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The present research describes the post wear SEM images of nano-CuO doped alumina ceramics in the light of three different image processing algorithms, viz. entropy analysis, Sobel edge detection techniques and entropy filtered image histogram analysis. The 2 wt% CuO doped sintered alumina has shown the best performance towards wearing due to maximum bulk density, hardness and fracture toughness. These materialistic properties are fitted to correlate with the lowest value of entropy and edge density index at the level of 2 wt% doping. The entropy filtered image histogram of 2 wt% doping is the closest to the Gaussian Bell shape distribution with the lowest skewness factor. These three-image computed methods validate their suitability in developing a real-life implementation for a possible wear resistance estimator.

A Novel Approach to Evaluate the Wear Behavior of CuO Doped Sintered Alumina Ceramics Using Image Processing Models