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Laser powder bed fusion (LPBF) holds promise to efficiently produce metal parts. However, LPBF incurs stochastic melt pool (MP) spattering, which would roughen workpiece in-process surface, thus weakening inter-layer bonding and causing issues like porosity, powder contamination, and recoater intervention. Understanding the consequential effect of MP spattering on layer surface remains difficult due to the lack of process monitoring capability for concurrently tracking MP spatters and characterizing layer surfaces. In this work, using our lab-designed LPBF-specific fringe projection profilometry and an off-axis camera, we quantify the correlation between MP spattering and in-process layer surface roughness for the first time to reveal the influence of MP spatters on process anomaly and part defects. A method of extracting and registering MP spattering metrics is developed by machine learning of the in-situ imaging data. Each image is analyzed to obtain the MP center location and the spatter count and ejection angle. These MP spatter signatures are registered for each monitored MP across each layer. Regression modeling is used to correlate registered MP spatter signature and its processing parameters with layer surface topography measured by the in-situ FPP. We find that the attained MP spatter feature profile can help predict the layer surface roughness more accurately (> 50% less error), in contrast to the conventional approaches that would only use nominal process setting. This is because the spatter information can reflect key process changes including the deviations in actual laser scan parameters and their effects. The results corroborate the importance of spatter monitoring and the distinct influence of spattering on layer surface roughness. Our work paves a foundation to thoroughly elucidate and effectively control the role of MP spattering in defect formation during LPBF.

Melt pool (MP) temperature is one of the determining factors and key signatures for the properties of printed components during metal additive manufacturing (AM). The state-of-the art measurement systems are hindered by both the equipment cost and the large-scale data acquisition and processing demands. In this work, we introduce a novel coaxial high-speed single-camera two-wavelength imaging pyrometer (STWIP) system as opposed to the typical utilization of multiple cameras for measuring MP temperature profiles through a laser powder bed fusion (LPBF) process. Developed on a commercial LPBF machine (EOS M290), the STWIP system is demonstrated to be able to quantitatively monitor MP temperature and variation for 50 layers at high framerates (> 30,000 fps) during a print of five standard fatigue specimens. High performance computing is employed to analyze the acquired big data of MP images for determining each MPs average temperature and 2D temperature profile. The MP temperature evolution in the gage section of a fatigue specimen is also examined at a temporal resolution of 1ms by evaluating the derived MP temperatures of the printed samples first, middle and last layers. This paper is first of its kind on monitoring MP temperature distribution and evolution at such a large, detailed scale for longer durations in practical applications. Future work includes MP registration and machine learning of MP-Part Property relations.