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Automatic classification approach to weld defects based on PCA and SVM

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To improve the accuracy of automatic defect classification, a novel algorithm has been developed based on the principal component analysis (PCA) and support vector machine (SVM) methods. The original defect data are transformed to principal component space by the PCA algorithm and then the optimal dataset is selected. Then, the SVM is used for defect classification. For estimating the actual classification accuracy of the proposed method in a concrete system, the bootstrap method is introduced. The experimental result demonstrates that the accuracy of the new method is 90.75%, which promotes the evaluation accuracy by 3.24% and 4.93% compared with the SVM and MLP-ANN, respectively. Furthermore, the new method takes less computing time than the MLP-ANN method.
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Document Type: Research Article

Publication date: October 1, 2013

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