Dutta, Samik and Pal, Surjya K. and Sen, Ranjan (2016) On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression. Precision Engineering, 43. pp. 34-42.

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In this paper, a method for on-machine tool condition monitoring by processing the turned surface images has been proposed. Progressive monitoring of cutting tool condition is inevitable to maintain product quality. Thus, image texture analyses using gray level co-occurrence matrix, Voronoi tessellation and discrete wavelet transform based methods have been applied on turned surface images for extracting eight useful features to describe progressive tool flank wear. Prediction of cutting tool flank wear has also been performed using these eight features as predictors by utilizing linear support vector machine based regression technique with a maximum 4.9% prediction error.

Item Type: Article
Depositing User: Dr. Sarita Ghosh
Date Deposited: 10 Jul 2017 07:21
Last Modified: 10 Jul 2017 07:21
URI: http://cmeri.csircentral.net/id/eprint/397

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