Prediction of properties of electroless nickel plating with diamond powder based on artificial neural network

ObjectivesTo improve the quality of chemical plating on diamond micropowders, an experimental analysis was conducted on the influence of key process parameters on the plating quality during the chemical plating process. The experimental results were then predicted using artificial neural networks.MethodsNickel plating experiments were carried out on the surfaces of M1/2, M6/12, and M20/30 micron diamond powders using the electroless plating method. The effects of electroless plating process parameters—such as diamond particle size, concentration of sodium hypophosphite, plating solution temperature, and plating solution pH—on the coating properties were investigated. The performance of the coatings were evaluated as follows: (1) The deposition rate of the coating was expressed as the difference in the quality of diamond powder before and after electroless plating per unit time. (2) The coating density was expressed as the mass of the coating per unit volume. (3) Each group of coated diamond powders was immersed in hydrochloric acid solution with a mass fraction of 10% for 24 hours, and the corrosion weight loss of diamond powder was used to indicate the coating's corrosion resistance of the coating—where higher corrosion weight loss indicates poorer corrosion resistance. Data on the influences of process parameters, such as diamond particle size, sodium hypophosphite concentration, plating solution temperature, and plating solution pH on coating performance were used as the training set. Both BP and GRNN artificial neural networks were applied to predict the deposition rate, coating density, and corrosion resistance under four different conditions. The accuracy of the models was evaluated by comparing experimental data with predicted values. ResultsThe BP neural network model and the GRNN model can be used to predict the coating performance of micron diamond powders after training on sample data. The absolute relative error between the predicted coating performance values of the BP neural network model and the experimental values was less than 15.00%, with an average absolute relative error of 9.14%. The absolute relative error between the predicted coating performance values and experimental values of the GRNN model was less than 10.00%, with an average absolute relative error of 5.07%. In predicting the performance of electroless nickel plating on diamond micro powders, the predictive performance of GRNN is superior to that of BP neural network.ConclusionsThe prediction error values of BP neural network and GRNN for the chemical plating performance of diamond micropowder are both less than 10.00%, which proves that they can be used to predict the relevant results and reduce the number of experiments to obtain optimal process parameters. And the prediction error of GRNN is smaller than that of BP neural network, which proves that the performance of GRNN in prediction experiments is better than that of BP neural network.