A control method for improving the set precision of the coiling temperature of automobile beam steel

Abstract

The application relates to a control method for improving the coiling temperature setting precision of automobile girder steel, and belongs to the technical field of hot continuous rolling plate production methods.The technical scheme of the application is as follows: the strip steel parameters under different cooling processes are separately set, a CTC model selects a corresponding model parameter table according to a laminar flow cooling code, a single model table is applied to control the same steel grade when the cooling process is different, and mutual interference of model setting and self-learning is avoided.The application has the beneficial effects that the effect is remarkable, the problem of large strip steel coiling temperature control deviation caused by different cooling processes is solved, and product degradation quality accidents caused by girder steel cooling process conversion are avoided.

Description

TECHNICAL FIELD

[0001] The application relates to a control method for improving the coiling temperature setting precision of automobile beam steel, and belongs to the technical field of hot continuous rolling plate production methods. BACKGROUND

[0002] Hot-rolled beam steel is applied to structural members such as longitudinal beams, lining beams and cross beams of automobile chassis. The automobile chassis is subjected to various impacts and torsional complex stresses during driving, and the service condition of the chassis is quite harsh. Therefore, the beam steel is required to have high strength, good plasticity, toughness and cold bending performance. In actual production, the plasticity and toughness of high-strength automobile beam steel are generally low, which causes cracking, rebounding difficulty to control and tearing and delamination during punching, and the problems are common in various steel enterprises, the waste rate is high, and the application and promotion of high-strength automobile beam steel in the automobile manufacturing industry are seriously affected.

[0003] The hot-rolled beam steel is different in cooling strategy according to different use positions of the strip steel on the automobile, so that different laminar cooling codes are used for two rolling plans of the same steel grade and the same specification. In the hot-rolling production, the strip steel is subjected to cooling processes such as front interval cooling, front 3 / 4 cooling and rear interval cooling in the laminar cooling area. The temperature drop of the strip steel is different in each area of the laminar rollerway due to the different cooling modes, and the performance of the strip steel is different under different cooling modes, so that the requirements of different users are met. The mathematical model sets the temperature control parameters of the strip steel according to the steel grade, the model learns from the strip steel according to the coiling deviation of the strip steel, and the set parameters have heredity. In actual production, the model has a large deviation in setting the cooling water quantity for different cooling processes. The strip steel is subjected to parameter setting in the same CTC model table in the initial rolling stage of the hot-rolled beam steel due to the change of the cooling process. The coiling temperature of the strip steel deviates from the target temperature more, and the coiling temperature has a great influence on the metallographic structure of the hot continuous rolling strip steel, and is one of the main process parameters for determining the processing performance and mechanical properties of the finished strip steel. The control mode causes the product performance of the beam steel to be inconsistent, and is prone to cause accidents such as steel stacking, and has a high failure rate in production and great damage to the equipment. SUMMARY

[0004] The application aims to provide a control method for improving the coiling temperature setting precision of automobile beam steel. The parameters of the strip steel under different cooling processes are set separately, the CTC model selects the corresponding model parameter table according to the laminar cooling code, the same steel grade is controlled by using separate model tables when the cooling process is different, the mutual interference of model setting and self-learning is avoided, the effect is remarkable, the problem of large coiling temperature control deviation of the strip steel caused by different cooling processes is solved, the product degradation quality accident caused by the change of the cooling process of the beam steel is avoided, and the above problems in the background art are effectively solved.

[0005] The technical scheme of the present application is: a control method for improving the set precision of the coiling temperature of automobile beam steel, comprising the following steps:

[0006] (1) confirming different laminar cooling modes in the rolling process of the beam steel, and distinguishing different cooling codes;

[0007] (2) collecting the historical rolling data of the beam steel, and taking the cooling code corresponding to the largest number of rolled steel coils as the original steel grade code, i.e. the steel grade code SGP in the FSU and FDTC mathematical models and the steel grade code CTC_SGP in the CTC model are the same code;

[0008] (3) statistically analyzing the steel grade code CTC_SGP in the hot rolling process, and setting other cooling codes of the beam steel to other unused codes;

[0009] (4) adjusting the model parameter table and the model self-learning model table corresponding to the CTC_SGP steel grade code of the newly generated beam steel;

[0010] (5) simulating the strip corresponding to the newly added CTC_SGP steel grade code under the rolling plan of the beam steel in actual production;

[0011] (6) optimizing and adjusting the parameters of different model tables according to the coiling temperature control deviation of the beam steel.

[0012] In the step (1), the historical rolling data is used to statistically analyze the laminar cooling process in the production of the beam steel, the temperature drop of the strip in the laminar region is analyzed, the cooling process of the strip is optimized, the coiling temperature fluctuation is reduced, the cooling efficiency corresponding to the newly applied cooling code is set, and the opening sequence and opening state of the laminar region header are configured.

[0013] In the step (2), the historical rolling data of the beam steel is collected, the strip is classified by the cooling code in the data table, the number of steel coils produced by each cooling code is counted, the used CTC_SGP steel grade code in the CTC mathematical model is counted in the database, the CTC_SGP code is classified, and part of the code is used as an expansion code for the beam steel under different cooling modes.

[0014] In the step (3), the steel grade code is set in the CTC mathematical model, in order to reduce the influence of the conversion of the CTC_SGP steel grade code on the model setting precision, the CTC_SGP steel grade code corresponding to the cooling code with the largest rolling coil number is applied to the original code, that is, the cooling mode steel SGP and the CTC_SGP are the same code; the CTC_SGP steel grade code corresponding to the remaining cooling codes of the large beam steel is set to the unused code in the CTC model, so as to prevent the problem of mutual interference of parameters during rolling caused by the sharing of one CTC_SGP code by two steel grades.

[0015] In the step (4), the model parameter table corresponding to the newly created CTC_SGP code and the self-learning model table are set, because the cooling efficiency of different cooling codes is different, the newly added model table parameters are set to have certain differences, according to the control characteristics of the steel strips corresponding to each CTC_SGP code, the cooling efficiency and the self-learning gain value parameters are set correspondingly, and the prediction precision of the model is improved.

[0016] In the step (5), the slab PDI information is issued by the first three systems before the production of the large beam steel, including the slab rolling target thickness, width, finishing temperature, coiling temperature and laminar cooling code, the courseT pre-calculation is performed on the slab after the plan is issued, the laminar cooling water quantity and the steel strip speed data in the model log file are checked, the data are compared and analyzed with the data of the steel strip with good coiling temperature control after rolling, and the set parameters are adjusted.

[0017] In the step (6), after the rolling of the large beam steel is completed, there is a certain deviation in the coiling temperature control of part of the newly added CTC_SGP code steel strip, or the coiling temperature of the steel strip fluctuates greatly, at this time, the problems are analyzed according to the model feedback log, and the model setting parameter table and the feedback control self-learning parameter table are optimized as a whole.

[0018] The beneficial effects of the present application are: by separately setting the steel strip parameters under different cooling processes, the CTC model can select the corresponding model parameter table according to the laminar cooling code, the same steel grade is controlled by applying a separate model table when the cooling process is different, the mutual interference of model setting and self-learning is avoided, the effect is remarkable, the problem of large deviation of the coiling temperature control of the steel strip caused by different cooling processes is solved, and the occurrence of the product degradation quality accident caused by the change of the cooling process of the large beam steel is avoided. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 It is the work flow chart of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the embodiment of the application clearer, the technical scheme in the embodiment of the application will be clearly and completely described below in conjunction with the drawings in the embodiment. Obviously, the described embodiment is only a part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiment in the application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the application.

[0021] A control method for improving the coiling temperature setting precision of automobile beam steel, comprising the following steps:

[0022] (1) confirming different laminar cooling modes in the rolling process of the beam steel, and distinguishing different cooling codes;

[0023] (2) collecting the historical rolling data of the beam steel, and taking the cooling code corresponding to the largest number of rolled steel coils as the original steel grade code, i.e. the steel grade code SGP in the FSU and FDTC mathematical models and the steel grade code CTC_SGP in the CTC model are the same code;

[0024] (3) counting the steel grade code CTC_SGP rolled by hot rolling, and setting other cooling codes of the beam steel to other unused codes;

[0025] (4) adjusting the model parameter table and the model self-learning model table corresponding to the CTC_SGP steel grade code of the newly generated beam steel;

[0026] (5) in actual production, simulating the strip corresponding to the newly added CTC_SGP steel grade code under the rolling plan of the beam steel;

[0027] (6) according to the coiling temperature control deviation of the beam steel, corresponding optimization and adjustment are made on the parameters of different model tables.

[0028] In the step (1), the historical rolling data is applied to count the laminar cooling process in the production of the beam steel, the temperature drop of the strip in the laminar region is analyzed, the cooling process of the strip is optimized, the coiling temperature fluctuation is reduced, the cooling efficiency corresponding to the newly applied cooling code is set, and the opening sequence and opening state of the laminar region header are configured.

[0029] In the step (2), the historical rolling data of the beam steel is collected, the strip is classified by the cooling code in the data table, the production coil number of each cooling code strip is counted, the used CTC_SGP steel grade code in the CTC mathematical model is counted in the database, the CTC_SGP code is classified, and part of the code is divided to be used as the expansion code of the beam steel under different cooling modes.

[0030] In the step (3), the steel grade code is set in the CTC mathematical model, in order to reduce the influence of the transformation of the CTC_SGP steel grade code on the model setting precision, the CTC_SGP steel grade code corresponding to the cooling code with the largest rolling coil number is applied to the original code, that is, the cooling mode steel SGP and the CTC_SGP are the same code; the CTC_SGP steel grade code corresponding to the remaining cooling codes of the Daguang steel is set as the unused code in the CTC model, so as to prevent the problem of mutual interference of parameters during rolling caused by the sharing of one CTC_SGP code by two steel grades.

[0031] In the step (4), the model parameter table and the self-learning model table corresponding to the newly created CTC_SGP code are set, because the cooling efficiency of different cooling codes is different, the newly added model table parameters are set to have certain differences, according to the control characteristics of the strip corresponding to each CTC_SGP code, the cooling efficiency and the self-learning gain value parameters are set correspondingly, and the prediction precision of the model is improved.

[0032] In the step (5), the slab PDI information is issued by the first three systems before the production of the Daguang steel, including the slab rolling target thickness, width, finishing temperature, coiling temperature and laminar cooling code, the courseT pre-calculation is performed on the slab after the plan is issued, the laminar cooling water quantity and the strip speed data in the model log file are checked, the data are compared and analyzed with the data of the well-controlled strip after rolling and coiling, and the set parameters are adjusted.

[0033] In the step (6), after the rolling of the Daguang steel is completed, there is a certain deviation in the coiling temperature control of part of the newly added CTC_SGP code strip, or the coiling temperature fluctuation of the strip is large, at this time, the problems are analyzed according to the model feedback log, and the model setting parameter table and the feedback control self-learning parameter table are optimized as a whole.

[0034] In practical application, the application realizes the separate setting and self-learning of the Daguang steel under different cooling modes by applying the newly added CTC_SGP steel grade code in the CTC mathematical model, and can effectively avoid the abnormal coiling temperature control caused by the change of the cooling mode of the Daguang steel, including the following process:

[0035] 1, setting of the laminar cooling mode of the Daguang steel

[0036] There are 6 codes of laminar cooling process in the production of large beam steel, i.e. 1, 4, 6, 20, 21 and 22, which correspond to the cooling modes of front interval cooling, front concentrated cooling, rear interval cooling, etc. The opening sequence of header for each cooling mode is different, and the temperature drop of the strip in each area is also different, so the cooling efficiency of each code model table is set differently. According to the coiling temperature control in the actual production of large beam steel, the opening sequence number and the opening and closing of the cooling header corresponding to each code are configured in the model table. Taking the cooling code 6 as an example, this cooling mode is front interval cooling, and the parameter table is set to open the upper header in the sequence number 1.3.5…151, and the lower header in the sequence number 2, 4, 6…152. The last two groups of 21 and 22 headers are the cooling water fine adjustment section, which are opened from back to front. The first 7 groups of headers are set to open one and close one for interval cooling of the strip. The other cooling modes are set according to the cooling process requirements.

[0037] 2. Rolling data collection of large beam steel

[0038] A new data table is created to collect the rolling data of large beam steel, which includes a large amount of model setting and feedback control data such as large beam steel grade code SGP, finishing mill threading speed, CTC model steel grade code CTC_SGP, laminar cooling code patCode, and cooling efficiency. This table includes all the temperature control information of the rolled large beam steel, which can quickly find the temperature control situation of large beam steel under different cooling codes. In the model database, the used CTC_SGP is counted, which has 1000 codes. In practical application, the first 800 codes are used as the general code of mathematical model for each steel grade, i.e. FSU, FDTC and CTC mathematical model, and the last 200 codes are the expansion code of CTC mathematical model applied according to the cooling characteristics of special steel grade.

[0039] 3. Setting of CTC model expansion code of large beam steel

[0040] The data table is newly built, and the beam steel rolling volume and the strip temperature control hit rate under different cooling modes are counted. In order to reduce the influence of the transformation of CTC_SGP steel code on the model setting precision, the CTC_SGP steel code corresponding to the cooling code with the most rolling volume is applied to the original code. Under this cooling mode, the strip SGP and the CTC_SGP are the same code, that is, the universal steel code of all mathematical models, and the CTC_SGP is set as the extended code under the other cooling modes. Taking 700L beam steel as an example, the steel code SGP is 601, the rolling and coiling of the cooling code 20 of this steel are more, and therefore the CTC_SGP code corresponding to the cooling code 20 is set as 601. The CTC_SGP code corresponding to the other cooling codes is set respectively on the basis of ensuring that the CTC_SGP code is not repeatedly used.

[0041] 4. Setting of CTC mathematical model parameters

[0042] The newly built CTC_SGP steel code is applied to the setting of the CTC model parameters, and the FSU and FDTC mathematical models still apply the beam steel SGP code in the calculation, and the called parameters are not changed, and therefore the FSU and FDTC model parameters do not need to be re-set. For the CTC model, the newly built CTC_SGP steel code is a new steel rolling, and the series of parameter tables are the initial values of the model. In order to prevent the model from setting a large deviation of the coiling temperature, the called model parameters need to be re-set. Taking the newly added CTC_SGP code 805 as an example, the original 601 code corresponding parameters are copied to the parameter table corresponding to 805 in the actual operation. The parameter table includes the cooling water cooling efficiency, the cooling efficiency self-learning value zlw12, the strip model temperature self-learning value tmpVrnCt after rolling, etc.

[0043] 5. Simulation calculation of newly built code beam steel before production

[0044] The newly built CTC_SGP code beam steel is a new steel in the CTC mathematical model. Taking the newly added CTC_SGP code 805 as an example, the original steel code 601 corresponding cooling efficiency parameter table is copied in the actual application. Since there is a large difference in the cooling mode, the control still has a large deviation, and therefore the simulation calculation of the strip before rolling is needed. After the three-level issues the PDI information of the beam steel in this cooling mode, the slab is simulated and calculated on the HMI, the CTC model log file generated by the calculation is applied, the rolling speed of the strip and the number of open laminar cooling water header groups are compared with the data of the previously rolled and well-controlled strip, and if the data deviation is large, the coiling temperature self-learning value and the cooling efficiency self-learning value are adjusted. After the adjustment, the simulation calculation is performed again to ensure that the setting data of the beam steel to be rolled is close to the data of the previously well-controlled strip.

[0045] 6,Parameter optimization after new code girder steel production

[0046] After the rolling of the new CTC model steel code girder steel is completed, the model table is further optimized according to the actual control of the coiling temperature in the log log generated by the model. Taking the newly added CTC_SGP code 805 girder steel as an example, when the coiling temperature of the strip fluctuates greatly, the feedback control gain value can be appropriately reduced. Taking the 6.0mm thickness specification as an example, the gain value is adjusted from the initial value 0.40 to 0.25, and the adjusting water quantity of the laminar cooling water is reduced; the CTC mathematical model collects the coiling temperature of the strip after rolling to feed forward set the coiling temperature, when the strip after rolling temperature and the target temperature deviate, the CTC model sets the water quantity too large to cause the coiling temperature overshoot, the feed forward setting gain value should be appropriately reduced, taking the 6.0mm thickness specification as an example, the feed forward gain value is adjusted from the initial value 0.30 to 0.25. Other parameters such as self-learning gain value, head and tail self-learning value, etc. are adjusted according to the coiling temperature control condition.

[0047] The present application aims at the problem of low coiling temperature setting precision caused by the changeable cooling process of girder steel, and separately sets the strip parameters under different cooling processes, realizes that the CTC model selects the corresponding model parameter table according to the laminar cooling code, and controls the same steel grade with different cooling processes by using separate model tables, avoiding the mutual interference of model setting and self-learning. The method has remarkable effect after optimization, solves the problem of large deviation of strip coiling temperature control caused by different cooling processes, and avoids the occurrence of product degradation quality accidents caused by the change of girder steel cooling process.

[0048] The above examples are only used to illustrate but not to limit the technical solutions of the present application. Although the present application has been described in detail with reference to the above examples, those skilled in the art should understand that the present application can still be modified or equivalently replaced without departing from the spirit and scope of the present application. Any modification or partial replacement should be covered in the scope of the claims of the present application.

Claims

1. A control method for improving the accuracy of the set roll temperature of a motor vehicle beam steel, characterized by The method comprises the following steps: (1) confirming different laminar cooling modes in the rolling process of the beam steel and distinguishing different cooling codes; (2) collecting the rolling data of the beam steel by creating a new data table, wherein the data table comprises a beam steel grade code SGP, a CTC model steel grade code CTC_SGP and a laminar cooling code patCode; (3) counting the steel grade code CTC_SGP after hot rolling; applying the original code of the CTC_SGP steel grade code corresponding to the cooling code with the largest rolling coil number, that is, the cooling mode of the steel SGP is the same as the CTC_SGP; the CTC_SGP steel grade code corresponding to the remaining cooling codes of the beam steel is set as a code not used in the CTC model, which is a newly created CTC_SGP code; (4) copying the original SGP model table to the newly created CTC_SGP corresponding model parameter table, and adjusting the parameters of the model parameter table and the self-learning model table corresponding to the newly created CTC_SGP steel grade code of the beam steel; (5) in actual production, after the rolling plan of the beam steel is issued, the steel corresponding to the newly created CTC_SGP steel grade code is simulated and calculated; The PDI information of the slab is issued by the three-level system before the production of the beam steel, including the rolling target thickness, width, finishing temperature, coiling temperature and laminar cooling code of the slab. After the plan is issued, the course T of the slab is pre-calculated, the data of the laminar cooling water flow and the strip speed are checked in the model log log file, and the data are compared and analyzed with the data of the well-controlled strip, and the set parameters are adjusted. If the data deviation is large, the coiling temperature self-learning value and the cooling efficiency self-learning value are adjusted, and then the simulation calculation is performed again to ensure that the set data of the beam steel to be rolled is close to that of the well-controlled strip before. (6) After the rolling of the beam steel is completed, the model table parameters are optimized and adjusted according to the coiling temperature control deviation of the beam steel.

2. The control method for improving the set accuracy of the coiling temperature of an automobile beam steel according to claim 1, characterized by: In step (1), the laminar cooling process in the production of the beam steel is counted by using the historical rolling data, the temperature drop of the strip in the laminar region is analyzed, the cooling process of the strip is optimized, the coiling temperature fluctuation is reduced, the cooling efficiency corresponding to the newly applied cooling code is set, and the opening sequence and opening state of the laminar region header are configured.

3. The control method for improving the set accuracy of the coiling temperature of an automobile beam steel according to claim 1, characterized by: In step (4), the model parameter table and the self-learning model table corresponding to the newly created CTC_SGP code are set. Since the cooling efficiencies of different cooling codes are different, the newly added model table parameters have certain differences. According to the control characteristics of the strip corresponding to each CTC_SGP code, the cooling efficiency and the self-learning gain value parameters are set correspondingly to improve the prediction accuracy of the model.

4. The control method for improving the set accuracy of the coiling temperature of an automobile beam steel according to claim 1, characterized by: In step (6), after the rolling of the beam steel is completed, the strip with the newly added CTC_SGP code has a certain deviation in the coiling temperature control or the coiling temperature fluctuation of the strip is large. At this time, the problems are analyzed according to the model feedback log, and the model setting parameter table and the feedback control self-learning parameter table are optimized as a whole.

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