Performance analysis of plasma spray Ni60CuMo coatings on a ZL109 via a back propagation neural network model.

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]