Prediction of Deposition Parameters in Manufacturing of Ni-Based Coating Using ANN

Qualities of coatings deposited by High-velocity oxy-fuel (HVOF) spray technique are sometimes greatly influenced by the deposition parameters. It is difficult to research and develop a comprehensive model of the HVOF spray process because of the complex chemical and thermodynamic processes involved. The aim of this study is to use a back propagation neural network to create a predictive model for the mechanical properties of NiCrSiBFe coatings deposited by HVOF. The impact of the deposition parameters with respect to the intermediate process is also examined in this study. The change in porosity, nano-hardness, and sliding wear rate of coatings under various powder feed rate, stand-off distance, and oxygen gas flow rate were predicted using back propagation neural network algorithm. Similar trends are seen when comparing the predicted and experimental results, indicating that the developed model correctly predicted the properties of NiCrSiBFe coatings. The average errors for porosity, nano-hardness, and sliding wear rate are 1.816%, 1.997%, and 4.405%, respectively. The developed back propagation model can therefore be applied to coating operating practice for spray performance prediction, and also for parameter management and optimisation.