On-Line Detection of Porosity in Gas Tungsten Arc Welding of Aluminum Alloy Based on Spectrum Features

The real-time detection of porosity in welding process is an important problem to be solved in intelligent welding manufacturing. A new on-line detection method for porosity of aluminum alloy in robotic arc welding based on arc spectrum is proposed in this paper. First, k-shape and the improved k-means were used for the initial feature selection of the preprocessed arc spectrum to reduce the data dimension. Second, the secondary feature selection was carried out based on the importance of features to further reduce feature redundancy. Then, the optimal sample label library was established by combining the final characteristic parameters and the X-ray pictures of welds. Finally, an on-line detection method of porosity in gas tungsten arc welding of aluminum alloy based on light gradient boosting machine (LightGBM) was proposed. Compared with extreme gradient boosting (XGBoost) and categorical boosting (CatBoost), this method can achieve better detection performance. The new method proposed in this paper can be used to detect other welding defects, which is helpful to the development of intelligent welding technology. © Shanghai Jiao Tong University 2022.