Modeling Melt Pool Flow in Laser Powder Bed Fusion with a Modified TENO-SPH Method

Abstract

Laser Powder Bed Fusion (LPBF) is a pivotal additive manufacturing technology whose final quality is determined by the complex interplay of process parameters such as laser power and scanning speed. Relying solely on physical experimentation to control internal defects (e.g., pores and cracks) is both inefficient and costly, making numerical simulation an essential tool for process optimization. Among available computational methods, Smoothed Particle Hydrodynamics (SPH) is particularly suited for modeling melt pool dynamics due to its inherent ability to handle free surfaces and evolving interfaces. However, both conventional SPH and the SPH method with kernel gradient correction (KGC) exhibit significant pressure oscillations in melt pool simulations, which degrade the accuracy of key predictions such as pool morphology and temperature distribution. To mitigate this, the high-order Targeted Essentially Non-Oscillatory (TENO) reconstruction scheme can be incorporated to improve numerical accuracy. Yet, the TENO-SPH approach may still exhibit stability issues when simulating intense melt pool flows. In this work, we develop a modified TENO-SPH framework by integrating KGC for the simulation of melt pool flow in LPBF. Numerical tests confirm that the proposed KGC-TENO-SPH model accurately captures melt pool flow behavior and pressure distribution, thereby providing a reliable tool for optimizing process parameters and controlling internal defects.