LAUSR

Accurate characterization of keyhole pool behavior is important to improve the penetration recognition and quality detection during the variable polarity plasma arc welding (VPPAW) of aluminum alloy. However, the low-level hand-crafted visual/acoustic features are often incapable of sufficiently representing the dynamic characteristics of keyhole pool under complex welding conditions. In this paper, we developed an end-to-end visual-acoustic penetration recognition (VAPR) framework based on a hybrid convolutional neural network (CNN) and extreme learning machine (ELM), which consists of three consecutive phases: (1) visual-acoustic data preparation, (2) multi-features extraction, and (3) penetration classification. Specifically, we applied a flexible dual-sensor acquisition system for synchronously collecting/preprocessing the visual-acoustic signals and exploring the internal correlation between the visual-acoustic features and keyhole pool behavior as well as weld penetration status. Then we established two individual CNNs for learning high-level visual-acoustic features directly from the raw visual images and 2-D time-frequency spectrograms. With the visualization of CNN-based deep learning features, we also explained the physical meanings of visual-acoustic features. To further improve the prediction performance of CNN model, we employed the ELM model as a strong tool to classify penetration status. By comparing with other state-of-the-art methods, our hybrid CNN-ELM approach with visual-acoustic fusion has a superior performance in terms of the classifying accuracy. The VAPR framework in this paper will provide some guidance for post-process quality inspection and realize an adaptive control of VPPAW process.