Advanced applications in manufacturing engineering 9780081024140, 9780081024157, 1051051061, 0081024142, 0081024150

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ADVANCED APPLICATIONS IN MANUFACTURING ENGINEERING

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Woodhead Publishing in Materials

ADVANCED APPLICATIONS IN MANUFACTURING ENGINEERING

Edited by

MANGEY RAM Department of Mathematics, Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India

J. PAULO DAVIM Department of Mechanical Engineering, University of Aveiro, Portugal

Woodhead Publishing is an imprint of Elsevier The Officers’ Mess Business Centre, Royston Road, Duxford, CB22 4QH, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, OX5 1GB, United Kingdom Copyright © 2019 Elsevier Ltd. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-08-102414-0 (print) ISBN: 978-0-08-102415-7 (online) For information on all Woodhead publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Matthew Deans Acquisition Editor: Glyn Jones Editorial Project Manager: Mariana L. Kuhl Production Project Manager: Sojan P. Pazhayattil Designer: Mark Rogers Typeset by Thomson Digital

CONTENTS

List of Contributors ix Editors’ Biography xiii Prefacexv Acknowledgmentxvii

1. Methodology for Selection and Application of Eco-Efficiency Indicators Fostering Decision-Making and Communication at Product Level—The Case of Molds for Injection Molding

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Paulo Peças, Uwe Götze, Rita Bravo, Fanny Richter, Inês Ribeiro 1.1  Motivations and brief chapter overview 1 1.2  Literature survey and contributions 3 1.3  Suggested methodology and its application 9 1.4 Conclusions 46 1.5  Appendix A 47 References50

2. Fabrication of Magnetic Tunnel Junctions

53

Jitendra P. Singh, Richa Bhardwaj, Aditya Sharma, Baljeet Kaur, Sung O. Won, Sanjeev Gautam, Keun Hwa Chae 2.1  Magnetic tunnel junction 53 2.2  Junction size 54 2.3  Growth of multilayer structure 56 2.4  Molecular beam epitaxy (MBE) 56 2.5 Lithography 61 2.6  Patterning of Fe/MgO/Fe system 65 2.7  Fabrication of device using pseudo/metal masking procedure 70 2.8 Conclusions 72 References72

3. Effect of Equipment’s Failure on Gas Turbine Power Plant

79

Nupur Goyal, Mangey Ram, Shubham Amoli, Shivam Jagga 3.1 Introduction 79 3.2  Mathematical modeling details 81 3.3  Particular cases and their numerical computation 85 3.4  Results, discussion, and conclusion 102 References102

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4. Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

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Paulo Peças, Inês Ribeiro, Elsa Henriques, Ana Raposo 4.1 Introduction 105 4.2  Additive manufacturing in mold manufacturing 106 4.3  Means and methods 108 4.4  The process-based models and their outputs 117 4.5  Results of life cycle engineering assessment 126 4.6 Conclusion 132 4.7  Appendix 1 134 References138

5. Manufacturing Engineering Requirements in the Early Stages of New Product Development—A Case Study in Two Assembly Plants

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Mariam Nafisi, Magnus Wiktorsson, Carin Rösiö, Anna Granlund 5.1 Introduction 141 5.2  Theoretical framework 143 5.3  Research approach 148 5.4  Empirical findings 151 5.5  Analysis and discussion 158 5.6 Conclusion 164 References165

6. Development of SMEs Coping Model for Operations Advancement in Manufacturing Technology

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Michael K. Adeyeri, Sesan P. Ayodeji, Bassil O. Akinnuli, Peter K. Farayibi, Olatunji O. Ojo, Kehinde Adeleke 6.1 Introduction 169 6.2  Advances in manufacturing technology and its tools 171 6.3  Small and medium scale industries 173 6.4  Coping models for SMES placement in AMT 179 6.5 Conclusion 188 References189

7. Applications of Computational Methods in Manufacturing Processes

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Mohit Pant, Sahil Garg 7.1 Introduction 191 7.2  Meshfree methods 193 7.3  Application of computational methods to manufacturing processes 196 7.4  Numerical implementation 207 7.5 Conclusions 223 References225

Contents

8. Study of Turbulent Plane Circular Jet for Modulation of Recirculation Zone Behind a Cubical Obstruction

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Manas Kumar Bhukta, Goutam Kumar Bose, Kaustav Debnath 8.1 Introduction 231 8.2  Formulation of the problem 233 8.3  Result and discussion 237 8.4 Conclusion 248 References249

Index251

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LIST OF CONTRIBUTORS Kehinde Adeleke Department of Mechanical Engineering, Faculty of Engineering, Adeleke University, Ede, Nigeria Michael K. Adeyeri Department of Industrial and Production Engineering, The Federal University of Technology, Akure, Nigeria Bassil O. Akinnuli Department of Industrial and Production Engineering, The Federal University of Technology, Akure, Nigeria Shubham Amoli Department of Mechanical Engineering, Graphic Era Deemed to be University, Dehradun, India Sesan P. Ayodeji Department of Industrial and Production Engineering, The Federal University of Technology, Akure, Nigeria Richa Bhardwaj Advanced Analysis Center, Korea Institute of Science and Technology, Seoul, South Korea Manas Kumar Bhukta Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India Goutam Kumar Bose Department of Mechanical Engineering, Haldia Institute of Technology, Haldia, India Rita Bravo IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal Keun Hwa Chae Advanced Analysis Center, Korea Institute of Science and Technology, Seoul, South Korea Kaustav Debnath Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Shibpur, India Peter K. Farayibi Department of Industrial and Production Engineering, The Federal University of Technology, Akure, Nigeria Uwe Götze Chemnitz University of Technology, Chemnitz, Germany Sahil Garg Department of Mechanical Engineering, National Insitute of Technology Hamirpur, Hamirpur, India

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Sanjeev Gautam Dr. S.S. Bhatnagar University Institute of Chemical Engineering & Technology, Panjab University, Chandigarh, India Nupur Goyal Department of Mathematics, Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India Anna Granlund School of Innovation, Design and Engineering, Mälardalen University, Eskilstuna, Sweden Elsa Henriques IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal Shivam Jagga Department of Mechanical Engineering, Graphic Era Deemed to be University, Dehradun, India Baljeet Kaur Department of Physics, Panjab University, Chandigarh, India Mariam Nafisi School of Innovation, Design and Engineering, Mälardalen University, Eskilstuna, Sweden Olatunji O. Ojo Department of Industrial and Production Engineering, The Federal University of Technology, Akure, Nigeria Mohit Pant Department of Mechanical Engineering, National Insitute of Technology Hamirpur, Hamirpur, India Paulo Peças IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal Carin Rösiö School of Innovation, Design and Engineering, Mälardalen University, Eskilstuna; Department of Industrial Engineering and Management, School of Engineering, Jönköping University, Jönköping, Sweden Mangey Ram Department of Mathematics, Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India Ana Raposo IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal Inês Ribeiro IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal Fanny Richter Chemnitz University of Technology, Chemnitz, Germany Aditya Sharma Advanced Analysis Center, Korea Institute of Science and Technology, Seoul, South Korea

List of Contributors

Jitendra P. Singh Advanced Analysis Center, Korea Institute of Science and Technology, Seoul, South Korea Magnus Wiktorsson School of Innovation, Design and Engineering, Mälardalen University, Eskilstuna, Sweden Sung O. Won Advanced Analysis Center, Korea Institute of Science and Technology, Seoul, South Korea

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EDITORS’ BIOGRAPHY Mangey Ram received the Ph.D. degree major in Mathematics and minor in Computer Science from G.B. Pant University of Agriculture and Technology, Pantnagar, India. He has been a Faculty Member for around 10 years and has taught several core courses in pure and applied mathematics at undergraduate, postgraduate, and doctorate levels. He is currently a professor at Graphic Era Deemed to be University, Dehradun, India. Before joining the Graphic Era, he was a Deputy Manager (Probationary Officer) with Syndicate Bank for a short period. He is Editor-in-Chief of International Journal of Mathematical, Engineering and Management Sciences; Editor, Guest Editor & Member of the editorial board of many journals. He is a regular Reviewer for international journals, including IEEE, Elsevier, Springer, Emerald, John Wiley, Taylor & Francis, and many other publishers. He has published 131 research publications in IEEE, Springer, Elsevier, Emerald, World Scientific, and many other national and international journals of repute and also presented his works at national and international conferences. His fields of research are reliability theory and applied mathematics. Dr. Ram is a senior member of the IEEE, life member of Operational Research Society of India, Society for Reliability Engineering, Quality and Operations Management in India, Indian Society of Industrial and Applied Mathematics, member of International Association of Engineers in Hong Kong, and Emerald Literati Network in the United Kingdom. He has been a member of the organizing committee of a number of international and national conferences, seminars, and workshops. He has been conferred with “Young Scientist Award” by the Uttarakhand State Council for Science and Technology, Dehradun, in 2009. He has been awarded the “Best Faculty Award” in 2011 and recently Research Excellence Award in 2015 for his significant contribution in academics and research at Graphic Era. J. Paulo Davim received the Ph.D. degree in Mechanical Engineering in 1997, the M.Sc. degree in Mechanical Engineering (materials and manufacturing processes) in 1991, the Dipl.-Ing Engineer’s degree (5 years) in Mechanical Engineering in 1986, from the University of Porto (FEUP), the Aggregate title (Full Habilitation) from the University of Coimbra in 2005 and the D.Sc. degree from London Metropolitan University in 2013. He is Eur Ing by FEANI-Brussels and Senior Chartered Engineer by the Portuguese Institution of Engineers with an MBA and Specialist title in Engineering and Industrial Management. Currently, he is a professor at the Department of Mechanical Engineering of the University of Aveiro, Portugal. He has more than 30 years of teaching and research experience in Manufacturing, Materials and Mechanical Engineering with special emphasis in Machining & Tribology. He has also interest in Management & Industrial Engineering and Higher Education for Sustainability & Engineering Education. He has guided large numbers of postdoc, Ph.D., and masters students, as well as, coordinated xiii

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and participated in several research projects. He has received several ­scientific awards. He has worked as evaluator of projects for international research agencies as well as examiner of Ph.D. thesis for many universities. He is the Editor-in-Chief of several international journals, Guest Editor of journals, books Editor, book Series Editor, and Scientific Advisory for many international journals and conferences. Presently, he is an Editorial Board member of 25 international journals and acts as reviewer for more than 80 prestigious Web of Science journals. In addition, he has also published as editor (and coeditor) of more than 100 books and as author (and coauthor) of more than 10 books, 80 book chapters, and 400 articles in journals and conferences (more than 200 articles in journals indexed in Web of Science core collection/h-index 45/6000+ citations and SCOPUS/h-index 52+/8000+ citations).

PREFACE The manufacturing industry has been one of the basic drivers for modern rapid global economic development. This development has consequence in many economic benefits to and enhancement of quality of life for many people all over the world. This rapid development also creates many opportunities and challenges for both industrialists and academics, and have completely changed in this global design and manufacture environment. More of the designs and manufacturing tasks can now be undertaken within a computer environment using simulation and virtual reality technologies. Manufacturing engineering is the processes for new products, improving manufacturing yield, implementing automated manufacturing and production facilities, and establishing quality and safety programs.The goal of the manufacturing engineering is to provide manufacturing engineers for problem solving, for establishing manufacturing processes, and for improving existing production lines in a bold or complex one. This holds both conventional and emerging manufacturing tools and processes used in the automotive, aerospace, and defense industries and their supply chain industries. Both industry and the academic have an urgent need to equip themselves with the latest knowledge, in technology for engineering design and manufacture. Through this book Advanced Applications in Manufacturing Engineering the engineers and academician have to gain a great knowledge and help them in the manufacturing engineering. The book is meant for those who wish to take manufacturing engineering as a subject of study. The material is intended for an audience at the level of postgraduate or senior undergraduate students. That’s why manufacturing engineering is now as well recognized and rapidly developing branch of engineering. Mangey Ram Dehradun, India J. Paulo Davim Aveiro, Portugal

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ACKNOWLEDGMENT The editors acknowledge Elsevier and the editorial team for their adequate and professional support during the preparation of this book. Also, we would like to acknowledge all the chapter authors and the reviewers for their availability for work on this book project. Mangey Ram Graphic Era Deemed to be University, India J. Paulo Davim University of Aveiro, Portugal

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CHAPTER 1

Methodology for Selection and Application of Eco-Efficiency Indicators Fostering Decision-Making and Communication at Product Level—The Case of Molds for Injection Molding Paulo Peças*, Uwe Götze**, Rita Bravo*, Fanny Richter**, Inês Ribeiro* *IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal **Chemnitz University of Technology, Chemnitz, Germany

1.1  MOTIVATIONS AND BRIEF CHAPTER OVERVIEW In this chapter, a methodology for the selection and communication of eco-efficiency indicators for products is proposed, and its application in the mold manufacturing and plastic injection molding sector is demonstrated. The proposed methodology aims to contribute to the extension of the use of the eco-efficiency concept to products—for decision-making processes on the one hand and as a metrics for intracompany and intercompany communication and reporting on the other hand. Respecting the existing standards and guidelines [1,2], the methodology strengthens accuracy and comprehensiveness in the set of indicators proposed and simultaneously provides to the user (company, organization, etc.) a shorter and better manageable list of adequate indicators compared to the large lists of indicators suggested by the normative documents. So, the understanding of “what an eco-efficiency is about” is fundamental as well as to point out why the authors of this chapter find this concept and the corresponding metrics highly useful for product/system design decisions. As one of the most important barriers for its use at the product level seems to be the indicators selection which can be very time consuming and very tangling, the authors suggest a methodology for selecting and applying these indicators. In addition, not all the indicators proposed by the normative documents are useful and/or applicable for product decisions and some pertinent indicators to assess product performance are not proposed by those documents (mainly the ones related with economic performance). Eco-efficiency is a commonly referred metrics to assess the environmental impact and the value of the overall activities of a company simultaneously.This concept belongs to the sustainable development assessment realm and guides companies to decrease the environmental impact of the resources consumed and emissions of the production system Advanced Applications in Manufacturing Engineering. http://dx.doi.org/10.1016/B978-0-08-102414-0.00001-X Copyright © 2019 Elsevier Ltd. All rights reserved.

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and/or increase the value of the activities’ output. It should also be used to support the decision-making on new products development and on products improvement, as proclaimed by the normative documents that define eco-efficiency [1,2] and to constitute a coherent system of indicators for decision-making and reporting in companies including the company as a whole as well as its products. Nevertheless, for the application of eco-efficiency at product level, there are several inhibiting factors, despite the significant research and normative work published. Some of these limiting factors are presented as follows: (1) co-existence of different normative documents with different definitions and scopes of eco-efficiency, (2) existence of large lists of possible eco-efficiency indicators, (3) not-clear and fuzzy classifications of those indicators, (4) nonexistence of uniform and clearly defined rules for calculating the final eco-efficiency ratio, (5) difficulty of applying indicators in the early life cycle phases of innovative and not completely defined products, (6) the value indicators suggested are usually related to a fixed and relatively short period of time (e.g., 1 year) that for the product level is insufficient because a lot of product decisions affect the products’ life cycle, and (7) the application at product level requires the consideration of a wide range of factors and a lot of details in information gathering and calculation. Contrarily, when eco-efficiency is applied to a whole production system, the information required is usually available: on the one hand total energy consumed, aggregate materials consumed, etc. for the environmental impact, financial indicators being suggested namely gross value-added, economic value-added, etc. that are already regularly calculated in the frame of financial accounting by a lot of companies on the other hand. Besides hindering a more frequent use of eco-efficiency at product level due to unclear guidance and being highly time consuming, these facts also cause indecision concerning the data to be retrieved from the production system. Thus, this inhibits an accurate as well as comprehensive use of eco-efficiency indicators for intracompanies and intercompanies comparison of the same or similar products. The methodology proposed in this chapter aims to tackle these barriers by facilitating the identification and selection of the most adequate set of indicators. Yet, the proposed methodology considers and respects the normative documents because they are a good and a widely acceptable starting basis.The methodology comprises two main steps: Step (1) the creation of a short list of indicators relevant for the type of product and Step (2) the selection and application of a small set of indicators especially important for the company specific decision-making process and reporting, using organized, and summarized profiles displaying the relevant information. The simplification rationale of the first step is based on the elimination of a large number of indicators suggested by normative documents that are not useful for the type of product under study. Nevertheless, the referred shorter list of indicators is even composed by 2–3 dozens of possible indicators.The rationale of the second step is to organize a shorter list in a language commonly used by the company: by type of processes,

The Case of Molds for Injection Molding

consumables, materials, and emissions (for the environment related indicators) and by those as well as type of cost drivers, product costs, product sales return etc. (for the value related indicators). The company should choose an appropriate small set of indicators for the specific decision-making and reporting tasks under analysis; this small set of indicators is not necessarily static, meaning that different small sets can/should be selected depending on the issue to be analyzed for decision-making and on the aim of reporting. The methodology is applied to molds for plastic parts injection molding. The result of the first step is a (shorter) list of eco-efficiency related indicators that can be used by any company of the mold making and injection molding sector.The result of the second step is demonstrated by comparing the performance of two types of molds on a life cycle perspective, from the mold manufacturing to the mold end-of-life, including the use phase of the mold (the injection molding process), namely the plastic material used, wasted and recycled in that phase. The methodology is applicable for other products as well by the replication of the first step, creating a shorter list adequate to the product of interest and then conducting the second step. By making easier the selection and handling of eco-efficiency indicators, the proposed methodology contributes to the use of this metrics in intracompany comparison and communication of sustainability related performance (e.g., comparing different products performance, assessing evolution of performance along time) and also intercompany communication (e.g., by including some of these indicators in products data sheets, benchmarking with other companies of the same activity sector). In addition, the structure and logics of indicators selection and reporting proposed in the methodology can be further extended to company level, being only necessary to adapt the level of detail of the indicators (toward a more macro level) and the type of the economic performance indicators (using other financial related indicators).

1.2  LITERATURE SURVEY AND CONTRIBUTIONS Eco-efficiency belongs to the realm of sustainability assessment methods despite it just considers two pillars of sustainability: the environmental and the economic assessment. So it is important to remember one of the most cited definitions of sustainable development (presented in 1983 by the World Commission on Environment and Development, later known as the Brundtland Commission): “sustainable development is a development that meets the needs of the present without compromising the ability of future generations to meet their own needs” [3]. There is still nowadays some debate as to the exact meaning of sustainability, where some critics argue that this definition is rather vague, leaving room for interpretation. Nevertheless, there is agreement that sustainable development concerns social, economic, and environmental goals [4–6] that are understood as the three pillars of sustainability [7]. The simultaneous encompassing of these aspects promoted the development of several methods and tools with a life cycle perspective

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that are continuously evolved to improve handling the multidimensionality and corresponding complexity of sustainability and the “challenge of gathering and allocating lifecycle information” [8–10]. The concept of eco-efficiency was firstly introduced by Schaltegger and Sturm, in 1989, as the idea of “creating more value with less impact” [11], being now one of the focal points especially in reporting for environmentally conscious companies. Ideally, it should be considered at all levels of decision-making on industrial activities, from product design to manufacturing and disposal [12]. Eco-efficiency is one of the methods dealing simultaneously with two of those pillars, furnishing an assessment based on indicators [13]. The performance measuring by indicators is one of the positive aspects of eco-efficiency having a high potential for use in decision-making processes in organizations.Yet identifying one suitable set of eco-efficiency indicators at product level has proven to be a challenge. The existing approaches for the generation and selection of those indicators are so extensive and complex that they are inhibiting most of the organizations to take advantage of the potential of the use of indicators to measure sustainability related performance [14]. Nevertheless, those approaches are consistent, credible, and scientifically-supported. Some initiatives proposed by organizations and programs should be mentioned: the International Standards Organization’s International Standard on Environmental Management (ISO 14000), the World Business Council for Sustainable Development (WBCSD), the Organisation for Economic Co-operation and Development (OECD), and the European Environment Agency (EEA) [14,15–18].These initiatives and programs propose different indicators for the economic, environmental and/ or social aspects of sustainability, aggregated on different levels, resulting in a very large (and so hardly manageable) number of indicators [14].These lists composed by hundreds of indicators hamper a company or organization to select the most proper and relevant indicators [14,17]. Furthermore, its definitions and scope present some differences depending on the source (publishing organization) [19], as demonstrated in Table 1.1. From the analysis of Table 1.1, two main groups emerge, differentiated by the application of a life cycle perspective in an eco-efficiency study, which is only mentioned by ISO 14045 and the WBCSD. Another important difference is the field of application. The OECD document describes eco-efficiency as suitable for evaluating a whole company or sector whereas the ISO 14045, WBCSD and EEA documents also include the comparison between products and product systems. WBCSD recommends the use of seven principles (Table 1.2) to guide the continuous improvement of eco-efficiency performance: an organization should simultaneously improve itself concerning several principles [17]. The other normative documents do not refer to these or other principles. Additionally to these multiple ways to define eco-efficiency, there is also no agreement upon methods to calculate eco-efficiency, indicating that there is still need of further developments [11,20]. One of the most mentioned methods in literature is to

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Table 1.1  Eco-efficiency definitions and scopes present in several normative documents Source

Definition

Scope

ISO 14045 (2011) [1]

“Eco-efficiency is a quantitative management tool that enables the consideration of life cycle environmental impacts of a product system alongside its product system value to a stakeholder” “Eco-efficiency is achieved by the delivery of competitively priced goods and services that satisfy human needs and bring quality of life, while progressively reducing ecological impacts and resource intensity throughout the life-cycle to a level at least in line with the Earth’s estimated carrying capacity” “Eco-efficiency is the efficiency with which ecological resources are used to meet human needs, defined as the ratio between the value of products and services produced by a company, business sector or economy, and the environmental pressure of the company, sector or economy” “A concept and strategy enabling sufficient delinking of the ‘use of nature’ from economic activity needed to meet human needs (welfare) to allow it to remain within carrying capacities; and to permit equitable access and use of the environment by current and future generations”

• Life cycle perspective • Focus on a product and on the impacts during life cycle

WBCSD 1 (2000) [2,18]

OECD (2010) [19]

EEA (2010) [19]

• Life cycle but allows a single phase perspective • Suggests the comparison among products

• No explicit life cycle perspective • Evaluates a whole company or sector

• No explicit life cycle perspective • Suggests the comparison among products

Table 1.2  Seven eco-efficiency principles [15] Objectives

Eco-efficiency principles

Optimize the use of resources

Reduce material consumption Reduce energy consumption Enhance recyclability Reduce dispersion of toxic substances Maximize use of renewable resources Extend product life Increase service intensity

Reduce environmental impact Increase product or service value

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calculate eco-efficiency in the form of a ratio between a value indicator (as a measure of the economic dimension) and an environmental impact indicator [21,17]: Eco-efficiency=

Products or Services Value Environmental Influence

(1.1)

However, few authors also reverse the ratio, presenting an environmental impact per value unit indicating that a lower ratio will suggest a better product, service, company, etc. in terms of eco-efficiency [11,22,23]. However, most authors consider this last ratio as counter-intuitive since, when thinking of eco-efficiency, a better product or service is considered more eco-efficient if it has a higher ratio [2,24]. Regarding the indicators suggested for the two sides of the ratio the normative documents have some similarities and interdependencies. These four normative documents provide a list of hundreds of environmental performance indicators (EPIs) for the ratio denominator, and around 20 value indicators for the ratio numerator, declaring that those indicators are just suggestions, meaning that even other indicators (beside the hundreds suggested) can be used. In some cases, the use of one normative document is not enough, that is, the WBCSD initiative requires the use of the ISO 14031 standard [1,19]. As regards to eco-efficiency ratios, these references do not propose or recommend a concrete proposal for one or more eco-efficiency ratios.These normative documents present solely examples and mention that any combination between value and environmental indicators is a measure of eco-efficiency [1,2]. This situation, being part of the motivation for the development of the methodology presented in this chapter, is also concerning other authors, mainly regarding the EPIs. Some authors recommend that instead of the long list of EPIs suggested by the normative documents only the indicators related with the most significant environmental aspects for the study should be used [25,26]. Those authors named these indicators as key environmental performance indicators (KEPIs) and define them as “a set of simple and understandable indicators meaningful to the study.” Following those authors, the number of indicators should be reduced to a more manageable number focusing on the purpose of a particularly study or sector. In line with this rationale some authors proposed a method that includes significance analysis for all the environmental aspects and indicators [Eco-Efficiency Integrated Methodology for Production Systems (EcoProsys) [25,27,28]]. The user of this method has to attribute a score of importance to each environmental aspect (e.g., each material used, each machine, each consumable, each process, etc.) for each one of the seven eco-efficiency principles mentioned by WBCSD [17] (Table 1.2). In addition, the user has to mention which eco-efficiency principles are intended to be improved in the study/analysis. On the basis of this information the methodology identifies the significant environmental aspects to be considered in the study/analysis, that is, the significant EPIs or KEPIs are identified. However, despite this interesting approach reduces the environmental indicators to a few suggested dozens,

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the result is still a very large number of possible combinations of eco-efficiency ratios: the user of this approach has to select among hundreds of possible eco-efficient ratios (all the possible combinations of value and environmental indicators, suggested “automatically” by the EcoProsys methodology). Besides the large number of combinations, some of the automatically suggested eco-efficiency ratios have no relevance, neither for decision-making nor for intra and interorganization comparisons and communication, for example, the ratio between the added-value of cutting and the environmental impact of welding, etc. So, the EcoProsys methodology does not fully contribute to apply ecoefficiency to product level and to simplify the use of eco-indicators, despite it encloses several positive and interesting aspects related with the application of eco-efficiency as an assessment metrics for production systems. Another issue regarding EPIs is related with the units used for it. The normative documents [1,2] use physical units (kg, kWh, etc.) to express the environmental impact. Nevertheless, some authors argue that this approach causes two types of limitations on the use of eco-efficiency: (1) the impact of energy depends on its origin so the use of the kWh consumed is not enough to define accurately the environmental impact and (2) for eco-efficiency ratios where the aim is to assess the overall eco-efficiency it is necessary to add the environmental impact of the use of material, energy consumption, emissions, etc., so the use of the physical units inhibits such calculation [29,30]. Accordingly, these authors suggest the use of eco-indicators that transform each specific environmental impact in an environmental influence measured by a single-score based on life cycle assessment databases (e.g., ReCiPe). Following these authors Figge and Hahn and Olsthoorn et al. [29,30], the use of eco-indicators allows the aggregation of environmental aspects impacts in useful and meaningful indicators, enabling an easy comparison between different products/systems. However, the choice of eco-indicators is subjective, and the results are only meaningful for products with the same function and similar manufacturing process and materials [31]. Furthermore, the WBCSD warns that this aggregation of aspects into one single final result must be done carefully, as it might conceal important information [15]. Not only the environmental dimension but also the value dimension raises some methodical questions. First of all, the term “products or services value” Eq. (1.1) leaves some room for interpretation and infilling. The WBCSD [17,11] suggests a set of indicators including volume-, mass-, function-related and monetary indicators with the latter ones comprising gross sales or margins, net sales, earnings before interest and taxes (EBIT), value-added, investments and costs (this is shown in more detail in Section 1.3.2.2). However, the WBCSD normative document [17] is very broad about the value indicators that can be used, some of the suggested indicators do not seem to be very useful for product decisions (e.g., value-added) and they all do not refer to the life cycle of products which is the decisive time perspective for sustainability-oriented communication and decision-making. Thus, there is still a need for a suitable set of value

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indicators for measuring eco-efficiency at product level. Hence, several gaps exist for the operationalization of the use of eco-efficiency at the product level, among others the existence of several definitions and scopes of eco-efficiency as well as very large and not uniformly structured lists of suggested indicators, nonuniform rules for the aggregate eco-efficiency ratio as well as its nominator and denominator (see also Section 1.1) and, finally, the missing of product-related value indicators. In order to contribute to closing the gaps outlined above, a methodology is proposed to facilitate the creation of a shorter list of indicators specific for a type of product and the selection of the most adequate (and reduced) set of indicators for a particular analysis and evaluation or reporting of product performance in a company. In this way, by facilitating the identification and selection of performance indicators the proposed methodology contributes to the wide spreading of the use of eco-efficiency (indicators) in decision-making at product level as well as to fostering the use of eco-efficiency related metrics for inter and intracompany comparisons. The product that the group selected to demonstrate the methodology is molds that are used for the plastic parts production by injection molding, a very relevant industrial sector which is still growing in applications and diversity. Actually plastics are increasingly used worldwide, in areas ranging from construction to healthcare or electronics, due to their low cost, versatility, and durability. However, plastic products present a significant environmental concern, considering the greenhouse gas emissions associated, in particular, with the production of plastic and the impact of improper disposal of the products in end-of life [32]. Furthermore, this industrial sector consumes a great deal of material and energy in both the mold manufacturing and injection molding phases [32,33]. Consequently, this increasing concern on the environmental impact of plastics is also referring to the plastic injection molding industry, and inherently to the mold performance that depends on its design [34,35]. In fact, different mold designs cause distinct performances of the injection molding process (mold use phase), particularly in energy and material consumption resulting in considerable effects on costs and environmental impact [21]. But other life cycle phases, especially the end of life phase, are also affected. So the consideration of several life cycle phases in the eco-efficiency assessment is essential promoting the consideration of a life cycle perspective in the selection and use of the indicators. Concluding, the eco-efficiency assessment and its future implementation in the decision-making processes are of particular significance in sectors such as the plastic injection molding industry. However, even with several life cycle studies developed in this sector [33,34,36], demonstrating the importance of mold design for the injection molding process’ resources consumption and overall performance, no eco-efficiency indicators for the sustainable design of molds or for the injection molding phase can be found [25,37]. Therefore, this chapter also contributes by proposing a shorter list of indicators for eco-efficiency assessment for the sector of mold making for injection molding of plastic

The Case of Molds for Injection Molding

parts. To adapt it to the specifities of this industrial sector, life cycle as well as productrelated indicators are included. In addition, this reduced list of indicators is applied to a decision-making process of mold design contributing to clarify the results interpretation and use for decision-making. Additionally, the discussion about the results obtained for the several type of indicators gives insights about how these indicators can be applied by the company to use eco-efficiency as an internal performance assessment (between departments or processes) and for benchmarking with other companies of the same activity sector that use the same indicators.The following section describes the methodology proposed in this chapter as well as the results of its application.

1.3  SUGGESTED METHODOLOGY AND ITS APPLICATION 1.3.1  Overview of the proposed methodology The sequential steps of the proposed methodology to select appropriate eco-efficiency indicators are presented in Fig. 1.1. The Step (1) consists of the creation of a list of environmental impact indicators and value indicators adequate to a specific type of product (that sometimes can be equivalent to a sector of activity).These lists are shorter than the long lists existing in the normative documents cited above and more appropriate to be applied at product level. The environmental impact indicators list includes “only” the indicators relevant for the processes, raw materials, consumables and other resources, and emissions typically used/emitted in a type of product, meaning that it can be used by companies that produce this type of products (or belong to that activity sector if this is the case). In this chapter the Step (1) is demonstrated for injection molds, that is, for the mold manufacturing and injection molding of plastic parts sector (presented in Section 1.3.2). For other types of products (or other sectors of activity), with other processes, materials, etc., the Step (1) should be performed analogously probably resulting in a different product (sector)-specific list. The generation of this list of indicators is based on the available normative documents summarised in Table 1.1. The user of the methodology should define the scope of the use of this list regarding the need of having a life cycle perspective and/or comparing products and/or analysing processes, resources, and emissions deeply. According to Table 1.1 the OECD [2] initiative is more adequate if “just” a macro-level analysis is to be performed (analysis of an organization as a whole, or an entire sector), and the remaining ones can be used if the analysis to be performed is at micro-level (product, processes, and system).The WBCSD [2] initiative and/or the ISO 14045 standard [1] are proper when a life cycle perspective is required. The WBCSD [2] and/or EEA initiatives [19] fit well if the list of indicators will be used to compare products or systems alternatives, namely in decision-making processes. Despite the methodology proposed accepts these four normative documents as guides for the indicators list creation, the list of the WBCSD [2,19] initiative is the one recommended here for the application of the

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Figure 1.1  Sequence and steps of the methodology proposed to apply eco-efficiency at product level.

The Case of Molds for Injection Molding

eco-efficiency metrics at product level. This one covers the critical aspects for a comprehensive assessment at product level: life cycle perspective and enabling a comparison among different products. The Step (2) consists of creating the eco-efficiency profile of a product based on the Step (1) lists. The eco-efficiency profile is composed by three profiles. The environmental profile includes the KEPIs found relevant to the products’ specific study or analysis depending on its aim and requirements [among the indicators list of Step (1)].The value profile includes the most monetary value indicators found relevant for the same study or analysis. So, two even shorter lists are created just with the indicators really important for the specific product under analysis. Finally, the third profile is the eco-efficiency ratios profile that includes quotients between indicators of the two other created profiles. Here the user has the opportunity to include in this profile only the meaningful and representative eco-efficiency ratios, avoiding combinations of indicators with no meaning or significance. The simultaneous use and interpretation of the three profiles are important because the eco-efficiency metrics is focusing a ratio, so it is important to understand if a better result is obtained by decreasing of environmental impact and/or increasing the value. The eco-efficiency profile can then be used in, at least, three ways. By comparing this profile for several alternatives of design, manufacturing, etc. of the product, the ecoefficiency logics can be applied in decision-making processes to select the most appropriate material, process, production sequence, etc. The eco-efficiency profile can also be used for internal comparisons in an organization to assess the evolution of eco-efficiency ratios along time and/or to compare the performance of similar products produced by the company. Furthermore, it allows to perform intercompany comparisons (using the same indicators and the same data retrieving and treatment methods) for internal use (e.g., benchmarking with similar products produced by other companies) and to communicate its products’ performance and the evolution of its indicators along time to the society. Therefore the proposed methodology facilitates the operationalization of the use of eco-efficiency indicators at the product level by providing a way to not-extensive lists of indicators and enabling to understand the impact of decisions (past and future) on the eco-efficiency performance of products.

1.3.2  Step (1)—Selection of indicators for the injection molding sector This section describes how Step (1) of the methodology is applied to the mold manufacturing and injection molding activity sector. As referred above, the eco-efficiency assessment and performance reporting of a product must be done in a life cycle perspective to assure that all the products’ impacts are considered. For the injection molding molds this situation is obvious because the design options and the manufacturing process and steps selected influence significantly the

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performance of the product in the use phase (the injection molding of the plastic part) and the end-of-life (e.g., energy consumed, material consumed, wastes, disposal requirements, etc.). All the production steps are relevant and should be included with a high level of detail about materials, processes and emissions.There is also the need to compare different product alternatives for decision-making as well as for internal reporting and comparison. Therefore, the normative document serving as the basis to create the short list is that of the WBCSD initiative, as explained in the previous section. At this stage of eco-efficiency analysis it is necessary to define a functional unit to be used as reference for the value and environmental impact indicators.This unit is a measure of the function of the system and it is used as a reference to which the inputs and outputs are related. In this case, the functional unit selected is the plastic part produced by the mold, meaning that in general the value and the environmental impact of producing the mold, using and disposal the mold will be assessed referring to the quantity of plastic parts a mold can perform during its life. This implies the necessity to define a specific plastic part to be produced with given characteristics concerning mass, complexity etc. If more than one type of plastic part is expected to be produced, a representative part should be defined or the mass of the parts should be selected as the reference instead of their numbers (assuming that the mass is the factor that primarily influences resource consumption, production time etc.). However, the plastic part life cycle will not be fully included in the analysis, only the life cycle phases related with the mold use phase will be included (plastic part material consumed and plastic part production by injection molding). Despite the plastic part is the functional unit to be used in most of the indicators, there are indicators that will explicitly refer to the mold, so as if the mold would be the functional unit (e.g., energy consumed during mold production). This is just a practical use of the indicators for sake of significance of the set of indicators. As the mold is defined to be able to produce a fixed number of plastic parts, there is an unambiguous relationship between the mold and the plastic parts and the coherence with the functional unit is kept. A general life cycle of such an injection molding mold is presented in Fig. 1.2. For the material acquisition phase, all the impacts caused to have the material available for the use by the mold manufacturing company have to be aggregated. This includes the effects of molds material extraction, processing, transports, etc. The end of molds life should be considered when the mold has no use for the owner (no need of plastic part

Figure 1.2  Mold Life Cycle.

The Case of Molds for Injection Molding

production) and not regarding its remaining mechanical life. The disposal phase should also include the impact of the mold and plastic part materials. The inclusion of all life cycle phases will promote a responsible decision-making and the achievement of significant figures because the indicators are based on a life cycle based assessment. This is an important difference between applying eco-efficiency to product level and to company level. Usually at company level only the impacts generated by the processes performed by the company are considered without regarding the consequences of the company decision outside the “company’s doors.” 1.3.2.1  Key environmental performance indicators In the report “measuring eco-efficiency” [17], the WBCSD proposes the use of the ISO 14031 standard to study the environmental influence as it best covers material consumption and environmental protection. Hence, ISO 14031 is the proposed baseline for listing all the existing EPIs in this approach. After that, the classification proposed by WBCSD is used to structure the EPIs in different types of performance assessment [38]. The knowledge and expertise about mold manufacturing and injection molding are used to select the EPIs relevant to the type of product under study (injection molds) on a life cycle perspective—the KEPIs. A brief description of the ISO 14031 and of the WBCSD normative documents contents is done before formulating the approach used by the proposed methodology. 1.3.2.1.1  The ISO 14031 standard and the list of EPIs

The EPIs presented in the ISO 14031 standard have the three purposes : (1) comparing the environmental performance of products, services or companies over time, (2) indicating possible improvement opportunities by their assessment; and (3) communicating environmental influences [39–42]. The full list of types of EPIs, as suggested by the ISO 14031 standard, is presented in Appendix Table  1.1. The ISO 14031 standard uses the term “scope” to classify the EPIs in different areas of impact.This ISO standard indicates that each activity that interacts with the environment, named environmental aspect (e.g., every process or task that consumes energy and/or material that causes emissions, etc.) has to be assigned to one of the listed EPIs [41]. At company level, the environmental aspects used are just a few ones, usually related with a section or department because the final goal is to have the overall assessment of the production system. For example, the energy consumed by the different types of equipment is aggregated just in one figure representing the energy consumed by the section or department (or even only by the overall production system) over a fixed period. A similar calculation is done about materials, water, or other resources consumed. As a result, the number of environmental aspects is usually similar to the number of EPIs. This macro-level approach is enough to assess the eco-efficiency at company level. Contrarily, at product level, the interaction of every activity with the environment must

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be considered in a higher level of detail, identifying each particular source of resource consumption and emissions. This detailed information will be relevant for the comparison of product design or production alternatives and also for the identification and communication of the sources of environmental impact. In addition, at product level the several life cycle phases must/should be considered, meaning additional environmental aspects to be assessed. For example, the energy consumed must be assessed not only for each process used in the product manufacturing but also for the product use phase and even for the product end-of-life phase. So this causes an “explosion” of environmental aspects to be considered and assessed for each EPI if no selection approach is used. This is what is proposed in the present methodology, which by the use of proper and specific KEPIs will reduce the number of indicators to be taken into account. 1.3.2.1.2  The WBCSD classification of EPIs

As described above, the WBCSD normative document [17] proposes the use of the ISO 14031 standard [41] to organize the environmental aspects in types of EPIs and types of environmental scope aiming to assure comprehensiveness in the analysis. On the basis of this “complete” list of indicators, the WBCSD document [17] recommends the classification of the EPIs in two types of indicators: • Business specific indicators (BSIs): to be used internally in the company, depending mostly on the type of processes and technologies used by a particular company; there is usually no preoccupation to be “understandable” and/or aligned with other companies or organizations because they are to be used intracompany. • Generally applicable indicators (GAIs): to be used internally in the company but also externally, meaning that a globally accepted metrics must be used (thus offering no doubts to the one that consults it); the following indicators are proposed by WBCSD: Energy consumption, Materials consumption, Water consumption, Greenhouse gas emissions and Ozone depleting substance emissions. According to WBCSD [17], the GAIs often are an aggregation of the BSIs. Nevertheless, if an indicator defined by the company uses a specific metrics, not generally known, it must be considered as BSI even if it aggregates several environmental aspects.WBCSD also recommends that both types of EPIs should be used to interpret eco-efficiency because the potential aggregation in GAIs might conceal relevant information. As referred before, a special recommendation is given by WBCSD about the use of eco-indicators that summarize environmental influences from several environmental aspects—it should be considered that the use of aggregated eco-points might conceal important information about a product or a company’s eco-efficiency performance [17,18]. 1.3.2.1.3  The selection of the KEPIs

The methodology proposed in this chapter uses the two above described normative documents as the basis to define the indicators for measuring the environmental impact of eco-efficiency assessment of products. The approach used to define the KEPIs

The Case of Molds for Injection Molding

Figure 1.3  Approach for the choice of key environmental performance indicators.

(Fig. 1.3) begins with the selection of the EPIs from the ISO 14031 standard list that are technologically related with the product under analysis. This selection should be done by experts on the product in question who are able to identify the inputs and outputs involved during the product life cycle based on their experience. Additionally, the list of selected EPIs should be done for a representative product [a standard product containing no special features or components and/or the most common product (with the highest volume)] to enable its application for several specific products of that type. A representative injection mold is the product used in this chapter to demonstrate the application of the methodology. In this case, the authors of this chapter acted as “experts” and used their knowledge and experience within the sector of injection molding for selecting the EPIs from the 14031 standard list by primarily excluding indicators that are not applicable to or relevant for molds manufacturing or injection molding.The result is presented in Table 1.3 (first two columns of Table 1.3).The following sub-step is to identify the environmental aspects of each (aggregate) process of each life cycle phase for each EPI found relevant (columns 3, 4, and 5 of Table 1.3). In this way, all the activities that occur during the product life cycle with impact on the environment are listed (and no more than those). The list of Table 1.3 might not be considered as “short”, but it is shorter than the list of all the indicators proposed by ISO 14031. In the short list of EPIs proposed in Table 1.3 for the manufacturing phase, only computer numerical control (CNC) machining, EDM, and Grinding were considered because these are the most common mold manufacturing processes. In the case of a specific product for which a process or an activity is applied that is not included in this general short list suggested for a representative type of product, this process or activity should be added. This could be the case for the following entities: injection molds requiring thermal treatment of the mold cavity; mold parts done by additive manufacturing; the use of robotic arms for the manipulation of the parts in the injection molding phase; and process and activities not considered in the short list of Table 1.3. Some additional comments on the short list presented in Table 1.3 seem to be necessary. For the energy consumed the environmental aspects used are “total energy used”

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Eco-efficiency principles Scope

EPI

Life cycle phase

Material

Amount of material used

Raw material acquisition Mold manufacturing

Process

CNC EDM Grinding

Amount of material processed, recycled, or reused

Mold use

Injection

Mold manufacturing

CNC

EDM Mold use

Injection

Mold end of life Amount of packaging material disposed or reused

Raw material acquisition Mold manufacturing Mold use

CNC EDM Grinding Injection

Aspect

1

Steel to manufacture mold Cutting tool Wire Electrode Wheel Lubricant fluid Polymer used to produce parts Mold material recycled

×

×

×

Mold material recycled Polymer recycled or reused Mold material recycled Packaging

×

×

×

×

Packaging Packaging Packaging Packaging

× × × ×

× × × × × ×

× ×

2

3

4

×

×

5

6

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Table 1.3  Short list of EPIs selected from the ISO 14031 list for injection molds with the environmental aspects identified for every aggregate process of each life cycle phase

Amount of ancillary material recycled or reused

Mold manufacturing

CNC

Amount of raw material reused Amount of water used Amount of water reused

Mold use

Injection

Recycled or nonused cutting fluid Nonused filters Recycled or nonused dielectric fluid Recycled or nonused deionized water Nonused filters Recycled or nonused lubricant fluid Reused polymer

Mold manufacturing Mold manufacturing

EDM

Deionized water

×

EDM

×

Amount of hazardous materials used

Mold manufacturing

CNC

Amount of energy used

Mold manufacturing

Reused deionized water Cutting fluid Filters Dielectric fluid Filters Lubricant fluid Total energy Total energy Total energy Total energy

EDM

Grinding

Mold use

Grinding CNC EDM Grinding Injection

×

×

× ×

× ×

×

×

×

× ×

× ×

×

×

×

× × × × ×

× × × × × × × × × (Continued  )

The Case of Molds for Injection Molding

Energy

EDM

×

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Eco-efficiency principles Scope

EPI

Life cycle phase

Supply and distribution

Average fuel consumption of the vehicle fleeta

Raw material acquisition Mold use

Process

Injection

Mold end of life Products

Physical facilities and equipment

Rate of defective products

Mold use

Injection

Product use duration

Mold use

Injection

Mold end of life Mold manufacturing

CNC

Number of operating hours of a specific equipment per year

Number of unforeseen emergency occurrences or operations per year

EDM

Mold use Mold manufacturing

Grinding Injection CNC EDM Grinding

Aspect

Average fuel consumption Average fuel consumption Average fuel consumption Number of defective parts produced Cycle time Number of shots until mold maintenance Mold life Milling Turning Drilling EDM Electrode machining Grinding Injection Machine emergencies Machine emergencies Machine emergencies

1

2

3

4

5

6

7

× × × ×

×

× ×

×

× × × × × × × × × × ×

Advanced Applications in Manufacturing Engineering

Table 1.3  Short list of EPIs selected from the ISO 14031 list for injection molds with the environmental aspects identified for every aggregate process of each life cycle phase (cont.)

Mold use

Total area of land used for production

Injection

Raw material acquisition Mold manufacturing

CNC EDM Grinding

Mold use

Injection

Mold end of life Mold Manufacturing

CNC EDM Grinding

Mold use

Injection

× ×

×

×

×

×

×

×

×

× × × × ×

×

The Case of Molds for Injection Molding

Number of hours of preventive maintenance of equipment per year

Machine emergencies Mold emergencies Space allocated to raw materials Space allocated to the machines Space allocated to the machines Space allocated to the machines Space allocated to the machines Space allocated to the mold in end of life Machine maintenance Machine maintenance Machine maintenance Machine maintenance Mold maintenance

(Continued  )

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Eco-efficiency principles Scope

EPI

Life cycle phase

Process

Aspect

1

Waste

Total waste to final destination

Mold Manufacturing

CNC

Nonrecycled mold material

×

×

Unusable cutting tool Nonrecycled mold material Unusable wire Unusable electrode Contaminated deionized water Unusable grinding wheel Nonrecycled polymer Contaminated cutting fluid Unusable filters Contaminated dielectric fluid Contaminated filters Contaminated lubricant fluid

×

×

×

×

× × ×

× × ×

×

×

×

×

EDM

Grinding

Amount of waste controlled by authorization

Mold use

Injection

Mold Manufacturing

CNC EDM

Grinding

2

3

4

×

×

×

× ×

× ×

× ×

×

×

×

×

×

×

5

6

7

Correlation of each environmental aspect with the eco-efficiency principles: 1-reduced material intensity; 2-reduced energy intensity; 3-reduced dispersion of toxic substances; 4-enhanced recyclability; 5-maximized use of renewables; 6-extended product life; 7-increased service intensity. CNC, Computer numerical control. a Transport of the products to the customer.

Advanced Applications in Manufacturing Engineering

Table 1.3  Short list of EPIs selected from the ISO 14031 list for injection molds with the environmental aspects identified for every aggregate process of each life cycle phase (cont.)

The Case of Molds for Injection Molding

by each mold manufacturing process and injection phase. In fact, some of these processes need several auxiliary equipment each one with a specific consumption of energy. Nevertheless, the authors found not relevant to separate these consumption in different indicators, so the aspect considered includes the energy of the main and auxiliary equipment. Regarding the environmental aspect “average fuel consumption”, representing the fuel consumption for delivering the mold or parts to the customer, the EPI selected from the ISO 14031 list is “consumption of the vehicle fleet”, even though the mold manufacturing and/or injection molding companies usually do not have a vehicle fleet for product distribution.This is motivated by nonexistence of another EPI in the list that represents better this environmental aspect. Before proceeding to the definition of the KEPI, a verification of completeness is proposed in this methodology: to verify that all the eco-efficiency principles are covered by the shorter EPIs list (Table 1.2). The selected EPIs identified for all the processes and life cycle phases are cross-checked with the eco-efficiency principles: If an EPI allows to measure the performance concerning a specific eco-efficiency principle, this is signed with an × in Table 1.3. It should be verified that all the principles are represented by at least one EPI. If there is one or more principles not covered by any EPI, the shorter list should be reflected and additional EPIs should be selected from the ISO 14031 list to assure its completeness. According to Table 1.3, all eco-efficiency principles are comprised, additionally all selected EPIs refer to at least one principle. The last sub-step is the definition of the KEPIs. The definition of KEPIs aims to facilitate the Step (2) of the methodology, especially the determination of eco-efficiency ratios, and the development of the eco-efficiency profile. KEPIs are quantifiable metrics that reflect the essential environmental performance of a system, avoiding the use of a longer list with no representative environmental impacts [43]. The KEPIs concept is not included in the normative documents used as the basis of the methodology, but several publications recommend its use [25,26,28]. These authors state that KPIs must be related with the same functional unit (the plastic part in this case; sometimes the injection mold is used, this is possible because it refers to a fixed amount of produced plastic parts during life cycle), and should allow an accurate assessment of the performance, should be understandable and unambiguous and should allow for comparison with regulatory requirements. The methodology proposed in this chapter follows the WBCSD division of KEPIs in BSIs and GAIs allowing the creation of a list of indicators for a detailed/specific analysis of the product’s environmental aspects to be mainly used for comparison and communication at intracompany level (BSIs), and of another list of indicators that are generally accepted for comparison and communication at intercompany level (GAIs). The indicators of both lists can be used for decision-making, depending on the aim of the study or improvement. By this division the proposed methodology aims to assure completeness and significance by including the BSIs and by listing the GAIs.

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In addition to those two lists of KEPIs referred, the proposed methodology also includes another division regarding the aggregation level (Levels 1 and 2, see below). With this division it is intended to allow the comprehensive understanding of the environmental impacts of products life cycle phases. This results in 4 lists of KEPIs described as follows (see Tables 1.4 and 1.5 for the injection molds): a) Business specific indicators Level 1: KEPIs expressing the quantities of resources consumed related to the environmental aspects involved in the products life cycle; this list is very detailed with almost no aggregation of indicators—aggregation of environmental aspects in one KEPI is done when there is more than one environmental aspect in the same manufacturing process (for the same type of EPI). Note: Here all the EPIs are relevant, so they are called KEPIs; Nevertheless, this Level 1 list is still extensive, so the Level 2 was created allowing the aggregation of several KEPI in one KEPI to facilitate the analysis. b) Business specific indicators Level 2: KEPIs aggregating KEPIs of BSIs Level 1 of the same life cycle phase for each type of EPI; this list is shorter than the one for Level 1 and facilitates the analysis per life cycle phase, but it keeps the detail per type of EPI. c) Generally applicable indicators Level 1: KEPIs aggregating KEPIs of BSIs Level 2 in the following indicators: energy consumption, materials consumption, water consumption, fuel consumption, emissions, effective production and use time and space allocated; but keeping the division by life cycle phase (these indicators were defined on the base of the ISO 14031 types of scope of the EPIs list and the GAIs type of indicators as proposed by WBCSD); this list is structured according to these types of indicators and also the life cycle phase; if in each life cycle phase the indicator is related with only one type of material, energy source, emission, etc. the KEPI can be expressed in physical units, if not, the eco-indicators must be used. d) Generally applicable indicators Level 2: KEPIs aggregating KEPIs of GAIs Level 1 in the following indicators: impact of energy consumption, impact of materials consumption, impact of water consumption, impact of emissions, effective production, and use of time and space allocated; this list has only six indicators with a very high level of aggregation; normally these KEPIs must be expressed in eco-indicators because usually for each life cycle phase different resources are used (that is why these KEPIs are named with the term “impact”). Tables 1.4 and 1.5 present the short lists, that is, the result of Step (1) of the proposed methodology applied to the product “injection mold”. These tables also include the list of EPIs, mold life cycle phases, aggregate processes, and environmental aspects, along with the Levels 1 and 2 BSIs and GAIs. It is important to note that the ISO 14031 standard proposes EPIs that cannot be included into any of the GAIs proposed by the WBCSD, namely, indicators related to time and space allocated. Nevertheless, the authors of the proposed methodology decided to include them for reasons of completeness in the description of the product impact.

Table 1.4  Business-specific indicators (level 1 and level 2) EPI

Life cycle phase

Amount of material used

Raw material acquisition Mold manufacturing

Process

CNC EDM Grinding

Amount of material processed, recycled, or reused

Mold use

Injection

Mold manufacturing

CNC

Mold use

Amount of packaging material disposed or reused

Raw material acquisition

Steel to manufacture mold Cutting tool Wire Electrode Wheel Polymer used to produce parts Mold material recycled

EDM

Mold material recycled

Injection

Polymer recycled or reused Mold material recycled Packaging

Business-specific indicators (level 1)

Business-specific indicators (level 2)

Amount of material to manufacture mold

Raw material acquisition (start of life) Material consumption (mold manufacturing)

Material consumption (CNC) Material consumption (EDM) Material consumption (grinding) Material consumption (mold use) Amount of recycled mold material (CNC) Amount of recycled mold material (EDM) Amount of recycled polymer (mold use) Amount of recycled mold material (end of life) Amount of packaging material (start of life)

Material consumption (mold use) Amount of mold material recycled (mold manufacturing) Amount of polymer recycled (mold use) Amount of mold material recycled (end of life) Amount of packaging material (start of life) (Continued  )

The Case of Molds for Injection Molding

Mold end of Life

Aspect

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EPI

Amount of ancillary material recycled or reused

Life cycle phase

Process

Aspect

Mold manufacturing

CNC

Packaging

EDM

Packaging

Grinding

Packaging

Mold use

Injection

Packaging

Mold manufacturing

CNC

Recycled or nonused cutting fluid Nonused Filters Recycled or nonused dielectric Fluid Nonused Filters Recycled or nonused lubricant fluid Reused polymer

EDM

Grinding Amount of raw material reused Amount of hazardous materials used

Mold use

Injection

Mold Manufacturing

CNC

Cutting fluid Filters

EDM

Dielectric fluid Filters

Grinding

Lubricant fluid

Business-specific indicators (level 1)

Business-specific indicators (level 2)

Amount of packaging material (CNC) Amount of packaging material (EDM) Amount of packaging material (grinding) Amount of packaging material (mold use) Amount of ancillary material recycled or reused (CNC)

Amount of packaging material (mold manufacturing)

Amount of packaging material (mold use) Amount of ancillary material recycled or reused (mold manufacturing)

Amount of ancillary material recycled or reused (EDM) Amount of ancillary material recycled or reused (grinding) Amount of polymer reused (mold use) Amount of hazardous materials used CNC Amount of hazardous materials used (EDM) Amount of hazardous materials used (grinding)

Amount of polymer reused (mold use) Amount of hazardous materials used (mold manufacturing)

Advanced Applications in Manufacturing Engineering

Table 1.4  Business-specific indicators (level 1 and level 2) (cont.)

Rate of defective products

Mold use

Injection

Total waste to final destination

Mold manufacturing

CNC

EDM

Grinding Mold use

Injection

Nonrecycled polymer

Mold manufacturing

CNC

Contaminated cutting fluid

EDM

Grinding

Unusable filters Contaminated dielectric fluid Contaminated filters Contaminated lubricant fluid

Rate of defective products (mold use)

Rate of defective products (mold use)

Total waste to final destination (CNC)

Total waste to final destination (mold manufacturing)

Total waste to final destination (EDM)

Total waste to final destination (grinding) Total waste to final destination (mold use) Amount of waste controlled by authorization (CNC)

Total waste to final destination (mold use) Amount of waste controlled by authorization (Mold manufacturing)

Amount of waste controlled by authorization (EDM) Amount of waste controlled by authorization (grinding) (Continued)

The Case of Molds for Injection Molding

Amount of waste controlled by authorization

Number of defective parts produced Nonrecycled mold material Unusable cutting tool Nonrecycled mold material Unusable wire Unusable electrode Contaminated deionized water Unusable grinding wheel

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Business-specific indicators (level 1)

Business-specific indicators (level 2)

Energy consumption (CNC) Energy consumption (EDM) Energy consumption (grinding) Energy consumption (mold use) Amount of water used (EDM)

Energy consumption (mold manufacturing)

EPI

Life cycle phase

Process

Aspect

Amount of energy used

Mold manufacturing

CNC

Total energy

EDM

Total energy

Grinding

Total energy

Mold use

Injection

Total energy

Amount of water used

Mold manufacturing

EDM

Deionized water

Amount of water reused

Mold manufacturing

EDM

Reused deionized water

Amount of water reused (EDM)

Average fuel consumption of the vehicle fleet

Raw material acquisition

Average fuel consumption

Transport

Average fuel consumption

Mold end of life

Average fuel consumption

Average fuel consumption (start of life) Average fuel consumption (transport after production) Average fuel consumption (end of life) Number of machine operating hours per year (CNC) Number of machine operating hours per year (EDM)

Number of operating hours of a specific equipment per year

Mold manufacturing

CNC EDM

Milling Turning Drilling EDM Electrode machining

Energy consumption (mold use) Amount of water used (mold manufacturing) Amount of water reused (mold manufacturing) Average fuel consumption (start of life) Average fuel consumption (transport after production) Average fuel consumption (end of life) Number of machine operating hours per year (Mold Manufacturing)

Advanced Applications in Manufacturing Engineering

Table 1.4  Business-specific indicators (level 1 and level 2) (cont.)

Number of unforeseen emergency occurrences or operations per year

Grinding

Grinding

Mold use

Injection

Injection

Mold manufacturing

CNC

Machine emergencies

EDM

Machine emergencies

Grinding

Machine emergencies

Injection

Machine emergencies

Mold use

Mold emergencies Mold manufacturing

CNC

Machine maintenance

EDM

Machine maintenance

Number of machine operating hours per year (mold use) Number of emergencies per year (Mold Manufacturing)

Number of emergencies per year (mold use)

Number of hours of preventive maintenance per year (mold manufacturing)

(Continued)

The Case of Molds for Injection Molding

Number of hours of preventive maintenance of equipment per year

Number of machine operating hours per year (grinding) Number of machine operating hours per year (mold use) Number of machine emergencies per year (CNC) Number of machine emergencies per year (EDM) Number of machine emergencies per year (grinding) Number of machine emergencies per year (mold use) Number of mold emergencies per year (injection) Number of hours of preventive maintenance of machine per year (CNC) Number of hours of preventive maintenance of machine per year (EDM)

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EPI

Life cycle phase

Mold use

Process

Aspect

Grinding

Machine maintenance

Injection

Machine maintenance

Mold maintenance

Product use duration

Mold use

Injection

Cycle time Number of shots until mold maintenance

Mold end of life

Mold life

Business-specific indicators (level 1)

Number of hours of preventive maintenance of machine per year (grinding) Number of hours of preventive maintenance of machine per year (mold use) Number of hours of preventive maintenance of mold per year (mold use) Cycle Time (mold use) Number of cycles until mold maintenance (mold use) Mold life (end of life)

Business-specific indicators (level 2)

Number of hours of preventive maintenance per year (mold use)

Cycle Time (mold use) Number of cycles until mold maintenance (mold use) Mold life (end of life)

Advanced Applications in Manufacturing Engineering

Table 1.4  Business-specific indicators (level 1 and level 2) (cont.)

Total area of land used for production

Raw material acquisition

Space allocated to raw materials

Mold manufacturing

Space allocated to the machine Space allocated to the machine Space allocated to the machine Space allocated to the machine

CNC EDM Grinding

Mold use Mold end of life

Injection

Space allocated to the mold in end of life

Space occupied by raw materials (start of life) Space allocated to machines (CNC) Space allocated to the machines (EDM) Space allocated to the machines (grinding) Space allocated to the machines (mold use) Space allocated to the molds (end of life)

Space allocated to raw materials (start of life) Space allocated to the machines (mold manufacturing)

Space allocated to the machines (mold use) Space allocated to molds (end of life)

CNC, Computer numerical control.

The Case of Molds for Injection Molding

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Table 1.5  Generally applicable indicators (Level 1 and Level 2) Business-specific indicators (Level 2)

Raw material acquisition (start of life) Amount of packaging material (start of life) Material consumption (mold manufacturing) Amount of mold material recycled (mold manufacturing) Amount of packaging material (mold manufacturing) Amount of ancillary material recycled or reused (mold manufacturing) Amount of hazardous materials used (mold manufacturing) Total waste to final destination (mold manufacturing) Amount of waste controlled by authorization (mold manufacturing) Material consumption (mold use) Amount of polymer recycled (mold use) Amount of packaging material (mold use) Amount of polymer reused (mold use) Rate of defective products (mold use) Total waste to final destination (mold use) Amount of mold material recycled (end of life) Energy consumption (mold manufacturing) Energy consumption (mold use) Amount of water used (mold manufacturing) Amount of water reused (mold manufacturing) Average fuel consumption (start of life) Average fuel consumption (transport after production) Average fuel consumption (end of life)

Generally applicable indicators (Level 1)

Generally applicable indicators (Level 2)

Material consumption (start of life)

Impact of material consumption

Material consumption (mold manufacturing)

Material consumption (mold use)

Material consumption (end of life) Energy consumption (mold manufacturing) Energy consumption (mold use) Water consumption (mold manufacturing) Average fuel consumption (start of life) Average fuel consumption (transport after production) Average fuel consumption (end of life)

Impact of energy consumption Impact of water consumption Impact of emissions

The Case of Molds for Injection Molding

Table 1.5  Generally applicable indicators (Level 1 and Level 2) (cont.) Business-specific indicators (Level 2)

Number of machine operating hours per year (mold manufacturing) Number of emergencies per year (mold manufacturing) Number of hours of preventive maintenance per year (mold manufacturing) Number of emergencies per year (mold use) Number of machine operating hours per year (mold use) Number of hours of preventive maintenance per year (mold use) Cycle time (mold use) Number of cycles until mold maintenance (mold use) Mold life (end of life) Space allocated to raw materials (start of life) Space allocated to the machines (mold manufacturing) Space allocated to the machines (mold use) Space allocated to the molds (end of life) Total environmental impact (all life cycle phases)

a

Generally applicable indicators (Level 1)

Generally applicable indicators (Level 2)

Effective production time (mold manufacturing)

Effective production and use timea

Effective time of mold use until end of life (mold use)

Total space allocated

Space allocateda

Life cycle impact (sum of impact of all life cycle phases)

Indicators can be omitted from the environmental profile of the company.

Still, these KEPIs influence resource consumption and emissions and should therefore be present in the environmental profile for decision-making tasks but are not the most relevant to be used in eco-efficiency ratios. The space allocated is measured by the space occupied by all machines and areas that are passed by the mold throughout its life cycle. There is one KEPI included in the Level 2 GAIs’ list that is not proposed by ISO 14031, that is, the total environmental impact of the product during its life cycle. This KEPI is the sum of all environmental impacts referring to that list measured in ecopoints. This indicator is very useful for the eco-efficiency ratios because it represents the overall influence of the product in the environment. Despite the referred skepticism of the normative document regarding the use of an eco-indicator, the authors of the

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proposed methodology recommend its use as the only way to have a quantitative representation of the overall environmental impact. These eco-indicators are used together with physical-based indicators in the eco-efficiency profiles proposed by this methodology to avoid concealing relevant information. The buildup of the environmental profile is based on these lists of KEPIs.The company applying the methodology has to decide to use all or a part of them. In general, the BSIs of both levels are mainly to be used for decision-making processes of product (re)design or continuous improvement of production performance. Some of the GAIs are also recommended for decision-making because of their immanent aggregation facilitating the assessment of the aggregated impact of alternative solutions (e.g., material consumed in the mold manufacturing phase). In addition, as a general recommendation, the Level 1 GAIs are suitable for intracompany comparison and communication because these indicators reveal the aggregated consumptions and emissions. So they can be used to monitor the performance along time (in addition, some of the Level 2 BSIs’ KEPIs might be useful for this aim).The Level 2 GAIs are mainly suitable for decision-making and reporting because they assess the impact of the product at a highly aggregated level and use eco-points that are comparable among similar products using different materials and processes in one or more companies. 1.3.2.2  Value indicators Analogously to the EPIs (or the KEPIs in the case of the proposed methodology), the value indicators have three main purposes in the eco-efficiency assessment: they are used as numerators of the eco-efficiency measuring ratios, they are used to build up the value profile (one of the profiles of the eco-efficiency profile), and, based on that, they allow for analyses of the different elements of the value and the factors creating it. In the classical definition of the eco-efficiency ratio Eq. (1.1), the numerator designation is “product or service value” [1,2,19]. Nevertheless, the eco-efficiency concept is in fact a relation between the economic performance and the environmental performance; so indicators for the numerators of the eco-efficiency ratios proposed by the eco-efficiency’s normative documents do not have to be exclusively related with value measurements [1,2,19]. In addition, these normative documents’ application framework is at company level, as referred before, so the indicators suggested refer to value and economic performance measurements at company level (and not at product level). This is also the case for the normative document selected to guide the proposed methodology, that is, the WBCSD [17,11] that proposes a set of indicators for the value profile with indicators that are not only directly related with value but also others indirectly related. This list of indicators is presented in Table 1.6. The WBCSD classifies the indicators in four different “aspects,” which should be interpreted as the “scope” of the EPIs list of the ISO 14031 standard [41] (Table 1.3). Among this list of indicators, the indicator “cost” is included, which cannot be used directly in the eco-efficiency ratio numerator, being exclusively to be used in the value profile.

The Case of Molds for Injection Molding

Table 1.6  Set of indicators for value assessment suggested by WBCSD [17] Scope

Example indicator

Volume

Units sold (e.g., number) Employees (e.g., labor hours) Quantity sold (e.g., kilograms) Quantity produced or provided (e.g., kilograms) Gross sales or gross margins Net sales (gross sales—sales returns—sales allowances—discounts) EBIT (gross sales—operating expenses) Value added (net sales-cost of goods purchased) Investments Costs (e.g., cost of goods sold, production, energy) Product performance (e.g., laundry loads washed) Product durability/lifetime (e.g., vehicle miles traveled)

Mass Monetary figures

Function

EBIT, earnings before interest and taxes.

The WBCSD normative document [17] is very broad about the value indicators that can be used, referencing this list as a suggestion of indicators and mentioning that “additional financial value indicators” can be used. As a result, there are several publications referring to the use of the WBCSD normative document that propose the use of such type of indicators, for example, gross domestic product, gross value-added and earnings before interest, taxes, depreciation, and amortization [44,45], among others. The WBCSD normative document [17] suggests some of the indicators of this list for the numerator of the eco-efficiency ratios. Using the division of the EPIs in GAIs and BSIs, the Quantity of goods or services produced or provided and the Net sales are suggested as GAIs’ indicators because, according to WBCSD, the way they are measured by the companies is similar; the EBIT, gross margin, and value added are suggested as BSIs by WBCSD because “…there are still wide differences in the meaning and measurement for these financial measures…” [17] (the authors do not fully agree with this statement due to existing rules for calculating figures such as EBIT). Concluding, an application of the eco-efficiency metrics at product level based on the existing normative document for the value indicators does not face a problem resulting from a high number of indicators. The barrier in this case is the inadequacy of most of the indicators to be applied at product level, mainly the monetary ones. Those indicators express the performance of the company resulting from all of its financial and commercial activities and not necessarily exclusively the performance of the products. Additionally, they refer to one period and usually to all or at least larger groups of products. So in the proposed methodology, a different set of value indicators is proposed. 1.3.2.2.1  The selection of the product-related value indicators

The proposed indicators for Value assessment are listed in Table 1.7. This list contains indicators that unequivocally measure the product value and others that are related

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Table 1.7  Set of indicators for value assessment suggested by the proposed methodology for the injection mold Scope

Example indicator

Unit-related indicators (parts)

Cost per unit (mold cost + plastic part production) Price per unit Contribution margin per unit (price–variable costs per unit) Profit per unit (price–costs per unit)a Costs (e.g., cost of a specific month, year, etc.) Sales (e.g., sales of a specific month, year, etc.) Profita (e.g., profit of a specific month, year, etc.)

Period-related indicators (mold use or part production phase) Life cycle phaserelated indicators (referring to the molds life cycle) Life cycle-related indicators (molds usage or part production phase)

Cost of development phase (e.g., design, prototyping, etc.) Costs of mold production phase (e.g., materials, processes, energy, labor, etc.) Costs of usage phase (e.g., materials, processes, energy, labor, etc.) Costs of EOL phase (of the mold) Plastic parts produced during molds life timea Life cycle costs (measured as NPV) Life cycle sales (measured as NPV) Life cycle profit (measured as NPV)a

NPV, Net-present value. a These are the ones to be used in the eco-efficiency ratios (measures of value) and the remaining ones are to be used only in the value profile.

with—mainly they are influencing—the economic performance of the product but are not measures of value. These latter ones allow for analyzing eco-efficiency and deriving starting points for improving it. The existence of two types of indicators is similar to the list proposed by WBCSD (Table 1.6) comprising the full set of indicators to be used in the value profile and the ones that measure value and can also be used as nominator in the eco-efficiency ratios. Four types of scope are proposed. The unit-related indicators intend to measure the economic performance per plastic part produced being the profit per plastic part unit the indicator that measures the value of the product (the mold). This value indicator, together with the environmental impact per plastic part, can be used in an early design phase or during the development phase of the mold to support decision-making processes related with eco-efficiency performance of the product design and production alternatives. The period-related indicators are important to analyse the performance mainly after the mold is already in use. Therefore, these types of indicators allow the comparison of the mold performance during several time periods (e.g., 6 months, 1 year, etc.) aiming to understand the evolution of eco-efficiency performance (if used together with the environmental impact of the production of the part for the same period in the ecoefficiency ratios). So this type of indicators is useful for intracompany communication and comparisons.

The Case of Molds for Injection Molding

The phase-related indicators allow for assessing the relevance of the several life cycle phases in terms of cost and in terms of sales (just for the use phase).These indicators give a detailed view of the contribution of each phase to the economic performance that, together with the Level 1 of the GAIs of the environmental performance (also per life cycle phase), permits to assess both the performances simultaneously. The use of ecoefficiency ratios is not meaningful, but the analysis of the relative contribution of each phase will allow the identification of the contribution/importance of each phase to the total environmental impact and cost. This is useful for decision-making among product alternatives during the product development phase and even in an early design phase. The life cycle related indicators aim to assess the life cycle performance of the product. The value is assessed by the life cycle profit and by the capacity of the mold production during its life span.The life cycle profit indicator, together with the (total) life cycle environmental performance, forms an eco-efficiency ratio that retrieves the eco-efficiency performance of the product on a life cycle perspective.This eco-efficiency ratio is very useful for decisionmaking processes and also for intracompany communication about the overall product performance.The number of plastic parts that the mold can produce during its lifetime can be used with the corresponding environmental indicator to assess the expected eco-efficiency of the product (the mold).This eco-efficiency ratio is very useful for intercompany communication, for the communication to the society about the performance of the product, and to allow benchmarking (if the other companies follow the same calculation rules).This indicator in the form of an eco-efficiency ratio has the advantage of not revealing any detail about profit or cost of the product, information that the company usually wants to keep in-house.

1.3.3  Step (2)—Application to a case study 1.3.3.1  Case study description Two mold design alternatives, able to produce similar plastic parts, are considered to demonstrate how the methodology can be applied. These alternatives are mainly differentiated by their feeding system (cold runners and hot runners). The goal of the eco-efficiency analysis is to present the eco-efficiency profiles and discuss the molds’ performance on eco-efficiency. These two types of feeding system are primarily distinguished by the material consumption. The injection molding with a cold runners mold requires more plastic material. The plastic solidifies in the runners during the cooling phase of the injection cycle and must be removed after ejection. Contrarily, the use of a hot runner mold allows the plastic material to remain in the runners and only the final part is ejected. Hence, it is possible to estimate, a priori, that the mold with cold runners will present an increase in plastic material consumption and in energy consumption during injection, when compared to the mold with hot runners. In other words, the use of cold runners requires to inject more material and a higher cycle time, so more energy is required. Consequently, higher environmental impact is expected with cold runner molds. However, molds with hot runners entail a more complicated technology, which

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Table 1.8  Part characteristics (information given by an industrial company) Characteristic

Part

Material Part volume Projected area Maximum thickness Runner diameter Maximum material recycle rate Reject rate Complexity Part life Expected annual production volume Market price

PBT 3.73 cm3 279 mm2 3 mm 6 mm 30% 1% High 8 years 6,500,000 parts/year 0.083 €/part

PBT, Polybutylene terephthalate.

will undoubtedly increase the mold costs and the efforts in production. Thus it is important to consider both the mold production and injection phases of a molds life cycle. Following the methodology, the eco-efficiency definition chosen is the one presented by the WBCSD [15,16], as the comparison is made between products in a life cycle perspective.With the definition selected, it is then necessary to characterize the products (the mold with hot runners and with cold runners), the manufacturing processes and resources necessary for the mold production, the plastic part production process and resources necessary (the mold use phase), and the end-of-life issues. The two mold design alternatives considered are molds wherein the only difference is the type of feeding system (cold runners or hot runners). Both molds are used to produce the same plastic part, with the characteristics presented in Table 1.8. These molds are machined using CNC, both wire and electrode EDM and Grinding. The mold alternative characteristics are presented in Table 1.9, including the mold dimensions and the processing times for each manufacturing process. In Appendix Table  1.2, detailed data related with mold manufacturing and mold use regarding resources consumed are presented. Table 1.9  Mold alternatives data (information given by an industrial company)

Parts produced during mold life Dimensions Number of cavities CNC time Electrode EDM time Wire EDM time Grinding time Additional data presented in Appendix Table  1.2. CNC, Computer numerical control.

Cold runners

Hot runners

5.2E + 7 24.6 × 34.6 cm 8 203 h 400 h 291 h 150.5 h

5.2E + 7 24.6 × 39.6 cm 8 223 h 400 h 291 h 150.5 h

The Case of Molds for Injection Molding

Gathering all the information necessary for the value and environmental impact indicators for both mold design alternatives is a basis for building up the environmental and the value profiles that will be presented in the next section as part of Step (2) of the proposed methodology. In addition, the eco-efficiency ratios are also presented, composing three sets of information of the eco-efficiency profile. 1.3.3.2  Environmental profile The environmental profile for the molds of the case study is presented in Tables 1.10 and 1.11. This profile was based on the “shorter” list of indicators created for this type of product in Step (1) of the methodology (Tables 1.4 and 1.5). But some of the indicators listed in these Step (1) tables were not used in the environmental profile of the case study because they were considered to be not relevant for the analysis for the following reasons: • Some of the operations/processes listed in Step (1) do not occur or are not used in the case study under analysis (e.g., packaging processes) • Some of the operations/processes were considered to be not relevant for the analysis by the decision makers (despite they might have impact and influence on the performance; this case study was done with information gathered in an industrial company, and no information was disclosed about some processes/tasks, e.g., transport); • About some operation/processes, information is not yet available (this is the case, e.g., for auxiliary materials). This lack of information should be avoided for the sake of comprehensiveness of the information, mainly if the aim is to communicate or report the results at an intercompany level or to the society. Nevertheless, the path to a complete data gathering system takes time and energy, and involves several stakeholders, so at the moment, these profiles must be generated with limited information. In this particular case, the main aim is to compare the eco-efficiency performance of two types of molds, so this lack of information does not affect the decision-making process because the missing indicators are certainly equal or similar for both alternatives. The BSIs for this case study are presented in Table 1.10. One of the objectives of the use of Level 2 of these indicators is to aggregate the impact of several aspects of a KEPI in a single score.When the several aspects of the same KEPI have different physical dimensions, the use of an eco-indicator is necessary to calculate the aggregated impact. For this case study, the aspects of each KEPI have the same physical unit, so there was no need to make use of eco-indicators. The aggregated results are presented in the physical unit of the KEPIs bearing the advantage of facilitating the interpretation by technical personal. The GAIs are listed in Table 1.11. The Ecoinvent database was used, and the ecoindicators were calculated by the ReCiPe method (end-point scoring) in accordance with the proposed methodology recommendations—the GAI aggregation characteristic

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Business-specific indicators (Level 1)

Mold design alternatives Cold runners

Hot runners

Amount of material to manufacture mold (kg) Material consumption (mold use) (kg) Amount of recycled polymer (injection) (kg) Amount of hazardous materials used (CNC) (dm3) Amount of hazardous materials used (grinding) (dm3) Rate of defective products (mold use) (parts/h)

47.93

57.67

88,482.55

53,376.93

26,680.85

16,070.36

0.23

0.26

0.38

0.38

16.61

19.57

Total waste to final destination (mold use) (kg) Energy consumption (CNC) (kWh) Energy consumption (EDM) (kWh) Energy consumption (grinding) (kWh) Energy consumption (injection) (kWh)

61,801.70

37,306.57

2,036.67

2,236.67

7,228.80

7,228.80

1,017.56

2,229.19

95,673.35

81,207.28

Business-specific indicators (Level 2)

Mold design alternatives Cold runners

Hot runners

Raw material acquisition (start of life) (kg) Material consumption (mold use) (kg) Amount of polymer recycled (mold use) (kg) Amount of hazardous materials used (mold manufacturing) (kg)

47.93

57.67

88,482.55

53,376.93

26,680.85

16,070.36

0.62

0.64

Rate of defective products (mold use) (parts/h) Total waste to final destination (mold use) (kg) Energy consumption (mold manufacturing) (kWh)

16.61

19.57

61,801.70

37,306.57

10,283.03

11,694.65

Energy consumption (mold use) (kWh)

95,673.35

81,207.28

Advanced Applications in Manufacturing Engineering

Table 1.10  Business-specific indicators for the case study, considering the mold production and the mold use for the production of 6.5E6 plastic parts (1 year of mold use)

450.00

450.00

Number of machine operating hours per year (CNC) (h) Number of machine operating hours per year (EDM) (h) Number of machine operating hours per year (grinding) (h) Number of machine operating hours per year (mold use) (h) Number of hours of preventive maintenance of mold per year (mold use) (h) Cycle time (injection) (s) Number of cycles until mold maintenance (mold use) Mold Life (parts) Space occupied by machine (CNC) (m2) Space occupied by machine (EDM) (m2) Space occupied by machine (grinding) (m2) Space occupied by machine (mold use) (m2)

203.00

223.00

691.00

691.00

330.00

330.00

3,953.44

3,355.67

6.66

6.66

2.17 1,000,000

1.84 1,000,000

5.2E + 7 20.00

5.2E + 7 20.00

40.00

40.00

20.00

20.00

15.00

15.00

Amount of water used (mold manufacturing) (dm3) Number of machine operating hours per year (mold manufacturing) (h)

450.00

450.00

1,224

1,244

Number of machine operating hours per year (mold use) (h) Number of hours of preventive maintenance per year (mold use) (h) Cycle time (mold use) (s) Number of cycles until mold maintenance (mold use) Mold life (parts) Space allocated to the machines (mold manufacturing) (m2)

3,953.44

3,355.67

6.66

6.66

2.17 1,000,000

1.84 1,000,000

5.2E + 7 80.00

5.2E + 7 80.00

Space allocated to the machines (mold use) (m2)

15.00

15.00

The Case of Molds for Injection Molding

Amount of water used (EDM) (dm3)

CNC, Computer numerical control.

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Table 1.11  Generally applicable indicators for case study, considering the mold production, and the mold use for the production of 6.5E6 plastic parts (1 year of mold use) Generally applicable Indicators (Level 1)

Mold design alternatives Cold runners

Hot runners

Material consumption (start of life) (Pt) Material consumption (mold manufacturing) (Pt) Material consumption (mold use) (Pt) Energy consumption (mold manufacturing) (Pt) Energy consumption (mold use) (Pt)

54.64

65.75

0.04

0.04

201,038.22

121,355.33

533.07

606.25

4,959.71

4,209.79

Water consumption (mold use) (Pt) Effective production time (mold manufacturing) (h) Effective time of mold use (mold use) (h) Total space allocated (m2)

0.07

0.07

1,224.00

1,244.00

3,960.10

3,362.33

95.00

95.00

a

Generally applicable indicators (Level 2)

Mold design alternatives Cold runners

Hot runners

Impact of material consumption (Pt)

201,092.90

121,421.11

Impact of energy consumption (Pt)

5,492.78

4,816.04

Impact of water consumption (Pt) Effective production and use timea (h)

0.07

0.07

5,184.10

4,606.33

Space allocateda (m2) Total environmental impact

95.00

95.00

206,585.75

126,237.22

Not included in the total environmental impact. This information might be useful to be displayed or available and is recommended by ISO14031.

The Case of Molds for Injection Molding

usually demands for the use of eco-indicators to allow the aggregation of the environmental impacts from different sources (aspects). The specific impacts (eco-indicators) calculated using the software SimaPro and the ReCiPe impact assessment method are presented in Appendix Table  1.3. These figures are then multiplied with the aggregated quantities from Level 2 BSIs and summed, allowing the calculation of GAI Level 1 figures. As previously explained, these results are then aggregated, originating the indicators proposed by the WBCSD (Level 2 GAI). The mold with hot runners causes less overall environmental impact as expected; it requires less material, time, and energy to produce the plastic parts.The low influence of the mold manufacturing phase in the life cycle environmental impact is revealed by the fact that the mold with hot runners has higher environmental impact in its manufacturing phase (around 10%–15% more than the cold runner mold) but has around 40% less life cycle environmental impact. So the indicators with high contribution to the overall environmental impact are by far the plastic material consumed followed by the energy consumed in the injection molding process. 1.3.3.3  Value profile To present the value profile, first the costs for each mold design alternative must be calculated. With the information available regarding the mold manufacturing processes, labor requirements, bought components, etc., it is possible to calculate the total production costs, as presented in Table 1.12. These costs can be gathered in several ways, being Table 1.12  Total mold production cost for mold alternatives

Mold design alternatives Cold runners

Hot runners

4,485 € 17,658 € 1,002 € 1,362 € 750 €

4,623 € 17,958 € 1,018 € 1,363 € 750 €

Main machine cost Tooling cost Fixed overhead cost Building cost Maintenance cost Subcontracts/bought components

10,528 € 1,603 € 4,414 € 659 € 8 € 8,405 €

10,755 € 1,621 € 4,489 € 693 € 8 € 13,569 €

Total

50,873 €

56,846 €

Variable costs

Project cost Labor cost Energy cost Process material cost Material cost Fixed costs

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Table 1.13  Monetary data related with the several mold life cycle phases referring to 1 year of mold use, that is, for a production of 6,500,000 plastic parts (using NPV)

Mold development and production cost (NPV) Injection molding process cost (without plastic material) Plastic material cost Mold EOL revenue (NPV) Plastic parts EOL cost (NPV) Mold sales price

Cold runner

Hot runner

11,689.53 46,142.68

13,062.00 37,837.00

€/mold.year €/mold.year

202,167.79 6.09 1,456.00 58,642.00

104,555.00 7.33 1,456.00 65,556.39

€/mold.year €/mold.year €/mold.year €/mold

EOL, End of life; NPV, net-present value.

recommended to be for the same time period than the data retrieved for the environmental indicators. In this case, they were obtained directly from the company in which the case study is based. The mold with hot runners has a higher manufacturing cost because of the cost of the hot runner system that is bought from a supplier. There are also differences in the other cost factors but these are not as important as the costs of the runner system. In fact, the mold with hot runners uses more energy, materials, labor, and tools than the cold runner mold (Table 1.12). But the mold with hot runners has a better performance, consuming less energy and injection equipment time and also less material.Therefore in the case the mold is sold to another company and not used indoor to produce the plastic parts, the hot runner mold has a higher sales price because of its better performance (Table 1.13). The end of life (EOL) costs of the molds are slightly different because of the different mass of materials but have very low impact in the overall costs. The EOL costs of the plastic parts sold are the same for the two alternatives because the parts were considered to be the same. The Value indicator results are presented in Table 1.14. The unit-related indicators are expressed in monetary units per plastic part sold during mold lifetime, which was considered to be of 5.2E+07 plastic parts.The profit per unit is higher for the mold with hot runners, because for the same price per plastic units, it allows lower cost per unit. The Period-related indicators are difficult to apply in this case, because there is no information about real utilization of both alternatives. So this group of indicators was used to compare the two molds considering only the mold production phase and selling price of the mold to another company that does the injection molding. Again, the mold with hot runners reveals a higher profit, but this difference is smaller than considering also the mold use phase, meaning that the market price of the molds does not fully reflect the much higher potential of molds with hot runners. As regards the Phase-related indicators, the cost of the use phase is responsible for more than 90% of the costs, meaning that a higher emphasis in the design and mold

The Case of Molds for Injection Molding

Table 1.14  Value profile: value indicators for the case study Scope

Indicator

Cold runner

Hot runner

Unit-related indicators (parts)

Cost per unit (mold cost + plastic part production) Price per unit

0.040

0.024

€/plastic part

0.083

0.083

Contribution margin per unit (price—variable costs per unit) Profit per unit (price—costs per unit)a Costs of the mold Mold price Profit of the mold salea

0.045

0.061

€/plastic part €/plastic part

0.043

0.059

50,873.00 58,642.00 7,769.00

56,846.00 65,556.39 8,710.00

€/plastic part €/mold €/mold €/mold

Cost of development phase (e.g., design, prototyping, etc.) Costs of mold production phase (e.g., materials, processes, energy, labor, etc.) Costs of usage phase (e.g., materials, processes, energy, labor, etc.) Costs of EOL phase (of the mold) Costs of EOL phase (of the parts) Plastic (good) parts produced during molds lifetimea Life cycle costs (measured as NPV) Life cycle sales (measured as NPV) Life cycle profit (measured as NPV)a

4,485.00

4,623.00

€/mold

46,388.00

52,223.00

€/mold

1,986,483.75

1,139,136.00

€/mold

−100.00

−100.00

€/mold

11,648.00

11,648.00

€/mold

52,000,000.00

52,000,000.00

parts

261,448.67

155,454.00

539,500.00

539,500.00

278,051.33

384,046.00

€/mold. year €/mold. year €/mold. year

Period-related indicators (only mold production) Phase-related indicators

Life cycle-related indicators (molds usage or part production phase)

The functional unit is the plastic part, but in some indicators the use of the mold as the reference unit makes information clearer; the transfer of those values for the functional unit is done by the quotient between the figure and the number of parts done by the mold during its life cycle (5.2E + 07 parts). EOL, End of life; NPV, net-present value. a These are the ones to be used in the eco-efficiency ratios (measures of value) and the remaining ones are to be used only in the value profile.

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manufacturing phase can, despite increasing those phases’ costs, allow a dramatic much higher impact in the mold use phase, compensating the increase of the design and mold manufacturing phase costs. The life cycle related indicators show a similar trend, with the mold with hot runner being the one with better performance in profit, that is, the indicator that measures the product value. From both the environmental and value results, it is possible to predict that the alternative with hot runners will be the one with better eco-efficiency ratios. However, for methodology demonstration, the results for eco-efficiency ratios will be presented in the following section. 1.3.3.4  Eco-efficiency ratios’ profile The eco-efficiency ratios used for the case study are listed in Table 1.15. These ratios should be analyzed always together with the other two components of the eco-efficiency profile, the value and the environmental impact profiles. The first ratio measures the relation between the mold production capacity during its lifetime and the environmental impact caused by that production, on a life cycle perspective. The mold with cold runners has an eco-efficiency performance that is 61% of the one of the mold with hot runners. This ratio is not very useful to compare the Table 1.15  Eco-efficiency ratios profile for the case study Value and environmental indicators

Plastic parts produced during mold life cycle Total environmental impact

Eco-efficiency ratios

Cold runner

Hot runner

Cold runner

Hot runner

52,000,000.00

52,000,000.00

31.46

51.49

1,652,686.00

1,009,897.76

parts/pt

Cold/Hot = 0.61 Profit per plastic part Environmental impact per plastic part

0.0428 0.0318

0.0591 0.0194

1.35

3.0

€/pt

Cold/Hot = 0.44 Profit of the mold sale Environmental impact of the mold production

7,769.19 587.75

8,710.39 672.14

13.22

12.96

€/pt

Cold/Hot = 1.02 Life cycle profit Total environmental impact

278,050.09 1,652,686.00

384,046.00 1,009,897.76

0.17

0.38

Cold/Hot = 0.44

€/pt

The Case of Molds for Injection Molding

two alternatives because both molds produce the same quantity of parts during their life cycle, so the difference is relative to the already identified difference in environmental impacts of the two alternatives (in the environmental profile). Nevertheless, this ratio might be useful to compare molds for similar plastic parts for intercompany analysis and also for intracompany communication, because it does not reveal any confidential information related with cost or profit. For clearly different plastic parts, this ratio should be normalized for the part volume or for the part weight. In addition, clearly different mold/part geometric complexity, surface quality requirements, etc. might cause the inadequacy of molds comparison based on this eco-efficiency ratio. The second and the fourth ratios aim at relating the value indicator profit with the environmental impact, with one of them being relative to the unitary value and the other to the life cycle value.The results show that the eco-efficiency performance of the mold with cold runners is even worse in this case than in the first ratio, its eco-efficiency performance being only 44% of the mold with hot runners. The reason relies on the fact that the cold runner mold has a worse behavior than the mold with hot runners in both sides of the ratio, so the overall performance is lower. These two ratios give a very comprehensive idea of the two alternative differences that together with the information presented in the environmental and value profile allow to understand the origin of these differences, and so contribute to the decision-making process. The last eco-efficiency ratio to comment is the third one in Table 1.15, related only with the mold development and production phases and assuming that the mold is sold after its manufacturing (this is a recurrent scenario in the mold making industry: the injection molding operations are often not done by the mold manufacturer). In this case, the potential benefits of using the mold with hot runners (in the use phase of the mold) will occur outside of the company that produced the mold (so his customer will benefit from a better mold in several aspects). In fact, the sales price of the mold with hot runners is higher than the one with cold runners (Table 1.14); but in terms of eco-efficiency ratio, the mold with cold runners has an higher eco-efficiency performance; the slightly lower profit is compensated by a lower environmental impact of the mold with cold runners because it is a much more simpler mold. This is a very dangerous conclusion, and this ratio was used in the profile exactly to emphasize it; the origin of this result is the exclusion (or noninclusion) of a life cycle perspective in this ratio.Without counting with the performance of the product in its use phase in the eco-efficiency ratio, there is the danger to penalize the products with higher energy and resources (and economic) efficiency because their production phase is in general more demanding in terms of cost and resources consumption. In reverse, the use of the life cycle perspective indicators will contribute to demonstrate which solutions have the higher overall eco-efficiency performance. Here the results strongly support the realizations of molds with hot runners, because both the ecological and economical life cycle performances are by far better.

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1.4 CONCLUSIONS Eco-efficiency is a key concept for companies to reach a more sustainable development, considering not only the added value aspect of its activities but also the environmental impacts. However, the study of eco-efficiency is still no easy task, despite recent studies and publications, as no uniform, generally accepted definition and framework for its measurement is available, and it is focused on eco-efficiency at company level. This gap is particularly observed in the mold manufacturing and injection molding industry, where little work has been found on the subject although the eco-efficiency concept is predominantly significant for mold manufacturing and injection molding companies, as a result of the highly significant plastic consumption and the expected increase in the number of products and developments in this sector. This chapter contributes to the discussion of the concept of eco-efficiency, presenting its origins and different definitions, concluding that there is no universal definition or measuring method for eco-efficiency. As such, a methodology for the choice of one definition of eco-efficiency is proposed, followed by a proposed set of environmental and value indicators and eco-efficiency ratios—ratios between value and environmental impact—for the particular sector and focusing on the product level. This specific focus implied the necessity to introduce specific—unit-, period-, life cycle phase- and life cycle-related—value indicators for enabling a comprehensive evaluation of product performance.With the environmental and value indicators, the value and environmental profiles can be presented and the eco-efficiency ratios can be calculated and included in the eco-efficiency profile. The single ratios of the profile can be individually used for internal and external comparisons, communication, and decision-making (including decisions about design alternatives); as a whole, they present the most meaningful product-related information in terms of value, environmental impact, and eco-efficiency for the company. Having presented and discussed the methodology, this chapter also includes a case study referring to the mold manufacturing and injection molding industry. The inclusion of the case study aims at applying the methodology, proposing environmental indicators for this sector, which is an area relatively unexplored as of today, and demonstrating the steps toward the final eco-efficiency profile. In this case study, the comparison of two different mold designs (cold runners and hot runners) clearly shows that the mold design with hot runners would be the best in terms of eco-efficiency, as the value is higher for this design alternative, and the impact of material consumption is lower due to the lower levels of material and energy consumption. Although the applicability of the concept has been shown by the case study, there is still a lot of room for further work, such as specifying, extending, improving, and validating the methodology and its indicators and ratios with a view on the manifold possible fields of application.

The Case of Molds for Injection Molding

1.5  APPENDIX A Appendix Table 1.1  Environmental performance indicators according to the ISO 14031 standard Scope

EPI (ISO 14031 standard) (2005) [41]

Material

Quantity of materials used per unit of product Quantity of processed, recycled or reused materials Quantity of packaging materials discarded or reused per unit of product Quantity of auxiliary materials recycled or reused Quantity of raw materials reused in the production process Quantity of water per unit of product Quantity of water reused Quantity of hazardous materials used in the production process Quantity of energy used per year or per unit of product Quantity of energy used per service or customer Quantity of each type of energy used Quantity of energy generated with by-products or process streams Quantity of energy units saved due to energy conservation programs Amount of hazardous materials used by contracted service providers Amount of cleaning agents used by contracted service providers Amount of recyclable and reusable materials used by contracted service providers Amount or type of wastes generated by contracted service providers Number of pieces of equipment with components designed for easy disassembly, recycling and reuse Number of operating hours of a specific equipment by year Number of emergency occurrences (e.g., explosions) or unforeseen operations (e.g., operation suspension) per year Total land area used for production purposes Land area used to produce a unit of energy Average fuel consumption of the vehicle fleet Number of vehicles in fleet with pollution abatement technology Number of hours of preventive maintenance of equipment per year Average fuel consumption of the vehicle fleet Number of freight deliveries by mode of transportation per day Number of vehicles in fleet with pollution abatement technology Number of business trips saved through other means of communication Number of business trips by mode of transportation

Energy

Services supporting the organization’s operations Physical facilities and equipment

Supply and delivery

Products

Number of products introduced in the market with reduced hazardous properties Number of products which can be reused or recycled Percentage of a product’s content that can be reused or recycled Rate of defective products Number of units of by-products generated per unit of product Number of units of energy consumed during use of product Product use duration Number of products with instructions regarding environmentally safe use and disposal (Continued  )

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Appendix Table 1.1  Environmental performance indicators according to the ISO 14031 standard (cont.)

Services provided by the organization

Wastes

Emissions

Amount of cleaning agent used per square meter (for a cleaning services organization) Amount of fuel consumption (for an organization whose service is transportation) Quantity of licenses sold for improved processes (for a technology licensing organization) Number of incidents or insolvencies in environmental credit risk (for a financial services organization) Quantity of materials used during after-sales servicing of products Quantity of waste per year or per unit of product Quantity of hazardous, recyclable or reusable waste produced per year Total waste for disposal Quantity of waste stored on site Quantity of waste controlled by permits Quantity of waste converted to reusable material per year Amount of hazardous waste saved by replacing material Quantity of specific emissions per year Quantity of specific emissions per unit of product Quantity of waste energy released to air Quantity of air emissions that contribute to depletion of the ozone layer Quantity of emissions into the atmosphere with potential to cause global climate change Quantity of specific material discharged per year Quantity of specific material discharged to water per unit of product Quantity of waste energy released to water Quantity of material sent to landfill per unit of product Quantity of effluent per service or customer Noise measured at a certain location Quantity of radiation released Amount of heat, vibration, or light emitted

The Case of Molds for Injection Molding

Appendix Table 1.2  Mold data relevant for the environmental information Mold design alternatives EPI

Amount of material used

Life cycle phase

Process

Raw material acquisition

Cold runners

Hot runners

Steel to manufacture mold Polymer used to produce parts Polymer recycled or reused

47.93

57.67

kg

88,482.55

53,376.93

kg

26,680.85

16,070.36

kg

Aspect

Mold use

Injection

Amount of material processed, recycled or reused Amount of hazardous materials used Rate of defective products

Mold use

Injection

Mold manufacturing

CNC Grinding

Cutting fluid Lubricant fluid

0.23 0.38

0.26 0.38

dm3 dm3

Mold use

Injection

65,657

65,657

parts

Total waste to final destination Amount of energy used

Mold use

Injection

Number of defective parts produced Nonrecycled polymer

61,801.70

37,306.57

kg

Mold manufacturing

CNC EDM Grinding Injection EDM

Total energy Total energy Total energy Total energy Dielectric fluid CNC EDM Grinding Injection

2,036.67 7,228.80 1,017.56 95,673.35 450

2,236.67 7,228.80 2,229.19 81,207.28 450

kWh kWh kWh kWh dm3

203 691 330 3,953.44

223 691 330 3,355.67

h h h h

Mold maintenance

6.66

6.66

h

Amount of water used Number of operating hours of a specific equipment per year Number of hours of preventive maintenance of equipment per year

Mold use Mold manufacturing Mold manufacturing Mold use

CNC EDM Grinding Injection

Mold use

Injection

(Continued )

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Appendix Table 1.2  Mold data relevant for the environmental information (cont.) Mold design alternatives EPI

Duration of use of the product

Total area of land used for production

Life cycle phase

Process

Aspect

Mold use

Injection

Cycle time Number of shots until mold maintenance Mold life Space occupied by machine Space occupied by machine Space occupied by machine Space occupied by machine

Mold end of life Mold manu- CNC facturing EDM Grinding Mold use

Injection

Cold runners

Hot runners

2.17 1,000,000

1.84 1,000,000

s

8 20

8 20

years m2

40

40

m2

20

20

m2

15

15

m2

CNC, Computer numerical control; EPI, environmental performance indicators.

Appendix Table 1.3  Specific environmental impacts Specific impact (ReCiPe)

Steel Electricity (high voltage) Deionized water Lubricant oil PBT PBT (recycling) PBT (incineration)

1.14 Pt/kg of steel 0.0144 Pt/MJ 0.000156 Pt/kg of water 63.2 Pt/m3 0.50 Pt/kg of PBT –0.46 Pt/kg of PBT 2.738 Pt/kg of PBT

PBT, Polybutylene terephthalate.

REFERENCES [1] ISO 14045:2012, International Organization for Standardization, 2012. [2] M. Lehni, S. Schmidheiny, B. Stigson, Eco-Efficiency: Creating More Value With Less Impact, World Business Council for Sustainable Development, Geneva, (2000). [3] B.D. Paul, A history of the concept of sustainable development: literature review, Ann. Univ. Oradea Econ. Sci. Ser. 17 (2) (2008) 576–580. [4] A. Azapagic, S. Perdan, Indicators of sustainable development for industry: a general framework, Process Saf. Environ. Prot. 78 (4) (2000) 243–261.

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[5] M. Braungart, W. McDonough, A. Bollinger, Cradle-to-cradle design: creating healthy emissions-a strategy for eco-effective product and system design, J. Clean. Prod. 15 (13) (2007) 1337–1348. [6] M. Hauschild, H. Wenzel, L. Alting, Life cycle design – a route to the sustainable industrial cultur?, CIRP Ann. Manuf. Technol. 48 (1) (1999) 393–396. [7] A. Szekeres, J. Jeswiet, Defining sustainability: critical factors in sustainable material selection, in: Reengineering Manufacturing for Sustainability, 2013. [8] K. Gupta, R.F. Laubscher, J.P. Davim, N.K. Jain, Recent developments in sustainable manufacturing of gears: a review, J. Clean. Prod. 112 (2016) 3320–3330. [9] H. Zhang, K.R. Haapala, Integrating sustainable manufacturing assessment into decision making for a production work cell, J. Clean. Prod. 105 (2015) 52–63. [10] T. Buchert, A. Kaluza, F.A. Halstenberg, K. Lindow, H. Hayka, R. Stark, Enabling product development engineers to select and combine methods for sustainable design, Proc. CIRP 15 (2014) 413–418. [11] T. Silalertruksa, S.H. Gheewala, P. Pongpat, Sustainability assessment of sugarcane biorefinery and molasses ethanol production in Thailand using eco-efficiency indicator, Appl. Energy 160 (2015) 603–609. [12] H.X.Tan, Z.Yeo, R. Ng,T.B.Tjandra, B. Song, A sustainability indicator framework for Singapore small and medium-sized manufacturing enterprises, Proc. CIRP 29 (2015) 132–137. [13] I.A. Pissourios, An interdisciplinary study on indicators: a comparative review of quality-of-life, macroeconomic, environmental, welfare and sustainability indicators, Ecol. Indic. 34 (2013) 420–427. [14] T. Hák, S. Janoušková, B. Moldan, Sustainable development goals: a need for relevant indicators, Ecol. Indic. 60 (2016) 565–573. [15] H.A.Verfaillie, R. Bidwell, Measuring Eco-Efficiency: A Guide to Reporting Company Performance, World Business Council for Sustainable Development, Geneva, (2000). [16] WBCSDEco-Efficiency. Creating More Value with Less Impact, World Business Council for Sustainable Development, (2000). [17] L. Neufeld, F. Stassen, R. Sheppard, T. Gilman, The New Plastics Economy: Rethinking the Future of Plastics, World Economic Forum, (2016). [18] S. Esteves, E.J. Lourenço, N. Moita, P. Peças, I. Ribeiro, E. Henriques, J. P. Pereira, Injection moulding process indicators to foster a more sustainable production of plastic parts, in: Energetisch-wirtschaftliche Bilanzierung–Diskussion der Ergebnisse des Spitzentechnologieclusters eniPROD Energy-related Technologic and Economic Balancing and Evaluation–Results from the Cluster of Excellence eniPROD, 2014. [19] P. Peças, I. Ribeiro, R. Folgado, E. Henriques, A life cycle engineering model for technology selection: a case study on plastic injection moulds for low production volumes, J. Clean. Prod. 17 (9) (2009) 846–856. [20] R. Folgado, P. Peças, E. Henriques, Life cycle cos for technology selection: a case study in manufacturing of injection moulds, Int. J. Prod. Econ. 128 (1) (2010) 368–378. [21] T. Spiering, S. Kohlitz, H. Sundmaeker, C. Herrmann, Energy efficiency benchmarking for injection moulding processes, Robot. Comput. Int. Manuf. 36 (2015) 45–59. [22] A. Elduque, D. Elduque, C. Javierre, Á. Fernández, J. Santolaria, Environmental impact analysis of the injection molding process: analysis of the processing of high-density polyethylene parts, J. Clean. Prod. 108 (2015) 80–89. [23] A.J. Baptista, E.J. Lourenço, J.P. Pereira, F. Cunha, B. Marques, E.J. Silva, P. Peças, Eco-efficiency framework as a decision support tool to enhance economic and environmental performance of production systems, in: Energetisch-wirtschaftliche Bilanzierung–Diskussion der Ergebnisse des Spitzentechnologieclusters eniPROD Energy-related Technologic and Economic Balancing and Evaluation–Results from the Cluster of Excellence eniPROD, 2014. [24] W. Li, S. Kara, F. Qureshi, Characterising energy and eco-efficiency of injection moulding processes, Int. J. Sustain. Eng. 8 (1) (2015) 55–65. [25] K. Müller, A. Holmes, M. Deurer, B.E. Clothier, Eco-efficiency as a sustainability measure for kiwifruit production in New Zealand, J. Clean. Prod. 106 (2015) 333–342. [26] M.Z. Hauschild, Better-but is it good enough? On the need to consider both eco-efficiency and ecoeffectiveness to gauge industrial sustainability, Proc. CIRP 29 (2015) 1–7. [27] M. Lehni, S. Schmidheiny, B. Stigson, History and Definitions of Eco-efficiency, 2000. Available from: http://prepare-net.com/sites/default/files/history_and_definition_of_eco-efficiency.pdf.

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[28] A. Sproedt, J. Plehn, P. Schönsleben, C. Herrmann, A simulation-based decision support for eco-efficiency improvements in production systems, J. Clear Prod. 105 (2015) 389–405. [29] F. Figge,T. Hahn, Sustainable value added – measuring corporate contributions to sustainability beyond eco-efficiency, Ecol. Econ. 48 (2) (2004) 173–187. [30] X. Olsthoorn, D. Tyteca, W. Wehrmeyer, M. Wagner, Environmental Indicators for business: a review of the literature and standardisation methods, J. Clean. Prod. 9 (5) (2001) 453–463. [31] M. Winter, C. Herrmann, Eco-efficiency of alternative and conventional cutting fluids in external cylindrical grinding, Proc. CIRP 15 (2014) 68–73. [32] F. Figge, T. Hahn, Value drivers of corporate eco-efficiency: management accounting information for the efficient use of environmental resources, Manag. Account. Res. 24 (4) (2013) 387–400. [33] J. Hrˇebícˇek, P. Misarˇová, J. Hyršlová, Environmental key performance indicators and corporate reporting, in: International conference EA-SDI, 2007. [34] A. Baptista, E. Lourenço, J. Pereira, F. Cunha, E. Silva, P.P. Peças, ecoPROSYS: an eco-efficiency framework applied to a medium density fiberboard finishing line, Proc. CIRP 48 (2016) 170–175. [35] A. Baptista, E. Lourenço, E. Silva, M. Estrela, P. Peças, Integration of eco-efficiency and efficiency assessment methodologies: the efficiency framework, in: International Conference on Sustainable Design and Manufacturing – SDM 2017, Bologna, 2017, pp. 613–623. [36] J. Korol, D. Burchart-Korol, M. Pichlak, Expansion of environmental impact assessment for eco-efficiency evaluation of biocomposites for industrial application, J. Clean. Prod. 113 (2016) 144–152. [37] A. Audenaert, S.H. De Cleyn, M. Buyle, LCA of low-energy flats using the Eco-indicator 99 method: Impact of insulation materials, Energy Build. 47 (2012) 68–73. [38] V. Veleva, M. Ellenbecker, Indicator of sustainable production: framework and methodology, J. Clean. Prod. 9 (6) (2001) 519–549. [39] C. Jasch, Environmental performance evaluation and indicators, J. Clean. Prod. 8 (1) (2000) 79–88. [40] I.I. Issa, D.C. Pigosso, T.C. McAloone, H. Rozenfeld, Leading product-related environmental performance indicators: a selection guide and database, J. Clean. Prod. 108 (2015) 321–330. [41] N P ISO 14031:2005, Instituto Português da Qualidade, 2005. [42] A. Scipioni, A. Mazzi, F. Zuliani, M. Mason, The ISO 14031 standard to guide the urban sustainability measurement process: an Italian experience, J. Clean. Prod. 16 (12) (2008) 1247–1257. [43] DEFRA, Trucost, Environmental Key Performance Indicators – Reporting Guidelines for UK Business, Department for Environment, Food and Rural Affairs (DEFRA), London, (2016). [44] M. Camarero, J. Castillo-Giménez, A.J. Picazo-Tadeo, C. Tamarit, Is eco-efficiency in greenhouse gas emissions converging among European Union countries?, Empir. Econ. 47 (1) (2014) 143–168. [45] K. Charmondusit, S. Phatarachaisakul, P. Prasertpong, The quantitative eco-efficiency measurment for small and medium enterprises: a case study of wooden toy industry, Clean Technol. Environ. Policy 16 (5) (2014) 935–945.

FURTHER READINGS [46] M. Robaina-Alves, V. Moutinho, P. Macedo, A new frontier approach to model the eco-efficiency in European countries, J. Clean. Prod. 103 (2015) 562–573. [47] C. Rattanapan, T.T. Suksaroj, W. Ounsaneha, Development of eco-efficiency indicators for rubber glove product by material flow analysis, Proc. Soc. Behav. Sci. 40 (2012) 99–106. [48] K. Czaplicka-Kolarz, D. Burchart-Korol, P. Krawczyk, Eco-efficiency analysis methodology on the example of the chosen polyoefins production, J. Achiev. Mater. Manuf. Eng. 43 (1) (2010) 469–475. [49] L. Alting, Life cycle engineering and design, CIRP Ann. Manuf. Technol. 44 (2) (1995) 569–580. [50] J.L.Taulo, A.B. Sebitosi, Material and energy flow analysis of the Malawian tea industry, Renew. Sustain. Energy Rev. 56 (2016) 1337–1350. [51] E. Costa, R. Sousa, S. Bragança, A.C. Alves, An industrial application of the SMED methodology and other lean production tools, in: Proceedings of Integrity, Reliability and Failure, Funchal, 2013.

CHAPTER 2

Fabrication of Magnetic Tunnel Junctions Jitendra P. Singh*, Richa Bhardwaj*, Aditya Sharma*, Baljeet Kaur**, Sung O. Won*, Sanjeev Gautam†, Keun Hwa Chae*

*Advanced Analysis Center, Korea Institute of Science and Technology, Seoul, South Korea **Department of Physics, Panjab University, Chandigarh, India †Dr. S.S. Bhatnagar University Institute of Chemical Engineering & Technology, Panjab University, Chandigarh, India

2.1  MAGNETIC TUNNEL JUNCTION Magnetic tunnel junctions (MTJ) are well known spintronic devices, which are utilized in numerous applications. These junctions find applications in magnetic random access memory (MRAM) [1], nonvolatile logic fan-out architecture [2], reading heads for hard disk drive [3], writing heads [4], and random number generator [5–8].Thus these devices have offered a path to the researcher towards miniaturization of devices with large magnetic memory. As a general description, MTJ are formed when an insulating barrier is sandwiched between two ferromagnetic layers of different coercivities. As most of the ferromagnets have oxidizing nature, hence, a capping layer (a layer having conducting nature) is attached to one of the ferromagnet [9]. Fig. 2.1 shows a schematic of typical MTJ, where capping layer, barrier layer, and ferromagnet layers are clearly shown. Insulating or barrier layer is kept in such a manner that electron can effectively flow across this barrier [1,9]. Now a days, the most common barrier material is MgO [10], however, materials such as Al2O3 [11], NaCl [12], ZnO [13], and Mg3B2O6 [14] are also significantly used. Researchers also utilize titanates [15,16] and ferrites [17] as barrier layer material. Barriers with graphene as insulating layer are also under investigation by various groups [18,19]. Common ferromagnetic layers are Fe, Co, and CoFeB [20]. MTJ with heusler alloys [21,22] and rare earth metals [23] are also being extensively studied in recent years. Moreover, MTJ with magnetic oxides as ferromagnetic electrodes are also under progress for the fabrication of these devices [24,25]. When spins have same orientation in upper and lower ferromagnets, the tunneled electrons passing through the barrier will experience lower resistance (Rp). Perhaps the situation may also get reversed, if spin-orientation in two ferromagnets is opposite. Thus tunneled electrons experience higher electrical resistance in this case. Hence, such combination of ferromagnets and an insulator (shown in Fig. 2.1) exhibits spin-orientation dependent electrical behavior, which is forbidden in common conducting or magnetic materials [26,27]. Advanced Applications in Manufacturing Engineering. http://dx.doi.org/10.1016/B978-0-08-102414-0.00002-1 Copyright © 2019 Elsevier Ltd. All rights reserved.

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Figure 2.1  Schematic of magnetic tunnel junction, which contains a barrier layer, lower ferromagnetic electrode and upper ferromagnetic electrode.

When spin-orientation of such a combination of ferromagnets and insulator is tuned by applying magnetic field, then one can observe a change in the electric resistance of the device with an effect of applied magnetic field, that is, magneto-resistance (MR) effect [9–11]. The MR effect in these devices is related to tunneling phenomena, hence, this effect is known as TMR effect [9]. Thus these devices exhibit tunneling magnetoresistance (TMR) that depends on the spin polarization of ferromagnetic layer and also on the thickness of barrier layer [28]. Several groups successfully demonstrated this effect experimentally in such combinations [9–23] and technological development of this effect can be seen by successful implementation of numerous memory devices [1–8]. In Fig. 2.2, MR of a Fe/MgO/Fe/IrMn MTJ at two different temperatures 293 K and 20 K are shown [29]. This figure clearly reflects the change of electric resistance when magnetic field is applied. Higher resistance of device is observed when spins are antiparallel; however, lower resistance of device appears when spins are in parallel states in both ferromagnetic layers. These devices are based on the principle of spin dependent tunneling. Spin dependent tunneling through insulating barrier leads to huge value of TMR. A TMR value of ∼1000% was predicted [30,31] theoretically, however, a value of 604% could be achieved experimentally till date [32]. Thus these devices exhibit interesting phenomena; however, the deposition of these devices is not an easy task. In this chapter, we will describe the fabrication of these devices. Before discussing the fabrication of these devices, we will highlight the importance of junction area.

2.2  JUNCTION SIZE One necessary condition for the application of MTJ as MRAM elements or read head sensors in future storage technology is the possibility to form micrometer-scale junctions. Another concern for fabricating these devices is to achieve low dimensions and to work for longer duration. It is reported that nanometer-sized magnetic cells can have a thermal stability high enough for data retention over 10 years

Fabrication of Magnetic Tunnel Junctions

Figure 2.2  Typical tunnel magnetoresistance curve for a Fe(001)/MgO(001)/Fe(001) having MgO thickness of 2.3 nm measured at two different temperatures. Data taken from Macmillan, S. Yuasa, T. Nagahama, A. Fukushima, Y. Suzuki, K. Ando, Giant room-temperature magnetoresistance in single-crystal Fe/MgO/Fe magnetic tunnel junctions. Nature 3 (2004) 868–871. Copyright (2003).

[18–23,32,33]. Thus MTJs are fabricated down to the dimensions of nanometers levels using lithography process [34,35]. A schematic of a Fe/MgO/Fe/Co MTJ after lithography process is shown in Fig. 2.3. Junction area of rectangular device is 50 × 100 µm2. Requirement of a memory application is that the resistance of junction area should have a definite value for a given set of barrier parameters. Thus resistance area (RA) product, which is the product of junction area and resistance of barrier, is defined to understand the characteristics of junction of MTJ [36,37]. An upper limit is about 20 kΩ µm2 to the RA product of MRAM cells is set based on the operational requirements on noise and access time [38–40].Therefore, researchers also focused to evaluate RA product as well as TMR of these devices [41–45]. Therefore, number of groups are working in the field of MTJ fabrication and are putting efforts to scale down these devices [41–45]. Researcher reported TMR = 12% and RA = 1 kΩ µm2 in RF plasma oxidized Al barrier [41], natural in situ oxidation (TMR = 12%, RS = 2 kΩ µm2 [42]), and multiple oxidation of successive layers of Al in O2 (TMR = 6%, RS = 960 Ω µm2 [43]). These several issues look lithography as an alternate solution to minimize the junction area. Thus the fabrication of MTJ is a twostep process. In the first step, multilayer structures are grown using suitable deposition technique. In the second step, grown multilayers are fabricated into devices. We have described these steps one by one.

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Figure 2.3  MTJ device after lithography process. Two MTJS having diameter 50 µm are shown in figure.

2.3  GROWTH OF MULTILAYER STRUCTURE Growth of a multilayer structure is extremely important. Dedicated deposition chamber with ultra-high vacuum (10−10–10−11 Torr) for deposition of multilayer structure is required [21–25].

2.4  MOLECULAR BEAM EPITAXY (MBE) MBE is well known tool for the deposition of MTJ.This technique is effective in maintaining stoichiometry and orientation of different layers. Thus researchers focused their attention to grow MTJ using this technique. Using this technique, trilayer heterostructure La1−xSrxMnO3 as the ferromagnet and CaTiO3 as insulating layer was deposited. The deposited structure exhibits magnetoresistance ∆R/R(H)∆R/R(H) of as much as 450% in 200 Oe applied field at 14 K, and which persists up to ∼250 K [46]. Mia et al. deposited Fe/MgO/Fe structure using MBE method. These authors reported TMR value ∼120% and RA product of the order of 106 Ω–µm2 [47]. Yuasa et al. reported the fabrication of fully epitaxial Fe/MgO/Fe MTJ using this technique, which has MR ratio of 88% (T = 293 K) and RA product of few kΩ–µm2 [48]. A RHEED image of the Fe surface (Fig. 2.4A) showed good crystallinity and flatness of the epitaxial Fe(001). The RHEED pattern (Fig. 2.4B) showed high-quality epitaxial growth of the

Fabrication of Magnetic Tunnel Junctions

Figure 2.4  RHEED images of (A) Fe(001) bottom electrode layer ([108] azimuth) and (B) MgO(001) tunnel barrier layer ([98] azimuth). Data taken from S. Yuasa, A. Fukushima, T. Nagahama, K. Ando, Y. Suzuki, High tunnel magnetoresistance at room temperature in fully epitaxial Fe/MgO/Fe tunnel junctions due to coherent spin-polarized tunneling, Jap. J. Appl. Phys. 43 (4B) (2004) L588–L590.

MgO(001) layer [48]. Later, this group reported a value of 180% of TMR and RA product of 25 kΩ–µm2 for Fe(001)/MgO(001)/Fe(001) junctions deposited using same technique [29]. Similarly, Fe/MgO/Fe MTJ with significant values of TMR were fabricated by a number of groups using this technique [49–52]. Epitaxially MgO(100)/Fe/MgO/Fe/ Co/Pd MTJ elaborated by MBE, with insulating layer’s thickness reduced to 0.8 nm (Fig. 2.5). These junctions show a low resistance ∼4 kΩ–µm2 and TMR up to 17%, and a very small interlayer magnetic coupling [53]. Number of groups also utilize this technique to grow MTJ structure like Fe/MgO/ Gd [54], heusler based MTJ [55], MgO–EuO composite tunnel barriers [56], and Co/ MgO/Co tunnel junctions [57].

2.4.1  E-beam evaporation Our group utilizes e-beam evaporation method for growing Fe/MgO/Fe structures [9,58,59]. In Fig. 2.6, e-beam evaporation set-up installed at Inter University Accelerator Center is depicted. This set-up allows substrate heating upto 500°C as well as online monitoring of thickness using quartz crystal monitor [60]. Our group has reported the fabrication of Fe/MgO/Fe multilayer using e-beam evaporation.The detailed procedure

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Figure 2.5  TEM views of MgO(100)/Fe/MgO/Fe/Co/Pd multilayer with 0.8 nm MgO barrier. Data taken from E. Popova, J. Faure-Vincent, C. Tiusan, C. Bellouard, H. Fischer, M. Hehn, F. Montaigne, M. Alnot, S. Andrieu, A. Schuhl, E. Snoeck, V. da Costa, Epitaxial MgO layer for low-resistance and coupling-free magnetic tunnel junctions. Appl. Phys. Lett. 81 (6) (2002) 1035–1037, AIP Publishing.

Figure 2.6  Schematic of e-beam evaporation set-up at IUAC, New Delhi. Data taken from V. Singh, S.R. Abhilash, B.R., Behera, D. Kabiraj, Fabrication of thin self-supporting platinum targets using evaporation techniques. Nucl. Inst. Meth. Phys. Res. A. 635 (1) (2011) 20–23. Copyright (2011) from Elsevier.

Fabrication of Magnetic Tunnel Junctions

of the growth of these structures is reported in the Ref. [58,59]. TEM image of grown Fe/Mgo/Fe structure is shown in Fig. 2.7 [9]. These structures show the presence of Fe-oxides at interfaces rather than formation of perfect interfaces [61]. Our group has also grown Fe/MgO/Fe/Co MTJ structures [62,63] and MgO/Fe/MgO structures [64] using this technique; however, vacuum level of the order of 10−8 Torr results in interface oxidation. This condition should be avoided for the fabrication of good quality MTJ.

2.4.2  Sputtering deposition As multilayer structure typical to MTJ consists of ferromagnetic and insulating layer, hence a combination of DC and RF sputtering is another choice for growing these structures. Fig. 2.8 shows schematic of Six-Gun RF sputtering set-up, which is used for the deposition of CoFeB/MgO/CoFeB structure [65,66]. In recent years, this technique is effectively used for the deposition of multilayer structure of this CoFeB based MTJ. MgO tunnel barriers with CoFe electrodes are grown using sputtering method, which exhibit TMR values of up to approximately 220% at room temperature and approximately 300% at low temperatures [67]. In Fig. 2.9, HRTEM image of grown multilayer structure is shown. Ferromagnetic and barrier layers are very clear in the HRTEM image.These structure exhibit crystallinity as observed from well distinguishable lattice for each layer. As these MTJ exhibit highest value of TMR (by the end of 2004), hence, number of researchers utilized this technique to develop MTJ with varying compositions of ferromagnetic CoFeB electrodes [68–72]. Ikeda et al. reported a value of TMR ∼604% at room temperature using this technique in 2008 [32]. This is the maximum value of

Figure 2.7  (A) HRTEM image showing a layered structure of Fe/MgO/Fe multilayer deposited on MgO (buffer)/Si (100), (B–D) shows the crystalline nature of Fe, MgO, and Fe layers in the structures. Data taken from J.P. Singh, B. Kaur, S. Gautam, W.C. Lim, K. Asokan, K.H. Chae, Chemical effects at interfaces of Fe/ MgO/Fe magnetic tunnel junction. Superlattices Microstruct. 100, 560–586. Copyright (2016) from Elsevier.

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Figure 2.8  Six-Gun RF sputtering set-up at Taiwan Spin Research Center, National Chung Cheng University, Taiwan.

Figure 2.9  Transmission electron micrographs of magnetic tunnel junctions. Data taken from Macmillan, S.S.P. Parkin, C. Kaiser, A. Panchula, P.M. Rice, B. Hughes, M. Samant, S.H. Yang, Giant tunnelling magnetoresistance at room temperature with MgO (100) tunnel barriers, Nat. Mater. 3 (12) (2004) 862–867. Copyright (2004).

TMR reported in MTJ till date. Utilizing this technique same group has obtained TMR value of the order of 120%, high thermal stability at dimension as low as 40 nm diameter and a low switching current of 49 µA in Ta/CoFeB/MgO/CoFeB/Ta. Thus, these parameters persist pathways to utilize these MTJs in spintronic devices [73].

2.4.3  Ion beam sputtering deposition Various research groups also utilized Ion beam sputtering to deposit these structures [74–79]. In Fig. 2.10, procedure utilized by Cardoso et al. is depicted [74].These authors have reported TMR values up to 110%, with RA products of 100–400 Ω  µm2 for CoFeB/MgO/CoFeB MTJ. Singh and Chaudhary grew NiFe/Mg/MgO/CoFe MTJ using this technique [76–78]. TMR value of 1% was reported by these authors for ion beam sputtered MTJ [77]. Thus these are the well-known methods to deposit multilayer structure, which needs lithography process to further fabricate these devices in well-defined junctions. Some

Fabrication of Magnetic Tunnel Junctions

Figure 2.10  (A) Schematic representation of the electrical fields used to extract the ions from the guns, (B) geometry used for the metallic film depositions, not assisted (top) and for MgO assisted deposition (bottom), where the sample is tilted into the assist beam direction. Data taken from S. Cardoso, R.J. Macedo, R. Ferreira, A. Augusto, P. Wisniowski, P.P. Freitas, Ion beam assisted deposition of MgO barriers for magnetic tunnel junctions, J. Appl. Phys. 103 (2008) 07A905, AIP Publishing.

other deposition techniques such as pulsed laser deposition [80,81] and atomic layer deposition [82–85] are also utilized by researchers for growing oxide heterostructures in context of MTJ.

2.5 LITHOGRAPHY Lithography is well known phenomena in electronic industry and utilized for the fabrication of semiconductor devices [86,87]. Though optical lithography fulfils the device size requirement in semiconductor industry [88]; however, e-beam lithography is also utilized. E-beam lithography followed by advanced etching procedure (ion beam milling, reacting ion etching etc.) is preferred [89,90], to design devices free from chemical impurity and mechanical damage [88,90]. Depending on the size of fabricated device, two kinds of lithography is defined: (1) Microlithography for growing features smaller than 10 µm and (2) Nanolithography features smaller than 100 nm. With the development of technological advances and device requirements, MTJ with junction size scaling down to few nm are under fabrication. Thus the fabrication of MTJ comes under the category of nanolithography.

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2.5.1 Photolithography Photolithography is one of these methods, often applied to semiconductor manufacturing of microchips. Photolithography is also commonly used for fabricating micro-electro-mechanical-systems (MEMS) devices. A typical lithography process contains several steps to fabricate device from layer grown on substrate (wafer). These steps are shown in Fig. 2.11 and can be summarized as follows: 1. Cleaning of surface layer as described by Radio Corporation of America [91]. 2. Heating to drive off any moisture that may be present on the wafer surface. 3. Application of photoresist by spin coating. 4. Exposure of photoresist by pattern of intense light. The exposure to light causes a chemical change that allows some of the photoresist to be removed by a special solution, called “developer” by analogy with photographic developer. Positive photoresist, the most common type, becomes soluble in the developer when exposed; with negative photoresist, unexposed regions are soluble in the developer. 5. Etching. 6. Photoresist removal. Thus lithography process requires two most crucial steps: (1) exposure and (2) etching. Etching is removal of photoresist from layer. A simple and cost effective approach of etching is done using chemicals and known as chemical etching [92,93]. Now a days

Figure 2.11  The schematic diagram showing the formation of a polymeric relief using lithography. The resist pattern is used to subsequently modify the underlining substrate (Reproduced). Data taken (modified) from L.F. Thompson, An introduction to lithography, in: Introduction to Microlithography, ACS Symposium Series, American Chemical Society, Washington, DC, 1983. Copyright (1983), American Chemical Society.

Fabrication of Magnetic Tunnel Junctions

v­ arious methods are utilized for etching procedure. These methods are plasma etching [94], reactive ion etching [95], and ion beam milling [96]. These approaches are commonly known as dry etching, however, chemical etching procedure is known as wet etching. Other major step is exposure. Generally, the nature of radiation used for exposure defined the category of lithography. When ultra-violet radiation is used for exposure, then such a process is known as Photolithography. This method is not only applicable for the fabrication of semiconductor devices but also equally applicable for the fabrication of MTJ.There are reports of micron-sized Ni–Fe/Al2O3/Co [96], Co75Fe25/Al2O3/ Co75Fe25 [97], and Fe/MgO/Fe [98] junction using photolithography. This technique is recently used by Chen et al. to grow micron-sized junction on flexible substrate [99,100]. Fabrication of MTJ is a typical process as the number of layers for pattering is more than three.Thus each layer needs pattering. In case of NiFe/Al2O3/Co fabrication,

Figure 2.12  Patterning process for NiFe/Al2O3/Co magnetic tunnel junction. Patterning of NiFe, Al2O3, SiO2, Co, and Ti/Cu electrode is depicted in (A), (B), (C), (D), and (E). Data taken from T. Miyazaki, S. Kumagai, T. Yaoi, Spin tunneling in Ni–Fe/Al2O3/Co junction devices. J. Appl. Phys. 81 (8) (1997) 3753–3757, AIP Publishing.

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patterning of NiFe, Al2O3, and Co layer is required. The schematic of pattering process is shown in Fig. 2.12. Patterning of each layer passes through several steps involved in Fig. 2.11. Thus patterning of MTJ device is tedious job and needs a lot of expertise.

2.5.2  E-beam lithography Methods like, electron beam lithography, are capable of much greater patterning resolution (as small as a few nanometers). Electron beam lithography is also important for it is used in the manufacture of photomasks. Electron beam lithography as it is usually practiced as a form of maskless lithography, in which a mask is not required to generate the final pattern. Instead, the final pattern is created directly from a digital representation on a computer, by controlling an electron beam as it scans across a resist-coated substrate. Electron beam lithography has the disadvantage of being much slower than photolithography. Thus most of the MTJ uses e-beam lithography [64–79]. In Fig. 2.13, schematic process of MTJ fabrication using lithography process is depicted [101]. This process is utilized to fabricate Ni80Fe20/Co75Fe25/Al-O/Co75Fe25/Ta MTJ. In this procedure, a part of the MTJ structure was removed by using e-beam lithography and Ar ion milling (Fig. 2.13A). After this, substrate was covered with a thick Al2O3/Cu film (Fig. 2.13B) and liftoff in organic solvent, Pt layer of thickness 10 nm was vacuum-evaporated obliquely on both sides of Al2O3/Cu films. Finally, junction area was defined by Ar ion milling, in which the Pt films were used as etching masks with 100 µm × 10 nm (Fig. 2.13C).

Figure 2.13  Schematic illustration of the nanofabrication process. Various steps of this process are depicted in (A), (B), and (C). Data taken from T. Niizeki, H. Kubota, Y. Ando, T. Miyazaki, Nanofabrication of magnetic tunnel junctions by using electron beam lithography, J. Magn. Magn. Mater. 272–276 (2004) 1947–1948. Copyright (2004) from Elsevier.

Fabrication of Magnetic Tunnel Junctions

2.6  PATTERNING OF FE/MGO/FE SYSTEM In case of etching, chemical etching gives significant results; however, ion milling is preferred now a days. Thus to understand the phenomena of MTJ fabrication, we first describe the procedure for Fe/MgO/Fe/Au MTJ using chemical etching and e-beam lithography. The Fe/MgO/Fe MTJ is selected as it is the simple system and will provide the detailed information of this lithography procedure. A process containing almost 21 steps has been depicted by following figures lithography process. These processes are shown in terms of various steps for better understanding of readers. Step 1: Growth of multilayer structure.

Step 2: Deposition of photoresist (PR) on the structure.

Step 3: Masking of PR.

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Step 4: Exposure of radiation on PR.

Step 5: As a result of exposure, desired structures are grown in PR.

Step 6: Etching of Au layer.

Step 7: Etching of Fe layer using appropriate etching agent.

Fabrication of Magnetic Tunnel Junctions

Step 8: Etching of MgO barrier layer.

Step 9: Removal of photoresist.

Step 10: Deposition of PR.

Step 11: Use of another mask to define lower layer and exposure to radiation.

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Step 12: After exposure structures are formed through PR.

Step 13: Etching of lower electrode.

Step 14: Removal of PR.

Step 15: Deposition of silica for insulation among several devices.

Fabrication of Magnetic Tunnel Junctions

Step 16: Deposition of PR.

Step 17: Mask to grow contact pads and exposure through radiation.

Step 18: Formation of structure for lower contact pads.

Step 19: Silica etching and formation of structure for contact pads.

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Step 20: Contact layer deposition and removal of PR.

Step 21: Deposition of PR and exposure through mask to deposit contact pads of ∼1 mm.

2.7  FABRICATION OF DEVICE USING PSEUDO/METAL MASKING PROCEDURE Thus there is a need to pass through a complicated lithography process to fabricate these devices. A number of such treatments also affect the properties of these devices. Hence, researcher utilized technology, which is free from complicated lithography process. In the beginning of this technology, researchers utilize the fabrication of these junctions, which is popularly known as pseudo-masking. Moodera et al. carried out this methodology to develop the procedure for growing CoFe/Al2O3/NiFe and CoFe/Al2O3/Co junctions [102,103]. These junctions could obtain almost 11% of TMR at room temperature. The procedure used by them is depicted in Fig. 2.14. This procedure is motivated by the Jullieres’s observation of TMR in Fe/Ge/Co junction who oxidized Ge in dry oxygen to minimize magnetic coupling [104]. This group also utilizes metal masking to deposit Gd/Gd2O3/NiFe junctions [105]. Development in masking process is also carried out by the researcher with time. Ootuka et al. reported fabrication method based on Si3N4 membrane [106], which is replacement for metal mask and allows in situ deposition of MTJ. This process also avoids the situation where vacuum breaking is required for designing junctions as reported by Moodera group [102,103] and Julliers [104]. The process of designing a membrane mask is shown in Fig. 2.15. Thus the fabrication of pseudo-masking methods to grow MTJ without lithography process, however, mask formation needs e-beam lithography as depicted in Fig. 2.15. In 2004, Perkin et al. used pseudo masking to fabricate these devices, which

Fabrication of Magnetic Tunnel Junctions

Figure 2.14  Schematic illustration of fabrication process of CoFe/Al2O3/NiFe magnetic tunnel junction. Schematic is illustrated on the basis of description given by J.S. Moodera, L.R. Kinder, T.M. Wong, R. Meservey, Large magnetoresistance at room temperature in ferromagnetic thin film tunnel junctions, Phys. Rev. Lett. 74 (16) (1995) 3273–3276.

Figure 2.15  Process of making a deposition mask (Reproduced). Data taken (modified) from Y. Ootuka, K. Ono, H. Shimada, S.-H. Kobayashi, A new fabrication method of ultra-small tunnel junctions, Physica B 227 (1–4) (1996) 307–309. Copyright (1996) from Elsevier.

gives almost 220% TMR value at room temperature [67]. Later, this technique was also utilized by Barraud et al. to grow Co/Al2O3/Co junction on Si and organic substrate [107] and on Kapton substrate by a different group [108]. Thus MTJ fabrication is a complicated and multistep procedure as described by this figure. In this schematic diagram, it is clear that masking, etching, and choice of radiation

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are of utmost importance. With the technological advancement a significant improvement is observed in the lithography process that produces good quality junction with perfect shape and smaller junction size. Lithography techniques such as ion beam lithography [109], X-ray lithography [110], and free electron lithography [111] are utilized by researchers. However, the use of these techniques is limited due to typical instrumentation associated with these techniques [112,113].

2.8 CONCLUSIONS MTJ are important candidates of spintronic devices. The fabrication of these junctions is basically a two-step procedure. In first step, different layers are grown using a specified technique such as MBE, e-beam evaporation, a combination of DC–RF sputtering, or ion beam sputtering. These techniques provide good epitaxy for Fe/MgO/Fe like structure (MBE) and CoFeB/MgO/CoFeB structure (sputtering), and hence these are preferred for growing these structures. Pattering of these multilayer structures is carried out using e-beam lithography, which results in the formation of MTJ down to 100 nm. E-beam lithography results in edge formation; hence, the need of disappearance motivated researchers to go through ion beam lithography, X-ray lithography, and free electron beam lithography. Though pattering using these techniques is difficult task; however, efforts are undertaken by various groups to facilitate these facilities for the fabrication of these junctions. Development of these techniques will lead to further minituatraion of memory devices.

ACKNOWLEDGMENT Authors acknowledge the financial support provided by the Korea Institute of Science and Technology (KIST-2V06030). One of the authors, JPS, is thankful to Prof. Gung Chern, National Chung Cheng University, Cha-Yi, Taiwan to access RF-sputtering for deposition of CoFeB/MgO/CoFeB structures.

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CHAPTER 3

Effect of Equipment’s Failure on Gas Turbine Power Plant Nupur Goyal*, Mangey Ram*, Shubham Amoli**, Shivam Jagga**

*Department of Mathematics, Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India **Department of Mechanical Engineering, Graphic Era Deemed to be University, Dehradun, India

3.1 INTRODUCTION High population growth, economic growth, urbanization, and industrial expansion, especially in the developing countries, result in increase in energy demand.The word’s energy demand is projected to grow significantly over the next 10 years.The major energy demand is to be met by using nonrenewable fossil fuels with limited supply. The most conversant item of turbomachinery is the gas turbine. It is a heat engine, which converts fuel (chemical) energy to mechanical energy, which will further be used for power generation, oil and gas process plants, aviation, as well domestic and smaller related industries. If high-power density, quick starting, and low weight are required, then gas turbines are used. A gas turbine, also called a combustion turbine, is a rotary engine that extracts energy from a flow of hot gas produced by combustion of gas or fuel oil in a stream of compressed air. It has an upstream air compressor with radial or axial flow mechanically coupled to a downstream turbine and a combustion chamber in between. Gas turbine may also refer to just the turbine element. Energy is released when compressed air is mixed with fuel and ignited into the combustor.The resulting gases are directed over the turbine blades, spinning the turbine, and mechanically powering the compressor. Finally, the gases are passed through a nozzle, generating additional thrust when accelerating the hot exhaust gases by expansion back to atmospheric pressure. Energy is extracted in the form of shaft power, compressed air, and thrust, in any combination, and used to power aircraft, trains, ships, electrical generators, and even tanks. Reliability work on gas turbine power plant introduces two basic concepts that have central importance, namely, modeling of the designed plant and their state transition probability. To determine the plant’s reliability, there are two key factors, namely, failure distribution of equipment and plant configuration (Fig. 3.1) [1,2]. Reliability of the plant can be improved through the renewal of its subsystems that have failed. A system can be renewed by several forms either by repairing or by replacing [3]. Researchers and practitioners have paid much attention for modeling the reliability of repairable system. A system is called a repairable system when, after Advanced Applications in Manufacturing Engineering. http://dx.doi.org/10.1016/B978-0-08-102414-0.00003-3 Copyright © 2019 Elsevier Ltd. All rights reserved.

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Figure 3.1  Configuration diagram.

failing to perform at least one of its required operations, it can be restored to perform satisfactorily all of its required functions by any method, other than the replacement of the entire system. Dhillon and Yang [4] presented a model representing two identical units in parallel and one on standby system with human errors. The model separates critical and noncritical human errors, and a unit can fail either due to a nonhuman error or a human error. Conformist reliability theory carries out the assumptions of the probability theory and binary states of a subsystem or system as working (fully or partially) and failed. Reliability of a system deliberates the probability of the system in which system working state to failed state has no transition [5]. Ram et al. [6] have also analyzed a standby system with waiting for repair policy. They founded the reliability measures of a general standby system in which different failures occur such as equipment failure, standby failure, human error, and failure in waiting time to repair. Ram [7] studied the different area of reliability in which it has great importance and also discussed its applications in major areas. A lot of research has been done in the field of system reliability, but no one considered the reliability measures of the gas turbine power plant. Zaini [8] developed a model with high degree of efficiency both thermodynamically and mechanically. The importance of this study is that it increased the performance of gas turbine in terms of efficiency.To increase the performance in terms of efficiency, the gas generator speed has been controlled. Al-Doori [9] designed a cycle model of gas turbine power plant and deeply studied its parameters, design, and operation condition on the power output, compression work, specific fuel consumption, and thermal efficiency. The modification improved the efficiency of the gas turbine power plant and compared the results with non-intercooled gas turbine power plant. Thirunavukarasu [10] focused on development of a dynamic gas turbine model to stimulate both single shaft and twin shaft engines.This is done by integrating the gas turbine model with a power generation and distribution system and a thermal system. Comparison of variable speed operation results of single shaft and twin shaft gas turbine engines has shown that the efficiency increases as load decreases. Mohanty and Venkatesh [11] determined the effect of various operating parameters such as maximum temperature and pressure of Rankine cycle, turbine inlet temperature, and pressure ratio of Brayton cycle on the net output work and thermal efficiency of the combine cycle using MATLAB.

Effect of Equipment’s Failure on Gas Turbine Power Plant

A mathematical model of the gas turbine power plant has been designed in this study. To derive the probability expression of reliability and availability, Markov process [12], supplementary variable technique [13,14] and Laplace transform have been employed. The authors also generalized the probability of each transition state. To overcome the effect of each failure on the modeled gas turbine power plant, some particular cases of reliability and availability have been discussed, while Ram and Goyal [15] have discussed the reliability of an MTTF of gas turbine with the incorporation of coverage factor. The results have been explained by taking some numerical examples and demonstrated graphically.

3.2  MATHEMATICAL MODELING DETAILS 3.2.1 Notations All the notations used in the proposed work are described in Table 3.1.

3.2.2 Assumptions In the proposed research work, the following assumptions have been used 1. Initially, the power plant is in good working condition. 2. The plant covers three types of states, namely, good, degraded, and failed. 3. Only one transition is allowed at one time. 4. The sufficient repair facility is available. 5. When the failed component is repaired, plant works like a new one. Table 3.1 Notations t/s

Time scale/Laplace transform variable

P (s)

Laplace transformation of P (t ).

λNF / λNA / λCC / λT /

Failure rate for nonavailability of fuel/ nonavailability of air/ combustion chamber/turbine/generator/rotating shaft/ generator convertor/compressor of gas turbine. The probability of the stage Si at time t when i = 0, 1.

λG / λR / λGC / λC Pi (t ) Pj ( x , t )

Pup (t )

The probability density function of the state Sj, at epoch time t, and has an elapsed repair time of x; when j = 2, 3, 4, 5, 6, 7, 8, respectively. Repair rate from complete/partially failed state to the good working state. Upstate system probability at time t or availability of the system.

Rl (t )

The reliability of the system at time t.

µ (x ) / φ

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3.2.3  System description In this work, authors focused on the study of gas turbine power plant. Gas turbine plant consists of many components, but we consider some main components such as compressor, combustion chamber, turbine, and generator.The configuration of the plant with the specification of considered components is shown in Fig. 3.1. Fresh air is first compressed by compressor, which then enters into the combustion chamber where fuel is injected into the highly pressurized air inside the combustion chamber and burned essentially at constant pressure. This gas at high pressure and high temperature enters into the turbine, where gas expands up to the ambient pressure and is thrown out to the surroundings and provides the rotary motion to the shaft.The same shaft is connected to the generator, which converts this gas into the electrical energy by rotary motion of turbine shaft and generator convertor. In this process, many possibilities of working occur in the plant, and all the possible transitions are shown in transition state diagram as in Fig. 3.2. If any one of the turbine, compressor, generator, and combustion chamber fails, then the system goes into the state of complete failure. Due to the nonavailability of air and fuel, the system also goes into the state of complete failure. If the rotating shaft connecting the compressor, turbine, and generator axially fails, then the whole system also completely fails. The failure of generator convertor does not lead to the complete failure of the system, rather it brings the plant to a degraded state Table 3.2

Figure 3.2  Transition state diagram.

Effect of Equipment’s Failure on Gas Turbine Power Plant

Table 3.2  State description State

Description

S0 S1 S2 S3 S4 S5 S6 S7 S8

Good working condition of the system. Degraded state of the system due to failure of generator convertor. Complete failed state of the system due to failure of rotating shaft. Complete failed state of the gas turbine due to nonavailability of fuel. Complete failed state of the system due to nonavailability of air. Complete failed state of the system due to the failure of combustion chamber. Complete failed state of the system when turbine has been failed. Complete failed state of the system when compressor has been failed. Complete failed state of the system when generator has been failed.

3.2.4  Formulation and solution of the model By the probability consideration, we can obtain the following set of difference differential equations, possessing the present mathematical model: 8 ∞ ∂  P t P t ( x ) + + + + + + + + = + λ λ λ λ λ λ λ λ φ µ ∫ ∑ Pi (x, t ) dx NF NA CC T C G R GC  0 ( ) 1( )  ∂t  0 i =2 (3.1)

∂  (3.2)  ∂t + λNF + λNA + λCC + λT + λC + λG + λR + φ  P1 (t ) = λGC P0 (t ) ∂ ∂  (3.3)  ∂t + ∂x + µ ( x ) Pi ( x, t ) = 0; i = 2,3,4,5,6,7,8 Boundary condition (3.4) Pi (0, t ) = λξ [P0 (t ) + P1 (t )] ; i = 2,3,4,5,6,7,8; ξ = R , NF , NA,CC ,T ,C ,G Initial Condition

{

Pi (0) = 1 i = 0 (3.5) 0 i ≥1 Taking the Laplace transformation of Eqs. (3.1)–(3.4), using Eq. (5), we have ∞

8

0

i =2

[ s + λNF + λNA + λCC + λT + λC + λG + λR + λGC ] P 0 ( s ) = 1 + φ P 1 ( s ) + ∫ µ (x )∑ P i (x, s ) dx

(3.6)

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(3.7) [ s + λNF + λNA + λCC + λT + λC + λG + λR + φ ] P 1 ( s ) = λGC P 0 ( s )  ∂  (3.8) s + ∂x + µ ( x ) P i ( x, s ) = 0; i = 2,3,4,5,6,7,8 (3.9) P i (0, s ) = λξ P 0 ( s ) + P 1 ( s ); i = 2,3,4,5,6,7,8; ξ = R , NF , NA,CC ,T ,C ,G After solving Eqs. (3.6)–(3.9), we get the probability of each transition state as s+B (3.10) P 0 (s) = K

λ (3.11) P 1 ( s ) = GC P 0 ( s ) s+B

{

}

 1 − S µ (s)  s + A + φ P P 0 (s) ; i ( s ) = λξ   (3.12) s   s+B i = 2,3,4,5,6,7,8; ξ = R , NF , NA,CC ,T ,C ,G where, A B K

= λNF + λNA + λCC + λT + λC + λG + λR + λGC ; = A − λGC + φ ;

= ( s + A)( s + B ) − {φλGC + ( A − λGC ) ( s + A + φ ) S µ ( s )} .

The Laplace transformation of the probability of upstate and downstate of the gas turbine power plant 1

P up ( s ) = ∑ P j ( s ) j =0 (3.13)  λ  = 1 + GC  P 0 ( s )  s+B 8

P down ( s ) = ∑ P j ( s ) j =2 (3.14)  1 − S µ (s)  s + A + φ P 0 (s) = ( A − λGC )   s   s+B

{

}

Effect of Equipment’s Failure on Gas Turbine Power Plant

3.3  PARTICULAR CASES AND THEIR NUMERICAL COMPUTATION 3.3.1  Availability analysis Availability of the plant can be developed by focusing on an ostentatious plan on growing testability and maintainability and not on reliability. The availability of the system depends on the system organization as well as to the component’s availability. This value decreases as the probability of its component failures increases [16,17]. It can be calculated by taking the inverse Laplace transformation of Eq. (3.13) and setting the value of failure and repair rates as λNF = 0.006 , λNA = 0.200 , λCC = 0.011 , λT = 0.050 , λG = 0.200 , λC = 0.050 , λGC = 0.150 , µ( x ) = 1 , φ = 1 [18–20]. 1. Effect of λNF : Suppose that we have sufficient fuel, then availability of gas turbine power plant is revealed in Table 3.3 and the comparison is shown in Fig. 3.3. 2. Effect of λNA : Suppose that enough air is available, then availability of gas turbine power plant is revealed in Table 3.4 and the comparison is shown in Fig. 3.4. 3. Effect of λCC : Suppose that there is no problem in combustion chamber, then availability of gas turbine power plant is revealed in Table 3.5 and the comparison is shown in Fig. 3.5. 4. Effect of λT : Suppose that plant cannot fail due to turbine, then availability of gas turbine power plant is revealed in Table 3.6 and the comparison is shown in Fig. 3.6. 5. Effect of λG : Suppose that generator is working properly, then availability of gas turbine power plant is revealed in Table 3.7 and the comparison is shown in Fig. 3.7.

Table 3.3  Availability of the plant with respect to nonavailability of fuel Availability Pup(t) Time (t)

λNF = 0.006

λNF = 0

Availability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.722 0.663 0.651 0.648 0.647 0.647 0.647 0.647 0.647 0.647

1.000 0.725 0.666 0.653 0.650 0.650 0.650 0.650 0.650 0.650 0.650

0 0.41 0.45 0.31 0.31 0.46 0.46 0.46 0.46 0.46 0.46

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Figure 3.3  Availability of the plant with respect to nonavailability of fuel.

Table 3.4  Availability of the plant with respect to nonavailability of air Availability Pup(t) Time (t)

λNA = 0.200

λNA = 0

Availability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.722 0.663 0.651 0.648 0.647 0.647 0.647 0.647 0.647 0.647

1.000 0.810 0.761 0.748 0.745 0.744 0.744 0.744 0.743 0.743 0.743

0 10.86 12.88 12.97 13.02 13.04 13.04 13.04 12.92 12.92 12.92

Effect of Equipment’s Failure on Gas Turbine Power Plant

Figure 3.4  Availability of the plant with respect to nonavailability of air.

Table 3.5  Availability of the plant with respect to combustion chamber Availability Pup(t) Time (t)

λCC = 0.011

λCC = 0

Availability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.722 0.663 0.651 0.648 0.647 0.647 0.647 0.647 0.647 0.647

1.000 0.727 0.668 0.655 0.653 0.652 0.652 0.652 0.652 0.652 0.652

0 0.69 0.75 0.61 0.76 0.77 0.77 0.77 0.77 0.77 0.77

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Figure 3.5  Availability of the plant with respect to combustion chamber.

Table 3.6  Availability of the plant with respect to turbine Availability Pup(t) Time (t)

λT = 0.050

λT = 0

Availability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.722 0.663 0.651 0.648 0.647 0.647 0.647 0.647 0.647 0.647

1.000 0.743 0.686 0.673 0.670 0.669 0.669 0.669 0.669 0.669 0.669

0 2.83 3.35 3.27 3.28 3.29 3.29 3.29 3.29 3.29 3.29

Effect of Equipment’s Failure on Gas Turbine Power Plant

Figure 3.6  Availability of the plant with respect to turbine.

Table 3.7  Availability of the plant with respect to generator Availability Pup(t) Time (t)

λG = 0.200

λG = 0

Availability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.722 0.663 0.651 0.648 0.647 0.647 0.647 0.647 0.647 0.647

1.000 0.810 0.761 0.748 0.745 0.744 0.744 0.744 0.743 0.743 0.743

0 10.86 12.88 12.97 13.02 13.04 13.04 13.04 12.92 12.92 12.92

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Figure 3.7  Availability of the plant with respect to generator.

6. Effect of λC : Suppose that the compressor of the plant is working perfectly, then availability of gas turbine power plant is revealed in Table 3.8 and the comparison is shown in Fig. 3.8. 7. Effect of λR : Suppose that the rotating shaft is perfect, then availability of gas turbine power plant is revealed in Table 3.9 and the comparison is shown in Fig. 3.9. Table 3.8  Availability of the plant with respect to compressor Availability Pup(t) Time (t)

λC = 0.050

λC = 0

Availability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.722 0.663 0.651 0.648 0.647 0.647 0.647 0.647 0.647 0.647

1.000 0.743 0.686 0.673 0.670 0.669 0.669 0.669 0.669 0.669 0.669

0 2.83 3.35 3.27 3.28 3.29 3.29 3.29 3.29 3.29 3.29

Effect of Equipment’s Failure on Gas Turbine Power Plant

Figure 3.8  Availability of the plant with respect to compressor.

Table 3.9  Availability of the plant with respect to rotating shaft Availability Pup(t) Time (t)

λR = 0.028

λR = 0

Availability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.722 0.663 0.651 0.648 0.647 0.647 0.647 0.647 0.647 0.647

1.000 0.734 0.676 0.663 0.660 0.659 0.659 0.659 0.659 0.659 0.659

0 1.63 1.92 1.81 1.82 1.82 1.82 1.82 1.82 1.82 1.82

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Figure 3.9  Availability of the plant with respect to rotating shaft.

8. Effect of λGC : Suppose that generator convertor is working properly, then availability of gas turbine power plant is revealed in Table 3.10 and the comparison is shown in Fig. 3.10.

Table 3.10  Availability of the plant with respect to generator convertor Availability Pup(t) Time (t)

λGC = 0.150

λGC = 0

Availability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.722 0.663 0.651 0.648 0.647 0.647 0.647 0.647 0.647 0.647

1.000 0.722 0.663 0.651 0.648 0.647 0.647 0.647 0.647 0.647 0.647

0 0 0 0 0 0 0 0 0 0 0

Effect of Equipment’s Failure on Gas Turbine Power Plant

Figure 3.10  Availability of the plant with respect to generator convertor.

3.3.2. Reliability analysis The reliability of a system can be formulated through the different probabilistic approach based on mature scientific philosophy. Plant’s reliability is one of the crucial quality characteristics which convention with the performance of its each component of its. Reliability represents the probability of nonfailure components, subsystems, and system to perform their required functions for a precise time period in specified environmental condition ([12,21]). Therefore repair facility has not taken account into the reliability evaluation. Setting all the repair rates zero and assigning the value of failure rates as λNF = 0.006, λNA = 0.200, λCC = 0.011, λT = 0.050 , λG = 0.200 , λC = 0.050, λGC = 0.150 [18–20] in Eq. (3.13), later on takes the inverse Laplace transformation. 1. Effect of λNF : Suppose that we have sufficient fuel, then reliability of gas turbine power plant is revealed in Table 3.11 and the comparison is shown in Fig. 3.11. 2. Effect of λNA : Suppose that enough air is available, then reliability of gas turbine power plant is revealed in Table 3.12 and the comparison is shown in Fig. 3.12.

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Table 3.11  Reliability of the plant with respect to nonavailability of fuel Reliability Rl(t) Time (t)

λNF = 0.006

λNF = 0

Reliability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.580 0.336 0.195 0.113 0.066 0.038 0.022 0.013 0.007 0.004

1.000 0.583 0.340 0.198 0.116 0.068 0.039 0.023 0.013 0.008 0.004

0 0.51 1.18 1.51 2.59 2.94 2.56 4.35 0.00 12.50 0.00

Figure 3.11  Reliability of the plant with respect to nonavailability of fuel.

Effect of Equipment’s Failure on Gas Turbine Power Plant

Table 3.12  Reliability of the plant with respect to nonavailability of air Reliability Rl(t) Time (t)

λNA = 0.200

λ NA = 0

Reliability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.580 0.336 0.195 0.113 0.066 0.038 0.022 0.013 0.007 0.004

1.000 0.708 0.502 0.355 0.252 0.178 0.126 0.089 0.063 0.045 0.032

0 18.08 33.07 45.07 55.16 62.92 69.84 75.28 79.36 84.44 87.50

Figure 3.12  Reliability of the plant with respect to nonavailability of air.

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3. Effect of λCC : Suppose that there is no problem in combustion chamber, then reliability of gas turbine power plant is revealed in Table 3.13 and the comparison is shown in Fig. 3.13.

Table 3.13  Reliability of the plant with respect to combustion chamber Reliability Rl(t) Time (t)

λCC = 0

λCC = 0.011

Reliability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.580 0.336 0.195 0.113 0.066 0.038 0.022 0.013 0.007 0.004

1.000 0.586 0.344 0.201 0.118 0.069 0.040 0.024 0.014 0.008 0.005

0 1.02 2.32 2.98 4.24 4.35 5.00 8.33 7.14 12.50 20.00

Figure 3.13  Reliability of the plant with respect to combustion chamber.

Effect of Equipment’s Failure on Gas Turbine Power Plant

4. Effect of λT : Suppose that plant cannot fail due to turbine, then reliability of gas turbine power plant is revealed in Table 3.14 and the comparison is shown in Fig. 3.14. Table 3.14  Reliability of the plant with respect to turbine Reliability Rl(t) Time (t)

λT = 0.050

λT = 0

Reliability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.580 0.336 0.195 0.113 0.066 0.038 0.022 0.013 0.007 0.004

1.000 0.610 0.372 0.226 0.138 0.084 0.051 0.031 0.019 0.012 0.007

0 4.92 9.68 13.72 18.11 21.43 25.49 29.03 31.58 41.67 42.86

Figure 3.14  Reliability of the plant with respect to turbine.

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5. Effect of λG : Suppose that generator is working properly, then reliability of gas turbine power plant is revealed in Table 3.15 and the comparison is shown in Fig. 3.15.

Figure 3.15  Reliability of the plant with respect to generator.

Table 3.15  Reliability of the plant with respect to generator Reliability Rl(t) Time (t)

λG = 0.200

λG = 0

Reliability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.580 0.336 0.195 0.113 0.066 0.038 0.022 0.013 0.007 0.004

1.000 0.708 0.502 0.355 0.252 0.178 0.126 0.089 0.063 0.045 0.032

0 18.08 33.07 45.07 55.16 62.92 69.84 75.28 79.36 84.44 87.50

Effect of Equipment’s Failure on Gas Turbine Power Plant

6. Effect of λC : Suppose that the compressor of the plant is working perfectly, then reliability of gas turbine power plant is revealed in Table 3.16 and the comparison is shown in Fig. 3.16.

Table 3.16  Reliability of the plant with respect to compressor Reliability Rl(t) Time (t)

λC = 0.050

λC = 0

Reliability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.580 0.336 0.195 0.113 0.066 0.038 0.022 0.013 0.007 0.004

1.000 0.610 0.372 0.226 0.138 0.084 0.051 0.031 0.019 0.012 0.007

0 4.92 9.68 13.72 18.11 21.43 25.49 29.03 31.58 41.67 42.86

Figure 3.16  Reliability of the plant with respect to compressor.

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7. Effect of λR : Suppose that the rotating shaft is perfect, then reliability of gas turbine power plant is revealed in Table 3.17 and the comparison is shown in Fig. 3.17.

Table 3.17  Reliability of the plant with respect to rotating shaft Reliability Rl(t) Time (t)

λR = 0.028

λR = 0

Reliability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.580 0.336 0.195 0.113 0.066 0.038 0.022 0.013 0.007 0.004

1.000 0.596 0.356 0.212 0.126 0.075 0.045 0.027 0.016 0.010 0.006

0 2.68 5.62 8.02 10.32 12.00 15.56 18.52 18.75 30.00 33.33

Figure 3.17  Reliability of the plant with respect to rotating shaft.

Effect of Equipment’s Failure on Gas Turbine Power Plant

8. Effect of λGC : Suppose that generator convertor is working properly, then reliability of gas turbine power plant is revealed in Table 3.18 and the comparison is shown in Fig. 3.18.

Table 3.18  Reliability of the plant with respect to generator convertor Reliability Rl(t) Time (t)

λGC = 0.150

λGC = 0

Reliability increment in %

0 1 2 3 4 5 6 7 8 9 10

1.000 0.580 0.336 0.195 0.113 0.066 0.038 0.022 0.013 0.007 0.004

1.000 0.580 0.336 0.195 0.113 0.066 0.038 0.022 0.013 0.007 0.004

0 0 0 0 0 0 0 0 0 0 0

Figure 3.18  Reliability of the plant with respect to generator convertor.

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3.4  RESULTS, DISCUSSION, AND CONCLUSION The variation of availability and reliability of the gas turbine power plant with respect to time for its components is explained in detail with their respective graphs. Availability as well as reliability of gas turbine power plant in various cases has been calculated using Markov Process and supplementary variable technique. In this study, we can find that there is a significant difference in the reliability and availability calculations of the gas turbine power plant when the failure rates of its subsystem increases or decreases. This difference is shown in various graphs of reliability and availability. As one can see from Figs. 3.3–3.9 and 3.11–3.17, the availability and reliability of the gas turbine power plant increases as the failure rates of nonavailability of fuel, nonavailability of air, combustion chamber, turbine, generator, compressor, and rotating shaft increases. While as shown in Figs. 3.10 and 3.18, there is no change in availability and reliability of the plant in case of increment or decrement in the failure rate of generator convertor. It is an interesting finding, which means it requires preventive maintenance. From the observation of all graphs, we find that the results are indeed better when failure rates of the subsystems of the gas turbine power plant decreases. This is an important finding for the engineers and practitioners. They should be very careful in the determination of the reliability of the gas turbine power plant based on its failure rates. In future, authors hope to find the other parameters such as MTTF and MTTR of the gas turbine power plant.

REFERENCES [1] H. Wang, h. Pham, Survey of reliability and availability evaluation of complex networks using Monte Carlo techniques, Microelectron. Reliab. 37 (2) (1997) 187–209. [2] S. Gupta, P.C.Tewari, Simulation modelling and analysis of a complex system of a thermal power plant, J. Ind. Eng. Manag. 2 (2) (2009) 387–406. [3] R.K.Tuteja, S.C. Malik, Reliability and profit analysis of two single-unit models with three modes and different repair policies of repairmen who appear and disappear randomly, Microelectron. Reliab. 32 (3) (1992) 351–356. [4] B.S. Dhillon, N.Yang, Availability of a man-machine system with critical and non-critical human error, Microelectron. Reliab. 33 (10) (1993) 1511–1521. [5] K. Kumar, J. Singh, P. Kumar, Fuzzy reliability and fuzzy availability of the serial process in butter-oil processing plant, J. Math. Stat. 5 (3.1) (2009) 65–71. [6] M. Ram, S.B. Singh, V.V. Singh, Stochastic analysis of a standby system with waiting repair strategy, IEEE Trans. Syst. Man Cybern. Syst. 43 (3) (2013) 698–707. [7] M. Ram, On system reliability approaches: a brief survey, Int. J. Syst. Assur. Eng. Manag. 4 (2) (2013) 101–117. [8] M.A. Zaini, The Study on the Performance of the Gas Turbine for Power Generation. (Doctoral dissertation, UMP). 2008. [9] W.H.A.R. Al-Doori, Parametric performance of gas turbine power plant with effect intercooler, Mod. Appl. Sci. 5 (3) (2011) 173. [10] E. Thirunavukarasu, Modeling and Simulation Study of a Dynamic Gas Turbine System in a Virtual Test Bed Environment. (Doctoral dissertation, University of South Carolina), 2013.

Effect of Equipment’s Failure on Gas Turbine Power Plant

[11] D.K. Mohanty,V.Venkatesh, Performance analysis of a combined cycle gas turbine under varying operating conditions, Mech. Eng. Int. J. 1 (2) (2014). [12] B.S. Dhillon, C. Singh, Engineering Reliability: New Techniques and Applications, Wiley, New York, (1981) pp. 329–334. [13] D.R. Cox,The analysis of non-Markovian stochastic processes by the inclusion of supplementary variables, Math. Proc. Camb. Philos. Soc. 51 (3) (1955) 433–441 Cambridge University Press. [14] E.A. Oliveira, A.C.M. Alvim, P.F. e Melo, Unavailability analysis of safety systems under aging by supplementary variables with imperfect repair, Ann. Nucl. Energy 32 (2) (2005) 241–252. [15] M. Ram, N. Goyal, Gas turbine assimilation under copula-coverage approaches, 2015In Research Advances in Industrial Engineering. . [16] A. Avižienis, J.C. Laprie, B. Randell, C. Landwehr, Basic concepts and taxonomy of dependable and secure computing, IEEE Trans. Dependable Secure Comput. 1 (1) (2004) 11–33. [17] M. Samrout, F.Yalaoui, E. Châtelet, N. Chebbo, New methods to minimize the preventive maintenance cost of series–parallel systems using ant colony optimization, Reliab. Eng. Syst. Safe. 89 (3) (2005) 346–354. [18] N. Goyal, M. Ram, A. Bhardwaj, A. Kumar, Thermal Power Plant Modelling with Fault Coverage Stochastically, Int. J. Manuf. Mater. Mech. Eng. 6 (3) (2016) 28–44. [19] N. Goyal, M. Ram, A. Kaushik, Performability of Solar Thermal Power Plant under Reliability Characteristics, Int. J. Syst. Assur. Eng. Manag. 8 (2) (2017) 479–487. [20] N. Goyal, M. Ram, Stochastic modelling of a wind electric generating power plant performance under multi-approaches, Int. J. Qual. Reliab. Manag. 34 (1) (2017) 103–127. [21] W.Q. Meeker, L.A. Escobar, Reliability: the other dimension of quality, Qual. Technol. Quant. Manag. 1 (1) (2003).

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CHAPTER 4

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection Paulo Peças, Inês Ribeiro, Elsa Henriques, Ana Raposo IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

4.1 INTRODUCTION Additive manufacturing (AM) is nowadays a hot topic that many believe will be disruptive to the manufacturing sector [1,2] with benefits to sustainable development. AM can be defined as a range of manufacturing technologies that are capable of translating virtual solid models into physical 3D parts by the deposition of successive layers of material [3,4], without additional fixtures or cutting tools. Rather than subtracting (remove) or forming raw materials as usually happens in more traditional technologies, AM technologies join (add) the material to achieve a final geometry. Furthermore, AM technologies can process a large variety of metals, ceramics, polymers, and composites and are able to construct complex geometric parts in a single step, meaning that many previously separated parts can be consolidated into a single object, and the number of stages of conventional manufacturing processes can be reduced. The potential to save time and resources and to generate less waste is significant during manufacturing and also on the use phase of the final products due to designs topologically optimized [5]. AM technologies tend to be energetically efficient and, as so, tend to reduce the energy-related environmental and economic impacts [6–8]. However, the comparison of the energy efficiency of AM and conventional technologies is not so straightforward. As shown by Chen et al. [9], the embodied energy in the final product (energy required for the extraction, processing and transportation of the raw materials, and to make the product) depends largely on the boundaries of the analysis and is a function of the production volume. The benefits of AM technologies acquire importance given that environmental and economic sustainability is becoming an established competitive focus for any company, which often means having a strategy for minimizing the environmental consequences and impacts of the business activities [10]. There is a direct positive impact of good environmental records in the business overall performance and in the generation of profit and growth [11]. In general, the reduction of environmental impacts is achieved by decreasing the consumption of resources and/or the polluting emissions and waste, Advanced Applications in Manufacturing Engineering. http://dx.doi.org/10.1016/B978-0-08-102414-0.00004-5 Copyright © 2019 Elsevier Ltd. All rights reserved.

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which is well matched with cost reduction. Fostered by a managerial and cultural evolution, the improvement of the environmental and economic performances in manufacturing is based and dependent on selecting the most appropriated materials and technologies in the development phase of more and more efficient and eco-friendly products [12]. Beyond the potential environmental and economic benefits, AM can also offer specific technical gains, as far as it allows producing high complex geometries with dimensional accuracy and good surface finishing [3,13], together with the appropriated material properties. It opens up the possibility of producing multifunctional parts with complex shapes and geometric/technical properties that have been difficult or even impossible to obtain using material removal or forming processes. The evolution and spreading of AM has been conducive to design innovation. Moreover, as far as it introduces speed, versatility, and adaptability to the manufacturing processes, it enlarges the techno-economic advantages when small production volumes and just-in-time manufacturing are required [14,15]. This chapter studies the potential of AM technologies for injection molds with conformal cooling systems. The analysis is based on a case study intending to compare the performance of conventional machining and two AM technological alternatives in providing cooling solutions for a plastic injection mold. Assuming that the effects of the benefits and drawbacks of the options have different dimensions and time horizons, the comparison will be made based on the life cycle perspective of the mold. A Life Cycle Engineering (LCE) approach is followed, which allows the integrated evaluation of the economic, environmental, and technical performance throughout the mold life. Such approach is typically associated with an engineering-focused analysis allowing to compare, usually for the same manufacturing and business conditions, technological alternatives [16,17], and design options [18–20]. Furthermore, sensitivity analyses are performed to understand the robustness and most suitable alternative for different production contexts, taking advantage of the process modeling based on engineering relations.

4.2  ADDITIVE MANUFACTURING IN MOLD MANUFACTURING Complex geometries and dimensional accuracy are two strong requirements in special tooling production, especially as regards to plastics injection molds. The conventional machining technologies involve equipment programming for intricate tools paths and extra steps of part loading/unloading and fixing [21]. AM allows releasing the geometrical constraints and the production of complex features in a single step [13], although some surface finishing operations might be required. In particular, the special characteristics of AM technologies are appropriated to generate conformal cooling channels in injection molds, which have been difficult to execute with the conventional techniques

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[22,21]. Making use of cooling lines (inside the mold core and cavity) curving and closely outlining the geometry of the molding cavity, conformal cooling promotes a faster and more uniform temperature control and, as so, significantly increases the performance of the injection cycle, on both production rate and quality of the plastic part [23]. In fact, several studies confirm that conformal cooling provides a better injection molding performance than the simpler approaches with straight cooling channels produced by drilling machines [24–26]. The conformal cooling channels are closer to the part being molded and an equal distance can be set between the part surface and the closest channel, resulting in a uniform cooling of the molded part. A homogeneous temperature in the part promotes its quality insofar as defects (e.g., warpage), sink marks, differential shrinkage, and weld lines, are minimized. Cooling channels that better contour the molding cavity also contribute to a 40%–60% lower cooling time [24]. Playing a role in the part quality and in the reduction of the injection cycle time, conformal cooling is likely to increase the injection molding productivity and to reduce the production costs of molded parts as well as the energy and material consumption. Among several existent AM technologies and equipment [27], the Direct Metal Laser Sintering (DMLS) is able to produce molds parts (core and cavity of the mold or chunks of them) with conformal cooling channels inside it. DMLS is based on a layer-by-layer deposition of metallic powders. Their subsequent laser sintering in a shape defined by a 3D model selectively binds the material together to create a solid structure [28,29]. Another technology that can be used to produce conformal cooling channels is the Vacuum Furnace Brazing (VBm) of metal plates. This technique makes use of halfchannels machined on the plate surfaces that are then piled and joined by brazing in a furnace [30]. In contrast with the typical AM method (e.g., the referred DMLS), VBm requires extra steps to previously machine the half-channels and afterwards to finish the geometry of the molding cavity [30]. It can be considered a hybrid process, because it uses conventional (machined metal plates) and AM technologies (the rough shape is obtained by piling up successive layers of material). The production of the molding components (core and cavity) of injection molds by DMLS or VBm technologies requires distinct equipment, energy, consumables, and even material, from the ones used when conventional machining is employed. Consequently, the economic, technical, and environmental performances of the different ways of producing the channels for the cooling system (allowing distinct geometries) are expected to be different along the different phases of the mold life, from its production and use phase (production of parts by injection molding) to its end of life. In that sense, it is important to analyze the technological options in a life cycle perspective. Despite the potential benefits of producing molds with conformal cooling, most mold makers do not have in-house the mentioned technologies to produce the contouring

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channels. In fact, conformal cooling is not so far spread in the industry. Without any published study but based on a permanent contact with several mold manufacturing companies, the authors attribute this situation mainly to two aspects: (1) the difficulty and even inadequacy of conventional machining technologies to produce effective conformal cooling channels and (2) the high investment in equipment and cost of raw materials related with the AM technologies of metals. Furthermore, conformal cooling is not always an advantage as far as conventional engineering and mold design solutions can assure the cycle time and quality specified for most final parts geometries. Strict conformal cooling is only really needed in complex plastic part geometries, frequently with thin walls and intricate features, difficult to cool quickly and uniformly. In fact, in the industrial context, conventional drilling processes are frequently used to produce cooling channels somehow similar to a “pure” conformal cooling system (for instance, a contouring channel is approximated by a sequence of elementary straight channels). Any conformal cooling solution increases the mold production cost, making it economically viable only for very high production volumes, in which the mold high cost is diluted over a large number of final parts. This introduces an additional dimension of analysis as regards technology selection: the influence of the expected number of plastic parts to be produced. In addition, the performance of each technological solution for conformal cooling varies differently with the size and complexity of the mold, meaning that sensitivity analysis is recommended when benchmarking alternatives.

4.3  MEANS AND METHODS This section presents the methodology followed to analyze the performance of different conformal cooling solutions. The analysis is based on a case study approach involving three technologies.The two AM-based alternatives considered in this work use the VBm and DMLS technologies for the production of the mold with conformal cooling. A third alternative relies on conventional machining technologies, where the difficulty of closely following the molding surface is evident.

4.3.1  Case study The comparison of the three alternatives of producing a conformal cooling system is based on a case study consisting in the production of a plastic part, made of polypropylene (PP), by injection molding.The part presents geometrical characteristics related with a difficult efficient and smooth cooling inside the mold: circular shape, low thickness, and small space between the part walls (Fig. 4.1). The geometry of the cooling systems and the core and cavity of the molds are presented in Table 4.1 for the three mold construction alternatives. It should be noted that only the production of the mold cavity and core were considered, because the remaining functional systems (mold structure, injection, and ejection systems) are essentially the same for the three alternatives.

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

Figure 4.1  Plastic part dimensions and dimensions. Maximum height: 56.5 mm; Maximum diameter: 90.6 mm; Minimum thickness: 6 mm.

Table 4.1  Design of the conformal cooling systems. The cooling system based on conventional machining technologies is tentative for achieving a “pure” conformal cooling Description

Components of the molding cavity

Conformal cooling system based on conventional technologies

Conformal cooling system based on AM technologies

Cavity

Core

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4.3.2  Conformal cooling technologies Conventional machining is very limited in producing molding components with contouring channels. On the basis of milling and drilling, the channels are created with a layout as similar as possible to the conformal system, to achieve a fast and smooth cooling and the resulting benefits of a low cycle time and a good quality of the plastic part. However, as the drilled channels are straight and incapable to contour the molding surface uniformly, for the same mold volume, the number of channels is lower due to the geometrical constraints and so some degradation in the cooling performance is expected. The built channels materialize a tentative to achieve a “pure” cooling system, which for this geometry is only possible to reach by the AM technologies. In opposition, AM technologies, namely DMLS and VBm, can easily generate the required internal free-form channels, even though conventional milling is required to achieve the specified tolerances and quality of the molding surfaces (in DMLS) and to produce the half channels of the elementary plates (in VBm).The alternative technologies considered to produce cooling channels are described in the following paragraphs (Fig. 4.2). DMLS begins with the digital solid models of the core and cavity of the mold (Fig. 4.3), which are processed to create “build paths” that reproduce the models through the consolidation of powder material, layer by layer, by means of an energy source. A

Figure 4.2  Images of the conformal cooling channels position in the mold cavity and core. (A) Using conventional machining technologies—from left to right: the cavity, the core, the mold with the plastic part in blue and (B) Using AM technologies—from left to right: the exterior of the cavity, the bottom of the core, and the mold with the plastic part in blue.

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

Figure 4.3  3D models developed for DMLS. Orange and green geometries represent empty space (cooling channels). (A) Core and (B) Cavity.

typical DMLS machine integrates a powder bed system, an Yb-fiber Laser energy source that casts a single layer of powder metal, with a thickness between 20 and 60 µmm upon a previous layer with a step thickness between.The process is repeated until the solid 3D component is completed. Because the material goes through rapid temperature cycles and steep temperature gradients occur between the successive layers, a heat treatment should be performed afterwards to reduce the thermal tensile stresses [31]. VBm follows a 3D construction process in which metallic plates are joining by brazing in a vacuum furnace. The metallic bond of the plates is achieved by using a filler metal and heating the base material at an appropriate temperature (without melting). Because the melting point of the filler metal is lower than the temperature of the base material in solid state, the joint is filled by capillary action and a diffusion bonding of

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Figure 4.4  3D models of the plates with half-channels machined by milling. (A) Core and (B) Cavity.

the plates is achieved [32]. The production of conformal channels using VBm technology requires auxiliary steps. The material is first sliced into several plates, where the half channels are machined (Fig. 4.4 presents the 3D models of the set of plates required for the mold core and cavity). Then the plates have to be prepared for vacuum brazing: they are cleaned for a proper capillary action and the filler metal is applied. The whole set of plates is then assembled and introduced into a vacuum furnace, where it is heated, keeping a uniform temperature, in a controlled atmosphere. Initially, the furnace is at a low atmosphere pressure (vacuum) of an inert gas. When the cooling starts, nitrogen is injected to equal the pressure inside the furnace with the outside pressure and to eliminate residuals. Once the cooling is completed, the component is removed and cleaned from residuals.

4.3.3 Methods The analysis follows a life cycle approach, grounded on process-based models (PBM) [18][25], which, handling each step of the manufacturing processes, supply information on the material, energy consumption and resources utilization to Life Cycle Cost (LCC) and Life Cycle Assessment (LCA) models settled for the economic and environmental assessments, respectively (Fig. 4.5). The technical performance of the final plastic part is also considered based on quality-related attributes. The results of the LCE analysis combine then these three dimensions of analysis (economic, environmental, and technical) for a global evaluation. A PBM consists in a process and operations modules. The first relies on engineering, technological, and scientific principles to relate the features of the final part, such as geometry and material, to the technical parameters of the manufacturing process. These parameters include the cycle time, amount of raw material, type and quantity of

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

Figure 4.5  Process-based model integration with LCC and LCA.

consumables, and the nominal dimensions and power of the required equipment. The operations module relates the process requirements with the operations context, computing the operational resources, such as the amount of machining tools and other consumables, equipment time, number of operators, energy, and other resources needed to achieve the production volume.The PBM outputs are the required amount of resources. Figs. 4.6 and 4.7 present the type of PBM inputs and outputs for the three mold production alternatives and for the injection molding process (the mold use phase), respectively. The PBM content is detailed explained in Section 4.4. On the basis of the calculated consumptions and resources time-usage for each manufacturing step, costs are determined for each life cycle phase considered, by applying cost factors. The total cost is the sum of variable costs (material, energy, and labor) and fixed costs (equipment and building/space costs), represented by Eq. (4.1), in which Cfxxi stands for the cost factor of item i of the resource type xx. In Section 4.4.2, the used financial relations to calculate the cost for each life cycle phase, that is, the LCC, are discussed. Total Cost =

nmat

∑Material i =1 nl



ncons

i

nen

× Cfmati + ∑Consumablesi × Cfconsi + ∑Energyi × Cfeni i =1

i =1

nequi

nb

i =1

i =1

+∑Labouri × Cfli + ∑Equipmenti × Cfequii + ∑Building i × Cfbi i =1

(4.1)

LCA integrates all the environmental impacts generated along the life cycle phases. The inventory of the materials and energy flows (outputs from the PBM) are translated to environmental impact through the use of an eco-indicator database (according to their

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Figure 4.6  PBM inputs and intermediate calculations for outputs of mold production.

impact in the different environmental damaged categories). The elementary impacts are combined in three higher level categories: human health, ecosystems, and resources, which are then submitted to a weighting system to get the aggregated eco-indicator [28]. This process was supported by the SimaPro software, and ReCiPe (H), an update of EI’99 and CML 2000 methods, was used as the weighting system. The total score of environmental impacts is represented by Eq. (4.2) (EI stands for the eco-indicator):



Total EI =

nmat

ncons

nen

∑Material × EI + ∑Consumables × EI + ∑Energy × EI i

i =1

i

i

i =1

i

i

i

(4.2)

i =1

A common scope, boundaries, and inventory system are used for the cost and environmental evaluation of the mold life cycle (Fig. 4.8). The material production phase regards the production of raw material for mold manufacturing. The mold manufacturing is related with the several manufacturing steps required to transform the raw material

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

Figure 4.7  PBM inputs and outputs of injection molding process.

Figure 4.8  Mold Life Cycle.

into the final mold. It should be remembered that only the components involved in the molding surfaces (core and cavity) with their cooling channels were considered, because the remaining functional systems of the mold are the same. The use phase of the mold refers to the injection molding process for the production of the plastic part. The material consumed here is the plastic injected into the mold. The main difference of the alternatives in the use phase relies in the different performance of the mold cooling system, resulting in different injection cycle times. Because the same injection molding

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Figure 4.9  Methodology used for the technical performance assessment.

equipment was considered, the energy consumed differs among the alternatives due to a different cycle time. For the mold end of life phase, it is assumed that the mold material (steel for all the alternatives despite minor differences in alloying elements) is sold for recycling as well as the process scrap material resulting from the machining processes. Several decision-making methods can be applied to compare the technical performance of the final parts molded by the alternative molds, from graphic theory and matrix approaches to methods based on multiple attribute decision-making (MADM). In common, all of them rely on know-how and expertise of professionals and users to determine the relevant functional attributes for the application, and, on a comparison basis, assess the performance of the alternatives within this set of attributes. Fig. 4.9 outlines the methodology based on MADM principles that was followed to compare the technical performance of the parts produced by the different mold alternatives. Finally, the individual assessment of the economic, environmental, and technical performance of the technological options is integrated in a global evaluation, based on ternary diagrams, disclosing the best choices as a function of the importance given to each dimension of the analysis (Fig. 4.10). The visual representation of the final comparison

Figure 4.10  Method for the LCE assessment.

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

allows an easy interpretation of the performance in each dimension (Ribeiro, Peças, and Henriques, Incorporating tool design into a comprehensive LCC framework usign the case of injection molding, [18]) (Peças, Henriques, and Ribeiro, Integrated Approach to Product and Process Design Based on Life Cycle Engineering, [33]) and also of the domain (range of importance weights) of each “best alternative”. The data for the case study were collected by different means and from different sources. Observation of industrial operations and manufacturing data logs was the base to prepare the framework of the PBM and the general data. Then interviews involving industrial experts allowed the discussion of the processes and the fine-tuning of the data. In parallel, computer-aided software applications, namely MasterCam and Moldflow, were used to simulate milling and injection molding processes, respectively. Through these process simulations, the relevant operation parameters were obtained, allowing a sort of data triangularization with the industrial reality.

4.4  THE PROCESS-BASED MODELS AND THEIR OUTPUTS This section is dedicated to the deployment of the content of the PBM, namely the technological and production-related correlations between the part and process design parameters and the time and resources consumed. The overview of the PBM is schematized in Figs. 4.6 and 4.7, so here its content is described for the three alternatives. The links of PBM outputs to the economic and environmental evaluation are also discussed. The section ends with the results of the PBM regarding the required production time and the resources list for the molds and plastic parts production, within the three alternatives of conformal cooling.

4.4.1  Process-based model technical relations and assumptions This section presents the technical relations and assumptions used to compute the resources for the life cycle inventory that will feed the cost and environmental assessments. Each life cycle phase is described, the mold production processes being significantly different for each alternative. 4.4.1.1  Brazing-alternative—mold produced by Vacuum Furnace Brazing Brazing-Alternative consists of a mold with conformal cooling system produced by the VBm technology. The sequential steps and processes involved in the mold production (Fig. 4.11) are explained in the following paragraphs.

Figure 4.11  Overview of the mold production for the Brazing-Alternative.

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Figure 4.12  Dimensions of a single plate with the overthicknesses for machining.

The quantification of the raw material required depends on the dimension of the molding components. For this alternative, the raw material must be transformed into slices by cutting, roughing, and finishing machining operations. In the resulting slices, the half channels are then machined—these slices are the plates to pile prior to brazing by the VBm technology. As the mold has a revolution geometry, a cylindrical raw material bar was considered. The total length and diameter of the bar depend not only on the core and cavity dimensions, but also on the machining overthickness, which is the thickness to removing during the roughing and finishing machining operations. A roughing operation is needed to remove residual contaminants on the surface of the raw material and also to guarantee a flat surface: it removes a roughing thickness. A finishing operation is performed to obtain the required roughness: it removes a finishing thickness. The final dimensions of the raw material result from adding these overthicknesses to the external (or final required) dimensions of the mold components. Fig. 4.12 illustrates the final dimensions of a single plate and the required overthickness for the roughing and finishing operations. The final diameter of the plate is the mold cavity/core diameter, Dca. To obtain the required diameter, DRM, of the raw material bar, the roughing required overthickness, d, must be added. The designed length (or thickness) of each VBm plate is the distance between two channels in consecutive levels (in height): in fact the distance of the centers of the half channels to be machined in each plate (Fig. 4.4). To obtain slice of raw material with the required length (or thickness), the roughing overthickness, d, and the finishing overthickness, f, have to be added to the plate design length (or thickness), Lp.The roughing overthickness is in fact the raw material wasted during the bar slice cutting. One should note that the finishing overthickness must be counted twice and the roughing overthickness is wasted 2n-1 times,

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

n being the number of slices. The number of plates depends on the number of cooling channels located along the cavity and core height. Eqs. (4.3) and (4.4) allow to compute the required raw material bar length, LRM, and diameter, DRM, in which Lca and Lco stand for the design length or the mold cavity and core, respectively:

L RM = L ca + L co + 2d (n − 1) + 2 fn

(4.3)



DRM = Dca + 2d

(4.4)

So the volume of raw material (VVBm RM) that is necessary to purchase can be computed by Eq. (4.5):

π ( DRM ) = × L RM 4 2



VRM VFBm

(4.5)

In the case study under analysis, along the length of the cavity, cooling channels are located in five different levels, which means that the steel bar must be cut into six slices. Similarly, the mold core component has two different locations of cooling channels, so the remaining steel bar must be divided into three slices. After having generated by saw cutting the required slices, surfaces are finishing and the half channels are machined by milling on each flat surface. The mold production time, TP, is the sum of the operation time, Top, and setup time Tsetup. The operation time has several components: the slices cutting (roughing) time, the slices finishing time, the half channels machining time, the brazing time in the vacuum furnace, and the mold finishing time. The setup time has four components: the cutting operations setup (saw equipment), the milling operations setup (CNC milling equipment), and the component preparation to brazing time and the vacuum furnace setup. Eqs. (4.6)–(4.8) represent the time computing with self-explained variables.

TP = Top + Tsetup

(4.6)



Top = Tslices _ cutting +Tslices _ finishing + Tchannels _ mach + Tbrasing + Tmould _ finishing

(4.7)



Tsetup = Tsetuo _ saw + Tsetup _ CNC + Tsetup _ brasing + Tsetup _ furnace

(4.8)

The values considered for these times are presented in Appendix Table  4.1. They were estimated by MasterCam software and validated in close contact with a mold making company. The setup times were also gathered on an industrial environment visiting and observing similar operations in a mold making company and in a company that uses furnace brazing. The machining operation requires cutting tools, cutting fluids, and energy. The tools wearing were not considered significant for this study, and as regards their cost, they are

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assumed to be included in the cost of the equipment. The energy consumed by the saw machine and the CNC milling machine was roughly estimated by Eq. (4.9)–(4.11). The cutting fluid consumption was estimated by Eq. (4.12), and the average fluid rate consumption assumed was 1.1 mL/h as presented in Appendix Table  4.1. Please note that even economically negligible, cutting fluids have a potentially significant environmental impact.

E = E saw + ECNC

(4.9)



E saw = Tslices _ cutting ×Powersaw _ equipment

(4.10)



ECNC = (Tslices _ finishing + Tchannels _ mach + Tmold _ finiching ) × PowerCNC _ equipment

(4.11)



Fluid = Tslices finishing + Tchannelsmach + Tmold finishing × Cons _ rate

(

)

(4.12)

The production time and energy consumption in the vacuum brazing process are expected to be relevant and considerably more complex than in the machining processes, so further detail needs to be provided. Before the assembly of the two sets of plates to be introduced into the furnace, a preparation step is required. It consists in cleaning the plates, using a degreasing fluid to eliminate eventual residuals, followed by the application of the filler metal and stop-off material. The sum of these operations times is the component preparation to brazing time of Eq. (4.8) (Tsetup_brazing). Several materials are consumed to prepare the plates for brazing and the overall quantities depend on the surface areas of the plates, namely a degreasing agent to prepare the surface, a filler metal to join the metal plates by thermal diffusion (brazing) and a stop-off material to be placed in the half machined channels. The consumption rate and the required consumables volumes are presented in Appendix Table  4.1. The assembled parts are then introduced in a furnace for a thermal cycle in an Argon and Nitrogen furnace atmosphere. The consumption of these gases is also presented in Appendix Table  4.1. The brazing thermal cycle consists in three phases: increasing temperature, constant temperature, and decreasing temperature. Its cycle time depends on many variables such as the thickness of the part, the amount of parts (or volume of parts) in the furnace, the part material, the furnace type, and the required time for the filler metal to flow through the joints.Table 4.2 presents the thermal cycle temperatures and duration considered for the case under analysis, which allows calculating the brazing time in the vacuum furnace, Tbrazing of Eq. (4.7). On the basis of a thermal cycle, the energy consumption during brazing (E) is expected to be relevant. It can be estimated by the sum of the consumption in the three main phases of the thermal cycle above mentioned:

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection



Heating

Const.

Cooling

600

600

20

−920 0

500

0

−580

3.8

4.5

6.55

6.85 9.85 10.05

0.5

0.7

2.05

0.3

Cooling

100

Const.

Cooling

Const.

Heating

Const.

Const.

Temp. (°C) ∆Temp. (°C) Time (h) ∆Time (h)

Heating

Vacuum (Initial)

Table 4.2  Brazing thermal cycle used in the case study

20

1023 1023 1098 1098 1020 1020 100

0

1003 0

75

0

−78

0

0.25

2.12

2.7

2.9

3.2

3.3

0.25

1.87

0.58

0.2

0.3

0.1

E = Eincreasing _ temp + Econstant _ temp + Edecreasing _ temp

3

0.2

(4.13)

The energy consumed to increase the furnace temperature, Eincreasing_temp, is estimated by the sum of the energy to heating the furnace to a target temperature, the energy required to heat the material mass inside the furnace, the energy for losses in the furnace, and the energy consumed by auxiliary equipment during that period, as expressed in Eq. (4.14). The energy consumed during the period at constant temperature, Econstant_temp, is calculated by the energy losses of the furnace and auxiliary equipment during that time (Eq. (4.15)). Finally, the energy consumed during temperature decreasing, Edecreasing_temp, is calculated by the energy consumed by auxiliary equipment during that time, as expressed in Eq. (4.16).

Eincreasing _ temp = Eheat _ furnace + Eequip _ losses + Eauxiliary _ equip

(4.14)



Econstant _ temp = Eequip _ losses + Eauxiliary _ equip

(4.15)



Edecreasing _ temp = Eauxiliary _ equip

(4.16)

The characteristics of the furnace (Appendix Table  4.1) used together with these equations allowed the calculation of the several energy parcels. The results obtained regarding the production time and the consumption of energy, materials and consumables are showed and discussed simultaneously using all the alternatives in Section 4.4.2 of this chapter. 4.4.1.2  Laser-alternative—mold produced by Direct Metal Laser Sintering The second alternative comprises the conformal cooling system produced by DMLS. The main sequential steps and processes involved in the mold production (Fig. 4.13) are overviewed in the following paragraphs.

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Figure 4.13  Overview of the mold production for the Laser-Alternative.

For the production of the mold core and cavity by DMLS, the 3D model of these components is pos-processed by a software allowing the construction of the shapes layer by layer by laser sintering of the metallic powder. This process occurs in an atmosphere of Nitrogen to protect the melting pool from oxidation and contaminations. The amount of metal powder for the DMLS is determined by the mold dimensions and overthicknesses to take into account contractions of the metal powder in the sintering process and molding surface finishing (already discussed in the previous section). Eq. (4.17) expresses the simple calculation of the required volume of metal powder, VRM, based on the volume of the mold considering the core and cavities dimensions plus the overthickness for their final surface finishing by milling, Vmold_finish, and on the expected percentage of contraction of the metal powder, C, that depends on the type of material. Information on the powder alloy, contraction values and total volume required is presented in Appendix Table  4.2.

VRM = Vmold _ finish × (1 + C )

(4.17)

The operation time of the DMLS process is dependent on the scanning speed (Sscan), which regulates the construction rate as a function of the type of equipment, part shape, and type of metallic alloy. For this process, the setup time was neglected. The production time, TP, and the energy consumed, E, to produce the core and the cavity can be estimated by Eqs. (4.18) and (4.19).The values used for each variable and the computing results are presented in Appendix Table  4.2.

−1 TP = VRM ×S scan

(4.18)



E = TP × (PowerLaser _ equip + PowerDMLS _ aux _ equip )

(4.19)

As already mentioned, after obtaining the mold cavity and core by DMLS, a heat treatment is needed, followed by a finishing process by milling to reach the required dimensional tolerances and surface quality. These processes were previously described for the Brazing-Alternative. The same types of inputs are required for the Laser-Alternative, which are presented in Appendix Table  4.2, together with some of the results achieved.

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

Figure 4.14  Overview of the mold production for the Conventional-Alternative.

4.4.1.3  Conventional-alternative—mold produced by conventional technologies The Conventional-Alternative deals with an approximation of a conformal cooling system based on a set of linear cooling channels that with the appropriated design somehow follow the contour of the molding surface. As far as only linear channels are involved, the full mold can be produced by conventional technologies, namely drilling, milling, and lathing. The sequential steps and processes involved in the mold production (Fig. 4.14) are explained in the following paragraphs. To obtain the mold core and cavity, a steel cylindrical bar is assumed as raw material, so the material volume required is very similar to the one necessary for the BrazingAlternative. The main difference is that less roughing and finishing milling operations are needed as far as the bar slicing is eliminated. The steel bar length required, LRM, and its diameter, DRM, can be computed by Eqs. (4.20) and (4.21), in which d and f are the overthicknesses for roughing and finishing and Lca, Lco, and Dca are the lengths and diameter of the mold cavity and core, respectively. The total material volume required, VRM, can be obtained by Eq. (4.22).

L RM = L ca +L co + 2d + 2 f

(4.20)



DRM = Dca + 2d

(4.21)

π ( DRM ) = × L RM 4 2



VRM

(4.22)

The cavities production and surface finishing are performed by machining operations. The production time of the machining processes, together with energy and cutting fluids consumption can be estimated in a similar way to the machining phase of the Brazing-Alternative (Eqs. (4.6)–(4.12)). Certainly, the input values are different. For instance, because in this case, the channels are obtained by several drilling operations and only the external shapes are generated by milling/lathing, the machining time is expected to be significantly different. The inputs of each machining operation as well as of materials and consumables are presented in Appendix Table  4.3. 4.4.1.4  Injection molding phase The mold use phase is the injection molding process, that is, the plastic parts production. This process is common for the three alternatives. The mold useful life is defined by the

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number of injection shots (injection cycles) it will go through. The usually accepted life for a steel mold injecting PP, operated and maintained in the appropriated conditions, is around one million cycles. So, this was the value estimated for the mold useful life in all the alternatives, assuming that the Brazing and Laser alternatives allow the manufacturing of molds with mechanical and reliability characteristics similar to conventional molds made of machined steel. It should be noted that this is clearly a reasonable assumption as far as DMLS process can deal with metallic powders to produce the conventionally used steels for injection molds applications with excellent mechanical properties (high value of yield and tensile strength, toughness, ductility and impact strength, high fatigue limit, high compressive strength, hardness, and wear resistance). A similar claim is true also for the Brazing alternative mold, because the strength of the metallic bond of the plates can reach the mechanical properties of the basic materials. The injection molding cycle time is the sum of elementary times from opening and closing the mold and filling the mold cavity, to packing and cooling the plastic part. The cooling time is the one that mostly influences the total cycle time and its reduction is a main reason for using conformal cooling solutions. In the estimation of the different elementary times for each alternative, the two mold design configurations were simulated by Moldflow software.The cycle time obtained for the molds with the conformal cooling channels produced by AM technologies was 16.4 s/part, and a cycle time of 29.4 s was achieved for the Conventional-Alternative. These results make clear the importance of the “real” conforming cooling, allowing a more efficient cooling together with a more effective process of obtaining a plastic part free of quality defects. A setup time was considered to launch the injection molding process, including the time to prepare and install the mold on the injection machine and to set the processes parameters. The same setup time was assumed for all the alternatives. In the injection molding, energy is consumed to melt the material and to fill the mold cavities. However, as pointed out by Ribeiro et al. (Ribeiro, Peças, and Henriques, Modeling the energy consumption in the injection molding process, [34]), energy consumption is highly related with the type and installed power of the injection machine. To estimate the energy consumed, the model proposed by Ribeiro et al. (Ribeiro, Peças, and Henriques, Modeling the energy consumption in the injection molding process, [34]) was used. The inputs of the model and the total energy consumed are presented in Appendix Table  4.4. The other inputs and the resources and time consumed for the use phase of the alternative molds are also presented in that table. 4.4.1.5  End of life phase At their end of life, the three alternative molds, all made of steel even if with different origins, go to recycling. The scraps (metallic chips) that resulted from the several machining operations of the mold production can also go to recycling. So, the total volume of material to recycle is very similar in all the alternatives: in the Laser-Alternative, it

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

is essentially the volume of powder used to produce the mold, and for the other two alternatives, it is the acquired raw material.

4.4.2  Discussion of results The equations from Sections 4.4.1.1–4.4.1.5, allowing the calculation of the time and resources required and schematically illustrated in Figs. 4.6 and 4.7, are the core of the PBM. The results obtained for the three alternatives under analysis, that is, the resources required for all alternatives throughout their molds life cycle, are presented in Table 4.3. The Brazing-Alternative requires more materials, consumables, and energy than the others in the mold production phase. The Laser-Alternative requires more time to produce the mold but less energy, materials, and consumables. In the use phase, that is, in the injection molding, the lower cycle time of the two AM-based alternatives allows a considerably lower part production time than the Conventional-Alternative. At the end of life, the Brazing-Alternative recovers more material only because it uses a higher quantity. After obtaining what we can call the inventory of the resources consumption (or used in a time-based way), they have to be translated into costs and environmental impacts, to then conclude on the economic and environmental assessment. Throughout the mold life cycle, different costs are incurred in difference periods, and therefore the computation of costs cannot be a simple product of quantities and price factors and the sum of cost parcels. In fact, the mold in the use phase is an investment to Table 4.3  Results from the process-based model and intermediate calculations Alternatives Life cycle phase

Raw material acquisition Mold production

Mold use (part production) End of life (+production scrap) a

Steel powder.

Time and resources

VBm

Laser

Conventional

Steel (kg/mold)

24.5

7.8a

16

Operation time (h/mold) Setup time (h/mold) Cutting fluid (dm3/mold) Nitrogen (m3/mold) Decreasing (l/mold) Stop-off (m2/mold) Argon (m3/mold) Energy (kWh/mold) Operation time (s/part) Setup time (h/batch) Material PP (cm3/part) Energy (kWh/part) Steel (kg)

36.69 12 0.0251 9.375 100 0.7 4.8 2716.32 16.4 0.5 36 0.028 −24.5

53.32 3 0.0009 26.265 0 0 0 417.5 16.4 0.5 36 0.028 −7.8

45.11 4.75 0.1045 0 0 0 0 2101.82 29.4 0.5 36 0.038 −16

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launch the plastic parts production by injection molding that will be used for the production of approximately 1 million parts. Depending on the market demand per time period, the production of these parts may be spread over several years (to a maximum of the considered mold life). Therefore, the cost of the mold is allocated to the part production as an annual equivalent fixed cost, the number of years being dependent on the annual production volume and mold useful life measured in number of injection cycles. The mold recycling at its end of life is also added by computing its present value and converting it to an equivalent annual cost. A mold use phase of 8 years was assumed.The unit costs (cost factors) of the resources consumed throughout the mold life are in Appendix Table  4.5.The financial relations used to obtain the cost for the three alternatives are listed in Appendix Table  4.6 and Appendix Table  4.7. Regarding the environmental impacts, the PBM outputs for the three alternatives, namely raw materials, consumables, and energy consumed, are used in Eq. (4.1) to obtain the impact of each alternative and the relevance of each life cycle phase. The used ecoindicators are listed in Appendix Table  4.8 and were obtained, as referred, by the SimaPro software and ReCiPe (H), an update of EI’99 and CML 2000 methods. The elementary impacts are combined in human health, ecosystems, and resources categories, which are then submitted to a weighting system to get the aggregated eco-indicator (Peças, Henriques, and Ribeiro, Integrated Approach to Product and Process Design Based on Life Cycle Engineering, [33]). This allows for the analysis of the category of environmental impact of each alternative. The next section analyzes and discusses the economic and environmental assessments.

4.5  RESULTS OF LIFE CYCLE ENGINEERING ASSESSMENT The LCE assessment is based on the analysis of three dimensions: economic, environmental, and technical. In this section, each of the results obtained for each dimension is compared for the three alternatives. As mentioned, for the economic and environmental analysis, the LCC and LCA methods are used, based on the inventory obtained from the PBM. Regarding the technical assessment, the part quality attributes are presented, MADM is here applied, and the results are then discussed. The integration of the three analysis dimensions is represented using the mapping of the best solutions among the alternatives as a function of the importance domains of the three dimensions of analysis. The parametrical characteristics of the PBM allow to show the influence of variables considered relevant. So, sensitivity analysis to the production volume and to the mold size are presented and discussed.

4.5.1  Economic and environmental assessment For an annual production volume of 2000 plastic parts over 8 years, Table 4.4 presents the costs and environmental impacts of each alternative mold.This significantly low production

Table 4.4  LCC and LCA results for the three alternatives Alternatives LCC (in euros)

Use phase

End of life

VBm

Laser

Conventional

VBm

Laser

Conventional

Material Consumables Energy Labor Equipment Tooling Building Maintenance

159.78 452.87 253.98 475.71 1,292.58 61.92 25.66 164.03

1380.46 404.47 35.31 685.86 3,929.47 2.24 1.95 114.70

108.40 0.45 196.61 495.57 1,153.18 127.26 38.38 115.32

87.81 20.06 154.01 – – – – –

28.18 0.00 23.72 – – – – –

59.57 0.01 118.78 – – – – –

Total per mold

2,886.51

6,581.29

2,235.15

261.88

51.90

178.36

Cost per year

642.48

1466.60

497.68

Material Injected (PP) Energy Labor Equipment Building Maintenance

93.05 5.39 49.65 25.39 2.78 2.54

93.05 5.39 49.65 25.39 2.78 2.54

93.05 7.19 86.96 44.47 4.88 4.45

16.33 3.27 – – – –

16.33 3.27 – – – –

16.33 4.36 – – – –

Total per year

178.80

178.80

240.99

19.60

19.60

20.69

Material

−1.61

−0.52

−1.09

−2.58

−0.83

−1.75

Total per year

819.67

1,644.89

733.62

366.71

98.85

256.88

0.41 8.95

0.82 4.46

0.37 10.00

0.18 2.70

0.05 10.00

0.13 3.85

LCC and LCA per part Normalized score for LCE

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

Mold production

LCA (in points)

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volume, when compared to the usual volumes in injection molding, was in fact the required real volume in the company where this case study was based. Nevertheless, in the sensitivity analysis Section 4.5.3, the results will be presented for higher production volumes. In this production scenario, the mold production phase is the one with higher impact on costs and on the environment. The Laser-Alternative has the higher mold cost but in the environmental dimension, it is the one with lower impacts in the mold production phase. The best score of this alternative in this phase is related with the less quantity of material and energy required; its worst score in economic terms is derived from the very high cost of the metal powder and the high equipment investment. The ConventionalAlternative has the lowest mold production cost because it requires less material for the mold than the Brazing-Alternative and uses the same type of machining equipment. The Brazing alternative has the highest environmental impact derived from the higher energy and material consumption. In the mold use phase, both AM-based alternatives exhibit the same behavior and, when compared to the Conventional one, present a lower cost and a lower environmental impact because of more efficient heat transfers allowing a reduction on the injection cycle time. Looking into the total LCC and LCA per part, the Conventional-Alternative is the best in cost and in environmental impact, followed closely by the Brazing-Alternative in both cases. These results are explained by the very low production volume per year. In fact, in this context, the mold has a large influence in the overall life cycle performance. As after 8 years the production was assumed to stop, one can say that part of the molds useful life go to recycling and potentially lost. In the context of normal yearly production rates of injection molding, sometimes millions of parts per year, the injection molding becomes the most relevant phase both in terms of costs and environmental impacts. In such a scenario, the Conventional-Alternative would have the worst total life cycle results, because its higher cycle time would cause, comparatively with the other alternatives, higher costs and higher environmental impacts. Regarding the LCA analysis, the comparison of the environmental damage categories among the alternatives is convenient (Fig. 4.15).The damage category most affected by the resources required for each alternative (essentially steel and energy) is the resources category, followed by the human health category. The lower energy consumption of Laser-Alternative causes a reduced percentage on human health impact category. Nevertheless, the hierarchy is the same in the three alternatives.

4.5.2  Technical assessment To accomplish the LCE analysis, the three alternatives should be assessed from a technical point of view. The quality of the part was selected as a relevant technical aspect to differentiate the alternatives. The different shape of the cooling systems causes differences in the plastic part thermodynamic cycles and temperature gradients. These aspects

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

Figure 4.15  Damage categories results for the three alternatives (mold production).

affect not only the cycle time, as already observed, but also influences the probability of occurrence of defects in the injected part. The first step in the technical assessment is to define the quality attributes and relate them with the technical characteristics. Three attributes were considered important in a plastic part obtained by injection molding: nonexistence of surface defects, the mechanical resistance of the part, and its dimensional accuracy. The same importance weights were given to the attributes as no special requirements were found for the plastic part use phase.To measure these attributes, several related technical characteristics were evaluated through the use of the Moldflow software for the two types of mold’s cooling systems. The technical characteristics investigated were the warpage, weld lines, residual stresses, and air traps. The results are illustrated in Table 4.5 where the higher quality of the AMbased conformal cooling system reveals, once more, its potential in achieving higher performance regarding the injection molding process: in every technical characteristic, the result is significantly better.

Table 4.5  Technical characteristics Technical characteristics

Conventional-alternative

Alternatives laser/brazing

Warpage (mm) Weld lines (#) Residual stress (MPa) Air traps (#)

0.737 6 30.88 211

0.592 0 11.458 34

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Table 4.6  Relation between quality attributes and technical characteristics of the plastic part Technical characteristics Quality attributes

Surface defects Strength Dimensional Accuracy

Weight of attributes

Warpage

33 (3)% 33 (3)% 33 (3)%

– – 33 (3)%

Weld lines

Residual stresses

Air traps

Weight of characteristics

16 (6)% 16 (6)% –

– 16 (6)% –

16 (6)% – –

Then, it is necessary to match each quality attribute with the related technical characteristics, by spreading each attribute importance weight by the characteristics that have an effect on it. The dimensional accuracy is clearly related with the warpage level and was not considered to be affected by the others characteristics. The surface defects are clearly affected by the weld lines and air traps, so an even weight distribution was performed. A similar even weight distribution was given to the mechanical strength attribute-related characteristics, the weld lines, and the residual stresses. The weighting distribution is presented in Table 4.6. The result is an importance weight of 33.3% for warpage and weld lines characteristics, and a weight of 16.7% for the residual stresses and for the air traps. The technical performance of each alternative can then be evaluated by determining its total score. This total score is the sum for all attributes of the adimensional value of each technical characteristic multiplied by the respective weight (Fig. 4.9). The adimensionalization was done by the ratio between the value of each characteristic and the best among the alternatives, and transforming it to a scale from 0 to 10 points (0 for the worst technical performance and 10 for the best). The results are presented in Table 4.7. As expected, the conformal cooling system produced by the AM alternatives presents a higher technical score, meaning that parts made by the Laser and Brazing alternatives have a higher quality, taking into account this specific set of attributes measured by considered technical characteristics than the Conventional alternative. The difference in the results between the types of two cooling systems shows the efficiency level of molds with conformal cooling produced by AM technologies. Table 4.7  Technical assessment score Technical characteristics Warpage

Weld lines

Residual Stresses

Air traps

Weight of characteristics Alternatives

Total score

33 (3)%

33 (3)%

16 (6)%

16 (6)%

Conventional Laser/Brazing

3.60 10.00

2.71 3.3 (3)

0.00 3.3 (3)

0.62 16.6 (6)

0.27 16.6 (6)

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

4.5.3  The Life Cycle Engineering-integrated analysis The evaluations performed on a life cycle perspective involving the economic, environmental, and technical performance dimensions could not reach a best solution among the different alternatives, because the best solution in one dimension is frequently the worst in another. In fact, the analysis can go further by integrating the three dimensions to allow a global multidimensional evaluation. The ternary diagrams, introduced by Ribeiro et al. [15], are used for the integrated analysis, because they provide a way of mapping the best solutions as a function of the importance given by decision-makers to each dimension of analysis. To build the ternary diagrams, the final results on the three dimensions have to be normalized as described in Eqs. (4.23)–(4.25). In fact, the results presented previously in Tables 4.4 and 4.7 are already normalized respecting these equations. min [LCC i ]i =1 3



Normalized of LCC i : nLCC i =

LCC i min [LCAi ]i =1

(4.23)

3



Normalized of LCAi : nLCAi =

LCAi

Normalized of Technical Performance : nTechPerf i =

TechPerf i 3 max [TechPerf i ]i =1

(4.24)

(4.25)

The final score of alternative i can then be computed by Eqs. (4.26) and (4.27) (Ribeiro, Peças, and Henriques, Life Cycle Engineering Framework for Technology and Manufacturing Process Evaluation, [35]), in which the importance of each dimension of analysis is modeled by multiplicative importance weights:

Scorei = w1 • nLCC i + w 2 • nLCAi + w 3 • nTechPerf i

(4.26)



w1 + w 2 + w 3 = 100%

(4.27)

where w1, w2, and w3 are the importance weights given to economic, environmental, and technical performance dimensions, respectively. The results can then be mapped in ternary diagrams. Fig. 4.16 shows these diagrams, one a production volume of 2000 parts per year and the other for 100,000 parts per year. For very low production volumes, the Conventional-Alternative is only the best one when a high importance is given to cost (higher than 60%) and low importance is given to the technical performance (below 15%). The Brazing-Alternative is the best solution when a high importance is given to the environmental performance, being replaced by the Laser-Alternative when an increased importance is attributed to the technical performance. In the context of 100,000 units produced per year, the Brazing alternative is

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Figure 4.16  Analysis to the production Volume-First ternary diagram to a production volume of 2000 parts per year and second ternary diagram to a production volume of 100,000 parts per year.

the best one if an importance higher than 85% is given to cost. The Laser-Alternative occupies most of the best solutions space, only loosing potential when a high importance is given to cost. For this production volume, the Conventional-Alternative is never a best option, as expected by the results presented. The sensitivity analysis on the production volume was performed taking advantage on the parametric structure of the developed PBM. Regarding only the cost dimension, the increasing of the production volume results in a significant cost per part reduction that is verified in all alternatives. This is a typical behavior of the capital nature of molds. The alternatives Brazing and Laser are economically better than the Conventional one for a production volume above 60,000. This situation can be explained by the longer cycle time of injection molding in the Conventional-Alternative. The AM-based alternatives have the same technical performance, and because the Laser-Alternative has a higher investment on equipment and mold material, the Brazing-Alternative is the most economic option from 5000 parts until the maximum production volume in the mold lifetime.

4.6 CONCLUSION This chapter contributes to a better understating of the potential of AM technologies in the manufacturing of molds with conformal cooling systems. Two AM alternatives are compared with the conventional way of producing such type of molds (i.e., milling, drilling, lathing, etc). One of them is a pure AM technology for metals, the DMLS process and the other is a hybrid alternative making use of the brazing of stacked plates

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

with half cooling channels previously machined on the flat surfaces of the plates. The comparison is comprehensive as all life cycle phases are analyzed and compared on the economic, environmental, and technical dimensions, being then integrated and globally assessed using a LCE approach. A case study regarding a specific plastic part to be produced by injection molding was explored. Because of the specific part geometry, an efficient cooling system is difficult to be constructed in the mold by conventional technologies, and conformal cooling solutions, only possible with AM construction technologies, become an option that deserves to be exploited and analyzed. On the basis of the case study, the technological relations between the mold and part manufacturing processes with the design parameters were built up in a Process Based Model (PBM). Its outputs allow the estimation of the economic and environmental performance. The technical assessment was obtained considering the different quality attributes and related technical characteristics of the plastic part obtained by the two types of cooling systems. Using the parametric structure of the PBM, sensitivity analyses were performed to understand the most suitable alternative for different production contexts. The results can briefly be described as really positive for the conformal cooling solution based on AM technologies. The most common part quality challenges in injection molding, such as burn marks, entrapped air, and shape deviations, can be overcome, and the injection cycle times are significantly reduced based on a better thermal management allowed by the more effective design freedom of the cooling system. When the mold alternatives are compared, considering a life cycle perspective and the aggregation of economical, functional, and environmental criteria, only for very small production volumes per year (the baseline case under analysis aimed 2000 parts per year), the conventional alternative emerged as the best one. However, this happens only if a high importance is given to cost and a very low importance is given to the environmental and technical performances. In fact, the lower cost of the mold is the main advantage of this alternative, but its low productivity during the use phase (much higher cycle time in the injection molding and lower part quality) is an important drawback that only can be acceptable if a low production volume of the final parts is intended. The DMLS-based alternative is the best one when a very high importance is given to the environmental impact. The reason relies in the very low energy consumed and in the low mold production time. The high equipment cost is its main weakness causing a higher cost of the mold. However, since for high production volumes of plastic parts the cost of the mold is largely diluted, this alternative became selectable. If average to higher production volumes of plastic parts are needed and a high importance is given to the economic dimension, the option based on a mold constructed by brazing stacks of steel plates emerges as the best one. The mold’s lower cost than the DMLS-based alternative is the main reason for this behavior.

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Even if the presented analysis has been performed based on a case study, the methodology followed is general enough to allow other analysis. Also, due to the mold cooling criticality for the sample part used, it is believed that the outcomes are demonstrative of the AM potential for conformal cooling application. Additional work toward the mold production and parts injection would certainly allow fine tuning the results; however, the global conclusions are robust enough to accommodate some uncertainty on the data used in this study. As a general conclusion, one can say that taking advantage of emerging AM technologies for metallic materials, molds with conformal cooling systems with channels that entirely contour the molding surfaces can become the current one in the industry, especially when high productivity molds are intended and a high quality is required for the final plastic part. Moreover, further research on AM-based technologies applied to mold design and manufacturing is expected to push mold thermal management to a new domain. This can be done taking advantage of flexible lattice and functional structures inside the mold to decrease its production time and material usage significantly and to increase the active control through cooling and heating of the thermal cycles of the molding surface. Intensive research is needed in this field, which can lead to higher productivity and cheaper molds.

4.7  APPENDIX 1 Appendix Table 4.1  Mold production inventory-related data—brazing-alternative Brazing—Alternative Operation time

Setup time

Resources

– Saw Power: 7 kW Velocity: 0.25 m/h Area: 0.387 m2 Milling Power: 53 kW Area: 16.79 m2 –

– 5 h

– 0.5 h

24.5 Kg steel Energy: 35.2 kWh

20.86 h

4.5 h

–

4 h

Furnace Power: 300 kW Area: 0.5795 m2 Milling Power: 53 kW Area: 16.8 m2

10.05 h

2 h

0.78 h

1 h

Cutting fluid: 24.2 cm3 Energy: 1105.9 kWh Degreasing: 100 L Filler metal: 0.1 m2 Stop-off: 0.7 m2 Argon: 4.8 m3 Nitrogen: 9.375 m3 Energy: 1533.7 kWh Cutting Fluid: 0.9 cm3 Energy: 41.52 kWh

Stage

Equipment data

Material acquisition Raw material preparation Channels and cavities manufacturing Preparation of components for brazing Vacuum brazing Finishing

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

Appendix Table 4.2  Mold production inventory-related data—laser-alternative Laser—Alternative Stage

Equipment data

Material acquisition DMLS

– Laser Power: 7.5 kW Velocity: 20 cm3/h Area: 0.105 m2 – Milling Power: 53 kW Area: 16.9 m2

Heat treatment Finishing

Operation time

Setup time

– 52.53 h

– 2 h

Powder steel: 7.8 Kg Energy: 376 kWh Nitrogen: 26.3 m3

– 0.79 h

– 1 h

– Cutting fluid: 0.9 cm3 Energy: 41.5 kWh

Resources

Appendix Table 4.3  Mold production inventory-related data—conventional-alternative Conventional—Alternative Stage

Equipment data

Material acquisition Raw material preparation

– Saw Power: 7 kW Velocity: 0.25 m/h Area: 0.387 m2 Milling Power: 53 kW Area: 16.79 m2 Drilling Power: 1.4 kW Area: 2 m2 Milling Power: 53 kW Area: 16.79 m2

Cavities manufacturing Channels manufacturing Finishing

Operation time

Setup time

Resources

– 1.11 h

– 0.5 h

Steel: 16 Kg Energy: 7.82 kWh

18 h

1 h

5 h

3.25 h

21 h

–

Cutting fluid: 21.2 cm3 Energy: 971.86 kWh [2,0]Cutting fluid: 5.9 cm3 Energy: 7.14 kWh Cutting fluid: 24.3 cm3 Energy: 1115 kWh

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Appendix Table 4.4  Inventory-related data for the injection molding phase Injection molding Alternative

Brazing Laser Conventional

Equipment data

Injection machine power: 24.2 kW Area: 15 m2

Cycle time

Setup time

16.4 s/part 29.4 s/part

0.5 h/ batch

Resources

Energy: 0.028 kWh/part Energy: 0.038 kWh/part

Volume: 36 cm3/ part

Appendix Table 4.5  Price factors for mold production and use (injection molding) phases

Material Consumables

Labor

Equipment

Heat treatment Maintenance

Energy Building

Description

Value

Steel 2343 Powder steel CL50 WS Cutting fluid Degreasing loctite 7012 Filler Metal-AWS Bni Stop-off Material (STOP PASTE 25) Argon 5.0, 99.999% pure Nitrogen 5.0, 99.999% pure Saw Milling Preparation Furnace Laser Saw Milling Furnace Laser Injection Machine

6.5 €/kg 175 €/kg 8.89 €/m3 1.9 €/L 2.2 €/m2 36.5 €/L 17.9 €/m3 16.1 €/m3 1.00 €/month 1.00 €/month 1.30 €/month 1.30 €/month 1.30 €/month 30.500 € 207.000 € 548.798 € 610.920 € 59.270 € 3.4 €/kg 2.000 € 13.250 € 2.460 € 1.313 € 0.0935 €/kWh 634 €/m2

Thermocouple Heating zone Software (DMLS) Laser

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

Appendix Table 4.6  Financial relations for the economic assessment and symbols description Driver cost

Symbol

Financial relation

Material cost Consumables cost Labor cost

C material C consumables C Labour

C material = mm • f material C consumables = mc • f consumables

Energy cost

C energy

Equipment cost

C equipment

 N wages N labour rsocial f labour   t setup   × V p tc + C labour =   N batch  Dyear H day    C energy = E × f energy

C equipment

Building cost

C building C building

Tooling cost

C tooling

Maintenance cost

C maintenance

1  I equipment  1 −  1+ i =  1 1 −  n +1 + 1 i + 1 i ( ) 

1  I building  1 −  1+ i =  1 1 −  n +1 + 1 i (1 + i ) 

 VP  t + t setup  c  N batch   ×  Dyear H day    VP  t + t setup  c  N batch   ×  Dyear H day  

C tooling = tc × f tooling C maintenance =

t setup  CmanualV p  tc +  N batch  Dyear H day 

C maintenance = 10%C equipment Symbol mm mc

Description Amount of material Amount of consumables

Symbol I n

N wages

Number of wages

i

Description Initial investment Number of year of equipment/ building life Interest rate

N labour rsocial

Number of operators Social rate

Cmanual Tsetup

Maintenance annual cost Setup time

D year

Number of days per year

H day

Number of hours per day

N batch

Number of batches

fi

Price factor

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Appendix Table 4.7  Exogenous variables of mold production and use phases Exogenous variables

Value

Working days per year Working hours per day Number of paid months per year Social costs Interest rate Equipment life Building life

250 days/year 7 14 1.23% 15% 8 years 30 years

Appendix Table 4.8  Eco indicators from simapro software with recipe method, distributed by impact category, in specific points per related quantity Category Product

Human health

Ecosystem

Resources

Total

Steel tool (points/Kg) Lubricating oil (points/Kg) Filler metal-AWS Bni (points/Kg) Braze stop off (points/Kg) Degreasing agent (points/Kg) Nitrogen (points/Kg) Argon (points/Kg) Electricity (points/MJ)

1.24 0.04 2.43 0.05 0.01 0.01 0.01 0.007

0.233 0.02 0.28 0.02 0.3 0.008 0.005 0.003

2.098 0.18 1.18 0.08 0.01 0.01 0.009 0.005

3.57 0.26 3.89 0.17 0.16 0.03 0.02 0.015

REFERENCES [1] I.J. Petrick, T. Simpson, 3D Printing disrupts manufacturing: how economies of one create new rules of competition, Res. Technol. Manage. 56 (6) (2013) 12–16. [2] M. Attaran, The rise of 3-D printing: the advantages of additive manufacturing over traditional manufacturing, Bus. Horiz. 60 (5) (2017) 677–688. [3] I. Gibson, D. Rosen, B. Stucker, Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping and Direct Digital Manufacturing, Springer, (2014). [4] M. Schmidt, M. Merklein, D. Bourell, D. Dimitrov, G.N. Levy, Laser based additive manufacturing in industry and academia, CIRP Ann. 66 (2) (2017) 561–583. [5] C. Chu, G. Graf, D. Rosen, Design for additive manufacturing of cellular structures, Comput. Aided Des. Appl. 5 (2008) 686–696. [6] J. Madan, M. Mani, K. Lyons, Energy performance ealuation and improvement of unitmanufacturing processes: injection molding case study, Cleaner Prod. 105 (2015) 157–170. [7] Y. Zhai, J.L. Lagoy, Additive manufacturing: making imagination the major limitation, Metals Mater. Soc. 66 (2014) 808–816. [8] J. Kruth, Material incress manufacturing by rapid prototyping techniques, CIRP Ann. 40 (1991) 603– 614. [9] D. Chen, S. Heyer, S. Ibbotson, K. Salonitis, J. Steingrímsson, S. Thiede, Direct digital manufacturing: definition, evolution, and sustainability implications, J. Cleaner Prod. 107 (2015) 615–625. [10] C.M. Gomas, J.M. Kneipp, I. Kruglianskas, L. Rosa, R. Bichueti, Management for sustainability in companies of the mining sector: an analysis of the main factors related with the business performance, J. Cleaner Prod. 84 (2014) 84–93.

Additive Manufacturing in Injection Molds—Life Cycle Engineering for Technology Selection

[11] Commission of the European Communities Commission, Green Paper: Promoting a European Framework for Corporate Social Responsibility, 2001. [12] L. Probst, E. Minfardini, L. Frideres, D. Demetri, L. Schnabel, A. Kauffman, et al. Advanced Manufacturing: Environmentally Friendly Technologies and Energy Efficiency, Business Innovation Observatory, (2013). [13] P. Zelinski, Additive Manufacturing Expands Engineering Freedom While Reducing Material Use and Cost, 2012. Available from: http://www.mmsonline.com/articles/why-is-additive-manufacturingimportant. [14] W. Frazier, Metal additive manufacturing a review, J. Mater. Eng. Perform. 23 (6) (2014) 1917–1928. [15] S. Ford, M. Despeisse, Additive manufacturing and sustainability: an exploratory study of the advantages and challenges, J. Cleaner Prod. 137 (2016) 1573–1787. [16] I. Ribeiro, P. Peças, A. Silva, E. Henriques, Life cycle engineering methodology applied to material selection, a fender case study, J. Cleaner Prod. 16 (2008) 1887–1899. [17] P. Peças, I. Ribeiro, A. Silva, E. Henriques, Comprehensive approach for informed life cycle-based materials selection, Mater. Des. 43 (2013) 220–232. [18] I. Ribeiro, P. Peças, E. Henriques, A life cycle framework to support materials selection for ecodesign: a case study on biodegradable polymers, Mater. Des. 51 (2013) 300–308. [19] I. Ribeiro, P. Peças, E. Henriques, Incorporating tool design into a comprehensive life cycle cost framework usign the case of injection molding, J. Cleaner Prod. 53 (2013) 297–309. [20] I. Ribeiro, J. Kaufmann, A. Schmidt, P. Peças, E. Henriques, U. Götze, Fostering selection of sustainable manufacturing technologies – a case study involving product design, supply chain and life cycle performance, J. Cleaner Prod. 112–114 (2016) 3306–3319. [21] W. Morrow, H. Qi, I. Kim, J. Mazumder, S. Skerlos, Environmental aspects of laser-based and conventional tool and die manufacturing, J. Cleaner Prod. 15 (2007) 932–943. [22] GPI: Prototype & Manufacturing Services, Inc., 2018. Available from: http://gpiprototype.com/services/conformal-cooling-dmls.html. [23] G.-l. Wang, G.-q. Zhao, X.-x. Wang, Heating/cooling channels design for an automative interior part and its evaluation in rapid heat cycle molding, Mater. Des. 59 (2014) 310–322. [24] R. Beard, Ratio analysis: injection molding, MoldMaking Technol. (2014). [25] R. Beard, R. Beard, Financial justification of conformal cooling, MoldMaking (2014). [26] D. Dimla, M. Camilotto, F. Miani, Design and optimization of conformal cooling channels in injection moulding tools, J. Mater. Process. Technol. 164–165 (2005) 1294–1300. [27] H. Stauss, AM Envelope: The Potential of Additive Manufacturing for Façade Construction, 2013. [28] D. Gu, Laser Additive Manufacturing of High-Performance Maerials, Springer, (2015). [29] 3D Printing: An Overview, 2018. Available from: https://www.techpats.com/blog/3d-printing-technologies-overview. [30] M. Knights, Conformal Mold Cooling Offers Design Freedom, Savings. (Plastics Machinery Magazine), 2015. Available from: http://www.plasticsmachinerymagazine.com. [31] N. Karapatis, Y. Guidoux, P. Gygax, R. Glardon, Thermal Behaviour of Parts Made by Direct Metal Laser Sintering, Swiss Federal Institute of Technology, (2013). [32] M. Schwartz, Brazing. ASM International, 2003. [33] P. Peças, E. Henriques, I. Ribeiro, Integrated approach to product and process design based on life cycle engineering, In: Handbook of Research on Trends in Product Design and Development:Technological and Organizational Perspectives, Business Science Reference, (2010). [34] I. Ribeiro, P. Peças, E. Henriques, Modelling the energy consumption in the injection moulding process, Int. J. Sustain. Manuf. (2012) 263–268. [35] I. Ribeiro, P. Peças, E. Henriques, Life cycle engineering framework for technology and manufacturing process evaluation, In: Technology and Manufacturing Process Selection: The Product Life Cycle Perspective, Springer, (2014) pp. 217–237.

FURTHER READING [36] D. Bourell, J.P. Kruth, M. Leu, G. Levy, A. Clare, Materials for additive manufacturing, CIRP Ann. 66 (2) (2017) 659–681.

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CHAPTER 5

Manufacturing Engineering Requirements in the Early Stages of New Product Development—A Case Study in Two Assembly Plants Mariam Nafisi*, Magnus Wiktorsson*, Carin Rösiö*,**, Anna Granlund*

*School of Innovation, Design and Engineering, Mälardalen University, Eskilstuna, Sweden **Department of Industrial Engineering and Management, School of Engineering, Jönköping University, Jönköping, Sweden

5.1 INTRODUCTION As product life cycles get shorter and customer demands become more diverse, manufacturing companies are pressed to release new products onto the market faster and more frequently [1]. Therefore new product development (NPD) is crucial for the survival and prosperity of companies in different sectors, whether they offer products or services [2,3]. New products need to be superior to their predecessors in terms of quality, function, features, and price [4]. Manufacturing enterprises should integrate their innovative products with their manufacturing system capabilities [5]. A mismatch between the product design and manufacturing process capabilities makes it impossible for the company to manufacture a high-quality product cost effectively. In practice, this misalignment leads to an unavoidable increase in manufacturing costs and to launch delays. Therefore it is important to take manufacturing and assembly into account as early as possible during product design [6]. To reduce time to volume and to improve coordination between design and manufacturing, product development and manufacturing development activities should be carried out in parallel and should be integrated [7]. The integration of design engineering and the manufacturing process ensures a reliable product without defects [8]. Several concepts are introduced to enable integration, including but not limited to concurrent engineering (CE), integrated design and engineering, and design for manufacturing/design for assembly (DFM/DFA) [9]. The success of a new product in the market relies on the success of its respective development project.Therefore NPD projects are of high importance for companies and take up a lot of resources and budgetary funds. Despite all the effort that companies put into their NPD projects, many new products fail for different reasons [2,10] and do not meet their financial objectives or launch dates. Advanced Applications in Manufacturing Engineering. http://dx.doi.org/10.1016/B978-0-08-102414-0.00005-7 Copyright © 2019 Elsevier Ltd. All rights reserved.

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NPD projects vary in their content, purpose, and extent, and every project has unique characteristics and different challenges. However, a common characteristic of NPD projects is that they are information-intensive [11]. NPD projects require collaboration and information exchange at the right time. Various functions are involved in the activities at each project stage, which necessitates cross-functional contact and communication. One important aspect of NPD projects is the interface between manufacturing and product development functions. Historically, manufacturing has had less stature than other functions involved in NPD, even though manufacturing involvement in NPD activities has been shown to have advantages [12]. It is important that manufacturing system capabilities, limitations, and requirements are communicated to the product design team early in the NPD project to minimize design rework and extra cost. Manufacturing has the knowledge and expertise that is required to move from product innovation to mass production [12]. While limitations in a manufacturing system should not hinder innovativeness in new products, manufacturing capabilities, limitations, potentials, and goals should be considered when new products are being planned and designed [13,14]. Manufacturing can provide product design teams with input about what is feasible and what is not [12]. As indicated in previous studies, the link between product design and manufacturing should be established early in the NPD project to ensure that interdependencies among them are recognized and resolved [8,15]. Problems that are discovered late (e.g., during detailed design or manufacturing) are costly to fix [14]. Tackling this issue requires effective design–manufacturing integration and communication early in the NPD process.This is to ensure that manufacturing requirements are communicated to product design teams to avoid design rework and achieve better product fit for the manufacturing system. Many integration mechanisms are discussed in the literature, with the focus being on integration management, enablers, and barriers [16–18]. While providing useful insight regarding integration, many of these mechanisms remain at a general project level, and there are no practical guidelines about integrating manufacturing requirements in NPD activities. In addition, there is little research addressing manufacturing involvement in the fuzzy front end of NPD with respect to challenges and proper integration mechanisms. Therefore the purpose of this chapter is to investigate types of manufacturing requirements and how these requirements are integrated into the fuzzy front end of NPD projects when an existing manufacturing system is to produce the new product. We begin with mapping and analyzing manufacturing requirements in two industrial cases for this type of NPD project. We then investigate what mechanisms are used to communicate and integrate these requirements. The chapter concludes by reflecting on the suitability/efficiency of these mechanisms for defining, communicating, and following up various kinds of requirements throughout NPD projects.

Manufacturing Engineering Requirements in the Early Stages of New Product Development

5.2  THEORETICAL FRAMEWORK There is an extensive body of literature focusing on various dimensions of NPD. For the purpose of this chapter, we present the literature in the following three sections: NPD process, types of manufacturing requirements to integrate in NPD, and mechanisms for defining, communicating, and following up requirements throughout NPD projects.

5.2.1  Early stages of NPD The NPD process is defined as a series of activities that start with product planning and end with production ramp-up of the final product [19], see Fig. 5.1. Several functions within the company are involved in this process, with various tasks and responsibilities during each of the stages. Sales and marketing, design, and manufacturing are the prominent functions involved in the NPD process [19]. In this chapter, “design” and “manufacturing” functions are defined similar to the definition by Ulrich and Eppinger [19], meaning that design is a function in the company that has the lead role in defining the physical form of the product to satisfy customers. Product designers belong to this function. Manufacturing is the function that is responsible for designing and operating or coordinating the manufacturing system to produce the product. Manufacturing system refers to a collection of machines and human resources to perform processing and/ or assembly operations on raw material or parts [20]. This model is generic, showing the NPD process as a linear, stage-gate process with each stage starting when the previous one has finished. In practice, there are activities in these stages that happen simultaneously or iteratively, making the management of NPD projects a challenge. Therefore the traditional and sequential way of managing NPD projects does not support the complex NPD process [1,9]. The generic model fits the process used for the development of market-pull products. In cases of complex products, such as airplanes and automobiles, the model should be adapted to fit the complexities involved in that type of development process. In the industrial context, when an NPD project starts, manufacturing system development begins as a concurrent subproject along with the NPD. Other functions, such as purchasing, sales and marketing, service, etc., are also affected, but a description of these functions is outside the scope of this chapter. Communication and information exchange between the subprojects are of utmost importance. Fig. 5.2 illustrates the concurrency and dependence between the subprojects. Design and manufacturing (functions) are responsible for product development and manufacturing system development, respectively. Adapted from Ulrich and Eppinger

Figure 5.1  New product development process, as used in this chapter [19].

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Figure 5.2  Product development and manufacturing system development in an NPD project [21].

[19], Table 5.1 summaries the responsibilities of design and manufacturing during the concept development (CD) and product planning stages. With shortened product life cycles, companies aim at reducing product realization times to achieve more efficient developments and make superior products [22]. Any time delays can impose extra costs on the company. This has led to the emergence of some practices to improve the NPD process. For instance, companies reorganized their NPD processes and moved from a sequential path with minimal interaction between involved departments and stakeholders toward an integrated path. To further improve the NPD, in terms of shorter development time and improved product quality and price, new paradigms were introduced and practiced, such as CE [23,24] and integrated product development, which advocates integration between various stakeholders in NPD projects [25]. At their core, both these concepts emphasize creating cross-functional project teams and working in iterations. Koufteros et al. [26] state that CE can enhance information flow and reduce uncertainty in an organization. In doing so, companies could save time in their NPD process and improve the quality of the products and NPD results [22]. Because NPD is characterized as complex Table 5.1  Typical tasks and responsibilities of design and manufacturing functions during concept development and system-level design stages

Design

Manufacturing

Concept development

System-level design

• Investigate feasibility of product concepts • Develop industrial design concepts • Build and test prototypes • Estimate manufacturing costs • Assess production feasibility

• Develop product architecture • Define major subsystems and interfaces • Refine industrial design • Preliminary component engineering • Identify suppliers for key components • Perform make–buy analysis

Source: Adapted from K.T. Ulrich, S.D. Eppinger, Product Design and Development, fifth ed., McGraw-Hill/Irwin, Boston, MA, 2012.

Manufacturing Engineering Requirements in the Early Stages of New Product Development

and information-intensive [11], many researchers have emphasized the importance of communication and coordination between various stakeholders in the NPD process, particularly between the design and manufacturing departments [27]. Better coordination and communication helps minimize the occurrence of an “over the wall design” in which product design and manufacturing departments have little contact while a new product is under development. In response to yet more disruptive innovation, new NPD strategies and frameworks have been called for that more strongly combine simplicity, velocity, and flexibility. The adoption of stage-gate models and the inclusion of the so-called agile method from software engineering are emphasized by Cooper [28], among others. The agile method in software development that took off during the 1990s was established in the Manifesto for Agile Software Development [29] and present basic principles and values for software development projects, including managing customer needs and using short development cycles to evolve requirements by iteration and adaptation throughout the project life cycle [30]. However, product complexity, the number of functions involved, and the degree of technology innovation may affect the applicability of agile practices for NPD. In complex situations, the traditional NPD stage-gate model necessitates a great deal of effort to identify and detail requirements and specifications in the initial planning phases [31]. With the agile method, however, the strategy is to iteratively develop the requirements during the development process, which can generate rework, failures, and cost overruns for highly integrated products with interdependent components and systems [32]. Therefore various authors have argued that the agile method is more suitable for small projects and small and collocated teams as opposed to traditional NPD practices that are suited to large and complex programs [33]. In contrast, other authors argue for the combination of stage-gate and agile management approaches. Since the initial development of the agile method, Boehm and Turner [34] have argued that the challenge is to find the balance between agility and discipline. The result is a recent set of studies focused on understanding and exploring hybrid product development approaches [30,35,36].

5.2.2  Assembly requirements for a new product In recent decades, requirements engineering (RE) has been established as a distinct field of research and practice. Originating from software engineering, it has evolved to include a broader perspective and to be used in other applications and has become critical in developing informal, fuzzy individual statements of requirements to a formal specification that is understood and accepted by all stakeholders [37]. RE is important in NPD activities because NPD projects are based on requirements that define what results the stakeholders expect [38]. Wiesner et al. [38] assert that RE is the key to the success or failure of NPD projects because the project outcomes are the results of the requirements

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that were initially set. Formulating the problem is to a great extent the most important step in solving it. As defined in ISO 9000:2015, a requirement is a need or expectation that is stated, generally implied or obligatory [39]. In this chapter, the term “manufacturing requirements” includes needs, expectations, wishes, and demands that originate from the manufacturing function during NPD projects. NPD projects have various stakeholders, each of which have requirements that should be discussed and considered in the project. Establishing a balance between the requirements is often a challenge, especially when there are contradicting requirements. One difficulty in RE is that requirements can be unclear, which can lead to misunderstandings [40]. According to Ref. [37], RE consists of four stages: • Requirements elicitation: understanding the current situation and need for change • Requirements negotiation: defining the context within which deliberation occurs • Requirements specification: describing functional and nonfunctional goals • Requirements validation: validating against stakeholders’ goals. Product specifications are determined in the early stages of NPD. Important decisions about the product are made at these stages, which have consequences for the stakeholders and downstream processes. Therefore it is crucial that stakeholders are involved and that their requirements are treated appropriately. In the planning and CD stages, stakeholders and downstream departments should be sufficiently involved to communicate and understand requirements and to come to an agreement.The decisions made at these stages will influence stakeholders and downstream processes, and therefore it is important that the stakeholders are involved in decision making in the front end of NPD projects. In the assembly stage, which is the last stage in the production chain, the results of earlier stages in the chain can often be seen. Any quality problems in the product design or manufacturing of the parts that are to be assembled can lead to trouble in the assembly process. Therefore a successful final product requires a successful assembly process, in addition to successful component manufacturing and product design. In other words, all the steps before final assembly should be done perfectly for a perfect final product. Requirements standards and textbooks typically classify requirements into functional requirements (required operations and/or data) and nonfunctional requirements (quality requirements). In contrast, Glinz [41] argues that this classic categorization is inconsistent and ambiguous and proposes a new classification of requirements based on four facets: kind (e.g., function, performance, or constraint), representation (e.g., operational, quantitative, or qualitative), satisfaction (e.g., hard or soft), and role (e.g., prescriptive or assumptive). Another dimension is that many researchers have implicitly or explicitly described specifications as being separable in terms of criteria that must be fulfilled and criteria that one wants to be fulfilled [42]. For application in assembly system development,Wiktorsson et al. [43] show industrial cases with requirements categorized into four groups: functional requirements (musts in terms of performance), internal design constraints (musts

Manufacturing Engineering Requirements in the Early Stages of New Product Development

in terms of design solutions due to internal reasons), external design constraints (musts in terms of design solutions due to external reasons), and winning criteria (wants in terms of assembly system capabilities). Concerning the explicit content of assembly systems’ requirements, previous studies on assembly systems shed light on the components of assembly systems and increase the understanding of the dynamics between the components [44,45]. Asadi et al. [46] studied assembly requirements in the context of NPD and identified seven categories of assembly requirements—assembly principles and targets, logistics, quality, cost, safety and ergonomics, environment, and a product’s physical interfaces and characteristics. However, this research did not investigate which of these requirements were handled and communicated at the beginning of NPD projects.

5.2.3  Mechanisms, methods, and tools for DFM DFM involves designing products in such a way that they are easy to manufacture. The concept exists in many engineering disciplines, but the specific methods and tools differ depending on the manufacturing technology. Areas such as assembly, machining, printed circuit board manufacturing, and other automatic assembly systems have different schemes for evaluating manufacturing costs and improving manufacturability. These schemes help to precisely define various tolerances, rules, and common manufacturing checks related to DFM. In addition to the specific product design, other aspects influence the manufacturability, such as the form and type of raw materials and the impact of other manufacturing processes on, for example, tolerances and surfaces. To ensure manufacturability, especially for machining applications, a large set of computer-aided manufacturing (CAM) tools have been developed and used over the years. CAM systems together with product lifecycle management systems provide manufacturing process planners with correct data for process planning. Some guidelines have been developed to overcome the “over the wall design” difficulties, with DFM/DFA being one of the most prominent paradigms. In most of the available literature on DFA/DFM [47,48], the product aspects are stressed, meaning that designing the product for ease of assembly is emphasized. DFA/DFM guidelines are intended to help designers design components that fulfill specific, often contradicting, requirements (e.g., performance, function, or tolerance requirements) and that are easy enough to assemble or manufacture [48]. For instance, a product design that is easy to assemble might result in a more complicated manufacturing process that is more expensive. Similarly, a product design that is easy to assemble might be difficult to disassemble, making it a poor design from a service and maintenance perspective. DFA/DFM has assisted designers in designing more assembly-friendly components or parts, but it is not enough to ensure a suitable design from an assembly system perspective [49]. Therefore it is necessary to include the assembly department early in NPD projects to get their requirements and feedback on the concept.

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Various types of supporting tools are available to designers who use DFA/DFM in their design work. Computer-aided design (CAD) tools enable designers to check the assembly interfaces using 3-D models. Today, it is possible to simulate assembly stations and use mannequins to check how easily an assembly task can be performed. Design guidelines and checklists provide instructions for the appropriate design of technical products [50]. They are company-specific and are devised with consideration to the company’s products and its structure. Nonetheless, they are too generic, leaving the translation of the general rules into useful recommendations to the designer [50]. Using manufacturing knowledge to support design decisions can be a way to address the aforementioned disadvantage. Recent research has indicated that model-based manufacturing (MBM) can be a way of integrating manufacturing knowledge earlier in the product lifecycle. MBM, which is part of the larger model-based enterprise concept, utilizes model-based definitions (MBDs) in manufacturing products. The MBM uses digital communication between the design and manufacturing functions, enabling a more collaborative product development environment [51]. Failure modes and effect analysis (FMEA) is another tool used during NPD projects. By performing FMEA during the project, possible failures in a design can be identified. The consequences of the risks are classified according to severity [52]. FMEA requires a cross-functional team of experts to ensure that all risks are identified in the analysis.

5.3  RESEARCH APPROACH The aim of the study was to create an empirical view of integrating manufacturing requirements early in the NPD process. To achieve this, a multiple case study research method was selected. The study was conducted with two purposes in mind. The first was to identify what requirements were important for the assembly plants when the new product was being developed. The second was to investigate how these requirements were communicated and ensured during the NPD process. In the following sections, the research method, data collection, and analysis are explained.

5.3.1  Case study method The case study method is a powerful and widely used research method where the aim of the researcher is to study a phenomenon within its natural context and to gain a deep understanding of the phenomenon [53,54]. This research strategy focuses on understanding the dynamics present within single settings [55]. It is suitable to answer “how” and “why” research questions. The type of design for the case study was a holistic multiple case design, with one unit of analysis (manufacturing requirements in the early stages of NPD) and two contexts (case companies), as presented in Fig. 5.3.

Manufacturing Engineering Requirements in the Early Stages of New Product Development

Figure 5.3  Multiple case study design with a single unit of analysis.

In this study, manufacturing requirements in NPD projects at two manufacturing companies were studied. The participating companies are global manufacturers in the automotive sector with a long and successful history and global production networks. They are large companies, with over 30,000 employees worldwide. The design and development of the products take place centrally in one location in Europe. The manufacturing of components and assembly are distributed in several locations globally. The two companies offer automotive products in several different ranges to address customer needs. Their products are complex and modular, consisting of a large number of parts and variants. Manufacturing is customer (order)-driven, and customers are given the possibility of specifying their desired product as they wish it to be. For this study, one NPD project was selected at each of the companies. Each project was strategically important for its respective company. In addition, the products did not previously exist in the companies’ manufacturing systems. This entailed close collaboration between product development teams and the manufacturing function to ensure the fit between product design and manufacturing capabilities. Choosing a case project from two companies helps enrich the empirical context, thus increasing literal replication [54]. An overview of the cases and companies is presented in Table 5.2. A more detailed description of the products and cases is presented in Section 5.4. Table 5.2  Case company profile and case characteristics

Operations

Case A

Case B

Development and manufacturing of heavy vehicles Development and manufacturing of personal vehicles

Component in NPD project

Type of product

NPD project model

Exhaust system part

Complex

Engine part

Complex

Adaptation of the generic model Adaptation of the generic model

Design– manufacturing proximity

Located in the same city but various locales Located in different cities in the same county, some 160 km apart

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5.3.2  Data collection Several different tools were utilized for data collection in the two cases. In case A, the data was collected over a course of 4 months using observations, interviews, and studying company documents. Six in-depth, semi-structured interviews were carried out by one of the authors using an interview guide. All the interviews were recorded and later transcribed. The length of the interviews ranged between 40 and 120 min. The questions aimed at gaining an understanding of the communication of manufacturing requirements in the early stage of the NPD process, that is, during the CD stage. The specific focus was on identifying what requirements were communicated and how it was done. In addition to interviews, participant observations were used to gather data.Various project meetings, risk analysis sessions, design reviews, and test assemblies were attended by one of the authors, which made it possible to observe the dynamics of cross-functional interactions between individuals from different functional areas. During the course of the study, company documents were also studied, which provided a third source of data. Company documents included both general documents, such as guidelines, checklists, and standards, and documents with specific information about the case project. Using different sources of data served to ensure and increase construct validity [54]. To increase reliability, a case study protocol was developed and used. Case B was conducted in a similar way to case A. The authors attended two extensive discussion sessions with one of the technology project managers when the study was initiated. The sessions lasted 2–3 h each. This was followed by three in-depth semistructured interviews with two technology project managers and a product preparator, conducted by one of the authors. Documents provided by the company were the third source of data used in the study. Due to confidentiality concerns, project-specific documents were not available; therefore general documents (e.g., guidelines and methods) were studied. Table 5.3 shows the details of the interviews in case A (six interviews) and case B (three interviews). Table 5.3  Details for each interview in cases A and B Interviewee

Position

Duration of the interview (min)

A1 A2 A3 A4 A5 A6 B1 B2 B3

Design engineer Design engineer Design engineer Project leader Product layout engineer Product introduction engineer Technology project manager Technology project manager Product preparator

42 41 40 60 50 120 120 70 45

Manufacturing Engineering Requirements in the Early Stages of New Product Development

5.3.3  Data analysis The data analysis stage began at the same time as the data collection, in agreement with Saunders et al. [56]. The recorded interviews were transcribed by one of the authors, followed by the analysis of the obtained text. The emphasis during the analysis was on identifying categories and themes related to manufacturing requirements and integration mechanisms. The categories were formulated with a focus on requirements and methods and were used to make a matrix of categories. The data obtained in the study was placed in the matrix for the easier interpretation of data and identification of trends in the data. Reliability and validity are important aspects that determine the quality of any research.Validity has three aspects—internal validity, external validity, and construct validity.Yin [54] proposes several tactics in different research phases to ensure that validity and reliability are achieved and that research quality is satisfactory. To ensure that construct validity is satisfied, multiple sources of data are used in the data collection phase of the study. External validity can be rather difficult to achieve because there were two companies in the study, each with one case. Saunders et al. [56] state that reliability refers to whether one’s data collection and analytical procedures would produce consistent findings if they were repeated on another occasion or by another researcher. Yin [54] suggests that a case study protocol and case study database be developed during the data collection phase of case study research to increase the reliability. Such a study protocol was developed in the study.

5.4  EMPIRICAL FINDINGS In this section, the findings are presented in relation to the purpose of the chapter. Each case is presented separately, with a more detailed description of the company and project. Subsequently, the findings from the cases are presented by representation of the company’s NPD process, the considered manufacturing requirements, and the applied mechanisms for communicating and integrating the requirements.

5.4.1  Case A: Exhaust component in heavy vehicle industry The company where case A was studied is a leading global manufacturer of heavy vehicle and industrial engines. It has a long history of implementing lean principles and has developed a company-specific lean-based improvement program in the form of a production system governed by specific principles and based on the Toyota Production System. The main building blocks in their production system (here referred to as XPS: company-specific production system) are elimination of waste, focus on customers, and respect for the individual. Production is demand-driven, and continuous improvement of operations is emphasized. The priorities of the companies are defined in agreement with the main elements of their XPSs and the prevailing mindset. In order of importance,

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safety/health/environment, quality, on-time delivery, and cost determine the priorities in terms of decisions and activities at these companies. Again, this is grounded in the main elements of each company’s XPS. The company offers a wide range of products for different purposes and markets. Its products are modular and complex, consisting of a large number of parts. Strategic parts and components are manufactured in-house, while less strategic ones are purchased.The assembly of components and final assemblies are done manually to a great extent. The modularity and availability of part variants makes it possible for the company to offer their customers a wide range of products to fit their specific purpose, which gives the company a competitive advantage. In case study A, the CD stage of an NPD project at the company was studied. This project aimed at developing an exhaust component that would be introduced to the final chassis assembly in a later stage. The project was significant for the company because the component did not have any previous equivalent in the product or production range. This meant that the product design and development required more time and effort due to the novelty of the component. Additionally, the production sites had to be more actively involved in the NPD project to ensure that the component would fit the manufacturing process. 5.4.1.1  The NPD process The NPD process used in the company is depicted in its abstract form as a linear stagegate model, as shown in Fig. 5.4. The content and activities in the process model resemble those in the generic model by Ulrich and Eppinger [19] to a large extent. The transition from each stage to the next is decided in various steering group meetings in which managers from different organizational functions, such as product development, production, and product introduction, are present. There are some general milestones that are common in an NPD project for all the involved functions (e.g., design, manufacturing, purchasing, and sales and marketing). In addition, there are specific milestones defined by the involved functions, in accordance to and synchronized with the general milestones. NPD projects are begun for different reasons. For example, there can be a customer demand or a market opportunity that results in an NPD project. Legal requirements, quality, and cost improvement can also lead to the initiation of an NPD project. During the CD stage, a project team is created to work on the business case and the conceptual design.The goal during the CD stage is to develop a product concept that is feasible so that the project

Figure 5.4  NPD process model in case A.

Manufacturing Engineering Requirements in the Early Stages of New Product Development

Figure 5.5  Typical project organization at company A.

can go through to the next stage. One important task during the CD stage is eliminating and minimizing risks associated with the concept and project.The project team should be able to show that the remaining risks will not jeopardize the project and can be managed in the later stages of the project.To achieve this, the company includes various functions to make sure that no risks are unattended. Fig. 5.5 shows an example of project organization in company A, which shows the various functions involved in an NPD project. Production units, which are important stakeholders in the NPD process, are represented during the CD stage by a function called product introduction. In addition to representing production units in NPD projects, product introduction units are also responsible for global process preparation and validation.They are also involved in longterm development of common global methods and best practices in production. Fig. 5.6 illustrates how product introduction works as a link between design (CD teams) and manufacturing (assembly sites). 5.4.1.2  Considered requirements from manufacturing In case A, the product introduction unit was responsible for communicating manufacturing requirements to design teams. At a general level, these requirements concerned either the assembly part on its own or how it fitted the surrounding systems, namely, the assembly and material handling processes. Therefore we have grouped the requirements into three categories—physical properties of the parts, assembly process and material handling, and line feeding. Some of these requirements were expressed and discussed in

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Figure 5.6  Product introduction as a link between concept development (CD) teams and assembly sites [57].

project meetings by the assembly representative as the project proceeded. Others were mentioned in the company’s procedures and standards and were officially known and accepted. An example of the latter is the requirement of low risk of injury, mentioned in the company’s ergonomics checklist. Below, the three categories together with the encompassed requirements are listed. Physical properties of the parts • Appropriate weight of the product/component • No risk of injury to the assembly operators • Low sensitivity to damage (e.g., if dropped during assembly) Assembly process • Easy access to assembly points/places for the assembly operator • Confirmation of the correct assembly (e.g., clicking sound) • Possible to hang/place the part to have both hands free for assembly • Impossible for the assembly operator to pick a wrong part or assemble parts incorrectly • Utilization of the current production system (as much as possible) • Similar assembly sequence for the new and existing products • Appropriate assembly times (considering the takt time) • Physical limitations in the assembly facility Material handling and line feeding • Minimum number of part variants • Possible to use current material handling tools • Possible to use existing pallets/shelves in the assembly line The findings show that not all requirements in each category were brought up and discussed by the assembly representative in the project meetings during the CD stage. For instance, in the category “physical properties of the parts,” the weight limit was clearly mentioned in the company’s ergonomics standards and the DFA checklist. In

Manufacturing Engineering Requirements in the Early Stages of New Product Development

the category “assembly process,” many of the requirements were mentioned in the DFA checklist and were discussed and followed up by the assembly representative. On the contrary, in the category “material handling and line feeding,” requirements were rarely discussed in the CD stage or provided as an input for the product designers. Instead, the assembly departments waited for the product concept to mature to give feedback to the design function with respect to material handling aspects. Requesting a change in the product is costly both in terms of time and resources if the product design is shown not to meet the requirements in later stages. 5.4.1.3  Mechanisms used for the verification and communication of requirements In case A, several mechanisms were used to address the assembly requirements presented in the previous section. They are listed below, irrespective of their importance or the extent to which they were used. • Test assemblies: Digital test assemblies (DTAs) are employed in the CD stage. According to company guidelines, at least one DTA should be done in the CD stage. Less often, physical test assemblies (PTAs) are done using Styrofoam models or 3-D printed models to create the desired interfaces. • Risk assessments: One of the most important tools used by various departments to understand risks and to suggest measures to tackle the risk associated with the NPD project, it includes strength, weakness, opportunity, and threat (SWOT) analysis and various FMEAs. • DFA principles: These are mostly focused on reducing the number of parts, “pokayoke” and ensuring access. Logistics and the handling of parts were scarcely discussed in the DFA checklists. • Ergonomics standards of the company: These are devised to help designers consider the principles of ergonomics in their designs. • CAD systems: These are extensively used in design work, design review, and project meetings.

5.4.2  Case B: engine component in the automotive industry The company where case B was studied is a global manufacturer of personal vehicles with a long and successful history of innovation in design and production. The company has a network of production plants in several countries, but most of the product development activities are concentrated in one location. Company B’s company-specific production system (here referred to as XPS) is inspired by lean principles. According to the company’s philosophy, safety, quality, and care for the environment are greatly valued. Cost and delivery constitute the core focus of the company, while improvement, safety, morale, and environment are the enablers to achieve them. In case study B, the CD stage of an NPD project was studied. The project involved the development and introduction of an engine component previously purchased from

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Figure 5.7  The NPD process model in case B.

a supplier. The production of the component entailed two sets of processes—machining of the parts constituting the component and assembly of the parts to achieve the final component. Therefore machining and assembly workshops had to be in close contact with design teams in this NPD project. 5.4.2.1  The NPD process The company’s product development system is depicted as a linear process consisting of four stages, as seen in Fig. 5.7. Various functions are involved in NPD, such as design, manufacturing, sales and marketing, purchasing, and quality assurance (QA). One department at company B has the role of linking product development with manufacturing. It is responsible for finding the most optimal balance between product design and manufacturing requirements. 5.4.2.2  Considered requirements from manufacturing The engine plant has developed a “manufacturing requirement” guideline that covers several important aspects in the assembly process. The guideline contains restriction and process strategies that apply to new or redesigned parts, and guideline items are either “demands” or “recommendations.” The guideline covers important aspects in nine categories: parts supplier, assembly process, fasteners and joints, quality requirements, endof-line testing, cleanliness, environment and ergonomics, product mix/common process, and factory-specific requirements. The guideline even includes some general product requirements per component type. These requirements are identified from a process perspective and aim at making the assembly process easier and reducing safety risks and quality problems. The importance of the requirements is justified by how they help in meeting the company’s core values and the production system principles. Table 5.4 shows the requirements in the “Assembly process” category. These requirements could in fact be attributed to one of the categories of the “assembly process” or “properties of the part.” In this case, the material handling and line feeding aspects were not mentioned in the guideline. The interviewees believed that the material handling aspects, even though important, were not treated sufficiently during the CD stage. 5.4.2.3  Mechanisms used for verification and communication in case B For the most part, case B utilized mechanisms similar to those in case A to verify and communicate manufacturing requirements. These mechanisms are listed below.

Manufacturing Engineering Requirements in the Early Stages of New Product Development

Table 5.4  Requirements in the assembly process as defined by company B Demanded or recommended

What

Why

Design of the parts shall support a generic assembly sequence Parts should handle the forces during the processes

To achieve an economical and optimized yet flexible balance

Demanded

To avoid damage to parts that may lead to quality defects or in the worst case personal injury To avoid increased cost or quality defects because of extra handling, extra packaging, demand for additional separate areas, etc Improves quality, as it means a quality risk to interrupt an assembly operation and pass it over to another assembler. Also reduces cost in terms of man-hours and assembly tools To avoid disturbances in the process because of unreadable part markings To avoid damaging the product or the assembler

Demanded

To decrease man-hours and cost

Recommended

To facilitate assembly and visual control

Recommended

To improve quality and work environment and reduce man-hours

Recommended

Parts shall be assembled directly in the final product (i.e., avoid subassemblies) Parts shall be assembled completely without being interrupted by other assembly operations Markings on parts should be visible and undamaged The assembly, positioning, and transport interfaces shall be robust and common for different variants of a part Supporting and fixating tools should be avoided Create color difference between parts, especially if visual control is needed It should be possible to assemble parts without lubricants

Demanded

Demanded

Demanded Demanded

• CAD systems • Design review sessions are the most important mechanism used in case B. Production had the possibility of participating in design reviews and giving feedback on the design. • Digital and PTAs • Risk assessments: product FMEA and later process FMEA • Ergonomics guideline of the company • Manufacturing requirements description • Quality function deployment (QFD), used at the beginning of the NPD project to integrate customer requirements into the design

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The description of manufacturing requirements had a similar function as the DFA checklist used in case A but was more detailed. It had a specific section focusing on plant-specific requirements, which made it a better tool for communicating manufacturing requirements. QFD was used in case B but not in case A. The rest of the above mechanisms were common in both cases.

5.5  ANALYSIS AND DISCUSSION In this section, the NPD process is discussed from a manufacturing perspective. It includes the involvement and integration of manufacturing in NPD projects and the mechanisms to enable the incorporation of manufacturing requirements early in NPD projects.

5.5.1  The applied development processes The case companies have both developed an adaptation of a generic stage-gate-based NPD model to manage their NPD projects. It was not clear if either of the companies have discussed the applicability of stage-gate models with comprehensive early requirement engineering phases or if more agile development methods with an emergent requirement definition would be suitable. Both companies also have a company-specific lean-based improvement program in the form of a production system (an XPS) governed by specific principles and based on the Toyota Production System.

5.5.2  Requirement types considered Several characteristics in the performance of an assembly system are influenced by the design of parts. For instance, total cost, quality, and delivery precision are determined and influenced by the design and quality of the ingoing parts. Koren [5] states that the quality that the customer sees depends to a large extent on the quality of the final assembly. As a stakeholder, the assembly system has certain requirements of the new product. The incorporation of the requirements in the product design work can potentially lead to a better design for the assembly system. During the studied NPD processes in the cases, it was concluded that the requirements communicated between manufacturing and product development concerned not only the specific assembly system itself but also derived from sources impacting the assembly system, as illustrated in Fig. 5.8. Legislation and regulation provided rigid requirements that had to be met. Limitations on or conditions regarding the use of chemicals or certain materials in the product are examples of these requirements. Other requirements in fact originated from other parts of the supply chain, which had an impact on the manufacturing process. The principles mentioned in the companies’ XPS was another source of requirements that the manufacturing function formulated during NPD projects. The existing manufacturing system and its capabilities and the future

Manufacturing Engineering Requirements in the Early Stages of New Product Development

Figure 5.8  Requirements concerning current and future assembly systems, with internal or external origin.

manufacturing system are other sources of requirements. Langowitz [58] argues that to avoid mismatches between new products and the manufacturing system, new products may be designed to match the existing manufacturing system or the manufacturing system may be prepared in advance to accommodate the new product. Both solutions require close collaboration between the design and manufacturing functions. By analyzing the lists of requirements from the two cases and relating these to the theoretical framework, including structures for RE and critical characteristics of manufacturability, the requirements were identified as stemming from different perspectives. First, the requirements originated from a company function, which could be the assembly operation itself, or (as illustrated in Fig. 5.9) from other parts of the manufacturing system, such as logistics, machining etc., or even external stakeholders, such as suppliers or distributors. Second, the source of the requirement could either be to align the product design with the current system or an anticipated future system, or the requirement could be based on internal or external standards or legislation. Third was what anticipated effect the requirement was intended to lead to. This could preferably be aligned to the key performance dimensions defined in the XPS of the company, such as safety, cost, quality, etc. These three perspectives reflected the manufacturing perspective of the requirement, its origin, and effect. In addition to these, the requirements also could be described according to how they influenced the product and the form of the requirement, as described in the RE literature. All these perspectives are illustrated in Fig. 5.9 and are summarized as follows: • Functional belonging (e.g., assembly operator, material handling, maintenance, and QA) • Source (e.g., current system, a known future system, internal standards and principles, and external sources or legislation/regulation) • Anticipated effect (e.g., lead time, cost, quality, safety, and ergonomics)

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Figure 5.9  Manufacturing requirements had different perspectives, such as belonging to various manufacturing functions, having internal or external sources and having different anticipated performance impacts (effect). They were also linked to various product characteristics and were communicated in different forms.

• Product impact (e.g., form, function, weight, material, appearance, etc.) • Form of requirement (e.g., hard/soft, legislation/internal standard/recommendation, and qualitative/quantitative) In the studied cases, during the planning, CD, and early system-level design stages, manufacturing requirements mostly concerned physical properties of parts and their impact on assembly processes. An important aspect, for instance, was ease of assembly or operator’s access to the assembly interface.These requirements were clearly emphasized in companies’ XPS. In Fig. 5.10, one example from illustrating a requirement in case A concerning the weight of parts can be seen.This requirement relates to the assembly and material handling functions. In fact, it originates from the company’s XPS, which emphasizes health and safety in the workplace in light of the existing environment, health, and safety guidelines. This requirement is classified as a “qualitative,” “hard” requirement, as it is demanded that the requirement be satisfied. Large parts, especially in the heavy automotive sector, cannot comply with this requirement. In such cases, manufacturing should provide the operators with lifting or assistive equipment to make sure health and safety are not compromised.

5.5.3  Mechanisms used for verification and communication The case companies use several tools and methods during the CD stage for communicating assembly requirements and expectations. Some of these tools are generic and

Manufacturing Engineering Requirements in the Early Stages of New Product Development

Figure 5.10  One example of a manufacturing requirement concerning weight limits on parts.

are used by various companies, while others could be specifically developed at the case companies to fit their needs. Some of these tools are useful for collecting requirements from the assembly department (the focus of the chapter), while others are useful in integrating various stakeholders in the NPD activities. Both companies have cross-functional project teams with representatives from various departments. In the CD stage, the companies keep the working groups small and encourage frequent meetings to increase working efficiency. Once the concept is agreed upon and the design goes to the more detailed stage, the working groups become bigger and the frequency of meetings decreases. Because we were interested in investigating how the requirements from assembly were communicated and treated in the early stages of the studied NPD projects, it was relevant to study how the tools employed by the companies captured or covered the assembly requirements. We used the three categories of assembly requirements mentioned in Section 5.4.1.1. Table 5.5 illustrates the tools used during the early stages of NPD projects and the assembly requirement categories that were identified in the empirical study. The “X” denotes what requirement categories were covered by the tools. It was expressed during the interviews and was seen in the observations (in both case studies) that material handling requirements were underattended in the early stages of the NPD projects. These tools handle the requirements at different levels and in fact serve different purposes, as illustrated in Fig. 5.11.

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Table 5.5  Tools used early in NPD projects versus assembly requirement categories they address Physical properties of the parts

SWOT FMEA QFD Ergonomics checklist DTA and PTA DFA checklist Design reviews

X X X X X X

Assembly process

Material handling and line feeding

X X X X X

Figure 5.11  Three types of mechanisms (tools and methods) used for manufacturing requirements.

Some of them, such as SWOT, FMEA, and QFD, can be used to define and elicit assembly requirements for a specific part or component. Others, such as DFA and ergonomics checklists, are useful in communicating general requirements. They can provide insight to the design engineers about the requirements that apply in the company or in the production system.There is also a third category that can be useful to assess how well a design meets a certain requirement, for example, digital and physical test assemblies. These tools are usually based on analyzing the current manufacturing system and analyzing discrepancies when introducing a new type of a similar product. Many of the decisions in the early stages of the NPD are made intuitively based on previous experiences using practice-oriented tools, such as risk analyses and FMEA [59]. The difficulty arises when the new product has not been internally manufactured before, which was the situation in our studied cases. Given the fact that the production site had no previous experience with or knowledge of the particular components, it was a challenge for the assembly representatives to provide input to the product development team early on. According to their NPD process model, at the beginning of an NPD project, assembly departments perform SWOT and FMEA analyses. The results provide insight regarding what requirements assembly has, which are important to be incorporated and addressed during the product design. Normally, product designers use the results of these analyses together with failure reports from the earlier designs to design parts that are

Manufacturing Engineering Requirements in the Early Stages of New Product Development

better than their predecessors. In the cases we studied, the product under development did not have any earlier version. That is, there was no similar product in the production program, so the assembly departments could not perform any analysis based on a similar existing product. Therefore the SWOT and FMEA analyses resulted in risks that the participants in the analyses could deem to be potentially risks, without having any prior experience of similar cases. This was mentioned as a challenge by the product introduction team members who did this analysis together with the production team. Other than the results from the SWOT and FMEA, the assembly departments did not provide any list of requirements to design (or to product designers) as an input. In other words, there was not a list of assembly requirements that the design and assembly departments agreed upon. Instead, the requirements were orally discussed in project meetings by the product introduction engineer, who had the responsibility of representing assembly and their concerns. This highlighted the significant role of the product introduction engineer in discussions. Both companies assigned experienced engineers as the product introduction engineers to represent assembly in project meetings.The assembly representative in the project had to be actively involved in project meetings to understand the product design and to assess its consequences for the assembly system. If the product design had any conflicts with the assembly requirements, the assembly representative had to discuss the matter with designers to find an appropriate solution. If the product introduction engineers deemed it necessary to be able to assess a design or resolve a conflict or needed input from production, they would discuss it with production engineers or the line organization. Otherwise, they would make the decision based on their experience and expertise. The results of the study showed that consulting the production engineers or the line organization happened rarely during the CD stage. During the detailed product design stage, however, the assembly function was more involved in the NPD project, mostly during the assembly system modification.

5.5.4  Toward a future classification and support structure for manufacturing requirements Available guidelines for DFA and DFM (e.g., those developed by Boothroyd et al. [6]) provide best practices for design. These guidelines are generic and are best suited for greenfield applications where the manufacturing system will be designed according to the product. In the case of legacy manufacturing systems, considering the manufacturing system is a challenge for two reasons. First, the consideration of the legacy manufacturing system should not limit the innovativeness of the new product. Therefore, establishing a balance between the manufacturing system and the new design is the first challenge. The second challenge pertains to how the manufacturing system requirements should be communicated and integrated in the product design. The latter might be overcome by introducing a systematic way of working with requirements,

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especially from the manufacturing side. It is important to be able to systematically elicit requirements from the manufacturing perspective. Additionally, manufacturing should be able to make a distinction between “demanded” and “recommended” requirements. This would help designers to define a boundary for the product design. Manufacturing can also help designers to understand the source and effects of manufacturing requirements. The model in Fig. 5.9 can be a useful tool for manufacturing to produce a map of their requirements. Documentation and formal agreement on requirements between product design and manufacturing can potentially improve the way of working in the early stages of NPD.

5.6 CONCLUSION The design–manufacturing interface is very crucial in NPD projects. Decisions made during the product development impact the downstream processes, such as manufacturing. To ensure that the new product is fit for manufacturing, it is important to understand manufacturing requirements early in the NPD process. In this chapter, we investigated how assembly requirements were handled in the early stages of two NPD projects at two manufacturing companies. First, assembly requirements during the early stages of NPD were identified based on the empirical data. A comparison between the empirical findings and the requirements in the literature revealed that some assembly requirements were not expressed or discussed by the assembly representatives in the study. Most notably, logistics and material handling aspects were rarely discussed between the assembly representatives and the product development department during CD. Additionally, it was noted that the companies lacked a systematic way to identify and express their assembly requirements. This made it difficult for assembly representatives in NPD projects to set requirements for the new product and to follow them up. As a result, it was up to the assembly representative in the projects to decide and act according to their experience and expertise, which resulted in divergence in the results of various projects at the case companies. An analysis of the tools and methods used in the early stages of NPD indicated that they served three different purposes: (1) to elicit (collect) requirements, for example, using FMEA and SWOT, (2) to communicate the requirements via DFA/DFM checklists, ergonomics checklists, and standards, and (3) to evaluate and assess the fulfillment of requirements, for example, test assemblies and CAD systems. The results also indicated that these tools and methods were established in the companies to varying degrees. For instance, DFA and ergonomics checklists were known and used within product development teams but were designed as general guidelines. Therefore they needed to be completed with project-specific requirements in each project. This indicated that the companies needed more tools to adequately address assembly requirements. In addition, the lack of a set of agreed-upon assembly requirements handed over to the design teams

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was noticed. The case companies expressed that the lack of formal and strict requirements from assembly enabled designers to be more innovative and more able to think outside of the box and not be limited by manufacturing requirements. There are some limitations associated with this research.The results are based on two case studies at two companies in the automotive sector. This affects the generalizability of the research outside this context [53,54]; however, it allows greater depth in the study. More cases, possibly from other industries, can help improve generalizability of the study. More research is necessary to develop further tools and mechanisms that support the integration of manufacturing requirements early in NPD projects. MBD might play such a role, especially with the recent trend of digitalization. Additionally, it is vital to create a culture of close collaboration between design and manufacturing in companies by regarding manufacturing input as adding value to product design work and not limiting it.

REFERENCES [1] A.F. Sommer, C. Hedegaard, I. Dukovska-Popovska, K. Steger-Jensen, Improved product development performance through agile/stage-gate hybrids: the next-generation stage-gate process?, Res. Technol. Manag. 58 (2015) 34–45. [2] R.G. Cooper, Predevelopment activities determine new product success, Ind. Mark. Manag. 17 (1988) 237–247. [3] P. Tuli, R. Shankar, Collaborative and lean new product development approach: a case study in the automotive product design, Int. J. Prod. Res. 53 (2015) 2457–2471. [4] G. Prudhomme, P. Zwolinski, D. Brissaud, Integrating into the design process the needs of those involved in the product life-cycle, J. Eng. Des. 14 (2003) 333–353. [5] Y. Koren, The Global Manufacturing Revolution: Product-Process-Business Integration and Reconfigurable Systems, John Wiley & Sons, (2010). [6] G. Boothroyd, P. Dewhurst, W.A. Knight, Product Design for Manufacture and Assembly, third ed., CRC Press, New York, (2010). [7] E.B. Magrab, S.K. Gupta, F.P. McCluskey, P. Sandborn, Integrated Product and Process Design and Development: The Product Realization Process, CRC Press, (2009). [8] M.L. Swink, R. Calantone, Design-manufacturing integration as a mediator of antecedents to new product design quality, IEEE Trans. Eng. Manag. 51 (2004) 472–482. [9] V. Paashuis, H. Boer, Organizing for concurrent engineering: an integration mechanism framework, Integr. Manuf. Syst. 8 (1997) 79–89. [10] R.G. Cooper, New products: what separates the winners from the losers and what drives success, in: PDMA Handbook of New Product Development, 2013, pp. 3–34. [11] J. Eriksson, Collaborative Product Development: A Collaborative Decision-Making Approach, 2009. [12] R. Calantone, C. Dröge, S.Vickery, Investigating the manufacturing–marketing interface in new product development: does context affect the strength of relationships?, J. Oper. Manag. 20 (2002) 273–287. [13] O. Isaksson, T.C. Larsson, A.Ö. Rönnbäck, Development of product-service systems: challenges and opportunities for the manufacturing firm, J. Eng. Des. 20 (2009) 329–348. [14] M. Sandberg. Design for Manufacturing: Methods and Applications Using Knowledge Engineering, 2007. [15] M. O’Driscoll, Design for manufacture, J. Mater. Process. Technol. 122 (2002) 318–321. [16] R. Dekkers, C.M. Chang, J. Kreutzfeldt, The interface between “product design and engineering” and manufacturing: a review of the literature and empirical evidence, Int. J. Prod. Econ. 144 (2013) 316–333. [17] J. Nihtilä, R&D–Production integration in the early phases of new product development projects, J. Eng. Technol. Manag. 16 (1999) 55–81.

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[18] A. Vandevelde, R. Van Dierdonck, Managing the design-manufacturing interface, Int. J. Oper. Prod. Manag. 23 (2003) 1326–1348. [19] K.T. Ulrich, S.D. Eppinger, Product Design and Development, fifth ed., McGraw-Hill/Irwin, Boston, MA, (2012). [20] M.P. Groover, Automation, Production Systems and Computer-Integrated Manufacturing, Pearson Education India, (2016). [21] M. Nafisi, M. Wiktorsson, C. Rösiö, Manufacturing involvement in new product development: an explorative case study in heavy automotive component assembly, Proc. CIRP 50 (2016) 65–69. [22] S.Valle, D.Vázquez-Bustelo, Concurrent engineering performance: incremental versus radical innovation, Int. J. Prod. Econ. 119 (2009) 136–148. [23] C.H. Loch, C. Terwiesch, Communication and uncertainty in concurrent engineering, Manag. Sci. 44 (1998) 1032–1048. [24] J. Stahl, H. Luczak, R. Langen, M. Weck, P. Klonaris, T. Pfeifer, Concurrent engineering of work and production systems, Eur. J. Oper. Res. 100 (1997) 379–398. [25] D. Gerwin, N.J. Barrowman, An evaluation of research on integrated product development, Manag. Sci. 48 (2002) 938–953. [26] X. Koufteros, M.Vonderembse,W. Doll, Concurrent engineering and its consequences, J. Oper. Manag. 19 (2001) 97–115. [27] K.B. Clark, S.C. Wheelwright (Eds.), The product development challenge: competing through speed, quality, and creativity, Harvard Business School Press, Boston, MA, 1995. [28] R.G. Cooper, Perspective: the stage-gate® idea-to-launch process—update, what’s new, and nexgen systems, J. Prod. Innov. Manag. 25 (2008) 213–232. [29] K. Beck, M. Beedle, A.Van Bennekum, A. Cockburn,W. Cunningham, M. Fowler, J. Grenning, J. Highsmith, A. Hunt, R. Jeffries, Manifesto for Agile Software Development, 2001. [30] J.B. Barlow, J. Giboney, M.J. Keith, D. Wilson, R. Schuetzler, P.B. Lowry, A.Vance. Overview and Guidance on Agile Development in Large Organizations, 2011. [31] V.Vinekar, C.W. Slinkman, S. Nerur, Can agile and traditional systems development approaches coexist? An ambidextrous view, Inf. Syst. Manag. 23 (2006) 31–42. [32] D.Turk, R. France, B. Rumpe. Limitations of Agile Software Processes. arXiv preprint arXiv:1409.6600, 2014. [33] G. Lee, W. DeLone, J.A. Espinosa, Ambidextrous coping strategies in globally distributed software development projects, Commun. ACM 49 (2006) 35–40. [34] B. Boehm, R. Turner, Balancing Agility and Discipline: A Guide for the Perplexed, Portable Documents, Addison-Wesley Professional, (2003). [35] C.G. Cobb, Making Sense of Agile Project Management: Balancing Control And Agility, John Wiley & Sons, (2011). [36] M. Špundak, Mixed agile/traditional project management methodology–reality or illusion?, Proc. Soc. Behav. Sci. 119 (2014) 939–948. [37] P. Loucopoulos, Requirements engineering, Design process improvement, Springer, 2005, pp. 116–139. [38] S. Wiesner, M. Peruzzini, J.B. Hauge, K.-D. Thoben, Requirements engineering, in: Concurrent Engineering in the 21st Century, Springer, 2015, pp. 103–132. [39] ISO 9000:2015(en), Quality management systems — Fundamentals and vocabulary [WWW Document], n.d. URL https://www.iso.org/obp/ui/#iso:std:iso:9000:ed-4:v1:en:term:3.8.5. (Accessed 18 October 2017). [40] L. Almefelt, F. Andersson, P. Nilsson, J. Malmqvist, Exploring requirements management in the automotive industry, in: DS 31: Proceedings of ICED 03, the 14th International Conference on Engineering Design, Stockholm, 2003. [41] M. Glinz, Rethinking the notion of non-functional requirements, in: Proceeding of Third World Congress for Software Quality, pp. 55–64. [42] G. Paul, W. Beitz, Engineering Design: A Systematic Approach, Springer, London, (1996). [43] M. Wiktorsson, T. Andersson, M. Broman, A note on the specification of assembly systems, Int. J. Prod. Res. 38 (2000) 3997–4002. [44] E. Aresu, Design process management for successful assembly systems, Production Systems, Department of Mechanical Engineering, Linköpings Universitet, (2001).

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[45] N. Asadi, Supporting Flexibility in an Assembly System Through Product Design, Mälardalen University, (2015). [46] N. Asadi, J. Schedin, A. Fundin, M. Jackson, Considering assembly requirement specifications in product development: identification and approach, in: 24th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM), May 20–23, 2014, San Antonio, Texas, USA, 2014, pp. 969–976. [47] J.D. Booker, K.G. Swift, N.J. Brown, Designing for assembly quality: strategies, guidelines and techniques, J. Eng. Des. 16 (2005) 279–295. [48] M. Fleischer, J.K. Liker,The hidden professionals: product designers and their impact on design quality, IEEE Trans. Eng. Manag. 39 (1992) 254–264. [49] K. Agyapong-Kodua, R. Darlington, S. Ratchev, Towards the derivation of an integrated design and manufacturing methodology, Int. J. Comput. Integr. Manuf. 26 (2013) 527–539. [50] H. Meerkamm, M. Koch, Design for X, Design Process Improvement, Springer, (2005) pp. 306–323. [51] T.D. Hedberg Jr., N.W. Hartman, P. Rosche, K. Fischer, Identified research directions for using manufacturing knowledge earlier in the product life cycle, Int. J. Prod. Res. 55 (2017) 819–827. [52] K.B. Zandin, Maynard’s Industrial Engineering Handbook, fifth ed., McGraw-Hill, (2001). [53] C. Karlsson, Researching Operations Management, Routledge, (2010). [54] R.K.Yin, Case Study Research: Design and Methods, Sage publications, (2014). [55] K.M. Eisenhardt, Building theories from case study research, Acad. Manag. Rev. 14 (1989) 532–550. [56] M. Saunders, P. Lewis, A. Thornhill, Research Methods for Business Students, Pearson, Harlow, England; New York, USA, (2012). [57] M. Nafisi, M. Wiktorsson, Ensuring manufacturability in early stages of new product development: a study of two practices, in: 24th EurOMA Conference, July 1–5, 2017, Edinburgh, Scotland, 2017. [58] N.S. Langowitz, An exploration of production problems in the initial commercial manufacture of products, Res. Pol. 17 (1988) 43–54. [59] N. Lakemond, T. Magnusson, G. Johansson, K. Säfsten, Assessing interface challenges in product development projects, Res. Technol. Manag. 56 (2013) 40–48.

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CHAPTER 6

Development of SMEs Coping Model for Operations Advancement in Manufacturing Technology Michael K. Adeyeri*, Sesan P. Ayodeji*, Bassil O. Akinnuli*, Peter K. Farayibi*, Olatunji O. Ojo*, Kehinde Adeleke** *Department of Industrial and Production Engineering, The Federal University of Technology, Akure, Nigeria **Department of Mechanical Engineering, Faculty of Engineering, Adeleke University, Ede, Nigeria

6.1 INTRODUCTION Technological advancement has widely cut across every facet of the socioeconomy and manufacturing sectors with significant and laudable changes being recorded. With this rapid transformation, the manufacturing cutting edge has greatly influenced products’ definition, quality, geometry, demand, customers’ taste, usability, and reusability as asserted by Cheng and Bateman [1]. Cheng and Bateman [1] posited that the manufacturing technological growth emancipated from mass production to flexible manufacturing (FM), from FM to computer integrated manufacturing (CIM), from CIM to lean manufacturing which extends to just in time manufacturing (JIT), and JIT stems to concurrent engineering and this leads to agile manufacturing. The make-up of these transformations with passage of time are shown in Fig. 6.1 as adapted from Cheng and Bateman [1]. Qin et al. [2] noted that the advances in manufacturing or its transformation could be summarily described as being complex, computerized, automated, and evolving. Based on this, these authors classified manufacturing systems into: single station manned cells; single station automated cells; manual assembly system; automated assembly system; cellular manufacturing system; flexible manufacturing system; and reconfigurable manufacturing system. Ocampo et al. [3] elucidated further by classifying manufacturing operational tools based on: 1. Design and engineering: computer aided design (CAD), computer aided manufacturing (CAM), computer aided engineering (CAE), computer aided process planning (CAPP), product life cycle management (PLM), concurrent engineering, failure mode and effects analysis (FMEA) design of experiments (DOE); 2. Management: Enterprise Resource Planning (ERP), Material Requirement Planning (MRP), Manufacturing Resource Planning (MRP II), Manufacturing Execution Systems (MES), preventive maintenance, quality management, inventory Advanced Applications in Manufacturing Engineering. http://dx.doi.org/10.1016/B978-0-08-102414-0.00006-9 Copyright © 2019 Elsevier Ltd. All rights reserved.

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Figure 6.1  Development in manufacturing technology. (Reproduced with permission from K. Cheng, R.J. Bateman, E-Manufacturing: characteristics, applications and potentials, Pro. Nat. Sci. 18 (2008) 1323–1328).

­ anagement, supervisory control, productivity analysis, Supply Chain Management m (SCM), Customer Relationship Management (CRM); automation [Computer Numerical Control (CNC), Programmable Logic Controllers (PLC), Flexible Manufacturing Systems (FMS), Automatic Guided Vehicles (AGV), Automatic Storage and Retrieval Systems (ASRS), Robots Automatic Quality Inspection (machine vision, sensors), Bar code/RFID Systems]; and 3. Process improvement [Statistical Process Control (SPC), Total Quality Management (TQM), Jidoka (Error detection automation), Define, Measure, Analyze, Improve, Control (DMAIC), Value Stream Mapping (VSM), Total Productive Maintenance (TPM), Methods Engineering and JIT]. The explicit discussion on these shall be found in the subsequent section of this chapter. All the manufacturing processes serve as an end to meeting customers’ needs, thus calling for their sustainability which is adjudged the best approach in gaining competitive advantage in manufacturing economy by Singh et al. [4]. Also, sustainability of the production processes calls for integration of eco-friendly plans into the production cycles. Few of the manufacturing approaches are sustainably inclined while some do not really fit into the manufacturing advancement concepts. Globally, the impact of SME industries in manufacturing economy cannot be underestimated as these are very

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pivotal to its growth [4]. Therefore, the present chapter contribution on manufacturing advancement technology is being centered on how small and medium scale engineering or industries can be sustained. Based on this, this chapter seeks to establish a platform where the less privileged manufacturing enterprises or group such as small and medium scale could be assisted in finding their feet in this competitive manufacturing world through dynamic and sustainable decision model. The chapter is thus divided into the following sections. Section 6.2 discusses the manufacturing advances and their respective tools, while Section 6.3 x-rays the SMEs characteristics.The coping model development and its discussion is fully discussed in Section 6.4.The concluding remarks are presented in Section 6.5.

6.2  ADVANCES IN MANUFACTURING TECHNOLOGY AND ITS TOOLS Manufacturing operations logically coordinate money (capital), materials, machines, manpower, and methods (major 5 Ms) to deliver products for market consumptions [5]. Advances in manufacturing technology have evolved over time to improve all the phases involved in traditional manufacturing by utilizing innovative/cutting-edge practices and technologies. These phases of manufacturing include engineering design, manufacturing/execution, process and quality control, and environmental-health safety. In addition, intensive attentions are given to the attainment of zero-defect production, less waste, less energy consumption, operation flexibility, price reduction, competitive market advantage, and close alignment of supply chain with demand. Modern day manufacturing employs diverse well-known tools otherwise known as manufacturing operations management and high-tech innovations (or technologies) in achieving the aforementioned focuses. In fact, advances in manufacturing are adjudged to be built on three pillars of manufacturing technology which are robots, computers, and production equipment, without denial of the importance of advances in nontechnology areas such as quality management, lean manufacturing/production, continuous improvement, and workforce training [6]. These nontechnology areas are essentially used to increase effectiveness and performance of companies [3]. The tools employed in advancing manufacturing are broadly classified into two categories namely technologies and methodologies as indicated in Fig. 6.2. It is important to note that the subdivisions of these tools are not limited to those indicated in Fig. 6.2. Technological tools are significantly based on the synergetic integration of electronics, mechanical, and computer-based systems to aid product design, operations, and control of manufacturing systems. These tools are the hardware and computer programs used to increase flexibility in design, fabrication, assembly and operations [3]. A few of these tools include CAM, CAE, and material resource planning (MRP). Ease of maintenance, increased reliability, reduced development costs, and time-to-market are attained with the adoption of computer-based or software product line manufacturing approach [7].

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Figure 6.2  Advanced manufacturing tools: technologies and process improvement methodologies.

Digital manufacturing technologies which facilitate flexible, scalable, and modular manufacturing of customized parts are also classified as technological-based tools [8]. Likewise, various manufacturing concepts aimed at improving flexibility, reconfigurability, and meeting up with delivery time are important technological tools. For instance, the concept of agile manufacturing has been introduced into advanced manufacturing to satisfy the demand for low and high volumes of products by integrating hardware and information flows, computer systems, and manufacturing systems with flexibility and reconfigurability [9,10]. The capacities for product design, development, and manufacturing have been revolutionized in modern manufacturing via technological tools. For instance, the integration of additive processes and computer-based design optimization has led to the mantra, “complexity is free” in manufacturing [11]. Thus, complex structures can be designed and optimized for performance with the use of advanced computer-based technologies. Superior quality and shorter delivery cycles are competitive advantages that can be

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attained if innovation in production technology or powerful competitive technology is strategically implemented [12]. In fact, holistic design paradigm can be attained when computational tools are combined with stochastic methodology and material awareness to facilitate concurrent optimization of design structure, material selection and fabrication process [11]. However, other integrations such as information technology, cloud and ubiquitous manufacturing are important for the improved performance of manufacturing technologies. Cloud manufacturing (CM) and ubiquitous manufacturing (UbiM) employ ubiquitous and convenient network access to a joint array of configurable manufacturing resources, which include manufacturing equipment and software [13]. The utilization of information technology (or information system) is adjudged to facilitate collaboration between new manufacturing, virtual manufacturing, virtual enterprise, concurrent engineering, rapid prototyping, and enterprise integration and management [14]. Methodological tools are sets of organized process improvement and managerial tools targeted at improving manufacturing performance. Examples of these tools are TQM and Six Sigma as indicated in Fig. 6.2. Most manufacturing companies that employ innovative technologies integrate one or two of the improvement methodologies into their operations with the aims of improving quality/performance, reducing waste, and so on. Economic and reliable schedules are essential for optimum efficiency in the management of industrial operations of manufacturing companies and these can be achieved via the use of computer-based manufacturing scheduling tools to overcome limited short- and long-term memory of human planners [15].

6.3  SMALL AND MEDIUM SCALE INDUSTRIES Small and medium scale industries activities stemmed from manufacturing, agricultural, and services that are value addition oriented. Some agricultural products such as timber, cocoa, coffee, palm fruits, cassava and just to mention a few will be economically valueless after being harvested if not processed, milled, turned, and transformed through the necessary machinery. Also, the solid earth minerals (crude oil, bitumen, iron ores gold, quartz, precious stones, etc.) will be of no importance if not extracted, beneficiated, and polished. Most of the value addition processes are easy to set up through the use of indigenous machines, thereby encouraging more entrepreneurs to invest in manufacturing activities at small and sustainable level. Complementing this fact, Singh [4] disclosed that in Malaysia, small and medium industries occupied 96.6% of their manufacturing sector. However, the small and medium industries may find it difficult to cope with the trend in technological advancement owing to limited resources and lack of finances available to them. With the advancement in technology toward Industry 4.0, where majority of manual activities are preplaced with robots and automated systems, and manufactured outputs are smart products with embedded systems, SMEs may not be able to compete favorably to deliver desirable products for consumers [2]. Moreover,

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majority of the production activities in SMEs are either manual or semiautomated, and are ­time-consuming; hence, the financial resources and machineries available may not be capable to catch up with new technological development. Earlier, mass production for consumers was in vogue, but in recent times, this has become obsolete, as individual consumer demands differ significantly, thus pushing the boundaries of manufacturing toward mass customization [16]. Furthermore, there is reduction in product lifetime as consumers are demanding new products which can better serve them well and there is increase in the level of product/component complexity over the years as shown in Fig. 6.3 as presented in [16]. At present, most SMEs are struggling with their few resources to survive and are not adequately prepared for such trends in the consumer requirements, short product life cycle, product complexity, and market demands. More so, the rapid change in the consumer requirements and competition among the SMEs and even large scale industries to have a good market share, production lead time has to be reduced. Hence, methodology for production lead time reduction becomes necessary. As defined by United Nations, small-scale manufacturing industries (SSMI) are production organizations capable of employing ten or more persons. However, there

Figure 6.3  Manufacturing trends over the last few decades. (Reproduced with permission from P. J. Bartolo, I. Gibson, History of stereolithographic processes. In Stereolithography: Materials, Processes and Applications, New York, Springer Science and Business Media, 2011).

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are several other definitions of SSMI based on number of employees, capital investment, and annual turnover; and the definition of SSMI is thus country dependent. SSMI, in United Kingdom, is defined as an organization with less than 200 employees on their payroll and having an annual revenue of £2 million. However, in Nigeria, SSMI is defined as companies engaged in production or semiproduction or repair type activities, employing a maximum of 50 employees, and with initial capital not more than ₦500,000 or approximately $2000 [17]. There is no clear difference in the definition of medium scale industries and hence the classification of small and medium scale industries (SMEs).

6.3.1  Characteristics of small and medium scale industries As countries of the world, especially the developing countries look forward to being globalized, it is important for them to put in place schemes and instruments to enable them attain their desired level of global outlook. The ability of these countries to unite with the international economy through investment and trade is a salient factor for their attainment of their globalization. One of the instruments capable to boosting both domestic economy and global outlook is the small and medium industries, as these account for more than 90% of the industrial firm and the largest employer of the nation’s workforce. Hence, it is necessary to provide an enabling environment and coping strategies for the small and medium industries to thrive to promote the local content policies and there is also a need to identify the challenges facing these industries and opportunities available to them has to be developed, most especially the SMEs need to adopt new manufacturing techniques which will enable them adapt to the market situation rapidly. Summarily, the small and medium scale enterprise characteristics are as listed: 1. It makes use of resources that are indigenous 2. It is bound by little investment capital 3. Its turnover is less than €50,000,000 [18] 4. It is susceptible to continuous change as customers’ taste vary 5. It is flexible in adapting to change 6. Shorter break-even point as investment returns period takes shorter time 7. Lack of expertise 8. Lack of method and procedure SMEs operate a batch production system where different products are produced in a particular quantity, hence they need to develop an effective material utilization and management system. Moreover, SMEs can as well adopt a direct material reuse approach by developing products with adaptable geometrical features such that already-made parts of products that are obsolete can be adapted to manufacture parts of other new products. This can be achieved by adopting different manufacturing techniques such as additive and manufacturing technologies as depicted in Fig. 6.4 [19].

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Figure 6.4  Adapting existing part using different manufacturing technologies to produce new part. (Reproduced with permission from V.T. Le, H. Paris, G. Mandil, D. Brissaud, A direct material reuse approach based on additive and subtractive manufacturing technologies for manufacture of parts from existing components. Procedia CIRP – The 24th CIRP Conference on Life Cycle Engineering, 61 (2017) 229–234).

However, there are several manufacturing technologies and tools which can help SMEs stay profitable and cope with the challenges of product development, consumer requirements, and market demands.The identified manufacturing tools include: lean manufacturing, JIT manufacturing, additive and subtractive manufacturing, total quality management and reconfigurable manufacturing.With these tools, it is anticipated that the SMEs will not only cope with market situation, their turnover on investment will be significantly raised.

6.3.2  Reconfigurable/modular/flexible manufacturing SMEs would benefit significantly by adopting a reconfigurable manufacturing system approach, as this allows the production configuration to be easily modified as there is change in consumer requirements over time [20]. As change is inevitable, so also the new products are evolving and this will continue to be, as consumers always wish to stay current and up-to-date in terms of technology. Therefore, there is need for SMEs to adopt manufacturing technology which is modularized, flexible, reconfigurable, and scalable to allow modification necessary to cater for the change in product design and development for consumers [8]. Hence for a product, the production line must be modified to track the dynamics of the product life cycle. In order for the SMEs to survive and prosper in the competitive market, it is important for them to adopt agile manufacturing strategy in which their production system can react quickly and efficiently to changes in market propelled by consumer demands [21]. This is expected to promote low production cost, increasing market share, and timely introduction of new products into the market.

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6.3.3  Additive manufacturing/rapid prototyping Additive manufacturing/rapid prototyping is a solid free-form fabrication technology capable of producing physical models and functional components by depositing the material in a layer-by-layer fashion using the 3-dimensional CAD component model file [11]. There are several technologies associated with additive manufacturing and can be basically classified as plastic-based (fused deposition modeling), metallic-based (selective laser melting), and paper-based (laminated object manufacturing) amongst others as illustrated in Fig. 6.5 as culled from author [22]. These technologies are capable of assisting SMEs in the area of rapid development of new products with lower production lead time. Hence, mass customization of products become easy and SMEs will be able to meet consumer needs timely [23]. It is worthy to note that for SMEs to cope with the increasing market demand instability and shorter product life cycles, the adoption of additive manufacturing in their product development will enhance their manufacturing flexibility and capability to meet the market requirement within a short period of time. This is essential for the SMEs to stay in healthy competition with their counterparts and even the large-scale industries to have a fair market share. This novel technology is capable of introducing unlimited product design freedom into the manufacturing operations of SMEs and empower them in the area of

Figure 6.5  Categories of additive manufacturing processes. (Reproduced with permission from R. Singh, S. Singh, Additive manufacturing: an overview. materials science and materials engineering (2017) doi:10.1016/B978-0-12-803581-8.04165-5) [22]

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Figure 6.6  Product mix, volume, and new product flexibility achievable through additive manufacturing. (Reproduced with permission from P. Spalt, T. Bauernhansl, A framework for integration of additive manufacturing technologies in production networks. Procedia CIRP, 57 (2016) 716–721).

high ­product mix flexibility such that new products variants are manufactured easily and quickly for a good time market entry as illustrated in Fig. 6.6 [24]. Owing to the enhanced manufacturing capabilities of additive manufacturing techniques, it was observed that SMEs in Sweden are responsible for 78% users of additive manufacturing machines which is making them to have a decreased time-to-market product development [12].

6.3.4  Lean manufacturing Lean manufacturing approach would provide a huge benefit for SMEs if adopted in their production facilities. The lean principles search through all the production activities and even the supply chain of raw materials and finished goods to identify and eliminate nonvalue added activities such as overproduction. Among several applicable lean tools, the SMEs can apply lean approach through the development of value stream map of the current state of the SMEs operations and establishment of a workable future state map, application of kaizen continuous improvement methodology, application of 5S principle and six-sigma to produce zero or near-zero nondefective products and adoption of statistical control charts for monitoring the quality of the products to easily identify the sources of variability in their operations. These are expected to raise the return on investment for the SMEs and help them remain competitive in the global market [25]. Moreover, the production lead time will be drastically reduced as majority of the nonvalue added activities would have been eradicated. Furthermore, lean manufacturing are better applied to

Development of SMEs Coping Model for Operations Advancement in Manufacturing Technology

Figure 6.7  Synergizing lean and agile manufacturing approaches to adapt SMEs to demand fluctuations. (Reproduced with permission from J. Prince, J.M. Kay, Combining lean and agile characteristics: creation of virtual groups by enhanced production flow analysis. Int. J. Prod. Econ. 85 (2003) 305–318).

consistent demand situations, and can be combined with agile manufacturing approach, as illustrated in Fig. 6.7, as modified from Prince and Kay [26], which can adequately provide the necessary support for the operations/production activities of SMEs at the face of rapidly fluctuating demand for products and services [26].

6.3.5  Just-In-Time (JIT) manufacturing JIT manufacturing will offer good benefits for SMEs as it promotes zero or near-zero inventory system. It also promotes the manufacture of required products in adequate quantity needed without having to keep finished goods or work-in-progress in any store. It ensures that raw materials needed are readily available with the suppliers that can make a delivery within a limited period of time. This manufacturing approach will reduce the production cost through the elimination of the inventory cost, thus the product price will become highly competitive to boost patronage and hence increase sales volume [25].

6.4  COPING MODELS FOR SMES PLACEMENT IN AMT A lot of models and approaches have been proposed for SMEs either to promote improved quality and performance or to harness all the possible components of wealth creation. Commercial ERP packages are designed and well suited for large-scale

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c­ompanies/industries; they can as well be beneficial for the SMEs but the general understanding is that they are difficult to implement in SMEs. In light of this, researches focusing on the integration of ERP into SMEs have become paramount interest to improve the competitiveness and performance of SMEs. Authors [27] designed a conceptual ERP model for SMEs in Iran. Promotion of innovative and entrepreneurial performance by SMEs is envisioned via the adoption, development, and implementation of ERP technology in this part of the world. Likewise, the prospects of aligning information technology with business objectives especially for SMEs have led to the proposition of models by researchers. Alyahya and Suhaimi [28] proposed a conceptual information technology-business strategic alignment model for SMEs in Saudi Arabia. The proposed model is projected to aid future researches to leverage on the development of more operative strategic alignment methodologies for SMEs. Asikhia and Rensburg [29] identified the domains of wealth creation for SMEs and established a conceptual framework/model for this. Human resources, innovation, technology, organizational infrastructure, cost economies and strategy were identified as the paramount wealth creation domains for SMEs. Husband and Mandal [30] proposed a conceptual model for the integration of quality management into SMEs. It was reported that quality methods’ application and validity can be employed by interest groups such as SME owners and other stakeholders. Banerjee and Dasgupta [31] gave a verbal ­conceptual model to manage branding initiatives for SMEs.Yan [32] revealed the theoretical framework of competitive advantage for SMEs in China. It was expounded that the success of this framework depended on identification of appropriate/applicable strategy, industry structure (market entry barriers and competitive pressure), and development of core capability (entrepreneur, marketing, and innovation capabilities). SMEs can cope with advanced manufacturing technology by appraising the essential tools of manufacturing. A critical assessment of the two major tools shows that technological tools are the foundations on which methodological tools are built on or leveraged. As a result, manufacturing strategies need to be critically scrutinized by SMEs to ascertain the appropriate and minimum technologies required to keep up with the competitive market demands and quality. For instance, additive manufacturing is one of the foremost renaissances in global manufacturing and product improvement owing to the fact that complex geometries can be optimized and produced [11]. Kianian et al. [12] opined that the use of additive manufacturing (AM) by SMEs will enable complete management of product chain from the innovation stage to manufacturing level and this will assist SMEs to keep up with international market. Also, additive manufacturing (AM) is adjudged to reduce initial capital required to fulfill economies of scale and its flexibility focuses on reducing the capital required to fulfill economies of scope [12]. This is extremely beneficial to the SMEs because lower capital risk is involved in AM. The integration and use of AM by SMEs will consequently bring about competitiveness in international/global market.

Development of SMEs Coping Model for Operations Advancement in Manufacturing Technology

It is paramount to point out that SMEs cannot survive or cope with international market if minimum technological innovations are not inculcated into their manufacturing strategies. Skill acquisition and introduction of new technologies are attributes that should be continuously sort for by SMEs in today’s market environment. Thus, the new conceptual coping strategy model that considers the relevance of SMEs in manufacturing technology advances is thus presented in the preceding subsection.

6.4.1  Coping model flowchart formulation If the manufacturer population bidding for the production of product PN is n, then the probability of any prospective bidder to secure the contract is as expressed in Eq. (6.1)

Pr( N ) =

1 n

(6.1)

There are other factors which any prospective manufacturing bidder would like to add to itself so as to stand competitive and be at added advantage in securing the contract as the criteria imposed in picking the best bidder (manufacturer) may be too demanding. For SMEs to be able to compete favorably, the manufacturing firm must be able to meet up with the product’s production guidelines governing the behavior of its manufacturability as declared in Eq. (6.2).

M OP = f (ma , rm , Pd , m p , E , S fc )

(6.2)

where MOP is manufacturing operation which is modeled as a function of machine availability (ma), raw material (rm), product design (Pd), man power (mp), energy availability, and shop floor control (Sfc). If these factors are constant with the exception of machine availability, Eq. (6.2) therefore becomes Eq. (6.3). M op = kma (6.3) Where k is a constant of proportionality. Inferentially, Eq. (6.3) established that as machines are available, manufacturing operations are bound to be better in efficiency, timely in operation, and achievable in general. In view of this, SMEs could be involved in competing for contracts of deliverable products provided they know how to use their limited resources in strategizing for machines procurements or advanced manufacturing activities needed for the production of the products. In view of this, as availability of fund is paramount to the definition of SMEs, the following mathematical expressions are therefore strategized to assist in decision taking. The description of the notations used are as stated: SMEcapita lim it represents the maximum capital limit for SMEs, M cos t stands for the manufacturing cost required in producing the ordered product. While other notations retail their initial meaning.

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1. If M a ≈ M op , and M cos t ≤ SMEcapital lim it then proceed on manufacturing of products ordered. 2. If M a < M op, and M cos t > SMEcapital lim it with lead time and customization in focus, then make use of additive manufacturing. 3. If M a < M op, and M cos t > SMEcapital lim it with continuous varying of products geometry, sizes, and modifications, then adopt reconfigurable manufacturing. 4. If M a < M op, and M cos t > SMEcapital lim it with consistency in product’s demand, lean manufacturing is preferred. 5. If M a < M op, and M cos t > SMEcapital lim it with focus on zero inventory, then JIT manufacturing is recommended. Transforming the model strategies listed earlier, the resulting outcomes led to the iconic models expressed in Figs. 6.8 and 6.9. Considering the established SMEs model of Figs. 6.8 and 6.9, the coping strategy gives a clear guidance on how it can be adopted and implemented.With the activities numbering indicated in the model flowchart, once an order is received as depicted in activity (1) one, the order is read, interpreted with details of manufacturing parameters highlighted. The

Figure 6.8  SMEs coping model flowchart formulation.

Development of SMEs Coping Model for Operations Advancement in Manufacturing Technology

Figure 6.9  Continuation of SMEs coping model flowchart formulation shown in Fig. 6.8.

next stage of the coping model is to determine suitable manufacturing approach for choice of products. The approach describes what should form the basis for the manufacturing of ordered product(s).After this, peculiarity of the existing production factory is established.At this 4th activity level, the existing manufacturing operations’ uniqueness, dimension, facilities, and tools are spelt out to form the basis in answering the decision statement question “is existing production factory suitable for the present task?” The response to the question determines which route to follow. Process route 6 is implemented if the answer to the

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decisional question is “yes.”Activity 6 is therefore implemented.Activity 7 is activated, if the answer to the decisional question of activity 5 is “no.” Identification of features to be added which are needed to meet up with the new customer’s taste or coping with the manufacturing operation advancement in related to the old existing manufacturing tools is done. The outcome of the identified features are thereafter valued in terms of cost to assist in determining if the features are within the SME tolerated cost range. If it is beyond the cost, the entrepreneur needed to revisit the process, restrategize and may need to outsource for some of the salient features. Activity 9 (which is taking decision on which manufacturing strategy to adopt) would be called into play if the answer to the decisional question of activity 8 is “yes.” The manufacturing strategy is to give room for the best operational method to adopt in adapting to the manufacturing advancement technology. To identify the best strategy to adopt, four decisional questions as given in activities 11 to 14 were structured. Decisional question of activity 11 as quoted: “is lower lead time and mass customization paramount?” will assist in linking or marrying the positive response of “yes” to “adopt additive or rapid manufacturing.” But if the response to the activity 11 is “no,” then activity 12 of decisional question “is process characterized with continuous and varying modifications?” is thus asked. The use of reconfigurable or modular or flexible manufacturing is proposed for adoption if the answer to the decisional question of activity 12 is yes. If not, this will call for decisional question of activity 13. The decisional question 13 deals with customers’ demand, and the question is “is demand for product consistent?” if products’ demand is consistent, lean manufacturing is proposed as indicated in activity 17. Else, it calls for last decisional question “is production aimed at zero inventory?” If “yes,” JIT manufacturing is adopted. Otherwise, activity 10 is implemented for further strategizing. Any of the activities 15, 16, 17, and 18 that is opted for based on the decision outcome will lead to initialization of production set up in activity 6. Once this is actuated, the manufacturing process is thus monitored to guide against flaws, defects, and failure. Hence, this necessitates the assessment of products’ quality and conformance as contained in activity 20. For clarity, decisional question to know if products are in conformance to standard is asked in activity model question 21. If products are in conformance, then they are delivered to customers. Otherwise, the model suggests that activity 10 of restrategizing should be carried out. The feedback from end users are also considered and integrated into the model as this helps in manufacturing advancement and building of long-term customer/entrepreneur relationship. If customers are satisfied, then production can continuously increase. But if products are not satisfactorily okay by customers, model suggests that activity 8 needs to be called into play.

6.4.2  Case study scenario Mike-Meek firm is a Small Manufacturing Engineering (SME) firm that specializes in the production of chairs. A sample of their product is shown in Fig. 6.10. The firm received order for production of five thousand (5000) executive armrest chairs with the following features and attributes:

Development of SMEs Coping Model for Operations Advancement in Manufacturing Technology

Figure 6.10  Initial sample of Mike-Meek’s products (arm rest chair) before the new order is received.

1. pivoting armrest; 2. posture stabilizer; 3. ease of adjustment; 4. sharp edges free; 5. provision for accommodating varying users with arm height and width controls; and 6. accommodation of varieties of arm positions. The task is to determine if Mike-Meek would be able to meet up with the customer’s demand. This case study shall be implicitly discussed using the developed coping model so as to determine its suitability. For the firm to meet up with this requisite order, the firm either has to outsource or procure some machine modules as the new customer’s order is quite different from. Solution The case study is to test the suitability and robustness of developed coping model template given in Figs. 6.8 and 6.9. The preliminary steps to be taken by the firm are to carry out: 1. preliminary design of how the new customer’s order would be using appropriate tools such as CAD to conceptualize the design; and

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2. examining the finite element analysis (FEA) to predict the performance of the ­product during the design cycle. The final output of the design process as ordered by customer led to the product shown in Fig. 6.11. Considering the sample shown in Fig. 6.10, it is evident that the nature of chairs produced by Mike-Meek firm are simply executive but not sophisticated. Comparing this with the new order received, it shows that the manufacturing firm must adopt the model described in this chapter. Marrying the scenario with the model by considering the numberings of activities stated, it is evident that activities 1, 2, and 3 have been implemented. Existing peculiarity established by considering the new order to the old product implies that there are similarities. The coping model activities consideration application for Mike-Meek factory is as shown in Table 6.1. With reference to Table 6.1, after the product has reached the model activity 20 where product’s quality and conformance to standards are being checked, once it passes this test, the chairs are now delivered to the customers. The outcome of the product design displaying its flexibility and adjustment as specified in the order received is as shown in Fig. 6.12. In the considered case study, as CAD tools used have validated the flexibility required as stated in the received order, then without any iota of doubt, when produced, it will as well meet up with the specified requirements.

Figure 6.11  The output design of customer’s new order.

Development of SMEs Coping Model for Operations Advancement in Manufacturing Technology

Table 6.1  The coping model application to Mike-Meek firm for decision making on choice of manufacturing type Model activity number in reference to Fig. 6.7

Marrying of Mike-Meek’s existing provisions to the coping model of Fig. 6.7

Model activity 1

Executive armchair

Model activity 2

Executive armchair

All the components parts are noted for production

Model activity 3

Fabrication and assembly work

This is being aided by CAD modeling

Model activity 4

There are: • availability of forming machine • shaping machine • guillotine • welding machine • jigs and fixtures • hand tools • upholstery materials

The list is expected to cover all available tools and machines own by Mike-Meek

Model activity 5

The existing production layout/ factory is 75% suitable. In other words, it needs additional modules

Answer to the decisional question is “No”

Model activity 7

The identified machines or modules features to be added are: • tube end forming machine • CNC roll bender • arm adjustment mechanism • chair control mechanism

Model activity 8

The proposed cost for the procureThere is no need for ment of new modular machine(s) restrategizing summed to the initial facility is still within the SME scope In other words the whole plant setup cost is within the SME cost

Model activities 9, 11, 12, The suitable manufacturing strat13 and 14 egy is decided with the aid of the decisional questions of activity 11, 12 13 and 14

Model activity 6

Production set up is initialized

Model activity 19

Manufacturing process is monitored

Remark if any

Since process is characterized with varying modification, reconfigurable or modular or flexible manufacturing should be used for the given order

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Figure 6.12  The output design of customer’s new order showing arms positioning flexibility/ adjustment. (A) Lowered. (B) Raised state.

6.5 CONCLUSION The basic manufacturing operation practices have been discussed by clearly itemizing their unique characteristics. The chapter considered lean manufacturing, additive manufacturing, JIT, and reconfigurable or modular/flexible manufacturing as the most viable and probable that SMEs could cope with based on marrying their technologies with practicable methodologies. The developed coping model for SMEs placement in advanced manufacturing technology shows that for a SME to survive in a competitive environment, competitive and manufacturing strategies need to be carefully integrated, practiced, and assessed to achieve the right performance. Any adopted manufacturing actions (from the decision made) as discussed in the model is sufficient to integrate convenient and affordable technologies into product production. Otherwise, possible and reliable outsourcing window or collaboration is recommended. It is assumed that this concept if properly harnessed, would reduce the number of moribund industries, establish industrial linkages between the SMEs and rejuvenate the already failed ones. Further works could be integrated to embrace manufacturing dynamism and production optimization in a varying world market. Also, key performance indicators for assessing SMEs activities could be incorporated.

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REFERENCES [1] K. Cheng, R.J. Bateman, E-Manufacturing: characteristics, applications and potentials, Prog. Natural Sci. 18 (2008) 1323–1328. [2] J. Qin, Y. Liu, R. Grosvenor, A categorical framework of manufacturing for Industry 4.0 and beyond, Procedia CIRP 52 (2016) 173–178. [3] J.R. Ocampo, J.C. Hernández-Matías, A. Vizán, A method for estimating the influence of advanced manufacturing tools on the manufacturing competitiveness of Maquiladoras in the apparel industry in Central America, Comp. Ind. 87 (2017) 31–51. [4] S. Singh, E.U. Olugu, S.T. Musa, Development of sustainable manufacturing performance evaluation expert system for small and medium enterprises, 13th Global Conference on Sustainable Manufacturing—Decoupling growth from resource use, Procedia CIRP 40 (2016) 608–613. [5] A. Bhattacharya, W. Cheffi, P.K. Dey, Recent advances in manufacturing operations management, J. Manufac. Technol. Manage. 27 (1) (2016)doi: 10.1108/JMTM-12-2015-0109. [6] C.T. Leondes, Control and Dynamic Systems Advances in Theory and Applications, Academic press, United States. (Chapter 1), 1992. [7] M.A. Noor, R. Rabiser, P. Grunbacher, Agile product line planning: A collaborative approach and a case study, J. Sys. Soft. 81 (2008) 868–882. [8] S. Scholz, T. Mueller, M. Plasch, H. Limbeck, R. Adamietz, T. Iseringhausen, D. Kimmig, M. Dickerhof, C.Woegerer, A modular flexible scalable and reconfigurable system for manufacturing of microsystems based on additive manufacturing and e-printing, Rob. Comp. Int. Manu. 40 (2016) 14–23. [9] N. Costantino, M. Dotoli, M. Falagario, M.P. Fanti, A.M. Mangini, A model for supply management of agile manufacturing supply chains, Int. J. Prod. Econ. 135 (2012) 451–457. [10] Y.Y.Yusuf, M. Sarhadi, A. Gunasekaran, Agile manufacturing: The drivers, concepts and attributes, Int. J. Prod. Econ. 62 (1999) 33–43. [11] B.H. Jared, M.A. Aguilo, L.L. Beghini, B.L. Boyce, B.W. Clark, A. Cook, B.J. Kaehr, J. Robbins, Additive manufacturing: toward holistic design, Scripta Materialia 135 (2017) 141–147. [12] B. Kianian, S. Tavassoli, T.C. Larsson, O. Diegel, The adoption of additive manufacturing technology in Sweden, Procedia CIRP 40 (2016) 7–12. [13] Y. Lin, T. Chen, A ubiquitous manufacturing network system, Rob. Comp. Int. Manu. 45 (2017) 157– 167. [14] E. Adrian, M. Coronado, M. Sarhadi, C. Millar, Defining a frame work for information systems requirements for agile manufacturing, Int. J. Prod. Econ. 75 (2002) 57–68. [15] M. Dios, J.M. Framinan, A review and classification of computer-based manufacturing scheduling tools, Comp. Ind. Eng. 99 (2016) 229–249. [16] P.J. Bartolo, I. Gibson, History of stereolithographic processes, Stereolithography: Materials, Processes and Applications, Springer Science and Business Media, New York, (2011). [17] T.A. Odetayo, A.R. Onaolapo, Socio-economic characteristics of small scale enterprises and microfinance bank officials in Osun State, Nigeria, Am. J. Bus. Manage. 5 (1) (2016) 31–40. [18] A. Moeuf, S. Tamayo, S. Lamouri, R. Pellerin, A. Lelievre, Strengths and weaknesses of small and medium sized enterprises regarding the implementation of lean manufacturing, IFAC-PapersOnLine 49-12 (2016) 071–076. [19] V.T. Le, H. Paris, G. Mandil, D. Brissaud, A direct material reuse approach based on additive and subtractive manufacturing technologies for manufacture of parts from existing components, Procedia CIRP – The 24th CIRP Conference on Life Cycle Engineering Vol. 61 (2017) 229–234. [20] K. Jackson, K. Efthymiou, J. Borton, Digital manufacturing and flexible assembly technologies for reconfigurable aerospace production systems, Procedia CIRP 52 (2016) 274–279. [21] A. Gunasekaran, Design and implementation of agile manufacturing systems, Int. J. Prod. Econ.Vol. 62 (1999) 1–6. [22] R. Singh, S. Singh, Additive manufacturing: an overview, Mater. Sci. Mater Eng. (2017)doi: 10.1016/ B978-0-12-803581-8.04165-5. [23] H. Gaub, Customisation of mass-produced parts by combining injection molding and additive manufacturing with industry 4.0 technologies, Rein. Plastics 60 (2016) 401–404. [24] P. Spalt, T. Bauernhansl, A framework for integration of additive manufacturing technologies in production networks, Procedia CIRP Vol. 57 (2016) 716–721.

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[25] F.A. Abdulmalek, J. Rajgopal, Analysing the benefits of lean manufacturing and value stream mapping via simulation: a process sector case study, Int. J. Prod. Econ. 107 (2007) 223–236. [26] J. Prince, J.M. Kay, Combining lean and agile characteristics: creation of virtual groups by enhanced production flow analysis, Int. J. Prod. Econ. 85 (2003) 305–318. [27] Q.V. Damirchi, G. Rahimi, Design a conceptual ERP model for small and medium enterprises of Iran, Inter. J. Contem. Res. Bus. 3 (5) (2011) 850–860. [28] M. Alyahya, M.A. Suhaimi, A conceptual model for business and information technology strategic alignment from the perspective of small and medium enterprises, Int. J. Bus. Hum. Tech. 3 (7) (2013) 83–90. [29] O.U. Asikhia, M.J.V. Rensburg, SMEs wealth creation model: a conceptual framework, Afr. J. Hosp. Tour. Leis. 4 (1) (2015) 1–19. [30] S. Husband, P. Mandal, A conceptual model for quality integrated management in small and medium size enterprises, Int. J. Quality Relia. Manage. 16 (7) (1999) 699–713. [31] S. Banerjee, P. Dasgupta, Branding in small and medium enterprises: a conceptual model to manage branding initiatives, 11th International Conference of the Society for Global Business & Economic Development, May 2009, At Bratislava, Slovak Republic, 2009. [32] S.Yan, A theoretical framework of competitive advantage for SMEs in china under new normal economy, Euro. Sci. J. 11 (34) (2015) 1–12.

CHAPTER 7

Applications of Computational Methods in Manufacturing Processes Mohit Pant, Sahil Garg Department of Mechanical Engineering, National Insitute of Technology Hamirpur, Hamirpur, India

7.1 INTRODUCTION The birth of industrial revolution introduced a new sphere of knowledge to mankind in the field of manufacturing processes and metallurgical sciences. Earlier the failures in engineering components were mitigated by the use of better materials but little was focused on defects caused during manufacturing processes. Advanced materials such as functionally graded materials (FGMs) employ sophisticated manufacturing techniques [1,2] for their manufacturing.These manufacturing processes employ advanced machinery and skilled engineers. Henceforth, the cost associated with these processes is high. Owning to high costs the margin of failure of the processes being very lean and here the computational methods come into play. A generalized visualization of manufacturing process can be seen in Fig. 7.1. The areas of reserach, analysis, design, prototype, and testing attract vast costs. Hence, it is imperative to use computational methods rather than investing time and money in experimentation. A literature survey of application of computational techniques reveals that most of the researchers have used finite element method (FEM) based software for the analysis of various aspects of manufacturing processes. FEM has been most widely used in maching with the help of Johnson–Cook (J–C) model [4] to simulate the effect of machining parameters. In welding, the analysis of heat transfer and joint strength has been achieved using computational tools [5]. FEM applications to metal forming have also proven to be effective [6,7]. Although, researchers have not shirked from elaborating the disadvantages of FEM packages in their inability to handle large deformations, costly remeshing, volumetric locking, and inaccuracy in modeling oscillations in stresses. Meshfree methods (MMs) have answered the call to successfully remove all kinds of difficulties associated with FE (Finite Element) adaptation of manufacturing processes. It would not be wrong to say that MMs are future of design and analysis in the field of engineering. MMs such as smooth particle hydrodynamics (SPH) [8], element free Galerkin method (EFGM) [9], reproducing kernel particle method (RKPM) [10], meshless local Petrov–Galerkin (MLPG) [11], and extended finite element method (XFEM) [12] Advanced Applications in Manufacturing Engineering. http://dx.doi.org/10.1016/B978-0-08-102414-0.00007-0 Copyright © 2019 Elsevier Ltd. All rights reserved.

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Figure 7.1  Visualization of manufacturing process. (https://www.megapixl.com/manufacturingprocess-illustration-15811926 [3]).

provide better workability over mesh-based methods of analysis by eliminating the need of remeshing and redistribution of nodal arrangements [13]. EFGM scores over other meshless methods of fracture analysis by eliminating the need of remeshing and redistribution of nodal arrangements. EFGM also provides higher rates of convergence, higher adaptivity and can handle large material distortions easily. MLPG is advantageous to other meshless techniques as it requires no shadow elements like EFG and no special procedure for integration is needed. XFEM uses local partition of unity (PU) based extrinsic enrichment for modeling discontinuities. The nodes are enriched using additional functions based on the backgroud of problem. These techniques provide a comprehensive reduction in cost and engineering effort which would otherwise be highly inefficienct owning to cost of manufacturing process [14–16]. Motivated by applications and futuristic advantages of computational tools to manufacturing industry present work is an attempt to comprehend and prevent catastrophic stresses in structures arising due to practical causes, for example, heat generation during machining, flow analysis in mold, residual thermal stresses during solidification of the cast, heat affected zone in welding, and bulk deformations in forming. The contribution of this work will provide a platform to analyze and imitate manufacturing problems in various entities using meshfree techniques.The work will successfully highlight the advatages of MMs over FEM and will pitch the advantage of use of computational tools over experimental analysis. The work is organized as follows: Section 7.2 provides basic mathematical formulations to prominent MMs, Section 7.3 is organised in separate reviews of use of computational tools in machining, welding, casting, and forming. In the end, few problems

Applications of Computational Methods in Manufacturing Processes

are discussed to demonstrate the capability of meshless techniques, Section 7.4 discusses numerical examples to demonstrate the applicability of MMs and finally the work is concluded with conclusion in Section 7.5 discussing the benifits of MMs to a particular manufacturing process.

7.2  MESHFREE METHODS 7.2.1  Principle of MMs A generalized layout of a MM can be described as discretization of a domain into a number of data points to obtain the unknown without the need of defining the connectivity between data points. These set of data points called as nodes can be scattered randomly within the domain and these data points also define the boundary of the domain. The integration of these data points is achieved by using Gaussian quadrature scheme.

7.2.2  Basic approximations and procedure The basic steps in Meshfree formulation are same as FEM except for the formation of shape function and imposition of boundary conditions. The basic approximations for a field variable u in any boundary value problem can be written as: n



u h ( x ) = ∑∅i ( X ) ui = ∅T ( X ) U s

(7.1)

i =1

where ∅i are the shape functions and the ui’s are the nodal values at particle i located at position xi and n is the set of nodes included in the local support of domain for which ∅i ( X ) ≠ 0 . Us is the vector that collects all the field variables at these nodes. Note, that the above form is identical to an FEM approximation. However, in contrast to FEM, the shape functions in Eq. (7.1) are only approximants and not interpolants, since ui ≠ u ( X i ) .

7.2.3  Classification of MMs There are a number of versions of MMs developed so far and since this is in development stage, some new ones will continue to appear in the future. According to the approaches to arrive at the discrete governing equations, they largely fall into three categories. The first category is the MMs based on strong form formulation, second is based on weak form formulation, and the last one is mixed of both, for example, based on strong–weak form formulation as shown in Fig. 7.2. 7.2.3.1  Based on strong form formulation Many problems in engineering are modeled using partial differential equations (PDEs). The set of PDEs describing such problems is often referred to as the strong form of the problem.

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Figure 7.2  Classifications according to formulation.

7.2.3.1.1  Smooth particle hydrodynamics

Gingold and Monaghan [8] are credited for developing SPH method, the oldest MM, uses a kernel approximation for a single function u(x) in a domain Ω by

u h ( x ) = ∫ w ( x − y, h ) u ( y ) d Ω y

(7.2)

Where uh(x) is the approximation, w(x − y, h) a kernel or weight function, and h a measure of the size of the support. The discrete form was obtained by numerical quadrature of the right-hand side in the following type:

u h ( x ) = ∑w ( x − x I ) uI ∆VI = ∑φI ( x ) uI I

(7.3)

I

where ∆VI is the volume, for (3D), or area, for 2D, or length, for 1D, associated with node I, and φI(x) = w(x − xI) ∆VI the SPH shape function of the approximation. 7.2.3.1.2  Reproducing kernel particle method

The RKPM employed by Gosz and Liu [17] is an improvement of the continuous SPH approximation.To increase the order of completeness of the approximation, a correction function c(x, x − y) is introduced into the approximation:

( x ) = ∫ c ( x, x − y ) φα ( x − y ) u ( y ) dΩy

(7.4)

The correction function is obtained by imposing the reproducing conditions, that is, the reproducing equation should exactly reproduce polynomials and can be expressed by a linear combination of polynomial basis functions; α is the dilation parameter of the kernel function φα(x − y).

Applications of Computational Methods in Manufacturing Processes

7.2.3.1.3  Collocation method

In this method, the strong form description of the governing equation and the boundary conditions are used and discretized by collocation techniques as presented by Zhu and Atluri [18]. Consider a set of n nodes in a domain Ω and boundary Γ. The approximation of field variable is given by Eq. (7.1) and any of the shape function can be used.The discrete equations are obtained by enforcing the equations on the set of nodes in the domain but do not include the boundary nodes. The equations can be written as

Lu h ( x I ) = f ( x I ) , I ε Ω − Γ

(7.5)



u (xI ) = u (xI ) , I ε Γ

(7.6)

The above is a set of algebraic equations in the unknowns uI. u represents the prescribed nodal displacement on boundary. There are two major advantages served by the collocation method, namely (1) the final system of equation constructed is more efficient due to elimination of need of integration and (2) calculation of shape functions is constrained to nodal points only in contrast to integration points in other methods.Talking about disadvantages that come along is the use of higher order equations for shape functions which puts load on the solver also the omission of Kronecker delta property causes difficulties in imposing boundary conditions and the stiffness matrix is sometimes nonsymmetric. 7.2.3.2  Based on weak form formulation A weak form is a weighted-integral statement of a differential equation; in which the differentiation is distributed among the dependent variable and the weight function, and it includes the natural boundary conditions of the problem. 7.2.3.2.1  Element free Galerkin method

One can always write the weighted-integral form of a differential equation, whether the equation is linear or nonlinear. The weak form can be developed if the equations are second order or higher. The method of weighted residuals can be used to approximate the weighted-integral form of any equation. If the trail and test functions are same then the method is better known as EFGM [9]. 7.2.3.2.2  Meshfree Petrov–Galerkin method

The trail and test functions in Galerkin methods are given by: N

N

I =1

I =1

u h ( x ) = ∑∅I ( x ) uI ; δu h ( x ) = ∑Ψ I ( x ) δuI

(7.7)

If different shape functions are used for the approximation of the test and trial functions, that is, ∅I ≠ Ψ I , then a Meshfree Petrov–Galerkin method [11], is obtained. The

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advantage over EFGM is that it does not require any background cells for numerical integration. Also no special integration scheme is needed to evaluate the boundary and volume integrals. 7.2.3.2.3  Point interpolation method

The radial basis point interpolation [19], form is written as:



n

m

i =1

j =1

u h ( x ) = ∑Bi (r ) ai + ∑p j ( x ) b j

(7.8a)

with the constraint condition n



∑p i =1

ij

( x ) ai = 0, j = 1tom

(7.8b)

where Bi(r) is the radial basis functions, n is the number of nodes in the neighborhood of x, pj(x) is monomials in the space coordinates xT = (x, y), m is the number of polynomial basis functions, coefficients ai and bi are interpolation constants. In the radial basis function Bi(r), the variable is only the distance, r, between the interpolation point x and a node xi. 7.2.3.3  Based on weak–strong form formulation The key idea of the Meshfree weak–strong method is that in establishing the discretized system equations; both the strong form and the local weak form are used for the same problem, but for different groups of nodes that carry different types of equations/conditions. The local weak form is used for all the nodes that are on or near boundaries with derivative boundary conditions. The strong form is used for all the other nodes.

7.3  APPLICATION OF COMPUTATIONAL METHODS TO MANUFACTURING PROCESSES The previous section nicely explains the brief of mathematical formulation of various MMs. This section is divided into four major categories. Application of advanced computational techniques to four major sections of manufacturing sectors has been identified. The first section is dedicated to simulation and analysis of machining process and study of cutting tool. In the next subsection the analysis of welding with respect to mechanical properties of joints and heat transfer analysis is presented. The third subsection indentifies the prowess of computational tools in understanding the application of fluid flow analysis to improve the casting operation. Finally this section concludes with the advantages provided by meshfree tools to handle large deformations that take place in forming process.

Applications of Computational Methods in Manufacturing Processes

7.3.1 Machining The concept of machining involves many processes like turning, milling, grinding etc. A vast literature on analysis of cutting forces is available which has helped researchers develop computational tools to mirror and analyze the machining process. The advantage of computational tools is that the user can define mechanical properties of tool and workpiece easily and can obtain the optimum parameters without actual experimental process. Table 7.1 provides information about the work carried by researchers by employing various computational tools to analyze machining processes. 7.3.1.1  Application of MMs to cutting tool analysis One of the most researched causes of machine tool failure is the wear of the tool due to excessive heat generation. These looses are mitigated by use of coolant and heat resistant alloy coating for tools. These advanced coating materials have gradually varying properties along the volume resulting in amalgamation of ideal properties of two or more materials(e.g., toughness of metal and refractoriness of ceramic) in one and are called as FGMs. Japan is recognized for bringing the FGM concept to picture [33] and, since then the application of FGMs is numerous [34–38]. The machining of heat resistant super alloys can be achieved effectively by using FGM coated inserts to evade excessive heat generation and cut the costs of coolant consumption.The analysis of thermal stresses to avoid consequential failures is important for achieving desired productivity. The work presented aims to provide a generalized model for analysis of cracks in FGMs under thermoelastic forces generated during machining process. EFGM and XFEM have been employed to perform the analysis. The variation of temperature in a FGM domain causes displacement which consequentially produces thermal stresses.The effect of gradation of mechanical properties on thermal stress intensity factor (SIF) is studied. Modified thermal interaction integral is used to calculate the mixed mode SIFs. The manufacturing process employed to achieve the desired properties in FGMs is considerably high; hence it is of paramount importance to study the risk of failures involved with FGMs during manufacturing or machining. FGMs or composites can be used to manufacture machine tools and during high-speed machining process the generation of thermal stresses can cause rapid wear in tool material. El-Wardany et al. [39] used finite element analysis to study the factors influencing the temperature distribution in composite materials used for making tools.Wang et al. [40] studies the tool flank wear using a thermal model based on Green’s function in conjugation with microstructurebased method using orthogonal hard turning. Cho and Park [41] employed finite element analysis of thermoelastic characteristics of machine tools made of FGMs. Yadav et al. [42] used finite element model to study failures such as micro cracks developed due to thermal stresses by estimating the temperature field and thermal stresses due to Gaussian distributed heat flux of a spark during EDM. The use of simulation techniques

197

198

Source

Work done

Liu and Guo Residual stresses gener[20] ated due to variation of sequential cuts and tool chip friction in a machined layer were analyzed

Theory/problem

Method/algorithm used

• Plain strain conditions. Thermoelastic• Semiinfinite elements at the plastic coupling bottom of the workpiece FEM model • Large deformation is produced during chip formation and to control this a four noded bilinear element is used for creation of workpiece conjugated with hour-glass control algorithm to reduce integration burden Ee et al. [21] Prediction of residual • Improvisation of Johnson– Thermoelasticstresses Cook Model to represent the plastic coupling material as non-Newtonian FEM model fluid • Remeshing technique • Simulating thermomechanical effect Outeiro The work deals with • J–C model Elastic-viscoplastic et al. [22] investigation of machine • The cutting parameters under FEM surface draped with reorthogonal conditions were sidual stresses under the studies using coated (ISO P05effects of tool geometry, P25) tungsten carbide tools and cutting parameters, and uncoated (ISO M10-M30) coating materials in case • Workpiece modeled with 3000 of AISI 316L steel isoparametric quadrilateral elements and tool is modeled as rigid and discretized into 1000 elements

Remark

The simulation helped in establishing optimized parameters for second cut

Hydrostatic stress component computed using FEM shows high improvement in accuracy

Uncut chip thickness has the strongest influence on residual stresses

Advanced Applications in Manufacturing Engineering

Table 7.1  Summary of research papers dealing with machining

Source

Work done

Theory/problem

Method/algorithm used

• Material properties are modeled at nodes • Use of kernel approximation for numerical integration

SPH

Umbrello et al. [4]

Analyze effect of selection of J–C constitutive equation material constants in FE modeling

FEM

Kortabarria et al. [23]

To predict residual stresses in Inconel 718

Madaj and Píška [24]

A2024-T351 aluminum alloy experiencing Orthogonal cutting is simulated

• Five sets of material constants used in J–C model • Orthogonal cutting of AISI 316L • Model based on constant shear hypothesis for FE code • The work takes place in two steps: • Firstly, to obtain the temperature, stress and strain fields Deform 3D v10.2 nose turning model is used. After that the values of these quantities are employed in a machining model running on multi revolution Abaqus/ Standard v6.12 • The saw-toothed chip formation in framework of Johnson–Cook failure parameters D1–D5 and SPH density was investigated • Workpiece was modeled using SPH and tool was modeled using FEM

SPH successfully mirrored the machining process and predicted the machining forces with approximately 10% and 30% errors in normal and tangential components respectively The results obtained validate the accuracy of FEM model

FEM

The multirevolution model proposed performed better than nose turning models

SPH

SPH was able to predict chip shape and cutting forces with accuracy

Applications of Computational Methods in Manufacturing Processes

Limido et al. Simulation of orthogonal [15] cutting to predict cutting forces

Remark

(Continued ) 199

200

Source

Work done

Uhlmann et al. [25]

Meshfree method used for • FPM cutting simulation based cutting simulation citing on J–C model parameters the problems faced by FEM simulation

Ali et al. [26]

Stenberg and Proudian [27] Rüttimann et al. [28]

Theory/problem

Method/algorithm used

FPM

Remark

The simulation of unbroken and broken chips is done by the use of the Johnson–Cook material law without and with ductile damage. FPM is computationally efficient as it can exploit advantages of multi-core system compared to FEM To simulate milling process • The total number of elements FEM FEM can predict the feed cutting and estimate the cututilized is approximately about force and surface roughness in ting force and surface 11,286. The number of nodes is good concurrence with the roughness with variation 11,512 and the total number of experimental results. of feed rate in case of variables in the model is about FEM can lead to reduced matitanium alloy 23,026. The mesh of workpiece chining time and manufacturused to be approximately the ing cost as well. This is because element size of 0.05 lm and the accuracy of both values cutting tool geometry was of the cutting force for the meshed with the minimum experimental and predicted element size of 0.2 µm model was about 97% To obtain residual stresses • DEFORM was used since Thermomechanical Difficulty in extracting residual using FE simulation in it has the ability to provide FEM stresses due to remeshing turning using DEremeshing problem associated with FEM FORM Simulation of cutting • SPH implementation in LSSPH SPH method was able to reprobehavior of single DYNA with a total lagrangian duce stagnant zones in agreehexaoctahedral diamond formulation ment to results reported in cutting grains • Node to surface contact search literature and observed qualitaalgorithm to establish contact tively in the experiment between surface and grains

Advanced Applications in Manufacturing Engineering

Table 7.1  Summary of research papers dealing with machining (cont.)

Zhang et al. [29]

Understanding selection of • Three metal cutting models J–C model parameters namely: Lagrangian, ALE, and CEL

Boldyrev et al. [30]

Investigate material model • Hexahedral elements with FEM parameters and failure uniform meshing criteria on cutting forces • FEM with Langrangian aparising in the cutting proach, element erosion, and process of 6061-T6 deletion with failure strain 0.5 aluminum 2D and 3D chip forma• Basic and normal set up for FEM tion simulations using 2D simulation with three times simufact forming bigger elements for basic setup • For 3D simulations hexahedral and tetrahedral meshes were used

Kahwash et al. [32]

FEM

Simulation of orthogonal • Newton–Raphson method to EFGM cutting process of unidisolve nonlinear system of equarectional composites tions • Penalty approach • MLS approximation

Remark

The simulation results predict that the best set of Johnson– Cook model parameters is not unique for the three numerical models of metal cutting because the accuracy of these models depend not only on constitutive model but also on chip separation phenomena Johnson–Cook constitutive model and failure criteria are able to give more accurate results than kinematic and isotropic hardening 2D simulations reflect experimental results within 20% deviation. 3D simulations with hexahedral mesh yield similar results but tend to overestimate the cutting forces. 3D simulations with tetrahedral mesh do not yield relevant results Model was able to predict the relation between cutting forces, fiber orientation and rake angle

ALE, Arbitrary Eulerian–Lagrangian; CEL, couple Lagrangian–Eulerian; EFGM, element free Galerkin method; FE; FEM, finite element method; FPM, finite pointset method; J–C, Johnson–Cook; MLS, moving least square; SPH, smooth particle hydrodynamics.

Applications of Computational Methods in Manufacturing Processes

Work done

Segebade et al. [31]

Theory/problem

Method/algorithm used

Source

201

202

Advanced Applications in Manufacturing Engineering

has been employed in manufacturing analysis as well [43]. Gurgel et al. [44] used EFGM to find optimal machining parameters. Limido et al. [15] applied SPH a type of MM to machining process as it can handle large-scale deformations easily as compared to meshbased methods. Bhemuni et al. [45] used computer aided engineering based simulation to simulate hard turning operation for analyzing various machining parameters.Very few literature works are dedicated to analysis of fracture in cutting tools using computational tools. Present study is an effort to extend the domain of meshfree tools to analysis fracture behavior of coatings used for cutting tool applications. In the area of fracture mechanics steady state analysis of thermoelastic problems in cracked structures was performed by dell’Erba and Aliabadi [46] using dual boundary element method (BEM). Static fracture analysis of FGMs was presented by Marur and Tippur[47]. Rao and Rahman [48] presented two new methods for calculation of SIFs for FGMs under mechanical loading using EFGM. Zhao et al. [49] investigated the thermal resistance of functionally graded ceramic tool materials made of Al2O3-TiC and Al2O3-(W, Ti)C. Thermal interaction integral for FGMs was presented by Amit and Kim [50] and they used FEM for analysis of results. BEM was employed by Ekhlakov et al. [51] for the analysis of thermoelastic fracture of FGMs under thermal shock. Zhang and Ma [52] used numerical manifold method to analyze fracture in isotropic FGMs. The XFEM has been widely used for thermoelastic fracture analyzes of FGMs [53–56]. Bayesteh et al. [57] employed extended iso-geometric analysis (XIGA) recently for analysis of thermoelastic fracture of FGMs. Bhardwaj et al. [58] used nonuniform rational basis spline (NURBS) based XIGA for simulation of cracked FGM plates by first order shear deformation theory under a variety of loading and boundary conditions.

7.3.2 Welding The process of welding is characterized by fusion of materials thereby providing a joint between two structures.Welding technique encompasses high power to melt the materials and the joint thus achieved has to provide sound mechanical properties to consider it workable. Computational aspects of welding date back to 1982 [59] when researchers studied a simple arc welding process using previous experimental data. Computational tools such as computational fluid dynamics analysis can provide simulation of dissimilar metal welding process by simulating the mixing of two materials. Furthermore, the mechanical properties of joint can be mapped into modeling tools including EFGM and their strength, fracture toughness etc. can be estimated. Table 7.2 shows information about the work carried by researchers by employing various computational tools to analyze welding processes.

7.3.3 Casting One of the oldest manufacturing processes can be dated back to ancient times till now is casting. The applications of casting range from automotive components, spacecraft

Table 7.2  Summary of research papers dealing with welding Source

Work done

Alfaro et al. [60]

Simulation of welding procedure

Wang et al. [62]

Simulation of explosive welding

Shibahara and Atluri [63]

Heat conduction analysis of weld bead

Theory/problem

Method/ algorithm used

Remark

Friction stir welding

• The coupling of rigid NEM The method provides the ability plastic material with heat to track data points throughtransfer mechanism was out the simulation achieved by block iterative semiimplicit method, together with a fixed point algorithm to treat the nonlinear coupling Friction • Elastic-plastic deformation Particle method The model is used to predict stir spot model to calculate plastic with the the effect of plastic flow in welding flow movingrelation to pin geometry. The • Various pin geometries to particle triangular pin has enhanced test the model are considsemiimplicit material flow and cylindriered method cal pin causes material flow at periphery in upward direction Explosive • Burn fractions to simulate MPM Fluid and solid interaction is welding detonation of explosive solved with ease compared to FEM. Simulation of wavy interface was not achieved Generalized • The model is analyzed by MLPG The method was able to predict evaluating the effect of the changes in temperature support size and testing distribution with changes in function with 861 and material properties 1891 nodal points and it was established that a higher number of nodes mitigate the effect of MLPG parameters

Applications of Computational Methods in Manufacturing Processes

Hirasawa et al. Investigation of [61] plastic flow and material mixing w.r.t. tool geometry

Welding procedure

(Continued ) 203

204

Welding procedure

Source

Work done

Wang et al. [64]

Simulation of explosive welding of titanium and its alloys

Pan et al. [65]

The welding zone Friction stir entities namely welding grain size distribution, temperature distribution, material hardness, evolution, and texture affecting due to tool rotational, and translational speeds are studied Simulation of crack Generalized • 3D FEM using quadratic propagation of a tetrahedral finite elements surface crack in • 2D XFEM using Mk facweld tor • 3D Mk factor formulae

Tanaka et al. [66]

Explosive/ impact welding

Theory/problem

Method/ algorithm used

• The size of particle seSPH using lected for SPH simulation AUTODYN is considered to be 20 µm software to capture jetting particles and interface profiles • Free boundary is assumed for its ability to reflect back stress waves and produce effective simulations • Material is modeled as SPH non-Newtonian fluid and the elastic deformations are ignored • Interfacial modeling between tool and sheet along with sheet and backing plate is done

FEM and XFEM

Remark

The modeling adjustments made to SPH for impact welding process helped in understanding of wave formation and was able to simulate shear stress and plastic strain

The work has listed down several advantages of MMs like SPH over FEM or finite difference methods in the areas of tracking interfaces, material mixing and avoiding the costly remeshing procedure

The work takes the advantage of ability of computational methods to analyze cracks in a weld joint, thereby eliminating the need of expensive procedures like nondestructive testing

Advanced Applications in Manufacturing Engineering

Table 7.2  Summary of research papers dealing with welding (cont.)

Source

Work done

Welding procedure

Timesli et al. [67]

Simulation of material mixing

Friction stir welding

ReséndizFlores and SaucedoZendejo [68]

Xiao et al. [69]

• A new algorithm combining a time discretization, a space discretization, a homotopy transformation, a perturbation technique, and a continuation method Simulation of Generalized • Gaussian weight function moving heat • Modifications by addition source and deletion of particles based on the mutual spacing between particles and discontinuities Simulation of weld- Friction stir • Lagrangian thermomechaning procedure welding ically coupled 3D FEM to predict the model with a rigid viscoeffect of process plastic material behavior parameters on • The workpiece and temperature distool were meshed with tribution, strain tetrahedral elements with distribution, and 6000 and 10000 elements material flow respectively Simulation of FSW Friction stir • Penalty method to enforce process is done welding boundary conditions using MM be• Smoothening technique to cause heat transfer impart numerical stability is usually accompanied with the material flow in FSW, the meshless method, which can easily treat large deformation

Method/ algorithm used

Remark

High order The number of matrix trianimplicit algogulations are greatly reduced rithm based and the algorithm can solve on MLS apthe problems of nonlinearity proach without iterations FPM

FEM

Meshless particle method

FPM is advantageous since it does not need background numerical integration procedure as in FE or other MMs. Easier to impose boundary conditions than FEM Simulation model was able to assist and predict the experimental observations

The method consumes less time and is independent of particle distribution as compared to FEM

205

(Continued )

Applications of Computational Methods in Manufacturing Processes

Buffa et al. [5]

Theory/problem

206

Welding procedure

Source

Work done

Nassiri et al. [70]

Investigation of Impact CP-Ti & Cu110 welding impact welding parameters which are difficult to predict experimentally due to large deformations

Theory/problem

• To model flyer and base plate using SPH, the particles with the same size were used whereas an extremely fine mesh was defined at the interface in the ALE method to capture the wavy pattern

Method/ algorithm used

SPH and ALE

Remark

The methodology helped avoiding problems related to mesh tangling and distortion during large deformations. The ALE mesh can optimize according to moving boundary resulting in higher accuracy

ALE, Arbitrary Lagrangian–Eulerian; FE; FEM, finite element method; FPM, finite pointset method; FSW, friction stir welding process; MLPG, meshless local Petrov–Galerkin; MLS; MMs, meshfree methods; MPM, material point method; NEM, natural element method; SPH, smooth particle hydrodynamics; XFEM, extended finite element method.

Advanced Applications in Manufacturing Engineering

Table 7.2  Summary of research papers dealing with welding (cont.)

Applications of Computational Methods in Manufacturing Processes

components, and many industrial and domestic components. Various casting defects arise due to faulty mold design and improper cooling. The defects such as cracks, porosity arising from residual stresses, and escaping of trapped gases can be avoided with the help of computational tools. Meshfree techniques have huge impacts in the field of heat transfer analysis. Henceforth, modeling of solidification of molten material can be achieved and comprehended. Table 7.3 provides information about the work carried by researchers by employing various computational tools to analyze casting processes.

7.3.4 Forming The process of forming involves large deformation of materials by processes extrusion, rolling, forging etc. Forming is one area where the application of meshfree techniques can be highly beneficial. MM such as EFGM have a capability to handle large deformations with ease when compared to FEM. The application of high compressive stresses to shape the material can produce cracks. EFGM and XFEM have proved their mettle in analysis of fracture problems with extreme accuracy and reliability. Table 7.4 provides information about the work carried by researchers by employing various computational tools to analyze welding processes.

7.4  NUMERICAL IMPLEMENTATION This section is aimed at representing the capability and multitude of MMs.The problems selected for simulation will provide the researchers a glimpse of implementation of MMs to various manufacturing techniques. The coding of work employing EFGM, XFEM, and Coupled FE-EFG has been done on MATLAB.

7.4.1  Mathematical formulation for FGM Let us assume a two-dimensional domain with minor displacements, bounded by Γ (Fig. 7.3). To determine the mathematical modeling for FGM it is considered that the mechanical properties of the material such as modulus of elasticity E, thermal conductivity k, Poisson’s ratio v, and coefficient of thermal expansion β follow some gradation. Here, we can define the gradation as given by the following equations:

E = E ( x1 , x 2 ) = E ( x )

(7.9)



ν = ν ( x1 , x 2 ) = ν ( x )

(7.10)



k = k( x1 , x 2 ) = k( x )

(7.11)



β = β ( x1 , x 2 ) = β ( x )

(7.12)

207

Table 7.3  Summary of research papers dealing with casting

Source

Work done

Chan et al. [71]

A computational model To evaluate errors in to simulate the continuity and esflow and effect of tablish the pressure interfaces during the correction equafilling of 3D metaltion the continucasting molds ity equations are differentiated with respect to pressure Two realistic die models The die geometry undergoing HPDC and the gating are investigated using system were 3D simulations mainly analyzed to achieve efficient cast Extension of SPH Die design and filling modeling of HPDC process to both 3D and to realistic die geometries

Cleary et al. [72]

Cleary et al. [73]

Mirbagher et al. [74]

Problem type

Theory/problem

Method/ algorithm used

• VOP method • Van leer scheme representation of flow

CFD

• Eulerian techniques for modeling interfacial flows are the marker and VOF methods

SPH

• VOP methods.

SPH

The effects of back Major casting defects • MAC and SOLA-VOF under the pressure under such as mold erocategory of CFD modeling were the heat and mass sion, gas trapping, employed transfer outline are cold shut, and imincorporated in a purities involved computational model were considered in the work

Remark

The casting geometry is improved with the help of simulation results as it helped in analyzing the surface behavior and implementation of remedial procedures for wave of air voids The details of flow process along with the wave behavior at free surface are efficiently captured by SPH

3D isothermal modeling of two relatively simple real components demonstrate that die filling is far from a uniform front fill SOLA-VOF The work provided ways numerical to cut down the time algorithm, and cost in the casting utilizing process by improvethe FDM ments in casting design

Source

Work done

Cleary et al. [75]

A meshfree based The SPH based • Lagrangian simulation technique, computational model simulated solutions VOF method to analyze the flow were tested on through narrow mold filling of sevsections and sudden eral real automosharp bends to study tive components the fluid motion and splashing phenomena

Vertnik et al. Simulation of heat [76] transfer in directchill casting of aluminium alloys

Problem type

Theory/problem

Transient heat trans- • RBFs, BEM port in direct-chill • Continuum formulation is emcasting of aluminiployed to generate thermal field um alloys • Additional nodes are defined to govern the movement of block

Method/ algorithm used

SPH

Classical meshless Kansa method

Remark

Along with cost cutting and time saving application this work established the domain of SPH based simulations in HPDC process with lower rejection rate, reduced scrap and providing that lower thickness shell used can provide faster cycle times Addition and deletion of nodes near the boundary poses problems, as selection of nodes is difficult. The method is an improvement over the conventional Kansa method by reducing the number of equation for solution. Addition of nodes to define movement of block and overlapping influence areas represent the novelty of handling growth of computational domain (Continued )

Table 7.3  Summary of research papers dealing with casting (cont.)

Source

Work done

Zhang et al. [77]

A MM based algorithm Large-scale deforto mirror the thermation and crack mal stresses involved problems during the solidification of continuous casted billet

Problem type

Zhang et al. [78]

A square bland present in a mold during continuous casting is simulated and analyzed for solid shell growth

Cleary [79]

Simulation of low pressure die casting parameters

Theory/problem

• The work model employs integrated solution of casting procedure by combining the thermal and mechanical components involved in the process

Method/ algorithm used

Remark

FPM and the The mold involved in MLPG continuous casting based elasprocess may develop tic-plastic fracture due to evoluanalysis tions of stress and strain model during the solidification process Solidification • To achieve the simulation process FPM The simulation estabsimulation during following adjustments were made lished FPM as a potencontinuous casting I. Neumann boundary was treated tial tool for modeling The heat extracwith Onate stabilization scheme solidification process tion to solidify II. MLS approach to discretize shell is achieved by space water cooling III. For latent heat calculations enthalpy method is used. Predicting: Amount • Problems emerge when solidifiSPH Different flow behaviors of volumetric recation fronts merge and cut off controlling the rate duction of cooled regions of still liquid metal from of oxide formation. metal, path and the new metal supply Small-scale solidificaarea for formation • Lennard-Jones forces to model tion can also be modof oxide, rate of boundaries. eled compared to FE feed, solidification • Particle has its temperature drop tools front dynamics below the solidus distribution of • Modeling of solidification is done residual pressure in when temperature of the metal solidified mateand if it has more than two of its rial and of cavity neighbors being part of the “solid” defect formation metal phase, then its governing equation from Navier–Stokes changes to one more appropriate for solidification

Source

Work done

Problem type

Yi et al. [80]

Simulation of HPDC filling process

Modeling of the location of free surface and other interrelated phenomena

Kosec and Sarler [81]

Vertnik and Sarler [16]

Theory/problem

• VOP in each computational mesh. • Treatment of inlet particles was modeled differently than fluid particles. • Modeling inlet boundary by set of inlet virtual particles and inlet control particles. • Leap-frog algorithm to perform numerical integration Simulation of solidifica- Calculation of mac- • Solidification models are moving tion process rosegregation with interfaces with high gradients of mesosegragates in physical properties, strong couthe cast plings between the conservation equation Simulation of turbulent fluid flow and solidification in casting

Continuous casting process of steel

• Various boundary conditions at different parts of casting process • The mixture continuum model is used to treat the solidification system • Modeling of the mushy zone is done as a Darcy porous media with Kozeny–Karman permeability relation • The incompressible turbulent flow of the molten steel is described by the LRN k–ε turbulence model

Method/ algorithm used

SPH and FDM

Remark

Effective in dealing large deformation of free surface flow. SPH provides better results than FDM

LRBFCM

Effect of this stabilization tool on the results and show high sensitivity of the results on the selection of the upwind magnitude Meshless Comparison of results method with experimental and based on ANSYS based simularadial basis tion are well in agreefunction ment

(Continued )

Table 7.3  Summary of research papers dealing with casting (cont.)

Problem type

Source

Work done

Cao et al. [82]

Simulation based design Simulation of mold and optimization of filling process casting process

• Modification of link list algorithm SPH • Two types of particles are defined: Type-I placed on solid boundaries to exercise Lennard–Jones force on fluid particles and Type-II act as ghost particles fixed inside solid domain

Xu and Yu [83]

Fluctuation-free pressure and velocity field

• SPH exhibits the drawback of low SPH accuracy and large and random pressure oscillation

Flow problem

Theory/problem

Method/ algorithm used

Remark

The domain of influence of SPH particle is cut near the boundary causing loss in accuracy. The variation of filling rates of molds results, exhibits the ability of SPH numerical methodology Shear-thinning behavior of polymer melts is nicely displayed

ANSYS, Analysis systems; 3D,Three-dimensions; BEM, boundary element method; CFD, computational fluid dynamics; FDM, finite difference method; FE; FPM, finite pointset method; HPDC, high-pressure die casting; LRBFCM, local radial basis function collocation method; LRN, low-Reynolds number; MAC, marker and cell; MLPG, meshless local Petrov–­ Galerkin; MLS; MM, meshfree method; RBFs, radial basis functions; SOLA-VOF, solution algorithm-volume of fluid; SPH, smooth particle hydrodynamics; VOP, volume-of-fluid.

Table 7.4  Summary of research papers dealing with forming Method/ Algorithm used

Source

Work done

Forming process

Theory/Problem

Chen et al. [84]

Meshless method based simulation and analysis of metal forming process

Ring Compression test and upsetting

• Penalty approach in conjugation with Coulomb law of friction to model contact

RKPM

Lepadatu et al. Numerical prediction of [7] extrusion die wear

Extrusion die

• Archard model

FEM

Lu et al. [85]

Selective enrichment procedure based meshfree method is developed to capture post-buckling behavior and wrinkling in sheet metal forming

Plate/shell

• The particles to be enriched are selected on the basis of location rather than enriching the whole domain to capture the buckling mode and therefore postbuckling behavior

RKPM

Xiong et al. [86]

Simulation of bulk metal forming processes

Compression of cylinder and bars, backward extrusion

• RKPM in conjugation with flow formulation for slightly compressible rigidplastic materials

RKPM

Remark

Mesh distortion difficulty in the FE analysis is overcome by the usage of a smooth kernel function with flexibly adjustable support size Pressure distribution in the die is localized in the active part of the die with maximum value in the corner zones The method first of all is very time efficient owning to selective enrichment. Since higher order enrichment function can be used in user defined area smooth results are obtained which makes the stress-based buckling predictor more accurate and reliable The method is able to predict strain and flow rates better than FEM procedure (Continued )

Table 7.4  Summary of research papers dealing with forming (cont.) Method/ Algorithm used

Source

Work done

Forming process

Theory/Problem

Xiong et al. [87]

Steady and nonsteady state analysis of bulk forming processes

Flat rolling, compression of rods and heading of cylindrical billets

• Continuous evaluation of shape of workpiece is the main novelty of this work by calculation of true nodal velocity at every step

RKPM

Xiong and Martins [88]

Simulation of bulk metal forming processes

Heading and backward extrusion

RKPM

Guan et al. [89]

Massive metal forming process simulation

Meshless analysis techniques for metal forming processes with arbitrarily shaped die

• A new adaptive cell procedure to produce new background cells using optimal traingulation at end of each deformation step to overcome cell distortion • Volumetric strain is mapped using lower order equations to reduce load on solver • Contact or detachment of nodes from die is determined using normal nodal force or normal stress • Initial velocity field is generated using direct iteration and after that Newton–Raphson method is employed to calculate stiffness matrix

Rigid/viscoplastic EFGM

Remark

This advancement handles large deformation without the conventional loopholes in FE based algorithms, that is, mesh-breaking, locking etc. providing results that are in close consensus with both FE predictions and experimental data Results show that adaptive triangular background cells are capable of efficiently handling large plastic deformations without the need of remeshing The work is done to establish the method as replacement for rigid visco/plastic FEM owning to its advantages. The results obtained are in concurrence with literature

Method/ Algorithm used

Source

Work done

Forming process

Theory/Problem

Liu et al. [90]

Numerical simulation of extrusion processes

Plane-strain upsetting and backward extrusion

Hanoglu et al. [91]

Thermomechanical analysis of hot shape rolling of steel

Steel billet

Cui and Li [92]

Edge-based gradient smoothing technique for the simulation of metal forming processes

Backward extrusion, ring compression, upsetting analysis

• Modified penalty approach Coupled to eliminate volumetric FE-EFG locking • Elements with high distortion are converted to EFG data points whereas lower distortion elements are ceased as FE nodes • Bounding box algorithm based on maximum and minimum coordinate points of distorted elements to perform conversion • The temperature distribuRBFCM tion of steel slice in thermal model is used to calculate mechanical stresses via thermal strains • The new form of the billet is modeled by TFI and ENG at each deformation step to determine the node distribution • Workpieces are discretized Edge based using triangular elements smoothwhere the nodes of elements ened share the support domain FEM for integration purposes.The strain field required to perform the numerical integration is smoothened by the use of gradient smoothening technique over the whole domain

Remark

This method is different from standard FE-EFG practice as it uses conversion algorithm to impart stability to FE analysis

The displacement and temperature distribution at each node represent the solution. The problem is simulated by FEM using DEFORM and the results of MM are concurrent with FEM The method is flexible compared to FEM as the integration elements are able to share to support domains. The linear approximation function reduces the overall time consumed in integration process

(Continued )

Table 7.4  Summary of research papers dealing with forming (cont.) Method/ Algorithm used

Source

Work done

Forming process

Theory/Problem

Wang and Yuan [93]

Numerical simulation for coupled deformation between sheet metal and flexible-die

Sheet metal, flexible-die

• The elastoplastic deformation of sheet metal was analyzed with FEM and the bulk deformation of flexible-die was analyzed with EFGM

Coupled FE-EFG

Labergere et al. [94]

Fracture analysis in metal forming process

Blanking

FEM

Yuan et al. [95]

Simulation of viscous pressure forming

VPF

• Crack path detection by diffuse elements • Bezier curve smoothening to represent crack • Thermo-elasto-visco-plastic modeling • For integration a scheme based on elastic prediction plastic correction radial return is used • Element deletion to represent ductile crack. • The FEM procedure employs shell element • Penalty method used to describe contact and friction

Coupled FE-EFG

Remark

Simulation results are consistent with those attained by DEFORM-2D and experiments. It shows that the FEM-EFGM model and key techniques for frictional contact are effective and accurate Several schemes to enhance the computational efficiency of FE tool were demonstrated. The dependency on mesh size remain an issue which can be handled using MMs

FEM is sued for sheet metal deformation and EFG is used for visco-elastoplastic bulk deformation. Sheet deformation is vastly affected by viscosity. Coupled FE-EFG approach also helps in analyzing viscous pressure bulging

EFGM, Element free Galerkin method; ENG, elliptic node generation; FE; FE-EFG, finite element-element free Galerkin; FEM, finite element method; MM, meshfree method; RBFCM, radial basis function collocation method; RKPM, reproducing kernel particle method; TFI, transfinite interpolation; VPF, viscous pressure forming.

Applications of Computational Methods in Manufacturing Processes

Figure 7.3  Domain along with essential and natural boundary conditions.

The governing equilibrium equations [9] are given as:

∇.σ + b = 0 in Ω



(7.13)

The Neumann and Dirichilet boundary conditions are considered to be following:

u = u on Γu



σ .n = t



on Γt

(Essential)

(7.14)

(Natural)

(7.15)

where, σ is the stress tensor which is defined as σ = D( x )[ε − εT ], D(x) is the material property matrix, ε is the strain vector, εT = β∆T is the thermal strain vector, b is the body force vector, u is the displacement vector, t is the traction force, and n is the unit normal. 7.4.1.1  Computation of thermal interaction integral The path independent J-integral for a isotropic cracked body, is given as [48]



J=



∫ W δ

ij

Γ

− σ ij

∂ui   n j dΓ ∂x 1 

(7.16)

ε

where W = ∫ σ dε represents the strain energy density, σ stands for stress, and n is the 0

outward unit normal vector to an discretional curve surrounding the crack tip (Fig. 7.4). In problems concerning linear elastic material models, it is known that W = σ ijεij /2.The

Figure 7.4  Crack surrounded by an arbitrary path Γ of area A0.

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Advanced Applications in Manufacturing Engineering

contour integral in Eq. (7.16) can be transformed into an area integral using Green’s theorem. J=





∫ σ A

ij

 ∂q ∂ui dA − W δij  ∂x 1  ∂x j

(7.17)

where A is the area enclosed by the curve and q is a weight function picked in such a way that it has a value of unity at the crack tip, zero along the boundary of the domain, and random at other places. The nonequilibrium formulation for FGMs presented by Goli et al. [96] is very well derived and hence used in this work for computing the SIFs. The final form of M-integral can be written as: M=

∫ {(σ A

act aux ij i ,1

u

+ σ ijaux uiact,1 ) − (σ ikactε ikauxδ1 j } q j dA +

∫ {σ

A

u − c ijkl ,1εklact _ mεijaux + σ ijauxε ijact,1_ th } qdA

aux act ij , j i ,1

(7.18)

7.4.2  Results and discussions First problem is related to modeling of a layered material with a small crack. This problem will furnish the following contributions: • Establish the ability of MMs to model complex material properties • Analysis of machine tools with coatings can be achieved on similar terms as very high temperatures are generated during machining and internal defects near the tool edges may behave differently. • The analysis of defects or discontinuities in heat affected zone, weld zone of dissimilar materials can be done. • Thermomechanical analysis has been widely discussed in the literature. This example of thermal barrier coating (TBC) is based on entrapping high stress oscillation around the crack tip in thermomechanical environment which is difficult with FEM. The second example deals with elastoplastic deformation of a die. The problem will provide the following contributions: • Establish the ability of MMs in handling large deformations. • Provide a ground for future research to analyze forming processes using MMs. 7.4.2.1  TBC with single edge crack Advanced technological equipments operating under elevated temperatures namely turbocharger casing, machine tools, spacecraft parts, aero engines, ducting, and nozzles, etc require materials to provide amalgamation of desired mechanical properties such that the necessary refractoriness, strength, and toughness can be achieved. TBC is one such example of sophisticated class of materials called FGMs usually applied to metallic surfaces used for above-mentioned purposes. Applications of coatings to cutting tools have increased [97] globally. The increased demand of coated tools motivates the researchers

Applications of Computational Methods in Manufacturing Processes

Figure 7.5  A crack in a functionally graded thermal barrier coating.

to constantly improve the design and properties of FGM based coatings keeping in mind all possible causes of failures. Applications of functionally graded TBCs to inserts employed for high-speed machining [98,99] at elevated temperature along with corrosive environment make them highly susceptible for crack initiation. Study of thermal stresses developed due to manufacturing process [100] is important to avoid future failures of components. The problem selected here provides an insight about the prowess of EFGM and XFEM in handling crack present in a TBC. Fig. 7.5 shows a functionally graded TBC deposited on the bond coat and the metallic substrate. The FGM coating consist of 100% Zirconium-Yttria at X1 = 0 and 100% nickel-chromium-aluminum-Zirconium bond coat at X1 = W1.The metallic substrate is made up of nickel-based super-alloy.The material properties of different constituents of TBC are listed in Table 7.5. Fig. 7.5 shows the dimensions of the FGM based TBC domain selected for the problem along with the boundary conditions under thermal load. The initial conditions of the problem assumes the system under constant temperature (T0 = 1000°C). The problem considers top and bottom edge of the TBC domain to be constrained from exchanging heat from the surroundings. After the temperature difference is applied at the boundaries the system achieves a steady state with temperature T1 = 0.2T0 and T2 = 0.5T0 at left and right edges, respectively. The gradation of Young’s modulus (E), Table 7.5  Properties of TBC constituents Material property

Zirconia-Yttria (FGM)

Bond coat NiCrAlY

Metallic substrate (Ni)

E (GPa) v β (°C−1) k (W/mk)

27.6 0.25 10.01 × 10−6 1

137.9 0.27 15.16 × 10−6 25

175.8 0.25 13.91 × 10−6 7

FGM, Functionally graded materials.

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Advanced Applications in Manufacturing Engineering

Poisson’s ratio (v), and thermal expansion coefficient (β) for the FGM coating region (0