Steel strip manufacturing method and steel strip

Abstract

This steel strip manufacturing method for manufacturing a steel strip product by adjusting an operation condition of at least one process includes: acquiring actual result data about an operation condition of at least one process executed before a to-be-adjusted process for which an operation condition is to be adjusted; adjusting and setting the operation condition to be applied to the to-be-adjusted process so that a predicted value of an index representing the quality of the steel strip product is a preset target value; and processing the steel strip by applying the set operation condition to the to-be-adjusted process. The predicted value is calculated using a prediction model. The prediction model outputs the predicted value of the index representing the quality of the steel strip product when the input operation condition is applied to the to-be-adjusted process on the basis of actual result data that has been input. The prediction model is generated using data associating, for each unit length in the longitudinal direction of the steel strip product, actual result data for operating conditions in at least one process and a measurement value of the index representing the quality of the steel strip product.

Description

Steel strip manufacturing method and steel strip

[0001] The present disclosure relates to a steel strip manufacturing method and a steel strip. In particular, the present disclosure relates to a steel strip manufacturing method and a steel strip suitable for use as a member in industrial fields such as for automobiles.

[0002] In the manufacturing process of a steel strip, a slab is manufactured in a continuous casting process, and a hot-rolled steel strip is manufactured from the slab in a hot rolling process. The hot-rolled steel strip can be processed into a cold-rolled steel strip through a cold rolling process and a continuous annealing process. Also, the hot-rolled steel strip can be processed into a hot-dip galvanized steel strip through a cold rolling process and a continuous annealing hot-dip galvanizing process.

[0003] In the manufacture of a steel strip, in order to obtain desired quality, optimal manufacturing conditions are set in each process. However, in the long manufacturing process from steelmaking to the final process, it is difficult to manufacture all manufacturing conditions as targeted. Furthermore, as time passes, the state of each facility used in the manufacturing process also changes. As a result, the variation in the quality of the manufactured steel strip can increase.

[0004] Regarding the variation in the quality of the manufactured steel strip, Patent Document 1 discloses a method for accurately predicting the quality of a product in consideration of changes in the manufacturing conditions and manufacturing environment of the product. Also, Patent Document 2 discloses a method for manufacturing a heat-treated steel sheet having a specific chemical steel composition and a specific microstructure to be achieved within a heat treatment line. Further, Patent Documents 3 and 4 disclose 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 that accurately predict the steel sheet phase fraction in a high-temperature state and promptly reflect the predicted variation in the phase fraction in annealing conditions.

[0005] Japanese Patent Application Laid-Open No. 2019-74969 Japanese Patent Application Laid-Open No. 2022-46622 International Publication No. 2024-070279 Japanese Patent Application Laid-Open No. 2024-048291

[0006] However, the quality prediction method disclosed in Patent Document 1 is limited to predicting product quality based on training data derived from a large amount of manufacturing experience, and is not sufficient for dynamically controlling quality and reducing quality variability. Furthermore, the technology disclosed in Patent Document 2 is a method for controlling tensile properties through the target microstructure and expected microstructure for each product, and it is necessary to link the microstructure to the properties. In addition, since calculations are performed from the microstructure during the manufacturing process, a large amount of microstructure data from the manufacturing process and the product is necessary to perform predictions accurately. Furthermore, the technologies disclosed in Patent Documents 3 and 4 require correlating actual data (actual values ​​of operating conditions and temperature history) with the phase fraction during annealing in order to generate a prediction model, and a large amount of microstructure data from the manufacturing process and the product is necessary to perform predictions accurately.Therefore, a steel strip manufacturing method is needed that reduces quality variability in a way different from these conventional technologies.

[0007] Therefore, this disclosure was developed in view of the above-mentioned situation and aims to provide a steel strip manufacturing method that can reduce quality variability and improve product yield by appropriately changing the operating conditions of the steel strip manufacturing process based on the quality predicted by a predictive model. Furthermore, this disclosure aims to provide a steel strip with reduced quality variability produced by the manufacturing method described above.

[0008] (1) A steel strip manufacturing method according to one embodiment of the present disclosure is a steel strip manufacturing method for manufacturing a steel strip product by adjusting the operating conditions of at least one process, and includes: acquiring actual data of operating conditions in at least one process that is executed before the adjustment target process to which the operating conditions of the adjustment target process are to be adjusted; adjusting and setting the operating conditions to be applied to the adjustment target process so that the predicted value of an index representing the quality of the steel strip product, assuming that the steel strip to be processed in the adjustment target process is processed in the adjustment target process, becomes a predetermined target value; and processing the steel strip by applying the set operating conditions to the adjustment target process. The predicted value is calculated using a prediction model. The prediction model is configured to accept input of actual data relating to the steel strip to be processed and operating conditions to be applied to the adjustment target process, and to output a predicted value of an index representing the quality of the steel strip product, assuming that the input operating conditions are applied to the adjustment target process to process the steel strip. The prediction model is generated using data that associates actual operating conditions data in at least one process with measured values ​​of an index representing the quality of the steel strip product for each unit length in the longitudinal direction of the steel strip product.

[0009] (2) In the steel strip manufacturing method described in (1) above, the adjustment target process may be a continuous annealing process using a continuous annealing facility that includes a heating zone, a soaking zone and a cooling zone. The steel strip manufacturing method may include obtaining actual data of operating conditions applied in at least one of the processes of ironmaking, steelmaking, hot rolling or cold rolling which is performed before the continuous annealing process, and adjusting and setting the operating conditions applied to the continuous annealing process so that the predicted value of an index representing the quality of the steel strip product, assuming that the steel strip to be processed is processed in the continuous annealing process, becomes a predetermined target value. The prediction model may be configured to accept input of actual data relating to the steel strip to be processed and operating conditions to be applied to the continuous annealing process, and to output a predicted value of an index representing the quality of the steel strip product, assuming that the input operating conditions are applied to the continuous annealing process to process the steel strip to be processed.

[0010] (3) In the steel strip manufacturing method described in (2) above, the operating conditions of the continuous annealing process may include the operating conditions of the homogenized zone or the cooling zone.

[0011] (4) In the steel strip manufacturing method described in (1) above, the adjustment target process may be a continuous annealing process using a continuous annealing facility including a heating zone, a soaking zone, an induction heating device, and a cooling zone. The steel strip manufacturing method may include obtaining actual operating data applied to at least one of the processes of ironmaking, steelmaking, hot rolling, or cold rolling which is performed before the continuous annealing process, or actual operating data applied to a process using the heating zone or the soaking zone, which is equipment prior to the induction heating device in the continuous annealing process, and adjusting and setting the operating conditions applied to the continuous annealing process so that the predicted value of an index representing the quality of the steel strip product, assuming that the steel strip to be processed is processed in the continuous annealing process, becomes a predetermined target value. The prediction model may be configured to accept input of actual data relating to the steel strip to be processed and operating conditions to be applied to the continuous annealing process, and to output predicted values ​​of an index representing the quality of the steel strip product, assuming that the input operating conditions are applied to the continuous annealing process to process the steel strip.

[0012] (5) In the steel strip manufacturing method described in (4) above, the operating conditions of the continuous annealing process may include the operating conditions of the induction heating device or the cooling zone.

[0013] (6) In the steel strip manufacturing method described in any one of (2) to (5) above, the temperature of the steel strip at the exit of the homogenizing zone may be 600°C or higher and 1000°C or lower. The residence time of the steel strip in the homogenizing zone may be 1000 seconds or less. The temperature of the steel strip at the exit of the cooling zone may be 0°C or higher and 800°C or lower.

[0014] (7) The steel strip manufacturing method described in any one of (2) to (6) above may further include inputting the standard operating conditions of the continuous annealing equipment as the operating conditions of the continuous annealing equipment into the prediction model.

[0015] (8) In the steel strip manufacturing method described in any one of (2) to (7) above, the continuous annealing equipment may be configured to perform a hot-dip galvanizing process or an alloying process.

[0016] (9) In the steel strip manufacturing method described in any one of (1) to (8) above, the indicators representing the quality of the steel strip product may include at least one of the following: tensile strength, yield strength, elongation, hole expansion ratio, bendability, r value, hardness, fatigue properties, impact value, delayed fracture value, wear value, chemical treatment properties, high temperature properties, low temperature toughness, corrosion resistance, magnetic properties, surface properties, or plating properties.

[0017] (10) In the steel strip manufacturing method described in any one of (1) to (9) above, the prediction model may be a statistical model or a machine learning model.

[0018] A steel strip (11) according to one embodiment of the present disclosure is manufactured by the steel strip manufacturing method described in any one of (1) to (10) above.

[0019] (12) The steel strip described in (11) above may have a microstructure containing 1% or more and 100% or less of one or more phases selected from ferrite, martensite, bainite, pearlite, or retained austenite.

[0020] This disclosure provides a steel strip manufacturing method that can improve product yield by accurately predicting the quality after a continuous annealing process and appropriately changing the operating conditions of the steel strip manufacturing process based on the predicted quality. Furthermore, by using actual data applied to processing at unit length intervals in the longitudinal direction of the steel strip for the construction of the prediction model, it is possible to create a highly accurate prediction model without using a large amount of microstructure data. In addition, the variation in the quality of steel strips manufactured by the above-described steel strip manufacturing method is reduced.

[0021] Figure 2 is an example of a manufacturing process in the steel strip manufacturing method according to this disclosure. Figure 3 is a block diagram showing an example configuration of the steel strip manufacturing system according to this disclosure. Figure 4 is a block diagram showing an example configuration when the continuous annealing equipment is further equipped with an induction heating device. Figure 5 is a flowchart showing an example of a procedure for the steel strip manufacturing method according to this disclosure. Figure 6 is a graph showing the composition of the first element of each steel strip used in the first embodiment. Figure 7 is a graph showing the composition of the second element of each steel strip used in the first embodiment. Figure 8 is a graph showing the hot rolling temperature of each steel strip used in the first embodiment. Figure 9 is a graph showing the composition of the third element of each steel strip used in the second embodiment. Figure 10 is a graph showing the composition of the first element of each steel strip used in the third embodiment. Figure 10 is a graph showing the composition of the second element of each steel strip used in the third embodiment. Figure 10 is a graph showing the hot rolling temperature of each steel strip used in the third embodiment. Figure 11 is a graph showing the composition of the second element of each steel strip used in the third embodiment. Figure 1 third element of each steel strip used in the fourth embodiment. This graph shows the hot rolling temperatures of each steel strip used in the fourth embodiment.

[0022] The steel strip manufacturing method and embodiments of the steel strip relating to this disclosure will be described below with reference to the drawings. Each drawing is schematic and may differ from the actual one. Furthermore, the following embodiments are illustrative of an apparatus or method for realizing the technical idea of ​​this disclosure and do not limit the configuration to those described below. In other words, the technical idea of ​​this disclosure can be modified in various ways within the technical scope described in the claims.

[0023] As illustrated in Figure 1, the manufacturing process in the steel strip manufacturing method according to this disclosure includes a pig ironmaking process S1, a steelmaking process S2, a hot rolling process S3, a cold rolling process S4, and a continuous annealing process S5. The steel strip product manufactured by the manufacturing process in the steel strip manufacturing method according to this disclosure is a thin steel strip. Furthermore, the steel material manufactured is assumed to be a cold-rolled steel sheet.

[0024] In the ironmaking process S1, the raw material iron ore is charged into the blast furnace together with limestone and coke, and molten pig iron is produced. The pig iron extracted from the blast furnace is then subjected to compositional adjustments, such as carbon content, in a converter, and the final compositional adjustments are carried out through secondary refining.

[0025] In the steelmaking process S2, as a method for preparing a steel slab having a desired component composition, for example, molten steel having the above component composition is produced by melting the steel material. The melting method is not particularly limited and may be a known melting method such as converter melting or electric furnace melting. Next, a steel slab is produced by solidifying the obtained molten steel. The method for producing a steel slab from molten steel is not particularly limited and may be a continuous casting method, ingot casting method or thin slab casting method. From the viewpoint of preventing macrosegregation, it is preferable to produce a steel slab using a continuous casting method. In a continuous casting machine, refined steel is cast to produce a slab, or intermediate material called a cast slab.

[0026] In the hot rolling process S3, the slab is heated in a heating furnace, and hot rolling is performed by a rolling mill to produce hot-rolled steel.

[0027] In the cold rolling process S4, the hot-rolled steel material is cooled in a cooling process in a cooling device, and cold rolling is performed by a rolling mill to produce cold-rolled steel strip.

[0028] In the continuous annealing process S5, the cold-rolled steel material is subjected to continuous annealing treatment to produce steel strip products.

[0029] The manufacturing process may include other processes as appropriate, such as pickling, skin passing, winding, or inspection.

[0030] (Example of configuration of steel strip manufacturing system 1) As shown in Figure 2, the steel strip manufacturing system 1 according to this disclosure comprises a control device 10, a continuous annealing machine 20, a management PC (Personal Computer) 30, and a performance database 40. The control device 10 controls the continuous annealing machine 20. The continuous annealing machine 20 is used to execute the continuous annealing process S5 shown in Figure 1.

[0031] The management PC 30 manages steel strip information. Steel strip information includes information that identifies one or more of the following: the product name of the steel strip, the steel type, the symbol used to manage the steel strip, the thickness of the steel strip, or the width of the steel strip. The steel strip information also includes the operating conditions set for the continuous annealing equipment 20.

[0032] The management PC 30 may be implemented as a computer such as a desktop PC, notebook PC, or tablet PC. The management PC 30 may be replaced by at least one server. The management PC 30 is not limited to these examples and may be replaced by various devices. The management PC 30 may be configured to include a communication module that is capable of communicating with other devices such as the control device 10.

[0033] The management PC 30 may be implemented in an on-premises environment or using cloud services. The management PC 30 may also be implemented in a hybrid form combining an on-premises environment and cloud services.

[0034] The performance database 40 stores performance data in association with steel strip information. The performance data represents the actual operating conditions when the steel strip is processed at each stage of the manufacturing process in the steel strip manufacturing method. In the hot rolling process S3 or the cold rolling process S4, the actual operating conditions may change depending on the rolling direction of the rolled steel plate, that is, the position in the longitudinal direction of the steel strip. Therefore, the actual operating conditions of the rolling mill in the hot rolling process S3 or the cold rolling process S4 may be acquired for each unit length in the longitudinal direction of the steel strip. The unit length may be set as appropriate.

[0035] The performance database 40 may be configured to include at least one server or storage device. The performance database 40 may be configured to include, for example, semiconductor memory, magnetic memory, or optical memory. The performance database 40 may be configured to include an electromagnetic storage medium such as a magnetic disk. The performance database 40 may be configured to include a communication module that is configured to communicate with other devices such as the control device 10.

[0036] The performance database 40 may be implemented in an on-premises environment or using cloud services. The performance database 40 may also be implemented in a hybrid form combining an on-premises environment and cloud services.

[0037] The manufacturing system 1 may further include equipment used to perform each of the steps S1 to S4 of the manufacturing process shown in Figure 1. The performance database 40 may acquire performance data from each piece of equipment and store it in association with the steel strip information.

[0038] <Control device 10> The control device 10 comprises an acquisition unit 11, a calculation unit 12, a storage unit 13, and an output unit 14.

[0039] The acquisition unit 11 may be connected to other devices such as the management PC 30 or the performance database 40 via a network such as a LAN (Local Area Network), or directly without a network. The acquisition unit 11 may also be configured to include a communication interface for connecting to other devices such as the management PC 30 or the performance database 40 via various wired or wireless communication methods.

[0040] The acquisition unit 11 may include an input device that receives input from a user, such as a manager or worker of the steel strip manufacturing system 1. The input device may include, for example, a keyboard or physical keys, or a pointing device such as a touch panel, touch sensor, or mouse. The input device is not limited to these examples and may include various other devices. The acquisition unit 11 may be connected to an external input device.

[0041] The arithmetic unit 12 may be configured to include one or more processors. The processors may include general-purpose processors such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). The processors may include dedicated processors specialized for specific processing. The processors are not limited to these and may include any processor. The processors may realize the functions of the control device 10 by reading and executing programs stored in the memory unit 13, which will be described later. The arithmetic unit 12 may be configured to include one or more dedicated circuits. The dedicated circuits may include ASICs (Application Specific Integrated Circuits) or FPGAs (Field Programmable Gate Arrays), etc.

[0042] The storage unit 13 may be configured to include one or more memories. The memory may include, for example, a semiconductor memory, a magnetic memory, or an optical memory, but is not limited thereto and may include any memory. The storage unit 13 may be configured to include an electromagnetic recording medium such as a hard disk drive (HDD). The storage unit 13 may store the program executed by the arithmetic unit 12. The storage unit 13 may function as a work memory of the arithmetic unit 12.

[0043] The output unit 14 may be communicably connected to the continuous annealing facility 20 via a network such as a LAN or directly without passing through a network, and may be configured to output information or data to the continuous annealing facility 20. The output unit 14 may be configured to include a communication interface for communicably connecting to the continuous annealing facility 20 in various communication methods, wired or wireless.

[0044] The output unit 14 may be configured to include a display device. The display device may include various displays such as, for example, a liquid crystal display. The output unit 14 may be configured to include other various devices or interfaces without being limited to these examples.

[0045] The control device 10 may be realized as a computer such as a desktop PC, a notebook PC, or a tablet PC. The control device 10 may be configured to include at least one server. The control device 10 may include various devices without being limited to these examples. Also, the control device 10 may be realized in an on-premises environment or may be realized using a cloud service.

[0046] <Continuous annealing facility 20> The continuous annealing facility 20 includes a heating zone 21, a soaking zone 22, and a cooling zone 24.

[0047] The heating zone 21 is a facility for heating a steel plate, and heats the steel plate so as to raise the temperature to a preset temperature within the range of 500°C to 1000°C according to the steel grade. In the heating zone 21 according to the present disclosure, a direct-fired heating furnace method is adopted so that the temperature can be raised to a certain level in a short time and the surface state can be controlled. The direct-fired heating furnace can reduce the volume of the furnace due to its high heating capacity, and in addition, enables flexible control of the oxidation-reduction reaction on the steel plate surface in consideration of the subsequent plating process.

[0048] The soaking zone 22 is a facility for holding the steel plate heated in the heating zone 21 at a predetermined temperature, and is necessary for controlling the recrystallization, recovery, or reverse transformation of the structure of the steel strip.

[0049] When the temperature of the steel plate on the outlet side of the soaking zone 22 is less than 600°C, recrystallization or reverse transformation does not proceed sufficiently, and the workability or strength required for the steel strip product cannot be obtained. On the other hand, when the temperature of the steel plate on the outlet side of the soaking zone 22 exceeds 1000°C, the required workability or strength cannot be obtained due to excessive coarsening of the α phase, excessive generation of austenite, or coarsening of the prior austenite grain size. Therefore, the temperature of the steel plate on the outlet side of the soaking zone 22 is preferably 600°C or more and 1000°C or less.

[0050] Even if the residence time of the steel strip in the soaking zone 22 exceeds 1000 seconds, the effect of controlling the structure of the steel plate by the soaking zone saturates. Since the longer the residence time in the soaking zone 22, the higher the equipment cost, in order to reduce the equipment cost, the residence time of the steel strip in the soaking zone 22 is preferably 1000 seconds or less, more preferably 500 seconds or less, and still more preferably 250 seconds or less.

[0051] The lower limit of the residence time of the steel strip in the soaking zone 22 is not limited, but from the viewpoint of homogenizing the structure in the width direction of the steel strip, the residence time of the steel strip in the soaking zone 22 is preferably 1 second or more, more preferably 10 seconds or more.

[0052] In the present disclosure, as the heating method of the soaking zone 22, from the viewpoints of efficiency and heating uniformity, radiation heating, that is, a radiant tube heating method is adopted.

[0053] The cooling zone 24 is equipment for cooling the steel sheet to a predetermined temperature. Cooling methods include gas jet cooling, roll cooling, or water cooling. Different cooling methods may be combined, or the cooling conditions for the same type of cooling method may be changed, in order to control the temperature history, i.e., the cooling history, of the steel sheet. The quality of the steel strip can be controlled by controlling the steel sheet structure when it reaches the cooling stop temperature based on the cooling history.

[0054] The lower limit of the cooling stop temperature is assumed to be 0°C. Normally, when cooling using a water cooling method, the cooling stop temperature will be above 0°C. Cooling steel plates below 0°C requires special equipment, leading to increased equipment costs.

[0055] The upper limit for the cooling stop temperature is assumed to be 800°C. If the cooling stop temperature exceeds 800°C, the quality of the steel sheet controlled by the uniforming chamber 22 changes, making it difficult to control the quality required for the steel strip product. Therefore, the cooling stop temperature is 800°C or lower, preferably 700°C or lower, and more preferably 600°C or lower.

[0056] Based on the above, the cooling stop temperature in this disclosure is 0°C or higher and 800°C or lower.

[0057] As illustrated in Figure 3, the continuous annealing equipment 20 may further include an induction heating device 23 between the uniform heating zone 22 and the cooling zone 24. The induction heating device 23 is a device that heats the steel sheet to a predetermined temperature. The purpose of this process is to heat the entire steel sheet uniformly to a predetermined temperature in a short time. The induction heating device 23 is necessary to control the recrystallization, recovery, or reverse transformation of the steel strip's structure. Furthermore, by reflecting temperature fluctuations in the uniform heating zone 22 in changes to the operating conditions of the induction heating device 23, a quick response can be achieved, minimizing variations in the quality of the steel strip product. Compared to the radiant heating method, the induction heating device 23 has the advantage of a higher heating capacity for the steel sheet and a higher responsiveness to temperature control.

[0058] The temperature of the steel sheet in the induction heating device 23 may be between 600°C and 1000°C. If the temperature of the steel sheet in the induction heating device 23 is below 600°C, recrystallization or reverse transformation will not proceed sufficiently, and the desired workability or strength of the steel strip product cannot be obtained. On the other hand, if the temperature of the steel sheet in the induction heating device exceeds 1000°C, the desired workability or strength cannot be obtained due to excessive coarsening of the α phase or excessive formation of austenite, and coarsening of the prior austenite grain size. Therefore, it is preferable that the temperature of the steel sheet in the induction heating device 23 be between 600°C and 1000°C.

[0059] Furthermore, the heating rate in the induction heating device 23, i.e., the rate of change of temperature over time, is preferably 5°C / s or more and 200°C / s or less. If the heating rate is less than 5°C / s, the characteristic of high responsiveness to temperature control is lost, making rapid response difficult. On the other hand, if the heating rate exceeds 200°C / s, uniformity in the width direction of the steel strip may be lost. Therefore, the heating rate of the induction heating device 23 is preferably 5°C / s or more and 200°C / s or less.

[0060] In this disclosure, when heating a steel strip in such a temperature range, the temperature may exceed the Curie point at which the magnetism of the steel sheet changes. Therefore, the induction heating device 23 is preferably of the transverse type.

[0061] <Other Manufacturing Equipment> The steel strip manufacturing system 1 may also include a hot-dip galvanizing facility after the cooling zone 24 of the continuous annealing facility 20. The hot-dip galvanizing facility can perform hot-dip galvanizing on steel sheets. The hot-dip galvanizing may be performed using known methods. The hot-dip galvanizing facility may include a snout, bath rolls, or wiping device as needed. The wiping device blows wiping gas from nozzles positioned on both sides of the steel sheet to blow away excess molten zinc adhering to the surface of the steel sheet, thereby controlling the amount of adhesion. If the temperature of the steel sheet before the hot-dip galvanizing process is lower than the plating bath temperature, it is preferable to heat it to the plating bath temperature using a reheating facility. The hot-dip galvanizing process may be included in the continuous annealing process. In other words, the continuous annealing facility 20 may be configured to perform the hot-dip galvanizing process.

[0062] The steel strip manufacturing system 1 may include an alloying treatment facility following the hot-dip galvanizing facility. The alloying treatment facility heats the steel sheet and performs an alloying treatment to alloy the zinc deposited on the steel sheet with the steel sheet. The alloying treatment may be performed using known methods. The alloying treatment process may be included in the continuous annealing process. In other words, the continuous annealing facility 20 may be configured to perform the alloying treatment process.

[0063] The steel strip manufacturing system 1 may further include, after the alloying treatment equipment, other equipment such as a heating zone, a heat-retaining zone, a cooling zone, a temper rolling mill, or a straightening machine, in order to improve the quality of the final product and ensure manufacturing stability. These pieces of equipment may be used according to the quality required for the product.

[0064] (Example of operation of the control device 10) In the steel strip manufacturing system 1 according to the present disclosure, the calculation unit 12 of the control device 10 sets a process in the steel strip manufacturing process to be adjusted, i.e., the adjustment target process, predicts the value of an index representing the quality of the steel strip processed in the adjustment target process, and sets the operating conditions of the adjustment target process to control the equipment used in the adjustment target process so that the predicted value becomes the target value set for each steel strip product. If the adjustment target process is a continuous annealing process, the calculation unit 12 may predict the value of an index representing the quality of the steel strip processed in the continuous annealing equipment 20 used in the continuous annealing process, and set the operating conditions of the continuous annealing equipment 20 to control the continuous annealing equipment 20 so that the predicted value becomes the target value set for each steel strip product. As shown in Figure 3, if the continuous annealing equipment 20 is equipped with an induction heating device 23, the calculation unit 12 may, as part of the continuous annealing process, designate the induction heating process using the induction heating device 23 as the adjustment target process, predict the value of an index representing the quality of the steel strip processed by the induction heating device 23, and control the induction heating device 23 by setting the operating conditions of the induction heating device 23 so that the predicted value becomes the target value determined for each steel strip product.

[0065] Indicators representing the quality of steel strip may include at least one of the following: tensile strength, yield strength, elongation, hole expansion ratio, bendability, r value, hardness, fatigue properties, impact value, delayed fracture value, wear value, chemical treatment properties, high-temperature properties, low-temperature toughness, corrosion resistance, magnetic properties, surface properties, or plating properties.

[0066] If the continuous annealing equipment 20 does not include an induction heating device 23 as illustrated in Figure 2, the operating conditions of the continuous annealing equipment 20 include at least one operating condition of the heating zone 21, the uniform zone 22, or the cooling zone 24, preferably including the operating conditions of the uniform zone 22 and the cooling zone 24. If the continuous annealing equipment 20 includes an induction heating device 23 as illustrated in Figure 3, the operating conditions of the continuous annealing equipment 20 include at least one operating condition of the heating zone 21, the uniform zone 22, the induction heating device 23, or the cooling zone 24, preferably including the operating conditions of the induction heating device 23 and the cooling zone 24. If the continuous annealing equipment 20 includes equipment used in a hot-dip galvanizing process, equipment used in an alloying process, or other equipment used in a process after the alloying process, the operating conditions of the continuous annealing equipment 20 may include at least one operating condition of these pieces of equipment.

[0067] The calculation unit 12 predicts an index representing the quality of the steel strip using a prediction model. The prediction model may be configured to output predicted values ​​for an index representing the quality of the steel strip product, assuming that the operating conditions input to the prediction model are applied to the continuous annealing equipment 20 and the steel strip to be processed is processed by applying the operating conditions input to the prediction model to the continuous annealing equipment 20 and processing the steel strip. If the continuous annealing equipment 20 is equipped with an induction heating device 23 as illustrated in Figure 3, the prediction model may be configured to output a predicted value of an index representing the quality of the steel strip product when the operating conditions input to the prediction model are applied to the continuous annealing equipment 20 and the steel strip is processed, assuming that the operating conditions input to the prediction model are applied to the continuous annealing equipment 20 and the steel strip is processed, based on actual data from when the steel strip to be processed in the continuous annealing equipment 20 was processed in a process prior to the continuous annealing process, or in a facility prior to the induction heating device 23 in the continuous annealing process, such as the heating zone 21 or the uniform zone 22.

[0068] The prediction model may be structured as a statistical model or a machine learning model. The prediction model may be structured for each type of steel strip product. The types of steel strip products may be classified, for example, based on the tensile strength grade of the steel strip. The elements for classifying steel strip products are not limited to the tensile strength grade, but may include mechanical properties other than tensile strength, steel type, or surface properties, etc.

[0069] When the prediction model is constructed as a statistical model, it may be generated for each steel strip product by statistically processing data that associates the actual data of each process stored in the actual database 40 with the measured values ​​of an indicator representing the quality of the steel strip product.

[0070] If the predictive model is configured as a machine learning model, it may be generated for each steel strip product by performing machine learning using training data that associates the actual data of each process stored in the actual database 40 with the measured values ​​of an indicator representing the quality of the steel strip product.

[0071] When the actual data for the hot rolling process S3, cold rolling process S4, or continuous annealing process S5 is acquired for each unit length in the longitudinal direction of the steel strip, it is associated with the measured value of an index representing the quality of the steel strip product for each unit length in the longitudinal direction. When the predictive model is configured as a statistical model, it is generated by statistically processing data that associates the actual data of each process with the measured value of the index for each unit length in the longitudinal direction of the steel strip product. When the predictive model is configured as a machine learning model, it is generated by performing machine learning using training data that associates the actual data of each process with the measured value of the index for each unit length in the longitudinal direction of the steel strip product. In processes that continuously process the steel strip in the longitudinal direction, operating conditions may fluctuate during processing. Since operating conditions are related to quality, fluctuations in operating conditions affect quality. The unit length in the longitudinal direction of the steel strip product is preferably 10 m or less, and more preferably 3 m or less. By generating a predictive model that takes into account the actual data for each unit length in the longitudinal direction of the steel strip, the prediction accuracy of the index representing quality is improved.

[0072] The calculation unit 12 may calculate a predicted value by assuming that arbitrary operating conditions are applied to the continuous annealing equipment 20 to process the steel strip to be processed, repeating this process while changing the arbitrary operating conditions until the absolute value of the difference between the predicted value and the target value falls below a predetermined value, and setting the operating conditions at which the absolute value of the difference between the predicted value and the target value falls below the predetermined value as the operating conditions to be applied to the continuous annealing equipment 20. The predetermined value may be set as appropriate. The calculation unit 12 may input the standard operating conditions of the continuous annealing equipment 20 as arbitrary operating conditions into the prediction model, and evaluate the predicted value of an index representing the quality of the steel strip processed by the continuous annealing equipment 20 by applying the input standard operating conditions.

[0073] The calculation unit 12 may compare the predicted values ​​for each of the multiple operating conditions, calculated by assuming that each of the multiple operating conditions is applied to the continuous annealing equipment 20 to process the steel strip to be processed, with the target values, and set the operating conditions that minimize the difference between the predicted values ​​and the target values ​​as the operating conditions to be applied to the continuous annealing equipment 20. The calculation unit 12 may input the standard operating conditions of the continuous annealing equipment 20, or operating conditions that are a part of the standard operating conditions, into the prediction model as multiple operating conditions, and evaluate the predicted values ​​of an index representing the quality of the steel strip processed in the continuous annealing equipment 20 by applying the input operating conditions.

[0074] By setting the operating conditions applied when processing the steel strip in the continuous annealing equipment 20 based on highly accurate predicted values ​​of indicators, the quality of the steel strip products manufactured through the continuous annealing process is stabilized.

[0075] The following describes an example of operation in which the control device 10 executes a steel strip manufacturing method including the procedure illustrated in the flowchart of Figure 4 to set the operating conditions of the continuous annealing equipment 20, and the continuous annealing equipment 20 processes the steel strip. The steel strip manufacturing method may be implemented as a steel strip manufacturing program to be executed by the processor constituting the calculation unit 12 of the control device 10. The steel strip manufacturing program may be stored in a non-temporary computer-readable medium.

[0076] The calculation unit 12 acquires steel strip information related to the steel strip to be processed from the management PC 30 using the acquisition unit 11 (step S11).

[0077] The calculation unit 12 obtains performance data from the performance database 40 for processes prior to the continuous annealing process of the steel strip to be processed, based on the steel strip information (step S12).

[0078] The calculation unit 12 assumes operating conditions to be applied to the continuous annealing equipment 20 (step S13).

[0079] The calculation unit 12 uses a prediction model to apply assumed operating conditions and calculates a predicted value for an index representing the quality of the steel strip when it is processed in the continuous annealing equipment 20 (step S14).

[0080] The calculation unit 12 determines whether the absolute value of the difference between the predicted value and the target value of the indicator representing the quality of the steel strip is less than a predetermined value (step S15). If the absolute value of the difference between the predicted value and the target value is not less than the predetermined value (step S15: NO), that is, if the absolute value of the difference between the predicted value and the target value is greater than or equal to the predetermined value, the calculation unit 12 returns to the procedure in step S13 and repeats the prediction procedure in step S14 and the determination procedure in step S15, assuming different operating conditions.

[0081] If the absolute value of the difference between the predicted value and the target value is less than a predetermined value (step S15: YES), the calculation unit 12 sets the operating conditions assumed in step S13 as the operating conditions to be applied to the continuous annealing equipment 20 (step S16). The calculation unit 12 may output the operating conditions to be set for the continuous annealing equipment 20 from the output unit 14.

[0082] The calculation unit 12 controls the continuous annealing equipment 20 under the set operating conditions to process the steel strip (step S17). After executing the procedure in step S17, the calculation unit 12 finishes executing the flowchart in Figure 4. The calculation unit 12 may set the operating conditions of the continuous annealing equipment 20 in real time by executing the procedure in the flowchart in Figure 4 for each unit length of steel strip loaded into the continuous annealing equipment 20.

[0083] The calculation unit 12 may assume multiple operating conditions in the procedure of step S13 in Figure 4, and calculate predicted values ​​of the indicator for each of the multiple operating conditions in the procedure of step S14. In this case, the calculation unit 12 may calculate the absolute difference between the predicted value and the target value of the indicator corresponding to each of the multiple operating conditions in the procedure of steps S15 and S16, and set the operating condition that minimizes the absolute value of the difference as the operating condition to be applied to the continuous annealing equipment 20.

[0084] (Component Composition of Steel Strip Products) Next, the elemental component composition of the steel strip products manufactured by the manufacturing method relating to this disclosure will be described. In the following description, "%", which is the unit of component content, means "mass%".

[0085] The carbon content is set to be between 0.0001% and 0.4%. Carbon is included to increase the strength in tensile tests and to ensure the desired strength. If the carbon content is less than 0.0001%, the strength targeted in this disclosure cannot be ensured. If the carbon content exceeds 0.4%, the toughness and weldability are likely to decrease, so it is preferable to set the carbon content to be between 0.0001% and 0.4%.

[0086] The Si content is specified as 0.01% or more and 4.0% or less. Si is a strengthening element that provides solid solution strengthening without reducing the ductility of the steel sheet. If the Si content is less than 0.01%, it is difficult to ensure the desired effect. On the other hand, if the Si content is too high, it leads to a significant increase in rolling load during hot rolling and cold rolling. It is also highly likely to reduce surface properties or weldability. Therefore, it is preferable to have a Si content of 0.01% or more and 4.0% or less.

[0087] The Mn content is set to be between 0.1% and 5.0%. Mn is included not only as a strengthening element through solid solution strengthening, but also to improve the hardenability of the steel and ensure the desired strength. If the Mn content is less than 0.1%, it is difficult to ensure the strength targeted in this disclosure. On the other hand, if the Mn content is excessive, there is a high possibility of deterioration of weldability or plating adhesion, and an increase in rolling load during hot rolling and cold rolling. Therefore, it is preferable to set the Mn content to be between 0.1% and 5.0%.

[0088] The phosphorus (P) content is specified as 0.3% or less. P is an element that has a solid solution strengthening effect and increases the strength of steel sheets. However, excessive addition of P can degrade formability. Therefore, it is preferable to keep the P content at 0.3% or less. Furthermore, due to production technology constraints, the P content is set at 0.002% or more.

[0089] The sulfur (S) content is specified as 0.030% or less. S, as an impurity element in steel, is highly likely to impair the formability or weldability of steel sheets. Therefore, it is preferable to keep the sulfur content at 0.030% or less.

[0090] The Al content should be 2.0% or less. Al acts as a deoxidizing element. Excessive Al content can lead to reduced processability or deterioration of surface properties. Therefore, it is preferable to keep the Al content at 2.0% or less. Furthermore, to obtain a sufficient deoxidizing effect, it is preferable to have an Al content of 0.001% or more.

[0091] The nitrogen (N) content is specified as 0.010% or less. N is a common impurity found in steel. Excessive N content may reduce weldability. Therefore, it is preferable to keep the N content at 0.010% or less. While there is no particular lower limit to the N content, due to production technology constraints, the N content is set at 0.0006% or higher.

[0092] The Ti content is set to 0.5% or less. Ti contributes to increased strength through the formation of fine carbides, nitrides, or carbonitrides during hot rolling and annealing. On the other hand, if Ti is present in excess, the amount of coarse Ti-based precipitates such as TiN, Ti(C,N), Ti(C,S), or TiS increases, which may degrade the toughness. Therefore, it is preferable to keep the Ti content at 0.5% or less. From the viewpoint of obtaining the above-mentioned strength-enhancing effect, it is preferable to have a Ti content of 0.001% or more as needed.

[0093] The Nb content is set at 0.5% or less. Nb contributes to improved hardenability and to increased strength through the formation of fine carbides, nitrides, or carbonitrides during hot rolling or annealing. On the other hand, excessive Nb content may lead to the formation of coarse carbides and nitrides, potentially reducing toughness or weldability. Therefore, it is preferable to keep the Nb content at 0.5% or less. From the viewpoint of obtaining the above-mentioned strength-enhancing effect, it is preferable to have an Nb content of 0.001% or more as needed.

[0094] The V content is set at 0.5% or less. Like Nb or Ti, V contributes to improved hardenability and also contributes to increased strength through the formation of fine carbides, nitrides, or carbonitrides during hot rolling or annealing. On the other hand, excessive V content may lead to the formation of coarse carbides and nitrides, potentially reducing toughness or weldability. Therefore, it is preferable to keep the V content at 0.5% or less. From the viewpoint of obtaining the above-mentioned strength-enhancing effect, it is preferable to have a V content of 0.001% or more as needed.

[0095] The B content is set to 0.01% or less. B contributes to increased strength by improving hardenability through segregation at austenite grain boundaries. To obtain this effect, it is preferable to have a B content of 0.0001% or more as needed. On the other hand, excessive B content may reduce weldability, so if B is included, it is preferable to have a B content of 0.01% or less.

[0096] The Cr content is specified as 2.0% or less. Cr is an element that enhances hardenability and increases strength. To obtain these effects, it is preferable to have a Cr content of 0.0005% or more, if necessary. On the other hand, if the Cr content exceeds 2.0%, excessive Cr-based carbides may be formed, potentially impairing hardenability. It may also degrade the surface properties. Therefore, when Cr is included, it is preferable to keep the Cr content at 2.0% or less.

[0097] The Ni content is specified as 2.0% or less. Ni is an element that enhances hardenability and therefore contributes to increased strength. To obtain this effect, it is preferable to have a Ni content of 0.005% or more as needed. An excessive increase in Ni content leads to an increase in cost. Therefore, when Ni is included, it is preferable to have a Ni content of 2.0% or less.

[0098] The Mo content is specified as 2.0% or less. Mo is an element that enhances hardenability and therefore contributes to increased strength. To obtain this effect, it is preferable to have a Mo content of 0.010% or more as needed. On the other hand, if the Mo content exceeds 2.0%, excessive Mo-based carbides may be formed, potentially impairing hardenability. It may also degrade toughness or weldability. Therefore, when Mo is included, it is preferable to keep the Mo content at 2.0% or less.

[0099] The Sb content is specified as 0.1% or less. Sb is an effective element in suppressing the reduction in strength of steel sheets by inhibiting decarburization or denitrification. Furthermore, since Sb is also effective in suppressing spot weldable cracks, it is preferable to have an Sb content of 0.002% or more as needed. On the other hand, if the Sb content exceeds 0.1%, the castability decreases. Therefore, when Sb is included, it is preferable to keep the Sb content at 0.1% or less.

[0100] The Sn content is specified as 0.1% or less. Like Sb, Sn is an effective element in suppressing the reduction in strength of steel sheets by inhibiting decarburization or denitrification. Furthermore, since Sn is also effective in suppressing spot weldable cracks, it is preferable to have a Sn content of 0.002% or more as needed. On the other hand, if the Sn content exceeds 0.1%, the castability decreases. Therefore, when Sb is included, it is preferable to have an Sb content of 0.1% or less.

[0101] The copper (Cu) content is specified as 2.0% or less. Cu is an element that enhances hardenability, thus increasing strength. To obtain this effect, it is preferable to have a Cu content of 0.005% or more, if necessary. On the other hand, if the Cu content exceeds 2.0%, there is a risk of deterioration of weldability or toughness, or a decrease in castability. Therefore, when including Cu, it is preferable to keep the Cu content at 2.0% or less.

[0102] The Ta content is set to 0.1% or less. Like Ti, Nb, and V, Ta contributes to increased strength by forming fine carbides, nitrides, or carbonitrides during hot rolling or annealing. To obtain such an effect, it is preferable to have a Ta content of 0.001% or more as needed. On the other hand, if the Ta content exceeds 0.1%, there is a risk that coarse precipitates or inclusions will be excessively formed, increasing defects on the surface and inside the steel sheet. Therefore, when Ta is included, it is preferable to have a Ta content of 0.1% or less.

[0103] The W content is set to 0.2% or less. Like Ti and Nb, W contributes to increased strength by forming fine carbides, nitrides, or carbonitrides during hot rolling or annealing. To obtain such an effect, it is preferable to have a W content of 0.001% or more as needed. On the other hand, if the W content exceeds 0.2%, a large amount of coarse precipitates or inclusions may be generated, potentially degrading formability or weldability. Therefore, when W is included, it is preferable to keep the W content at 0.2% or less.

[0104] The Mg content is specified as 0.1% or less. Mg is an element that is effective in improving toughness by refining inclusions such as sulfides or oxides. To obtain such an effect, it is preferable that the Mg content be 0.0001% or more. On the other hand, if the Mg content exceeds 0.1%, the surface quality deteriorates. Therefore, when Mg is included, it is preferable that the Mg content be 0.1% or less.

[0105] The Zn content is specified as 0.1% or less. Zn is an element that effectively improves toughness by spheroidizing the shape of inclusions. To obtain such an effect, it is preferable that the Zn content be 0.001% or more. On the other hand, if the Zn content exceeds 0.1%, excessive coarse precipitates or inclusions may be formed, and deterioration of the surface properties may become apparent. Therefore, when including Zn, it is preferable that the Zn content be 0.1% or less.

[0106] The Co content is specified as 1.0% or less. Like Zn, Co is an effective element for spheroidizing the shape of inclusions and improving toughness. To obtain such effects, it is preferable to have a Co content of 1.0% or more. On the other hand, if the Co content exceeds 1.0%, coarse precipitates or inclusions may be excessively formed, and deterioration of the surface properties may become apparent. Therefore, when including Co, it is preferable to keep the Co content at 1.0% or less.

[0107] The Zr content is specified as 0.2% or less. Zr is an effective element for spheroidizing the shape of inclusions and improving toughness. Zr also improves castability. To obtain these effects, it is preferable to have a Zr content of 0.001% or more. On the other hand, if the Zr content exceeds 0.2%, coarse precipitates or inclusions may be excessively formed, and deterioration of the surface properties may become apparent. Therefore, when Zr is included, it is preferable to keep the Zr content at 0.2% or less.

[0108] The Ca content is specified as 0.1% or less. Ca contributes to improved toughness through the refinement of inclusions. To obtain this effect, it is preferable to have a Ca content of 0.0001% or more as needed. On the other hand, if the Ca content exceeds 0.1%, there is a risk of excessive formation of coarse precipitates or inclusions, which may degrade the surface quality. Therefore, when Ca is included, it is preferable to have a Ca content of 0.1% or less.

[0109] The content of Se, Te, Ge, As, Sr, Cs, Hf, Pb, and Bi, as well as the content of REM (Rare Earth Metal), should be 0.1% or less. Se, Te, Ge, As, Sr, Cs, Hf, Pb, and Bi, as well as REM, are all effective elements for improving the hole-expanding properties of steel sheets. To obtain such effects, it is preferable that the content of Se, Te, Ge, As, Sr, Cs, Hf, Pb, and Bi, as well as REM, be 0.0001% or more. On the other hand, if the content of Se, Te, Ge, As, Sr, Cs, Hf, Pb, and Bi, as well as REM, exceeds 0.1%, excessive coarse precipitates or inclusions may be formed, and deterioration of the surface properties may become apparent. Therefore, when containing at least one of Ce, Se, Te, Ge, As, Sr, Cs, Hf, Pb, and Bi, and REM, it is preferable that the content of Se, Te, Ge, As, Sr, Cs, Hf, Pb, and Bi, and REM be 0.1% or less.

[0110] Steel strip products contain Fe and unavoidable impurities as elements other than those mentioned above.

[0111] (Structural Structure of Steel Strip Products) Next, the structural structure of steel strip products manufactured by the manufacturing method relating to this disclosure will be described.

[0112] The steel strip product may contain 1% or more and 100% or less of one or more phases selected from ferrite, martensite, bainite, pearlite, or retained austenite. In this disclosure, the steel structure will vary depending on the target quality of each steel strip product. For example, increasing the fraction of soft ferrite can ensure elongation. Also, increasing the fraction of hard martensite can ensure strength. As described above, the steel structure may be adjusted according to the target quality. Note that martensite includes fresh martensite obtained by normal quenching and so-called tempered martensite obtained by tempering fresh martensite.

[0113] Here, the area ratio of the steel microstructure is measured at a depth of 1 / 4 of the thickness of the steel plate, as described below. First, a sample is cut from the steel plate so that the cross-section parallel to the rolling direction of the steel plate, i.e., the L-section, becomes the observation surface. Next, the observation surface of the sample is polished using diamond paste. Next, the observation surface of the sample is polished using alumina. Then, the microstructure is revealed by etching the observation surface of the sample with nital. Then, using a scanning electron microscope (SEM), a 16 × 15 grid is placed at 4.8 μm intervals on an SEM image with a magnification of 1500x, in an area of ​​actual length 82 μm × 57 μm, and the area ratio of each microstructure is investigated by point counting, which counts the number of points on each phase.

[0114] Ferrite is a region that appears black in SEM images and has a massive morphology. Ferrite is a structure composed of crystal grains with a BCC lattice. Martensite is a region that appears white to light gray. Martensite is a hard structure that is formed by a transformation from austenite below the Ms point. Bainite is a region that appears black to dark gray and has massive or amorphous morphology. Bainite is a hard structure in which fine carbides are dispersed within needle-like or plate-like ferrite. Bainite is formed from austenite at relatively low temperatures. Bainite also contains a relatively small number of carbides. Pearlite is formed from austenite at relatively high temperatures and consists of layered ferrite and cementite.

[0115] Furthermore, the area fraction of retained austenite is measured at a depth of 1 / 4 of the steel plate thickness, as described below. First, the steel plate is mechanically ground in the thickness direction, i.e., in the depth direction, to a depth of 1 / 4 of the plate thickness, and then chemically polished with oxalic acid. The surface exposed by polishing is used as the observation surface. Next, the observation surface is observed by X-ray diffraction. CoKα rays are used as the incident X-rays, and the ratio of the diffraction intensity of each surface (200), (220), and (311) of fcc iron, i.e., austenite, to the diffraction intensity of each surface (200), (211), and (220) of bcc iron is calculated. Then, the volume fraction of retained austenite is calculated from the ratio of the diffraction intensities of each surface. Then, assuming that the retained austenite is three-dimensionally homogeneous, the volume fraction of retained austenite is considered to be the area fraction of retained austenite.

[0116] (Examples) Examples of the steel strip manufacturing method according to the present disclosure are described below. However, the scope of the present disclosure is not limited to the examples.

[0117] <First Embodiment> In the first embodiment, the rate of deviation from the standard tensile strength of steel strip products manufactured using the continuous annealing equipment 20 illustrated in Figure 2, which does not have an induction heating device 23, was evaluated. The actual data used to generate the prediction model consisted of tensile strength measurements of the steel strip products and data in which the longitudinal position was aligned every 1m along the longitudinal direction of each process.

[0118] In the first embodiment, 2,000 steel slabs were prepared by continuous casting. These slabs were then subjected to hot rolling, pickling, and cold rolling processes. Furthermore, these slabs were cast at different times. The hot rolling, pickling, and cold rolling processes for each slab were performed at different times.

[0119] The graph in Figure 5A shows the measured values ​​of the composition of the first element contained in 2000 steel slabs used in the first embodiment. The first element is one of the elements described above as elements that may be contained in the steel strip. The horizontal axis of the graph in Figure 5A is a number assigned sequentially to identify the steel slab. The vertical axis is the normalized value of the composition of the first element contained in the steel slab identified by the number on the horizontal axis. The normalization of the composition of the first element was performed so that the range from the lower limit to the upper limit of the content of the first element, which is set as a quality standard for steel slabs, falls within the range of -1 to +1.

[0120] The graph in Figure 5B shows the measured values ​​of the composition of the second element contained in the 2000 steel slabs used in the first embodiment. The second element is one of the elements described above as elements that may be contained in the steel strip, and is different from the first element. The horizontal axis of the graph in Figure 5B is a number assigned sequentially to identify the steel slab. The vertical axis is the normalized value of the composition of the second element contained in the steel slab identified by the number on the horizontal axis. The normalization of the composition of the second element was performed so that the range from the lower limit to the upper limit of the content of the second element, which is set as a quality standard for steel slabs, falls within the range of -1 to +1.

[0121] The graph in Figure 5C shows the temperatures obtained when the hot rolling process was performed on 2000 steel slabs used in the first embodiment. The horizontal axis of the graph in Figure 5C represents sequential numbers assigned to identify the steel slabs. The vertical axis represents the normalized temperature obtained when the hot rolling process was performed on the steel slabs identified by the numbers on the horizontal axis. The normalization of the temperature obtained when the hot rolling process was performed was carried out so that the range from the lower limit to the upper limit of the temperature set as the control standard for the rolling mill used in the hot rolling process falls within the range of -1 to +1.

[0122] As shown in the graphs in Figures 5A and 5B, the composition of the first and second elements contained in the steel slabs both meet the quality standards for steel slabs, but there is variation. Furthermore, as shown in the graph in Figure 5C, the temperature during the hot rolling process on the steel slabs meets the control standards for the rolling mill, but there is variation.

[0123] In the first embodiment, 1,000 steel strips were randomly selected from 2,000 steel strips obtained from 2,000 steel slabs before being loaded into the continuous annealing equipment 20. For each steel strip, operating conditions were set to be applied to the continuous annealing equipment 20 during processing so that the predicted tensile strength of each steel strip, assuming it had been processed in the continuous annealing equipment 20, would be the target value, i.e., within the standard value. Each steel strip was then processed in the continuous annealing equipment 20 using the set operating conditions. On the other hand, in the method according to the first comparative example, the remaining 1,000 steel strips were processed by applying the same operating conditions set in the continuous annealing equipment 20 to all of them.

[0124] In both the first embodiment and the first comparative example, post-treatments such as plating and temper rolling were performed after the processing in the continuous annealing equipment 20.

[0125] Table 1 shows the percentage of out-of-spec tensile strength for steel strip products manufactured using the methods of the first embodiment and the first comparative example. Compared to the case where operating conditions were not changed for each steel strip, as in the first comparative example, the percentage of out-of-spec tensile strength was reduced when operating conditions were changed for each steel strip according to actual data, as in the first embodiment.

[0126]

[0127] <Second Embodiment> In the second embodiment, the plating quality, i.e., the rate of non-plating occurrence, of steel strip products manufactured using the continuous annealing equipment 20 illustrated in Figure 2, which does not have an induction heating device 23, was evaluated. In the second embodiment, as in the first embodiment, 2000 steel strips were prepared. Furthermore, the actual data used to generate the prediction model was data in which the longitudinal position of the steel strip product was aligned every 1m in the longitudinal direction with the external quality evaluation position of the steel strip product.

[0128] The graph in Figure 6A shows the measured values ​​of the composition of the third element contained in 2000 steel slabs used in the second embodiment. The third element is one of the elements described above as elements that may be contained in the steel strip. The third element may be the same as the first or second element in the first embodiment, or it may be a different element. The horizontal axis of the graph in Figure 6A is a number assigned sequentially to identify the steel slab. The vertical axis is the normalized value of the composition of the third element contained in the steel slab identified by the number on the horizontal axis. The normalization of the composition of the third element was performed so that the range from the lower limit to the upper limit of the third element content, which is set as a quality standard for steel slabs, falls within the range of -1 to +1.

[0129] The graph in Figure 6B shows the temperatures obtained when the hot rolling process was performed on 2000 steel slabs used in the second embodiment. The horizontal axis of the graph in Figure 6B represents the sequential numbers assigned to identify the steel slabs. The vertical axis represents the normalized temperature obtained when the hot rolling process was performed on the steel slabs identified by the numbers on the horizontal axis. The normalization of the temperature obtained when the hot rolling process was performed was carried out so that the range from the lower limit to the upper limit of the temperature set as the control standard for the rolling mill used in the hot rolling process falls within the range of -1 to +1.

[0130] According to the graph in Figure 6A, the composition of the third element contained in the steel slab meets the quality standards for the steel slab, but there is variation. Furthermore, according to the graph in Figure 6B, the temperature during the hot rolling process on the steel slab meets the control standards for the rolling mill, but there is variation.

[0131] In the second embodiment, 1,000 steel strips were randomly selected from 2,000 steel strips obtained from 2,000 steel slabs before being charged into the continuous annealing equipment 20. For each steel strip, operating conditions were set to be applied to the continuous annealing equipment 20 during processing so that the predicted value of the plating properties of each steel strip, assuming it had been processed in the continuous annealing equipment 20, would be the target value, i.e., a value within the specifications. Each steel strip was then processed in the continuous annealing equipment 20 using the set operating conditions. On the other hand, in the method according to the second comparative example, the remaining 1,000 steel strips were processed by applying the same operating conditions set in the continuous annealing equipment 20 to all of them.

[0132] In both the second embodiment and the second comparative example, post-treatments such as plating and temper rolling were performed after the processing in the continuous annealing equipment 20.

[0133] Table 2 shows the rate of non-standard plating quality, i.e., the rate of unplated steel strip products, produced by the methods of the second embodiment and the second comparative example. Compared to the case where operating conditions were not changed for each steel strip, as in the second comparative example, the rate of unplated steel strips was reduced when operating conditions were changed for each steel strip according to actual data, as in the second embodiment.

[0134]

[0135] <Third Embodiment> In the third embodiment, the rate of deviation from the standard tensile strength as a quality measure for steel strip products manufactured using a continuous annealing apparatus 20 illustrated in Figure 3, which is equipped with an induction heating device 23, was evaluated. In the third embodiment, as in the first embodiment, 2000 steel strips were prepared. The actual data used to generate the prediction model consisted of tensile strength measurements of the steel strip products and data from which the longitudinal position was aligned every 1m along the longitudinal direction of each process.

[0136] The graph in Figure 7A shows the measured values ​​of the composition of the first element contained in 2000 steel slabs used in the third embodiment. The first element is one of the elements described above as elements that may be contained in the steel strip. The horizontal axis of the graph in Figure 7A is a number assigned sequentially to identify the steel slab. The vertical axis is the normalized value of the composition of the first element contained in the steel slab identified by the number on the horizontal axis. The normalization of the composition of the first element was performed so that the range from the lower limit to the upper limit of the content of the first element, which is set as a quality standard for steel slabs, falls within the range of -1 to +1.

[0137] The graph in Figure 7B shows the measured values ​​of the composition of the second element contained in 2000 steel slabs used in the third embodiment. The second element is one of the elements described above as elements that may be contained in the steel strip, and is different from the first element. The horizontal axis of the graph in Figure 7B is a number assigned sequentially to identify the steel slab. The vertical axis is the normalized value of the composition of the second element contained in the steel slab identified by the number on the horizontal axis. The normalization of the composition of the second element was performed so that the range from the lower limit to the upper limit of the content of the second element, which is set as a quality standard for steel slabs, falls within the range of -1 to +1.

[0138] The graph in Figure 7C shows the temperatures obtained when the hot rolling process was performed on 2000 steel slabs used in the third embodiment. The horizontal axis of the graph in Figure 7C represents sequential numbers assigned to identify the steel slabs. The vertical axis represents the normalized temperature obtained when the hot rolling process was performed on the steel slabs identified by the numbers on the horizontal axis. The normalization of the temperature obtained when the hot rolling process was performed was carried out so that the range from the lower limit to the upper limit of the temperature set as the control standard for the rolling mill used in the hot rolling process falls within the range of -1 to +1.

[0139] The graph in Figure 7D shows the temperature of the uniform zone 22 when a continuous annealing process was performed on 2000 steel slabs used in the third embodiment. The horizontal axis of the graph in Figure 7D is a number assigned sequentially to identify the steel slabs. The vertical axis is the normalized value of the temperature of the uniform zone 22 when a continuous annealing process was performed on the steel slabs identified by the numbers on the horizontal axis. The normalization of the temperature of the uniform zone 22 when the continuous annealing process was performed was carried out so that the range from the lower limit to the upper limit of the temperature defined as the control standard for the uniform zone 22 falls within the range of -1 to +1.

[0140] As shown in the graphs in Figures 7A and 7B, the composition of the first element and the composition of the second element contained in the steel slab both meet the quality standards for the steel slab, but there is variation. Furthermore, as shown in the graph in Figure 7C, the temperature when the hot rolling process is performed on the steel slab meets the control standards for the rolling mill, but there is variation. Furthermore, as shown in the graph in Figure 7D, the temperature of the uniform zone 22 when the continuous annealing process is performed on the steel slab meets the control standards for the uniform zone 22, but there is variation.

[0141] In the third embodiment, 1,000 steel strips were randomly selected from 2,000 steel strips obtained from 2,000 steel slabs before being loaded into the continuous annealing equipment 20. For each steel strip, operating conditions were set to be applied to the induction heating device 23 of the continuous annealing equipment 20 during processing, so that the predicted tensile strength of each steel strip, assuming it had been processed in the continuous annealing equipment 20, would be the target value, i.e., within the standard value. Each steel strip was then processed in the continuous annealing equipment 20 using the set operating conditions. On the other hand, in the method according to the third comparative example, the remaining 1,000 steel strips were processed by applying the same operating conditions set in the continuous annealing equipment 20 to all of them.

[0142] In both the third embodiment and the third comparative example, post-treatments such as plating and temper rolling were performed after the processing in the continuous annealing equipment 20.

[0143] Table 3 shows the percentage of out-of-spec tensile strength for steel strip products manufactured using the methods of the third embodiment and the third comparative example. Compared to the case where operating conditions were not changed for each steel strip, as in the third comparative example, the percentage of out-of-spec tensile strength was reduced when operating conditions were changed for each steel strip according to actual data, as in the third embodiment.

[0144]

[0145] <Fourth Embodiment> In the fourth embodiment, the plating quality, i.e., the rate of non-plating occurrence, of steel strip products manufactured using a continuous annealing apparatus 20 illustrated in Figure 3, which is equipped with an induction heating device 23, was evaluated. In the fourth embodiment, as in the first embodiment, 2000 steel strips were prepared. Furthermore, the actual data used to generate the prediction model was data in which the longitudinal position of the steel strip product was aligned every 1m in the longitudinal direction with the external quality evaluation position of the steel strip product.

[0146] The graph in Figure 8A shows the measured values ​​of the composition of the third element contained in 2000 steel slabs used in the fourth embodiment. The third element is one of the elements described above as elements that may be contained in the steel strip. The third element may be the same as the first or second element in the third embodiment, or it may be a different element. The horizontal axis of the graph in Figure 8A is a number assigned sequentially to identify the steel slab. The vertical axis is the normalized value of the composition of the third element contained in the steel slab identified by the number on the horizontal axis. The normalization of the composition of the third element was performed so that the range from the lower limit to the upper limit of the third element content, which is set as a quality standard for steel slabs, falls within the range of -1 to +1.

[0147] The graph in Figure 8B shows the temperatures obtained when the hot rolling process was performed on 2000 steel slabs used in the fourth embodiment. The horizontal axis of the graph in Figure 8B represents the sequential numbers assigned to identify the steel slabs. The vertical axis represents the normalized temperature obtained when the hot rolling process was performed on the steel slabs identified by the numbers on the horizontal axis. The normalization of the temperature obtained when the hot rolling process was performed was carried out so that the range from the lower limit to the upper limit of the temperature set as the control standard for the rolling mill used in the hot rolling process falls within the range of -1 to +1.

[0148] According to the graph in Figure 8A, the composition of the third element contained in the steel slab meets the quality standards for the steel slab, but there is variation. Furthermore, according to the graph in Figure 8B, the temperature during the hot rolling process on the steel slab meets the control standards for the rolling mill, but there is variation.

[0149] In the fourth embodiment, 1,000 steel strips were randomly selected from 2,000 steel strips obtained from 2,000 steel slabs before being loaded into the continuous annealing equipment 20. For each steel strip, operating conditions were set to be applied to the induction heating device 23 of the continuous annealing equipment 20 during processing, so that the predicted value of the plating properties of each steel strip, assuming it had been processed in the continuous annealing equipment 20, would be the target value, i.e., within the standard value. Each steel strip was then processed in the continuous annealing equipment 20 using the set operating conditions. On the other hand, in the method according to the fourth comparative example, the remaining 1,000 steel strips were processed by applying the same operating conditions set in the continuous annealing equipment 20 to all of them.

[0150] In both the fourth embodiment and the fourth comparative example, post-treatments such as plating and temper rolling were performed after the processing in the continuous annealing equipment 20.

[0151] Table 4 shows the rate of non-standard plating quality, i.e., the rate of unplated steel strips, produced by the methods of the fourth example and the fourth comparative example. Compared to the case where operating conditions were not changed for each steel strip, as in the fourth comparative example, the rate of unplated steel strips was reduced when operating conditions were changed for each steel strip according to actual data, as in the fourth example.

[0152]

[0153] (Summary) As described above, according to the steel strip manufacturing method of this disclosure, an indicator representing the quality of the steel strip can be accurately predicted from the actual data of the process prior to the continuous annealing process. Then, by appropriately setting the operating conditions of the continuous annealing equipment 20 based on the predicted value of the indicator, the variation in the quality of the steel strip product is reduced and the yield of the steel strip product is improved.

[0154] While embodiments of this disclosure have been described based on the drawings and examples, it should be noted that those skilled in the art can make various modifications or alterations based on this disclosure. Therefore, it should be noted that these modifications or alterations are included within the scope of this disclosure. For example, the functions included in each component or step can be rearranged in a logically consistent manner, and multiple components or steps can be combined into one or divided. Embodiments relating to this disclosure can also be realized as programs executed by a processor in the device or as storage media recording such programs. These should also be understood to be included within the scope of this disclosure.

[0155] According to this disclosure, it becomes possible to manufacture steel strips with excellent quality stability, improve the yield of manufactured products, and have great industrial value.

[0156] 1. Prediction System 10. Prediction Device (11: Acquisition Unit, 12: Calculation Unit, 13: Storage Unit, 14: Output Unit) 20. Continuous Annealing Equipment (21: Heating Zone, 22: Sort Zone, 23: Induction Heating Device, 24: Cooling Zone) 30. Management PC 40. Performance Database

Claims

A method for manufacturing steel strip products by adjusting the operating conditions of at least one process, To obtain actual operating condition data for at least one process that is executed before the process that is subject to adjustment, The operating conditions applied to the adjustment target process are adjusted and set so that the predicted value of the indicator representing the quality of the steel strip product, assuming that the steel strip to be processed in the adjustment target process is processed in the adjustment target process, becomes a predetermined target value. The set operating conditions are applied to the adjustment target process to process the steel strip. Includes, The aforementioned predicted values ​​are calculated using a prediction model. The prediction model is configured to accept input of actual data relating to the steel strip to be processed and operating conditions to be applied to the adjustment process, and to output predicted values ​​of an index representing the quality of the steel strip product when the input operating conditions are applied to the adjustment process and the steel strip to be processed is processed. A method for manufacturing steel strips, wherein the prediction model is generated using data that associates actual operating conditions in at least one process with measured values ​​of an index representing the quality of the steel strip product for each unit length in the longitudinal direction of the steel strip product. 　 The aforementioned adjustment target process is a continuous annealing process using a continuous annealing facility that includes a heating zone, a soaking zone, and a cooling zone. To obtain actual operating data of the operating conditions applied in at least one of the following processes, which is performed prior to the continuous annealing process: the ironmaking process, the steelmaking process, the hot rolling process, or the cold rolling process. The operating conditions applied to the continuous annealing process are adjusted and set so that the predicted value of the indicator representing the quality of the steel strip product, assuming that the steel strip to be processed is subjected to the continuous annealing process, becomes a predetermined target value. Includes, The steel strip manufacturing method according to claim 1, wherein the prediction model is configured to accept input of actual data relating to the steel strip to be processed and operating conditions to be applied to the continuous annealing process, and to output a predicted value of an index representing the quality of the steel strip product when it is assumed that the input operating conditions are applied to the continuous annealing process to process the steel strip to be processed. 　 The steel strip manufacturing method according to claim 2, wherein the operating conditions of the continuous annealing process include the operating conditions of the homogenized zone or the cooling zone. 　 The process to be adjusted is a continuous annealing process using a continuous annealing facility that includes a heating zone, a soaking zone, an induction heating device, and a cooling zone. To obtain actual operating data of the operating conditions applied in at least one of the processes performed prior to the continuous annealing process, such as the ironmaking process, steelmaking process, hot rolling process, or cold rolling process, or to obtain actual operating data of the operating conditions applied in the process using the heating zone or soaking zone, which are facilities prior to the induction heating device in the continuous annealing process, The operating conditions applied to the continuous annealing process are adjusted and set so that the predicted value of the indicator representing the quality of the steel strip product, assuming that the steel strip to be processed is subjected to the continuous annealing process, becomes a predetermined target value. Includes, The steel strip manufacturing method according to claim 1, wherein the prediction model is configured to accept input of actual data relating to the steel strip to be processed and operating conditions to be applied to the continuous annealing process, and to output a predicted value of an index representing the quality of the steel strip product when it is assumed that the input operating conditions are applied to the continuous annealing process to process the steel strip to be processed. 　 The steel strip manufacturing method according to claim 4, wherein the operating conditions of the continuous annealing process include the operating conditions of the induction heating device or the cooling zone. 　 The temperature of the steel strip at the exit side of the homogenized zone is 600°C or higher and 1000°C or lower. The residence time of the steel strip in the aforementioned homogeneous zone is 1000 seconds or less. The temperature of the steel strip at the exit side of the cooling zone is 0°C or higher and 800°C or lower. A method for manufacturing steel strip according to any one of claims 2 to 5. 　 The method for manufacturing steel strip according to any one of claims 2 to 6, further comprising inputting the standard operating conditions of the continuous annealing equipment as the operating conditions of the continuous annealing equipment into the prediction model. 　 The steel strip manufacturing method according to any one of claims 2 to 7, wherein the continuous annealing equipment is configured to perform a hot-dip galvanizing process or an alloying process. 　 A method for manufacturing a steel strip according to any one of claims 1 to 8, wherein the indicators representing the quality of the steel strip product include at least one of the following: tensile strength, yield strength, elongation, hole expansion ratio, bendability, r value, hardness, fatigue properties, impact value, delayed fracture value, wear value, chemical treatment properties, high temperature properties, low temperature toughness, corrosion resistance, magnetic properties, surface properties, or plating properties. 　 The steel strip manufacturing method according to any one of claims 1 to 9, wherein the prediction model is a statistical model or a machine learning model. 　 A steel strip manufactured by the steel strip manufacturing method described in any one of claims 1 to 10. 　 The steel strip according to claim 11, having a microstructure comprising 1% or more and 100% or less of one or more phases selected from ferrite, martensite, bainite, pearlite, or retained austenite.

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