ANN Approach to Predict the Flow Stress of CMn (Nb-Ti-V) Micro Alloyed Steel

Abstract

The flow behavior of CMn (Nb-Ti-V) micro alloyed steel was studied by hot compression tests in a wide range of temperatures (700 °C to 1050 °C, Step 50 °C), strain rates (0.000734 s-1 , 0.0029 s-1 , and 0.0146 s-1) and true strain of 0 to 0.8. Based on the experimental true stress-plastic strain data, the artificial neural network (ANN) methods were employed to predict the flow stress of CMn (Nb-Ti-V). The ANN model was trained with Levenberg-Marquardt (LM) algorithm. The optimal LM neural network model with two hidden layer network with ten neurons in the first and ten neurons in the second gives the best predictions is developed. It is demonstrated that the LV neural network model has better performance in predicting the flow stress. The results can be further used in mathematical simulation of hot metal forming processes.