Determination method, determination device, partial steel material generation method, and partial steel material

Abstract

To manufacture steel materials that meet required material properties at lower costs.SOLUTION: A determination method acquires information regarding manufacturing performance at each position of a steel material, estimates material properties at multiple positions of the steel material based on the information regarding manufacturing performance, and determines test piece collection positions that meet the quality requirements of the steel material based on the material properties at the multiple positions.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a determination method, a determination device, a partial steel material generation method, and a partial steel material. [Background technology]

[0002] In steel production lines, material properties are measured to confirm the required quality and ensure quality. However, since the material properties of steel are measured at the position where a test piece is taken (see, for example, Patent Document 1), it is difficult to measure the material properties of the steel over the entire length and width of the steel. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2004-156973 Summary of the Invention [Problem to be solved by the invention]

[0004] For example, in the case of thin coil steel, mechanical test specimens are sometimes taken from the leading and trailing ends of the steel material to measure its material properties. If the material properties obtained from the taken test specimen do not meet the order requirements (due to a material deviation), the coil must be rewound on the recoiling line (RCL), and test specimens taken at a different position must be tested again. This results in reduced yield due to excess cutting, reduced yield due to threading the strip through the RCL, and increased process costs. This problem is not limited to thin coil steel, but is common to all steel products. Furthermore, it is sufficient if the required material properties are met, and it is not always necessary to take test specimens and conduct testing.

[0005] The present invention has been made in consideration of the above-mentioned circumstances, and provides a technique that makes it possible to manufacture steel materials that satisfy required material properties at lower costs. [Means for solving the problem]

[0006] [1] One aspect of the present invention is a method for determining whether a test piece should be taken at a position on a steel material, whether it should be manufactured at each position on the ...

[0007] [2] One aspect of the present invention is the determination method described in [1] above, in which information regarding the manufacturing performance is obtained at each position along the longitudinal direction of the steel material, material properties at a predetermined position along the longitudinal direction of the steel material are estimated, and the longitudinal position of the test piece extraction position of the steel material is determined.

[0008] [3] One aspect of the present invention is the determination method described in [2] above, in which information about the manufacturing performance is also obtained at each position along the short side of the steel material, material properties are estimated in the longitudinal direction of the steel material and at predetermined positions along the short side, and the longitudinal position of the test piece extraction position of the steel material is determined.

[0009] [4] One aspect of the present invention is the determination method described in [1] above, wherein the steel is a thick steel plate, and the test piece collection position is determined so as not to include any position in the steel to be shipped where the material properties do not satisfy the specified requirements.

[0010] [5] One aspect of the present invention is the determination method described in [1] above, wherein the steel material is a coil of thin plate, and if the quality requirements are not actually met in the test, the steel material is recoiled on a recoiling line to determine a new test specimen collection position.

[0011] [6] One aspect of the present invention is a determination device that includes a control unit that acquires information regarding manufacturing performance at each position of a steel material, estimates material properties at multiple positions of the steel material based on the information regarding manufacturing performance, and determines a test piece collection position that meets the quality requirements of the steel material based on the material properties at the multiple positions.

[0012] [7] One aspect of the present invention is a method for producing partial steel material, which includes obtaining information regarding manufacturing performance at each position of a steel material, estimating material properties at multiple positions of the steel material based on the information regarding manufacturing performance, determining test piece extraction positions that meet the quality requirements of the steel material based on the material properties at the multiple positions, and producing partial steel material, which is a partial steel material, by cutting out the steel material according to the determined test piece extraction positions.

[0013] [8] One aspect of the present invention is a partial steel material produced by acquiring information regarding manufacturing performance at each position of a steel material, estimating material properties at multiple positions of the steel material based on the information regarding manufacturing performance, determining test piece extraction positions that meet the quality requirements of the steel material based on the material properties at the multiple positions, and cutting out the steel material according to the determined test piece extraction positions. [Effects of the Invention]

[0014] According to the present invention, it is possible to manufacture steel materials that satisfy required material properties at lower costs. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a schematic block diagram showing the system configuration of a determination system 100 according to the present invention. [Figure 2] 1 is a schematic block diagram showing a specific example of the functional configuration of an iron and steel system 10. FIG. [Figure 3] 2 is a schematic block diagram showing a specific example of the functional configuration of the learning device 20. FIG. [Figure 4] FIG. 10 is a diagram illustrating a specific example of training data. [Figure 5] 10 is a flowchart showing a specific example of processing by the learning device 20. [Figure 6] 2 is a schematic block diagram showing a specific example of the functional configuration of a determination device 30. FIG. [Figure 7]FIG. 10 is a diagram showing a specific example of location record information used in the present embodiment. [Figure 8] FIG. 10 is a diagram showing the estimation results (TS) by the material property estimation unit 332. [Figure 9] FIG. 10 is a diagram showing the estimation result (YP) by the material property estimation unit 332. [Figure 10] 10 is a flowchart showing a specific example of processing by the determination device 30. [Figure 11] FIG. 2 is a diagram illustrating an outline of an example of the hardware configuration of an information processing device 90 applied to the present embodiment. [Figure 12] FIG. 10 is a diagram showing a modified example of the determination device 30 configured in this manner. [Figure 13] FIG. 10 is a diagram showing a specific example of location record information used in a modified example. [Figure 14] 10A and 10B are diagrams showing specific examples of estimation results of material properties at each position. [Figure 15] 10A and 10B are diagrams showing specific examples of estimation results of material properties at each position. DETAILED DESCRIPTION OF THE INVENTION

[0016] FIG. 1 is a schematic block diagram showing the system configuration of a determination system 100 of the present invention. The determination system 100 determines the positions (hereinafter referred to as "test positions") where tests are to be performed to obtain steel that satisfies required material properties (quality requirements) based on information (hereinafter referred to as "production performance information") related to the manufacturing performance of the steel to be determined (hereinafter referred to as "target steel"). The test positions must be determined to ensure that the material properties of the steel obtained as a product meet the requirements. For example, if the leading and trailing ends of the steel to be obtained as a product are used as test positions and both the leading and trailing end test positions meet the requirements, it can be said that the steel (the steel obtained as a product) between the leading and trailing ends, where operation is more stable than the leading and trailing ends, also meets the requirements. Therefore, for example, the steel obtained as a product may be between the leading and trailing end test positions. In this case, the determination system 100 determines at least two (multiple) test positions as test positions. However, the relationship between the test positions and the steel material obtained as a product described above is merely one specific example, and the relationship between the steel material obtained as a product and the test positions may be defined in other ways as long as it is guaranteed that the material properties of the steel material obtained as a product meet the requirements. In the following explanation, the test positions will be explained using a specific example in which test pieces are obtained by cutting the steel material. Information that associates multiple positions on the target steel material with manufacturing performance information at each position is called position performance information. The target steel material may be, for example, a thin coil, a wire rod, a thick plate, or other types of steel material.

[0017] The manufacturing performance information includes measurements taken during the manufacturing process of the target steel material (process measurement values, described later) and information related to the manufacturing of the target steel material (setting information, described later). The test position of the target steel material may be a position from which a test piece is taken (hereinafter referred to as a "test piece taking position"), or may be a position where testing is performed on the actual product without taking a test piece. The determination system 100 estimates material properties for multiple positions on the target steel material based on, for example, the position performance information, and determines the test position based on the estimation results. Details of such a determination system 100 will be described below.

[0018] The determination system 100 includes an iron and steel system 10, a learning device 20, and a determination device 30. The iron and steel system 10 and the determination device 30 are communicatively connected via a network 70. The learning device 20 and the determination device 30 may also be communicatively connected via the network 70. The network 70 may be a network using wireless communication, or may be a network using wired communication. The network 70 may be configured using, for example, the Internet, or may be configured using a local area network (LAN). The network 70 may also be configured by combining multiple networks.

[0019] 2 is a schematic block diagram showing a specific example of the functional configuration of the steel system 10. The steel system 10 is a system that controls equipment (e.g., a blast furnace or continuous casting equipment) that produces target steel materials. The steel system 10 includes, for example, a communication device 11, a sensor group 12, a setting information storage device 13, and a control device 14.

[0020] The communication device 11 communicates data with other devices (for example, the determination device 30) via the network 70. The communication device 11 may be a device that performs wireless communication or a device that performs wired communication.

[0021] The sensor group 12 is configured using a plurality of sensors. The sensor group 12 acquires various values ​​(hereinafter referred to as "process measurement values") that can be measured in the manufacturing process of the target steel material. The process measurement values ​​are used as values ​​included in the manufacturing performance information. For example, the sensor group 12 may include a temperature sensor that measures temperature. The sensor group 12 may include a load sensor that measures load. The sensor group 12 may measure a plurality of types of temperatures depending on the process. For example, the sensor group 12 may measure a temperature in a hot rolling process (e.g., a coiling temperature) or a temperature in an annealing process (e.g., a heating temperature or a soaking temperature). The temperature sensors included in the sensor group 12 may be provided in equipment for each process so as to measure both the temperature in the hot rolling process and the temperature in the annealing process. A plurality of temperature sensors included in the sensor group 12 may be provided so as to measure a plurality of types of temperatures even in one process. For example, the sensor group 12 may include a temperature sensor that measures the heating temperature in the annealing process and a temperature sensor that measures the soaking temperature. Although the temperature sensor and the load sensor have been described as specific examples of sensors included in the sensor group 12, other sensors may be used. The sensor group 12 outputs measured values ​​to the control device 14.

[0022] The setting information storage device 13 is configured using a storage device such as a magnetic hard disk drive or a semiconductor storage device. The setting information storage device 13 stores data used by the control device 14. The setting information storage device 13 stores, for example, information related to the production of the target steel material (hereinafter referred to as "setting information"). The setting information may include, for example, information about the shape of the target steel material (e.g., information indicating the thickness and width), or may include information indicating the amount of material (e.g., carbon, manganese, etc.) used in the production of the target steel material. The setting information may also include any other information related to the target steel material.

[0023] If the setting information changes depending on the position of the target steel material, data associating each position with setting information corresponding to that position (hereinafter referred to as "position setting information") may be stored. Position setting information is one specific example of position performance information. For example, position setting information may be stored as data associating setting information corresponding to each position in the length direction (longitudinal direction) of the target steel material with each position. Furthermore, position setting information may be stored as data associating setting information corresponding to each position in the width direction of the target steel material as well as in the longitudinal direction with each position. For example, data associating setting information at each position across the entire length and width of the target steel material with information indicating each position may be stored as position setting information.

[0024] The control device 14 is configured using an information processing device such as a personal computer, a server device, or a PLC (Programmable Logic Controller). The control device 14 is configured using a processor such as a CPU (Central Processing Unit) and a memory (main storage device). The control device 14 functions when the processor executes a program. Note that all or part of the functions of the control device 14 may be realized using hardware such as an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array). The program may be recorded on a computer-readable recording medium. Examples of computer-readable recording media include portable media such as a flexible disk, a magneto-optical disk, a ROM, a CD-ROM, and a semiconductor storage device (e.g., an SSD: Solid State Drive), as well as storage devices such as a hard disk or semiconductor storage device built into a computer system. The program may be transmitted via a telecommunications line.

[0025] The control device 14 transmits, for example, information acquired from the sensor group 12 to the determination device 30 via the communication device 11. The control device 14 may process the information acquired from the sensor group 12 before transmitting it to the determination device 30. For example, the control device 14 may generate data correlating the information acquired from the sensor group 12 with each position in the longitudinal direction of the target steel material based on the timing at which each piece of information was acquired, and transmit the generated data to the determination device 30. Furthermore, the control device 14 may generate data correlating the information acquired from the sensor group 12 with each position in the width direction of the target steel material based on the position of the sensor at which each piece of information was acquired, and transmit the generated data to the determination device 30.

[0026] By configuring the control device 14 in this manner, data may be generated in which information obtained by the sensor group 12 at each position across the entire length and width of the target steel is associated with information indicating each position. Data in which information obtained by the sensor group 12 at each of multiple positions on the target steel is associated with each other in this manner is called position measurement information. Position measurement information is one specific example of position performance information. The control device 14 may generate position performance information including the setting information and process measurement values ​​for each position by associating the information obtained by the sensor group 12 with the setting information for each position on the target steel, and transmit the information to the determination device 30.

[0027] 3 is a schematic block diagram showing a specific example of the functional configuration of learning device 20. Learning device 20 is configured using an information processing device such as a personal computer or a server device. Learning device 20 includes a communication unit 21, a storage unit 22, and a control unit 23.

[0028] The communication unit 21 is a communication device. The communication unit 21 may be configured as, for example, a network interface. The communication unit 21 communicates data with other devices via the network 70 in accordance with the control of the control unit 23. The communication unit 21 may be a device that performs wireless communication or a device that performs wired communication.

[0029] The storage unit 22 is configured using a storage device such as a magnetic hard disk drive or a semiconductor storage device. The storage unit 22 stores data used by the control unit 23. The storage unit 22 may function as, for example, a teacher data storage unit 221 and a trained model storage unit 222.

[0030] The training data storage unit 221 stores training data used in the learning process executed by the learning device 20. Fig. 4 is a diagram showing a specific example of training data. The training data stored in the training data storage unit 221 includes a combination of manufacturing performance information (explanatory variables) and material properties (objective variables) obtained for the steel material for which each piece of manufacturing performance information is obtained.

[0031] The manufacturing performance information includes, for example, setting information and process measurement values. In the example of FIG. 4, the size of the target steel material and information about the manufacturing process are shown as specific examples of setting information. The thickness (mm) and width (mm) of the target steel material are used as specific examples of size. The actual values ​​of the input amounts (0.01%) of carbon, manganese, etc. are used as specific examples of information about the manufacturing process. For example, a value of 15 indicates that the input amount is 0.15%. In the example of FIG. 4, the coiling temperature (Celsius) in the hot rolling process, the heating temperature (Celsius) in the annealing process, the soaking temperature, etc. are used as specific examples of process measurement values.

[0032] Material properties are values ​​related to the characteristics (quality) required of the target steel material. Specific examples of material properties include material strength values ​​(MPa) such as YP (yield stress), TS (tensile strength), and EL (elongation). In the example of Figure 4, the values ​​of YP and TS are used. Since material properties change depending on the manufacturing performance information as described above, there is a correlation between the manufacturing performance information and material properties.

[0033] The trained model storage unit 222 stores a trained model obtained by a learning process using the training data stored in the training data storage unit 221.

[0034] The control unit 23 is configured using a processor such as a CPU and a memory. The control unit 23 functions as an information control unit 231 and a learning control unit 232 by the processor executing a program. All or part of the functions of the control unit 23 may be realized using hardware such as an ASIC, PLD, or FPGA. The above program may be recorded on a computer-readable recording medium. Examples of computer-readable recording media include portable media such as a flexible disk, a magneto-optical disk, a ROM, a CD-ROM, and a semiconductor storage device (e.g., an SSD), as well as storage devices such as a hard disk or semiconductor storage device built into a computer system. The above program may be transmitted via a telecommunications line.

[0035] The information control unit 231 controls the input and output of information. For example, the information control unit 231 acquires training data from other devices (information processing devices or storage media) and records the training data in the training data storage unit 221. For example, the information control unit 231 transmits the trained model stored in the trained model storage unit 222 to another device (for example, the determination device 30).

[0036] The learning control unit 232 executes a learning process using the training data stored in the training data storage unit 221. Specific examples of such a learning process include supervised learning for classification, such as a support vector machine, a random forest, or a neural network, or supervised learning for regression. The learning control unit 232 generates a trained model for outputting estimated values ​​of the material properties of the target steel material based on input manufacturing performance information of the target steel material, for example, by performing supervised learning. The learning control unit 232 records the generated trained model in the trained model storage unit 222. The trained model obtained by the learning control unit 232 may be transmitted to the determination device 30 and recorded in the estimation model storage unit 321 of the determination device 30. Such a trained model can obtain material properties as an output by providing manufacturing performance information as an input.

[0037] 5 is a flowchart showing a specific example of processing by the learning device 20. First, the information control unit 231 acquires training data (step S101). The training data may be input by a user, acquired via communication from another information device, or acquired from a recording medium connected to the learning device 20, for example. The learning control unit 232 executes a learning process using the training data and records a trained model in the trained model storage unit 222 (step S102).

[0038] 6 is a schematic block diagram showing a specific example of the functional configuration of the determination device 30. The determination device 30 is configured using an information processing device such as a personal computer or a server device. The determination device 30 includes a communication unit 31, a storage unit 32, a control unit 33, an input unit 34, and an output unit 35.

[0039] The communication unit 31 is a communication device. The communication unit 31 may be configured as, for example, a network interface. The communication unit 31 communicates data with other devices via the network 70 in accordance with the control of the control unit 33. The communication unit 31 may be a device that performs wireless communication or a device that performs wired communication.

[0040] The storage unit 32 is configured using a storage device such as a magnetic hard disk drive or a semiconductor storage device. The storage unit 32 stores data used by the control unit 33. The storage unit 32 may function as an estimation model storage unit 321, for example.

[0041] The estimation model storage unit 321 stores an estimation model used when the material property estimation unit 332 performs a material property estimation process. The estimation model may be configured using information on a trained model generated in advance by a learning process, for example. Such a learning process may be executed by another device (for example, the learning device 20) or by the device itself (the determination device 30). The estimation model does not necessarily have to be generated by a learning process. The estimation model may be configured using, for example, a lookup table that associates manufacturing performance information with material properties, or may be configured in another manner. When configured using a lookup table, a table may be used that determines that a heating temperature higher than 950 degrees is inappropriate as a material property, for example.

[0042] The control unit 33 is configured using a processor such as a CPU and a memory. The control unit 33 functions as an information control unit 331, a material property estimation unit 332, and a position determination unit 333 by the processor executing a program. Note that all or part of the functions of the control unit 33 may be realized using hardware such as an ASIC, PLD, or FPGA. The above program may be recorded on a computer-readable recording medium. Examples of computer-readable recording media include portable media such as flexible disks, magneto-optical disks, ROMs, CD-ROMs, and semiconductor storage devices (e.g., SSDs), as well as storage devices such as hard disks and semiconductor storage devices built into a computer system. The above program may be transmitted via a telecommunications line.

[0043] The information control unit 331 acquires manufacturing performance information at multiple positions of the target steel material from other devices such as the steel system 10. For example, the information control unit 331 may acquire position measurement information, position setting information, and position performance information. The information control unit 331 outputs information input by a user via the input unit 34 to the control unit 33. The information control unit 331 outputs information indicating the determination result obtained by the position determination unit 333. The output of the information indicating the determination result may be output from the output unit 35, for example, or may be performed by transmitting it from the communication unit 31 to another information processing device.

[0044] Fig. 7 is a diagram showing a specific example of position record information used in this embodiment. The position record information shows setting information (size and steelmaking process) and process measurement values ​​(measurement values ​​in the hot rolling process and annealing process) for a specific target steel material (the coil with coil number "101" in Fig. 7) at multiple positions (each position at 1 meter intervals in the longitudinal direction in Fig. 7).

[0045] The material property estimation unit 332 performs a material property estimation process for the target steel material using the estimation model stored in the estimation model storage unit 321 and the manufacturing performance information of the target steel material. The material property estimation process for the target steel material estimates the material properties of the target steel material. In this embodiment, the material property estimation unit 332 estimates the values ​​of YP and TS as specific examples of material properties. The material property estimation unit 332 may, for example, estimate statistical values ​​(e.g., standard deviation and average values) of the values ​​of each material property.

[0046] The material property estimation unit 332 estimates the material property of each position included in the position result information by using the manufacturing result information (e.g., process measurement values ​​and setting information) and the estimation model. The material property estimation unit 332 outputs the estimation result of the material property of each position to the position determination unit 333.

[0047] 8 and 9 are diagrams showing the estimation results by the material property estimation unit 332. FIG. 8 is a diagram showing TS at each position, and FIG. 9 is a diagram showing YP at each position. In FIGS. 8 and 9, the vertical axis indicates the value of the material property, and the horizontal axis indicates the longitudinal position of the target steel material. In FIGS. 8 and 9, as specific examples of the material property values, estimated values ​​of the 97.5% quantile, 2.5% quantile, and average value of each material property are shown. In FIG. 8, a value of 980 MPa is set as the lower limit of the pass criterion for TS, and a line is drawn at this value. In FIG. 9, a value of 565 MPa is set as the lower limit of the pass criterion for YP, and a value of 700 MPa is set as the upper limit, and a line is drawn at each value.

[0048] The position determination unit 333 determines the test position of the target steel material by using the values ​​of the material properties at each position of the target steel material. The position determination unit 333 may determine the test position based on a reference value input by the user via the input unit 34. Below, several specific examples of the processing of the position determination unit 333 will be described.

[0049] First, a first specific example will be described. For example, the test positions at which the test is performed may be determined so that the test passes with a predetermined probability. For example, when the test positions are determined so that the target steel material from which the estimation results shown in Figures 8 and 9 are obtained passes with a 50% probability, the test position at one end (leading end) is determined to be 12 m, and the test position at the other end (tail end) is determined to be 1283 m.

[0050] Tests are conducted at each test position. If the test result is unacceptable, the steel product is threaded onto the recoiling line (RCL) and tested again at another test position. If it is desired to reduce the cost of such a process, the predetermined probability may be set to a value higher than 50%. As a result, for example, based on the 97.5% quantile, the test position at one end (leading end) may be determined to be 80 m and the test position at the other end (tail end) may be determined to be 1130 m. By making such a determination, appropriate test positions are determined according to the set probability. The predetermined probability value may be input by a user via the input unit 34 or may be provided by communication from another information processing device.

[0051] Next, a second specific example will be described. The position determination unit 333 may calculate the pass probability when a test is performed at each position and determine the test position based on the result. For example, if the longitudinal position l [m] is defined as a parameter indicating the position and the pass rate p(l) of the mechanical test at that position, the pass rate p(l) can be defined as follows:

[0052]

number

[0053] In Equation 1, "upper" and "lower" are the upper and lower limits of the mechanical test value, respectively. The position determination unit 333 may determine the test position l based on p(l) so as to achieve a predetermined pass rate. The pass rate value may be input by the user via the input unit 34, or may be provided by communication from another information processing device. The position determination unit 333 may also generate and output a graph showing the relationship between the pass rate p(l) and the test position (l). In this case, the position determination unit 333 may acquire the test position (l) selected by the user based on the output graph as the determination result.

[0054] Next, we will explain a third specific example. If the mechanical test after the test fails, as mentioned above, the strip must be threaded on the recoiling line and retested, resulting in additional process costs. On the other hand, if the test position is determined based on a higher pass rate, the amount of steel that is not used for the intended purpose increases, resulting in relatively higher costs. Therefore, if the additional process cost is defined as W_1 and the steel price per meter is defined as W_2, the expected value J(l) of the manufacturing cost when testing at a longitudinal position l [m] can be defined as follows:

[0055]

number

[0056] The position determination unit 333 may determine the test position of the target steel material that has the optimum manufacturing cost by calculating the test position l that minimizes the expected value J(l) of the manufacturing cost using an optimization method. The expected value of the manufacturing cost may be input by the user via the input unit 34, or may be provided by communication from another information processing device. The position determination unit 333 may also generate and output a graph showing the relationship between the expected value J(l) and the test position (l). In this case, the position determination unit 333 may acquire the test position (l) selected by the user based on the output graph as the determination result.

[0057] In addition, when the steel plate is threaded through the recoiling line, several meters of steel may be cut off at the leading and trailing ends, and these several meters of steel may become unusable as products. The loss caused by such cutting may also be included in the additional process cost.

[0058] The input unit 34 is configured using an existing input device such as a keyboard, a pointing device (mouse, tablet, etc.), a button, a touch panel, etc. The input unit 34 is operated by a user when inputting a user instruction to the determination device 30. The input unit 34 may be an interface for connecting the input device to the determination device 30. In this case, the input unit 34 inputs an input signal generated in the input device in response to a user input to the determination device 30. The input unit 34 may be configured using a microphone and a voice recognition device. In this case, the input unit 34 acquires an acoustic signal generated by the user's speech, performs voice recognition on the words spoken by the user, and inputs character string information of the recognition result to the determination device 30. The voice recognition process may be performed by the control unit 33. The input unit 34 may be configured in any way as long as it is capable of inputting a user instruction to the determination device 30.

[0059] The output unit 35 outputs information in a form that can be recognized by the user. The output unit 35 may be, for example, an image display device such as a liquid crystal display or an organic EL (Electro Luminescence) display. The output unit 35 may be an interface for connecting an image display device to the determination device 30. In this case, the output unit 35 generates a video signal for displaying image data and outputs the video signal to the image display device connected to the output unit 35. The output unit 35 may be a device for outputting sound, such as a speaker. The output unit 35 may be an interface for connecting an audio output device, such as a speaker or headphones, to the determination device 30. In this case, the output unit 35 generates an audio signal for reproducing audio data and outputs the audio signal to the audio output device connected to the output unit 35. The output unit 35 may be configured as a touch panel integrated with the input unit 34.

[0060] 10 is a flowchart showing a specific example of the processing of the determination device 30. First, the information control unit 331 acquires position performance information from the steel system 10 (step S201). The material property estimation unit 332 estimates the material property of each position by performing a material property estimation process using the manufacturing performance information of each position (step S202). The position determination unit 333 determines the test position by performing a position determination process based on the material property of each position (step S203). The information control unit 331 outputs information indicating the determination result of the position determination unit 333 (step S204).

[0061] After this, the target steel material is cut out from an area determined according to the test, and a steel product (partial steel material) is produced according to the test results at the determined test position. For example, if tests are conducted at both a test position determined as the leading end and a test position determined as the tail end, and the test results show that the requirements are met, the steel material between the leading end test position and the tail end test position may be obtained as the steel product (partial steel material). The process of cutting out the partial steel material from the target steel material may be performed after the test if a test piece can be obtained without cutting out, or may be performed before the test if a test piece cannot be obtained without cutting out. To perform this process, the steel system 10 has equipment for obtaining test pieces from the target steel material and equipment for cutting out partial steel material from the target steel material.

[0062] The judgment system 100 configured in this manner uses the position history information of the target steel material to enable more accurate determination of an appropriate testing position. This makes it possible to manufacture steel material that satisfies the required material properties at lower cost. Specifically, this is as follows: The judgment system 100 estimates the material properties of each position based on information (position history information) that indicates the manufacturing history information of each position of the target steel material. Based on the estimated material properties of each position, a testing position that satisfies the required material properties is determined.

[0063] FIG. 11 is a diagram illustrating an outline of an example hardware configuration of an information processing device 90 applied to this embodiment. The information processing device 90 includes a processor 91, a main memory device 92, a communication interface 93, an auxiliary memory device 94, an input / output interface 95, and an internal bus 96. The processor 91, the main memory device 92, the communication interface 93, the auxiliary memory device 94, and the input / output interface 95 are communicably connected to each other via the internal bus 96. The information processing device 90 may be applied to, for example, the learning device 20 and the determination device 30. In this case, for example, the communication units 21 and 31 may be configured using the communication interface 93. For example, the memory units 22 and 32 may be configured using the auxiliary memory device 94. Furthermore, the control units 23 and 33 may be configured using the processor 91 and the main memory device 92.

[0064] (Variation) In this embodiment, the learning device 20 and the determination device 30 are configured as separate devices, but they may also be configured as an integrated device. FIG. 12 is a diagram showing a modified example of the determination device 30 configured in this manner. The memory unit 32 of the determination device 30 shown in FIG. 12 also functions as a teacher data memory unit 322. The control unit 33 of the determination device 30 shown in FIG. 12 also functions as a learning control unit 334. The teacher data memory unit 322 functions in the same way as the teacher data memory unit 221 of the learning device 20. The learning control unit 334 functions in the same way as the learning control unit 232 of the learning device 20.

[0065] The learning device 20 may be implemented using a plurality of information processing devices. For example, the learning device 20 may be implemented using a device such as a cloud. For example, in the learning device 20, the memory unit 22 and the control unit 23 may each be implemented in different information processing devices. For example, the memory unit 22 of the learning device 20 may be distributed and implemented across a plurality of information processing devices. The determination device 30 may be implemented using a plurality of information processing devices. For example, the determination device 30 may be implemented using a device such as a cloud. For example, in the determination device 30, the memory unit 32 and the control unit 33 may each be implemented in different information processing devices. For example, the memory unit 32 of the determination device 30 may be distributed and implemented across a plurality of information processing devices.

[0066] In the above-described position actual information, a specific position (e.g., the center) is fixed in the width direction, and actual information for each longitudinal direction is obtained at that width direction position. Alternatively, actual information may be obtained at multiple width direction positions for the same longitudinal direction position. FIG. 13 shows a specific example of position actual information used in this modified example. The position actual information in this modified example shows, for a specific target steel product (coil No. "101" in FIG. 13), setting information (size and steelmaking process) and process measurement values ​​(measurements in the hot rolling process and annealing process) for each combination of multiple longitudinal (length direction) positions and multiple transverse (width direction) positions. In the example of FIG. 13, multiple positions are defined at 1-meter intervals in the longitudinal direction, and multiple positions are defined at 0.1-meter intervals in the transverse direction. However, this spacing is merely an example, and the spacing in each direction may be defined in any way.

[0067] When such positional performance information is obtained, the material property estimation unit 332 estimates the material property at each position indicated by a combination of the longitudinal position and the lateral position. The material property estimation unit 332 outputs the estimation result of the material property at each position to the position determination unit 333. For example, the material property estimation unit 332 may estimate a value indicating the estimation result of the material property at each position in the longitudinal direction as shown in FIG. 8 or FIG. 9 at each position in the width direction.

[0068] Figures 14 and 15 show specific examples of the estimation results of material properties at each position. Figure 14 shows the pass rate value at each position indicated by a combination of the longitudinal position and the lateral position. Figure 14 shows the pass rate for the following three types: Pass rate below 0.90 Pass rate is 0.90 or higher and less than 0.95 Pass rate of 0.95 or higher The position determination unit 333 may determine the test position so as to satisfy the quality requirement, for example, a pass rate of 0.90 or more. In the example of FIG. 14, the test piece extraction position at the leading end and the test piece extraction position at the trailing end are determined so as to obtain a partial steel product with a pass rate of 0.90 or more. FIG. 15 is a diagram showing the regions divided into those with pass rates of less than 0.90 and those with pass rates of 0.90 or more. For example, at the leading end, the test piece extraction position is determined based on the rearmost (tail-end) position among the positions that do not satisfy the quality requirement. On the other hand, at the trailing end, the test piece extraction position is determined based on the forward-most (tip-end) position among the positions that do not satisfy the quality requirement.

[0069] Although an embodiment of the present invention has been described above in detail with reference to the drawings, the specific configuration is not limited to this embodiment, and includes designs within the scope of the gist of the present invention. [Explanation of symbols]

[0070] 100...Determination system, 10...Steel system, 11...Communication device, 12...Sensor group, 13...Setting information storage device, 14...Control device, 20...Learning device, 21...Communication unit, 22...Memory unit, 221...Teacher data storage unit, 222...Learned model storage unit, 23...Control unit, 231...Information control unit, 232...Learning control unit, 30...Determination device, 31...Communication unit, 32...Memory unit, 321...Estimation model storage unit, 33...Control unit, 331...Information control unit, 332...Material property estimation unit, 333...Position determination unit, 34...Input unit, 35...Output unit

Claims

1. Obtain information on the manufacturing performance of each steel position, Estimating material properties at a plurality of positions of the steel material based on the information on the manufacturing history; determining a test specimen collection position that satisfies the quality requirements of the steel material based on the material properties at the plurality of positions; Judgment method.

2. The information on the manufacturing performance is acquired at each position along the longitudinal direction of the steel material, Estimating material properties at predetermined locations along the longitudinal direction of the steel material; The method according to claim 1 , further comprising determining a longitudinal position of a test piece taken from the steel material.

3. Information about the manufacturing performance is also acquired at each position along the short side direction of the steel material, Estimating material properties at predetermined positions along the longitudinal direction and the lateral direction of the steel material; The method according to claim 2 , further comprising determining a longitudinal position of a test piece taken from the steel material.

4. The steel material is a thick steel plate, The determination method according to claim 1 , wherein the test piece sampling positions are determined so as not to include positions in the steel product to be shipped where the material properties do not satisfy predetermined requirements.

5. the steel material is a thin coil, 2. The method according to claim 1, wherein, if the quality requirements are not actually satisfied in the test at the test specimen collection position, the steel material is recoiled on a recoiling line to determine a new test specimen collection position.

6. a control unit that acquires information on manufacturing performance at each position of the steel material, estimates material properties at a plurality of positions of the steel material based on the information on manufacturing performance, and determines test piece collection positions that satisfy quality requirements for the steel material based on the material properties at the plurality of positions; A determination device comprising:

7. Obtain information on the manufacturing performance of each steel position, Estimating material properties at a plurality of positions of the steel material based on the information on the manufacturing history; determining a test specimen collection position that satisfies the quality requirements of the steel material based on the material properties at the plurality of positions; A partial steel material production method for producing a partial steel material by cutting out the steel material according to the determined test piece collection position.

8. Obtain information on the manufacturing performance of each steel position, Estimating material properties at a plurality of positions of the steel material based on the information on the manufacturing history; determining a test specimen collection position that satisfies the quality requirements of the steel material based on the material properties at the plurality of positions; A partial steel material is produced by cutting out the steel material according to the determined test piece collection position.

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