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Prediction of phosphorus content of electroless nickel–phosphorous coatings using artificial neural network modeling

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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 (\( 91^\circ {\text{C}},\:0.4\,{\text{Ni}}^{{{\text{ + + }}}} /{\text{H}}_{2} {\text{PO}}_{2}^{{ - - }} \)). 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.

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Acknowledgments

The study was financially supported by Isfahan University of Technology and Isfahan Industrial Complex. The authors are grateful to Dr. A. Saatchi the head of Materials Engineering Department for his assistance.

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Authors and Affiliations

  1. Department of Applied Information Technology, IT University of Gothenburg, Gothenburg, Sweden

    Sayed Yousef Monir Vaghefi

  2. Department of Materials Engineering, Isfahan University of Technology, Isfahan, Iran

    Sayed Mahmoud Monir Vaghefi

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  1. Sayed Yousef Monir Vaghefi
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  2. Sayed Mahmoud Monir Vaghefi
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Correspondence to Sayed Yousef Monir Vaghefi.

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Monir Vaghefi, S.Y., Monir Vaghefi, S.M. Prediction of phosphorus content of electroless nickel–phosphorous coatings using artificial neural network modeling. Neural Comput & Applic 20, 1055–1060 (2011). https://doi.org/10.1007/s00521-010-0473-6

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  • DOI: https://doi.org/10.1007/s00521-010-0473-6

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