Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing Using Generative Deep Diffusion

TL;DR

This paper introduces a generative deep learning diffusion model that efficiently upscales low-fidelity melt pool simulations to high-fidelity results, significantly reducing computational costs in additive manufacturing analysis.

Contribution

The work presents a novel probabilistic diffusion framework for upscaling coarse melt pool simulations to high-fidelity, enabling faster analysis without extensive high-resolution simulations.

Findings

Predicts melt pool depth within 3 μm accuracy

Reduces analysis time by 100x

Preserves key melt pool metrics in upscaled data

Abstract

Defects in laser powder bed fusion (L-PBF) parts often result from the meso-scale dynamics of the molten alloy near the laser, known as the melt pool. For instance, the melt pool can directly contribute to the formation of undesirable porosity, residual stress, and surface roughness in the final part. Experimental in-situ monitoring of the three-dimensional melt pool physical fields is challenging, due to the short length and time scales involved in the process. Multi-physics simulation methods can describe the three-dimensional dynamics of the melt pool, but are computationally expensive at the mesh refinement required for accurate predictions of complex effects, such as the formation of keyhole porosity. Therefore, in this work, we develop a generative deep learning model based on the probabilistic diffusion framework to map low-fidelity, coarse-grained simulation information to the high-fidelity counterpart. By doing so, we bypass the computational expense of conducting multiple high-fidelity simulations for analysis by instead upscaling lightweight coarse mesh simulations. Specifically, we implement a 2-D diffusion model to spatially upscale cross-sections of the coarsely simulated melt pool to their high-fidelity equivalent. We demonstrate the preservation of key metrics of the melting process between the ground truth simulation data and the diffusion model output, such as the temperature field, the melt pool dimensions and the variability of the keyhole vapor cavity. Specifically, we predict the melt pool depth within 3 $\mu m$ based on low-fidelity input data 4$\times$ coarser than the high-fidelity simulations, reducing analysis time by two orders of magnitude.