An infrared visual sensing detection approach for swing arc narrow gap weld deviation

The present work develops a weld deviation detection system based on infrared visual sensing for swing arc narrow gap weld tracking, proposes a local pattern recognition (LPR) algorithm to detect groove edge position, and experimentally investigates their adaptabilities. This approach captures welding image synchronously with the base current period of pulsed arc as the arc swings to stay at groove sidewall, and extracts weld deviation feature from the relative distances of electrode wire center to the opposite sidewall of arc staying position, thus reducing arc light disturbance. The LPR algorithm seeks the straightest line segment by finding the minimum standard variance of groove edge pixel distribution in moving LPR window, and can adaptively acquire sidewall edge true position from the straightest segment regardless of size-varying spatters. It is shown that this approach is obviously superior to conventional mean algorithm in the precision and efficiency of weld deviation detection.