Process control system and method of operation thereof

Abstract

The invention relates to a process control system and a method of operating the same. The process control system according to an embodiment of the present invention comprises: a first system for generating thickness information of an internal defect layer included in a carbon steel product; the second system is used for receiving the thickness information of the internal defect layer from the first system through a network and controlling an etching process by using the thickness information of the internal defect layer, and the etching process is used for removing at least one part of the internal defect layer from the carbon steel product. The first system provides a calculation module required by the second system for controlling the etching process for the second system, and the second system provides information required by the first system for updating the calculation module for the first system.

Description

[0001] This application is a divisional application of a Chinese patent application with an application date of February 18, 2021, an application number of 202180015231.8, and an invention title of "Process Control System and Its Operating Method". The patent application 202180015231.8 is the Chinese national stage application of the international application PCT / KR2021 / 002056. Technical Field

[0002] The present invention relates to a process control system and its operating method for controlling a process of removing an internal defect layer included in a carbon steel product. Background Art

[0003] The surfaces of automotive axle bearings, etc. produced using carbon steel products may be subjected to continuous and repeated loads. Therefore, carbon steel products used in the production of axle bearings, etc. require strict surface quality.

[0004] Internal defect layers, etc. included in carbon steel products need to have their thicknesses strictly managed, and the internal defect layers can be removed through an etching process, etc. The etching process may be a process of removing the internal defect layer by bringing an etching solution into contact with the carbon steel product.

[0005] Regarding such prior art, it can be easily understood by referring to Korean Patent Publication No. 10-2019-0124019. Summary of the Invention

[0006] Technical Problem

[0007] One technical problem to be solved by the technical idea of the present invention is to provide a process control system and its operating method that control an etching process based on the thickness of an internal defect layer included in a carbon steel product, thereby being able to improve the efficiency and productivity, etc. of the etching process.

[0008] Technical Solution

[0009] A process control system according to an embodiment of the present invention includes: a first system for generating thickness information of an internal defect layer included in a carbon steel product; and a second system for receiving the thickness information of the internal defect layer from the first system through a network and controlling an etching process using the thickness information of the internal defect layer, the etching process being for removing at least a part of the internal defect layer from the carbon steel product, the first system providing a calculation module required for the second system to control the etching process to the second system, and the second system providing information required for the first system to update the calculation module to the first system.

[0010] A process control system according to an embodiment of the present invention includes: a storage device for storing control data required for controlling an etching device, the etching device being used to remove at least a part of an internal defect layer contained in a carbon steel product; and a processor for controlling the etching device based on the control data. The carbon steel product includes a first region and a second region different from the first region, and the thickness of the internal defect layer contained in the first region and the thickness of the internal defect layer contained in the second region are different from each other. The control data includes a first conveying speed for the first region to pass through the etching device and a second conveying speed for the second region to pass through the etching device, and the first conveying speed and the second conveying speed are different from each other.

[0011] A process control system according to an embodiment of the present invention includes: a storage device for storing a calculation module, the calculation module being used to generate thickness information of an internal defect layer contained in the carbon steel product based on at least one of the composition, cooling rate, phase fraction, and temperature of the carbon steel product; a communication unit connected to a network; and a processor for transmitting at least one of the thickness information of the internal defect layer and control data for controlling an etching process to an external server through the communication unit, the external server being used to control the etching process for removing at least a part of the internal defect layer.

[0012] Advantages of the invention

[0013] According to an embodiment of the present invention, a process control system and an operation method thereof can be provided. During the period when each region defined in the length direction of the carbon steel product contacts the etching solution, the etching process is controlled based on optimal control data, so that the productivity and efficiency of the etching process can be improved.

[0014] In addition, according to an embodiment of the present invention, by measuring the thickness of the remaining internal defect layer contained in the pickled carbon steel product after the etching process is completed and using the measured thickness of the remaining internal defect layer, a calculation module for generating control data for controlling the etching process is enabled to learn, so that the etching process can be performed based on optimal control data.

[0015] The various beneficial advantages and effects of the present invention are not limited to the above content, and the advantages and effects of the present invention should be more easily understood during the description of the specific embodiments of the present invention. Brief description of the drawings

[0016] Figure 1 It is a diagram for explaining the preparation process of a hot-rolled steel sheet according to an embodiment of the present invention.

[0017] Figure 2 It is a diagram simply showing a hot-rolled steel sheet according to an embodiment of the present invention.

[0018] Figure 3 is a block diagram showing a process control system according to an embodiment of the present invention.

[0019] Figure 4 is simply a block diagram showing a process control system according to an embodiment of the present invention.

[0020] Figure 5 is a diagram for explaining an initial learning method of a calculation module included in a process control system according to an embodiment of the present invention.

[0021] Figure 6 is a diagram for explaining a learning model included in a process control system according to an embodiment of the present invention.

[0022] Figure 7 is a diagram for explaining an operation method of a process control system according to an embodiment of the present invention.

[0023] Figure 8 is a flowchart for explaining an operation method of a process control system according to an embodiment of the present invention.

[0024] Figure 9 is simply a block diagram showing a process control system according to an embodiment of the present invention.

[0025] Figure 10 is a diagram for explaining an operation method of a process control system according to an embodiment of the present invention.

[0026] Figure 11 is a diagram for explaining a learning method of a calculation module included in a process control system according to an embodiment of the present invention.

[0027] Figure 12 is a diagram for explaining an etching process according to an embodiment of the present invention.

[0028] Figure 13 is a chart provided to explain an etching process according to an embodiment of the present invention.

[0029] Figure 14 is a diagram for explaining an initial learning method of a calculation module included in a process control system according to an embodiment of the present invention.

[0030] Figure 15 is a diagram for explaining a learning method of a calculation module included in a process control system according to an embodiment of the present invention.

[0031] Figure 16 is a flowchart for explaining an operation method of a process control system according to an embodiment of the present invention.

[0032] Figure 17This is a diagram for explaining the operation method of the process control system according to an embodiment of the present invention. Detailed Embodiment

[0033] In this specification, carbon steel products are alloy products of iron and carbon, containing 0.01% to 2.0% carbon, and typically may include hot-rolled products, thick plate products, wire rod products, etc. For example, the hot-rolled product may be a hot-rolled coil obtained by hot-rolling a slab and coiling it in a coil shape or a hot-rolled steel plate made by cutting it into sheets, and may have a thickness of 1 mm to 25 mm. The thick plate product may be a product obtained by hot-rolling a slab and making it into a plate shape, and may have a thickness of 4 mm to 200 mm. The wire rod product may be a coiled product with a circular cross-section obtained by grooving rolling a steel billet in a hot state, and may have a cross-sectional diameter of 3 mm to 100 mm.

[0034] The process control system of the present invention can produce pickled carbon steel products (hereinafter referred to as "pickled carbon steel products") by controlling the conveying speed of the carbon steel products during the etching process of the carbon steel products, and thus can improve the surface quality of the carbon steel products.

[0035] Hereinafter, the technical idea of the present invention will be described centering on a hot-rolled steel plate or a hot-rolled coil in which the hot-rolled steel plate is coiled. However, the technical idea of the present invention is not limited to hot-rolled steel plates or hot-rolled coils, and within the range easily understandable by those skilled in the art, the technical idea of the present invention can be applied to all carbon steel products.

[0036] Hereinafter, with reference to the accompanying drawings, preferred embodiments of the present invention will be described in detail. The same reference numerals are used for the same structural elements in the drawings, and repeated descriptions of the same structural elements are omitted.

[0037] Figure 1 This is a diagram for explaining the manufacturing process of a hot-rolled steel plate according to an embodiment of the present invention.

[0038] Figure 1 This is a diagram simply showing a hot-rolled steel plate production apparatus, which is used to produce a hot-rolled steel plate, and cool and coil the hot-rolled steel plate to produce a hot-rolled coil. Refer to Figure 1 , a hot-rolling process can be performed, in which the slab heated in a heating furnace is rolled to a specified thickness by a rough rolling mill and a finishing mill (or a finishing rolling mill). The hot-rolled steel plate (strip) produced by the hot-rolling process can be moved to a cooling section, i.e., a cooling table of the Run Out Table (ROT). The hot-rolled steel plate can be cooled by the cooling water sprayed from the ROT cooling table, and the cooling temperature, cooling time, etc. can be determined according to the quality required for the hot-rolled steel plate.

[0039] After cooling, for ease of storage and / or transportation, the hot-rolled steel sheet can be coiled into a coil shape in a coiler. The hot-rolled coil (HC, Hot Coil) formed by coiling the hot-rolled steel sheet into a coil is placed in a yard, air-cooled, and then shipped. After that, an etching process can be performed to remove the internal defect layer contained in the hot-rolled steel sheet. In one embodiment, referring to Figure 1 the manufacturing process of the hot-rolled steel sheet and the etching process for removing the internal defect layer of the hot-rolled steel sheet, etc. can be carried out by different entities. In addition, in one embodiment, the manufacturing process of the hot-rolled steel sheet and the etching process for removing the internal defect layer of the hot-rolled steel sheet, etc. can also be carried out by the same entity.

[0040] Figure 2 FIG. is a diagram simply showing a hot-rolled steel sheet according to an embodiment of the present invention.

[0041] Referring to Figure 2 , the hot-rolled steel sheet can be divided into multiple regions in the length direction (X-axis direction). As an example, the hot-rolled steel sheet can include regions A, B, C, etc. arranged in sequence in the length direction. In Figure 2 one embodiment shown, region A can be the region that is coiled first in the hot-rolled steel sheet, and region C can be the region that is coiled last. Region B can be the region located between region A and region C in the length direction.

[0042] In one embodiment, the hot-rolled steel sheet can include surface defects. For example, the surface defects can include at least one of scale and internal defect layer. Scale is generated in the rolling process and can exist on the surface of the material. The internal defect layer can be contained below the surface of the material, i.e., inside the material, and the internal defect can be defined to mean an internal oxide layer and / or decarburized layer. The internal oxide layer can be generated during the process of oxidation caused by components such as chromium (Cr), manganese (Mn), silicon (Si), zinc (Zn), magnesium (Mg), aluminum (Al), etc. whose oxygen affinity is higher than that of iron (Fe) inside the base material. The decarburized layer can be generated during the process of carbon in the steel combining with oxygen in the atmosphere and scale and then being discharged into the atmosphere in the form of gas. The thickness of the internal defect can vary according to the composition of the hot-rolled steel sheet, the temperature when the hot-rolled steel sheet is coiled into a hot-rolled coil (HC), the cooling time after coiling, the width, thickness, and length of the hot-rolled steel sheet, etc. Internal defects such as the internal defect layer can be an important factor reducing the durability of products produced using the hot-rolled steel sheet.

[0043] As an example, the temperature of the hot-rolled coil (HC) to be coiled may be about 500 to 700 °C, and the hot-rolled coil (HC) in the coiled state can be cooled by an air-cooling method in a state of being exposed to air. In the hot-rolled coil (HC) in the coiled state, the A region and the C region exposed to the outside can be cooled relatively quickly, while the unexposed B region is cooled relatively slowly. Therefore, the thickness of the internal defect layer contained in the B region of the hot-rolled steel sheet may be greater than the thicknesses of the internal defect layers respectively contained in the A region and the C region of the hot-rolled steel sheet. Therefore, depending on the region of the hot-rolled steel sheet, the thickness of the internal defect layer may deviate. As an example, the thickness of the internal defect layer contained in the A region and / or the C region may be less than a specified reference thickness, and the thickness of the internal defect layer contained in the B region may be greater than the reference thickness.

[0044] In addition, in the width direction of the hot-rolled steel sheet, depending on the region, the thickness of the internal defect layer may also be different. As an example, in the width direction, the region adjacent to the edge of the hot-rolled steel sheet can be cooled relatively quickly, and there may also be a difference in the thickness of the internal defect layer under different cooling rates in the width direction.

[0045] The etching process for removing at least a part of the internal defect layer can be carried out by bringing the hot-rolled steel sheet into contact with an etching solution. As an example, the etching process can be carried out by transporting the hot-rolled steel sheet in a state of being immersed in the etching solution contained in an etching tank. Alternatively, the etching process can also be carried out by spraying the etching solution onto the surface of the hot-rolled steel sheet in the etching tank or brushing the surface of the hot-rolled steel sheet with a brush soaked with the etching solution. The etching process can be at least one of an acid pickling process, a dry etching process, and a wet etching process.

[0046] As an example, in the etching process, the etching process can be carried out by bringing the hot-rolled steel sheet into sufficient contact with the etching solution regardless of the region of the hot-rolled steel sheet, so as to sufficiently remove the internal defect layer contained in the hot-rolled steel sheet. However, the above-described method may cause an increase in the time of the etching process and / or the etching solution input into the etching process, thereby reducing the productivity.

[0047] According to an embodiment of the present invention, the thickness of the internal defect layer contained in the hot-rolled steel sheet along the length direction of the hot-rolled steel sheet can be calculated and / or actually measured, and based on the thickness information of the internal defect layer, the etching process can be controlled with the best efficiency. Therefore, the time of the etching process can be shortened and the usage amount of the etching solution can be reduced, thereby improving the productivity. In addition, the thickness deviation of the internal defect layer generated by region on the pickled steel sheet after the etching process is completed can be reduced.

[0048] Figure 3 It is a block diagram showing a process control system according to an embodiment of the present invention.

[0049] According to an embodiment of the present invention, the process control system 1 may include: a first system SYS1 for generating thickness information of an internal defect layer included in a hot-rolled coil formed by coiling a hot-rolled steel sheet in a roll shape; and a second system SYS2 for controlling an etching process for removing at least a part of the internal defect layer in the hot-rolled steel sheet released from the hot-rolled coil by using the thickness information of the internal defect layer received from the first system SYS1. In addition, the first system SYS1 may provide a calculation module required for the second system SYS2 to control the etching process. In addition, the second system SYS2 may acquire information required for the first system SYS1 to update the calculation module and provide it to the first system SYS1. The first system SYS1 and the second system SYS2 may communicate with each other through a network 30.

[0050] Referring to Figure 3 , the process control system 1 according to an embodiment of the present invention may be operated by a first entity performing a hot-rolling process and a second entity performing an etching process. In one embodiment, the first entity may produce a hot-rolled steel sheet and coil the hot-rolled steel sheet to prepare a hot-rolled coil, and the second entity may receive the hot-rolled coil from the first entity and perform an etching process for removing an internal defect layer of the hot-rolled steel sheet. Referring to Figure 3 , the first system SYS1 may be run by the first entity, and the second system SYS2 may be run by the second entity. However, according to an embodiment, the first system SYS1 and the second system SYS2 may also be run by the same entity. At this time, the network 30 may be an internal network of the entity that runs both the first system SYS1 and the second system SYS2.

[0051] Referring to Figure 3 , the first system SYS1 may include a first server 10, and the second system SYS2 may include a second server 20. The first server 10 and the second server 20 may communicate through the network 30, and the second system SYS2 may control an etching device 22 for removing at least a part of the internal defect layer of the hot-rolled steel sheet.

[0052] The network 30 may mean a wired Internet network, a wireless Internet network, or a wireless local area network (WLAN) such as Wi-Fi (wireless fidelity). The wired Internet network or the wireless Internet network may mean an open computer network structure that provides the Internet TCP / IP protocol and various services existing in its upper layer, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP (Simple Network Management Protocol), NFS (Network File Service), and NIS (Network Information Service), etc.

[0053] In one embodiment, the first system SYS1 may generate thickness information of an internal defect layer of a hot-rolled steel sheet produced in a hot-rolling process. The first system SYS1 may transmit the thickness information of the internal defect layer to the second system SYS2 via the network 30. The second system SYS2 may control the etching process performed by the etching device 22 by using the thickness information of the internal defect layer received via the network 30.

[0054] As an example, the first system SYS1 may include a first server 10, and the first server 10 may include a computing module 11 and a learning model for enabling the computing module 11 to perform learning, etc. In one embodiment, the thickness information of the internal defect layer may be generated by the computing module 11. The first system SYS1 may generate the thickness information by actually measuring the thickness of the internal defect layer of the hot-rolled steel sheet, or by calculating the thickness of the internal defect layer of the hot-rolled steel sheet. In one embodiment, the first system SYS1 may calculate the thickness of the internal defect layer by using at least one of the composition of the hot-rolled steel sheet, the phase fraction of the hot-rolled steel sheet, and the temperature of the hot-rolled steel sheet, so as to generate the thickness information of the internal defect layer.

[0055] The thickness information of the internal defect layer may include the thickness of the internal defect layer contained in the hot-rolled steel sheet in each of a plurality of regions defined in the length direction of the hot-rolled steel sheet. Therefore, the thickness information of the internal defect layer may include the thickness distribution of the internal defect layer shown along the length direction of the hot-rolled steel sheet, etc.

[0056] According to another embodiment, the thickness-related information of the internal defect layer can be generated by the calculation module 11. The thickness-related information of the internal defect layer can mean all forms of information processed in such a way that the thickness information of the internal defect layer can be extracted. When the first system SYS1 transmits the thickness-related information of the internal defect layer to the second system SYS2 via the network 30, the second system SYS2 can extract the thickness information of the internal defect layer included in the thickness-related information of the internal defect layer received via the network 30, or calculate the thickness information of the internal defect layer based on the thickness-related information, and control the etching process performed by the etching device 22 using the extracted or calculated thickness information of the internal defect layer.

[0057] The calculation module 21 of the second system SYS2 can generate the control data required to control the etching device 22. As an example, the calculation module 21 can use the thickness information of the internal defect layer received from the first system SYS1 to generate the control data. The control data can include at least one of the speed at which the hot-rolled steel sheet is conveyed by the etching device 22 during the etching process, the concentration of the etching solution in contact with the hot-rolled steel sheet in the etching device 22, the composition of the etching solution, the temperature of the etching solution, and the use or non-use of the promoter.

[0058] The calculation module 21 of the second system SYS2 can be at least one module in the calculation module 11 stored and managed by the first system SYS1 and received by the second system SYS2. The first system SYS1 can provide the module required to control the etching device 22 in the calculation module 11 to the second system SYS2. As an example, the first system SYS1 can use the learning model 12 to make the module required to control the etching device 22 learn and then provide the module to the second system SYS2. The first system SYS1 can provide the module to the second system SYS2 by means of transmission via the network 30. Alternatively, the module learned by the first system SYS1 can also be provided to the second system SYS2 by directly inputting and storing the module into the second system SYS2 by an operator.

[0059] The second system SYS2 can provide the first system SYS1 with the information required for the first system SYS1 to update the calculation module 11. As an example, when the etching apparatus 22 finishes the etching process on the hot-rolled steel sheet to produce the pickled steel sheet, the second system SYS2 can provide the first system SYS1 with at least one of the thickness of the remaining internal defect layer included in the pickled steel sheet and the control data measured in the etching apparatus 22 during the etching process. The first system SYS1 can use at least one of the thickness of the remaining internal defect layer and the control data received from the second system SYS2 to update the calculation module 11. As an example, the learning model 12 can use at least one of the thickness of the remaining internal defect layer and the control data to make the calculation module 11 learn, so as to update the calculation module 11.

[0060] After updating the calculation module 11, the first system SYS1 can transmit at least one of the updated calculation modules 11 to the second system SYS2 through the network 30. The second system SYS2 can use the calculation module 11 received from the first system SYS1 to update the stored calculation module 21. As an example, the second system SYS2 can overwrite the existing calculation module 21 with the calculation module received from the first system SYS1.

[0061] In one embodiment, the calculation module 11 of the first system SYS1 can be multiple calculation modules. The calculation module 11 can include a calculation module for generating the thickness information of the internal defect layer included in the hot-rolled steel sheet and a calculation module for generating control data for controlling the etching apparatus 22, etc. The first system SYS1 can transmit the calculation module for generating control data to the second system SYS2.

[0062] The learning model 12 can optimize the calculation module 11. As an example, the learning model 12 can include multiple learning models for making multiple calculation modules learn. In addition, the learning model 12 can use at least one of the thickness of the remaining internal defect layer and the control data received from the second system SYS2 to make the calculation module 11 learn, so as to achieve optimization. In one embodiment, the learning model 12 can make the calculation module 11 learn, so that the thickness of the remaining internal defect layer calculated by the calculation module 11 and the thickness of the remaining internal defect layer received from the second system SYS2 are consistent with each other or the difference between the two reaches below a specified reference value.

[0063] The calculation module 11 can be implemented by hardware such as a circuit, or can be implemented by software such as source code. The calculation module 11 can execute a specified calculation using the input value to generate an output value. As an example, the learning model 12 can make the calculation module 11 learn by adjusting the weighting values, coefficients, etc. in the calculation executed in the calculation module 11.

[0064] According to an embodiment, the second server 20 of the second system SYS2 may further include an additional calculation module. The additional calculation module may be an additional calculation module different from the calculation module 21 received from the first system SYS1, which may adjust at least a part of the control data output by the calculation module 21 and input it into the etching device 22. As an example, considering mechanical errors and operation delays existing in the etching device 22, etc., the additional calculation module may adjust at least a part of the control data output by the calculation module 21 and input it into the etching device 22. Therefore, when the second server 20 includes an additional calculation module, the control data output by the calculation module 21 and the control data input into the etching device 22 may be different from each other.

[0065] The additional calculation module may adjust the scale unit of the control data or convert the control data into a data format that can be input into the etching device 22, so that the control data output by the calculation module 21 can be applied to the etching device 22. According to an embodiment, the calculation module 21 may also directly adjust the scale unit, data format, etc. of the control data and output it to the etching device 22. At this time, the second server 20 may not include an additional calculation module either.

[0066] In one embodiment, the calculation module 21 of the second system SYS2 may receive the thickness information of the internal defect layer from the first system SYS1. The thickness information may include the thickness of the internal defect layer calculated and / or measured by the first system SYS1. The second system SYS2 may use the thickness information to determine the control data for controlling the etching device 22. In one embodiment, the control data may include the conveying speed of the hot-rolled steel sheet conveyed by the etching device 22, the concentration of the etching solution in contact with the hot-rolled steel sheet in the etching device 22, the temperature of the etching solution, the composition of the etching solution, and whether to use a promoter, etc. The etching device 22 may be automatically controlled by the control data output by the calculation module 21.

[0067] As an example, the control data input into the etching device 22 can optimize the etching process performed on each of a plurality of regions defined along the length direction of the hot-rolled steel sheet. For example, the conveying speed of the hot-rolled steel sheet in the etching process of a partial region near the end of the hot-rolled steel sheet and the conveying speed of the hot-rolled steel sheet in the etching process of other partial regions far from the end of the hot-rolled steel sheet may be different. As an example, the conveying speed of the hot-rolled steel sheet in the etching process of a partial region near the end of the hot-rolled steel sheet may be faster than the conveying speed of the hot-rolled steel sheet in the etching process of other partial regions far from the end of the hot-rolled steel sheet. In one embodiment, during the etching process, the scale on the surface of the hot-rolled steel sheet may be removed together.

[0068] In one embodiment, due to mechanical errors present in the etching apparatus 22 and signal delays during processes such as inputting and outputting control data, the control data output by the calculation module 21 may not match the control data actually measured in the etching apparatus 22 during the etching process. Taking the conveying speed of the hot-rolled steel sheet in the control data input to the etching apparatus 22 as an example, the conveying speed of the hot-rolled steel sheet input from the calculation module 21 to the etching apparatus 22 may not match the actual conveying speed at which the etching apparatus 22 conveys the hot-rolled steel sheet. In addition, the concentration of the etching solution and / or the temperature of the etching solution in the control data input to the etching apparatus 22 may change during the process of contact between the hot-rolled steel sheet and the etching solution.

[0069] In one embodiment of the present invention, mechanical errors and signal delays can be considered to control the etching apparatus 22. As an example, the second system SYS2 can obtain information including the thickness of the remaining internal defect layer in the pickled steel sheet after the etching process is completed and the control data measured in the etching apparatus 22 during the etching process, so that mechanical errors and signal delays can be reflected in the control data. The second system SYS2 can transmit the obtained information to the first system SYS1.

[0070] The learning model 12 of the first system SYS1 can utilize the information transmitted by the second system SYS2 to cause at least one of the calculation modules 11 to learn. After the learning of the calculation module 11 is completed, the first system SYS1 can transmit at least one of the calculation modules 11 that have completed learning, for example, the calculation module for generating control data for controlling the etching apparatus 22, to the second system SYS2. The second system SYS2 can utilize the calculation module received from the first system SYS1 to update the stored calculation module 21. Therefore, the calculation module 21 of the second system SYS2 can generate control data considering mechanical errors and signal delays and can control the etching apparatus 22 more accurately.

[0071] According to an embodiment, the second system SYS2 may also include a learning model. The learning model included in the second system SYS2 can cause the calculation module 21 included in the second system SYS2 to learn in a manner similar to the learning model 12 included in the first system SYS1. Details regarding this will be described later with reference to Figure 14 and Figure 15 will be described.

[0072] In addition, the thickness of the internal defect layer contained in the hot-rolled steel sheet can be directly calculated by the second system SYS2, and the etching process can be carried out using this thickness. At this time, the second system SYS2 can receive the information required to calculate the thickness of the internal defect layer from the first system SYS1. As an example, the first system SYS1 can collect information such as the cooling rate of the hot-rolled steel sheet, the composition of the hot-rolled steel sheet, and the oxygen partial pressure in the surroundings during the cooling of the hot-rolled steel sheet, and transmit this information to the second system SYS2. The calculation module 21 of the second system SYS2 can use the variables received from the first system SYS1 to predict the thickness of the internal defect layer contained in the hot-rolled steel sheet and control the etching device 22.

[0073] In one embodiment, as described above, the conveying speed of the hot-rolled steel sheet during the etching process can be adjusted using the calculated thickness of the internal defect layer. As an example, the hot-rolled steel sheet can be conveyed at a first conveying speed during the period when the first region where the thickness of the internal defect layer is predicted to be relatively small comes into contact with the etching solution. In addition, the hot-rolled steel sheet can be conveyed at a second conveying speed slower than the first conveying speed during the period when the second region where the thickness of the internal defect layer is predicted to be relatively large comes into contact with the etching solution.

[0074] In one embodiment of the present invention, the following operations can be implemented by being distributed between two different systems SYS1 and SYS2: These operations include operations for collecting information for predicting the internal defect layer of the hot-rolled steel sheet, operations for calculating the internal defect layer, operations for generating control data for controlling the etching device 22 based on the calculated internal defect layer, operations for re-learning the calculation modules 11 and 21 using the control data collected during the etching process performed by the etching device 22, etc. As an example, these operations can be appropriately distributed between the first system SYS1 and the second system SYS2 as needed. As an example, the operations can be allocated to the first system SYS1 and the second system SYS2 according to the performance of the first system SYS1 and the second system SYS2, so as to efficiently manage the load of the systems SYS1 and SYS2 and effectively operate the entire system 1.

[0075] For example, when the performance of the first system SYS1 is far superior to that of the second system SYS2, most of the operations can be processed by the first system SYS1, and the second system SYS2 can receive the control data generated by the first system SYS1 to control the etching device 22. When the performance of the first system SYS1 and the second system SYS2 is similar, the first system SYS1 can generate thickness information representing the thickness of the internal defect layer contained in the hot-rolled steel sheet, and the second system SYS2 can generate control data by using the thickness information of the internal defect layer. On the contrary, when the performance of the second system SYS2 is far superior to that of the first system SYS1, the first system SYS1 can collect the information of the hot-rolled steel sheet required for calculating the thickness of the internal defect layer and transmit it to the second system SYS2, and the second system SYS2 can calculate the thickness of the internal defect layer and generate control data to control the etching device 22.

[0076] To predict the thickness of the internal defect layer, the information of the hot-rolled steel sheet received by the second system SYS2 from the first system SYS1 may include information related to the phase fraction of the hot-rolled steel sheet, information related to the temperature of the hot-rolled steel sheet, and information related to the composition of the hot-rolled steel sheet, etc. The information related to the phase fraction may mean all forms of information processed in a way that can extract the phase fraction information, the information related to the temperature of the hot-rolled steel sheet may mean all forms of information processed in a way that can extract the temperature information, and the information related to the composition of the hot-rolled steel sheet may mean all forms of information processed in a way that can extract the composition information. Therefore, the second system SYS2 can use the information received from the first system SYS1 to first calculate the phase fraction, temperature, and composition of the hot-rolled steel sheet, etc., and then use these data to predict the thickness of the internal defect layer contained in the hot-rolled steel sheet.

[0077] Figure 4 It is a block diagram simply showing a process control system according to an embodiment of the present invention.

[0078] Referring to Figure 4 , the process control system 40 according to an embodiment of the present invention may include a first server 41 and a hot-rolled steel sheet production device 45, etc. The first server 41 can control and manage the hot-rolled steel sheet production device 45. The hot-rolled steel sheet production device 45 may be a device that rolls a slab heated in a heating furnace to produce a hot-rolled steel sheet, and cools and winds up the hot-rolled steel sheet.

[0079] The first server 41 may include a communication unit 42, a storage device 43, a processor 44, etc. The communication unit 42 can communicatively connect the first server 41 and the network. The storage device 43 can store the data required for the operation of the first server 41 and the management of the hot-rolled steel sheet production device 45, etc. The processor 44 can control the communication unit 42, the storage device 43, the hot-rolled steel sheet production device 45, etc.

[0080] As an example, the storage device 43 can store a calculation module for performing a specified calculation. In one embodiment, the calculation module can be linked with the hot-rolled steel sheet production device 45 and generate thickness information of an internal defect layer contained in the hot-rolled steel sheet by using information obtained from the hot-rolled steel sheet production device 45. In addition, the calculation module can generate control data required to control the etching device by an external system connected through the communication unit 42 and a network or the like. The etching device can be a device for removing at least a part of the internal defect layer contained in the hot-rolled steel sheet produced by the hot-rolled steel sheet production device 45.

[0081] For example, the control data can include the conveyance speed of the hot-rolled steel sheet conveyed by the etching device, the temperature and concentration of the etching solution in contact with the hot-rolled steel sheet in the etching device, the composition, and whether or not to use a promoter. As an example, in the thickness information of the internal defect layer generated by the calculation module, the first region of the hot-rolled steel sheet can have an internal defect layer with a first thickness, and the second region can have an internal defect layer with a second thickness different from the first thickness. The control data can include a first conveyance speed for conveying the hot-rolled steel sheet in the etching process of the first region and a second conveyance speed for conveying the hot-rolled steel sheet in the etching process of the second region, and the first conveyance speed and the second conveyance speed can be different from each other. As an example, when the first thickness is greater than the second thickness, the first conveyance speed can be slower than the second conveyance speed. Therefore, a sufficient etching process can be performed on a region with a relatively large thickness.

[0082] In one embodiment, the storage device 43 can be used to store a learning model required for the calculation module to perform learning. The learning model can be executed by the processor 44, and the processor 44 can optimize the calculation module by executing the learning model.

[0083] The processor 44 can be implemented by a CPU, an AP, an SoC, etc., and can control the storage device 43, the communication unit 42, the hot-rolled steel sheet production device 45, etc. As an example, the processor 44 can transmit at least one of the thickness information of the internal defect layer generated by the calculation module stored in the storage device 43 and the control data required for the external system to control the etching device to the external system.

[0084] When the processor 44 transmits the thickness information of the internal defect layer to the external system, the external system can directly generate control data for controlling the etching device based on the thickness information of the internal defect layer. At this time, the processor 44 can extract the calculation module for generating control data by using the thickness information of the internal defect layer from the storage device 43 and transmit it to the external system.

[0085] In addition, when the processor 44 transmits control data to the external system, the external system can control the etching device using the received control data. If necessary, additional operations such as changing the format of the control data can be performed in the external system. At this time, the processor 44 may not transmit to the external system the calculation module that generates control data using the thickness information of the internal defect layer.

[0086] In one embodiment, the calculation module stored in the storage device 43 may include a first module that calculates the phase fraction before the hot-rolled steel sheet is coiled in the hot-rolled steel sheet production device 45. The first module may calculate the phase fraction based on at least one of the process conditions of the hot-rolled steel sheet, such as the cooling rate of the hot-rolled steel sheet, the composition of the hot-rolled steel sheet, and the initial temperature of the hot-rolled steel sheet. The learning model in the storage device 43 may compare the phase fraction actually measured in the hot-rolled steel sheet with the phase fraction predicted by the first module through calculation, and cause the first module to learn.

[0087] In one embodiment, the calculation module stored in the storage device 43 may further include a second module that calculates the temperature change of the hot-rolled steel sheet. The second module may predict the temperature change using at least one of the phase fraction calculated by the first module, the elapsed time after coiling the hot-rolled steel sheet, and the composition of the hot-rolled steel sheet. The learning model may compare the temperature change calculated by the second module with the temperature change actually measured in the hot-rolled steel sheet, and cause the second module to learn.

[0088] In one embodiment, the calculation module stored in the storage device 43 may further include a third module that calculates the thickness of the internal defect layer contained in the hot-rolled steel sheet. As an example, the third module may calculate the thickness of the internal defect layer using at least one of the temperature change calculated by the second module, the composition of the hot-rolled steel sheet, and the oxygen partial pressure around the hot-rolled steel sheet. The learning model may compare the thickness of the internal defect layer predicted by the third module through calculation with the thickness of the internal defect layer measured from the hot-rolled steel sheet, and cause the third module to learn.

[0089] As an example, the third module may calculate the thickness of the internal defect layer in each of a plurality of regions defined along the length direction of the hot-rolled steel sheet. The thicknesses of the internal defect layer calculated in at least a part of the plurality of regions may be different from each other.

[0090] In one embodiment, the calculation module stored in the storage device 43 may further include a fourth module that calculates the thickness of the internal defect layer removed in an etching process for removing at least a part of the internal defect layer. The fourth module may calculate the thickness of the internal defect layer removed in the etching process based on the control data input to the etching device to control the etching device.

[0091] The learning model can compare the thickness of the remaining internal defect layer in the pickled steel sheet after the etching process is completed with the thickness of the internal defect layer predicted by the fourth module through calculation, and cause the fourth module to learn. As an example, the learning model can compare the difference between the thickness of the internal defect layer calculated by the third module and the thickness of the internal defect layer calculated by the fourth module with the thickness of the remaining internal defect layer contained in the pickled steel sheet. In order to cause the fourth module to learn, the first server 41 can receive the thickness of the remaining internal defect layer measured in the pickled steel sheet by an external system that controls the etching device.

[0092] In one embodiment, the calculation module stored in the storage device 43 may further include a fifth module for generating control data required for the external system to control the etching device. The control data generated by the fifth module may include at least one of the speed at which the hot-rolled steel sheet is conveyed by the etching device, the concentration of the etching solution that contacts the hot-rolled steel sheet in the etching device, the composition of the etching solution, the temperature of the etching solution, and the use or non-use of a promoter.

[0093] The first server 41 can receive control data from an external system for controlling the etching device so that the learning model can cause the fifth module to learn. The control data received by the first server 41 from the external system may be the control data actually measured by the external system from the etching device during the etching process. The learning model can compare the thickness of the internal defect layer calculated by the fourth module using the control data received from the external system with the thickness of the remaining internal defect layer actually measured from the pickled steel sheet, and cause the fifth module to learn.

[0094] Figure 5 It is a diagram for explaining an initial learning method of a calculation module included in a process control system according to an embodiment of the present invention.

[0095] Refer to Figure 5 As shown in FIG. 1, the thickness control system 100 of the internal defect layer of the hot-rolled steel sheet may include a calculation module 110 and a learning model 120. As an example, the calculation module 110 may include a thickness information generation module 111 and a control data generation module 112. In one embodiment, the calculation module 110 may include first to fifth modules M1 to M5, the thickness information generation module 111 may include first to third modules M1 to M3, and the control data generation module 112 may include a fourth module M4 and a fifth module M5. The first to fifth modules M1 to M5 may be implemented by hardware such as a circuit or by software such as source code.

[0096] For example, when the first to fifth modules M1 to M5 are implemented by software such as source code, the first to fifth modules M1 to M5 can perform calculations for calculating output data using input data. The calculations of the first to fifth modules M1 to M5 can be determined and optimized by the learning model 120.

[0097] The learning model 120 may include first to fourth learning models LM1 to LM4 and a feedback learning model LMT. The first learning model LM1 may be a model for initial learning of the first module M1, and the second learning model LM2 may be a model for initial learning of the second module M2. Similarly, the third learning model LM3 may be a model for initial learning of the third module M3, and the fourth learning model LM4 may be a model for initial learning of the fourth module M4. The feedback learning model LMT may be a model for learning at least one of the first to fifth modules M1 to M5 after completion of the etching process. In one embodiment, the feedback learning model LMT may also cause the first to fifth modules M1 to M5 to learn simultaneously.

[0098] The learning model 120 may cause the first to fifth modules M1 to M5 to learn by using a deviation correction method, reinforcement learning, or the like. When using the deviation correction method, the learning model 120 may cause the first to fifth modules M1 to M5 to learn by correcting the error between the measured value input from the outside and the calculated value output from the module. When the learning model 120 uses reinforcement learning, the learning model 120 may be a DNN (deep neural network), but is not necessarily limited to the example described above.

[0099] For one embodiment in which the learning model 120 uses reinforcement learning to cause each module to learn, it will be described later with reference to Figure 6 will be described. Next, with reference to Figure 5 , a specific method for initial learning of the process control system 100 will be described.

[0100] In one embodiment, the first module M1 may calculate a phase fraction before coiling a hot-rolled steel sheet. The first learning model LM1 may receive a first process condition CD1 and a first phase fraction value EPF. The first phase fraction value EPF may include an actual measured phase transformation fraction, a phase variable, or the like 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 a composition of the hot-rolled steel sheet.

[0101] The first learning model LM1 may input the first process condition CD1 into the first module M1. The first module M1 may calculate a second phase fraction value PPF obtained by calculating the phase fraction of the hot-rolled steel sheet using the first process condition CD1. The second phase fraction value PPF output from the first module M1 may be transmitted to the first learning model LM1.

[0102] The first learning model LM1 can compare the first phase fraction value EPF and the second phase fraction value PPF, and cause the first module M1 to learn. As an example, when the first phase fraction value EPF and the second phase fraction value PPF are inconsistent, the first learning model LM1 can adjust the weighted value and coefficient of the first calculation, etc., where the first calculation is the calculation of the phase fraction by the first module M1 receiving the first process condition CD1. The first learning model LM1 can adjust the weighted value and coefficient of the first calculation of the first module M1 so that the first phase fraction value EPF and the second phase fraction value PPF are consistent or the difference between the first phase fraction value EPF and the second phase fraction value PPF reaches a value below a specified value.

[0103] The second learning model LM2 can receive the second process condition CD2, the environmental condition FV representing the surrounding environment, and the first temperature value ET measured from the hot-rolled steel plate, etc. The second process condition CD2 can include the time point for measuring the temperature of the hot-rolled steel plate after coiling and the composition of the hot-rolled steel plate, etc. The environmental condition FV can include the value representing the surrounding environment that affects the cooling rate of the hot-rolled steel plate after coiling. For example, when the surrounding environment is air, the value of the environmental condition FV can be "1", when the surrounding environment is wind, the value of the environmental condition FV can be "2", and when the surrounding environment is water, the value of the environmental condition FV can be "3". The first temperature value ET can include the actually measured value of the hot-rolled coil temperature.

[0104] The second module M2 can receive the second process condition CD2 and the environmental condition FV from the second learning model LM2. As an example, the second module M2 can calculate the temperature of the hot-rolled steel plate using the second process condition CD2 and the environmental condition FV, and can output the second temperature value PT after calculating the temperature of the hot-rolled steel plate. The second temperature value PT can include the value calculated based on the time elapsed after coiling for the temperature of each region defined in the length direction of the hot-rolled steel plate, etc. The second module M2 can output the second temperature value PT to the second learning model LM2.

[0105] The second learning model LM2 can compare the first temperature value ET and the second temperature value PT, and cause the second module M2 to learn. For example, when the first temperature value ET and the second temperature value PT are inconsistent or the difference between the first temperature value ET and the second temperature value PT is greater than a specified value, the second learning model LM2 can adjust the weighting value and coefficient of the second calculation for calculating the second temperature value PT in the second module M2, etc. The second learning model LM2 can cause the second module M2 to learn so that the first temperature value ET and the second temperature value PT are consistent or the difference between the first temperature value ET and the second temperature value PT reaches a value below a specified value.

[0106] The third learning model LM3 can receive the composition ING of the hot-rolled steel sheet, the oxygen partial pressure OPP around the hot-rolled steel sheet, the output value of the second module M2, i.e., the second temperature value PT, and the first thickness ED of the internal defect layer measured from the hot-rolled steel sheet, etc. The first thickness ED of the internal defect layer can be the measured value after actually measuring the thickness of the internal defect layer of the hot-rolled steel sheet.

[0107] The third module M3 can receive the composition 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 as input data from the third learning model LM3. The third module M3 can calculate the second thickness PD of the internal defect layer. The second thickness ED can be the thickness of the internal defect layer that may exist in the hot-rolled steel sheet calculated by the third module M3 using at least one of the composition ING of the hot-rolled steel sheet, the oxygen partial pressure OPP around the hot-rolled steel sheet, and the second temperature value PT. The second thickness PD of the internal defect layer output by the third module M3 can be transmitted to the third learning model LM3.

[0108] The third learning model LM3 can compare the first thickness ED and the second thickness PD of the internal defect layer and make the third module M3 learn. As an example, when the first thickness ED and the second thickness PD of the internal defect layer are inconsistent or the difference between the two is greater than a specified value, the third learning model LM3 can adjust the weighted value and coefficient, etc., of the third calculation for calculating the second thickness PD of the internal defect layer in the third module M3. The third learning model LM3 can make the third module M3 learn so that the measured value, i.e., the first thickness ED, and the calculated value, i.e., the second thickness PD, of the internal defect layer are consistent or the difference between the two reaches below the specified value.

[0109] The fourth learning model LM4 can receive the conveying speed PV of the hot-rolled steel sheet, the characteristics AC of the etching solution used in the etching process, and the first thickness EPA of the internal defect layer removed by the etching device, etc. The characteristics AC of the etching solution can include the concentration of the etching solution in contact with the hot-rolled steel sheet, the temperature of the etching solution, the composition of the etching solution, and whether a promoter is used, etc. The conveying speed PV of the hot-rolled steel sheet can mean the speed at which the hot-rolled steel sheet moves during the period when the hot-rolled steel sheet is in contact with the etching solution. The first thickness EPA of the internal defect layer can mean the thickness of the internal defect layer actually removed in the etching process through the conveying speed PV of the hot-rolled coil and the characteristics AC of the etching solution.

[0110] The fourth module M4 can receive the conveying speed PV of the hot-rolled steel sheet and the characteristics AC of the etching solution from the fourth learning model LM4. The fourth module M4 can calculate a second thickness PPA1 of an internal defect layer that is expected to be removed by the etching device using at least one of the conveying speed PV of the hot-rolled steel sheet and the characteristics AC of the etching solution. In one embodiment, the fourth module M4 can receive the etching time determined according to the conveying speed PV and the characteristics AC of the etching solution as input values, and perform a calculation of the second thickness PPA1 of the internal defect layer using the input values. The fourth module M4 can output the second thickness PPA1 of the internal defect layer to the fourth learning model LM4.

[0111] The fourth learning model LM4 can compare the second thickness PPA1 of the internal defect layer with the first thickness EPA of the internal defect layer, and cause the fourth module M4 to learn. For example, when 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 between the two is greater than a specified value, the fourth learning model LM4 can adjust the weighted value, coefficient, etc. of the fourth calculation for calculating the second thickness PPA1 of the internal defect layer in the fourth module M4.

[0112] The fifth module M5 can generate control data for controlling the etching device. As an example, the fifth module M5 can repeatedly call the fourth module M4 by using an optimization method (such as the golden section method, etc.) to find the optimal control data. The fifth module M5 can select an optimization method for finding the optimal control data or modify the optimization method.

[0113] Figure 6 It is a diagram for explaining a learning model included in a process control system according to an embodiment of the present invention.

[0114] Refer to Figure 6 , when the learning model uses reinforcement learning to make each module learn, the learning model can be implemented by a deep neural network (DNN), etc. The learning model can include an input layer IL, a hidden layer HL, and an output layer OL. As an example, a plurality of nodes included in the input layer IL, the hidden layer HL, and the output layer OL can be connected to each other in a fully connected manner. The input layer IL can include a plurality of input nodes x1 to xi, and the number of input nodes x1 to xi can correspond to the number of input data. The output layer OL can include a plurality of output nodes y1 to yj, and the number of output nodes y1 to yj can correspond to the number of output data.

[0115] The hidden layer HL can include first to third hidden layers HL1 to HL3, and the number of hidden layers HL1 to HL3 can be deformed into various numbers. As an example, the learning model 120 can learn by adjusting the weighted values of each hidden node included in the hidden layer HL.

[0116] Figure 7 This is a diagram for explaining the operation method of a process control system according to an embodiment of the present invention.

[0117] Refer to Figure 7 , the process control system 200 according to an embodiment of the present invention may include computing modules 210 to 230. As an example, the process control system may be connected to an external system through a network, and the external system directly controls an etching device for removing at least a part of an internal defect layer of a hot-rolled steel sheet.

[0118] In one embodiment, the computing modules 210 to 230 may calculate the thickness of the internal defect layer contained 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 in the length direction of the hot-rolled steel sheet may be required. When the phase transformation is not completed before coiling the hot-rolled steel sheet, heat generation due to the phase transformation may occur after coiling. Therefore, the temperature change of the hot-rolled steel sheet after coiling may be calculated considering the phase fraction before coiling the hot-rolled steel sheet.

[0119] In one embodiment, the first module 210 may calculate the phase fraction PPF at different cooling times using the first process condition CD1. The first process condition CD1 may include the cooling rate of the hot-rolled steel sheet, the temperature of the hot-rolled steel sheet, the composition of the hot-rolled steel sheet, and the like.

[0120] During the cooling process of the hot-rolled steel sheet, a phase transformation from austenite to pearlite may occur, and phase transformation heat generation due to the phase transformation may occur. Due to the slow pearlite phase transformation of hot-rolled high-carbon steel, the phase transformation may not be completed during cooling, and further phase transformation may occur on the hot-rolled steel sheet after coiling. As a result, the hot-rolled steel sheet may be exposed to a high-temperature oxidation atmosphere for a long time after being coiled, and the thickness of the internal defect layer may increase.

[0121] The second module 220 may calculate the temperature PT at different times after coiling the hot-rolled steel sheet using the second process condition CD2 and environmental conditions FV, etc. As an example, the second module 220 may calculate the temperature PT in each region in the length direction of the hot-rolled steel sheet. The second process condition CD2 may include the time point for measuring the temperature of the hot-rolled steel sheet after coiling and the composition of the hot-rolled steel sheet, and the like.

[0122] In order to calculate the temperature of the hot-rolled steel sheet after coiling, it is also necessary to consider the phase transformation heat generation described above. Therefore, the second module 220 can receive the phase fraction PPF from the first module 210. Based on the phase fraction PPF, the phase fraction at the coiling time point can be known, and the amount of phase change further generated on the hot-rolled steel sheet after coiling can be known. Therefore, the second module 220 can consider the amount of phase change further generated on the hot-rolled steel sheet after coiling and calculate the temperature PT of the hot-rolled steel sheet according to the elapsed time after coiling the hot-rolled steel sheet. In Figure 7 In an embodiment represented in

[0123] The third module 230 can calculate the thickness PD of the internal defect layer contained in the hot-rolled steel sheet by using the composition ING of the hot-rolled steel sheet and the oxygen partial pressure OPP around the hot-rolled steel sheet, etc. In an embodiment, the third module 230 can receive the temperature PT according to the elapsed time after coiling in each region of the hot-rolled steel sheet from the second module 220 and calculate the thickness PD of the internal defect layer in each of the regions of the hot-rolled steel sheet.

[0124] Figure 8 is a flowchart for explaining an operation method of a process control system according to an embodiment of the present invention.

[0125] Referring to Figure 8 , the process control system can transmit at least a part of the calculation module to an external system (S110). As an example, the main body for transmitting at least a part of the calculation module to the external system can be a system for producing a hot-rolled steel sheet and coiling the hot-rolled steel sheet in the form of a hot-rolled coil. In addition, the external system for receiving at least a part of the calculation module can be a system for receiving the hot-rolled coil and performing an etching process.

[0126] The external system can use the calculation module received in step S110 to perform an etching process. As an example, the calculation module transmitted to the external system in step S110 can be a module for generating control data for controlling the etching process. The process control system can receive from the external system the thickness of the remaining internal defect layer contained in the pickled steel sheet after the etching process is completed and the control data measured in the etching device during the etching process, etc. (S120). As an example, the control data received in step S120 can be a value actually measured in the etching device, and thus may be different from the value input to the etching device by the external system for controlling the etching device.

[0127] The process control system can utilize the control data received in step S120 to calculate the thickness of the internal defect layer expected to be removed by the etching device (S130). In addition, the process control system can compare the thickness of the internal defect layer calculated in step S130 with the thickness of the remaining internal defect layer received in step S120, and based on the comparison result, cause the calculation module to learn (S140). The process control system can include a learning model required for the learning of the calculation module, and the learning model can adjust weighting values, coefficients, etc. utilized in the calculations of at least one calculation module.

[0128] After the learning is completed, the process control system can transmit at least one of the calculation modules to an external system (S150). The external system can utilize the calculation module received from the process control system to update the stored calculation module in an overwriting manner or the like. Therefore, the external system can perform the etching process in an optimized manner considering the thickness distribution of the internal defect layer contained in the hot-rolled steel sheet.

[0129] Figure 9 is a block diagram simply showing the process control system according to an embodiment of the present invention.

[0130] Referring to Figure 9 , the process control system 50 can include a second server 51, an etching device 55, etc. The second server 51 can control and manage the etching device 55. The etching device 55 can receive the hot-rolled coil produced by the aforementioned hot-rolled steel sheet production device 45, release the hot-rolled coil in a coiled state as a hot-rolled steel sheet, and then perform an etching process on the hot-rolled steel sheet to produce a pickled steel sheet.

[0131] The etching device 55 can be at least one of a pickling device, a dry etching device, and a wet etching device. When the etching device 55 is a pickling device, the internal defect layer can be removed by immersing the hot-rolled steel sheet in an acidic etching solution. Alternatively, the internal defect layer can be removed by spraying an etching solution onto the surface of the hot-rolled steel sheet, or by brushing the surface of the hot-rolled steel sheet with a brush dipped in the etching solution.

[0132] The second server 51 can include a communication unit 52, a storage device 53, a processor 54, etc. The communication unit 52 can communicatively connect the second server 51 and the network. As an example, the second server 51 and the first server 41 can communicate through the communication unit 52. As described in the foregoing with reference to Figure 4 , the first server 41 can be a server for controlling the hot-rolled steel sheet production device 45. The storage device 53 can store data required for the operation of the second server 51 and the control of the etching device 55. The processor 54 can control the communication unit 52, the storage device 53, the etching device 55, etc.

[0133] In one embodiment, the storage device 53 may store control data required to control the etching device 55. As an example, the control data stored in the storage device 53 may be data obtained by the processor 54 executing a calculation module stored in the storage device 53 or data received from an external system connected through the communication unit 52. As an example, when storing control data in the storage device 53 in the absence of a calculation module, the control data may be transmitted from an external system for managing the production of hot-rolled steel sheets.

[0134] The storage device 53 may include a calculation module for generating control data. The calculation module may execute calculations for controlling the etching process performed in the etching device 55. As an example, the calculation module may generate control data by considering the thickness distribution of the internal defect layer included in the hot-rolled steel sheet, etc., and the control data can control the etching device 55 to efficiently remove the internal defect layer. In one embodiment, the calculation module stored in the storage device 53 may be a calculation module received from an external system through the communication unit 52, and the external system may be a system for controlling a hot-rolled steel sheet production device. However, the external system is not necessarily limited to a system for controlling a hot-rolled steel sheet production device, and according to the embodiment, the calculation module may be received from various external systems.

[0135] As an example, the calculation module may generate control data by using the thickness information of the internal defect layer received by the second server 51 through the communication unit 52. The processor 54 may input the thickness information of the internal defect layer to the calculation module stored in the storage device 53 and execute the calculation module to obtain control data. The thickness information of the internal defect layer may include the thickness distribution of the internal defect layer shown along the length direction of the hot-rolled steel sheet.

[0136] According to an embodiment, the calculation module stored in the storage device 53 may also generate control data by using the information of the hot-rolled steel sheet received through the communication unit 52. At this time, the calculation module may calculate the thickness information of the internal defect layer included in the hot-rolled steel sheet by using the phase fraction of the hot-rolled steel sheet, the temperature of the hot-rolled steel sheet, and the composition of the hot-rolled steel sheet, etc. In addition, the calculation module may generate control data by using the thickness information of the internal defect layer. As an example, the calculation module stored in the storage device 53 may include a thickness information generation module for calculating the thickness information and a control data generation module for generating control data.

[0137] The control data may include at least one of the conveyance speed of the hot-rolled steel sheet conveyed by the etching device 55, the concentration of the etching solution that contacts the hot-rolled steel sheet in the etching device 55, the temperature of the etching solution, the composition of the etching solution, and the use or non-use of a promoter. As an example, in the thickness information of the internal defect layer, the thicknesses of the internal defect layers respectively shown in the first region and the second region of the hot-rolled steel sheet may be different from each other. At this time, the first region and the second region may be regions defined at different positions along the length direction of the hot-rolled steel sheet.

[0138] In the control data, the first conveyance speed of the hot-rolled steel sheet during the period of contacting the etching solution while passing through the etching device 55 in the first region may be different from the second conveyance speed of the hot-rolled steel sheet during the period of contacting the etching solution while passing through the etching device 55 in the second region. As an example, when the thickness of the internal defect layer in the first region is smaller than the thickness of the internal defect layer in the second region, the first conveyance speed may be faster than the second conveyance speed.

[0139] The calculation module for generating the control data may determine the conveyance speed of the hot-rolled steel sheet based on the thickness of the internal defect layer. In one embodiment, the calculation module may perform a prescribed calculation using the thickness of the internal defect layer to determine the conveyance speed of the hot-rolled steel sheet. In addition, in one embodiment, the calculation module may also compare a prescribed reference thickness with the thickness of the internal defect layer and determine the conveyance speed of the hot-rolled steel sheet based on the comparison result.

[0140] In one embodiment, the second server 51 may obtain the information required to update the calculation module from the etching device 55 and feedback the information obtained from the etching device 55 to the external system through the communication unit 52. Here, the external system may be a system for storing and managing the calculation module and including a learning model for updating the calculation module. When the storage device 53 includes a learning model for updating the calculation module, the update operation of the calculation module using the information obtained from the etching device 55 may also be performed in the second server 51.

[0141] As an example, the external system may be a system for controlling the production process of the hot-rolled steel sheet. The external system may use the information fed back from the second server 51 to update at least a part of the calculation module stored in the external system. As an example, the weighting values, coefficients, etc. used when at least a part of the calculation module stored in the external system performs calculations may be adjusted.

[0142] Furthermore, the external system may transmit at least one of the updated computing modules to the second server 51, for example, the computing module for generating control data. The second server 51 may use the new computing module received from the external system to update the existing computing module stored in the storage device 53. As an example, the second server 51 may update the computing module by overwriting the existing computing module with the new computing module. Therefore, the etching process can be performed using the computing module optimized according to various conditions of the hot-rolled steel sheet, which is the object of the etching device 55 and / or the etching process, thereby improving the efficiency and productivity of the etching process.

[0143] In another embodiment, the second server 51 may obtain the information required to update the computing module from the etching device 55, and directly update the computing module stored in the storage device 53 using the information obtained from the etching device 55. In addition, as an example, the storage device 53 may also store only the control data for controlling the etching device 55 in the absence of an additional computing module. At this time, the second server 51 may feedback the information required to update the computing module obtained from the etching device 55 to the external system, and receive the new control data generated by the updated computing module in the external system and store it in the storage device 53.

[0144] Figure 10 It is a diagram for explaining the operation method of the process control system according to an embodiment of the present invention.

[0145] Refer to Figure 10 , the process control system 300 may include computing modules 310 and 320. As an example, the computing modules 310 and 320 may be modules stored in the process control system 300 for controlling the etching device to execute. In addition, the computing modules 310 and 320 may also be stored in an external system connected to the process control system 300 through a network.

[0146] The computing modules 310 and 320 may be transmitted and stored in the process control system 300 in a state after learning through the learning model of the external system. In one embodiment, the computing modules 310 and 320 stored in the process control system 300 for controlling the etching device to execute may be modules received from the external system. Taking an embodiment shown in Figure 5 as an example, it is described as follows: In the computing module 110 shown in Figure 5 , the control data generation module 112 may be transmitted and stored in the process control system 300 as the computing modules 310 and 320.

[0147] The process control system 300 may determine the control data for controlling the etching device. The process control system 300 may receive the thickness information PD of the internal defect layer. As previously referred to Figure 9Similarly, in an embodiment described, the thickness information PD of the internal defect layer received by the process control system 300 may be the thickness of the internal defect layer calculated to possibly be included in the hot-rolled steel sheet, and may include the thickness distribution of the internal defect layer in different regions of the hot-rolled steel sheet. The process control system 300 may calculate the control data PPV2 based on the thickness information PD of the internal defect layer.

[0148] In an embodiment, the calculation modules 310 and 320 may include a first module 310 and a second module 320, etc. The second module 320 may receive the thickness information PD of the internal defect layer. The thickness information PD of the internal defect layer may be the thickness of the internal defect layer expected to exist in the hot-rolled steel sheet, and this thickness information PD may also be inconsistent with the thickness of the internal defect layer actually existing in the hot-rolled steel sheet. In addition, the second module 320 may receive the thickness target value GD of the remaining internal defect layer. The remaining internal defect layer may be the internal defect layer existing in the pickled steel sheet after the etching process is completed. As an example, when the internal defect layer is to be completely removed by the etching process, the thickness target value GD of the remaining internal defect layer may be 0. According to the embodiment, the thickness target value GD of the remaining internal defect layer may also be greater than 0. The second module 320 may periodically call the first module 310 to calculate the optimal control data PPV2.

[0149] The second module 320 may output the initial value of the control data to the first module 310. The initial value of the control data may include the characteristics AC of the etching solution measured from the tank containing the etching solution in the etching device and the conveying speed PPV1 of the hot-rolled steel sheet, etc. The initial value of the control data may be determined as an arbitrary value, or may be determined by the value measured in real time from the etching device. When the initial value of the control data is determined as an arbitrary value, the initial value of the control data may be determined by an external system operating the hot-rolled steel sheet production device or the process control system operating the etching device. The first module 310 may calculate the thickness PPA2 of the internal defect layer expected to be removed by the etching device by using at least one of the characteristics AC of the etching solution and the conveying speed PPV1.

[0150] The second module 320 can receive the thickness PPA2 of the internal defect layer from the first module 310. The second module 320 can determine whether the characteristics AC of the etching solution and the arbitrarily set delivery speed PPV1 conform to the optimal control data PPV2 based on the thickness PPA2 of the internal defect layer. As an example, the second module 320 can compare the difference between the thickness information PD of the internal defect layer and the target thickness value GD of the remaining internal defect layer with the thickness PPA2 of the internal defect layer calculated by the first module 310. When the comparison results are inconsistent, the second module 320 can adjust at least one of the characteristics AC of the etching solution and the delivery speed PPV1 and input it to the first module 310. In the above-described manner, the second module 320 can change at least one of the characteristics AC of the etching solution and the delivery speed PPV1 and call the first module 310 until the difference between the thickness information PD of the internal defect layer and the target thickness value GD of the remaining internal defect layer is consistent with the thickness PPA2 of the internal defect layer calculated by the first module 310, or the difference between the two reaches a specified value or less.

[0151] [Table 1] is a table for explaining the method by which the second module 320 generates the optimal control data PPV2. For ease of explanation, it is assumed in [Table 1] that the characteristics AC of the etching solution are the same. The second module 320 can calculate the target removal thickness GPA indicating how much of the internal defect layer should be removed based on the difference between the thickness information PD of the internal defect layer expected to be present in the hot-rolled steel sheet and the target thickness value GD of the remaining internal defect layer.

[0152] The X region, Y region, and Z region defined in [Table 1] can be regions defined at different positions in the length direction of the hot-rolled steel sheet. As an example, the X region and the Z region can be closer to the ends of the hot-rolled steel sheet in the length direction compared to the Y region. In other words, the Y region can be arranged between the X region and the Z region in the length direction. Referring to [Table 1], in the first region, the target removal thickness GPA can be determined to be 1 μm (= 3 μm - 2 μm), which is calculated as the difference between the thickness information PD of the internal defect layer present in the hot-rolled steel sheet and the target thickness value GD of the remaining internal defect layer.

[0153] Referring to [Table 1], when the conveying speed PPV1 of the first module 310 is 15 mpm, the thickness PPA2 of the internal defect layer expected to be removed by the etching device may be 2 μm. Since the thickness PPA2 of the internal defect layer expected to be removed by the etching device is greater than the target removal thickness GPA, the second module 320 may increase the conveying speed PPV1 to 20 mpm, which is faster than 15 mpm, and transmit it to the first module 310. When the conveying speed PPV1 is 20 mpm and the thickness PPA2 of the internal defect layer expected to be removed by the etching device calculated by the first module 310 is not equal to 1 μm, the second module 320 may change the conveying speed PPV1 to a value different from 20 mpm and output this value to the first module 310. On the contrary, when the conveying speed PPV1 is 20 mpm and the thickness PPA2 of the internal defect layer calculated by the first module 310 is 1 μm, the thickness PPA2 of the internal defect layer expected to be removed by the etching device is consistent with the target removal thickness GPA. Therefore, the second module 320 may determine the optimal conveying speed for the X region of the hot-rolled steel sheet to be 20 mpm.

[0154] [Table 1]

[0155] Item X Region Y Region Z Region PD 3μm 10μm 3μm GD 2μm 2μm 2μm GPA 1μm 8μm 1μm PPV1 15mpm 10mpm 15mpm PPA2 2μm 4μm 2μm PPV2 20mpm 5mpm 20mpm

[0156] mpm = meter per minute (meters per minute)

[0157] In Figure 10 In an embodiment represented by, the calculation modules 310 and 320 of the process control system 300 are taken as an example to illustrate the generation of control data, but it is not necessarily limited to this form. According to an embodiment, the control data may also be generated by a calculation module of an external system connected to the process control system 300 through a network. At this time, the process control system 300 may receive the control data generated by the external system through the network and control the etching device using the received control data.

[0158] Therefore, the process control system 300 may generate control data including the conveying speed of the hot-rolled steel sheet based on the thickness information PD of the internal defect layer. As an example, as described above, the thickness of the internal defect layer of the hot-rolled steel sheet may vary according to a plurality of regions defined along the length direction. As an example, the thickness of the internal defect layer contained in the region cooled in a state exposed to the outside may be smaller than the thickness of the internal defect layer contained in the region not exposed to the outside and slowly cooled.

[0159] As an example, the etching process may be performed by bringing the hot-rolled steel sheet into sufficient contact with the etching solution regardless of the region of the hot-rolled steel sheet, so as to be able to sufficiently remove the internal defect layer contained in the hot-rolled steel sheet. However, the method described above will increase the time of the etching process and / or the etching solution input into the etching process, so the productivity may be reduced.

[0160] According to an embodiment of the present invention, the thickness of the internal defect layer can be calculated along the length direction of the hot-rolled steel sheet, and control data capable of controlling the etching process with optimal efficiency can be calculated based on the calculated thickness information of the internal defect layer. Therefore, the time of the etching process can be shortened and the usage amount of the etching solution can be reduced, thereby improving productivity. In addition, the thickness deviation of the internal defect layer generated along the length direction or the like on the pickled steel sheet after the etching process is completed can be reduced.

[0161] As an example, in the length direction of the hot-rolled steel sheet, a region where the thickness of the internal defect layer is less than a preset reference thickness t can be defined as a first region, and a region where the thickness of the internal defect layer is greater than the reference thickness t can be defined as a second region. At this time, the conveying speed of the hot-rolled steel sheet during contact with the etching solution in the first region can be faster than the conveying speed of the hot-rolled steel sheet during contact with the etching solution in the second region. Alternatively, without separately setting the reference thickness t, the conveying speeds for the first region and the second region can be determined by receiving the thickness information of the internal defect layer and calculating and determining the conveying speed.

[0162] As a result, the second region can be in contact with the etching solution for a longer time than the first region. Therefore, the thickness deviation of the remaining internal defect layer included in the pickled steel sheet after the etching process is completed can be minimized.

[0163] According to the embodiment, a plurality of reference thicknesses t can be set. As an example, when there are two reference thicknesses t1 and t2, the hot-rolled steel sheet can be divided into at least three regions according to the thickness of the internal defect layer. In other words, when the number of reference thicknesses is n, the hot-rolled steel sheet can be divided into n + 1 regions. When the magnitudes of the reference thicknesses are defined as shown in Mathematical Formula 1, the conveying speed of the hot-rolled steel sheet during contact with the etching solution in each of the n + 1 regions included in the hot-rolled steel sheet can be defined as shown in Mathematical Formula 2.

[0164] [Mathematical Formula 1]

[0165] t1 < t2 < t3 <... < tn

[0166] [Mathematical Formula 2]

[0167] Conveying speed of the first region > conveying speed of the second region >... > conveying speed of the (n + 1)th region

[0168] According to the embodiment, in addition to the conveying speed of the hot-rolled steel sheet, the etching rate can also be adjusted. For example, a region with a relatively small thickness of the internal defect layer can be brought into contact with an etching solution having a relatively small etching rate, and a region with a relatively large thickness of the internal defect layer can be brought into contact with an etching solution having a relatively high etching rate. Alternatively, the etching rate can also be adjusted by changing the area of the brush dipped in the etching solution or the pressure of the brush.

[0169] Figure 11 It is a diagram for explaining the learning method of a calculation module included in a process control system according to an embodiment of the present invention. Figure 12 It is a diagram for explaining an etching process according to an embodiment of the present invention. Figure 13 It is a chart provided for explaining an etching process according to an embodiment of the present invention.

[0170] First, referring to Figure 12 , the etching apparatus 500 can perform an etching process according to control data PPV2 determined according to an embodiment described above with reference to Figure 10 and the like, thereby removing at least a part of the internal defect layer of the hot-rolled steel sheet 510. Referring to Figure 12 , during the etching process performed by the etching apparatus 500, the gauges MI1 and MI2 can measure the thickness ERD of the remaining internal defect layer in at least a part of the pickled steel sheet 520 after the etching process is completed, and collect control data EPV in a tank TK or the like where the etching process is performed. As an example, since the thickness ERD of the remaining internal defect layer can be measured in a plurality of regions, position information PI can be collected together. The control data EPV collected by the first gauge MI1 is the control data EPV actually measured in the etching apparatus 500 including the tank TK, and due to mechanical errors, signal delays, process errors, etc., the value of the control data EPV may be different from the value of the control data PPV2 input to the etching apparatus 500. The first gauge MI1 can obtain at least one of the conveying speed of the hot-rolled steel sheet 510 and the characteristics of the etching solution in contact with the hot-rolled steel sheet 510 in the tank TK.

[0171] Referring to Figure 12 , the etching apparatus 500 can include a coiler CL, an uncoiler UCL, a tank TK, a first gauge MI1, a second gauge MI2, and the like. The uncoiler UCL can cause the hot-rolled steel sheet 510 in the form of a strip obtained by unwinding the hot-rolled coil HC to enter the tank TK. The hot-rolled steel sheet 510 can be subjected to an etching process while passing through the etching solution accommodated in the tank TK. Alternatively, during the period when the hot-rolled steel sheet 510 passes through the tank TK, the hot-rolled steel sheet 510 can be etched by an etching solution sprayed around the hot-rolled steel sheet 510 and / or a brush dipped in the etching solution.

[0172] During the etching process, the etching apparatus 500 can be controlled according to the control data PPV2. As an example, according to the control data PPV2, the conveyance speed of the hot-rolled steel sheet 510 can be different in each region in the length direction of the hot-rolled steel sheet 510. During the etching process performed by the etching apparatus 500, the first measuring instrument MI1 connected to the tank TK can actually measure the control data EPV at at least one measurement time point. As described above, due to mechanical errors, operation delays, etc., at least a part of the control data PPV2 input to the etching apparatus 500 and the control data EPV actually measured by the first measuring instrument MI1 from the etching apparatus 500 can have different values from each other. The first measuring instrument MI1 can transmit the actually measured control data EPV to the external system 400.

[0173] Referring to Figure 11 , the external system 400 can be a system that manages the calculation modules 411 to 415 (410), and can include a learning model LMT for causing the calculation module 410 to learn and update. As an example, the external system 400 can use the information collected by the first measuring instrument MI1 and the second measuring instrument MI2 in the etching apparatus 500 to update the calculation module 410. The external system 400 can control the etching apparatus 500 through the control data generated by the updated calculation module 410, so as to operate the etching apparatus 500 in an optimal state.

[0174] In Figure 13 a shown embodiment, the horizontal axis represents the position in the length direction of the hot-rolled steel sheet, and the vertical axis represents the thickness PD of the internal defect layer and the conveyance speed. As an example, the thickness PD information of the internal defect layer can be the thickness value of the internal defect layer that may exist in the hot-rolled steel sheet calculated by at least a part of the calculation module 410 included in the external system 400.

[0175] Referring to Figure 13 , the thickness PD of the internal defect layer can be calculated to be greater in the Y region than in the X region and the Z region. Similar to the description made with reference to Table 1 above, the X region, the Y region, and the Z region can be regions that appear in sequence in the length direction, and the X region and the Z region can be regions closer to the ends of the hot-rolled steel sheet in the length direction than the Y region. The Y region can be the region between the X region and the Z region.

[0176] In one embodiment, the computing module 410 of the external system 400 may calculate the conveying speed OPV for the hot-rolled steel sheet based on the thickness PD of the internal defect layer. The conveying speed OPV may be the speed at which the hot-rolled steel sheet 510 is actually conveyed in the etching device 500 during the etching process according to the control data PPV2 input to the etching device 500. Since the thickness of the internal defect layer can be calculated to be greater in the Y region than in the X region and the Z region, the conveying speed OPV can be relatively slow during the contact of the Y region with the etching solution so as to be able to sufficiently remove the internal defect layer in the Y region.

[0177] The actual conveying speed EPV may be a value actually measured in the etching device 500 during the etching process based on the conveying speed OPV. The error and operation delay of the etching device 500, etc. can be reflected in the actual conveying speed EPV. Therefore, the actual conveying speed EPV may be different from the conveying speed OPV set by the control data PPV2.

[0178] As an example, in order to completely remove the internal defect layer, the conveying speed TPV can be uniformly maintained and the etching process can be carried out based on the thickness of the internal defect layer present in the second region D2. However, at this time, since the progress speed of the etching process slows down, the productivity may be reduced. On the contrary, according to an embodiment of the present invention, the etching process can be carried out by applying the conveying speed OPV calculated differently for each region of the hot-rolled steel sheet. Therefore, not only can the internal defect layer be effectively removed, but also the time of the etching process and the amount of the etching solution input into the etching process can be reduced. Therefore, the productivity of the etching process can be improved.

[0179] Refer again to Figure 12 , before the etching device 500 uses the coiler CL to coil the pickled steel sheet 520 after the etching treatment to prepare the pickled steel coil PC, the second measuring instrument MI2 may measure the position information PI of the pickled steel sheet 520 and the thickness ERD of the remaining internal defect layer at the position corresponding to the position information PI.

[0180] According to the embodiment, at least one measuring position can be specified on the pickled steel sheet 520 after the etching treatment, and a part of the pickled steel sheet 520 can be cut at the measuring position for sampling. The cross-section of the sample taken from the pickled steel sheet 520 can be observed through a microscope to measure the thickness of the remaining internal defect layer. The measuring position corresponds to the position information PI of the pickled steel sheet 520, and the thickness of the remaining internal defect layer corresponds to the thickness ERD of the remaining internal defect layer at the position corresponding to the position information PI.

[0181] Refer again to Figure 11, the learning model 420 can receive the thickness ERD of the remaining internal defect layer measured by the second gauge MI2 of the etching apparatus 500 at at least one measurement position of the pickled steel sheet 520. The fifth module 415 can receive the control data EPV actually measured from the etching apparatus 500 during the etching process. As an example, the fifth module 415 can receive the control data EPV actually measured through the network from the process control system including the etching apparatus 500.

[0182] The fifth module 415 can output the actually measured control data EPV to the fourth module 414. The fourth module 414 can calculate the thickness PPA2 of the internal defect layer expected to be removed by the etching apparatus 500 based on the actually measured control data EPV. The fifth module 415 can receive the thickness PPA2 of the internal defect layer from the fourth module 414. The fifth module M5 can calculate the expected thickness PRD of the remaining internal defect layer based on the thickness PPA2 of the internal defect layer expected to be removed by the etching apparatus 500. The expected thickness PRD of the remaining internal defect layer can be the thickness of the internal defect layer expected to remain in the pickled steel sheet 520 after the etching process according to the actually measured control data EPV.

[0183] The learning model LMT can receive the expected thickness PRD of the internal defect layer from the fifth module 415. The learning model LMT can compare the actually measured thickness ERD of the remaining internal defect layer from the pickled steel sheet 520 with the expected thickness PRD of the remaining internal defect layer. The learning model LMT can cause at least one of the calculation modules 411 - 415 to learn so that the actually measured thickness ERD of the remaining internal defect layer from the pickled steel sheet 520 and the expected thickness PRD of the remaining internal defect layer are consistent or the difference between the two reaches a specified value or less. For example, the learning model LMT can also cause all the calculation modules 411 - 415 to learn simultaneously, or can selectively cause only a specific model to learn.

[0184] According to an embodiment, a learning model can also be included in the Figure 9 process control system 50 including a server and an etching apparatus. The learning model included in the Figure 9 process control system 50 can cause the calculation modules included in the Figure 9 process control system 50 to learn. Next, with reference to Figure 14 and Figure 15 the method by which the learning model in the Figure 9 process control system 50 causes the calculation modules included in the Figure 9 process control system 50 to learn will be described.

[0185] Figure 14 is a diagram for explaining the initial learning method of the calculation modules included in the process control system of an embodiment of the present invention.

[0186] Reference Figure 14 As shown in Figure 14 , the process control system 600 may include a computing module 610 and a learning model 620, which may be stored in a storage device. As an example, the computing module 610 may be a control data generation module. The learning model 620 may include a first learning model LM1 and a feedback learning model LMT. The first learning model LM1 may be a model for enabling the first module M1 to perform initial learning, and the feedback learning model LMT may be a model for enabling at least one of the first module M1 and the second module M2 to perform learning after the etching process is completed. In one embodiment, the feedback learning model LMT may also enable the first module M1 and the second module M2 to perform learning simultaneously. In addition, according to an embodiment, the first learning model LM1 and the feedback learning model LMT may not be distinguished, and one learning model may perform the learning of both the first module M1 and the second module M2.

[0187] The first learning model LM1 may receive the conveying speed PV of the hot-rolled steel sheet, the characteristics AC of the etching solution used in the etching process, and the first thickness EPA of the internal defect layer removed by the etching device, etc. The characteristics AC of the etching solution may include the concentration of the etching solution in contact with the hot-rolled steel sheet, the temperature of the etching solution, the composition of the etching solution, and whether a promoter is used, etc. The conveying speed PV of the hot-rolled steel sheet may mean the speed at which the hot-rolled steel sheet moves during the contact between the hot-rolled steel sheet and the etching solution. The first thickness EPA of the internal defect layer may mean the thickness of the internal defect layer actually removed in the etching process carried out by the conveying speed PV of the hot-rolled coil and the characteristics AC of the etching solution.

[0188] The first module M1 may receive the conveying speed PV of the hot-rolled steel sheet and the characteristics AC of the etching solution from the first learning model LM1. The first module M1 may use at least one of the conveying speed PV of the hot-rolled steel sheet and the characteristics AC of the etching solution to calculate the second thickness PPA1 of the internal defect layer expected to be removed by the etching device. In one embodiment, the first module M1 may receive the etching time determined according to the conveying speed PV and the characteristics AC of the etching solution as input values, and use the input values to perform a calculation for calculating the second thickness PPA1 of the internal defect layer. The first module M1 outputs the second thickness PPA1 of the internal defect layer to the first learning model LM1.

[0189] The first learning model LM1 may compare the second thickness PPA1 of the internal defect layer with the first thickness EPA of the internal defect layer, and enable the first module M1 to perform learning. For example, when the first thickness EPA of the internal defect layer and the second thickness PPA1 of the internal defect layer are inconsistent or the difference between the two is greater than a specified value, the first learning model LM1 may adjust the weighted value and coefficient of the first calculation used in the first module M1 for calculating the second thickness PPA1 of the internal defect layer, etc.

[0190] The second module M2 can generate control data for controlling the etching apparatus. As an example, the second module M2 can repeatedly call the first module M1 by using an optimization method (e.g., the golden section method, etc.) to find the optimal control data. The second module M2 can select or modify the optimization method for finding the optimal control data.

[0191] Figure 15 It is a diagram for explaining the learning method of the calculation module included in the process control system according to an embodiment of the present invention.

[0192] Refer to Figure 15 , the process control system 700 can be a system for managing the calculation modules 711 and 712 (710), and can include a learning model LMT for enabling the calculation module 710 to learn. As an example, the process control system 700 can use the information collected by the first measuring instrument MI1 and the second measuring instrument MI2 in the etching apparatus 500 to enable the calculation module 710 to learn. By controlling the etching apparatus 500 with the control data generated by the calculation module 710 after completing the learning, the etching apparatus 500 can be operated in an optimal state.

[0193] The learning model 720 can receive the thickness ERD of the remaining internal defect layer measured by the second measuring instrument MI2 of the etching apparatus 500 at at least one measurement position of the pickled steel sheet 520. The second module 712 can receive the control data EPV actually measured from the etching apparatus 500 during the etching process.

[0194] The second module 712 can output the actually measured control data EPV to the first module 711. The first module 711 can calculate the thickness PPA2 of the internal defect layer expected to be removed by the etching apparatus 500 based on the actually measured control data EPV. The second module 712 can receive the thickness PPA2 of the internal defect layer from the first module 711. The second module M2 can calculate the expected thickness PRD of the remaining internal defect layer based on the thickness PPA2 of the internal defect layer expected to be removed by the etching apparatus 500. The expected thickness PRD of the remaining internal defect layer can be the thickness of the internal defect layer expected to remain in the pickled steel sheet 520 after the etching process according to the actually measured control data EPV.

[0195] The learning model LMT can receive the predicted thickness PRD of the internal defect layer from the second module 712. The learning model LMT can compare the actually measured thickness ERD of the remaining internal defect layer and the predicted thickness PRD of the remaining internal defect layer from the pickled steel sheet 520. The learning model LMT can cause at least one of the calculation modules 711 and 712 to learn so that the thickness ERD of the remaining internal defect layer measured from the pickled steel sheet 520 and the predicted thickness PRD of the remaining internal defect layer are consistent or the difference between the two reaches a specified value or less. For example, the learning model LMT can also cause all the calculation modules 711 and 712 to learn simultaneously, or can selectively cause only a specific model to learn.

[0196] Figure 16 is a flowchart for explaining the operation method of a process control system according to an embodiment of the present invention.

[0197] Refer to Figure 16 , the process control system for controlling the etching process can receive a calculation module (S210) from an external system. The calculation module received in step S210 can generate control data for controlling the etching process. The process control system can use the control data generated by the calculation module to perform the etching process (S220). At least a part of the internal defect layer contained in the hot-rolled steel sheet can be removed through the etching process.

[0198] The process control system can transmit to the external system the control data actually measured in the etching apparatus performing the etching process and the thickness of the remaining internal defect layer contained in the pickled steel sheet after the etching process is completed (S230). The control data transmitted to the external system in step S230 can be a value actually measured from the etching apparatus. The thickness of the remaining internal defect layer can be measured by taking a local area of the pickled steel sheet as a sample and using microscopic examination or the like.

[0199] The external system can cause the calculation module to learn using the control data received in step S230, the thickness of the remaining internal defect layer, etc. After the learning is completed, the process control system can receive at least one of the calculation modules after the learning is completed from the external system (S240). The process control system can use the calculation module received in step S240 to update the existing calculation module (S150). Therefore, by reflecting process errors that may occur in the etching apparatus or the like, the etching process can be accurately controlled.

[0200] As an example, the external system can mean a system for managing and controlling the production process of hot-rolled steel sheets. This is an exemplary embodiment, and the claims are not limited thereto.

[0201] Figure 17 is a diagram for explaining the operation method of a process control system according to an embodiment of the present invention.

[0202] Reference Figure 17 The first system SYS1 for preparing hot-rolled steel sheets can generate thickness information PD of the internal defect layer (S310). The thickness information PD of the internal defect layer generated in step S310 can be the thickness of the internal defect layer calculated based on at least one of the temperature at different elapsed times after coiling measured in each region of the hot-rolled steel sheet defined in the length direction, the composition of the hot-rolled steel sheet, and the oxygen partial pressure around the hot-rolled steel sheet. The first system SYS1 can transmit the thickness information PD of the internal defect layer to the second system SYS2 via a network (S320).

[0203] The second system SYS2 can generate control data PPV for controlling the etching process of the hot-rolled steel sheet by using the thickness information PD of the internal defect layer (S330). The second system SYS2 can perform an etching process for removing at least a part of the internal defect layer of the hot-rolled steel sheet according to the control data PPV (S340). The second system SYS2 can obtain position information PI indicating a specific position of the hot-rolled steel sheet, the thickness ERD of the remaining internal defect layer measured at the position corresponding to the position information PI, control data EPV, etc. during the etching process (S350). The control data EPV obtained in step S350 is the data actually measured from the etching device during the etching process, and may be inconsistent with the control data PPV generated in step S330 due to various factors such as process errors and signal delays. The second system SYS2 can transmit the information obtained in step S350 to the first system SYS1 (S360).

[0204] The first system SYS1 can calculate the predicted thickness PRD of the remaining internal defect layer by using the information received in step S360 (S370). The first system SYS1 can compare the predicted thickness PRD of the remaining internal defect layer calculated in step S370 with the thickness ERD of the remaining internal defect layer measured at at least one position (S380). The first system SYS1 can cause at least one of the calculation modules included in the first system SYS1 to learn so that the predicted thickness PRD of the remaining internal defect layer calculated at a specific position of the hot-rolled steel sheet is consistent with the measured thickness ERD of the remaining internal defect layer or the difference between the two reaches a specified value or less (S390).

[0205] The first system SYS1 can transmit at least one of the calculation modules after completion of learning to the second system SYS2 (S400). The second system SYS2 can update the calculation module pre-stored in the second system SYS2 by using the calculation module received from the first system SYS1 (S410).

[0206] The present invention is not limited to the above embodiments and drawings, and the present invention is defined by the appended claims. Therefore, within the scope of the technical idea of the present invention described in the claims, those skilled in the art can make various forms of substitution, deformation and change, which should also fall within the scope of the present invention.

[0207] The present application also relates to the following aspects:

[0208] 1. A process control system, comprising:

[0209] A first system for generating thickness information of an internal defect layer contained in a carbon steel product; and

[0210] A second system for receiving the thickness information of the internal defect layer from the first system through a network and controlling an etching process using the thickness information of the internal defect layer, the etching process being used to remove at least a part of the internal defect layer from the carbon steel product,

[0211] The first system provides a calculation module required for the second system to control the etching process to the second system,

[0212] The second system provides information required for the first system to update the calculation module to the first system.

[0213] 2. The process control system according to aspect 1, wherein

[0214] The first system provides the calculation module to the second system, and the calculation module is used to determine at least one of the conveying speed of the carbon steel product in the etching process, the concentration of the etching solution in contact with the carbon steel product in the etching process, the temperature of the etching solution, the composition of the etching solution, and the use of a promoter.

[0215] 3. The process control system according to aspect 1, wherein

[0216] The second system provides at least one of the thickness of the remaining internal defect layer contained in the pickled carbon steel product after the etching process is completed and the control data measured in the etching device performing the etching process to the first system.

[0217] 4. The process control system according to aspect 1, wherein

[0218] The second system controls the etching process by adjusting at least one of the conveying speed of the carbon steel product conveyed by the etching device performing the etching process, the concentration of the etching solution in contact with the carbon steel product in the etching device, the temperature of the etching solution, the composition of the etching solution, and the use of a promoter.

[0219] 5. The process control system according to aspect 4, wherein,

[0220] The carbon steel product includes a first region and a second region. In the first region, the internal defect layer has a first thickness, and in the second region, the internal defect layer has a second thickness different from the first thickness.

[0221] The second system transports the carbon steel product at a first transport speed during contact of the first region with the etching solution, and transports the carbon steel product at a second transport speed different from the first transport speed during contact of the second region with the etching solution.

[0222] 6. The process control system according to aspect 5, wherein,

[0223] The first thickness is less than the second thickness.

[0224] The first transport speed is faster than the second transport speed.

[0225] 7. The process control system according to aspect 1, wherein,

[0226] The etching process is at least one of a pickling process, a dry etching process, and a wet etching process.

[0227] 8. The process control system according to aspect 1, wherein,

[0228] The first system is used to store a calculation module and a learning model for enabling the calculation module to learn.

[0229] 9. The process control system according to aspect 1, wherein,

[0230] The first system is used to actually measure the thickness of the internal defect layer contained in the carbon steel product to generate thickness information of the internal defect layer.

[0231] 10. The process control system according to aspect 9, wherein,

[0232] The first system is used to respectively actually measure the thickness of the internal defect layer in a plurality of regions defined in the length direction of the carbon steel product.

[0233] 11. The process control system according to aspect 1, wherein,

[0234] The first system is used to calculate the thickness of the internal defect layer by using at least one of the phase fraction of the carbon steel product, the composition of the carbon steel product, and the temperature of the carbon steel product.

[0235] 12. The process control system according to aspect 11, wherein,

[0236] The calculation module is configured to calculate the thickness of the internal defect layer in a plurality of regions defined in the length direction of the carbon steel product, respectively.

[0237] 13. A process control system, comprising:

[0238] A storage device configured to store control data required for controlling an etching device, which is used to remove at least a part of the internal defect layer contained in the carbon steel product; and

[0239] A processor configured to control the etching device based on the control data.

[0240] The carbon steel product includes a first region and a second region different from the first region, and the thickness of the internal defect layer contained in the first region is different from the thickness of the internal defect layer contained in the second region.

[0241] The control data includes a first conveyance speed for the first region to pass through the etching device and a second conveyance speed for the second region to pass through the etching device, and the first conveyance speed and the second conveyance speed are different from each other.

[0242] 14. The process control system according to aspect 13, wherein

[0243] The storage device is configured to store a calculation module for generating the control data.

[0244] 15. The process control system according to aspect 13, further comprising a communication unit connected to a network.

[0245] The processor is configured to receive, through the communication unit, a calculation module for generating the control data and store the calculation module in the storage device.

[0246] 16. The process control system according to aspect 13, further comprising a communication unit connected to a network.

[0247] The processor is configured to receive, through the communication unit, information related to the thickness of the internal defect layer and input the information related to the thickness of the internal defect layer into a calculation module for generating the control data to obtain the control data.

[0248] 17. The process control system according to aspect 13, further comprising a communication unit connected to a network.

[0249] The processor is configured to receive, through the communication unit, information about the carbon steel product and input the information about the carbon steel product into a calculation module for generating the control data to obtain the control data.

[0250] 18. The process control system according to aspect 17, wherein,

[0251] The information of the carbon steel product includes at least one of information related to the phase fraction of the carbon steel product, temperature-related information of the carbon steel product, and composition-related information of the carbon steel product.

[0252] 19. The process control system according to aspect 13, further comprising a communication unit connected to a network,

[0253] The processor is configured to receive the control data through the communication unit and store the control data in the storage device.

[0254] 20. The process control system according to aspect 13, wherein,

[0255] The control data further includes at least one of the concentration of the etching solution in contact with the carbon steel product in the etching device, the temperature of the etching solution, the composition of the etching solution, and whether a promoter is used.

[0256] 21. The process control system according to aspect 13, wherein,

[0257] The etching device is at least one of a pickling device, a dry etching device, and a wet etching device.

[0258] 22. The process control system according to aspect 13, wherein,

[0259] The thickness of the internal defect layer included in the first region is less than the thickness of the internal defect layer included in the second region, and the first conveying speed is faster than the second conveying speed.

[0260] 23. The process control system according to aspect 13, wherein,

[0261] The processor is configured to receive the conveying speed of the carbon steel product, the characteristics of the etching solution used in the etching process, and the first thickness of the internal defect layer removed by the etching device,

[0262] The storage device includes a calculation module, and the calculation module is configured to calculate a second thickness of the internal defect layer expected to be removed by the etching device by using at least one of the conveying speed of the carbon steel product and the characteristics of the etching solution,

[0263] The processor is configured to compare the second thickness of the internal defect layer with the first thickness of the internal defect layer and cause the calculation module to learn.

[0264] 24. The process control system according to aspect 13, wherein,

[0265] the storage device includes a calculation module, and the calculation module is configured to receive the control data measured from the etching device and calculate the predicted thickness of the remaining internal defect layer expected to be removed by the etching device based on the measured control data.

[0266] the processor is configured to compare the predicted thickness of the remaining internal defect layer with the thickness of the remaining internal defect layer measured from the pickled carbon steel product after the etching process is completed, and cause the calculation module to learn.

[0267] 25. A process control system, comprising:

[0268] a storage device for storing a calculation module, the calculation module generating thickness information of an internal defect layer contained in the carbon steel product based on at least one of the composition, cooling rate, phase fraction, and temperature of the carbon steel product;

[0269] a communication unit connected to a network; and

[0270] a processor for transmitting at least one of the thickness information of the internal defect layer and control data for controlling an etching process to an external server through the communication unit, the external server being configured to control the etching process for removing at least a part of the internal defect layer.

[0271] 26. The process control system according to aspect 25, wherein,

[0272] the storage device is configured to store a learning model for causing the calculation module to learn.

[0273] 27. The process control system according to aspect 25, wherein,

[0274] the calculation module includes a first module configured to calculate the phase fraction before coiling the carbon steel product.

[0275] 28. The process control system according to aspect 27, wherein,

[0276] the processor is configured to compare the phase fraction measured from the carbon steel product with the phase fraction calculated by the first module based on at least one of the temperature and the composition of the carbon steel product, and cause the first module to learn.

[0277] 29. The process control system according to aspect 27, wherein,

[0278] the calculation module further includes a second module configured to calculate the temperature change of the carbon steel product.

[0279] 30. The process control system according to aspect 29, wherein,

[0280] The processor is configured to compare the temperature change calculated by the second module using at least one of the phase fraction calculated before coiling the carbon steel product, the elapsed time after coiling the carbon steel product, and the composition of the carbon steel product with the temperature change measured from the carbon steel product, and cause the second module to learn.

[0281] 31. The process control system according to aspect 29, wherein,

[0282] The calculation module further includes a third module, and the third module is configured to calculate the thickness of the internal defect layer contained in the carbon steel product.

[0283] 32. The process control system according to aspect 31, wherein,

[0284] The processor is configured to compare the thickness of the internal defect layer calculated by the third module using at least one of the temperature change calculated based on the elapsed time after coiling the carbon steel product, the composition of the carbon steel product, and the oxygen partial pressure around the carbon steel product with the thickness of the internal defect layer measured from the carbon steel product, and cause the third module to learn.

[0285] 33. The process control system according to aspect 31, wherein,

[0286] The third module is configured to calculate the thickness of the internal defect layer in multiple regions respectively distinguished along the length direction of the carbon steel product.

[0287] 34. The process control system according to aspect 31, wherein,

[0288] The calculation module further includes a fourth module, and the fourth module is configured to calculate the thickness of the internal defect layer removed in the etching process based on the control data.

[0289] 35. The process control system according to aspect 34, wherein,

[0290] The processor is configured to compare the thickness of the remaining internal defect layer contained in the pickled carbon steel product after completing the etching process with the thickness of the internal defect layer calculated by the fourth module, and cause the fourth module to learn.

[0291] 36. The process control system according to aspect 34, wherein,

[0292] The computing module further includes a fifth module for generating the control data.

[0293] 37. The process control system according to aspect 36, wherein

[0294] the processor is configured to compare the thickness of the internal defect layer calculated by the fourth module using the control data measured by the external server from the etching apparatus performing the etching process with the thickness of the remaining internal defect layer measured from the pickled carbon steel product after the etching process is completed, and cause the computing module to learn.

[0295] 38. The process control system according to aspect 25, wherein

[0296] the control data includes at least one of the conveying speed of the carbon steel product in the etching process, the concentration of the etching solution in contact with the carbon steel product in the etching process, the temperature of the etching solution, the composition of the etching solution, and the use of a promoter.

[0297] 39. The process control system according to aspect 25, wherein

[0298] the carbon steel product includes a first region and a second region, in the first region the internal defect layer has a first thickness, and in the second region the internal defect layer has a second thickness different from the first thickness,

[0299] the control data includes a first conveying speed for conveying the first region and a second conveying speed for conveying the second region in the etching process, and the first conveying speed and the second conveying speed are different from each other.

[0300] 40. The process control system according to aspect 25, wherein

[0301] the etching process is at least one of a pickling process, a dry etching process, and a wet etching process.

Claims

1. A process control system, comprising: A first system for generating thickness information of an internal defect layer contained in a carbon steel product; And A second system for receiving the thickness information of the internal defect layer from the first system via a network and controlling an etching process using the thickness information of the internal defect layer, the etching process being used to remove at least a part of the internal defect layer from the carbon steel product, The first system provides a calculation module required by the second system to control the etching process to the second system, The second system provides information required by the first system to update the calculation module to the first system, Wherein the calculation module has a plurality of modules; Wherein the first system or the second system has a plurality of learning models for enabling each of the plurality of modules to learn, and a feedback learning model for enabling at least one of the plurality of modules to learn after completion of the etching process.

2. The process control system according to claim 1, wherein, The first system provides the calculation module to the second system, and the calculation module is used to determine at least one of the conveying speed of the carbon steel product in the etching process, the concentration of the etching solution in contact with the carbon steel product in the etching process, the temperature of the etching solution, the composition of the etching solution, and the use of a promoter.

3. The process control system according to claim 1, wherein, The second system provides at least one of the thickness of the remaining internal defect layer contained in the pickled carbon steel product after completion of the etching process and the control data measured in the etching device performing the etching process to the first system.

4. The process control system according to claim 1, wherein, The second system controls the etching process by adjusting at least one of the conveying speed of the carbon steel product conveyed by the etching device performing the etching process, the concentration of the etching solution in contact with the carbon steel product in the etching device, the temperature of the etching solution, the composition of the etching solution, and the use of a promoter.

5. The process control system according to claim 4, wherein, The carbon steel product includes a first region and a second region. In the first region, the internal defect layer has a first thickness, and in the second region, the internal defect layer has a second thickness different from the first thickness. The second system conveys the carbon steel product at a first conveying speed during the contact between the first region and the etching solution, and conveys the carbon steel product at a second conveying speed different from the first conveying speed during the contact between the second region and the etching solution.

6. The process control system according to claim 5, wherein, The first thickness is less than the second thickness, The first conveying speed is faster than the second conveying speed.

7. The process control system according to claim 1, wherein, The etching process is at least one of a pickling process, a dry etching process, and a wet etching process.

8. The process control system according to claim 1, wherein, The first system is used to store a computing module and a learning model for enabling the computing module to perform learning.

9. The process control system according to claim 1, wherein, The first system is used to actually measure the thickness of the internal defect layer contained in the carbon steel product to generate thickness information of the internal defect layer.

10. The process control system according to claim 9, wherein, The first system is used to respectively actually measure the thickness of the internal defect layer in a plurality of regions defined in the length direction of the carbon steel product.

11. The process control system according to claim 1, wherein, The first system is used to calculate the thickness of the internal defect layer by using at least one of the phase fraction of the carbon steel product, the composition of the carbon steel product, and the temperature of the carbon steel product.

12. The process control system according to claim 11, wherein, The computing module is used to respectively calculate the thickness of the internal defect layer in a plurality of regions defined in the length direction of the carbon steel product.

13. A process control system, comprising: A storage device for storing control data required for controlling an etching device, the etching device being used to remove at least a part of the internal defect layer contained in a carbon steel product; And A processor for controlling the etching device based on the control data, The carbon steel product includes a first region and a second region different from the first region, the thickness of the internal defect layer contained in the first region and the thickness of the internal defect layer contained in the second region are different from each other, The control data includes a first conveying speed for the first region to pass through the etching device and a second conveying speed for the second region to pass through the etching device, and the first conveying speed and the second conveying speed are different from each other, Wherein the storage device is used to store a computing module for generating the control data and a learning model for optimizing the computing module, Wherein the computing module has a plurality of modules; Wherein the learning model has a plurality of learning models for enabling each of the plurality of modules to perform learning, and a feedback learning model for enabling at least one of the plurality of modules to perform learning after the etching process is completed.

14. The process control system according to claim 13, further comprising a communication unit connected to a network, The processor is used to receive the computing module for generating the control data through the communication unit and store the computing module in the storage device.

15. The process control system according to claim 13, further comprising a communication unit connected to a network, The processor is used to receive information related to the thickness of the internal defect layer through the communication unit and input the information related to the thickness of the internal defect layer into a computing module for generating the control data to obtain the control data.

16. The process control system according to claim 13, further comprising a communication unit connected to a network, The processor is used to receive information of the carbon steel product through the communication unit and input the information of the carbon steel product into a computing module for generating the control data to obtain the control data.

17. The process control system according to claim 16, wherein, the information of the carbon steel product includes at least one of information related to the phase fraction of the carbon steel product, information related to the temperature of the carbon steel product, and information related to the composition of the carbon steel product.

18. The process control system according to claim 13, further comprising a communication unit connected to a network, wherein the processor is configured to receive the control data through the communication unit and store the control data in the storage device.

19. The process control system according to claim 13, wherein, the control data further includes at least one of the concentration of the etching solution in contact with the carbon steel product in the etching device, the temperature of the etching solution, the composition of the etching solution, and whether a promoter is used.

20. The process control system according to claim 13, wherein, the etching device is at least one of a pickling device, a dry etching device, and a wet etching device.

21. The process control system according to claim 13, wherein, the thickness of the internal defect layer included in the first region is less than the thickness of the internal defect layer included in the second region, and the first conveying speed is faster than the second conveying speed.

22. The process control system according to claim 13, wherein, the processor is configured to receive the conveying speed of the carbon steel product, the characteristics of the etching solution used in the etching process, and the first thickness of the internal defect layer removed by the etching device, the storage device includes a calculation module, and the calculation module is configured to calculate a second thickness of the internal defect layer expected to be removed by the etching device by using at least one of the conveying speed of the carbon steel product and the characteristics of the etching solution, the processor is configured to compare the second thickness of the internal defect layer with the first thickness of the internal defect layer and cause the calculation module to learn.

23. The process control system according to claim 13, wherein, the storage device includes a calculation module, and the calculation module is configured to receive the control data measured from the etching device and calculate the expected thickness of the remaining internal defect layer expected to be removed by the etching device based on the measured control data, the processor is configured to compare the expected thickness of the remaining internal defect layer with the thickness of the remaining internal defect layer measured from the pickled carbon steel product after the etching process is completed and cause the calculation module to learn.

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