Skip to main content
padlock icon - secure page this page is secure

Automatic classification approach to weld defects based on PCA and SVM

Buy Article:

$22.00 + tax (Refund Policy)

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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: CLASSIFICATION; DEFECTS; FEATURES; PCA; SVM

Document Type: Research Article

Publication date: October 1, 2013

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more