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

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

The flexible measurement method of grain sizes of steel plate internal structure during rolling process

Technical field

The invention belongs to technical field of steel rolling, be specially adapted to the roughing mill of heavy and medium plate mill, hot continuous rolling process and the flexible measurement method of finishing mill grain sizes of steel plate internal structure during rolling process.

Background technology

Computer technology and mechanical simulation combine, and make the control of steel physical dimension reach higher level, but the online direct Detection ﹠ Controling of microstructure evolution parameter are in the state that falls behind relatively always.Utilize the organization factors of true measuring instrument and equipment online in real time, direct test material, still need after some time.Soft-measuring technique organically combines control theory and process mechanism, can calculate the parameter that those can not be surveyed or be difficult to detect continuously, replaces online measuring instrument to a certain extent.At present soft-measuring technique has become the strong instrument of automatic monitoring and process optimization, and is listed in following control field and needs one of several important directions of primary study.Use the microstructure evolution of soft-measuring technique on-line monitoring rolled piece, will promote the understanding of people, promote the further application of controlled rolling and controlled cooling (TMCP) technology in board rolling Physical Metallurgy rule in the material processing.

Grain refinement is as unique a kind of schedule of reinforcement that had not only improved intensity but also improved toughness, is one of target of pursuing of ferrous materials of new generation always.Because the appearance of TMCP The Application of Technology and powerful formula rolling equipment moves the necessary means that becomes Development of New Generation ultra-fine grain steel under the industrial condition behind final rolling temperature reduction and the load distribution.Accurately prediction α crystallite dimension to improving production technology, improves the steel product quality level and is extremely important.But from the computing method of present prediction α crystallite dimension, existing experimental formula not only range of application is narrow, can not reflect Physical Metallurgy mechanism, and does not take into full account deformation, cooling etc. and be not suitable for the prediction of controlled rolling and controlled cooling process.

At present, aspect the prediction of hot-strip structure property, some models have been developed abroad.China minority steel mill package import foreign system, summarize from effect and model, mainly there is following deficiency in prior art: 1) model mostly adopts the experience regression model, lack the support of brand-new Physical Metallurgy theory, versatility is poor, range of application is narrow, is difficult to adapt to the requirement of changes in process parameters under the controlled rolling and controlled cooling condition; 2) the on-line system technical indicator concentrates on the precision of prediction of final mechanical property, differentiation to the microstructure parameter lacks necessary the description, the demand that people monitor in real time to rolling all fronts microstructural parameter be can not satisfy, the on-line optimization and the control of technological procedure and steel grades are unfavorable for.The present invention in conjunction with Heavy Plate Production reality, has proposed the new method of a cover high-precision forecast α crystal grain from the transition kinetics principle.

Summary of the invention

Deficiency at the prior art existence, the invention provides the method for the inner crystallite dimension online soft sensor of a kind of operation of rolling light plate, method of the present invention is that Physical Metallurgy mechanism and database, infotech are combined, its objective is the real time on-line monitoring of realizing steel plate interior microscopic tissue, for optimizing technological procedure and chemical constitution, improving the steel performance quality provides foundation.

The inventive method may further comprise the steps:

(1) select, determine model parameter, adopt thermal simulation and hot rolling experiment, the researching high-temperature deformational behavior is drawn stress-strain diagram and static softening curve to the influence of static state and dynamic recrystallization; Adopt continuous cooling transformation and deformation induced heat of transformation simulated experiment, measure phase transformation mark and α crystallite dimension under different distortion and the cooling condition, utilize mathematical regression and match means to determine model parameter.

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

Computer control system comprises following part to the processing of data: stove affirmation, the affirmation of coming out of the stove, rolling mill technology Model Calculation, cooling control technology Model Calculation, database storing, report generation are confirmed, gone into to raw data typing (PDI typing), raw data.Along with the flow process of data, data structure progressively forms.During the PDI typing because the steel billet of same heat (batch) number has same raw data, so the steel-making heat (batch) number is a unique index as the identify label of one group of steel billet; When going into stove, steel billet gone into the stove serial number in order to distinguish to every block of steel billet distributes one; Be that every block of steel billet distributes an identification card number when steel billet is come out of the stove.From the steel billet every different processing course of steel billet experience of control cold junction bundle of coming out of the stove, so the identification card number of steel billet becomes unique index.

Organize models is according to rolled piece PDI information, from controlled rolling process machine data storehouse, extract detected parameters and the passage temperature (apart from surperficial 3/4 thickness) of process automation calculating, rolling quiescent interval and heating parameters, the chemical constitutions etc. such as rolling schedule, mill speed and roll-force that obtain by basic automatization, from control cold process machine, extract related datas such as opening cold, final cooling temperature, actual cooling velocity, cool time, above data are calculated as model initial input parameter.

(3) prediction austenite grain size and differentiation thereof:

1. calculate the crystallite dimension of operation of rolling dynamic recrystallization

The crystallite dimension equation of dynamic recrystallization dynamics and dynamic recrystallization can be described by equation (1)～(4):

$X_D=1-\exp [-k{\left(\frac{\mathrm{\ϵ}-{\mathrm{\ϵ}}_c}{{\mathrm{\ϵ}}_{0.5}}\right)}^{n_d}]---\left(1\right)$

d _DRX＝DZ ^m (2)

${\mathrm{<figure-callout\; id="2"\; label="D\; DG"\; filenames="C20051004613000121.png"\; state="\{\{state\}\}">D</figure-callout>}}_{\mathrm{DG}}^{}={\mathrm{<figure-callout\; id="2"\; label="D\; DRX"\; filenames="C20051004613000121.png"\; state="\{\{state\}\}">D</figure-callout>}}_{\mathrm{DRX}}^{}+A_{\mathrm{DG}}{\left(C_{\mathrm{ep}}\right)}^{\mathrm{\&phi;}}.\exp (-\frac{Q_{\mathrm{dg}}}{T})\&CenterDot;{t_r}^{\mathrm{\&theta;}}---\left(3\right)$

$Z=\stackrel{.}{\mathrm{\&epsiv;}}\exp (Q_d/\left(\mathrm{RT}\right))---\left(4\right)$

Wherein, X _DBe the dynamic recrystallization rate; Z is the Zener-Hollomen parameter, is provided by formula (4), and this equation is called as the Sellars-Tegart relational expression.Wherein, ε _cAnd ε _0.5Be respectively the dynamic recrystallization critical strain and 50% pairing strain takes place, calculate by deformation condition, temperature and initial austenite crystallite dimension etc.Q _d(kJ/mol) be the dynamic recrystallization activation energy, n _dBe the constant relevant, by trying to achieve with the relation equation of flow stress, rate of deformation, deformation temperature and chemical constitution etc. with chemical constitution.d _DRXBe dynamic recrystallization crystallite dimension, d _DGBe the crystallite dimension of dynamic recrystallization after growing up, C _EqBe carbon equivalent, Q _DgBe the dynamic recrystallization activation energy of growing up, A _DGBe constant, all the other parameters are constant.

2. calculate the crystallite dimension of rolling tempus intercalare static state, inferior dynamic recrystallization

As ε＜ε _cThe time, static state takes place in mild carbon steel distortion back, and crystallization is softening again.For the static state research of crystallization kinetics again, according to the Avrami equation:

$X_s=1-\exp [-C{\left(\frac{t}{t_{0.5}^s}\right)}^{n_s}]---\left(5\right)$

In the formula, X _SBe the dynamic recrystallization rate; n _sBe constant; t _0.5 ^SFor static state again percent crystallization in massecuite reach for 50% time, can by with strain, austenite grain size and again crystallization kinetic parameter determine; T is the operation of rolling passage quiescent interval, is calculated by process model.

Static recrystallization crystal particle dimension d _SRXCalculate according to equation (6):

$d_{\mathrm{SRX}}=D_s{\mathrm{\&epsiv;}}^{{\mathrm{\&lambda;}}^{\&prime;}}{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="C20051004613000121.png"\; state="\{\{state\}\}">d</figure-callout>}}_0^{{\mathrm{\&eta;}}^{\&prime;}}\exp \left(\frac{Q_{\mathrm{ds}}}{\mathrm{RT}}\right)---\left(6\right)$

Wherein, d _SRXBe static recrystallization crystal particle dimension; D _s, λ ', η ' they are constant; R is a gas parameter, and T is a temperature, Q _DsBe static state crystal growth activation energy again.

As ε＞ε _cThe time, dynamic recrystallization will take place in the mild carbon steel deformation process, and inferior dynamic recrystallization takes place afterwards.Than static state crystallization again, inferior dynamic recrystallization carries out more rapid, and it is more tiny to form crystal grain.Inferior dynamic recrystallization dynamics and crystallite dimension illustrate X by equation (7) and (8) respectively _MBe inferior dynamic recrystallization rate; t _0.5 ^MFor percent crystallization in massecuite again reaches for 50% time, relevant with strain rate and deformation temperature; d _MDRXBe inferior dynamic recrystallization crystallite dimension; D ', m ', n _mBe constant.

$X_M=1-\exp [-C{\left(\frac{t}{t_{0.5}^M}\right)}^{n_m}]---\left(7\right)$

d _MDRX＝D′Z ^m′ (8)

3. calculate the austenite average grain size and grow up

The austenite average grain size in milling train exit 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 wherein _NFor the austenite grain size of crystallization does not again take place, can determine by the non-recrystallization model.

${\left(d_{\mathrm{SG}}\right)}^{N_g}={\left(d_A\right)}^{N_g}+A_{\mathrm{NG}}\exp (-\frac{Q_{\mathrm{gg}}}{\mathrm{RT}})\&CenterDot;t^{{\mathrm{\&theta;}}^{\&prime;}}---\left(10\right)$

Recrystal grain is grown up by equation (10) expression, wherein d _SGBe the recrystallization crystal particle dimension after growing up; Q _GgBe the activation energy of growing up; N _g, A _NG, and θ ' be constant.

Because the passage of board rolling in interim, if austenite can not be fully softening, with the overstrain Δ ε that keeps to a certain degree, therefore, should add the overstrain of preceding a time when calculating the passage strain:

ε＝ε _i+Δε (11)

Δε＝ε _i-1(1-X _S) (12)

ε wherein _I-1, ε _iIt is the accumulation strain of i-1 and i passage.

4. calculate the austenite grain size of milling train outlet, enter down a time, the differentiation of each the passage austenite grain size of 1.～3. cycle calculations operation of rolling in the repeating step (3) finally obtains the austenite grain size that finish to gauge exports.

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

In γ → α phase transformation in earlier stage, phase transformation is carried out with " nucleation is grown up " mechanism, and its kinetics equation is (13); In the phase transformation later stage, meet " position is saturated " mechanism, its kinetics equation is (14), Transformation Mechanism t switching time _NGRelevant with forming core, growth rate, can return in conjunction with experimental result by theoretical model and determine.

${\mathrm{<figure-callout\; id="1"\; label="X\; F"\; filenames="C20051004613000121.png"\; state="\{\{state\}\}">X</figure-callout>}}_F=1-\exp (-\frac{\mathrm{\&pi;}}{3}{I}_S{S}_{\mathrm{\&gamma;}}{G}_F^3\&CenterDot;t^4)---\left(13\right)$

X _F2＝1-exp(-2S _γG _F·t) (14)

X wherein _F1And X _F2Be respectively the transformation ratio in γ → α phase transformation early stage and later stage; S _γBe unit volume austenite equivalence crystal boundary surface area; I _SAnd G _FBe respectively the forming core and the growth rate of α phase.G wherein _FAdopt the formula of Hillert to calculate.I _SShown in equation (15), in the formula, K ₁, K ₂Be constant, k is a Boltzmann constant, and ε is strain.Δ G _vFor the α forming core volume free energy under the deformation condition not changes, adopt super constituent element model to calculate Δ μ _dFor the distortion stored energy, can calculate by relation equation with dislocation desity, flow stress.

$I_S=\frac{K_1{D}_C}{{\left(\mathrm{kT}\right)}^{1/2}}\&times;\exp [-\frac{\exp (-\mathrm{\&lambda;\&epsiv;}).{\mathrm{<figure-callout\; id="2"\; label="K"\; filenames="C20051004613000121.png"\; state="\{\{state\}\}">K</figure-callout>}}_2}{\mathrm{RT}{(\mathrm{\&Lambda;}{G}_v-\mathrm{\&Lambda;}{\mathrm{\&mu;}}_d)}^2}]---\left(15\right)$

Ferrite crystal grain sum n after γ → α phase transformation in the unit volume austenite _FCan be expressed as:

$n_F={\&Integral;}_0^{\mathrm{NG}}{I}_S{S}_{\mathrm{\&gamma;}}[1-X_F\left(t\right)]\mathrm{dt}---\left(16\right)$

X wherein _FBe the ferritic phase variability.

So mean diameter d of ferrite crystal grain _FCan be expressed as:

$d_F={\left(\frac{2}{{3n}_F}\right)}^{1/3}---\left(17\right)$

Deformation induced phase transformation (DIFT) may take place in the low-temperature space operation of rolling, because α crystal grain is separated out in the operation of rolling can be according to isothermal processes, adopt equation (13) (14) (15) to explain its transition kinetics, wherein constant λ needs that chamber thermal simulation experiment and result of calculation match are determined by experiment in the nucleation rate.Deformation induced transformation ratio is greater than 5%, thinks that the DIFT process takes place, otherwise thinks and do not take place.

Pro-eutectoid ferrite for continuous cooling process changes, adopt Scheil superposition rule to handle, be about to continuous cooling transformation and be processed into small isothermal phase change sum, promptly calculate according to equation (18), wherein X represents the phase transformation volume fraction of tissue, and constituent is organized in the j representative.

$X_n^j=X_{n-1}^j+\mathrm{\&Delta;}{X}_n^j---\left(18\right)$

At this, the inventive method is considered the DIFT phase transformation in the zerolling first in the controlled rolling process, and itself and continuous cooling transformation (CCT) are combined, as final α crystallite dimension, and shown in equation (19), d wherein _FD, d _FCBe respectively the crystallite dimension that DIFT and CCT process produce,

Be average alpha crystallite dimension, X _FD, X _FCBe respectively DIFT and CCT process α transformation ratio.

$\stackrel{\&OverBar;}{d_{\mathrm{\&alpha;}}}=X_{\mathrm{FD}}\&CenterDot;d_{\mathrm{FD}}+X_{\mathrm{FC}}\&CenterDot;d_{\mathrm{FC}}---\left(19\right)$

The present invention has three positive effects: 1, can be with very high precision, and stable, predict and the steel plate interior tissue comprise the differentiation of austenite and ferrite grain size apace, realize the online soft detection of operation of rolling microstructure; 2, help on-line optimization and control rolling, process for cooling, improve the steel internal organizational structure, produce performance homogeneous, stable hot-rolled product; 3, the soft-measuring technique of microstructure reduces test sample to realizing the high accuracy prediction of product room-temperature mechanical property, and it is significant to shorten the production cycle.

Description of drawings

Fig. 1 is the process flow diagram of the inventive method;

Fig. 2 is system's input parameter hum pattern;

Fig. 3 is the variation synoptic diagram of Medium and Heavy Plate Rolling process austenite grain size;

Fig. 4 is austenite grain size measured value and calculated value contrast synoptic diagram;

Fig. 5 is 5 ℃/s for cooldown rate, and the finish rolling start rolling temperature was to the synoptic diagram that influences of organizational parameter when finished product thickness was respectively 12mm and 18mm;

Fig. 6 is for being 5 ℃/s when cooldown rate, and finished product thickness is respectively 12mm, and intermediate blank thickness was to the synoptic diagram that influences of organizational parameter when the finish rolling start rolling temperature was 850 ℃;

Fig. 7 is for being 850 ℃ when the finish rolling start rolling temperature, and cooldown rate was to the synoptic diagram that influences of organizational parameter when finished product thickness was respectively 14mm and 18mm;

Fig. 8 is the calculated value of α crystallite dimension and the comparison synoptic diagram of measured value.

Embodiment

The α that Fig. 1 proposes for the present invention is the calculation process that develops of crystallization and α crystallite dimension again, below in conjunction with accompanying drawing the inventive method is described in detail.

(1) selecting, determine model parameter, is example with mild carbon steel, adopts single pass and two pass time compression experiment, and the researching high-temperature deformational behavior is drawn stress-strain diagram and static softening curve to the influence of static state and dynamic recrystallization; Adopt continuous cooling transformation and deformation induced heat of transformation simulated experiment, measure phase transformation mark and α crystallite dimension under different distortion and the cooling condition, utilize the mathematical regression means to determine model parameter, as shown in table 1.

Parameter	Parameter value	Parameter	Parameter value	Parameter	Parameter value	Parameter	Parameter value
								D	16000	n s	0.6	C	0.69	θ′	1
m	-0.23	D s	343	D′	26000	λ	1.6
								A DG	39000	λ′	-0.5	m′	0.23	K 1	2.07×10 11
φ	1.43	η′	0.4	Ng	10	K 2	6.33×10 -15
								θ	0.3	n m	1.5	A NG	1.31×10 52

Table 1

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

Select to calculate for the rolled piece of 3C4602P00A2 for PDI number.At first from the process machine data, call data such as steel grade alloying component, billet size, controlled rolling and cooling control technology as input information, as shown in Figure 2.This steel grade chemical constitution (ms-%) is: 0.18C-0.40Si-1.43Mn-0.019P-0.010S; Rolling schedule is: 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).The passage temperature of being calculated by process automation (apart from surperficial 3/4 thickness) is respectively (unit: ℃): 1110; 1082; 1053; 1030; 1016; 1005; 1001; 980; 952; 928; 852; 835; 826; 810.The rolling quiescent interval of each passage is 5s.

(3) prediction austenite grain size and differentiation thereof;

With finish rolling first passage is example, calculates according to 1.～3. method in the step (3): austenite dynamic recrystallization rate X _D=0; Static state is percent crystallization in massecuite X again _S=84%, static recrystallization crystal particle dimension is 20.21 μ m; Finish rolling first passage outlet average grain size is 21.94 μ m, and entering the preceding crystallite dimension of next passage after growing up is 24.48 μ m.

According in the step (3) 4., enter down a time, repeat the differentiation of each the passage austenite grain size of 1.～3. cycle calculations operation of rolling in this step, finally obtain the austenite grain size of finish to gauge outlet.Figure 3 shows that the differentiation situation of austenite grain size in the steel plate rolling process.Dynamic recrystallization generation and reduction ratio and rolling temperature are closely related, and at the higher rough rolling step of temperature, the dynamic recrystallization bating effect is apparent in view; Static and inferior dynamic recrystallization is very complete the rough rolling step generation, but in of the reduction (temperature in be about 850 ℃) of finish rolling process owing to rolling temperature, static state percent crystallization in massecuite again descends, cause the overstrain between passage slightly to increase, last passage austenite grain size is 23 μ m before the phase transformation.

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

On the basis that austenite grain size calculates, calculate according to the method for this step that to generate phase composition in this example be F+P, the ferrite volume fraction is 70.9%, and the pearlite mark is 29.1%, wherein the DIFT number turnover is 0, and ferrite grain size is 12.63 μ m.All crystal grains size result of calculation is deposited in automatically to be for PDI number in the text of filename, in order to query analysis.

Verify as follows to the accuracy of the inventive method:

(1) utilize method of the present invention that the simulated experiment of multi-pass continuous rolling is calculated.The technological procedure of this experiment is illustrated by Fig. 2,1230 ℃ of wherein roughing process heating-up temperatures, the distortion of 5 passages.Designed five kinds of different temperature schedules of F1-F5 in the simulated experiment of finish rolling process, finish rolling finishes to quench to measure austenite grain size.As shown in Figure 3, along with the decline of finish rolling stage deformation temperature, austenite grain size reduces as can be seen.What the reduction of finish rolling stage deformation temperature made static crystallization again becomes not really abundant, and overstrain increases, and dislocation desity improves, increased deformation energy, crystallization driving force again increases, and nucleation rate is accelerated, suppressed grain coarsening simultaneously, these all are the reasons that causes the austenite crystal refinement.The calculated value and the measured value that it can also be seen that austenite grain size from figure coincide better, illustrate that the inventive method has higher precision.

(2) according to cut deal TMCP industry rolling condition, in line computation the influence of different technical parameters to the room temperature microstructure, and combine with experimental result, the research cooling controlling and rolling controlling process is to Q235 cut deal tissue and Effect on Performance, for definite new rolling procedure and cooling system provides guidance.Table 2 is a cut deal cooling controlling and rolling controlling process parameter.

	Technological parameter
			The steel billet heating	1150℃×2～3h
The finish rolling stage is depressed distribution (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；	⑥45→36→28→22→20； ⑦93→73→57→45→36→28→22→ 20； ⑧45→36→28→24→22→20
			Temperature schedule	Finish rolling open rolling: 810～860 ℃; Finish rolling finish to gauge: 700～780 ℃; Open cold: 680～750 ℃; Cold eventually: 550～710 ℃

Table 2

For mild carbon steel, the main control parameters of cut deal TMCP technology has: finish rolling (II stage) rolling temperature, reduction system and cooldown rate etc.The control of final rolling temperature mainly treats that by roughing and finish rolling are two stage temperature and middle cooling realize that the formulation of reduction system moves after mainly considering to add large deformation and load distribution in the mill capacity scope.The two influences each other again simultaneously, and for example intermediate blank treats that temperature thickness is exactly both to have reflected to depress distribution system, directly influences the temperature variation of finish rolling process again.The temperature in finish rolling stage and distortion play an important role to austenite recrystallization process and strain accumulation.The control of cooldown rate mainly realizes by control roller table speed, cooling water flow.Quickening cooling can crystal grain thinning, increases substantially the performance level of steel.

Fig. 5 shows the steel plate finish rolling start rolling temperature of 18mm and 12mm finished product thickness to rolling the influence of back crystallite dimension.As can be seen from the figure, along with the reduction of final rolling temperature, ferrite grain size significantly reduces.The thickness of 18mm and 12mm is compared, and the ferrite grain size of 18mm is thicker under the same terms.

Fig. 6 shows the influence of intermediate blank thickness to room temperature texture's crystallite dimension.As can be seen from the figure, intermediate blank thickness is bigger, and ferrite crystal grain is thinner; Along with intermediate blank thickness reduces, it is big that ferrite grain size becomes.Fig. 7 shows the influence of cooldown rate to crystallite dimension.Along with the increase of cooldown rate, ferrite grain size attenuates.Equally, when finished product thickness was big, ferrite grain size was bigger.

(3) in α forming core and growth process, consider the double action of equivalent deformation protruding rank effect and equivalent grain boundary area, set up and describe thermal deformation austenite phase transformation process kinetics model, under simulation on-line rolling technology and the member condition, γ → α phase transition process and final crystallite dimension situation, and compare with experimental result, to verify its accuracy.Fig. 8 shows the comparison of the calculated value and the measured value of crystallite dimension, and wherein the measured value data are from the metallographic testing result of on-the-spot finished product sheet material, and steel grade is Q235, Q345 etc.The result shows that the calculated value of ferrite grain size and measured value coincide good.

Claims (2)

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.

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