Genetic optimized Al–Mg alloy constitutive modeling and activation energy analysis. (15th April 2023)

• Record Type: Journal Article
• Authors: Chen, Wenning; Li, Sijia; Bhandari, Krishna Singh; Aziz, Shahid; Chen, Xuewen; Jung, Dong Won
• Is Part Of: International journal of mechanical sciences. Volume 244(2023)
• Publication Date: 2023-04-15
• Subjects: Flow behavior -- Constitutive modeling -- Genetic algorithm -- Al–Mg alloy -- Neural network -- Activation energy; Mechanical engineering -- Periodicals; Génie mécanique -- Périodiques; Mechanical engineering; Maschinenbau; Mechanik; Zeitschrift; Periodicals; 621.05
• DOI: 10.1016/j.ijmecsci.2022.108077
• Languages: English
• ISSNs: 0020-7403

Abstract

Abstract: 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 ∼ 773 K and strain rate 0 . 0003 ∼ 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 α, 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. Graphical abstract: Highlights: The Arrhenius-type model compensated by polynomial function was built. Establishing the genetic optimized Arrhenius-type model by parameter optimization. A simple structure but high-precision BP-ANN model was proposed. The deformation mechanism was analyzed by genetic optimized activation energy.