Accelerating High-Fidelity Thermal Process Simulation of Laser Powder Bed Fusion via the Computational Fluid Dynamics Imposed Finite Element Method (CIFEM)

Abstract

The current work proposes a finite element method (FEM) to accelerate scanwise thermal process simulation of the laser powder bed fusion (L-PBF) process with computational fluid dynamics (CFD) resolution near the melt pool. Termed the CFD imposed FEM (CIFEM), the transient thermal fields from a high-fidelity CFD simulation and inferred by deep learning are imposed as temperature values rather than utilizing a conventional heat source model as in existing FEM-based process simulations. These fields are enforced only within a relatively small computational region encompassing the melt pool, while heat diffusion effects elsewhere are solved via the FEM. For a wide range of laser power and scan speeds covering the conduction, transition, and keyhole melting regimes, 29 of the 30 total CIFEM-simulated melt pool sizes lie within two standard deviations of the experimental melt pool sizes. Compared with the CFD simulations, the thermal fields obtained by CIFEM possess 7.44% mean absolute relative error (MARE), significantly less than the 43.76% MARE on three representative test cases simulated using the Goldak heat source model calibrated to the measured melt pool dimensions. In terms of computational efficiency, the CIFEM model running on a GPU card with 4,608 Compute Unified Device Architecture (CUDA) cores is 28.2× more efficient than the CFD simulations running on 24 CPU cores in parallel.

Keywords

• Computational fluid dynamics
• Finite element method
• Deep learning
• Heat source
• Laser powder bed fusion