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SS304 is a commercial grade stainless steel which is used for various engineering applications like shafts, guides, jigs, fixtures, etc. Ceramic coating of the wear areas of such parts is a regular practice which significantly enhances the Mean Time Between Failure (MTBF). The final coating quality depends mainly on the coating thickness, surface roughness and hardness which ultimately decides the life. This paper presents an experimental study to effectively optimize the Atmospheric Plasma Spray (APS) process input parameters of Al2O3-40% TiO2 ceramic coatings to get the best quality of coating on commercial SS304 substrate. The experiments are conducted with a three-level L18 Orthogonal Array (OA) Design of Experiments (DoE). Critical input parameters considered are: spray nozzle distance, substrate rotating speed, current of the arc, carrier gas flow and coating powder flow rate. The surface roughness, coating thickness and hardness are considered as the output parameters. Mathematical models are generated using regression analysis for individual output parameters. The Analytic Hierarchy Process (AHP) method is applied to generate weights for the individual objective functions and a combined objective function is generated. An advanced optimization method, Teaching-Learning-Based Optimization algorithm (TLBO), is applied to the combined objective function to optimize the values of input parameters to get the best output parameters and confirmation tests are conducted based on that. The significant effects of spray parameters on surface roughness, coating thickness and coating hardness are studied in detail.

journal of surface engineered materials and advanced technology

Parameter Optimization of Amalgamated Al&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;-40% TiO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; Atmospheric Plasma Spray Coating on SS304 Substrate Using TLBO Algorithm