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Condition monitoring of the grinding wheel is very si gnificant in the industry as it plays a direct role in determining the final surface quality of the ground ed components. But most of the current research in t he field is focused on qualitative assessment of the grinding wh eel condition. Application of machine vision and ima ge processing are becoming more common in the manufacturing secto r today. Various image processing techniques can be successfully utilized for distinguishing the loaded portion of the grinding wheel from the rest of the wheel. Here, we present the development and validation of an innova ti e software system based on image processing for e valuating quantitatively the percentage of grinding wheel loa ding. The software converts the RGB image captured usi ng a USB microscope into the grayscale image that has pixel light intensity varying from 0 to 255. The high pix el intensity of the loaded chips in the grinding wheel is used for setti ng a threshold value for image segmentation (global thresholding). Based on the threshold value set, the software conver ts the grayscale image into a binary image of white and black pixels in which the black pixels corresponds to the loaded portion of the wheel. The percentage of wheel loading s determined as the ratio of black pixels to the total number of pixels. The results were validated with the equivale nt Matlab® package and shows close agreement. This innovative, economic al system has the capability of providing a reliabl e solution for the quantitative measurement of grinding wheel loading.

Image Processing Based Tailor - made Software Package for the Condition Monitoring of Grinding Wheel