Recognition of Weld Penetration During K-TIG Welding Based on Acoustic and Visual Sensing

Abstract

In the field of welding process control, on-line monitoring of welding quality based on multi-sensor information fusion has attracted more attention. In order to recognize the penetration state of the Keyhole mode Tungsten Inert Gas welded joint in real time, an acoustic and visual sensing system was established in this paper. The acoustic and visual features that characterize the penetration state of the welded joints in 34 dimensions were extracted and the variation of the acoustic signal and the keyhole geometry were analyzed. In addition, the weighted scoring criterion based on the Fisher distance and the maximum information coefficient (Fisher–MIC) and Support Vector Machine (SVM) model based on cross-validation (CV) are designed as the feature selection method. The feature selection method can evaluate the penetration recognition accuracy of different feature subsets. The experiment results show that the maximum recognition accuracy was 97.1655%, which was performed by the 10-dimension optimal feature subset and the CV–SVM model with particle swarm optimization (PSO–CV–SVM). It is proved that the selected acoustic and visual features can well characterize the penetration state of the welded joints, and the feature selection method and PSO–CV–SVM model have superior performance.

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