No abstract

No abstract

A multilayer feedforward neural network with two hidden layers was designed and developed for prediction of the phosphorus content of electroless Ni-P coatings. The input parameters of the network were the pH, metal turnover, and loading of an electroless bath. The output parameter was the phosphorus content of the electroless Ni-P coatings. The temperature and molar rate of the bath were constant (91 C; 0:4 Ni þþ =H 2 PO ÀÀ 2 ). The network was trained and tested using the data gathered from our own experiments. The goal of the study was to estimate the accuracy of this type of neural network in prediction of the phosphorus content. The study result shows that this type of network has high accuracy even when the number of hidden neurons is very low. Some comparison between the network's predictions and own experimental data are given.

No 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 decomposercomposer 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.