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.
Tool Condition Monitoring in Turning by Applying Machine Vision
Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received April 18, 2015; final manuscript received October 5, 2015; published online November 19, 2015. Assoc. Editor: Robert Gao.
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Dutta, S., Pal, S. K., and Sen, R. (November 19, 2015). "Tool Condition Monitoring in Turning by Applying Machine Vision." ASME. J. Manuf. Sci. Eng. May 2016; 138(5): 051008. https://doi.org/10.1115/1.4031770
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