Deep learning enhanced digital twin for Closed-Loop In-Process quality improvement

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. (C) 2020 CIRP. Published by Elsevier Ltd. All rights reserved.