Analyzing the mechanisms of Al2O3-TiO2 coating for enhanced slurry erosion resistance on AISI410 stainless steel

In this research, the optimization of D-gun process parameters for depositing Al2O3-TiO2 coatings on SS410 steel has been explored, with a specific focus on enhancing resistance to slurry erosion. A hybrid intelligent approach based on an artificial neural network (ANN) is employed to quantitatively analyze the influence of jet velocity, impingement angle, and slurry concentration on erosion performance. The findings reveal the significant impact of these parameters, with higher jet velocities and slurry concentrations, coupled with lower impingement angles, leading to increased mass loss, underscoring the need for precise parameter optimization. The ANN model has been developed which is further optimized by the amended slime mold algorithm (ASMA) for accurate predictions for optimal parameter selection to enhance coating durability. Additionally, metallurgical and mechanical characterizations offer quantitative insights into material properties, including porosity percentage, microhardness, surface roughness, and bond strength, all of which play critical roles in erosion resistance. Through SEM imaging, erosion mechanisms such as lip and crater formation, plows, erosive grooves, and crowded pits are quantitatively identified, shedding light on erosive wear patterns. Comparative analysis of coated samples quantitatively underscores the varying levels of erosive damage and resistance, emphasizing the essential role of coating composition and process parameters.