LAUSR

Ultrathin sheets edge welding is a critical technology for aircraft, aerospace, or much high-end equipment manufacturing industrial applications. However, its online quality monitoring is still challenging due to the complicated and multicoupled transport phenomena in the mesoscale molten pool. This article presents an intelligent methodology for real-time monitoring of edge welds forming quality using microvision sensing and support vector machine based on swarm optimized and computationally inexpensive floating selection (SOCIFS-SVM). The clear images of the mesoscale molten pool are continuously acquired by using the passive microvision sensor. Based on the analysis for the dynamic behavior of the mesoscale molten pool and the morphology of the formed edge welds, a robust image processing procedure is developed to extract the single-frame spatial features and interframe correlation features from the microvision image sequence of the molten pool. A Kalman filter is applied to suppress the noise caused by the pulse current, etc. Then, the SOCIFS-SVM-based approach is established to predict three typical types of welding states. Experimental results with multiple groups of different welding parameters show that the proposed method can effectively and robustly identify the lack of fusion, humping, and sound weld with the highest accuracy of 96.09%. In addition, the established model can still achieve mean accuracy of 94.98% for additional untrained data, indicating that the model has strong generalization ability. The proposed real-time quality monitoring method makes online defect diagnosis of ultrathin sheets edge welding possible and provides a basis for online quality closed-loop control.