A novel multilayer neural network model for heat treatment of electroless Ni-P coatings

Abstract

A novel multilayer neural network was designed and implemented for prediction of the hardness of electroless Ni-P coatings. Heat treatment, a process for adjusting the hardness of electroless Ni-P coatings, was modeled. Three neural network models, a multilayer preceptron, a radial basis functions network, and a novel model, called the decomposer-composer model, were implemented and applied to the problem. The input parameters were the phosphorus content of the coatings, and the temperature and duration of the heat treatment process. The models output was the hardness of electroless Ni-P coatings. The training and test data were extracted from a number of experimental projects. The decomposer-composer model achieved better result and performance compared to the other models.