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

作　　者：蒋浩强 陈善本 许靖远 JIANG Haoqiang;CHEN Shanben;XU Jingyuan(School of Materials Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]School of Materials Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2024年第2期339-348,共10页上海交通大学学报（英文版）

基　　金：the National Natural Science Foundation of China(Nos.61873164 and 51575349)。

摘　　要：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.

关 键 词：porosity detection robotic arc welding arc spectrum 

分 类 号：TG441.7[金属学及工艺—焊接]