Tool condition monitoring by SVM classification of machined surface images in turning
IR@CMERI: CSIR- Central Mechanical Engineering Research Institute (CMERI), Durgapur
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| Title |
Tool condition monitoring by SVM classification of machined surface images in turning
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| Creator |
Bhat, Nagaraj N.
Dutta, Samik Vashisth, Tarun Pal, Srikanta Pal, Surjya K. Sen, Ranjan |
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| Description |
Tool condition monitoring has found its importance to meet the requirement of quality production in industries. Machined surface is directly affected by the extent of tool wear. Hence, by analyzing the machined surface, the information about the cutting tool condition can be obtained. This paper presents a novel technique for multi-classification of tool wear states using a kernel-based support vector machine (SVM) technique applied on the features extracted from the gray-level co-occurrence matrix (GLCM) of machined surface images. The tool conditions are classified into sharp, semi-dull, and dull tool states by using Gaussian and polynomial kernels. The proposed method is found to be cost-effective and reliable for online tool wear classification.
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| Publisher |
Springer
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| Date |
2016
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| Type |
Article
PeerReviewed |
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| Identifier |
Bhat, Nagaraj N. and Dutta, Samik and Vashisth, Tarun and Pal, Srikanta and Pal, Surjya K. and Sen, Ranjan (2016) Tool condition monitoring by SVM classification of machined surface images in turning. The International Journal of Advanced Manufacturing Technology, 83 (9-12). pp. 1487-1502.
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| Relation |
http://cmeri.csircentral.net/429/
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