Control method for improving setting precision of coiling temperature of automobile beam steel

Abstract

The invention relates to a control method for improving automobile beam steel coiling temperature setting precision, and belongs to the technical field of hot continuous rolling plate production methods. According to the technical scheme, strip steel parameters under different cooling processes are independently set, a CTC model selects a corresponding model parameter table according to laminar cooling codes, the same steel type is controlled by applying the independent model table when the cooling processes are different, and mutual interference of model setting and self-learning is avoided. The method 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 the occurrence of product degradation quality accidents caused by the transformation of the girder steel cooling process is avoided.

Description

Technical Field

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

[0002] Hot-rolled beam steel is used in the longitudinal beams, lining beams, cross beams and other structural parts of the automobile chassis. During driving, the automobile is subjected to various impacts, torsion and other complex stresses, which places very stringent requirements on the service conditions of the frame, requiring the beam steel 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, resulting in cracking during stamping, difficult to control springback, and tearing and delamination during punching, which are common problems in major steel companies. The scrap rate is high, which seriously affects the application and promotion of high-strength automobile beam steel in the automobile manufacturing industry.

[0003] Hot-rolled beam steel adopts different cooling strategies according to the different parts of the strip used in the car to meet the needs of different users for product performance. Different laminar cooling codes will appear in two rolling plans of the same steel grade and specification. In hot rolling production, the strip has cooling processes such as front-stage interval cooling, front-stage 3 / 4 cooling, and rear-stage interval cooling in the laminar cooling area. Due to different cooling methods, the temperature drop of the strip in each area of ​​the laminar roller is different. There are certain differences in the performance of the strip under different cooling methods, which meets the needs of different users. The mathematical model sets the temperature control parameters of the strip according to the steel grade. The model self-learns the lower roll strip according to the control deviation of the upper roll strip, and the setting parameters are hereditary. In actual production, the model sets the cooling water volume deviation for different cooling processes. In the initial stage of hot-rolled beam steel rolling, the parameters of the strip with different cooling processes are set in the same CTC model table. Due to the change of cooling process, the strip coiling temperature deviates from the target temperature. The coiling temperature has a great influence on the metallographic structure of the hot-rolled strip, and is one of the main process parameters that determine the processing performance and mechanical properties of the finished strip. This control method results in inappropriate performance of beam steel products and is very likely to cause accidents such as steel piling. The failure rate in production is high and the damage to equipment is serious. Summary of the invention

[0004] The purpose of the present invention is to provide a control method for improving the setting accuracy of the coiling temperature of automobile beam steel. By individually setting the strip parameters under different cooling processes, the CTC model selects a suitable model parameter table according to the laminar cooling code, and uses a separate model table to control the same steel type when the cooling process is different, avoiding the mutual interference of model setting and self-learning, and the effect is significant. The problem of large deviation in strip coiling temperature control caused by different cooling processes is solved, and the occurrence of product degradation quality accidents caused by changes in the cooling process of beam steel is avoided, which effectively solves the above-mentioned problems existing in the background technology.

[0005] The technical solution of the present invention is: a control method for improving the setting accuracy of the coiling temperature of automobile beam steel, comprising the following steps: (1) Confirm the different laminar cooling methods in the beam steel rolling process and distinguish the different cooling codes; (2) Collect historical rolling data of beam steel. The cooling code with the largest number of rolled steel coils corresponds to the original steel grade code, that is, the steel grade code SGP in the FSU and FDTC mathematical models is the same as the steel grade code CTC_SGP in the CTC model; (3) Statistics are collected for the hot-rolled steel grade code CTC_SGP, and other cooling codes for beam steel are set to other unused codes; (4) Adjust the parameters of the model parameter table and model self-learning model table corresponding to the CTC_SGP steel grade code of the newly generated beam steel; (5) In actual production, after the beam steel rolling plan is issued, simulation calculations are performed on the strip steel corresponding to the newly added CTC_SGP steel grade code; (6) According to the temperature control deviation of beam steel coiling, the parameters of different model tables are optimized and adjusted accordingly.

[0006] In the step (1), historical rolling data is used to collect statistics on the laminar cooling process in beam steel production, the temperature drop of the strip in the laminar flow area is analyzed, the strip cooling process 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 flow area header are configured.

[0007] In the step (2), historical rolling data of beam steel is collected, strip steel is classified by cooling code in a data table, and the number of strip steel coils produced for each cooling code is counted; CTC_SGP steel grade codes used in the CTC mathematical model are counted in a database, CTC_SGP codes are classified, and some codes are divided out as expansion codes specifically for beam steel under different cooling methods.

[0008] 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 accuracy, the CTC_SGP steel grade code corresponding to the cooling code with the largest number of rolled coils is used as the original code, that is, the SGP of the strip steel in this cooling mode and the CTC_SGP are the same code; the CTC_SGP steel grade codes corresponding to the remaining cooling codes of the beam steel are set to the codes not used in the CTC model, so as to prevent the problem of mutual interference of parameters during rolling caused by two steel grades sharing one CTC_SGP code.

[0009] In the 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 parameter settings 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 accordingly to improve the prediction accuracy of the model.

[0010] In the step (5), the three-level system before beam steel production issues slab PDI information, including slab rolling target thickness, width, final rolling temperature, coiling temperature and laminar cooling code. After the plan is issued, the courseT of the slab is pre-calculated, the laminar cooling water volume and strip speed data in the model log file are checked, and the data is used to compare and analyze with the data of the rolled strip with good coiling temperature control, and the set parameters are adjusted.

[0011] In the step (6), after the beam steel is rolled, some newly added CTC_SGP code strips have certain deviations in coiling temperature control, or the coiling temperature of the strips fluctuates greatly. At this time, the existing 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.

[0012] The beneficial effects of the present invention are as follows: by individually setting the strip parameters under different cooling processes, the CTC model selects a suitable model parameter table according to the laminar cooling code, and a separate model table is used to control the same type of steel under different cooling processes, thereby avoiding mutual interference between model setting and self-learning, and the effect is significant. The problem of large deviation in strip coiling temperature control caused by different cooling processes is solved, and the occurrence of product degradation quality accidents caused by changes in the cooling process of beam steel is avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a work flow chart of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the invention implementation cases clearer, the technical solutions in the implementation cases of the present invention will be clearly and completely described below in conjunction with the drawings in the implementation cases. Obviously, the implementation cases described are only a small part of the implementation cases of the present invention, rather than all the implementation cases. Based on the implementation cases in the present invention, all other implementation cases obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0015] A control method for improving the setting accuracy of automobile beam steel coiling temperature comprises the following steps: (1) Confirm the different laminar cooling methods in the beam steel rolling process and distinguish the different cooling codes; (2) Collect historical rolling data of beam steel. The cooling code with the largest number of rolled steel coils corresponds to the original steel grade code, that is, the steel grade code SGP in the FSU and FDTC mathematical models is the same as the steel grade code CTC_SGP in the CTC model; (3) Statistics are collected for the hot-rolled steel grade code CTC_SGP, and other cooling codes for beam steel are set to other unused codes; (4) Adjust the parameters of the model parameter table and model self-learning model table corresponding to the CTC_SGP steel grade code of the newly generated beam steel; (5) In actual production, after the beam steel rolling plan is issued, simulation calculations are performed on the strip steel corresponding to the newly added CTC_SGP steel grade code; (6) According to the temperature control deviation of beam steel coiling, the parameters of different model tables are optimized and adjusted accordingly.

[0016] In the step (1), historical rolling data is used to collect statistics on the laminar cooling process in beam steel production, the temperature drop of the strip in the laminar flow area is analyzed, the strip cooling process 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 flow area header are configured.

[0017] In the step (2), historical rolling data of beam steel is collected, strip steel is classified by cooling code in a data table, and the number of strip steel coils produced for each cooling code is counted; CTC_SGP steel grade codes used in the CTC mathematical model are counted in a database, CTC_SGP codes are classified, and some codes are divided out as expansion codes specifically for beam steel under different cooling methods.

[0018] 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 accuracy, the CTC_SGP steel grade code corresponding to the cooling code with the largest number of rolled coils is used as the original code, that is, the SGP of the strip steel in this cooling mode and the CTC_SGP are the same code; the CTC_SGP steel grade codes corresponding to the remaining cooling codes of the beam steel are set to the codes not used in the CTC model, so as to prevent the problem of mutual interference of parameters during rolling caused by two steel grades sharing one CTC_SGP code.

[0019] In the 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 parameter settings 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 accordingly to improve the prediction accuracy of the model.

[0020] In the step (5), the three-level system before beam steel production issues slab PDI information, including slab rolling target thickness, width, final rolling temperature, coiling temperature and laminar cooling code. After the plan is issued, the courseT of the slab is pre-calculated, the laminar cooling water volume and strip speed data in the model log file are checked, and the data is used to compare and analyze with the data of the rolled strip with good coiling temperature control, and the set parameters are adjusted.

[0021] In the step (6), after the beam steel is rolled, some newly added CTC_SGP code strips have certain deviations in coiling temperature control, or the coiling temperature of the strips fluctuates greatly. At this time, the existing 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.

[0022] In practical applications, the present invention realizes the individual setting and self-learning of beam steel under different cooling modes by applying the newly added CTC_SGP steel grade code in the CTC mathematical model, which can effectively avoid the abnormal coiling temperature control caused by the change of the cooling mode of the beam steel, including the following process: 1. Setting of laminar cooling method for beam steel There are 6 cooling codes in the laminar cooling process of beam steel production, namely 1, 4, 6, 20, 21, and 22, which correspond to cooling methods such as front-stage interval cooling, front-stage concentrated cooling, and rear-stage interval cooling. The opening sequence of each cooling method is different, and the temperature drop of the strip in each area is quite different, so the cooling efficiency is set differently in the model table of each code. According to the coiling temperature control of beam steel in actual production, the opening sequence number of the cooling manifold corresponding to each code and the opening and closing of the manifold are configured in the model table. Taking cooling code 6 as an example, the cooling method is front-stage interval cooling. The opening sequence number of the upper manifold is set as 1.3.5...151 in the parameter table, and the opening sequence number of the lower manifold is set as 2,4,6...152. The last two groups of 21 and 22 manifolds are cooling water fine-tuning sections, and the opening sequence is from back to front; the first 7 groups of manifolds are set to open one and close one, and the strip is intermittently cooled. Other cooling methods are set according to the requirements of the cooling process.

[0023] 2. Beam steel rolling data collection A new data table is created to collect the rolling data of beam steel. The data table includes a large number of model settings and feedback control data such as the beam steel grade code SGP, the finishing mill threading speed, the CTC model steel grade code CTC_SGP, the laminar cooling code patCode, and the cooling efficiency. This table includes all the temperature control information of the rolled beam steel, and can quickly find the temperature control of the beam steel under different cooling codes. The used CTC_SGP is counted in the model database. There are 1,000 codes in total. In actual applications, the first 800 are used as general codes for mathematical models of various steel grades, namely the general codes for FSU, FDTC and CTC mathematical models, and the last 200 are extended codes in the CTC mathematical model that are applied according to the cooling characteristics of special steel grades.

[0024] 3. Setting of expansion code for beam steel CTC model In the newly created data table, the number of rolled coils of beam steel and the hit rate of strip temperature control under different cooling methods are counted. In order to reduce the influence of changing CTC_SGP steel code on the accuracy of model setting, the CTC_SGP steel code corresponding to the cooling code with the largest number of rolled coils is applied with the original code. Under this cooling method, the strip SGP and CTC_SGP are the same code, that is, the universal steel code for all mathematical models, and the CTC_SGP expansion code is set for the remaining cooling methods. Taking 700L beam steel as an example, the steel code SGP is 601. There are many rolled coils with cooling code 20 for this steel type, so the corresponding CTC_SGP code is 601 when cooling code 20 is set; other cooling codes such as cooling code 1 are set to 805 for CTC_SGP, and the SGP code is not changed to 601. On the basis of ensuring that the CTC_SGP code will not be reused, the CTC_SGP codes corresponding to the remaining cooling codes are set separately.

[0025] 4. Setting of CTC mathematical model parameters The newly created CTC_SGP steel grade code is used alone for setting CTC model parameters. The FSU and FDTC mathematical models still use the beam steel SGP code during calculations. The calling parameters have not changed, so there is no need to reset the FSU and FDTC model parameters. For the CTC model, the newly created CTC_SGP steel grade code is for rolling of new steel grades. This series of parameter tables are the initial values ​​of the model. To prevent the model from having large deviations in the coiling temperature setting, the called model parameters need to be reset. Taking the newly added CTC_SGP code 805 as an example, in actual operation, the parameters corresponding to the original 601 code are copied to the parameter table corresponding to 805. The parameter table includes cooling water cooling efficiency, cooling efficiency self-learning value zlw12, and model temperature self-learning value tmpVrnCt after strip rolling.

[0026] 5. New code beam steel pre-production simulation calculation The newly added CTC_SGP code beam steel is a new steel type in the CTC mathematical model. Taking the newly added CTC_SGP code 805 as an example, the cooling efficiency and other parameter tables corresponding to the original steel type code 601 are copied in actual application. Due to the large difference in cooling methods, there is still a large deviation in control, so it is necessary to simulate the strip steel before rolling. After the third level sends the PDI information of the beam steel with this cooling method, the HMI simulates the slab, and uses the CTC model log file generated by the calculation to compare the strip steel rolling speed and the number of laminar cooling water opening header groups with the previously rolled well-controlled strip steel data. If the data deviation is large, adjust the coiling temperature self-learning value and cooling efficiency self-learning value, etc. After adjustment, simulate again to ensure that the beam steel to be rolled is close to the set data of the previously well-controlled strip steel.

[0027] 6. New code beam steel post-production parameter optimization After the rolling of the beam steel with the newly created CTC model steel grade code is completed, the model table is further optimized according to the actual control of the strip coiling temperature in the log log generated by the model. Taking the newly added CTC_SGP code 805 beam steel as an example, when the strip coiling temperature 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 of 0.40 to 0.25, and the adjustment water volume of the laminar cooling water is reduced; the CTC mathematical model collects the strip final rolling temperature to feed forward the coiling temperature. When the strip final rolling temperature deviates from the target temperature and the CTC model sets too much water volume, causing the coiling temperature to overshoot, the feedforward setting gain value should be appropriately reduced. Taking the 6.0mm thickness specification as an example, the feedforward gain value is adjusted from the initial value of 0.30 to 0.25. Other parameters such as self-learning gain value, head and tail self-learning value, etc. are adjusted accordingly according to the coiling temperature control situation.

[0028] The present invention aims at the problem that the coiling temperature setting accuracy is low due to the variable cooling process of beam steel, and the strip parameters under different cooling processes are set separately, so that the CTC model selects the appropriate model parameter table according to the laminar cooling code, and controls the same steel type with a separate model table when the cooling process is different, avoiding the mutual interference of model setting and self-learning. The method has significant effects after optimization and implementation, and solves the problem of large deviation in strip coiling temperature control caused by different cooling processes, and avoids the occurrence of product downgrade quality accidents caused by the change of beam steel cooling process.

[0029] The above embodiments are only used to illustrate rather than limit the technical solutions of the present invention. Although the present invention is described in detail with reference to the above embodiments, those skilled in the art should understand that the present invention can still be modified or replaced by equivalents. Any modification or partial replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A control method for improving the setting accuracy of the coiling temperature of automobile beam steel, characterized in that The following steps are involved: Confirm the different laminar cooling methods in the beam steel rolling process and distinguish the different cooling codes; Collect historical rolling data of beam steel, the cooling code with the largest number of rolled steel coils corresponds to the original steel grade code, that is, the steel grade code SGP in the FSU and FDTC mathematical models is the same as the steel grade code CTC_SGP in the CTC model; Statistics are collected on the hot-rolled steel grade code CTC_SGP, and other cooling codes for beam steel are set to other unused codes; Adjust the parameters of the model parameter table and model self-learning model table corresponding to the CTC_SGP steel grade code of the newly generated beam steel; In actual production, after the beam steel rolling plan is issued, simulation calculation is performed on the strip steel corresponding to the newly added CTC_SGP steel grade code; According to the temperature control deviation of beam steel coiling, the parameters of different model tables are optimized and adjusted accordingly.

2. A control method for improving the setting accuracy of the coiling temperature of automobile beam steel according to claim 1, characterized in that: In the step (1), historical rolling data is used to collect statistics on the laminar cooling process in beam steel production, the temperature drop of the strip in the laminar flow area is analyzed, the strip cooling process 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 flow area header are configured.

3. A control method for improving the setting accuracy of the coiling temperature of automobile beam steel according to claim 1, characterized in that: In the step (2), historical rolling data of beam steel is collected, strip steel is classified by cooling code in a data table, and the number of strip steel coils produced for each cooling code is counted; CTC_SGP steel grade codes used in the CTC mathematical model are counted in a database, CTC_SGP codes are classified, and some codes are divided out as expansion codes specifically for beam steel under different cooling methods.

4. A control method for improving the setting accuracy of the coiling temperature of automobile beam steel according to claim 1, characterized in that: 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 accuracy, the CTC_SGP steel grade code corresponding to the cooling code with the largest number of rolled coils is used as the original code, that is, the SGP of the strip steel in this cooling mode and the CTC_SGP are the same code; the CTC_SGP steel grade codes corresponding to the remaining cooling codes of the beam steel are set to the codes not used in the CTC model, so as to prevent the problem of mutual interference of parameters during rolling caused by two steel grades sharing one CTC_SGP code.

5. A control method for improving the setting accuracy of the coiling temperature of automobile beam steel according to claim 1, characterized in that: In the 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 parameter settings 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 accordingly to improve the prediction accuracy of the model.

6. A control method for improving the setting accuracy of the coiling temperature of automobile beam steel according to claim 1, characterized in that: In the step (5), the three-level system before beam steel production issues slab PDI information, including slab rolling target thickness, width, final rolling temperature, coiling temperature and laminar cooling code. After the plan is issued, the courseT of the slab is pre-calculated, the laminar cooling water volume and strip speed data in the model log file are checked, and the data is used to compare and analyze with the data of the rolled strip with good coiling temperature control, and the set parameters are adjusted.

7. A control method for improving the setting accuracy of the coiling temperature of automobile beam steel according to claim 1, characterized in that: In the step (6), after the beam steel is rolled, some newly added CTC_SGP code strips have certain deviations in coiling temperature control, or the coiling temperature of the strips fluctuates greatly. At this time, the existing 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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