# NDT Method for Line Laser Welding Based on Deep Learning and One-Dimensional Time-Series Data
###### Abstract
First, directional lights were projected onto the edges of the seams to generate unique artificial light and shadow (LS) features. Then, image processing algorithms based on threshold and edge extraction were used to calculate the accurate edges of the seams to achieve weld recognition. Finally, a welding seam inspection platform was built, and the welding seam  
Figure 2: Flow chart for the proposed method. identification and deviation correction experiments were carried out. The experimental results showed that the proposed detection method could effectively identify the edge of the seam [14]. Common weld detection algorithms are likely to be disturbed by the noise from the spatter and arc during the welding process. A weld seam recognition algorithm based on structured-light vision has been proposed to overcome this challenge. The algorithm can be divided into three steps: initial laser center line recognition, online laser center line detection and weld feature extraction. A Laplacian of Gaussian filter is used for the recognition of the laser center line. Then, an algorithm based on the NURBS-snake model detects the laser center line online in a dynamic region of interest (ROI). Using the line obtained from the previous step, feature points are determined through segmentation and straight-line fitting, while the position of the weld seam can be calculated according to the feature points. The accuracy, efficiency and robustness of the recognition algorithm have been verified with experiments [15]. However, the above-mentioned welding detection methods have turned out to have low detection accuracy, and the features of the structure can hardly be applied in other contexts. With the rapid development of machine-learning technology, a variety of machine learning-based NDT methods have emerged in the field of additive industrial manufacturing [16]. Machine learning (ML) has been applied to various aspects of AM to improve the whole design and manufacturing workflow, especially in the era of industry 4.0. [PERSON] et al. discussed the application of various types of machine-learning techniques to various aspects of additive manufacturing [17]. Various machine-learning methods have been proposed in the field of welding defect detection. [PERSON] et al. proposed a new method for weld defect classification based on the analytic hierarchy process (AHP) and Dempster-Shafer (DS) evidence theory. The method based on DS evidence theory was presented to improve the accuracy of classification and included calculation of the standard values of features based on frequency histogram analysis and an improved Dempster's rule for combination based on WF. A case study on the classification of steam turbine weld defects was provided to illustrate and evaluate the proposed techniques. [18]. [PERSON] et al. proposed a system for detection and classification of defective welds using weld surface images. The weld surfaces produced under different welding condition were captured by a digital camera and processed to extract features. The features from the weld surface image were extracted with a maximally stable extremal region algorithm and used as the input for classification of the weld joint. The support vector machines algorithm has been used for classification of welds using features from surface images [19]. [PERSON] et al. proposed a system for monitoring the welding of galvanized steel sheets with a spectrometer. In this study, the Fisher criterion was used to achieve defect feature ranking, and the k-nearest neighbor algorithm was used to select the feature ranking and achieve defect classification [20]. [PERSON] et al. proposed a method based on machine learning and the random forest (RF) algorithm to classify porosity and achieved high accuracy.