NDT Method for Line Laser Welding Based on Deep Learning and One-Dimensional Time-Series Data

Welding testing is particularly important in industrial systems, but there are still some deficiencies in terms of testing performance, anti-noise capability and defect identification in current mainstream welding non-destructive testing technologies. With the development of structured-light non-destructive testing technology, deep learning technology, signal processing technology and other fields, various possibilities have emerged that make it possible to propose new ideas for welding non-destructive testing. This study used a laser sensor to propose a non-destructive method for testing welding defects in seam contours. In order to solve the problems of low sampling rates and poor recognition accuracy in traditional methods of welding defect detection, the proposed method introduces image coding into laser sensors and applies deep-learning algorithms to the classification and detection of weld defect images. By preprocessing the weld seam by encoding one-dimensional data as two-dimensional images, this method develops a framework for the detection and classification of pre-coded laser weld seam images. After taking the original extracted weld image center trajectory data as one-dimensional sequence data, we utilized the method of encoding one-dimensional time series data as two-dimensional time-series images. In doing so, the one-dimensional laser data can be encoded into the corresponding two-dimensional images and, with the application of a deep neural network, welding defect classification and detection can be realized. Experimentation was used to verify that the proposed method is of higher accuracy than traditional methods for classifying and detecting defects directly from two-dimensional welding images.

Automated summary

This study proposes a new non-destructive welding testing method using a laser sensor, which introduces image coding and deep learning algorithms for defect classification and detection. The proposed method achieves higher accuracy compared to traditional methods for directly classifying and detecting defects from two-dimensional welding images.