Innovative Approach to Evaluate the Wearing of Nano-TiO2-Doped Alumina Ceramics in the Light of Image Modeling

Abstract The present research is emphasized on the microscopic observation of post-wear surface of nano-TiO2-doped alumina ceramics to access wearing by promising image processing algorithms, namely, entropy analysis, Sobel edge detection technique, and entropy filtered image histogram analysis in relation to the extent of doping. The experimental results of specific wear-rate showed an indicator with the extent of microfracturing of grains, plowing of materials and debris formation on the wear track after a long wear cycle in terms of entropy level, edge density index, and entropy filtered image, and the nature of histogram at different doping levels. The lowest value of entropy level and edge density index is shown at the level of 1 wt%. TiO2-doped alumina ceramics due to the presence of low number of granularity and microfracture grains on the wear track cause the lowering of specific wear-rate. The histogram of entropy filtered image for 1 wt% doping is more uniformly distributed with the highest frequency and lowest skewness factor over a wide range of intensity values.