An Experimental Investigation into the Amalgamated Al2O3-40% TiO2 Atmospheric Plasma Spray Coating Process on EN24 Substrate and Parameter Optimization Using TLBO

Surface coating is a critical procedure in the case of maintenance engineering. Ceramic coating of the wear areas is of the best practice which substantially enhances the Mean Time between Failure (MTBF). EN24 is a commercial grade alloy which is used for various industrial applications like sleeves, nuts, bolts, shafts, etc. EN24 is having comparatively low corrosion resistance, and ceramic coating of the wear and corroding areas of such parts is a best followed practice which highly improves the frequent failures. The coating quality mainly depends on the coating thickness, surface roughness and coating hardness which finally decides the operability. This paper describes an experimental investigation to effectively optimize the Atmospheric Plasma Spray process input parameters of Al2O3-40% TiO2 coatings to get the best quality of coating on EN24 alloy steel substrate. The experiments are conducted with an Orthogonal Array (OA) design of experiments (DoE). In the current experiment, critical input parameters are considered and some of the vital output parameters are monitored accordingly and separate mathematical models are generated using regression analysis. The Analytic Hierarchy Process (AHP) method is used to generate weights for the individual objective functions and based on that, a combined objective function is made. An advanced optimization method, Teaching-Learning-Based Optimization algorithm (TLBO), is practically utilized to the combined objective function to optimize the values of input parameters to get the best output parameters. Confirmation tests are also conducted and their output results are compared with predicted values obtained through mathematical models. The dominating effects of Al2O3-40% TiO2 spray parameters on output parameters: surface roughness, coating thickness and coating hardness are discussed in detail. It is concluded that the input parameters variation directly affects the characteristics of output parameters and any number of input as well as output parameters can be easily optimized using the current approach.

Parameter Optimization of Amalgamated Al2O3-40% TiO2 Atmospheric Plasma Spray Coating on SS304 Substrate Using TLBO Algorithm

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.

Yttria-stabilized-zirconia hollow spheres prepared by atmospheric plasma spray

Yttria-stabilized-zirconia （YSZ） hollow spheres are widely utilized for their novel physical and chemical properties. However, developing a simple and low-cost method for preparing such hollow spheres still remains a great challenge. In this paper, an atmospheric plasma spray （APS） method is introduced, and the formation mechanism of hollow 7YSZ （ZrO2-7wt%Y2O3） spheres is presented. The hollow sphere morphology was observed by scanning electron microscopy （SEM） when agglomerated and sintered 7YSZ powders were used. Additionally, additive composition changes, phase transformations, and the thermal behavior of 7YSZ powders were analyzed by energy dispersive spectroscopy （EDS）, X-ray diffractometry （XRD）, thermogravimetric analysis （TG） and differential scanning calorimeter analysis （DSC）. Furthermore, the phase transformations of agglomerated and sintered 7YSZ powders, 7YSZ hollow spheres that annealed at various temperatures for different times are analyzed.

Development of an Emulator for the Plasma Process Control

This work aims at developing an automatic system for the control of the APS （air plasma spraying） plasma process in which some instability phenomena are present. APS is a versatile technique to produce coatings of powder material at high deposition rates. Using this technique, powder particles are injected into a plasma jet, where they are melted and accelerated towards a substrate. The coating microstructures and properties depend strongly on the characteristics of the plasma jet, which can be controlled by the adjustment of the process parameters. However, the imeractions among the spray variables, render optimization and control of this process are quite complex. Understanding relationships between coating properties and process parameters is mandatory to optimize the process technique and the product quality. We are interested in this work to build an on-line control model for the APS process based on the elements of artificial intelligence and to build an emulator that replicates the dynamic behavior of the process as closely as possible.