ANN and RSM models approach for optimization of HVOF coating

Abstract

High-Velocity Oxygen Fuel (HVOF) coating process is employed in many industries, not only for extending the life of the material, but also to improve or restore the dimensions/surface properties of the component, by spraying molten or semi-molten powder materials over the surface of the component. Porosity and Hardness are the significant properties required to assess the quality of coatings. In this research, response surface methodology (RSM) and artificial neural network (ANN) are used for optimizing the powder composition to obtain the desired response. Based on the mixture design, twenty-five HVOF coatings were performed and the data were used for training and testing the ANN. The composition of five powders, Chromium Carbide (Cr3C2), Nickel (Ni), Chromium (Cr), Boron (B) and Silicon (Si) were varied and performed HVOF coating to obtain minimum porosity and maximum hardness to serve in high temperature oxidation environment. Optical Microscope, X-ray diffraction, Scanning electron microscope, and Vickers hardness tester were used to carry out the cross-section analysis on the coated samples. Optimized powder composition was identified to achieve a dense coating. Response value obtained by RSM and ANN models indicate that the values obtained by “ANN Model” exhibit a better prediction over “RSM Model”.