Effects of Process and Heat Source Parameters on Temperature Evolution in Thin-wall Wire Arc Additive Manufacturing using Explainable Deep Learning

Abstract

The wire arc additive manufacturing (WAAM) process involves a multitude of uncertain parameters, making WAAM a complex system for analysis. To comprehensively investigate their effects and conduct sensitivity studies, a parametric approach is essential. However, such an approach necessitates a substantial number of simulations, each of which is time-consuming and can last up to a few days. In response, we construct a deep learning-based surrogate model trained on the data created by the validated finite element (FE) method. This surrogate model is used to conduct sensitivity analysis via the SHapley Additive exPlanations (SHAP) method. The findings indicate that the positioning of the laser and its proximity to individual nodal points during the printing process are crucial features in predicting temperature evolution. Additionally, using the FFNN model instead of solely the FE model significantly reduces the computational cost (3272 times) of predicting the temperature field. This innovative approach promises to streamline the exploration of the intricate parameter space of WAAM, offering valuable insights for enhanced process optimization and control of complex manufacturing processes.