Editorial: Zero defect manufacturing in the era of industry 4.0 for achieving sustainable and resilient manufacturing

Abstract

Companies are paying particular attention to product quality to ensure that all their customers are satisfied. Traditional quality improvement (QI) methods such as Lean Manufacturing (LM), Six Sigma (SS), Theory of Constraints (TOC), Total Quality Management (TQM), and Lean Six Sigma (L6S) are well-established production systems that have the goal of improving product quality without learning from defects, as they simply trace and remove them (Psarommatis et al., 2020b). Also, they do not take full advantage of recent & innovative data-driven technologies. Finally, the notion of prediction and its impact is absent from the core of those methods. Therefore, these methods can only provide a limited response and support to the new scenarios that need to be addressed to implement an effective digital and green transition.A recent approach, the so-called Zero Defect Manufacturing (ZDM), exploits I4.0 technologies and aims at addressing the limitations of the more traditional QI methods (Psarommatis et al., 2021). It builds on the ability to incorporate digital technologies such as AI, ML, or big industrial data into QI control loops which intelligently predict and prevent defects at both the product and process levels, ultimately increasing their autonomy. It enables comprehensive feedforward and feedback control loops to be implemented, and it leverages the ability to integrate Predictive Maintenance solutions, which in turn, contribute to jointly achieving resilient and sustainable objectives.ZDM is considered by both researchers and the industry as a viable replacement for the traditional QI methods (Psarommatis et al., 2020a(Psarommatis et al., , 2021. ZDM is not one method but rather a toolbox for decreasing and mitigating failures within manufacturing processes and "to do things right the first time". ZDM covers both product and process quality. This concept had only partially been implemented so far due to many technological and economic limitations that restricted its rollout. For instance, the equipment required for data recording used to be very expensive and companies did not invest in it.However, the landscape has changed. Nowadays, increased computing power and data storage, significantly reduced sensor prices, combined with new digital technologies have made the implementation of the concept of ZDM easier than ever before. On one hand, the evolution of Industry 4.0 digital and automation technologies, such as intelligent machines, IIoT, digital and cognitive twins, AI, etc. has allowed responses to unexpected events and disruptions to become smarter and faster. On the other hand, the availability of the large volumes of data needed for the development of machine learning-based quality control strategies has allowed "industrialized" AI to work properly within factories and across global value chains.ZDM coupled with digital technologies has the potential to become the new standard for companies towards sustainable and resilient manufacturing, characterized by zero defects and zero wastes (Psarommatis et al., 2021). However, a large effort is still required to increase the flexibility and autonomy at the equipment and digital ZDM control loop level and to leverage the zero defect, circular, and green manufacturing approach . This process requires advanced solutions and techniques allowing the integration and coordination of intelligent automation and digital intelligence technologies for advanced manufacturing. (Psarommatis and May, 2022)The manufacturing industry should therefore master such integration complexity and data-driven flexibility across the full product and process lifecycle (engineering, planning, commissioning, operation, and servicing) to leverage the costeffective implementation of closed-loop cognitive feedforward and feedback control loops that satisfy ZDM optimization and emerging resiliency requirements (Ameri et al., 2021;Psarommatis and Bravos, 2022;.The first paper, "Zero Defect Manufacturing terminology standardization: definition, improvement and harmonization" by João Sousa, Artem Nazarenko, Christian Grunewald, Foivos Psarommatis, Francisco Fraile, Olga Meyer and João Sarraipa presents a methodological approach to provide a common agreement on the ZDM concept and associated terminology taking place within an open CEN-CENELEC Workshop. The methodology has the support of ISO 704, ISO 860, and ISO 10241-1/2. This work shows that the terminology for ZDM has a significant overlap with the terminology of quality management, metrology, dependability, statistics, non-destructive inspection, and condition monitoring.The second paper, "A review on the advanced maintenance approach for achieving the zerodefect manufacturing system" by Hongbae Jun presents a review on advanced maintenance approaches for achieving ZDM. The advanced maintenance approach, which is often called by various terms such as predictive maintenance, condition-based maintenance plus (CBM+), and PHM (Prognostics and Health Management), requires various interdisciplinary knowledge and systematic integration. In this study, we will review previous works mainly focusing on advanced maintenance subject among ZDM research works, and briefly discuss the challenging issues for applying PHM technologies to the ZDM.The third paper, "RMPFQ: A Quality-oriented Knowledge Modelling Method for Manufacturing Systems Towards Cognitive Digital Twins" by Xiaochen Zheng, Pierluigi Petrali, Jinzhi Lu, Claudio Turrin and Dimitris Kiritsis presents a semantic modelling method named RMPFQ (Resource, Material, Process, Function/Feature, Quality) aiming to interlink the main influential factors related to product quality during manufacturing processes. The proposed RMPFQ model is formalized with an application ontology following the IOF-Core middle-level and BFO top-level ontologies. Based on this ontology, a semantic-driven digital twin architecture is designed and mapped to the recently proposed Cognitive Digital Twin concept. A correlation matrix is designed to quantify the relationships among RMPFQ elements thus to facilitate the industrial applications.The fourth paper, "Semantic Systems Engineering Frameworks for Zero-Defect Engineering and Operations in the Continuous Process Industries" by David B. Cameron, Arild Waaler, Erlend Fjøsna, Monica Hole and Foivos Psarommatis presents a framework for implementing ZDM in the process industry supply chain. The framework brings together modelling techniques and models from the following disciplines: system engineering, computer-aided process engineering, automation and semantic technologies. These contributions are synthesized into an information fabric that allows engineering firms to work in new ways. Operators and contractors can use the fabric to move from document-driven engineering to data-based processes. The fabric captures requirements and intent in design so that facilities can be delivered and started-up and operated with zero defects in the design and construction. The information is also a vital support for safe and efficient maintenance and operations.The fifth paper, "Defect detection on optoelectronical devices to assist decision making: a real industry 4.0 case study" by George P. Moustris, George Kouzas, Spyros Fourakis, Georgios Fiotakis, Apostolos Chondronasios, Abd Al Rahman M. Abu Ebayyeh, Alireza Mousavi, Kostas Apostolou, Jovana Milenkovic, Zoi Chatzichristodoulou, Jeremy Butet, Stéphane Blaser, Olivier Landry and Antoine Muller presents an innovative approach, based on industry 4.0 concepts, for monitoring the life cycle of optoelectronical devices, by adopting image processing and deep learning techniques regarding defect detection. The proposed system comprises defect detection and categorization during the front-end part of the optoelectronic device production process, providing a two-stage approach; the first is the actual defect identification on individual components at the wafer level, while the second is the pre-classification of these components based on the recognized defects.The final paper, "A Systematic Review on Machine Learning Methods for Root Cause Analysis towards Zero-Defect Manufacturing" by Konstantinos Papageorgiou, Aikaterini Rapti, Theodosios Theodosiou, Elpiniki Papageorgiou, Nikolaos Dimitriou, Dimitrios Tzovaras and George Margetis presents a literature review protocol and reports the latest advances in Root Cause Analysis (RCA) toward Zero-Defect Manufacturing (ZDM). The most recent works are reported to demonstrate the use of machine learning methodologies for root cause analysis in the manufacturing domain. The popularity of these technologies is then summarized and presented in the form of visualizing graphs. This enables us to identify the most popular and prominent methods used in modern industry.