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Laser powder bed fusion (LPBF) holds promise to efficiently produce metalparts. However, LPBF incurs stochastic melt pool (MP) spattering, which wouldroughen workpiece in-process surface, thus weakening inter-layer bonding andcausing issues like porosity, powder contamination, and recoater intervention.Understanding the consequential effect of MP spattering on layer surfaceremains difficult due to the lack of process monitoring capability forconcurrently tracking MP spatters and characterizing layer surfaces. In thiswork, using our lab-designed LPBF-specific fringe projection profilometry andan off-axis camera, we quantify the correlation between MP spattering andin-process layer surface roughness for the first time to reveal the influenceof MP spatters on process anomaly and part defects. A method of extracting andregistering MP spattering metrics is developed by machine learning of thein-situ imaging data. Each image is analyzed to obtain the MP center locationand the spatter count and ejection angle. These MP spatter signatures areregistered for each monitored MP across each layer. Regression modeling is usedto correlate registered MP spatter signature and its processing parameters withlayer surface topography measured by the in-situ FPP. We find that the attainedMP spatter feature profile can help predict the layer surface roughness moreaccurately (> 50% less error), in contrast to the conventional approaches thatwould only use nominal process setting. This is because the spatter informationcan reflect key process changes including the deviations in actual laser scanparameters and their effects. The results corroborate the importance of spattermonitoring and the distinct influence of spattering on layer surface roughness.Our work paves a foundation to thoroughly elucidate and effectively control therole of MP spattering in defect formation during LPBF.

Influence of Spattering on In-process Layer Surface Roughness during Laser Powder Bed Fusion