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An approach for process optimisation of the Automated Fibre Placement (AFP) based thermoplastic composites manufacturing using Machine Learning, photonic sensing and thermo-mechanics modelling

Journal Article


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.

Publication Date


  • 2022

Citation


  • Islam, F., Wanigasekara, C., Rajan, G., Swain, A., & Prusty, B. G. (2022). An approach for process optimisation of the Automated Fibre Placement (AFP) based thermoplastic composites manufacturing using Machine Learning, photonic sensing and thermo-mechanics modelling. Manufacturing Letters, 32, 10-14. doi:10.1016/j.mfglet.2022.01.002

Scopus Eid


  • 2-s2.0-85123929338

Start Page


  • 10

End Page


  • 14

Volume


  • 32

Issue


Place Of Publication


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.

Publication Date


  • 2022

Citation


  • Islam, F., Wanigasekara, C., Rajan, G., Swain, A., & Prusty, B. G. (2022). An approach for process optimisation of the Automated Fibre Placement (AFP) based thermoplastic composites manufacturing using Machine Learning, photonic sensing and thermo-mechanics modelling. Manufacturing Letters, 32, 10-14. doi:10.1016/j.mfglet.2022.01.002

Scopus Eid


  • 2-s2.0-85123929338

Start Page


  • 10

End Page


  • 14

Volume


  • 32

Issue


Place Of Publication