Efficient weld bead recognition for robotic grinding using laser vision and machine learning

This study aims to identify the geometry of weld beads from point cloud data to enhance robotic grinding planning. Robotic grinding is widely used in industry due to the hazardous working conditions and health risks associated with manual operations. However, certain grinding tasks require precise recognition of the work area for effective path planning. The processing and identification of weld bead data from point clouds remain active research challenges because of varying geometries. By using a precise single-line laser vision system, many studies have focused on fitting the base metal surface to extract weld beads. Nevertheless, advanced machine learning algorithms offer the potential for more efficient classification of weld bead points. This study compares the performance of three classification algorithms, random forest, k-nearest neighbors, and support vector machine, in identifying weld bead profiles and positions. Both flat and curved base metal surfaces were analyzed with the proposed inputs from the filtered point data of the scanned line profiles. These inputs included (1) the vertical distance to the next point, (2) the vertical distance to the highest point, (3) the horizontal distance to the highest point, (4) the point height relative to the scanner datum, and (5) the slope to the next point for each point on the profiles. The proposed inputs enhanced performance, and the results show that the random forest algorithm achieved the best accuracy of 98.98% for peripheral welds and 98.78% for parallel welds. The standard deviations of the measured maximum heights were 0.0129 mm for peripheral welds and 0.1613 mm for parallel welds, with respective maximum heights of 2.354 mm and 3.375 mm. Furthermore, a grinding error of just 0.066 mm was achieved, underscoring the effectiveness of these algorithms in accurately identifying weld bead geometries for robotic grinding applications.