Abstract: A 4×9×1 type of BP neural network model was set up to predict the Al2O3 contents in Ni-Al2O3 composite coatings by using the artificial neural network technology.The content and 3D surface pattern were analyzed by using XRD diffraction and atomic force microscopy (AFM).The results show that when the number of hidden layers is 9, the minimum root mean square error is 1.13;, the fitting similarity R is about 0.99937, which indicates that the BP neural network model can accurately predict the Al2O3 contents.When the duty ratio is 60;, the cathode current density of 4 A/dm2, pH=4, the bath temperature of 55 ℃, Ni-Al2O3 composite coating has a dense structure, and the crystalline is fine.

Key words: A 4×9×1 type of BP neural network model was set up to predict the Al2O3 contents in Ni-Al2O3 composite coatings by using the artificial neural network technology.The content and 3D surface pattern were analyzed by using XRD diffraction and atomic force microscopy (AFM).The results show that when the number of hidden layers is 9, the minimum root mean square error is 1.13;, the fitting similarity R is about 0.99937, which indicates that the BP neural network model can accurately predict the Al2O3 contents.When the duty ratio is 60;, the cathode current density of 4 A/dm2, pH=4, the bath temperature of 55 ℃, Ni-Al2O3 composite coating has a dense structure, and the crystalline is fine.