Surrogate Model for Prediction of Temperature Distribution in WAAM Using Deep Learning

Abstract

A Faster and data-driven method by deep learning, compare with a traditional method by FEM, is proposed for thermal simulation of Wire Arc Additive Manufacturing (WAAM). In this paper, cross sectional temperature distributions of WAAM-made parts are predicted and shows a good agreement with FEM method.