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

• Published in: Precision engineering Vol. 36; no. 3; pp. 458 - 466
• Main Authors: Dutta, S., Datta, A., Chakladar, N. Das, Pal, S.K., Mukhopadhyay, S., Sen, R.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.07.2012; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Density; Exact sciences and technology; GLCM; Homogeneity; Image contrast; Image processing; Industrial metrology. Testing; Mechanical engineering. Machine design; Pattern recognition. Digital image processing. Computational geometry; Pixel pair spacing; Power spectral density; Surface layer; Texture; Texture analysis; Tool condition monitoring; Turning; Power spectral density; Turning; Texture analysis; Pixel pair spacing; Tool condition monitoring; GLCM; Image texture; Grey level image; Image processing; Spectral energy distribution; Roughness; Machining; Digital processing; Spacing; Digital image; Rough surface; Time variation; Operating conditions; Image analysis; Tool wear; Homogeneity; Penetration rate; Cutting tool; Monitoring
• ISSN: 0141-6359; 1873-2372
• DOI: 10.1016/j.precisioneng.2012.02.004

▶ Accurate GLCM analysis is dependent on the appropriate selection of pixel pair spacing (pps) value for periodic texture images. ▶ Automatic computation of appropriate pps value has been done. ▶ A study of the condition of the cutting tool using GLCM technique with optimum pps has been done. ▶ Contrast and homogeneity were two texture descriptors found to be suited, where contrast is the most suited descriptor. ▶ The selection of optimum pps value can be done for any periodic texture images 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.