the need for manual error checking, Poka-Yoke can help increase efficiency and productivity. It can lead to cost savings and faster completion. • Increasing competitiveness: Poka-Yoke can help organizations become more competitive in their markets by improving quality and efficiency. It can lead to increased sales and market share. • Increased customer satisfaction: By providing higher products and services quality, Poka-Yoke can help organizations better meet customer needs and expectations, which can lead to increased customer satisfaction and loyalty. • Improving employee satisfaction: By reducing errors and simplifying processes, PokaYoke can help improve the work environment and increase employee satisfaction and morale.

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All these advantages point to the necessity of designing workplaces so that the work surface is adapted to the employee as much as possible and serves to simplify work. It can be achieved using poka-yoke principles. Our further research will focus on combining ergonomics with the use of a pick-to-light system and testing it in laboratory conditions. Acknowledgment. The research was partially supported by the Polish National Agency for Academic Exchange within the project “Strengthening the scientific cooperation of the Poznan University of Technology and Sumy State University in the field of mechanical engineering” (agreement no. BPI/UE/2022/8-00). This work was also supported by the projects KEGA 038TUKE-4/2022 and VEGA 1/0268/22 granted by the Ministry of Education, Science, Research and Sport of the Slovak Republic.

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14. Niemann, J., Reich, B., Stöhr, C.: Lean six sigma. In: Niemann, J., Reich, B., Stöhr, C. (eds.) Lean Six Sigma, pp. 11–61. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-66263008-2_3 15. Burduk, A., Łapczy´nska, D., Kocha´nska, J., Musiał, K., Wi˛ecek, D., Kuric, I.: Waste management with the use of heuristic algorithms and internet of things technology. Sensors 22, 8786 (2022). https://doi.org/10.3390/s22228786 16. Lasinska, N.: Hybrid management methodology for transport projects related to rolling stock. J. Eng. Sci. 8(2), B7–B11 (2021). https://doi.org/10.21272/jes.2021.8(2).b2 17. Bhasin, S.: Lean Management Beyond Manufacturing. Springer Cham (2015). https://doi. org/10.1007/978-3-319-17410-5 18. Balog, M., Knapˇcíková, L.: Advances of intelligent techniques used in industry 4.0: proposals and testing. Wirel. Netw. 27(3), 1665–1670 (2019). https://doi.org/10.1007/s11276-019-020 64-w 19. Monkova, K., et al.: Condition monitoring of Kaplan turbine bearings using vibro-diagnostics. Int. J. Mech. Eng. Robot. Res. 9(8), 1182–1188 (2020). https://doi.org/10.18178/ijmerr.9.8. 1182-1188 20. Poka - Yoke: prevention of inconsistencies in the production process. http://katedry.fmmi. vsb.cz/639/qmag/mj41-cz.pdf. Accessed 23 Nov 2022 21. Harisha, N.: Poka Yoke. https://www.slideshare.net/NagiripatiHarisha/pokayoke-report-inpdf-form. Accessed 24 Nov 2022

Agile in the Context of Manufacturing SMEs Angelina Iakovets(B)

, Michal Balog , and Kamil Židek

Technical University of Košice, 1, Bayerova Street, Prešov 08001, Slovak Republic [email protected]

Abstract. Flexible production management, as well as lean production, are the trend of our time. Small and medium-sized enterprises are under pressure from large enterprises since the latter has a well-established system and the resources to carry out experimental and pilot projects without harming the enterprise. Smaller enterprises are forced to respond flexibly to changes in the technology market and demand in cramped conditions. The study included an analysis of modern methods for assessing the effectiveness of Industry 4.0 technologies implementation and an analysis and comparison of the principle of lean production and Agile. Experts of a typical SME evaluated modern agile methods according to the selected methods, and the factors with the highest risk for the enterprise were also selected. During the study, the Agile and Lean methods were compared to select the appropriate method of assessing the attractiveness of Agile methodologies for MSP production management. Selected assessing methods could help introduce not only Agile or Lean components but other modern trends in management and manufacturing processes. Keywords: Sustainable Manufacturing · Lean · Management · Industry 4.0 · Industry 5.0 · DEMATEL

1 Introduction Since the beginning of 2000, manufacturing development has been actively developing according to the Industry 4.0 trend [1], but starting in 2019, enterprises began moving to the next development stage – Industry 5.0. According to the fifth concept, enterprises should ensure distributed value chain and remote manufacturing using eco-friendly and energy-safe technologies [2]. The value chain principle is based on constant control of each process to provide maximum value for the least possible cost. Constant control and measurement of each activity can be ensured by automating all processes in the company, which is a costly and time-consuming process [3]. The most popular form of automating paperwork, the next widespread automation, is the introduction of IoT components into the enterprise’s information system. Mentioned automation processes require not only devices but software. The rapid development of the software is the next stage of growth that helps to reduce development time and provide quick integration of smart components into the manufacturing process without time losses and with low costs. Software development today, in most cases, is managed by Agile methods of developing software. According © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 132–141, 2023. https://doi.org/10.1007/978-3-031-32767-4_13

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to the developers’ statements of this method: the method allows a short time to provide a product that will fully meet the customer’s requirements. Manufacturing plant projects involve a considerable number of variables and steps that need to be taken into account. The clarity and smoothness of processes are confidence in the future for any manufacturing enterprise. The question arises: Can another project management method ensure the enterprise’s competitiveness and provide the necessary flexibility to customer requirements in the context of digitalization. A flexible response of manufacturing enterprises to a changeable environment should have been provided by Lean production, where the IT sector, in turn, used Agile [4]. Based on the growing interest and popularity of Agile, there is a need to study this technique to help her find her place in the manufacturing industry [5].

2 Literature Review Lean manufacturing is a complex process using flexible techniques, but mainly it is a structural process of manufacturing that seeks to reduce the amount of waste produced. One of the main goals is to secure the value chain of all processes (from material to final product). The researchers emphasize that under conditions of high digitalization of processes, it is possible to use Manufacturing Agile [5], where its concept is built on: core competencies, multi-skilled and flexible people, empowerment, teamwork, and continuous improvement, information technology and communication [6], concurrent engineering and rapid prototyping, virtual enterprises and change management [7]. The key enablers of flexible manufacturing are virtual enterprise formation tools/metrics; physically distributed manufacturing architecture and teams; rapid partnership formation tools/metrics; concurrent engineering; integrated product/production/business information system; rapid prototyping tools, and electronic commerce. A conceptual framework for developing an agile manufacturing system based on customization and system integration with the help of business process redesign, legal issues, concurrent engineering, computer-integrated manufacturing, cost management, total quality management, and information technology (Fig. 1) [8]. Manufacturing Agile is explored in research by Angappa Gunasekaran [9], where the author assessed the effectiveness of investing in agile versus other manufacturing systems. For these purposes, he used decision trees, spreadsheets, and comparisons based on the initial investment. This initial approach showed that agile could be used by manufacturing enterprises, but still not without a combination with traditional methods of organizing production processes to ensure stability [8]. Since creating a product is a rather complex and multi-stage process, it is important to understand the fundamental differences between the well-established Lean model and the Agile model for the manufacturing sector. Because SMEs are the most vulnerable segment of enterprises in this industry, it is essential to understand the principles of modern Agile tools. Comparison of these methods will help to understand the fundamental difference between methods and, in the case of implementation, gets the information of fundamental principles and build appropriate strategy. Lean aimed at time loss elimination, and control value chain, to provide the satisfaction of customers and flexibility of the manufacturing process in the shortest time with optimal use of own sources (Fig. 2).

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Fig. 1. A conceptual model to illustrate the concept and enablers of agile manufacturing [9].

According to Agile Manifesto [4], Agile uses methods such as Agile Scaling Professional (ASP), Development & Operations (DevOps), Disciplined Agile Delivery (DAD), Dynamic System Development Method (DSDM), Feature-Driven Development (FDD), Kanban, Lean Software Development (Lean SD), Leaner Style Sheets (LeSS), Agile Model Driven Development (MDD), Microsoft Solutions Framework (MSF), Personal Software Process (PSP), Open Unified Process (OpenUP), Rapid application development (RAD), Rational Unified Process (RUP), Scaled Agile Framework (SAFe), SCRUM, Team Software Process (TSP), Unified Process (UP), Extreme Programming (XP) [11], where Kanban and Lean SD are modified according to features of IT projects [12]. The next part of the research aims to assess, using modern methods for evaluating innovations. These factors may influence the adoption of Agile in the context of manufacturing SMEs.

3 Research Methodology The introduction of new technologies and management techniques is always associated with an assessment of the attractiveness of the proposed solution. Agile project management methodology is primarily an IT project management methodology. IT solutions

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Fig. 2. House of Lean [10].

are a part of the concept of Industry 4.0. Therefore, it makes sense to turn to assessment methods for Industry 4.0 components when assessing Agile attractiveness for a manufacturing enterprise. Generally, Agile and Lean are opposite principles where Lean is an ordered, clear sequential process, and Agile is close to being an ordered chaos, where processes have a sequence, but making changes is still not so orderly. It is worth noting that despite this difference, the goals of these methods are still similar and correspond to the concepts of Industry 4.0 and 5.0 (Table 1). Table 1. Comparison of core principles of Lean and Agile [5]. Agile principles

Lean principles

General principles of both

Iterate faster

Reduce waste

Productivity

Bottom-up

Anti-complexity

Increase quality

Flexibility

Establish standard procedures Empower people

Welcome changes (including Continuously improve technological)

Respond to customer demands

Digitalization produces enormous pressure on workers and enterprises [13], duties such as ensuring quick design, integration, and adaptation to new intelligent technologies

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to be competitive in the market. Small and medium-sized enterprises are the most vulnerable category of enterprises in the manufacturing sector, which, among other things, are under pressure from significant production concerns. The EU manufacturing market has shown that large corporations are putting enormous pressure on SMEs. In contrast, the last ones should respond with the same flexibility to market changes but with poorer resources [14]. A variety of assessment methods have been developed to evaluate the feasibility and risks of implementing Industry 4.0 components, and some of them are presented below. The Agile assessment can be realized with the help of one of these methods since the previously mentioned principles and the initial scope of Agile is the IT field that has been actively promoted as part of the Industry 4.0 trend. The author of the Unified Theory of Acceptance and Use of Technology (UTAUT), Mohammad I. Ahmad, proposes using UTAUT as a tool for integrating IT into an enterprise’s processes. Ahmad’s theory was established on eight theories: The theory of Reasoned Action (TRA), the Technology Acceptance Model (TAM), the Motivational Model (MM), the Theory of Planned Behavior (TPB), a model combining the Technology Acceptance Model and the Theory of Planned Behavior (C-TAM-TPB), the model of PC utilization, the Innovation Diffusion Theory (IDT), and the Social Cognitive Theory (SCT) [15]. The Unified Theory of Acceptance and Use of Technology Mode diagram is presented in Fig. 3.

Fig. 3. Unified Theory of Acceptance and Use of Technology Mode diagram [15].

Since then, studies of another group of scientists empirically validate and/or theoretically contribute to his theory. For example, Balkrishna E. Narkhede et al. researched implementation barriers to lean-agile manufacturing systems [16]. The research authors worked on problems of lean integration principles, where problems were determined

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with the help of Interpretive Structural Modeling (ISM) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL). The same approach is used by scientists who study the problems of implementing Industry 4.0 components in enterprises [17]. Chang et al. say that technology–organization–environment (TOE) relating to Industry 4.0 technology application evaluation is more multifaceted than other methods and requires comprehensive analysis. In this study, authors applied a multiple-criteria decision-making (MCDM) approach, a decision-making trial, and an evaluation laboratory (DEMATEL) and set up using an influence network relations digraph (INRD). The DEMATEL-based analytic network process (DANP) was used to indicate the influence weights linking the above aspects and elements. Lastly, they used the modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique applied influence weights to assess the aspects/elements in the gaps identified and to investigate how to reduce the gaps to estimate the application of Industry 4.0 technology by SMEs [17]. While the methods used are based on peer review of proposed changes, they are also relevant for evaluating the effectiveness of IT solutions. For comparison, in the IT field, methods such as the Usability test [18] or the MOM [19] tests are used, which have a similar appearance, but still not such a detailed analysis of the factors of influence. Within the framework of the proposed study, it is proposed to combine the experience of the mentioned scientists to evaluate Agile manufacturing in a manufacturing SME environment.

4 Results and Discussion For experimental evaluation, a typical small industrial Slovakian enterprise was chosen. The enterprise characteristics: 49 full-time employees, annual turnover of up to 10 million euros, mass production, changing product range, fluctuating orders, permanent market. The company is engaged in the so-called turnkey production, which means that specialists prepare and calculate all product parameters from components and materials to installation conditions. This approach ensures the enterprise’s flexibility to customers’ requirements and expands the scope of entrepreneurial activity. The enterprise is interested in implementing new approaches to projects to provide a faster product project design and a more flexible response to market changes. The enterprise uses software solutions for its activities and Industry 4.0 components but still has a place for software solutions for the decision-making process. There were selected a couple of projects for testing Manufacturing Agile. The leading group of employees involved in such a process completed the evaluation of Manufacturing Agile. There were used MCDM and DEMATEL combination, which means for building the final graph of the effect of the implementation of Manufacturing Agile on the manufacturing process by using g9 criteria (Fig. 4) [17]. g9 is a set of factors related to the Industry 4.0 technology application within each aspect that is subsequently evaluated by 5 experts (team members). The final DEMATEL MCDM analysis is represented in Table 2. According to Table 2, four factors can affect the final implementation of Agile Manufacturing. G-factors that affect the implementation process are illustrated in Fig. 5 by red dots.

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Fig. 4. Clarification of elements and aspects [17].

Table 2. Results of the DEMADEL MCDM analysis. g

Ri

Ci

Ri+Ci

Ri-Ci

Identify

g1

−1,074056452

−1,21574

−2,28979

0,141678689

Cause

g2

−1,050806351

−1,26364

−2,31444

0,212831682

Cause

g3

−1,093927204

−1,39214

−2,48607

0,298217185

Cause

g4

−1,038345997

−0,70807

−1,74641

−0,330279275

Effect

g5

−1,401240483

−1,24782

−2,64906

−0,153424613

Effect

g6

−1,263981545

−1,37058

−2,63457

0,106602168

Cause

g7

−1,224013195

−1,12547

−2,34949

−0,098540982

Effect

g8

−0,385152907

−0,49963

−0,88478

0,114472839

Cause

g9

−1,260927624

−0,96937

−2,2303

−0,291557693

Effect

The red dots are g4 (financial commitment), g5 (organizational readiness), g7 (competitive pressure), and g9 (environmental uncertainty), which can be seen in Fig. 5. According to the graph, it can be seen that factor g4 has the most significant impact on the implementation process. This factor is essential when introducing any technical innovations, as technologies develop very quickly, as does the software needed to ensure the transparency of all processes. The rapid development of technology creates pressure

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Fig. 5. Graphical illustration of the DEMADEL MCDM analysis results.

on enterprises, and they, in turn, are forced to improve to be competitive constantly. DEMATEL MCDM method is based on the assessment of the influence of factors one on another, and that is why necessary to, therefore, it is essential to find not only the most influential factors but also to emphasize their relationship (Fig. 6).

Fig. 6. Graphic scheme diagram of the influence of factors on each other.

According to Fig. 6. it can be seen that despite the strongest effect providing the g4 factor, factor g9 creates a strong effect. g9 is the environmental uncertainty factor (Fig. 4.). Trends in project management and tools for managing and planning processes are constantly developed, as a bright example: circular economy or reverse logistics. For example, mentioned methods were supported by the Slovakian government [20], and most enterprises that use plastic bottles for their product were supposed to change their packages and organize the process of return of packages back. Such uncertainties arise unexpectedly, and in case of poor awareness, the enterprise may suffer losses (financial, non-financial, and even competitive position in the market).

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5 Conclusions The proposed study showed that Manufacturing Agile is possible in MSP conditions but subject to two main conditions: a high-quality enterprise information base and a high level of digitalization of enterprise processes. Only under the condition of meeting these two conditions will the enterprise be able to develop a solid basis to implement Agile without losses effectively. The proposed assessment method can be combined with other types of enterprise analysis to assess the enterprise’s readiness for such changes. Based on the data collected, it can be concluded that if the enterprise is ready to accept the risk of uncertainty of the market and if there is a strong foundation, Agile can be a helpful tool for providing a higher level of flexibility to customer needs and thereby cover a larger market share. Acknowledgment. This work was supported by the Slovak Research and Development Agency under contract No. APVV-19-0590, and also by the projects VEGA 1/0700/20, KEGA 022TUKE4/2023 granted by the Ministry of Education, Science, Research and Sport of the Slovak Republic.

References 1. A Minoseg szolgalataban. https://www.isoforum.hu/media/programnaptar/files/E3Konczos neSzMea.pdf. Accessed 01 Nov 2022 2. European Commission. https://research-and-innovation.ec.europa.eu/research-area/indust rial-research-and-innovation/industry-50_en. Accessed 01 Nov 2022 3. Paschek, D., Luminosu, CT., Ocakci, E.: Industry 5.0 challenges and perspectives for manufacturing systems in the society 5.0. In: Draghici, A., Ivascu, L. (eds.) Sustainability and Innovation in Manufacturing Enterprises. ASST, pp. 17–63. Springer, Singapore (2022). https:// doi.org/10.1007/978-981-16-7365-8_2 4. Apke, L.: Understanding the Agile Manifesto: A Brief & Bold Guide to Agile Kindle Edition. Kindle Edition (2014) 5. Tulip. https://tulip.co/ebooks/agile-manufacturing/download/. Accessed 21 Oct 2022 6. Gunasekaran, A.: Agile manufacturing: enablers and an implementation framework. Int. J. Prod. Res. 36(5), 1223–1247 (1998). https://doi.org/10.1080/002075498193291 7. Vyshnavi, T.S., Chetan, N.: A review on Implementation of agile in manufacturing industries using key enablers. Int. Res. J. Eng. Technol. (IRJET) 3(03), 1929–1933 (2016) 8. Sharp, J.M., Irani, Z., Desai, S.: Working towards agile manufacturing in the UK industry. Int. J. Prod. Econ. 62, 155–169 (1999). https://doi.org/10.1016/S0925-5273(98)00228-X 9. Angappa, G.: Agile manufacturing: enablers and an implementation framework. Int. J. Prod. Res. 36, 1223–1247 (1998). https://doi.org/10.1080/002075498193291 10. Höök, M., Stehn, L.: Lean principles in industrialized housing production: the need for a cultural change. Lean Constr. J. 2008, 20–33 (2008) 11. Wrike. https://www.wrike.com/project-management-guide/faq/what-are-the-different-typesof-agile-methodologies/. Accessed 2022/15/21 12. Leaninfo. http://www.leaninfo.ru/2009/03/26/lean-software-development/. Accessed 2022/15/21 13. Eurofond. https://www.eurofound.europa.eu/data/digitalisation/research-digests/employ ment-impact-of-digitalisation. Accessed 2022/15/21

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14. Brodny, J., Tutak, M.: Digitalization of small and medium-sized enterprises and economic growth: evidence for the EU-27 countries. J. Open Innov. Technol. Mark. Complex 8(2), 67 (2022). https://doi.org/10.3390/joitmc8020067 15. Mohammad, A.: Unified theory of acceptance and use of technology (UTAUT): a decade of validation and development. J. Assoc. Inf. Syst. 17(5) (2014). https://doi.org/10.17705/1jais. 00428 16. Narkhede, B.E., Raut, R.D., Roy, M., Yadav, V.S., Gardas, B.: Implementation barriers to leanagile manufacturing systems for original equipment manufacturers: an integrated decisionmaking approach. Int. J. Adv. Manuf. Technol. 108(9–10), 3193–3206 (2020). https://doi. org/10.1007/s00170-020-05486-5 17. Chang, S.-C., Chang, H.-H., Lu, M.-T.: Evaluating industry 4.0 technology application in SMEs: using a hybrid MCDM approach. Mathematics 9, 414 (2021). https://doi.org/10.3390/ math9040414 18. Usability Geek. https://usabilitygeek.com/usability-testing-mobile-applications/. Accessed 2022/20/10 19. Fitzpatrick, R.: The MOM test. Robfitz LTD, England (2019) 20. 2019 Coll. The temporary version of the regulation effective from 01.01.2022 to 31.12.2022. https://www.slov-lex.sk/pravne-predpisy/SK/ZZ/2019/302/. Accessed 11 Feb 2022

Efficiency of the Production Process in the New Production Facility Matúš Matiscsák(B)

, Peter Trebuˇna , Marek Kliment , Marek Mizerák , and Jozef Trojan

Technical University of Košice, 9, Park Komenského, 042 00 Košice, Slovakia [email protected]

Abstract. The paper’s main objective is to optimize a specific product’s production process in an unnamed company. We used PLM software Tecnomatix Plant Simulation from Siemens to optimize the production process. While solving the optimization, it was found that it could not be done in the current production hall, and it would be necessary to build a new production hall. For this reason, two simulations were created. The first simulation deals with the current production process of a particular product. The second simulation deals with the predictive production process in the new production hall. To better visualize the new plant’s production process and appearance, the walls and production equipment are created in the 3D model and arranged according to the layout. As last, the results of both simulations will be compared. Based on the comparison, we will get approximate results by how much we will be able to optimize the production process in the new production hall. Keywords: Simulation Effectiveness · Predictive Manufacturing · Lean Production · Sustainable Manufacturing

1 Introduction In the 21st century, most successful companies and firms use various simulation programs to design production. These programs allow observing the entire production process in minute detail, while the simulation can be stopped at any moment and restarted [1]. Careful simulation observation can reveal bottlenecks, errors, and various shortcomings that could arise in real production. Using simulation programs, we can ensure that before real production starts, the production will be free of errors and downtime and as efficient as possible [2]. The paper is divided into three interrelated units. The first unit introduces the readers to the issues of the paper. Important theoretical insights used are described as a basis for the research. In the second unit, we discuss the optimization of the production process of a particular product. We solved the optimization using simulations in PLM software Tecnomatix Plant Simulation to minimize the company’s operating costs and avoid unnecessary downtime in actual production. At the end of the second unit, the results of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 142–151, 2023. https://doi.org/10.1007/978-3-031-32767-4_14

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our research are evaluated. The last unit summarizes the contribution contained in the paper. Through this article, we want to highlight the importance of linking real production with the digital model. Based on the created digital model, we can verify many optimization variants using simulations, which would be very difficult to confirm in actual production, especially with significant investments many companies do not have available.

2 Literature Review The main initiators of industrial engineering methods are industrial engineers, who focus on finding solutions to reduce costs or increase efficiency, quality, and productivity at different stages of the production process [3]. This includes fundamental topics such as Industry 4.0, Product Lifecycle Management, and optimization, with the most important being to know the manufacturing process down to the smallest detail [4]. The manufacturing process is a hierarchy of interrelated activities that add value. These repeatable activities always lead to achieving the company’s desired goal [5]. Inputs, outputs, resources, and duration define a production process. Inputs are transformed into outputs during activities [6]. That is, inputs to a production process are items needed to carry out the process, e.g., materials, raw materials, energy, equipment, tools, or employee labor [7]. The output of the process is the final product intended for the final consumer [8]. Manufacturing processes can be considered a major global profit generator for businesses. This is also where the greatest optimization potentials arise. Enterprises take these rationalization measures themselves to a certain extent. Still, after some time, they reach a threshold, crossing which could jeopardize the continuity of production and delivery of finished products to customers because they do not have sufficient optimization know-how and thus no basis for responsible and rational decision-making [9, 10]. Nowadays, when a company wants to have high efficiency in the production process, it must incorporate a more complex area of digitalization [11]. It is essential since these areas can be linked and possibly improved and accelerated. The integration of simulation into the process analysis area is offered as one of the most effective solutions where we can take advantage of the flexibility, versatility, and many other benefits of simulation [6]. The potential of the TX Plant Simulation software is in its dynamics and hierarchical structure. This allows working with an already created model, modifying it, and creating material flow simulation models that can reflect a variety of logistics and production systems operating on different principles [12].

3 Research Methodology In this article, we mapped a particular product’s manufacturing process, finding all the key parameters that affect the current production. To evaluate the efficiency of the manufacturing process, we used Tecnomatix Plant Simulation software from Siemens [13].

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We described the manufacturing process of a specific product in detail using the simulation in TX Plant Simulation. The product’s current and expected production process in the new production hall is simulated to compare the results. Currently, the production process takes place on two floors. During it, there are bottlenecks in production, and inefficient production, because part of the product manufacturing process starts on one floor. Then the product manufacturing process is completed on the second floor. The predictive manufacturing process will differ from the current one in that it will take place in a new production hall. Another difference is that the entire production process will occur on only one floor. At present, the production process takes place on two floors. The last and most important difference is that the new production hall will be used to produce new machines, some of which will already be fully automatic. 3.1 Simulation of the Current Production Process of the Product The simulation of the current production process of a specific product does not take into account sometimes since the entire production process takes about three days. A more accurate expression of the duration of the whole production process is within 48 h. The simulation of the production process of a particular product starts with the reception of frozen raw material on the ground floor. The 3D model of the ground floor is shown in Fig. 1.

Fig. 1. 3D model of the ground floor. Source: own processing.

Subsequently, the frozen raw material is unpacked from the transport package and stored in the defrosting box. After thawing, the worker unpacks and checks the quality of the thawed raw material (see Fig. 2). After a thorough inspection, the raw material is moved to the cooking chamber. After cooking, it is cooled in a cooling chamber, and further cooled with water.

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Fig. 2. Unpacking and quality control. Source: own processing.

The raw material, cooked and cooled in this way, is moved to the first floor, where the product’s production process continues. The machines shown in Fig. 3 are colored differently. Important machines for our product are marked in blue and green.

Fig. 3. 3D model of the first floor. Source: own processing.

The simulation of the production process of the product continues with lacquering. After the lacquering period has expired, the lacquer is drained, and the raw material is allowed to drain. At this point in the production process, time 0 is set in the simulation for specific operations. The first operation where the simulation sets the time is grinding the cooked raw material. Figure 4 shows a green machine where sterilized vegetables are rinsed. The vegetables are drained in a blue container. The process of weighing semi-finished products and additives is shown in Fig. 5, while weighing specific products takes place on a blue scale.

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Fig. 4. Rinsing and dripping process. Source: own processing.

Fig. 5. Weighing of raw materials. Source: own processing.

The weighed container with the mixture is transferred to the machine for mixing, which takes about 5 min. The mixed mixture is transferred to the reservoir. Subsequently, it comes to filling the specific product into consumer packaging. The worker packs the required product pieces in a paper carton and places them on a pallet. The process is shown in Fig. 6. The last step in the product’s production process is wrapping the pallet with stretch film and sending it to the dispatch warehouse.

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Fig. 6. Filling and packaging process. Source: own processing.

3.2 Predictive Simulation of the Product Manufacturing Process Predictive simulation of the production process of a product does not take into account sometimes since the entire production process takes about three days. A more accurate expression of the duration of the whole production process is within 48 h. Figure 7 demonstrates a 2D model of the production process of a product.

Fig. 7. 2D model of the production process of a product. Source: own processing.

The production process simulation starts with the receipt of the frozen raw material and its subsequent unpacking from the transport packages, with the processing time set to 0 in the simulation. Also, the transfer time to the warehouse is omitted, followed by the transfer time of the raw material to the daily intermediate warehouse. Another process

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where no time is considered is the thawing of frozen raw material. The thawing process will take approximately 8 h, but this is still being worked out. This process should take less than 8 h. After thawing, the raw material is taken out and checked for quality. This time is also not accounted for in the simulation. After unpacking and checking the quality of the raw material, it is the turn to cook and cool. These times are also set to 0 in the simulation. The approximate cooking time will be up to an hour and a half. The cooling process will also take up to an hour and a half. Once the raw material is cooked and cooled, it will move on to painting. The actual varnishing time will be 12 h, while in the simulation, the time is set to 0. The following process is the grinding of the raw material, and this is the first process where the time is set in the simulation. While the raw material is being milled, the ingredients and the remaining raw materials required to produce the product are being prepared and weighed. The weighed mixture in the flip-flop will move to a new, fully automatic line that will automatically tip the flip-flop. Then mixing, filling, labeling, foreign object inspection, packaging, cartooning, and palletizing will occur. Once the pallet is complete, wrapping it in the stretch film is the final step. The pallet, wrapped in this way, is ready for dispatch. To better visualize the production process of a particular product and the production hall, we can see in Fig. 8 the 3D model.

Fig. 8. 3D model of the production process of a product. Source: own processing.

The 3D model of the production process was created in Tecnomatix Plant Simulation.

4 Results and Discussion We evaluate the results of the efficiency of the production process of a specific product using the statistical report offered by Tecnomatix Plant Simulation software.

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First, we evaluate the simulation results of the current manufacturing process. In both cases, the simulation time is set to 8 h. Also, the placement of the packed product cartons on the pallet is the same in both simulations. Figure 9 shows the result of the current manufacturing process.

Fig. 9. Result of the current production of the product. Source: own processing.

Consequently, Fig. 9 demonstrates the result of the predictive manufacturing process of the product in the new production hall (Fig. 10).

Fig. 10. Result of predictive product manufacturing. Source: own processing.

Based on the results, we can see that we could produce 11 pallets of a particular product in the new production hall, which is 5 pallets more than the current production. The result of the simulation of the production process in the new hall is only predictive and will differ slightly from reality. In the simulation, only estimated times were used for some production steps. Technical failure or other adverse phenomena may also affect the final production result. The simulation was created to verify the future production capacity of the new line.

5 Conclusions When optimizing the production process, it is essential to consider all the factors influencing it [14]. We need to analyze the reason for the bottleneck, how we can remove it, and whether removing it will create a new bottleneck in production. For this reason, we must think about the smallest details to avoid failure [15]. Our work shows that the optimization of the production process of a particular product will not be possible in the current production hall, mainly because of the poor layout, which has created bottlenecks in production. Adjusting the layout would require a significant investment in the current hall. For this reason, a more useful and efficient solution would be to build a new production facility. In the new production hall, the production process will be more efficient, and we will be able to eliminate the bottlenecks

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that arise in the current production process. Building a new production hall can shift the product’s production process from two to only one floor. By doing this, we can reduce the transfer time between different workplaces. The second most important factor is implementing new machines that can be fully automated. By using simulations, we have avoided an unnecessary investment that could have been inefficient and costly. We were also able to prevent the unnecessary shutdown of the production process and thus avoid the financial losses that would have occurred. The following steps of our research will be the detailed design of the production process in the new production hall. Using the PLM software Tecnomatix Plant Simulation, we will create detailed simulations and analyze them with the current production process to create the most efficient production plant without any bottlenecks. Acknowledgment. This article was created by the implementation of the grant projects: APVV17-0258 “Digital engineering elements application in innovation and optimization of production flows”; APVV-19-0418 “Intelligent solutions to enhance business innovation capability in the process of transforming them into smart businesses”; VEGA 1/0438/20 “Interaction of digital technologies to support software and hardware communication of the advanced production system platform”; KEGA 001TUKE-4/2020 “Modernizing Industrial Engineering education to Develop Existing Training Program Skills in a Specialized Laboratory”; VEGA 1/0508/22 “Innovative and digital technologies in manufacturing and logistics processes and system”; KEGA 020TUKE4/2023 “Systematic development of the competence profile of students of industrial and digital engineering in the process of higher education”.

References 1. Pekarˇcíková, M., Trebuˇna, P., Dic, M., Král, Š: Modelling and simulation in the Tecnomatix Plant Simulation environment. Acta Simulatio 7(1), 1–5 (2021). https://doi.org/10.22306/ asim.v7i1.59 2. Straka, M., Khouri, S., et al.: Utilization of computer simulation for waste separation design as a logistics system. Int. J. Simul. Model. 17(4), 83–596 (2018). https://doi.org/10.2507/IJS IMM17(4)444 3. Kábele, P., Edl, M.: Increasing the efficiency of the production process due to using methods of industrial engineering. In: Ivanov, V., et al. (eds.) DSMIE 2019. LNME, pp. 126–137. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-22365-6_13 4. Prester, J., Buchmeister, B., Palˇciˇc, I.: Effects of advanced manufacturing technologies on manufacturing company performance. Strojniski vestnik J. Mech. Eng. 64(12), 763−771 (2018). https://doi.org/10.5545/sv-jme.2018.5476 5. Koblasa, F., Šírová, E., Králiková, R.: The use of process thinking in the industrial practice – preliminary survey. Tehnicki Vjesnik Techn. Gaz. 26(3), 786−792 (2019). https://doi.org/10. 17559/TV-20150617135306 6. Rosova, A., Behun, M., Khouri, S., Cehlar, M., Ferencz, V., Sofranko, M.: Case study: the simulation modeling to improve the efficiency and performance of production process. Wirel. Netw. 28, 863–872 (2020). https://doi.org/10.1007/s11276-020-02341-z 7. Buckova, M., Krajcovic, M., Edl, M.: Computer simulation and optimisation of transport distances of order picking processes. In: 12th International Scientific Conference of Young Scientists on Sustainable, Modern and Safe Transport, Procedia Engineering, vol. 192, pp. 69−74 (2017). https://doi.org/10.1016/j.proeng.2017.06.012

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8. Kiyko, S., Druzhinin, E., Prokhorov, O., Ivanov, V., Haidabrus, B., Grabis, J.: Logistics control of the resources flow in energy-saving projects: case study for metallurgical industry. Acta Logistica 7(1), 49–60 (2020). https://doi.org/10.22306/al.v7i1.159 9. Drastich, A.: Optimization of material flow by simulation methods. Acta Logistica 4(4), 23–26 (2017). https://doi.org/10.22306/al.v4i4.76 10. Aloini, D., Dulmin, R., Mininno, V.: Modelling and assessing ERP project risks: a Petri Net approach. Eur. J. Oper. Res. 220(2), 484–495 (2012). https://doi.org/10.1016/j.ejor.2012. 01.062 11. Grznár, P., Gregor, M., Gola, A., Nielsen, I., Mozol, S., Seliga, V.: Quick workplace analysis using simulation. Int. J. Simul. Model. 21(3), 465–476 (2022). https://doi.org/10.2507/IJS IMM21-3-612 12. Trebuˇna, P., Pekarˇcíková, M., Edl, M.: Digital value stream mapping using the Tecnomatix Plant Simulation software. Int. J. Simul. Model. 18(1), 19–32 (2019). https://doi.org/10.2507/ IJSIMM18(1)455 13. Malega, P., Gazda, V., Rudy, V.: Optimization of production system in plant simulation. Simul. Trans. Soc. Model. Simul. Int. 98, 295–306 (2022). https://doi.org/10.1177/003754972110 38908 14. Fedorko, G., Vasil, M., Bartosova, M.: Use of simulation model for measurement of MilkRun system performance. Open Eng. 9(1), 600–605 (2019). https://doi.org/10.1515/eng-20190067 15. Lai, X., Shui, H., Ding, D., Ni, J.: Data-driven dynamic bottleneck detection in complex manufacturing systems. J. Manuf. Syst. 60, 662–675 (2021). https://doi.org/10.1016/j.jmsy. 2021.07.016

Implementation of Intelligent Transport Systems in an Urban Agglomeration: A Case Study Joanna S˛ek1

, Piotr Trojanowski1(B) , Łukasz Gilewicz1 , Bartosz Gapinski2 , and Artem Evtuhov3

1 West Pomeranian University of Technology in Szczecin, 17, Piastów Avenue, 70-310

Szczecin, Poland [email protected] 2 Poznan University of Technology, 3, Piotrowo Street, 61-138 Poznan, Poland 3 Sumy State University, 2, Rymskogo-Korsakova Street, Sumy 40007, Ukraine

Abstract. The development of road transport requires cities to have a multifaceted traffic management system. It is particularly true in city centers, where public transport modes (buses and trams) are also being added in addition to individual vehicles. The article is based on field research carried out, for which the case study was the city center of Szczecin. The article proposes a three-stage research methodology, consisting of an inventory of the current state of the selected infrastructure section, surveys of traffic density and travel time, and analysis of the data obtained (field research), which are the key to selecting locally appropriate ITS-class devices. The actions taken resulted in selecting ITS systems according to criteria such as improvement of traffic safety and fluidity, improvement of the overall traffic management and information system, and improvement in the functioning of public transport. As part of the work, many devices and systems were identified that can significantly improve the collective and individual mobility of the studied street section. These include intersection monitoring, accommodating signaling, a tram priority system, and passenger and parking information boards. This article aims to present the possibilities of implementing ITS systems in city centers, taking into account the specific individual road conditions and the need to improve vehicle flow management, using the example of a ready-made proposal for the city of Szczecin, using the minimum necessary equipment. Keywords: ITS · Traffic Studies · Cities Mobility · Public Transport · Sustainable Transportation

1 Introduction With the increasing number of vehicles, transport within city limits, especially in city centers, is becoming increasingly difficult. One of the solutions used to increase traffic safety, improve mobility in city centers and increase the efficiency of urban traffic management is the implementation of solutions and devices belonging to the group of intelligent transport systems. The concept of ITS implementation proposed in the article was developed based on an analysis of the current state of a selected road section in the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 152–161, 2023. https://doi.org/10.1007/978-3-031-32767-4_15

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very center of Szczecin, which, due to the significant volume of car traffic and means of public transport and the lack of possibilities to expand the infrastructure, requires a proper, holistic approach to traffic management. The street section chosen for the study does not meet many contemporary transport standards, e.g., very few devices are responsible for assisting and directing traffic, and the traffic organization that has remained unchanged for many years only exacerbates the problem. The main aim of the article is to prepare a concept for the application of ITS, which can be directly implemented on the road section selected for the study.

2 Literature Review Transport systems, consisting of, i.e. road infrastructure and means of transport, are an indispensable part of any country’s economy. Hence, they are of interest to many researchers. Research related to transport systems addresses their problems on numerous levels. One of the problems is the reliability of system operation. It can be considered both in the context of the reliability of means of transport, including the application of innovative solutions [1], reliability of road infrastructure [2], management decisionmaking [3], processes related to cargo manipulation in freight transport [4] or location of vehicle diagnostic and repair centers [5]. An equally important research area related to transport systems [6] is safety analysis. The development of telematics, including telecommunications, information technology, and automation, has made it possible to make a qualitative leap in the approach to developing transport systems [7]. Such solutions are used at all levels of different systems [8, 9], ranging from issues related to the development of the automotive industry [10], communication between individual traffic participants [11], through the collection of data on the current state of the road infrastructure [12], to advanced technologies for controlling individual elements [13], as well as entire systems [14]. Urban agglomerations, due to their large population centers and the associated high number of road users, are very sensitive areas regarding traffic management [15]. Consequently, all kinds of technical solutions to support decision-making in matters of organization [16] and infrastructure management [17] are increasingly being implemented [18]. In practice, a major challenge for those deciding on road infrastructure development is the appropriate construction of an ITS structure [19] based on which a comprehensive traffic control system would operate in a selected area. This is due to the multitude of ITS solutions currently operating on the market [20], the lack of clearly formulated implementation schemes, and the definition of rational selection criteria. Based on the literature analysis, it was concluded that there is a need to develop a methodology facilitating the implementation of ITS solutions in urban agglomerations.

3 Research Methodology The wide range of subsystems, the need to define selection criteria, and the considerable differences in road infrastructure make it necessary to carry out a detailed study to

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determine the level of relevance of the various solutions that can be introduced into the transport system. The proposed research methodology is divided into three stages. The first comprises a series of preparatory activities aimed at developing a concept for ITS implementation, which thus constitutes the second stage of the research. The last, third stage is openended and consists of proposed solutions that can be introduced within the ITS system. A simplified diagram of the research methodology is presented in Fig. 1.

Fig. 1. Simplified scheme of the research methodology.

3.1 Stage I - Creation of the Database The selection of the section for the ITS project is carried out according to the following criteria:

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– transit importance for the city - both of local and all-city importance, enabling the passage of passenger vehicles, heavy goods vehicles, and public transport, – paid parking zone - the selection of a section close to the paid parking zone allows the implementation of devices supporting the parking system, – high intensity of public transport – many public transport lines are in the selected section. The selected infrastructure section’s current condition, i.e., an infrastructure inventory, is analyzed after prior fragmentation into sections of no more than 300 m. The analysis includes information on the layout and division of streets, the location of intersections, traffic lights, public transport stops, the division of traffic lanes, paid parking zones, the layout of tracks, and the general urban context. The traffic volume survey was carried out based on a modified method by the General Directorate of National Roads and Motorways. Modifications concern the following areas: – exclusion of vehicles such as agricultural tractors and motorbikes - due to their marginal contribution to traffic, – the inclusion of rail public transport vehicles, – the combination of truck tractors with and without semi-trailers in one group. Travel time study - field studies. Split between passenger vehicles and public transport vehicles. Selection of frequency and hours of measurement following the traffic characteristics of the section, i.e., including both peak and off-peak times. The definition of the concept’s objective for the selected section can only occur after a preliminary analysis of the results obtained in the previous steps of stage one. The identification of the problems of the section in question, including the identification of weak points, forms the basis for the creation of the ITS implementation concept. 3.2 Stage II - Creating a Concept for Its Implementation The main idea behind the concept is to implement ITS systems in such a way that it is possible to improve the transport system in a given section. These primary concerns: – general improvement of safety, – increase in the scope of operation and improvement of the information system for passengers and drivers, – improving traffic flow, – improving the functioning of public transport. 3.3 Stage III - Definition of Its Elements At this stage, the ITS structure possible to implement in the analyzed road infrastructure area is defined. The results of previous research and analyses determine the selection of individual elements. The purpose of the third stage is to propose specific solutions, the implementation of which will contribute to the realization of the assumptions presented in the earlier stages of the research methodology.

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4 Results and Discussion For a long time now, various processes and studies have been conducted on a large scale to improve urban mobility. However, there are still places where traffic flow needs to be improved or where modern ITS solutions have already been introduced, but where they do not fully meet the intended purpose. This study aimed to assess the feasibility of implementing ITS to improve urban traffic flow in the example of Szczecin. It should be noted that ITS is of particular importance in city centers, where there is no possibility of significant interference with the road system, in areas that are characterized by increased traffic volumes at certain times of the day, etc. ITS can be used to improve traffic flow. Since the current road infrastructure in towns and cities is not adapted to the level of traffic, the separation and implementation of the relevant elements belonging to ITS is a necessary solution for improving the safety of all traffic users. According to the proposed research methodology, the first stage concerns the creation of a database necessary to develop the concept of ITS implementation in the surveyed city. In addition to the basic criteria influencing the selection of the street section to be surveyed, issues of previously introduced ITS solutions in the given area of the city were also taken into account, as well as the possible inclusion of the given section in Szczecin’s road infrastructure development plans. The road section between the junction of Bohaterów Warszawy Avenue, 26 Kwietnia Street and Bolesława Krzywoustego Street and Zwyci˛estwa Square was selected for conceptual studies and divided into four sections (200–300 m length each) for further detailed analysis. It should be noted that this street section is very important for the city as it is one of the main road arteries in the city center. The next step was to analyze the current state of the selected infrastructure section, which provided information on the layout and division of streets, the location of intersections, traffic lights, as well as the course of tracks, and the location of public transport stops. An additional source of information was the traffic volume survey of the selected section, which was carried out on 7th January 2022. Two observers in two rounds carried out the measurements – the first during the traffic peak (13:00–15:00) and the second during the off-peak period (16:00–18:00). One of four measurement forms is presented in Table 1. Based on the analysis of the measurements obtained, the following conclusions were reached: – about 80% of all passing vehicles were passenger cars, while vans and lorries made up a small proportion (about 11%), – the frequency of buses was lower than that of trams, with buses passing on average every second phase of the traffic light cycle change, and trams passing on average in each phase of the traffic light cycle, also two per phase, – in the first phase of measurements, traffic was more intense in the direction towards the city center, while in the second phase of measurements (traffic rush hour), there was a decrease in traffic towards the city center and an increase in traffic towards the city center, – the number of running buses and trams was similar in both measurement phases.

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Table 1. Traffic measurement form - section 4 - corner of Wojska Polskiego Avenue and Bolesława Krzywoustego Street (north side). Location of research:

4) Corner of Wojska Polskiego Avenue and Bolesława Krzywoustego Street (north side)

Direction of vehicle movement: West - Bolesława Krzywoustego Street Observer:

Łukasz Gilewicz

Date: 07.01.2022

Measurement phase:

Measurement time

Passengers vehicle

Delivery vehicles

Heavy goods vehicles

Buses

Trams

I

14:40 – 14:45

40

0

1

0

1

17

1

0

0

1

28

4

0

1

1

17

1

1

0

1

10

0

0

1

0

Total

112

6

2

2

4

17:10 – 17:15

20

0

0

1

1

28

2

1

0

0

38

1

1

1

2

35

0

0

0

1

18

3

1

1

1

139

6

3

3

5

II

Total

The second study measured the time required to travel along a selected section using a car and a means of public transport - the tramway. The results of the individual measurements are presented in Table 2. Analysis of the measurement data allows the following conclusions to be drawn: – average arrival times by car and tram are similar, as the tram travels on a shared route with road vehicles for about half of its route, – the average travel speed of the tram is slightly higher. The final element of the first stage of the work was to define the conceptual goal of the selected section. As a result of the traffic volume and travel time surveys, a fairly high number of unsafe situations and driver errors were also observed, which had a negative impact on traffic safety and fluidity. The analysis of the results of the infrastructure inventory of the selected road section also revealed equipment deficiencies, i.e., support facilities for the information system for drivers and passengers. On this basis, the aim of the project was to implement intelligent transport systems devices to improve road safety and traffic flow and the information system for public transport passengers and individual drivers.

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Table 2. Average travel times for the selected modes of transport on the surveyed street section. No.

Number of journey phase

Type of vehicle

Average travel time [min]

The length of surveyed street section [m]

Average driving speed [km/h]

1

I

Passenger car

4:21

980

13.52

2

II (traffic peak) Passenger car

4:38

980

12.69

3

I

Tram

4:29

980

13.12

4

II (traffic peak) Tram

4:25

980

13.31

Because the selected section is of great traffic importance for road users, stage II of the research methodology proposed the implementation of a number of ITS devices according to the functions performed, i.e.: – improving traffic safety (monitoring at junctions, accommodating signaling, tram priority system), – improvement of the information system for passengers and drivers (passenger and parking information boards), – improving traffic flow and the quality of tram transport operation (accommodating signaling, tram priority system). The third stage involves introducing and justifying the selection of locally appropriate ITS devices proposed at stage two. The first solution proposed to improve traffic safety is to monitor sensitive areas (junctions) using eleven CCTV cameras. It will enable real-time observation and recording of all dangerous events in the area of intersections including vehicles driving contrary to traffic regulations. The camera subsystem will be supplemented by a dedicated accommodative signaling subsystem, which will manage the traffic lights in the area of Plac Kosciuszki. This system is designed to adapt the phases of individual traffic lights to changing traffic conditions, e.g., to regulate the phases of traffic signal changes during and after the traffic rush hour and to increase the capacity of this section as much as possible. The third element in this group is the tram priority subsystem - the receiving equipment must be connected and synchronized with the traffic light control system. In this case, it is planned to reduce the travel time of trams on a selected section. An appropriate interplay between the accommodating signaling subsystem and tram priority should enable a smoother passage for all users. The second group of ITS devices aims to improve the transmission of information to passengers and other road users. To this end, it is planned to complete the missing passenger information boards for public transport users and to install 9 new parking information boards. The boards will display information on the number of available parking spaces in selected car parks, which should contribute to a better turnover of parking vehicles in the city center.

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5 Conclusions The conducted field studies allowed determining the basic parameters of road traffic. The share of motor vehicles and trucks was determined, respectively 80% and 11%, concerning all road users. The general traffic volume was also analyzed concerning their direction (to and from the city center) and the detailed intensity, including averaged travel times for passenger cars and trams. It allowed concluding that the average travel times by the indicated means of transport are similar. Safe and efficient travel in city centers is currently one of the most important transport problems. A study carried out in January 2022 in Szczecin showed that it is still possible to identify strategic road sections that are not adapted to the prevailing individual and collective vehicle traffic (including trams). As a result of the study, it was proposed to implement solutions such as: – the deployment of an additional eleven CCTV cameras, – an installation of a dedicated accommodative signaling subsystem, which will manage the traffic lights in the area of Plac Kosciuszki, – include the tram priority subsystem - the receiving equipment must be connected and synchronized with the traffic light control system, – complete the missing passenger information boards for public transport users, – an installation of nine new parking information boards. The ITS devices proposed in the study, layout, and location are intended to fulfill the expected tasks with a minimum of additional equipment. The main idea is to improve traffic flow in the city center for passenger vehicles, delivery vehicles, and others, but also for public transport vehicles (buses and trams). Future research directions will focus on continuing research into urban traffic safety and optimizing the use of ITS. Acknowledgment. The research was partially supported by the Polish National Agency for Academic Exchange within the project “Strengthening the scientific cooperation of the Poznan University of Technology and Sumy State University in the field of mechanical engineering” (agreement no. BPI/UE/2022/8-00).

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Tracking of Trucks Using the GPS System for the Purpose of Logistics Analysis Peter Trebuˇna , Marek Mizerák(B) , Miriam Pekarˇcíková , Marek Kliment , and Matúš Matiscsák Technical University of Košice, 9, Park Komenského, Košice 042 00, Slovak Republic [email protected]

Abstract. This article deals with and describes the issue of truck logistics analysis using GPS localization technology. Nowadays, it is still a progressive technology that works on the principle of data collection in real-time, so its results are up to date. The Mapon program is used for the evaluation and analysis of this issue. It analyzes selected monitored parameters such as route planning, fuel consumption, driver behavior, breaks, breakdowns, etc. Based on this program, it is possible to optimize parameters and preferentially reduce the costs associated with the transportation of cargo or avoid the movement of freight vehicles empty, which results in inefficient transport and underutilization of resources, and economic loss. Creating a report is the main task of this program, which will be described in the article. The article will discuss a GPS tracking system’s other uses and benefits: operational planning, anti-theft, assistance services, and real-time truck location. Thanks to this option, the ability to guide the driver according to the requirements and also faster assistance in an emergency. Keywords: Technology · Data · Vehicle · Reporting · Sustainable Transportation

1 Introduction Today, without optimal management models, optimizing all processes of movement of goods, services, resources and other objects would be extremely difficult. With the advent of information technology and systems that process large amounts of information, various mathematical formulas and techniques are being developed to simplify logistics tasks. At the same time, the main goal of any logistics system is to reduce the total cost of delivering goods, services, and end users. In economics, the primary role of logistics can be defined as the effective management of material flows. The turbulent development of this science was in the 1930s in the United States. At this time, scientists, economists, and business people primarily devoted themselves to logistics as a science that effectively coordinates the interaction of logistics, production, distribution, transportation, communication structure, and market. The basis of the development was the idea of the need to integrate supply, production, and distribution systems, in which the optimal function would be to supply the company with raw materials [1], and supplies would be coordinated by production, storage, and distribution [2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 162–171, 2023. https://doi.org/10.1007/978-3-031-32767-4_16

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2 Literature Review Industrial engineering is built on the principles of Industry 4.0. It includes both localization technologies and the principles of GPS systems, which nowadays are essential parameters for tracking logistics on our roads [3]. Nowadays, even in transport companies, great emphasis is placed on an overview of the driver’s behavior when driving and transporting loads, as well as parameters such as fuel consumption and driver breaks [4]. All this also affects distribution networks and stops on routes [5], as described in the articles [6, 7]. The ideas of these authors partially inspired this post. The article explains the term GPS scientifically and its meaning for transport companies in case of traffic accidents or vehicle theft [8]. As part of route analyses, the freight forwarder can determine route plan changes, evaluate the route’s optimality, or change parameters [9]. It is necessary to accept the risks associated with transport [10], as described in the article [11]. This means that the freight forwarder and the driver should always know the nature of the transported cargo and pay attention to, for example, the consumption of materials, the method of transportation for flammable materials, etc. [12]. These are all risks of trucking on roads and highways. Mapoon intelligent software reacts to all these risks and parameters. The article derives all analyses from its working environment cooperating with fuel gauges. It thus creates accurate and realistic driving information. Of course, driver cards also contribute to this, as described in the post [13].

3 Research Methodology With the help of truck tracking via GPS, the use of the vehicle fleet can be significantly optimized. The truck tracking system serves for safety and provides a better overview of the vehicle fleet. Truck trackers are easy to integrate and offer a wide range of features. However, there are legal and technical issues involved in truck tracking systems – for example, whether GPS tracking of trucks is allowed and how the data can be downloaded. GPS trackers are the means of choice for locating vehicles in fleet management and tracking their movements. Susceptible devices can be easily installed in any vehicle. The truck tracking system can be programmed with geofencing and a virtual fence. In this case, the vehicle automatically triggers an alarm when the truck leaves the planned route. Figure 1 shows a GPS device used to track trucks [14]. When installing a navigation device on a vehicle, a navigation unit with sensors is used, which is connected to the vehicle’s onboard computer. Installation of this device takes approximately 3–4 h. After that, the serial number of this navigation device will be added to the program, and it will start displaying the data.

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Fig. 1. GPS device - which is installed in the truck.

3.1 Operational Planning In a vehicle fleet or a logistics company, optimal operational planning is essential for trouble-free operation. However, observing the maintenance dates is necessary to avoid unforeseen vehicle breakdowns. Proper offenses such as speeding should ideally be traced back to the driver. In the end, effective protection of the vehicle fleet against theft is also necessary. GPS truck tracking realizes all these scenarios. Such truck tracking systems consist of a GPS module and software that can be installed on a desktop computer, smartphone, or tablet. GPS is a satellite global positioning system that transmits the vehicle’s exact location. Driving direction and idle times can also be checked in real-time. A GPS truck tracker has many uses. 3.2 Real-Time GPS Truck Tracking Fleet managers can use GPS to track trucks to determine when a vehicle will arrive at customers, partners, or suppliers and give them an exact delivery date. These, in turn, have the advantage of coordinating their operational planning accordingly. GPS truck tracking saves time and money and significantly increases customer satisfaction. Since driving times can also be checked, a GPS tracker in the truck is used for easy and efficient billing. Data can flow into payroll systems through an interface in the truck tracking system, and payroll can be created more easily and with fewer errors [15]. 3.3 Protection Against Theft and Assistance in Road Traffic In case of a theft, the truck’s GPS tracking status or driving data may be sent to investigative authorities. In most cases, the systems allow you to set the alarm, which immediately informs the responsible employees in case of unwanted or suspicious vehicle

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movement. The quick action triggered by a truck tracking system can significantly assist in investigating theft or other unauthorized vehicle use. Ultimately, it also helps drivers if their truck is equipped with GPS: it can recognize traffic jams and other traffic obstacles in time and display new routes. The driver’s driving times are fully recognized based on the truck’s GPS location, and the system warns of legally prescribed and necessary break times [16]. 3.4 Data Processing of GPS Systems of Tracked Trucks In addition to route data, high-performance GPS tracking for trucks also records other data that helps companies manage their fleet effectively: – – – –

tank content and fuel consumption, mileage and operating hours, tire pressure, speed.

With recorded tank content and fuel consumption, it is possible to identify the necessary refueling times and initiate measures in case of increased consumption. This may indicate a vehicle malfunction, and a service visit may be required. This also applies to falling tire pressure, which can be dangerous for drivers. Early information can interrupt the journey. Mileage and operating hours make it easy to follow scheduled maintenance. Since the data flows into the software, the responsible personnel are informed about it in time - so they can schedule vehicles from the fleet for the maintenance period. If the truck is equipped with GPS, there is a small module in the engine compartment or the driver’s cabin. It transmits data permanently and in real-time via radio and enables GPS tracking of the truck. These are also stored locally in the digital tachographs and can be downloaded or sent to the fleet software automatically. A fixed interval can be set for this, for example, once a week or once a month – depending on the company’s individual needs. 3.5 Mapon Mapon provides the possibility to monitor the fuel consumption of its fleet, digitizes the inspection of vehicles, and monitors, with the help of its software and hardware, the driver behind the tachograph. Mapon can also offer solutions for construction activities, courier companies, passenger transport, corporate fleets, agriculture, set up various reports, etc. The program allows one to see exactly where the vehicle is currently located. You can get information about the vehicle’s location 24/7, the current fuel consumption, and the driver’s mode of operation. The program also has an application for smartphones and tablets. When planning and assigning tasks, it helps to see the current driving time, distance, rest time, and tachograph forecast for drivers. Mapon provides device status monitoring, so you are always informed if something goes wrong and the device loses signal or stops data transmission.

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3.6 Fuel Consumption Analysis Using Mapon Program Figure 2, given below, shows the device with which the forwarder can determine the fuel level in the tanks.

Fig. 2. Fuel level measurement sensor.

For example, Fig. 3 shows the refueling schedule of a separate transport unit from 03/30/2022 to 04/01/2022. According to the schedule, we can see when and how many liters the truck refueled. The program also provides data for the beginning of the period and the end date. In addition, the report shown in Fig. 3 provides information on the fuel consumption for the given period, the average fuel consumption per 100 km, and the number of kilometers driven. The parent company needs such information to analyze fuel costs, as fuel costs are the most significant cost sector for carriers. On average, a truck can travel 600 km per day and 12,000 km per month. The average fuel consumption is normally 30 L per 100 km, so one truck per month requires at least 4 tons of diesel. It will also play an important role in pricing. If the customer orders transportation from Budapest to Košice and his cargo of 86 m3 weighs 10 tons, the transportation price will be different from the price of cargo with the same volume and route but with a weight of 25 tons. Another factor affecting truck fuel consumption is the driver’s experience. Experienced drivers have a slower truck driving pace and fewer hard braking and gas moments. While a driver with short work experience spends more fuel due to lack of experience – experienced drivers have a slower truck speed and fewer moments of hard braking and gas. At the same time, a driver with short work experience will spend more fuel due to shortage. Another factor that affects fuel consumption is weather and season. In winter, a truck (as well as a car) needs more fuel. The reason is the low temperature. The truck has to warm up and heat the cabin where the driver sits or the strong incoming

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Fig. 3. Fuel level monitoring.

wind. When driving against the wind, the truck consumes more fuel than when driving into the wind. Therefore, it is a priority for carriers to have the correct information about fuel needs.

4 Results and Discussion Figure 4. Shows the truck report on 03/16/2022. The system informs about how the vehicle moved. The pause lasted from 00:00 to 00:41, and the truck started moving in Romania. The program shows us the truck’s route and the numbering of highways. From this report, it is possible to analyze how quickly the truck got from A to B, using toll roads or bypassing them. You can also see where the driver deviated from the route. Based on these reports, travel documents are created, which are submitted to the accounting office for invoicing the business trip. The information on which country and how long the driver stays is particularly important for accounting to calculate allowances correctly. This program instantly generates such information that saves a lot of time. In addition, in logistics, shippers or consignees often ask carriers for this type of reporting to see if their goods have been delayed without good reason. You can see a “ranking” of the trucks in its fleet using the report analysis. Here, the Mapon program shows which trucks use fuel sparingly and which exceed the norm. This will help to solve the problem of unnecessary costs. If the truck suddenly exceeds the fuel consumption standard, replacing the filters or providing other services and replacement parts may be necessary. Excessive fuel consumption can also be related to the transport of heavy loads or inappropriate placement of the load on the semi-trailer. The main task of this type of analysis is to show the deviation from the norm. In Fig. 5, given below, we can see the table for monitoring the activity of the selected driver with the designation of the greatest and least efficient. As part of anonymity, providing comprehensive information in the contribution is impossible.

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Fig. 4. Reporting the operation of the truck on 16.03.2022.

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Fig. 5. Behavior of the driver while driving.

5 Conclusions There are no limits to development and improvement. Every day in the world, people work to improve the development of various technologies. It also applies to logistics and navigation systems and programs in it. Ten years ago, to find out where the truck was, the dispatcher had to call each driver and ask where they were. As a result, the company suffered significant financial losses for international calls, and the process took quite a long time. Today, logistics managers can see the real-time location of all their trucks by simply opening a navigation service provider’s website. The Mapon program constantly adds new functions to expand the range of services. Navigation systems are one of the components of the logistics industry. In addition, there are programs for searching loads, a program for accounting costs for the company, and separate programs for the service of tractors and semi-trailers for reporting drivers. Then you can make a general analysis of all the data. A possible proposal for the future would be for one platform to provide all these services in one information package. Acknowledgment. This article was supported by projects VEGA 1/0438/20 “Interaction of digital technologies to support software and hardware communication of an advanced production system platform”, KEGA 001TUKE-4/2020 “Modernization of industrial engineering teaching to develop the skills of the existing training program in a specialized laboratory”, APVV-17–0258

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“Digital engineering elements application in innovation and optimization of production flows”, APVV-19–0418 “Intelligent solutions to enhance business innovation capability in the process of transforming them into smart businesses”, VEGA 1/0508/22 (2022–2025) “Innovative and digital technologies in manufacturing and logistics processes and systems”.

References 1. M Demissie L Kattan 2022 Estimation of truck origin-destination flows using GPS data Transport Res E-Log 159 1 1 5 https://doi.org/10.1016/j.tre.2022.102621 2. SI You SG Ritchie 2018 A GPS Data Processing Framework for Analysis of Drayage Truck Tours KSCE J. Civ. Eng. 22 4 1454 1465 https://doi.org/10.1007/s12205-017-0160-6 3. Prester, J., Buchmeister, B., Palˇciˇc, I.: Effects of advanced manufacturing technologies on manufacturing company performance. Strojniski vestnik – Journal of Mechanical Engineering 64(12), 763−771 (2018). DOI:https://doi.org/10.5545/sv-jme.2018.5476 4. V Pavel M Maleki M Kargar 2022 A cluster-driven classification approach to truck stop location identification using passive GPS data J. Geogr. Syst. 24 4 657 677 https://doi.org/ 10.1007/s10109-022-00380-y 5. R Aziz 2016 Identifying and Characterizing Truck Stops from GPS Data P Perner Eds Advances in Data Mining. Applications and Theoretical Aspects ICDM 2016 16th Industrial Conference, ICDM 2016, New York, NY, USA, July 13–17, 2016. Proceedings New York, NY USA 2016 07 18 2016 07 20 Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence) LNCS (LNAI) 9728 Springer Cham 168 182 https://doi.org/10.1007/ 978-3-319-41561-1_13 6. KD Corro T Akter S Hernandez 2019 Comparison of Overnight Truck Parking Counts with GPS-Derived Counts for Truck Parking Facility Utilization Analysis Transp. Res. Rec. 2673 8 377 387 https://doi.org/10.1177/0361198119843851 7. D Yuniar L Djakfar A Wicaksono A Efendi 2020 Truck Driver Behavior and Travel Time Effectiveness Using Smart GPS Civil Eng. J. Tehran 6 4 724 732 https://doi.org/10.28991/ cej-2020-03091504 8. JJ Bartholdi A Lasso HD Rarliff Y Oliver 2019 Using GPS to measure truck service times in a container terminal Marit. Econ. logistics 21 1 146 155 https://doi.org/10.1057/s41278017-0097-1 9. P Trojanowski J Trojanowska 2021 Reliability of Road Transport Means as a Factor Affecting the Risk of Failure – The Transport Problem Case Study V Ivanov J Trojanowska I Pavlenko J Zajac D Perakovi´c Eds Advances in Design, Simulation and Manufacturing IV DSMIE 2021 Proceedings of the 4th International Conference on Design, Simulation, Manufacturing: The Innovation Exchange, DSMIE-2021, June 8–11, 2021, Lviv, Ukraine – Volume 1: Manufacturing and Materials Engineering Lviv Ukraine 2021 06 08 2021 06 11 Lecture Notes in Mechanical Engineering LNME Springer Cham 253 261 https://doi.org/10.1007/978-3-03077719-7_26 10. Arunmozhi, A., Park, J.: Comparison of HOG, LBP and Haar-like features for on-road vehicle detection. In: 2018 IEEE International Conference on Electro/Information Technology (EIT), pp. 0362–0367. Rochester, MI, USA (2018). https://doi.org/10.1109/eit.2018.8500159 11. V Ivanov I Pavlenko I Kuric M Kosov 2019 Mathematical Modeling and Numerical Simulation of Fixtures for Fork-Type Parts Manufacturing L Knapˇcíková M Balog Eds Industry 4.0: Trends in Management of Intelligent Manufacturing Systems EAI/Springer Innovations in Communication and Computing EICC Springer Cham 133 142 https://doi.org/10.1007/9783-030-14011-3_12

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12. MI Asborno S Hernandez T Akter 2020 Multicommodity port throughput from truck GPS and lock performance data fusion Marit. Econ. logistics 22 2 196 217 https://doi.org/10.1057/ s41278-020-00154-7 13. P Camargo SY Hong V Livshits 2017 Expanding the Uses of Truck GPS Data in Freight Modeling and Planning Activities Transp. Res. Rec. 2646 68 76 https://doi.org/10.3141/264 6-08 14. EA Nevland K Gingerich P Park 2020 A data-driven systematic approach for identifying and classifying long-haul truck parking locations Transp. Policy 96 48 59 https://doi.org/10.1016/ j.tranpol.2020.04.003 15. T Soroúorpte A Sumalee HW Ho 2020 Statistical estimation of freight activity analytics from Global Positioning System data of trucks Trans. Res E-Log 140 101986https://doi.org/10. 1016/j.tre.2020.101986 16. L Filina-Dawidowicz D Mo˙zdrze´n S Stankiewicz 2020 Integrated Approach for Planning of Intermodal Food Transport Chains Considering Risk Factors G Rodriguez Morales ER Fonseca C. JP Salgado P Pérez-Gosende M Orellana Cordero S Berrezueta Eds Information and Communication Technologies TICEC 2020 8th Conference, TICEC 2020, Guayaquil, Ecuador, November 25–27, 2020, Proceedings Guayaquil Ecuador 2020 11 25 2020 11 27 Communications in Computer and Information Science CCIS 1307 Springer Cham 319 332 https://doi.org/10.1007/978-3-030-62833-8_24

Manufacturing Technology

Preliminary Study of Mass Material Removal for Aluminum Alloy by Low Pressure Abrasive Water Jet Frantisek Botko1(B) , Jozef Zajac1 , Svetlana Radchenko1 Dominika Botkova1 , and Dagmar Klichova2

,

1 Technical University of Kosice, 1, Bayerova, Presov 080 01, Slovak Republic

[email protected] 2 Institute of Geonics, The Czech Academy of Sciences, 1768/9, Studentska,

Ostrava 708 00, Czech Republic

Abstract. Nowadays, great demands are placed on the improvement of technological processes from the perspective of quality and also from the environmental point of view. Most of the recent studies of control depth machining are realized with pressures above 100 MPa. The presented article focuses on preliminary testing of control depth cut machining by abrasive water jet technology with low pressure 50 MPa and low abrasive mass flow. The study shows promising results for the high-strength aluminum alloy AW7075. Experiments were realized using 5 levels of traverse speed (from 100–500 mm.min−1 ) and 4 levels of abrasives mass flow (20–50 g.min−1 ). The maximal mass material removal observed was 0,12 g pre one pass, and the maximal depth observed was 843 µm, both with traverse speed 100 mm.min−1 but with different abrasive mass flow. According to the obtained results can be designed more complex experiments to determine possibilities of control depth machining using an abrasive water jet with 50 MPa pressure. Keywords: AWJ Machining · Control Depth Machining · Traverse Speed · Abrasive Mass Flow · Groove · Sustainable Manufacturing

1 Introduction Nowadays, it emphasizes improving manufacturing processes and finding alternative approaches to producing engineering components. Controlled depth machining using an abrasive water jet (also known as AWJ milling) could be the alternative for producing thin-walled components, machining hard-to-cut materials, and producing narrow grooves. The material removal mechanism differs from conventional machining processes and thus gives new approaches to the machining process. The main difference between AWJ control depth machining and milling is minimal lateral force and the absence of tool wear. Material removal rate and depth of the cut can be regulated by changing numerous technologic conditions of AWJ, like traverse speed, abrasive mass flow, standoff distance, water pressure, a ratio of nozzle orifice diameter to focusing tube © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 175–184, 2023. https://doi.org/10.1007/978-3-031-32767-4_17

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diameter, and many others. For this reason, it is important to deeply study the effects of the combinations of technological conditions on the resulting tool mark in the material.

2 Literature Review The phenomena of water jet disintegration effect on various materials are a hotspot of interest for many scientists, as [1] where the modulated water jet is discussed, and [2] which summarizes water jet machining applications. Applying conventional technologies, in addition to their benefits, also brings some undesirable effects, such as the generation of dust and vapors [3, 4], the creation of a heat-affected zone, the limited lifetime of tools [5], and many other. Applying water jet machining to replace conventional technologies makes removing some undesirable effects possible and maintaining sustainable processes possible [6]. Due to the different modifications, water jet technology can be used in various forms and applications. Hloch et al. [7] studied the effect of standoff distance of nozzle orifice in PWJ machining on the resulting depth and morphology, authors [8] studied the erosion effect of the pulsed water jet on the brass surface, Raj et al. and [9] compared continual and pulsed water jet machining and [10] summarized information about water jet with slurry injection machining. The presented article is focused on the study of low-pressure abrasive water jet (50 MPa) material removal at various traverse speeds and abrasive mass flow. Since most researchers perform AWJ control depth machining (AWJ milling) at pressures above 100 MPa, this preliminary study investigates the possibility of applying lower pressures for this operation. The study [11] shows that in AWJ machining, the depth of cut depends on the traverse speed. In fact of this, there are difficulties in retaining a uniform depth of cut during profile machining. The authors [11] also stated that strategies are highly needed for the industrial application of AWJ control depth machining. Sourd et al. [12] studied the influence of AWJ machining parameters (feed speed, pump pressure, and overlap) on the depth of cut of CFRP 3D composite. Azarsa et al. [13] deal with free-standing structures with high aspect ratios. The authors [13] studied the effect of traverse speed, the count of passes, water jet pressure, standoff distance, and mass flow of the abrasive on the final shape. Authors [14] proposed an analytical model for kerf profile prediction taking into account water jet pressure, traverse speed, abrasive rate, and stand-off distance of the cutting head. Du et al. [15] studied AWJ factors effect on ductile materials. Ramakrishnan [16] studied AWJ milling of titanium alloy and the resulting surface roughness and microhardness. Akkurt [17] studied the effects of different cutting processes on the microstructural and hardness of aluminum and aluminum alloy cut with different processes. A collective of authors [18] studied the fatigue behavior of composites machined using AWJ-controlled depth of cut.

3 Research Methodology A preliminary experimental study was performed using high-strength aluminum alloy AW 7075 T6 (Tables 1 and 2).

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Table 1. Chemical composition of AW 7075 [19]. Si

Fe

Cu

Mn

Mg

Cr

Zn

Ti

Al

0.4

0.5

1.2–2

0.3

2.1–2.9

0.18–0.28

5.1–6.1

0.2

rest

Table 2. Mechanical properties of AW 7075 [19]. Tensile strength [MPa]

Yield point [MPa]

Hardness [HB]

Density [kg.m−3 ]

530

470

150

2.8

Experiments were performed on machine tool Water Jet 3015 RT-3D with a working area of 3000 mm × 1500 mm with hydraulic pump PTV Jets −3.8/60. Mass removal was measured using Mettler Tolledo PL303 balance. Optical measurement was performed using Keyence VHX-5100. The experimental procedure consisted of 20 combinations of process parameters (traverse speed and abrasives mass flow) listed in Table 3 [20]. Each sample was preprepared with dimensions 100 × 30 × 5 mm. Machining was realized using fixtures for easier manipulation and repeatability of the experiment. For each combination of parameters were created 4 samples and average values were calculated. Before performing experimental machining, calibration of the abrasives feeder was realized to obtain results as accurately as possible. Mass material removal was identified using analytical balance Mettler Toledo. Samples were weighed before and after machining, and the difference

Fig. 1. Machining of experimental samples.

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was calculated as material removal. For better visualization of created grooves, measurements were performed on a confocal microscope Keyence. The performance of experimental machining is shown in the following figure (Fig. 1). Table 3. Technological conditions of experiment [20]. Sample no

Traverse speed [mm.min−1 ]

Abrasive mass flow [g.min−1 ]

p [MPa]

Df/Do

z

Abrasives

1

100

20

50

0.9/0.33

4

2

100

30

Australian garnet MESH 80

3

100

40

4

100

50

5

200

20

6

200

30

7

200

40

8

200

50

9

300

20

10

300

30

11

300

40

12

300

50

13

400

20

14

400

30

15

400

40

16

400

50

17

500

20

18

500

30

19

500

40

20

500

50

4 Results and Discussion Results obtained from measurements and analysis of experimental samples show smooth grooves with rounded bottoms created by a traverse of an abrasive water jet through experimental samples. The upper edges of the groove exhibit a small entry radius caused by the material removal process. The following figure (Fig. 2) shows the top view of sample 1 (right) and the 3D view (left). On the 3D view, can be observed waves in the direction of the traverse movement of the cutting head on the bottom of the groove created by the passing of the water jet.

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Fig. 2. Visualization of groove created by 1. Combination of factors (100 mm.min−1 and 20 g.min−1 ) [20].

The following figures (Fig. 3 and Fig. 4.) show the crosscut of sample 1 in the area of maximal measured depth 660 µm and the calculated area of crosscut 377 277 µm2 . This method can also be used to evaluate mass material removal by calculating the crosscut’s average area multiplied by the groove’s length.

Fig. 3. Area of the maximal depth of groove created by 1 combination of factors (100 mm.min−1 and 20 g.min−1 ) [20].

The graphical dependence of mass material removal on abrasive mass flow for different traverse speeds is shown in Fig. 5. The highest material removal was achieved

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Fig. 4. Crosscut of sample created by 1 combination of factors (100 mm.min−1 and 20 g.min−1 ) [20].

for the lowest traverse speed, 100 mm.min−1 , with 40 g.min−1 of abrasive flow. Small differences between different samples can be observed for higher traverse speeds of 200–500 mm.min−1 . For traverse speed, 200 mm.min−1 were values of mass material removal in the range of 0.035 to 0.0435 g according to abrasive mass flow. For traverse speed 300 mm.min−1 from 0,022 to 0,027 g, and for traverse speed 500 mm.min−1 0,013–0,018 g. Small differences between mass material removal at higher traverse speeds are given by the fact that there was applied low abrasives mass flow [20] (Fig. 6).

Fig. 5. Dependence of mass material removal on abrasive mass flow.

Graphical dependence of mass material removal dependence no traverse speed at different abrasives mass flows shows highest values for traverse speed 100 mm.min−1 in the range 0.08 to 0.12 g. With increasing traverse speed, mass material removal decreases.

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Fig. 6. Dependence of mass material removal on traverse speed.

Table 4 presents the maximal depth for selected combinations of factors. Table 4. Maximal depth for selected combinations of factors. Sample No.

Traverse speed [mm.min−1 ]

Abrasive mass flow [g.min−1 ]

Depth [µm]

1

100

20

660.000

4

100

50

843.000

5

200

20

366.000

8

200

50

430.000

9

300

20

272.000

12

300

50

293.000

13

400

20

211.000

16

400

50

230.000

17

500

20

213.000

20

500

50

195.000

Each traverse speed and minimal and maximal abrasives mas flow samples were analyzed using a confocal microscope Keyence VHX-5100. As is evident from measured values, the highest depths of cut are obtained for traverse speed 100 mm.min−1 . With increasing traverse speed, the depth difference between low and high abrasive mass flow is decreasing.

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5 Conclusions Problematics of control depth machining using an abrasive water jet is the focus of many researchers. Since in water jet machining, there is no presence of rigid tool. There is a high necessity to find an optimal combination of process factors to obtain the desired depth of cut and mass material removal. AWJ process is characterized by many process factors that can be controlled and thus create many combinations for obtaining desired results. The presented preliminary study was focused on the influence of two of the basic parameters of the AWJ process (traverse speed and abrasives mass flow) on the final mass material removal and maximal obtained depth of cut in high-strength aluminum alloy AW7075. Experiments were realized with a pump pressure of 50 MPa, the lower pressure used in most recent studies dealing with AWJ control depth machining (100 MPa and above). Obtained findings: – Maximal mass material removal (0,12 g) was obtained for traverse speed 100 mm.min−1 with 40 g.min−1 of abrasive flow – The maximal depth of cut (843 µm) was obtained for traverse speed 100 mm.min−1 with 50 g.min−1 of abrasive flow – With increasing traverse speed, there is a significant decrease in the abrasive mass flow effect on mass material removal – For abrasives mass flows in this preliminary study (20–50 g.min−1 ) at traverse speeds 300–500 mm.min−1 is the difference between low and high abrasive mass flow approximately 20 µm of depth – According to the preliminary findings at presented experimental conditions with traverse speeds 100 and 200 mm.min−1 can be applied to remove the material and traverse speeds 300–500 mm.min−1 can be applied to remove thinner layers of material Obtained results show potential for application in the creation of narrow grooves, which is challenging to achieve using conventional milling technology since the gap created by AWJ can be only 1 mm wide. For widening of knowledge in the area of AWJ controlled depth of cut with pump pressure 50 MPa is needed to perform experiments with more involved process factors such as a stand of distance, different configurations of focusing tube diameter to nozzle orifice diameter (Df/Do), the inclination angle of cutting head, abrasives with different MESH in combination with traverse speed and abrasives mass flow. AWJ-controlled depth machining has promising potential in machining hard-to-machine materials and alloys because there is an absence of tool wear. O Acknowledgment. This work was supported by the Slovak Research and Development Agency under contract no. APVV-20-0514 – Research of the influence of technological parameters of abrasive water jet machining on the surface integrity of tool steels, by the project KEGA 032TUKE4/2021 – Transfer and implementation of knowledge in the field of energy beam technologies into study programs of technical secondary schools supporting dual education granted by the Ministry of Education, Science, Research and Sport of the Slovak Republic. The article results

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from the Project implementation: Development of excellent research capacities in the field of additive technologies for the Industry of the 21st century, ITMS: 313011BWN5, supported by the Operational Program Integrated Infrastructure funded by the ERDF.

References 1. Kusnerova, M., et al.: Innovative approach to advanced modulated water jet technology. Tehn. Vjesnik-Tech. Gazette 19(3), 475–480 (2012) 2. Liu, X., Liang, Z., Wen, G., Yuan, X.: Waterjet machining and research developments: a review. Int. J. Adv. Manuf. Technol. 102(5–8), 1257–1335 (2019). https://doi.org/10.1007/ s00170-018-3094-3 3. Banon, F., Sambruno, A., Batista, M., Simonet, B., Salguero, J.: Surface quality and free energy evaluation of s275 steel by shot blasting, abrasive water jet texturing and laser surface texturing. Metals 10(2), 290 (2020). https://doi.org/10.3390/met10020290 4. Perec, A.: Environmental aspects of abrasive water jet cutting. Rocznik Ochrona Srodowiska 20, 258–274 (2018) 5. Hutyova, Z., Harnicarova, M., Zajac, J., Valicek, J., Mihok, J.: Experimental study of surface roughness of wood plastic composites after turning. Adv. Mater. Res. 856, 108–112 (2014). https://doi.org/10.4028/www.scientific.net/AMR.856.108 6. Bayraktar, S., ¸ Alparslan, C.: Sustainable abrasive jet machining. In: Gajrani, K.K., Prasad, A., Kumar, A. (eds.) Advances in Sustainable Machining and Manufacturing Processes, pp. 173−188. CRC Press, Boca Raton (2022). https://doi.org/10.1201/9781003284574 7. Hloch, S., et al.: Effect of pressure of pulsating water jet moving along stair trajectory on erosion depth, surface morphology and microhardness. Wear 452, 203278 (2020). https://doi. org/10.1016/j.wear.2020.203278 8. Lehocká, D., et al.: Pulsating water jet erosion effect on a brass flat solid surface. Int. J. Adv. Manuf. Technol. 97(1–4), 1099–1112 (2018). https://doi.org/10.1007/s00170-018-1882-4 9. Raj, P., Chattopadhyaya, S., Mondal, A.: A review on continuous and pulsed water jet machining. Mater. Today-Proc. 27, 2596–2604 (2020) 10. Molitoris, M., Pitel, J., Hosovsky, A., Tothova, M., Zidek, K.: A review of research on water jet with slurry injection. In: International Conference on Manufacturing Engineering and Materials, ICMEM 2016, pp. 333–339. Elsevier Science BV, Novy Smokovec (2016). https:// doi.org/10.1016/j.proeng.2016.06.675 11. Alberdi, A., Rivero, A., de Lacalle, L.N.L., Etxeberria, I., Suarez, A.: Effect of process parameter on the kerf geometry in abrasive water jet milling. Int. J. Adv. Manuf. Technol. 51(5–8), 467–480 (2010). https://doi.org/10.1007/s00170-010-2662-y 12. Sourd, X., Zitoune, R., Crouzeix, L., Salem, M., Charlas, M.: New model for the prediction of the machining depth during milling of 3D woven composite using abrasive waterjet process. Compos. Struct. 234, 111760 (2020). https://doi.org/10.1016/j.compstruct.2019.111760 13. Azarsa, E., Cinco, L., Papini, M.: Fabrication of high aspect ratio free-standing structuresusing abrasive water jet micro-machining. J. Mater. Process. Technol. 275, 116318 (2020). https:// doi.org/10.1016/j.jmatprotec.2019.116318 14. Ozcan, Y., Tunc, L.T., Kopacka, J., Cetin, B., Sulitka, M.: Modelling and simulation of controlled depth abrasive water jet machining (AWJM) for roughing passes of free-form surfaces. Int. J. Adv. Manuf. Technol. 114(11–12), 3581–3596 (2021). https://doi.org/10. 1007/s00170-021-07131-1 15. Du, M., Zhang, K., Liu, Y., Feng, L., Fan, C.: Experimental and simulation study on the influence factors of abrasive water jet machining ductile materials. Energy Rep. 8, 11840– 11857 (2022)

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16. Ramakrishnan, S.: Investigating the effects of abrasive water jet machining parameters on surface integrity, chemical state in machining of Ti-6Al-4V. Mater. Today Commun. 31, 103480 (2022) 17. Akkurt, A.: The effect of cutting process on surface microstructure and hardness of pure and Al 6061 aluminium alloy. Eng. Sci. Technol.-Int. J.-JESTECH 18(3), 303–308 (2015). https:// doi.org/10.1016/j.jestch.2014.07.004 18. Hejjaji, A., Zitoune, R., Toubal, L., Crouzeix, L., Collombet, F.: Influence of controlled depth abrasive water jet milling on the fatigue behavior of carbon/epoxy composites. Compos. A Appl. Sci. Manuf. 121, 397–410 (2019) 19. https://batz-burgel.com/en/metal-trading/aluminium-product-range/en-aw-7075/. Accessed 04 Nov 2022 20. Botko, F.: The influence of the technological parameters of the abrasive water jet on material removal during machining with controlled depth of cut at low pressure. Habilitation thesis, 99 p., Kosice (2023)

Analysis of Microcutting of VT8 Titanium Alloy and 12Cr2Ni4A Steel During Grinding with Cubic Boron Nitride Tatiana Chumachenko1(B) , Oleksandr Derevianchenko1 , Tatiana Nikolaieva1 Yevhen Omelchenko1 , and Alla Bespalova2

,

1 Odessa Polytechnic National University, 1, Shevchenko Ave., Odessa 65044, Ukraine

[email protected] 2 Odessa State Academy of Civil Engineering and Architecture, 4, Didrikhsona St.,

Odessa 65029, Ukraine

Abstract. The article is devoted to the analytical and experimental study of cutting with a single grain when grinding titanium alloy VT8 and carbon case-hardened steel 12Cr2Ni4A. Titanium has high strength, good corrosion resistance, and, at the same time, has a relatively small mass, which makes its use indispensable in areas where, along with their mass, good mechanical properties of products are essential. The machining of titanium is hampered by its very high chemical activity at elevated temperatures. A weld may form in the contact area. It changes the geometry of the cutting edge by the formed build-up. The object of the study is the unit cutting forces that occur during the grinding of titanium alloy and carbon case-hardened steel, as well as the associated values that affect the quality of the machined surface. The research used samples of titanium alloy VT8 and steel 12Cr2Ni4A. Grinding was carried out with cubic boron nitride (CBN) wheels. The results of this study will further allow the prediction of the parameters of the part processing mode to obtain the necessary indicators of the quality of the surface layer and the geometric dimensions of the part while increasing the productivity of the grinding wheel and minimizing energy costs. The study aims to establish the dependences of the change in unit cutting forces and the values emerging from them during the grinding of samples from VT8 and 12Cr2Ni4A. Keywords: Cutting Forces · Titanium Alloys · Grain Cutting · Grain Size · Heat Flow · Hardened Steel · Temperature · Industrial Innovation

1 Introduction Titanium alloys are one of the primary structural materials currently used in various industries: mechanical engineering, aviation, rocket and space technology, metallurgy, oil and gas industry. Their widespread use is associated with a set of properties inherent in titanium and its alloys-high specific strength, corrosion resistance in aggressive environments, non-magnetism, and good thermal stability at operating temperatures up to 500–600 °C. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 185–194, 2023. https://doi.org/10.1007/978-3-031-32767-4_18

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It is generally recognized that titanium is difficult to process efficiently. Lower machinability is associated with a lower thermal conductivity than metal and a high coefficient of friction, which increases the temperature in the cutting zone. The high chemical activity of titanium is the cause of increased chemical deterioration of the tool. In the process of processing titanium billet, the service period of the grinding disc is reduced by 15–20 times, and the cost of dressing reaches 60–70% of the total time spent on work. The research of the titanium alloy grinding process is to control the thermal processing mode to maintain the machined surface’s quality. The thermal process can be controlled by changing the composition of the grinding disc, assigning optimal processing modes, and applying cooling methods. Experimental research in this area is quite laborious and time-consuming since it is impossible to cover a wide range of combinations of processing modes and characteristics of grinding discs. Grinding is characterized by cutting forces when removing individual chips with abrasive grains; the layer thickness removed by one abrasive grain; thermal phenomena - instantaneous and contact temperatures. The cutting forces during grinding are formed due to the summation of the elementary cutting forces that develop when cutting with individual cutting grains. Enrolling individual forces during grinding is impossible since this would make it necessary to place a corresponding sensor under each grain, which is practically impossible. Thus, only the total cutting forces can be experimentally determined during grinding. The average value of the elementary cutting forces can be obtained by calculating if, in addition to measuring the total forces, the number of working grains in the contact patch of the disc with the workpiece. To determine the contact temperature of grinding, it is necessary to know the thermal power Q, which is formed in the zone of contact of the disc with the workpiece and is the sum of the thermal power of the grains of the wheel qi, which simultaneously work in the contact zone. In this work, an analytical study of the unit cutting forces of the titanium alloy VT8 and steel 12Cr2Ni4A was carried out. The experimentally obtained values of the total cutting forces confirm the reliability of the selected cutting grain model and the calculations’ correctness. The dependences of the change in the intensity of the heat flux of a single grain qi on the depth of grinding are calculated, based on which the values of temperatures T under the action of a single grain, the thickness of the flakes removed by the grain, and the roughness are obtained. The method of semi-artificial microthermocouple was used to measure temperatures during the grinding of titanium alloy VT8 and steel 12Cr2Ni4A. The analysis of individual cutting forces establishes the relationship between the thermophysical characteristics of the material being processed and the technical characteristics of the grinding wheel. It makes it possible to predict the temperature in the contact zone of the disc and the workpiece and, as a result, to assign rational machining modes. High temperatures during grinding cause the formation of burns on titanium alloys. It leads to developing the zone of oxide, hydroxide, nitride films and a zone of structural phase transformations. It is especially important for dry grinding, in which oxide films of various shades appear. Under the lies a layer that does not have a clear crystalline

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structure. The depth of this layer depends on the intensity of the burn and is tenths and even hundredths of a micron. Even when no noticeable structural changes were found in the surface layers of titanium alloys after grinding, it cannot be argued that the surface layer of titanium alloys does not have structural transformations after grinding. As a result of experiments on titanium alloy VT8 and hardened carbon steel 12Cr2Ni4A., it was found that by reducing the grain size of the circle, we get the effect of increasing the energy consumption of the grinding process, due to an increase in the magnitude of cutting forces. The cutting forces during the machining of titanium alloys are at the same level as those of steels. Their lower machinability is associated with lower thermal conductivity and a high coefficient of friction, increasing the cutting zone’s temperature. The above analysis points to the task set’s importance by analyzing the unit cutting forces when grinding parts made of titanium alloys.

2 Literature Review Thus, understanding the mechanics of titanium machining [1] and modeling have shown that the orientation of the tool has a decisive influence on the resulting cutting force and the component that is normal to the machined surface. It is also possible to predict the tool orientation at which cutting torque and energy dissipation are minimal. However, the process of grinding titanium alloys was not considered. The materials [2] considered the possibility of obtaining β-titanium alloys with desired characteristics. An alloy with this particular e/a value has a high elastic anisotropy, resulting in a unique dislocation-free plastic deformation mechanism and mechanical properties. This study [3] investigates the effect of Ti64 microstructure on grinding forces and surface roughness during surface grinding. In [4], the surface quality of the Ti–6Al–4V alloy was studied in terms of microhardness after surface grinding and surface roughness of a silicon carbide wheel under various grinding conditions. In [5], a cutting force model based on a predictive model for orthogonal cutting was developed to predict the force during the face milling of a Ti6Al4V titanium alloy. The study [6] discusses surface integrity issues when grinding aged C–250 steel with resin-bonded and electroplated CBN grinding wheels. Titanium alloys are not considered. Article [7] is devoted to proposing an analytical force model in the grinding of aging steel 3j33, based on the fact that the grinding process is divided into three stages, namely friction, plunge, and shear, in terms of the working state of the grain. These three stages are defined by the chip thickness model, in which the friction stage is derived from the Hertzian contact theory. Cutting forces when grinding with CBN wheels are not taken into account. The results showed [8] that the surface roughness of worn steel can be reduced by about 87% under cryogenic conditions compared to dry grinding. The issues of grinding with wheels made of superhard materials are not considered. The article [9] considers temperature and energy problems in MSS abrasive grinding. Cutting forces are not taken into account. In this article [10], an experimental study was carried out to determine the effect of grinding force, temperature, and grinding conditions such as grinding speed, workpiece processing speed, and grinding depth on the distribution of surface and deep residual stresses caused by grinding aging steel 3J33 with miniature CBN discs with a galvanized coating. Titanium alloys and body-hardened carbon steel

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are not considered. This article [11] discusses the influence of grinding conditions and grinding wheels on surface roughness and wear of grinding wheels when grinding tool steel VACO 180 on a 5-axis grinder. The experiment was designed to study the effect of changes in cutting speed, depth of cut, and the use of two different grinding wheels on these two values. Cutting forces are not taken into account. In [12], the developed numerical models of the evolution of chips and cutting forces during the processing of a polycrystalline workpiece material with a mechanochemical effect by changing the fracture energy are considered. The results of [13] describe the effect of aging treatment on cutting forces, surface topology, tool wear, and vibration. Problems with cutting forces during grinding are not affected. The work [14] studies the process of grinding titanium alloys using grinding wheels made of silicon carbide. Cutting forces have not been studied. In [15], the wear resistance of diamond and cubic boron nitride grains during grinding was studied. Cutting forces are not taken into account. Thus, analyzing the data available in the literature, we can conclude that in recent years much attention has been paid to finishing technologies [16] and the development of quality criteria for the surface layer [17]. However, there is little data on how the characteristics change after the action of single cutting forces, the values of which can be quite significant.

3 Research Methodology Features of the processes that occur in the material being processed (titanium alloy VT8 and steel 12Cr2Ni4A) under the action of cutting forces of grains of grinding wheels have not yet been studied in sufficient volume. Therefore, for the results of the control of such complex objects to be uniquely determined, it is necessary to study the parameters of the interaction of models of cutting grains with the material being processed and describe them mathematically. It is impossible to experimentally determine the cutting forces from each grain located in the contact spot of the wheel with the part. However, at the initial stage of the experiment, it is possible to establish the dependencies of how grinding modes, the technical characteristics of the grinding wheel, and the thermophysical characteristics of the material being processed affect the unit cutting forces. And the average value of the elementary cutting forces can be obtained by calculation if, in addition to measuring the total forces, the number of working grains in the contact spot of the circle with the part is determined. In addition, the tangential force Pz , or flakes forming force, determines the cutting power, forming a heat source. The force Py determines the depth of penetration of the cutting grains into the material and can lead to the formation of temporary stresses that can form cracks. Knowing the force Py and the area of the contact spot of the wheel with the part allows you to determine the specific pressure and correlate it with the processing modes that provide the necessary characteristics of the surface layer. It makes it possible to recalculate the processing modes of one grinding method to another. In this work, a statistically planned experiment was used, which made it possible to obtain full information about the studied process of grinding titanium alloy VT8 and steel 12Cr2Ni4A using circles of cubic boron nitride. Thus, the number of time-consuming experiments is minimized.

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Mathematical dependences for the numerical calculation of unit cutting forces Py and Pz (1,2) depending on the thermophysical characteristics of the material under study and the properties of grinding wheels are described in more detail in [18]. √ 2T λ π  Pz1 = √  (1) Rh V ατ 1 − e− 2aτ Py1 =

Pz1 0, 55

(2)

where λ – coefficient of thermal conductivity, J/m·s; α – coefficient of thermal diffusivity, m2 /s; τ – duration of the heat source, s; R – radius of curvature of the cutting part of the grain, m; T – temperature from a single grain, ºC; H –microhardness of the test sample, N/m2 . In this article, a model of the shape of the cutting part of the grain in the form of a sphere is adopted. The trajectory of grain movement was divided into the i-th number of sections. For each section, the cutting parameters are calculated: the length of the arc of the contact of the circle with the metal or the length of the grain trajectory – L, m; cutting forces – Pz , Py , N (Table); the amount of heat that is released by one grain – qi , W (Fig. 2); effective area of the contact spot – F, m2 ; the thickness of the chips removed by the grain – h, m (Fig. 4); total heat flow – Q, W; temperature – T °C (Fig. 3), roughness – Ra , μm (Fig. 5), provided that the temperature of the material under study in the i-th section will be equal to the maximum cutting temperature in the section i-1. The following grinding process parameters are set: flat grinding; titanium alloy VT8: coefficient of thermal activity ε = 7.3·103 J/m2 °C s0.5 ; thermal diffusivity. α = 8.9·10–6 m2 /s; hardness HB = 103; ultimate strength σv = 1080 MPa; thermal conductivity λ = 21.9 J/m·s·deg; steel 12Cr2Ni4A, cemented and hardened (annealed) for martensite: ε = 12.5·103 J/m2 °C s0.5 ; α = 8·10–6 m2 /s; hardness HRC = 60–62; σv = 1220 MPa. Grinding wheel LO 160/125 C10 100%; D = 450 mm; ε = 1.1·103 J/m2 °C s0.5 ; α = 5.3·10–6 m2 /s; grinding modes: Vw = 35 m/s; Vd = 0.25 m/s; t = 0.03mm; S = 2 mm. The total cutting forces were determined experimentally, which made it possible to test the mathematical model and obtain real values of the forces (Fig. 1). Cutting forces according to the method described in work [19]. Temperature measurements were carried out while grinding titanium alloy VT8 and steel 12Cr2Ni4A using the shear semi-artificial microthermocouple method. Microthermocouples were used to measure temperature and determine the number of cutting grains and the distance between them [18, 20]. Of the currently developed methods for measuring grinding temperatures, the most accessible and convenient is the method of a cutting semi-artificial microthermocouple, consisting of the metal being machined and a thermoelectrode located in the ground part, which gives a clear temperature distribution in the cutting zone and allows recording and measuring cutting temperatures from individual abrasive grains, directly in the grinding area. One of the electrodes of such a microthermocouple is a workpiece, and the second thermoelectrode is a constantan wire with a diameter of 20 mm. The diameter of the wire is much smaller than the distance between the cutting grains of almost any size, which prevents it from being cut into several grains at once.

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Since the formation of thermocouples “thermoelectrode–part” occurs during grinding, the signal of such thermocouples will correspond to the thermal transition temperature. It is necessary to determine the surface temperature of the part [20]. Both the surface temperature and the thermoelectrode end temperature can be described by the expression: √ 1, 12Q τ , (3) TS = Fε TS = Tth

εS + εth εS + εth ; orTS = Tth × E × j = k1 × k2 × E 2εS 2εS

(4)

where: Ts – determined surface temperature, ºC; Q – power of the heat source, W; τ – duration of the heat source, s; F – area of the contact spot of the wheel with the part, m2 ; ε – coefficient of thermal activity J/m2 ·°C ·s0.5 ; , c – specific heat capacity, J/kg·deg; λ – coefficient of thermal conductivity, J/m·s; ρ – sample density, kg/m3 ; Tth – temperature of the microthermocouple junction; εs – coefficient of thermal activity of the grounded surface, J/m2 ·°C ·s0.5 ; εth – coefficient of thermal activity of the microthermocouple connection, J/m2 ·°C ·s0.5 ; E – value of thermo-EMF; j is the coupling coefficient between the thermal transition temperature and the thermo-EMF current (thermo-EMF); k2 – loop gain; Thus, using the dependencies of contact heat transfer, we obtain an expression for determining the contact temperature of the surface.

4 Results and Discussion According to the above methods and analytical dependences, theoretical and experimental data were obtained. (Table 1, Fig. 1, 2, 3, 4 and 5). During the experiments, the influence of cutting fluids was not taken into account since there are quite a lot of them, and their influence requires a separate study. For grinding wheels made from CBN, by changing their grain size from 8 to 25, one can observe an increase in the value of unit cutting forces from 1.1 to 2.5 times, respectively. Table 1. Calculated values of unit cutting forces during grinding of titanium alloy VT8 and steel 12Cr2Ni4A. Steel t × 10–3 , m 12Cr2Ni4A wheel grit size /Titanium alloy 0.01 VT8 0.02

Pzsr × 10–4 , H

Pysr × 10–4 , H

(8)

(12)

(25)

(8)

1.7/1.6

2.4/2.3 4.4/4.1

3.1/3.06 4.6/3.5

8.0/7.8

3.4/3.3

4.8/4.6 8.6/8.3

6.2/6.1

15.7/15.3

0.03

3.9/3.7

5.6/5.5 10.2/9.9 7.2/7.1

0.04

4.3/4.13 5.8/5.6 11/10.3

7.7/7.5

(12) 8.8/7.6

(25)

10.2/8.7 18.6/18.2 10.7/9.1 19.4/18.9

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Fig. 1. Dependence of the change in the values of the total cutting forces Py and Pz (points experiment, line - calculation) on the grinding depth: a) titanium alloy VT8, b) steel 12Cr2Ni4A with different grain sizes. Grinding modes: Vw = 35 m/s; Vd = 0.25 m/s; S = 2.10−3 m/stroke. Grinding wheel (CBN) LO 125/100 S10 100%, LO 160/125 S10 100%, LO 250/160 S10 100%.

Fig. 2. Calculated dependences of the change in the heat flux intensity of a single grain qi on the grinding depth: a) titanium alloy VT8, b) steel 12Cr2Ni4A.Grinding modes: Vw = 35 m/s; Vd = 0.25 m/s; S = 2.10−3 m/stroke. Grinding wheel (CBN) LO 125/100 S10 100%, LO 160/125 S10 100%, LO 250/160 S10 100%.

This phenomenon can be explained as follows: by reducing the grain size of the grinding wheel, we observe a significant increase in the number of cutting grains per unit surface of the wheel and, consequently, in the contact zone of the grinding wheel with the workpiece. In this case, there is a decrease in the power load per grain, but an increase in the number of grains increases the cutting force. It should be noted that cutting forces also increase due to friction between the grinding surface and the wheel joint when using fine-grained wheels. The ratio between the cutting forces was determined experimentally: Py /Pz = 1.82.

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Fig. 3. Dependence of the change in temperature T under the action of a single grain on the depth of grinding (dots - experiment, line - calculation) with a different granularity of the wheel: a) titanium alloy VT8 and b) steel 12Cr2Ni4A. Grinding modes: Vw = 35 m/s; Vd = 0.25 m/s; S = 2.10−3 m/stroke. Grinding wheel (CBN) LO 125/100 S10 100%, LO 160/125 S10 100%, LO 250/160 S10 100%.

Fig. 4. Deepening value of the grain on the depth of grinding titanium alloy VT8 and steel 12Cr2Ni4A. Grinding modes: Vw = 35 m/s; Vd = 0.25 m/s; S = 2.10−3 m/stroke. Grinding wheel (CBN) LO 125/100 S10 100%, LO 160/125 S10 100%, LO 250/160 S10 100%.

Fig. 5. Dependence of the change in roughness on the grinding depth: titanium alloy VT8 and steel 12Cr2Ni4A. Grinding modes: Vw = 35 m/s; Vd = 0.25 m/s; S = 2.10−3 m/stroke. Grinding wheel (CBN) LO 125/100 S10 100%, LO 160/125 S10 100%, LO 250/160 S10 100%.

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Surface grinding with CBN wheels with a grain size of 12 gives the following practical values of cutting forces: Py = 8.376 N, Pz = 4.607 N – for titanium alloy VT8; Py = 9.861 N, Pz = 5.423 N – for steel 12Cr2Ni4A. For VT8 and steel 12Cr2Ni4A, the depth of grain penetration into the metal h and the roughness Ra are pretty close (Fig. 4 and Fig. 5). A direct proportional relationship exists between a decrease in grain size in a wheel and an increase in cutting forces. Thus, we get the effect of increasing the energy consumption of the grinding process. The analysis of individual cutting forces in the processing of titanium alloys and steel 12Cr2Ni4A will allow for further research to assign rational grinding modes with CBN wheels, which are guaranteed to lead to minimal energy consumption and a high-quality surface layer of the part.

5 Conclusions When choosing a grinding wheel for processing titanium alloys, it is necessary to choose wheels with the maximum grit that meets the requirements for surface roughness and labor protection requirements. The rotation speed of the grinding wheel should be selected within 30 m/s. Further research in this area will make it possible to create a database of processing modes that do not deteriorate the quality of the machined titanium alloy surface.

References 1. Lazoglu, I., Ehsan Layegh Khavidaki, S., Mamedov, A.: Mechanics of Titanium Machining. In: Davim, J. (eds.) Machining of Titanium Alloys. Materials Forming, Machining and Tribology, pp. 57–78. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-439 02-9_3 2. Xing, H., Sun, J.: Deformation defects in a metastable β titanium alloy. In: Richter, S., Schwedt, A. (eds.) EMC 2008 14th European Microscopy Congress 1–5 September 2008, Aachen, Germany, pp. 675–676. Springer, Berlin, Heidelberg (2008). https://doi.org/10.1007/ 978-3-540-85226-1_338 3. Phi, H.T., Hoang, G.V., Bui, T.N., Nguyen, T.K., Truong, S.H.: The effect of microstructure on the cutting forces and microhardness in the surface grinding of titanium alloys. In: Long, B.T., Kim, Y.-H., Ishizaki, K., Toan, N.D., Parinov, I.A., Vu, N.P. (eds.) MMMS 2020. LNME, pp. 525–533. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69610-8_72 4. de Mello, A.V., de Silva, R.B., Machado, A.R., Gelamo, R.V., Diniz, A.E., de Oliveira, R.F.M.: Surface grinding of Ti-6Al-4V alloy with SiC abrasive wheel at various cutting conditions. Procedia Manuf. 10, 590–600 (2017). https://doi.org/10.1016/j.promfg.2017.07.057 5. Yun, C.: Cutting forces and tool wear in dry milling of Ti6Al4V. And Mat. Res. 565, 454–459 (2012). https://doi.org/10.4028/www.scientific.net/AMR.565.454 6. Shen, S., Li, B., Guo, W.: Surface integrity in grinding of C-250 maraging steel with resinbonded and electroplated CBN grinding wheels. Int. J. Adv. Manuf. Technol. 103(1–4), 1079– 1094 (2019). https://doi.org/10.1007/s00170-019-03574-9 7. Weicheng, G., Beizhi, L., Shouguo, S., Qinzhi, Z.: Experimental study on force model in grinding of maraging steel 3J33. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 233(10), 3475– 3486 (2018). https://doi.org/10.1177/0954406218814041

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8. Balan, A.S.S., et al.: Effect of cryogenic grinding on fatigue life of additively manufactured maraging steel. Materials 14, 1245 (2021). https://doi.org/10.3390/ma14051245 9. Zishan, D., Beizhi, L., Omar, F., Yamin, S., Steven, Y., Lianga, B.: Investigation of temperature and energy partition during maraging steel micro-grinding. Procedia CIRP 56, 284–288 (2016). https://doi.org/10.1016/j.procir.2016.10.084 10. Shouguo, S., Beizhi, L., Weicheng, G.: Residual stresses distributions in grinding of 3J33 maraging steel with miniature electroplated CBN Wheel. MATEC Web Conf. 256, 01002 (2019). https://doi.org/10.1051/matecconf/201925601002 11. Jindrich, F., Tomas, B., Miroslav, Z.: Grinding of maraging steel 1.2709 with SiC grinding wheels and effect of grinding conditions on the surface roughness and wear of the wheels. Manuf. Technol. 20(1), 18–22 (2020). https://doi.org/10.21062/mft.2020.018 12. Zhongpeng, Z., Yan, J.L., Jiayi, Z., Xin, J., Hao, W.: Ultra-precision micro-cutting of maraging steel 3J33C under the influence of a surface-active medium. J. Mat. Process. Technol. 292, 117054 (2021). https://doi.org/10.1016/j.jmatprotec.2021.117054 13. Demir, H., Guldibi, A.S.: Aging effect on microstructure and machinability of corrax steel. Eng. Technol. Appl. Sci. Res. 10(1), 5168–5174 (2020). https://doi.org/10.48084/etasr.3265 14. Kremen, Z.I.: A new generation of high-porous vitrified cBN wheels. Ind. Diamond Rev. 63(4), 53–56 (2003) 15. Zieli´nski, B., Nadolny, K., Zawadka, W., Chaci´nski, T., Stachurski, W., Batalha, G.F.: Effect of the granularity of cubic boron nitride vitrified grinding wheels on the planar technical blades sharpening process. Materials 15(22), 7989 (2022). https://doi.org/10.3390/ma1522 7989 16. Orgiyan, A., Ivanov, V., Tonkonogyi, V., Balaniuk, A., Kolesnik V.: The efficiency of dynamic vibration dampers for fine finishing boring. In: Tonkonogyi, V., Ivanov, V., Trojanowska, J., Oborskyi, G., Pavlenko, I. (eds.) Advanced Manufacturing Processes IV. InterPartner 2022. LNME, pp. 140–149. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16651-8_14 17. Kunitsyn, M., Usov, A., Sikirash, Y.: Impact of the heterogeneous structure of magnetic hard alloys on the quality characteristics of the surface layer during grinding processing. In: Tonkonogyi, V., Ivanov, V., Trojanowska, J., Oborskyi, G., Pavlenko, I. (eds.) InterPartner 2021. LNME, pp. 405–414. Springer, Cham (2022). https://doi.org/10.1007/978-3-03091327-4_40 18. Lebedev, V., Klimenko, N., Uryadnikova, I., Chumachenko, T., Ovcharenko, A.: Definition of the amount of heat released during metal cutting by abrasive grain and the contact temperature of the ground surface. Eastern-Eur. J. Enterpr. Technol. 5/7(83), 43–50 (2016). https://doi. org/10.15587/1729-4061.2016.81207 19. Durgumahanti, P., Singh, V., Venkateswara, P.: A new model for grinding force prediction and analysis. Int. J. Mach. Tools Manuf 50(3), 231–240 (2010). https://doi.org/10.1016/j.ijm achtools.2009.12.004 20. Bezpalova, A., Lebedev, V., Chumachenko, T., Frolenkova, O., Klymenko, N.: Methods for measuring grinding temperatures. In: Tonkonogyi, V., Ivanov, V., Trojanowska, J., Oborskyi, G., Pavlenko, I. (eds.) InterPartner 2021. LNME, pp. 141–150. Springer, Cham (2022). https:// doi.org/10.1007/978-3-030-91327-4_14

An Impact of the Cutting Fluid Supply on Contact Processes During Drilling Eshreb Dzhemilov1(B) , Eskender Bekirov1 , Alper Uysal2 and Ruslan Dzhemalyadinov1

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1 Crimean Engineering and Pedagogical University, Simferopol 295015, Republic of Crimea

[email protected] 2 Yildiz Technical University, 34349 Be¸sikta¸s, Istanbul, Turkey

Abstract. The article discusses machining blind holes with twist drills, which is one of the most common machining operations. Typical problems for this process are difficulty to chip removal and coolant supply to the machining zone. Also, when chips are removed along the spiral grooves, they rub against the hole’s surface, deform, and, as a result, the surface quality deteriorates. For a comprehensive assessment of the drilling process and the influence of various cutting fluids, it is necessary to control all parameters directly in the material removal process. The paper proposes a method for controlling contact loads and cutting temperature along the generatrix of the workpiece when using various compositions of cutting fluids and assessing their effect on the roughness of the resulting surface. The influence of the supply method of the studied cutting fluids is also shown. So the supply of liquids to the cutting zone through the internal channels of the tool contributes to a significant decrease in the cutting temperature and, by reducing the friction coefficient, helps to reduce the surface roughness compared to the most used method of supply-irrigation. Keywords: Process Innovation · Drilling · Contact Pressures · Cutting Temperatures · Surface Roughness · Cutting Fluids

1 Introduction Most machine parts undergoing machining have holes. The most common method of obtaining holes in a solid material is drilling. This process has been widely studied and does not present any particular difficulties. Drilling blind holes in metal alloys are often carried out with the supply of cooling lubricants to the machining zone. Constant chip removal and high specific pressures in the machining zone do not guarantee that lubricants will reach the tool-workpiece interface. Therefore, it is essential to select lubricants properly, considering modern requirements for the greening of production and methods of its supply directly to the chip formation zone. For a qualitative assessment of the drilling process, it is necessary to consider not only the output parameters, such as surface roughness and tool life but also dynamically changing indicators of contact pressures and temperatures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 195–204, 2023. https://doi.org/10.1007/978-3-031-32767-4_19

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2 Literature Review At this stage of product development, there is a tendency to reduce negative factors in manufacturing machine parts associated with cutting fluids based on oil products. One of the directions is the complete rejection of process media and the transition to dry cutting [1]. However, this method is not suitable for a number of machining operations [2] and often requires either the use of a special tool [3] or switching to high-speed machining [4, 5]. The second way to reduce the negative impact is the use minimum quantity lubrication technology. The authors in [6] propose using the MQL method combined with Arduino Uno control. In [7], the efficiency of using a process media with the addition of nanofluid supplied by the MQL method for turning austenitic steel was experimentally shown. The use of additives in the form of nanofluid [8] provides a sustainable approach when using MQL. The authors in [9] showed the positive effect of MQL technology when turning AISI 304 stainless steel on reducing the roughness of the resulting surface. The supplied liquid contained nanoparticles of two sizes, 30 and 40 nm. The authors proposed a new approach to predicting the resulting surface roughness by changing the size of the supplied particles in the composition of the cutting fluid and using machine learning. In the study [10], the authors showed the effect of MQL and the addition of nano-additives (nanoMQL) to the applied liquid on reducing the height of microroughness of the machined surface and tool wear when turning ER7 steel. According to the presented results, using MQL technology reduces roughness by more than 24% and wear by 34%. Adding nanoparticles to the composition leads to an improvement of 34% and 37.6%, respectively. Aslan and al., in their article, presented a comparative assessment of dry cutting and when using minimal lubrication technology. It is noted that when machining Strenx 900 structural steel, oil supplied in fine droplets decreases roughness by more than 30%, tool life increases by more than 90%, and cutting forces decrease by 28% [11]. In [12] shows the effect of a hybrid nanofluid based on palm oil by adding graphene nanoparticles and hexagonal boron nitride fed into the treatment zone using MQL technology. The authors note a decrease in surface roughness when machining Inconel 718 by more than 40% compared to dry cutting. Tool life increases by more than 50%. Other ways to increase the treatment are presented in [13]. Despite all the advantages of MQL, this technology is poorly suited for processes occurring in closed-cutting conditions, such as drilling blind holes. Constant chip removal makes it difficult for small doses of lubricants to enter the treatment area. The third way to reduce the negative impact is by using environmentally friendly cutting fluids. The basis of such formulations is usually fatty acids of plant and animal origin. Work [14] shows the effect of vegetable oils and pork fat on reducing the wear of a high-speed steel cutting plate during turning. Ecological compositions of lubricants are also created based on non-edible oils of vegetable origin. In [15], the authors developed two formulations based on Jatropha and Pongamia oil. The authors note the positive effect of vegetable oils on reducing cutting forces and surface roughness. The authors present similar results in a review article [16]. The effectiveness of biodegradable oils in metalworking processes compared to mineral oils has been noted.

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For operations in closed-cutting conditions, such as drilling blind holes, the key factor in increasing the durability of the tool and the quality of the resulting surface will be the effective cooling of the working area. This article examines the influence of various technological media supplied to the treatment area by irrigation and through the internal channels of the tool.

3 Research Methodology Experimental studies were carried out on a radial drilling machine model 2K522. The tool used was a twist drill with channels for the internal supply of cutting fluid. Drilling was carried out in two structural materials: low-alloy steel 41Cr4 and extra-hard steel 100Cr6. The following process media were selected to cool the working area: waterbased – Aquafreeze 6, oil-based – MOBILMET 423, and rapeseed oil. The coolant supply to the cutting zone was carried out in two ways: through the internal channels of the tool with a pressure of 5 bar and through irrigation. A specially designed experimental setup simultaneously records contact pressures and temperatures along the workpiece generatrix in one pass of the tool (Fig. 1). The cutting temperature was measured using an artificial thermocouple, the junctions of which were located opposite the strain gauges. To estimate the temperature parameters along the entire length of the cylinder generatrix during drilling, the readings recorded on different thermocouple junctions were switched as the drill went deeper. The data recording rate was fifty readings per second. The temperature readings were chosen according to the maximum values, that is, at the moment of elastic-plastic deformation of the cut layer of the allowance. Machining modes are selected according to average production values for two types of materials: cutting speed – 18.84 m/min (600 r/min), feed – 0.1 mm/rev. Roughness parameters after drilling were measured using a profilometer TR-200 model.

4 Results and Discussion When machining steel 100Cr6, using the water-miscible cutting fluid Aquafriz-6 has a noticeable effect on reducing contact pressures (Fig. 2). It is due to the lower viscosity compared to oil liquids and, as a result, more reliable penetration into the contact zone while creating a boundary layer that facilitates chip withdrawal.

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Fig. 1. Experimental set-up.

A similar change pattern in contact pressure is also observed while machining a low-alloy steel 41Cr4 (Fig. 3). When supplying technical fluids through the channels of the tool, the influence of rapeseed oil is noticeable. It can be explained by the fact that the oil is guaranteed to enter the contact zone and change the actual shear angle of chip formation. When using the irrigation method, due to the destructurization of fatty acids under the influence of high temperatures, rapeseed oil loses its lubricating effect.

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Fig. 2. Change in contact pressure along the length of the generatrix of the workpiece when machining steel 100Cr6 with coolant supply: a – by the method of irrigation, b – through the internal channels of the tool.

A more pronounced method of supplying cutting fluid affects the cooling of the cutting zone. Aquafriz-6 also has the greatest effect on reducing the cutting temperature by watering and feeding through the tool’s internal feces (Figs. 4–5). The graphs show the relative (without considering the temperature gradient) characteristic of the analysis of cutting fluids at temperature. Due to the water base, Aquafreeze-6 leads to significant heat removal from the contact zone. The supply of technological media through the channels of the tool contributed to a more than twofold decrease in the height of microroughness after drilling steel 100Cr6 (Fig. 6, b) and steel 41Cr4 (Fig. 7, b).

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Fig. 3. Change in contact pressure along the length of the generatrix of the workpiece when machining steel 41Cr4 with coolant supply: a – by the method of irrigation, b – through the internal channels of the tool.

Analyzing the data obtained, it can be noted that the main factor affecting the contact pressure indicators is the directly removed layer of material and the pressure of the tool itself on the hole walls. The difference in the efficiency of supplying lubricating process fluids only slightly affects the change in contact pressure indicators. The slight influence of oily technological media can be explained by a higher viscosity and, as a result, a lower penetrating power directly in the zone of contact between the tool and the workpiece. In addition to the greater penetrating ability of the process fluid Aquafriz-6, its water base, with an increase in temperature in the contact zone, leads to more efficient heat removal due to water evaporation, and its oil base continues to maintain a lubricating function.

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Fig. 4. Influence of cutting fluids on cutting temperature when drilling steel 100Cr6.

Fig. 5. Influence of cutting fluids on cutting temperature when drilling steel 41Cr4.

Also, the temperature data presented in the study are relative due to the presence of a gradient during the measurement. The actual temperature in the contact zone is much higher. The smoke point of oils used as process fluids starts from 250 °C. Thus, in the chip formation zone, they are completely destructured, leaving graphite as one of the combustion products of oils. Further research will be aimed at developing a model to improve the efficiency of the technological media used based on dynamic analysis of output parameters (contact

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Fig. 6. Influence of technological media on the roughness after drilling steel 100Cr6 supplied: a) by irrigation, b) through the internal channels of the tool.

Fig. 7. Influence of technological media on the roughness after drilling steel 41Cr4 supplied: a) by irrigation, b) through the internal channels of the tool.

pressures, temperatures, cutting forces) and subsequent correction of cutting conditions to improve the quality of blind holes.

5 Conclusion The following conclusions can be drawn based on the analysis of the obtained data. Internal supply of cutting fluid through the channels of the tool makes it possible to ensure its guaranteed entry into the machining zone. For drilling blind holes in metal alloys, the main characteristic affecting the machining quality is the temperature in the cutting zone. Reducing the viscosity of the process medium used and the presence of endothermic components in the composition contributes to decreased temperature in

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the cutting zone. The use of oil technological media requires adjustment of production cutting conditions to ensure that the temperature in the contact zone is below the values of their smoke point.

References 1. Zhang, N., Yang, F., Liu, G.: Cutting performance of micro-textured WC/Co tools in the dry cutting of Ti-6Al-4V alloy. Int. J. Adv. Manuf. Technol. 107(9–10), 3967–3979 (2020). https://doi.org/10.1007/s00170-020-05161-9 2. Song, X., Takahashi, Y., He, W., Ihara, T.: Study on the protective effect of built-up layer in dry cutting of stainless steel SUS30. In: 34th ASPE Annual Meeting, American Society of Precision Engineering, pp. 138−148. American Society for Precision Engineering, Pittsburgh, Pennsylvania, USA (2019) 3. Duan, R., Deng, J., Ai, X., Liu, Y., Chen, H.: Experimental assessment of derivative cutting of micro-textured tools in dry cutting of medium carbon steels. Int. J. Adv. Manuf. Technol. 92(9–12), 3531–3540 (2017). https://doi.org/10.1007/s00170-017-0360-8 4. Song, Y., Cao, H., Wang, Q., Zhang, J., Yan, C.: Surface roughness prediction model in highspeed dry milling CFRP considering carbon fiber distribution. Compos. B Eng. 245, 110230 (2022). https://doi.org/10.1016/j.compositesb.2022.110230 5. Takemoto, S., Sato, M., Matsuno, T., Yamamoto, K.: Chip Combustion in High-speed Dry Cutting of Titanium Alloy. In: Proceedings of International Conference on Leading Edge Manufacturing in 21st century (LEM21), p. 116. The Japan Society of Mechanical Engineers, Tokyo, Japan (2017). https://doi.org/10.1299/jsmelem.2017.9.116 6. Muttaqin, A.Z., Darsin, M., Kharisma, Y.R., Syuhri, A., Trfiananto, M.: Development of Minimum Quantity Lubricant (MQL) automation in applying cutting fluid on lathes. Jurnal POLIMESIN 20(2), 83–86 (2022). https://doi.org/10.30811/jpl.v20i2.2618 7. Touggui, Y., Uysal, A., Emiroglu, U., Dzhemilov, E.: An experimental and statistical investigation on cutting forces in turning of AISI 304 stainless steel under dry, MQL and nanofluid MQL conditions. In: Ivanov, V., Trojanowska, J., Pavlenko, I., Zajac, J., Perakovi´c, D. (eds.) DSMIE 2021. LNME, pp. 513–522. Springer, Cham (2021). https://doi.org/10.1007/978-3030-77719-7_51 8. Govardhan, K., Narender, G., Sarma, G.S.: Numerical simulation of viscous dissipation and chemical reaction in MHD of Nanofluid. J. Eng. Sci. 6(2), F15–F23 (2019). https://doi.org/ 10.21272/jes.2019.6(2).f3 9. Dubey, V., Sharma, A.K., Pimenov, D.Y.: Prediction of surface roughness using machine learning approach in MQL turning of AISI 304 steel by varying nanoparticle size in the cutting fluid. Lubricants 10(5), 81 (2022). https://doi.org/10.3390/lubricants10050081 10. Çamlı, K.Y., et al.: Performance of MQL and Nano-MQL lubrication in machining ER7 steel for train wheel applications. Lubricants 10(4), 48 (2022). https://doi.org/10.3390/lubricant s10040048 11. Aslan, A., Salur, E., Kunto˘glu, M.: Evaluation of the role of Dry and MQL regimes on machining and sustainability index of strenx 900 steel. Lubricants 10(11), 301 (2022). https:// doi.org/10.3390/lubricants10110301 12. Makhesana, M.A., Patel, K.M., Bagga, P.J.: Evaluation of surface roughness, tool wear and chip morphology during machining of nickel-based alloy under sustainable hybrid nanofluidMQL Strategy. Lubricants 10(11), 315 (2022). https://doi.org/10.3390/lubricants10110315 13. Hovorun, T.P., et al.: Physical-mechanical properties and structural-phase state of nanostructured wear-resistant coatings based on nitrides of refractory metals Ti and Zr. Funct. Mat. 26(3), 548–555 (2019). https://doi.org/10.15407/fm26.03.548

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14. Yakubov, C., Dzhemalyadinov, R., Skakun, V.: Improving material cutting by application of natural origin oil. In: International Conference on Modern Trends in Manufacturing Technologies and Equipment (ICMTMTE 2018), p. 01130. EDP Sciences, Sevastopol, Russia (2018). https://doi.org/10.1051/matecconf/201822401130 15. Jeevan, T.P., Jayaram, S.R.: Performance evaluation of jatropha and pongamia oil based environmentally friendly cutting fluids for turning AA 6061. Adv. Tribol. 1, 2425619 (2018). https://doi.org/10.1155/2018/2425619 16. Deshpande, V., Jyothi, P.N.: A review on sustainable eco-friendly cutting fluids. J. Sustain. Environ. Manag. 1(2), 306–320 (2022). https://doi.org/10.3126/josem.v1i2.45383

Modeling and Surface Modification of AISI 321 Stainless Steel by Nanosecond Laser Radiation Sergey Dobrotvorskiy1(B) , Borys A. Aleksenko1 , Mikołaj Ko´sci´nski2 Yevheniia Basova1 , and Vadym Prykhodko1

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1 National Technical University “Kharkiv Polytechnic Institute”, 2, Kyrpychova St.,

Kharkiv 61002, Ukraine [email protected] 2 Poznan University of Life Sciences, 38/42, Wojska Polskiego, 60637 Poznan, Poland

Abstract. Increasing the hydrophobicity of technological surfaces in terms of sustained economic growth is a priority for developing mechanical engineering quality, including green energy. The use of methods of laser action on the surfaces of metals makes it possible to increase the efficiency of such surfaces, providing the conditions for reducing the coefficient of friction and the hydrodynamic resistance during the flow of processing fluid to modernize hydraulic turbines and other things effectively. The paper considers issues related to forming the AISI 321 steel surface structure after exposure to nanosecond laser radiation. It describes the main features of the energy impact on the sample to obtain a hydrophilic and superhydrophobic surface. Based on the proposed methodology, an experimental study was carried out, and definitions were made of the primary indicators that determine the efficiency of the energy impact on the working environment and the formation of sandwich structures. A reasonable estimation of the laser radiation efficiency is given while forming the sample’s surface structure. It allows correcting rational modes for creating a surface with desired properties to provide the highest hydrophobicity and hydrophilicity. A new mechanism for the formation of surface superhydrophobicity is proposed and discussed. The practical value of the study is in the study of ways to improve the efficiency of surfaces by possibly intensifying their hydrophilic and superhydrophobic characteristics after energy exposure to a nanosecond laser. Keywords: Nanolaser · LIPSS · AFM · Electrostatic Effect · Hydrophobicity · Energy Efficiency · Industrial Growth

1 Introduction In connection with the development of technologies that allow you to create a controlled relief of the texture and structure of the surface, lately, there is an increasing interest in creating surfaces with a predictable degree of hydrophobicity. Superhydrophobic surfaces are beginning to be actively used in creating surfaces and coatings. This, in turn, makes it possible to prevent the icing of elements of wind turbines, aircraft, and process structures. It is also a promising approach to create surfaces that are self-cleaning from © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 205–215, 2023. https://doi.org/10.1007/978-3-031-32767-4_20

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drip contamination, reduce the coefficient of friction, and reduce hydrodynamic resistance during the liquid flow. In the case of a hydrodynamic flow of a thick liquid around a superhydrophobic surface, macroscopic slippage and a decrease in the flow friction resistance are observed. This may be relevant for the efficient upgrading of hydraulic turbines. In addition, superhydrophobic surfaces are also of considerable interest when applying colors and special protective coatings. This is because, in this case, the spreading efficiency of applied coatings (colors) plays an important role. Structural elements of hydraulic, wind, and aircraft are often made of stainless chromium-nickel austenitic steels. As a rule, a feature of the operation of these items is poor environmental conditions, which leads to a decrease in the efficiency of their use [1]. Therefore, giving the surface of critical turbine elements (for example, turbine blades) hydrophobic properties of the required level can increase the efficiency of their use and is an up-to-date but not very trivial task. Currently, the most promising method for creating surfaces’ hydrophobic steel properties is pulsed laser radiation with varying degrees of extremely short duration and high intensity of laser pulses. Practice shows that lasers of the nanosecond, picosecond, and femtosecond ranges of pulse duration are most suitable for such a modification. However, such a great variety in laser types, in the time range, in the schemes used for scanning and overlapping the laser beam, in different laser exposure intensities, and various physicochemical mechanisms of substance removal leads to the formation of a considerable number of LIPSS (Laser Induced Periodic Surface Structures). And this, in turn, makes it difficult to find rational processing modes, which is confirmed by significant differences in the research results of various authors. This ensures that, firstly, to reduce the cost of developing technologies, it will be helpful to conduct computer simulations to find rational processing conditions and proceed to natural experiments. Secondly, studies on the creation of LIPSS on the surface of stainless steels are up-todate but far from being achieved. This paper aims to clarify some of the gaps in these studies.

2 Literature Review The study of surface modification and the creation of LIPSS structures is currently receiving great attention. This is because LIPSS structures give the surface unique properties that are radically different from unprocessed surfaces. Byung-Chan Kim et al. conducted a prospective study investigating the laser texturing of an AISI 4140 mold steel metal surface aimed at creating a superhydrophobic surface later applied to transfer a superhydrophobic pattern to a polymer [2]. Gupta R.K. and others presented a study that deals with water droplets to improve the corrosion resistance of mild steel substrate through surface deposition of a thin film of austenitic stainless steel. Thus, the researchers’ results showed that under the influence of the laser beam on the surface, the water droplet and cavitation erosion resistance (WDER and CER) of these materials improved significantly, which was a consequence of increasing surface hardness and formation of fine-grained microstructures [3]. The authors presented similar results in a paper showing the AISI 420 stainless steel laser hardening process [4]. These results correlate with the results obtained in the article by Fiorucci M.P. etc. [5]. The mentioned study aimed to analyze the influence of operating parameters during the micromachining 316L and Ti6Al4V stainless steel process using a

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nanosecond laser with a target texturing of the surface of bio metallic implants, which promotes osseointegration [5]. Cao, L., and others have completed the research to obtain a superhydrophobic surface on AISI 4130 steel after laser irradiation. Based on the paper’s results [6, 7], one can also note that metals and many metal oxides can strongly adsorb silanes when a chelating functionality such as a diamine or di-carboxylate is present. The possibility of using an organofunctional silane with reactivity had been caused by the difficulty associated with the stability of such a surface can be maintained under limited operating conditions [7]. This may explain that a considerable number of scientific works are aimed at studying the possibility of the direct laser formation of hydrophobic surfaces by femtosecond, picosecond, and nanosecond pulses [8, 9]. However, there is no single approach to research methods. For example, the sequential laser ablation and chemical etching process for the fabrication of ratchet microstructures on AISI 316L stainless steel was proposed by Cai Y. and others [10]. It was noted in the work that the laser ablation has formed a certain processed layer, which has been covered with an oxide layer. Prospects for using laser surface structuring methods for the anti-icing of aviation materials are qualitatively considered in the works of [11–13]. The importance of surface roughness formation after laser structuring has been emphasized in the papers [11, 14, 15]. The article [14] is devoted to an experimental study of the effect of roughness on contact angle measurements. It proposes a genetic algorithm for determining an empirical regression model for combining roughness and wettability contact angles. The authors noted that a significant change in wettability is associated with forming textures on surfaces of samples with contact angles in the range of 30–110°. In investigation [16], the functionalization of AISI 316L surfaces by nanosecond Nd:YAG laser texturing and adsorption of superhydrophobic fuoroalkylsilane functionalized 30 nm silica nanoparticles have been described. Of scientific interest is the work [17], where an ultrashort pulse laser was used for studying the ablation characteristics of stainless steel and cemented tungsten carbide. It has been shown that the ablation process is affected by pulse duration, the number of pulses, and the radiation wavelength. Comparative studies [18] have been undertaken on the stress corrosion cracking susceptibility of laser-treated alloy 304 L samples from stainless steel with a traditionally milled analog in a chloride environment. The authors found that the samples processed by the laser demonstrated more than 12 times longer crack initiation time due to predominantly ferritic duplex microstructure. It could also be noted that laser surface treatment allows for texturing tool steels [19] for manufacturing complex parts [20]. The study deserves attention because the nanosecond fiber optic laser system was used to scan and texturize surfaces. In work for the D2 tool, steel is protected by argon to prevent oxidation have been provided. The authors emphasize [21] that in the LIPSS formation, as the surface’s processing speed scales with the average power, nanosecond-pulsed lasers can be cost-effective and lead to a reduced processing time. It leads to sustainable economic growth, especially for industry requirements of treating large surface areas. In research works [21, 22], the high efficiency of the nanostructures was noted. From the analysis of the literary search on the LIPSS creation was found that hydrophilic surfaces are formed immediately after laser beam irradiation, and only after

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a long time, from 17 to 50 days can they become hydrophobic. Thus, it has been established that the problem of controlled aging of surfaces is extremely relevant. However, a unified approach to the mechanism of LIPSS creation, aging, and the formation of hydrophobicity has proven intractable and requires further research.

3 Research Methodology Experimental studies were carried out on AISI 321 austenitic chromium-nickel-stainless steel plates with dimensions of 35x55x1 mm in the state of delivery. The surface was not subjected to additional processing. The samples had a typical steel chemical composition (in weight%): Fe 67.0; Cr 17.0–19.0; Ni 9.0–11.0; Mn < 2.0; Si < 0.8; Ti 0.6–0.8. To obtain a structured surface, a laser setup with an IR ytterbium fiber laser (wavelength 1064 nm) was used. Scanning of the laser beam on the surface along the X–axis and transversely along the Y–axis was carried out by a BM 2500 + biaxial galvanometric scanner. The laser radiation had a pulse duration of 100 ns, a repetition rate of 20– 100 kHz, and a peak power of up to 1 mJ in the TEM00 mode. The samples were scanned with a raster at a linear speed of up to 5 mm/s with a parallel line density of up to 50 µm. The wetting angle of distilled water droplets was determined using an Ossila contact angle goniometer. The quality of the structure and surface roughness was determined by force atomic microscopy. A model of thermal laser action on a stainless-steel plate was built in the Comsol Multiphysics computing environment using the Heat Transfer (ht) module. The heat flux F to the material surface is calculated as follows: ⎛  ⎞ 2 (x − xcoor )2 + (y − ycoor )2 2Plaser ⎠, exp⎝− (1) F= π R2spot R2spot where Plaser – is the laser power, Rspot – is the beam radius, xcoor , ycoor – is the coordinate of the plate where the beam is focused at a given time by scanning. The calculated value of the heat flux F [W/m2 ] is multiplied by the emissivity coefficient of the material, which is 0.8. Heating is calculated in the “Heat Transfer” module according to the equations by finite element method: ρCp

∂T + ρCp u · ∇T + ∇q = Q + Qted , ∂t q = −k∇T ,

(2) (3)

where – the main heat flux of the Q and the additional heat flux from the imposition of pulses Qted ; ρ – the density; u – the laser beam scanning velocity; Cp – the specific heat capacity, k – the coefficient of thermal conductivity, T – temperature, q – the heat flux, ∇T – is the temperature gradient. Absorbed energy q0 is determined by the following formula: −n · q = q0

(4)

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Heat transfer from the plate surface is calculated through radiation to the environment according to the equation:   (5) −n1 · q = εσ T 4 ambient − T 4 , where ε – the emissivity, σ – Stefan–Boltzmann constant, Tambient – the temperature of the ambient medium (20 °C), n− material absorption coefficient, n1 – emissivity. The laser’s beam moves relative to the plate surface along two horizontal coordinates. Positioning the laser’s beam and synchronizing its radiation in time and space is given by a set of functions. As a result, both the dynamics of the interaction of the laser radiation energy were obtained with the plate and the dynamics of the destruction processes. The impact was carried out sequentially at 2 and 4 points, respectively. The distance between the impact points is 50 µm.

4 Results and Discussion Modifications were subjected to samples after aging in the wild for 6 months. That is, there were no typical relaxation processes for the first 17–30 days. Figures 1, 2 and 3 show the results of scanning the surface using an atomic force microscope. In the topography of the surface, a line structure is clearly distinguished (Fig. 3, which correlates with the theoretical structure shown in Fig. 1, c, d) due to the alternation of scanning lines and the overlap of laser spots. The bottom of the slots is the least susceptible to modification. This is apparently because, after the end of the movement of the evaporation front, the slot lower part is subjected to a shock pulse from the expansion plasma, and a structure close to the original matrix is retained (Fig. 1, a). This is seen in Fig. 2 when examining phases where the bottom surface of the rows looks uniform. Interlinear protrusions are formed both by the displacement of the melt, by the impulse wave from the plasma’s expansion, and partly from the deformation at the initial stage of heating and melting (Fig. 1, b). The phase composition of the overshot differs significantly from the bottom of the slot. Processing modes in the nanosecond range are characterized by forming metal oxides that are part of the composition AISI 321 and, to some extent, carbides of these metals. During aging, the surface acquired the properties of superhydrophobicity since the contact angles of wetting reach a value of 126.51° for drops of distilled water on a horizontal surface (Fig. 4). Figures 5 show the dynamics of the water droplets’ movement along a structured surface along the scanning lines with a vertical arrangement of the simple. Drop 1, which started first, formed a static electric charge on its surface while moving and stopped. Therefore, drop 2, catching up with the first and having the same potential, tries to bypass the first drop. At the same time, the moving drop interacts with the weakly moving drop 3. But even the subsequent merging of 2 and 3 (Fig. 5, c) and 2–3–4 (Fig. 5, d) does not disturb the movement of drop 1, which has the highest electric potential. Usually, continuous hydrophobic surfaces are negatively charged in water, probably due to the adsorption of hydroxyl groups. Some of these charges are not neutralized on the reverse side of the sliding drop and remain on the surface as remnants of interfacial charges spontaneously form at the water–solid interface. As a result, the drop must have a positive

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Fig. 1. Modeling the structuring process of AISI 321 steel using a nanosecond laser: a – the model of craters’ development during a laser pulse; b – initial stage metal deformation in the irradiation zone; c – scheme of the development of a conical structure during evaporation during a laser pulse at 4 points; d – scheme of the development of a conical structure during evaporation during a laser pulse at 2 points (the destruction of the plate surface after successive impacts).

charge. The hydrophobic surface formed by laser radiation has a non–uniform and local coverage by structures with semiconductor and dielectric structures (dark areas in Fig. 2, a). Therefore, the formation of the type of charge can be of a local and probabilistic nature. From the point of view of the prospects of using the obtained results for the surface of turbine blades, two aspects can be the most interesting. Possibility of generating electrical energy when flowing around a liquid or vapor and reducing friction losses due to the effect of liquid slippage on a superhydrophobic surface of the turbines’ elements. This correlates with the study’s findings [23], and emphasizes that the prospects of electricity generation processes at the nanoscale conductive or semiconductor layers in contact with moving water drops are promising. The most successful is the use of conductive or semiconductor–layered materials in contact with moving water droplets, such as nanotubes, graphene, and insulator– semiconductor architectures. Also, a good result was obtained by plasma deposition of thin metal films on dielectrics with subsequent oxidation of the film to FeOx., CrOx,

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Fig. 2. The result of structuring steel AISI 321 using a nanosecond laser: a – the structure of the formed surface with elements of melting; b – distribution of metal oxides and carbides.

Fig. 3. The structured surface of AISI 321 steel after scanning by Nanosecond Laser Radiation (Rq = 1210 nm, Ra = 958 nm).

Fig. 4. The adsorption properties behavior of the formed surface: a – an adsorbed drop of distilled water on the formed surface; b – measurement of the contact angles of wetting on the formed surface.

and NiOx states. Indeed, experiments show the important role of intraoxide electron

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Fig. 5. Dynamics of sliding of water drops on the formed surface: a – 10 s after the start of the movement; b – 15 s after the beginning of the movement; c – 25 s after starting to move; d – 35 s after starting to move.

transfer for nanolayers of Fe and Ni since their thermal oxides contain several degrees of oxidation of metals and a much lower value for chromium oxides. The deposition of films of hydrophobic materials Teflon or polydimethylsiloxane on gold and SiO2 also confirms the dependence of the generation of electric charges on the dielectric properties of materials [24]. Unlike the above examples, the processes occurring under the influence of nanosecond laser radiation have significant differences [25, 26]. Many factors should be taken into account. The shape of the laser beam energy distribution in time can be represented as an asymmetric Gaussian distribution with a flatter right–hand side after the maximum point, combined with an abrupt leading edge and a flatter trailing edge. It has been known that the energy distribution in the TEM00 mode over the beam cross-section also has Gaussian distribution (Fig. 1). At the initial moment up to about 10–11 –10–12 s, a two-temperature heating mode is realized when the electron temperature is higher than the ion (lattice) temperature. By the time the temperatures equalize to the Boltzmann state, nanoclusters and nanostructures are formed in the surface layer of the material, which causes deformations. Thus, the first deformation wave is formed. At the initial phase of Boltzmann heating (before the moment of melting), the deformation of the material increases, moving from the center to the edges and deep into the sample. Thus, the sample undergoes a second deformation wave. As a result of wave summation, a stress-strain state of the material arises from compression with the possibility of partial implementation of plastic deformation and, as a result, the formation of a deformation roller (Fig. 2). As the melting phase begins in the middle of the trailing edge of the pulse, the boundary regions near the melt boundaries lead to a reverse wave of tensile stresses. A compression wave again replaces it due to the expansion plasma’s spread wave. It should be noted that thermochemical oxidation reactions stimulated by laser radiation proceed simultaneously with these processes. The formation of oxides having a cluster structure with the possibility of further transformation over time under natural conditions is possible. It is worth emphasizing that the picture is complicated both by the overlap of pulses during scanning and by the expansion of the plasma from adjacent pulses. In addition, rapid crystallization at the cooling stage goes with the dissolution of the initial carbide particles of the composition M23 C6

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– in particular, (CR, Fe)23 C6 – and, therefore, a saturation of the crystal lattice in the surface layer atoms of carbon [27] and chromium [28]. Thus, the interline protrusions and the areas adjacent to them can be represented as a sandwich structure consisting of a metal base, with a transitional stress-strain layer with a layer of nanoclusters and nanostructures, recrystallization layer with carbides, and the last layer with oxides with different levels of electrical conductivity and dielectric permittivity. Because of this, such sandwich surfaces can generate an electric charge when droplets move.

5 Conclusions Nanosecond laser radiation is promising for modifying the surface of AISI 321 steel to create both hydrophilic and superhydrophobic surfaces. Computer modeling by the finite element method of surface modification processes makes it possible to visually clarify the features of the modification mechanism and find a rational processing mode for creating a surface with desired properties. Experimental studies have established that after structuring the surface of AISI 321 steel with a nanosecond laser, the roughness Rq = 1210 nm, Ra = 958 nm is achieved under rational modes. At the same time, the surface has a superhydrophobic character since the contact angles of wetting reach a value of 126.51° for drops of distilled water on a horizontal surface. The non–uniform distribution of oxides and carbides over the surface and the multi-layer surface’s properties in the depth of the material after laser exposure are noted. The sandwich structure contains a base metal, a stress-strain zone of the metal with elements of plastic deformation, formed at the stages of two-temperature and Boltzmann heating. Above is a zone containing nanostructures formed by shock waves. Then comes the stress-strain layer of melt recrystallization containing carbides and a final layer containing metal oxides formed from stimulation of oxidative processes by laser radiation. The process of relaxation and rearrangement of a part of this sandwich structure in time can explain the effect of the transition of the steel surface from a hydrophilic state to a superhydrophobic one. The possibility of creating modified surfaces of austenitic steels by nanosecond laser radiation is established when water drops to move along the surface, which generates an electrostatic charge, which can be used to create new types of energy sources without moving elements. This electrostatic effect can be used to increase water slippage along the profile of a turbine blade and reduce friction costs. In the future development of this research, it is planned to study and improve the technology of controlling the surface layer, which will be based on the creation of LIPSS structures, and possibly will provide controlled hardness and adhesion of surfaces. Acknowledgment. The results were partially obtained within the project “Development of a methodology for optimal design and manufacture of highly efficient, highly reliable turbomachines, taking into account various operating modes” (State Reg. No. 0121U107511).

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Improvement of the Quality of Wear Zones for Cutting Tools Textures Classes Recognition Based on Convolutional Models Oleksandr Fomin(B)

, Oleksandr Derevianchenko , Natalya Volkova , and Natalia Skrypnyk

Odessa Polytechnic National University, 1, Shevchenko Ave, Odessa 65044, Ukraine [email protected]

Abstract. For modern metalworking machine tool systems, which operate with limited operator participation, there is a need to develop methods for automatic control and recognition of the states of cutting tools. It provides the possibility of predicting their residual resource and timely replacement. In recognizing the cutting tools (CT) states, data mining methods are increasingly used: decision trees, genetic algorithms, fuzzy logic, and neural networks. Therefore, the task of improving the quality of CT wear zones textures classes’ recognition (based on convolutional models’ application) is relevant. The article aims to create a new fuzzy neural net classifier to improve the quality of cutting tool wear zones recognition texture classes. The scientific novelty consists in creating convolutional models and a new fuzzy neural net classifier of CT wear zone textures – for recognizing wear mechanisms on the flanks of cutters in finishing turning conditions. Practical usefulness consists in improving the quality of textures classes of CT wear zones recognition to the level of almost 100%. An example of a corrective influence on the turning process is a change in the feed rate S – in the case of concentrated wear traces recognition on the near-vertex section of the cutter’s flank surface. Keywords: Finishing Turning Conditions · CT States Classifier · Convolutional Model · Industrial Growth · Manufacturing Innovation

1 Introduction For modern metalworking machine tool systems, which operate with limited operator participation, there is a need to develop automatic control and recognition of the states of cutting tools. It provides the possibility of predicting their residual resource and timely replacement. In the tasks of recognizing the cutting tools (CT) states, methods of data mining are increasingly used: decision trees, genetic algorithms, fuzzy logic, and neural networks (NN) [1]. Therefore, the task of improving the quality of CT wear zones textures classes’ recognition (based on convolutional models application) is relevant. Technical diagnosis of CT states in the process of processing parts is an important task in the conditions of automated industrial production, as the wear of the cutting © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 216–225, 2023. https://doi.org/10.1007/978-3-031-32767-4_21

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tool affects the quality of manufacturing high-precision parts [1]. Timely detection of tool wear prevents wear of equipment, reduction in the quality of processing parts, and reduction in the number of defective products. CT image processing in computer diagnostic systems consists of successive stages, the most important of which is the segmentation stage. The quality of computer diagnostic systems depends on the quality of segmentation. Images of CT wear zones are characterized by texture. Depending on the characteristics they described, textures are divided into the following classes of textures: statistical, spectral, structural, and combined [2]. When the texture type is established, applying appropriate texture segmentation methods gives high indicators of segmentation quality. Therefore, the article aims to create a new fuzzy neural net classifier to improve the quality of cutting tool wear zones recognition texture classes.

2 Literature Review Finishing turning and boring are widely used precision cutting operations [3]. When fine boring holes use long and stepped boring bars, bending and torsional vibrations occur [4]. It leads to the appearance of features in the shapes and textures of the wear zones of the rear surfaces of the boring cutters. For such machining operations characterized by small depths of cut, it is important to recognize the texture classes of worn cutter surfaces. The textures of worn surfaces of abrasive tools (grinding wheels) [5] also represent CT states and need to recognize the corresponding classes. Modern machine tools increasingly use image processing operations [6]. Vision systems are widely used to monitor the state of cutting tools on many types of machine tools, in particular, on lathes with numerical control [7]. Increasingly widespread use in metalworking is methods and models of the fuzzy neural network [8], fuzzy logic application [9], recurrent neural networks [10], fuzzy neural network [11], and others. All these methods and models correspond to the level of technology in industry 4.0 [12]. The literature review indicates the article’s subject matter is relevant and promising.

3 Research Methodology To recognize CT states, neural networks of varying complexity can be built: Singlelayer, Feed Forward (FF), or multi-layer Deep Feed Forward (DFF) with a different number of neurons. As the complexity of the network structure increases, the training time increases. Single-layer FF neural networks have been around for a long time, but more powerful DFF solutions are replacing them. Unlike FF, in which weight coefficients transmit signals from one neuron to another, increasing or decreasing it depending on the value of the weight coefficient, DFF works with hierarchical models in which features are expressed as a multi-level composition of primitives (Fig. 1). The images of textural wear zones of the rear surfaces of the cutters, necessary for the experiments, were obtained using a special stand developed by the authors [1]. Such

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a stand is a prototype of a device that includes a vision system. The device provides the possibility of obtaining the above images directly on the machine during periods of interruption of processing. The classical problems of diagnosing the CT states using NN, based on the training set, as a rule, are solved using the recognition error backpropagation algorithm [2]. In the experiments, 3 sets of images of the textures of the wear zones of the rear surfaces of cutters for finishing (fine or precision turning) were used; images of the groove (concentrated) wear marks, adhesive wear marks, and abrasive wear marks. An example of a corrective influence on the turning processes is a change in the feed rate S – in the case of concentrated wear traces recognition on the near-vertex section of the cutter flank surface. Neural network topology and scheme of features identification of CT texture classes at each network level are represented in Fig. 1.

Fig. 1. Neural network topology (a) and scheme of features identification of CT texture classes at each network level (b)

The quantity of images in the study sample is 30 (10 images of each class); the quantity of hidden layers in the neural network is 3. The disadvantage of this approach is the need to parameterize images to obtain a training sample U.

4 Results and Discussion Parametrization is a rather complicated engineering and creative stage; the result largely depends on the quality of execution. In addition, the parameterization stage can take significant time and slow down the diagnostic process. So, in each of the hidden layers of the DFF, the problem of identifying features of the corresponding level is solved (Fig. 2). Due to this structure, DFFs can model complex non-linear relationships: additional layers allow elements to be composed of lower levels, potentially modeling complex data with a lower dimensionality than a FF network with similar indicators. DFF makes it possible to work with initial diagnostic information in the form of images of wear surfaces, representing it as a set of pixels of different brightness (Fig. 2).

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Fig. 2. Algorithm for building a neural network using the Keras library: 1 – initialization of the modeling environment; 2 – formation of a training dataset; 3 – building a neural network model; 4 – compilation of models; 5 – initialization of model parameters; 6 – model training; 7 – obtaining a working model; 8 – CT states diagnosing; 9 – visualization of diagnostic results.

This work implements the CT state classifier using the Keras software tools (https:// keras.io). Keras is a Python library of functions that reduce the cognitive load on the developer/engineer by using consistent and simple APIs, minimizing the number of user actions required for typical use cases. Once the model is defined, it must be trained. Based on the input, we add 3 layers to extract 48 features and an output layer to calculate 3 classes with soft max activation. The results of the sequential formation of three diagnostic models for recognizing the CT wear zone texture classes are presented in Fig. 3, 4 , and 5.

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Fig. 3. Creation of diagnostic model Single layer NN FF for recognition classes of CT wear zones textures: 1 – structure of the model; 2 – parameters of the model learning process; 3 – iterative process of training the model; 4 – reliability of diagnosing model.

Fig. 4. Creation of diagnostic model Multi-layer NN DFF for recognition classes of CT wear zones textures: 1 – structure of the model; 2 – parameters of the model learning process; 3 – iterative process of training the model; 4 – reliability of diagnosing model.

As a result of implementing the fuzzy NN based on DFF, training was completed in 237 iterations (Table 1), and the probability of correct recognition increased by 4.3%

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Fig. 5. Creation of diagnostic model Multi-layer fuzzy NN DFF for recognition classes of CTwear zone textures: 1 – structure of the model; 2 – parameters of the model learning process; 3 – iterative process of training the model; 4 – reliability of diagnosing model.

compared to the NN DFF. The above confirms the operability of the DFF neural network classifier and prospects for its application. As shown in Fig. 4 and Fig. 5, losses are significantly reduced, and the accuracy is almost 100%. Table 1. Comparison of the reliability of diagnosing for various NS topologies. Network type

Reliability of diagnosis Diagnosis errors (%) Number of iterations (%)

Single layer NN FF

88.5

11.5

150

Multi-layer NN DFF

93.1

6.9

274

Multi-layer fuzzy NN 97.4 DFF

2.6

237

Preliminary experiments have shown that, along with proven convolutional diagnostic models in the form of neural networks for recognizing texture classes of CE wear zones, traditional models based on convolutional transformations of texture images demonstrate good efficiency. Let us proceed to the analysis of the possibility of using these transformations for recognizing classes of textures of CT wear zones. Along with well-established convolutional diagnostic models in the form of neural networks, traditional models based on convolutional image trans-formations demonstrate good performance. Let us analyze the possibility of using these transformations to recognize CT wear zone texture classes. Technical diagnosis of the cutting tool’s state

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in processing parts is an essential task in automated industrial production conditions, as the cutting tool’s wear affects the quality of manufacturing high-precision parts [13]. Timely detection of tool wear prevents wear of equipment, reduction in the quality of processing parts, and reduction in the number of defective products. Image processing in computer diagnostic systems consists of successive stages, the most important of which is the segmentation stage. The quality of computer diagnostic systems depends on the quality of segmentation. Images of CT wear zones are characterized by texture. Depending on the characteristics by which they are described, textures are divided into the following classes of textures: statistical, spectral, structural, and combined [14]. When the texture type is established, applying appropriate texture segmentation methods gives high indicators of segmentation quality. A hybrid method of identifying texture areas using multifractal indicators and spectral texture features as elements of the identification vector was proposed in the research [13]. This method was applied to CT wear zone images, which can be described by spectral, statistical, and combined (spectral-statistical) models of textures (which ensured high quality of determination of the considered textures). However, in practice, CT images during processing in technical diagnostic systems contain textures, and various texture models, including structural ones, can describe that. CT image shows a structural model of the texture (Fig. 6, a) and a model image of the structural texture (Fig. 1, b). A hybrid method of identifying texture [13] does not provide a high-quality definition of the structural type of the texture. This paper proposes the method of structural texture identification on CT images based on multifractal indicators [13] and the response value of a generalized comb filter. The response of the generalized comb filter is obtained as a convolution of the image line with a generalized comb filter, which gives the maximum response when determining the boundary of the structural texture. The stages of the method are presented below: 1. Pre-processing using homomorphic filtering. 2. Evaluation of multifractal indicators for a rectangular fragment of the texture. 3. Calculation of the response value of the generalized comb filter for a rectangular fragment of the texture. 4. Formation of a secondary identification vector. As a result of execution stages 2–3, the primary identification vector is formed: I = (Hx, Hy, Hxy, Cx, Cy, Cxy, g), where Hx, Hy, Cx, Cy are multifractal indicators of H1, C1 in the directions x, y, and Hxy, Cxy – diagonal multifractal indicators, g – response value of the generalized comb filter. As a result of filtering with a comb filter, the passband of which is the union of the passbands of a set of bandpass filters, the value of the texture feature is calculated, and the transition to the intensity values of images occurs by processing the image on a single scale. The developed method was applied to CT images containing areas of structural texture (Fig. 6, a). At the pre-processing stage, the image was filtered with a homomorphic filter. A rectangular fragment of the image was selected to calculate the elements of the primary identification vector on the CT image, which contained an area of structural texture (Fig. 6, c) in which the calculation was performed. For the selected fragment of

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the texture, the elements of the primary identification vector have the following values: Hx = 0.456; Hy = 0.453; Hxy = 0.315; Cx = 0.225; Cy = 0.216; Cxy = 0.232, g = 1.489. The result of the classification of CT texture images in the space of two main components for different types of textures is presented in Fig. 6, d-f.

Fig. 6. CT image that contains areas of structural texture (a); model image of structural texture (b); CT image after homomorphic filtering (c); results of the CT texture images classification in the space of two main components: d – statistical texture; e – spectral texture; f – structural texture.

To evaluate the quality of the work of the developed method of identification for CT texture images, the probability of correct detection [15] and the type of texture were determined (Table 2). Table 2. Probability of correct detection of the type of texture. Texture type

Spectral

Statistical

Structural

Spectral

0.88

0.01

0.09

Statistical

0.02

0.96

0.01

Structural

0.1

0.02

0.9

The analysis of the obtained results showed that the probability of correct detection of homogeneous textural areas of the spectral texture on the images of the CT wear zones was 0.88, with a false alarm probability of 0.12. The probability of correct detection of homogeneous textural areas of a statistical texture is 0.96, with a false alarm probability

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of 0.04; the probability of correct detection of the regions of a structural texture was 0.9, with a probability of a false alarm of 0.1. A comparative analysis of the obtained results with the results of work [13] showed that the probability of correct detection of the statistical texture was not increased by 0.1, and the probability of correct detection of the spectral texture decreased by 0.5. At the same time, the proposed method allows the detection of the structural texture with a probability of 0.9. Therefore, the method of identifying CT wear textures, based on multifractal indicators, was further developed by including the response value of the generalized comb filter to the primary identification vector, which made it possible to classify the type of CT structural texture with high probability.

5 Conclusions Studies have shown that fuzzy networks showed the best results in the classification of CT states in comparison with single-layer FF and multi-layer DFF networks. Thus, the proposed fuzzy neural network model can be used for CT state diagnosing based on the textures of wear zones, making the quality control system resistant to possible distortions in the texture of the wear zone. Achieved improving the quality of wear zones of cutting tools textures classes recognition based on convolutional models application: reliability of diagnosis is 97.4%; diagnosis errors are 2.6%; the number of iterations is 237. The method of identifying CT wear textures, based on multifractal indicators, made it possible to classify the type of CT structural texture with high probability. The results of quality recognition of the CT wear zone textures classes demonstrate the advantages of convolutional models, both neural network and traditional, over statistical and neural network models. Convolutional models of CT texture classes are inferior in learning speed (LS) to traditional models. However, LS models are not critical, as it is an offline procedure performed once. The trained models work online, providing the required quality of the CT texture classes’ recognition in production conditions.

References 1. Derevianchenko, O., Fomin, O.: Complex Recognition Approach for Cutting Part of Cutters in Finishing Turning. In: Ivanov, V., Trojanowska, J., Pavlenko, I., Zajac, J., Perakovi´c, D. (eds.) DSMIE 2021. LNME, pp. 21–30. Springer, Cham (2021). https://doi.org/10.1007/9783-030-77719-7_3 2. Gaurav, D.S., Nikhil, P.T.: Analytical and systematic study of artificial neural network. Int. Res. J. Eng. Technol. 9(3), 653–658 (2022) 3. Orgiyan, A., Oborskyi, G., Ivanov, V., Balaniuk, A., Kolesnik, V.: Improvement of the Efficiency of Fine Boring for Stepped Holes with a Large Diameter Range. In: Ivanov, V., Trojanowska, J., Pavlenko, I., Rauch, E., Perakovi´c, D. (eds) Advances in Design, Simulation and Manufacturing V. DSMIE 2022. Lecture Notes in Mechanical Engineering, vol. 1, pp. 322–331. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06025-0_32

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4. Orgiyan, A., Oborskyi, G., Ivanov, V., Balaniuk, A., Matzey, R.: Interaction of Flexural and Torsional Shapes Vibrations in Fine Boring with Cantilever Boring Bars. In: Ivanov, V., Trojanowska, J., Pavlenko, I., Zajac, J., Perakovi´c, D. (eds.) DSMIE 2021. LNME, pp. 481– 489. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77719-7_48 5. Tonkonogyi, V., Sidelnykova, T., Daši´c, P., Yakimov, A., Bovnegra, L.: Improving the Performance Properties of Abrasive Tools at the Stage of Their Operation. In: Karabegovi´c, I. (ed.) NT 2019. LNNS, vol. 76, pp. 136–145. Springer, Cham (2020). https://doi.org/10.1007/9783-030-18072-0_15 6. Rifai, A.P., Fukuda, R., Aoyama, H.: Image based identification of cutting tools in turningmilling machines. Jpn. Soc. Precis. Eng 2(85), 159–166 (2019). https://doi.org/10.2493/jjspe. 85.159 7. Sun, W.-H., Yeh, S.-S.: Using the machine vision method to develop an on-machine insert condition monitoring system for computer numerical control turning machine tools. Materials 11(10), 2–17 (2018). https://doi.org/10.3390/ma11101977 8. Chungchoo, C., Saini, D.: On-line tool wear estimation in CNC turning operations using fuzzy neural network model. Int. J. Mach. Tools Manuf. 1(42), 29–40 (2019). https://doi.org/ 10.1016/S0890-6955(01)00096-7 9. Ghencea, D.P., Anania, F.D., Zapciu, M.: Research of fuzzy logic application on surfaces roughness prediction under finishing milling process. IOP Conf. Series Mater. Sci. Eng. 1018(1), 012020 (2021). https://doi.org/10.1088/1757-899X/1018/1/012020 10. Zhang, J., Zeng, Y., Starly, B.: Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis. SN Appl. Sci. 3(4), 442 (2021). https:// doi.org/10.1007/s42452-021-04427-5 11. Lin, C., Jhang, J., Chen, S.: Tool wear prediction using a hybrid of tool chip image and evolutionary fuzzy neural network. Int. J. Adv. Manuf. Technol. 118, 921–936 (2022). https:// doi.org/10.1007/s00170-021-07291-0 12. Mukku, V.D., Lang, S., Reggelin, T.: Integration of LiFi technology in an industry 4.0 learning factory. Procedia Manuf. 31, 232–238 (2019). https://doi.org/10.1016/j.promfg.2019.03.037 13. Volkova, N.P., Krylov, V.N.: Hybrid texture identification method. Herald Adv. Inf. Technol. 4(2), 123–124 (2021). https://doi.org/10.15276/hait.02.2021 14. Humeau-Heurtier, A.: Texture feature extraction methods: a survey. IEEE Access 7, 8975– 9000 (2019). https://doi.org/10.1109/ACCESS.2018.2890743 15. Lin, H.-D., Chen, H.-L.: Detection of surface flaws on textured LED lenses using wavelet packet transform based partial least squares techniques. Int. J. Innovative Comput. Inf. Control 15(3), 905–921 (2019). https://doi.org/10.24507/ijicic.15.03.905

Conductivity Measurement Verification of Additively Manufactured and Bulk Metals Matúš Geˇlatko1(B)

, Michal Hatala1 , Radoslav Vandžura1 and Dušan Manduˇlák3

, Martin Kasenˇcák2 ,

1 Technical University of Košice With Seat in Prešov, 1, Bayerova St., 080 01 Prešov, Slovakia

[email protected]

2 First Welding Company, Inc., 14, Kopˇcianska, 851 01 Bratislava, Slovakia 3 1. Prešovská nástrojáreˇn, Ltd., 2407/2, Lubochnianska, ˇ ˇ 080 06 Prešov - Lubotice, Slovakia

Abstract. Eddy current testing method has the predisposition to be a sufficient tool for the evaluation of surface and subsurface layers in additively manufactured metal components. Electrical conductivity is the most significant material parameter influencing the penetration depth of eddy currents. Its value is missing in AM powder materials data sheets and differs from conventional ones. The described experiment checks the eddy current testing device’s ability for material conductivity measurement. The main purpose is to compare measured conductivity in AM stainless steel material with measured conductivity within recent research on the same material and check a measurement deviation on various materials. The eddy current testing device’s ability to distinguish various materials was checked during the preliminary experiment. During the main experiment, a device was calibrated for conductivity measurement, and various materials were inspected using its special conductivity regime. Parameters important for deviation expression were calculated and analyzed. Some similarities and differences were found with other research in the field. Based on the derived conclusions, the method used can determine conductivity. Higher deviations were obtained in the case of high-conductivity materials. However, exceptions can be special alloys. Furthermore, the calculation of standard penetration depth seems essential for verifying the influence of deviation, which also concerns the mentioned comparison of AM stainless steel conductivity measurements. Keywords: Eddy Current Testing · Conductivity · Selective Laser Melting · Standard Penetration Depth · Manufacturing Innovation

1 Introduction Additive manufacturing (AM) of metal components has a high potential for creating parts in highly specific and non-standard shapes compared with components made by conventional technologies. Same as in the case of these technologies, defects also restrict the final quality of AM products. Therefore, it has been a high effort in recent years to create methodologies of non-destructive testing, which provide many superlatives of detection for identifying these materials. Successful identification of defects will © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 226–237, 2023. https://doi.org/10.1007/978-3-031-32767-4_22

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consequently allow optimizing AM processes for their presence suppression and their subsequent incorporation to engineering assemblies realized using detachable and nondetachable joints. Eddy current (EC) testing is well-known for its high-sensitivity defects detection in surface layers of conductive materials. Its ability to detection of artificial defects, representing frequently occurring real defects in AM products, in SS 316L stainless steel made by Selective Laser Melting (SLM) technology, was checked during the recent research [1], with promising experimental results. Due to its unknown electric conductivity value, one of the open issues within this experiment is the standard penetration depth calculation of eddy currents into the evaluated SS 316L material. It is a key parameter for mentioned penetration depth calculation and was determined using the eddy current testing device NORTEC 600 used for the whole experiment. After its determination and consequent penetration depth calculation, an interesting phenomenon occurred. Eddy currents could detect artificial defects in the reliable quality of obtained data below the calculated penetration depth. The experiment’s aim, described in this article, is to check the ability of the NORTEC 600 device for conductivity measurement of various materials, determine a deviation of measurement, and compare the obtained value of SS 316L material with the conductivity value obtained within recent research.

2 Literature Review Metal component creation reached by additive manufacturing (AM) has become highly popular in the recent decade, which is confirmed by increasing numbers of publications, focused on reviews [2, 3], even in such an unconventional field as aerospace [4]. A significant review of non-destructive testing methods settings, their application in the AM field, and consequent optimization of process parameters is a study by Malekipour E. and El-Mounayri H. [5]. With the research in the field of additive manufacturing technologies, non-destructive testing methods application for their evaluation is the point of interest. Defects’ presence in the subsurface layers of an evaluated AM stainless steel was a point of interest in an experiment by Dai T. et al. [6], using the ultrasonic device on the base of laser energy. A porosity was investigated in the alloy with an aluminum base, made by additive manufacturing technology, during the application of X-ray tomography [7]. The microstructure of special alloy material with the label Inconel 625 was investigated during the study made by Li, S. and collective [8], for which X-ray diffraction was chosen. Even untraditional non-destructive method, such as acoustic emission, was selected for research on microdefects identification in SLM sample [9]. Also, residual stresses in AM products were a point of interest for inspection, using ultrasonic testing [10] and X-ray diffraction [11]. Whereas the eddy current testing method is comparative, heterodyne eddy current dependency on artificial defects was investigated in AM stainless steel [12]. Variations in the density of additively manufactured samples were investigated, during which the conductivity was determined using the multi-frequency EC technology [13]. Within the research by Stoll P. et al. [14], eddy current probes were inserted in the material produced by AM to investigate its structural health. Titanium alloy was the subject of interest for eddy current testing of subsurface flaws during the special additive/subtractive

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hybrid manufacturing (ASHM) [15], where process parameters´ influence was monitored. Additionally, it was found that measuring the conductivity of AM materially is better than using the value of a conventionally made one. Also, while the study focused on the effect of heat treatment on AM aluminum alloy (AlSi10Mg) samples, conductivity determination was run using the eddy current testing device [16]. Inconel 738LC alloy was subjected to the detection of defects in its subsurface layers within the experiment performed by Guo S. et al. [17]. 99,9% pure copper, made by selective electron beam melting, was investigated with a focus on its electrical conductivity using eddy current testing [18]. Electrical conductivity, measured by the eddy current method, was information about microstructural variations in different materials processed by friction stir welding and tungsten arc welding. Obtained data were also compared with hardness values [19]. Studies by Yu Y. and co-authors showed that a method has predispositions for coating thickness and its conductivity identification, as in the case of monolayer coating [20], so in the case of multi-layered coating [21]. The eddy-current technique was even used for conductivity and thickness determination of ultra-thin metallic coating within the research made by Xu, J. et al. [22] Noncontact eddy current testing method of conductivity was proposed during the research [23], and the influence of related parameters was investigated.

3 Research Methodology In the first experiment stage, 10 samples of various conductivity values were chosen. To ensure the variability of obtained data, different material kinds were selected. From steels, AISI 4140 alloy steel, AISI 304 stainless steel, SS 316L stainless steel made by Selective Laser Melting (SLM), and EOS Maraging steel MS1 made using Direct Metal Laser Sintering (DMLS). In the case of Aluminum alloys, conventionally made 7075-T6 alloy and AlSi10Mg are made using Selective Laser Melting (SLM). From the group of special alloy materials, titanium alloy with label ASTM B265 Grade 5, nickel-based alloy Inconel 625, and nickel-copper alloy Monel 400. From high-conductive materials, copper-based alloy with label CW004A was selected. Eddy’s current measurement machine, used for the investigation, was a portable EC testing device with the label NORTEC 600, manufactured by OLYMPUS COMPANY (Fig. 1). A device works based on the electromagnetism principle. It is primarily dedicated to conductive material testing. It includes defect identification, such as cracks or porosity, coating thickness measurement, or material conductivity determination. During the preliminary experiment, single-coil (absolute) probe INDETEC ndt MTW100.S3.A1N (P1) was used, which provides identification in the frequency range between values 10 and 100 kHz. During the main part of the conductivity determination, a special OLYMPUS SPO-887L (P2) probe was used with a fixed 60 kHz excitation frequency [24]. The preliminary experiment involved checking the eddy current testing device’s ability to distinguish various materials. IMPEDANCE regime was chosen, excitation frequency of INDETEC ndt MTW100.S3.A1N probe was set to 100 kHz to ensure better sensitivity and signal deviation curves distinguishing.

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Fig. 1. NORTEC 600 device with probes

3.1 Calibration Process The calibration was crucial and the first important step for the main part of the experiment, which represents the conductivity measurement of all samples. After connecting the OLYMPUS SPO-887L conductivity probe and turning on the device, a special conductivity regime is automatically set. During the calibration process, low and high conductivity limits are set, as the fundamentals for the following conductivity calculations of material samples, chosen for the investigation. The calibration comprises four steps (Fig. 2). According to the electric conductivity values of used materials, stated in material sheets, GR5 ASTM B265 was chosen for low limit. Its value is 1.03% IACS (The International Annealed Copper Standard). The highest electric conductivity of chosen materials (101.42% IACS), corresponding to the copper alloy CW004A [25], was selected as a high limit. 3.2 Experimental Process During the experimental procedure, a probe was attached to the evaluated material samples, and a device calculated the value of their conductivity (σ), which was displayed. Measurements were done three times to probe each material, and the whole process, including the calibration, was repeated three times (9 measurements overall). The identification was made at a room temperature equal to 21 °C. Obtained data were recorded and processed. In the first stage, the arithmetic mean of all measurements was calculated for each material according to the following equation [26]   (1) x = (n; i = 1)xi /n where x¯ is the arithmetic mean of measured values, x i represents measured values, and n is the number of values. This parameter was used as a base for the following calculations. A difference (measurement error) between the average value of all measurements and the conductivity value of the data sheet was determined by using an equation [26] ε = X − x = X

(2)

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Fig. 2. Calibration process for conductivity measurement

where ε is the measurement error, X represents the data sheet conductivity value, x¯ represents the measured average value, and ΔX is their difference. Also, a standard deviation was calculated for measured conductivity values by applying the next equation [26]  (3) s = (n; i = 1) (xi )2 /(n − 1) where s refers to standard deviation, n is the number of values, and x i represents measured values. Additionally, maximum and minimum measured conductivity values were selected. The average and maximum deviation from data sheet conductivity were expressed in percentage values, and data were transformed to standard penetration depth values.

4 Results and Discussion A preliminary experiment showed various impedance signal curve deviations based on different probed material structures (Fig. 3). Signal curves of materials with higher predicted conductivity occupied the right side of the screen. The highest deviation was in the case of copper alloy CW004A, which has the highest conductivity. The next are alloys based on aluminum (7075-T6 and AlSi10Mg), which signal curves partially overlap each other, which is the consequence of the high similarity of these two materials. On the other side of the display, materials with the base material in steel are present (Maraging steel MS1 and AISI 4140 alloy steel). It can be predicted that these materials will show low conductivity potential during the experiment. Curves of remaining materials occupied the middle part of the display. Again, a similarity can be found between materials on the base of the same material (AISI 304 and SS 316L). They are not included in the area with AISI 4140 alloy steel because of their non-magnetic character. Also, signal curves of a group of special alloy materials are present in this area. The strongest peak was obtained in the case of MONEL 400 alloy and vice versa. The lowest was obtained by GR5 ASTM B265 titanium alloy. This preliminary experiment confirmed the predicted potential of eddy current testing device for diversifying materials.

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Fig. 3. Signal-to-material-structure deviation

During the main experiment, the determined and calculated parameters are summarized in Table 1. The first parameter monitored was a verification of the conductivity of samples used for the calibration process. Namely, GR5 ASTM B265 titanium alloy and CW004A copper alloy were probed in the same number of repetitions as other materials. The average measured conductivity is equal to that of the data sheet (1.03) in the case of B265 material, and for the material CW004A, a deviation is equal to 0.69% IACS. Average deviations expressed in percentage are 0% and 0.68%, respectively. Therefore, a calibration process can be considered satisfactory. The highest deviation was reached in the case of AISI 4140 alloy steel, which average percentage deviation was 87.24%. There was a significant difference between the datasheet value (6.84% IACS) and the measured values. All these values were equal to 1% IACS because the measurement device displays the lowest conductivity value equal to 1% IACS. Avg. ε and Max. ε are in [%], rest are in [% IACS]. It can be predicted that this material has an even lower conductivity level. A reason can be that the used sample material does not correspond to the material described in the datasheet, despite its same label. The same phenomenon occurred when probing the Maraging Steel MS1 material. Similarly, it can be predicted lower conductivity than 1% IACS. However, in this case, a value is not compared with some reference value because the manufacturer does not state the electric conductivity value in a material data sheet. A significant deviation was reached from the group of materials not included in the calibration process in the case of special alloys. Values of difference between the average measured and reference conductivity were the same for both materials (MONEL 400 and INCONEL 625), equal to 0.63% IACS. For the first mentioned, the average percentage deviation is 20%. As the difference in the case of INCONEL 625 corresponds to lower conductivity values, the average percentage deviation is more significant and reaches 47.01%. Such deviations can be ascribed to the fact that these materials are not commonly used as electric conductors, and reference value can be indicative. Other

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M. Geˇlatko et al. Table 1. Determined and calculated parameters of the main experiment x¯

ε (ΔX) s

GR5 B265 1.03 [25]

1.03

0

0.0165831 1.05

1

0.00

2.91

INC. 625

1.34 [25]

1.97

0.63

0.0120185 1.99

1.95

47.01

48.51

AISI 304

2.39 [25]

2.36

0.03

0.0548736 2.46

2.29

1.26

4.18

MON. 400 3.15 [25]

2.52

0.63

0.2086864 2.74

2.23

20.00

29.21

SS 316L

3.40 [1]

3.52

0.12

0.0234521 3.57

3.49

3.53

5.00

AISI 4140

7.84 [25]

1

6.84

0

1

87.24

87.24

7075-T6

33.48 [25]

39.49

6.01

0.3299537 39.96

39.15

17.95

19.35

AlSi10Mg

37.76 [27]

40.69

2.93

0.4142999 41.34

40.13

7.76

9.48

CW004A

101.42 [25] 102.11 0.69

1.3747909 104.73

100.86

0.68

3.26

MS1

-

0

1

-

-

Material

X

1

-

Max. σ

1

1

Min. σ

Avg. ε Max. ε

elevated values of deviations were reached at samples made from the material on the aluminum base. For AlSi10Mg, a difference was 2.93% IACS, and for 7075-T6, a difference was 6.01% IACS. The percentage expression values are 7.76% and 17.95%, respectively. Such higher deviations can result from higher conductivity values, with a higher potential for measurement deviation. Also, it must be mentioned that the manufacturer of AlSi10Mg states its electrical conductivity value (37.76% IACS) at 40 °C [27], and the experiment was carried out at 21 °C. The lowest deviations were reached in the case of stainless-steel samples. Namely, a difference for AISI 304 was 0.03% IACS and 1.26% average deviation. In the case of SS 316L, values were 0.12% IACS and 3.53% average deviation. It is important to note that reference conductivity for SS 316L is not the datasheet value because the manufacturer does not state it. A chosen value was measured during the calibration process of previous research on this material [1]. Therefore, this deviation can be considered a deviation between two separate determinations of the electric conductivity of mentioned material. Considering the standard deviation parameter, the highest was reached for verification measurements of CW004A copper alloy. Also, higher values were reached for aluminum-based materials and MONEL 400. The lowest standard deviation was present during the measurements of the INCONEL 625 sample. AISI 4140 and MS1 steel standard deviations are equal to 0. Still, it cannot be considered valuable because the lowest displayed limit is 1% IACS, and it is most likely that these two materials have lower conductivity. Obtained data are interpreted in the following diagrams. For better distinction, diagrams were separated. The first (Fig. 4) includes materials with lower conductivity (up to 10% IACS), and the second (Fig. 5) includes materials with higher conductivity (above 10% IACS), which corresponds to aluminum and copper alloys. MS1 material was excluded from the depiction due to the missing reference value. Also, average percentage deviations are depicted. For a deviation expression of comprehensive conductivity measurement of materials without calibration samples, the calculated average difference between reference and

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Fig. 4. Measured conductivity deviation of materials with lower conductivity

Fig. 5. Measured conductivity deviation of materials with higher conductivity

measured conductivity equals 2.85% IACS, and the average percentage deviation is 30.20%. If the AISI 4140 steel is excluded, the value is decreased to 18.8%. Also, SS 316L and MS1 materials were excluded from the comprehensive average deviation due to the missing value of reference conductivity. The average difference for verification measurements of calibration samples is 0.34% IACS, and the same value represents percentage expression (0.34%). Additionally, for a better depiction of obtained data, electric conductivity values were translated to the standard penetration depth of eddy currents value, known as a depth in evaluated material, where eddy currents have 37% density of their density on a sample surface. Its calculation is based on the following equation [28]   (4) δ = (2/ (μ × ω × σ )) = (1/ (π × f × μ × σ )) where μ [H·m−1 ] is the permeability, ω [rad·s−1 ] refers to the angular frequency, σ [S·m−1 ] represents conductivity, and f [Hz] is the frequency. Calculated values are summarized in Table 2. It can be seen that significant deviations are present in the case of AISI 4140, INCONEL 625, and MONEL 400 materials. As stated before, if a deviation is elevated and the conductivity level of material is higher, such as in aluminum alloys, after the calculation of standard penetration depth, a deviation is not as significant as it seems.

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M. Geˇlatko et al. Table 2. Comparison of calculated standard penetration depth values

Material

δ (datasheet) [mm]

δ (measured) [mm]

Difference [mm]

GR5 ASTM B265

6.51

6.51

0

INCONEL 625

5.71

4.71

1

AISI 304

4.27

4.3

0.03

MONEL 400

3.72

4.16

0.44

SS 316L

3.58

3.52

0.06

AISI 4140

2.36

6.61

4.25

7075-T6 Al

1.14

1.05

0.09

AlSi10Mg

1.08

1.04

0.04

CW004A

0.66

0.65

0.01

MS1

-

6.61

-

By comparing δ in SS 316L material performed in last research [1] and during this experiment, a difference is equal only to 0.06 mm, which can be considered satisfactory. Some similarities and differences can be found with the conclusions of research from the field of electric conductivity measurement using eddy currents. Various material structure changes reached by the welding showed differences in electrical conductivity [19], confirming the prediction that conductivity changes with structure. Another study [13] agrees with this phenomenon, and the lower conductivity is described as the consequence of less metal in the evaluated volume of material. Therefore, less conductivity can be predicted for the same material made by AM technology compared to conventional. It is confirmed by the study [15], where measured conductivity was lower for AM sample. However, within the recent research [1] and this experiment, SS 316L produced by SLM technology had a higher conductivity value in both cases, compared with conventionally made. The measured conductivity value for as-built AlSi10Mg, within the mentioned experiment [16], was 28.5% IACS. Compared with recent research [1], the value for the same material was 34.48% IACS. In this study, it was equal to 40.69% IACS. The reason for such deviation can be different used AM technology (DMLS within the experiment [16] and SLM in this study) or different metal powder manufacturers.

5 Conclusions The paper describes an experiment focused on the electrical conductivity determination of various materials, using the eddy current testing device, with subsequent parameters calculation, to check the accuracy of obtained data, compared with recent research. Based on obtained data, some important conclusions can be formulated. The preliminary experiment showed the potential of using an eddy current testing device for distinguishing various materials, conductivity not excluding, as shown in the main experimental part.

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Higher measurement deviations were reached for well-known materials for their high conductivity value. The exception was AISI 4140 alloy steel, which deviation reached a significant value. However, the such deviation should be ascribed to bad reference conductivity value setting rather than measurement device. Also, a pretty high deviation was reached in the case of unique alloy materials, which can be ascribed to their low application as conductors, and reference conductivity values may not be accurate. Although some deviations were significant, mainly in the case of high-conductive materials, their influence was not so serious after the standard penetration depth calculation. A difference between the measured electrical conductivity value for SS 316L within recent research and described experiment in this paper showed a negligible deviation value. Some questions are still open, according to conclusions, and follow-up research should be done. It is necessary to verify the experiment’s data, with measurements done using a device intended for conductivity measurement. Extraordinary attention should be paid to special alloy materials, where measurements reached a quite high deviation despite their low-conductive potential. Regarding materials produced by additive manufacturing, which was the reason for this experiment, their conductivity needs to be checked with the same conventional materials within a more extensive study, including a larger number of samples. Acknowledgements. This work was funded by the Slovak Research and Development Agency under contract No. APVV-21-0228 and the projects VEGA 1/0391/22 and KEGA 028TUKE4/2021 granted by the Ministry of Education, Science, Research and Sport of the Slovak Republic. Paper is the result of the Project implementation: Development of excellent research capacities in the field of additive technologies for the Industry of the 21st century, ITMS: 313011BWN5, supported by the Operational Program Integrated Infrastructure funded by the ERDF.

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Substantiation of Chip Removal Models During Milling of Closed Grooves Oleksandr Gnytko(B)

and Anna Kuznetsova

National Aerospace University, 17, Chkalov St., Kharkiv 61070, Ukraine [email protected]

Abstract. The timely chip evacuation from the cutting zone is of the utmost importance due to the possibility of repeated chip cutting, which is actually abrasive. The authors proposed three main schemes for forced chip removal from closed T-shaped slots: due to the forced removal of chips from the space between the teeth of the cutter (supply of a lubricating-cooling technological medium through the channels in the tool body); due to the forced removal of chips from the space outside the cutting tool (cooling agent supply through the channels of the external nozzle); a combination of the two previous methods. The theoretical foundations of the proposed chip removal devices have been developed to determine the rational parameters of their functioning based on five mathematical models. Confirmation of the reliability of the developed theoretical provisions and the adequacy of the created mathematical models requires appropriate experimental studies. When conducting experimental studies, mechanical processing of T-shaped grooves 12 in steel and cast iron blanks was performed. For this, milling cutters with a diameter of 21 mm were used for processing cast iron and with a diameter of 21 mm for processing steel 45. Verifying the hypothesis about the adequacy of the developed mathematical models is carried out using the Fisher F-criterion. The simulation error is estimated by calculating the absolute and relative errors of the mismatch between the theoretical curve and the curve approximating the experimentally obtained data. Keywords: T-Shaped · Tool Body · Teeth · Mathematical · Adequacy · Fisher · Sustainable Manufacturing

1 Introduction One of the prerequisites for ensuring high cutting performance is the timely removal of chips from the working area [1], since when a separated chip (which is actually an abrasive [2]) re-enters the cutting area [3], intensive tool wear [4], an increase in cutting energy consumption [5], and many other negative phenomena take place. Of particular relevance is the timely removal of chips when milling closed profile grooves (T-shaped, dovetail type), curly labyrinths, etc. [6], since the resulting groove space, due to its closedness, is quickly filled with separated chips, which begins to interact with cutting tools [7] or functional media [8] and area [9], thus deteriorating their performance. In this case, the untimely removal of chips from the cutting zone is a factor that significantly © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 238–250, 2023. https://doi.org/10.1007/978-3-031-32767-4_23

Substantiation of Chip Removal Models

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limits the productivity of milling closed profile slots. It determines the practical need to develop effective devices for forced chip removal when milling closed profile slots.

2 Literature Review The chip removal process consists of the following steps: removing chips from the cutting zone, removing chips from the machine, removing chips from a group of machines, collecting chips, and processing them. There are the following ways to remove chips from the machine: mechanical with the help of conveyors, scrapers, auto-operators, brushes, and gravitational, in which the chips are fed onto the inclined surfaces of the fixture and machines, and then dumped onto the conveyor under the machines; flushing chips with an emulsion jet, suction chips with compressed air; electromagnet chip removal. There may be cases of combined use of the considered methods. Means of forced removal of chips from the cutting zone in relation to some types of processing are known and widely used. Although various methods can be used for chip removal, including methods that use air or air/coolant mixture jets by feeding it through the channels in the tool body to remove chips [6], by gravitational forces [10], unique tool geometry [11], thermal [12] and plasma treatment [13], use of electric [14], magnetic [15], electrons [16] or electromagnetic fields [17], for now, these issues are considered only for the metal and CFRP drilling [18], low-frequency vibration support drilling [19] of the deep hole at small diameters, grinding [20], end milling [21], 3D finite element modeling of chip formation and deleting processes [22], chip removal at nano-scales [23] and other ways of machining [24]. So, in deep drilling, forcing chip removal from the working area is used through a pressure supply of a lubricating-cooling technological medium (LCTM) to the cutting zone through an axial channel in the body of the drill, etc. However, standard solutions and approaches to determining the rational parameters of such devices cannot be used when milling closed profile slots due to significant differences in the physical picture of the workflow and the required values of the parameters of chip removal devices: – – – – – –

in the type of chips formed and the features of their shape; in the schemes of accumulation and movement of chips; in the configuration and volume of the working space of the cutting tool; in the trajectory of the chips; in terms of the speed of movement and accumulation of chips; in cutting conditions, etc.

Therefore, research aimed at creating effective devices for the timely evacuation of chips from the working area when milling closed profile slots is relevant.

3 Research Methodology The authors proposed three main schemes for forced chip removal (and developed corresponding technical solutions that have patent protection): (i) due to the forced removal

240

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of chips from the space between the teeth of the cutter (supply of LCTM through the channels in the tool body); (ii) due to the forced removal of chips from the space outside the cutting tool (cooling agent supply through the channels of the external nozzle); (iii) a combination of the two previous methods. The theoretical foundations of the proposed chip removal devices have been developed to determine the rational parameters of their functioning based on 5 mathematical models [6]. Confirmation of the reliability of the developed theoretical provisions and the adequacy of the created mathematical models (MM) requires appropriate experimental studies. Since there is no data on the conduct of experimental studies in this direction, it is also necessary to develop a methodology for their conduct. Due to their closed configuration, the milling of closed profile slots is characterized by limited access to the processing zone, which significantly complicates the creation of devices for the forced removal of separated chips. The most problematic in this respect is the processing of T-slots. Therefore, the effectiveness of the proposed technical solutions should be considered in their example. When conducting experimental studies, mechanical processing of T-shaped grooves 12 in steel and cast-iron blanks was performed. For this, milling cutters with a diameter of ∅21mm were used for processing cast iron and a diameter of ∅21mm for processing steel 45. Accepted values of cutting modes are given in Tables 1 and 2. Table 1. Values of steel 45 machining cutting conditions (T-shaped cutter ∅21 mm, z = 6). S m , mm/min

64

82

104

130

160

200

S z , mm/tooth

0,026

0,043

0,055

0,069

0,085

0,106

n, rpm

410

315

315

315

315

315

V, m/min

27

20

20

20

20

20

Table 2. Values of cast iron machining cutting conditions (T-shaped cutter ∅21 mm, z = 8). S m , mm/min

64

82

104

130

160

64

S z , mm/tooth

0,031

0,050

0,063

0,100

0,166

0,031

n, rpm

255

205

205

160

120

255

V, m/min

17

14

14

10

8

17

Grooving was carried out on a universal milling machine 6M76P-1. A universal tensometric dynamometer UDM600 was fixed on the machine table. A fixture with a workpiece was attached to it. The signals generated by the dynamometer were transmitted to the recording device - the H338–1 recorder. The specificity of the issue under consideration determines the need to study the formation of force factors in implementing the working process of slot milling. Without forced removal of chip elements from the processing zone, they are likely to be re-cut by the tool, which causes a corresponding

Substantiation of Chip Removal Models

241

change in cutting force. An analysis of the change in the cutting force during processing allows one to judge the “behavior” of the chips (the direction of movement, the presence in the corresponding zones, etc.). The adopted and implemented bench design and instrumental complex make it possible to measure the values of power parameters that differentially characterize the accumulation and movement of chips in the entire possible range of changes in the design and operating parameters of the devices (systems) under study. Figure 1 shows a schematization of the cutting force components during processing with a T-shaped cutter. From which, it follows that the horizontal components Phi of the cutting force on the teeth of the cutter, located on opposite sides of the pre-machined rectangular groove (pos. 1), are directed oppositely. Therefore, the value n  Phi (n is the number of cutter teeth that are simultaneously of the resulting vector i=1

processed) is determined by their difference, and the measurement of the value

n 

Phi

i=1

does not allow one to judge the true instantaneous values of the Phi components.

Fig. 1. Scheme for measuring the components of cutting forces.

The vertical components Pvi of the cutting force on the teeth of the cutter, located on opposite sides of the pre-machined rectangular groove, are directed in the same way. n  Pvi is determined by their sum, and Therefore, the value of the resulting vector the measurement of the value

n 

i=1

Pvi makes it possible to more correctly judge the true

i=1

Fig. 2. Scheme for processing experimental data.

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instantaneous values of the Pvi components. Figure 2 shows an example of implementing n  Pvi in time (t). the change in the sum of forces i=1

Section 1 corresponds to the change in the sum of cutting forces Pv1 over the length of implementation l1 (machining without re-entry of chips into the cutting zone). The record of length l1 is performed during the time t. The number of revolutions the cutter makes during the time t - k, the length of processing - L. The recalculation of the values of l1 into t is carried out using the speed of pulling the tape recorder. Based on t, the values of k and L are determined through the feed Sm and the spindle speed n (Tables 1, 2). Section 3 - change in Pv2 at the length of the implementation of the record l3 (processing when chips re-enter the cutting zone). Section 2 is transitional (at the moment the chips enter the cutting zone). Verifying the hypothesis about the adequacy of the developed mathematical models is carried out using the Fisher F-criterion. The model is adequate if the condition  2 2 Srep ≤ F(α, fad ; frep ), (1) F = Sad 2 is the dispersion of adequacy; S 2 where: Sad rep - dispersion of reproducibility; F(α, fad ; frep ) - Fisher’s test at a given level of significance α; fad - number of degrees 2 ;f 2 of freedom Sad rep is the number of degrees of freedom Srep . Approximation of the obtained experimental data is carried out by the polynomial regression method, implemented using MathCAD software. The essence of the method is to find a single polynomial of arbitrary order or a set of second-order polynomials that best approximate the neighborhoods of sample points defined in the corresponding vectors. The simulation error is estimated by calculating the absolute and relative errors of the mismatch between the theoretical curve and the curve approximating the experimentally obtained data. Below are the methods and results of confirmation of the adequacy of the three developed mathematical models (MM).

1. MM1 study of filling the space between the teeth of the cutter with chips during the cutting process. Allows you to determine the number of revolutions of the cutter N, corresponding to the filling of the space between the teeth (provided that the chips are not removed due to inertia forces). The final dependence of the model has the form    tan βt h13 [tan βt h1+2(l tan ωh )] l h1[tan βt h1+(tan βt h1+2(l tan ωh ))] + 3 2 4 , N= Sz (d − a) · l · kl

(2)

where: N is the number of revolutions of the cutter; S z - feed per cutter tooth, mm; d cutter diameter, mm; a - the width of the pre-machined groove, mm; l is the height of the cutting part of the cutter, mm; k l is the coefficient of chip loosening; h1 is the length of the front surface of the cutter tooth, mm; βt - the angle between the front surface of the cutter tooth and the surface of the back of the head of the next tooth, deg; ωh - helical groove angle of the cutter, deg.

Substantiation of Chip Removal Models

243

The analysis of the above dependence made it possible to establish that the filling of the space between the cutter teeth occurs quite quickly, on average, in 0.4…5 revolutions (in the recommended standard range of geometric parameters and modes). Table 3 shows the data for assessing the level of adequacy of MM1, while Fig. 3 shows the theoretical and experimental curves. Table 3. Initial data for establishing MM1 adequacy levels. Workpiece material

Gray cast iron

2 Adequacy dispersion Sad

0.003

Number of degrees of freedom fad

2

2 Reproducibility dispersion Srep

0.007

Number of degrees of freedom frep

8

Estimated value - Fisher’s criterion

2.33

Table value - Fisher’s test (at a significance level q = 0.05)

19.37

Fig. 3. Graphs of the dependence of the number of revolutions of the cutter N until the space between the teeth is filled as a function of the feed per tooth S z (the material being processed is cast iron, groove 12, cutter diameter d c = 21 mm).

An analysis of the results of experimental studies showed that in the studied range of parameter changes, the filling of the space between the teeth of the cutter ∅ 21 during the processing of cast iron occurs after 1.19… 0.40 revolutions of the cutter concerning the theoretical values of 1.43… 0.43 revolutions. Table 4 shows the data for estimating the MM1 error.

244

O. Gnytko and A. Kuznetsova Table 4. Initial data for estimating the error MM1 (workpiece material cast iron).

Estimated values

Feed per tooth, mm 0,050

0,063

0,100

0,166

Experimentally recorded values of N, rev

1,19

1,03

0,66

0,40

Estimated values of N, rev

1,43

1,14

0,71

0,43

0,05

0,03

7,6

7,5

Absolute error abs

0,24

Relative error δrel , %

20

0,11 10,7

abs.av = 0.11 revolution δrel.av = 11.4%

2. MM2 study of the chip element’s movement along the cutter tooth’s front surface under inertial forces and the hydrodynamic force of forced action. Allows you to determine the instantaneous value of the speed of movement of the chip element as a result of inertial and (or) forced action on it within the tool (cutter). The final dependencies of the model have the form v1 = α1 · C1 · eα1 ·t + α2 · C2 · eα2 ·t α1,2 =

−A ±

√

A2 − 4B D · α2 D · α1 , C1 = − , C2 = , 2 B · (α2 − α1 ) B · (α2 − α1 )

A = 2ω · ff , B = −ω2 cos α · (cos δ − sin δ · ff ),

(3)

D =(2Fa /ρc · Sz (d − a) · l) × (cos β − sin β · ff ) − g · fb (1 + sin α + ff )    + ω2 rc − (h1 − 3 3(Sz (d − a) · l)/π /2) cos α · (cos δ − sin δ · ff ), where t is the time of movement of the chip element along the front surface of the cutter tooth, s; ω – angular velocity of the chip element in a portable rotational motion, deg; f f - coefficient of friction of the chip element on the front surface of the cutter tooth; α – the front angle of the cutter tooth, deg; δ – the angle of inclination of the vector of centrifugal force of inertia, deg; F a – forced action force on the chip element, N; β – angle of inclination of the force vector F a to the front surface of the cutter tooth, deg; ρc is the density of the chip element, N/m3 ; g is the free-fall acceleration, m/s2 ; f b is the coefficient of friction of the chip element along the bottom surface of the groove; r c – cutter radius, m. The analysis of this model made it possible to establish that when machining steels and cast irons in the recommended standard range of geometric parameters and modes, in 30… 100% of cases, there is no possibility of self-removal of chip elements due to inertia forces (v1 < 0). When processing steel billets (with the modes given in Tables 1 and 2), in 100% of cases, the chip elements are removed from the space between the

Substantiation of Chip Removal Models

245

teeth of the cutter due to inertia forces. When machining cast iron, self-removal occurs only at a machine spindle speed of 255 rpm. At n < 255 rpm, v1 < 0. The obtained experimental data confirmed the simulation results. 3. Technological operations of milling T-slots structurally usually consist of two transitions - pre-processing (milling a rectangular groove with a disk or end mill) and final processing (milling with a T-shaped cutter). When processing with a T-shaped cutter, the separated chips are located on both sides of the cutter - in a rectangular groove (Fig. 4, a) and the resulting T-shaped one (Fig. 4, b).

Fig. 4. The scheme of filling the grooves with chips during milling: a) rectangular groove; b) T-slot.

MM4 of the process of filling the groove space with chips. It allows you to set the length of the groove milling until the chip elements re-enter the processing zone due to the backwater from the accumulated chips (Fig. 4). A. For a rectangular groove (Fig. 4 a) L1 =

2 mve1 , 2 · Fa

(4)

where L 1 is the length of processing with a T-shaped cutter until the chip elements reenter the cutting zone as a result of the occurrence of backwater from the side of the chip accumulated in a rectangular groove, m; ve1 is the speed of the chip element at the exit from the space between the teeth, m/s. B. For a T-slot (Fig. 4, b) 2 3 [(xn + d 2 − π d 8) − c tan ε 2]dc + a 4[cot ε(xn − a 2) − c] + cot ε · a 16 L2 = 2 (d − a) · l · kl − (d · c + cot ε · a 4) (5)

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O. Gnytko and A. Kuznetsova

where L 2 is the length of processing until the chip elements re-enter the cutting zone as a result of the backwater in the T-slot, m; x n is the distance over which the chip element moves outside the cutter under the action of inertial forces, m; c is the height of the T-shaped groove, c; ε - angle of repose of the bulk array of chips, deg.

4 Results and Discussion The performed analysis made it possible to establish that the chips fill the space of each of the grooves after 0.4…30 mm of processing in the recommended standard range of geometric parameters and modes. Tables 5 and 6 show data for assessing the level of adequacy of MM4a and MM4b. Table 5. Initial data for establishing MM4a adequacy levels. Workpiece material

Steel 45

Gray cast iron

2 Adequacy dispersion Sad

0.413

0.055

Number of degrees of freedom fad

3

2 Reproducibility dispersion Srep

0.254

Number of degrees of freedom frep

10

Estimated value - Fisher’s criterion

1.62

Table value - Fisher’s test (at a significance level q = 0.05)

3.71

0.032

1.72

Table 6. Initial data for establishing MM4b adequacy levels. Workpiece material

Steel 45

Gray cast iron

2 Adequacy dispersion Sad

0.075

0.35

Number of degrees of freedom fad

3

2 Reproducibility dispersion Srep

0.061

Number of degrees of freedom frep

10

Estimated value - Fisher’s criterion

1.23

Table value - Fisher’s test (at a significance level q = 0.05)

3.71

Figure 5 and 6 show the theoretical and experimental curves.

0.24

1.46

Substantiation of Chip Removal Models

247

Fig. 5. Graphs of the dependence of the cutter L 1 displacement (filling a rectangular groove) before the chip circulation occurs as a function of feed per tooth S z (material being machined - a) Steel 45, b) cast iron, groove 12, d c = 21 mm).

Fig. 6. Graphs of the dependence of the cutter L 2 displacement (filling a T-shaped groove) before the chip circulation occurs as a function of feed per tooth S z (material being machined - a) Steel 45, b) cast iron, groove 12, d c = 21 mm).

Tables 7–10 show the data for estimating the error of modeling MM4a,b. Table 7. Initial data for estimating the error MM4a (workpiece material steel 45). Estimated values

Feed per tooth, mm 0.026

0.043

0.055

0.069

Experimentally recorded values of L 1 , mm

6.10

3,00

3.40

3.68

Estimated values of L 1 , mm

8.9

3.62

3.67

3.71

Absolute error abs

2.8

0.62

0.27

0.03

Relative error δrel , %

31

17

7.3

0.8

abs.av = 0.79 mm δrel.av = 12.5%

248

O. Gnytko and A. Kuznetsova Table 8. Initial data for estimating the error MM4a (workpiece material cast iron).

Estimated values

Feed per tooth, mm 0.031

0.050

0.063

0.100

Experimentally recorded values of L 1 , mm

1.067

0.520

0.693

0.542

Estimated values of L 1 , mm

0.940

0.574

0.572

0.585

Absolute error abs

0.127

0.054

0.121

0.043

Relative error δrel , %

12

9.4

17

2.7

abs.av = 0.07 mm δrel.av = 9.2% Table 9. Initial data for estimating the error MM4b (workpiece material steel 45). Estimated values

Feed per tooth, mm 0.026

0.043

0.055

0.069

Experimentally recorded values of L 2 , mm

3.95

3.83

3.46

3.46

Estimated values of L 2 , mm

4.93

3.28

3.29

3.29

Absolute error abs

0.98

0.55

0.17

0.17

Relative error δrel , %

20

14

5

5

abs.av = 0.39 mm δrel.av = 9.4% Table 10. Initial data for estimating the error MM4b (workpiece material cast iron). Estimated values

Feed per tooth, mm 0.031

0.050

0.063

0.100

Experimentally recorded values of L 2 , mm

3.24

0.273

0.347

0.217

Estimated values of L 2 , mm

2.98

0.19

0.19

0.20

Absolute error abs

0.26

0.083

0.157

0.017

Relative error δrel , %

8

30

45

7.8

abs.av = 0.12 mm δrel.av = 24%

An analysis of the results of experimental studies showed that the movement of the cutter L 1 before the re-entry of chips into the cutting zone as a result of backwater is 6.1…4.0 mm for steel, 1.0…0.5 mm for cast iron. The movement of the cutter L 2 is 3.9…3.2 mm for steel, 3.2…0.2 mm for cast iron. The obtained data agree with the theoretical ones (Fig. 5, 6; Tables 7–10).

5 Conclusions The adequacy of the developed mathematical models was proved using Fisher’s Fcriterion. The adequacy of the models was made within the studied range of parameters

Substantiation of Chip Removal Models

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when processing steel workpieces ∅21 mm with the number of teeth z = 6 (minute feed S m = 64…200 mm/min, cutting speed V = 27…20 m/min) and cast iron workpieces with the number of teeth z = 8 (minute feed S m = 64…160 mm/min, cutting speed V = 17…8 m/min). The simulation error was estimated by calculating the absolute and relative errors of the mismatch between the theoretical curve and the curve approximating the experimentally obtained data. The developed mathematical models can be used to determine the rational parameters of devices for forcing chip removal from the cutting zone. A promising direction is improving existing research in the framework of new, more intense cutting conditions in processing workpieces from aluminum alloys.

References 1. Stephenson, D.A., Hughey, E., Hasham, A.A.: Air flow and chip removal in minimum quantity lubrication drilling. Procedia Manufact. 34, 335–342 (2019). https://doi.org/10.1016/j.pro mfg.2019.06.171 2. Azam, S., Ahmadloo, E.: Analysis of chip removal operations via new quick-stop device. Mater. Manuf. Processes 31(13), 1782–1791 (2016). https://doi.org/10.1080/10426914.2015. 1127959 3. Balázs, B.Z., Takács, M.: Finite element modelling of thin chip removal process. IOP Conference Series: Mater. Sci. Eng. 426, 012002 (2018). https://doi.org/10.1088/1757-899X/426/1/ 012002 4. Chen, S.-H., Luo, Z.-R.: Study of using cutting chip color to the tool wear prediction. Int. J. Adv. Manuf. Technol. 109(3–4), 823–839 (2020). https://doi.org/10.1007/s00170-020-053 54-2 5. Takács, M.: Validation of 3d finite element simulation of chip removal process performed by unique insert geometry. Key Eng. Mater. 581, 505–510 (2014). https://doi.org/10.4028/www. scientific.net/kem.581.505 6. Gnytko, O., Kuznetsova, A.: Theoretical research of the chip removal process in milling of the closed profile slots. Arch. Mater. Sci. Eng. 113(2), 69–76 (2022). https://doi.org/10.5604/ 01.3001.0015.7019 7. Xu, C., Yiwen, W., Jiazhong, X., Xianli, L.: Calculation of negative-pressure chip in suctiontype internal chip removal system and analysis of influencing factors. Int. J. Adv. Manuf. Technol. 99(1–4), 201–209 (2018). https://doi.org/10.1007/s00170-018-2443-6 8. Ruzaikin, V., Lukashov, I.: Experimental method of ammonia decomposition study based on thermal-hydraulic approach. Results Eng. 15, 100600 (2022). https://doi.org/10.1016/j.rin eng.2022.100600 9. Ruzaikin, V., Lukashov, I., Fedorenko, T., Abashin, S.: The equilibrium contact angle of ammonia-stainless steel interface. Results Eng. 16, 100691 (2022). https://doi.org/10.1016/j. rineng.2022.100691 10. Banerjee, A., Feng, H., Bordatchev, E.: Geometry of chip formation in circular end milling. Int. J. Adv. Manuf. Technol. 59, 21–35 (2012). https://doi.org/10.1007/s00170-011-3478-0 11. Liu, Y., Li, S., Li, H., Qin, X., Xing, Y., Liu, H.: The design and performance evaluation of assisted chip removal system in helical milling of CFRP/Ti stacks. Int. J. Adv. Manuf. Technol. 108(5–6), 1297–1308 (2020). https://doi.org/10.1007/s00170-020-05421-8 12. Baranov, O., Košiˇcek, M., Filipiˇc, G., Cvelbar, U.: A deterministic approach to the thermal synthesis and growth of 1D metal oxide nanostructures. Appl. Surf. Sci. 566, 150619 (2021). https://doi.org/10.1016/j.apsusc.2021.150619

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13. Guo, B., et al.: Single-Crystalline metal oxide nanostructures synthesized by PlasmaEnhanced thermal oxidation. Nanomaterials 9(10), 1405 (2019). https://doi.org/10.3390/nan o9101405 14. Breus, A., Abashin, S., Lukashov, I., Serdiuk, O.: Anodic growth of copper oxide nanostructures in glow discharge. Arch Mater. Sci. Eng. 114(1), 24–33 (2022). https://doi.org/10.5604/ 01.3001.0015.9850 15. Breus, A., Abashin, S., Serdiuk, O.: Carbon nanostructure growth: new application of magnetron discharge. J. Achievements Mater. Manuf. Eng. 109(1), 17–25 (2021). https://doi.org/ 10.5604/01.3001.0015.5856 16. Baranov, O., Romanov, M., Fang, J., Cvelbar, U., Ostrikov, K.: Control of ion density distribution by magnetic traps for plasma electrons. J. Appl. Phys. 112(7), 073302 (2012). https:// doi.org/10.1063/1.4757022 17. Baranov, O., Filipiˇc, G., Cvelbar, U.: Towards a highly-controllable synthesis of copper oxide nanowires in radio-frequency reactive plasma: fast saturation at the targeted size. Plasma Sources Sci. Technol. 28, 084002 (2019). https://doi.org/10.1088/1361-6595/aae12e 18. Xu, C., Wang, Y., Xu, J., Liu, X.: Design of internal-chip-removal drill for CFRP drilling and study of influencing factors of drilling quality. Int. J. Adv. Manuf. Technol. 106(5–6), 1657–1669 (2019). https://doi.org/10.1007/s00170-019-04698-8 19. Bleicher, F., Reiter, M., Brier, J.: Increase of chip removal rate in single-lip deep hole drilling at small diameters by low-frequency vibration support. CIRP Ann. 68(1), 93–96 (2019). https:// doi.org/10.1016/j.cirp.2019.04.028 20. Barrenetxea, D., Alvarez, J., Marquinez, J.I., Sanchez, J.A.: Grinding with controlled kinematics and chip removal. CIRP Ann. 65(1), 341–344 (2016). https://doi.org/10.1016/j.cirp. 2016.04.097 21. Kopaˇc, J., Pušavec, F.: Development and manufacturing of customized milling cutters for individual tool-making industry. J. Achievements Mater. Manuf. Eng. 82(1), 26–32 (2017). https://doi.org/10.5604/01.3001.0010.2076 22. Ozel, T., Llanos, I., Soriano, J., Arrazola, P.: 3D finite element modelling of chip formation process for machining Inconel 718: comparison of FE software predictions. Mach. Sci. Technol. Int. J. 15(1), 21−46 (2011). https://doi.org/10.1080/10910344.2011.557950 23. Liu, H., Guo, Y., Li, D., Wang, J.: Material removal mechanism of FCC single-crystalline materials at nano-scales. Chip removal & ploughing. J. Mater. Process. Technol. 294, 117106 (2021). https://doi.org/10.1016/j.jmatprotec.2021.117106 24. Kumar, K.S., Paulraj, G.: Analysis and optimization of fixture under dynamic machining condition with chip removal effect. J. Intell. Manuf. 25(1), 85–98 (2012). https://doi.org/10. 1007/s10845-012-0677-y

Surface Roughness Assessment After Milling of Pure and Carbon Black Reinforced Polypropylene Materials Ahmet Hakan Akin1 , Yusuf Furkan Yapan1 , Yaroslav Kusyj2 and Alper Uysal1(B)

, Vitalii Ivanov3,4

,

1 Yildiz Technical University, Besiktas, Istanbul 34349, Turkey

[email protected]

2 Lviv Polytechnic National University, 12, Stepana Bandery St., Lviv 79013, Ukraine 3 Sumy State University, 2, Rymskogo-Korsakova St., Sumy 40007, Ukraine 4 Technical University of Košice, 1, Bayerova St., Prešov 08001, Slovak Republic

Abstract. Polypropylene (PP) material and its composites are preferred for many applications due to their advanced properties. Although PP material products are produced in a near-net shape, milling, which is a secondary machining process, is required to obtain dimensional tolerances, appropriate geometric shapes, and quality surfaces. For this reason, the chip removal process gains importance so that PP can be used in structural parts and assembled with different parts. In this study, milling of pure polypropylene (Pure PP) and carbon black reinforced polypropylene (CB-PP) composite investigated the effect of cutting speed (70, 100, and 130 m/min) and feed (0.4, 0.5, and 0.6 mm/rev) on the average surface roughness (Ra), chip morphology, and exit burr formation. In addition, the Ra value was predicted by mathematical modeling of the Ra. A relationship was established between the Ra and cutting parameters such as cutting speed, feed, and radial depth of cut. The experimental results showed that the Ra value decreased as the cutting speed increased and the exit burr area increased. Also, as the feed increased, the Ra value and the exit burr size increased. It was seen that the deviation of mathematical modeling from experimental results was 11% on average, and it was usable. It was observed that CB-PP gave better Ra than pure PP under the same cutting parameters. Keywords: Sustainable Manufacturing · Process Innovation · Milling · Carbon Black · Polypropylene · Surface Roughness · Burr Formation

1 Introduction Polypropylene (PP) materials and their composites are preferred for many applications because of their advanced properties [1]. Furthermore, PP has the lowest density among all thermoplastics, so it has become popular and charming in numerous cases [2]. Since the composite material products, in which PP is used as a matrix, are produced in a nearnet shape, it is thought that machining operations will not be needed. However, chip © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 251–263, 2023. https://doi.org/10.1007/978-3-031-32767-4_24

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removal operations, which are secondary machining processes such as turning, milling, and drilling, are required to obtain dimensional tolerances, appropriate geometric shapes, and quality surfaces [3]. Machining is necessary as a finishing operation for polymerbased composites [4]. For this reason, the chip removal process gains importance so that PP and its composites can be used in structural parts and assembled with different parts. This study investigated in milling of pure polypropylene (Pure PP) and carbon black reinforced polypropylene (CB-PP) composites by influences of cutting speed and feed on the Ra, chip morphology, and top burr formation. Thus, the milling performances of pure PP and CB-PP composite were determined.

2 Literature Review Considerable work has been reported on the milling of PP and its composites in the literature. Shukor et al. [5] investigated the effects of milling parameters such as feed rate, depth of cut, and speed on the surface roughness of PP. They reported that the depth of cut was ranked first to be the highest of the three parameters, followed by feed rate and cutting speed. Bajpai and Singh [6] analyzed drilling forces in terms of variation in the feed rate and cutting speed with two different drill point geometries (solid and hollow drill geometry) on sisal fibers-reinforced PP composite lamin. Zhu et al. [7] reported that the wood-plastic composite with PP presented the highest cutting temperature, cutting force, and tool wear, followed by polyethylene and polyvinyl chloride wood-plastic composites. Shagwira et al. [8, 9] focused on optimizing with the Taguchi method the input parameters such as machining speed, feed rate, and depth of cut used in the CNC milling of PP + 60 wt.% quarry dust composite (QDC) and PP + 80 wt.% QDC. For the contribution of 60 wt.% QDC, they reported that feed rate is the most influential factor which affects the material removal rate, followed by the depth of cut and cutting speed, respectively. For the contribution of 80 wt.% QDC, it was reported that the main factors for both the material removal rate and average surface roughness (Ra ) are the depth of cut as the ratios of 49.5% and 37.8%, respectively, feed rate as the ratios of 33.7% and 21.8%, respectively and spindle speed as the ratios of 12.7% and 20.8%, respectively. Codolini et al. [10] investigated the influence of milling, water jet, and laser cutting on the uniaxial tensile properties of mineral-filled PP by correlating the Ra . The researchers reported that milling composed the best edge quality and repeatable experimental results in the tensile test specimens. Tran et al. [11] investigated the effects of cutting parameters on the specific cutting energy, thrust force, Ra , fine dust, and ultrafine dust emission in dry drilling of a new hybrid biocomposite material made of miscanthus fibers and biochar-reinforced PP. Mudhukrishnan et al. [12, 13] investigated the influence of drilling parameters on the exit delamination of the drilled hole in carbon fiber-reinforced PP (CFR-PP) and glass fiber-reinforced PP (GFR-PP) composites using three different drill tools made with high-speed steel, tipped carbide, and solid carbide. They observed that the delamination factor increases with increasing feed rate and decreases with increasing drill speed for all three drill materials. Ya¸sar et al. [14] reported that the thrust forces decreased while Ra values increased when the chitosan filler ratio and feed rate increased in chitosan-filled PP composites’ milling. Kuram [15] carried out the micro-milling of pure PP and GFRPP and reported that the micro-milling performance of reinforced PP composite was poor

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as for pure PP. Even though the glass fiber reinforcement enhanced the mechanical and thermal properties of polymers, the reinforcement disturbed the micro-machinability of polymers. The chip removal process of polymer composites can be improved by changing the machining parameters, which may change the absolute material’s properties [16, 17]. Surface integrity plays a significant role in material functionality and depends on many machining parameters [18]. Estimating the Ra before the part is machined would be profitable, as this will also help refine the chip removal parameters by selecting improved milling plans and tooling. A numerical model was presented by Franco et al. [19] to predict the surface profile and Ra as a function of factors such as feed, cutting tool geometry, and tool errors in the face milling process for structural carbon steel. Another model Wojciechowski et al. [20] proposed includes the effects of machining parameters, tool wear, and dynamic phenomena related to instantaneous tool deflections. Also, Wojciechowski et al. [21] also proposed a numerical model for hardened steel that predicts the Ra produced in ball end milling, including surface inclination angle, tool wear, and minimum uncut chip thickness. Pelayo et al. [22] suggested a numerical model for Ra values for Ti-6Al-4V and Al7075 T6 in the milling process. Close agreement between the predicted Ra values and the experimental ones was found. In most cases, the relative error was found to be below 10%. In general, for metallic materials, good agreement was found between the theoretical and experimental results obtained for Ra . However, there are a limited number of studies in the literature on predicting Ra in the machining processes of the PP and its composite materials, as well as ensuring the reliability of the technological equipment [23, 24]. Dhokia et al. [25] developed a Ra prediction model based on neural networks to extract information about the machining of PP materials, and the Ra was predicted successfully. Abhishek et al. [26] proposed a hybrid technique of adaptive network-based fuzzy inference system and genetic algorithm for estimating axial force and Ra in the drilling of glass-reinforced polymer composite. A regression model was developed using experimental data for estimating the Ra in the drilling process of PP composite by Mudhukrishnan et al. [13]. The developed model has a high R-squared value which shows the strong relationship between the model and the response variables. When the studies in the literature are analyzed, this work fills the gap in the milling of pure polypropylene (Pure PP) and carbon black reinforced polypropylene (CB-PP) composites.

3 Research Methodology 3.1 Experimental Section This study is based on the measurement, determination, and interpretation of the surface roughness, exit burr size, and chips formed on the surface of the plastic material pure polypropylene (Pure PP) and carbon black reinforced polypropylene (CB-PP) as a result of the milling process. Plain pure PP material is Capilene® brand, and CB-PP material is Premix brand PRE-ELEC® PP 1399 model. The technical properties of these materials used in experimental studies are given in Table 1.

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Properties

Pure PP

CB-PP

Specific weight (g/cm3 )

0.92

0.98

Yield strength (MPa)

22

26

Young’s modulus (MPa)

1100

1400

Strain failure (%)

> 50

30

Yield strain (%)

10

10

Bending temperature (°C)

100–112

83

Volume resistivity (·cm)

> 1013

103 –107

Surface resistance ()

> 1013

105 –108

The workpiece is 150 × 150 × 10 mm in size and was melted in a plastic injection machine at 220 °C and produced in a mold at 60 °C temperature by applying 70 MPa injection pressure. The PP granules were dried at 60 °C for 2 h before melting. The experimental setup is shown in Fig. 1. Milling experiments were conducted on the Biesse Rover B model woodworking CNC milling machine. All experiments used a WC (Tungsten carbide) double edges end mill of the Leitz brand with a diameter of 9 mm was used.

Fig. 1. Schematic representation of the experimental setup.

The total and cutting lengths were 55 mm and 25 mm, respectively. All experiments were conducted by employing the same tool holder and at the same tool overhang value. The workpieces were fixed to the CNC worktable by screwing from two corners on an MDF plate. Milling experiments were carried out on a total of 18 experiments by opening channels along the plate under three different cutting speeds (70, 100, and 130 m/min),

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three different feeds (0.4, 0.5, and 0.6 mm/rev), and 1.5 mm constant radial depth of cut given in Table 2. The cutting parameters were selected based on the manufacturer’s recommendations and the state-of-the-art in machining PP materials. After the experiment, the Mitutoyo Surftest SJ-210 model surface roughness measuring device was used to measure the surface roughness of the milled samples. The images of chips and burrs formed during the milling process were investigated using SOIF XLB45-B3 model digital stereo microscope. Table 2. Milling experiment plan for pure PP and CB-PP. Material

Exp. No

Cutting speed (m/min)

Feed (mm/rev)

Radial Depth of Cut (mm)

PP

1

70

0.4

1.5

2

70

0.5

1.5

3

70

0.6

1.5

4

100

0.4

1.5

5

100

0.5

1.5

6

100

0.6

1.5

7

130

0.4

1.5

8

130

0.5

1.5

9

130

0.6

1.5

10

70

0.4

1.5

11

70

0.5

1.5

12

70

0.6

1.5

13

100

0.4

1.5

14

100

0.5

1.5

15

100

0.6

1.5

16

130

0.4

1.5

17

130

0.5

1.5

18

130

0.6

1.5

CB-PP

3.2 Mathematical Modelling of Surface Roughness Reported studies revealed the significance of surface roughness (Ra ) in determining a product’s quality. Until now, a limited number of works have been conducted on the predictive modeling of Ra for slot-milled operations on PP and its composites [27]. In this study, the Ra model aims to determine a relationship between Ra and the cutting parameters (feed, cutting speed, and radial depth of cut) for pure PP and CB-PP milling. A universally accepted empirical relationship has been proposed by Sagherian

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and Elbestawi [28], which allows the estimation of Ra in milling, which correlates proportionally with the radial depth of cut and feed and inversely proportionally with cutting speed. The proposed mathematical model is given in Eq. 1.   a b /Sc RPredicted = K * F * D (1) a where F, D, and S are feed, radial depth of cut, and cutting speed, respectively. K, a, b, and c are constants. Dhokia et al. [25] also used this mathematical model for PP material, and model constants were optimized using a genetic algorithm. The prediction model was tested on the experimental validation set. The mean deviation of the prediction result from the measured results was 8.43% which is small and presents the efficacy of the prediction model. Therefore, this study found the constant model parameters using the pseudo-code for a genetic algorithm written in Matlab. The percent deviation between the Ra Experimental and the Ra Predicted is given in Eq. 2.    Experimental /RPredicted ∗100 (2) % deviation = RPredicted − Ra a a The purpose of applying the optimization is to obtain the constants for the Ra Experimental prediction model by minimizing the error value between the Ra and the . RPredicted a

4 Results and Discussion 4.1 Experimental Surface Roughness The Ra results obtained in the experiments are given in Fig. 2. When the results were examined, the roughness value of CB-PP was measured lower than pure PP at all cutting speeds and feeds, which shows that the reinforcement of carbon black to pure PP provides an improvement in the roughness values. As it is known from the previous studies in the literature, the Ra value improved as the feed decreased and the cutting speed increased. In the milling of pure PP material, while the feed was constant at 0.4 mm/rev, the Ra decreased from 2.276 µm to 2.192 µm by increasing the cutting speed from 70 m/min to 100 m/min. Moreover, the Ra decreased from 2.192 µm to 2.170 µm by increasing the cutting speed from 100 m/min to 130 m/min. In the milling of CB-PP material, while the feed was constant at 0.4 mm/rev, the Ra reduced from 2.253 µm to 2.188 µm by increasing the cutting speed from 70 m/min to 100 m/min. The parameter Ra decreased from 2.188 µm to 2.148 µm by increasing the cutting speed from 100 m/min to 130 m/min.

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Fig. 2. The surface roughness results obtained in the experiments.

4.2 Chip Morphology Investigation of the chip forms in machining operations is essential for better understanding the cutting parameters’ effects. Chip formation is affected by many factors, including the cutting tool’s rake angle, cutting parameters, and workpiece material [29]. In general, as the cutting speed increases, the chips begin to be in a flatter form than “c” in terms of geometric shape, while their folds increase as the feeds increase at the same cutting speed. The chip morphology obtained in the experiments is given in Fig. 3 and Fig. 4 for pure PP and CB-PP, respectively. It has been observed that spiral and long comma chip morphologies occurred in Pure PP material, while only long comma chip morphologies occurred in CB-PP. It was observed that the chips of pure PP were affected by heat and deformed more, while the chips that occurred due to the good thermal conductivity of CB-PP were less deformed. 4.3 Exit Burr Formation Exit burr size depends on many factors, such as material, cutting speeds, feed, the wear of the tooltip, and temperature. To examine the effect of the selected parameters in this study on the exit burr formation form, the burr forms that occurred in the CB-PP material are given in Fig. 4 and Fig. 5, respectively, at different cutting speeds and feed. Quantitative analysis of the top burr was challenging because of the wavy nature of the top burr. For this reason, qualitative analysis was performed in this work in the fashion of conducted study by Kuram [15]. It was seen in these figures that there was a relationship between cutting speed, feed, and burr size. As the cutting speed increased (Fig. 5), the

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Fig. 3. Chip morphologies for pure PP in different cutting parameters.

Fig. 4. Chip morphologies for CB-PP in different cutting parameters.

exit burr size increased. Also, the exit burr size increased as the feed increased at the same cutting speed (Fig. 6). In addition, the surface of the slot bases was examined, given in the below figures. Friction between the PP workpiece and tool caused heat generation at the contact zone, which softened the polymer matrix. Consequently, burr occurred on the slot surface, which reduced the surface quality [30–32].

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Fig. 5. Exist burr formation for respectively increasing cutting speed.

Fig. 6. Exist burr formation for respectively increasing feed.

4.4 Prediction of Surface Roughness The determined constant parameters of the Ra mathematical model are given in Table 3. values were calculated for each After iterations determined the parameters, the RPredicted a experiment. Table 3. Optimized constant parameters of surface roughness mathematical model. K

a

b

c

0.38

0.28

0.17

0.01

The experimental and predicted Ra and the percentage deviation values between the values are given in Fig. 7. When the experimental and predicted Ra values were examined, an average of more percentage deviation occurred in PP than in CB-PP. The mean deviation of the Ra Predicted model over the Ra Experimental is obtained as 11.1%.

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Fig. 7. Percent deviation between measured and predicted surface roughness values.

5 Conclusions In this study, it was aimed to fill the gap in the milling of pure polypropylene (Pure PP) and carbon black reinforced polypropylene (CB-PP) composite by examining the effects of cutting speed and feed on the chip morphology, top burr formation, experimental and predicted surface roughness values. After evaluating the data collected through the present study, the following conclusions are given below: • As the cutting speed increases at the same feed, the surface roughness decreases for pure PP and CB-PP. • The surface roughness value increases for pure PP and CB-PP as the feed increases at the same cutting speed. • The surface roughness of CB-PP material is low when pure PP and CB-PP milled under the same machining parameters are compared. • Based on the experimental results, the optimal cutting parameters were obtained as the cutting speed of 130 m/min and the feed of 0.40 mm/rev for both PP materials. • The exit burr sizes increased under the same feed and different cutting speeds. • The exit burr sizes increased under the same cutting speed and different feeds. • Spiral and long comma chip morphologies occurred in Pure PP material, while only long comma chip morphologies occurred in CB-PP. • The mean deviation of the predicted surface roughness model over the experimental surface roughness is obtained as 11.1%. This mathematical model gives an idea about the surface roughness of the materials that are planned to be processed.

Acknowledgment. The research was partially supported by the Slovak Research and Development Agency (project SK-UA-21–0060) and the Ministry of Education, Science, Research and

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Sport of the Slovak Republic (projects VEGA 1/0080/20 and KEGA 028TUKE-4/2021). The results were partially obtained within the project “Intensification of production processes and development of intelligent product quality control systems in smart manufacturing” ordered by the Ministry of Education and Science of Ukraine (State Reg. No. 0122U200875).

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Improvement of a Two-Stage Drive for Multioperational Milling Machine Oleg Krol1(B)

, Volodymyr Sokolov1

, and Petko Tsankov2

1 Volodymyr Dahl East Ukrainian National University, 59-a, Central Ave.,

Severodonetsk 93400, Ukraine [email protected] 2 Trakian University, 38, Graf Ignatiev Street, Yambol, Bulgaria

Abstract. The issues of creating drive mechanisms for drilling-milling-boring machine tools with a vertical spindle axis of rotation are considered. 3D modeling of the spindle assembly structures with a power bevel gear and the rotary table with a power worm gear, as the main forming elements in processing housing parts, has been carried out. The toolkit of the integrated computer-aided design system and specialized 3D operations in the express mode of multivariate modeling were used. A new approach is proposed to improve the kinematic parameters of the shaping unit drive devices for the machine tool in a two-criteria formulation. A variant of partitioning the gear ratio of a two-stage worm-bevel drive into stages from the condition of their equal strength is considered, under which favorable conditions for lubricating the wheels of both stages are simultaneously realized. An analytical form is presented for determining the energy parameters at various drive stages on the base. The main indicator of the performance of worm and bevel gears, respectively, is the contact endurance of the wheel’s teeth of these gears, provided that the contact stresses are equal. A numerical analysis was carried out for several combinations of worm wheel materials and bevel wheel tooth hardness. Keywords: 3D Modeling · Stage · Gear Ratio · Uniform Strength · Product Innovation · Manufacturing Investment

1 Introduction In modern machine-tool building, the share of multifunctional metal-cutting machinetool complexes that implement the production of various structures with complex shaping movements is increasing [1]. The efficiency of the process of creating a metal-cutting machine depends on the level of quality of forming units [2]. The spindle unit of a vertical milling machine [3] with a drive bevel gear, which is the executive housing of the machine’s carrier system, has a decisive influence on the main output parameters and machine performance as a whole [4]. At the same time, additional modular equipment [5] is being used more and more. Expanding technological capabilities – various tool magazines, overhead rotary tables, and spindle heads with built-in precise positioning units [6]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 264–273, 2023. https://doi.org/10.1007/978-3-031-32767-4_25

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In the context of the production of an increasing number of machine tool sizes and a constant change in the configuration of machined parts [7], it is promising to design and manufacture a line of rotary tables equipped with hydromechanical drives [8]. A rotary table with a driven worm gear significantly impacts the machine’s flexibility and positioning accuracy [7]. The calculation of the balance of the relative compliance of the carrier system of the SVM1F4 machine tool showed that the workpiece and its elastic connections with the turntable are up to 22% (of the carrier system total compliance) when loaded by the weight of the nodes and loaded in the processing zone equal to 10 kN in three coordinates (X, Y, Z) [6]. The relative flexibility of the rotary table is up to 36%, and the elastic connections of the rotary table with the cross table are up to 10% under the same loading conditions. The design of machine tool gear drives [9] based on the use of power bevel and worm gears is faced with the problem of uneven strength of their steps, which indicates the irrational use of the bearing capacity of steps, and hence the gearboxes as a whole [10]. The design and engineering analysis of the structures of the forming units of a machine tool is a multicriteria task, including decision-making related to the level of contact stresses, dimensions, lubrication conditions, etc. [11]. So, when assessing the performance of the main power drives of a vertical milling machine, it is essential to determine the stress-strain state of the bevel gear of the main drive and the worm gear of the turntable drive [12]. The resulting stress maps of mechanical transmission elements allow the designer to search for ways to achieve one of the main criteria for structure effectiveness– equal strength.

2 Literature Review Quite a lot of research is devoted to the problems of improving the designs of machine tool drives with different visions of solving these design problems. The paper [13] considers the problem of finding the minimum mass of a two-stage gear drive, which includes three assembly units of shafts mounted on tapered rolling bearings. Gears and corresponding fixing elements are mounted on the shafts under consideration, which are considered in formulating the problem [13]. The authors have developed a set of Cambrian v.3.2 optimal design programs based on the Monte Carlo method to determine the minimum mass of the structural elements of the considered gearbox. At the same time, the problem of rational design of drive gears was solved according to the criterion of the minimum volume of the internal cavity of the gearbox housing. The authors note a certain similarity of the optimal decisions made at various stages of the considered gearbox. The paper [14] considers the problem of optimal design of a two-stage bevel-helical gearbox according to the minimum mass criterion. Such a design option simultaneously provides a condition for operability in terms of the characteristics of the contact strength of the wheels. This task is reduced to finding the minimum dimensions, particularly the length of the gearbox. The authors [14] formed an analytical form that reflects the dependence of the length of the gearbox on the allowable contact stress, the geometric parameters of the wheels, and the division of the gear ratio into steps. A CAD-9 program has been developed for designing a bevel-helical and two-stage spur gearbox according

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to an expanded and coaxial scheme. The design results confirm the monotonous decrease in the gear ratio by stages while minimizing the gearbox’s dimensions (and hence the mass). Another software product was proposed by the American Association AGMA, which is used in the initial stages of designing gearboxes based on various gear designs [15]. This software called “Design optimization Gearbox KISSsoft” aims to solve the same problem presented in [13] using the objective function of minimizing the internal volume of the gearbox housing. Limiting values of contact and bending stresses arising in the contact zone of the gearbox’s mechanical gears are considered limitations. As a result of using this software product [15], the main parameters of the gear wheels of the reducer and, in particular, the rational value of the module and the width of the crown gear elements are determined. Under the conditions of this formulation (objective function and constraints), kinematic characteristics and their division into gear stages are obtained. Formulation of the problem. To improve the design of a two-stage gearbox based on the search for the optimal ratio of gear ratios that achieve equal strength of the elements of bevel and worm gears and increased performance under lubrication conditions.

3 Research Methodology 3.1 Three-Dimensional Models of Forming Nodes for Multioperational Machine As a design object, a specialized multi-operational CNC machine of the second standard size, milling-drilling-boring type, based on the SVM1F4 model, is considered. The machine can process vertical, horizontal, and inclined planes, shaped surfaces, holes, and grooves by various technological methods: milling, drilling, and reaming. The design of the considered CNC machine (Fig. 1) has such specific units as a hydraulic unit [16] for precise positioning of the spindle and modeling elements that implement the main shaping movement [17, 18]. The drive of the main movement uses an adjustable drive based on a DC motor and a thyristor voltage converter. For automated manipulation of workpieces and cutting tools of various sizes and shapes, this machine uses additional modular equipment and fixtures [19], which allow the implementation of different technological operations without reinstalling the workpieces [20]. To analyze the performance of the design and select the optimal design option for a specialized multi-operational CNC machine tool (model SVM1F4) equipped with an automatic tool changer and a rotary table, 3D models of the main forming units and the machine drive was created in Creo Parametric CAD (Fig. 1) [21]. While creating three-dimensional models of forming nodes, advanced features of the system were used, in particular, generative design and real-time modeling. Based on the geometric core of the Creo system, a geometric model is formed, and the geometric parameters of the created object are determined [22]. To simplify the process of building a 3D model and performing engineering analysis in the CAE system, the functionality for removing holes and roundings from the model is effectively used, which leads to a simplification of the model and the successful application of the express procedure for the multivariate design of forming units.

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An effective tool is also the rounding modification procedure, typical for such complex parts as the housing of forming units processed on milling and grinding machines [23]. At the same time, the rounding design has been improved when creating elements of the original 3D model. In this case, you can use the geometric kernel to build previously inaccessible combinations of fillets when designing molds for the table housing [24]. For such a relatively complex three-dimensional operation as the construction of surface intersection curves, the corresponding Creo CAD Helical Sweep algorithm was used when the intersection curve passes through the surface pole or coincides with one of its reference objects [22]. It is also possible to reduce the design time for three-dimensional models of complex surfaces of worm gear elements by introducing a unique technique for constructing gear rims by simulating gear milling of worm wheels [25]. When constructing them (Fig. 1, d), the multivariate mode of forming the teeth of the coils is quite effective, including the option “Build without teeth (coils)”. In addition, the Tapered Extrude operation makes it possible to tilt the side surfaces when building a model of a bevel gear with a circular tooth [26]. When building and researching with the help of these options, prerequisites are created for multivariate design and simulation of various designs of mechanical transmissions. A three-dimensional model reflecting the design features of the forming units, with extensive use of the capabilities of the geometric and parametric cores of Creo CAD, is shown in Fig. 1. 3.2 Partitioning the Gear Ratio of the Worm-Bevel Drive One of the disadvantages of multi-stage machine drives [27] is the uneven strength of the stages, which indicates the irrational use of the carrying capacity of individual stages, and hence the drives as a whole. To increase the carrying capacity, let’s consider how to carry out the procedure for partitioning the gear ratio of the Worm-Bevel Drive (WBD) into its steps from the condition of their equal strength (Fig. 2). On Fig. 2 shows the kinematic scheme of the WBD drive. The task is reduced to finding the gear ratios of the first stage UI - worm gear [28], and the second stage UII – bevel gear on tapered bearings, from the condition of equal strength, that is: T3(I ) = T3(II ) . Here T3(I ) and T3(II ) are torques (N · m) on the 3rd shaft of the WB gearbox, found by the main criterion for the performance of worm and bevel gears, respectively - the contact endurance of the teeth of the wheels of these gears, provided that the contact stresses σH (i) = [σH ]i , i = I , II (MPa).   3 aw 1 [σH ]I · z2 /q 2 = UII · · · ; 5300 1 + z2 /q KHI   3 de2 θH [σH ]II 2 T3(II ) = · · . UII 2120 1000 · KHII 

T3(I )

(1)

(2)

Index I refers to the worm gear; index II refers to the bevel gear). Here UII = U /UI – the relationship between the gear ratios U of the gearbox and its stages UI and UII ; aw – center distance, mm (determined from the condition of contact endurance; q – worm diameter coefficient; z2 – the number of teeth of the worm wheel.

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Fig. 1. 3D modeling of the machine forming units: a – spindle head; b – rotary table; c – worm transmission; d – bevel gearing.

Fig. 2. Kinematic scheme of the worm-bevel drive.

The recommended ratio of parameters for power worm gears [26, 29]: z2 /q = 4; KH – load factor, load factors are taken as averaged values in the usual ranges for these gears: KH (I ) ≈ (1.1...1.2), KH (II ) ≈ (1.3...1.4); de2 – external dividing diameter of the bevel gear; θH – coefficient of worm deformation: θH = c1 + c2 · uII , where constants cI and cII are taken depending on the hardness of the teeth of the bevel gear [30]. After substituting the indicated parameters into Eqs. (1) and (2), the desired condition for equal strength takes the form: U 2 · UI = 0.038 · c1 · U1 + c2 · U



[σH ]II [σH ]I

2   de2 3 · . aw

(3)

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3.3 Effective Lubrication of the Drive Mechanical Gears The ratio of dimensions (de2 /aw )3 , included inequality (3). It is advisable to assign from the condition of equality of the diameters of the bevel wheel de2 and the dividing diameter of the worm wheel de2 : de2 = d2 .

(4)

which will ensure the same immersion depth in the oil bath; (Fig. 2, the oil level is shown by a dotted line). We will find the parameters d2 and de2 use the main criteria for the performance of worm and bevel gears – the contact endurance of the teeth at the maximum level of their loading, that is, at σH (I ) = [σH ]I and σH (II ) = [σH ]II :    1 + z2 /q 3 5300 · · KH (I ) · T2 = [σH ]I ; (5) σH (I ) = z2 /q aW  1000 · KH (II ) · T3 · UII = [σH ]II . (6) σH (II ) = 2120 · 3 θH · de2 For equality (5): aW = CI · U −1/3 · UI

1/3

q = z2 /4; where CI = 5 ·

 3

5300 4·[σH ]I

2

;

KH (I ) ≈ 1, 1;

· KH (I ) = const .[3]



5300 4·[σH ]I

2

T2 ≈ T3 /UII

(7)

· KH (I ) = const . At the

same time:

(8)

where . For equality (6): KH (II ) ≈ 1, 3;

1/3

de2 = CII · UII 

where CII = 1650 ·

3

KH (II ) [σH ]2II

−1/3

· θH

;

θH = c1 + c2 · UII = c1 + c2 · U · UI−1 . (9)

= const; c1 and c2 – constants depend on the hardness of

the gear teeth and the bevel gear wheel (Table 1). Here: M1 /M2 – martempering gear teeth (1) and wheels (2) of the bevel gear; HFC1 /M2 – HFC hardening (HFCH) gear teeth and martempering wheel teeth; HFC1 /HFC2 – HFC hardening gear and wheel teeth.

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Hardening gears/wheels

c1

c2

M1/M2

1.22

0.21

HFC1/M2

1.13

0.13

HFC1 /HFC2

0.81

0.15

To determine the center distance aw according to the equal strength criterion, we represent it as a function of d2 , that is aw = aw (d2 ): aw = 0.5 · m · (z2 + q) = 0.625 · d2 . Since de2 = d2 , then aw = 0.625·de2 . As a result, (de2 /aw )3 = (de2 /0.625 · de2 )3 ≈ 4.1. Another criterion for finding the center distance aw of a worm gearbox is the maximum heat transfer to the value of the average oil temperature. This is not only the area of the free surface of the gearbox but also the criterion for the lubrication effectiveness in the oil bath. After substituting this value U into Eq. (3), we obtain the desired equation for calculating the gear ratio UI of the gearbox WBD, which ensures the equal strength of both stages of the gearbox U2 ≈ 0.156 · UI · (c1 · UI + c2 · U )



[σH ]II [σH ]I

2 .

(10)

4 Results and Discussion Numerical analysis of dependence (10) was performed for 4 combinations of worm gear materials and bevel gear teeth hardness. The results are presented in Table 2. The limiting value U = 220 taken in the calculations is explained by the fact that for gear ratios of worm and bevel gears in power drives, it is recommended to be limited to the values UI < UI (max) = 80 and U2(max) = 7; (dashes in the table correspond to the values UI > Umax ) For the convenience of using this method of partitioning the gear ratio into stages of the WBD gearbox, Eq. (10) is approximated by a linear function (approximation error is less than 2%). Worm wheel teeth made of high tin bronze: UI = 0.35 · U – cemented bevel gear teeth; UI = 0.47 · U – HFCH bevel gear teeth. Tinless bronze worm wheel teeth: UI = 0.37 · U – cemented bevel gear teeth; UI = 0.49 · U – HFCH bevel gear teeth. The upper values Ui in Table 2 refer to case-hardened bevel gear teeth, and the lower values refer to HFC hardened. The values U2 = U /UI for the quantities U and UI indicated in the table limits within: UII ≈ 2.0...2.9. Comparing these approximations shows that the main values lead to the greater influence exerted on the teeth material of the worm and bevel gears elements and the choice of their hardening method.

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Table 2. Values for WBD gearboxes from the condition of equal strength of steps. U

Worm gear teeth material High tin bronze

Tinless bronze

60

21/28

22/30

80

28/37

29/39

100

35/46

37/49

120

41/56

44/59

140

48/65

51/69

160

55/74

59/79

180

62/-

66/-

5 Conclusions A comprehensive project of a two-stage worm-bevel drive of a multioperational machine has been developed. A set of 3D models of a spindle unit equipped with a hydraulic unit for precise spindle positioning and controlled by a drive based on a DC motor with a thyristor voltage converter has been created. Three-dimensional modeling was carried out using effective three-dimensional operations based on the modification of fillets when modeling the drive housing, a method of imitating gear milling of worm wheels when constructing gear rims. Tapered Extrude operations with the option of tilting the side surfaces when building a model of bevel gear with a circular tooth. When designing a three-dimensional model of the hydraulic unit, the Multibody Design procedure was used. An idea was put forward, and an algorithm was implemented for breaking down the gear ratio of a worm-bevel drive into stages from the condition of their equal strength. To provide numerical results, an analytical form is proposed, which also considers another criterion - effective lubrication in an oil bath, which increases the carrying capacity of the drive under consideration. Calculated dependencies for gear ratios UI and UII stages of the DWB are obtained that satisfy this condition under the current restrictions on the values UI and UII . At the same time, two variants of bevel gear are considered – with straight and circular teeth. Numerical analysis of the kinematics for the gear ratios on the drive stages was performed for 4 options for combining the worm gears’ materials and the hardness of the teeth of the bevel gears. At the same time, the limiting values of gear ratios in machine tools’ power drives were considered.

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18. Monkova, K.: Condition monitoring of Kaplan turbine bearings using vibro-diagnostics. Int. J. Mech. Eng. Robot. Res. 9 (8), 1182–1188 (2020). https://doi.org/10.18178/ijmerr.9.8.11821188 19. Bass, D., Riedl, R., Slagle, N.: 4th and 5th Axis Rotary Table Mechanical Engineering Department. California Polytechnic State University, San Luis Obispo (2016) 20. Karpus, V.E., Ivanov, V.A.: Choice of the optimal configuration of modular reusable fixtures Russ. Eng. Res. 32(3), 213–219 (2012). https://doi.org/10.3103/S1068798X12030124 21. Shin, R.H.: Parametric Modeling with Creo Parametric 7.0. SDC Publisher, Mission (2020) 22. Wyndorps, P.: 3D design with Creo Parametric and windchill: PTC Creo 4.0 and PTC. EuropaLehrmittel, Taschenbuch (2022). [in German] 23. Syzyi, Y., Ushakov, O., Slipchenko, S., Basova, Y., Ivanova, M.: Simulation of the contact temperature in the cylindrical plunge grinding process. Diagnostyka 21(2), 77–86 (2020). https://doi.org/10.29354/diag/122532 24. Ivanov, V., Pavlenko, I., Kuric, I., Kosov, M.: Mathematical modeling and numerical simulation of fixtures for fork-type parts manufacturing.In: Knapˇcíková, L., Balog, M., Eds Industry 4.0: Trends in Management of Intelligent Manufacturing Systems EAI/Springer Innovations in Communication and Computing EICC, pp. 133–142. Springer, Cham (2019). https://doi. org/10.1007/978-3-030-14011-3_12 25. Radzevich, S.: Theory of Gearing: Kinematics, Geometry, and Synthesis, 2nd Edn. CRC Press Boca Raton, Florida (2018) 26. Litvin, F.L., Qi, F., Fuentes, A.: Computerized Design, Generation, Simulation of Meshing and Contact of Face-Milled Format Cut Spiral Bevel Gears. NASA/CR-2001 (2001) 27. Demianenko, M., et al.: Impact of dynamic characteristics of gears on the reliability of prilling equipment. In: Knapcikova, L., Perakovi´c, D., Perisa, M., Balog, M. (eds.) Sustainable Management of Manufacturing Systems in Industry 4.0. EAI/Springer Innovations in Communication and Computing, pp. 197–211. Springer, Cham (2022). https://doi.org/10.1007/978-3030-90462-3_13 28. Shevchenko, S., Mukhovaty, A., Krol, O.: Geometric aspects of modification of tapered roller bearings. Procedia Eng. 150, 1107–1112 (2016). https://doi.org/10.1016/j.proeng.2016.07. 7.221 29. Krivosheya, A.V., Voznyy, V.V., Melnyk, V.E.: Analysis of the gear tooth gearing by the module m = 2.625 mm of hydraulic pumps. J. Eng. Sci. 4(1), A11–A15 (2017). https://doi. org/10.21272/jes.2017.4(1).a2 30. Solid cutters for bevel gears. Standard ASP 2023-special S390, Padova (2014)

Information Technologies of the Analysis for Models to Ensure Quality Characteristics of the Working Surfaces During Mechanical Processing Maksym Kunitsyn(B)

, Anatoly Usov , and Yuriy Zaychyk

Odessa Polytechnic National University, 1, Shevchenko Ave., Odessa 65044, Ukraine [email protected]

Abstract. In this work, information technology was developed to analyze and synthesize models to ensure the quality characteristics of the working surfaces of products during mechanical processing. For this, the problem of thermoelasticity for bodies weakened by inhomogeneities is solved. The work aims to create information technologies about the state of the working surfaces of products during their mechanical processing. It considers the influence of inhomogeneities in the materials of structural elements on their functional gradient properties to ensure high efficiency in designing the appropriate type of technological systems. Mathematical models of the dynamics for thermomechanical processes accompanying the mechanical processing of products from materials of the heterogeneous structure were improved. A spatially non-stationary formulation of the problem was used based on systems of differential equations of thermoelasticity in partial derivatives and discontinuity conditions on defects of the elastic-deformation characteristics of the processed material. This allows for improving the accuracy of the identification of mathematical models generally. The developed mathematical models of the system for evaluating the effectiveness of the functioning of technological complexes of mechanical processing make it possible to determine the relationship between the parameters of the state of the processed surfaces and the main controlling technological characteristics that provide the necessary properties of functionally gradient materials. Keywords: Structural · Integral · Thermal · Problem Crack · Layer · Surface · Defects · Stress State · Process Innovation

1 Introduction The most effective among the available arsenal of methods for researching physical processes occurring in environments of heterogeneous structure and electromagnetic signals in environments with variable characteristics, as well as the formation of defects on the working surfaces of products made of structural materials with various types of inhomogeneities of hereditary origin, so far remains the development information technologies for the analysis and synthesis [1] of models for ensuring the quality characteristics of the working surfaces of products [2] during mechanical processing [3]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 V. Ivanov et al. (Eds.): DSMIE 2023, LNME, pp. 274–285, 2023. https://doi.org/10.1007/978-3-031-32767-4_26

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2 Literature Review The functional purpose of structural elements depends significantly on the defectiveness of the structure of the materials from which they are made. In natural materials, there are many micro defects, the development of which (under the influence of load) leads to the loss of functional capacity [3]. In this work, based on the analysis and synthesis of models for ensuring the quality characteristics of the working surfaces of products during mechanical processing, the information technology of functional relationships of technological parameters with thermomechanical ones is presented. Processes accompanying processing [4]. Thanks to the method of singular integral equations, a unified approach to solving problems of thermoelasticity for bodies weakened by inhomogeneities is presented [5]. The work aims to create an information technology that considers the influence of inhomogeneities in the materials of construction elements on their functional gradient properties, including strength. The choice of studying structural elements’ strength and destruction depends on the object’s size. Micro-examinations are related to inhomogeneities formed in the surface layer when receiving the workpiece in manufacturing structural elements [6]. Taking defectiveness into account allows us to adequately consider the mechanism of destruction of objects as a process of crack development [7]. Studying the limited state of fundamental elements weakened by defects and the construction of information technology will create the prerequisites for a good display of the destruction mechanism. In addition to the deterministic approach, it is also necessary to consider a probabilistic statistical analysis of the effect of material inhomogeneities of structures on their functional properties [8].

3 Research Methodology Let us consider some provisions necessary for modeling systems in a non-homogeneous space. The inner and outer regions separated by a system of closed contours included in the contour L are denoted by S + and S − , respectively, while we consider that the outer region S − contains an infinitely distant point of the complex plane C. Let the function f (t) ∈ H (μ) (L), where H (μ) (L) is the set of Hölder continuous functions on the contour, that is, such that meet the conditions [9]: |f (t2 ) − f (t1 )| ≤ A|t2 − t1 |μ ; A > 0; 0 < μ < 1. The constant A here is called the Hölder constant, and μ is the Hölder exponent. At the same time, at the points z ∈ L of the complex plane C, there is a Cauchy-type integral as the main value: F(z) =

1 2π i

 L

f (τ )d τ ,z ∈ / L. τ −2

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Moreover, at the points z ∈ L, this integral exists in the usual sense, and on the contour L, the relation [10] is valid:  1 1 1 f (τ )d τ f (τ ) − f (t) L = d τ + f (t), 2π i τ − t 2π i τ −t 2 L

The joint application of the basic provisions of the flat problem of the theory of thermoelasticity and the theory of functions of a complex variable or the method of singular integral equations allows for estimating the stress-strain state near a defect of the crack or hard inclusion type. In the asymptotic approximation, the combined stress tensor and displacement vector near the vertices of a rectilinear hard inclusion or crack is given as follows [11]:      KI± r 2(x + ρ ∗ ) + 1 cos β2 − cos 3β u 2   4G − = ∗ v ρ 2π 2(x − ρ ∗ ) − 1 sin β2 − sin 3β 2 K± − ρII∗



r 2π

   3/2 + 0 r , 2(x + ρ ∗ ) + 1 sin β2 + sin 3β 2   − 2(x − ρ ∗ ) − 1 cos β2 − cos 3β 2

where (u, v) – the components of the vector of elastic displacements; KI± , KII± – stress intensity coefficients (coefficients for the singular part of stresses); the signs “+” and “−” correspond to the right and left vertices of the linear crack-like defect. If ρ∗ = −1, the given formulas show the known asymptotic distribution near the crack, and if ρ∗ = x, where x = 3−4μ for plane strain and x = (3 − μ)/(1 − μ) We obtain the corresponding asymptotics for the plane stress state in the case of solid inclusion [4, 12]. The simulation results make it possible to effectively assess the influence of foreign fillers on the loss of strength of an elastic body containing the specified imperfections. In turn, the exact determination of the order and character of the singularity of stresses near the vertices of an acute-angle imperfection in an elastic material, presented in an analytical form, is required in fracture mechanics for the formulation and recording of the appropriate criterion strength ratios. The Riemann boundary value problem consists of the following: it is necessary to find a piecewise holomorphic function (z) from the jump line C, which has a finite order at infinity, subject to the boundary condition: + (t) = G(t)− (t) + g(t), t ∈ C,

(1)

where G(t), g(t) – functions of class H0 given on C, which are called the coefficient and the free term of the problem. Equality (1) must hold for all normal points of line C. Consider the previous corresponding (1) homogeneous Riemann boundary value problem with the condition: + (t) = G(t)− (t), t ∈ C.

(2)

A solution (t) of a homogeneous problem [13] is called a canonical solution if not only (t) but also 1/(t) – piecewise holomorphic functions.

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The singular integral equation is equivalent to the Riemann boundary value problem: + (x) = G(x)− (x) + g(x)

(3)

4 Results and Discussion The strength and destruction of real structural elements are significantly influenced by their materials’ heterogeneity and defectiveness (structural, technological, and deformation damage). Therefore, when determining the bearing capacity of such elements, there is a need to consider the micro-heterogeneity and defectiveness of materials and products. Among various defects, cracks, pointed cavities, and extraneous inclusions are essential, as they cause a significant concentration of stresses in structural elements. Developing such defects into an unstable trunk crack leads to local or complete destruction. Taking defectiveness into account makes it possible to accurately determine structural elements’ bearing capacity. The crack formation intensity is determined by the properties of structural components and their orientation in the matrix of metals prone to this type of defect. Therefore, it is advisable to consider the influence of structural parameters on the main criterion of local destruction of K1C . To control the quality of a part during machining, it is necessary to study the patterns of formation of the thermomechanical state of the surface layer, taking into account its heterogeneity. The solution to these problems allows us to analyze the processes accompanying the mechanical processing of products and synthesize devices for automatic stabilization of the qualitative characteristics of the working surfaces of structural elements. When choosing and justifying the mathematical model, it was considered that thermal and mechanical phenomena accompany the process of machining parts. However, temperature fields have a predominant effect on the stress-strain state of the surface layer. Considering that the bulk of the surface layer of the metal during processing is in an elastic state, it is possible to use the model of a thermoelastic body, which reflects the relationship between mechanical and thermal phenomena at finite values of heat fluxes. Since information about the distribution of temperatures and stresses along the depth and in the direction of tool movement is essential for studying the thermomechanical state of working surfaces, a plane problem is considered. When compiling the calculation scheme (Fig. 1), it is assumed that the part can be represented in the form of piecewise homogeneous conditional layers with different properties located on the main matrix material, which makes it possible to study thermomechanical processes when processing parts with several types of coatings, thickness ak applied to the base material. Such a scheme predetermines the thermal and deformation conditions for the conjugation of layers along their interfaces - ak . The influence of inhomogeneities arising in the surface layer of the product during the technological process is considered in the model by the presence of inclusions and defects in the surface layer (Fig. 1). The system of equations that determine the thermal and stress-strain state when grinding the surface of parts with coatings, the upper layer of which has inhomogeneities such as inclusions and cracks, contains:

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Fig. 1. Calculation scheme for determining the mutual influence of defects on the intensity of the formation of cracks of the treated surface from the thermomechanical load formed during grinding.

Equation of non-stationary heat conduction:  2  ∂ T ∂ 2T ∂T 0≤x a∗ ∂x λ ∂x

σx (x, y, τ )|x=0 = ττ y (x, y, τ ) x=0 = 0

(8) (9)

where q(y, τ) is the intensity of the heat flux formed as a result of the interaction of the tool with the part; λ is the coefficient of thermal conductivity of the processed material; 2a∗ is the length of the contact zone of the tool with the machined surface; γ is the coefficient of heat exchange with the environment; σx , τxy – normal and shear stresses. Conditions for pairing layers (coatings): – for temperature fields: k−1

k

T (ak − 0, y, τ ) =T (ak + 0, y, τ ) σ

k−1

λk−q ∂∂xT (ak − 0, y, τ ) = λk ∂∂xT (ak + 0, y, τ ) – for deformation fields: k−1 υ j (ak k−1 σ x (ak k−1 τ xy (ak

k

− 0, y) = υ j (ak + 0, y) k

− 0, y) = σ x (ak + 0, y)

(10)

k

− 0, y) = τ xy (ak + 0, y)

where λk – the thermal conductivity of the k-th layer; αk – the thickness of the k-th layer; vj – components of displacements in the k-th layer. For surface layers with structural and technological inhomogeneities, the discontinuity conditions for the solution, depending on the type of defect, will be: – on inclusions: v = 0, σx  = 0;   v = 0, τxy = 0; – on crack-like defects: σx  = 0, v = 0;   τxy = 0, v = 0,

(11)

where u, v, σx , τxy  – the jumps of the displacement and stress components. Of the available fracture criteria that take into account the local physical and mechanical properties of inhomogeneous materials, the most acceptable for this case are the criteria of the force approach associated with the use of the concept of stress intensity factor (SIF). When loading leads to the fact that the stress intensity KI becomes equal to the limiting value KIc , then the crack-like defect turns into a main crack. Modeling the influence of the initial piecewise homogeneity of processed materials (parts with coatings) on thermomechanical processes is carried out by discontinuous

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solutions. By them, we mean such solutions that satisfy the Fourier thermal conductivity and Lame elasticity equations everywhere except for the boundaries of defects. When crossing the boundary, the displacement and stress fields suffer discontinuities of the first kind, i.e., their jumps appear u, v, σx , τxy . The solution of the thermal problem (4), (7), (8), (10) is carried out using the integral Fourier transforms in the variable y, and Laplace transforms in τ to the function T (x, y, τ) in I (k = 0) layer. Discontinuous solutions also estimate the stress-strain state of a layered half-plane. The interfaces x = ak (k = 0) are considered as defects, when passing through which the displacement and stress fields suffer discontinuities. The construction of discontinuous solutions of the Lame equations with given jumps is carried out using the Trefftz functions [14]:

υ = ψ1 + (x − a)ψ0 v = ψ2 + (x − a)ψ 0

ψ0 =



ψ1 +ψ 2 3−4η , e





(12)

= ψ1 + ψ2 + ψ0

where ψ0 (x, y) = 0, ψj (x, y) = btk T (j) , (j = 1, 2) Stresses are found by the formulas:





σx = (1 − μ)ψ0 + ψ1 + (x − ak )ψ0 σy = −μψ0 + ψ 2 + (x − ak )ψ0 τxy = ψ12 + 2(x − ak )ψ 0 + ψ2 + ψ0

(13)

The application of the generalized Fourier transforms in the variables x, y to Eqs. (5), (6), (9), (10), taking into account (12), makes it possible to obtain recursive relations relating displacements and directions in the k-th layer with stresses and displacements, formed in the first layer under the action of non-stationary temperature fields. The effect of inhomogeneities in the surface layer of structural elements on the intensity of cracking and chipping during machining is studied as follows. Under conditions of uneven heating, thermal deformations occur in the surface layer, which causes thermal stresses. Under the influence of these stresses, concentrated in the locations of defects, the formation of defects (cracks) occurs [15]. Of most significant interest is the behavior of stresses in the vicinity of the vertices of defects such as cracks, pointed inclusions, and structural imperfections, i.e., stress features at y → ±lk . The nature of the stress field near the end of the defect, obtained in the framework of the classical theory of elasticity, is determined by the stress intensity factors KI = iKII . Thus, the study of the stress intensity at the vertices of a defect with a length of 2l, located at a depth of σ∗ , when the heat flux q is set on the surface of the body (x = 0, |y| ≤ a∗ ), made it possible to establish the limiting value of this flow q∗ , at which the specified defect begins to develop into a main crack: √ 2 3λ(1 − ν)K4C q∗ = √ α2 El π lσ ∗

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For defect-free machining of steels and alloys with crack-like defects and inclusions, when choosing machining modes and tool characteristics, one should be guided by the limiting values of the heat flux formed during machining so that hereditary defects do not leave the equilibrium state. The surface layer of processed materials contains heterogeneities and imperfections of hereditary origin, which have varying degrees of randomness. Therefore, in the study of the causes of the appearance of defects, in addition to the deterministic one, a probabilistic-statistical approach is necessary. A stochastic model of crack formation during the machining of metals of a heterogeneous structure is built based on an integrated approach based on the results of a deterministic theory of the development of individual defects and methods of probability theory. The surface layer is considered as a medium weakened by random non-interacting defects (cracks), and inclusions, the determining parameters of which are random variables with known laws of their probabilistic distribution. The surface layer’s destruction probability is investigated depending on various types of the probability distribution of defects’ sizes (length, depth) and their orientation. The probabilistic characteristics of the limiting heat flux are considered from the same positions. It has been established that an increase in the homogeneity of the material leads to a rise in the heat flux value corresponding to a fixed failure probability. From the established functional relationships between the kinetics of thermomechanical phenomena, the hereditary inhomogeneity of the surface layer, and the quality indicators of processed products have been obtained. Heat flow is the main criterion for the limiting equilibrium of crack-like defects. To elucidate the role of the heat stress of micro-cutting in the mechanism of crack initiation, taking into account the heterogeneity of the material being processed, the mechanical and thermal characteristics of the impulse components of machining were determined [16]. It has been established that the heat flux from them is a variable value √ determined by the formula q = c τ , where c is a coefficient that takes into account the thermophysical properties of the metal and processing modes, τ is the contact time of the pulsed component of the tool with the machined surface of the product. Experimental studies confirm the established pattern of changes in the heat flux density from the impulse component. The influence of the design parameters of the tool on the thermomechanical state of the surface layer was determined using the model problem (4), (7), and the boundary conditions in the form: √  n   c τ H (y) − H y2a∗ σ y + kl − vkp τ (14) q(y, τ ) = k=0 λ where H (y) – the Heaviside function; σ(y) – Dirac delta function; n is the number of grains passing in the √ contact zone during the time; λ – thermal conductivity of the product material; c τ – heat flow from a single grain-pulse component of the tool; vg , vkp , tgr – processing modes, 2a∗ – the length of the arc of contact between the tool and the workpiece; l ∗ – the distance between impulse components. The maximum values of the instantaneous temperature TM were obtained theoretically and experimentally confirmed, from the pulse component to the constant component -TK , which are used

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further as criteria values in determining the conditions for forming defects such as burns and their depth. The influence of processing modes on the qualitative characteristics of the surface layer was studied depending on the dominant factors. The adequacy of the theoretically established dependences of the contact temperature on the processing parameters and the physical and mechanical properties of the processed material was checked. The controlled values were: microhardness, residual stresses, tensile strength, and the presence of microcracks on the treated surface. The structural stress state of the surface layer of the material, which is formed during its processing, affects the fracture toughness KIc . Of the technological parameters, the influence of the depth of processing as the dominant factor in the occurrence of thermomechanical phenomena on the value of the crack resistance coefficient was studied. The decrease in crack resistance is associated with increased tensile residual stresses. The study of the role of the heterogeneity of the coating structure in the mechanism of crack resistance reduction was carried out using the theoretically established criterion of local failure in the form of the following inequality: l0