Controlling surface roughness in laser-based powder bed fusion of metals

Laser-based powder bed fusion of metals (PBF-LB/M) allows for intricate geometries and complex part designs, making it a key technology for advanced manufacturing. However, surface roughness is inevitable, affecting part properties such as mechanical strength, thermal performance, and wear resistance. While reducing roughness is often aimed for, some applications require specific surface textures rather than mere minimization. Existing research has explored roughness modification, but a comprehensive understanding of influencing factors is still lacking. This study introduces a holistic approach by developing a modeling approach based on supervised machine learning and feature importance evaluation to determine the impact on surface texture. The study examines how parameters such as laser power, scan speed, build platform position, overhang angle, and especially the volume of process parameter adaptation in overhang angles influence surface texture in Ti-6Al-4V components. A machine learning based predictive model lays the foundation to modify surface texture in a controlled manner. The results show that surface orientation relative to shielding gas flow influences the surface characteristics, with surfaces facing against the gas flow experiencing higher surface texture. Application of optimized downskin parameters also enables nearly identical surface texture on the upskin and downskin of 45° geometries. This constant surface texture, independent of the surface orientation, is especially advantageous in internal cooling channels. The findings contribute to a predictive analytical model for surface roughness control in PBF-LB/M.