An approach for process optimisation of the Automated Fibre Placement (AFP) based thermoplastic composites manufacturing using Machine Learning, photonic sensing and thermo-mechanics modelling. (April 2022) - Islam, Faisal; Wanigasekara, Chathura; Rajan, Ginu; Swain, Akshya; Prusty, B. Gangadhara
Abstract: The automated fibre placement (AFP) process is a complex manufacturing technique with many variables which affect the final part quality. Inverse Machine Learning (ML) models can be used as decision-aid tools for optimising thermoplastic composites manufacturing. However, a common challenge of ML application in manufacturing is the acquisition of relevant and sufficient data. To overcome this small-data learning problem, a hybrid approach has been proposed here which combines the benefits of ML algorithms such as the Artificial Neural Networks (ANN), virtual sample generation (VSG) methods, physics-based numerical simulations and data obtained from experiments and photonic sensors, to enhance the manufacturing process.