Genetic optimized Al-Mg alloy constitutive modeling and activation energy analysis

As an intelligent global optimization method, the genetic algorithm has tremendous potential for improving flow behavior modeling and analysis. Based on flow stress-true strain curves of Al-Mg AA5005 alloy under temperature 563 similar to 773 K and strain rate 0.0003 similar to 0.03 s-1, a phenomenological model named Arrhenius-type (A-T) was established to describe the flow behavior. On this basis, the genetic optimized A-T (GA-T) model with higher precision was obtained by optimizing A-T parameters a, n, Q and ln A. To reduce the large computing power consumed by unnecessary complex topological network structure when conducting simulations by the back propagation artificial neural network (BP-ANN) model, a genetic optimized BP-ANN (GBP-ANN) model was designed through determining the initial values of weights, biases and hyper parameters. The presented GBP-ANN model inherits the advantage of the BP-ANN model's high accuracy as well as maintaining the simplest structure. The statistical analysis demonstrates that the GBP-ANN model possesses the best flow behavior description ability among three established models. Moreover, the GBP-ANN also shows a better generalization performance than the GA-T model. Lastly, with the help of the GA-T model, the activation energy map was plotted to determine the desirable deformation condition analyze the deformation mechanism. Our work presents a combination of GBP-ANN model and genetic optimized Q analysis, thus shedding new light on high accuracy flow behavior modeling and deformation mechanism analysis.

Automated summary

As an intelligent global optimization method, the genetic algorithm has high potential for improving flow behavior modeling and analysis. A phenomenological model named Arrhenius-type (A-T) was established to describe the flow behavior of Al-Mg AA5005 alloy, and a genetic optimized A-T (GA-T) model with higher precision was obtained. A genetic optimized BP-ANN (GBP-ANN) model was designed to reduce computing power consumption and achieve high accuracy flow behavior description.