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

Abstract

The hot behaviour of micro-alloy steel CMn (Nb-Ti-V) was studied using hot compression tests in a wide range of temperatures (700 to 1050 °C, step 50 °C), deformation rates (0.000794, 0.0029 and 0.01436 s-1) and true deformation rates of 0 to 0.8. Based on experimental stress-deformation data, artificial neuron network (ANN) methods were used to predict flow stress CMn (Nb-Ti-V). The optimal ANN model was developed using Levenberg-Marquardt algorithm, and vas formed with two hidden layers with ten neurons in the first and ten neurons in the second. This model has been shown to be more effective in predicting flow stress and results can also be used in the mathematical simulation of hot metal formation processes.

Keywords

• Flow Stress
• Micro Alloyed Steel
• Artificial Neural Network
• Hot Compression Tests