Dutta, S. and Datta, A. and Das Chakladar, N. and Pal, S.K. and Mukhopadhyay, S. and Sen, R. (2012) Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique. Precision Engineering, 36. pp. 458-466. ISSN 0141-6359

Full text not available from this repository.

Abstract

With the advancement of digital image processing, tool condition monitoring using machine vision is gaining importance day by day. In this work, online acquisition of machined surface images has been done time to time and then those captured images were analysed using an improvised grey level cooccurrence matrix (GLCM) technique with appropriate pixel pair spacing (pps) or offset parameter. A novel technique has been used for choosing the appropriate pps for periodic texture images using power spectral density. Also the variation of texture descriptors, namely, contrast and homogeneity, obtained from GLCM of turned surface images have been studied with the variation of machining time along with surface roughness and tool wear at two different feed rates.

Item Type:	Article
Subjects:	Image processing for tool condition monitoring
Depositing User:	Dr. Sarita Ghosh
Date Deposited:	15 Apr 2016 09:36
Last Modified:	15 Apr 2016 11:53
URI:	http://cmeri.csircentral.net/id/eprint/154

Actions (login required)

View Item