新型近β钛合金Ti-7333高温变形行为研究

对新型近β钛合金Ti-7333进行等温压缩试验,并对合金的流变行为进行研究.研究结果表明:Ti-7333的流变应力对变形参数的变化十分敏感,随着温度的升高和应变速率的下降,流变应力显著减小;合金的变形以动态回复为主,动态再结晶为辅.基于Mecking和Bergstrom提出的合金热变形过程中的位错密度演变模型建立了Ti-7333合金的本构模型,准确地描述了合金热变形过程中的流变应力,并且模型中参数数量较少,便于应用.     

基于摩擦修正的TB6合金流变应力行为研究及本构模型建立

TB6合金是一种高强高韧近β钛合金。采用Gleeble-3500热模拟试验机对铸态TB6钛合金进行了等温热压缩变形试验,变形温度范围为700~900℃,应变速率范围为0.001~1.000 s-1,研究了铸态TB6合金热变形流变应力行为,分析了热压缩后的金相显微组织,基于摩擦修正后的流变应力曲线采用双曲正弦形式的修正Arrhenius关系对TB6钛合金的本构模型进行回归。结果表明:铸态TB6合金的热变形行为对变形温度和应变速率较为敏感,随着变形温度的降低和应变速率的增加流变应力显著增大;其热变形机制以动态回复和动态再结晶为主;得到铸态TB6钛合金热变形本构方程,比较回归模型计算的应力值与实测值其平均相对误差仅为1.48%,因此采用Z参数的双曲正弦函数形式能够较为精确地预测铸态TB6合金高温变形时的流变应力。以上研究为TB6钛合金塑性加工过程的模拟和控制提供了理论基础。     

Al-Cu-Mg-Ag合金热压缩变形行为的预测

采用了热模拟实验机研究了Al-Cu-Mg-Ag耐热铝合金的热压缩变形行为。实验的温度和应变速率分别为340～500℃,0.001～10 s-1。分别用了本构方程和人工神经网络来对Al-Cu-Mg-Ag合金的流变行为进行了分析和模拟。神经网络的结构是3-20-1;输入参数是温度,应变速率和应变;输出参数是流变应力。结果表明该合金的流变曲线出现加工硬化、过渡、软化和稳态流变这4个阶段,流变应力随着应变速率的增加而增大,随着变形温度的下降而减少。用所建立的神经网络模型预测了变形温度和应变速率对流变应力的影响,预测的结果与热压缩变形的基础理论吻合得很好,而且该模型可以很好地描述Al-Cu-Mg-Ag合金的流变应力,在应变速率为0.001～10 s-1的条件下,其平均相对误差分别为3.68%,3.98%,1.53%,3.53%和2.04%。这表明神经网络的预测性能优良,具有很强的推广能力。同时通过本构方程和神经网络的预测结果比较看出神经网络模型的相关系数比较高,而且神经网络比本构方程有更好的预测性能。神经网络可以预测不同应变下的相应的流变应力,但是本构方程只可以根据不同的应变速率和温度来预测峰值应力。     

氢化钛粉末冶金制备的近α型Ti-1100合金的高温压缩性能

以氢化钛粉为原料,采用粉末冶金法-热等静压法制备高温钛合金Ti-1100,并进行了等温压缩试验,通过压缩样品应力应变曲线进行压缩变形行为分析,再结合Arrhenius双曲正弦本构模型建立热压缩本构方程.通过应力应变曲线分析,发现应变速率在0.01 s-1时,所有样品在加工硬化后均表现出稳态流变行为;而应变速率为1 s-1、温度在900℃或1000℃时,流变应力随着变形达到稳态流变状态后,呈增加趋势.应变速率为0.01、0.1、1 s-1时的热压缩变形激活能分别为96、165、232 kJ/mol.硬度测试结果表明显微硬度随温度和应变速率增加稍有降低趋势,当温度为950℃,应变速率为0.1 s-1时,合金的硬度普遍较小,热加工性能最佳.     

00Cr26Mo4超级铁素体不锈钢的热变形行为

在Gleeble-3500热模拟试验机上进行高温压缩实验,研究00Cr26Mo4超级铁素体不锈钢在变形温度为1 050～1 250℃、应变速率为0.01～10 s~(-1)条件下的热变形行为。采用幂函数、指数函数和双曲正弦模型模拟该材料的热变形参数,建立了相应的热变形本构方程。结果表明,在热压缩过程中,流变应力随变形温度的升高而降低,随应变速率的升高而增加,流变应力并未出现明显峰值,材料的软化机制仅有动态回复。探究了幂函数、指数函数和双曲线函数3种模型与00Cr26Mo4钢本构关系的相关性。结果表明,双曲正弦函数模型更符合00Cr26Mo4超级铁素体不锈钢热加工流变应力应变曲线变化规律,并基于双曲正弦函数模型建立了00Cr26Mo4钢的本构方程,计算了热变形激活能238.836 kJ/mol。     

基于神经网络的BT20钛合金本构关系模型

在Thermecmastor-Z型热模拟试验机上对BT20钛合金进行了变形温度800～1 100℃及应变速率0.001～70 s-1的热模拟压缩实验。以实验数据为基础,运用BP神经网络算法原理,建立了BT20钛合金在高温变形条件下的应力与应变、应变速率和变形温度关系的预测模型,并对模型的泛化能力进行了误差评价。结果表明:通过BP神经网络建立的合金本构关系模型具有较高的预测精度,预测结果的相对误差均在3%以内,能很好地满足实际应用的需求。此外,该模型能够客观、真实地描述BT20钛合金的高温动态变形行为,为材料高温本构关系模型的建立提供了快捷、有效的工具。     

医用镍钛合金的制备与热压缩变形行为

采用真空感应熔炼法制备了医用Ti-50. 7%Ni合金(原子数分数), 测试了铸态合金的成分、相变点、微观组织和硬度, 并采用Gleeble-3800热模拟实验机在变形温度750~950℃、应变速率0. 001~1 s-1, 应变量为0. 5的条件下对Ni-Ti合金进行高温压缩变形, 分析其流动应力变化规律, 建立了高温塑性变形本构关系和热加工图.结果表明: 当变形温度减小或应变速率增大时, Ni-Ti合金的流动应力会随之增大.应变速率为1 s-1时, 合金的真应力-真应变曲线呈现出锯齿状特征.根据热加工图, 获得了Ni-Ti合金的加工安全区和流变失稳区, 进而确定其合理的热变形温度范围为820~880℃, 真应变速率低于0. 1 s-1.从而为制定镍钛合金的锻造工艺参数提供理论和数据基础.      

TC11钛合金本构关系神经网络模型的建立

利用MATLAB软件建立了反映材料热变形本构关系的神经网络模型,该模型中采用遗传算法优化其权值和阈值提高了网络收敛的稳定性。并采用Themecmastor-Z型热加工模拟试验机上进行的TC11钛合金等温恒应变速率压缩试验获得的试验数据进行训练,建立了TC11钛合金热变形本构关系的BP神经网络模型,并进行了预测,预测误差小于10%。     

TC4钛合金基于Voce方程的稳定流变本构模型研究

提出了一种包含流变软化的本构的构建方法。通过采用圆柱体试样的热压缩模拟试验,在塑性应变速率为0.1 s-1和20 s-1之间时,观察到TC4钛合金在750～950℃范围内均存在流变应力随着塑性应变降低的流变软化现象。采用双Voce方程对试验数据拟合得到了大塑性变形条件下的稳定流变应力。采用LevenbergMarquardt非线性拟合算法得到了TC4钛合金包含流变软化的本构方程。并且发现Levenberg-Marquardt非线性拟合算法求得的本构方程参数比线性拟合误差更小。结果表明文中提出的流变应力计算方法规避了变形不稳定区域对特征变形抗力判断的干扰,得到了符合指数函数的材料高温稳定流变本构模型,在新型金属材料热加工工艺开发中具有较强的应用价值。     

纳米TiB增强高铌TiAl合金的热变形行为

以Ti-45Al合金粉、Nb粉、Al粉和TiB2合金粉为原料,采用放电等离子烧结法制备含纳米TiB增强相的Ti-45Al-7Nb-1B合金,通过热模拟实验研究该合金在900~1 200℃、应变速率为0.001~1 s-1条件下的热变形行为,推导出高温变形流变本构方程,并建立基于动态材料模型的热加工图。结果表明:含纳米TiB增强相的Ti-45Al-7Nb-1B合金的高温流变应力与变形条件之间的关系可用双曲正弦函数描述,其高温变形激活能为497.95k J/mol,在高应变速率(0.1 s-1)条件下变形时,材料发生失稳变形,最佳变形参数区间为1 000~1 130℃/0.001~0.01 s~(-1)。     

Modified Arrhenius-type Constitutive Model and Artificial Neural Network-based Model for Constitutive Relationship of 316LN Stainless Steel during Hot Deformation

Hot compression experiments of 316 LN stainless steel were carried out on Gleeble-3500 thermo-simulator in deformation temperature range of 1 223-1 423 K and strain rate range of 0.001-1 s-1. The flow behavior was investigated to evaluate the workability and optimize the hot forging process of 316 LN stainless steel pipes. Constitutive relationship of 316 LN stainless steel was comparatively studied by a modified Arrhenius-type analytical constitutive model considering the effect of strain and by an artificial neural network model. The accuracy and effectiveness of two models were respectively quantified by the correlation coefficient and absolute average relative error. The results show that both models have high reliabilities and could meet the requirements of engineering calculation. Compared with the analytical constitutive model, the artificial neural network model has a relatively higher predictability and is easier to work in cooperation with finite element analysis software.     

FNN model for carbide size of M50 steel during hot deformation

Isothermal compression of M50 steel was carried out on a Gleeble-3500 simulator at the deformation temperatures ranging from 1223 to 1423 K and the strain rates ranging from 10 to 70 s^??1. The relationship between the deformation temperature, strain rate, strain and the carbide size of M50 steel was acquired by simulating the isothermal compression via finite element method, and a fuzzy neural network model for predicting the carbide size during hot deformation was established. The maximum and average difference between the experimental and the predicted carbide size were 9.2 and 4.1% respectively. Applying the present fuzzy neural network model, the effect of the deformation temperature, strain rate and strain on the carbide size of M50 steel during hot deformation was analysed.     

An overview on constitutive modelling to predict elevated temperature flow behaviour of fast reactor structural materials

This overview emphasized the aspects of formulation and application of various constitutive models developed by us in recent past viz. Johnson- Cook (JC), modified Zerilli-Armstrong (MZA), strain compensated Arrhenius type model and artificial neural network (ANN) model to predict elevated temperature flow behaviour of fast reactor structural materials. It has been shown that the JC model is not able to represent the high temperature flow behaviour of both alloy D9 and the modified 9Cr-1Mo as it does not incorporate the coupled effect of strain and temperature, and of strain rate and temperature. The new materials model based on Zerilli-Armstrong (ZA) equation considers the coupled effects of temperature and strain and of strain rate and temperature on the flow stress and hence has the capability to predict flow stress over a wider domain temperature and strain rate in comparison with JC model. The formulation and application of strain compensated Arrhenius type constitutive model to predict high temperature flow behaviour of alloy D9 and modified 9Cr-1Mo has been discussed. Development and application of a generic ANN based constitutive model to predict high temperature deformation behaviour of austenitic stainless steels has been highlighted. Finally, a comparative analysis of the merits and shortcomings of these models has been made.     

常见的钛合金热变形本构模型分析及未来发展

概括总结了钛合金热变形本构模型的三种建立途径,分别介绍了并联概率模型、多项式模型、Arrhenius方程、动态回复与动态再结晶本构模型、Johnson-Cook模型、Zerrilli-Armstrong模型、人工神经网络模型等钛合金本构模型的建立方法、优缺点以及应用现状。结合材料的微观组织结构与宏观变形行为建立本构模型,统一本构模型建立方法及模型形式,依据具体实验条件修正本构模型,建立精确高效的计算系统将成为钛合金本构模型发展的主要方向。     

F45MnVS钢热变形本构方程

 为了建立可以满足计算精度的F45MnVS钢高温塑性变形本构关系模型,利用Gleeble-3500试验机完成了热模拟等温压缩试验,获得了变形温度为800~1 000 ℃、应变速率为0.01~10 s-1、变形量为0~70%时的金属流变行为。结果表明,应力随应变的变化具有明显动态再结晶特征,应力随变形温度的降低、应变速率的增加而增大;基于对Arrhenius方程和Zener-Hollomon参数的解析,获得了热变形激活能Q,建立了峰值应力本构模型;基于应力-位错关系和动态再结晶动力学,建立了加工硬化-动态回复和动态再结晶两个阶段的机理型本构模型,用于描述不同变形温度和应变速率时应力与应变之间的关系;采用所建模型完成了不同变形条件的应力应变预测,与试验结果的对比分析表明,相关系数为0.997,吻合度高。     

基于RBF的轴承钢变形抗力的预测

以凸轮式高速形变试验机得到的试验数据为基础,利用Matlab人工神经网络工具箱,建立了轴承钢的变形抗力与其化学成分、变形温度、变形速率及变形程度对应关系的RBF神经网络预测模型.分析了变形温度和变形速率对轧制压力网络模型精度的影响.得出随着变形温度的增加,网络的预测误差逐渐增大;随着变形速率的增大,网络的预测误差逐渐减...     

Pharmaceutical granulation and tablet formulation using neural networks

Current-day pharmaceutical formulation may be trial and error in nature due to the absence of a clear relationship between the formulation characteristics (output variables) and the material and process variables (input variables). Neural networks are networks of adaptable nodes, which through a process of learning from task examples, store experiential knowledge and make it available for prediction. Prediction of a model granulation and tablet system characteristics from the knowledge of material and process variables utilizing neural networks is the basis of this presentation. The formulation design contained the following variables: granulation equipment, diluent, method of binder addition, and the binder concentration. The material, process, granulation evaluation, and tablet evaluation data of the formulations were used as the data set for training and testing of the neural network models. A comparison of the neural network prediction performance with that of regression models was also done. Both the granulation model and the tablet model converged fairly rapidly in the training step. In the testing step, the predictions for all granulation model variables (geometric mean particle size, flow value, bulk density, and tap density) were satisfactory. In the tablet model, the predictions for disintegration and thickness were also satisfactory. The predictions for hardness and friability were less than satisfactory. Two situations where the neural network may not perform adequately are discussed. The neural network prediction is better or comparable for all the predicted variables in this study compared to regression methods. The results clearly show the applicability of neural networks to formulation modeling.     

Constitutive Modeling for Flow Behaviors of Superaustenitic Stainless Steel S32654 during Hot Deformation

Hot deformation behavior of superaustenitic stainless steel S32654 was investigated with hot compression tests at temperatures of 950-1 250 ℃ and strain rates of 0.001-10s~(-1).Above 1 150 ℃,with strain rate lower than 0.1s~(-1),the flow curves exhibit nearly steady-state behavior,while at higher strain rate,continuous flow softening occurs.To provide a precise prediction of flow behavior for the alloy,the constitutive modeling considering effect of strain was derived on the basis of the obtained experimental data and constitutive relationship which incorporated Arrhenius term and hyperbolic-sine type equation.The material constantsα,n,Q and lnA are found to be functions of the strain and can be fitted employing eighth-order polynomial.The developed constitutive model can be employed to describe the deformation behavior of superaustenitic stainless steel S32654.     

Cloud Neural Fuzzy PID Hybrid Integrated Algorithm of Flatness Control

In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neural network and fuzzy integration. By indeterminacy artificial intelligence, the problem of fixing the membership functions of input variables and fuzzy rules was solved in an actual fuzzy system and the nonlinear mapping between variables was implemented by neural network. The algorithm has the adaptive learning ability of neural network and the indetermi- nacy of a cloud model in processing knowledge, which makes the fuzzy system have more persuasion in the process of knowledge inference, realizing the online adaptive regulation of PID parameters and avoiding the defects of the traditional PID controller. Simulation results show that the algorithm is simple, fast and robust with good control performance and application value.