AI Modeling for High-Fidelity Heat Transfer and Thermal Distortion Forecast in Metal Additive Manufacturing

Abstract

Abstract In this study, a novel AI-based modeling approach is introduced to estimate high-fidelity heat transfer calculations and predict thermal distortion in metal additive manufacturing, specifically for the multi laser powder bed fusion (ML-PBF) process. The effects of start position and printing orientation on deformation and stress distribution in parts produced using ML-PBF additive manufacturing process were investigated. A total of 512 simulations were executed, and the maximum and minimum deformation values were recorded and compared. A significant improvement e.g., 53% in deformation was observed between the best and worst printing cases. A low-fidelity modeling framework, based on a feedforward neural network was developed for the rapid prediction of thermal displacement with high accuracy. The model with unknown test cases demonstrated a strong positive correlation (R = 0.88) between high-fidelity and network-predicted low-fidelity outputs. The simplicity, computational efficiency, and ease of use of the developed model make it a valuable tool for preliminary evaluation and optimization in the early stages of the design process. By adjusting controlling factors and identifying trends in thermal history, the model can be scaled to a high-fidelity model for increased accuracy, significantly reducing development time and cost. The findings of this study provide valuable insights for designers and engineers working in the field of additive manufacturing, offering a better understanding of deformation/thermal displacement control and optimization in the PBF process using multiple lasers.