Accurate prediction of melt pool dynamics in laser powder‐bed fusion (LPBF) depends on a realistic representation of laser energy absorption under varying process conditions. However, most computational models assume a constant laser absorptivity, neglecting its dependence on energy input. This limitation is particularly pronounced for nickel–titanium (NiTi) shape memory alloys, where temperature‐dependent thermophysical properties and evaporation‐driven compositional changes govern melt pool behavior and functional phase transformations. In this article, we determine an energy‐dependent laser absorptivity for NiTi using a combined experimental–computational framework. Single‐track LPBF experiments over a range of process parameters provide melt pool geometries used as reference data for inverse identification. A high‐fidelity smoothed particle hydrodynamics thermofluid model, incorporating temperature‐dependent material properties, is employed to reconstruct melt pool evolution. By minimizing discrepancies between simulated and experimental melt pool dimensions, the effective laser absorptivity is inferred as a function of input energy. The resulting energy‐dependent absorptivity coefficients improve the fidelity and interpretability of the process model, enabling accurate prediction of transient melt pool dynamics and solidified track geometries. These results highlight the advantages of energy‐dependent absorptivity for predictive mesoscale LPBF simulations and provide a physics‐guided pathway toward improved process modeling and control of functional materials such as NiTi.
Afrasiabi et al. (Sat,) studied this question.