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- W4313334031 endingPage "108077" @default.
- W4313334031 startingPage "108077" @default.
- W4313334031 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∼0.03s−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 lnA. 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." @default.
- W4313334031 created "2023-01-06" @default.
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- W4313334031 date "2023-04-01" @default.
- W4313334031 modified "2023-10-18" @default.
- W4313334031 title "Genetic optimized Al–Mg alloy constitutive modeling and activation energy analysis" @default.
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- W4313334031 doi "https://doi.org/10.1016/j.ijmecsci.2022.108077" @default.
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