Predicting flow strength of austenitic steels with an IPANN model using different training strategies

Model construction and training strategies of an IPANN were developed to improve the prediction accuracy of the hot strength of a series of austenitic steels with different carbon content deformed under a wide range of conditions. The prediction accuracy is largely dependent on the training schemes and model structure because the flow strength varies with deformation conditions and chemical compositions in a very complex way. The scheme for selecting training data of every independent input was optimised, so that a generalised model could be achieved with less training data. With the strategies introduced in this work, the effect of the carbon content and deformation was accurately presented in both the work hardening and dynamic recrystallisation regimes.