This study investigates the effects of process parameters in the powder bed fusion–laser beam (PBF-LB) process on IN939 samples. The parameters examined include laser power (160, 180, and 200 W), laser scanning speed (400, 800, and 1200 mm/s), and hatch distance (50, 80, and 110 μm). The study focuses on how these parameters affect surface roughness, relative density, defect formation, and the microstructure of the samples. Surface roughness analysis revealed that the average surface roughness (Sa) values of the sample ranged from 4.6 μm to 9.5 μm, while the average height difference (Sz) varied from 78.7 μm to 176.7 μm. Furthermore, increasing the hatch distance from 50 μm to 110 μm while maintaining constant laser power and scanning speed led to a decrease in surface roughness. Relative density analysis indicated that the highest relative density was 99.35%, and the lowest was 93.56%. Additionally, the average porosity values were calculated, with the lowest being 0.06% and the highest reaching 9.18%. Although some samples had identical average porosity values, they differed in porosity/mm² and average Feret size. Variations in relative density and average porosity were noted in samples with the same volumetric energy density (VED) due to different process parameters. High VED led to large, irregular pores in several samples. Microcracks, less than 50 μm in length, were present, indicating solidification cracks. The microstructural analysis of the XZ planes revealed arc-shaped melt pools, columnar elongated grains aligned with the build direction, and cellular structures with columnar dendrites. This study provides insights for optimizing PBF-LB process parameters to enhance the quality of IN939 components.

Powder bed fusion–laser beam (PBF-LB) of nickel–titanium (NiTi) has attracted increasing interest in aerospace, biomedical, and energy applications owing to its shape memory and superelastic properties, combined with the capability to fabricate complex geometries. However, the strong sensitivity of NiTi to thermal history and process variability makes predictive modeling and process parameter selection challenging. In this work, a physics-guided decision-support framework is developed for melt pool prediction and stability assessment during the PBF-LB processing of NiTi. A high-fidelity thermal finite element model incorporating CALPHAD-derived, temperature-dependent material properties was calibrated using a subset of experimental measurements and independently validated against additional experimental melt pool data. The calibrated model demonstrated good agreement with experiments, yielding mean absolute percentage errors of 4.22% and 5.63% for melt pool width and depth, respectively, on the validation dataset. A multi-output random forest surrogate trained on the validated simulation dataset enabled rapid prediction of melt pool geometric features, achieving test-set $R^2$ values exceeding 0.95, together with low MAE and RMSE values, while five-fold cross-validation confirmed robust predictive performance. The proposed framework integrates surrogate predictions with physics-based melt pool stability criteria to rapidly identify physically feasible processing conditions, thereby providing a computationally efficient foundation for future supervisory process control strategies.