KR20220083206A - System for manufacturing hot rolled steel and operating method of the same - Google Patents

Abstract

A hot-rolled steel sheet production system according to an embodiment of the present invention includes arithmetic modules for determining process parameters for controlling a pickling process of a hot-rolled steel sheet, and a storage for storing learning models for learning the arithmetic modules, and a communication unit connected to a network and a processor that controls the learning models to learn the operation modules, and transmits at least one of the operation modules to an external server operating a pickling processing device that performs the pickling process through the communication unit. .

Description

Hot-rolled steel sheet production system and its operating method

The present invention relates to a hot-rolled steel sheet production system and an operating method thereof.

Steel plate includes general steel and high carbon steel, and bearing shells for automobiles produced using steel plate can receive continuous and repeated loads on the surface. Therefore, the steel sheet used for the production of bearing shells and the like is required to have strict surface quality.

It is necessary to strictly control the thickness of the internal defect layer present in the steel sheet, and a pickling process for removing the internal defect layer, etc. may be applied to the steel sheet. The pickling process is a process of removing surface defects by washing the hot-rolled steel sheet with an acidic solution such as hydrochloric acid. In the pickling process, the internal defect layer can be removed by sufficiently washing the hot-rolled steel sheet.

Registered Patent Publication No. 10-2077281

One of the tasks to be achieved by the technical idea of the present invention is to predict the thickness of an internal defect layer occurring in a hot-rolled steel sheet, and learn an arithmetic module that controls the pickling process according to the thickness of the internal defect layer to operate a pickling processing device. It is intended to provide a hot-rolled steel sheet production system capable of improving the productivity of the hot-rolled steel sheet production process and an operating method thereof by providing it to the side.

A hot-rolled steel sheet production system according to an embodiment of the present invention is connected to a storage and network for storing operation modules that determine process parameters for controlling a pickling process of a hot-rolled steel sheet, and learning models for learning the operation modules. A communication unit, and a processor that controls the learning models to learn the operation modules, and transmits at least one of the operation modules to an external server that operates a pickling processing device that performs the pickling process through the communication unit do.

The method of operating a hot-rolled steel sheet production system according to an embodiment of the present invention includes learning arithmetic modules that determine process parameters for controlling a pickling process, wherein the pickling process is a process of removing an internal defect layer of a hot-rolled steel sheet , transmitting at least some of the arithmetic modules to an external system including a pickling processing device performing the pickling process, the thickness of the residual internal defect layer included in the pickling steel sheet on which the pickling process is completed, and the pickling process receiving from the external system the process parameters measured by the pickling treatment device during the process, and using the received process parameters to calculate the thickness of the internal defect layer calculated by at least one of the calculation modules from the external system and learning at least one of the operation modules by comparing it with the received thickness of the residual internal defect layer.

According to an embodiment of the present invention, by controlling the pickling treatment apparatus to apply optimal process parameters to each of the regions of the hot-rolled steel sheet, it is possible to provide a hot-rolled steel sheet production system capable of improving the productivity of the pickling process, and an operating method thereof. can

In addition, by measuring the thickness of the internal defect layer of the pickling steel sheet that has completed the pickling process, and learning operation modules that determine the process parameters using the measured thickness of the internal defect layer, the optimal process parameters can be more accurately determined. .

The various and beneficial advantages and effects of the present invention are not limited to the above, and will be more easily understood in the course of describing specific embodiments of the present invention.

1 is a view for explaining a manufacturing process of a hot rolled steel sheet according to an embodiment of the present invention.
2 is a view simply showing a hot-rolled steel sheet according to an embodiment of the present invention.
3 and 4 are block diagrams illustrating a hot-rolled steel sheet production system according to an embodiment of the present invention.
5 is a view for explaining an initial learning method of the hot-rolled steel sheet production system according to an embodiment of the present invention.
6 is a view for explaining a learning model included in the hot-rolled steel sheet production system according to an embodiment of the present invention.
7 is a view for explaining a method of operating a hot-rolled steel sheet production system according to an embodiment of the present invention.
8 is a view for explaining an operating method of a hot-rolled steel sheet production system according to an embodiment of the present invention.
9 is a view for explaining a method of operating a hot-rolled steel sheet production system according to an embodiment of the present invention.
10 is a view for explaining an operating method of a hot rolled steel sheet production system according to an embodiment of the present invention.
11 is a flowchart for explaining an operating method of a hot-rolled steel sheet production system according to an embodiment of the present invention.
12 and 13 are views simply showing an automated pickling system interlocked with a hot-rolled steel sheet production system according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The same reference numerals are used for the same components in the drawings, and duplicate descriptions of the same components will be omitted.

1 is a view for explaining a manufacturing process of a hot-rolled steel sheet according to an embodiment of the present invention.

1 may be a view simply showing a hot-rolled steel sheet production apparatus for producing a hot-rolled steel sheet, cooling and winding the hot-rolled steel sheet to produce a hot-rolled coil. Referring to FIG. 1 , a hot rolling process of rolling a slab heated in a heating furnace to a predetermined thickness using a roughing mill and a finishing mill (or a finishing mill) may be performed. The hot-rolled steel sheet (strip) produced through the hot rolling process may be transferred to a cooling zone of a run-out table (ROT) cooling section. The hot-rolled steel sheet may be cooled by cooling water sprayed from the ROT cooling zone, and the cooling temperature, cooling time, etc. may be determined according to the quality required for the hot-rolled steel sheet.

After cooling, the hot-rolled steel sheet may be wound in a coil form in a winder for convenience of storage and/or movement. Hot-rolled coil (HC, Hot Coil), in which a hot-rolled steel sheet is wound in the form of a coil, is stored in the yard, cooled in air, and then shipped as a product. After that, a pickling process is performed to remove the internal defect layer present in the hot-rolled steel sheet. can In one embodiment, the manufacturing process of the hot-rolled steel sheet described with reference to FIG. 1 and the pickling process for removing the internal defect layer of the hot-rolled steel sheet may be performed by different entities.

2 is a view simply showing a hot-rolled steel sheet according to an embodiment of the present invention.

Referring to FIG. 2 , the hot-rolled steel sheet may be divided into a plurality of regions along the longitudinal direction (X-axis direction). For example, the hot-rolled steel sheet may include a first region, a second region, and a third region that are sequentially arranged along the longitudinal direction. 2 , the first region, the second region, and the third region may be sequentially wound. In other words, the first area may be wound first, then the second area may be wound, and the third area may be wound last. The first to third regions are regions defined according to a winding order, and the length and/or area of each of the first to third regions is not limited as illustrated in FIG. 2 .

In one embodiment, the hot rolled steel sheet may include surface defects. For example, the surface defects may include at least a portion of scale, an internal oxide layer, and a decarburized layer. Scale develops during the rolling process and can exist on the surface of the material. The internal defect layer may be included below the surface of the material, that is, within the material, and may be defined as a concept including an internal oxide layer and a decarburization layer. The internal defect layer contains components such as chromium (Cr), manganese (Mn), silicon (Si), zinc (Zn), magnesium (Mg), and aluminum (Al) that have higher oxygen affinity than iron (Fe) in the base material. Oxidation can occur in the process. The decarburized layer may occur in the process of combining carbon in steel with oxygen in the atmosphere and scale and then discharging into the atmosphere in the form of gas. The thickness of the internal defect layer may vary depending on the components of the hot-rolled steel sheet, the temperature when the hot-rolled steel sheet is wound with a hot-rolled coil (HC), the cooling time after winding, and the width, thickness, and length of the hot-rolled steel sheet. Surface defects, such as internal defect layers, can be a factor that deteriorates the durability of products produced using hot-rolled steel sheets.

As an example, the temperature of the wound hot-rolled coil HC may be about 500 to 600° C., and the wound hot-rolled coil HC may be cooled by air cooling in a state exposed to air. In the hot-rolled coil HC in the wound state, the first region and the third region exposed to the outside may be cooled relatively quickly, whereas the second region not exposed to the outside may be cooled relatively slowly. Accordingly, the thickness of the internal defect layer occurring in the second region of the hot-rolled steel sheet may be greater than the thickness of the internal defect layer occurring in each of the first region and the second region of the hot-rolled steel sheet. Accordingly, there may be variations in the thickness of the internal defect layer according to regions of the hot-rolled steel sheet.

In addition, according to embodiments, the thickness of the internal defect layer may vary according to regions even in the width direction of the hot-rolled steel sheet. For example, regions adjacent to the edge of the hot-rolled steel sheet in the width direction may be cooled relatively quickly, and a difference in the thickness of the internal defect layer according to the cooling rate may appear in the width direction as well.

The pickling process for removing the internal defect layer may be performed by transferring the hot-rolled steel sheet in a state in which the hot-rolled steel sheet is immersed in the pickling solution. For example, the pickling process may be performed for a sufficient time regardless of the area of the hot-rolled steel sheet so that the internal defect layer included in the hot-rolled steel sheet can be sufficiently removed. However, the above method may lead to an increase in the time of the pickling process and/or an increase in the pickling solution input to the pickling process, resulting in a decrease in productivity.

In one embodiment, the thickness of the internal defect layer generated in the hot-rolled steel sheet may be determined according to the components of the hot-rolled steel sheet, the temperature of the hot-rolled steel sheet during winding, the temperature change of the hot-rolled steel sheet after winding, and the like. Therefore, when the main body producing the hot-rolled steel sheet and the main body performing the pickling process are separated, the main body producing the hot-rolled steel sheet predicts the thickness of the internal defect layer in advance and provides it to the main body performing the pickling process, The process can be carried out efficiently.

According to an embodiment of the present invention, it is possible to predict the thickness of the internal defect layer included in the hot-rolled steel sheet, and calculate process parameters for controlling the pickling process with optimum efficiency according to the predicted thickness of the internal defect layer. The process parameters may be provided to the main body performing the pickling process. Alternatively, the main body producing the hot-rolled steel sheet predicts the thickness of the internal defect layer and provides it to the main body performing the pickling process, and the main body performing the pickling process uses the thickness of the internal defect layer to control the pickling process It is also possible to calculate the necessary process parameters.

The thickness of the internal defect layer may also be provided by learning and tuning an operation module for controlling the pickling process based on the process parameters using a machine learning model or the like at the main producer side of the hot-rolled steel sheet according to embodiments. . Accordingly, productivity can be improved by shortening the time of the pickling process and reducing the amount of the pickling solution used. In addition, it is possible to reduce the thickness variation of the internal defect layer appearing for each area in the pickling steel sheet that has completed the pickling process.

3 and 4 are block diagrams illustrating a hot-rolled steel sheet production system according to an embodiment of the present invention.

Referring first to FIG. 3 , the hot-rolled steel sheet production system 1 according to an embodiment of the present invention may include a first system 10 and a network 20 connected to the first system 10 , etc. . The first system 10 may be operated by an entity that produces a hot-rolled steel sheet and winds the hot-rolled steel sheet to produce a hot-rolled coil.

3 , the network 20 may mean a wired Internet network, a wireless Internet network, or a wireless local area network (WLAN) such as Wi-Fi (wireless fidelity). can The wired Internet network or the wireless Internet network includes the Internet TCP/IP protocol and various services existing in the upper layer, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System). ), Simple Mail Transfer Protocol (SMTP), Simple Network Management Protocol (SNMP), Network File Service (NFS), Network Information Service (NIS), etc. may refer to an open computer network structure.

In one embodiment, the first system 10 may include arithmetic modules 11 that determine process parameters for controlling the pickling process. Also, the first system 10 may include a learning model 12 for learning at least some of the operation modules 11 .

For example, the first system 10 may be connected to the second system through the network 20 . The second system may include a pickling treatment device for performing the pickling process of the hot-rolled steel sheet, and may be operated by a second main body that is different from the first main body and performs the pickling process. As an example, the pickling treatment device of the second system unwinds the hot-rolled coil on which the hot-rolled steel sheet is wound to form a strip again, transports the strip-shaped hot-rolled steel sheet and immersed in the pickling solution inside the pickling tank to proceed with the pickling process. The pickling steel sheet on which the pickling process is completed may be wound up again in the form of a pickling coil.

The first system 10 may transmit at least one of the arithmetic modules 11 to the second system so that the second system can effectively control the pickling processing apparatus. For example, the calculation module transmitted to the second system may determine process parameters for controlling the pickling processing apparatus. The process parameters may include the concentration and temperature of the pickling solution contained in the pickling tank of the pickling treatment device, whether or not an accelerator is used, whether or not a brush in contact with the surface of the hot-rolled steel sheet is used, and the transport speed of the hot-rolled steel sheet in the pickling treatment device. can For example, the calculation module transmitted to the second system may adjust the feeding speed of the hot-rolled steel sheet during the pickling process in consideration of the thickness deviation of the internal defect layer existing in the hot-rolled steel sheet. For example, in the hot-rolled steel sheet, the feed rate decreases while the area where the thickness of the inner defect layer is expected to be large is immersed in the pickling solution, and the area where the thickness of the inner defect layer is expected to be small in the hot-rolled steel sheet is immersed in the pickling solution. During this time, the feed rate can be increased.

The first system 10 may train and transmit the arithmetic modules 11 to the second system so that the pickling process performed in the second system is optimized. In addition, the first system 10 may receive feedback from the second system with data necessary for learning the operation modules 11 . The first system 10 may optimize the operation modules 11 by learning the operation modules 11 using the data fed back from the second system. The data fed back to the first system 10 includes device parameters acquired by the second system from the pickling treatment device during the pickling process, and residual internal defects measured at at least one location of the pickling steel sheet on which the pickling process is completed. at least one of the thicknesses of the layers.

In operation, the operation modules 11 of the first system 10 may predict the thickness of the internal defect layer included in the hot-rolled steel sheet. For example, the calculation modules 11 may predict the thickness of the internal defect layer according to the longitudinal direction of the hot-rolled steel sheet. The first system 10 may transmit the thickness of the internal defect layer predicted by the operation modules 11 to the second system through the network 20 .

As described above, the second system may receive and store at least some of the operation modules 11 of the first system 10 . In an embodiment, the calculation module of the second system may determine process parameters for controlling the pickling processing apparatus by using the thickness of the internal defect layer predicted by the first system 10 . The pickling processing apparatus may be controlled by process parameters determined by the calculation module of the second system.

For example, the process parameters input to the pickling treatment apparatus may be determined as values capable of optimizing the pickling process for each of regions defined along the longitudinal direction of the hot-rolled steel sheet. For example, the feed rate of the hot-rolled steel sheet during the pickling process for the first region or the third region corresponding to the end of the hot-rolled steel sheet is determined by the hot-rolling during the pickling process for the second region located between the first region and the third region. It may be different from the feed speed of the steel plate. For example, the transport speed of the hot-rolled steel sheet during the pickling process for the first region or the third region may be faster than the transport speed of the hot-rolled steel sheet during the pickling process for the second region located between the first region and the third region. . In one embodiment, during the pickling process, scale present on the surface of the hot-rolled steel sheet may be removed together.

The calculation modules 11 of the first system 10 may include a first module for predicting the phase fraction before winding the hot-rolled steel sheet. In addition, the calculation modules 11 may include a second module for predicting a temperature in each of the regions along the longitudinal direction of the hot-rolled steel sheet. In addition, the calculation modules 11 may include a third module for predicting the thickness of the internal defect layer included in each of the regions along the longitudinal direction of the hot-rolled steel sheet.

In an embodiment, the second module may predict the temperature using the phase fraction calculated by the first module. The temperature predicted by the second module may be a temperature predicted in each of the regions according to the elapsed time after the hot-rolled steel sheet is wound in the form of a hot-rolled coil (HC). Also, the third module may predict the thickness of the internal defect layer included in each of the regions using the temperature calculated by the second module. The thickness of the internal defect layer predicted by the third module is transmitted to the second system, and the calculation module of the second system uses the thickness of the internal defect layer calculated by the third module to determine process parameters for controlling the pickling treatment apparatus. can decide

In addition, in one embodiment, the calculation modules 11 of the first system 10 may include a fourth module for estimating the thickness of the internal defect layer to be removed by the pickling processing apparatus, based on at least one of the process parameters. can In addition, the calculation modules 11 may include a fifth module that determines process parameters for controlling the pickling processing apparatus. For example, the fifth module calculates the process parameters using the thickness of the internal defect layer calculated by the fourth module to be removed by the pickling treatment device, and the thickness of the internal defect layer calculated by the third module to be present in the hot-rolled steel sheet. can decide The first system 10 transmits the fourth module and the fifth module to the second system, and the second system stores the fourth module and the fifth module and can be used to control the pickling processing apparatus.

The learning model 12 may train at least one of the operation modules 11 . For example, the learning model 12 may train the operation modules 11 by comparing a predetermined reference value with an output value output by at least one of the operation modules 11 . The learning model 12 may train the operation modules 11 until the reference value and the output value match or the difference between the reference value and the output value is within a predetermined range. For example, the learning model 12 may calculate the difference between the reference value and the output value using a predetermined loss function, and train the operation modules 11 according to whether the difference is within a predetermined range. In an embodiment, the learning model 12 may use a mean squared error, a cross entropy error, etc. as a loss function for calculating the difference between the reference value and the output value.

In an embodiment, the second system may acquire data required for the learning model 12 to learn at least one of the computation modules 11 , and feed it back to the first system 10 . For example, the second system may acquire device parameters corresponding to process parameters input to the pickling processing device at at least one measurement time point while the pickling processing device performs a pickling process. The apparatus parameters may have different values from the process parameters due to mechanical errors and/or operation delays of the pickling processing apparatus.

If the feed rate of the hot-rolled steel sheet among the process parameters input to the pickling treatment device is described as an example, the feed rate of the hot-rolled steel sheet input to the pickling treatment device and the actual feed rate at which the hot-rolled steel sheet is transferred from the pickling treatment device may not match. can In addition, the concentration of the pickling solution and/or the temperature of the pickling solution among the process parameters input to the pickling treatment apparatus may change during the pickling treatment of the hot-rolled steel sheet. Accordingly, device parameters may be obtained by measuring the actual transport speed of the hot-rolled steel sheet, the concentration of the pickling solution, the temperature of the pickling solution, whether or not an accelerator is used, and the like at at least one measurement time point during the pickling process.

In addition, the second system may acquire the thickness of the residual internal defect layer included in the pickling steel sheet on which the pickling process has been completed. For example, the thickness of the remaining internal defect layer may be obtained by designating at least one measurement position on the pickling steel sheet on which the pickling process is completed, and measuring the thickness of the internal defect layer at the measurement position. According to embodiments, the measurement location may be designated as a plurality of different locations along the longitudinal direction of the pickling steel sheet.

The second system transmits the position information of the pickling steel sheet indicating the measurement position, the thickness of the residual internal defect layer obtained at the measurement position, and the device parameters obtained during the pickling process for the measurement position through the network 20 to the first system 10 ) can be transmitted.

The learning model 12 of the first system 10 may learn at least some of the operation modules 11 by using the data received from the second system. As described above, the learning model 12 may learn at least some of the operation modules 11 using a loss function. As an example, by applying the thickness of the residual internal defect layer according to the position of the pickling steel sheet transmitted by the second system as a reference value, a fourth module for predicting the thickness of the internal defect layer among the calculation modules 11 can be learned . When learning of the operation modules 11 is completed, the first system 10 may transmit at least one of the operation modules 11, for example, the fourth module and the fifth module, to the second system. have.

The second system may update the previously stored operation module by using the new operation module received from the first system 10 . For example, the second system may overwrite an existing operation module with a new operation module received from the first system 10 .

4 may be a block diagram simply showing the first system 30 included in the hot-rolled steel sheet production system according to an embodiment of the present invention. The first system 30 may include a first server 31 and a hot-rolled steel sheet production apparatus 35 , and the like, and the first server 31 may control and manage the hot-rolled steel sheet production apparatus 35 . The hot-rolled steel sheet production apparatus 35 may be a device for producing a hot-rolled steel sheet by rolling a slab heated in a heating furnace, and cooling and winding the hot-rolled steel sheet.

The first server 31 may include a communication unit 32 , a storage 33 , a processor 34 , and the like. The communication unit 32 may connect the first server 31 and the network to be able to communicate. The storage 33 may store data required for operation of the first server 31 and management of the hot-rolled steel sheet production apparatus 35 . The processor 34 may control the communication unit 32 and the storage 33 , the hot-rolled steel sheet production apparatus 35 , and the like.

For example, the storage 33 may store arithmetic modules executing a predetermined operation and learning models for optimizing the arithmetic modules. At least one of the arithmetic modules may be linked with the hot-rolled steel sheet production apparatus 35 , and may execute an operation using data obtained from the hot-rolled steel sheet production apparatus 45 . In addition. At least one of the operation modules may execute an operation for controlling a device included in the first system 30 and another system connected through a network. In one embodiment, at least one of the arithmetic modules is included in the first system 30 and the second system connected through the network, and pickling for performing the pickling process of the hot-rolled steel sheet produced by the hot-rolled steel sheet production apparatus 35 Calculations can be performed to optimally control the processing unit.

5 is a view for explaining an initial learning method of the hot-rolled steel sheet production system according to an embodiment of the present invention.

Referring to FIG. 5 , the first system SYS1 may include operation modules 110 and a learning model 120 . The calculation modules 110 may include a prediction module 111 and a parameter calculation module 112 . In an embodiment, the operation modules 110 may include first to fifth modules M1-M5, and the prediction module 111 may include first to third modules M1-M3. In addition, the parameter calculation module 112 may include a fourth module M4 and a fifth module M5. The first to fifth modules M1 - M5 may be implemented as hardware such as a circuit, or may be implemented with software such as source code.

For example, when the first to fifth modules M1-M5 are implemented as software such as source code, the first to fifth modules M1-M5 calculate output data using input data. operation can be executed. The operation of each of the first to fifth modules M1-M5 may be determined and optimized by the learning model 120 .

The learning model 120 may include first to fourth learning models LM1-LM4 and a feedback learning model LMT. The first learning model LM1 may be a model for initially learning the first module M1 , and the second learning model LM2 may be a model for initially learning the second module M2 . Similarly, the third learning model LM3 may be a model for initially learning the third module M3, and the fourth learning model LM4 may be a model for initially learning the fourth module M4. can The feedback learning model LMT may be a model for learning at least one of the first to fifth modules M1-M5 after the pickling process is completed. In an embodiment, the feedback learning model LMT may simultaneously learn the first to fifth modules M1-M5.

The learning model 120 may learn each of the first to fifth modules M1-M5 using a deviation correction method, reinforcement learning, or the like. When the deviation correction method is used, the learning model 120 may train the first to fifth modules M1-M5 by correcting the error between the externally input measurement value and the predicted value output from the module. When the learning model 120 uses reinforcement learning, the learning model 120 may be a deep neural network (DNN), but is not necessarily limited to such an example.

An embodiment in which the learning model 120 learns each module using reinforcement learning will be described later with reference to FIG. 6 . In addition, a learning method of the feedback learning model (LMT) will be described later with reference to FIG. 10 . Hereinafter, an initial learning method of the first system SYS1 will be described in detail with reference to FIG. 5 .

In an embodiment, the first module M1 may predict the phase fraction before winding the hot-rolled steel sheet. The first learning model LM1 may receive the first process condition CD1 and the first phase fraction value EPF. The first phase fraction value EPF may include a phase transformation fraction or amount of phase transformation actually measured under the first process condition CD1. The first process condition CD1 may include a cooling rate of the hot-rolled steel sheet, a temperature of the hot-rolled steel sheet, and components of the hot-rolled steel sheet.

The first learning model LM1 may input the first process condition CD1 to the first module M1 . The first module M1 may calculate a second phase fraction value PPF obtained by predicting the phase fraction of the hot-rolled steel sheet by using the first process condition CD1 . The second phase fraction value PPF output by the first module M1 may be transmitted to the first learning model LM1 .

The first learning model LM1 may learn the first module M1 by comparing the first phase fraction value EPF and the second phase fraction value PPF. For example, if the first phase fraction value EPF and the second phase fraction value PPF do not match, the first learning model LM1 receives the first process condition CD1 from the first module M1 and receives the phase The first function for predicting the fraction may be adjusted. The first learning model LM1 is configured such that the first phase fraction value (EPF) and the second phase fraction value (PPF) match, or the difference between the first phase fraction value (EPF) and the second phase fraction value (PPF) is The first function of the first module M1 may be adjusted to be less than or equal to a predetermined value. In this case, the first learning model LM1 may calculate a difference between the first phase fraction value EPF and the second phase fraction value PPF by using a predetermined loss function. In an embodiment, the first learning model LM1 may learn the first module M1 in a manner that adjusts a weight, etc. used by the first module M1 for calculation.

The second learning model LM2 may receive the second process condition CD2 , the environmental condition FV indicating the surrounding environment, and the first temperature value ET measured from the hot-rolled steel sheet. The second process condition CD2 may include a time point at which the temperature of the hot-rolled steel sheet is measured after winding, and components of the hot-rolled steel sheet. The environmental condition (FV) may include a factor value expressing the surrounding environment that affects the cooling rate of the hot-rolled steel sheet after winding. For example, if the surrounding environment is air, the factor value of the surrounding environment may be '1', if the surrounding environment is wind, the factor value of the surrounding environment may be '2', and if the surrounding environment is water, the factor value of the surrounding environment may be '3'. The first temperature value ET may include a value actually measured at the temperature of the hot-rolled coil.

The second module M2 may receive the second process condition CD2 and the environmental condition FV from the second learning model LM2 . For example, the second module M2 may predict the temperature of the hot-rolled steel sheet using the second process condition CD2 and the environmental condition FV, and output the second temperature value PT at which the temperature of the hot-rolled steel sheet is predicted. can do. The second temperature value PT may include a temperature of each of the regions defined in the longitudinal direction of the hot-rolled steel sheet, a value predicted according to an elapsed time after winding, and the like. The second module M2 may output the second temperature value PT as the second learning model LM2.

The second learning model LM2 may train the second module M2 by comparing the first temperature value ET and the second temperature value PT. Like the first learning model LM1 , the second learning model LM2 may compare the first temperature value ET and the second temperature value PT using a loss function. For example, if the first temperature value ET and the second temperature value PT do not match or the difference between the first temperature value ET and the second temperature value PT is greater than a predetermined value, the second learning The model LM2 may adjust the second function for calculating the second temperature value PT in the second module M2 . The second learning model LM2 is configured such that the first temperature value ET and the second temperature value PT coincide or the difference between the first temperature value ET and the second temperature value PT is less than or equal to a predetermined value. It is possible to train the second module M2 to become

The third learning model LM3 is the component ING of the hot-rolled steel sheet, the oxygen partial pressure (OPP) around the hot-rolled steel sheet, the second temperature value PT that is the output value of the second module M2, and the inside measured from the hot-rolled steel sheet. The first thickness ED of the defect layer may be input. The first thickness ED of the internal defect layer may mean a value actually measured in the thickness of the internal defect layer of the hot-rolled steel sheet.

The third module M3 receives, as input data, the component ING of the hot-rolled steel sheet, the oxygen partial pressure (OPP) around the hot-rolled steel sheet, and the second temperature value PT of the hot-rolled steel sheet from the third learning model LM3. can The third module M3 may calculate the second thickness information PD of the internal defect layer. The second thickness information PD is obtained by the third module M3 from the hot-rolled steel sheet using at least one of the component ING of the hot-rolled steel sheet, the oxygen partial pressure (OPP) around the hot-rolled steel sheet, and the second temperature value PT. It may be the thickness of the internal defect layer predicted to be present. The second thickness information PD of the internal defect layer output by the third module M3 may be transmitted to the third learning model LM3 .

The third learning model LM3 may learn the third module M3 by comparing the first thickness ED of the internal defect layer and the second thickness information PD. For example, if the first thickness ED and the second thickness information PD of the internal defect layer do not match or the difference is greater than a predetermined value, the third learning model LM3 is configured in the third module M3. A third function for calculating the second thickness information PD of the defect layer may be adjusted. The third learning model LM3 operates the third module M3 so that the first thickness ED, which is the measured value of the internal defect layer, and the second thickness information PD, which is the predicted value, match or the difference becomes less than or equal to a predetermined value. can learn

The fourth learning model (LM4) receives the feed rate (PV) of the hot-rolled steel sheet, the characteristics of the pickling solution contained in the pickling tank (AC), and the first thickness of the internal defect layer removed by the pickling treatment unit (EPA). can The characteristics (AC) of the pickling solution may include the concentration of the pickling solution in the pickling tank, the temperature of the pickling solution, whether or not an accelerator is used, and the like. The feed rate (PV) of the hot-rolled steel sheet may mean a speed at which the hot-rolled steel sheet moves in the pickling tank. The first thickness EPA of the internal defect layer may mean the thickness of the internal defect layer that is actually removed in the pickling process performed by the feed rate (PV) of the hot-rolled coil and the characteristic (AC) of the pickling solution.

The fourth module M4 may receive the feed rate PV of the hot-rolled steel sheet and the characteristic AC of the pickling solution from the fourth learning model LM4. The fourth module (M4) calculates the second thickness (PPA1) of the internal defect layer expected to be removed by the pickling treatment device using at least one of the feed rate (PV) of the hot-rolled steel sheet and the characteristic (AC) of the pickling solution. can In one embodiment, the fourth module (M4) receives the pickling time determined according to the transport rate (PV) and the characteristic (AC) of the pickling solution as input values, and uses the input values to remove the internal defect layer 2 You can run an operation to calculate the thickness (PPA1). The fourth module M4 may output the second thickness PPA1 of the internal defect layer as the fourth learning model LM4 .

The fourth learning model LM4 may train the fourth module M4 by comparing the second thickness PPA1 of the internal defect layer with the first thickness EPA of the internal defect layer. For example, if the first thickness EPA of the internal defect layer and the second thickness PPA1 of the internal defect layer do not match or the difference is greater than a predetermined value, the fourth learning model LM4 is configured by the fourth module M4 A weight, a variable, etc. used to calculate the second thickness PPA1 of the internal defect layer may be adjusted.

The fifth module M5 may determine process parameters input to the pickling treatment apparatus. For example, the fifth module M5 may find optimal process parameters by repeatedly calling the fourth module M4 using an optimization technique (eg, a golden partition method, etc.). The fifth module M5 may select an optimization technique for finding optimal process parameters or may modify the optimization technique.

For example, the fifth module (M5) determines the transport rate (PV) for transferring the hot-rolled steel sheet in the pickling process as the first rate and the concentration included in the characteristics (AC) of the pickling solution as the first concentration, and then the fourth The module M4 may be called to receive the second thickness PPA1 of the internal defect layer expected to be removed by the pickling processing unit. If the second thickness (PPA1) of the internal defect layer does not match the target of the pickling process, the fifth module (M5) corrects at least one of the transport rate (PV) and the characteristic (AC) of the pickling solution, and then 4 The second thickness PPA1 of the internal defect layer may be transmitted by calling the module M4. In this way, by repeatedly calling the fourth module (M4) while modifying the feed rate (PV) and/or the characteristics (AC) of the pickling solution, process parameters optimized for the goal to be achieved in the pickling process are set to the fifth module (M5) ) can be determined.

6 is a view for explaining a learning model included in the hot-rolled steel sheet production system according to an embodiment of the present invention.

Referring to FIG. 6 , when the learning model trains each module using reinforcement learning, the learning model may be implemented as a deep neural network (DNN) or the like. The learning model may include an input layer (IL), a hidden layer (HL), and an output layer (OL). For example, a plurality of nodes included in the input layer IL, the hidden layer HL, and the output layer OL may be fully connected to each other. The input layer IL may include a plurality of input nodes x1-xi, and the number of input nodes x1-xi may correspond to the number of input data. The output layer OL may include a plurality of output nodes y1-yj, and the number of output nodes y1-yj may correspond to the number of output data.

The hidden layer HL may include first to third hidden layers HL1 to HL3 , and the number of the hidden layers HL1 to HL3 may be variously modified. For example, the learning model 120 may be trained by adjusting the weights of each of the hidden nodes included in the hidden layer HL. Also, unlike the embodiment shown in FIG. 6 , at least one of the hidden nodes of the hidden layers HL1 to HL3 may recurrently transmit an output value to the hidden node of the same layer.

7 is a view for explaining an operating method of a hot rolled steel sheet production system according to an embodiment of the present invention.

Referring to FIG. 7 , the hot-rolled steel sheet production system 200 according to an embodiment of the present invention may include operation modules 210 to 230 . For example, the operation modules 210 - 230 may be stored and executed in the first system among the systems included in the hot-rolled steel sheet production system 200 . The first system may be a system connected to the second system directly controlling the pickling processing apparatus through a network.

In an embodiment, the operation modules 210 to 230 may predict the thickness of the internal defect layer included in the hot-rolled steel sheet. In one embodiment, in order to calculate the thickness of the internal defect layer, the cooling rate of the hot-rolled steel sheet along the longitudinal direction of the hot-rolled steel sheet may be required. If the phase transformation is not completed before winding the hot-rolled steel sheet, heat may occur due to the phase transformation after winding. Therefore, it is possible to calculate the temperature change of the hot-rolled steel sheet after winding in consideration of the phase fraction before the winding of the hot-rolled steel sheet.

In an embodiment, the first module 210 may calculate the phase fraction PPF according to the cooling time by using the first process condition CD1 . The first process condition CD1 may include a cooling rate of the hot-rolled steel sheet, a temperature of the hot-rolled steel sheet, and components of the hot-rolled steel sheet.

In a hot-rolled steel sheet, a phase transformation from austenite to pearlite may occur during the cooling process, and transformation heat in which the temperature rises due to the phase transformation may occur. In hot-rolled high-carbon steel, the phase transformation may not be completed during cooling due to slow pearlite transformation, and additional phase transformation may occur in the hot-rolled steel sheet after winding. As a result, the hot-rolled steel sheet may be exposed to a high temperature oxidizing atmosphere for a long time after being wound, and the thickness of the internal defect layer may increase.

The second module 220 may predict the temperature PT according to the elapsed time after the hot-rolled steel sheet is wound by using the second process condition CD2 and the environmental condition FV. For example, the second module 220 may predict the temperature PT in each of the regions along the longitudinal direction of the hot-rolled steel sheet. The second process condition CD2 may include a time point at which the temperature of the hot-rolled steel sheet is measured after winding, and components of the hot-rolled steel sheet.

In order to predict the temperature of the wound hot-rolled steel sheet, since the above-described transformation heat must also be taken into consideration, the second module 220 may receive the phase fraction (PPF) from the first module 210 . Based on the phase fraction (PPF), it is possible to know the phase fraction at the time of winding, and the amount of phase transformation additionally occurring in the hot-rolled steel sheet after winding. Accordingly, the second module 220 may predict the temperature PT of the hot-rolled steel sheet according to the elapsed time after the hot-rolled steel sheet is wound in consideration of the amount of phase transformation additionally occurring in the wound hot-rolled steel sheet. In the embodiment shown in FIG. 8 , time t1 may represent a temperature at which transformation heat due to phase transformation is reflected.

The third module 230 may predict the thickness PD of the internal defect layer included in the hot-rolled steel sheet by using the component (ING) of the hot-rolled steel sheet, the oxygen partial pressure (OPP) around the hot-rolled steel sheet, and the like. In one embodiment, the third module 230 receives the temperature PT according to the elapsed time after winding in each of the regions of the hot-rolled steel sheet from the second module 220, and in each of the regions of the hot-rolled steel sheet, internal The thickness (PD) of the defect layer can be predicted.

However, according to embodiments, without the first module 210 for predicting the phase fraction (PPF) and the second module 220 for predicting the temperature (PT) of the hot-rolled steel sheet based on the phase fraction (PPF), the second 3 The module 230 may predict the thickness PD of the internal defect layer. The temperature of the hot-rolled steel sheet is actually measured and provided to the third module 230 so that the third module 230 can predict the thickness PD of the internal defect layer without the first module 210 and the second module 220 . can do. Hereinafter, it will be described with reference to FIG. 8 .

8 is a view for explaining a method of operating a hot-rolled steel sheet production system according to an embodiment of the present invention.

In the embodiment shown in FIG. 8 , the third module 230 may predict the thickness PD of the internal defect layer present in the hot-rolled steel sheet without the first module 210 and the second module 220 . As described with reference to FIG. 7 , in order for the third module 230 to predict the thickness PD of the internal defect layer, data on the temperature PT of the hot-rolled steel sheet may be required. In the embodiment shown in FIG. 8 , the temperature change of the surface of the hot-rolled coil HC may be directly measured using the infrared camera 240 installed on the top of the hot-rolled coil HC. The controller 250 interworking with the infrared camera 240 may generate data including a temperature change of the hot-rolled coil HC according to time and transmit it to the third module 230 . According to embodiments, the temperature change may be first measured using the infrared camera 240 before the hot-rolled steel sheet is wound into the hot-rolled coil HC.

In FIG. 8 , it has been described that the temperature of the hot-rolled coil HC is measured using one infrared camera 240 , but the number and location of the infrared cameras 240 may be variously modified according to embodiments. For example, a plurality of infrared cameras 240 may be disposed to measure temperatures at different positions of the hot-rolled coil HC. In this case, according to embodiments, some infrared cameras 240 may measure the temperature of the hot-rolled coil HC from the side, not the top, of the hot-rolled coil HC.

The infrared camera 240 detects thermal infrared radiation emitted from the hot-rolled coil HC, and the controller 250 detects a temperature change of the hot-rolled coil HC using the thermal infrared detected by the infrared camera 240 . have. The first module 210 for predicting the phase fraction by directly measuring the temperature change of the hot-rolled coil HC using the infrared camera 240 and transmitting it to the third module 230, and the temperature change according to the phase fraction Without the second module 220 for predicting , the third module 230 may predict the thickness PD of the internal defect layer. Accordingly, it is possible to reduce the amount of computation as a whole in the system. In addition, since the actual temperature measured from the hot-rolled coil HC and/or the hot-rolled steel sheet may be used in the learning process of the third module 230 , the accuracy of the third module 230 may be improved.

However, only the surface temperature of the hot-rolled coil (HC) or the hot-rolled steel sheet can be measured with the thermal infrared detected by the infrared camera 240 . Accordingly, the control unit 250 may further include a calculation module for calculating the internal temperature as well as the surface temperature of the hot-rolled coil (HC) or the hot-rolled steel sheet using the thermal infrared rays detected by the infrared camera 240 .

9 is a view for explaining a method of operating a hot-rolled steel sheet production system according to an embodiment of the present invention.

Referring to FIG. 9 , the hot-rolled steel sheet production system 300 may include operation modules 310 and 320 . As an example, the operation modules 310 and 320 are stored in the first server of the first system for producing the hot-rolled steel sheet among the systems included in the hot-rolled steel sheet production system 300, and are learned by the first server. It can be modules. In addition, the operation modules 310 and 320 may be connected to the first system through a network, and may be stored in the second system including a pickling processing device for performing a pickling process of the hot-rolled steel sheet. For example, the operation modules 310 and 320 may be transmitted to the second system in a state that has been trained by the learning model of the first system, and may be stored in the second system.

The hot-rolled steel sheet production system 300 may determine process parameters for controlling the pickling treatment apparatus. The hot-rolled steel sheet production system 300 may receive the thickness PD of the internal defect layer. The thickness PD of the internal defect layer received by the hot-rolled steel sheet production system 300 is similar to the embodiment described with reference to FIG. 7 above, not the thickness of the internal defect layer actually measured in the hot-rolled steel sheet, but a hot-rolled steel sheet It may be the thickness of the internal defect layer expected to be included, and may include a change in the thickness of the internal defect layer according to regions of the hot-rolled steel sheet. The hot-rolled steel sheet production system 300 may calculate the optimal process parameters PPV2 based on the thickness PD of the internal defect layer.

In an embodiment, the operation modules 310 and 320 may include a first module 310 and a second module 320 , and the like. The second module 320 may receive the thickness PD of the internal defect layer. The thickness PD of the internal defect layer is the thickness of the internal defect layer expected to exist in the hot-rolled steel sheet, and may not exactly match the thickness of the internal defect layer that is actually present in the hot-rolled steel sheet. Also, the second module 320 may receive the target thickness GD of the residual internal defect layer from the outside. The residual internal defect layer may be an internal defect layer existing in the hot-rolled steel sheet after the pickling process is completed. For example, when the internal defect layer is completely removed by the pickling process, the target thickness GD of the remaining internal defect layer may be zero. In some embodiments, the target thickness GD of the residual internal defect layer may be greater than zero. The second module 320 may periodically call the first module 310 to calculate the optimal process parameters PPV2 .

The second module 320 may output initial values of the process parameters to the first module 310 . The initial value of the process parameters may include the characteristic (AC) of the pickling solution measured from the pickling tank, and the feed rate (PPV1) of the hot-rolled steel sheet. The initial values of the process parameters may be determined as arbitrary values, or may be determined by values measured in real time from the pickling treatment apparatus. When the initial values of the process parameters are arbitrarily determined, the initial values of the process parameters may be determined by the first system operating the hot-rolled steel sheet production apparatus or the second system operating the pickling treatment apparatus. The first module 310 may calculate the thickness (PPA2) of the internal defect layer expected to be removed by the pickling processing device by using at least one of the characteristics (AC) of the pickling solution and the optional transport rate (PPV1). . Alternatively, the first module 310 is expected to remain after the pickling process using the difference between the thickness (PD) of the internal defect layer predicted to exist in the hot-rolled steel sheet and the thickness (PPA2) of the internal defect layer expected to be removed. It is also possible to calculate the predicted thickness of the internal defect layer.

The second module 320 may receive the thickness PPA2 of the internal defect layer from the first module 310 . The second module 320 determines whether the characteristic AC of the pickling solution and the arbitrarily set feed rate PPV1 match the optimal process parameters PPV2 based on the thickness PPA2 of the internal defect layer. The second module 320 may call the fourth module M4 until the characteristics AC of the pickling solution and an arbitrary feed rate PPV1 match the optimal pickling process parameters PPV2.

[Table 1] is a table for explaining how the second module 320 calculates the optimal process parameters PPV2. For convenience of explanation, it is assumed that the characteristics (AC) of the pickling solution in [Table 1] are the same. The second module 320 has a target removal thickness indicating how much of the internal defect layer should be removed based on the difference between the thickness PD of the internal defect layer expected to exist in the hot-rolled steel sheet and the target thickness GD of the remaining internal defect layer. (GPA) can be calculated.

The first region, the second region, and the third region defined in [Table 1] may be regions continuously arranged in the longitudinal direction in the hot-rolled steel sheet, and the first region and the third region are regions adjacent to the end of the hot-rolled steel sheet can take Referring to [Table 1], the target removal thickness (GPA) in the first region is 1 μm, which is the difference between the thickness PD of the internal defect layer expected to exist in the hot-rolled steel sheet and the target thickness GD of the residual internal defect layer. (=3 μm-2 μm) can be determined.

Referring to [Table 1], in the first module 310, the thickness PPA2 of the internal defect layer expected to be removed by the pickling processing apparatus when the transport speed PPV1 is 15mpm may be 2 μm. Since the thickness PPA2 of the internal defect layer expected to be removed by the pickling treatment apparatus is greater than the target removal thickness GPA, the second module 320 increases the feed rate PPV1 to 20 mpm, which is faster than 15 mpm, to increase the first It can output to the module 310 . If the thickness PPA2 of the internal defect layer expected to be removed by the pickling treatment device when the feed rate PPV1 is 20mpm is calculated by the first module 310 as a value other than 1 μm, the second module 320 is The feed rate PPV1 may be changed back to a value different from 20 mpm and outputted to the first module 310 . On the other hand, when the feed rate (PPV1) is 20mpm, if the thickness of the internal defect layer calculated by the first module 310 (PPA2) is 1 μm, the thickness of the internal defect layer expected to be removed by the pickling treatment device (PPA2) Since α coincides with the target removal thickness (GPA), the second module 320 may determine a feed rate of 20 mpm among the optimal process parameters for the first region of the hot-rolled steel sheet.

items first area second area 3rd area PD 3μm 10μm 3μm GD 2μm 2μm 2μm GPA 1μm 8μm 1μm PPV1 15mpm 10mpm 15mpm PPA2 2μm 4μm 2μm PPV2 20mpm 5mpm 20mpm

mpm = meter per minute

In the embodiment shown in FIG. 9 , it has been described that the calculation modules 310 and 320 of the second system calculate the optimal process parameters, but the present invention is not limited thereto. According to embodiments, the calculation modules of the first system connected to the second system through a network may calculate optimal process parameters. The second system may receive the optimal process parameters calculated by the first system through the network, and control the pickling treatment apparatus using the received optimal process parameters.

10 is a view for explaining an operating method of the hot-rolled steel sheet production system according to an embodiment of the present invention.

Referring to FIG. 10 , the first system 400 may include operation modules 411-415 ( 410 ) and a learning model 420 . The first system 400 may be connected to a second system including a pickling treatment device for performing a pickling process for removing the internal defect layer of the hot-rolled steel sheet through a network or the like.

In one embodiment, the first system 400 is configured to correspond to the thickness (ERD) of the residual internal defect layer included in the hot-rolled steel sheet and the process parameters at at least one measurement time point during the pickling process by the pickling processing apparatus. Device parameters (EPV) may be received. In an embodiment, there may be a plurality of measurement time points, and may be set according to a predetermined period. The device parameters EPV are process parameters actually measured by the pickling processing device, and may have different values from the process parameters input to the pickling processing device due to a mechanical error, operation delay, or the like. For example, the feed speed of the hot-rolled steel sheet included in the process parameters may be different from the feed speed of the hot-rolled steel sheet included in the device parameters EPV.

During the pickling process, the pickling treatment device may unwind and transport the hot-rolled steel sheet wound in a coil shape and immerse it in the pickling solution. The pickling steel sheet passed through the pickling solution can be wound up again. For example, before the pickling steel sheet is wound again, the pickling processing apparatus may measure the thickness (ERD) of the residual internal defect layer in the pickling steel sheet. In addition, the pickling processing apparatus may transmit to the fifth module M5 the apparatus parameters EPV applied to control the pickling process for a specific position of the pickling steel sheet in which the thickness (ERD) of the residual internal defect layer is measured. .

According to an embodiment, at least one measurement position may be designated on the pickling-treated pickling steel sheet, and a sample may be collected by cutting a portion of the pickling steel sheet at the measurement position. The thickness of the residual internal defect layer can be measured by observing the cross section of the sample taken from the pickling steel plate under a microscope. The measurement position may correspond to the position information PI of the pickling steel sheet, and the thickness of the residual internal defect layer may correspond to the thickness ERD of the residual internal defect layer at a position corresponding to the position information PI. have.

The fifth module 415 may output the device parameters EPV to the fourth module 414 . The fourth module 414 may calculate the thickness PPA2 of the internal defect layer expected to be removed by the pickling processing apparatus based on the apparatus parameters EPV. The fifth module 415 may receive the thickness PPA2 of the internal defect layer from the fourth module 414 . The fifth module M5 may calculate the expected thickness PRD of the residual internal defect layer based on the thickness PPA2 of the internal defect layer expected to be removed by the pickling processing apparatus. The expected thickness PRD of the residual internal defect layer may be the thickness of the residual internal defect layer expected to remain in the pickling steel sheet after the pickling process is performed according to the device parameters EPV.

The learning model LMT may receive the predicted thickness PRD of the internal defect layer from the fifth module 415 . The learning model LMT may compare the thickness ERD of the residual internal defect layer directly measured from the pickling steel sheet with the expected thickness PRD of the residual internal defect layer calculated by the fourth module 414 . In the learning model (LMT), the actually measured thickness (ERD) of the residual internal defect layer and the expected thickness (PRD) of the residual internal defect layer calculated by the fourth module 414 match or the difference is less than or equal to a predetermined value. As much as possible, at least some of the operation modules 411-415 may be trained. For example, the learning model LMT may train all of the operation modules 411-415 at the same time, or may selectively train only a specific model.

11 is a flowchart illustrating an operating method of a hot-rolled steel sheet production system according to an embodiment of the present invention.

Referring to FIG. 11 , the hot-rolled steel sheet production system may transmit at least some of the arithmetic modules to an external system ( S110 ). For example, a subject that transmits at least some of the operation modules to the external system may be a first system that produces a hot-rolled steel sheet and winds the hot-rolled steel sheet in the form of a hot-rolled coil. In addition, the external system that receives at least some of the operation modules may be a second system that receives the hot-rolled coil and performs the pickling process.

The external system may perform the pickling process using the calculation modules received in step S110. The hot-rolled steel sheet production system may receive, from an external system, the thickness of the residual internal defect layer included in the pickling steel sheet on which the pickling process has been completed, and process parameters measured by the pickling treatment apparatus during the pickling process ( S120 ). For example, the process parameters received in step S120 may have values actually measured by the pickling treatment device, and thus may be different from values input to the pickling treatment device by an external system to control the pickling treatment device. The process parameters received in step S120 may be defined as device parameters.

The hot-rolled steel sheet production system may use the process parameters received in step S120 to calculate the thickness of the internal defect layer expected to be removed by the pickling treatment apparatus (S130). In addition, the hot-rolled steel sheet production system may compare the thickness of the internal defect layer calculated in step S130 with the thickness of the residual internal defect layer received in step S120 and train the operation modules based on the comparison result ( S140 ). The hot-rolled steel sheet production system may include a learning model required for learning of the calculation modules, and the learning model may adjust weights, coefficients, etc. used for calculation in at least one of the calculation modules.

When the learning is completed, the hot-rolled steel sheet production system may transmit at least one of the operation modules to the external system (S150). The external system may perform the pickling process in a manner optimized for the thickness deviation of the internal defect layer existing in the hot-rolled steel sheet by controlling the pickling processing apparatus using the calculation module received from the hot-rolled steel sheet production system.

12 and 13 are diagrams simply showing an automated pickling system interlocked with a hot-rolled steel sheet production system according to an embodiment of the present invention.

As described above, the hot-rolled steel sheet production system according to an embodiment of the present invention may predict the thickness of the internal defect layer included in the produced hot-rolled steel sheet, and transmit it to an external system performing the pickling process. In other words, the external system for receiving the thickness of the internal defect layer predicted by the hot-rolled steel sheet production system may be an automated pickling system including a pickling processing device for performing a pickling process on the hot-rolled steel sheet.

In addition, as described with reference to FIG. 11, the hot-rolled steel sheet production system according to an embodiment of the present invention receives the thickness of the remaining internal oxide layer from the pickling steel sheet on which the pickling process has been completed, and the thickness of the internal defect layer predicted previously and Comparisons can be used to train computational modules. In order to measure the thickness of the residual internal defect layer in the pickling steel sheet, a sample obtained by cutting a portion of the pickling steel sheet at at least one measurement location designated for the pickling steel sheet may be used.

However, the method of taking a sample from the pickled steel sheet and predicting the thickness of the residual internal defect layer therefrom may cause physical damage to the pickled steel sheet. In addition, in order to minimize the physical damage of the pickled steel sheet, the number of measurement locations has to be reduced, and in this case, it may not be possible to accurately measure the thickness of the residual internal defect layer across the pickled steel sheet. In the pickling processing apparatuses 500 and 600 according to the embodiments shown in FIGS. 12 and 13 , the surface state is detected using the surface defect detection apparatuses 430 and 530 rather than a method of directly collecting a sample. By doing so, the thickness of the residual internal defect layer can be measured.

First, referring to FIG. 12 , the pickling processing apparatus 500 may include a coiler (CL), an uncoiler (UCL), a pickling tank (TK), a first measuring instrument (MI1), a second measuring instrument (MI2), etc. have. The uncoiler UCL may introduce the hot-rolled steel sheet 510 in the form of a strip from which the hot-rolled coil HC is unwound into the pickling tank TK. The hot-rolled steel sheet 510 may be subjected to pickling treatment while passing through the pickling solution accommodated in the pickling tank TK. The pickling tank TK stores a pickling solution necessary for the pickling process, and according to embodiments, a brush contacting the surface of the hot-rolled steel sheet 510 may be further included.

During the pickling process, the pickling processing apparatus 500 may be controlled according to the optimal process parameters PPV2 according to the above-described embodiments. For example, according to the process parameters PPV2 , the transport speed of the hot-rolled steel sheet 510 may vary in each of the regions along the longitudinal direction of the hot-rolled steel sheet 510 . The first measuring instrument MI1 connected to the pickling tank TK may measure the device parameters EPV at at least one measurement time point while the pickling processing apparatus 500 performs a pickling process. The device parameters EPV correspond to the process parameters PPV2, but at least some of the device parameters EPV and the process parameters PPV2 may have different values due to a mechanical error, an operation delay, etc. . The device parameters EPV obtained by the first measuring instrument MI1 may be transmitted to the fifth module 415 as described above with reference to FIG. 10 .

The second measuring instrument MI2 may designate at least one measurement position on the pickling steel plate 520 , and cut a portion of the pickling steel plate 520 at the measurement position to collect a sample. By observing the cross section of the sample taken from the pickling steel plate 520 under a microscope, the thickness of the residual internal defect layer can be measured. The measurement position may correspond to the position information PI of the pickling steel sheet 520, and the thickness of the residual internal defect layer is in the thickness ERD of the residual internal defect layer at the position corresponding to the position information PI. may be applicable.

In one embodiment shown in FIG. 12, the surface state of the hot-rolled steel sheet 510 is detected by photographing the surface image, or the surface state of the hot-rolled steel sheet 510 is detected by measuring the glossiness of the surface of the hot-rolled steel sheet 510. The surface defect detection apparatus 530 may be included in the pickling processing apparatus 500 . The surface defect detection apparatus 530 may detect a surface state and glossiness of the hot-rolled steel sheet 510 in at least one of a width direction and a length direction. Since it is possible to determine the amount of the internal defect layer removed from the hot-rolled steel sheet 510 by the pickling process of the pickling treatment apparatus 500 from the surface state detected by the surface defect detection apparatus 530, the second measuring instrument MI2 It is possible to reduce the amount of the sample collected by , or not selectively use the second measuring instrument MI2.

The operation of the pickling processing apparatus 600 according to the embodiment shown in FIG. 13 may be similar to that described with reference to FIG. 12 . However, in the embodiment shown in FIG. 13 , the surface defect detection device 630 may be disposed at the rear end of the pickling tank TK. Accordingly, the surface defect detection apparatus 630 may detect the surface state and/or glossiness of the pickling steel sheet 620 that has passed through the pickling tank TK.

12, since the surface defect detection device 630 is disposed at the rear end of the pickling tank TK, the state of the pickling steel sheet 620 in which the pickling process is completed by passing through the pickling tank TK accurately can be detected. Accordingly, the second measuring instrument MI2 may be omitted or the number of samples collected by the second measuring instrument MI2 may be reduced.

In addition, unlike the second measuring instrument MI2 that measures the thickness (ERD) of the residual internal defect layer with samples taken at specific locations of the pickling steel plate 620 , the surface defect detecting device 630 is the pickling steel plate 620 . ) to obtain an image of the entire surface or glossiness. Accordingly, the thickness of the residual internal defect layer in the longitudinal direction and/or the width direction of the pickled steel sheet 620 can be sufficiently measured.

As such, by measuring the thickness of the residual internal defect layer over the entire pickling steel sheet 620 , it is possible to increase the learning amount for optimizing the fourth module 414 and the fifth module 415 . As the learning amount of the fourth module 414 and the fifth module 415 increases, the fifth module 415 executes and outputs the optimal process parameters (PPV2) in the system interlocked with the pickling processing apparatus 600 accuracy can also be improved.

The present invention is not limited by the above-described embodiments and the accompanying drawings, but is intended to be limited by the appended claims. Accordingly, various types of substitution, modification and change will be possible by those skilled in the art within the scope not departing from the technical spirit of the present invention described in the claims, and it is also said that it falls within the scope of the present invention. something to do.

Claims (18)

a storage for storing arithmetic modules for determining process parameters for controlling the pickling process of the hot-rolled steel sheet, and learning models for learning the arithmetic modules;
a communication unit connected to the network; and
a processor that controls the learning models to learn the operation modules, and transmits at least one of the operation modules to an external server that operates a pickling processing device that performs the pickling process through the communication unit; A hot-rolled steel sheet production system comprising a.
According to claim 1,
The processor controls the learning models to learn the arithmetic modules using loss functions that compare the output value of each of the arithmetic modules with an actual value measured from the hot-rolled steel sheet,
The loss functions calculate a difference between the output value and the measured value using at least one of a mean square error and a cross entropy error.
According to claim 1,
The calculation modules, hot-rolled steel sheet production system including a first module for predicting the phase fraction before winding the hot-rolled steel sheet.
4. The method of claim 3,
The processor is configured to compare a first phase fraction value measured in the hot-rolled steel sheet with a second phase fraction value calculated by the first module from at least one of a temperature of the hot-rolled steel sheet and a component of the hot-rolled steel sheet. Hot rolled sheet production system with learning module.
According to claim 1,
The arithmetic module, the hot-rolled steel sheet production system including a second module for predicting the temperature of each of the plurality of regions along the longitudinal direction of the hot-rolled steel sheet.
6. The method of claim 5,
The processor is configured to use at least one of a first temperature value measured from the hot-rolled steel sheet, a phase fraction predicted before winding the hot-rolled steel sheet, an elapsed time after winding the hot-rolled steel sheet, and a component of the hot-rolled steel sheet. A hot-rolled steel sheet production system for learning the second module by comparing the second temperature value calculated by the second module.
According to claim 1,
The calculation modules include a third module for predicting the thickness of the internal defect layer included in each of the plurality of regions along the longitudinal direction of the hot-rolled steel sheet.
8. The method of claim 7,
The processor includes: a first thickness of the internal defect layer measured from the hot-rolled steel sheet, a temperature predicted according to an elapsed time after winding in each of the regions, a component of the hot-rolled steel sheet, and an oxygen partial pressure around the hot-rolled steel sheet A hot-rolled steel sheet production system for learning the third module by comparing the second thickness of the internal defect layer calculated by the third module using at least one in each of the regions.
8. The method of claim 7,
The third module is a hot-rolled steel sheet production system for predicting the thickness of the internal defect layer included in the hot-rolled steel sheet by using a temperature change output by an infrared camera that captures hot infrared rays emitted from the surface of the hot-rolled steel sheet.
According to claim 1,
A hot-rolled steel sheet production system for receiving at least one of a transport speed of the hot-rolled steel sheet from the pickling treatment device and a characteristic of a pickling solution contained in a pickling tank of the pickling treatment device through the communication unit.
11. The method of claim 10,
The calculation modules include a fourth module for calculating the thickness of the internal defect layer to be removed by the pickling processing apparatus based on at least one of the transport speed and the characteristics of the pickling solution.
12. The method of claim 11,
The processor, hot rolling for learning the fourth module using the first thickness of the residual internal defect layer measured from the pickling steel sheet on which the pickling process is completed, and the second thickness of the internal defect layer calculated by the fourth module steel plate production system.
12. The method of claim 11,
The arithmetic modules include a fifth module for determining the process parameters.
14. The method of claim 13,
The processor, the thickness of the internal defect layer calculated by the external server using the process parameters measured from the pickling treatment device during the pickling process, and the external from the pickling steel sheet subjected to the pickling process on the hot-rolled steel sheet Receives the thickness of the remaining internal defect layer measured by the server from the external server,
A hot-rolled steel sheet production system for learning at least one of the arithmetic modules by using the thickness of the internal defect layer and the thickness of the remaining internal defect layer.
15. The method of claim 14,
The processor, using the thickness of the residual internal defect layer determined using the surface state of the pickling steel sheet detected by the external server by shooting the surface image of the pickling steel sheet, learning at least one of the operation modules Hot rolled steel sheet production system.
14. The method of claim 13,
The processor, a hot-rolled steel sheet production system for transmitting the fourth module and the fifth module to the external server through the communication unit.
learning operation modules that determine process parameters for controlling a pickling process, wherein the pickling process is a process of removing an internal defect layer of a hot-rolled steel sheet;
transmitting at least some of the arithmetic modules to an external system including a pickling processing device performing the pickling process;
receiving, from the external system, the thickness of the residual internal defect layer included in the pickling steel sheet on which the pickling process has been completed, and the process parameters measured by the pickling treatment device during the pickling process; and
At least one of the calculation modules is learned by comparing the thickness of the internal defect layer calculated by at least one of the calculation modules using the received process parameters with the thickness of the residual internal defect layer received from the external system. making; Operating method of a hot-rolled steel sheet production system comprising a.
18. The method of claim 17,
When the learning of the operation modules is completed, the operating method of the hot-rolled steel sheet production system for transmitting at least one of the operation modules to the external system.

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