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Over the last years, the manufacturing industry has seen constant growth and change, as companies have been able to meet higher customer expectations mostly due to the advancements brought by the Industry-4.0 era. Additionally, the digital and green manufacturing transition and the increasing demand for mass ...

Over the last years, the manufacturing industry has seen constant growth and change, as companies have been able to meet higher customer expectations mostly due to the advancements brought by the Industry-4.0 era. Additionally, the digital and green manufacturing transition and the increasing demand for mass customization impose new rules for the commercial success and sustainable production of a product.
The manufacturing industry has had to confront itself with highly dynamic and hyperconnected scenarios to effectively manage product circularity, minimal production environmental footprint, closed-loop PLM, resource optimization and manufacturing asset, and process and product repurposing. The effective management of “quality” and “waste” in both the product and process level has therefore become instrumental in industrial competitiveness.

Companies are paying particular attention to product quality to ensure that all of 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. Traditional QI methods, however, cannot autonomously learn from defects, as they simply trace and remove them. 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. 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.

Zero Defect Manufacturing is considered by both researchers and the industry as a viable replacement for the traditional QI methods. 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. 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.
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 cost-effective implementation of closed-loop cognitive feedforward and feedback control loops that satisfy ZDM optimization and emerging resiliency requirements.

This collection on Zero Defect Manufacturing follows and builds on the success of the article collection on "Data-Driven Cognitive Manufacturing - Applications in Predictive Maintenance and Zero Defect Manufacturing" that has received more than 68,000 views so far. It intends to bring together research articles highlighting Industry 4.0 ZDM frameworks, methods, and technologies motivating advancements in the domain of resilient and sustainable product manufacturing and autonomous manufacturing process quality assurance. It provides a perfect opportunity for researchers to examine previously unexplored aspects, propose and develop new ideas, share insights, and inspire new research paths.
Original research papers, review papers and industrial case studies are welcome to submit on themes such as, but not limited to:

• Artificial intelligence (incl. machine learning, deep learning, collaborative learning) for ZDM
• Knowledge-based systems for ZDM
• Decision support systems for ZDM implementation
• ZDM Standards and interoperability
• ZDM technologies
• Advanced defects detection systems
• Virtual metrology applications
• Rework of defected products
• Predictive maintenance
• Defects Prediction
• Methodologies for defect prevention
• Product life cycle
• Data-driven Manufacturing
• Intelligent Maintenance Systems
• Digital Twins
• Cognitive Digital Twins
• Scheduling for ZDM
• Industrial implementation case studies
• Engineering modeling and simulation
• Solutions and standards for the digital transformation
• Manufacturing systems for Industry 4.0
• Resilient Manufacturing Systems

Keywords: ZDM, Zero Defect Manufacturing, Sustainable Manufacturing, Zero Waste, Quality Assurance, Quality Management System, Inspection, Detection, Prediction, Repair, Industry 4.0, ZDM technologies, Prevent, Data Driven Manufacturing, Intelligent Maintenance Systems

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