A part-scale, feature-based surrogate model for residual stresses in the laser powder bed fusion process

Abstract

Over the past decade, the Laser Powder Bed Fusion (LPBF) process has been widely used in the fabrication of industrial parts with advanced functions. It is known that the complex thermal processing of the material during the LPBF process has a significant influence on product quality. While high fidelity simulation models can account for the effects of processing, they are generally too computationally expensive to be directly used in the design of components. Consequently, in this paper we propose a surrogate model for Simulation Models of the residual stress at the part-scale based on a Convolutional Neural Network (CNN) with a 3D U-Net architecture. In order to model the wide range of geometries that can arise during the design process, we developed a feature-based approach in which we trained our CNN on combinations of three basic types of geometric features: circular struts, square struts, and walls. Data augmentation was utilized to account for orientation invariance. Several benchmarks were designed to test the performance of the surrogate model. Results demonstrated that a CNN with a 3D U-Net architecture can accurately predict the residual stress for the features designed. The average training and testing errors are 5.3% and 6.6%, respectively. Prediction performance for the benchmark parts led to validation errors of 14.4%− 28.3% due to their complex geometries. Nevertheless, this strategy led to a significant reduction in runtime, demonstrating that the proposed feature-based surrogate model has the potential to replace high fidelity process simulations for the design of practical engineering parts manufactured using LPBF.