Artificial neural network model of constitutive relations
for shock-prestrained copper
for shock-prestrained copper
(Department of Material Science and Engineering,
Central South University, Changsha 410083, P.R.China)
Central South University, Changsha 410083, P.R.China)
Abstract: Data from the deformation on Split-Hopkinson Bar were used for constructing an artificial neural network model. When putting the thermodynamic parameters of the metals into the trained network model, the corresponding yielding stress can be predicted. The results show that the systematic error is small when the objective function is 0.5, the number of the nodes in the hidden layer is 6 and the learning rate is about 0.1, and the accuracy of the rate-error is less than 3%.
Key words: shock-prestrain; constitutive relations; artificial neural network model