WP7 aims to establish data analytics models to extract maximal fidelity metrology information from the manufacturing supply chain. It primarily looks at the multistage manufacturing product and process data and links these to dimensional and surface metrology. It is part of Theme II on Metrology Data Analytics and aligns with Objective 7 of the proposal.
Obajemu O, Mahfouf M, , McLeay T and Kadirkamanathan V. An interpretable machine learning–based approach for process to areal surface metrology informatics. IEEE Trans Industrial Informatics, In Review 2020.
Papananias M, McLeay T, Mahfouf M, and Kadirkamanathan V. A Bayesian framework to estimate part quality and associated uncertainties in multistage manufacturing. Computers in Industry, 105:35-47, 2019.
Papananias M, McLeay TE, Mahfouf M and Kadirkamanathan V. An intelligent metrology informatics system based on neural networks for multistage manufacturing processes. Proceedia CIRP, Sheffield, June 2019.
Obajemu O, Mahfouf M, McLeay TE, Jiang X, Scott PJ and Kadirkamanathan V. A new Gaussian process-based approach for uncertainty propagation in surface metrology profile parameters estimation. Proceedings of the 19th Euspen International Conference, Bilbao, Spain, June 2019.