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 magnitudes depend on their position in the input space. From the population of possible models, a committee of models is found to give the most reliable estimates. The results from the neural network model were found to be consistent with known modles, and reasonable estimates are obtained beyond the scope of the experimental data.
ISIJ International, Vol. 39, 1999, 999-1005.
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