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2222材料工程  2022, Vol. 50 Issue (6): 170-177    DOI: 10.11868/j.issn.1001-4381.2021.000624
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于GA改进BP神经网络预测热变形流变应力模型的建立
汪雅婷1, 黎俊良2, 袁楷峰1, 陈广义1,*()
1 佛山科学技术学院 机电工程与自动化学院,广东 佛山 528200
2 西安热工研究院有限公司,西安 710054
Establishment of hot deformation flow stress prediction model based on GA improved BP neural network
Yating WANG1, Junliang LI2, Kaifeng YUAN1, Guangyi Chen1,*()
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
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摘要 

应力-应变曲线对研究金属热变形过程中的加工硬化、动态再结晶和动态回复的变化具有重要的意义,而预测不同热变形参数下的应力-应变曲线有助于研究热加工过程中金属的可加工性和不稳定性。在应变速率为0.01~3 s-1以及变形温度为1000~1200 ℃条件下,利用Gleeble-3500热模拟试验机对Nb-V-Ti微合金钢进行热压缩实验,研究了Nb-V-Ti微合金钢的热变形行为。建立BP神经网络模型和基于GA改进BP神经网络模型,分别预测在应变速率0.5 s-1、变形温度1050 ℃和应变速率1 s-1、变形温度1100 ℃条件下的流动应力行为并验证模型效果。研究结果表明:经GA改进后的BP神经网络模型对测试数据的应力-应变曲线与实验曲线具有很好的吻合,相关系数分别达0.99202和0.99734,误差仅为2.7816%和2.1703%,预测结果与实验结果相对误差在[-2, 2]范围内,证明了模型的预测可靠性,且适用于较广的应变范围,为工业生产轧制工艺提供理论指导。

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汪雅婷
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袁楷峰
陈广义
关键词 热变形流变应力遗传算法BP神经网络预测模型    
Abstract

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.

Key wordsthermal deformation    flow stress    genetic algorithm    BP neural network    prediction model
收稿日期: 2021-07-08      出版日期: 2022-06-20
中图分类号:  TG142.1  
通讯作者: 陈广义     E-mail: 943903188@qq.com
作者简介: 陈广义(1963—),男,教授,硕士,主要研究方向为智能检测与智能控制,联系地址:广东省佛山市南海区狮山镇广云路佛山科学技术学院仙溪校区机电工程与自动化学院(528200),E-mail:943903188@qq.com
引用本文:   
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.
链接本文:  
http://jme.biam.ac.cn/CN/10.11868/j.issn.1001-4381.2021.000624      或      http://jme.biam.ac.cn/CN/Y2022/V50/I6/170
Fig.1  不同应变速率下不同温度的应力-应变曲线
(a)=0.01 s-1;(b) =0.1 s-1;(c) =0.5 s-1;(d) =1 s-1;(e) =3s-1
Fig.2  不同变形温度下不同变形速率的应力-应变曲线
(a)1000 ℃;(b)1050 ℃;(c)1100 ℃;(d)1150 ℃;(e)1200 ℃
Fig.3  GA改进型BP神经网络模型结构
Strain rate/s-1 T/℃
1000 1050 1100 1150 1200
0.01 C C C C C
0.10 C C C C C
0.50 C M C C C
1 C C M C C
3 C C C C C
Table 1  测试数据和训练数据的划分
Fig.4  确定过程中隐含层节点数与训练集均方根误差的关系曲线
Fig.5  不同样本中BP神经网络模型和GA改进型BP神经网络模型输出值的训练误差曲线
(a)=0.5 s-1, T=1050 ℃; (b) =1 s-1, T=1100 ℃
Fig.6  BP神经网络(a)及GA改进型BP神经网络(b)预测应力值与实验数据曲线对比曲线
Fig.7  不同条件时,BP神经网络(1)和GA改进BP神经网络(2)预测的应力值与实验值线性关系图、相对误差比值与相对误差值分布图
(a) =0.5 s-1, T=1050 ℃;(b) =1 s-1, T=1100 ℃
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