Prediction of HVAF thermal spraying parameters and coating properties based on improved WOA-ANN method

In this study, high-velocity air fuel (HVAF) thermal spray technology was utilized to deposit Hastelloy C276 powder onto 316L stainless steel substrates. The effects of various spray process parameters such as propane pressure, air pressure, spray distance, and powder feeding rate on the microstructure, microhardness, and corrosion resistance of the coatings were investigated. In addition, the coupling effects among multiple spray parameters were considered by employing a combined approach with the Improved Whale Optimization Algorithm (IWOA) and Artificial Neural Network (ANN) model, and the mapping relationships between the spray process parameters and coating porosity, microhardness, and corrosion current density was established. The results revealed that the prepared C276 coating exhibits lower porosity, higher microhardness, and exceptional corrosion resistance. And the IWOA-ANN model outperforms the ANN model and WOA-ANN model in the prediction accuracy and stability for all three performance measures, particularly for porosity and microhardness. Furthermore, the comparison between experimental results and predicted results validates the reliability and accuracy of the trained IWOA-ANN model, demonstrating its efficacy in predicting HVAF spray parameters and coating properties.