CN1330930C

CN1330930C - Flexible measurement method for grain sizes of steel plate internal structure during rolling process

Info

Publication number: CN1330930C
Application number: CNB200510046130XA
Authority: CN
Inventors: 许云波; 吴迪; 刘相华; 王国栋; 于永梅
Original assignee: Northeastern University China
Current assignee: Northeastern University China
Priority date: 2005-03-29
Filing date: 2005-03-29
Publication date: 2007-08-08
Anticipated expiration: 2025-03-29
Also published as: CN1664500A

Abstract

The present invention relates to a method for softly measuring the grain size of an inner structure of a steel board in the rolling process, which can be combined with a physical metallurgy mechanism, a database and the information technology. The present invention has the purpose that the real-time online monitoring of a microstructure in the steel board is achieved, and can provide a criterion for optimizing the technology process and chemical components and improving the steel performance and quality. The method comprises the following steps that a certainty model parameter is selected; the real-time communication is established with a processing machine; a technology parameter and dynamic data of an alloy component are called from the database of the processing machine so as to be input as an initial parameter. The prediction of the grain size and the evolution of an austenite comprises the following steps that the grain size of dynamic recrystallization in the rolling process can be calculated; the grain size of static and secondary dynamic recrystallization in the rolling intermission period can be calculated; the average grain size and the growth of the austenite can be calculated; the grain size of the austenite of a finally rolling outlet can be obtained; the grain size of a ferrite which changes a phase can be predicted.

Description

Soft Measurement Method of Grain Size in Internal Structure of Steel Plate During Rolling

技术领域technical field

本发明属于轧钢技术领域，特别适用于中厚板轧机、热连轧过程的粗轧机和精轧机轧制过程钢板内部组织晶粒尺寸的软测量方法。The invention belongs to the technical field of steel rolling, and is particularly suitable for the soft measurement method of the grain size of the internal structure of steel plates in the rolling process of medium and thick plate rolling mills, rough rolling mills and finishing mills in the hot continuous rolling process.

背景技术Background technique

计算机技术和力学模拟相结合，使钢材外形尺寸控制达到较高水平，但是组织演变参数在线直接检测与控制一直处于相对落后的状态。利用真实检测仪表和设备在线实时、直接检测材料的组织状况，尚需假以时日。软测量技术将控制理论和工艺机理有机的结合起来，能够连续计算那些不可测或难以检测的参数，在一定程度上取代在线检测仪表。目前软测量技术已经成为自动监测和过程优化的有力工具，并被列为未来控制领域需要重点研究的几个重要方向之一。应用软测量技术在线监测轧件的组织演变，必将促进人们对材料加工过程中物理冶金规律的了解，推动控轧控冷(TMCP)技术在板带轧制中的进一步应用。The combination of computer technology and mechanical simulation has made the control of steel shape and size reach a higher level, but the online direct detection and control of microstructure evolution parameters has been in a relatively backward state. It will take time to use real testing instruments and equipment to directly detect the organizational status of materials in real time online. Soft-sensing technology organically combines control theory and process mechanism, can continuously calculate those unmeasurable or difficult-to-detect parameters, and replaces online detection instruments to a certain extent. At present, soft sensor technology has become a powerful tool for automatic monitoring and process optimization, and is listed as one of the important research directions in the field of future control. The application of soft-sensing technology to monitor the microstructure evolution of rolled pieces on-line will surely promote people's understanding of the physical and metallurgical laws in the material processing process, and promote the further application of controlled rolling and controlled cooling (TMCP) technology in strip rolling.

晶粒细化作为唯一一种既提高强度又改善韧性的强化方式，一直是新一代钢铁材料所追求的目标之一。由于TMCP技术的应用和强力式轧制设备的出现，精轧温度降低和负荷分配后移已成为工业条件下开发新一代超细晶粒钢的必要手段。准确预测α晶粒尺寸，对改进生产工艺，提高钢材质量水平具有极其重要的意义。但从目前预测α晶粒尺寸的计算方法来看，已有的经验公式不仅应用范围窄，不能反映物理冶金机制，且没有充分考虑形变、冷却等并不适合于控轧控冷过程的预测。Grain refinement, as the only strengthening method that can improve both strength and toughness, has been one of the goals pursued by the new generation of steel materials. Due to the application of TMCP technology and the emergence of heavy-duty rolling equipment, the reduction of finish rolling temperature and the rearward shift of load distribution have become the necessary means to develop a new generation of ultra-fine grain steel under industrial conditions. Accurately predicting the α grain size is of great significance for improving the production process and improving the quality of steel. However, judging from the current calculation method for predicting the α grain size, the existing empirical formula not only has a narrow application range, but cannot reflect the physical metallurgical mechanism, and does not fully consider deformation, cooling, etc., and is not suitable for the prediction of the controlled rolling and cooling process.

目前，在热轧带钢组织性能预测方面，国外开发了一些模型。我国少数钢厂成套引进了国外系统，从应用效果和模型综述来看，现有技术主要存在以下的不足：1)模型大都采用经验回归模型，缺乏全新的物理冶金理论的支撑，通用性差，应用范围窄，很难适应控轧控冷条件下工艺参数变化的要求；2)在线系统技术指标集中在最终力学性能的预测精度，对显微组织参数的演变缺乏必要描述，不能够满足人们对轧制全线微观结构参数实时监测的需求，不利于工艺规程和钢种成分的在线优化和控制。本发明从相变动力学原理出发，结合中厚板生产实际，提出了一套高精度预测α晶粒的新方法。At present, some models have been developed abroad in terms of the prediction of the microstructure and properties of hot-rolled strip steel. A small number of steel mills in my country have introduced complete sets of foreign systems. Judging from the application effect and model overview, the existing technologies mainly have the following deficiencies: 1) Most of the models use empirical regression models, lacking the support of new physical metallurgical theories, poor versatility, and poor application. The range is narrow, and it is difficult to adapt to the requirements of process parameter changes under the conditions of controlled rolling and controlled cooling; 2) The technical indicators of the online system focus on the prediction accuracy of the final mechanical properties, and lack the necessary description of the evolution of microstructure parameters, which cannot meet people's expectations for rolling. The demand for real-time monitoring of the microstructural parameters of the whole production line is not conducive to the online optimization and control of process regulations and steel composition. The present invention starts from the principle of phase transformation dynamics and combines with the actual production of medium and thick plates, and proposes a set of new methods for predicting α grains with high precision.

发明内容Contents of the invention

针对现有技术存在的不足，本发明提供一种轧制过程中钢板内部晶粒尺寸在线软测量的方法，本发明的方法是将物理冶金机理和数据库、信息技术相结合，其目的是实现钢板内部微观组织的实时在线监测，为优化工艺规程和化学成分，提高钢材性能质量提供依据。Aiming at the deficiencies in the prior art, the present invention provides a method for on-line soft measurement of the grain size inside the steel plate during the rolling process. The method of the present invention combines physical metallurgical mechanism with database and information technology, and its purpose is to realize the The real-time online monitoring of the internal microstructure provides a basis for optimizing the process specification and chemical composition and improving the performance and quality of steel.

本发明方法包括以下步骤：The inventive method comprises the following steps:

(1)选择、确定模型参数，采用热模拟和热轧实验，研究高温变形行为对静态及动态再结晶的影响，绘制应力应变曲线和静态软化曲线；采用连续冷却转变和形变诱导相变热模拟实验，测定不同变形及冷却条件下的相变分数和α晶粒尺寸，利用数学回归和拟合手段确定模型参数。(1) Select and determine model parameters, use thermal simulation and hot rolling experiments to study the influence of high temperature deformation behavior on static and dynamic recrystallization, draw stress-strain curves and static softening curves; use continuous cooling transformation and deformation-induced phase transition thermal simulation In the experiment, the phase transformation fraction and α grain size under different deformation and cooling conditions were measured, and the model parameters were determined by means of mathematical regression and fitting.

(2)建立与过程机的实时通讯，从过程机数据库中在线调用工艺参数及合金成分动态数据，作为参数输入；(2) Establish real-time communication with the process machine, call process parameters and alloy composition dynamic data online from the process machine database, and input them as parameters;

计算机控制系统对数据的处理包括以下部分：原始数据录入(PDI录入)、原始数据确认、入炉确认、出炉确认、轧机工艺模型计算、控冷工艺模型计算、数据库存储、报表生成。随着数据的流动过程，数据结构逐步形成。PDI录入时由于同一炉号的钢坯拥有同样的原始数据，所以炼钢炉号作为一组钢坯的身份标识即唯一索引；钢坯入炉时为了加以区别为每块钢坯分配一个入炉顺序号；钢坯出炉时为每块钢坯分配一个身份证号。从钢坯出炉到控冷结束每块钢坯经历不同的加工历程，所以钢坯的身份证号成为唯一索引。The processing of data by the computer control system includes the following parts: original data entry (PDI entry), original data confirmation, furnace entry confirmation, furnace exit confirmation, rolling mill process model calculation, controlled cooling process model calculation, database storage, and report generation. With the flow of data, the data structure is gradually formed. When PDI is entered, because the steel billets with the same furnace number have the same original data, the steelmaking furnace number is used as the identity of a group of steel billets, that is, the unique index; when the billets are put into the furnace, a sequential number is assigned to each billet in order to distinguish them; Each billet is assigned an ID number when it comes out of the furnace. From the billet out of the furnace to the end of controlled cooling, each billet undergoes different processing processes, so the ID number of the billet becomes the only index.

组织模型根据轧件PDI信息，从控轧过程机数据库中提取由基础自动化得到的压下规程、轧制速度及轧制力等检测参数和过程自动化计算的道次温度(距表面3/4厚度)、轧制间歇时间及加热参数、化学成分等，从控冷过程机中提取开冷、终冷温度、实际冷却速度、冷却时间等相关数据，将以上数据作为模型初始输入参数进行计算。According to the PDI information of the rolled piece, the organizational model extracts the reduction schedule, rolling speed, rolling force and other detection parameters obtained from the basic automation from the database of the controlled rolling process machine, and the pass temperature calculated by the process automation (3/4 thickness from the surface ), rolling interval time, heating parameters, chemical composition, etc., extract relevant data such as start cooling, final cooling temperature, actual cooling speed, cooling time, etc. from the controlled cooling process machine, and use the above data as the initial input parameters of the model for calculation.

(3)预测奥氏体晶粒尺寸及其演变：(3) Predict austenite grain size and its evolution:

①计算轧制过程动态再结晶的晶粒尺寸① Calculate the grain size of dynamic recrystallization during rolling

动态再结晶动力学及动态再结晶的晶粒尺寸方程可由方程(1)～(4)描述：The kinetics of dynamic recrystallization and the grain size equation of dynamic recrystallization can be described by equations (1) to (4):

${X x}_{D D.} = = 11 - - exp exp [[- - k k {((\frac{ϵ ϵ - - {ϵ ϵ}_{c c}}{{ϵ ϵ}_{0.5 0.5}}))}^{{n no}_{d d}}]] - - - - - - ((11))$

d_DRX＝DZ^m (2)d _DRX = DZ ^m (2)

${D D.}_{}^{DG DG} = = {D D.}_{}^{DRX DRX} + + {A A}_{DG DG} {(({C C}_{ep ep}))}^{φ φ} . . exp exp ((- - \frac{{Q Q}_{dg d g}}{T T})) \cdot \cdot {t t}_{r r}^{θ θ} - - - - - - ((33))$

$Z Z = = \overset{. .}{ϵ ϵ} exp exp (({Q Q}_{d d} / / ((RT RT)))) - - - - - - ((44))$

其中，X_D为动态再结晶率；Z为Zener-Hollomen参数，由公式(4)给出，此方程被称为Sellars-Tegart关系式。其中，ε_c和ε_0.5分别为动态再结晶临界应变和发生50％所对应的应变，由变形条件、温度和初始奥氏体晶粒尺寸等计算得到。Q_d(kJ/mol)为动态再结晶激活能，n_d为与化学成分有关的常量，通过与流变应力、变形速率、变形温度和化学成分等的关系方程求得。d_DRX为动态再结晶晶粒尺寸，d_DG为动态再结晶长大后的晶粒尺寸，C_eq为碳当量，Q_dg为动态再结晶长大激活能，A_DG为常数，其余参数为常数。Among them, X _D is the dynamic recrystallization rate; Z is the Zener-Hollomen parameter, which is given by the formula (4), and this equation is called the Sellars-Tegart relationship. Among them, ε _c and ε _0.5 are the critical strain of dynamic recrystallization and the strain corresponding to 50% occurrence, respectively, which are calculated from deformation conditions, temperature and initial austenite grain size. Q _d (kJ/mol) is the activation energy of dynamic recrystallization, and _nd is a constant related to chemical composition, which is obtained through the relationship equation with flow stress, deformation rate, deformation temperature and chemical composition. d _DRX is the grain size of dynamic recrystallization, d _DG is the grain size after dynamic recrystallization growth, _Ceq is carbon equivalent, Q _dg is the activation energy of dynamic recrystallization growth, A _DG is a constant, and other parameters are constants .

②计算轧制间歇期间静态、亚动态再结晶的晶粒尺寸② Calculate the grain size of static and metadynamic recrystallization during the rolling interval

当ε＜ε_c时，低碳钢变形后发生静态再结晶软化。对于静态再结晶动力学的研究，按照Avrami方程：When ε< _εc , static recrystallization softening occurs after deformation of low carbon steel. For the study of static recrystallization kinetics, follow the Avrami equation:

${X x}_{s the s} = = 11 - - exp exp [[- - C C {((\frac{t t}{{t t}_{0.5 0.5}^{s the s}}))}^{{n no}_{s the s}}]] - - - - - - ((55))$

式中，X_S为动态再结晶率；n_s为常数；t_0.5 ^S为静态再结晶率达到50％的时间，可通过与应变、奥氏体晶粒尺寸和再结晶动力学参数而确定；t为轧制过程道次间歇时间，由过程模型计算得到。In the formula, X _S is the dynamic recrystallization rate; n _s is a constant; t _0.5 ^S is the time for the static recrystallization rate to reach 50%, which can be determined by strain, austenite grain size and recrystallization kinetic parameters; t is the intermittent time of the rolling pass, which is calculated by the process model.

静态再结晶晶粒尺寸d_SRX根据方程(6)来计算：The static recrystallized grain size d _SRX is calculated according to equation (6):

${d d}_{SRX SRX} = = {D D.}_{s the s} {ϵ ϵ}^{{λ λ}^{' '}} {d d}_{00}^{{η η}^{' '}} exp exp ((\frac{{Q Q}_{ds ds}}{RT RT})) - - - - - - ((66))$

其中，d_SRX为静态再结晶晶粒尺寸；D_s、λ′、η′为常数；R为气体参数，T为温度，Q_ds为静态再结晶长大激活能。Among them, d _SRX is the static recrystallization grain size; D _s , λ′, η′ are constants; R is the gas parameter, T is the temperature, and Q _ds is the activation energy of static recrystallization growth.

当ε＞ε_c时，低碳钢变形过程将发生动态再结晶，之后发生亚动态再结晶。较静态再结晶而言，亚动态再结晶进行更为迅速，形成晶粒更为细小。亚动态再结晶动力学和晶粒尺寸分别由方程(7)和(8)示出，X_M为亚动态再结晶率；t_0.5 ^M为再结晶率达到50％的时间，与应变速率和变形温度有关；d_MDRX为亚动态再结晶晶粒尺寸；D′、m′、n_m为常数。When ε> _εc , dynamic recrystallization will occur in the deformation process of low carbon steel, and then metadynamic recrystallization will occur. Compared with static recrystallization, metadynamic recrystallization proceeds more rapidly and forms finer grains. The metadynamic recrystallization kinetics and grain size _are shown by equations (7 ⁾ and (8), respectively, X _M is the metadynamic recrystallization rate; Temperature-related; d _MDRX is the sub-dynamic recrystallization grain size; D', m', _nm are constants.

${X x}_{M m} = = 11 - - exp exp [[- - C C {((\frac{t t}{{t t}_{0.5 0.5}^{M m}}))}^{{n no}_{m m}}]] - - - - - - ((77))$

d_MDRX＝D′Z^m′ (8)d _MDRX = D′Z ^m ′ (8)

③计算奥氏体平均晶粒尺寸及其长大③ Calculate the average grain size and growth of austenite

轧机出口处的奥氏体平均晶粒尺寸可表示为：The average grain size of austenite at the exit of the rolling mill can be expressed as:

d_A＝[d_DRX·X_D+d_N(1-X_D)]·(1-X_S(M))+d_SRX(MDRX)·X_S(M) (9)d _A =[d _DRX X _D +d _N (1-X _D )] (1-X _S(M) )+d _SRX(MDRX) X _S(M) (9)

其中d_N为未发生再结晶的奥氏体晶粒尺寸，可通过未再结晶模型确定。where d _N is the austenite grain size without recrystallization, which can be determined by the non-recrystallization model.

${(({d d}_{SG SG}))}^{{N N}_{g g}} = = {(({d d}_{A A}))}^{{N N}_{g g}} + + {A A}_{NG NG} exp exp ((- - \frac{{Q Q}_{gg gg}}{RT RT})) \cdot \cdot {t t}^{{θ θ}^{' '}} - - - - - - ((1010))$

再结晶晶粒长大由方程(10)表示，其中d_SG为长大后的再结晶晶粒尺寸；Q_gg为长大激活能；N_g、A_NG、和θ′均为常数。The recrystallized grain growth is expressed by Equation (10), where d _SG is the recrystallized grain size after growth; Q _gg is the growth activation energy; N _g , _ANG , and θ′ are all constants.

由于板带轧制的道次间隔期间内，如果奥氏体不能充分软化，将保留一定程度的残余应变Δε，因此，在计算道次应变时应加上前一道次的残余应变：Since during the pass interval of strip rolling, if the austenite cannot be fully softened, a certain degree of residual strain Δε will be retained. Therefore, the residual strain of the previous pass should be added to the calculation of pass strain:

ε＝ε_i+Δε (11)ε=ε _i +Δε (11)

Δε＝ε_i-1(1-X_S) (12)Δε=ε _i-1 (1-X _S ) (12)

其中ε_i-1、ε_i为第i-1和第i道次的累积应变。Where ε _i-1 and ε _i are the cumulative strains of the i-1th and i-th passes.

④计算轧机出口的奥氏体晶粒尺寸，进入下一道次，重复步骤(3)中的①～③循环计算轧制过程各道次奥氏体晶粒尺寸的演变，最终得到终轧出口的奥氏体晶粒尺寸。④ Calculate the austenite grain size at the exit of the rolling mill, enter the next pass, repeat steps ① to ③ in step (3) to calculate the evolution of the austenite grain size in each pass during the rolling process, and finally obtain the final rolling exit Austenite grain size.

(4)预测相变后铁素体晶粒尺寸：(4) Predict the grain size of ferrite after phase transformation:

在γ→α相变前期，相变以“成核长大”机制进行，其动力学方程为(13)；在相变后期，符合“位置饱和”机制，其动力学方程为(14)，相变机制转换时间t_NG与形核、长大速率有关，可通过理论模型结合实验结果回归确定。In the early stage of the γ→α phase transition, the phase transition proceeds with the mechanism of "nucleation and growth", and its kinetic equation is (13); in the late phase transition, it conforms to the "position saturation" mechanism, and its kinetic equation is (14), The transition time t _NG of the phase transition mechanism is related to the nucleation and growth rates, which can be determined by regression using theoretical models combined with experimental results.

${X x}_{F f 11} = = 11 - - exp exp ((- - \frac{π π}{33} {I I}_{S S} {S S}_{γ γ} {G G}_{}^{F f} \cdot &Center Dot; {t t}^{44})) - - - - - - ((1313))$

X_F2＝1-exp(-2S_γG_F·t) (14)X _F2 ＝1-exp(-2S _γ G _F t) (14)

其中X_F1和X_F2分别为γ→α相变前期和后期的相变率；S_γ为单位体积奥氏体等效晶界表面积；I_S和G_F分别为α相的形核及长大速率。其中G_F采用Hillert的公式计算。I_S由方程(15)所示，式中，K₁、K₂为常数，k为波尔兹曼常数，ε为应变。ΔG_v为未变形条件下的α形核体积自由能变化，采用超组元模型进行计算，Δμ_d为变形存储能，可通过与位错密度、流变应力的关系方程来计算。Among them, X _F1 and X _F2 are the phase transformation rates in the early stage and late stage of γ→α phase transformation, respectively; S _γ is the equivalent grain boundary surface area of austenite per unit volume; I _S and G _F are the nucleation and growth of α phase, respectively rate. Where G _F is calculated using Hillert's formula. I _S is shown by Equation (15), where K ₁ and K ₂ are constants, k is Boltzmann's constant, and ε is strain. ΔG _v is the free energy change of α nucleation volume under undeformed conditions, which is calculated using the supercomponent model, and Δμ _d is the deformation storage energy, which can be calculated through the relationship equation with dislocation density and flow stress.

${I I}_{S S} = = \frac{{K K}_{11} {D D.}_{C C}}{{((kT kT))}^{11 / / 22}} \times \times exp exp [[- - \frac{exp exp ((- - λϵ λϵ)) . . {K K}_{22}}{RT RT {((Λ Λ {G G}_{v v} - - Λ Λ {μ μ}_{d d}))}^{22}}]] - - - - - - ((1515))$

γ→α相变后单位体积奥氏体内的铁素体晶粒总数n_F可表示为：The total number of ferrite grains n _F in unit volume of austenite after γ→α transformation can be expressed as:

${n no}_{F f} = = {&Integral; &Integral;}_{00}^{NG NG} {I I}_{S S} {S S}_{γ γ} [[11 - - {X x}_{F f} ((t t))]] dt dt - - - - - - ((1616))$

其中X_F为铁素体相变率。Where X _F is the ferrite transformation rate.

因此铁素体晶粒的平均直径d_F可表示为：Therefore, the average diameter d _F of ferrite grains can be expressed as:

${d d}_{F f} = = {((\frac{22}{{33 n no}_{F f}}))}^{11 / / 33} - - - - - - ((1717))$

低温区轧制过程可能发生形变诱导相变(DIFT)，由于α晶粒在轧制过程析出可按照等温处理，采用方程(13)(14)(15)来表述其相变动力学，其中形核速率中常数λ需要通过实验室热模拟实验和计算结果拟合确定。形变诱导相变率大于5％，认为DIFT过程发生，否则认为不发生。Deformation-induced transformation (DIFT) may occur during the rolling process in the low temperature region. Since the precipitation of α grains during the rolling process can be treated according to isothermal treatment, equations (13)(14)(15) are used to describe the phase transformation kinetics, where the nucleation The rate constant λ needs to be determined through laboratory thermal simulation experiments and calculation results. If the deformation-induced phase transition rate is greater than 5%, the DIFT process is considered to occur, otherwise it is considered not to occur.

对于连续冷却过程的先共析铁素体转变，采用Scheil迭加法则处理，即将连续冷却相变处理成微小等温相变之和，即按照方程(18)来计算，其中X代表组织的相变体积分数，j代表组织组成物。For the proeutectoid ferrite transformation in the continuous cooling process, the Scheil superposition rule is used to process the continuous cooling phase transition into the sum of small isothermal phase transitions, which is calculated according to equation (18), where X represents the phase transition of the structure Volume fraction, j represents tissue composition.

${X x}_{n no}^{j j} = = {X x}_{n no - - 11}^{j j} + + Δ Δ {X x}_{n no}^{j j} - - - - - - ((1818))$

在此，本发明方法在控轧过程中首次考虑低温轧制中的DIFT相变，并将其与连续冷却转变(CCT)结合起来，作为最终的α晶粒尺寸，如方程(19)所示，其中d_FD、d_FC分别为DIFT和CCT过程产生的晶粒尺寸，

为平均α晶粒尺寸，X_FD、X_FC分别为DIFT和CCT过程α相变率。Here, the method of the present invention considers the DIFT phase transformation in low-temperature rolling for the first time in the process of controlled rolling, and combines it with the continuous cooling transformation (CCT) as the final α grain size, as shown in equation (19) , where d _FD , d _FC are the grain sizes produced by DIFT and CCT processes, respectively,

is the average α grain size, X _FD , X _FC are the α phase transition rates of DIFT and CCT processes, respectively.

$\overset{&OverBar; &OverBar;}{{d d}_{α α}} = = {X x}_{FD FD} \cdot &Center Dot; {d d}_{FD FD} + + {X x}_{FC FC} \cdot &Center Dot; {d d}_{FC FC} - - - - - - ((1919))$

本发明具有三个明显效果：1、能够以很高的精度，稳定、快速地预测钢板内部组织，包括奥氏体和铁素体晶粒尺寸的演变，实现轧制过程显微组织的在线软检测；2、有助于轧制、冷却工艺的在线优化和控制，改善钢材内部组织结构，生产出性能均一、稳定的热轧产品；3、显微组织的软测量技术对实现产品室温力学性能的高精度预报，减少检测样品，缩短生产周期具有重要意义。The present invention has three obvious effects: 1. It can stably and quickly predict the internal structure of the steel plate with high precision, including the evolution of austenite and ferrite grain size, and realize the on-line softening of the microstructure during the rolling process. 2. Contribute to the online optimization and control of the rolling and cooling process, improve the internal structure of the steel, and produce hot-rolled products with uniform and stable properties; 3. The soft measurement technology of the microstructure has a great impact on the mechanical properties of the product at room temperature It is of great significance to reduce the number of test samples and shorten the production cycle.

附图说明Description of drawings

图1为本发明方法的流程图；Fig. 1 is the flowchart of the inventive method;

图2为系统输入参数信息图；Fig. 2 is a system input parameter information diagram;

图3为中厚板轧制过程奥氏体晶粒尺寸的变化示意图；Fig. 3 is a schematic diagram of the variation of austenite grain size in the rolling process of medium and heavy plates;

图4为奥氏体晶粒尺寸实测值和计算值对比示意图；Figure 4 is a schematic diagram of the comparison between the measured value and the calculated value of the austenite grain size;

图5为冷却速率为5℃/s，成品厚度分别为12mm和18mm时精轧开轧温度对组织参数的影响示意图；Figure 5 is a schematic diagram of the influence of the finishing rolling temperature on the microstructure parameters when the cooling rate is 5°C/s and the thickness of the finished product is 12mm and 18mm respectively;

图6为当冷却速率为5℃/s，成品厚度分别为12mm，精轧开轧温度为850℃时中间坯厚度对组织参数的影响示意图；Fig. 6 is a schematic diagram of the influence of the thickness of the intermediate billet on the structural parameters when the cooling rate is 5°C/s, the thickness of the finished product is 12mm, and the starting temperature of the finish rolling is 850°C;

图7为当精轧开轧温度为850℃，成品厚度分别为14mm和18mm时冷却速率对组织参数的影响示意图；Figure 7 is a schematic diagram of the influence of cooling rate on the microstructure parameters when the finish rolling start temperature is 850°C and the thickness of the finished product is 14mm and 18mm respectively;

图8为α晶粒尺寸的计算值与实测值的比较示意图。Fig. 8 is a schematic diagram of the comparison between the calculated value and the measured value of the α grain size.

具体实施方式Detailed ways

图1为本发明提出的α再结晶及α晶粒尺寸演变的计算流程，下面结合附图对本发明方法作详细描述。Fig. 1 is the calculation process of α recrystallization and α grain size evolution proposed by the present invention, and the method of the present invention will be described in detail below in conjunction with the accompanying drawings.

(1)选择、确定模型参数，以低碳钢为例，采用单道次和双道次压缩实验，研究高温变形行为对静态及动态再结晶的影响，绘制应力应变曲线和静态软化曲线；采用连续冷却转变和形变诱导相变热模拟实验，测定不同变形及冷却条件下的相变分数和α晶粒尺寸，利用数学回归手段确定模型参数，如表1所示。(1) Select and determine the model parameters. Taking low-carbon steel as an example, single-pass and double-pass compression experiments are used to study the influence of high-temperature deformation behavior on static and dynamic recrystallization, and draw stress-strain curves and static softening curves; The thermal simulation experiments of continuous cooling transformation and deformation-induced phase transformation measured the phase transformation fraction and α grain size under different deformation and cooling conditions, and determined the model parameters by means of mathematical regression, as shown in Table 1.

参数 parameters 参数值 Parameter value 参数 parameters 参数值 Parameter value 参数 parameters 参数值 Parameter value 参数 parameters 参数值 Parameter value D D 16000 16000 n_s n _s 0.6 0.6 C C 0.69 0.69 θ′ θ′ 1 1 m m -0.23 -0.23 D_s D _s 343 343 D′ D' 26000 26000 λ λ 1.6 1.6 A_DG A _DG 39000 39000 λ′ λ' -0.5 -0.5 m′ m' 0.23 0.23 K₁ K ₁ 2.07×10¹¹ 2.07×10 ¹¹ φ φ 1.43 1.43 η′ η' 0.4 0.4 Ng Ng 10 10 K₂ K ₂ 6.33×10^-15 6.33×10 ^-15 θ θ 0.3 0.3 n_m n _m 1.5 1.5 A_NG A _NG 1.31×10⁵² 1.31×10 ⁵²

表1Table 1

选择PDI号为3C4602P00A2的轧件进行计算。首先从过程机数据中调用钢种合金成分、坯料尺寸、控轧及控冷工艺等数据作为输入信息，如图2所示。该钢种化学成分(ms-％)为：0.18C-0.40Si-1.43Mn-0.019P-0.010S；压下规程为：220.00→202.24→183.03→172.00→163.01→142.79→123.23→102.85→83.78→62.02→53.97→47.22→42.38→38.56→36.00(mm)。由过程自动化计算的道次温度(距表面3/4厚度)分别为(单位：℃)：1110；1082；1053；1030；1016；1005；1001；980；952；928；852；835；826；810。各道次轧制间歇时间为5s。Select the rolled piece whose PDI number is 3C4602P00A2 for calculation. First, the data of steel alloy composition, billet size, controlled rolling and controlled cooling process are called from the process machine data as input information, as shown in Figure 2. The chemical composition (ms-%) of this steel grade is: 0.18C-0.40Si-1.43Mn-0.019P-0.010S; 62.02→53.97→47.22→42.38→38.56→36.00 (mm). The pass temperature (3/4 thickness from the surface) calculated by the process automation is (unit: ℃): 1110; 1082; 1053; 1030; 1016; 1005; 1001; 980; 952; 810. The intermission time of each rolling pass is 5s.

(3)预测奥氏体晶粒尺寸及其演变；(3) Predict austenite grain size and its evolution;

以精轧第一道次为例，根据步骤(3)中①～③的方法计算得到：奥氏体动态再结晶率X_D＝0；静态再结晶率X_S＝84％，静态再结晶晶粒尺寸为20.21μm；精轧第一道次出口平均晶粒尺寸为21.94μm，长大后进入下一道次前晶粒尺寸为24.48μm。Taking the first pass of finish rolling as an example, it is calculated according to the method ①～③ in step (3): dynamic recrystallization rate of austenite X _D =0; static recrystallization rate X _S =84%, static recrystallization The grain size is 20.21 μm; the average grain size at the exit of the first pass of finish rolling is 21.94 μm, and the grain size before entering the next pass after growing is 24.48 μm.

根据步骤(3)中的④，进入下一道次，重复本步骤中的①～③循环计算轧制过程各道次奥氏体晶粒尺寸的演变，最终得到终轧出口的奥氏体晶粒尺寸。图3所示为钢板轧制过程中奥氏体晶粒尺寸的演变情况。动态再结晶发生与压下率和轧制温度密切相关，在温度较高的粗轧阶段，动态再结晶软化效果比较明显；静态及亚动态再结晶在粗轧阶段发生的非常完全，但在精轧过程由于轧制温度的降低(入口温度为850℃左右)，静态再结晶率下降，导致道次间的残余应变略有增加，相变前最后一道次奥氏体晶粒尺寸为23μm。According to ④ in step (3), enter the next pass, repeat ①～③ in this step to calculate the evolution of austenite grain size in each pass of the rolling process, and finally obtain the austenite grain at the exit of final rolling size. Fig. 3 shows the evolution of austenite grain size during steel plate rolling. The occurrence of dynamic recrystallization is closely related to reduction rate and rolling temperature. In the rough rolling stage with higher temperature, the softening effect of dynamic recrystallization is more obvious; the static and sub-dynamic recrystallization occurs very completely in the rough rolling stage, but in the finish During the rolling process, due to the reduction of rolling temperature (entry temperature is about 850°C), the static recrystallization rate decreases, resulting in a slight increase in residual strain between passes, and the austenite grain size of the last pass before phase transformation is 23 μm.

(4)预测相变后铁素体晶粒尺寸；(4) Predict the ferrite grain size after phase transformation;

在奥氏体晶粒尺寸计算的基础上，按照本步骤的方法计算得到本实例中生成相组成为F+P，铁素体体积分数为70.9％，珠光体分数为29.1％，其中DIFT转变率为0，铁素体晶粒尺寸为12.63μm。将所有晶粒尺寸计算结果自动存入以PDI号为文件名的文本文件中，以备查询分析。On the basis of the calculation of austenite grain size, according to the method of this step, the phase composition generated in this example is F+P, the volume fraction of ferrite is 70.9%, the fraction of pearlite is 29.1%, and the DIFT transformation rate is 0, and the ferrite grain size is 12.63 μm. All grain size calculation results are automatically stored in a text file with the PDI number as the file name for query and analysis.

对本发明方法的精确性进行验证如下：The accuracy of the inventive method is verified as follows:

(1)利用本发明的方法对多道次连续轧制的模拟实验进行计算。该实验的工艺规程由图2示出，其中粗轧过程加热温度1230℃，5道次变形。精轧过程的模拟实验中设计了F1-F5五种不同的温度制度，精轧结束淬火以测定奥氏体晶粒尺寸。如图3所示，可以看出随着精轧阶段变形温度的下降，奥氏体晶粒尺寸减小。精轧阶段变形温度的降低使静态再结晶的发生变得不很充分，残余应变增大，位错密度提高，增大了变形储能，再结晶驱动力增大，形核速率加快，同时抑制了晶粒粗化，这些都是导致奥氏体晶粒细化的原因。从图中还可以看出奥氏体晶粒尺寸的计算值和实测值吻合较好，说明本发明方法具有较高的精度。(1) Using the method of the present invention to calculate the simulation experiment of multi-pass continuous rolling. The process schedule of the experiment is shown in Figure 2, in which the heating temperature of the rough rolling process is 1230°C, and the deformation is carried out in 5 passes. In the simulation experiment of the finishing rolling process, five different temperature systems of F1-F5 were designed, and the quenching at the end of finishing rolling was used to determine the austenite grain size. As shown in Figure 3, it can be seen that the austenite grain size decreases as the deformation temperature decreases in the finish rolling stage. The reduction of the deformation temperature in the finishing rolling stage makes the occurrence of static recrystallization insufficient, the residual strain increases, the dislocation density increases, the deformation storage energy increases, the recrystallization driving force increases, and the nucleation rate accelerates. These are the reasons for the refinement of austenite grains. It can also be seen from the figure that the calculated value of the austenite grain size is in good agreement with the measured value, indicating that the method of the present invention has relatively high precision.

(2)根据中厚板TMCP工业轧制条件，在线计算了不同工艺参数对室温显微组织的影响，并与实验结果相结合，研究控轧控冷工艺对Q235中厚板组织和性能的影响，为确定新的轧制规程和冷却制度提供指导。表2为中厚板控轧控冷工艺参数。(2) According to the TMCP industrial rolling conditions of medium and heavy plates, the influence of different process parameters on the microstructure at room temperature was calculated online, and combined with the experimental results, the influence of controlled rolling and controlled cooling process on the structure and properties of Q235 medium and heavy plates was studied , to provide guidance for determining new rolling regulations and cooling regimes. Table 2 shows the process parameters of controlled rolling and controlled cooling for medium and heavy plates.

工艺参数 Process Parameters 钢坯加热 billet heating 1150℃×2～3h 1150°C×2～3h 精轧阶段压下分配(mm)Reduction distribution in finishing rolling stage (mm) ①57→45→36→28→22→17→14→12→12；②28→22→17→14→12→12；③30→24→18→16→14→12→12；④18→16→14→12→12；⑤24→18→14→12→12； ①57→45→36→28→22→17→14→12→12; ②28→22→17→14→12→12; ③30→24→18→16→14→12→12; ④18→16→14→ 12→12; ⑤24→18→14→12→12; ⑥45→36→28→22→20；⑦93→73→57→45→36→28→22→20；⑧45→36→28→24→22→20 ⑥45→36→28→22→20; ⑦93→73→57→45→36→28→22→20; ⑧45→36→28→24→22→20 温度制度temperature system 精轧开轧：810～860℃；精轧终轧：700～780℃；开冷：680～750℃；终冷：550～710℃ Finish rolling start rolling: 810～860°C; finish rolling finish rolling: 700～780°C; start cooling: 680～750°C; final cooling: 550～710°C

表2Table 2

对低碳钢而言，中厚板TMCP技术的主要控制参数有：精轧(II阶段)轧制温度、压下制度和冷却速率等。精轧温度的控制主要通过粗轧和精轧两阶段的待温及中间冷却来实现，压下制度的制定主要考虑轧机能力范围内加大变形程度和负荷分配后移。同时二者又相互影响，例如中间坯待温厚度就是既反映了压下分配制度，又直接影响精轧过程的温度变化。精轧阶段的温度和变形对奥氏体再结晶过程及应变积累有重要作用。冷却速率的控制主要是通过控制辊道速度、冷却水流量来实现的。加速冷却可以细化晶粒，大幅度提高钢材的性能水平。For low carbon steel, the main control parameters of TMCP technology for medium and heavy plates are: finishing rolling (stage II) rolling temperature, reduction system and cooling rate, etc. The control of the finish rolling temperature is mainly realized through the waiting temperature and intermediate cooling in the two stages of rough rolling and finish rolling. At the same time, the two influence each other. For example, the thickness of the intermediate billet to be warmed not only reflects the reduction distribution system, but also directly affects the temperature change in the finishing rolling process. The temperature and deformation in the finish rolling stage play an important role in the austenite recrystallization process and strain accumulation. The control of the cooling rate is mainly realized by controlling the speed of the roller table and the flow rate of cooling water. Accelerated cooling can refine grains and greatly improve the performance level of steel.

图5示出了18mm和12mm成品厚度的钢板精轧开轧温度对轧后晶粒尺寸的影响。从图中可以看出，随着精轧温度的降低，铁素体晶粒尺寸显著减小。18mm和12mm的厚度相比，相同条件下18mm的铁素体晶粒尺寸较粗大一些。Figure 5 shows the influence of the finishing rolling temperature on the grain size after rolling for steel plates with a finished thickness of 18 mm and 12 mm. It can be seen from the figure that as the finish rolling temperature decreases, the ferrite grain size decreases significantly. Compared with the thickness of 18mm and 12mm, the ferrite grain size of 18mm is larger under the same conditions.

图6示出了中间坯厚度对室温组织晶粒尺寸的影响。从图中可以看出，中间坯厚度较大，铁素体晶粒较细；随着中间坯厚度减小，铁素体晶粒尺寸变大。图7示出了冷却速率对晶粒尺寸的影响。随着冷却速率的增加，铁素体晶粒尺寸变细。同样，成品厚度较大时，铁素体晶粒尺寸较大。Figure 6 shows the effect of the thickness of the intermediate billet on the grain size of the room temperature structure. It can be seen from the figure that the thickness of the intermediate billet is larger and the ferrite grains are finer; as the thickness of the intermediate billet decreases, the size of the ferrite grains becomes larger. Figure 7 shows the effect of cooling rate on grain size. As the cooling rate increases, the ferrite grain size becomes finer. Likewise, the ferrite grain size is larger when the finished product thickness is larger.

(3)在α形核及长大过程中，考虑等效变形凸阶效应和等效晶界面积的双重作用，建立描述热变形奥氏体相变过程动力学模型，模拟在线轧制工艺和成分条件下，γ→α相变过程及最终晶粒尺寸状况，并与实验结果进行比较，以验证其准确性。图8示出了晶粒尺寸的计算值和实测值的比较，其中实测值数据来自现场成品板材的金相检测结果，钢种为Q235、Q345等。结果表明，铁素体晶粒尺寸的计算值与实测值吻合良好。(3) In the process of α nucleation and growth, considering the dual effects of the equivalent deformation convex step effect and the equivalent grain boundary area, a kinetic model is established to describe the phase transformation process of hot deformed austenite, and the online rolling process and simulation are simulated. Under the compositional conditions, the γ→α phase transition process and the final grain size are compared with the experimental results to verify its accuracy. Figure 8 shows the comparison between the calculated value and the measured value of the grain size, wherein the measured value data comes from the metallographic inspection results of the finished plate on site, and the steel types are Q235, Q345 and so on. The results show that the calculated ferrite grain size is in good agreement with the measured value.

Claims

1, a kind of flexible measurement method of grain sizes of steel plate internal structure during rolling process is characterized in that the inventive method may further comprise the steps:

(1) selects, determines model parameter;

(2) real-time communication of foundation and process machine, calling technological parameter and alloying component dynamic data are imported as initial parameter from process machine data storehouse;

(3) prediction austenite grain size and differentiation thereof, comprise: the crystallite dimension of 1. calculating operation of rolling dynamic recrystallization, 2. calculate the crystallite dimension of rolling tempus intercalare static state, inferior dynamic recrystallization, 3. calculate the austenite average grain size and grow up, 4. obtain the austenite grain size of finish to gauge outlet;

(4) ferrite grain size after the prediction phase transformation.

2, the flexible measurement method of grain sizes of steel plate internal structure during rolling process according to claim 1, it is characterized in that described in the step (2) that calling technological parameter and alloying component dynamic data are meant the information according to rolled piece PDI from process machine data storehouse, from controlled rolling process machine data storehouse, extract rolling schedule, mill speed, roll-force, passage temperature; Rolling quiescent interval and heating parameters, chemical constitution; From control cold process machine data storehouse, extract and open cold, final cooling temperature, actual cooling velocity, cool time and import as initial parameter.