Laser powder bed fusion process optimization of CoCrMo alloy assisted by machine-learning

Gaussian process regression (GPR) model of machine learning method was employed to identify the optimal process window for high-performance CoCrMo alloy in laser powder bed fusion (LPBF), considering density (>= 99%) and surface roughness (<= 7 mu m) as key parameters. Additionally, the study examined the impact of LPBF parameters on morphology and distribution of defect and surface roughness. Results revealed a tongue-shaped optimal process window, with scanning speed having a greater influence on density than laser power. High laser power reduced surface roughness, and a combination of medium-to-high laser power (160-340 W) and moderate scanning speed (600-1500 mm/s) achieved low surface roughness (Ra <= 7 mu m). The mean absolute error confirmed the reliability of the optimized process window predicted by GPR.