Carbon fiber-reinforced plastic (CFRP) composites are prone to damage during both manufacturing and operational phases, making the classification and identification of defects critical for maintaining structural integ...
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Carbon fiber-reinforced plastic (CFRP) composites are prone to damage during both manufacturing and operational phases, making the classification and identification of defects critical for maintaining structural integrity. This paper presents a novel dual-modal feature classification approach for the eddy current detection of CFRP defects, utilizing a parallelreal-imaginary/swintransformer (PRI-SWT) model. Built using the transformer architecture, the PRI-SWT model effectively integrates the real and imaginary components of sinusoidal voltage signals, demonstrating a significant performance improvement over traditional classification methods such as Support Vector Machine (SVM) and Vision transformer (ViT). The proposed model achieved a classification accuracy exceeding 95%, highlighting its superior capability in terms of addressing the complexities of defect detection. Furthermore, the influence of key factors-including the real-imaginary fusion layer, the number of layers, the window shift size, and the model's scale-on the classification performance of the PRI-SWT model was systematically evaluated.
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