Estimation of Hot Torsion Stress Strain Curves in Iron Alloys Using a Neural Network Analysis
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The hot torsion stress-strain curves of steels have been modelled using a neural network, within a Bayesian framework. The analysis is based on an extensive database consisting of detailed chemical composition, temperature and strain rate from new hot torsion experiments. Non-linear functions are obtained, describing the variation of stress-strain curves with temperature and chemical composition. Predictions are associated with error bars, whose magnitude depends on their position in the input space. From the population of possible models, a "committee of models" is found to give the most reliable estimate. The results from the neural network model where found to be consistent with known models, and reasonable estimates are obtained beyond the scope of the experimental data.
- Transactions of the Iron and Steel Institute of Japan
Transactions of the Iron and Steel Institute of Japan 39(10), 999-1005, 1999-10
The Iron and Steel Institute of Japan