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- W4207059360 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." @default.
- W4207059360 created "2022-01-26" @default.
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- W4207059360 date "2022-04-01" @default.
- W4207059360 modified "2023-09-28" @default.
- W4207059360 title "An approach for process optimisation of the Automated Fibre Placement (AFP) based thermoplastic composites manufacturing using Machine Learning, photonic sensing and thermo-mechanics modelling" @default.
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- W4207059360 doi "https://doi.org/10.1016/j.mfglet.2022.01.002" @default.
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