A novel approach to analyze the microstructure of porous ceramics by image processing technique

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The present investigation focuses on quantifying microstructural attributes of porous sintered calcia (CaO) ceramics in relation to grains, pores, and their distribution. In this investigation, a Python -based digital image processing computer program is used to analyze microscopic images more accurately. After detecting the grain boundaries accurately, grain pores and boundary pores are successfully separated which led to calculate average grain-pores per grain and average boundary-pore per unit boundary line with comparison among three different microstructures. Accuracy and processing time are compared with commonly used linear intercept method (LIM) and bounding box method (BBM). It is found that the Maxwell distribution fits well in all cases and from the distribution curve root mean square (RMS) size, most probable size, and average size of grains and pores are calculated. Maximum grain growth is found for the case of 2 wt% La 2 O 3 addition. Among the three microstructures, the RMS grain size of 2 wt% La 2 O 3 added calcia is 50.84 μm compared to 24.3 μm and 33.66 μm for without additive and 2 wt% CeO 2 addition respectively. The best porous microstructure with the highest grain size is achieved by the addition of 2 wt% La 2 O 3 in calcia matrix.