Continuous annealing equipment, continuous annealing method, manufacturing method of cold-rolled steel sheet, and manufacturing method of plated steel sheet

Abstract

To provide a continuous annealing facility, a continuous annealing method, a cold rolled steel sheet production method and a plated steel sheet production method which predict a steel sheet phase fraction in a high temperature state at high precision and swiftly reflect annealing conditions with respect to the variation of the predicted phase fraction.SOLUTION: Provided is a continuous annealing facility which comprises a heating zone (6), a soaking zone (7) and a cooling zone (8) in this order. A space between the soaking zone (7) and the cooling zone (8) is provided with: at least one induction heating device (9); and a control device for setting the operational conditions of the induction heating device(s) based on a phase fraction during annealing obtained by a phase fraction prediction model.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to continuous annealing equipment, 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 continuous annealing equipment, 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 for automotive structural materials, etc. [Background technology]

[0002] In the production of automotive steel sheets, continuously cast slabs are extensively processed by hot rolling and cold rolling until they reach the final thickness. The subsequent annealing process restores the cold-worked structure, recrystallizes, and grows the grains, and further controls the transformed structure to adjust the balance between strength and workability.

[0003] In recent years, a continuous annealing furnace has been generally used for annealing, in which a continuous strip of steel sheet is continuously heated, soaked, and cooled while being transported. After the cooling, hot-dip galvanizing and overaging treatments are performed depending on the application of the steel sheet.

[0004] The heating means for annealing furnaces is generally a radiant tube burner, which uses gas combustion to heat a metal tube (radiant tube) and then indirectly heats the steel sheet with the radiant heat. In the production of hot-dip galvanized steel sheets, a direct-fire furnace, which heats the steel sheet by directly spraying the flame of gas combustion onto it, is sometimes used in the front stage of the heating furnace to ensure good coating properties.

[0005] Radiant tube furnaces heat steel sheets using radiant heat from the radiant tubes and furnace walls, resulting in a very large heat source volume and large thermal inertia. This makes it difficult to quickly respond to changes in the set temperature. Furthermore, the temperature rise rate of the steel sheet is slow toward the end of heating, and a certain soaking time is required for structural control. This increases the required furnace length and further increases thermal inertia, further delaying the response to the target temperature. As a result, during the continuous processing of coils, the temperature of some steel sheets may not fall within the specified annealing temperature range, resulting in problems such as reduced yield due to variations in mechanical properties and reduced productivity due to changes in line speed for temperature control.

[0006] Furthermore, the transformation behavior of steel sheets cannot be uniquely determined by temperature alone; it is affected by the conditions of the preceding processes, such as casting, hot rolling, and cold rolling. Therefore, even if the annealing temperature is precisely controlled, the mechanical properties of the final product may fall outside the target range. To solve this problem, it is desirable to understand the phase fractions of the steel sheet structure after annealing.

[0007] To address the above-mentioned problems, Patent Document 1 discloses a technique for improving responsiveness by installing an induction heating device between a preheating zone and a heating zone (direct-fired heating furnace) or between heating zones consisting of multiple direct-fired heating furnaces to supplement heating capacity. Patent Document 2 discloses a technique for determining the phase fraction of a steel sheet from changes in magnetic properties. Patent Document 3 discloses a technique for predicting changes in the phase fraction of a steel sheet from the results of measuring the steel sheet temperature and controlling annealing conditions to achieve a structure that achieves desired mechanical properties. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] Japanese Patent Application Publication No. 11-061277 [Patent Document 2] Japanese Patent Application Laid-Open No. 2000-144262 [Patent Document 3] Special Publication No. 2020-509243 Summary of the Invention [Problem to be solved by the invention]

[0009] However, in the technology of Patent Document 1, in order to prevent excessive oxidation of the steel sheet surface, the steel sheet temperature at the outlet of the direct heating furnace is low, and heating is also required in a radiant tube furnace. This makes it difficult to shorten the furnace length, and changes in furnace temperature setting have a wide impact. Furthermore, although it claims that the effects of furnace temperature fluctuations can be absorbed by quickly controlling the output of the induction heating device, the direct heating furnace following the induction heating device places emphasis on controlling the oxidation of the steel sheet, making it difficult to flexibly control the steel sheet temperature. Therefore, the effect of absorbing the effects of furnace temperature fluctuations cannot be fully demonstrated.

[0010] The technology of Patent Document 2 measures changes in the phase fraction of a steel sheet, and makes it possible to control manufacturing conditions based on the measurement results. However, controllability in conventional annealing furnaces is low; that is, even if the transformation rate can be measured, the controllability of the furnace cannot keep up with large changes in annealing conditions.

[0011] The technology in Patent Document 3 can predict the phase fraction, but controls the structure by reflecting the predicted phase fraction results in the cooling conditions. However, controlling only the cooling conditions results in low responsiveness, making it difficult to increase speed and precision.

[0012] In view of the above problems, the present disclosure aims to provide continuous annealing equipment, a continuous annealing method, a method for manufacturing a cold-rolled steel sheet, and a method for manufacturing a plated steel sheet, which are capable of accurately predicting the phase fraction of a steel sheet at high temperatures and quickly reflecting fluctuations in the predicted phase fraction in annealing conditions. [Means for solving the problem]

[0013] (1) A continuous annealing facility according to an embodiment of the present disclosure includes: A continuous annealing facility for steel sheets including a heating zone, a soaking zone, and a cooling zone in this order, at least one induction heating device between the soaking zone and the cooling zone; and a control device that sets the operating conditions of the induction heating device based on the phase fraction during annealing obtained by the phase fraction prediction model.

[0014] (2) As one embodiment of the present disclosure, in (1), The phase fraction prediction model is a machine learning model generated using training data in which the components, dimensions, and temperature of the steel sheet and the operating conditions of a continuous annealing furnace are used as input variables, and the phase fraction of the steel sheet during annealing is used as an output variable.

[0015] (3) As one embodiment of the present disclosure, in (2), The dimensions of the steel plate include the plate thickness of the steel plate, The temperature of the steel sheet includes the temperature of the steel sheet immediately before the start of heating and the maximum temperature reached by the induction heating device, The operating conditions of the continuous annealing furnace include the conveying speed of the steel sheet.

[0016] (4) A continuous annealing method according to an embodiment of the present disclosure, A continuous annealing method for a steel sheet, which comprises passing through a heating zone, a soaking zone, and a cooling zone in this order, At least one induction heating device is provided between the soaking zone and the cooling zone, The method includes a step of setting operating conditions of the induction heating device based on the phase fraction during annealing obtained by the phase fraction prediction model.

[0017] (5) As an embodiment of the present disclosure, in (4), The phase fraction prediction model is a machine learning model generated using training data in which the components, dimensions, and temperature of the steel sheet and the operating conditions of a continuous annealing furnace are used as input variables, and the phase fraction of the steel sheet during annealing is used as an output variable.

[0018] (6) As an embodiment of the present disclosure, in (5), The dimensions of the steel plate include the plate thickness of the steel plate, The temperature of the steel sheet includes the temperature of the steel sheet immediately before the start of heating and the maximum temperature reached by the induction heating device, The operating conditions of the continuous annealing furnace include the conveying speed of the steel sheet.

[0019] (7) As an embodiment of the present disclosure, in any one of (4) to (6), The induction heating device heats the steel sheet at a rate of 10°C / s or more and 200°C / s or less, The steel sheet begins to be cooled in the cooling zone within 10 seconds after heating by the induction heating device is completed.

[0020] (8) A method for producing a cold-rolled steel sheet according to an embodiment of the present disclosure includes: The cold-rolled steel sheet is annealed by an annealing method adjusted by the continuous annealing method of (7).

[0021] (9) A method for producing a plated steel sheet according to an embodiment of the present disclosure includes: (8) The surface of the steel sheet annealed by the method for producing a cold-rolled steel sheet is plated.

[0022] (10) As an embodiment of the present disclosure, in (9), The plating treatment is an electrogalvanizing treatment, a hot-dip galvanizing treatment, or a hot-dip galvannealing treatment. [Effects of the Invention]

[0023] According to the present disclosure, there are provided continuous annealing equipment, a continuous annealing method, a method for manufacturing a cold-rolled steel sheet, and a method for manufacturing a plated steel sheet, which are capable of accurately predicting the phase fraction of a steel sheet at high temperatures and quickly reflecting fluctuations in the predicted phase fraction in annealing conditions. As a result, it becomes possible to manufacture thin steel sheets and the like having target mechanical properties more stably than with conventional annealing furnaces. [Brief explanation of the drawings]

[0024] [Figure 1] FIG. 1 is a schematic diagram showing a hot-dip galvanizing process equipped with continuous annealing equipment according to one embodiment. [Figure 2] FIG. 2 is a diagram showing an example of the temperature history of a steel sheet. [Figure 3] FIG. 3 is a flowchart showing an example of a continuous annealing method. DETAILED DESCRIPTION OF THE INVENTION

[0025] Hereinafter, 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 according to an embodiment of the present disclosure will be described with reference to the drawings.

[0026] <Equipment configuration> FIG. 1 shows a portion of a hot-dip galvanizing process equipped with continuous annealing equipment according to this embodiment. In this embodiment, the steel material produced using the hot-dip galvanizing process is a thin steel sheet. The steel material produced is a cold-rolled steel sheet. The arrows in FIG. 1 indicate the line traveling direction. Hereinafter, the upstream side in this traveling direction may be referred to as the "front" and the downstream side as the "rear." The continuous annealing equipment includes a payoff reel 1, a welding machine 2, an electrolytic cleaning device 3, an entry looper 4, a preheating zone 5, a heating zone 6, a soaking zone 7, and a cooling zone 8. The continuous annealing equipment also includes an induction heating device 9 (hereinafter sometimes referred to as "IH"). The continuous annealing equipment includes at least one induction heating device 9. In this embodiment, the cooling zone 8 includes a first cooling zone 8A and a second cooling zone 8B. The continuous annealing equipment may further include a zinc plating tank (zinc pot 11) in which the thin steel sheet cooled to a predetermined temperature is immersed, an alloying zone, a holding zone, a final cooling zone, a temper rolling equipment, an outlet looper, a tension reel, etc.

[0027] The steel sheet wound into a coil in the previous process is unwound on a payoff reel 1. The unwound steel sheet passes through a preheating zone 5 and then enters a continuous annealing furnace. The continuous annealing equipment comprises, following the preheating zone 5, a heating zone 6, a soaking zone 7, and a cooling zone 8, in that order. In the continuous annealing equipment, an induction heating device 9 is provided between the soaking zone 7 and the cooling zone 8.

[0028] Furthermore, the continuous annealing equipment is equipped with 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 transformation rate prediction model. The continuous annealing equipment according to this embodiment is not limited to the configuration shown in FIG. 1 and can be modified depending on the target quality of the steel sheet to be manufactured. For example, a zinc pot 11 and an alloying zone may be provided following the continuous annealing furnace, or the zinc pot 11 may be omitted. Furthermore, the continuous annealing equipment can be partially divided. For example, the soaking zone 7 may be divided, and the atmosphere or temperature of each divided soaking zone 7 may be independently controlled.

[0029] <Details of components> FIG. 2 illustrates the temperature history of a steel sheet when annealed in a typical conventional annealing furnace and when annealed in the annealing furnace of this embodiment. The vertical axis represents the steel sheet temperature. The horizontal axis represents the passage time. The line speed is 100 mpm. The steel sheet has a thickness of 1 mm. The temperature history of this embodiment is shown by a solid line, with the corresponding process steps written below. The temperature history of the conventional technology is shown by a dotted line, with the corresponding process steps written above. As shown in FIG. 2, the annealing furnace of this embodiment requires less time for the process than the conventional technology due to the configuration described below.

[0030] (Pre-tropical zone) The thin steel sheet discharged from the payoff reel 1 at a temperature between room temperature and about 100°C first enters the preheating zone 5 and is heated to about 200°C. The temperature of the thin steel sheet may simply increase by heating. In this embodiment, the preheating zone 5 uses a system that utilizes high-temperature exhaust gas generated in the heating zone 6.

[0031] (heating zone) Next, the steel sheet enters the heating zone 6, where it is heated to a temperature of approximately 600 to 700°C. The temperature of the thin steel sheet may simply increase with heating. In this embodiment, a direct-fired heating furnace is used in the heating zone 6 to raise the temperature to a certain level in a short time and to control the surface condition. A direct-fired heating furnace has high heating capacity and allows the furnace volume to be small, and also allows for flexible control of the oxidation-reduction reaction on the steel sheet surface, taking into account the subsequent plating process.

[0032] (Soaking temperature) The soaking zone 7 primarily functions to promote the recrystallization of the α phase. However, it is acceptable for the temperature to pass through the A1 transformation point at some points during this process. The heating method for the soaking zone 7 is preferably a radiant heating method using gas combustion (radiant tube heating) due to its high efficiency and heating uniformity. The soaking zone 7 may be composed of a large single furnace shell. Alternatively, the soaking zone 7 may have two or more compartments, each separated by a heat insulating wall or the like, and each compartment may be maintained or heated within a predetermined target temperature range. In this embodiment, the soaking zone 7 maintains or slowly heats the steel sheet so that the temperature is in a temperature range (approximately 600 to 730°C) lower than the A1 transformation point, thereby ensuring a residence time in the recrystallization temperature range and suppressing the retention of unrecrystallized α phase. Here, the A1 transformation point is the temperature at which austenite transformation occurs, and the set furnace temperature is, for example, 730°C. In other words, the austenite phase begins to form at temperatures above the A1 transformation point.

[0033] If the temperature of the steel sheet in the soaking zone 7 is too low, the recrystallization of the α phase will not proceed, and sufficient workability will not 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 will become coarse, resulting in insufficient strength. The A1 transformation point may vary slightly depending on the composition of the steel sheet. Therefore, it is preferable to predict the recrystallization temperature range in advance by measurement, calculation, or simulation, and set it taking into account the error range during annealing.

[0034] If the steel sheet's residence time in the soaking zone 7 is too short, the α-phase recrystallization will not progress sufficiently, and if it is too long, the crystal grains will become coarse, resulting in reduced mechanical properties. Therefore, the optimal residence time was experimentally determined in advance and found to be approximately 20 to 60 seconds. If it is less than 20 seconds, the α-phase recrystallization will not progress sufficiently, resulting in poor workability, and if it exceeds 60 seconds, coarse crystal grains will form in some areas, resulting in uneven strength. Therefore, in the soaking zone 7, the steel sheet's temperature is held in a temperature range below the A1 transformation point for at least 20 seconds but not more than 60 seconds.

[0035] When slow heating is performed in the soaking zone 7, it is sufficient that the residence time in the recrystallization temperature range is ensured for the above-mentioned period. However, when slow heating is performed in the soaking zone 7, the heating rate is preferably 5°C / s or less. This is because if the heating rate is rapid and exceeds 5°C / s, it becomes impossible to suppress the retention of the unrecrystallized α phase.

[0036] In the soaking zone 7, after the residence time in the recrystallization temperature range has been ensured, slow heating to a temperature equal to or higher than the A1 transformation point may be performed as long as the temperature is less than 750°C. However, it is desirable that the heating rate be 5°C / s or less.

[0037] The continuous annealing equipment according to this embodiment includes a soaking zone 7 using a radiant tube furnace. However, the equipment may be configured without such a soaking zone 7 as long as it has the function of ensuring a heat retention time sufficient to promote α-phase recrystallization. For example, the soaking zone 7 is not limited to one including a radiant tube furnace. The continuous annealing equipment may not include the soaking zone 7, and the heating zone 6 may have the function of ensuring a heat retention time sufficient to promote α-phase recrystallization. The continuous annealing equipment may also include a heat retention device instead of the soaking zone 7. When the continuous annealing equipment does not include the soaking zone 7, the induction heating device 9 may be provided between the part that functions as the soaking zone 7 (in the above example, the part that ensures the heat retention time of the heating zone 6 or the heat retention device) and the cooling zone 8.

[0038] (Induction heating device) Next, the induction heating device 9 adjusts its output to rapidly heat the steel sheet so that the temperature of the steel sheet is within a temperature range (approximately 750 to 900°C) that is equal to or higher than the A1 transformation point and lower than the A3 transformation point. Here, the A3 transformation point is the upper limit temperature at which the γ phase fraction can be suppressed. The purpose of this process is to uniformly heat the entire steel sheet to a temperature equal to or higher than the A1 transformation point in a short period of time, which also contributes to the miniaturization of the entire equipment. Here, the A1 transformation point and the A3 transformation point may vary slightly depending on the composition of the steel sheet. Therefore, it is preferable to predict the appropriate temperature range in advance by measurement, calculation, or simulation, and set it while taking into account the error range during annealing.

[0039] In the case of DP steel, which is one of the targets of the continuous annealing equipment according to this embodiment, the temperature reached by the induction heating device 9 has a significant impact on the mechanical properties of the final product. Therefore, the heating method of the induction heating device 9 must be able to quickly respond to temperature control commands. Furthermore, when heating in this temperature range, the Curie point at which the magnetic properties of the steel sheet change is exceeded, so it is desirable that the induction heating device 9 be of the transverse type.

[0040] Furthermore, since the soaking zone 7 maintains the α-phase recrystallization region, a long heating time can lead to coarsening of the α-grains. Therefore, an induction heating device 9 capable of rapid heating is desirable. The induction heating device 9 may increase the temperature at a rate of 10°C / s or more and 200°C / s or less. A heating rate of less than 10°C / s can lead to coarsening of the α-grains, while a heating rate of more than 200°C / s can result in localized high-temperature areas in the width direction, preventing uniformity. It is more preferable for the induction heating device 9 to increase the temperature at a rate of 20°C / s or more and 100°C / s or less. A heating rate of 20°C / s or more can shorten the line length, while a heating rate of 100°C / s or less can further reduce the risk of buckling deformation of the steel sheet due to thermal stress.

[0041] If the induction heating device 9 is used to rapidly heat the steel sheet to the target annealing temperature, the transformation from the α phase to the γ phase may not reach equilibrium immediately after heating. However, if the steel sheet is held at a temperature close to the target annealing temperature for too long, the transformation from the α phase to the γ phase will proceed more than necessary, making material quality control more complicated. Therefore, after the target annealing temperature is reached, it is most desirable to enter the steel sheet into the cooling zone 8 as quickly as possible with a holding time of 0 seconds, and it is desirable to start cooling within 5 seconds. To avoid a non-equilibrium state, cooling should start within 10 seconds at the latest, even if the steel sheet is held. In other words, the steel sheet begins cooling in the cooling zone 8 within 10 seconds after heating by the induction heating device 9 is completed.

[0042] In order to satisfy these conditions, the induction heating device 9 is installed at the connection between the soaking zone 7 and the cooling zone 8. Installing the induction heating device 9 at the connection part is also possible in an existing furnace as an additional induction heating device 9.

[0043] (cooling zone) The cooling zone 8 is a facility for cooling the steel sheet to a predetermined temperature, and gas jet cooling, roll cooling, water cooling (water quenching), etc. may be used as the cooling means. As in this embodiment, the cooling zone 8 may be divided into a plurality of zones, such as a first cooling zone 8A and a second cooling zone 8B, and the thermal history of the steel sheet during cooling may be controlled by combining different cooling means or by changing the cooling conditions of the same type of cooling means.

[0044] (thermometer) For example, a thermometer 12 for measuring the surface temperature of the steel sheet may be installed at the connection point of each band. The thermometer 12 makes it possible to grasp the approximate temperature history of the steel sheet during heat treatment. For example, in the case of a long equipment length, such as the soaking zone 7, a thermometer 12 may be installed within the band to check the temperature history along the way. The temperature measurement method is not particularly limited, but for example, a radiation thermometer that measures the temperature by detecting infrared rays emitted by the steel sheet is suitable. In the case of a radiation thermometer, since it may be affected by reflected infrared light emitted by the surrounding furnace body, a cover may be provided between the measurement part of the radiation thermometer and the detection part of the steel sheet. Furthermore, since it may be affected by the emissivity of the steel sheet surface, a multi-reflection measurement method utilizing the wedge-shaped space between the transport roll in the furnace and the steel sheet may be adopted.

[0045] (hot-dip galvanizing bath) A hot-dip galvanizing bath is provided following the cooling zone 8, and the steel sheet discharged from the cooling zone 8 can be hot-dip galvanized. Hot-dip galvanizing may be carried out according to a conventional method, and a snout, a bath roll, etc. may be provided as necessary.

[0046] (Alloying equipment) An alloying treatment facility may be provided following the hot-dip galvanizing bath. In the alloying treatment facility, the steel sheet is heated and subjected to alloying treatment. The alloying treatment may be carried out according to a conventional method.

[0047] (Other facilities) Following the alloying equipment, in order to improve the quality of the final product and to improve the manufacturing stability and efficiency, a holding zone, a final cooling zone, a temper rolling equipment, a straightener, an outlet looper, a tension reel, etc. These equipments may be provided and used according to the quality required for the product, and are not particularly limited.

[0048] <Phase fraction prediction model> A phase fraction prediction model is used to predict the phase fraction of a steel sheet during annealing. The phase fraction prediction model accurately predicts the phase fraction of a steel sheet using various input variables related to the transformation of the steel sheet.

[0049] In this embodiment, the phase fraction prediction model uses operating conditions and temperature as input variables and the phase fraction of the steel sheet during annealing as an output variable. A database stored in a host computer containing actual values and set values (hereinafter also referred to as "actual data") from operations in the annealing process of thin steel sheets is used to generate the phase fraction prediction model. From the actual data stored in the database, actual values of the operating conditions and the temperature history in the annealing furnace are used as input actual data sets, and multiple pieces of training data (learning data) are prepared. The phase fraction prediction model uses the actual values of the operating conditions and the temperature history in the annealing furnace as input actual data sets, and the phase fraction during annealing corresponding to the operating conditions and the temperature history as output actual data. Then, the phase fraction prediction model is generated by machine learning using the multiple pieces of training data. Here, in this embodiment, the phase fraction prediction model also includes information about the steel sheet as input variables. That is, the phase fraction prediction model uses, for example, the composition, dimensions, and temperature of the steel sheet and the operating conditions of the continuous annealing furnace as input variables, and the phase fraction of the steel sheet during annealing as an output variable.

[0050] (input variables) In this embodiment, when constructing a machine learning model, the input variables used are specifically (a) "alloy composition and thickness of steel plate," (b) "operation parameters," and (c) "temperature history of steel plate."

[0051] Regarding (a) above, "alloy composition" refers to the composition of the steel sheet, and is information that determines the transformation temperature and the phase fraction before and after transformation. "Steel sheet thickness" refers to the thickness of the steel sheet transported to the annealing furnace, and is information that affects the temperature rise and cooling rate during annealing. For example, the steel sheet dimensions, which are input variables of the phase fraction prediction model, include the steel sheet thickness.

[0052] Regarding (b) above, the "operational parameters" include, as an essential item, the conveying speed of the steel sheet conveyed through the annealing furnace. This is because the conveying speed of the steel sheet affects annealing conditions such as the heating rate, cooling rate, and soaking time. For example, the operational conditions of a continuous annealing furnace, which are input variables of the phase fraction prediction model, include the conveying speed of the steel sheet. The "operational parameters" may also include, for example, at least one of the pass schedule of the hot rolling process, the cooling start temperature at the hot rolling run-out table, the cooling end temperature at the hot rolling run-out table, the coiling temperature of the hot rolled coil and the pass schedule of the cold rolling, the set furnace temperature of the annealing furnace, and the amount of gas input to the burner. These items enable the processing rate of the steel sheet (i.e., the amount of processing strain before annealing) and the microstructural state before annealing to be understood, thereby improving the prediction accuracy of the transformation temperature and phase fraction. The coiling temperature of the hot rolled coil is particularly useful. The "operational parameters" may also include the cold rolling reduction, which is a useful variable. The actual values for these items are information linked to the materials charged into the annealing furnace in the actual equipment, but information obtained when rolling is performed from steel ingots produced in a laboratory melting furnace may also be used.

[0053] Regarding (c) above, the "temperature history of the steel sheet" refers to the surface temperature of the steel sheet measured in the annealing furnace. The "temperature history of the steel sheet" includes, as essential items, the steel sheet temperature immediately before the start of heating and the maximum temperature reached by the induction heating device 9. The steel sheet temperature immediately before the start of heating may be the steel sheet temperature 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 transformation temperature can be determined, thereby improving prediction accuracy. For example, the steel sheet temperature, which is an input variable of the phase fraction prediction model, includes the steel sheet temperature immediately before the start of heating and the maximum temperature reached by the induction heating device 9. Here, the "temperature history of the steel sheet" may be discrete temperature data measured or calculated at the connection points of the annealing furnace components. Furthermore, to improve prediction accuracy and yield, a model for predicting the surface temperature of the steel sheet at locations that are not measured may be introduced. The model for predicting the surface temperature of the steel sheet (hereinafter referred to as the "steel sheet temperature prediction model") may be a physical model for numerical analysis or a machine learning model. In the case of a machine learning model, training data may be used in which the annealing furnace operating conditions (line speed, furnace temperatures at each position in the heating zone 6 and the soaking zone 7), the alloy composition and cross-sectional shape of the steel sheet (sheet thickness, sheet width) are used as input historical data, and the measurement results of the steel sheet temperature at each position in the annealing furnace are used as output historical data. A steel sheet temperature prediction model may be generated by machine learning using such training data. The input data for the steel sheet temperature prediction model may further include information on upstream processes. The information on the upstream processes may include at least one of the following cooling conditions: reheating temperatures in the hot rolling process and cold rolling process, rolling conditions such as pass schedules, cooling start temperatures and cooling stop temperatures at the finish exit side in the hot rolling process, and coil winding temperature.

[0054] (output variable) In this embodiment, when constructing a machine learning model, the output variable is a specifically measured phase fraction. The phase fraction may be measured by conducting a laboratory experiment in which a steel sheet is annealed and the phase fraction is determined from microstructural observation, but the method is not limited thereto. For example, a data table of phase fractions for various annealing conditions may be created in advance by conducting experiments under different annealing conditions. In this embodiment, to predict the phase fraction at the maximum temperature reached by the induction heating device 9, a steel sheet that has reached the maximum temperature is rapidly cooled in a laboratory experiment to freeze the structure, and the phase fraction is determined by cross-sectional microstructural observation. Alternatively, a steel sheet may be annealed under various conditions in an actual annealing furnace, and the cooled steel sheet may be sampled and the phase fraction before cooling may be determined by cross-sectional microstructural observation. The phase fraction during annealing can be determined by actual operation or laboratory experiments, or by numerical analysis using a physical model. Either method can determine the thermal history and phase fraction of a steel sheet during annealing under different operating conditions. Here, the phase fraction of the steel sheet during annealing particularly refers to the phase fraction of the steel sheet from immediately after the end of annealing in the induction heating device 9 until immediately before the start of cooling.

[0055] As described above, the phase fraction prediction model is generated, but actual data is collected offline. As actual data, thermal histories and phase fractions are calculated for various operating condition data sets in which the operating conditions are varied, and multiple training data based on the actual data are stored in a storage device or the database.

[0056] (Machine Learning) The method for generating the machine learning model is not limited 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, and support vector regression may be used. An ensemble model combining multiple methods may also be used. Furthermore, as the phase fraction prediction model, a machine learning model whose output is binarized may be used, which determines whether or not the phase fraction of the steel sheet is within a predetermined allowable range (pass or fail), rather than the calculated value of the phase fraction. In this case, a classification model such as k-nearest neighbor or logistic regression may be used.

[0057] <Operation method (control method)> 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 is adjusted, making it possible to manufacture products with stable mechanical properties.

[0058] 3 is a flowchart showing an example of a continuous annealing method performed in the continuous annealing facility in this embodiment. A control method for heat treating a steel sheet using the phase fraction prediction model will be described.

[0059] (Setting initial conditions and predicting phase fractions) The initial setting of the operating conditions is performed before the products to be controlled are charged into the annealing furnace. The operating conditions of the annealing furnace may be standard conditions prepared in advance for each product size and grade, or may be based on past production results.

[0060] (Determination of phase fraction and condition modification) The predicted phase fraction value for the initial setting obtained by the phase fraction prediction model described above ("Predicted phase fraction value" in Figure 3) is compared with the target phase fraction required for the product (steel plate). If the predicted value falls outside the set tolerance range ("Is it within target range?" in Figure 3, No), the operating conditions of the annealing furnace are reset. The resetting of the operating conditions is performed with priority given to the output and temperature of the induction heating device 9, which have high responsiveness and control accuracy. If the change can be accommodated by changing the IH heating amount ("Are IH operating conditions compatible?" in Figure 3, Yes), the changed heating amount is included in the input data set, and the predicted phase fraction is compared with the target again.

[0061] If the phase fraction falls within the target range after repeating this process of prediction, judgment, and resetting (Yes for "Is it within target range?" in Figure 3), those operating conditions are reflected in the control. If it cannot be kept within the target range (No for "Within IH output control range" in Figure 3), the operating conditions of the cooling zone 8 are also changed ("Cooling zone operating conditions" in Figure 3). Although it is less responsive to changes in operating conditions than the induction heating device 9, by controlling it in combination with the induction heating device 9 via the control flow described above, it is possible to minimize the amount by which the product's mechanical properties deviate from the target.

[0062] In this way, prediction, judgment, and resetting are repeated until the phase fraction falls 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.

[0063] (Update during operation) The above-described prediction, determination, and resetting processes are performed to initially set the operating conditions prior to the heat treatment of the product. After that, when the actual heat treatment of the product begins, the expected thermal history may deviate due to fluctuations in the line speed or the furnace temperature caused by the accuracy of the combustion control of the annealing furnace. Therefore, even after the heat treatment of the product begins, the phase fraction prediction model may be updated using newly obtained actual values (such as the value of the "annealing furnace thermometer" in Figure 3).

[0064] (Target material) The continuous annealing equipment according to this embodiment anneals steel materials, including DP steel, to stably obtain target mechanical properties. Specific examples of the steel sheet to be annealed include those containing, by weight percent, 0.03% to 0.25% C, 0.01% to 2.50% Si, 0.50% to 4.00% Mn, 0.100% to 0.0500% S, 0.005% to 2.000% sol. Al, and 0.100% to 0.100% N, as needed, and optionally containing Cr, Cu, Ni, Sb, Sn (each 1.00% or less), Mo, V, Ti, Nb (each 0.50% or less), Ta (0.10% or less), Mg, Zr (each 0.050% or less), B, Ca, and REM (each 0.0050% or less), with the balance being Fe and unavoidable impurities. However, the steel sheet to be used is not limited as long as it is necessary to control the two-phase structure of α-phase and γ-phase.

[0065] Here, the steel sheet may be subjected to annealing treatment in a continuous annealing facility, followed by plating treatment, alloying treatment, temper rolling, and shape correction treatment. The plating treatment and alloying treatment may be conventional methods that satisfy the quality required for the surface properties of the product, and are not particularly limited.

[0066] Furthermore, if any shape disturbance occurs, the steel sheet may be subsequently temper rolled and passed through a straightening machine for straightening. The temper rolling and straightening may be carried out under conditions that merely straighten the shape without affecting the mechanical properties of the steel sheet, and are not limited thereto.

[0067] (Example) Table 1 shows the conditions and results for producing steel sheets using a conventional continuous annealing facility and the continuous annealing facility according to the present embodiment (see Figure 1). To examine the variability in the mechanical properties of the products, 30 coils of each product were produced at multiple strength levels. Three strength levels (780 MPa, 980 MPa, and 1180 MPa) were produced, and 10 slabs of each strength level (1.0, 1.5, and 2.0 mm) were produced. These 10 slabs of each grade and thickness were all cast in different lots using a continuous casting machine. Therefore, although within the production control range, the chemical compositions of each slab varied, and the transformation behavior was not uniform.

[0068] [Table 1]

[0069] Each slab was hot-rolled, pickled, annealed as needed, and cold-rolled using conventional methods. Then, it was heat-treated using laboratory annealing, conventional annealing equipment, and the annealing equipment disclosed herein. It was then cooled, plated, and other post-treatments were performed. The steel sheet conveying speed was 60 to 120 mpm. However, in the case of Inventive Example 1, the steel sheet conveying speed was 30 to 150 mpm. The steel sheet temperature at the exit of heating zone 6 (direct flame heating) was kept within the range of 600 to 700°C to control the surface oxidation-reduction reaction. It was then introduced into radiant tube-type soaking zone 7 for further heating. The residence time in the recrystallization zone was 20 to 60 seconds. The heating / soaking time after the recrystallization zone was 100 to 200 seconds. The steel sheet temperature at the exit of soaking zone 7 (entrance to cooling zone 8) was controlled within the range of 750 to 850°C. In particular, the furnace temperature of soaking zone 7 was controlled so that the steel sheet temperature at the exit reached the target value for each grade. The cooling conditions may be set to achieve the strength required for each grade, followed by a plating process and, if necessary, a conventional alloying process. Appropriate processes were carried out depending on the application.

[0070] Immediately before winding the coated coil, an inline mechanical property measuring device (IMPOC manufactured by EMG) was installed and strength measurements were performed at a frequency of 15 to 30 points per minute (every 4 m of steel plate spacing). Strength was measured along the entire length of the coil. For the 780 MPa, 980 MPa, and 1180 MPa grades, the required strength ranges were 780 MPa, 980 MPa, and 1180 MPa or higher, respectively. Therefore, any portion where the measured tensile strength did not reach the required strength range was deemed to be a defective material.

[0071] Tensile test pieces were taken from the head and tail of the final coil product to measure the mechanical properties. The tensile test pieces conformed to JIS No. 5. The tensile test conformed to JIS Z2241.

[0072] Each standard strength (TS S The maximum fluctuation range of the tensile strength within one coil was calculated as a ratio to the maximum tensile strength (TS) of the measured specimens (780 MPa, 980 MPa, or 1180 MPa). MAX )-Minimum Tensile Strength (TS MIN The tensile strength fluctuation range is given by ΔTS [%] = 100 × (TS MAX -TS MIN ) / TS s is given by

[0073] Furthermore, any sections that were judged to be defective were cut off in a subsequent process. The ratio of the weight (or length) of the cut-off steel sheet to the weight (or length) of one coil was calculated as the defect rate [%]. The tensile strength variation range and defect rate were determined for each coil. The average values for 10 coils of each grade are shown as evaluation results in Table 1.

[0074] Comparative Example 1 is an example in which neither the induction heating device 9 nor the phase fraction prediction model was used. Because the annealing conditions were not optimized for each coil, the fluctuation range of tensile strength was large, at 10% or more for all grades. Furthermore, because the response when adjusting the furnace temperature was not fast, the steel sheet annealing progressed under inappropriate annealing conditions when switching grades, and there were many areas of the material that did not meet the standard strength, mainly around the front and rear ends of the coil.

[0075] Comparative Example 2 is an example in which a phase fraction prediction model was applied. The phase fraction prediction model predicts the phase fraction immediately before the start of cooling using the steel sheet composition, sheet thickness, and temperature history of the steel sheet, as well as the steel sheet conveying speed, as input variables. The annealing conditions were adjusted based on the predicted phase fraction. Although the optimal annealing conditions could be found for each coil and the failure rate of the material test was slightly reduced, the tensile strength variation range remained high because adjusting the annealing conditions took time. Here, the input variable of the phase fraction prediction model in Comparative Example 2 was the line speed (i.e., the steel sheet conveying speed, denoted as "LS" in Table 1).

[0076] Comparative Example 3 is an example of production using annealing equipment with the configuration shown in Figure 1, in which an induction heater 9 was installed at the outlet of the soaking zone 7. The introduction of the induction heater 9 made it easier to achieve the target annealing conditions with high precision, and some improvement was observed in the tensile strength variation range. However, because the operating conditions of the induction heater 9 are determined by the set conditions such as the operating conditions of the furnace, the annealing conditions were sometimes not optimal, and a certain degree of material testing error remained.

[0077] Comparative Example 4 is an example in which a phase fraction prediction model was further applied to Comparative Example 3. Comparative Example 4 is an example in which the final mechanical properties were determined by controlling the cooling conditions based on the predicted phase fraction. By introducing the induction heating device 9 and optimizing the cooling conditions, the steady-state portion of the coil was generally within the acceptable range, but the delayed response when switching the annealing conditions was not improved, resulting in material failures, particularly at the tip and tail ends of the coil.

[0078] Inventive Example 1, an induction heating device 9 and a phase fraction prediction model were introduced, and the conditions of the induction heating device 9 were changed based on the predicted phase fraction. Since the induction heating device 9 used in this example has a heating capacity of 15 to 150°C / s, the heating rate was adjusted within that range based on the predicted phase fraction. As a result, optimal annealing conditions were found for each coil, and responsiveness when adjusting the annealing conditions was improved. Inventive Example 1, the failure rate of the material test was significantly improved.

[0079] In Example 2, the temperature rise rate of the induction heating device 9 in Example 1 was adjusted to be limited to a preferred range. As a result, the conveying speed of the steel sheet had to be finely adjusted, but the failure rate of the material test was improved as in Example 1. Furthermore, in Example 2, it became possible to suppress an upward fluctuation in tensile strength due to over-annealing, and the tensile strength fluctuation range was also improved.

[0080] Inventive Example 3, as a result of phase fraction prediction, it was found that suitable annealing conditions could not be obtained within the heating rate range of the equipment capacity of the induction heating device 9, and therefore the cooling conditions in the cooling zone (here, the refrigerant injection pressure) were adjusted. Therefore, in Inventive Example 3, changes in the annealing conditions did not follow suit, especially at the head and tail ends of the coil. In Inventive Example 3, the fluctuation range of the tensile strength was larger than in Inventive Example 2.

[0081] In Example 4, the input variables of the phase fraction prediction model were the alloy composition, thickness, temperature history, and conveying speed of the steel sheet, as well as the hot rolling coiling temperature and cold rolling total reduction rate. This improved the accuracy of phase fraction prediction, and as a result of appropriately changing the temperature rise of the induction heating device 9 based on the predicted values, the tensile strength variation range and the rate of material failure were improved.

[0082] In this example, by introducing the induction heating device 9 and the phase fraction prediction model and adjusting the annealing operation conditions by prioritizing mainly the conditions of the induction heating device 9 based on the predicted phase fraction value, it was confirmed that it became possible to quickly adjust to the optimum annealing conditions and improve the yield of steel sheets.

[0083] As described above, the continuous annealing equipment, the continuous annealing method, the method for manufacturing a cold-rolled steel sheet, and the method for manufacturing a plated steel sheet according to the present embodiment can accurately predict the phase fraction of a steel sheet at high temperatures by virtue of the above-described configurations. Furthermore, fluctuations in the predicted phase fraction can be quickly reflected in the annealing conditions, thereby improving the product yield.

[0084] Although the embodiments of the present disclosure have been described based on the drawings and examples, it should be noted that those skilled in the art would easily be able to make various modifications or alterations based on the present disclosure. Therefore, it should be noted that these modifications and alterations are included within the scope of the present disclosure. For example, the functions included in each component or step can be rearranged so as not to cause logical inconsistencies, and multiple components or steps can be combined or divided into one. The embodiments of the present disclosure can also be realized as a program executed by a processor included in an apparatus or a storage medium on which a program is recorded. It should be understood that these are also included within the scope of the present disclosure.

[0085] Although the zinc pot 11 has been described as a galvanizing bath in which the steel sheet is immersed in the above embodiment, other coating processes may be performed, such as electrogalvanizing, hot-dip galvanizing, or galvannealing.

[0086] Furthermore, for example, a processor of the process computer may read and execute a program stored in a storage unit (e.g., a memory) of the process computer, thereby generating a phase fraction prediction model and performing calculations using the phase fraction prediction model. Furthermore, the phase fraction prediction model may be stored in a storage unit of the process computer. [Explanation of symbols]

[0087] 1 Payoff Reel 2. Welding machine 3. Electrolytic cleaning equipment 4 Inlet looper 5 Pre-tropical zone 6 Heating Zone 7. Equal Temperature 8 Cooling Zone 8A First Cooling Zone 8B Second cooling zone 9 Induction heating device 11 Zinc pot 12 Thermometer

Claims

1. A continuous annealing facility for steel sheets including a heating zone, a soaking zone, and a cooling zone in this order, at least one induction heating device between the soaking zone and the cooling zone; a control device that sets operating conditions of the induction heating device based on the phase fraction during annealing obtained by the phase fraction prediction model, the phase fraction prediction model is a machine learning model generated using training data in which the components, dimensions, and temperature of the steel sheet and the operating conditions of a continuous annealing furnace are used as input variables, and the phase fraction of the steel sheet during annealing is used as an output variable; The dimensions of the steel plate include the plate thickness of the steel plate, the temperature of the steel sheet includes the temperature of the steel sheet measured at a location corresponding to an entrance of the annealing equipment, an entrance of the heating zone, an entrance of the soaking zone, or an entrance of the induction heating device, and a maximum temperature reached by the induction heating device; The continuous annealing facility, wherein the operating conditions of the continuous annealing furnace include a conveying speed of the steel sheet.

2. A continuous annealing method for a steel sheet, which comprises passing through a heating zone, a soaking zone, and a cooling zone in this order, At least one induction heating device is provided between the soaking zone and the cooling zone, setting operating conditions of the induction heating device based on the phase fraction during annealing obtained by the phase fraction prediction model; the phase fraction prediction model is a machine learning model generated using training data in which the components, dimensions, and temperature of the steel sheet and the operating conditions of a continuous annealing furnace are used as input variables, and the phase fraction of the steel sheet during annealing is used as an output variable; The dimensions of the steel plate include the plate thickness of the steel plate, the temperature of the steel sheet includes the temperature of the steel sheet measured at a location corresponding to an entrance of the annealing equipment, an entrance of the heating zone, an entrance of the soaking zone, or an entrance of the induction heating device, and a maximum temperature reached by the induction heating device; The continuous annealing method, wherein the operating conditions of the continuous annealing furnace include a conveying speed of the steel sheet.

3. The induction heating device heats the steel sheet at a rate of 10°C / s or more and 200°C / s or less, 3. The continuous annealing method according to claim 2, wherein cooling of the steel sheet in the cooling zone begins within 10 seconds after heating by the induction heating device is completed.

4. A method for producing a cold-rolled steel sheet, comprising annealing the steel sheet, which is a cold-rolled steel sheet, by an annealing method adjusted by the continuous annealing method according to claim 3.

5. A method for producing a plated steel sheet, comprising plating a surface of the steel sheet annealed by the method for producing a cold-rolled steel sheet according to claim 4.

6. The method for producing a plated steel sheet according to claim 5, wherein the plating treatment is an electrogalvanizing treatment, a hot-dip galvanizing treatment, or a hot-dip galvannealing treatment.

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