1 School of Mechatronic Engineering and Automation, Foshan University, Foshan 528200, Guangdong, China 2 Xi'an Thermal Power Research Institute Co., Ltd., Xi'an 710054, China
Stress-strain curves are of great significance in studying the changes of work hardening, dynamic recrystallization and dynamic recovery of metal during hot deformation, and predicting the stress-strain curves under different thermal deformation parameters is helpful to study the machinability and instability of metal in hot working process. The thermal deformation behavior of Nb-V-Ti microalloy steel was studied by hot compression experiments at strain rates of 0.01-3 s-1 and deformation temperatures of 1000-1200 ℃ on Gleeble-3500 thermal simulation testing machine. The BP neural network model and GA improved BP neural network model were established to predict the stress-strain curves at the strain rate of 0.5 s-1 and deformation temperature of 1050 ℃, and the strain rate of 1 s-1 and deformation temperature of 1100 ℃. The results show that the BP neural network model improved by GA is in good agreement with the stress-strain curves of the test data and the experimental curves. The correlation coefficients are 0.99202 and 0.99734 respectively, and the errors are only 2.7816% and 2.1703%. The relative errors between the predicted results and the experimental results are within the range of [-2, 2]. It is proved that the model is reliable and applicable to a wide range of strain, which provides theoretical guidance for rolling process in industrial production.
汪雅婷, 黎俊良, 袁楷峰, 陈广义. 基于GA改进BP神经网络预测热变形流变应力模型的建立[J]. 材料工程, 2022, 50(6): 170-177.
Yating WANG, Junliang LI, Kaifeng YUAN, Guangyi Chen. Establishment of hot deformation flow stress prediction model based on GA improved BP neural network. Journal of Materials Engineering, 2022, 50(6): 170-177.
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2022, Vol. 50 
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