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《Proceedings of the 6th International Conference on Physical and Numerical Simulation of Materials Processing》2010年
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Neural network modelling of hot deformation of austenite

M.J.Peet  A.A.Shirzadi  
【摘要】: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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