Dutta, Samik and Pal, Surjya K. and Sen, Ranjan (2016) Tool Condition Monitoring in Turning by Applying Machine Vision. Journal of Manufacturing Science and Engineering, 138 (5). 051008 (17 pages).

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In this paper, a method for predicting progressive tool flank wear using extracted features from turned surface images has been proposed. Acquired turned surface images are analyzed by using texture analyses, viz., gray level co-occurrence matrix (GLCM), Voronoi tessellation (VT), and discrete wavelet transform (DWT) based methods to obtain information about waviness, feed marks, and roughness from machined surface images for describing tool flank wear. Two features from each texture analyses are extracted and fed into support vector machine (SVM) based regression models for predicting progressive tool flank wear. Mean correlation coefficient between the measured and predicted tool flank wear is found as 0.991.

Item Type: Article
Depositing User: Dr. Sarita Ghosh
Date Deposited: 11 Jul 2017 11:16
Last Modified: 11 Jul 2017 11:16
URI: http://cmeri.csircentral.net/id/eprint/430

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