In Situ Defect Detection in Selective Laser Melting using a Multi-feature Fusion Method

Abstract

Defects in each layer during selective laser melting (SLM) process are closely associated with the final forming quality and performance of the part. The in-situ monitoring of the layer-wise images is a reliable method for controlling and retracing the quality during the SLM process. In order to improve the accuracy and efficiency of defect detection in the SLM process as well as investigate the relations between the structures of thin-wall parts and defects, a feature fusion-based method is proposed for detecting geometric deformation, debris and local bulge defects on the forming layer. Edge features, texture features and geometric features in the image of forming layer are extracted using histogram of oriented gradients (HOG), gray level co-occurrence matrix (GLCM) and Hu invariant moments. These three features are weighted and formed a feature vector as the inputs of support vector machine (SVM) algorithm to achieve classifications of defects. The performance of different feature fused strategies and classifiers to characterize defects shows that the proposed feature fusion method combined with SVM classifier is able to achieve an average accuracy of 97.11% and an average F1score of 96.91%. The forming layer has severe defects of geometric deformation, debris or local bulge when the thickness of the thin-wall part reaches its limited 0.5mm.