Filter-PCA-Based Process Monitoring and Defect Identification During Climbing Helium Arc Welding Process Using DE-SVM

In-process defect detection for climbing helium arc welding of aluminum alloy is challenging due to the highly complex nature of the heat and mass transfer process. This article presents a process monitoring and defects identification method for weld quality monitoring during climbing helium arc welding based on molten pool visual sensing. High contrast molten pool images were acquired by the designed passive vision sensor. Then, an image processing algorithm based on Otsu's method and visual saliency features was proposed to extract multiregion features from the acquired images in real-time. An optimal feature subset selection approach based on filter and principal component analysis (Filter-PCA) was developed to select the components related to welding defects. Finally, a novel prediction model using Filter-PCA and the SVM based on differential evolution algorithm (DE-SVM) was established to identify undercut weld, snake-like weld, and sound weld. The experiment results obtained in the actual production line of 2219 aluminum alloy rocket propellent tank show that the proposed method is remarkably effective and robust. This research provides a technical approach for early identification and early warning of defects during climbing helium arc welding of aluminum alloy. It also provides a basis for stability and online control of weld formation quality under time-varying welding pose.