CN114417236A - Data evaluation-based quality optimization control method for steel rolled product - Google Patents

Abstract

The invention belongs to the technical field of steel rolling, and particularly relates to a quality optimization control method of a steel rolled product based on data evaluation. According to the method, data evaluation is carried out on the samples on the basis through data acquisition and processing of multiple samples, and the samples which are applicable to model correction and have the highest accuracy are screened out and used as source data; the correction of the model core parameters, such as roll gap, rolling force and rolling speed, directly related to the product quality in the steel production process is realized through model recalculation; in the correction process, the smooth coefficient is optimally selected, the correction efficiency is guaranteed, meanwhile, the prediction precision of the model is improved, the prediction result of the model is enabled to be faster and more accurate to be close to an actual measurement value, the control effect of quality indexes such as the thickness of steel products is improved, and the purpose of improving the high-quality control of the products in the same batch is finally achieved.

Description

A quality control method for steel rolling product quality optimization based on data evaluation

technical field

The invention belongs to the technical field of steel rolling, in particular to a method for optimizing the quality of steel rolling products based on data evaluation.

Background technique

Product thickness is a very important quality index in the steel production process. High-precision head thickness control accuracy is the basis for full-length strip thickness control. High-precision head thickness helps to quickly enter the subsequent automatic thickness control (AGC). process, so as to ensure the thickness control accuracy of the full length of the strip. In the actual strip production process, the thickness gauge on the rolling line is generally installed at the exit of the last stand. Due to the limitation of the installation environment and investment cost, the thickness gauge is generally not installed between the serial stands. As a result, the thickness of the strip head cannot be measured in real time. When the measurement data is obtained, the strip head has completed the production process. At this time, the strip head thickness has been determined and cannot be corrected. If the head thickness is abnormal, the There will be head quality objections in a larger length range, which will affect the head control accuracy of the full length of the strip. If the adjustment is not made in time, the thickness control accuracy of the next strip will also be affected, thereby affecting the product quality of the entire production batch. In the actual production process, when the strip passes through the measuring instrument, the actual detection data related to the thickness obtained is collected and stored through the data acquisition system, and the model is corrected based on the measured process data, and the key parameters in the model are optimized and adjusted, which can improve the product. thickness control accuracy. However, in the actual production process, the accuracy of the collected data is easily affected by the stability of the belt threading, which directly affects the quality of the model correction. Therefore, the data must be evaluated, and the highest quality production process data can be further used for the model. Correct use.

In the aspect of strip rolling thickness control, predecessors have done a lot of research work; Chinese patent CN101804420B proposes a method for finishing rolling thickness control in hot-rolled sheet production. For the process of entering closed-loop control, an AGC series connection is designed. Double-loop system realizes the automatic roll gap control function of AGC; Chinese patent CN104741388A, proposes a thickness control method for hot tandem finishing rolling, introduces Smith prediction compensation into the monitoring AGC control system, and uses the GM method to directly control the roll gap of the rolling mill. The soft measurement significantly improves the response rolling speed, stability and control precision of the control system. Gao Lei (Hot Working Technology, 2013, 42(11), 92-95) based on the basic theory of rolling, optimized and revised the thickness control model to improve the thickness prediction accuracy. Wang Jian (Journal of Central South University (Natural Science Edition), 2014, 45(10), 3398-3407) used the self-learning method to study the self-learning module of the preset model of a hot tandem finishing mill in a factory, and improved the The rolling force prediction accuracy provides a basis for thickness control; Qian Jingxue (Henan Metallurgy, 2019, 27(153), 49-53) analyzed the influence of the sampling rules of the self-learning model of the thickness control system on the thickness control accuracy of the head. The effect of model self-learning is improved by modifying the time of thickness self-learning head sampling.

In the above research process, to improve the control accuracy of thickness control through the optimization of the AGC system, it must be implemented according to the deviation of the actual measured value of the thickness after the strip passes through the thickness gauge and enters the closed loop of thickness control. The control effect is difficult to guarantee; the control of head thickness must be realized by model correction, but in the traditional model correction process, the data collected in the production process is not screened and processed, because the measured data in the production process is easy to Affected by the external environment, there is a large deviation, and it is difficult to reflect the actual production state. If the model is corrected using data with a large error, it will have the opposite effect. The resulting thickness control accuracy will not be improved, but will decrease, eventually causing the model to be unsuitable, resulting in an unstable thickness control effect and not utilizing the quality control of the product.

SUMMARY OF THE INVENTION

In view of the deficiencies of the prior art, the present invention proposes a method for optimizing the quality of rolled steel products based on data evaluation, which is applied to the process of finishing intermediate steel billets to prepare finished steel strips, as shown in FIG. 1 . The program flow is as follows:

Step 1: Determine the number l of samples in the rolling process after the strip is finished, and determine the number N of target sampling points included in each sample.

In the present invention, one sample is collected during the belt threading process, and one sample is collected during the rolling process after the belt threading is completed. Each sample contains N target sampling points, and the value of N is determined according to the intermediate billet size of the steel product to be rolled and the size of the finished strip. Specifically, the value of N can be determined by the following methods:

The number of samples l in the rolling process after the strip is completed can be 4-8. In the subsequent steps, an optimal sample should be selected from the l samples, and the data measurement value therein should be used for calculation.

Step 2: The strip starts to pass through each rack in turn, and the measured data generated in the production process is collected and stored:

Step 2.1: Collection and storage of strip head data during belt threading:

Let the number of stands used for rolling be n. Before finishing rolling starts, the model setting system will calculate the rolling schedule according to the requirements of the production target value, that is, the set values of parameters such as reduction ratio, rolling speed, roll gap and rolling force of each stand.

Then the threading and rolling process begins. The head of the strip passes through the first frame, the strip threading process begins, and the strip passes through each frame and the measuring instruments set at each frame in turn along the rolling direction.

In the process of belt threading, according to the fixed data sampling period, the sampling points after the strip head passes through the measuring instrument are counted in turn, and the measured data of the sampling point measured by the measuring instrument are collected and recorded at the same time; When the point count is equal to the number of target sampling points N, the average value of the collected measured data is calculated, and the average value of the measured data is stored as the measured value, which is used as the measured data sample of the belt passing process.

In order to avoid the fluctuation of the measurement value caused by the impact of the steel bite when the strip enters each rack during the belt threading process, the first m sampling points (m can be 3-5) passing through the measuring instrument are not collected, but are collected from the m+th sampling point. 1 sampling point starts to be recorded and stored, and the sample contains N sampling points (ie m+1th to m+Nth sampling points) in the process of tape passing.

The above collection and storage process is performed at each rack in sequence until the strip head passes through the last measuring instrument, and the above collection and storage process is also completed at the last measuring instrument.

During the belt threading process, the data collected and stored at least include the rolling speed when the strip head passes through the measuring instruments at each stand, and may also include the roll gap at each stand, rolling force, and Looper angles between racks, etc.

Step 2.2: Collection and storage of strip data during rolling after strip threading:

The head of the strip passes through the final measuring instrument, and after completing the data collection and storage during the strip threading process and the strip threading process, it starts to collect and store the strip steel measured data during the rolling process after the strip threading is completed: according to the fixed data Sampling period: Count the sampling points passing through the measuring instrument, and collect and record the actual measured data of the sampling point measured by the measuring instrument. When the number of sampling points passing through the measuring instrument is equal to the number of target sampling points N, calculate the current sample respectively. The average value of all measured data, and the average value of the measured data in the current sample is stored as the measured value, and the number of samples is recorded as 1; then, the sampling point is cleared, and the counting is restarted, the number of samples + 1, and so on, until the completion l sample data collection and storage process.

During the rolling process after strip threading, the data collected and stored at least include the rolling speed, roll gap, rolling force and looper angle at each stand, as well as the strip temperature and strip temperature at the exit of the last stand. Thickness and strip width.

Step 3: Validation and screening of data in collected samples

Step 3.1: Evaluate the validity of the data from the samples collected during and after the threading process

If any data in the sample exceeds the validity interval, go to step 7, the system alarms, and the process ends; if all the data in the sample is within the validity interval, go to step 3.2.

Step 3.2: Screen the optimal sample from the l sample data obtained in step 2.2 as a sample for calculation. The optimal samples can be screened by the following methods:

The sample with the smallest fluctuation degree of sample data is selected as the optimal sample. The calculation formula of the fluctuation degree of sample data δ _aim is as follows:

Among them, select p data types as the index for calculating the fluctuation degree of the sample data, preferably, the strip temperature, strip thickness, strip width, and looper angle at each rack at the exit of the last rack can be used. δj is the fluctuation degree of the jth index in the sample, where c _{act,j,k is} the kth measured value of the jth index, c _aim,j is the target of the jth index M _j is the number of measured data values corresponding to each index. For example, the strip temperature, strip thickness, and strip width at the exit of the last frame have only one measured data value each, and because there are n machines The number of loopers between the racks is n-1, and the index of the looper angle at each rack has n-1 data measurement values.

Step 4: Use the data of the selected optimal sample to determine the calculated value of the process parameters

Step 4.1 Determine the calculated value of the outlet thickness of each rack

Calculate the exit thickness of each stand according to the strip thickness at the exit of the last stand in the optimal sample obtained in step 3.2, the rolling speed at each stand and the looper angle;

For the calculated value h of the outlet thickness at the i-th rack where i is 1 to n-1 _{, i} is:

In the formula, f _i is the forward slip value of the i-th rack, and f _n is the forward slip value of the last rack; the forward slip value can be calculated by the model setting system through the calculation method in the prior art.

h _n and v _n are the measured values of the thickness and rolling speed of the exit strip at the last stand, that is, the n-th stand in the optimal sample, respectively, and v _i is the optimal sample at the i-th stand. Measured value of rolling speed; l _θi is the strip length from the ith stand to the i+1th stand calculated according to the measured value of the looper angle θ _i at the ith stand in the optimal sample, and l _θs is The strip steel target length between the i-th frame and the i+1-th frame is calculated according to the target looper angle θ _s ; the calculated value h of the exit thickness of the last frame is _{calculated, and n} directly adopts h _n .

In the above method, the method for calculating the strip length l _θ between two adjacent frames according to the looper angle θ between the two frames is as follows:

As shown in Figure 2, the strip length l _θ between the two racks is the sum of the distance AB between the highest point of the looper and the exit of the previous rack, and the distance BC between the highest point of the looper and the entrance of the next rack, according to Figure 2 The schematic diagram is not difficult to get:

Among them, L ₁ is the horizontal distance from the previous stand to the fulcrum of the looper, L ₂ is the height from the fulcrum of the looper to the rolling plane, _L is the horizontal distance between the two stands, RL is the length of the looper arm, and r is the length of the looper arm. Looper roll radius.

Step 4.2 Determine the calculated outlet temperature for each rack

Distribute the temperature deviation at the exit of the finishing rolling to each stand, and calculate the temperature value of the strip passing through each stand:

The calculated value T of the outlet temperature of the i-th rack is _{calculated, and the calculation formula of i} is:

In the formula, T _i is the set value of the rolling temperature of the i-th stand during the strip threading process in step 2.1, T is the _measured value of the strip temperature at the exit of the last stand in the optimal sample, and T _target is the final machine. Rack outlet strip temperature target value.

Step 4.3 Determine the outlet width value of each rack

The width spread is ignored in the finishing rolling process, so the measured value of the strip width at the exit of the last stand in the optimal sample is taken as the exit width value when the strip passes through each stand.

Step 4.4 Determine the calculated value of each rack roll gap

Use the bouncing equation to determine the calculated value of the roll gap at each frame, and the _calculation formula of the calculated value S of the roll gap at each frame is as follows:

In the formula, h _{is calculated} as the calculated value of the outlet thickness at the stand to be calculated, which is obtained from step 4.1; P ₀ is the zero-adjusted rolling force, M is the stiffness of the rolling mill, which are all values that have been determined according to the performance of the rolling mill; P The _{actual measurement} is the measured value of the rolling force at the stand to be calculated in the optimal sample.

Step 4.5 Determine the calculated value of rolling force of each stand

From steps 4.1 to 4.3, the calculated value of the outlet thickness of each stand, the calculated value of the outlet temperature of each stand, the value of the exit width of each stand, and the measured value of the rolling speed, etc., calculate and determine the rolling force of each stand. Calculated value P _{is calculated} ; in the prior art, there are various methods for calculating the rolling force based on these known conditions.

Preferably, the calculated value of rolling force of each stand can be determined according to the Sims formula:

P _calculation = 1.15σ _s l _c Q _P w/1000

In the formula: w—the exit strip width of the rack to be calculated, mm, is the value of the exit width of each rack determined in step 4.3;

σ _s —Deformation resistance at the frame to be calculated, MPa;

a1～ _{a6 —} regression coefficient, its value depends on steel grade;

T_计算为步骤4.2确定的所要计算机架的出口温度计算值；T—the thermodynamic temperature at the rack to be calculated, dimensionless,

T _{is calculated} as the calculated value of the outlet temperature of the desired computer rack determined in step 4.2;

—变形速率，s^-1，

其中v为最优样本中，所要计算的机架处的轧制速度测定值；

— deformation rate, s ^-1 ,

where v is the measured value of rolling speed at the stand to be calculated in the optimal sample;

Δh—所要计算的机架的压下量，mm，为所计算的机架入口厚度计算值与出口厚度计算值的差值；对于第1机架，入口厚度为带钢初始厚度h₀，之后每一个机架的入口厚度计算值为上一个机架的出口厚度计算值，各机架出口厚度计算值按照步骤4.1获得；ε—engineering strain, %,

Δh—the reduction of the frame to be calculated, mm, is the difference between the calculated value of the inlet thickness of the frame and the calculated value of the outlet thickness; for the first frame, the inlet thickness is the initial thickness h ₀ of the strip, and then The calculated value of the inlet thickness of each rack is the calculated value of the outlet thickness of the previous rack, and the calculated value of the outlet thickness of each rack is obtained according to step 4.1;

ε＝0.4时的变形抗力，MPa；σ ₀ — at the deformation temperature of 1000°C,

Deformation resistance at ε=0.4, MPa;

l _c —Contact arc length after crushing is considered for the frame to be calculated, mm,

R'—roll flattening radius of the stand to be calculated, mm;

R—the roll radius of the stand to be calculated, mm, is a known quantity;

Q _P —Stress state influence coefficient of the rack to be calculated:

h _m — the average thickness of the strip, mm, is the average value of the calculated value of the inlet thickness and the calculated value of the outlet thickness at the frame to be calculated;

b ₀ to b ₄ are regression coefficients related to the rack.

It should be noted that, in the above steps 4.4 and 4.5, for the convenience of description, the subscript i for distinguishing different stands is not added, but it is not difficult to understand that the calculated values of roll gap and rolling force are corresponding to each stand. The calculated values, measured values and known parameters of the rack are calculated separately.

Step 5: Determine the calculated values of the correction coefficients of the roll gap model, rolling force model and rolling speed model of each stand

Step 5.1: Calculate the Roll Gap Model Correction Factor

Use the calculated value S of the roll gap calculated in step 4.4 to _calculate , and the measured value S of the roll gap of the optimal sample screened in step 3.2 to be _measured , and calculate the correction coefficient of the roll gap model:

The calculation formula of the correction coefficient of the roll gap model:

Step 5.2: Calculate the rolling force model correction factor

Use the calculated value P of the rolling force obtained in step 4.5 to _calculate , and the measured value P of the rolling force of the optimal sample screened in step 3.2 is _measured , and the correction coefficient of the rolling force model is calculated:

The calculation formula of the correction coefficient of the rolling force model:

Step 5.3: Calculate the rolling speed model correction factor

Use the measured value v of the rolling speed during the strip threading process obtained in step 2.1 _{- the strip threading process} , and the measured value v of the rolling speed in the optimal sample of the rolling process after the strip threading obtained in step 3.2, to calculate the rolling Speed model correction factor

The calculation formula of the correction coefficient of the rolling speed model:

Step 6: Update the correction factor of each rack model

Step 6.1: Calculate the smoothing coefficient α according to the degree of deviation between the old value of the correction coefficient and the calculated value of the correction coefficient; the calculation formula is as follows:

和

Δ_计算为步骤5.1-5.3计算得到的模型修正系数计算值，包括

和

Δ_max为对应的模型修正系数最大值，包括辊缝、轧制力、轧制速度的模型修正系数最大值

和

Δ_min为对应的模型修正系数最小值，包括辊缝、轧制力、轧制速度的模型修正系数最小值

和

In the formula: Δ _old is the old value of the model correction coefficient, including the old value of the model correction coefficient of the roll gap, rolling force and rolling speed

and

Δ is _calculated as the model correction coefficient calculated in steps 5.1-5.3, including

and

_Δmax is the maximum value of the corresponding model correction coefficient, including the maximum value of the model correction coefficient of roll gap, rolling force and rolling speed

and

_Δmin is the minimum value of the corresponding model correction coefficient, including the minimum value of the model correction coefficient of roll gap, rolling force and rolling speed

and

For the three correction coefficients of roll gap, rolling force and rolling speed, if _{Δcalculation ≥Δmax} , then _{Δcalculation} is replaced and becomes the new _Δmax value; if _{Δcalculation≤Δmin} , then _{Δcalculation} is replaced and becomes the new Δmax _value . The new _Δmin value is then used for the calculation of the smoothing coefficient α; ω is the proportional coefficient, which can be 0.5-0.9.

Step 6.2: Calculate the new value of the model correction coefficient

The model coefficients are corrected in a smoothing manner, and the new value of the smoothed model correction coefficient _Δnew is calculated as follows:

_Δnew = _Δold +α·( _{Δcalculation} - _Δold )

Step 6.3: Transfer the calculated new value of the model correction coefficient to the model setting system, and add the roll gap correction coefficient and the setting result of the model setting system as the new roll when the next strip is calculated for the rolling schedule. The setting value of the rolling force is multiplied by the rolling force correction factor and the model setting result as the new rolling force setting value, and the rolling speed model correction factor is multiplied by the model setting result as the new rolling speed setting. value for strip production.

See Figure 4 for the calculation and update process of the correction coefficients of each rack model in steps 6.1-6.3.

初始的

可以分别取0.05和-0.05；对于轧制力和轧制速度，则可以初始模型修正系数取1，初始的最大值和最小值分别取1.05和0.95，之后在每次轧制过程中，不断对模型修正系数进行更新，对轧制规程持续进行优化。For each rolling, the new value of the model correction coefficient of the previous time becomes the old value of this time, and a correction and update are performed by the above method. The model correction coefficient and the maximum and minimum value of the model correction coefficient can take the default values at the beginning. For example, for the roll gap, the initial model correction coefficient value can take 0 as the initial value.

Initially

It can be taken as 0.05 and -0.05 respectively; for the rolling force and rolling speed, the initial model correction coefficient can be taken as 1, and the initial maximum and minimum values are taken as 1.05 and 0.95 respectively. Model correction factors are updated to continuously optimize the rolling schedule.

Step 7: End.

The invention collects and processes the data of multiple samples, and on this basis, evaluates the data of the samples, selects the samples with the highest accuracy for model correction, and uses this as the source data; Correction of the core parameters of the model, which are directly related to the product quality, roll gap, rolling force and rolling speed; in the correction process, the smoothing coefficient is optimally selected to ensure the correction efficiency and improve the prediction accuracy of the model. The prediction result of the model is faster and more accurate close to the measured value, so as to achieve the control effect of improving the quality indicators such as the thickness of steel products, and finally achieve the purpose of improving the high-quality control of the same batch of products.

Description of drawings

FIG. 1 is a schematic flow chart of the method in the present invention.

Figure 2 is a schematic diagram of various parameters involved in the calculation of the strip length between two adjacent racks.

FIG. 3 is a layout diagram of equipment and instruments in the finishing rolling area of the strip rolling process in the embodiment of the present invention.

FIG. 4 is a flow chart of calculating the correction coefficient of each rack model according to the present invention.

FIG. 5 is a diagram showing the effect of improving the thickness accuracy after the setting parameters are corrected in the embodiment of the present invention.

Detailed ways

Below in conjunction with embodiment, the scheme of the present invention is described in detail:

This embodiment optimizes the quality of rolled products in the hot tandem finishing rolling process of Q235A steel: the hot tandem finishing area consists of 7 stands, the 7th stand is the last stand, and the roll radius is 380mm. The distance between racks is 5000mm. As shown in FIG. 3 , the strip 9 passes through 7 stands (2 is the first stand in the figure) from the entrance of the finishing rolling area in sequence, and then reaches the finishing rolling exit to complete the strip threading process. Thermometer 1 and thermometer 6 are respectively arranged at the entrance of the finishing rolling area and the exit of the finishing rolling area to measure the temperature of the strip, and a width measuring instrument 7 is installed at the exit of the finishing rolling, that is, the exit of the last stand to measure the actual width of the strip. The outlet is also equipped with a thickness gauge 8 to measure the actual thickness of the strip, and each stand is equipped with a displacement sensor 3, a pressure sensor 4 and a rolling speed sensor 5 to measure the roll gap and rolling force when the strip passes through each stand. And the linear speed of the roll (ie rolling speed), an angle encoder is installed on the looper 10 between the adjacent stands to measure the angle of the looper in the production process; the outlet thickness gauge 8 is the final measuring instrument.

The inlet temperature of the current product is 1020°C, the thickness of the finishing rolled intermediate billet is 32.0mm, and the width of the intermediate billet is 1200mm; the target value of the looper angle is 20°, the target value of the temperature of the finished strip is 880°C, and the target value of the thickness is 2.00mm , the width target value is 1200mm; the data sampling period is 200ms, and the tape threading speed is 10×10 ³ mm/s.

The rolling schedule used in the production process is calculated by the model setting system. The strip starts the production process according to this rolling schedule. The rolling schedule is shown in Table 1.

Table 1

parameter name 1 rack 2 racks 3 racks 4 racks 5 racks 6 racks 7 racks Inlet thickness/mm 32.00 16.65 9.44 5.69 3.90 3.01 2.38 Outlet thickness/mm 16.65 9.44 5.69 3.90 3.01 2.38 2.00 Reduction rate/% 48.0 43.3 39.7 31.5 22.8 20.9 16.0 Rolling speed/m/s 1.05 2.01 3.32 5.01 6.58 8.42 10.00 temperature/℃ 989.0 971.5 954.3 937.4 920.8 904.5 888.5 Roll gap/mm 19.43 11.89 7.93 5.30 3.52 2.78 1.88 Rolling force/kN 24692 22680 21449 16399 11053 10397 7298

Step 1: It is determined that the number of samples in the belt threading process is 1, and the number of samples in the rolling process after the belt threading is completed is 5.

The number of target sampling points included in the sample for model correction is determined based on the intermediate and finished strip dimensions of the strip.

The number of target sampling points included in each sample is:

In order to avoid the fluctuation of the measurement value caused by the impact of the steel bite when the strip enters each rack during the belt threading process, the first 5 sampling points are not collected, and the recording and storage are started from the 6th sampling point. Each sample contains a total of N = 20 sample points (ie, the 6th to 25th sample points).

Step 2: The strip starts to pass through each rack in turn, and the measured data generated in the production process is collected and stored.

Step 2.1: Collection and storage of strip head data during belt threading

The strip head passes through the first frame, and the strip threading process begins. The strip passes through each frame and the measuring instruments set at each frame in turn along the rolling direction; according to the fixed data sampling period, the strip head passes through in turn. The sampling points behind the measuring instrument are counted, and the measured data of the sampling point measured by the measuring instrument are collected and recorded at the same time; when the sampling point count through the measuring instrument is equal to the number of target sampling points 20, the average value of the collected measured data is calculated, And store the average value of the measured data as the measured value, as the measured data sample of the belt wearing process;

The above collection and storage process is performed at each rack in sequence until the strip head passes through the last measuring instrument, and the above collection and storage process is also completed at the last measuring instrument.

The sample data of the strip steel head during the belt threading process collected are shown in Table 2. The looper angle at the first rack refers to the looper angle between the first and second racks, and so on.

Table 2

parameter name 1 rack 2 racks 3 racks 4 racks 5 racks 6 racks 7 racks Rolling speed/m/s 1.11 2.02 3.34 5.00 6.56 8.43 10 Roll gap/mm 19.43 11.89 7.93 5.30 3.52 2.78 1.88 Rolling force/kN 24002 22122 20889 16291 11088 9982 7302 Loop angle/° 14.6 16.8 17.0 19.4 20.4 18.2 /

Step 2.2: Collection and storage of strip data after strip-threading

After the strip head passes through the last measuring instrument (thickness gauge 8) at the outlet, the actual measurement data of the strip in the rolling process after the strip is completed starts to be collected and stored: according to the fixed data sampling period, The sampling point is counted, and the measured data of the sampling point measured by the measuring instrument is collected and recorded. When the sampling point count through the measuring instrument is equal to the number of target sampling points N, the average value of all the measured data in the current sample is calculated respectively, and The average value of the measured data in the current sample is stored as the measured value, and the number of samples is recorded as 1; then, the sampling point is cleared, and the counting is restarted, and the number of samples is +1 until the data collection and storage process of 5 samples is completed. The collected data of 5 samples of strip steel in the rolling process after strip threading is completed are shown in Table 3.

table 3

Step 3: Validation and screening of data in collected samples

Step 3.1: Evaluate the validity of the data from the samples collected during and after the threading process

If any data in the sample exceeds the validity interval, go to step 7, the system alarms, and the process ends; if all the data in the sample is within the validity interval, go to step 3.2.

The validity interval range of each process parameter is shown in Table 4. It can be seen that all the data of the sample are within the validity interval range.

Table 4

Step 3.2: Select the optimal sample from the 5 sample data obtained in step 2.2 as the sample for calculation; the specific method is to select the sample with the smallest fluctuation degree of the sample data as the optimal sample, and calculate the fluctuation degree of the sample data δ _aim The formula is as follows:

Among them, p data types are selected as indicators for calculating the fluctuation degree of the sample data. In this embodiment, the strip temperature, strip thickness, strip width, and looper angle at each rack outlet are used in this embodiment. The data type is used as the indicator; δj is the fluctuation degree of the jth indicator in the sample, where c _{act,j,k is} the kth measured value of the jth indicator, c _aim,j is the target value of the jth indicator ; M _j is the number of data measurement values corresponding to each index, and there is only one data measurement value for the strip temperature, strip thickness, and strip width at the exit of the last frame, and because this embodiment has 7 machines. If there are 6 loopers between the racks, there are 6 data measurement values for the index of the looper angle between the racks.

The calculation results of the data fluctuation degree of the five samples in this embodiment are shown in Table 5. The data fluctuation degree corresponding to sample 5 is 0.018, which is smaller than that of other sample data fluctuation programs, and is selected as the optimal sample.

table 5

sample number 1 2 3 4 5 Fluctuation of sample data 0.159 0.112 0.080 0.044 0.018

Step 4: Use the data of the selected optimal sample to determine the calculated value of the process parameters

Step 4.1 Determine the calculated value of the outlet thickness of each rack

Calculate the exit thickness of each stand according to the strip thickness at the exit of the last stand, the rolling speed at each stand and the looper angle in the optimal sample obtained in step 3.2; for the exit of the i-th stand where i is 1 to 6 Thickness calculation value h _{calculation, i} is:

In the formula, f _i is the forward sliding value of the i-th rack, and f ₇ is the forward sliding value of the last rack, which is calculated according to the model setting system; according to Table 3, h ₇ =2.00mm, v ₇ =10.00m /s; vi is the measured value of the rolling speed at the _i -th stand in the optimal sample; l _θi is the i-th machine calculated from the measured value of the looper angle θ _i at the i-th stand in the optimal sample The strip length from the rack to the i+1th rack, l _20° is the strip steel target length from the ith rack to the i+1th rack calculated according to the target looper angle of 20°. The calculated value h of the final frame outlet thickness is _{calculated, and} h ₇ = 2.00mm is directly used for 7.

The looper structure between racks and the corresponding parameters are shown in Figure 2. The calculated value of strip length between adjacent racks is shown in Table 6:

Table 6

Looper number 1 2 3 4 5 6 Strip length/mm 5007.1 5008.1 5007.4 5008.1 5007.8 5007.7

The specific calculation method of the strip length between adjacent frames is, when the looper angle is θ, the strip length between adjacent frames can be calculated according to the geometric relationship:

Taking the calculated value of the outlet thickness of the first frame in this embodiment as an example, when the looper angle of the first frame and the second frame is 19.5°, the strip length between the frames is:

When the target looper angle is 20.0°, the strip length between the frames is:

It can be seen from Table 3 that v ₁ =1.15m/s, and the calculated value of the thickness at the exit of the first frame is:

Step 4.2 Determine the calculated outlet temperature for each rack

The temperature deviation of the finishing rolling outlet is allocated to each stand, and the temperature value of the strip steel passing through each stand is calculated; the calculated value T of the outlet temperature of the i-th stand is _{calculated, and the calculation formula of i} is:

In the formula, T _i is the set value of the rolling temperature of the i-th stand during the strip threading process in step 2.1, and T is the _measured value of the strip temperature at the exit of the last stand in the optimal sample (sample 5), as shown in the table 3 shows 877°C and T _target is the last stand exit strip temperature target value, which in this example is 880°C. The number of racks is n=7.

Taking the first rack as an example, according to Table 1, T ₁ =989.0℃, the calculated value of the strip steel outlet temperature of the first rack is:

Step 4.3 Determine the outlet width value of each rack

In the finishing rolling process, the width spread is ignored, and the measured value of the finishing exit width in the optimal sample 5 is taken as the calculated value of the exit width when the strip passes through each stand; therefore, the strip exit width value of all stands is:

w _calculated = w _measured = 1205mm

Step 4.4 Determine the calculated value of each rack roll gap

Use the bouncing equation to determine the calculated value of the roll gap at each frame, and the _calculation formula of the calculated value S of the roll gap at each frame is as follows:

In the formula, h _{is calculated} as the calculated value of the outlet thickness at the stand to be calculated, obtained from step 4.1; P ₀ is the zero-adjusted rolling force, in this embodiment, P ₀ =8000kN, M is the stiffness of the rolling mill, in this embodiment M=6000kN/mm; P is the _measured value of rolling force at the stand to be calculated in the optimal sample, and in this embodiment, it is the measured value of rolling force of each stand of Sample 5 in Table 3.

Taking the first stand as an example, the calculated roll gap of the stand is:

Step 4.5 Determine the calculated value of rolling force of each stand

According to Sims formula, the calculated value of rolling force of each stand is determined, and the calculated value of rolling force P at each stand is _calculated as:

P _calculation = 1.15σ _s l _c Q _P w/1000

In the formula: w—the exit strip width of the rack to be calculated, mm, is the value of the exit width of each rack determined in step 4.3;

σ _s —Deformation resistance at the frame to be calculated, MPa;

a1～ _{a6 —} regression coefficient, its value depends on steel grade;

T_计算为步骤4.2确定的所要计算机架的出口温度计算值；T—the thermodynamic temperature at the rack to be calculated, dimensionless,

T _{is calculated} as the calculated value of the outlet temperature of the desired computer rack determined in step 4.2;

—变形速率，s^-1，

其中v为最优样本中，所要计算的机架处的轧制速度测定值；

— deformation rate, s ^-1 ,

where v is the measured value of rolling speed at the stand to be calculated in the optimal sample;

Δh—所要计算的机架的压下量，mm，为所计算的机架入口厚度计算值与出口厚度计算值的差值；对于第1机架，入口厚度为带钢初始厚度h₀，也就是精轧中间坯的厚度；之后每一个机架的入口厚度计算值为上一个机架的出口厚度计算值，各机架出口厚度计算值按照步骤4.1获得；ε—engineering strain, %,

Δh—the reduction amount of the frame to be calculated, mm, is the difference between the calculated value of the inlet thickness of the frame and the calculated value of the outlet thickness; for the first frame, the inlet thickness is the initial thickness h ₀ of the strip, also is the thickness of the finishing rolling intermediate billet; after that, the calculated value of the entrance thickness of each stand is the calculated value of the exit thickness of the previous stand, and the calculated value of the exit thickness of each stand is obtained according to step 4.1;

ε＝0.4时的变形抗力，MPa；对于本实施例的钢种为150.6MPa；σ ₀ — at the deformation temperature of 1000°C,

Deformation resistance when ε=0.4, MPa; for the steel grade of this embodiment, it is 150.6MPa;

l _c —Contact arc length after crushing is considered for the frame to be calculated, mm,

R'—roll flattening radius of the stand to be calculated, mm;

R—roll radius of the stand to be calculated, mm;

Q _P —Stress state influence coefficient of the rack to be calculated:

h _m — the average thickness of the strip, mm, is the average value of the calculated value of the inlet thickness and the calculated value of the outlet thickness at the frame to be calculated;

b ₀ to b ₄ are regression coefficients related to the rack.

For the steel grade and frame in this embodiment, the values of each regression coefficient are shown in the following table:

Regression coefficients a₁ a₂ a₃ a₄ a₅ a₆ value 2.878 3.665 0.1861 -0.1216 0.3795 1.402 Regression coefficients b₀ b₁ b₂ b₃ b₄ value 0.8049 -0.3393 0.2488 0.0393 0.0732

According to steps 4.1-4.5, the calculated values of each process parameter are shown in Table 7:

Table 7

parameter name 1 rack 2 racks 3 racks 4 racks 5 racks 6 racks 7 racks Calculated value of inlet thickness/mm 32.00 16.73 9.34 5.70 3.85 2.99 2.35 Calculated value of outlet thickness/mm 16.73 9.34 5.70 3.85 2.99 2.35 2.00 Calculated temperature/℃ 988.6 970.6 953.0 935.7 918.7 901.9 885.5 Calculated width/mm 1205 1205 1205 1205 1205 1205 1205 Roll gap calculation value/mm 19.45 11.75 7.87 5.29 3.52 2.69 1.92 Calculated value of rolling force/kN 23002 24238 20071 17201 11020 9982 7487

Step 5: Determine the calculated values of the correction coefficients of the roll gap model, rolling force model and rolling speed model of each stand

Step 5.1: Calculate the Roll Gap Model Correction Factor

Use the calculated value S of the roll gap calculated in step 4.4 to _calculate , and the measured value S of the roll gap of the optimal sample screened in step 3.2 to be _measured , and calculate the correction coefficient of the roll gap model:

The calculation formula of the correction coefficient of the roll gap model:

Taking the first frame as an example, the correction coefficient of the roll gap model is

The calculated value of the correction coefficient of the roll gap model at each stand is as follows:

Step 5.2: Calculate the rolling force model correction factor

Use the calculated value P of the rolling force obtained in step 4.5 to _calculate , and the measured value P of the rolling force of the optimal sample screened in step 3.2 is _measured , and the correction coefficient of the rolling force model is calculated:

The calculation formula of the correction coefficient of the rolling force model:

Taking the first stand as an example, the correction coefficient of the rolling force model is

The calculation value of the correction coefficient of the rolling force model is as follows:

Step 5.3: Calculate the rolling speed model correction factor

Use the measured value v of the rolling speed during the strip threading process obtained in step 2.1 _{- the strip threading process} (see Table 2), and the measured value of the rolling speed in the optimal sample of the rolling process after the strip threading obtained in step 3.2 v (see Table 3), calculate the rolling speed model correction factor

The calculation formula of the correction coefficient of the rolling speed model:

Taking the first stand as an example, the correction coefficient of the rolling speed model is

The calculation value of the correction coefficient of the rolling speed model is as follows:

Step 6: Model Correction Coefficient Update

Step 6.1: Calculate the smoothing coefficient according to the old value of the correction coefficient and the calculated value of the correction coefficient. The calculation formula is as follows:

和

Δ_计算为步骤5.1-5.3计算得到的模型修正系数计算值，分别代表

和

Δ_max为对应的模型修正系数最大值，分别代表

和

Δ_min为对应的模型修正系数最小值，分别代表

和

对于三个修正系数，均有若Δ_计算≥Δ_max，则Δ_计算替代并成为新的Δ_max值；若Δ_计算≤Δ_min，则Δ_计算替代并成为新的Δ_min值；ω为比例系数；此处取ω＝0.70；In the formula: Δ _old is the old value of the model correction coefficient, representing

and

Δ _{is calculated} as the calculated value of the model correction coefficient calculated in steps 5.1-5.3, representing

and

_Δmax is the maximum value of the corresponding model correction coefficient, representing

and

_Δmin is the minimum value of the corresponding model correction coefficient, representing

and

For the three correction coefficients, if _{Δcalculation≥Δmax} , then _{Δcalculation} is substituted and becomes the new _{Δmax value} ; if _{Δcalculation≤Δmin} , then _{Δcalculation is} substituted and becomes the new _Δmin value; ω is the proportional coefficient ; take ω=0.70 here;

The old value of the correction coefficient of the roll gap model in this embodiment

The old value of the correction coefficient of the rolling force model in this embodiment

The old value of the correction coefficient of the rolling speed model in this embodiment

计算值为1.06，大于

因此计算得到的第1机架轧制力平滑系数为：

Taking the smoothing coefficient of the rolling force correction coefficient of the first stand as an example,

The calculated value is 1.06, which is greater than

Therefore, the calculated smoothing coefficient of the rolling force of the first stand is:

The calculation results of the smoothing coefficients of the roll gap, rolling force and rolling speed of each stand are as follows:

parameter name 1 rack 2 racks 3 racks 4 racks 5 racks 6 racks 7 racks Roll Gap Smoothing Coefficient 0.14 0.51 0.41 0.07 0.00 0.45 0.28 Rolling force smoothing factor 0.38 0.41 0.35 0.21 0.07 0.07 0.00 Rolling speed smoothing factor 0.21 0.14 0.00 0.00 0.00 0.00 0.00

Step 6.2: Calculate the new value of the model correction coefficient

The model coefficients are corrected in a smoothing manner, and the _new value Δnew of the smoothed model coefficients is calculated as follows:

_Δnew = _Δold +α·( _{Δcalculation} - _Δold )

Take the calculation of the new value of the rolling force correction coefficient of the first stand as an example:

The new value of the calculated correction coefficient is: α=1.00+0.38×(1.06-1.00)=1.02

The calculation results of the new values of the model coefficients after smoothing are shown in the following table:

Step 6.3: Transfer the calculated new value of the model correction coefficient to the model setting system, and add the roll gap correction coefficient and the model setting result as the new roll gap setting value when the next strip is calculated for the rolling schedule , multiply the rolling force correction coefficient and the model setting result as the new rolling force setting value, and multiply the rolling speed rolling speed model correction coefficient and the model setting result as the new rolling speed setting value , for strip production.

Step 7: End.

Figure 5 shows the measurement results of the deviation between the thickness of the final stand outlet and the target value before and after the set values of the parameters such as roll gap, rolling force, and rolling speed are corrected using the method of this embodiment. It can be seen that compared with before the correction, for the first part of the sampling points, that is, the sampling points corresponding to the strip head, the thickness deviation value after the correction is greatly reduced, and the high-precision head thickness helps to quickly enter the subsequent automatic thickness control. (AGC) process, thereby ensuring the thickness control accuracy of the full length of the strip.

Claims (10)

1. a steel rolling product quality optimization control method based on data evaluation, is characterized in that, comprises the following steps: Step 1: Determine the number of samples l in the rolling process after the belt threading is completed, and determine the number of target sampling points N included in each sample; Step 2: The strip starts to pass through each rack in turn, and the measured data generated in the production process is collected and stored; Step 3: Validate and screen the data in the collected samples; Step 4: Use the data of the selected optimal sample to determine the calculated value of the process parameters; Step 5: Determine the calculated values of the correction coefficients of the roll gap model, rolling force model and rolling speed model of each stand; Step 6: Update the model correction coefficient of each rack; Step 7: End. 2. the steel rolling product quality optimization control method based on data evaluation according to claim 1, is characterized in that, comprises the following steps: Step 1: Determine the number of samples l in the rolling process after the belt threading is completed, and determine the number of target sampling points N included in each sample; Step 2: The strip starts to pass through each rack in turn, and the measured data generated in the production process is collected and stored: Step 2.1: Collection and storage of strip head data during belt threading: The number of stands used for rolling is n; The head of the strip passes through the first frame, the strip threading process begins, and the strip passes through each frame and the measuring instruments set at each frame in turn along the rolling direction; According to the fixed data sampling period, the sampling points after the strip head passes through the measuring instrument are counted in turn, and the measured data of the sampling point measured by the measuring instrument are collected and recorded at the same time; when the number of sampling points passing through the measuring instrument is equal to the target sampling point number When the target is N, calculate the average value of the collected measured data, and store the average value of the measured data as the measured value, as the measured data sample of the belt wearing process; Carry out the above-mentioned collection and storage process at each rack in turn, until the strip head passes through the last measuring instrument, and also complete the above-mentioned collection and storage process at the last measuring instrument; During the belt threading process, the collected and stored data includes the rolling speed at each stand during the belt threading process; Step 2.2: Collection and storage of strip data during rolling after strip threading: After the strip head passes through the last measuring instrument, it starts to collect and store the measured data of the strip during the rolling process after the strip is completed: according to the fixed data sampling period, the sampling points passing through the measuring instrument are counted and collected at the same time. Record the measured data of the sampling point measured by the measuring instrument. When the sampling point count through the measuring instrument is equal to the number of target sampling points N, calculate the average value of all the measured data in the current sample respectively, and average the measured data in the current sample. The value is stored as the measured value, and the number of samples is recorded as 1; then, the sampling point is cleared, and the counting is restarted, and the number of samples is +1 until the data collection and storage process of l samples is completed; During the rolling process after strip threading, the data collected and stored include the rolling speed, roll gap, rolling force and looper angle at each stand, as well as the strip temperature and strip thickness at the exit of the last stand and strip width; Step 3: Validation and screening of data in collected samples Step 3.1: Evaluate the validity of the data from the samples collected during and after the threading process If any data in the sample is outside the validity interval, go to step 7; if all the data in the sample are within the validity interval, go to step 3.2; Step 3.2: Screen the optimal sample from the l sample data obtained in step 2.2 as a sample for calculation; Step 4: Use the data of the selected optimal sample to determine the calculated value of the process parameters Step 4.1 Determine the calculated value of the outlet thickness of each rack Calculate the exit thickness of each stand according to the strip thickness at the exit of the last stand, the rolling speed at each stand and the looper angle in the optimal sample obtained in step 3.2; for the i-th stand where i is 1 to n-1 The calculated value of the outlet thickness h _{is calculated, and i} is:

In the formula, f _i is the forward slip value of the i-th frame, f _n is the forward slip value of the last frame; h _n and v _n are the strip thickness and rolling at the exit of the last frame in the optimal sample, respectively. Measured value of rolling speed, v _i is the measured value of rolling speed at the i-th stand in the optimal sample;

is the strip length between the i-th frame and the i+1-th frame calculated according to the measured value of the looper angle θ _i at the i-th frame in the optimal sample, and l _θs is calculated according to the target looper angle θ _s The target strip length from the i-th frame to the i+1-th frame; Calculate the calculated value h of the final frame outlet thickness _{, and h n} is directly used for _n ; Step 4.2 Determine the calculated outlet temperature for each rack The calculated value T of the outlet temperature of the i-th rack is _{calculated, and the calculation formula of i} is:

In the formula, T _i is the set value of the rolling temperature of the i-th stand during the strip threading process in step 2.1, T is the _measured value of the strip temperature at the exit of the last stand in the optimal sample, and T _target is the final machine. The target value of the strip temperature at the outlet of the rack; Step 4.3 Determine the outlet width value of each rack Take the measured value of the strip width at the exit of the last rack in the optimal sample as the exit width value when the strip passes through each rack; Step 4.4 Determine the calculated value of each rack roll gap Use the bouncing equation to determine the calculated value of the roll gap at each frame, and the _calculation formula of the calculated value S of the roll gap at each frame is as follows:

In the formula, h _{is calculated} as the calculated value of the outlet thickness at the stand to be calculated, obtained from step 4.1; P ₀ is the zero-adjusted rolling force, M is the stiffness of the rolling mill; P is _measured as the calculated stand in the optimal sample. The measured value of rolling force; Step 4.5 Determine the calculated value of rolling force of each stand From the calculated value of the outlet thickness of each stand, the calculated value of the outlet temperature of each stand, and the value of the exit width of each stand in steps 4.1 to 4.3, the calculated value P of the rolling force of each stand is _calculated and determined; Step 5: Determine the calculated values of the correction coefficients of the roll gap model, rolling force model and rolling speed model of each stand Step 5.1: Calculate the Roll Gap Model Correction Factor Use the calculated value S of the roll gap calculated in step 4.4 to _calculate , and the measured value S of the roll gap of the optimal sample screened in step 3.2 to be _measured , and calculate the correction coefficient of the roll gap model: The calculation formula of the correction coefficient of the roll gap model:

Step 5.2: Calculate the rolling force model correction factor Use the calculated value P of the rolling force obtained in step 4.5 to _calculate , and the measured value P of the rolling force of the optimal sample screened in step 3.2 is _measured , and the correction coefficient of the rolling force model is calculated: The calculation formula of the correction coefficient of the rolling force model:

Step 5.3: Calculate the rolling speed model correction factor Use the measured value v of the rolling speed during the strip threading process obtained in step 2.1 _{- the strip threading process} , and the measured value v of the rolling speed in the optimal sample of the rolling process after the strip threading obtained in step 3.2, to calculate the rolling Speed model correction factor

The calculation formula of the correction coefficient of the rolling speed model:

Step 6: Update the correction factor of each rack model Step 6.1: Calculate the smoothing coefficient according to the degree of deviation between the old value of the correction coefficient and the calculated value of the correction coefficient; the calculation formula is as follows:

In the formula: Δ _old is the old value of the model correction coefficient, including the old value of the model correction coefficient of the roll gap, rolling force and rolling speed

and

Δ is _calculated as the model correction coefficient calculated in steps 5.1-5.3, including

and

_Δmax is the maximum value of the corresponding model correction coefficient, including the maximum value of the model correction coefficient of roll gap, rolling force and rolling speed

and

_Δmin is the minimum value of the corresponding model correction coefficient, including the minimum value of the model correction coefficient of roll gap, rolling force and rolling speed

and

For the three correction coefficients, there are: if _{Δcalculation ≥Δmax} , then _{Δcalculation} replaces and becomes the new _Δmax value; if _{Δcalculation ≤Δmin} , then _{Δcalculation} replaces and becomes the new _Δmin value; ω is the ratio coefficient; Step 6.2: Calculate the new value of the model correction coefficient The model coefficients are corrected in a smoothing manner, and the new value of the smoothed model correction coefficient _Δnew is calculated as follows: _Δnew = _Δold +α·( _{Δcalculation} - _Δold ) Step 6.3: Transfer the calculated new value of the model correction coefficient to the model setting system, and add the roll gap correction coefficient and the model setting result as the new roll gap setting value when the next strip is calculated for the rolling schedule , multiply the rolling force correction coefficient and the model setting result as the new rolling force setting value, multiply the rolling speed model correction coefficient and the model setting result as the new rolling speed setting value, supply the strip Steel production and use; Step 7: End. 3. the steel rolling product quality optimization control method based on data assessment according to claim 1, is characterized in that, in described step 1, the determination method of the target sampling point number N that is included in each sample is as follows:

4. The steel rolling product quality optimization control method based on data evaluation according to claim 2, characterized in that, in the step 2.1, the first m sampling points are not collected, and start from the m+1th sampling point For collection and storage, the sample contains N sampling points in total, that is, the m+1th to m+Nth sampling points. 5. the steel rolling product quality optimization control method based on data evaluation according to claim 2, is characterized in that, the method for screening optimal sample in described step 3.2 is as follows: The sample with the smallest fluctuation degree of sample data is selected as the optimal sample. The calculation formula of the fluctuation degree of sample data δ _aim is as follows:

Among them, select p data types as the index used to calculate the fluctuation degree of the sample data; δj is the fluctuation degree of the jth index in the sample, where c _{act,j,k is} the kth measured value of the jth index , c _aim,j is the target value of the jth index; M _j is the number of data measurement values corresponding to each index. 6. The steel rolling product quality optimization control method based on data evaluation according to claim 5 is characterized in that, selecting the strip temperature, strip thickness, strip width and The four data types of the looper angle are used as indicators. The number of data measurement values corresponding to the strip temperature, strip thickness, and strip width at the exit of the last rack is one, and the data measurement corresponding to the looper angle at each rack The number of values is n-1. 7. The steel rolling product quality optimization control method based on data evaluation according to claim 2, characterized in that, in the step 4.1, according to the looper angle θ between the two stands, the belt between the two stands is calculated The method for steel length l _θ is as follows:

Among them, L ₁ is the horizontal distance from the previous stand to the fulcrum of the looper, L ₂ is the height from the fulcrum of the looper to the rolling plane, _L is the horizontal distance between the two stands, RL is the length of the looper arm, and r is the length of the looper arm. Looper roll radius. 8 . The method for optimizing the quality of rolled steel products based on data evaluation according to claim 4 , wherein the first m sampling points are not collected, wherein m is 3-5. 9 . 9 . The method for optimizing the quality of rolled steel products based on data evaluation according to claim 2 , wherein the number of samples 1 in the rolling process after the strip passing is completed is 4-8. 10 . 10. The steel rolling product quality optimization control method based on data evaluation according to claim 2, characterized in that, in the step 4.5, the method for determining the _calculated value P of the rolling force of each stand is as follows: Determine the calculated value of rolling force of each stand according to the Sims formula: P _calculation = 1.15σ _s l _c Q _P w/1000 Where: w—the exit strip width of the rack to be calculated, mm, is the value of the exit width of each rack determined in step 4.3; σ _s —the deformation resistance at the rack to be calculated, MPa;

a1～ _{a6 —} regression coefficient, its value depends on steel grade; T—the thermodynamic temperature at the rack to be calculated, dimensionless,

T _{is calculated} as the calculated value of the outlet temperature of the desired computer rack determined in step 4.2;

— deformation rate, s ^-1 ,

where v is the measured value of the rolling speed at the stand to be calculated in the optimal sample; ε—engineering strain, %,

Δh—the reduction amount of the frame to be calculated, mm, is the difference between the calculated value of the inlet thickness of the frame and the calculated value of the outlet thickness; for the first frame, the inlet thickness is the initial thickness h ₀ of the strip, and then The calculated value of the inlet thickness of each rack is the calculated value of the outlet thickness of the previous rack, and the calculated value of the outlet thickness of each rack is obtained according to step 4.1; σ ₀ — at the deformation temperature of 1000°C,

Deformation resistance at ε=0.4, MPa; l _c —Contact arc length after crushing is considered for the frame to be calculated, mm,

R'—roll flattening radius of the stand to be calculated, mm;

R—roll radius of the stand to be calculated, mm; Q _P —Stress state influence coefficient of the rack to be calculated:

h _m —the average thickness of the strip, mm, is the average value of the calculated value of the inlet thickness and the calculated value of the outlet thickness at the frame to be calculated; b ₀ to b ₄ are regression coefficients.

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