An ANFIS model for the prediction of flow stress of Ti600 alloy during hot deformation process

In this paper, an adaptive network-based fuzzy inference system (ANFIS) model has been established to predict the flow stress of Ti600 alloy during hot deformation process. This network integrates the fuzzy inference system with a back-propagation learning algorithm of neural network. The experimental results were obtained from Gleeble-1500 thermal-simulator at deformation temperatures of 800–1100 °C, strain rates of 0.001–10 s^?1, and height reduction of 70%. In establishing this ANFIS model, strain rate, deformation temperature and the strain are entered as input parameters while the flow stress are used as output parameter. After the training process, the fuzzy membership functions and the weight coefficient of the network can be optimized. A comparative evaluation of the predicted and the experimental results has shown that the ANFIS model used to predict the flow stress of Ti600 titanium alloy has a high accuracy and with absolute relative error is less than 17.39%. Moreover, the predicted accuracy of flow stress during hot deformation process of Ti600 titanium alloy using ANFIS model is higher than using traditional regression method, indicating that the ANFIS model was an easy and practical method to predict flow stress for Ti600 titanium alloy.     

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.     

Application of neural network and genetic algorithm to powder metallurgy of pure iron

In the present paper, soft computing techniques are applied to optimize the powder metallurgy processing of pure iron. An artificial neural network is trained to predict the stress resulting from a given trend in strain and sintering temperature. To prepare an appropriate model, pure iron powders are compacted and sintered at various temperatures. Subsequently, compression test is conducted at room temperature on the bulked samples. The sintering temperatures and the corresponding stress–strain records are used as sets of data for the training process. The performance of the network is verified by putting aside one set of data and testing the network against it. Eventually, by using a genetic algorithm, an optimization tool is created to predict the optimum sintering temperature for a desired stress–strain behavior. Comparison of the predicted and experimental data confirms the accuracy of the model.     

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.     

Flow curve prediction of an Al-MMC under hot working conditions using neural networks

The plastic flow behaviour of a particle-reinforced aluminium alloy matrix composite (AA2618 + Al₂O_3p) was studied by analysing the results of hot compression tests carried out in extended ranges of temperature and strain rate, typical of hot working operations. In general, for a given temperature and strain rate, the flow curves exhibit a peak, at relatively low strains, followed by flow softening; for a constant strain, the flow stress increases with increasing strain rate and decreasing temperature. The experimental data were used as an input for training artificial neural networks in order to predict the flow curves of the composite investigated. The comparison of the predicted stress–strain curves with the ones obtained by experimental testing, under conditions different from those used for the training stage, has proven the prediction generalisation capability of the artificial neural network-based models.     

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.     

金属塑性变形的本构模型研究

根据位错动力学理论,忽略动态应变时效因素,将塑性变形的流变应力分解为非热应力、热激活应力和粘拽阻力3部分,建立了一个基于物理概念的本构模型。对HSLA-65结构钢的力学行为进行了研究,试验温度为77～700K,应变率为0.001～0.1s-1,真实塑性应变超过60%。结果表明,塑性流变应力随温度的降低、应变和应变率的增加而增大;在一定的温度和应变率范围发生动态应变时效现象,并且随应变率的提高,该现象将移向更高的温区。通过模型预测与试验结果的比较可知,所给本构关系能很好地描述较宽的温度与应变率范围内的塑性流变应力。     

Prediction of metadynamic softening in a multi-pass hot deformed low alloy steel using artificial neural network

The metadynamic softening behaviors in 42CrMo steel were investigated by isothermal interrupted hot compression tests. Based on the experimental results, an efficient artificial neural network (ANN) model was developed to predict the flow stress and metadynamic softening fractions. The effects of deformation parameters on metadynamic softening behaviors in the hot deformed 42CrMo steel have been investigated by the experimental and predicted results from the developed ANN model. Results show that the effects of deformation parameters, such as strain rate and deformation temperature, on the softening fractions of metadynamic recrystallization are significant. However, the strain (beyond the peak strain) has little influence. A very good correlation between experimental and predicted results indicates that the excellent capability of the developed ANN model to predict the flow stress level and metadynamic softening, the metadynamic recrystallization behaviors were well evidenced.     

Artificial neural network modelling to predict hot deformation behaviour of as HIPed FGH4169 superalloy

Abstract

The hot deformation behaviour of as HIPed FGH4169 superalloy was studied by single stroke compression test on MMS-200 test machine at the temperatures of 950–1050°C and the strain rates of 0·004–10 s^?1. Based on the experimental results, a back-propagation artificial neural network model and constitutive equation method were established to predict the flow stress of FGH4169 superalloy. The predictability of two different models was compared. The correlation coefficients of experimental and predicted flow stress with the trained BP ANN model and constitutive equation were 0·9995 and 0·9808 respectively. The average root mean square error (RMSE) values of the trained ANN model and constitutive equation are 0·39 and 2·21 MPa respectively. And the average absolute relative error (AARE) values of the trained ANN model and constitutive equation are 1·79 and 7·47% respectively. The results showed that the ANN model is an effective tool to predict the flow stress in comparison with constitutive equation.     

基于GA改进BP神经网络预测热变形流变应力模型的建立EI北大核心CSCD

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。     

Neural network modelling of flow stress in Ti–6Al–4V alloy with equiaxed and Widmanstätten microstructures

Abstract

In the present study, artificial neural networks (ANNs) were used to model flow stress in Ti–6Al–4V alloy with equiaxed and Widmanstätten microstructures as initial microstructures. Continuous compression tests were performed on a Gleeble 3500 thermomechanical simulator over a wide range of temperatures (700–1100°C) with strain rates of 0˙001–100 s^–1 and true strains of 0˙1–0˙6. These tests have been focused on obtaining flow stress data under varying conditions of strain, strain rate, temperature, and initial microstructure to train ANN model. The feed forward neural network consisted of two hidden layers with a sigmoid activation function and backpropagation training algorithm was used. The architecture of the network includes four input parameters: strain rate ?, Temperature T, true strain ? and initial microstructure and one output parameter: the flow stress. The initial microstructure was considered qualitatively. The ANN model was successfully trained across (α+β) to β phase regimes and across different deformation domains for both of the microstructures. Results show that the ANN model can correctly reproduce the flow stress in the sampled data and it can predict well with the nonsampled data. A graphical user interface was designed for easy use of the model.     

Analysis of high temperature deformation behavior of a high Nb containing TiAl based alloy

The effects of deformation temperature and strain rate on the hot deformation behaviors of as-cast Ti-45Al-8.5Nb-(W,B,Y) alloy were investigated. The results indicated that when deformation temperature is below 1250 °C, the flow stress decreases with the increase of deformation temperature and decrease of strain rate, once deformation temperature reaches 1250 °C, the flow stress is not sensitive to strain rate any more. A neural network model was established to predict the flow stress of this high Nb containing TiAl based alloy during hot deformation. The predicted flow stress curves are in good agreement with experimental results.     

Prediction of flow stress in isothermal compression of Ti60 alloy using an adaptive network-based fuzzy inference system

Isothermal compression of Ti60 titanium alloy at the deformation temperatures ranging from 960 to 1110 °C, the strain rates ranging from 0.001 to 10 s⁻¹ and the height reductions of 60% were carried out on a Gleeble–3800 simulator. An adaptive network-based fuzzy inference system (ANFIS) model has been established to predict the flow stress of Ti60 alloy during hot deformation process. A comparative evaluation of the predicted and the experimental results has shown that the ANFIS model used to predict the flow stress of Ti60 titanium alloy has a high accuracy. The maximum difference and the average difference between the predicted and the experimental flow stress are 13.83% and 5.15%, respectively. The comparison between the predicted results based on the ANFIS model for flow stress and those using the regression method has illustrated that the ANFIS model is more efficient in predicting the flow stress of Ti60 alloy.     

一种建立颗粒增强金属基复合材料本构方程的新方法

采用基于人工神经网络的专家系统,建立了颗粒增强金属基复合材料的本构方程。以热模拟 试验得到的试验数据作为训练样本,比较准确地实现了工艺参数与流动应力之间关系的预测 。预测结果与实验数据误差小于10％。      

基于遗传BP网络的Mg-Sm-Zn-Zr合金应力预测模型及加工图

利用Gleeble-1500D试验机对新型Mg-Sm-Zn-Zr合金进行等温压缩实验,得到了该合金在350~450℃、0.001~1s-1条件下的真应力-应变曲线,应用遗传算法优化的BP神经网络建立起合金的应力预测模型,并对所建预测模型和考虑应变的Arrhenius本构模型进行了对比,采用预测数据并应用Murthy失稳准则绘制出该合金的热加工图,最后结合微观组织分析所绘制热加工图的合理性。结果表明,GA-BP模型预测值和实验值间的相关性系数为0.999,平均相对误差为1.469%,较应变补偿本构模型预测精度更高;热加工图设计合理,有效确认温度400~450℃、应变速率0.001~0.03s-1是最佳热加工范围,合金在该区域发生了动态再结晶。     

Prediction of flow stress in isothermal compression of Ti–6Al–4V alloy using fuzzy neural network

Isothermal compression of Ti–6Al–4V alloy at the deformation temperatures ranging from 1093 K to 1303 K with an interval 20 K, the strain rates ranging from 0.001 s⁻¹ to 10.0 s⁻¹ and the height reductions ranging from 20% to 60% with an interval 10% were carried out on a Thermecmaster-Z simulator. Based on the experimental results, a model for the flow stress in isothermal compression of Ti–6Al–4V alloy was established in terms of the fuzzy neural network (FNN) with a back-propagation learning algorithm using strain, strain rate and deformation temperature as inputs. The maximum difference and the average difference between the predicted and the experimental flow stress are 18.7% and 4.76%, respectively. The comparison between the predicted results based on the FNN model for flow stress and those using the regression method has illustrated that the FNN model is more efficient in predicting the flow stress of Ti–6Al–4V alloy.     

神经网络预测搅拌摩擦加工TC4超塑性行为

目的 研究搅拌摩擦加工工艺改性的Ti–6Al–4V双相钛合金的超塑性变形行为。方法 对360 r/min、30mm/min工艺条件下搅拌摩擦加工处理的TC4钛合金在不同的变形条件下进行超塑性拉伸实验,在实验数据的基础上构建以变形温度、应变速率和晶粒尺寸为输入参数且以峰值应力为输出参数的3–16–1结构的BP人工神经网络模型。应用所构建的BP人工神经网络模型对不同变形条件的Ti–6Al–4V钛合金的超塑性行为进行预测。结果 BP人工神经网络预测的精准度较高,实验应力值与预测应力值吻合度较高,相关系数R=0.991 3,相对误差为1.91%～12.48%,平均相对误差为5.92%。结论 该模型预测的准确性较高,能够客观真实地描述Ti–6Al–4V合金的超塑性变形行为。     

Dynamic Recrystallization Behavior of 13%Cr Martensitic Stainless Steel under Hot Working Condition

In this study,the effect of hot deformation on martensitic stainless steel was carried out in temperatures between 950 to 1100℃and strain rates of 0.001,0.01 and 0.1 s-1.Two important dynamic recrystallization parameters,the critical strain and the point of maximum dynamic softening,were derived from strain hardening rate vs stress curves.Then the calculated parameters were used to predict the dynamic recrystallized fraction.Our results show that critical stress and strain increase with decreasing deformation temperature and increasing strain rate.The hot deformation activation energy of the steel is also investigated in the present work with 413 kJ/mol.Our experimental flow curves are in fair agreement with the kinetics of dynamic recrystallization model.     

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%。     

Hot deformation behaviour and constitutive modelling of P92 heat resistant steel

Abstract

Hot compression tests are conducted in the present paper to investigate hot deformation behaviour of the newly developed heat resistant steel P92, which is used to fabricate main steam pipes for ultra supercritical power plants. Stress–strain curves at elevated temperatures and different strain rates are obtained. It is found that dynamic recrystallisation happens only when the temperature is above 1100°C and strain rate is below 0·1 s^?1. Otherwise, dynamic recovery is the main softening mechanism. Constitutive modelling with the hyperbolic sine, including an Arrhenius term, is used to predict peak and saturated stresses. Material constants for this model are determined. Results show that the model can be used to predict peak and saturated stresses. However, the model would fail in predicting flow stress with respect to strain; thus, a model containing nine independent parameters and the complete form of Spittel equation are utilised to predict flow stress curves softened by dynamic recrystallisation and dynamic recovery respectively since there are no unified equations. The predicted stress–strain curves are in good agreement with experimental results, which confirmed that the models developed in the present paper are effective and accurate for P92 steel.