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.
ABARGHOOEI H , ARABI H , SEYEDEIN S H , et al. Modeling of steady state hot flow behavior of API-X70 microalloyed steel using genetic algorithm and design of experiments[J]. Applied Soft Computing, 2016, 52, 471- 477.
PHANIRAJ M P , LAHIRI A K . The applicability of neural network model to predict flow stress for carbon steels[J]. Journal of Materials Processing Technology, 2003, 141 (2): 219- 227.
BARAGAR D L . The high temperature and high strain-rate behaviour of a plain carbon and an HSLA steel[J]. Journal of Mechanical Working Technology, 1987, 14 (3): 295- 307.
CHUN M S , BIGLOU J , LENARD J G , et al. Using neural networks to predict parameters in the hot working of aluminum alloys[J]. Journal of Materials Processing Technology, 1999, 86 (1/3): 245- 251.
MANDAL S , SIVAPRASAD P V , VENUGOPAL S , et al. Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion[J]. Applied Soft Computing, 2015, 9 (1): 237- 244.
SUBBARAO S M . Hindcasting of storm waves using neural networks[J]. Ocean Engineering, 2005, 32 (5/6): 667- 684.
SHENG C W , YANG H , SUN Z C , et al. Establishment of constitutive relation of TA15 titanium alloy based on BP neural network[J]. Journal of Plasticity Engineering, 2007, 14 (4): 101- 104.
MA S W , ZHANG Y , YANG M , et al. Effect of Zener-Hollomon parameters on hot deformation behavior of Cr4Mo4Ni4V high alloy steel[J]. Journal of Central South University (Science and Technology), 2021, 52 (2): 376- 388.
ZHANG S W , YANG M , LIANG Y L , et al. Constitutive equations and dynamic recrystallization behaviors of 20CrNi2Mo steel at stage of high temperature deformation[J]. Iron & Steel, 2017, 52 (8): 97- 106.
CHENG X N , GUI X , LUO R , et al. Constitutive equation and dynamic recrystallization behavior of 316L austenitic stainless steel for nuclear power equipment[J]. Materials Reports, 2019, 33 (11): 1775- 1781.
ZHONG M J , WANG K L , LU S Q , et al. Study on high temperature deformation behavior and BP neural network constitutive model of MoNb alloy[J]. Journal of Plasticity Engineering, 2020, 27 (12): 177- 182.
FENG R , WANG K L , LU S Q , et al. Constitutive relationship research on BT25 titanium alloy based on strain compensation and BP neural network[J]. Journal of Plasticity Engineering, 2020, 27 (12): 183- 190.
ZHANG Y W , FENG B , CHEN Y , et al. Fault diagnosis method for oil-immersed transformer based on XGBoost optimized by genetic algorithm[J]. Electric Power Automation Equipment, 2021, 41 (2): 200- 206.
LI D Q , XU L , HUANG X M , et al. Investigation on critical strain of dynamic recrystallization for 7A04 aluminum alloy[J]. Journal of Materials Engineering, 2013, (4): 23- 27.
ARUN BABU K , PRITHIV T S , GUPTA A , et al. Modeling and simulation of dynamic recrystallization in super austenitic stainless steel employing combined cellular automaton, artificial neural network and finite element method[J]. Computational Materials Science, 2021, 195, 110482.
WU J B , LIU G Q , XU L , et al. Prediction on hot deformation flow stress of medium-carbon micro-alloyed steels with vanadium based on artficial neural network[J]. Hot Working Technology, 2010, 39 (10): 51- 54.
CHANG R H , CAI Z Y , CHENG L R , et al. Flow stress prediction model and processing map of Mg-Sm-Zn-Zr alloy based on GA-BP neural network[J]. Materials Reports, 2017, 31 (3): 136- 149.
BABU K A , MANDAL S . Regression based novel constitutive analyses to predict high temperature flow behavior in super austenitic stainless steel[J]. Materials Science and Engineering: A, 2017, 703 (4): 187- 195.
HAGHDADI N , ZAREI-HANZAKI A , KHALESIAN A R , et al. Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy[J]. Materials & Design, 2013, 49, 386- 391.
ZHOU X , WANG K L , LU S Q , et al. Hot deformation behavior of Ti2041 alloy based on BP neural network and 3D processing map[J]. Rare Metal Materials and Engineering, 2021, 50 (4): 1233- 1240.