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Prediction of Deformation Behavior of Austenitic Stainless Steel 304 in Dynamic Strain Aging Regime
| Content Provider | Semantic Scholar |
|---|---|
| Author | Krishnamurthy, Nitin Singh, Yashjeet Gupta, Amit R. Das Singh, Shri Krishna |
| Copyright Year | 2013 |
| Abstract | The main focus of this paper is prediction of flow stress of Austenitic Stainless Steel 304 in the Dynamic Strain Aging (DSA) regime. For this purpose, a comparative study has been made on the capability of modified Zerilli Armstrong (ZA) model and the Artificial Neural Networks (ANN) model for representing the flow stress prediction in the DSA Regime. The DSA regime was identified by observing the serrations in the plot between true stress and true strain.The modified-ZA equation for prediction of flow behavior at elevated temperature of the material considers isotropic hardening, temperature softening, strain rate hardening, and the coupled effects of temperature and strain and of strain rate and temperature on the flow stress. Artificial Neural Network is another powerful tool to predict the flow stress behavior which uses a part of the data to train the network while the other is used to validate the model. Suitability of these models was evaluated by comparing the correlation coefficient and absolute average error of prediction. It was observed that the flow stress predictions of ZA model were not as accurate as compared to predictions of ANN model. The resultant value of the correlation coefficient for ZA Model was 0.8889 and that of ANN’s tested data was 0.9990. |
| Starting Page | 143 |
| Ending Page | 147 |
| Page Count | 5 |
| File Format | PDF HTM / HTML |
| DOI | 10.11127/ijammc.2013.02.025 |
| Volume Number | 3 |
| Alternate Webpage(s) | http://www.ijammc-griet.com/attach/1362723734_025.pdf |
| Alternate Webpage(s) | https://doi.org/10.11127/ijammc.2013.02.025 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
