Deep Learning based Super-resolution models for Accelerating Multiphysics Simulations of Laser Powder Bed Fusion

Laser Powder Bed Fusion is a method of additive manufacturing, where parts are constructed by iteratively fusing metal alloy powder, building complex 3D structures through laser melting. However, defects can form during the manufacturing process, where the meso-scale dynamics of the molten alloy near the laser, known as the melt pool, can directly contribute to the formation of undesirable porosity in the final part. Multiphysics simulation methods can describe the three-dimensional dynamics of the melt pool, but are computationally expensive at the resolutions required for accurate predictions. Therefore, in this work, we develop deep learning based super-resolution models to map low-resolution simulations of the melt pool temperature field to high-resolution simulations of the temperature field, avoiding the computational expense of performing multiple high-resolution simulations for analysis. To do so, we implement a 2-D diffusion model to upscale low-resolution cross-sections of the simulated melt pool to their corresponding high-resolution targets, by predicting the residual between the low-resolution and high-resolution images. We also implement a 3-D residual convolutional network super-resolution model to capture the full morphology of the melt pool. We demonstrate the preservation of key metrics of the melt pool physics between the ground truth simulation data and the super-resolution model output, such as the thermal field, the melt pool dimensions and the depth of the cavity formed by metal vaporization.