Laser Powder Bed Fusion Melt Pool Dynamics for Different Geometric Variations and Powder Layer Heights: High-Fidelity Multiphysics Modeling vs 2025 NIST Experiments

TL;DR

This study uses high-fidelity multiphysics simulations to analyze how powder layer height and geometry affect melt pool dynamics in metal additive manufacturing, validated against NIST experiments.

Contribution

It introduces a detailed open-source simulation framework that accurately models melt pool behavior under various process and geometric variations, validated with benchmark data.

Findings

Simulation results show excellent agreement with NIST experimental data.

Powder layer height significantly influences melt pool depth and width.

The model enables predictive process optimization and defect mitigation.

Abstract

Metal Laser Powder Bed Fusion (PBF-LB/M) is a leading additive manufacturing technique in which part quality and grain morphology are highly dependent on process parameters. Numerous studies of process variations, such as laser power, scan speed, and spot diameter, have demonstrated that they strongly influence melt pool dynamics; however, the effects of powder layer height and geometric variations remain less well understood. In this article, we focus on variations in powder layer height and part geometry to study their influence on melt pool dynamics. We employed a high-fidelity multiphysics simulation framework based on the open source finite volume method (FVM) solver package `LaserBeamFoam' built on `OpenFOAM' to study the variations in different melt pool metrics -- melt pool depth, width, bead height, overlap depth, overlap width, solidified area, and dilution area. The solver captures coupled phenomena of heat transfer, fluid flow, vaporization, recoil pressure, Marangoni convection, and realistic laser reflection behavior to accurately model the melt pool dynamics. Simulations are performed for different powder layer heights and geometric dimensions for direct comparison with benchmark experiments conducted at the National Institute of Standards and Technology (NIST) in 2025. Quantitative validation against NIST experiment demonstrates excellent agreement in all the melt pool metrics. These results highlight the predictive capability of physics-based PBF-LB models, paving the way for process optimization, defect mitigation, and the integration of simulation into digital twin frameworks for additive manufacturing.