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The use of advanced thermal spray technology for surface modification of aluminum alloy parts for automobiles can greatly improve the surface properties and service life of the parts while saving resources and reducing environmental pollution. In this paper, a Ni60A coating was prepared on the surface of aluminum alloy 109 (ZL109) by supersonic plasma spraying (HEPJet). The spraying process parameters were optimized based on the response surface methodology (RSM). The effects of the main factors such as spraying power, spraying distance, and Ar flow rate on the porosity and microhardness of the coating were analyzed by the Box–Behnken method (BBD). The response surface of the porosity and microhardness of the coating was constructed by establishing the appropriate mathematical model. The response surface of porosity and microhardness of the coating was constructed by establishing a relevant mathematical model. The results show that the established mathematical model is reliable, and the spray power has the greatest influence on the porosity and microhardness of the coating. The optimal porosity and microhardness of the coating predicted by the model are 0.85% and 971.1HV0.1, respectively, which is close to the experimental results. The porosity and microhardness of the coating measured by the experiment are 0.87% and 959.8HV0.1, respectively. The coatings prepared with optimal parameters have good compactness, small porosity, and high hardness. [ABSTRACT FROM AUTHOR]

Plasma spray coating properties frequently depend -to a great extent- on the spray parameters. However, it is difficult to analyze and obtain a comprehensive model of the entire plasma spray process due to the complex chemical and thermodynamic reactions that take place during the process. In this study, Ni60CuMo coatings were prepared on ZL109 substrates. A Back Propagation (BP) Neural Network model in the artificial neural network was used to predict the change in bonding strength, microhardness, and porosity of the coatings under different spraying distances, spraying powers, and powder feeding rates. The results show that the R -value of the trained network training is 0.8828. Comparison of experimental and predicted results reveals that both show similar trends, which verifies that the BP model can effectively predict the properties of Ni-based coatings. • Ni-based coatings were prepared on the surface of aluminum alloy by plasma spraying. • The microstructure and mechanical properties of the coatings were improved after spraying. • The BP model of artificial neural network was used to optimize the spraying parameters. • The parameter combination for preparing the optimal coating was obtained. [ABSTRACT FROM AUTHOR]