Prediction of metadynamic softening in a multi-pass hot deformed low alloy steel using artificial neural network
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The metadynamic softening behaviors in 42CrMo steel were investigated by isothermal interrupted hot compression tests. Based on the experimental results, an efficient artificial neural network (ANN) model was developed to predict the flow stress and metadynamic softening fractions. The effects of deformation parameters on metadynamic softening behaviors in the hot deformed 42CrMo steel have been investigated by the experimental and predicted results from the developed ANN model. Results show that the effects of deformation parameters, such as strain rate and deformation temperature, on the softening fractions of metadynamic recrystallization are significant. However, the strain (beyond the peak strain) has little influence. A very good correlation between experimental and predicted results indicates that the excellent capability of the developed ANN model to predict the flow stress level and metadynamic softening, the metadynamic recrystallization behaviors were well evidenced.
KeywordsArtificial Neural Network Flow Stress Root Mean Square Artificial Neural Network Model Dynamic Recrystallization
This work was supported by 973 Program (grant no. 2006CB705401), China Postdoctoral Science Foundation (grant no. 20070410302), the Postdoctoral Science Foundation of Central South University.
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