Multimodal signal segmentation technique based on morphological operators applied on synchronized optical data for Laser Powder Bed Fusion processes

Abstract

Recent approaches in online monitoring of Laser Powder Bed Fusion (LPBF) processes rely on multimodal analysis of several sensors. The reported defect detection accuracies are generally very high for laboratory conditions but hardly generalize to real-life situations with arbitrary geometries. Indeed, under such circumstances, the acquired signals turn out to be very complex — with many uninformative portions due to non-constant laser emissions. This issue makes the introduction of a segmentation technique a requirement to eliminate the sections of the signals acquired when the laser is “off.” To close this gap, we present a novel segmentation algorithm based on optical emission data and mathematical morphology operations to identify precisely the portions of the signals recorded only during laser emissions, regardless of the adopted scanning strategy. Experimental results with a commercially available LPBF machine using different scanning strategies and geometries show an average Intersection-over-Union of 87% compared to 63% obtained by thresholding.