Comparing predictions from constitutive equations and artificial neural network model of compressive behavior in carbon nanotube-aluminum reinforced ZA27 composites

Hybrid carbon nanotube-aluminum reinforced ZA27 composites under hot compressive forces were investigated in the temperature range of 473 - 523 K with strain rates of 0.01-10 s(-1). From the experimental data, the flow stress curves for increasing strain exhibit typical flow behavior associated with dynamic recrystallization softening. A comparison of predictions from an artificial neural network model and the constitutive equations to describe the hot compressive behavior was performed. Relative errors varied from -4.14% to 6.75% for the artificial neural network model and from -15.93% to 17.29% using the constitutive equations. The results indicate that the artificial neural network model was more accurate and efficient in predicting hot compressive behavior.