Motion feature based melt pool monitoring for selective laser melting process

Since various build defects in the Selective laser melting (SLM) process are found to be associated with the instability of melt pool, the melt pool monitoring is particularly important for the final product quality control. Previous studies focus on the geometric features to describe the changes of the size and shape of melt pool, which are not sufficient to characterize the dynamic variations of the melt pool during the build process monitoring. To solve this problem, a new motion feature is introduced to describe the moving melt pool. The melt pool and spatters are extracted by thresholds combined with the connected component analysis method. The distance between the centroid and the boundary of melt pool is calculated from the unfolded clockwise at a step angle, which constructs a high dimensional feature vector as the motion features. The k-means clustering algorithm is applied to cluster the motion features under varied process parameters, aiming at construct the link between the melt pool states and processing parameter for the quality control. The research results have shown that the extracted motion features can describe the variation of melt pool more accurately than the traditional geometric features, and they can distinguish the moving direction and melted states of over melting, partial melting and defects simultaneously. This research provides a new approach for intelligent online monitoring of the SLM process.