Features detection of Al alloy porosity during GTAW process based on arc spectrum and improved porosity-focus decision tree

Abstract

As one of the common internal defects in the aluminum welding process, porosity defect is still challenging for real-time control due to the complexity and diversity of the welding process. Arc spectrum contains large amount of welding process information and is effective for the detection of internal defects. This paper proposed a real-time porosity defect detection method for aluminum alloy in gas tungsten arc welding (GTAW) based on the improved gradient boosting decision tree and arc spectrum. The line and continuum spectrum are separated, and the arc blackbody radiation energy and electron temperature are extracted based on the new spectral separation algorithm. Considering the long-tail distribution characteristic of the training samples, a porosity-focus loss function and a parallel training structure were proposed. More targeted line spectrum features were extracted. The defect recall rate increased by 8.6 %, and the area under the receiver operating characteristic curve (AUC) increased by 5 %, without affecting the detection accuracy and response speed. The confidence level of the prediction results was improved. The model shows better robustness through testing experiments in the face of complex welding conditions and is of great importance to the internal quality monitoring of GTAW.