Deep learning enhanced digital twin for Closed-Loop In-Process quality improvement. Issue 1 (2020)

• Record Type: Journal Article
• Authors: Franciosa, Pasquale; Sokolov, Mikhail; Sinha, Sumit; Sun, Tianzhu; Ceglarek, Dariusz
• Is Part Of: CIRP annals. Volume 69:Issue 1(2020)
• Page Start: 369
• Page End: 372
• Publication Date: 2020
• Subjects: Digital Manufacturing System -- Assembly -- Digital Twin; Production engineering -- Research -- Periodicals; 670.5
• DOI: 10.1016/j.cirp.2020.04.110
• Languages: English
• ISSNs: 0007-8506

Abstract

Abstract: A digital twin framework is presented for assembly systems with compliant parts fusing sensors with deep learning and CAE simulations. Its underlying concept, 'process capability space, ' updates iteratively during evolving tasks of new product introduction with resulting model fidelity able to simulate dimensional, geometric and weld quality of parts and assemblies; isolate root causes of quality defects; and, suggest corrective actions for automatic defects mitigation; thereby, enabling 'Closed-Loop In-Process (CLIP) quality improvement' during assembly system development. Results, using the first fully digitally developed remote laser welding process for aluminium doors, yielded a right-first-time rate of >96% for door assembly cell development.