Bayesian neural network model for austenite formation in steels

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Abstract
The formation of austenite during the continuous heating of steels was investigated using neural network analysis with a Bayesian framework. An extensive database consisting of the detailed chemical composition, Ac1 and Ac3 temperatures, and the heating rate was compiled for this purpose, using data from the published literature. This was assessed using a neural network, with the aim of modelling the austenite start and finish temperatures. The results from the neural network analysis were consistent with what might be expected from phase transformation theory. MST/3373

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