| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 830485 | Materials & Design (1980-2015) | 2012 | 9 Pages |
In this paper, the applicability of artificial neural network (ANN) for the prediction of the oxidation kinetics of aluminized coating is presented. For developing the model, a consistent set of experimental data i.e. nanocrystalline Ni samples were aluminized by two steps aluminizing process and oxidized at 800, 900 and 1000 °C for various times are used. The exposure time and temperature of oxidation were used as the inputs of the model and the resulting mass gain of oxidized samples as the output of the model. Multi-layer perceptron neural network structure and back-propagation algorithm are used for the training of the model. After testing many different ANN architectures an optimal structure of the model i.e. 2-5-6-1 is obtained. Comparison of experimental and predicted values using the proposed ANN model shows that there is a good agreement between them with mean relative error less than 1.2%. This shows that the ANN model is an accurate and reliable approach to predict the oxidation behavior of aluminized nanocrystalline coatings.
Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► Oxidation behavior of aluminized nanocrystalline Ni was experimentally investigated. ► It is observed that the oxidation kinetics does not follow the parabolic law. ► ANN model was employed to predict the oxidation behavior of oxidized samples. ► It is found the ANN model is a reliable approach to predict the oxidized mass of coating.
