Abstract
In this research, the plastic flow behavior of Al–6%Mg alloy was studied by analyzing the results of hot compression tests in a range of temperature and strain rate. Then, an artificial neural network (ANN) model was trained at which the temperature, strain-rate, and strain parameters were used as the input layer and the flow stress as the output. The comparison of the predicted and experimental results of stress–strain curve proved the prediction capability of the ANN model
Contents
1. Introduction
2. Material and experimental procedures
3. Mathematical model
3.1. Correction due to friction
3.2. Correction due to heat of deformation
3.3. Correction of strain rate
3.4. Selection of the training data
4. Results and discussion
4.1. Correction of hot compression test data
4.2. Determination of high temperature constitutive equation
4.3. Neural network results
4.4. Comparison between the hyperbolic sine equation and ANN results
5. Conclusions
6. References