Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique

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• 作者：S. Dutta^a ; A. Datta^b ; N. Das Chakladar^b ; S.K. Pal^b ; ^{skpal@mech.iitkgp.ernet.in} ; S. Mukhopadhyay^c ; R. Sen^a
• 关键词：Tool condition monitoring ; Turning ; Texture analysis ; GLCM ; Pixel pair spacing ; Power spectral density
• 刊名：Precision Engineering
• 出版年：2012
• 期刊代码：145_01416359
• 类别：et
• 出版时间：July, 2012
• 卷：36
• 期：3
• 页码：458-466
• 文件大小：1947 K

摘要

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 co-occurrence 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.