The recognition of different welding defects is important for the assessment of the safety of welded structures. This study proposes a method to recognize defects in stainless steel welds based on multi-domain feature...
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The recognition of different welding defects is important for the assessment of the safety of welded structures. This study proposes a method to recognize defects in stainless steel welds based on multi-domain feature expression and self-optimization. This is because of the poor detection of feature-related information from signals, inadequate feature extraction by the convolutional network, the limited capability of intelligent techniques, network redundancy, and a lack of self-adaptive capability in prevalent methods, A 1D ultrasonic detection dataset of austenitic stainless steel welds in the time domain (TD) is first constructed and the 1D TD signals are rendered in the time-frequency domain, Gramain angular field, and the Markov transition field. The aim is to enrich the feature expression of 1D ultrasonic echo data of the weld defects. A comparison among a variety of lightweight convolutional neural networks on multi-spatial domain datasets is used to identify a combination of network and spatial domain datasets that are suitable for recognizing welding defects. Finally, a multi-scale depthwise separable convolution is designed, and is subjected to adaptive compression and parameter-adaptive optimization based on the sparrow search algorithm to construct the self-optimizing lightweight multi-scale MobileNetV3 (SLM-MobileNetV3) model. The results of experiments showed that the SLM-MobileNetV3 model has an accuracy of 97.26% for the recognition of five types of defects: inclusion, crack, porosity, incomplete penetration, and a lack of fusion. The time required for testing a single image was 2 ms. Experimental analysis showed that the proposed method can improve the accuracy of defect recognition while using few parameters and computational cost, and is generalizable.
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