Toward a concept of digital twin for monitoring assembly and disassembly processes

Abstract

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.

Keywords:

• defect generation
• digital twin
• prediction model
• quality control

Acknowledgments

The authors gratefully acknowledge Elena Martorana, M.Sc. and Giammarco Arcieri, M.Sc. for the fruitful collaboration on this project.

Additional information

Notes on contributors

Elisa Verna

Elisa Verna received the master of science degree in Industrial Engineering and Management in 2016 and the PhD in Management, Production and Design in 2021 from Politecnico di Torino, Italy. She is currently assistant professor at the Department of Management and Production Engineering (DIGEP) of the Politecnico di Torino. She is fellow of A.I.Te.M. (Associazione Italiana delle Tecnologie Manifatturiere) and E.N.B.I.S. (European Network for Business and Industrial Statistics). Her current research interests are quality engineering and management, statistical process control, and innovative production systems. In particular, she is focusing on the study, implementation and planning of quality inspection procedures in manufacturing processes, and on the development of defect generation models in manual and human–robot collaborative assembly and disassembly processes.

Stefano Puttero

Stefano Puttero is a second-year PhD student in Management, Production and Design at Politecnico di Torino. He received the master of science degree in “engineering and Management” from Politecnico di Torino, Italy, in 2021. His main research interests are quality control and management in human–robot collaboration and the development of innovative defect generation models in the collaborative environment. His research also focuses on the central role of humans in the collaborative environment and the relationship between human well-being and the generation of defects.

Gianfranco Genta

Gianfranco Genta received the master of science degree in Mathematical Engineering from Politecnico di Torino, Italy, in 2005 and the PhD degree in Metrology: Measuring Science and Technique from Politecnico di Torino in 2010. He is currently associate professor at the Department of Management and Production Engineering (DIGEP) of the Politecnico di Torino, where he teaches “Experimental Statistics and Mechanical Measurement” and “Industrial Quality Management”. He is research affiliate of CIRP (The International Academy for Production Engineering) and fellow of A.I.Te.M. (Associazione Italiana delle Tecnologie Manifatturiere). He is author and coauthor of three books and more than 70 publications on national/international journals and conference proceedings. His current research focuses on industrial metrology, quality engineering and experimental data analysis.

Maurizio Galetto

Maurizio Galetto received the master of science degree in Physics from University of Turin, Italy, in 1995 and the PhD degree in Metrology: Measuring Science and Technique from Politecnico di Torino, Italy, in 2000. He is currently head of department and full professor at the Department of Management and Production Engineering (DIGEP) of the Politecnico di Torino, where he teaches “Quality Engineering” and “Experimental Statistics and Mechanical Measurement”. He is associate member of CIRP (The International Academy for Production Engineering) and Fellow of A.I.Te.M. (Associazione Italiana delle Tecnologie Manifatturiere) and E.N.B.I.S. (European Network for Business and Industrial Statistics). He is member of the editorial board of the scientific international journal nanomanufacturing and metrology and collaborates as referee for many international journals in the field of industrial engineering. He is author and coauthor of four books and more than 100 published papers in scientific journals and international conference proceedings. His current research interests are in the areas of quality engineering, statistical process control, industrial metrology and production systems. At present, he collaborates in some important research projects for public and private organizations.