An efficient approach for defect detection in pattern texture analysis using an improved support vector machine

Manimozhi, I.; Janakiraman, S.

doi:10.1504/ijbidm.2021.115475

Cited by 1 publication

(2 citation statements)

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Section: Methodsmentioning

confidence: 99%

“…This method works by transforming the original variables of the data into a new set of uncorrelated variables, which means, high-dimensional data can be reduced to K dimensions by selecting the top K principal components, so as to achieving dimensionality reduction. 18,19 First, the data has to be preprocessed in order to remove noisy points. Then we have to calculate the covariance matrix of the preprocessed data.…”

Section: Flaws Characteristics Extraction Of Surface Defects and Data...mentioning

confidence: 99%

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Research on surface defect identification of steel balls based on improved K-CV parameter optimization support vector machine

Li,

Ren,

Feng

2023

Advances in Mechanical Engineering

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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 of the effective features data. The K-CV algorithm was employed in conjunction with an improved grid search method to find the optimal parameters “c” and “g.” This approach not only reduced the search time but also diminished the influence of individual samples on the model, thereby enhancing its robustness and ultimately improving the classification accuracy. The model’s performance was evaluated using a confusion matrix, and a comparison was made with three other machine learning models. The experimental results demonstrated the effectiveness of the proposed algorithm in classifying defects on highly reflective metal surfaces such as steel balls. The model achieved a classification accuracy of 97.15%.

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Section: Methodsmentioning

confidence: 99%

Section: Flaws Characteristics Extraction Of Surface Defects and Data...mentioning

confidence: 99%

Research on surface defect identification of steel balls based on improved K-CV parameter optimization support vector machine

Li,

Ren,

Feng

2023

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

An efficient approach for defect detection in pattern texture analysis using an improved support vector machine

Cited by 1 publication

References 0 publications

Research on surface defect identification of steel balls based on improved K-CV parameter optimization support vector machine

Research on surface defect identification of steel balls based on improved K-CV parameter optimization support vector machine

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