Artificial Intelligent Aided Analysis and optimization of High Velocity Oxy Fuel (HVOF) Sprayed Cr3C2−25NiCr Coatings

The coating properties are sensitive to the behaviors of in-flight particles which are mainly influenced by the operating parameters of the HVOF process. Due to the complex chemical and thermodynamic reactions, real-time optimizing the coating properties during the HVOF process is still a challenging issue.

This study focus on analyzing and predicting the HVOF sprayed Cr₃C₂−NiCr coatings by applying an Artificial Neural Network (ANN) model to optimize the operating parameters. Combined with an Accuraspray-g3 system which is applied to detect and acquire the temperature and velocity of in-flight particles, Cr₃C₂−NiCr powders were deposited via HVOF spraying system, in which the process parameters were automatically recorded during the spray process. The ANN model was built and trained with experimental data and corresponding coating properties to investigate the relationship between the operating parameters (gas flow rates, stoichiometric oxy/fuel ratio, spray distance, etc.) and the performance of coatings, which therefor contributes to optimize the mechanical and abrasive wear performance of coatings. The reliability and accuracy of the well-trained ANN model were verified by further experiments, which means the coatings with desired properties can be obtained according to the calculation results of the ANN model.

Keywords: HVOF spraying; ANN model; Cr₃C₂−NiCr coatings