Methods for analyzing steel materials, methods for creating strength prediction models for steel materials, programs, and analytical devices for steel materials.

Abstract

A method for analyzing steel materials that can automatically analyze steel materials, a method for creating a strength prediction model for steel materials, a program, and a steel material analysis device are provided. [Solution] The method for analyzing steel includes: a first acquisition step (S1) of acquiring the average grain size, strength and manufacturing conditions of the steel's microstructure; a calculation step (S2) of calculating the Hall-Petch parameters of the steel by inputting the acquired average grain size, strength and manufacturing conditions into a microstructure determination model with Hall-Petch parameters as the objective variable; and a determination step (S3) of determining whether the steel contains at least one of the martensite phase and the bainite phase based on the calculated Hall-Petch parameters of the steel.

Description

Technical Field

[0001] The present disclosure relates to a method for analyzing steel materials, a method for creating a strength prediction model of steel materials, a program, and an apparatus for analyzing steel materials.

Background Art

[0002] In the development of steel materials, at least the strength, which is a basic property of the steel material, is evaluated for the produced steel material. When the desired strength is obtained, various analyses are performed to identify the factors contributing to the manifestation of strength, and the manufacturing conditions for obtaining the target strength are estimated.

[0003] For example, the method of Patent Document 1 first obtains a non-linear relational expression between the material of a steel product and a plurality of material influencing factors using a neural network in order to reduce the variation in material. Then, the target value of the intentional control factor for obtaining the target material is obtained by substituting the remaining material influencing factors excluding the intentional control factor into this relational expression, and the steel product is manufactured by controlling the intentional control factor to the target value.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Here, regarding the analysis in the estimation of the above manufacturing conditions, if a person with expertise performs all the analyses and acquires information based on past experience, it is often possible to make a highly accurate prediction (accurate estimation of manufacturing conditions). However, there is a problem that the work efficiency is poor. On the other hand, if a person with little expertise performs the analysis and acquires information, although the work efficiency can be improved, inappropriate information may be obtained due to an incorrect analysis method. For example, if manufacturing conditions are estimated based on inappropriate information, problems such as delays in material development may occur.

[0006] Furthermore, in the method described in Patent Document 1, the accuracy of the predictive model obtained by the neural network depends on the number of data points and the explanatory variables used. To accurately predict material properties (e.g., strength), the quality of the information used as explanatory variables is crucial. Conventionally, improving the quality of information required human experience and knowledge, but as mentioned above, this presents work efficiency problems. Therefore, there is a need to automatically analyze steel materials without relying on human intervention.

[0007] In view of these circumstances, the purpose of this disclosure is to provide a method for analyzing steel materials that can automatically analyze steel materials, a method for creating a strength prediction model for steel materials, a program, and a steel material analysis device. [Means for solving the problem]

[0008] (1) A method for analyzing steel materials according to one embodiment of the present disclosure is: The first acquisition process involves obtaining the average grain size, strength, and manufacturing conditions of the steel material, A calculation step of calculating the Hall-Petch parameter of the steel material by inputting the acquired average particle size, strength, and manufacturing conditions into a microstructure determination model that uses the Hall-Petch parameter as the objective variable, The method includes a determination step of determining whether the steel material contains at least one of the martensite phase and the bainite phase, based on the calculated Hall-Petch parameters of the steel material.

[0009] (2) As one embodiment of the present disclosure, in (1), If it is determined that the steel material contains at least one of the martensite phase and the bainite phase, the method includes a second acquisition step of acquiring strain information of the steel material and the phase fraction of the phases contained in the steel material.

[0010] (3) A method for creating a strength prediction model for steel materials according to one embodiment of the present disclosure is: (2) The method for analyzing steel materials of (2) includes a strength prediction model creation step in which a strength prediction model is created in which the manufacturing conditions obtained in the first acquisition step, the strain information obtained in the second acquisition step, and the phase fraction are used as explanatory variables and the strength obtained in the first acquisition step is used as the objective variable.

[0011] (4) A program according to one embodiment of the present disclosure is Computer A first acquisition unit that acquires the average grain size, strength, and manufacturing conditions of the steel material, A calculation unit that calculates the Hall-Petch parameter of the steel material by inputting the acquired average particle size, strength, and manufacturing conditions into a microstructure determination model that uses the Hall-Petch parameter as the objective variable, Based on the calculated Hall-Petch parameters of the steel material, it functions as a determination unit that determines whether or not the steel material contains at least one of the martensite phase and the bainite phase.

[0012] (5) An analytical apparatus for steel materials according to one embodiment of the present disclosure is: A first acquisition unit that acquires the average grain size, strength, and manufacturing conditions of the steel material, A calculation unit that calculates the Hall-Petch parameter of the steel material by inputting the acquired average particle size, strength, and manufacturing conditions into a microstructure determination model that uses the Hall-Petch parameter as the objective variable, The system includes a determination unit that determines whether or not the steel material contains at least one of the martensite phase and the bainite phase based on the calculated Hall-Petch parameters of the steel material. [Effects of the Invention]

[0013] According to this disclosure, it is possible to provide a method for analyzing steel materials that can automatically analyze steel materials, a method for creating a strength prediction model for steel materials, a program, and a steel material analysis apparatus. [Brief explanation of the drawing]

[0014] [Figure 1]FIG. 1 is a schematic diagram showing a configuration example of a steel material analysis system including a steel material analyzer according to an embodiment of the present disclosure. [Figure 2] FIG. 2 is a flowchart showing the processing of a steel material analysis method according to an embodiment of the present disclosure.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, a steel material analysis method, a steel material strength prediction model creation method, a program, and a steel material analyzer according to an embodiment of the present disclosure will be described with reference to the drawings.

[0016] (Steel Material Analysis System) FIG. 1 is a block diagram of a steel material analysis system 1 including a steel material analyzer 10 according to the present embodiment. In the present embodiment, the steel material analysis system 1 includes a steel material analyzer 10, a database 30, and a display (display device). Here, the configuration of the steel material analysis system 1 is not limited to that shown in FIG. 1. For example, the steel material analysis system 1 may include an information processing device together with or instead of a display that displays data from the steel material analyzer 10. The information processing device may acquire data from the steel material analyzer 10 and perform further data processing (for example, statistical processing).

[0017] The database 30 stores various data obtained from the production and trial production of metal materials including steel materials. The data in the database 30 may be accumulated by a process computer that controls production equipment in a line for manufacturing metal materials (production line) or a computer that controls experimental conditions for trial-producing metal materials in a laboratory. Hereinafter, the metal material will be described as a steel material. Here, the steel material is not limited to a specific type.

[0018] (Steel Material Analyzer) The steel analysis device 10 automatically analyzes steel materials. Conventionally, once a desired strength (an example of a characteristic value) is obtained from manufactured steel materials, a person with expertise performs various analyses to identify the factors contributing to the strength. If all analyses are performed by an expert, the accuracy of identifying the contributing factors increases, but the work efficiency is poor. On the other hand, if the analysis is performed by someone with little expertise, the accuracy of identifying the contributing factors decreases. As described below, the steel analysis device 10 can automatically and accurately analyze steel materials without relying on human intervention (without going through work that uses human experience and knowledge).

[0019] The steel material analysis device 10 comprises a first acquisition unit 11, a calculation unit 12, a determination unit 13, a second acquisition unit 14, a strength prediction model creation unit 15, a verification unit 16, and an output unit 17. Here, the functional block comprising the second acquisition unit 14, the strength prediction model creation unit 15, and the verification unit 16 is sometimes referred to as the steel material strength prediction model creation device 20. The steel material strength prediction model creation device 20 executes a steel material strength prediction model creation method that creates a strength prediction model when predetermined conditions are met.

[0020] The first acquisition unit 11 is the input interface for the steel material analysis device 10. The first acquisition unit 11 acquires the average particle size, strength, and manufacturing conditions of the steel material's microstructure. Here, acquisition by the first acquisition unit 11 includes obtaining data through calculations based on the input data. In this embodiment, the first acquisition unit 11 acquires images of the steel material's microstructure (microstructure image), strength, and manufacturing conditions from the database 30, and calculates and acquires the average particle size from the microstructure image using known image processing techniques.

[0021] Here, the average grain size of the steel material is the average value of the grain size of the microstructure that can be identified in the microstructure image. The grain size may be measured according to the provisions of JIS. The microstructure image may be an image taken with a scanning electron microscope (SEM) or an optical microscope. If taken with an SEM, it is preferable to obtain a microstructure image at a magnification of 1000x. The strength is not limited to a specific type as long as it conforms to the Hall-Petch rule, and may be, for example, tensile strength or yield strength. A JIS No. 5 tensile test specimen may be taken from the steel plate so that the tensile direction is perpendicular to the rolling direction (C direction), and a tensile test may be performed in accordance with the provisions of JIS Z 2241 (2011) to determine the tensile strength and yield strength. The manufacturing conditions are the manufacturing conditions of the steel material, and include the composition of the steel material and heat treatment conditions.

[0022] The calculation unit 12 calculates the Hall-Petch parameters of the steel material by inputting the acquired average particle size, strength, and manufacturing conditions into a microstructure determination model. The microstructure determination model uses the average particle size, strength, and manufacturing conditions as explanatory variables and the Hall-Petch parameters as the dependent variable. The microstructure determination model may be created, for example, by extracting the above explanatory variables from actual manufacturing line data (past measured values) or laboratory experimental data (past measured values), and using the extracted data set for the dependent variable corresponding to the extracted explanatory variables.

[0023] For example, the organizational classification model can be calculated using the following formula (1), with reference to steel materials for which the correspondence between the explanatory variables and the objective variable is known in advance.

[0024]

number

[0025] Here, σ is the strength [GPa], K is the Hall-Petch parameter, and d is the grain size [μm]. The constant shown as 0.1 in equation (1) is an unknown value (offset value) and is determined together with K using data from at least three levels (three points) where σ and d are known. The microstructure determination model may be determined, for example, by producing three or more levels of steel material with the same composition, using three or more levels of annealing time, which is one of the heat treatment conditions, and obtaining the average grain size and strength for each. Alternatively, instead of heat treatment conditions, three or more levels of steel material may be produced based on composition, and the microstructure determination model may be determined by a similar method.

[0026] The determination unit 13 determines whether the steel material contains at least one of the martensite phase and the bainite phase based on the calculated Hall-Petch parameter of the steel material. If the steel material contains at least one of the martensite phase and the bainite phase, the value of the Hall-Petch parameter changes. Therefore, it is possible to determine whether at least one of the martensite phase and the bainite phase is contained in the steel material based on the value of the Hall-Petch parameter. The value of the Hall-Petch parameter is greater than 0.05 and less than 10 when at least one of the martensite phase and the bainite phase is contained. In other words, the value of the Hall-Petch parameter is 0.05 or less, or 10 or more, when the material consists only of other phases (phases that are neither martensite nor bainite). Also, for example, when the martensite phase is contained, the value of the Hall-Petch parameter is 0.6 [GPa·μm (1 / 2) It is greater than ].

[0027] The second acquisition unit 14 is the input interface for the steel material analysis device 10. When the second acquisition unit 14 determines that the steel material contains at least one of the martensite phase and the bainite phase, it acquires information on the strain of the steel material and the phase fraction of the phases contained in the steel material. Here, acquisition by the second acquisition unit 14 also includes obtaining information by calculation based on the input data.

[0028] Here, the information on the strain of the steel material is not limited to information obtained by a specific analytical method, but in this embodiment, it is information obtained by X-ray diffraction. In this embodiment, the second acquisition unit 14 acquires the information on the strain of the steel material from the database 30. The phase fraction is the ratio of each phase constituting the microstructure calculated using the microstructure image. The method for calculating the phase fraction is not limited to a specific method, but for example, machine learning-based image classification segmentation may be used. In this embodiment, the second acquisition unit 14 acquires the microstructure image from the database 30 and calculates and acquires the phase fraction from the microstructure image by image classification segmentation.

[0029] The intensity prediction model creation unit 15 creates an intensity prediction model using the manufacturing conditions acquired by the first acquisition unit 11 and the strain information and phase fraction acquired by the second acquisition unit 14 as explanatory variables, and the intensity acquired by the first acquisition unit 11 as the dependent variable. The intensity prediction model is a function obtained by, for example, a linear regression, local regression, machine learning, or deep learning algorithm. Here, the intensity prediction model may be created by combining these algorithms.

[0030] The validation unit 16 validates the intensity prediction model created by the intensity prediction model creation unit 15. The validation unit 16 may, for example, validate using new data in which the correlation between the explanatory variables and the dependent variable is known, or it may perform cross-validation.

[0031] The output unit 17 is the output interface of the steel material analysis device 10. The output unit 17 outputs the created strength prediction model. The output unit 17 may output the created strength prediction model to a database 30 or the like for storage. In addition, when the strength is calculated by the steel material analysis device 10 using the strength prediction model, the output unit 17 may output the calculated strength of the steel material to a display or information processing device.

[0032] Here, the steel analysis apparatus 10 is not limited to a specific device, but can be implemented as a computer, for example. A commercially available general-purpose computer can be used. The computer includes, for example, a storage device such as memory and a hard disk drive, a CPU and input / output devices. The first acquisition unit 11, calculation unit 12, determination unit 13, second acquisition unit 14, strength prediction model creation unit 15, verification unit 16 and output unit 17 may be implemented by software. For example, one or more programs may be stored in a storage device accessible by the computer's processor (CPU). The CPU may read the programs stored in the storage device, thereby causing the computer to function as the first acquisition unit 11, calculation unit 12, determination unit 13, second acquisition unit 14, strength prediction model creation unit 15, verification unit 16 and output unit 17.

[0033] (Methods for analyzing steel materials) Figure 2 is a flowchart showing the processing steps of the steel analysis method performed by the steel analysis apparatus 10 according to this embodiment. The steel analysis method generally includes a first acquisition step, a calculation step, and a determination step. The steel analysis method may further include a second acquisition step, a strength prediction model creation step, a verification step, and an output step.

[0034] In the first acquisition process (S1) performed by the first acquisition unit 11, the average grain size, strength, and manufacturing conditions of the steel material are acquired.

[0035] In the calculation step (S2) performed by the calculation unit 12, the Hall-Petch parameters of the steel material are calculated by inputting the acquired average particle size, strength, and manufacturing conditions into a microstructure determination model that uses the Hall-Petch parameters as the objective variable.

[0036] In the determination step (S3) performed by the determination unit 13, it is determined whether the steel material contains at least one of the martensite phase and the bainite phase based on the calculated Hall-Petch parameters of the steel material.

[0037] If the steel contains at least one of the martensite and bainite phases (Yes in S4), the process proceeds to S5. If the steel contains neither the martensite nor the bainite phase (No in S4), the process returns to S1 to reacquire a different microstructure image.

[0038] Here, if the steel material does not contain either the martensite or bainite phase, the conditions for acquiring the microstructure image may be incorrect, and therefore the conditions for acquiring the microstructure image may be changed. The conditions for acquiring the microstructure image include the conditions for observing the microstructure image. The observation conditions may include, for example, the magnification, field of view, and acceleration voltage set for an SEM, and for example, the magnification and field of view set for an optical microscope. If the steel material does not contain either the martensite or bainite phase, the pretreatment conditions for acquiring the microstructure image (polishing conditions, etching conditions, etc.) may be further changed. If the steel material does not contain either the martensite or bainite phase even when using the microstructure image obtained under the changed conditions, the series of processes may be terminated. Here, the initial conditions for acquiring the microstructure image may be general nital etching, SEM observation, and imaging at 1000x magnification, or other general conditions tailored to the type of steel may be set. When changing the acquisition conditions, a step may be added to set the magnification to include 30 or more crystal grains in the image.

[0039] In the second acquisition step (S5) performed by the second acquisition unit 14, if it is determined that the steel material contains at least one of the martensite phase and the bainite phase, information on the strain of the steel material and the phase fraction of the phases contained in the steel material are acquired.

[0040] In the strength prediction model creation process (S6) performed by the strength prediction model creation unit 15, a strength prediction model is created. The strength prediction model uses the manufacturing conditions obtained in the first acquisition process and the strain information and phase fraction obtained in the second acquisition process as explanatory variables, and the strength obtained in the first acquisition process as the dependent variable.

[0041] In the verification process (S7) performed by the verification unit 16, the intensity prediction model created in the intensity prediction model creation process is verified.

[0042] In the output process (S8) performed by the output unit 17, the created intensity prediction model, the intensity calculated by the intensity prediction model, and other information are output to the database 30, display, information processing device, etc.

[0043] (Examples) The effects of this disclosure will be described in detail below based on examples, but this disclosure is not limited to these examples. In the examples, the steel material analysis method using the steel material analysis apparatus 10 described above was performed.

[0044] In the example, steel sheets obtained through the following manufacturing processes (p1) to (p9) were used. (p1) is the steelmaking process. (p2) is the continuous casting process. (p3) is the heating process. (p4) is the hot rolling process. (p5) is the cooling process. (p6) is the winding process. (p7) is the cold rolling process. (p8) is the annealing process. (p9) is the pickling process.

[0045] Datasets for creating a microstructure determination model were prepared. Multiple model development datasets were prepared, consisting of average grain size calculated from microstructure images acquired under the conditions described below, manufacturing conditions, strength, and Hall-Petch parameters obtained using the acquisition method described below. Here, the manufacturing conditions include component composition, cold rolling ratio, annealing heating temperature, holding time during heating in annealing, cooling temperature immediately after annealing, and holding time during cooling immediately after annealing. The strength is the tensile strength obtained using the measurement method described below. The microstructure determination model with the Hall-Petch parameter (K) as the dependent variable is the regression equation shown in equation (2) below.

[0046]

number

[0047] Here, x1 is the grain size, and the average grain size is used. x2 is the composition, and the compositional composition is used. x3 is the cold rolling rate. x4 is the heating temperature for annealing. x5 is the holding time for heating during annealing. x6 is the cooling temperature performed immediately after annealing. x7 is the holding time for cooling performed immediately after annealing. x8 is the strength, and the tensile strength is used. Of these, x1 and x2 are essential factors for improving the accuracy of the Hall-Petch parameters.

[0048] The average grain size, strength, and manufacturing conditions of the steel material being evaluated were then obtained. Here, the average grain size was obtained from the microstructure image acquired by SEM. In addition, the manufacturing conditions, such as component composition, cold rolling rate, annealing heating temperature, holding time during annealing, cooling temperature immediately after annealing, and holding time during cooling immediately after annealing, were obtained, and the strength, specifically tensile strength, was obtained (first acquisition process).

[0049] The average particle size, strength, and manufacturing conditions obtained in the first acquisition process were input into a pre-prepared microstructure determination model to calculate the Hall-Petch parameters of the steel material to be evaluated (calculation process).

[0050] Prior studies (experiments) showed that the Hall-Petch parameter value was 0.6 [GPa·μm]. (1 / 2) If the value is greater than ], it can be determined that the martensite phase is present. Whether or not the martensite phase is present was determined based on the value of the Hall-Petch parameter. Here, the Hall-Petch parameter is 0.05 [GPa·μm (1 / 2) ] or less, or 10 [GPa·μm (1 / 2) In the above cases, the average particle size obtained based on the tissue image is determined to be inappropriate (determination step). If the average particle size is determined to be inappropriate, the process returns to the first acquisition step, and the average particle size is obtained using the newly acquired tissue image.

[0051] As described above, the microstructure image of the steel plate was obtained using a scanning electron microscope (SEM). For the microstructure observation, a specimen was taken so that the observation surface was at the 1 / 2 position of the plate thickness in the rolling direction cross section (L section). The surface of the specimen was polished, and then etching was performed using 3 vol% Nital solution corrosion to reveal the microstructure. After that, the microstructure was observed by SEM at a magnification of 1000x.

[0052] Furthermore, JIS No. 5 tensile test specimens were taken from the steel plate so that the tensile direction was perpendicular to the rolling direction (direction C). Tensile tests were performed on the taken specimens in accordance with the provisions of "JIS Z 2241 (2011)" with n=1, and the tensile strength was determined. n=1 means that one test was performed on one level of specimen.

[0053] For steel sheets with a single component composition, the annealing time was varied to three levels (100s, 1000s, and 10000s) for a specific annealing temperature. For each level, the average grain size was measured for n=1 on the microstructure images obtained using the method described above, and the Hall-Petch parameters were determined.

[0054] If the determination process determines that the material contains a martensite phase, information on the structural fraction of the martensite phase and the strain contained in the martensite phase, which are factors that generate tensile strength, is obtained (second acquisition process).

[0055] Here, the phase fraction of the martensite phase was obtained by the following procedure. First, a 100 [μm] × 100 [μm] SEM image (tissue image) was obtained. The tissue image may be obtained under the same conditions as the first acquisition step, or the tissue image obtained in the first acquisition step may be reused. The fraction of the martensite phase was obtained from the acquired tissue image using image analysis. In this example, the phase fraction of the martensite phase was obtained using machine learning-based image classification segmentation. In addition, information on the strain contained in the martensite phase was obtained by measuring the strain by performing X-ray diffraction measurements on a test specimen in a 1 [mm] × 1 [mm] area.

[0056] Next, a strength prediction model was created using the strain information and phase fraction obtained in the second acquisition process and the manufacturing conditions obtained in the first acquisition process as explanatory variables, and the strength obtained in the first acquisition process as the dependent variable (strength prediction model creation process). Furthermore, verification was performed using unknown samples to confirm the accuracy of the created strength prediction model (verification process).

[0057] By using the strength prediction model created in this way, it is possible to predict the strength of an unknown steel material, assuming it contains microstructures (phases) that affect strength, when the explanatory variables are arbitrarily changed, as shown in the examples in the table below. Based on the predicted strength, the next experimental conditions can be determined, leading to increased efficiency in steel material development. Here, in the table below, the Hall-Petch factor means that the Hall-Petch factor value obtained in a previously performed analysis of steel materials is used as the explanatory variable.

[0058] [Table 1]

[0059] Furthermore, in the table, the random process factor group consists of at least five manufacturing conditions randomly selected from the manufacturing conditions obtained in the steelmaking process, continuous casting process, heating process, hot rolling process, cooling process, coiling process, cold rolling process, annealing process, and pickling process. Process factor group A consists of at least five manufacturing conditions obtained in the steelmaking process, continuous casting process, heating process, hot rolling process, cooling process, coiling process, cold rolling process, annealing process, and pickling process. Specifically, process factor group A consists of at least five manufacturing conditions randomly selected from the steel sheet composition, the temperature conditions and rolling pass schedule of the hot rolling process, the rolling rate of the cold rolling process, and the temperature conditions and sheet feeding speed of the annealing process. Process factor group B consists of at least five manufacturing conditions obtained in the steelmaking process, continuous casting process, heating process, hot rolling process, cooling process, coiling process, cold rolling process, annealing process, and pickling process. Specifically, process factor group B consists of the steel sheet composition and at least five manufacturing conditions randomly selected from the temperature conditions and rolling pass schedule of the hot rolling process and the temperature conditions and sheet feeding speed of the annealing process. Process factor group C consists of the steel sheet composition and the annealing temperature conditions during the continuous annealing process, which are among the manufacturing conditions obtained in the steelmaking process, continuous casting process, heating process, hot rolling process, cooling process, winding process, cold rolling process, annealing process, and pickling process.

[0060] As described above, the steel analysis method, steel strength prediction model creation method, program, and steel analysis apparatus 10 according to this embodiment enable automatic and accurate analysis of steel without relying on human intervention, that is, without requiring human experience and knowledge. In other words, while conventional human analysis yielded significantly different results depending on the person performing the analysis, the strength prediction model of this disclosure allows for consistent analysis results regardless of the individual.

[0061] While embodiments of this disclosure have been described based on the drawings and examples, it should be noted that those skilled in the art will find it easy to make various modifications or alterations based on this disclosure. Therefore, it should be noted that these modifications or alterations are included within the scope of this disclosure. For example, the functions included in each component or step can be rearranged in a logically consistent manner, and multiple components or steps can be combined into one or divided. Embodiments relating to this disclosure can also be realized as storage media recording programs executed by a processor in the device. It should be understood that these are also included within the scope of this disclosure. [Explanation of symbols]

[0062] 1. Steel material analysis system 10. Analytical equipment for steel materials 11 First Acquisition Department 12 Calculation Section 13 Judgment section 14 Second Acquisition Department 15. Intensity Prediction Model Creation Section 16 Verification Department 17 Output section 20. Equipment for creating strength prediction models for steel materials 30 databases

Claims

1. The first acquisition process involves obtaining the average grain size, strength, and manufacturing conditions of the steel material, A calculation step of calculating the Hall-Petch parameter of the steel material by inputting the acquired average particle size, strength, and manufacturing conditions into a microstructure determination model that uses the Hall-Petch parameter as the objective variable, A method for analyzing steel, comprising: a determination step of determining whether the steel contains at least one of a martensite phase and a bainite phase based on the calculated Hall-Petch parameters of the steel.

2. A method for analyzing steel according to claim 1, comprising a second acquisition step of acquiring information on the strain of the steel and the phase fraction of the phases contained in the steel, when it is determined that the steel contains at least one of a martensite phase and a bainite phase.

3. A method for creating a strength prediction model for steel, comprising a strength prediction model creation step, in which the manufacturing conditions obtained in the first acquisition step, the strain information and the phase fraction obtained in the second acquisition step of the steel analysis method described in claim 2 are used as explanatory variables, and the strength obtained in the first acquisition step is used as the objective variable.

4. Computer A first acquisition unit that acquires the average grain size, strength, and manufacturing conditions of the steel material, A calculation unit that calculates the Hall-Petch parameter of the steel material by inputting the acquired average particle size, strength, and manufacturing conditions into a microstructure determination model that uses the Hall-Petch parameter as the objective variable, A program that functions as a determination unit that determines whether the steel material contains at least one of the martensite phase and the bainite phase, based on the calculated Hall-Petch parameters of the steel material.

5. A first acquisition unit that acquires the average grain size, strength, and manufacturing conditions of the steel material, A calculation unit that calculates the Hall-Petch parameter of the steel material by inputting the acquired average particle size, strength, and manufacturing conditions into a microstructure determination model that uses the Hall-Petch parameter as the objective variable, A steel material analyzer comprising: a determination unit that determines whether the steel material contains at least one of the martensite phase and the bainite phase based on the calculated Hall-Petch parameters of the steel material.

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