Journal
PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY
Volume 43, Issue -, Pages 34-42Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.precisioneng.2015.06.007
Keywords
Tool flank wear prediction; Machine vision; GLCM; Voronoi tessellation; Wavelet transform; Support vector regression
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Funding
- Steel Technology Centre, Indian Institute of Technology, Kharagpur
- CSIR [PSC0111]
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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. (C) 2015 Elsevier Inc. All rights reserved.
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