Research on surface defect identification of steel balls based on improved K-CV parameter optimization support vector machine
Lin Li,
Tian-ming Ren,
Ming Feng
Abstract:Surface defects generated during the production process of steel balls can lead to bearing failures, which makes it crucial to promptly detect and classify these defects. Defects classify is helpful for analysis and improving the production process. An algorithm that incorporates K-fold cross-validation (K-CV) with improved grid search is proposed to optimize the parameters of SVM, in order to detect surface defects with steel balls. Principal Component Analysis (PCA) was employed to reduce the dimensionality … Show more
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