Prediction of Medium Thermal Stability of 45 Steel after Impact Strengthening with BP Artificial Neural Network
The impact strengthened normalized 45 steel which was impact strengthened with free-fall type at room temperature have been aged at medium temperature.The steel was heated to 450℃,550℃and 650℃,respectively.Temperature of each group was kept for 10 min,20 min,30 min and 40 min,and the microhardness of each group was tested.Micro-structure of four kinds of samples heated to 650℃was observed.Taking the actual state parameters of samples as the learning sample,the three-layer BP neural network was trained.The results show that BPANN can predict the thermal stability of impact strengthened normalized 45 steel at medium temperature,and the error can be controlled within 3%-6%.Predicted values of BPANN are all larger than the measured ones,but the variation trend of predicted values is consistent with that of measured values.Prediction accuracy of the network can be improved by increasing the convergence rate of error function.Through observation on microstructure of the sample at 650℃,it can be determined that relevant contents involved in input layer of the network can make prediction result of BPANN reflect real state of the material.This work can reduce experimental cost and number of experiments,it's helpful to predict the thermal stability of impact strengthened normalized 45 steel at other heating temperatures.
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