A deep-learning-based in-situ surface anomaly detection methodology for laser directed energy deposition via powder feeding

Abstract

Laser Directed Energy Deposition via Powder Fed (LDED-PF) based Metal Additive Manufacturing suffers from inferior dimensional accuracy mainly due to thermal cycling, localized heat accumulation, inconsistency in the powder stream, deviation in the speed of the motion system and powder focus, etc. In-situ monitoring, along with defect detection algorithms, can be used for avoiding unpredictable build failures while minimizing the time and cost of exhaustive ex-situ characterization techniques. Existing surface quality assessment techniques for LDED-PF mainly focus on intermittent process monitoring, data acquisition, data post-processing, feature extraction, and error identification. The present work investigates the development of a novel in-situ monitoring software platform that can be used for the surface anomaly detection of LDED-PF parts using machine learning techniques. Despite the existing methods in the literature, this technique has shown high robustness and high confidence in defect detection regardless of the geometry, and varied density of point clouds. First, a novel method is developed to calibrate the laser line scanner with respect to the robotic end-effector with the sub 0.5 mm accuracy. Subsequently, 2D surface profiles obtained from the LDED-PF built part surface using the laser scanner are stitched together to create an accurate 3D point cloud representation. Further, the point cloud data is processed, and defect detection is carried out using unsupervised learning and supervised (deep) learning techniques. The overall accuracy and mean IoU of 91.3 % and 83.3 % are achieved, respectively. The study paves the way for the development of automatic tool path generation for the LDED-PF process to build high-quality components.