TWI711496B - Method for improving setting of temperature control rolling process - Google Patents

Abstract

A method for improving setting of a temperature control rolling process is described. In this method, a database is built to use steel plate materials, and differences of real time hardness and simulation calculation hardness to build a two-dimensional relational table. A difference between the real time hardness and the simulation calculation hardness is calculated according to the table while performing a next sequence rolling operation after a stopping rolling duration, so as to look up the corresponding R rolling force ratio mean value of the corresponding steel plate material. The value is multiplied by the simulation calculation rolling force to obtain a more accurate predicted rolling force of the steel plate for performing the next sequence rolling operation.

Description

Method for improving steel plate temperature control rolling process setting

The embodiment of the present invention relates to a temperature-controlled rolling process of a steel sheet, and particularly relates to a method for improving the temperature-controlled rolling process setting of a steel sheet.

In the manufacture of high-grade steel plates, in addition to the requirements for thickness accuracy after the completion of rolling, there are also requirements for the temperature at the time, which is called temperature control rolling. When performing the temperature-controlled rolling process of the steel plate, the thickness of the steel plate after each pass must be calculated in advance, especially when the steel billet is rolled to the rated thickness, the rolling must be suspended [this is the so-called sequence], After the steel billet is cooled to a predetermined temperature, it is rolled again, in order to reach the required plate temperature after the completion of the rolling.

Usually, a computer is used to simulate and calculate the required number of rolling passes, the time required to suspend rolling, and the temperature of the re-rolling delay based on the above-mentioned process requirements. Based on the information calculated by the above simulation, the rolling staff can know when to stop rolling and start rolling. However, such simulation calculations are based on theoretically set conditions. However, factors such as the temperature at which the steel billet is released from the furnace, the time-consuming rolling before rolling, the use of derusting water, and the heat taken away from the steel billet by the conveyor rollers, are all different from actual factors. The situation is different, so that the original simulation calculation result is no longer applicable, and the calculation must be recalculated.

In addition, after recalculation, if the number of rolling passes changes, the overall rolling time will change. This may not hit the rated finishing temperature, which will negatively affect the accuracy and quality of the steel plate.

Therefore, the object of the present invention is to provide a method for improving the setting of the steel plate temperature control rolling process, using the existing rolling data to find out the relevant factors of the reproducible rolling force error, and to summarize the correction values corresponding to different classifications and analyses. In order to improve the accuracy of rolling mill settings due to differences between actual and theoretical, and smoothly produce high-grade steel that meets the requirements.

According to the above-mentioned object of the present invention, a method for improving the setting of the steel plate temperature controlled rolling process is proposed. In this method, a data database is established to establish a two-dimensional relationship table based on the steel material and the difference between the hardness at the time and the simulated hardness. In the table, the corresponding R rolling force ratio of various steel plates must be constructed under different hardness differences. Among them, the R rolling force ratio is defined as the actual rolling force/simulation calculated rolling force. After the steel plate is stopped rolling, proceed to the next schedule. According to the data database, first calculate the difference between the current hardness and the simulated hardness. Then according to the hardness difference, find the R rolling force ratio of the corresponding steel plate material. The value is multiplied by the simulated rolling force to obtain the predicted rolling force of the steel plate for the next schedule rolling delay.

According to an embodiment of the present invention, the above-mentioned data database is constructed by summarizing the maximum, minimum, average, and error of the R-rolling force ratio of different steel plate materials in various hardness differences from 3000 rolling samples.

After the construction is completed and put into use, the variation of the error rate can be observed. After obtaining the predicted rolling force and actual rolling force of the next schedule of the steel plate, the error between the predicted rolling force and the actual rolling force can be calculated.

When the error exceeds a preset value, the relationship table between the R rolling force ratio and the hardness difference of the corresponding steel plate material must be adjusted according to the error.

The above default value is 10%.

When steel plate rolling is delayed, the mill gap must be set. When the steel plate enters the rolling mill, there will be a rolling force between the steel plate and the rolling mill, and the rolling force will stretch the rolling mill, which is the elastic tensor. When setting the rolling mill gap, the rolling mill elastic tensor must be deducted, otherwise the thickness of the rolled steel plate is incorrect. The rolling force is related to the hardness of the steel plate, and the correct steel plate hardness depends on the accurate steel plate temperature. However, during the rolling of the steel plate, due to environmental factors, such as the correctness of the initial rolling temperature, unexpected water spray, the cooling of the rolling line in contact with the steel plate, and the scale that will grow during the waiting period for cooling, etc. It will affect the accuracy of the temperature calculation mode.

Since all the factors that differ in theory and practice will affect the hardness of the two and cause the difference in rolling force, the embodiment of the present invention proposes a method for improving the temperature control rolling process setting of the steel plate, using the existing Rolling data, by means of big data data exploration, find out the relevant factors of reproducible rolling force error, and summarize the corresponding correction value based on this, so as to avoid misjudgment of excessive rolling force and abandon the original thickness configuration. Causes a change in the number of rolling passes.

Steel plate rolling is divided into three stages, namely the trimming stage, the widening stage, and the rolling stage. In the trimming stage, the defects of the steel blank are eliminated by rolling. In the widening stage, the width of the steel sheet is rolled. In the rolling stage, the thickness of the steel plate is rolled. It is usually necessary to rotate the steel blank 90 degrees between these three stages. Such a rotating system is manually operated, which is time-consuming and the operating conditions vary from person to person, which affects the time-consuming rolling before rolling. Since the rolling force is related to the hardness of the steel plate, and the hardness of the steel plate is related to the temperature of the steel plate, if the temperature of the steel blank out of the furnace is different from the temperature used in the simulation calculation, the temperature of the plate before the rest rolling will always be inaccurate. The correctness of the calculated hardness.

In addition, regarding the use of descaling water and the heat taken away from the steel plate by the conveyor roller, the simulation calculation only assumes that water will be sprayed at the beginning of each stage, but does not consider factors such as water volume and spray duration. Moreover, except for the water spray at each stage, the simulation calculation cannot grasp the water spray at other times, so the board temperature is theoretically overestimated.

Furthermore, the range of time required for rolling from a pause to restart is quite large. Although the existing temperature drop model can predict a short-term temperature, the prediction of a long-term temperature drop is quite inaccurate. This is caused by the original The biggest factor that needs to be recalculated because the simulated calculation results are no longer applicable. The number of rolling passes after recalculation may be changed. If the hardness is reported as more than less, the number of rolling passes after recalculation will increase. Conversely, if the hardness is too much under-reported, the number of rolling passes after recalculation will decrease. All of these will affect the overall rolling time, resulting in failure to hit the rated finishing temperature.

The delay of rolling after rest rolling and restarting rolling will load the setting of the number of subsequent rolling passes obtained by simulation calculation. The first setting will recalculate the rolling force based on the plate temperature. However, due to the influence of the above factors, the calculated rolling force often exceeds the limit, that is, exceeds the preset maximum rolling force. But in fact it is not always true, which means that the calculated rolling force exceeds the limit is an illusion. As a result, the settings of each pass need to be recalculated, and the number of passes after recalculation usually increases, which results in a delay in finishing rolling and affecting the hit of finishing temperature. Please refer to Table 1 below, which lists two simulation calculation examples. In the example of No. 97256, the original simulation calculation system calculated 4 rolling passes. After the recalculation, the rolling force 7820 has exceeded the maximum rolling force of 6000. Therefore, the number of passes is increased to 6 after the recalculation, and the rolling force is expected Reduced to 6011. However, for the rolling delay, the actual rolling force is only 4540, which is only 76% of the predicted rolling force. This means that the previously recalculated rolling force exceeds the limit and is an illusion, causing unnecessary recalculation. Table 1 Serial number Recalculate rolling force Maximum rolling force Original number New road number Predict rolling force Actual rolling force Rolling force ratio 97256 7820 6000 4 6 6011 4540 0.76 44354 8282 6000 6 8 6010 3394 0.56

The actual rolling force is usually less than the predicted rolling force. In addition, unless the rolling time is extremely short, the ratio of the measured value to the predicted value is generally less than 0.9. Based on this, it can be inferred that the actual hardness is less than the predicted hardness. Make the following derivation based on the above inference. In the following derivation, R_force represents the actual rolling force, C_force represents the predicted rolling force, R_Hard represents the actual hardness, C_Hard represents the predicted hardness, arc represents the arc length of the steel plate and the roll, and WIDo represents the width of the steel plate. xqf and xkf are rolling The correlation coefficient of the ductility mode, A13 and A14 are the hardness coefficients, and tmp_tm is the steel plate temperature. Among them, xqf is a coefficient related to the amount of deformation and deformation rate of steel, xkf is a coefficient related to arc, A13 is a coefficient related to temperature and steel, and A14 is a coefficient related to steel. Set R_force/C_force=0.9 Because R_Hard=R_force/arc/WIDo/xqf/xkf C_Hard=C_force/arc/WIDo/xqf/xkf →R_Hard=0.9*C_Hard Because Hard=exp(A13*tmp_tm+A14) →exp(A13*tmp_tm+A14_R) =0.9*exp(A13*tmp_tm+A14_C) →A13*tmp_tm+A14_R =log(0.9)+A13*tmp_tm+A14_C →A14_R=log(0.9)+A14_C

According to the above formula, adjust A14 first, that is, add log(0.9) to the hardness coefficient A14, and then calculate the predicted rolling force. Since log(0.9) is a negative value, this adjustment system reduces the hardness and lowers the predicted rolling force. Please refer to Table 2 below, which lists the adjusted results using the above derivation formula. In Table 2, the predicted rolling force before the implementation of O-Pforce; the predicted rolling force after the implementation of N-Pforce; the actual rolling force of rforce; the simulated rolling force; simul is the rolling force calculated by simulation; ratio is N-Pforce /O-Pforce, the ratio is 0.9, which is the hypothetical mechanism of this concept; actual WT table actual off-rolling time; expected WT table expected off-rolling time; WT difference indicates the difference between actual WT and expected WT, and the negative value indicates earlier Start rolling; the ratio of the predicted rolling force O-Pforce before the implementation of the rolling force ratio table to the simulated rolling force simul; and the R-rolling force ratio table the actual rolling force rforce and the simulated rolling force simul ratio. Table 2 Serial number O-Pforce N-Pforce rforce simul ratio Actual WT Expected WT WT difference Rolling force ratio R rolling force ratio 63877 6309 5660 6059 5801 0.9 115 166 -51 1.09 1.04 57459 6051 5430 6152 5998 0.9 120 127 -7 1.01 1.03 64055 6403 5743 6350 6092 0.9 119 165 -46 1.05 1.04 66730 6115 5481 5611 5613 0.9 116 197 -81 1.09 1.00 66729 6275 5623 5709 5572 0.9 151 191 -40 1.13 1.02 52116 6545 5868 6087 6168 0.9 155 264 -109 1.06 0.99 54854 6853 5975 6185 6177 0.9 153 242 -89 1.11 1.00 54614 6603 5929 5461 5631 0.9 275 544 -269 1.17 0.97

Analyzing the results of the implementation in Table 2 above, the new predicted rolling force N-Pforce has been reduced according to the original adjustment concept, and it is prevented from exceeding the upper limit, so there is no need to recalculate. However, compared with the new predicted rolling force N-Pforce and the actual rolling force rforce, there is no obvious correlation between the two. The inventor originally thought it was caused by the time difference of the rolling break, but after observation and analysis, it was found that this was not the case. For example, in the example of serial number 54614, the WT difference is -269, which means rolling is 269 seconds earlier than expected. It is acceptable that the actual rolling force of 5461 is smaller than the newly predicted rolling force of 5929; the example of serial number 66729 is 40 earlier than expected Second rolling, the actual rolling force of 5709 is larger than the newly predicted rolling force of 5623. It is acceptable, because the example of serial number 66729 starts rolling later than the example of serial number 54614, so the hardness is greater than the example of serial number 54614; but others are earlier than expected In the case where the rolling time is much longer than 40 seconds, the actual rolling force is greater than the newly predicted rolling force, and the range is greater. Therefore, the inventor believes that the original idea still has room for improvement.

The inventors further compared the hardness and the rolling force of the first rolling of the second procedure after the rest rolling and the actual hardness and the rolling force, and found that the hardness difference ratio is positively correlated with the R rolling force ratio. Among them, the hardness difference ratio is (the actual hardness-the simulated hardness)/the simulated hardness; and the R rolling force ratio is the actual rolling force/the simulated rolling force.

Please refer to Fig. 1, which is a schematic diagram showing the relationship between the difference between the hardness (XCEM) and the simulated calculated hardness of many steel plates during the second process rolling and the R rolling force ratio. As shown in Figure 1, when the rest rolling is finished and the second schedule rolling begins, the greater the difference between the hardness at that time and the simulated hardness, the greater the R rolling force ratio. For example, when the difference between the actual hardness and the simulated hardness is 0.4, it can be seen from Figure 1 that the R rolling ratio is between 1.16 and 1.32. In this way, not only can the predicted rolling force be qualitatively, that is, if the actual hardness is higher than the simulated hardness, the actual rolling force will be greater than the simulated rolling force, but also quantitatively, that is, when the hardness is 40% higher than the actual hardness The rolling force is between 1.16 times and 1.32 times of the simulated rolling force. However, there is still room for improvement in accuracy.

Figure 1 is divided into 8 zones according to the hardness difference. The distribution range and average value of the R rolling force ratio of each zone are shown in Table 3 below. It can be seen from Table 3 that there is a considerable gap between the maximum value and the minimum value of each zone. However, using the average value of the simulated rolling force and the R rolling force to calculate the rolling force, there are still some errors between the calculated rolling force and the actual rolling force. Although the error value listed in the last row of Table 3 is within the traditionally tolerable 20% range, there is still room for improvement in accuracy. table 3 Hardness difference ≤-5% -5% ~0% 0 ~5% 5% ~10% 10% ~15% 15% ~20% 20% ~30% ≥30% R rolling ratio 0.73 ~0.99 0.77 ~1.04 0.83 ~1.08 0.8 ~1.17 0.88 ~1.16 0.98 ~1.15 1.1 ~1.22 1.06 ~1.19 Mean 0.86 0.91 0.95 0.98 1.02 1.07 1.12 1.19 error 15% 14% 14% 19% 14% 7% 9% 12%

Therefore, factors other than the difference in hardness are further included in the analysis. These factors include the rolling time, thickness, temperature, and material of the steel plate. After analysis, the influence of the rolling time, thickness, and temperature of the steel plate is not significant. However, if the classification of the same hardness difference is sub-classified by the material of the steel plate, the overall effect will be more significant.

Please refer to Figure 2, which is a schematic diagram showing the relationship between the difference between the actual off-rolling time and the expected off-rolling time of many steel plates and the R rolling force ratio. It was originally estimated that the difference in the rolling rest time would affect the actual rolling force. If the difference between the actual rolling rest time and the expected rest rolling time is negative, it means that rolling is actually earlier than the scheduled time, so the plate temperature is higher, and the rolling The ductility will be smaller, and the R-rolling force ratio will be smaller, and vice versa, the R-rolling force ratio will be larger. However, observing the relationship diagram in Figure 2, it is found that the relationship between the two is not as speculated as mentioned above. For example, the distribution of the R-rolling force ratio corresponding to 100 seconds early rolling ranges from 0.85 to 1.25, and the distribution of the R-rolling force ratio corresponding to rolling 100 seconds later ranges from 0.95 to 1.27. It cannot produce effective discrimination.

The R rolling force ratio and hardness difference of nearly 3000 samples are summarized in Table 4 below, and the number of samples in Table 4 is more than that in Table 3. Comparing the data in Table 4 and Table 3 above, the error value of Table 4 is obviously larger than that of Table 3, so it is not applicable. However, the samples are further classified by steel plate material, such as the steel plate with material number 30002 in the sample, and the results of this classification are listed in the right side of Table 4 below. It can be found from Table 4 that the error value of the R rolling force ratio of the steel plate No. 30002 has been greatly improved, and they are all within 10%. Table 4 Poor hardness Biggest overall Overall smallest Overall mean Overall error 30002 max 30002 minimum 30002 mean 30002 error ≤-10% 0.99 0.73 0.86 15.1% 0.92 0.87 0.90 2.8% -10%~-5% 1.03 0.85 0.94 9.6% 0.99 0.9 0.95 4.8% -5%~0% 1.12 0.7 0.91 23.1% 1.06 0.94 1.00 6.0% 0%~5% 1.14 0.77 0.96 19.4% 1.11 0.94 1.03 8.3% 5%~10% 1.14 0.8 0.97 17.5% 1.14 0.96 1.05 8.6% 10%~15% 1.17 0.72 0.95 23.8% 1.16 1.02 1.09 6.4% 15%~20% 1.18 0.89 1.04 14.0% 1.18 1.05 1.12 5.8% 20%~30% 1.26 0.99 1.13 12.0% 1.25 1.11 1.18 5.9% 30%~45% 1.39 0.99 1.19 16.8% 1.39 1.18 1.29 8.2%

Please refer to Figure 3, which shows the maximum and minimum R rolling ratios corresponding to various hardness differences in Table 4, including the steel plate material number 30002 and the overall relationship diagram. It can be seen from Figure 3 that the curve 100 with the largest hardness difference and the R rolling ratio of steel plate material number 30002, and the curve 110 with the smallest hardness difference and the R rolling ratio of steel plate material number 30002 is between the hardness difference and the R rolling ratio as a whole Between the curve 120 where the hardness difference and the overall R rolling ratio are the smallest. Therefore, the distribution range of the sample can be reduced a lot after the steel plate material is reclassified.

According to the above-mentioned derivation and verification, as long as the hardness and rolling force of the steel plate of a material are obtained, the next procedure of the steel plate of this material, such as the rolling delay of the second procedure, is obtained first. The difference between the hardness at the time and the hardness calculated by the simulation, and then use the table of the relationship between the hardness difference and the R rolling ratio established for the steel plate corresponding to this material to obtain the predicted rolling force. For example, if you want to carry out the rolling delay of the second rule of the steel plate with material number 30002, if the difference between the hardness and the simulated calculated strain at the time is 0.4, the average value of the lowest R rolling ratio in Table 4 can be 1.29 Multiply the rolling force calculated by the simulation to get the predicted rolling force. The difference between the obtained predicted rolling force and the actual rolling force can be within 10%, so the predicted rolling force can be effectively reduced to underreport the phenomenon.

Based on the above, one embodiment of the present invention proposes a method for improving the temperature control rolling process setting of the steel sheet. Please refer to FIG. 4, which is a flowchart of a method for improving the temperature control rolling process setting of a steel plate according to an embodiment of the present invention. In this embodiment, when the method of improving the setting of the temperature-controlled rolling process of the steel plate is performed, step 200 may be performed first to establish a data database of specific factors related to the temperature-controlled rolling process of the steel plate to obtain a simulation The calculated correction amount. This data database can be established according to the following factors, including the steel plate material, the difference between the simulated calculated hardness and the hardness at the time of rolling, and the corresponding R rolling force ratio relationship table formed by the difference between the simulated calculated hardness and the hardness at the time of rolling. R rolling force ratio is defined as actual rolling force/simulation calculation rolling force, where the simulation calculation rolling force is the rolling force obtained through simulation calculation.

After completing the establishment of the data database related to the temperature-controlled rolling process of the steel plate, step 210 may be performed to obtain the current hardness of a steel plate after the rest rolling is completed for the next scheduled rolling delay. The material of this steel plate is one of the steel plate materials in the database.

Next, step 220 may be performed to calculate the hardness difference between the current hardness of the steel plate and the simulated hardness. In some demonstration examples, the hardness difference between the current hardness of the steel plate and the simulated hardness of the corresponding steel plate material is the hardness difference ratio, that is, (the actual hardness at the time-the simulated hardness)/the simulated hardness.

Then, step 230 can be performed to find the relation table of the R rolling force ratio and the hardness difference of the corresponding steel plate material in the data database according to the calculated hardness difference, thereby obtaining the R corresponding to the calculated hardness difference Average rolling force ratio.

After obtaining the corresponding average value of the R rolling force ratio, step 240 can be performed to multiply the found average value of the R rolling force ratio by the simulation calculation rolling force, so as to obtain the rolling delay time of the steel plate for the next procedure. Predict rolling force. That is to say, according to the material of the steel plate, the corresponding R rolling ratio and hardness difference relationship table is established, and the rolling delay in the next procedure can be used to find the R rolling force ratio by using the look-up table method.

In addition, the new sample data generated by rolling can be used for self-verification. When the rolling force error exceeds a preset value, it means that the rolling environment may have changed, and the original samples may have to be screened, and the relationship table between the R rolling force ratio and the hardness difference should be updated accordingly. As shown in Fig. 4, after obtaining the predicted rolling force of the steel plate for the next rolling schedule, step 250 can be performed to obtain the actual steel plate for the first rolling delay of the next rolling schedule. Rolling force. Then, step 260 may be performed to calculate the error between the predicted rolling force calculated in step 240 and the actual rolling force of the first rolling of the steel sheet. When the calculated rolling force error exceeds the preset value, the relationship table of R rolling force ratio and hardness difference of the corresponding steel plate material can be adjusted according to the rolling force error. This default value is equal to 10%.

First carry out qualitative analysis and observe the distribution tendency of error rate. If the overrun error rate is on the right side of the mean value of the R rolling force ratio, it means that the distribution of the R rolling force ratio tilts to the right, and the mean value of the R rolling force ratio must be increased. If there is a sample R rolling force ratio, if the difference between its R rolling force ratio and the over-limit R rolling force ratio (the actual rolling force and the newly predicted rolling force R ratio) is within the preset value, then Keep it, otherwise discard it, and recalculate the average R rolling force ratio based on the retained existing sample data. If the overrun error rate is located to the left of the mean value of the R-rolling force ratio, it means that the distribution of the R-rolling force ratio tilts to the left, and the mean value of the R-rolling force ratio must be reduced. The online adjustment function can be built in the entire improvement system to adjust the relationship table between the R rolling force ratio and the hardness difference according to the rolling force error rate during operation to achieve the intelligent effect of the data database self-correction.

The embodiment of the present invention can improve the inapplicability and recalculation of the original simulation calculation for the temperature control rolling process at the end of the rest rolling and start the rolling delay of the next schedule. This is usually the case when the hardness is underreported. In view of another case where the hardness is over-reported and the under-reported is low, the original simulation calculation result will not be judged to be unsuitable and recalculated before the first rolling of the next schedule, but the actual rolling force will exceed the limit after rolling. Therefore, it is still recalculated, which causes the number of rolling passes to change. Therefore, if the corresponding R rolling force is checked according to the hardness difference before rolling in the next procedure, and the predicted rolling force and hardness are corrected accordingly, if the revised predicted rolling force does not exceed the limit, you can Keep the original simulation calculation results and obtain the required finishing temperature.

It can be seen from the above-mentioned embodiments that one of the advantages of the present invention is that the method of the present invention for improving the temperature control of the steel plate temperature control rolling process setting uses the existing rolling data to find out the relevant factors of the reproducible rolling force error, and summarizes different classifications The correction value corresponding to the analysis can improve the accuracy of the rolling mill setting caused by the difference between actual and theoretical, and smoothly produce high-grade steel that meets the requirements.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention. Anyone with ordinary knowledge in this technical field can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to those defined by the attached patent application scope.

100: curve 110: Curve 120: Curve 130: Curve 200: step 210: Step 220: step 230: step 240: step 250: step 260: Step

In order to make the above and other objectives, features, advantages and embodiments of the present invention more comprehensible, the description of the accompanying drawings is as follows: [Figure 1] is a schematic diagram showing the relationship between the difference between the hardness of the second-routine rolling and the simulated calculated hardness of many steel plates and the R rolling force ratio; [Figure 2] is a schematic diagram showing the relationship between the difference between the actual off-rolling time and the expected off-rolling time of many steel plates and the R rolling force ratio; [Figure 3] shows the maximum and minimum R rolling ratios corresponding to various hardness differences in Table 4, including the steel plate material number 30002 and the overall relationship diagram; and [Fig. 4] is a flowchart showing a method for improving the temperature control rolling process setting of a steel plate according to an embodiment of the present invention.

Domestic deposit information (please note in the order of deposit institution, date and number) no Foreign hosting information (please note in the order of hosting country, institution, date and number) no

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Claims (2)

A method for improving the setting of the steel plate temperature control rolling process, including: establishing a data database, wherein the data database includes the steel plate material, the difference between the simulated calculated hardness and the hardness at the time of rolling, the simulated calculated hardness and the hardness at the time of the rolling R-rolling force ratio relationship table formed by the difference, where the R-rolling force ratio is defined as the actual rolling force/simulating the rolling force; obtaining the hardness of the rolling delay for the next schedule after a steel plate is suspended; calculating the hardness and simulation Calculate the difference in hardness; find the corresponding R-rolling force ratio based on the difference and the steel plate material; and multiply the R-rolling force ratio by the simulated rolling force to obtain the predicted rolling force of the steel plate for the next schedule. . The method for improving the temperature control rolling process setting of a steel sheet as described in claim 1, wherein a new sample generated by rolling is used to self-verify the data database. When the rolling force error value of the new sample exceeds the preset value, The R rolling force ratio relation table of the corresponding steel plate material is adjusted immediately, and the preset value is set to 10% in the use of steel plate rolling.

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