2021

Sinha, Sumit (2021) Building transformative framework for isolation and mitigation of quality defects in multi-station assembly systems using deep learning. PhD thesis, University of Warwick.

Sinha, Sumit, Franciosa, Pasquale and Ceglarek, Dariusz (2021) Object shape error correction using deep reinforcement learning for multi-station assembly systems. In: 2021 IEEE 19th International Conference on Industrial Informatics (INDIN), 21-23 Jul 2021, Palma de Mallorca, Spain ISBN 9781728143958. doi:10.1109/INDIN45523.2021.9557359

Sinha, Sumit, Franciosa, Pasquale and Ceglarek, Darek (2021) Object shape error response using Bayesian 3D convolutional neural networks for assembly systems with compliant parts. IEEE Transactions on Industrial Informatics , 17 (10). pp. 6676-6686. doi:10.1109/TII.2020.3043226

Sinha, Sumit, Franciosa, Pasquale and Ceglarek, Darek (2021) Object shape error response using Bayesian 3D convolutional neural networks for assembly systems with compliant parts. In: 18th IEEE International Conference on Industrial Informatics (INDIN), Coventry, 20-23 Jul 2020. Published in: 2020 IEEE 18th International Conference on Industrial Informatics (INDIN) pp. 104-109. ISBN 9781728149646. ISSN 2378-363X. doi:10.1109/INDIN45582.2020.9442245

Sinha, Sumit, Franciosa, Pasquale and Ceglarek, Dariusz (2021) Building a scalable and interpretable bayesian deep learning framework for quality control of free form surfaces. IEEE Access, 9 . pp. 50188-50208. doi:10.1109/ACCESS.2021.3068867

Sinha, Sumit, Franciosa, Pasquale and Ceglarek, Dariusz (2021) Point cloud object shape error datasets for root cause analysis of multi-station assembly systems. [Dataset]

2020

Franciosa, Pasquale, Sokolov, Mikhail, Sinha, Sumit, Sun, Tianzhu and Ceglarek, Dariusz (2020) Deep learning enhanced digital twin for Closed-Loop In-Process quality improvement. CIRP Annals - Manufacturing Technology, 69 (1). pp. 369-372. doi:10.1016/j.cirp.2020.04.110

2019

Sinha, Sumit, Glorieux, Emile, Franciosa, Pasquale and Ceglarek, Dariusz (2019) 3D convolutional neural networks to estimate assembly process parameters using 3D point-clouds. In: SPIE Optical Metrology, Munich, Germany, 24-27 Jun 2019. Published in: Proceedings of SPIE : Multimodal Sensing: Technologies and Applications, 11059 p. 10. ISSN 0277-786X. doi:10.1117/12.2526062