On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression
IR@CMERI: CSIR- Central Mechanical Engineering Research Institute (CMERI), Durgapur
View Archive Info| Field | Value | |
| Title |
On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression
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| Creator |
Dutta, Samik
Pal, Surjya K. Sen, Ranjan |
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| Description |
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.
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| Publisher |
Elsevier
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| Date |
2016-01
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| Type |
Article
PeerReviewed |
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| Identifier |
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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| Relation |
http://cmeri.csircentral.net/397/
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