Device for measuring mechanical properties, method for measuring mechanical properties, manufacturing apparatus for a substance, management method for a substance, and manufacturing method for a substance

Abstract

Provided are a mechanical property measurement device and a mechanical property measurement method capable of correctly measuring a mechanical property via a physical quantity. A mechanical property measurement device (100) includes: a physical quantity measurement unit (5) that measures a plurality of physical quantities of a measurement target object having a substance and a film on a surface of the substance; a calculation model generation unit (81) that selects a plurality of learning data from a learning data group based on a selection physical quantity that is at least two of the measured plurality of physical quantities, and generates a calculation model for calculating a mechanical property of the substance from the selected plurality of learning data; and a mechanical property calculation unit (82) that calculates the mechanical property of the substance using the generated calculation model and the at least two of the plurality of physical quantities, the selection physical quantity including at least one physical quantity measured using a first measurement signal and at least one physical quantity measured using a second measurement signal.

Description

Technical Field

[0001] This disclosure relates to measuring devices for mechanical properties, methods for measuring mechanical properties, manufacturing equipment for substances, methods for managing substances, and methods for manufacturing substances. Background Technology

[0002] In the manufacture of steel used in materials such as pipeline pipes, sampling inspection is sometimes conducted to check the mechanical properties of the steel. Sampling inspection involves removing a portion of the steel for inspection, processing it into a mechanical specimen, and then conducting tests—a so-called destructive test. In recent years, however, there has been a focus on non-destructively measuring or evaluating the mechanical properties of steel products themselves to ensure quality, rather than relying on sampling inspection. Therefore, attempts are being made to measure mechanical properties during or after steel manufacturing using various physical quantities related to the mechanical properties of the steel to be measured.

[0003] For example, Patent Document 1 describes a technique in which an alternating magnetic field is applied to a metallic material and the induced eddy current is detected, thereby detecting a high-hardness region that exists locally in the metallic material.

[0004] For example, Patent Document 2 describes a detection device having a first opening for insertion of a long condition on one side along its length and a second opening for insertion on the other side along its length, and a magnetic yoke member having a shape that is substantially axially symmetrical with respect to the axis passing through the first and second openings. The detection device of Patent Document 2 can reduce the dead zone at the length end of the long condition and can detect changes in magnetic properties with high precision.

[0005] For example, Patent Document 3 describes a technique for evaluating the film thickness of the coating material of a test subject based on the intensity of the eddy current induced by the test subject, and for determining the degree of degradation of the test subject based on information related to the reduction of the film thickness of the coating material.

[0006] Patent Document 1: Japanese Patent Application Publication No. 2008-224495

[0007] Patent Document 2: International Publication No. 2019 / 087460

[0008] Patent Document 3: Japanese Patent Application Publication No. 9-113488

[0009] In the current technology for measuring mechanical properties of steel by using sensors to measure the electromagnetic characteristics of the steel surface, the following problems exist: the inconsistency between the electromagnetic characteristics and the mechanical properties increases, making accurate calculations difficult. For example, for steel manufactured in a manner that yields the same mechanical properties, the electromagnetic characteristics of the steel surface measured by sensors may sometimes differ. Therefore, there is a need for a technology that can accurately measure mechanical properties in a non-destructive manner and be utilized in the steel manufacturing process. Summary of the Invention

[0010] This disclosure was made in view of the above circumstances, with the aim of providing a measuring device and a method for measuring mechanical properties that can accurately measure mechanical properties via physical quantities. Furthermore, another objective of this disclosure is to provide a material manufacturing apparatus and a material manufacturing method that can improve the yield of a material by accurately measuring its mechanical properties via physical quantities. Moreover, another objective of this disclosure is to provide a material management method that can provide high-quality materials by accurately measuring their mechanical properties via physical quantities.

[0011] In order to solve the above problems, as a result of an investigation into the relationship between the physical quantities and mechanical properties of the object being measured, the inventors discovered that these relationships affect the properties of the membrane possessed by the object being measured.

[0012] An embodiment of the present disclosure discloses a mechanical property measuring apparatus comprising: a physical quantity measuring unit that measures multiple physical quantities of a measuring object having a substance and a film on the surface of the substance; a calculation model generating unit that selects multiple learning data from a learning data set based on a selection physical quantity which is at least two of the measured multiple physical quantities, and generates a calculation model for calculating the mechanical properties of the substance based on the selected multiple learning data; and a mechanical property calculating unit that calculates the mechanical properties of the substance using the generated calculation model and at least two of the multiple physical quantities, wherein the selection physical quantity includes at least one physical quantity measured using a first measuring signal and at least one physical quantity measured using a second measuring signal.

[0013] An embodiment of the present disclosure discloses a method for measuring mechanical properties, comprising: a measurement step in which multiple physical quantities of a measurement object having a substance and a film on the surface of the substance are measured; a selection step in which multiple learning data are selected from a learning data set based on a selection physical quantity which is at least two of the measured multiple physical quantities; a generation step in which a calculation model is generated for calculating the mechanical properties of the substance based on the selected multiple learning data; and a calculation step in which the mechanical properties of the substance are calculated using the generated calculation model and at least two of the multiple physical quantities, wherein the selection physical quantity includes at least one physical quantity measured using a first measurement signal and at least one physical quantity measured using a second measurement signal.

[0014] An embodiment of the present disclosure discloses a material manufacturing apparatus comprising: a manufacturing apparatus for manufacturing a material; and a mechanical property measuring device, the mechanical property measuring device comprising: a physical quantity measuring unit for measuring multiple physical quantities of a test object having a material and a film on the surface of the material; a calculation model generating unit for selecting multiple learning data from a learning data set based on a selection physical quantity which is at least two of the measured multiple physical quantities, and generating a calculation model for calculating the mechanical properties of the material based on the selected multiple learning data; and a mechanical property calculating unit for calculating the mechanical properties of the material using the generated calculation model and at least two of the multiple physical quantities, wherein the selection physical quantity includes at least one physical quantity measured using a first measuring signal and at least one physical quantity measured using a second measuring signal, the measuring device measuring the mechanical properties of the material manufactured by the manufacturing apparatus.

[0015] An embodiment of the present disclosure discloses a method for managing a substance comprising: a measurement step in which multiple physical quantities of a measurement object having a substance and a film on the surface of the substance are measured; a selection step in which multiple learning data are selected from a learning data set based on at least two of the measured multiple physical quantities, including a selection physical quantity comprising at least one physical quantity measured using a first measurement signal and at least one physical quantity measured using a second measurement signal; a generation step in which a calculation model is generated for calculating the mechanical properties of the substance based on the selected multiple learning data; a calculation step in which the mechanical properties of the substance are calculated using the generated calculation model and at least two of the multiple physical quantities; and a management step in which the substance is classified based on the calculated mechanical properties of the substance.

[0016] An embodiment of the present disclosure discloses a method for manufacturing a substance comprising: a manufacturing step in which a substance is manufactured; a measurement step in which multiple physical quantities of the manufactured substance and a film on the surface of the substance are measured as measurement objects; a selection step in which multiple learning data are selected from a learning data set based on selection physical quantities, which are at least two of the measured multiple physical quantities; a generation step in which a calculation model is generated for calculating the mechanical properties of the substance based on the selected multiple learning data; and a calculation step in which the mechanical properties of the substance are calculated using the generated calculation model and at least two of the multiple physical quantities, wherein the selection physical quantities in the selection step include at least one physical quantity measured using a first measurement signal and at least one physical quantity measured using a second measurement signal.

[0017] According to an embodiment of the present disclosure, the mechanical property measuring apparatus and method can accurately measure mechanical properties using physical quantities. Furthermore, according to the material manufacturing equipment and method of the present disclosure, the yield rate of the manufactured material can be improved by accurately measuring mechanical properties using physical quantities. Moreover, according to the material management method of the present disclosure, high-quality materials can be provided by accurately measuring mechanical properties using physical quantities. Attached Figure Description

[0018] Figure 1 This is a block diagram of a measuring device for mechanical properties according to one embodiment of this disclosure.

[0019] Figure 2 This is a block diagram of the physical quantity measuring section.

[0020] Figure 3 This is a diagram showing a specific structural example of a sensor.

[0021] Figure 4 This diagram illustrates an example of the signal supplied to the excitation coil in order to generate an alternating magnetic field.

[0022] Figure 5 This is a flowchart illustrating the collection and processing of learning data.

[0023] Figure 6 This is a graph representing an example of a set of learning data.

[0024] Figure 7 It is a flowchart representing the measurement method of mechanical properties.

[0025] Figure 8It is a graph used to illustrate the relationship between the selected physical quantities and the learning data set.

[0026] Figure 9 It is a graph that compares the calculated mechanical properties with the measured values.

[0027] Figure 10 This is a graph that compares the calculated mechanical properties of the comparative example with the measured values.

[0028] Figure 11 This is a block diagram of a measuring device for mechanical properties involved in other embodiments.

[0029] Figure 12 This is a diagram illustrating an example of a steel manufacturing method. Detailed Implementation

[0030] (First Embodiment)

[0031] Figure 1 This is a block diagram of a mechanical property measuring device 100 according to the first embodiment of this disclosure. The measuring device 100 measures the object 101 (see reference 5) via the physical quantity measuring unit 5. Figure 2 Multiple physical quantities of the measured object 101 are used non-destructively to measure the substance 1 (refer to) the substance 1 of the measured object 101. Figure 2 The mechanical properties of a substance are measured. Here, mechanical properties refer to its mechanical characteristics, specifically properties relative to external forces such as tension, compression, or shear. Examples of mechanical properties include tensile stress, yield stress, and compressive stress (strengths), Vickers hardness and Leeb hardness (hardness), and brittleness. Physical quantities include objectively measurable quantities such as temperature, mass, and electromagnetic characteristics.

[0032] In this embodiment, steel is used as an example of material 1, but material 1 is not limited to steel. Hardness is used as an example of mechanical property, but mechanical properties are not limited to hardness. Electromagnetic characteristics are used as an example of multiple physical quantities, but multiple physical quantities are not limited to electromagnetic characteristics. Conventionally, electromagnetic characteristics such as permeability and magnetic retention are related to the mechanical properties of metals, and it is preferable to use electromagnetic characteristics to measure or evaluate mechanical properties. Eddy current testing or 3MA (Micromagnetic Multiparameter Microstructure and Stress Analysis) technology is preferred as a method for measuring electromagnetic characteristics. In particular, if the measurement signal described later uses an AC signal (AC current or AC voltage) superimposed with two or more frequencies, more electromagnetic characteristics can be obtained, which is preferable. Furthermore, by making one of the frequencies 200Hz or less, even if a film 2 (see reference) is formed on the surface of material 1... Figure 2 In this case, the alternating magnetic field also fully penetrates the surface of material 1, enabling more accurate measurement or evaluation of mechanical properties, and is therefore more preferred. When measuring the electromagnetic characteristics of the surface layer of material 1, the above-described measurement method is particularly preferred.

[0033] (Structure of a measuring device for mechanical properties)

[0034] like Figure 1 As shown, the measuring device 100 includes a physical quantity measuring unit 5, a control unit 8, a storage unit 10, and a display unit 11. The control unit 8 includes a calculation model generation unit 81, a mechanical property calculation unit 82, and a physical quantity measuring control unit 83. The storage unit 10 includes a learning data set 110. The learning data set 110 is used to generate a calculation model for calculating the mechanical properties of substance 1. Details of each element of the measuring device 100 will be described later.

[0035] Figure 2 This is a block diagram of the physical quantity measuring unit 5. The physical quantity measuring unit 5 includes a sensor 3 and a scanning unit 6. The sensor 3 measures the physical quantity of the object to be measured 101. The object to be measured 101 has a substance 1 and a film 2 formed on the surface of the substance 1. Details of each element of the physical quantity measuring unit 5 will be described later.

[0036] For example, when substance 1 is steel, an iron oxide film, known as oxide scale or black scale, is formed on the surface of the steel during the manufacturing process. Various types of iron oxide films exist, but commonly known ones include magnetite (Fe3O4), ferrous oxide (FeO), and hematite (Fe2O3). These oxide scales differ not only in their oxygen and iron compositions but also in their electromagnetic properties. For example, magnetite is magnetic, but ferrous oxide is not. Here, in order to measure the mechanical properties of substance 1 (especially the surface layer) as steel, physical quantities are measured from the surface. In other words, in this invention, substance 1 as steel and oxide scale as film 2 are both used as the object of measurement 101 to measure physical quantities.

[0037] Therefore, the oxide film 2, acting as a scale, affects the measurement of the steel material 1. Furthermore, the type and composition of the oxide film vary depending on the state of the steel during manufacturing. Also, the magnetic properties can sometimes be anisotropic due to differences in the steel's microstructure, resulting in variations in electromagnetic characteristics depending on the object being measured 101. Therefore, it is very difficult to measure or evaluate an object 101 that has both steel and an oxide film by simply establishing a relationship between the mechanical properties of the steel, such as hardness, and the electromagnetic characteristics of the object 101. In particular, when measuring the mechanical properties of the surface layer of the material 1, the electromagnetic characteristics of the oxide film 2 have a greater impact. Therefore, it is even more difficult to measure or evaluate an object 101 that has both steel and an oxide film by simply establishing a relationship between the mechanical properties of the steel's surface layer, such as hardness, and the electromagnetic characteristics of the object 101.

[0038] This remains true even when substance 1 is anything other than steel and film 2 is anything other than oxide scale. In particular, when film 2 has characteristics different from substance 1, it is very difficult to determine or evaluate the object 101, which has substance 1 and film 2 on its surface, simply by establishing a relationship between the mechanical properties of substance 1 and the multiple physical quantities of the object 101. Furthermore, when measuring the mechanical characteristics of the surface layer of substance 1, it is even more difficult to determine or evaluate the object 101, which has substance 1 and film 2 on its surface, simply by establishing a relationship between the mechanical characteristics of the surface layer of substance 1 and the multiple physical quantities of the object 101.

[0039] The storage unit 10 stores various information and programs for operating the measuring device 100. The various information stored in the storage unit 10 includes a learning data set 110, which is a collection of multiple learning data sets. The programs stored in the storage unit 10 include: a program that operates the control unit 8 as a computational model generation unit 81; a program that operates the control unit 8 as a mechanical characteristic calculation unit 82; and a program that operates the control unit 8 as a physical quantity measurement control unit 83. The storage unit 10 is, for example, composed of a semiconductor memory or a magnetic memory.

[0040] The display unit 11 displays various information about the mechanical properties of substance 1 to the user. In this embodiment, the display unit 11 is configured to include a display capable of displaying text, images, etc., and a touch screen capable of detecting contact from the user's finger, etc. The display can be a display device such as a liquid crystal display (LCD) or an organic electroluminescence display (OELD). The touch screen can be detected by any method such as electrostatic capacitance, resistive film, surface elastic wave, infrared, electromagnetic induction, or load detection. Here, as another example, the display unit 11 may also be configured as a display without a touch screen.

[0041] The control unit 8 controls the overall operation of the measuring device 100. The control unit 8 is configured with one or more processors. The processors may include at least one of a general-purpose processor that reads a specific program and executes a specific function, and a dedicated processor specialized for a specific process. The dedicated processor may also include an application-specific integrated circuit (ASIC). The processor may also include a programmable logic device (PLD). A PLD may also include a field-programmable gate array (FPGA). The control unit 8 may also include at least one of a system-on-a-chip (SoC) and a system-in-a-package (SiP) in cooperation with one or more processors. The control unit 8 functions as a calculation model generation unit 81, a mechanical characteristic calculation unit 82, and a physical quantity measurement control unit 83, based on the program read from the storage unit 10.

[0042] In addition, the control unit 8 collects learning data via the communication unit 7, establishes a correspondence between multiple learning data sets according to each item to generate a learning data set 110, and stores the learning data set 110 in the storage unit 10. Details of the learning data set 110 will be described later.

[0043] The computational model generation unit 81 selects multiple learning data from the learning data set 110 based on at least two of the multiple physical quantities of the object 101 measured by the physical quantity measurement unit 5. Hereinafter, the physical quantities used in the selection of the multiple learning data are referred to as the selection physical quantities. For example, it is assumed that the phase change of the current waveform, the amplitude of higher harmonics, and the incremental permeability, which are electromagnetic characteristic quantities, are all used as selection physical quantities. First, the computational model generation unit 81 acquires the learning data set 110 from the storage unit 10. Furthermore, the computational model generation unit 81 selects multiple learning data that are close to the combination of the acquired values ​​of the phase change of the current waveform, the amplitude of higher harmonics, and the incremental permeability. The computational model generation unit 81 generates a computational model based on the selected multiple learning data. The generated computational model is used by the mechanical characteristic calculation unit 82. Furthermore, examples of computational models used in this invention include: a regression model based on the k-nearest neighbor algorithm, a local linear regression model, and a regression model using a support vector machine. Among these, a regression model using K-nearest neighbors (KNN) and local linear regression (MLR) was preferred. The reason for this is that in typical regression models, biases in the overall distribution of the dataset can lead to decreased prediction accuracy. This is because models are usually constructed to minimize the overall error of the dataset. Here, the method of extracting the K nearest neighbors (KNN) from the data to be evaluated using KNN, and then constructing a regression model from these K nearest neighbors for evaluation, improves accuracy and is therefore preferred. Alternatively, MLR can achieve the same effect as KNN. For MLR, the dataset is weighted according to the order in which data close to the data to be evaluated has the greatest impact on the regression, while data further away have less impact, and the model is constructed sequentially for evaluation, thereby improving accuracy.

[0044] The mechanical property calculation unit 82 calculates the mechanical properties of material 1 using a calculation model generated by the calculation model generation unit 81 and at least two of a plurality of physical quantities measured by the physical quantity measurement unit 5. For example, the plurality of physical quantities includes the aforementioned electromagnetic characteristic quantities, the phase change of the current waveform, the amplitude of higher harmonics, and the incremental permeability, all of which are used in the calculation of the mechanical properties of material 1. The mechanical property calculation unit 82 obtains the generated calculation model from the calculation model generation unit 81. The mechanical property calculation unit 82 calculates the mechanical properties of material 1 by inputting the obtained values ​​of the phase change of the current waveform, the amplitude of higher harmonics, and the incremental permeability into the calculation model. The mechanical property calculation unit 82 may also output the calculated hardness of the steel to the display unit 11 for display to the user.

[0045] Here, when the computational model generation unit 81 generates the computational model, in the above example, all electromagnetic characteristic quantities are used as the selected physical quantities, but a combination of a portion of two or more electromagnetic characteristic quantities can also be used. Similarly, when the mechanical property calculation unit 82 calculates the mechanical properties of material 1, in the above example, all electromagnetic characteristic quantities are used, but a portion of two or more electromagnetic characteristic quantities can also be input into the computational model. In this case, the portion of electromagnetic characteristic quantities input into the computational model can also differ from the portion of electromagnetic characteristic quantities used when the computational model generation unit 81 generates the computational model. For example, the computational model generation unit 81 may generate the computational model using a combination of the phase change of the current waveform and the incremental permeability, while the mechanical property calculation unit 82 may input the phase change of the current waveform and the amplitude of higher harmonics into the computational model to calculate the mechanical properties of material 1.

[0046] The physical quantity measurement and control unit 83 controls the operation of the physical quantity measurement unit 5. For example, the physical quantity measurement and control unit 83 activates the sensor 3 to measure electromagnetic characteristic quantities.

[0047] (Structure of the physical quantity measuring unit)

[0048] Sensor 3 measures the physical quantity of the object 101, which has substance 1 and membrane 2. In this embodiment, a magnetic sensor is used as an example of sensor 3, but sensor 3 is not limited to a magnetic sensor. There can be one sensor 3, but there can be multiple sensors 3. Here, the measurement result of sensor 3 represents the physical quantity including the influence of membrane 2, that is, the physical quantity in a state where it has both substance 1 and membrane 2. In contrast, the mechanical properties calculated by mechanical property calculation unit 82 are related to substance 1 without membrane 2.

[0049] Figure 3 This diagram illustrates a specific structural example of sensor 3. Alternatively, sensor 3 could be, for example, a magnetic sensor, and include an excitation coil 31 and a magnetic yoke 32. Sensor 3 moves relative to the object being measured 101 while simultaneously applying an alternating magnetic field to the object 101. Figure 3 In the sensor shown, a single coil serves as both an excitation coil and a coil for measuring electromagnetic changes. Sensor 3 measures the influence of eddy currents and the like induced in the object 101 by an alternating magnetic field as changes in electromagnetic characteristic quantities. As another example, the sensor for measuring electromagnetic characteristic quantities can be configured such that an excitation coil is wound around a yoke, and additionally, an excitation coil and a coil for receiving signals are wound around it. As yet another example, the sensor for measuring electromagnetic characteristic quantities can also be configured such that an excitation coil is wound around a yoke, and a coil for measuring electromagnetic changes is independently placed between the yokes. The sensor for measuring electromagnetic characteristic quantities is not limited to any structure that includes an excitation coil, a coil for measuring electromagnetic changes, and a yoke. Figure 3 The structure shown.

[0050] Here, in steel, the surface electromagnetic characteristics can be used as the physical quantity to be measured. It is known that in steel, changes in the hysteresis curve and Barkhausen noise are correlated with mechanical properties such as tensile strength and hardness. Therefore, through... Figure 3 A magnetic sensor as shown is preferred for measuring the electromagnetic characteristics of the surface. Here, the hysteresis curve, also known as the BH curve, is a curve that represents the relationship between the strength of the magnetic field and the magnetic flux density. Furthermore, when an alternating current flows through a conductor, based on the phenomenon that the current density is higher at the surface of the conductor and lower further away from the surface (skin effect), the magnetic sensor can selectively measure the electromagnetic characteristics only on the surface of the object being measured. For the skin effect, the higher the frequency of the alternating current, the more easily the current concentrates at the surface. When the penetration depth is defined as the depth at which the current becomes approximately 0.37 times the surface current due to the skin effect, the relationship is given by the following equation (1). In equation (1), d is the penetration depth [m], f is the frequency [Hz], μ is the permeability [H / m], σ is the conductivity [S / m], and π is pi.

[0051] Mathematical Formula 1

[0052]

[0053] As shown in equation (1), the higher the frequency, the shallower the penetration depth. In other words, the lower the frequency, the deeper the penetration depth. Therefore, the penetration depth can be adjusted by adjusting the frequency according to the range of surface depth to be measured or evaluated. For example, when measuring or evaluating mechanical properties up to approximately 0.25 mm from the surface, the frequency is determined in a way that makes the penetration depth approximately 0.25 mm. Considering attenuation, it is preferable that 3 / 4 of the penetration depth relative to the surface depth is greater than 0.25 mm.

[0054] Figure 4 This represents an example of a signal given to the excitation coil 31 in order to generate an alternating magnetic field. Figure 4 The signal is a low-frequency signal superimposed with a high-frequency signal. By using such a signal, sensor 3 can efficiently measure electromagnetic characteristic quantities based on both low-frequency and high-frequency signals. For example, the low-frequency signal is a 150Hz sine wave. For example, the high-frequency signal is a 1kHz sine wave.

[0055] The scanning unit 6 moves the sensor 3 relative to the object being measured 101. The scanning unit 6 can also move the sensor 3 to an evaluation location specified by the physical quantity measurement and control unit 83. Furthermore, the scanning unit 6 can acquire information about the movement speed of the substance 1 and adjust it to move the sensor 3 at an appropriate relative speed.

[0056] (Choose a physical quantity)

[0057] Next, the preferred conditions for selecting the physical quantity will be explained. In this invention, the physical quantity to be selected is the most important concept. The physical quantity to be selected is at least two of the plurality of physical quantities measured by the physical quantity measuring unit 5. Furthermore, the physical quantity to be selected includes at least one physical quantity measured using a first measuring signal and at least one physical quantity measured using a second measuring signal.

[0058] In other words, the physical quantities of the object 101 measured by the physical quantity measuring unit 5 include one or more items measured using the first measuring signal and one item measured using the second measuring signal. Alternatively, when the measured physical quantity is an electromagnetic characteristic quantity, the first measuring signal may be an AC signal with a first frequency, and the second measuring signal may be an AC signal with a second frequency higher than the first frequency. In other words, the first measuring signal obtained from the physical quantity measuring unit 5 may be a low-frequency signal, and the second measuring signal obtained from the physical quantity measuring unit 5 may be a high-frequency signal.

[0059] Here, in the future Figure 4 When a low-frequency signal superimposed with a high-frequency signal is applied to the excitation coil 31, the electromagnetic characteristic quantity can also be a characteristic of the electrical signal observed by applying an alternating magnetic field to the object being measured 101. Specifically, the electromagnetic characteristic quantity can also be a characteristic associated with (1) the distortion of the current waveform, (2) the amplitude of the current waveform, (3) the phase change of the current waveform, (4) the amplitude of higher harmonics, (5) the phase change of higher harmonics, and (6) the incremental permeability. For example, the characteristic can also be (a) the maximum value, (b) the minimum value, (c) the average value, and (d) the magnetic retention force. Here, the incremental permeability is a value that represents the ease of magnetization under the applied magnetic field, and is shown in the magnetization curve representing the relationship between magnetic flux density and magnetic field through the gradient of the minor loop.

[0060] For example, when the object to be measured, 101, is steel with an oxide scale, it is preferable to apply a voltage or current to the excitation coil 31 of the electromagnetic sensor, which is a sine wave with a frequency of less than 150 Hz superimposed on a sine wave of more than 1 kHz. By making the low-frequency signal a sine wave of less than 150 Hz, the alternating magnetic field excited by the electromagnetic sensor can penetrate approximately 300 μm from the surface of the steel. Furthermore, the electromagnetic characteristic quantities measured using the low-frequency signal preferably include characteristics related to the phase change of the current waveform. Because the alternating magnetic field penetrates deeper when measured using the low-frequency signal, more information about the material 1 can be included than that of the membrane 2. Additionally, the phase change of the current waveform includes information related to the magnetic retention force. Therefore, by measuring the characteristics related to the phase change of the current waveform using a low-frequency signal, information about the magnetic retention force of the material 1 can be obtained. Furthermore, the electromagnetic characteristic quantities measured using the high-frequency signal preferably include characteristics related to the incremental permeability. Because the alternating magnetic field penetrates shallower when measured using the high-frequency signal, more information about the membrane 2 can be included than that of the material 1. Furthermore, the incremental permeability includes information about the magnetic properties of membrane 2 under a magnetic field that varies due to a low-frequency signal. Therefore, by measuring the properties associated with the incremental permeability using a high-frequency signal, information about the magnetic properties of membrane 2 can be obtained. Obtaining information about the magnetic properties of membrane 2 helps to compensate for the amount of influence of membrane 2 and accurately predict the properties of material 1. Preferably, the physical quantity selected includes at least one physical quantity measured using the first measurement signal and at least one physical quantity measured using the second measurement signal, in a manner that includes accurate information about both material 1 and membrane 2.

[0061] (Collection of learning data)

[0062] The mechanical property measuring device 100 according to this embodiment calculates the mechanical properties of substance 1 based on the physical quantities of the object to be measured 101 measured by the physical quantity measuring unit 5 and a plurality of selected learning data. For example, the object to be measured 101 is steel with oxide scale. For example, the physical quantities include electromagnetic characteristic quantities. For example, the mechanical property of substance 1 is the hardness of the steel. In the calculation of the mechanical properties of substance 1, a plurality of learning data are selected from a set of learning data prepared in advance for calculating the mechanical properties of the substance, and a calculation model is generated. In order to accurately measure the mechanical properties, it is necessary to select appropriate learning data based on physical quantities to generate an accurate calculation model. Therefore, it is preferable to pay appropriate attention to the collection of the set of learning data that forms the basis of the calculation model. The measuring system consisting of the measuring device 100 and the physical quantity measuring unit 5 collects learning data as follows, for example.

[0063] Figure 5 This is a flowchart illustrating the collection and processing of learning data. The control unit 8 sets the position of the object 101 to be measured, i.e., the evaluation location (step S1).

[0064] The control unit 8 causes the physical quantity measuring unit 5 to measure the physical quantity at the set evaluation location (step S2). Here, in the learning data, the physical quantity of the object 101 being measured is an explanatory variable.

[0065] The control unit 8 performs a pre-processing step (S3). Here, the pre-processing may, for example, remove the film 2 from the object to be measured 101 to measure the mechanical properties at the evaluation site. For example, if the object to be measured 101 is steel with an oxide scale on its surface, the oxide scale can be removed by etching or grinding. Alternatively, the pre-processing may include cutting the object to be measured 101 at the evaluation site to expose the cross-section of the material 1.

[0066] The control unit 8 measures the mechanical properties at the evaluation site (step S4). The learning data includes mechanical properties as the target variable. The mechanical property can be, for example, the hardness of the cross-section of the steel at the evaluation site. The mechanical property can be obtained by converting the Leeb hardness of the steel surface obtained using a spring-loaded hardness tester into the cross-sectional hardness using a conversion formula derived from past tests. In addition, for more accurate conversion, the converted value can be standardized for the thickness of the steel. That is, a process can be performed to convert the value to the thickness of the steel used as a reference. The thickness of the steel used as a reference is, for example, 28 mm. In addition, if the above-mentioned preprocessing involves cutting the object 101 to be measured at the evaluation site, the mechanical property can also be the Vickers hardness of the cut surface directly measured. The control unit 8 acquires the measured mechanical properties. The control unit 8 establishes a correlation between the management number of the substance 1 and the data tags, explanatory variables, and target variables of the evaluation site, etc., and stores it as learning data in the storage unit 10.

[0067] Figure 6This diagram illustrates an example of a learning data set 110 stored in the storage unit 10. The learning data set 110 may also include, for example, a data number serving as an identification number for the learning data and a plate number serving as an identification number for the steel, acting as a management number for the data tag. Furthermore, if the surface of the steel is defined with an X-axis and a Y-axis orthogonal to the origin, the learning data set 110 may also include the distance from the origin in the X-axis direction and the distance from the origin in the Y-axis direction as evaluation locations for the data tag. The learning data set 110 includes measured mechanical characteristics as target variables. The learning data set 110 includes physical quantities of the measured object 101 measured by the physical quantity measurement unit 5 as explanatory variables. Here, the physical quantity can be distinguished by the content measured using a first measurement signal and the content measured using a second measurement signal. Alternatively, if the physical quantity is an electromagnetic characteristic quantity, the first measurement signal may be an AC signal with a first frequency, and the second measurement signal may be an AC signal with a second frequency higher than the first frequency. In other words, it could also mean that the first measured signal is a low-frequency signal and the second measured signal is a high-frequency signal.

[0068] If the control unit 8 determines that not enough learning data for model generation has been collected (No in step S5), it returns to the processing of step S1 to collect more learning data.

[0069] If the control unit 8 determines that enough learning data for model generation has been collected and the collection is complete (as in step S5), it terminates a series of processes.

[0070] Here, the learning data set 110 stored in the storage unit 10 by the control unit 8, i.e., a collection of multiple learning data, may include target variables obtained through different measurement methods. In the example above, the learning data set 110 may include target variables obtained through at least two methods, such as directly measuring the Vickers hardness of the cut surface, converting the Leeb hardness of the steel surface into the hardness of the cross section, and further standardizing the converted value for the thickness of the steel. For example, the Vickers hardness may be correct, but measurement takes time in order to cut the steel. Here, by allowing the mixed existence of target variables obtained using different measurement methods, it is possible to generate a correct learning data set 110 within a realistic timeframe.

[0071] (Methods for measuring mechanical properties)

[0072] The mechanical property measuring device 100 according to this embodiment calculates the mechanical properties of the material 1 based on the physical quantities of the object to be measured 101 measured by the physical quantity measuring unit 5. For example, the object to be measured 101 is steel with an oxide scale. For example, the material 1 is steel. For example, the film 2 on the surface of the material 1 is an oxide scale. For example, the physical quantities include electromagnetic characteristic quantities. For example, the mechanical property of the material 1 is the hardness of the steel. For example, the sensor 3 is... Figure 2 and Figure 3 The magnetic sensor shown. A computational model is used in the calculation of the mechanical properties of substance 1. Generating an appropriate computational model is important for accurately measuring the mechanical properties. The mechanical property measuring device 100 according to this embodiment calculates the mechanical properties of substance 1 as follows. Figure 7 It is a flowchart representing the measurement method of mechanical properties.

[0073] The control unit 8 causes the physical quantity measuring unit 5 to measure the physical quantity of the object to be measured 101 (measurement step, step S11). At this time, the mechanical properties of the substance 1 (especially the surface layer) are measured, and the physical quantity is measured from the surface of the film 2 where the substance 1 exists. In other words, in this measurement method, the substance 1, which is steel, and the oxide scale, which is film 2, are both used as the object to be measured to measure the physical quantity. This is the same whether the substance 1 is other than steel or the film 2 is other than oxide scale. Specifically, the sensor 3 of the physical quantity measuring unit 5 is disposed on the surface of film 2. The measurement result of the sensor 3 indicates the physical quantity including the influence of film 2, that is, the physical quantity in a state where not only substance 1 but also film 2 exists. The scanning unit 6 causes the sensor 3 to move relative to the object to be measured 101. As a result, the sensor 3 applies an alternating magnetic field to the evaluation area of ​​the object to be measured 101 designated by the physical quantity measuring control unit 83. Sensor 3 measures the changes in electromagnetic characteristic quantities caused by eddy currents induced in the object to be measured 101 by the alternating magnetic field. Physical quantity measurement unit 5 sends the measured electromagnetic characteristic quantities as multiple physical quantities to control unit 8.

[0074] The control unit 8 selects multiple learning data from the learning data group 110 based on the physical quantity used as a selection criterion among at least two of the acquired physical quantities (selection step, step S12). Here, the control unit 8 selects content from the learning data that is close to the acquired physical quantity used for selection from the learning data that constitutes the learning data group 110 stored in the storage unit 10. Figure 8 This is a graph used to illustrate the relationship between the selected physical quantities and the learning data set 110. Figure 8 The black circles represent the learning data that makes up learning data group 110. Additionally, Figure 8The white circle represents the selected physical quantity. It is possible to create a local region centered on the selected physical quantity, targeting the first and second physical quantities of the selected physical quantity separately. The control unit 8 can select multiple learning data points contained within the local region.

[0075] Control unit 8 generates a computational model, which is used to calculate the mechanical properties of substance 1 based on multiple selected learning data (generation step, step S13). The computational model can be prepared as a linear regression model or a nonlinear regression model that links the explanatory variables of the learning data with the target variable. As a linear regression model, methods such as a general linear model or a general linear mixed model can be used. Here, from the viewpoint of improving computational accuracy, it is preferable that the computational model is generated using a local linear regression method in a way that matches the processing in step S12. Here, it is preferable that, in the generation of the computational model, the multiple learning data selected through the processing in step S12 are weighted according to their distance from the selected physical quantity. That is, it is preferable that the closer the data is to the selected physical quantity, the greater the weighting.

[0076] The control unit 8 calculates the mechanical properties of substance 1 based on the generated calculation model (calculation step, step S14). The control unit 8 uses the generated calculation model and at least two required physical quantities to calculate the mechanical properties of substance 1.

[0077] Here, the mechanical property of substance 1 can be, for example, the hardness of the cross-section of the steel at the evaluation location. The mechanical property can also be calculated by converting the Leeb hardness of the steel surface obtained using a spring-loaded hardness tester into a cross-sectional hardness value, for example, using a conversion formula derived from past tests. Furthermore, for a more accurate conversion, the calculated value can be standardized for the thickness of the steel. That is, a conversion to a value at the thickness of the steel used as a reference can be performed. The thickness of the steel used as a reference is, for example, 28 mm. Additionally, if the aforementioned preprocessing involves cutting the object 101 at the evaluation location, the mechanical property can also be the Vickers hardness of the cut surface directly measured.

[0078] The control unit 8 outputs the calculated mechanical properties of substance 1 to the display unit 11 (output step, step S15), ending a series of processes. The user can identify the mechanical properties of substance 1 displayed on the display unit 11. Based on the displayed mechanical properties of substance 1, the user can perform quality management of substance 1 or issue instructions to change the manufacturing parameters of substance 1.

[0079] As described above, the mechanical property measuring device 100 and the mechanical property measuring method performed by the measuring device 100 according to this embodiment can accurately measure mechanical properties via physical quantities using the aforementioned structure. In particular, when the membrane 2 has characteristics different from the material 1 for the multiple physical quantities being measured, a more suitable calculation model can be generated by the calculation model generation unit 81 or the selection and generation steps (steps S12 and S13), thus achieving the aforementioned effects to a greater extent. Furthermore, even when measuring the mechanical characteristics of the surface layer of the material 1, a more suitable calculation model can be generated by the calculation model generation unit 81 or the selection and generation steps (steps S12 and S13), thus achieving the aforementioned effects to a greater extent. Moreover, the aforementioned effects are also achieved in the case of the second embodiment described later.

[0080] (Example)

[0081] The effects of this disclosure will be specifically described below based on the embodiments, but this disclosure is not limited to these embodiments.

[0082] (First Embodiment)

[0083] In the first embodiment, the measuring device 100 is a device for measuring the surface hardness of steel. In this embodiment, substance 1 is steel. Film 2 is oxide scale formed on the surface of the steel. Sensor 3 is an electromagnetic sensor. The physical quantity of the object to be measured 101 is the electromagnetic characteristic quantity of the steel with oxide scale. In this embodiment, the mechanical property to be measured is the hardness of the cross-section of the steel at a depth of 0.25 mm.

[0084] Steel is manufactured by rough rolling continuously cast slabs followed by continuous online quenching via cooling. For data collection, the hardness of a section at a depth of 0.25 mm was measured on the steel produced using this process.

[0085] In this embodiment, an electromagnetic sensor capable of measuring electromagnetic characteristic quantities is configured in the measuring device 100 to measure the electromagnetic characteristic quantities of the surface layer of steel with oxide scale formed on its surface. Here, a manually operated trolley is used as the scanning unit 6. Eight electromagnetic sensors are arranged on the trolley. The eight electromagnetic sensors scan the entire surface of the steel.

[0086] A voltage is applied to the electromagnetic sensor, consisting of a sine wave with a first frequency superimposed with a sine wave with a second frequency higher than the first frequency. Here, the first frequency is set to 150Hz or less. The second frequency is set to 1kHz or more. Various electromagnetic characteristic quantities are extracted from the current waveform observed by the electromagnetic sensor. In this embodiment, 20 characteristic quantities are extracted as electromagnetic characteristic quantities, including current waveform distortion, amplitude and phase changes, amplitude and phase changes of higher harmonics, maximum, minimum, and average values ​​of incremental permeability, and magnetic retention force. Of these 20 characteristic quantities, 4 are physical quantities measured using a low-frequency signal meter, and 16 are physical quantities measured using a high-frequency signal meter. Here, the frequency of the applied sine wave is set to 150Hz or less so that the alternating magnetic field excited by the electromagnetic sensor penetrates approximately 300μm from the surface of the steel. In addition, incremental permeability is a value that represents the ease with which a magnetic field is applied, and it is represented by the gradient of a minor loop in the magnetization curve that represents the relationship between magnetic flux density and magnetic field.

[0087] Collect a sufficient amount of learning data and store the learning data set 110 in the storage unit 10. The sufficient amount of learning data is, for example, 100.

[0088] To calculate the surface hardness of steel, the measuring device 100 measures electromagnetic characteristic quantities using the physical quantity measuring unit 5. The selected physical quantities among the electromagnetic characteristic quantities are set to include at least one physical quantity measured using a low-frequency signal and at least one physical quantity measured using a high-frequency signal. Specifically, the selected physical quantities are set to include at least a characteristic related to the phase change of the current waveform using a low-frequency signal and a characteristic related to the incremental permeability using a high-frequency signal. Based on the selected physical quantities, the control unit 8 selects multiple learning data sets from the learning data group 110 in the storage unit 10. Using the selected multiple learning data sets, the control unit 8 generates a calculation model using a local linear regression method. Furthermore, the control unit 8 uses the generated calculation model to calculate the hardness.

[0089] Figure 9 This graph compares the hardness calculated in this embodiment with the measured value obtained by a hardness tester. The horizontal axis represents the actual surface hardness, which is the measured value obtained by cutting out the specimen and investigating it using a spring-loaded hardness tester. The vertical axis represents the predicted hardness, which is the hardness of the steel obtained in this embodiment, calculated using the generated calculation model. Here, hardness H0 and H1 are the lower and upper limits of the measured hardness, respectively. Figure 9As shown, the predicted hardness is almost identical to the actual surface hardness, and the measurement can be performed with an accuracy of approximately 9 Hv standard deviation. Therefore, it can be considered that the hardness calculated by the above method has the same level of accuracy as the hardness test. In addition, in the display unit 11, the hardness calculated in this embodiment is represented by light and dark colors. When mapped to the evaluation area of ​​the steel, the uniformity of the hardness of the steel surface can be visually confirmed.

[0090] in addition, Figure 10 This is a graph comparing the hardness calculated in a comparative example different from this embodiment with the measured value obtained by a hardness tester. Reference numerals are the same as... Figure 9 The same. In the comparative example, the physical quantities were selected to include only those measured using low-frequency signals, and a computational model was generated. For example... Figure 10 As shown, the predicted hardness is almost identical to the actual surface hardness, but... Figure 9 In comparison, there are sometimes discrepancies with the actual surface hardness. The accuracy is approximately 14 Hv standard deviation. Therefore, the physical quantities selected can include only those measured using low-frequency signals, but it has been confirmed that by selecting both physical quantities measured using low-frequency and high-frequency signals, a more accurate calculation model is generated.

[0091] (Second Embodiment)

[0092] As a second embodiment, an example is shown where, in a method for manufacturing thick steel plates, a mechanical property measurement method performed by a measuring device 100 is used to check the surface hardness. A specific manufacturing method is shown as an example... Figure 12 As shown. Figure 12 The manufacturing method of the thick steel plate 43 shown includes a rough rolling process S41, a finish rolling process S42, a cooling process S43, a surface hardness test process S45, a surface hardness retest process S46, and a removal process S47. A demagnetizing process S44 may be added as needed. If this step is added, the processes are performed in the following order: starting from the cooling process S43, followed by the demagnetizing process S44 and the surface hardness test process S45.

[0093] In the roughing rolling process S41, the steel sheet 41 is hot-rolled at a temperature of, for example, 1000°C or higher. In the subsequent finishing rolling process S42, it is hot-finished at a temperature of 850°C or higher, transforming the steel sheet 41 into a thick steel plate 42. The thick steel plate 42 is then cooled in the subsequent cooling process S43. Here, in the cooling process S43, cooling begins, for example, at a temperature of 800°C or higher for the thick steel plate, and continues until the temperature of the thick steel plate reaches approximately 450°C at the end of cooling.

[0094] In the surface hardness test step S45, the mechanical properties of the surface layer are measured on the entire surface of the cooled thick steel plate 42 using the testing method performed by the testing device 100. Then, based on the test results, the areas that are harder than the preset surface hardness are identified as solidified areas.

[0095] Here, when a steel plate is lifted using magnetic force, such as with a magnetic crane, a residual magnetic field remains in the portion attracted by the crane's magnet. When measuring mechanical properties by measuring electromagnetic characteristics, the accuracy of the measurement or evaluation of mechanical properties decreases if a residual magnetic field exists at least on the surface. Therefore, in cases where a process generates a residual magnetic field, it is preferable to add a demagnetizing step S44 before the surface hardness measurement step S45, thereby demagnetizing the residual magnetic field. In this case, the demagnetizing device uses a distance attenuation method to demagnetize the residual magnetic field on the surface to below 0.5 mT.

[0096] In the retesting step S46, the surface hardness of the cured portion detected in the surface hardness testing step S45 is retested. Here, the testing method performed by the testing device 100 retests the mechanical properties of the surface only relative to the cured portion including the surrounding area. Furthermore, if the surface hardness of the cured portion retested exceeds the aforementioned threshold and a determination is made again, the cured portion is determined to have a locally harder area, and the thick steel plate 42 is conveyed to the removal step S47.

[0097] Furthermore, in the removal process S47, the areas determined to be solidified through the remeasurement process S46 are removed. Specifically, the areas determined to be solidified are removed by grinding using a known grinding unit such as a grinding machine. After this removal process S47, the manufacturing of the thick steel plate 42 to the thick steel plate 43 is completed, and the thick steel plate 43 is transported to other processes (shipment process to the customer, steel pipe manufacturing process, etc.). In addition, it is preferable to use a known or existing thickness gauge to measure the wall thickness of the thick steel plate 42 at the grinding location for the areas that have been ground in the removal process S47 of the thick steel plate 42, to confirm whether it falls within the dimensional tolerances preset during steel plate manufacturing. Furthermore, it is preferable to measure the surface hardness of the solidified areas again using a known contact hardness tester after removing the solidified areas. Based on the measurement results, it is confirmed that the surface hardness is below the preset limit. If this can be confirmed, the manufacturing of the thick steel plate 42 to the thick steel plate 43 is completed.

[0098] On the other hand, if it is determined that there is no solidified part in the surface hardness test process S45 or that it is not a solidified part in the retest process S46, the manufacturing of the thick steel plate 43 is completed without going through the removal process S47, and the thick steel plate 43 is transported to other processes (shipment process to the customer, steel pipe manufacturing process, etc.).

[0099] Furthermore, the manufacturing method of the thick steel plate in this embodiment may also include an annealing process S48 (not shown) after the previous cooling process S43 and before the surface hardness testing process S45. In particular, when the manufactured thick steel plate 43 has a surface hardness (more specifically, the Vickers hardness measured from the top surface according to the ASTM A 956 / A956MA Standard Test Method for Leeb Hardness Testing of Steel Products) of 230 Hv or higher, and the thick steel plate 43 is prone to bending, it is preferable to perform the surface hardness testing process S45 after the cooling process S43, following the annealing process S48. By performing the annealing process S48, softening of the structure based on tempering can be expected. Softening of the structure inhibits the formation of solidified portions; therefore, as a result, a reduction in the removal area can be expected.

[0100] As described above, in the surface hardness test step S45, to confirm the hardness, the hardness is measured on the surface after the oxide scale has been removed, starting from the top surface, according to ASTM A 956 / A 956MA Standard Test Method for Leeb hardness Testing of Steel products. Here, in the rebound hardness test, the thickness of the object being tested affects the measured value. Therefore, a relationship is pre-established based on the Vickers hardness at a thickness investigation depth of 0.25 mm and the hardness value of the surface rebound hardness test. The hardness value determined as the solidified part can also be adjusted and determined based on the pre-established relationship to take into account the effect of thickness, using the cross-sectional hardness at 0.25 mm as a reference. In this example, the depth used as the reference is 0.25 mm, but the depth used as the reference is not limited.

[0101] Furthermore, in this embodiment, a method for removing the hardened portion determined by the surface hardness test step S45 at the surface of the thick steel plate 42 was described using a known grinding unit, but this is not a limitation of the present invention. If a method can remove the hardened portion, then known methods other than grinding (e.g., heat treatment) can also be used to remove it.

[0102] As in this embodiment, when the mechanical property measurement method performed by the measuring device 100 is used in the manufacturing method of the thick steel plate 43, the mechanical properties can be accurately measured by physical quantities, and therefore, the thick steel plate 43 as a high-quality material 1 can be provided. More specifically, a thick steel plate 43 with suppressed curing can be manufactured from the thick steel plate 42.

[0103] (Second Implementation)

[0104] Figure 11 This is a block diagram of a mechanical characteristic measuring device 100 according to the second embodiment of this disclosure. In the first embodiment, the learning data set 110 is stored in the storage unit 10 of the measuring device 100. In this embodiment, the learning data set 110 is stored in a database 12 located outside the measuring device 100. The control unit 8 can access the database 12 via the communication unit 7. In this embodiment, the control unit 8 stores the learning data set 110 in the database 12 via the communication unit 7. Furthermore, the control unit 8 retrieves the learning data set 110 from the database 12 via the communication unit 7. The other structures of the measuring device 100 are the same as in the first embodiment.

[0105] According to the mechanical property measuring device 100, the manufacturing equipment for the substance 1 equipped with the measuring device 100, the mechanical property measuring method performed by the measuring device 100, the management method for the substance 1 using the measuring method, and the manufacturing method, mechanical properties can be accurately measured via physical quantities, just like in the first embodiment. Furthermore, since the learning data set 110 is stored in a database 12 located outside the measuring device 100, it is possible to process learning data sets 110 exceeding the storage capacity of the internal storage unit 10.

[0106] Here, the communication method of the communication unit 7 can be a short-range wireless communication standard or a wireless communication standard connecting to a mobile phone network, and can be a wired communication standard. Short-range wireless communication standards may include, for example, WiFi (registered trademark), Bluetooth (registered trademark), infrared, and NFC (Near Field Communication). Wireless communication standards connecting to a mobile phone network may include, for example, LTE (Long Term Evolution) or 4th generation or later mobile communication systems. Furthermore, the communication method used in the communication between the communication unit 7 and the physical quantity measurement unit 5 may be, for example, a communication standard such as LPWA (Low Power Wide Area) or LPWAN (Low Power Wide Area Network).

[0107] This disclosure has been described based on the accompanying drawings and embodiments, but it should be noted that those skilled in the art can readily make various modifications and alterations based on this disclosure. Therefore, it should be understood that such modifications and alterations are included within the scope of this disclosure. For example, the functions contained in various methods, steps, etc., can be reconfigured logically without contradiction, and multiple methods and steps can be combined into one or divided.

[0108] The structures of the measuring device 100 and the physical quantity measuring unit 5 described in the above embodiments are illustrative and may not include all of the constituent elements. For example, the measuring device 100 may not include the display unit 11. Furthermore, the measuring device 100 and the physical quantity measuring unit 5 may include other constituent elements. For example, the physical quantity measuring unit 5 may be physically separated from the control unit 8 and the storage unit 10. In this case, the physical quantity measuring unit 5 can be electrically connected to the control unit 8 of the measuring device 100; this connection can be wired or wireless. Additionally, this connection may utilize known technology.

[0109] For example, this disclosure can also be implemented as a program describing the processing content that implements the various functions of the measuring device 100, or as a storage medium containing the program. It should be understood that the scope of this disclosure also includes these.

[0110] For example, the measuring device 100 according to the above-described embodiment uses Figure 1 The present invention has described a case where the measuring device 100 collects the learning data set 110, but the present invention is not limited thereto. Other physical measuring devices may also be used to collect the physical quantities of the object to be measured 101.

[0111] For example, the measurement device 100 described in the above embodiment is shown as an example of creating a computational model, but these can also be created by other information processing devices. In this case, such an information processing device acquires the learning data set 110 and creates a computational model. Furthermore, the information processing device transmits the created computational model to the measurement device 100. In other words, the computational model created by another device is installed in the control unit 8 of the measurement device 100 and used as part of the measurement device 100.

[0112] For example, in the above embodiment, an example of the sensor 3 scanning by the scanning unit 6 is shown, but the position of the sensor 3 can also be fixed. Even when the position of the sensor 3 is fixed, the scanning unit 6 can still move the object to be measured 101. Furthermore, the scanning unit 6 is described above as a manually operated trolley, but it can also be a trolley equipped with a mechanical drive device. Additionally, it can be a scanning unit 6 controlled by a control unit different from the control unit 8 of the measuring device 100 and capable of scanning. In particular, when installed within a manufacturing apparatus for the material 1, it is preferable to use one or more of a known scanning device, a new scanning device, a known scanning method, a new scanning method, a known control device, a new control device, a known control method, or a new control method to install the physical quantity measuring unit 5 according to the present invention. Furthermore, the control unit of the scanning unit 6 can also automatically scan in cooperation with the control unit (not shown) of other manufacturing equipment. Alternatively, it can also automatically scan using the control unit 8 of the mechanically-based measuring device 100. In this case, the scanning unit 6, the control unit of the scanning unit, the control unit of the manufacturing equipment, and the control unit 8 of the measuring device 100 can be electrically connected. Their connection can be wired or wireless. In addition, this connection can also utilize known or new technologies.

[0113] For example, in the above-described embodiment, the user's judgment can be input based on the mechanical properties of the displayed substance 1. The user can also input a judgment, such as whether it is superior or inferior, by touching the touchscreen with their finger on the display unit 11. The control unit 8 can also perform controls, such as deciding whether to perform the grinding process, based on the user's judgment. Furthermore, as another example, to improve the efficiency of the management process of substance 1, the control unit 8 can perform the judgment of the quality of substance 1 based on a set threshold, instead of the user.

[0114] Furthermore, in the above embodiments, examples of using steel as substance 1, electromagnetic characteristic quantities as physical quantities, and hardness as mechanical characteristics are described, but other combinations are also possible. For example, even if the physical quantity is temperature, the effects of the present invention are still achieved. For example, even if substance 1 is a metal or a compound, the effects of the present invention are still achieved. In particular, when the film 2 on the surface of the metal or compound has characteristics different from those of the metal or compound relative to the measured multiple physical quantities, a greater effect can be obtained. Here, examples of metals include iron, steel, nickel, cobalt, aluminum, titanium, or alloys containing any one or more of these metals. On the other hand, examples of compounds include inorganic compounds, organic compounds, or compounds containing any one or more of iron, steel, nickel, cobalt, aluminum, or titanium. Among these, if substance 1 is iron, steel, nickel, cobalt, an alloy containing any one or more of these metals, or a compound containing any one or more of these metals, the effects of the present invention can be more clearly obtained when using electromagnetic characteristic quantities as multiple physical quantities. In particular, when material 1 is steel, the mechanical properties are determined by the proportion of alloying elements contained in the steel, the quenching treatment, and the annealing treatment method. Therefore, as a physical quantity to be measured, at least one of the surface temperatures before and after quenching and annealing treatments can also be used.

[0115] (Application Example)

[0116] The mechanical characteristic measuring device 100 configured as described above and the mechanical characteristic measuring method performed by the measuring device 100 can be applied, for example, in the following equipment or situations.

[0117] Alternatively, the present invention can be used as part of an inspection apparatus for a manufacturing device constituting substance 1. That is, the surface of substance 1, manufactured by known, new, or existing manufacturing equipment, along with the film 2 on the surface of substance 1, can be measured using the mechanical property measuring device 100 according to the present invention. Furthermore, the inspection apparatus can inspect the mechanical properties of substance 1 based on the measurement results and, for example, pre-set mechanical properties. In other words, the mechanical property measuring device 100 according to the present invention measures substance 1 manufactured by the manufacturing equipment. Additionally, an inspection apparatus equipped with the mechanical property measuring device 100 according to the present invention can, for example, use pre-set mechanical properties to inspect substance 1 manufactured by the manufacturing equipment.

[0118] Alternatively, the present invention can be used as part of an inspection step included in a method for manufacturing substance 1. Specifically, the inspection step can be used to inspect substance 1 manufactured in a known, new, or existing manufacturing process while maintaining a film 2 on the surface of substance 1. Here, the inspection step includes the aforementioned measurement step, selection step, generation step, and calculation step as described in the present invention, using substance 1 with the film 2 on its surface as the measurement object 101, and calculating the mechanical properties of substance 1. Alternatively, the inspection step can use a mechanical property measuring device 100 as described in the present invention, using substance 1 with the film 2 on its surface as the measurement object 101, to calculate the mechanical properties of substance 1. More preferably, if the mechanical properties of substance 1 calculated by the calculation step or the measuring device 100 are not within a reference range, a condition-changing step can be included in the manufacturing method to change the manufacturing conditions of the manufacturing step in a manner that includes the reference range. Here, the reference range can also be a standard range of mechanical properties obtained statistically from previously manufactured substances 1. Manufacturing conditions are parameters that can be adjusted during the manufacturing process of substance 1. The manufacturing conditions can be selected, for example, the heating temperature, heating time, or cooling time of substance 1.

[0119] Based on the manufacturing equipment and method of substance 1, the mechanical properties can be accurately measured by physical quantities, thus enabling the manufacture of substance 1 with a high yield rate. Here, when the mechanical properties of substance 1 obtained by the mechanical property measuring device 100 or the calculation step are only the surface mechanical properties of substance 1, a more suitable calculation model can be generated by the calculation model generation unit 81 or by the selection step and the generation step (steps S12 and S13), thus achieving the aforementioned effects even more effectively.

[0120] Here, as an example of the manufacturing equipment for substance 1, the following can be cited. That is, a steel plate manufacturing equipment includes: a rolling mill that rolls steel sheets to obtain a steel plate; an inspection device equipped with a measuring device for mechanical properties according to the present invention, which measures the surface hardness of the steel plate by means of the measuring device, and determines the portion of the surface of the steel plate that is harder than a predetermined surface hardness as a solidified portion based on the measured surface hardness of the steel plate; and a removal device that removes the determined solidified portion from the surface of the steel plate.

[0121] Furthermore, if the aforementioned manufacturing equipment is equipped with a demagnetizing device between the rolling equipment and the inspection equipment to demagnetize the surface or the entire steel plate as needed, it can prevent a decrease in the accuracy of the measurement or evaluation of mechanical properties, and is therefore more preferred.

[0122] Furthermore, as an example of a method for manufacturing substance 1, the following can be cited. That is, a method for manufacturing a steel plate includes: a rolling step in which a steel sheet is rolled to obtain a steel plate; an inspection step in which the surface hardness of the steel plate is measured using a mechanical property measurement method according to the present invention, and based on the measured surface hardness of the steel plate, portions of the surface of the steel plate that are harder than a predetermined surface hardness are identified as solidified portions; and a removal step in which the identified solidified portions in the surface of the steel plate are removed.

[0123] Furthermore, if the manufacturing method includes a demagnetization step between the rolling step and the inspection step to demagnetize the surface or the entire steel plate, it can prevent a decrease in the accuracy of the measurement or evaluation of mechanical properties, and is therefore more preferred.

[0124] In the above-described steel plate manufacturing method, a rolling step is performed at 850°C or above in order to obtain a predetermined shape and mechanical properties in a continuous steel sheet. Alternatively, after the rolling step, quenching and annealing can be performed as heat treatment steps. It is known that electromagnetic characteristic quantities such as incremental permeability, magnetic retention force, and Barkhausen noise are related to the mechanical properties of steel. Therefore, in a state where the microstructure of the steel is determined by the above-described heat treatment steps, it is preferable to measure electromagnetic characteristic quantities as physical quantities of the object to be measured 101. At this time, the object to be measured 101 refers to the steel plate and the film on the surface of the steel plate. In addition, examples of films on the surface of the steel plate include iron oxide films such as oxide scale and black scale, organic films such as resin coatings, plating films, or chemically treated films. Furthermore, since the mechanical properties are determined during quenching and annealing, the physical quantities of the object to be measured 101 in the manufacturing method can also be further measured by measuring the temperature before and after quenching or the temperature before and after annealing.

[0125] Furthermore, the present invention can also be applied to a management method for substance 1, and substance 1 can be inspected, thereby managing substance 1. Specifically, for a pre-prepared substance 1 with a film 2 on its surface, inspection is performed in an inspection step, and management is carried out in a management step that classifies substance 1 based on the inspection results obtained through the inspection step. Here, the inspection step includes the aforementioned measurement step, selection step, generation step, and calculation step involved in the present invention, and calculates the mechanical properties of substance 1 using the pre-prepared substance 1 with a film 2 on its surface as the measurement object 101. Alternatively, the inspection step uses a mechanical property measuring device involved in the present invention to calculate the mechanical properties of substance 1 using substance 1 with a film 2 on its surface as the measurement object 101. In the subsequent management step, substance 1 can be managed. In the management step, based on the mechanical properties of substance 1 obtained through the calculation step or the mechanical property measuring device 100, the manufactured substance 1 is classified according to a pre-specified standard, thereby managing substance 1. For example, if substance 1 is steel and its mechanical property is the hardness of steel, the steel can be classified according to the grade corresponding to the hardness. Based on this management method for substance 1, the mechanical properties can be accurately measured via physical quantities, thus providing high-quality substance 1. Here, if the mechanical properties of substance 1 obtained by the mechanical property measuring device 100 or the calculation step are only the surface mechanical properties of substance 1, a more suitable calculation model can be generated by the calculation model generation unit 81 or by the selection step and the generation step (steps S12 and S13), thus achieving the aforementioned effects to a greater extent.

[0126] Furthermore, as an example of a management method for substance 1, the following can be cited. Specifically, a method for manufacturing a steel plate includes: an inspection step in which the surface hardness of the steel plate is measured using a mechanical property measurement method according to the present invention, and based on the measured surface hardness of the steel plate, portions of the surface of the steel plate that are harder than a predetermined surface hardness are identified as solidified portions; and a management step in which the steel plate is classified according to the area and / or location of the identified solidified portions on the surface of the steel plate.

[0127] Explanation of reference numerals in the attached figures

[0128] 1...Material; 2...Membrane; 3...Sensor; 5...Physical quantity measurement unit; 6...Scanning unit; 7...Communication unit; 8...Control unit; 10...Storage unit; 11...Display unit; 12...Database; 31...Excitation coil; 32...Magnetic yoke; 41...Steel sheet; 42...Thick steel plate; 43...Thick steel plate (without curing unit); 81...Computational model generation unit; 82...Mechanical property calculation unit; 83...Physical quantity measurement and control unit; 100...Measuring device; 101...Measured object; 110...Learning data set.

Claims

1. A measuring device for mechanical properties, characterized in that, have: The physical quantity measuring unit measures multiple physical quantities of a measuring object having a substance and a film on the surface of the substance; A computational model generation unit selects multiple learning data points from a learning data set based on a selection physical quantity, which is at least two of the measured multiple physical quantities, and generates a computational model for calculating the mechanical properties of the substance based on the selected multiple learning data points; and The mechanical property calculation unit uses the generated calculation model and at least two of the plurality of physical quantities to calculate the mechanical properties of the substance. The selected physical quantities include at least one physical quantity measured using a first measuring signal meter and at least one physical quantity measured using a second measuring signal meter.

2. The measuring device for mechanical properties according to claim 1, characterized in that, The plurality of physical quantities are physical quantities that are related to the mechanical properties of the substance.

3. The measuring device for mechanical properties according to claim 1 or 2, characterized in that, The plurality of physical quantities includes at least one of temperature, mass, and electromagnetic characteristic quantities.

4. The measuring device for mechanical properties according to any one of claims 1 to 3, characterized in that, The measuring device for the mechanical properties is based on metallic materials or compounds.

5. The measuring device for mechanical properties according to any one of claims 1 to 4, characterized in that, The aforementioned physical quantities are electromagnetic characteristic quantities. The first measurement signal is an AC signal with a first frequency. The second measurement signal is an AC signal with a second frequency that is higher than the first frequency.

6. The measuring device for mechanical properties according to claim 5, characterized in that, The electromagnetic characteristic quantity measured using the first measurement signal includes properties related to the phase change of the current waveform. The electromagnetic characteristic quantities measured using the second measuring signal include properties related to incremental permeability.

7. A method for measuring mechanical properties, characterized in that, include: The measurement step involves measuring multiple physical quantities of a measurement object having a substance and a film on the surface of the substance. In the selection step, multiple learning data are selected from the learning data set based on the physical quantity used as a selection criterion for at least two of the multiple measured physical quantities. A generation step, in which a computational model is generated, the computational model being used to calculate the mechanical properties of the substance based on the selected plurality of learning data; as well as The calculation step involves using the generated calculation model and at least two of the plurality of physical quantities to calculate the mechanical properties of the substance. The selected physical quantities include at least one physical quantity measured using a first measuring signal meter and at least one physical quantity measured using a second measuring signal meter.

8. A manufacturing apparatus for a substance, characterized in that, have: Manufacturing equipment, the materials it manufactures; and Measuring devices for mechanical properties; The measuring device for the mechanical properties includes: The physical quantity measuring unit measures multiple physical quantities of a measuring object having a substance and a film on the surface of the substance; A computational model generation unit selects multiple learning data points from a learning data set based on a selection physical quantity, which is at least two of the measured multiple physical quantities, and generates a computational model for calculating the mechanical properties of the substance based on the selected multiple learning data points; and The mechanical property calculation unit uses the generated calculation model and at least two of the plurality of physical quantities to calculate the mechanical properties of the substance. The selected physical quantities include at least one physical quantity measured using a first measuring signal and at least one physical quantity measured using a second measuring signal. The measuring device measures the mechanical properties of the material manufactured by the manufacturing equipment.

9. A method for managing materials, characterized in that, have: The measurement step involves measuring multiple physical quantities of a measurement object having a substance and a film on the surface of the substance. In the selection step, multiple learning data are selected from the learning data set based on at least two of the measured multiple physical quantities, i.e., based on the selection physical quantity including at least one physical quantity measured using a first measurement signal and at least one physical quantity measured using a second measurement signal. A generation step, in which a computational model is generated, the computational model being used to calculate the mechanical properties of the substance based on the selected plurality of learning data; The calculation step involves using the generated calculation model and at least two of the plurality of physical quantities to calculate the mechanical properties of the substance. as well as The management step involves classifying the substance based on its calculated mechanical properties.

10. A method for manufacturing a substance, characterized in that, have: Manufacturing steps, in which substances are manufactured; The measurement step involves using the manufactured substance and the film on the surface of the substance as the measurement objects, and measuring multiple physical quantities of the measurement objects. In the selection step, multiple learning data are selected from the learning data set based on the physical quantity used as a selection criterion for at least two of the multiple measured physical quantities. A generation step, in which a computational model is generated, the computational model being used to calculate the mechanical properties of the substance based on the selected plurality of learning data; as well as The calculation step involves using the generated calculation model and at least two of the plurality of physical quantities to calculate the mechanical properties of the substance. The physical quantities used for selection in the selection step include at least one physical quantity measured using a first measuring signal meter and at least one physical quantity measured using a second measuring signal meter.

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