Artificial neural network modelling of plating rate and phosphorus content in the coatings of electroless nickel plating

In this paper, a computer neural network has been developed for the simulation and prediction of plating rate and phosphorus content (P%) in the coatings, as a function of electroless plating bath composition and process parameters. Based on the optimized parameters, the model which is based on three layers artificial neural network (ANN) with back propagation learning algorithm was trained using datasets from orthogonal experiments. The results showed that the predicted value of neural network model coincided well with the experimental value. Therefore, a new way of optimizing process parameters and performance has been provided.