Artificial Neural Network to Predict the Hot Deformation Behavior of Super 13Cr Martensitic Stainless Steel
| Autor: | Dong Na Yan, Guanjun Qiao, De Ning Zou, Ying Han |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Normalization (statistics)
Materials science Artificial neural network business.industry Mechanical Engineering Martensitic stainless steel Structural engineering Strain rate Flow stress Overfitting engineering.material Deformation (meteorology) Condensed Matter Physics Compression (physics) Mechanics of Materials engineering General Materials Science business |
| Zdroj: | Materials Science Forum. 695:361-364 |
| ISSN: | 1662-9752 |
| Popis: | The hot deformation behavior of super 13Cr martensitic stainless steel was investigated using artificial neural network (ANN). Hot compression tests were carried out at the temperature range of 950°C to 1200°C and strain rate range of 0.1–50s–1at an interval of an order of magnitude. Based on the limited experimental data, the ANN model for the constitutive relationship existed between flow stress and strain, strain rate and deformation temperature was developed by back-propagation (BP) neural network method. A three layer structured network with one hidden layer and ten hidden neurons was trained and the normalization method was employed in training for avoiding over fitting. Modeling results show that the developed ANN model can efficiently predict the flow stress of the steel and reflect the hot deformation behavior in the whole deforming process. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|