Modeling Constitutive Relationship of Ti-555211 Alloy by Artificial Neural Network during High-Temperature Deformation

利用Gleeble-3800热模拟实验机,在应变速率0.001~1 s-1以及变形温度750~950 ℃范围内对Ti-555211合金进行等温恒应变速率压缩实验。基于人工神经网络的方法建立了Ti-555211合金热变形本构模型。模型的可靠性用平均相对误差和相关系数来确定。结果表明,所建立的本构模型与实验值的平均相对误差为1.60%,相关系数为0.99938,表明该模型能很好地预测该合金的本构关系。用神经网络来确定本构关系比传统的数学方程更加具有优势。热模拟实验结果表明,随着变形温度的升高和应变速率的减小,该材料的峰值应力有所减小,不连续屈服现象随着变形温度升高和应变速率的增大变得更加明显。流变曲线在不同的变形参数条件下表现形式也不同。     

Modeling Constitutive Relationship of Cu-0.4 Mg Alloy During Hot Deformation

For predicting the high-temperature deformation behavior in a Cu-0.4 Mg alloy, the true stress-strain data from isothermal hot compression tests on a Gleeble-1500 thermo-mechanical simulator, in a wide range of temperatures (500, 600, 700, 750, and 800 °C) and strain rates (0.005, 0.01, 0.1, 1, 5, and 10 s^?1), were employed to develop the Arrhenius-type constitutive model and the artificial neural network (ANN) constitutive model. Furthermore, prediction ability of the two models for high-temperature deformation behavior was evaluated. Correlation coefficients (R) between the experimental and predicted flow stress for the Arrhenius-type constitutive model and the ANN constitutive model are 0.9860 and 0.9998, respectively, and average absolute relative errors between the experimental and predicted flow stress for these two models are 5.3967% and 0.7401%, respectively. Results show that the ANN constitutive model can accurately predict the high-temperature deformation behavior over a wider range of temperatures and strain rates, while for the Arrhenius-type constitutive model there is greater divergence in the regime of high strain rates and low temperatures.     

Flow behavior of Al–6.2Zn–0.70Mg–0.30Mn–0.17Zr alloy during hot compressive deformation based on Arrhenius and ANN models

The hot deformation behavior of Al–6.2Zn–0.70Mg–0.30Mn–0.17Zr alloy was investigated by isothermal compression test on a Gleeble–3500 machine in the deformation temperature range between 623 and 773 K and the strain rate range between 0.01 and 20 s^?1. The results show that the flow stress decreases with decreasing strain rate and increasing deformation temperature. Based on the experimental results, Arrhenius constitutive equations and artificial neural network (ANN) model were established to investigate the flow behavior of the alloy. The calculated results show that the influence of strain on material constants can be represented by a 6th-order polynomial function. The ANN model with 16 neurons in hidden layer possesses perfect performance prediction of the flow stress. The predictabilities of the two established models are different. The errors of results calculated by ANN model were more centralized and the mean absolute error corresponding to Arrhenius constitutive equations and ANN model are 3.49% and 1.03%, respectively. In predicting the flow stress of experimental aluminum alloy, the ANN model has a better predictability and greater efficiency than Arrhenius constitutive equations.     

一种新型亚稳β钛合金热变形的本构模型(英文)

基于新型亚稳β钛合金Ti2448在温度1023～1123K、应变速率63～0.001s-1下的等温热压缩流动应力曲线特征,构建能够完整描述该合金流动应力与应变、应变速率、变形温度之间关系的本构模型。在此过程中,通过基于统一黏塑形理论改进双曲正弦函数,构建合金在高应变速率(≥1s-1)下发生动态回复(DRV)的模型;通过对标准的Avrami方程进行简化,表征了Ti2448在低应变率(1s-1)下发生的动态再结晶(DRX)软化机制。最终通过应用全局优化求解非线性方程的新方法确定模型中的相关参数。根据所建模型得到的预测曲线和实验曲线吻合得较好,能够有效预测Ti2448在热变形过程中的流动应力,为构建亚稳β钛合金热变形本构模型提供一种有效的方法。     

晶粒度对690合金热变形特性的影响

利用物理模拟实验方法对具有不同晶粒尺寸的690合金试样进行热压缩变形实验,变形温度范围为1100～1200℃,应变速率分别为0.1,1,10s-1,获得了合金的流变应力数据,并对合金变形后的组织特征进行了分析,建立了包含初始晶粒度参数的本构关系模型。结果表明:晶粒尺寸增大使690合金高温变形时的流变应力增加,发生动态再结晶的临界应变增大,动态再结晶体积分数减小,根据所建立的流变应力本构模型计算出的流变应力值与实验值相近,从而完善了690合金的热变形本构方程。     

690合金高温变形行为与动态再结晶模型

利用物理模拟实验方法对690合金进行恒温恒速压缩实验,变形温度范围为1050～1250℃,应变速率分别为0.1,1、5,10s-1,获得了合金的流变应力数据,并对合金变形后的组织特征进行了分析。建立了690合金高温热变形的本构方程和动态再结晶模型。结果表明:690合金高温变形时的流变行为可用Zener-Hollomon参数的双曲正弦函数来描述,所建立流变应力本构模型的预测值与实验值吻合较好,建立了690合金的动态再结晶模型,为热挤压过程中的组织控制提供理论依据。     

应变影响的AZ31B镁合金流动应力预测模型研究

本文采用热压缩试验获得了铸态AZ31B镁合金高温变形时的流变曲线,分析了变形温度和应变速率对流动应力的影响。结果表明：峰值应变随着应变速率增加和温度减小而增大,减小应变速率、适当提高变形温度对材料的动态回复和再结晶是有利的。利用多元回归分析建立了流动应力预测模型,该模型可以描述流动应力的应变敏感性,经验证发现使用其预测流动应力具有较高精度,相关系数高达0.9926,能较好地描述铸态AZ31B镁合金在热变形过程的流动行为。     

ARTIFICIAL NEURAL NETWORK MODEL OF CONSTTTUTIVE RELATIONSHIP FOR 2A70 ALUMINUM ALLOY

The hot deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over a wide range of temperatures 360-480℃ with strain rates of 0.01-1s-1 and the largest deformation of 60%, and the true stress of the material was obtained under the above-mentioned conditions. The experimental results shows that 2A70 aluminum alloy is a kind of aluminum alloy with the property of dynamic recovery; its flow stress declines with the increase of temperature, while its flow stress increases with the increase of strain rates. On the basis of experiments, the constitutive relationship of the 2A70 aluminum alloy was constructed using a BP artificial neural network. Comparison of the predicted values with the experimental data shows that the relative error of the trained model is less than ±3% for the sampled data while it is less than ±6% for the non- sampled data. It is evident that the model constructed by BP ANN can accurately predict the flow stress of the 2A70 alloy.     

应用智能方法建立Ti-6Al-2Zr-1Mo-1V合金的本构关系(英文)

利用Thermecmastor-Z热模拟机进行Ti-6Al-2Zr-1Mo-1V钛合金在不同工艺参数(变形温度800,850,900,1000,1050°C,应变速率0.01,0.1,1,10s-1)条件下的热模拟压缩试验,研究变形温度和应变速率对Ti-6Al-2Zr-1Mo-1V钛合金流变应力的影响。以试验数据为基础,应用BP神经网络算法原理,建立该合金的高温流动应力与变形温度、应变和应变速率对应关系的高温本构关系预测模型。结果表明,运用神经网络方法建立的Ti-6Al-2Zr-1Mo-1V钛合金本构关系模型具有较高的预测精度,与试验结果吻合良好。此外,运用Visual Basic可视化编程语言设计并开发了具有神经网络功能的用户界面。     

Arrhenius-Type Constitutive Model for High Temperature Flow Stress in a Nickel-Based Corrosion-Resistant Alloy

Hot deformation behavior of Nickel-based corrosion-resistant alloy (N08028) was studied in compression tests conducted in the temperature range of 1050-1200 °C and the strain rate range of 0.001-1 s^?1. The flow stress behavior and microstructural evolution were observed during the hot deformation process. The results show that the flow stress increases with deformation temperature decreasing and strain rate increasing, and that the deformation activation energy (Q) is not a constant but increases with strain rate increasing at a given strain, which is closely related with dislocation movement. On this basis, a revised strain-dependent hyperbolic sine constitutive model was established, which considered that the “material constants” in the original model vary as functions of the strain and strain rate. The flow curves of N08028 alloy predicted by the proposed model are in good agreement with the experimental results, which indicates that the revised constitutive model can estimate precisely the flow curves of N08028 alloy.