Prediction and control of structured porosity in laser powder bed fusion (LPBF) additive manufacturing using multi-scale modeling

This study presents a comprehensive multi-scale modeling framework for predicting and controlling process-induced porosity in Laser Powder Bed Fusion (LPBF) additive manufacturing. Three numerical approaches are developed and evaluated: a conduction-based finite element model, a hybrid geometric model with melt pool dimensions, and a multiphysics model. The use of partially molten powder particles is a significant innovation that improves prediction accuracy for porosity and surface morphology, especially under low-energy and high-hatch-spacing conditions. Model validation is carried out using high-resolution CT scans, SEM imaging, and buoyancy-based porosity measurements. The conduction and hybrid models offer computational efficiency and are appropriate for macro- to meso-scale investigations, but they have limited ability to capture precise pore morphology. The multiphysics model, incorporating melt pool fluid flow, surface tension effects, and partially molten powder particles, achieved porosity prediction within 5 % deviation of CT-based experimental values and closely matches observed pore structures across a range of laser energy densities (11.6–28.5 J/mm³). The study also maps process-structure interactions, emphasizing the impact of laser energy density and hatch spacing on pore properties. Overall, the proposed novel framework serves as a prediction and design tool for adjusting porosity in LPBF components to meet application-specific performance requirements.