# NDT Method for Line Laser Welding Based on Deep Learning and One-Dimensional Time-Series Data
###### Abstract  
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. deep learning; weld detection classification; line laser; one-dimensional sequential time series +
Footnote †: journal:  
The non-destructive testing methods include magnetic particle testing, eddy current testing, magneto-optical imaging testing, ultrasonic testing, infrared testing, penetrant testing and phased array ultrasonic testing, each of which has its own limitations [3; 4; 5; 6; 7]. The first three methods have certain requirements for the type of materials to be tested. Specifically, magnetic particle testing requires that the test piece be ferromagnetic material; eddy current testing is only applicable to the detection of surface and near-surface defects in conductive materials, as there are many interference factors involved in the detecting process; and magneto-optical imaging inspection produces images that are unclear, either for the object itself or the background, thus requiring a series of further image processing algorithms to improve the contrast of the magneto-optical image and highlight the welding characteristic information. Ultrasonic testing and infrared testing, on the other hand, have high requirements for the surface of the object to be tested. Ultrasonic testing does not work well on rough surfaces because a rough surface interferes with the ultrasonic projection effect and thus affects the accuracy of the test results. Infrared inspection cannot assess the shape, size or location of welding defects. The performance of penetration inspection is significantly affected by the imaging agent and testing environment. Phased array ultrasonic testing is a new technology in the field of non-destructive testing. It has the advantages of high sensitivity, high resolution and the ability to detect complex workpieces and deep defects. However, this technology also has its shortcomings. Defects are not intuitive, so it is difficult to characterize them, and this technology does not allow real-time detection. It is only suitable for detecting internal defects of more than 5 mm with regular shapes, and it has high operational requirements for operators.