Continuous annealing facility, continuous annealing method, method for producing cold-rolled steel sheet, and method for producing plated steel sheet

Abstract

Provided are a continuous annealing facility, a continuous annealing method, a method for manufacturing a cold-rolled steel sheet, and a method for manufacturing a plated steel sheet, whereby the phase fraction of a steel sheet in a high-temperature state is predicted with high accuracy and fluctuations in the predicted phase fraction are quickly reflected in annealing conditions. This continuous annealing facility for a steel plate is provided with a heating zone (6), a soaking zone (7), and a cooling zone (8) in this order, and is provided with: at least one induction heating device (9) between the soaking zone (7) and the cooling zone (8); and a control device for setting the operating conditions of the induction heating device on the basis of the phase fraction during annealing obtained from a phase fraction prediction model.

Description

Technical Field

[0001] The present disclosure relates to a continuous annealing device, a continuous annealing method, a method for manufacturing a cold-rolled steel sheet, and a method for manufacturing a plated steel sheet. The present disclosure particularly relates to a continuous annealing device, a continuous annealing method, a method for manufacturing a cold-rolled steel sheet, and a method for manufacturing a plated steel sheet for manufacturing a high-strength steel sheet used in structural materials for automobiles, etc. Background Art

[0002] In the production of thin steel sheets for automobiles, the continuously cast slabs are extensively processed by hot rolling and cold rolling before reaching the final thickness. In the subsequent annealing treatment, the cold worked structure is restored, recrystallized, grains grow, and the phase transformation structure is controlled to adjust the balance between strength and workability.

[0003] In recent years, in the annealing process, a continuous annealing furnace is generally used, which continuously heats, soaks and cools the steel sheets while conveying the steel sheets connected in a strip shape. In addition, hot-dip galvanizing and over-aging treatment are performed after the cooling, depending on the use of the steel sheets.

[0004] The heating mechanism of the annealing furnace is generally a radiant tube burner that heats a metal tube (radiant tube) by gas combustion and indirectly heats the steel sheet by its radiant heat. In addition, in the manufacture of hot-dip galvanized steel sheets, in order to ensure the coating properties, a direct-fire furnace is sometimes used in the front section of the heating furnace to heat the steel sheet by directly spraying the flame of gas combustion onto the steel sheet.

[0005] Since the radiant tube furnace uses the radiant heat from the radiant tube and the furnace wall to heat the steel plate, the volume of the heat source is very large and the thermal inertia is large. Therefore, it is difficult to quickly follow the changes in the set temperature. In addition, in the final stage of heating, the temperature rise rate of the steel plate is slow, and a certain amount of time is required to control the structure, so the required furnace length is extended, the thermal inertia becomes larger, and the tracking of the target temperature is delayed. As a result, during the continuous processing of the coil, the temperature of a part of the steel plate is not within the specified annealing temperature range, which will cause problems such as reduced yield due to deviations in mechanical properties and reduced productivity due to changes in the production line speed used to control the temperature.

[0006] In addition, the phase transformation behavior of the steel sheet is not determined solely by the temperature, but is also affected by the conditions of the previous processes, namely casting, hot rolling, and cold rolling. Therefore, even if the annealing temperature is controlled with high precision, there are cases where the mechanical properties of the final product exceed the target range. In order to solve this problem, it is preferable to understand the phase fraction of the steel sheet structure based on annealing.

[0007] In response to the above problems, Patent Document 1 discloses the following technology: by providing an induction heating device between the preheating zone and the heating zone (direct-fire heating furnace), or in the middle of a heating zone composed of multiple direct-fire heating furnaces, the heating capacity is compensated, thereby improving responsiveness. Patent Document 2 discloses a technology for grasping the phase fraction of a steel plate based on changes in magnetic properties. Patent Document 3 discloses the following technology: predicting the phase fraction change of a steel plate based on the measurement results of the steel plate temperature, and controlling the annealing conditions to obtain a structure with desired mechanical properties.

[0008] Prior art literature

[0009] Patent Literature

[0010] Patent Document 1: Japanese Patent Application Laid-Open No. 11-061277

[0011] Patent Document 2: Japanese Patent Application Publication No. 2000-144262

[0012] Patent Document 3: Japanese Patent Application No. 2020-509243 Summary of the invention

[0013] However, in order to prevent excessive oxidation of the steel plate surface, the technology of Patent Document 1 has a low steel plate temperature at the exit of the direct-fire heating furnace, and needs to be heated by a radiation tube furnace. Therefore, it is difficult to shorten the furnace length, and the influence range of the furnace temperature setting change is wide. In addition, although the influence of furnace temperature fluctuation can be absorbed by high-speed control of the output of the induction heating device, the direct-fire heating furnace after the induction heating device focuses on controlling the oxidation of the steel plate, and it is difficult to flexibly control the steel plate temperature. Therefore, the effect of absorbing the influence of furnace temperature fluctuation cannot be fully exerted.

[0014] The technology of Patent Document 2 measures the change in the phase fraction of the steel sheet and can control the manufacturing conditions based on the measurement results. However, the controllability of the conventional annealing furnace is low. That is, even if the phase transformation rate can be measured, the controllability of the furnace cannot keep up when the annealing conditions are greatly changed.

[0015] Although the technology of Patent Document 3 can predict the phase fraction, the predicted result of the phase fraction must be reflected in the cooling conditions to control the structure. If only the cooling conditions are controlled, the responsiveness is low and it is difficult to achieve high speed and high precision.

[0016] In view of the above problems, the present disclosure aims to provide a continuous annealing device, a continuous annealing method, a method for manufacturing a cold-rolled steel sheet, and a method for manufacturing a plated steel sheet, which can predict the phase fraction of a steel sheet in a high temperature state with high accuracy and quickly reflect the fluctuation of the predicted phase fraction in the annealing conditions.

[0017] (1) A continuous annealing facility according to one embodiment of the present disclosure is a continuous annealing facility for steel plates that includes a heating zone, a soaking zone, and a cooling zone in sequence, and includes:

[0018] at least one induction heating device between the soaking zone and the cooling zone, and

[0019] A control device for setting operating conditions of the induction heating device based on the phase fraction during annealing obtained by the phase fraction prediction model.

[0020] (2) As one embodiment of the present disclosure, in (1), the phase fraction prediction model is a machine learning model generated using teacher data with the composition, size and temperature of the steel plate and the operating conditions of the continuous annealing furnace as input variables and the phase fraction of the steel plate during annealing as output variables.

[0021] (3) As one embodiment of the present disclosure, in (2),

[0022] The size of the steel plate includes the thickness of the steel plate.

[0023] The temperature of the steel plate includes the temperature of the steel plate before heating starts and the maximum temperature reached by the induction heating device.

[0024] The operating conditions of the continuous annealing furnace include a conveying speed of the steel sheet.

[0025] (4) A continuous annealing method according to an embodiment of the present disclosure is a continuous annealing method for a steel plate that passes through a heating zone, a soaking zone, and a cooling zone in sequence, comprising the following steps:

[0026] At least one induction heating device is provided between the soaking zone and the cooling zone.

[0027] The operating conditions of the induction heating device are set based on the phase fraction during annealing obtained by the phase fraction prediction model.

[0028] (5) As one embodiment of the present disclosure, in (4),

[0029] The phase fraction prediction model is a machine learning model generated using teacher data having the composition, size, temperature of the steel plate and the operating conditions of the continuous annealing furnace as input variables and the phase fraction of the steel plate during annealing as an output variable.

[0030] (6) As one embodiment of the present disclosure, in (5),

[0031] The size of the steel plate includes the thickness of the steel plate.

[0032] The temperature of the steel plate includes the temperature of the steel plate before heating starts and the maximum temperature reached by the induction heating device.

[0033] The operating conditions of the continuous annealing furnace include a conveying speed of the steel sheet.

[0034] (7) As one embodiment of the present disclosure, in any one of (4) to (6),

[0035] The induction heating device heats the steel plate at 10°C / sec to 200°C / sec.

[0036] The steel plate starts cooling in the cooling zone within 10 seconds after the heating by the induction heating device is completed.

[0037] (8) A method for producing a cold-rolled steel sheet according to one embodiment of the present disclosure is to anneal the steel sheet as the cold-rolled steel sheet by an annealing method adjusted by the continuous annealing method of (7).

[0038] (9) A method for producing a plated steel sheet according to one embodiment of the present disclosure includes subjecting the surface of the steel sheet annealed by the method for producing a cold-rolled steel sheet according to (8) to a plating treatment.

[0039] (10) As one embodiment of the present disclosure, in (9),

[0040] The above-mentioned plating treatment is electrogalvanizing treatment, hot-dip galvanizing treatment or alloy hot-dip galvanizing treatment.

[0041] According to the present disclosure, a continuous annealing device, a continuous annealing method, a method for manufacturing a cold-rolled steel sheet, and a method for manufacturing a plated steel sheet can be provided, which can predict the phase fraction of a steel sheet at a high temperature with high accuracy and quickly reflect the fluctuation of the predicted phase fraction in the annealing conditions. Therefore, compared with the conventional annealing furnace, a thin steel sheet having target mechanical properties can be stably manufactured. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram showing a hot-dip galvanizing process provided with a continuous annealing facility according to one embodiment.

[0043] Figure 2 This is a diagram showing an example of the temperature history of a steel plate.

[0044] Figure 3 This is a flow chart showing an example of a continuous annealing method. DETAILED DESCRIPTION

[0045] Hereinafter, a continuous annealing facility, a continuous annealing method, a method for producing a cold-rolled steel sheet, and a method for producing a plated steel sheet according to an embodiment of the present disclosure will be described with reference to the drawings.

[0046] <Facility Configuration>

[0047] Figure 1 A part of the hot-dip galvanizing process of the continuous annealing equipment of this embodiment is shown. In this embodiment, the steel material manufactured by the hot-dip galvanizing process is a thin steel plate. In addition, the manufactured steel material is a cold-rolled steel plate. Figure 1 The arrow indicates the direction of travel of the production line. In the following, the upstream side of the travel direction is sometimes expressed as "front" and the downstream side is expressed as "rear". The continuous annealing equipment includes a uncoiler 1, a welding machine 2, an electrolytic cleaning device 3, an inlet-side looper 4, a preheating zone 5, a heating zone 6, a soaking zone 7 and a cooling zone 8. In addition, the continuous annealing equipment is equipped with an induction heating device 9 (Induction Heating device, hereinafter sometimes referred to as "IH"). The continuous annealing equipment is equipped with at least one induction heating device 9. In the present embodiment, the cooling zone 8 includes a first cooling zone 8A and a second cooling zone 8B. In addition, the continuous annealing equipment may further include a galvanizing tank (zinc pot 11) for immersion cooling of thin steel plates to a specified temperature, an alloying zone, a holding zone, a final cooling zone, a tempering rolling device, an outlet-side looper, a tension coiler, and the like.

[0048] The thin steel plate wound into a coil in the previous process is unwound by the uncoiler 1. The unwound thin steel plate passes through the preheating zone 5 and enters the continuous annealing furnace. The continuous annealing equipment is composed of a heating zone 6, a soaking zone 7 and a cooling zone 8 in sequence after the preheating zone 5. In the continuous annealing equipment, the induction heating device 9 is arranged between the soaking zone 7 and the cooling zone 8.

[0049] In addition, the continuous annealing equipment includes a mechanism for adjusting the output of the induction heating device 9 based on the predicted value of the γ phase fraction obtained by the phase transformation rate prediction model. Figure 1 The structure can be deformed in accordance with the target quality of the steel plate to be manufactured. For example, a zinc pot 11 and an alloying belt can be set after the continuous annealing furnace, or the zinc pot 11 can be omitted. In addition, the continuous annealing equipment can be partially divided. For example, the soaking zone 7 can be divided so that the divided soaking zones 7 can control the atmosphere or temperature independently.

[0050] <Details of components>

[0051] Figure 2 The temperature history of a steel plate when annealing is performed using a conventional annealing furnace and when annealing is performed using an annealing furnace according to the present embodiment is shown. The vertical axis is the temperature of the steel plate. The horizontal axis is the passing time. The production line speed is 100 mpm. In addition, the thickness of the steel plate is 1 mm. The temperature history in the present embodiment is represented by a solid line, and the corresponding process is recorded below. In addition, the temperature history of the prior art is represented by a dotted line, and the corresponding process is recorded above. Figure 2 As shown, in the annealing furnace of this embodiment, according to the structure described below, the process time is also shorter than that of the conventional technology.

[0052] (Pre-heat zone)

[0053] The steel sheet fed from the uncoiler 1 at room temperature to about 100° C. first enters the preheating zone 5 and is heated to about 200° C. The temperature of the steel sheet can be simply increased by heating. In this embodiment, the preheating zone 5 uses the high-temperature exhaust gas generated in the heating zone 6 .

[0054] (Heating belt)

[0055] Next, the steel plate enters the heating zone 6, and the temperature of the steel plate is heated to about 600-700°C. The temperature of the thin steel plate can be simply increased by heating. In this embodiment, the heating zone 6 adopts a direct-fire heating furnace method in order to heat up to a certain temperature in a short time and control the surface state. The direct-fire heating furnace not only has a high heating capacity and can reduce the volume of the furnace, but also can flexibly control the redox reaction on the surface of the steel plate in consideration of the subsequent plating process.

[0056] (tropical zone)

[0057] The main function of the soaking zone 7 is to recrystallize the α phase. However, there may also be a portion passing through the A1 phase transformation point at this time. As a heating method for the soaking zone 7, it is preferred to adopt a radiation heating (radiant tube heating) method based on gas combustion due to its high efficiency and heating uniformity. The soaking zone 7 can be composed of a larger single furnace shell. In addition, the soaking zone 7 can be a structure having more than two partitions, each partition is separated by an insulating wall, etc., and is maintained and heated in a target temperature area determined by each partition. In this embodiment, the soaking zone 7 is maintained or slowly heated in a temperature range (about 600 to 730°C) lower than the A1 phase transformation point of the steel plate, ensuring the residence time in the recrystallization temperature range, and suppressing the residue of the unrecrystallized α phase. Here, the A1 phase transformation point is the temperature at which the austenite phase transformation occurs. As an example, the furnace temperature is set to 730°C. In other words, the austenite phase begins to appear at a temperature above the A1 phase transformation point.

[0058] Here, if the temperature of the steel sheet is too low in the soaking zone 7, the recrystallization of the α phase will not proceed, and sufficient workability cannot be obtained. On the other hand, if the temperature of the steel sheet in the soaking zone 7 is above the A1 transformation point, the α recrystallized grains become coarse and the strength is insufficient. The A1 transformation point may fluctuate slightly depending on the composition of the steel sheet. Therefore, the recrystallization temperature range is preferably predicted in advance by measurement, calculation or simulation, and set after considering the error range in annealing.

[0059] If the residence time of the steel plate in the soaking zone 7 is too short, the recrystallization of the α phase will not proceed sufficiently. If it is too long, the crystal grains will become coarse, resulting in reduced mechanical properties. Therefore, the optimal residence time was studied experimentally in advance, and the results showed that it takes about 20 to 60 seconds. This is because if it is less than 20 seconds, the recrystallization of the α phase will not proceed sufficiently, and the workability will be poor. If it exceeds 60 seconds, local coarse crystal grains will be generated, resulting in uneven strength. Therefore, in the soaking zone 7, the temperature of the steel plate is maintained in a temperature range below the A1 phase transformation point for 20 to 60 seconds.

[0060] When slow heating is performed in the soaking zone 7, the residence time in the recrystallization temperature region only needs to ensure the above time. However, when slow heating is performed in the soaking zone 7, the heating rate is preferably 5°C / second or less. This is because if rapid heating is performed exceeding 5°C / second, it becomes impossible to suppress the retention of the non-recrystallized α phase.

[0061] In the soaking zone 7, after ensuring the residence time in the recrystallization temperature range, the temperature may be slowly heated to a temperature higher than the A1 transformation point if the temperature is lower than 750° C. However, the heating rate is preferably 5° C. / sec or lower.

[0062] Although the continuous annealing equipment of the present embodiment is provided with a soaking zone 7 of a radiant tube furnace, it can of course be configured without such a soaking zone 7 as long as it has a function of ensuring a heat retention time sufficient for the recrystallization of the α phase. For example, the soaking zone 7 is not limited to a radiant tube furnace. In addition, the continuous annealing equipment may have a function of ensuring a heat retention time of the heating zone 6 sufficient for the recrystallization of the α phase without having a soaking zone 7. In addition, the continuous annealing equipment may have a heat retention device instead of the soaking zone 7. In the case where the continuous annealing equipment does not have a soaking zone 7, the induction heating device 9 only needs to be provided between the portion having the function of the soaking zone 7 (the portion or the heat retention device that ensures the heat retention time of the heating zone 6 in the above example) and the cooling zone 8.

[0063] (Induction Heating Device)

[0064] Next, the induction heating device 9 adjusts the output so that the temperature of the steel plate is contained in a temperature range (about 750 to 900°C) above the A1 transformation point and below the A3 transformation point to quickly heat the steel plate. Here, the A3 transformation point is a temperature that can suppress the upper limit of the γ phase fraction. The purpose of this process is to uniformly heat the entire steel plate to a temperature above the A1 transformation point in a short time, which also contributes to the miniaturization of the entire equipment. Here, the A1 transformation point and the A3 transformation point fluctuate slightly depending on the composition of the steel plate. Therefore, the appropriate temperature range is preferably predicted in advance by measurement, calculation or simulation, and set after considering the error range in annealing.

[0065] In DP steel, which is one of the objects of the continuous annealing equipment of this embodiment, the reaching temperature of the induction heating device 9 has a great influence on the mechanical properties of the final product. Therefore, the heating method of the induction heating device 9 needs to respond quickly to the temperature control command. In addition, when heating in such a temperature range, since the Curie point of the magnetic change of the steel plate is exceeded, it is preferred that the induction heating device 9 is a horizontal type.

[0066] In addition, since the recrystallization region of the α phase is maintained in the soaking zone 7, if the heating takes time, it will lead to the coarsening of the α grains, so an induction heating device 9 that can heat quickly is also preferred. The induction heating device 9 can heat up at 10°C / second to 200°C / second. This is because if it is less than 10°C / second, it will lead to the coarsening of the α grains, and if it is greater than 200°C / second, a local high-temperature portion will be generated in the width direction and uniformity cannot be maintained. The induction heating device 9 is more preferably heated at 20°C / second to 100°C / second. This is because if the heating rate is more than 20°C / second, the length of the production line can be further shortened, and if it is less than 100°C / second, the risk of buckling and deformation of the steel plate due to thermal stress can be further reduced.

[0067] If the target annealing temperature is quickly heated to the target annealing temperature by the induction heating device 9, the phase transformation from the α phase to the γ phase immediately after heating will not reach the equilibrium state. However, if it is kept near the target annealing temperature for too long, the phase transformation from the α phase to the γ phase will proceed to a higher degree than necessary, making the material control complicated. Therefore, after reaching the target annealing temperature, it is best to enter the cooling zone 8 as quickly as possible with a holding time of 0 seconds, and it is preferred to start cooling within 5 seconds. In order to avoid a non-equilibrium state, even if it is kept, cooling must be started within 10 seconds at the latest. That is, the steel plate starts cooling in the cooling zone 8 within 10 seconds after the heating by the induction heating device 9 is completed.

[0068] In order to meet such conditions, the induction heating device 9 is arranged at the connection portion between the soaking zone 7 and the cooling zone 8. The induction heating device 9 is arranged at the connection portion, and the induction heating device 9 can also be added to the existing furnace body.

[0069] (Cooling Zone)

[0070] The cooling zone 8 is a device for cooling the steel plate to a predetermined temperature, and as a cooling mechanism, jet cooling, roller cooling, water cooling (water quenching), etc. can be used. As in the present embodiment, the cooling zone 8 can be divided into a plurality of zones such as a first cooling zone 8A and a second cooling zone 8B, and different cooling mechanisms can be combined, and the cooling conditions of the same cooling mechanism can be changed to control the thermal history of the steel plate during cooling.

[0071] (thermometer)

[0072] For example, a thermometer 12 for measuring the surface temperature of the steel plate may be provided at the connection portion of each belt. The thermometer 12 may be used to grasp the approximate temperature history of the steel plate during heat treatment. For example, in the case where the equipment is long like the soaking zone 7, a thermometer 12 may be provided inside the belt in order to confirm the temperature history in the middle. There is no particular limitation on the method of temperature measurement. For example, a radiation thermometer that measures the temperature by detecting infrared rays emitted by the steel plate is suitable. In the case of a radiation thermometer, since it is sometimes affected by the reflected light of infrared rays emitted by the surrounding furnace body, a cover may be provided between the measuring part of the radiation thermometer and the detecting part of the steel plate. In addition, since it is sometimes affected by the emissivity of the steel plate surface, a multi-reflection measurement method utilizing the wedge-shaped space between the conveying rollers in the furnace and the steel plate may be adopted.

[0073] (Hot dip galvanizing bath)

[0074] A hot dip galvanizing bath may be provided after the cooling zone 8, and hot dip galvanizing may be performed on the steel sheet discharged from the cooling zone 8. Hot dip galvanizing may be performed according to a conventional method, and a nozzle, a roll in the bath, etc. may be provided as necessary.

[0075] (Alloying equipment)

[0076] An alloying treatment device may be provided after the hot-dip galvanizing bath. The alloying treatment device is used to heat the steel sheet to perform the alloying treatment. The alloying treatment may be performed according to a conventional method.

[0077] (Other equipment)

[0078] Following the alloying equipment, in order to improve the quality of the final product and achieve manufacturing stability and efficiency, it is possible to further have a heat preservation zone, a final cooling zone, a tempering rolling equipment, a straightening machine, an exit side looper, a tension coiler, etc. These equipments can be set up and used according to the quality required for the product without any particular limitation.

[0079] <Phase fraction prediction model>

[0080] In order to predict the phase fraction of the steel sheet during annealing, a phase fraction prediction model is used. The phase fraction prediction model uses various input variables of the phase transformation of the steel sheet to predict the phase fraction of the steel sheet with high accuracy.

[0081] In this embodiment, the phase fraction prediction model uses operating conditions and temperature as input variables, and the phase fraction of the steel plate during annealing as an output variable. In the generation of the phase fraction prediction model, a database of the upper computer that stores the actual result values ​​and set values ​​(hereinafter also referred to as "actual result data") during the operation of the annealing process of the thin steel plate is used. According to the actual result data stored in the database, a plurality of teacher data (learning data) are prepared, which use the actual result values ​​of the operating conditions and the temperature history in the annealing furnace as input actual result data sets and the phase fraction in annealing corresponding to these operating conditions and temperature history as output actual result data. Then, the phase fraction prediction model is generated by machine learning using a plurality of teacher data. Here, in this embodiment, the phase fraction prediction model also includes information about the steel plate as an input variable. That is, the phase fraction prediction model, for example, uses the composition, size and temperature of the steel plate and the operating conditions of the continuous annealing furnace as input variables, and uses the phase fraction of the steel plate during annealing as an output variable.

[0082] (Input variable)

[0083] In this embodiment, when constructing a machine learning model, the input variables specifically used are (a) "alloy composition and thickness of the steel plate", (b) "operating parameters", and (c) "temperature history of the steel plate".

[0084] Regarding (a) above, "alloy composition" is the composition contained in the steel plate, which is information that determines the phase transition temperature and the phase fraction before and after the phase transition. "Steel plate thickness" is information that affects the heating and cooling rates during annealing by the thickness of the steel plate transported to the annealing furnace. For example, the size of the steel plate as an input variable of the phase fraction prediction model includes the thickness of the steel plate.

[0085] Regarding the above (b), the "operating parameters" include the conveying speed of the steel sheet conveyed in the annealing furnace as a necessary item. This is because the conveying speed of the steel sheet affects the annealing conditions such as the heating rate, cooling rate and soaking time. For example, the operating conditions of the continuous annealing furnace, which are the input variables of the phase fraction prediction model, include the conveying speed of the steel sheet. The "operating parameters" may include, for example, the pass schedule of other hot rolling processes, the cooling start temperature of the hot rolling output roller, the cooling end temperature of the hot rolling output roller, the coiling temperature of the hot rolling coil and the pass schedule of cold rolling, the set furnace temperature of the annealing furnace, and at least one of the gas supply to the burner. Based on these items, the processing rate of the steel sheet (i.e., the processing strain before annealing) and the organizational state before annealing can be grasped, and the prediction accuracy of the phase transformation temperature and the phase fraction is improved. In particular, the coiling temperature of the hot rolling coil is useful. In addition, the "operating parameters" may include the cold rolling reduction rate as a useful variable. As actual result values ​​of these items, information on the material charged into the annealing furnace in the test equipment is used, but information when rolling is performed on an ingot produced in a laboratory melting furnace may also be used.

[0086] Regarding the above (c), the "temperature history of the steel plate" is the surface temperature of the steel plate measured in the annealing furnace. The "temperature history of the steel plate" includes the temperature of the steel plate before the start of heating and the maximum temperature reached by the induction heating device 9 as essential items. The temperature of the steel plate before the start of heating can be the temperature of the steel plate measured at a location corresponding to the entrance of the annealing equipment, the entrance of the heating zone 6, the entrance of the soaking zone 7, or the entrance of the induction heating device 9. The more measurement points there are, the more accurately the phase change temperature can be grasped, so the prediction accuracy is improved. For example, the temperature of the steel plate as an input variable of the phase fraction prediction model includes the temperature of the steel plate before the start of heating and the maximum temperature reached by the induction heating device 9. Here, the "temperature history of the steel plate" can be data of discrete temperatures measured or calculated at the connection part of the constituent device of the annealing furnace. In addition, in order to improve the prediction accuracy and yield, a model for predicting the surface temperature of the steel plate at an unmeasured location can be introduced. The model for predicting the surface temperature of the steel plate (hereinafter referred to as the "steel plate temperature prediction model") can be a physical model for numerical analysis or a machine learning model. In the case of a machine learning model, teacher data can be used that uses the annealing furnace operating conditions (production line speed, furnace temperature at each position of the heating zone 6 and the soaking zone 7), the alloy composition and cross-sectional shape (plate thickness, plate width) of the steel plate as input actual result data and the measurement results of the steel plate temperature at each position in the annealing furnace as output actual result data. The steel plate temperature prediction model is generated by machine learning using such teacher data. The input data of the steel plate temperature prediction model may further include information on upstream processes. The information on upstream processes may include, for example, rolling conditions such as the reheating temperature of the hot rolling process and the pass schedule of the cold rolling process, the cooling start temperature and cooling stop temperature on the finishing rolling outlet side of the hot rolling process, and at least one of the cooling conditions such as the coiling temperature.

[0087] (Output variable)

[0088] In this embodiment, when constructing a machine learning model, the output variable uses a specifically measured phase fraction. Although the determination of the phase fraction is carried out by conducting a steel plate annealing experiment as a laboratory test and obtaining the phase fraction based on the microstructure observation, the method is not limited. For example, the annealing conditions can be changed to conduct experiments and a data table of the phase fraction under various annealing conditions can be prepared in advance. In this embodiment, in order to predict the phase fraction at the highest reaching temperature of the induction heating device 9, the steel plate that has reached the highest reaching temperature in the laboratory test is quenched to freeze the microstructure, and the phase fraction is obtained by cross-sectional microstructure observation. In addition, the phase fraction before cooling can be obtained by changing various conditions in the annealing furnace of the actual equipment and then annealing and cooling the steel plate, and observing the cross-sectional microstructure. In addition to obtaining the phase fraction in the annealing through actual operation or laboratory experiments, it can also be obtained through numerical analysis using a physical model. Either method can obtain the thermal history and phase fraction of the steel plate in the annealing relative to the operating conditions. Here, the phase fraction of the steel plate in the annealing particularly refers to the phase fraction of the steel plate just after the annealing of the induction heating device 9 is completed and before the cooling begins.

[0089] In summary, although the generation of the phase fraction prediction model can be performed, the collection of the actual result data is performed offline. As the actual result data, the thermal history and phase fraction of the operating condition data set with various changes in the operating conditions are obtained, and a plurality of teacher data based on the actual result data are prepared in the storage device or the above-mentioned database.

[0090] (Machine Learning)

[0091] There is no limitation on the method of generating a machine learning model as long as it can make predictions with the accuracy required for practical use. For example, commonly used methods such as neural networks (including deep learning), decision tree learning, random forests, support vector regression, etc. can be used. In addition, an integrated model that combines multiple methods can be used. In addition, as a phase fraction prediction model, a machine learning model that binarizes the output can be used. The machine learning model is not a calculated value of the phase fraction of the steel plate, but a judgment on whether it is within a predetermined allowable range of the phase fraction (qualified or unqualified). In this case, a classification model method such as the k-nearest neighbor method or logistic regression can be used.

[0092] <Operation method (control method)>

[0093] In this embodiment, the operating conditions of the induction heating device 9 are adjusted based on the phase fraction during annealing obtained (predicted) by the phase fraction prediction model. By performing such control, the annealing temperature of the steel sheet can be adjusted, and a product with stable mechanical properties can be manufactured.

[0094] Figure 3This is a flowchart showing an example of a continuous annealing method performed by the above-mentioned continuous annealing equipment in this embodiment. A control method when heat treating a steel plate using the above-mentioned phase fraction prediction model will be described.

[0095] (Setting of initial conditions and prediction of phase fractions)

[0096] The initial setting of the operating conditions is performed before the product to be controlled is loaded into the annealing furnace. The operating conditions of the annealing furnace can use the conditions prepared in advance as standard conditions for the size and grade of each product, and can adopt the actual results of past production.

[0097] (Phase fraction determination and condition correction)

[0098] The predicted value of the phase fraction for the initial setting obtained by the above-mentioned phase fraction prediction model ( Figure 3 The predicted value of the phase fraction is compared with the target phase fraction required by the product (steel plate). If the predicted value exceeds the set allowable range ( Figure 3 If the output and temperature of the induction heating device 9 with high responsiveness and control accuracy can be changed by changing the IH temperature rise amount ( Figure 3 If the answer to "Can IH operating conditions be met?" is Yes), the changed temperature rise is included in the input data set, and the phase fraction prediction is compared with the target again.

[0099] If such prediction, determination and resetting are repeated and the phase fraction falls within the target range ( Figure 3 If the value in "Is it within the target range?" is Yes), the operating condition is reflected in the control. If it does not fall within the target range ( Figure 3 If "Within IH output control range" is No), the operating conditions of cooling zone 8 are also changed accordingly ( Figure 3 The responsiveness to changes in operating conditions is low compared to the induction heating device 9, but by controlling the above control flow in combination with the induction heating device 9, the deviation of the mechanical properties of the product from the target can be minimized.

[0100] In this way, prediction, determination, and resetting are repeated until the phase fraction is within the target range. Once the operating conditions are determined, the determined operating conditions are output to the control device of the annealing furnace and reflected in the annealing conditions of the product.

[0101] (Update in progress)

[0102] The above-mentioned prediction, determination and reset process is used to perform the initial setting of the operating conditions before the heat treatment of the product. Then, when the heat treatment of the product actually starts, the assumed thermal history may deviate due to the furnace temperature fluctuation caused by the fluctuation of the production line speed or the accuracy of the combustion control of the annealing furnace. Therefore, even after the heat treatment of the product starts, the newly obtained actual result value ( Figure 3 The phase fraction prediction model is updated based on the value of the “temperature gauge in the annealing furnace”, etc.

[0103] (Target materials)

[0104] The continuous annealing equipment of the present embodiment is an equipment for annealing steel materials including DP steel to stably obtain target mechanical properties. Specific examples of the target steel plates include, in terms of weight %, steel plates containing 0.03% to 0.25% C, 0.01% to 2.50% Si, 0.50% to 4.00% Mn, 0.100% or less P, 0.0500% or less S, 0.005% to 2.000% Sol.Al, 0.100% or less N, Cr, Cu, Ni, Sb, Sn (all 1.00% or less), Mo, V, Ti, Nb (all 0.50% or less), Ta (0.10% or less), Mg, Zr (all 0.050% or less), B, Ca, REM (all 0.0050% or less) as required, and the remainder is composed of Fe and unavoidable impurities. However, the target steel sheet is not limited as long as the dual-phase structure of the α phase and the γ phase needs to be controlled.

[0105] Here, the steel sheet may be subjected to plating, alloying, temper rolling, and shape correction treatment after annealing in the continuous annealing equipment. The plating and alloying treatments may be conventional methods for satisfying the quality required for the surface characteristics of the product and are not particularly limited.

[0106] In addition, if the shape is irregular, it can be straightened by continuing the temper rolling and passing the plate through the straightening machine. The temper rolling and straightening can be performed without affecting the mechanical properties of the steel plate and only correcting the shape.

[0107] (Example)

[0108] Table 1 shows the results of using the conventional continuous annealing equipment and the continuous annealing equipment of the present embodiment (see Figure 1) Conditions and results of manufacturing thin steel plates. In order to investigate the deviation of the mechanical properties of the products, 30 sheets of each product of multiple strength grades were manufactured in coils. The manufacturing strength grades were 780MPa, 980MPa, and 1180MPa, and 10 sheets of each of the three thicknesses of 1.0, 1.5, and 2.0mm were manufactured at each strength grade. These 10 slabs of each grade and each thickness were cast in different batches in a continuous casting machine. Therefore, although it was within the scope of manufacturing management, there were deviations in the chemical composition of each slab, and the phase change behavior became uneven.

[0109]

[0110] Each slab is subjected to hot rolling, pickling, annealing and cold rolling as required by conventional methods, and then heat treated by laboratory annealing, conventional annealing equipment and the annealing equipment disclosed in the present invention, and then subjected to post-treatment such as cooling and plating. The conveying speed of the steel plate is 60 to 120 mpm. However, in the case of Inventive Example 1, the conveying speed of the steel plate is 30 to 150 mpm. The temperature of the steel plate at the outlet of the heating zone 6 (direct fire heating) falls within the range of 600 to 700°C to control the redox reaction on the surface. Then, it is introduced into the soaking zone 7 of the radiant tube method for further heating. The residence time in the recrystallization zone is 20 to 60 seconds. The heating / soaking time after the recrystallization zone is 100 to 200 seconds. The temperature of the steel plate at the outlet of the soaking zone 7 (the entrance of the cooling zone 8) is controlled in the range of 750 to 850°C. In particular, the furnace temperature of the soaking zone 7 is controlled in such a way that the temperature of the steel plate at the outlet reaches the target value of each grade. The cooling conditions may be set to achieve the strength required for each grade, and then a plating step and an alloying step by a conventional method may be performed as needed. Appropriate steps are performed according to each application.

[0111] Before winding the plated coil, an inline mechanical property measuring device (IMPOC manufactured by EMG) was set up to measure the strength at a frequency of 15 to 30 points / minute (at intervals of 4 m on the steel plate). The strength was measured over the entire length of the coil. At each level of 780 MPa, 980 MPa, and 1180 MPa, the required strength range was 780 MPa, 980 MPa, and 1180 MPa or more, respectively. Therefore, the portion where the measured tensile strength did not reach the required strength range was judged as a material test failure.

[0112] In addition, tensile test pieces are taken from the front and rear ends of the final product coil to measure the mechanical properties. Here, the tensile test pieces are based on JIS No. 5. In addition, the tensile test is based on JIS Z2241.

[0113] Calculate the value of each reference strength (TS SThe maximum fluctuation range of the tensile strength in a coil (TS 780 MPa, 980 MPa or 1180 MPa) is expressed as a ratio as the tensile strength fluctuation range. The maximum fluctuation range is determined by the maximum tensile strength (TS MAX )-Minimum tensile strength (TS MIN ) is calculated. The tensile strength fluctuation range is calculated by ΔTS[%] = 100 × (TS MAX -TS MIN ) / TS s Figure it out.

[0114] In addition, the parts judged as material test failures are cut off in the subsequent process. The ratio of the weight (or length) of the cut steel plate to the weight (or length) of one coil is calculated as the material test failure rate [%]. The tensile strength fluctuation range and material test failure rate are calculated for each coil. The average value of 10 coils of each grade is recorded as the evaluation result in Table 1.

[0115] Comparative Example 1 is an example in which the induction heating device 9 and the phase fraction prediction model are not used. Since the annealing conditions are not optimized for each coil, the fluctuation range of the tensile strength is more than 10% at any level, which is large. In addition, since the responsiveness when adjusting the furnace temperature is not fast, the steel plate is annealed under inappropriate annealing conditions when switching levels, and the test failure portion of the material that does not meet the benchmark strength is large, mainly at the front and rear ends of the coil.

[0116] Comparative Example 2 is an example of applying the phase fraction prediction model. In addition to the steel plate composition, plate thickness, and temperature history of the steel plate, the phase fraction prediction model also uses the conveying speed of the steel plate as an input variable to predict the phase fraction before the start of cooling. The annealing conditions are adjusted based on the predicted phase fraction. The optimal annealing conditions can be found for each coil. Although the material test failure rate is slightly reduced, the adjustment of the annealing conditions takes time, so the fluctuation range of the tensile strength remains high. Here, the input variable of the phase fraction prediction model of Comparative Example 2 uses the production line speed (i.e., the conveying speed of the steel plate, expressed as "LS" in Table 1).

[0117] Comparative Example 3 is a method in which an induction heating device 9 is provided at the outlet of the soaking zone 7. Figure 1 An example of manufacturing an annealing device with a structure of. By introducing the induction heating device 9, it is easy to achieve the target annealing conditions with high precision, and the fluctuation range of tensile strength is slightly improved. However, since the operating conditions of the induction heating device 9 are determined according to the setting conditions such as the operating conditions of the furnace, the annealing conditions are not optimal, and a certain amount of material remains and fails the test.

[0118] Comparative Example 4 is an example in which the phase fraction prediction model is further applied to Comparative Example 3. Comparative Example 4 is an example in which the final mechanical properties are determined by controlling the cooling conditions based on the predicted phase fraction. The introduction of the induction heating device 9 and the optimization of the cooling conditions made the stable portion of the coil approximately within the acceptable range, but the delay in the responsiveness when switching the annealing conditions was not improved, resulting in material test failures, especially at the leading and trailing ends of the coil.

[0119] Inventive Example 1 is an example of introducing an induction heating device 9 and a phase fraction prediction model and changing the conditions of the induction heating device 9 according to the predicted phase fraction. The induction heating device 9 used in this embodiment has a heating capacity of 15 to 150°C / second, so the heating rate is adjusted within this range based on the predicted phase fraction. As a result, the optimal annealing conditions are found for each coil, and the responsiveness when the annealing conditions are adjusted is improved. In Inventive Example 1, the material test failure rate can be greatly improved.

[0120] In Invention Example 2, the heating rate of the induction heating device 9 in Invention Example 1 was limited to an appropriate range and adjusted. In conjunction with this, the conveying speed of the steel plate was finely adjusted, but the material test failure rate was improved in the same manner as in Invention Example 1. In addition, in Invention Example 2, the increase fluctuation of the tensile strength caused by excessive annealing was suppressed, and the fluctuation range of the tensile strength was also improved.

[0121] Inventive Example 3 includes an example of adjusting the cooling conditions (here, the refrigerant injection pressure) of the cooling zone when the preferred annealing conditions cannot be obtained within the temperature increase rate range of the equipment capacity of the induction heating device 9 based on the results of the phase fraction prediction. Therefore, in Inventive Example 3, the setting change of the annealing conditions cannot be kept up, especially at the front end and the rear end of the coil. The vibration amplitude of the tensile strength of Inventive Example 3 is larger than that of Inventive Example 2.

[0122] Inventive Example 4, in addition to the alloy composition, plate thickness, temperature history and conveying speed of the steel plate, the hot rolling coiling temperature and the total cold rolling reduction ratio are further added to the input variables of the phase fraction prediction model. As a result, the prediction accuracy of the phase fraction is improved, and the temperature increase amount of the induction heating device 9 is appropriately changed based on the predicted value, resulting in an improvement in the tensile strength fluctuation range and the material test failure rate.

[0123] According to this embodiment, it can be confirmed that if the induction heating device 9 and the phase fraction prediction model are introduced, the annealing operation conditions are adjusted mainly based on the conditions of the induction heating device 9 based on the phase fraction prediction value, the annealing conditions can be quickly adjusted to the optimal annealing conditions, and the yield of the steel sheet is improved.

[0124] In summary, the continuous annealing equipment, continuous annealing method, cold-rolled steel sheet manufacturing method and plated steel sheet manufacturing method of the present embodiment can accurately predict the phase fraction of the steel sheet at high temperature through the above-mentioned configuration. In addition, the fluctuation of the predicted phase fraction can be quickly reflected in the annealing conditions, thereby improving the yield of the product.

[0125] The embodiments of the present disclosure are described based on the drawings and embodiments, but it is to be noted that those skilled in the art can easily make various deformations or modifications based on the present disclosure. Therefore, please note that these deformations or modifications are included in the scope of the present disclosure. For example, the functions included in each component or each step can be reconfigured to avoid logical contradictions, and multiple components or steps can be combined into one or divided. The embodiments of the present disclosure can also be implemented as a program executed by a processor possessed by the device or a storage medium for recording the program. It should be understood that these are also included in the scope of the present disclosure.

[0126] In the above embodiment, the zinc pot 11 is described as a galvanizing bath for dipping a thin steel sheet, but other plating treatments may be performed. The plating treatment may be, for example, electrogalvanizing, hot-dip galvanizing, or alloy hot-dip galvanizing.

[0127] In addition, for example, the generation of the phase fraction prediction model and the calculation of the phase fraction prediction model can be performed by the processor of the process computer reading and executing a program stored in the storage unit (e.g., memory) of the process computer. In addition, the phase fraction prediction model can be stored in the storage unit of the process computer.

[0128] Explanation of symbols

[0129] 1 Uncoiler

[0130] 2Welding Machine

[0131] 3Electrolytic cleaning device

[0132] 4. Inlet side looper

[0133] 5. Preheating

[0134] 6 Heating belt

[0135] 7 Tropical

[0136] 8 Cooling belt

[0137] 8A 1st cooling zone

[0138] 8B 2nd cooling zone

[0139] 9Induction heating device

[0140] 11 Zinc pot

[0141] 12 Thermometer

Claims

1. A continuous annealing equipment, which is a continuous annealing equipment for steel plates, which is equipped with a heating zone, a soaking zone and a cooling zone in sequence, and has: at least one induction heating device between the soaking zone and the cooling zone, and A control device sets the operating conditions of the induction heating device based on the phase fraction during annealing obtained by the phase fraction prediction model.

2. The continuous annealing equipment according to claim 1, wherein: The phase fraction prediction model is a machine learning model generated using teacher data having the composition, size, and temperature of the steel plate and the operating conditions of the continuous annealing furnace as input variables and the phase fraction of the steel plate during annealing as an output variable.

3. The continuous annealing equipment according to claim 2, wherein: The size of the steel plate includes the thickness of the steel plate. The temperature of the steel plate includes the temperature of the steel plate before heating starts and the maximum reaching temperature of the induction heating device. The operating conditions of the continuous annealing furnace include a conveying speed of the steel sheet.

4. A continuous annealing method, which is a continuous annealing method for a steel plate passing through a heating zone, a soaking zone and a cooling zone in sequence, comprising the following steps: At least one induction heating device is provided between the soaking zone and the cooling zone. The operating conditions of the induction heating device are set based on the phase fraction during annealing obtained by the phase fraction prediction model.

5. The continuous annealing method according to claim 4, wherein the phase fraction prediction model is a machine learning model generated using teacher data having the composition, size and temperature of the steel plate and the operating conditions of the continuous annealing furnace as input variables and the phase fraction of the steel plate during annealing as an output variable.

6. The continuous annealing method according to claim 5, wherein: The size of the steel plate includes the thickness of the steel plate. The temperature of the steel plate includes the temperature of the steel plate before heating starts and the maximum reaching temperature of the induction heating device. The operating conditions of the continuous annealing furnace include a conveying speed of the steel sheet.

7. The continuous annealing method according to any one of claims 4 to 6, wherein: The induction heating device heats the steel plate at 10°C / sec to 200°C / sec, The steel plate starts cooling in the cooling zone within 10 seconds after the heating by the induction heating device is completed. 8 . A method for producing a cold-rolled steel sheet, comprising annealing the steel sheet as the cold-rolled steel sheet by an annealing method adjusted by the continuous annealing method according to claim 7 . 9 . A method for producing a plated steel sheet, comprising subjecting the surface of the steel sheet annealed by the method for producing a cold-rolled steel sheet according to claim 8 to a plating treatment.

10. The method for producing a plated steel sheet according to claim 9, wherein: The plating treatment is electrogalvanizing, hot-dip galvanizing or alloy hot-dip galvanizing.

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