Keyhole status prediction based on voting ensemble convolutional neural networks and visualization by Grad-CAM in PAW

Abstract

K-PAW (Keyhole Plasma Arc Welding) has a wide range of applications in welding medium-thick metal workpieces. However, the welding processes are vulnerable due to the fragile force balance on the liquid metal around the keyhole, and burn-through or lack of fusion might be caused. In order to optimize the welding quality, considerable work has been devoted to penetration/keyhole status prediction by visual sensing and deep learning algorithms. However, a single network model is challenging to extract comprehensive features of weak-discrimination weld pool images. This paper focused on the correspondence between the topside weld pool images and the prediction/keyhole status. A novel prediction method has been proposed based on eight classical CNNs trained, compared, and fused. For these single models, the prediction accuracy is higher than 95 %, with the speed of faster than 10 frames per second (FPS). The model visualization by the Grad-CAM method was performed to show the focused feature regions clearly. Although regarded as black boxes, these prediction models are considered robust when the extracted features are consistent with the prior knowledge. KeyholeVot, a voting ensemble decision model, was established based on the selected three robust models (i.e., InceptionNetV3, InceptionResNetV2, and XceptionNet) and achieved 96.62 % evaluation accuracy.