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Quality is one of the key factors in the customer's selection process between competing products. Producing high-quality, defect-free products that meet consumer expectations is crucial for manufacturing companies to gain a competitive advantage. Accordingly, developing appropriate defect generation models is essential in modern manufacturing companies to predict defects and plan efficient quality control and production. On the other hand, with its ability to support new business models and decision support systems, digital twin technology is one of the new technologies emerging to support digital transformation. Faster optimization algorithms, more powerful computers, and a massive increase in available data are just some of the features of digital twins that can be used to advance simulation toward real-time quality control and optimization of products and production systems. This paper aims to model the generation of defects of product variants in assembly and disassembly processes and evaluate their integration within a digital twin system to prevent the occurrence of defects and ensure product quality. The proposed strategy is expected to improve the optimization, monitoring, and diagnostic capabilities of complex product variants' assembly and disassembly systems, realizing an upgrade from a single physical implementation to a combination of physical and digital. [ABSTRACT FROM AUTHOR]

The prediction of defects occurring during manufacturing processes is one of the strategies to be implemented by organizations to reach the goals of Zero-Defect Manufacturing (ZDM). In low-volume productions, characterized by a high level of complexity and customization, defects prediction may be challenging owing to the small amount of historical data typically available. This paper proposes a diagnostic tool that provides an in-line identification of critical steps of assembly processes. The method is based on a self-adaptive defect prediction model of the process, updated as new data are acquired. Assembly complexity of both the process and the design are used as predictors of the defect model. The methodology identifies critical assembly workstations where the respective average defectiveness deviates from the average defectiveness predicted by the model. Detecting critical workstations facilitates quality engineers in identifying the causes of non-conformities and undertaking appropriate corrective actions. The relevance of the method is emphasized by an application to a real case study related to the assembly of rotating ring wrapping machines used in end-of-line packaging. [ABSTRACT FROM AUTHOR]

Assembly processes in low-volume productions, i.e., single-units or small-sized-lots, are often characterized by a high level of customization and complexity. As a consequence, the scarcity of historical data and the difficulty in applying standard statistical techniques make process control extremely challenging. Accordingly, identifying effective diagnostic tools plays a key role in such productions. This paper proposes an innovative method for identifying critical workstations in assembly processes based on defect prediction models. Starting from the level of complexity in terms of assembly process and design, the method allows identifying the workstations whose defectiveness deviates, at a certain confidence level, from the predicted average value. Once the causes leading to significant nonconformities have been detected, appropriate corrective actions may be promptly undertaken to improve the process. An example of implementation of the method in wrapping machines production is presented and discussed. [ABSTRACT FROM AUTHOR]