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A new method in prediction of TCP phases formation in superalloys

Mousavi Anijdan, S. H ; Sharif University of Technology | 2005

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  1. Type of Document: Article
  2. DOI: 10.1016/j.msea.2005.01.012
  3. Publisher: 2005
  4. Abstract:
  5. The purpose of this investigation is to develop a model for prediction of topologically closed-packed (TCP) phases formation in superalloys. In this study, artificial neural networks (ANN), using several different network architectures, were used to investigate the complex relationships between TCP phases and chemical composition of superalloys. In order to develop an optimum ANN structure, more than 200 experimental data were used to train and test the neural network. The results of this investigation shows that a multilayer perceptron (MLP) form of the neural networks with one hidden layer and 10 nodes in the hidden layer has the lowest mean absolute error (MAE) and can be accurately used to predict the electron-hole number (Nv) and TCP phases formation in superalloys. © 2005 Elsevier B.V. All rights reserved
  6. Keywords:
  7. Electron energy levels ; Error analysis ; Multilayer neural networks ; Phase composition ; Phase transitions ; Multilayer perception (MLP) ; Network architecture ; Phase formation ; Topologically closed packed (TCP) phases ; Superalloys ; Superalloy
  8. Source: Materials Science and Engineering A ; Volume 396, Issue 1-2 , 2005 , Pages 138-142 ; 09215093 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0921509305000705