Physics-Based and Data-Driven ICME for Metal Additive Manufacturing—From Feedstock to Process Optimization

This presentation will showcase our recent progress in the physics-based and data-driven ICME for metal additive manufacturing. Firstly, a lattice Boltzmann method(LBM)-based model for the ultrasound atomization process is presented. The LBM can correlate the powder diameter probability distribution with ultrasound parameters, such as vibration frequency and magnitude. Then, we will present a sharp-diffusive interface multiphysics processes model developed to predict melt pool dynamics, thermal history, voids, and surface roughness that directly impact the printed part quality. Finally, we integrate the multiphysics processing with machine learning-accelerated computer vision to optimize cross-gas flow parameters for powder spatter mitigation.