Deep learning enhanced digital twin for Closed-Loop In-Process quality improvement. Issue 1 (2020) - Franciosa, Pasquale; Sokolov, Mikhail; Sinha, Sumit; Sun, Tianzhu; Ceglarek, Dariusz
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.