LAUSR

Abstract To improve the quality of narrow overlap welds and reduce cost during the high-strength production, it is essential to detect weld defects promptly by identification of the type of defects to provide solution accordingly. This paper proposes an integrated weld defect identification approach combing eddy current detection with 3D laser scanning based on Convolutional Neural Networks (CNN). The detection principle and equipment of the two detection methods are introduced. To fit the training process of CNN, two set of detection signals are preprocessed: a two-dimensional time-frequency diagram for eddy current signals using continuous wavelet transform and for laser images, weld edges are extracted and divided by region using image convolution and combining with integral graph. CNN model VGG16 is trained afterwards with data collected from one local manufacturer in Shanghai. It is discovered that performance of eddy current and laser image identification on different types of weld defects is different, and the accuracy can be increased with the two methods combined. Last, to achieve real-time detection of narrow overlap welding, a two-stage defect recognition model is built which greatly improves the efficiency of weld defect identification without affecting the accuracy of weld defect identification.