Dynamic behavior and modified artificial neural network model for predicting flow stress during hot deformation of Alloy 925. (December 2020)

• Record Type: Journal Article
• Authors: Zhu, Yulong; Cao, Yu; Liu, Cunjian; Luo, Rui; Li, Na; Shu, Gang; Huang, Guangjie; Liu, Qing
• Is Part Of: Materials today communications. Volume 25(2020)
• Publication Date: 2020-12
• Subjects: Alloy 925 -- Hot deformation -- Constitutive model -- Artificial neural network -- Genetic algorithm; Materials science -- Periodicals; 620.11
• DOI: 10.1016/j.mtcomm.2020.101329
• Languages: English
• ISSNs: 2352-4928

Abstract

Highlights: Nucleation mechanism mainly contains DDRX, PSN and twin-induced DRX. Some novel dynamic behavior can be attributed to the effect of DSA. The GA-BP ANN model constitutive model is best for predicting flow stress. Abstract: In this work, the hot deformation behavior of Alloy 925 with intermediate strain rates of 0.01s −1 -10s −1 ranging from 900 °C to 1150 °C has been investigated through the isothermal hot compression tests. Some features of the flow curves corresponding to negative strain rate sensitivity have been observed and discussed associated with the microstructural evolution, showing the nucleation mechanism of dynamic recrystallization and the occurrence of dynamic strain ageing. Besides, we have employed two typical constitutive models, namely, Arrhenius model and backpropagation artificial neural network (BP ANN) model to describe the flow behavior, and also developed a modified BP ANN model based on genetic algorithm (GA-BP ANN). The results show that the GA-BP ANN model has the highest accuracy and stability for predicting the flow stress. The correlation coefficient between the predicted and experimental values is 99.99 %, and the average absolute relative error is only 0.54 %. The comparative investigation on the predicted values of different ANN models reflects that GA can reduce the randomness of initial weights and thresholds of BP ANN and also can further increase the accuracy and stability of ANN model. Moreover, the increasing number of input training data can improve the prediction performance of neural network. For a single-layer neural network, 12 hidden layers can effectively ensure the reliability of the constitutive model.