Online monitoring of laser welding depth is increasingly important, with the growing demand for the precise welding depth in the field of power battery manufacturing for new energy vehicles. The indirect methods of welding depth measurement based on optical radiation, visual image and acoustic signals in the process zone have low accuracy in the continuous monitoring. Optical coherence tomography (OCT) provides a direct welding depth measurement during laser welding and shows high achievable accuracy in continuous monitoring. Statistical evaluation approach accurately extracts the welding depth from OCT data but suffers from complexity in noise removal. In this paper, an efficient method coupled DBSCAN (Density-Based Spatial Clustering of Application with Noise) and percentile filter for laser welding depth determination was proposed. The noise of the OCT data were viewed as outliers and detected by DBSCAN. After eliminating the noise, the percentile filter was used to extract the welding depth. By comparing the welding depth determined by this approach and the actual weld depth of longitudinal cross section, an average error of less than 5% was obtained. The precise laser welding depth can be efficiently achieved by the method.

The accurate and efficient segmentation of large-scale urban point clouds is crucial for many higher-level tasks, such as boundary line extraction, point cloud registration, and deformation measurement. In this paper, we propose a novel supervoxel segmentation approach to address the problem of under-segmentation in local regions of point clouds at various resolutions. Our approach introduces distance constraints from boundary points to supervoxel planes in the merging stage to enhance boundary segmentation accuracy between non-coplanar supervoxels. Additionally, supervoxels with roughness above a threshold are re-segmented using random sample consensus (RANSAC) to address multi-planar coupling within local areas of the point clouds. We tested the proposed method on two publicly available large-scale point cloud datasets. The results show that the new method outperforms two classical methods in terms of boundary recall, under-segmentation error, and average entropy in urban scenes.

Optical coherence tomography (OCT) offers a direct and precise measurement of keyhole depth in laser welding, facilitating its application for quality assurance and control. Nevertheless, in high-speed welding, the keyhole lags behind the processing beam, leading to a reduction in OCT measurement accuracy. In this paper, OCT is utilized to monitor keyhole morphology by employing a scanner to deflect the measuring beam and the keyhole lag is quantitatively analyzed. Bead-on-plate welds were conducted on stainless steel and aluminum alloys at varying welding speeds. Real-time OCT data collection was performed and reconstructed to generate keyhole morphology. The aluminum alloy exhibited fluctuating keyhole morphology and irregular seam surfaces. The determination of the keyhole lag was based on the longitudinal view of keyhole morphology. Results indicated a proportional increase in keyhole lag with welding speed, and this trend was consistent for both materials.