Machine learning based geometry reconstruction for quality control of laser welding processes

Abstract The increasing use of automated laser welding processes causes high demands on quality control. 2D or 3D sensor technology can be used for data acquisition to monitor the weld quality after laser welding. Compared to a 2D camera image, the 3D height data, e.g. acquired using optical coherence tomography, contains additional relevant information for quality inspection. However, the disadvantages are system complexity, higher costs, and longer acquisition times. Therefore, we compare image-based methods with the quality assessment based on height data. The first method uses feature vectors from grayscale images taken coaxially with the laser beam. The significant advantage is that a camera is often integrated into the laser system, so no additional hardware is required. In the second approach, we use an AI-based single-view 3D reconstruction method. The height profile is reconstructed from a camera image and used for further quality assessment. Thus, we combine the advantages of 2D data acquisition with higher accuracy in evaluating 3D data. In addition, we consider the usually low data availability in the industrial environment in the development of algorithms. We use a training data set with 95 samples and a test data set with 858 samples. The work uses the contracting process of copper wires to produce formed coil windings to illustrate the method. We analyze a data set with different defect types and compare the quality assessment using the height data acquired with OCT, the feature vectors from the camera images, and the reconstructed height data.