Infrared Visual Sensing Detection Approach of Swing Arc Narrow Gap Weld Deviation Based on Outlier Data Filtering

Narrow gap arc welding interferences pose notable challenges to accurate weld deviation detection in infrared visual sensing. An outlier data filtering (ODF) approach is proposed to precisely detect weld deviation between the groove and the torch centers after eliminating the welding-induced intense disturbances in images. The ODF approach develops a global pattern recognition (GPR) based algorithm to detect the groove center, and an optimal-estimation based position consistency filtering (PCF) algorithm to locate the torch center. In addition, ODF also modifies a welding image processing procedure to prepare the groove and the wire edges for the center detections. Experimental results show that the GPR algorithm removes the simulated welding spatters of ≤ 2.48 mm diameters on the groove edge, and the PCF algorithm accurately gains the torch center with the detection error of−0.022 ~ +0.035 mm. Finally, the ODF approach realizes a precise detection of the weld deviation with the detection error of−0.107 ~ +0.079 mm, demonstrating the effectiveness of the proposed approach regardless of welding interferences.