Adaptive vision-based detection of laser-material interaction for directed energy deposition

In-situ vision data acquisition, feature extraction, and analysis are ongoing challenges for quality assessment in directed energy deposition (DED). This work proposes a method for detecting target regions in the laser-material interaction zone based on a low-cost high-dynamic-range (HDR) vision sensor. Adaptive image thresholding, connected component analysis, and iterative energy minimization are used to identify target regions. The method is designed to be adaptive, in terms of obtaining parameters based on simple training data, and robust, in terms of feature detection performance subject to under-melt, conduction and keyhole melting mode phenomena. The performance of the proposed region detection scheme is quantitatively and qualitatively evaluated against annotated data. It was found that the True Positive Rate in detection was above 90%, while the False Detection Rate was less than 10%. Extensive experimental results show that the proposed scheme is able to detect and follow target regions under a variety of power levels and process conditions.