Fast and Accurate Defects Detection for Additive Manufactured Parts by Multispectrum and Machine Learning.

Abstract

Traditional defect detection methods for metal additive manufacturing (AM) have the problems of low detection efficiency and accuracy, while the existing machine learning detection algorithms are of poor adaptability and complex structure. To address the above problems, this article proposed an improved You Only Look Once version 3 (YOLOv3) algorithm to detect the surface defects of metal AM based on multispectrum. The weighted k-means algorithm is used to cluster the target samples to improve the matching degree between the prior frame and the feature layer. The network structure of YOLOv3 is modified by using the lightweight MobileNetv3 to replace the Darknet-53 in the original YOLOv3 algorithm. Dilated convolution and Inceptionv3 are added to improve the detection capability for surface defects. A multispectrum measuring system was also developed to obtain the AM surface data with defects for experimental verification. The results show that the detection accuracy in the test set by YOLOv3-MobileNetv3 network is 11% higher than that by the original YOLOv3 network on average. The detection accuracy for cracking defects of the three types of defects is significantly increased by 23.8%, and the detection speed is also increased by 18.2%. The experimental results show that the improved YOLOv3 algorithm realizes the end-to-end surface defect detection for metal AM with high accuracy and fast speed, which can be further applied for online defect detection.