Neural network modelling of hot deformation of austenite
M. J. Peet and A. A. Shirzadi
Proceedings of the 6th International Conference on Physical and Numerical Simulation of Materials Processing, (2010).
Abstract
The hot deformation behaviour of austenite in steels is a complicated process which depends on chemical composition, microstructure, temperature and strain rate. While many models have been developed to represent the flow stress as a function of these variables, it is not yet possible to predict the behaviour for a new alloy. Linear regression techniques are not capable of representing the data, however, neural networks are capable of modelling highly non-linear data. A neural network model was developed in this work using a large database of various steels. The model allows the calculation of error bars that depend upon the position of a prediction in the input space and the level of perceived noise in the data. The validity of the model was evaluated by comparing its outputs against those of the six carbon-manganese steels with different compositions.
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