ZK60镁合金热压缩变形行为

为了研究ZK60镁合金的热变形行为,采用Gleebe-1500热模拟机在变形温度为423～673K、应变速率为0.001～10s-1条件下对合金进行的热压缩试验.分析合金流变应力与应变速率、变形温度之间的关系,通过引入Z参数建立合金流变应力本构方程,并观察合金变形过程中的显微组织演变.结果表明:变形温度低于473K且应变速率大于0.1s-1时试样发生宏观开裂;在变形温度较高和应变速率较低时,合金真应力-真应变曲线具有动态再结晶特征.随变形温度升高和应变速率的降低流变应力减小,热压缩后的组织中再结晶现象越明显;应变速率越高,再结晶晶粒越细小.     

新型Ti-Al-Zr-Nb-Mo-Si钛合金热变形行为及基于BP神经网络模型的本构关系研究

通过热压缩模拟实验,研究了一种新型Ti-Al-Zr-Nb-Mo-Si高强度、高弹性模量钛合金在温度为950～1 150℃、应变速率为0.05～1 s~(-1)条件下的流变行为。真应力-真应变曲线表明,变形温度、应变速率对该合金的流变应力影响显著。基于实验数据,利用包含应变参量的双曲正弦型Arrhenius方程和BP人工神经网络模型分别构建了变形参数和流变应力的本构关系,并对两种模型进行了对比评价。结果表明,两种模型的平均相对误差值分别为11.21%和2.163%,整体上均可以较好地预测Ti-Al-Zr-Nb-Mo-Si钛合金热压缩流变应力;但相对传统Arrhenius方程,BP人工神经网络模型具有更高的精度和可靠性。     

基于改进Johnson-Cook模型的Mg-Gd-Y-Zn-Zr 合金高温流变行为研究

以Mg-Gd-Y-Zn-Zr合金为研究对象,分别在变形温度范围为250～400℃、应变速率范围为0.001～1 s-1的变形条件下,利用Gleeble-1500热模拟试验机,进行恒温等应变速率的热拉伸实验,研究该合金的高温流变行为.综合考虑温度、应变速率和应变在高温变形过程中的影响,建立了Mg-Gd-Y-Zn-Zr合金改进的Johnson-Cook本构模型.实验结果表明:Mg-Gd-Y-Zn-Zr合金的流变应力与变形温度、应变速率和应变呈非线性关系,应变速率的升高和变形温度的降低均会导致合金的流变应力明显升高.改进Johnson-Cook本构模型的预测数据与实验数据的平均相对误差(Δ)为4.5％,相关度(R)为0.994,所建立的本构模型能够准确地描述Mg-Gd-Y-Zn-Zr合金的高温流变行为.     

Mg-10Gd-3Y-0.6Zr-1Ag镁合金热压缩变形行为研究

为了考察Mg-10Gd-3Y-0.6Zr-1Ag镁合金在不同条件下的变形行为,采用Gleeble2000热模拟机对该合金进行研究,分析了该合金在变形温度350~500℃,应变速率0.001~1 s-1条件下流变应力的变化规律.研究结果表明:变形温度和应变速率对流变应力有显著的影响,流变应力随变形温度的升高和应变速率的降低而降低;在应变速率相同的情况下,合金在较高温度下变形时,流变应力随应变量的增加达到峰值后,基本呈稳态流变特征;采用双曲正弦模型计算出该合金的变形激活能和应力指数,建立了该合金相应的热变形本构关系.     

AZ31镁合金热变形特性研究

通过MTS试验机进行等温压缩实验,变形温度范围473～623 K、应变速率范围0.001～1 s-1,研究了AZ31镁合金的流变应力行为及其微观组织的演变规律.结果表明,变形温度、应变速率与峰值应力之间的相互关系可用指数模型来描述,其激活能约为138.13kJ/mol,而动态再结晶则是该合金在热变形过程中的主要软化机制和晶粒细化手段.     

ZK60镁合金加工图的构建及失稳分析

采用Gleeble-1500热模拟试验机对ZK60镁合金在变形温度为150～400℃,应变速率为0.001～10 s-1条件下的热变形行为进行研究,利用双曲正弦关系式描述了该合金在热变形过程中的稳态流变应力;根据合金动态模型,计算并分析了该合金的加工图.研究表明:利用加工图可确定出该合金热变形的流变失稳区,导致变形失稳的原因主要是孪生和局部流变;试验条件下热变形的最佳工艺参数为变形温度350℃,应变速率0.001 s-1,在该条件下合金发生完全再结晶,具有较好的塑性.     

Cu-Cr-Zr-Ce合金高温热变形行为研究

Cu-Cr-Zr系合金是一类高强度高导电集成电路用引线框架铜合金。在Gleeble-1500D热模拟实验机上,采用等温压缩实验研究了Cu-Cr-Zr-Ce合金在变形温度为600～800℃、应变速率为0.01～5s-1条件下的流变应力的相互变化规律,测定了其真应力-应变曲线,并利用光学显微镜分析了合金在热压缩过程中的组织演变规律。结果表明,Cu-Cr-Zr-Ce合金的真应力-真应变曲线呈现典型的动态回复特征,其流变应力和峰值应力随变形温度的降低和应变速率的提高而增大;且变形温度越高,应变速率越小,合金越容易发生动态回复和再结晶。在上述实验基础上,基于流变应力、应变速率和温度的相关性,计算出了该合金热压缩变形时的热变形激活能Q,并建立了其等温压缩塑性变形过程的流变应力与变形温度和应变速率之间关系的本构方程。     

Cu-Ni-Si-P合金动态再结晶研究及组织转变

在Gleeble-1500D热模拟试验机上,对Cu-2.0Ni-0.5Si-0.03P合金进行高温压缩实验,应变速率为0.01～5s-1、变形温度为600～800℃,对其高温等温压缩流变应力行为进行了研究.研究结果表明:随变形温度升高,合金的流变应力下降,随应变速率提高,流变应力增大.在应变温度为750、800℃时,合金热压缩变形流变应力出现了明显的峰值应力,表现为连续动态再结晶特征.可采用Zener-Hollomon参数的双曲正弦函数来描述Cu-2.0Ni-0.5Si-0.03P合金高温变形时的流变应力行为.从流变应力、应变速率和温度的相关性,得出了该合金高温热压缩变形时的应力指数n,应力参数α,结构因子A,热变形激活能Q和流变应力方程.合金动态再结晶的显微组织强烈受到变形温度的影响.     

Ti-5Mo-5V-1Cr-3Al合金的热压缩变形行为研究EI北大核心CSCD

采用恒应变速率热压缩模拟实验,对Ti-5Mo-5V-1Cr-3A1（简称1Cr）钛合金在应变速率0.001～1s-1、变形温度700～900℃条件下进行研究.结果表明：该材料的流变应力对温度与应变速率敏感：当变形温度为700～800℃时,真应力-真应变曲线呈现动态再结晶单曲线特征;当变形温度为800～900℃时,低应变速率（0.001s-1）的真应力-真应变曲线呈现动态再结晶多应力峰值曲线特征,高应变速率（0.01～1s-1）的真应力-真应变曲线呈现动态回复曲线特征.1Cr合金在等温压缩变形时的流变行为可用包含Zener-Holomon参数的Arrhenius本构方程描述,变形激活能为456kJ/mol.金相结果显示,材料在热压缩过程中的动态行为除了与变形速率、变形温度等加工参数相关外,也与相应温度、变形速率下材料的组织及相结构有关.合金在低应变速率0.001s 1下热压缩变形时,在接近相变点或以上（800～900℃）温度范围内仍呈现动态再结晶行为,这与材料在此阶段发生的应变诱发马氏体转变密切相关,马氏体相的析出促使材料在热变形时趋向于发生动态再结晶行为.     

喷射成形Nb合金化M3型高速钢热加工性能的研究

利用喷射成形工艺分别制备了M3及Nb合金化的M3高速钢;在变形温度为950～1150℃、应变速率为0.01～10s-1、最大真应变为0.7的条件下,利用Gleeble-1500热模拟试验机研究了MN合金的热压缩变形行为和组织演变情况,得到该合金的热变形激活能并构建了其热变形本构方程。结果表明,喷射成形制备的MN合金组织均匀细小,有利于热变形加工;在实验条件下,MN合金均表现出动态再结晶特征,变形温度和应变速率对合金流变应力的影响显著,流变应力随着变形温度的降低和应变速率的增加而增大;变形温度对变形后碳化物分布影响明显,温度越高,其分布越均匀。     

基于修正的Arrhennius方程钛合金高温本构方程研究

为了更准确地描述钛合金的高温变形行为,对Arrhennius方程进行修正得到钛合金高温本构方程.通过对一种新型钛合金在热模拟试验机上进行恒应变速率等温压缩实验,研究其在700~1 000℃、应变速率0.01~10 s-1条件下的热变形行为,分析了材料的真实应力-真实应变曲线.采用最小二乘拟合的数据回归处理,得到该钛合金在α+β双相区和β单相区的热变形激活能,并通过引入温度变量,获得了Arrhennius方程参数A随温度变化的函数关系,建立了该材料的高温流变应力本构方程.实验结果表明,随着变形增加,流变应力开始急剧增加,随后出现软化并趋于稳态,同时峰值应力对于温度和应变速率具有很强的敏感性.通过在Arrhenius方程中引入温度变量,有利于提高本构方程的准确性.     

Artificial neural network approach to predict the flow stress in the isothermal compression of as-cast TC21 titanium alloy

Isothermal compression of as-cast TC21 titanium alloy at the deformation temperatures ranging from 1000 to 1150 °C with an interval of 50 °C, the strain rates ranging from 0.01 to 10.0 s^?1 and the height reduction of 60% was conducted on a Gleeble-3500 thermo-mechanical simulator. Based on the experimental results, an artificial neural network (ANN) model with a back-propagation learning algorithm was developed to predict the flow stress in isothermal compression of as-cast TC21 titanium alloy. In the present ANN model, the strain, strain rate and deformation temperature were taken as inputs, and the flow stress as output. According to the predicted and experimental results, the maximum error and average error between the predicted flow stress and the experimental data were 4.60% and 1.58%, respectively. Comparison of the predicted results of flow stress based on the ANN model and those using the regression method, it was found that the relative error based on the ANN model varied from ?1.41% to 4.60% and that was in the range from ?13.38% to 10.33% using the regression method, and the average absolute relative error were 1.58% and 5.14% corresponding to the ANN model and regression method, respectively. These results have sufficiently indicated that the ANN model is more accurate and efficient in terms of predicting the flow stress of as-cast TC21 titanium alloy.     

Modeling constitutive relationship of Ti40 alloy using artificial neural network

Constitutive relationship equation reflects the highly non-linear relationship of flow stress as function of strain, strain rate and temperature. It is a necessary mathematical model that describes basic information of materials deformation and finite element simulation. In this paper, based on the experimental data obtained from Gleeble-1500 Thermal Simulator, the constitutive relationship model for Ti40 alloy has been developed using back propagation (BP) neural network. The predicted flow stress values were compared with the experimental values. It was found that the absolute relative error between predicted and experimental data is less than 8.0%, which shows that predicted flow stress by artificial neural network (ANN) model is in good agreement with experimental results. Moreover, the ANN model could describe the whole deforming process better, indicating that the present model can provide a convenient and effective way to establish the constitutive relationship for Ti40 alloy.     

Cr-Mo-B系机械工程用钢高温流变行为及热加工图

在变形温度为850~1150℃、应变速率为0.1~10s -1 的条件下,对Cr-Mo-B系机械工程用钢进行高温热压缩实验。基于真应力-应变曲线,建立输入参数为温度、变形速率、应变和输出参数为流变应力的人工神经网络(ANN)模型。结果表明:神经网络模型的预测精度高,其预测流变应力的均方根误差为1.3858。根据动态材料模型理论(DMM),构建并分析材料在真应变为0.5和0.7时的热加工图,确定了最佳热变形工艺参数:当真应变ε=0.5时,变形温度为1050~1150℃、应变速率为0.1~0.4s -1 区域的功率耗散因子η≥37.20%;当真应变ε=0.7时,变形温度为1000~1150℃、应变速率为0.1~0.6s -1 区域的功率耗散因子η≥35.80%。     

Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel

In order to study the workability and establish the optimum hot forming processing parameters for 42CrMo steel, the compressive deformation behavior of 42CrMo steel was investigated at the temperatures from 850 °C to 1150 °C and strain rates from 0.01 s⁻¹ to 50 s⁻¹ on Gleeble-1500 thermo-simulation machine. Based on these experimental results, an artificial neural network (ANN) model is developed to predict the constitutive flow behaviors of 42CrMo steel during hot deformation. The inputs of the neural network are deformation temperature, log strain rate and strain whereas flow stress is the output. A three layer feed forward network with 12 neurons in a single hidden layer and back propagation (BP) learning algorithm has been employed. The effect of deformation temperature, strain rate and strain on the flow behavior of 42CrMo steel has been investigated by comparing the experimental and predicted results using the developed ANN model. A very good correlation between experimental and predicted result has been obtained, and the predicted results are consistent with what is expected from fundamental theory of hot compression deformation, which indicates that the excellent capability of the developed ANN model to predict the flow stress level, the strain hardening and flow softening stages is well evidenced.     

Strain Softening and Hardening Behavior in AZ61 Magnesium Alloy

The deformation behavior of AZ61 Mg alloy during hot deformation has been investigated in wide temperature and strain rate range by a Gleeble simulator. Specimens are deformed in compression in the temperature range of 523～673 K and at strain rates of 0.001～1 s-1. It is found that the flow curves exhibit a peak and then decrease towards steady-state of classical DRX, which decrease with rising temperature and decreasing strain rate. The deformation behavior of the specimens can be attributed to the occurrence of strain hardening and softening. As stress decreases, the strain hardening rate declines at a fast rate when temperature rises or strain rate decreases. The shapes of θ-σ curves indicate some important features such as subgrain formation, the critical stress, the peak stress and steady stress. The onset of DRX can be determined by the point of inflection on θ-σ or Inθ-σ curves.     

Cu-2.32Ni-0.57Si-0.05P合金热压缩变形研究

在Gleeble-1500D热模拟试验机上,对Cu-2.32Ni-0.57Si-0.05P合金在应变速率为0.01～5s-1、变形温度为600～800℃、最大变形程度为60%条件下,进行恒温压缩模拟实验研究.分析了实验合金在高温变形时的流变应力、应变速率及变形温度之间的关系,研究了变形温度对合金显微组织的影响.计算了合金高温热压缩变形时的应力指数n、应力参数α、结构因子A以及平均热变形激活能Q.结果表明:合金的流变应力随变形温度升高而降低,随应变速率提高而增大.热变形过程的流变应力可用双曲正弦本构关系来描述.当变形温度高于750℃时,合金流变曲线呈现出明显的动态再结晶特征,合金显微组织为完全的动态再结晶组织.合金的热加工宜在应变速率为0.1～1s-1、温度为700～800℃范围内进行.     

漂珠/AZ91D镁合金复合材料的高温压缩变形行为

采用搅拌铸造法制备了漂珠(FAC)/AZ91D镁合金复合材料。研究了该复合材料的高温压缩变形行为,分析了压缩变形温度和应变率对FAC/AZ91D镁合金复合材料压缩变形行为的影响规律,并计算了其热变形激活能。结果表明:FAC/AZ91D镁合金复合材料的高温压缩真应力-真应变曲线分为4个阶段:弹性变形、加工硬化、峰值应力和稳态流变阶段。相同应变率下,FAC/AZ91D镁合金复合材料的峰值应力和稳态流变应力随压缩变形温度的升高而降低;相同压缩变形温度下,流变应力随应变率增大而升高。在相同应变率或相同压缩变形温度下,FAC/AZ91D镁合金复合材料的热变形激活能随压缩应变率或压缩变形温度的升高而增大,其热压缩行为可以用双曲正弦函数形式的Arrhenius关系来描述。压缩变形温度与应变率对FAC/AZ91D镁合金复合材料的高温压缩组织均有重要影响。提高压缩变形温度或增大应变率,均可加速动态再结晶的进程。     

Prediction of hot deformation behavior of Al–5.9%Cu–0.5%Mg alloys with trace additions of Sn

High temperature deformation behavior of Al–5.9wt%Cu–0.5wt%Mg alloys containing trace amounts (from 0 to 0.1 wt%) of Sn was studied by hot compression tests conducted at various temperatures and strain rates. The peak flow stress of the alloys increased with increase in strain rate and decrease in deformation temperature. The peak stress could be correlated with temperature and strain rate by a suitable hyperbolic-sine constitutive equation. The activation energy for hot deformation of the alloy without Sn content was observed to be 183.4 kJ mol⁻¹ which increased to 225.5 kJ mol⁻¹ due to 0.08 wt% of Sn addition. The Zener-Hollomon parameter (Z) was determined at various deforming conditions. The tendency of dynamic recrystallization increased with low Z values, corresponding to low strain rate and high temperature. The peak flow stresses at various processing conditions have been predicted by the constitutive modeling and correlated with the experimental results with fairly good accuracy. It was possible to predict 80, 75, 100, 100, 90, and 85% of the peak stress values within an error less than ±13%, for the investigated alloys. With addition of Sn content >0.04 wt%, peak flow stress increased significantly for all strain rate and temperature combinations. Scanning electron microscope revealed two types of second phases at the grain boundary of the undeformed alloy matrix, one being an Al–Cu–Si–Fe–Mn phase while the other identified as CuAl₂. The high strength and flow stress value of the alloy with 0.06 wt% of Sn content, may be attributed to the variation in amount, composition, and morphology of the Al–Cu–Si–Fe–Mn phase, as well as to the lower value of activation energy for precipitation reaction, as revealed from differential scanning calorimetric studies.     

2519A铝合金热压缩流变行为和显微组织分析

在Gleeble-1500D热模拟仪上进行热压缩实验,研究温度从300℃～450℃、应变速率为0．001～10s^-1时2519A铝合金热压塑行为,并用金相显微镜分析在不同热压缩条件下的组织形貌特征。结果表明,流变应力开始随着应变的增大而增大,出现峰值之后慢慢减小并慢慢趋于平稳。应力峰值随温度的增加而减小,随应变增大而增大,其热变形行为可用包含Zener-Hollomon参数的双弦本构关系来描述,得到平均激活能Q=223．11706kj／mol。合金在0．001s^-1～1s^-1。应变速率条件下软化机制主要为动态回复,而当应变速率上升到10s^-1后,合金微观组织出现局部动态再结晶。