Investigation of CNC Turning Tool Wearing using Image Processes
Nukman Bin Yusoff1, Abdulaziz S. Alaboodi2, Osama I. Alsultan3

1Dr. Nukman Bin Yusoff, Department of Mechanical Engineering, University, Malaya Kuala Lumpur Malaysia.
2Dr. Abdulaziz S. Alaboodi, Department of Mechanical Engineering, Qassim University, Buraydah Qassim Saudi Arabia.
3Eng. Osama I. Alsultan, Department of Mechanical Technology, Buraydah College of Technology, Buraydah, Qassim Saudi Arabia.
Manuscript received on 10 December 2014 | Revised Manuscript received on 20 December 2014 | Manuscript Published on 30 December 2014 | PP: 25-29 | Volume-4 Issue-7, December 2014 | Retrieval Number: G1889124714/14©BEIESP
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Abstract: Tool wear affects both spacemen dimensional precision and surface quality. Therefore, the prediction of tool wear amount during machining processes is very important in order to obtain high precision parts, which is reducing the manual fit operations, and production cost. Image processing analysis has been used to investigate tool wearing. One of the most common methods for image processing is texture analysis. That is the gray level co-occurrence matrix (GLCM), which have large number of texture features. In this paper, the relationship between GLCM texture features and the cutting tool wear in CNC turning operations has been investigated. Cutting tool wear has been represented by the machining time. A vision system has been employed to capture images for specimens with various machining time for the same cutting tool then images will analyzed by MATLAB functions codes, to calculate the texture features. Results showed that four texture features have good correlations with the machining time of the cutting tool.
Keywords: CNC, GLCM, Tool Wearing, Texture Features, Vision System, Image Processing.

Scope of the Article: Manufacturing Processes