Abstract
Abstract The extent of deformation induced martensite (DIM) is controlled by steel chemistry, strain rate, stress, strain, grain size, stress state, initial texture and temperature of deformation. In this research, a neural network model within a Bayesian framework has been created using extensive published data correlating the extent of DIM with its influencing parameters in a variety of austenitic grade stainless steels. The Bayesian method puts error bars on the predicted value of the rate and allows the significance of each individual parameter to be estimated. In addition, it is possible to estimate the isolated influence of particular variable such as grain size, which cannot in practice be varied independently. This demonstrates the ability of the method to investigate the new phenomena in cases where the information cannot be accessed experimentally. The model has been applied to confirm that the predictions are reasonable in the context of metallurgical principles, present experimental data and other recent data published in the literatures.
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