Theory-guided machine learning for thermal modeling of in-situ automated fiber placement of thermoplastic composites

Abstract

In-situ Automated Fiber Placement (AFP) of thermoplastic composites has several advantages over traditional manufacturing techniques, with the main benefit being eliminating secondary thermal processing. Without secondary heat treatment, the in-situ thermal history becomes the critical process parameter that governs bond development, crystallization kinetics, and the development of residual stresses. This work improves the thermal modeling of the in-situ Automated Fiber Placement (AFP) manufacturing process by leveraging Theory-Guided Machine Learning (TGML). A novel theory-guided neural network (TgNN) with theory-based pre-layer transforms models the three-dimensional temperature distribution during in-situ AFP manufacturing. The TgNN is fit on experimentally measured temperatures for various combinations of hot gas torch temperatures and heat source velocities. Feature engineering is implemented by applying theory-based pre-layer transforms to the input features time, the thermocouple coordinates, hot gas torch temperature, and heat source velocity. Compared to a theory-agnostic neural network, the TgNN with theory-based pre-layer transforms has improved predictive ability and requires fewer training data for equivalent performance. The trained model is computationally efficient and can be leveraged for online process control.