A Model to Predict Recrystallization Kinetics in Hot Strip Rolling Using Combined Artificial Neural Network and Finite Elements

A thermo-mechanical model has been developed to establish a coupled heat conduction and plastic flow analysis in hot-rolling process. This model is capable of predicting temperature, strain, and strain rate distributions during hot rolling as well as the subsequent static recrystallization fraction and grain size changes after hot deformation. Finite element and neural network models are coupled to assess recrystallization kinetics after hot rolling. A new algorithm has been suggested to create differential data sets to train the neural network. The model is then used to predict histories of various deformation variables and recrystallization kinetics in hot rolling of AA5083. Comparison between the theoretical and the experimental data shows the validity of the model.     

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

The static recrystallization 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 static recrystallized fractions. The effects of the deformation temperature, strain rate and deformation degree, as well as initial grain sizes, on the static recrystallization behaviors in two-pass hot compressed 42CrMo steel were investigated by the experiments and ANN model. A very good correlation between the experimental and predicted results from the developed ANN model has been obtained, which indicates that the excellent capability of the developed ANN model to predict the flow stress level and static recrystallization behaviors in two-pass hot deformed 42CrMo steel. The effects of strain rate, deformation temperature and degree of deformation on the static recrystallization behaviors are significant, while those of the initial austenite grain size are slight.     

基于模糊神经网络的Zr-2合金晶粒尺寸及流变应力模型

通过Zr-2合金在变形温度为750,800℃,变形速率为0.01,1,10 s-1,变形程度为50%,65%的热压缩试验获得数据,采用模糊神经网络方法建立Zr-2合金的晶粒尺寸及流变应力模型.模型的输入参数包括变形温度、变形程度、变形速率等热加工参数,模型的输出为晶粒尺寸和流变应力.结果表明,该模型避免了传统经验回归拟合复杂的数学公式计算,是简单而精确的建模方法,可用于优化热加工参数.     

THE HOT DEFORMATION BEHAVIOR AND DYNAMIC RECRYSTALLIZATION MODEL OF 35CrMo STEEL

The isothermal single-stage compression of 35CrMo structural steel has been carried out by using Gleeble 1500 simulator at the temperature range of 950℃ to 1150℃ and strain rate range of 0.01s-1 to 10s-1. The effect of hot deformation parameters, such as strain rate, deformed temperature and initial grain size on the flow stress behavior was investigated. The activation energy of tested alloy was calculated, which is 378.16kJ/mol; The relationships between the peak stress (σp), the peak stain (εp), the critical strain (εc) and Z parameter were established. The micro structure evolution shows the pre-existing austenite grain boundaries constitute the principal nucleation sites for dynamic recrystallization (DRX), and the initial austenite grain size affects the grain size of DRX slightly. The kinetic mathematical model of DRX of 35CrMo is: XDRX=1-exp(-3.23-2.28) and Ddyn = 2.252× 10Z-0.22.     

GH4169异型材单轧槽轧制工艺

采用轧制工艺生产GH4169合金异型材,结合实验条件,基于有限元模拟软件建立了单轧槽少道次轧制过程的三维刚塑性有限元模型。采用异型坯作为坯料,分析了轧制过程中孔型充满度、变形温度、等效应变和等效应力的分布情况。模拟结果表明,采用Φ160 mm×200 mm轧机时,初轧温度为1070℃,断面收缩率为45%,单轧槽两道次轧制成形,孔型充满度良好,等效应变约为0.3~1.4。结合模拟结果,在轧机上进行了热轧实验,轧件厚度满足尺寸要求,宽度比成品小2 mm,没有发生晶粒细化。这主要是由于多火次、多次数轧制,使得加热引起的晶粒长大程度大于小变形量引起的晶粒细化程度,使得晶粒未细化,宽度不够。     

应用人工神经网络构造Ti40合金加工图

以Gleeble-1500热模拟试验机获得的Ti40钛合金压缩试验数据为基础,应用人工神经网络对数据进行训练和预测,建立该合金的高温流动应力与应变、应变速率和温度对应关系的预测模型,其中,应变、应变速率（对数形式）和变形温度作为模型的输入参数,流动应力作为模型的输出参数。结果发现,运用BP反向传播算法进行训练的神经网络模型具有良好的预测功能,其预测值与实验测量值基本吻合。同时,采用神经网络模型预测的数据构造Ti40合金的加工图,其安全区和失稳区的范围与实测数据获得的加工图基本相符,并对各自区域的相应组织状态进行金相观察。     

Hot Deformation and Corrosion Resistance of High-Strength Low-Alloy Steel

The hot deformation characteristics and the corrosion behavior of a high-strength low-alloy(HSLA) steel were investigated at deformation temperatures ranging from 800 to 1100 ℃ and strain rates ranging from 0.1 to 10 s-1 using an MMS-200 thermal simulation testing machine. Based on the flow curves from the experiment, the effects of temperature and strain rate on the dynamic recrystallization behavior were analyzed. The flow stress decreased with increasing deformation temperature and decreasing strain rate. With the assistance of the process parameters, constitutive equations were used to obtain the activation energy and hot working equation. The hot deformation activation energy of HSLA steel in this work was 351.87 kJ/mol. The work hardening rate was used to determine the critical stress(strain) or the peak stress(strain). The dependence of these characteristic values on the Zener-Hollomon parameter was found. A dynamic recrystallization kinetics model of the tested HSLA steel was constructed, and the validity of the model was confirmed by the experimental results. Observation of the microstructures indicated that the grain size increased with increasing deformation temperature,which led to a lowered corrosion resistance of the specimens.     

Fe-18Mn-0.6C TWIP钢的热变形行为

在Gleeble-3500热模拟实验机上通过单道次压缩实验,研究了变形温度、应变速率和变形量对TWIP钢流变应力和临界动态再结晶行为的影响规律。结果表明,试验TWIP钢热变形的峰值应力随温度的升高而降低,随着应变速率的增大而升高;各种变形条件下,TWIP钢的奥氏体晶粒尺寸有很大差异,随着变形温度的升高,再结晶晶粒粗化,而应变速率和应变量的增加有利于晶粒细化;最后采用线性回归方法计算出TWIP钢的热变形激活能为443.3 kJ/mol,并求出了该钢种动态再结晶临界条件与Z参数之间的关系,以及动态再结晶动力学规律。     

热变形参数对Ti-15-3合金显微组织的影响及预测

通过等温压缩试验和金相分析研究了热变形参数对Ti-15-3(Ti-15V-3Cr-3Sn-3Al）合金固溶处理后显微组织的影响，等效晶粒尺寸随变形温度升高而增大，随变形程度和变形速率增加而减小，再结晶程度随变形温度升高而减小，随变形程度和变形速率增加而增大，采用人工神经网络的方法建立了等效晶粒尺寸及再结晶晶粒体积百分数与变形程度，变形速率和变形温度间的数学模型，预测值与实测值吻合较好，表明该方法很好地预测热变形参数对Ti-15-3合金固溶处理后显微组织的影响。     

变形条件对SiCp/2014Al复合材料力学行为和晶粒度的影响

研究了变形温度,应变速率和变形程度等热加工参数对ＳｉＣｐ／２０１４Ａｌ复合材料力学行为和晶粒度的影响。结果表明,该材料的流动应力对变形温度和应变速率比较敏感。变形每升高４０℃,流动应力降低５￣１０ＭＰａ,应变速率每提高一个数量级,流动应力增加１．３￣１．８们。提高应变整编有利于获得细晶组织。以足够的变形程度锻造以及适当提高锻后冷却速度均有利于获得细晶组织。根据实验结果确定了该材料的流动应力与热加工     

Application of mathematical modeling in two-stage rolling of hot rolled wire rods

The no-recrystallization temperature (T_nr) is an important parameter in the design of two-stage rolling schedule to obtain finer grain size. T_nr was obtained both by continuous cooling compression testing and tension-compression testing. However, due to the limitations of experimental installation, both compressing testing and tension-compression testing have a scaling down of practical pass strain and strain rate in rolling mill. The mathematical model that calculates mean flow stress (MFS) can eliminate these limitations and the pass strain and strain applied in mathematical model are approximately equal to the mean value of that in wire-rod rolling mill. Therefore, mathematical calculation is a new method to determine T_nr and the predicted T_nr is similar to experimental results. Due to the high strain rate and short interpass time at the finishing strain of wire rods mills, mathematical modeling is also an effective method to simulate microstructure-evolution in wire rods rolling. An expert system was established to study the microstructure evolution in two-stage rolling through the obtained dynamic recrystallization (DRX) model combined with metadynamic recrystallization (MRX) and static recrystallization (SRX) model in literature. In the present work, it is simplified that the complete metadynamic recrystallization (MRX) is achieved when strain for deformation exceeds critical strain ɛ_c. It was found that strain accumulation played an important role in finishing rolling. The recrystallization behavior during finishing rolling stage was repeated by static and dynamic model. The predicted austenite grain size and mean flow stress at each pass are expected to provide guidance for appropriate rolling schedule design.     

工艺参数对H型钢热轧过程奥氏体演化的影响

利用Gleeble-1500热模拟实验系统得到了Q235钢的高温流变应力数据,观测了完成变形瞬时原始奥氏体晶界,建立了流变应力及奥氏体再结晶数学模型。采用基于稳态判断的数值分析流程,对典型规格中翼缘E型钢多道次热轧成形过程进行了计算。分析了加热炉出炉温度、高压水除鳞强度、轧制变形速度、道次间隙时间等工艺参数对轧件内奥氏体晶粒演化的影响,并建立了估H 型钢腹板处奥氏体晶粒大小的数学模型。     

基于BP神经网络的镁合金晶粒尺寸及流变应力模型

通过等温压缩试验研究了热变形条件对AZ61B镁合金再结晶晶粒尺寸及其流变应力的影响，并采用人工神经网络方法分别建立了动态再结晶晶粒尺寸及流变应力的模型。利用反向传播算法对网络进行试探性训练研究，得到了最佳参数。结果表明，所建立的网络模型具有优良的性能，能精确预测AZ61B合金热变形条件下的再结晶晶粒尺寸及流变应力。     

17CrNiMo6钢的静态再结晶行为

采用Gleeble-1500热模拟试验机,在压缩温度为950~1050 ℃(间隔50 ℃)、预应变为0.1~0.2(间隔0.05)、应变速率为0.01~1 s^-1、不同原始晶粒尺寸和道次间隔时间条件下,对17CrNiMo6钢进行双道次热压缩试验。讨论了道次间隔时间、压缩温度、预应变、应变速率和原始晶粒尺寸对17CrNiMo6钢静态再结晶行为的影响。根据回归分析得到静态再结晶在不同变形条件下的流变应力曲线,结合压缩后试样的显微组织,建立了17CrNiMo6钢静态再结晶动力学模型和晶粒尺寸模型。结果表明,17CrNiMo6钢静态再结晶体积分数随压缩温度、间隔时间、预应变和应变速率增加而增大;静态再结晶晶粒尺寸随压缩温度和原始奥氏体晶粒尺寸增加而增大,随预应变和应变速率的增加而减小。通过对比所建模型的预测值与热压缩所得的试验值,发现二者较为吻合,验证了模型的准确性。     

氮强化高锰奥氏体钢热变形行为研究

利用Gleeble-3500热力模拟试验机在温度为1253～1423K,应变速率为0.1～10s-1的条件下对32Mn-7Cr-1Mo-0.3N奥氏体钢进行了热压缩变形试验,测定了其真应力-应变曲线,观察了变形后的组织.试验结果表明,流变应力和峰值应变随变形温度的降低和应变速率的提高而增大.真应变为0.6时,在1423K、应变速率在0.1～10s-1之间的试样均已发生完全动态再结晶;在1373K以下变形时,应变速率在0.1～10s-1之间,试样发生部分动态再结晶.动态再结晶晶粒尺寸随着变形温度的升高而增大,随着应变速率的升高而减小.32Mn-7Cr-1Mo-0.3N奥氏体钢的热变形激活能Q值为469.03kJ/mol,并获得热变形方程.     

基于Deform高线粗轧奥氏体晶粒细化研究

本文结合GCr15再结晶模型, 根据轧线实际孔型参数、轧线布置与轧制程序, 采用刚塑性有限元法, 利用模拟软件Deform对轴承钢线材GCr15粗轧进行了三维有限元模拟, 分析总结了粗轧过程中轧件温度场、等效应变和应变速率的变化规律, 得出粗轧过程动态、亚动态和静态再结晶的百分数和对应晶粒尺寸, 揭示了轧件在粗轧过程中再结晶规律及奥氏体晶粒细化规律, 并且证实了初始晶粒尺寸对粗轧过程奥氏体晶粒细化的影响规律。     

Modeling of grain size in isothermal compression of Ti-6Al-4V alloy using fuzzy neural network

Isothermal compression of Ti-6Al-4V alloy was conducted in the deformation temperature range of 1093-1303 K,the strain rates of 0.001,0.01,0.1,1.0,and 10.0 s-1,and the height reductions of 20%-60% with an interval of 10%.After compression,the effect of the processing parameters including deformation temperature,strain rate,and height reduction on the flow stress and the microstructure was investigated.The grain size of primary α phase was measured using an OLYMPUS PMG3 microscope with the quantitative metallography SISC IAS V8.0 image analysis software.A model of grain size in isothermal compression of Ti-6Al-4V alloy was developed using fuzzy neural net-work(FNN) with back-propagation(BP) learning algorithm.The maximum difference and the average difference between the predicted and the experimental grain sizes of primary α phase are 13.31% and 7.62% for the sampled data,and 16.48% and 6.97% for the non-sampled data,respectively.It can be concluded that the present model with high prediction precision can be used to predict the grain size in isothermal compression of Ti-6Al-4V alloy.     

Hot Deformation Behavior and Processing Maps of a High Al-low Si Transformation-Induced Plasticity Steel: Microstructural Evolution and Flow Stress Behavior

Hot deformation behavior of a high Al-low Si transformation-induced plasticity(TRIP) steel was studied by an MMS-300 thermo-simulation machine at the temperature range of 1050–1200℃ and strain rate range of 0.01–10s~(-1). The constitutive equations of the TRIP steel were established at high temperature by fitting the strain factor with a sixth-order polynomial. The instability during hot rolling was discussed using processing maps. The results reveal that two types of flow stress curves(dynamic recrystallization and dynamic recovery) were observed during the hot compression of the high Al-low Si TRIP steel. Flow stress decreased with increasing deformation temperature and decreasing strain rate. The predicted flow stress of experimental TRIP steel is in agreement with the experimental values with an average absolute relative error of 4.49% and a coefficient of determination of 0.9952. According to the obtained processing maps, the TRIP steel exhibits a better workability at strain rate of 0.1s~(-1) and deformation temperature of 1200℃ as compared to other deformation conditions.     

AZ31镁合金铸轧和常规轧制板的变形组织及形变特征

在变形温度为150～400 ℃、应变速率为0.3～0.000 3 s~(-1)条件下,在Gleeble1500热模拟机上采用等温拉伸试验对AZ31镁合金铸轧和常规轧制板的高温塑性及组织演变进行研究.结果表明:两种AZ31镁合金板的峰值应力和峰值应变均随着变形温度的降低和应变速率的增加而逐渐增大.铸轧板的应变硬化指数和应变速率敏感系数均大于常规轧制板的.在高温低应变速率变形条件下,铸轧板的晶界滑移引起的空洞尺寸、体积分数和密度均大于常规轧制板的.低应变速率下拉伸变形后的动态再结晶晶粒尺寸随温度的升高逐渐增加;不同变形条件下铸轧板的晶粒尺寸均小于常规轧制板的;再结晶晶粒尺寸和Z参数呈幂律关系.