基于GA改进BP神经网络预测热变形流变应力模型的建立

Establishment of hot deformation flow stress prediction model based on GA improved BP neural network

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。

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.