An image-based modeling technique for microstructure was created to simulate the coating wear process and to aid in the composition design of the abradable coating. A range of analysis tools were utilized for microstructure modeling, meshing, and wear analysis to develop a model of an abradable coating and analyze its wear under controlled conditions. The correlation between the percentage change in the form and composition of metal, oxide, and voids, as well as mechanical qualities like hardness, is investigated using computational models based on actual micrographic pictures of abradable coatings. Abrasion-resistant coatings such as NiCrAl-bentonite and other CoNiCrAl-BN polyester coatings were examined using microstructure photos. Various techniques were used to construct, mesh, and refine 3D coating finite element models from microstructure images, which were then enhanced by adding the blade. After that, the coating models were put through the simulated rub rig test. The effect of material parameter variation such as the changes in porosity level and hardness level on the abradability level of coatings is also presented here. As porosity increases, so does the erosion rate and abradability. A decrease in abradability occurs with an increase in hardness. Increased coating wear or decreased blade wear results in increased abradability. The abradability number of the CoNiCrAl-BN polyester coating coating, as determined by simulation, is higher than that of the NiCrAl-bentonite coating. Main outcomes of this investigation reveal that coatings with higher porosity (56%) and lower hardness (48 HR15Y) exhibit superior abradability (up to 11.34), enabling efficient, controlled material removal with minimal blade wear. In contrast, denser microstructures (46% porosity, 71 HR15Y hardness) show reduced abradability (∼6.56), resulting in higher blade damage due to increased contact stresses. Statistical analysis confirms abradability is directly proportional to porosity and erosion rate, and inversely proportional to hardness and interfacial bond strength. These findings provide a predictive, microstructure-driven design framework for optimizing abradable seal performance in gas turbine engines.