A technology maturity assessment framework for Industry 5.0 machine vision systems based on systematic literature review in automotive manufacturing

IF 7 2区 工程技术 Q1 ENGINEERING, INDUSTRIAL International Journal of Production Research Pub Date : 2023-10-17 DOI:10.1080/00207543.2023.2270588
Fotios K. Konstantinidis, Nikolaos Myrillas, Konstantinos A. Tsintotas, Spyridon G. Mouroutsos, Antonios Gasteratos
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The findings revealed that machine vision is utilised in each technological pillar of Industry 4.0, encompassing autonomous robots, augmented reality, predictive maintenance, additive manufacturing, and more. In analysing 22 vision-based applications in 47 automotive components, we clustered machine vision systems' architectural components and processing techniques, ranging from threshold-based methods to advanced reinforcement learning techniques suitable for the I5.0 environment. Leveraging the insights gathered, we propose the I5.0 technology maturity assessment framework for machine vision systems, evaluating nine functional components across five scaling technology levels. This framework serves as a valuable tool to identify weaknesses and opportunities for improvement, guiding machine vision integration into an intelligent factory.Keywords: Maturity assessmentmachine visionsystematic literatureautomotive manufacturingindustry 5.0zero defect manufacturing Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing not applicable – no new data generatedNotes1 https://fortune.com/fortune500/2021/.2 https://fortune.com/fortune500/2021/.3 https://bit.ly/ReviewedPapersAndAnalytics.Additional informationNotes on contributorsFotios K. KonstantinidisFotios Konstantinidis is a Team leader in Industry 5.0 & Smart Manufacturing at the Institute of Communication and Computer Systems (ICCS) of the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA) and holds a Ph.D. in Smart Manufacturing from the department of Production & Management Engineering at the Democritus University of Thrace (DUTh). He is currently leading a team of researchers and professionals with the objective of developing advanced industrial waste sorting systems. These systems utilize cutting-edge technologies such as hyperspectral & visual imaging, delta robots, air nozzles, X-ray sensors, and pretreatment units. Their focus areas include the efficient sorting of (bio)plastic waste, construction and demolition waste, metal scraps, mining characterization, and wood waste. Before this, Fotios worked as an I4.0 Technology Analyst, analysing the plants' maturity level and proposing I4.0 strategies for Fortune 500 companies. In contrast, he worked in the telecom industry at the Next-Generation Access networks. He has also organised workshops, delivered presentations at conferences/workshops, and published peer-reviewed journal papers throughout his career.Nikolaos MyrillasNikolaos Myrillas is a graduate of the Democritus University of Thrace. He holds a bachelor's degree in Production and Management Engineering. His research focuses on Industry 4.0 (I4.0) and advanced manufacturing technologies during the fourth industrial revolution. This was also the topic of his thesis, which was conducted as a final step of his studies. Nikolaos has worked in EYDAP S.A. - ATHENS WATER SUPPLY AND SEWERAGE COMPANY as an intern, where he gained exposure to the sustainable management practices of EYDAP through training on the exploitation of its renewable energy resource facilities. Nikolaos is not yet that experienced, but his love and passion for I4.0-related topics are guiding him.Konstantinos A. TsintotasKonstantinos Tsintotas (Senior Member, IEEE) received a bachelor's degree from the Department of Automation Engineering, Technological Education Institute of Chalkida (now National and Kapodistrian University of Athens), Psachna, Greece, in 2010, the master's degree in mechatronics from the Department of Electrical Engineering, Technological Education Institute of Western Macedonia (now University of Western Macedonia), Kila Kozanis, Greece, in 2015, and the Doctoral degree in robotics from the Department of Production and Management Engineering, Democritus University of Thrace, Xanthi, Greece, in 2021. He is currently a Postdoctoral researcher with the Laboratory of Robotics and Automation, Department of Production and Management Engineering, Democritus University of Thrace. His work is supported by several research projects funded by the European Commission and the Greek Government. His research interests include vision-based methods for modern and intelligent mechatronics systems. Details are available at: https://robotics.pme.duth.gr/ktsintotasSpyridon G. MouroutsosSpyridon Mouroutsos received the Diploma in Electrical Engineering from the Democritus University of Thrace, Greece (1981) and his Ph.D. in Systems Automation from the same University (1986). In 1986, he joined, as an Assistant Professor, the Electrical and Computer Engineering Department at the Democritus University of Thrace, Greece, where he currently serves as a Professor in Mechatronics, Systems Automation, and Standards. He has been a Referee, a Committee Member, or a Member of the Editorial Board for numerous International Scientific and Technical Journals and Conferences. Moreover, he has acted as an evaluator for National and EU research grant applications. His research interests include applications in Mechatronics, Systems Automation and Robotics, Intelligent and autonomous robots (humanoids, animated, underwater, flying, etc.), Data Fusion - sensors with applications in robotics and automation, Computer architectures - microprocessors and their applications and also Standards and CertificationAntonios GasteratosAntonios Gasteratos (Fellow member IET, Senior member IEEE) received the M.Eng. and Ph.D. degrees from the Department of Electrical and Computer Engineering, Democritus University of Thrace (DUTh), Xanthi, Greece, in 1994 and 1998, respectively. 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引用次数: 4

Abstract

AbstractWhen considering how an intelligent factory can ‘see,’ the answer lies in machine vision technology. To assess the current technological advancements of machine vision systems and propose a technology maturity assessment framework, a nine-phase Systematic Literature Review (SLR) strategy was implemented. As the automotive industry stands at the forefront of autonomous systems, we analysed 85 works across the entire automotive manufacturing life cycle. The findings revealed that machine vision is utilised in each technological pillar of Industry 4.0, encompassing autonomous robots, augmented reality, predictive maintenance, additive manufacturing, and more. In analysing 22 vision-based applications in 47 automotive components, we clustered machine vision systems' architectural components and processing techniques, ranging from threshold-based methods to advanced reinforcement learning techniques suitable for the I5.0 environment. Leveraging the insights gathered, we propose the I5.0 technology maturity assessment framework for machine vision systems, evaluating nine functional components across five scaling technology levels. This framework serves as a valuable tool to identify weaknesses and opportunities for improvement, guiding machine vision integration into an intelligent factory.Keywords: Maturity assessmentmachine visionsystematic literatureautomotive manufacturingindustry 5.0zero defect manufacturing Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing not applicable – no new data generatedNotes1 https://fortune.com/fortune500/2021/.2 https://fortune.com/fortune500/2021/.3 https://bit.ly/ReviewedPapersAndAnalytics.Additional informationNotes on contributorsFotios K. KonstantinidisFotios Konstantinidis is a Team leader in Industry 5.0 & Smart Manufacturing at the Institute of Communication and Computer Systems (ICCS) of the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA) and holds a Ph.D. in Smart Manufacturing from the department of Production & Management Engineering at the Democritus University of Thrace (DUTh). He is currently leading a team of researchers and professionals with the objective of developing advanced industrial waste sorting systems. These systems utilize cutting-edge technologies such as hyperspectral & visual imaging, delta robots, air nozzles, X-ray sensors, and pretreatment units. Their focus areas include the efficient sorting of (bio)plastic waste, construction and demolition waste, metal scraps, mining characterization, and wood waste. Before this, Fotios worked as an I4.0 Technology Analyst, analysing the plants' maturity level and proposing I4.0 strategies for Fortune 500 companies. In contrast, he worked in the telecom industry at the Next-Generation Access networks. He has also organised workshops, delivered presentations at conferences/workshops, and published peer-reviewed journal papers throughout his career.Nikolaos MyrillasNikolaos Myrillas is a graduate of the Democritus University of Thrace. He holds a bachelor's degree in Production and Management Engineering. His research focuses on Industry 4.0 (I4.0) and advanced manufacturing technologies during the fourth industrial revolution. This was also the topic of his thesis, which was conducted as a final step of his studies. Nikolaos has worked in EYDAP S.A. - ATHENS WATER SUPPLY AND SEWERAGE COMPANY as an intern, where he gained exposure to the sustainable management practices of EYDAP through training on the exploitation of its renewable energy resource facilities. Nikolaos is not yet that experienced, but his love and passion for I4.0-related topics are guiding him.Konstantinos A. TsintotasKonstantinos Tsintotas (Senior Member, IEEE) received a bachelor's degree from the Department of Automation Engineering, Technological Education Institute of Chalkida (now National and Kapodistrian University of Athens), Psachna, Greece, in 2010, the master's degree in mechatronics from the Department of Electrical Engineering, Technological Education Institute of Western Macedonia (now University of Western Macedonia), Kila Kozanis, Greece, in 2015, and the Doctoral degree in robotics from the Department of Production and Management Engineering, Democritus University of Thrace, Xanthi, Greece, in 2021. He is currently a Postdoctoral researcher with the Laboratory of Robotics and Automation, Department of Production and Management Engineering, Democritus University of Thrace. His work is supported by several research projects funded by the European Commission and the Greek Government. His research interests include vision-based methods for modern and intelligent mechatronics systems. Details are available at: https://robotics.pme.duth.gr/ktsintotasSpyridon G. MouroutsosSpyridon Mouroutsos received the Diploma in Electrical Engineering from the Democritus University of Thrace, Greece (1981) and his Ph.D. in Systems Automation from the same University (1986). In 1986, he joined, as an Assistant Professor, the Electrical and Computer Engineering Department at the Democritus University of Thrace, Greece, where he currently serves as a Professor in Mechatronics, Systems Automation, and Standards. He has been a Referee, a Committee Member, or a Member of the Editorial Board for numerous International Scientific and Technical Journals and Conferences. Moreover, he has acted as an evaluator for National and EU research grant applications. His research interests include applications in Mechatronics, Systems Automation and Robotics, Intelligent and autonomous robots (humanoids, animated, underwater, flying, etc.), Data Fusion - sensors with applications in robotics and automation, Computer architectures - microprocessors and their applications and also Standards and CertificationAntonios GasteratosAntonios Gasteratos (Fellow member IET, Senior member IEEE) received the M.Eng. and Ph.D. degrees from the Department of Electrical and Computer Engineering, Democritus University of Thrace (DUTh), Xanthi, Greece, in 1994 and 1998, respectively. From 1999 to 2000, he was a Visiting Researcher with the Laboratory of Integrated Advanced Robotics (LIRALab), DIST, University of Genoa, Genoa, Italy. He is currently a Professor and the Head of the Production and Management Engineering Department, DUTh. He is also the Director of the Laboratory of Robotics and Automation, DUTh, and teaches robotics, automatic control systems, electronics, mechatronics, and computer vision courses. He has authored more than 220 books, journals, and conference papers. His research interests include mechatronics and robot vision. Dr. Gasteratos is a Fellow member of IET. He has served as a reviewer for numerous scientific journals and international conferences. He is a Subject Editor of Electronics Letters and an Associate Editor of the International Journal of Optomechatronics. He has organised/co-organised several international conferences. More details about him are available at http://robotics.pme.duth.gr/antonis.
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基于系统文献综述的工业5.0机器视觉系统技术成熟度评估框架
当考虑智能工厂如何“看”时,答案在于机器视觉技术。为了评估当前机器视觉系统的技术进展并提出技术成熟度评估框架,实施了九阶段系统文献综述(SLR)策略。由于汽车行业处于自动驾驶系统的前沿,我们分析了整个汽车制造生命周期中的85项工作。研究结果显示,机器视觉在工业4.0的每个技术支柱中都得到了应用,包括自主机器人、增强现实、预测性维护、增材制造等。在分析47个汽车部件中的22个基于视觉的应用时,我们对机器视觉系统的架构组件和处理技术进行了聚类,从基于阈值的方法到适用于I5.0环境的高级强化学习技术。利用收集到的见解,我们提出了机器视觉系统的I5.0技术成熟度评估框架,评估了五个扩展技术级别的九个功能组件。这个框架是一个有价值的工具,可以识别弱点和改进机会,指导机器视觉集成到智能工厂中。关键词:成熟度评估机器视觉系统文献汽车制造业零缺陷制造披露声明作者未报告潜在利益冲突数据可用性声明数据共享不适用-没有新数据生成notes1 https://fortune.com/fortune500/2021/.2 https://fortune.com/fortune500/2021/.3 https://bit.ly/ReviewedPapersAndAnalytics.Additional信息贡献者说明fotios K. Konstantinidis fotios Konstantinidis是国立技术大学电气与计算机工程学院通信与计算机系统研究所(ICCS)的工业5.0和智能制造团队负责人在雅典(NTUA),并拥有博士学位,在德谟克利特大学(DUTh)生产与管理工程系智能制造。他目前领导着一个由研究人员和专业人员组成的团队,目标是开发先进的工业废物分类系统。这些系统采用了尖端技术,如高光谱和视觉成像、delta机器人、空气喷嘴、x射线传感器和预处理单元。他们的重点领域包括(生物)塑料废物、建筑和拆除废物、金属废料、采矿特性和木材废物的有效分类。在此之前,Fotios曾担任工业4.0技术分析师,分析工厂的成熟度水平,并为财富500强公司提出工业4.0战略。相反,他在电信行业的下一代接入网络工作。在他的职业生涯中,他还组织了研讨会,在会议/研讨会上发表演讲,并发表了同行评议的期刊论文。Nikolaos Myrillas毕业于色雷斯德谟克利特大学。他拥有生产和管理工程学士学位。主要研究方向为工业4.0和第四次工业革命时期的先进制造技术。这也是他论文的主题,这是他研究的最后一步。Nikolaos曾在雅典供水和污水处理公司(EYDAP S.A. - ATHENS WATER SUPPLY AND污水处理公司)实习,通过可再生能源设施开发方面的培训,他了解了EYDAP的可持续管理实践。Nikolaos还没有那么有经验,但他对4.0相关话题的热爱和热情正在引导着他。Konstantinos Tsintotas (IEEE高级会员),2010年毕业于希腊Psachna Chalkida技术教育学院(现为雅典国立和卡波迪斯特大学)自动化工程系学士学位,2015年毕业于希腊kla Kozanis西马其顿技术教育学院(现为西马其顿大学)电气工程系机电一体化硕士学位。并于2021年获得希腊色雷斯德谟克利特大学生产与管理工程系机器人博士学位。他目前是色雷斯德谟克利特大学生产与管理工程系机器人与自动化实验室的博士后研究员。他的工作得到了欧洲委员会和希腊政府资助的几个研究项目的支持。他的研究兴趣包括现代智能机电一体化系统的基于视觉的方法。详细信息请访问:https://robotics.pme.duth.gr/ktsintotasSpyridon。
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来源期刊
International Journal of Production Research
International Journal of Production Research 管理科学-工程:工业
CiteScore
19.20
自引率
14.10%
发文量
318
审稿时长
6.3 months
期刊介绍: The International Journal of Production Research (IJPR), published since 1961, is a well-established, highly successful and leading journal reporting manufacturing, production and operations management research. IJPR is published 24 times a year and includes papers on innovation management, design of products, manufacturing processes, production and logistics systems. Production economics, the essential behaviour of production resources and systems as well as the complex decision problems that arise in design, management and control of production and logistics systems are considered. IJPR is a journal for researchers and professors in mechanical engineering, industrial and systems engineering, operations research and management science, and business. It is also an informative reference for industrial managers looking to improve the efficiency and effectiveness of their production systems.
期刊最新文献
Deep learning and sequence mining for manufacturing process and sequence selection Low-carbon supply chain coordination through dual contracts considering pareto-efficiency Quantitative modelling approaches for lean manufacturing under uncertainty Managing inventory in customizable multi-echelon assembly systems Real-time vehicle relocation and staff rebalancing problem for electric and shared vehicle systems
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