Steel evaluation method and program

Abstract

The objective of the present invention is to provide an evaluation method with which it is possible to obtain steel having a stable quality, by using a new variable for evaluating dimensional change characteristics.　This steel evaluation method includes: acquiring a forging ratio (forging work ratio) when a steel ingot (21) is forged to obtain a forged material (22); acquiring the dimensions of a rectangular cuboid shaped sample (40) having one side in a direction of extension during forging, for the forged material (22); and calculating a variable related to isotropy of the sample (40) on the basis of the forging ratio and the dimensions. The variable is an isotropy variable. The dimensional change characteristics of samples (40) collected from each of a plurality of forged materials (22) are measured, the isotropy variable is calculated for each sample (40), and a graph employing the isotropy variable on a first axis and the dimensional change characteristic on a second axis is output on the basis of the isotropy variable and the dimensional change characteristics.

Description

Steel evaluation method and program

[0001] The present invention relates to a method and program for evaluating steel.

[0002] Steel used for bending, drawing, and punching plate materials is generally produced by subjecting cast steel ingots to hot processing such as forging and rolling, annealing, cutting, and quenching / tempering. Dimensional change is known to be an issue that occurs during the quenching and tempering process, and since large dimensional change leads to an increase in the number of processing steps after heat treatment, various studies have been conducted to suppress dimensional change.

[0003] For example, in Patent Document 1, the applicant of the present application discovered that in order to reduce dimensional change due to heat treatment that occurs after quenching and tempering, it is possible to suppress dimensional change due to heat treatment by reducing segregation, and proposed a cold die steel that can reduce segregation and suppress dimensional change by adopting a "segregation index K" that combines the concentration distribution rate of each element between the solid phase and the liquid phase and the change in specific gravity, and by optimally adjusting this value.

[0004] Furthermore, Patent Document 2 describes a cold die steel with excellent dimensional change controllability, which is made by adding appropriate amounts of Ni, Al, and Cu to a component composition that can suppress dimensional change as much as possible within a range that satisfies various properties by reducing the amount of primary carbides remaining in the as-cast state. According to the invention of Patent Document 1, Ni and Al form intermetallic compounds that precipitate during tempering (aging) in the secondary hardening region of the tool steel, thereby acting to suppress dimensional change in the contraction direction and offsetting expansion during heat treatment.

[0005] JP 2006-152356 A JP 2006-169624 A

[0006] G. A. Roberts, Tool Steel 5th (1998) p. 338 F. Rapatz, Die Edelstahl (1962), p. 945 Takayuki Shimizu, Koichiro Inoue, "Effect of Precipitated Carbide on Anisotropy of Dimensional Change during Heat Treatment of Cold Die Steel," Electric Steel Manufacturing, 2007, 78.4: 289

[0007] On the other hand, experimental results by the present applicant have shown that even alloys with the same composition exhibit variations in dimensional deformation characteristics. This variation, when the mold shape is rectangular, is expressed as anisotropy when the thickness, width, and length dimensions differ in the rate of change after quenching and tempering compared to the dimensions before quenching. Two metallurgical technical documents have reported on the observed dimensional deformation phenomenon (Non-Patent Documents 1 and 2). Furthermore, research on cold-work tool steels (cold-work die steels) has shown that this deformation is unlikely to occur in the absence of crystallized carbides in cold-work die steels. Furthermore, FEM analysis taking into account the distribution of crystallized carbides has reported that the presence of crystallized carbides results in anisotropy (Non-Patent Document 3). Therefore, stabilizing quality when evaluated by dimensional deformation has also been a challenge. While the above-mentioned deformation can be improved by eliminating elemental segregation, as described in Patent Document 1, the degree of impact on dimensional deformation varies depending on the element, and it took time to determine which elemental species should be focused on to improve segregation.

[0008] Therefore, an object of the present invention is to provide an evaluation method that uses new variables for evaluating dimensional change characteristics, thereby enabling steel of stable quality to be obtained.

[0009] The steel evaluation method involves obtaining the forging ratio when forging a steel ingot to obtain a forged material, obtaining the dimensions of a rectangular parallelepiped sample of the forged material with one side in the elongation direction during forging, and calculating a variable related to the isotropy of the sample based on the forging ratio and the dimensions.

[0010] According to the present invention, it is possible to provide an evaluation method for obtaining steel with stable quality.

[0011] 11 is an explanatory diagram illustrating an outline of a method for evaluating steel. It is an explanatory diagram illustrating the change in the unit cell before and after forging. It is an explanatory diagram illustrating the configuration of an information processing device. It is a graph showing the heat treatment dimensional change of Test Specimen No. 1-L. It is a graph showing the heat treatment dimensional change of Test Specimen No. 1-S. It is a graph showing the heat treatment dimensional change of Test Specimen No. 2-L. ​​It is a graph showing the heat treatment dimensional change of Test Specimen No. 2-S. It is a graph showing the heat treatment dimensional change of Test Specimen No. 3-L. It is a graph showing the heat treatment dimensional change of Test Specimen No. 4-L. It is a graph showing the relationship between isotropic variables and the sample standard deviation of the dimensional change rate. It is an example of an Ms point mapping image obtained by visualizing mapping data of Ms points. It is an example of a binarized image obtained by performing normalization processing and binarization processing on the image of FIG. 11. It is a diagram in which principal component vectors obtained by principal component analysis are superimposed on FIG. 11. It is a graph showing the relationship between isotropic variables and the first principal component contribution rate. It is a flowchart illustrating the processing flow of a program for calculating isotropic variables.

[0012] [Embodiment 1] Figure 1 is an explanatory diagram outlining a steel evaluation method. A steel ingot 21 is produced by mixing iron with various alloy elements to adjust the composition and casting the resulting product. The steel ingot 21 is then forged to produce a forged material 22 with a dense and strong structure. In the forging process, hot forging, cooling, and annealing are repeated multiple times. Forged material 22 that has been annealed but not quenched and tempered is sometimes called an annealed material, and forged material that has been quenched and tempered is sometimes called a quenched and tempered material.

[0013] The degree of forging is expressed by the forging ratio. The forging ratio is also called the forging ratio, and indicates the ratio of the cross-sectional area before and after forging. Details of the forging ratio are defined in JIS G0701-1957, "Expression of forging ratio for steel forging work," which is a Japanese Industrial Standard (JIS).

[0014] A sample 40 is prepared from the forged material 22. The sample 40 includes a dimensional change measurement sample 41 and a mapping sample 42. The dimensional change measurement sample 41 is used to measure the dimensional change characteristics. The mapping sample 42 is used to calculate the principal component contribution ratio. How to determine the dimensional change characteristics and the principal component contribution ratio will be described later.

[0015] Research by the applicant has revealed that there is a correlation between dimensional change characteristics and the principal component contribution ratio. Therefore, dimensional change characteristics can be evaluated by calculating the principal component contribution ratio. Calculating the principal component contribution ratio is easier to automate than actually measuring dimensional change characteristics, and data variation is less likely to occur. Therefore, the principal component contribution ratio can be used as a substitute characteristic for dimensional change characteristics.

[0016] However, to calculate the contribution ratio of the main components, it is necessary to perform element mapping using a field emission electron probe micro analyzer (FE-EPMA), as described below. FE-EPMA is an expensive instrument that can be used for a variety of measurements, so its use is costly.

[0017] Therefore, the applicant of the present application has developed a method for evaluating steel, which uses an isotropy variable Q calculated based on the forging ratio and the dimensions of the sample 40 as a substitute for the dimensional change characteristics. The calculation method of the isotropy variable Q will be described later. The isotropy variable is an example of a variable related to the isotropy of the sample 40.

[0018] The orientation in which each sample 40 was taken will be described using FIG. 1 . In the following description, the stretching direction during forging will be referred to as the L direction. Two directions perpendicular to the L direction will be referred to as the W direction and the T direction. The W direction is perpendicular to the T direction. In other words, the L direction, W direction, and T direction form an orthogonal coordinate system having three axes perpendicular to each other.

[0019] The dimensional change measurement sample 41 is a rectangular parallelepiped taken from the forged material 22 so that its sides are parallel to the L direction, W direction, and T direction. That is, the dimensional change measurement sample 41 has a rectangular parallelepiped shape with its first side facing the elongation direction during forging. The lengths of the sides of the dimensional change measurement sample 41 in the L direction, W direction, and T direction when cooled to room temperature are indicated by the symbols SL, SW, and ST.

[0020] The dimensional change measurement sample 41 is subjected to measurements of the dimensions before heat treatment, heat treatment such as quenching and tempering, and then measurements of the dimensions after heat treatment. The dimensional change characteristics are calculated from the dimensional changes before and after the heat treatment. Specific examples of the dimensional change characteristics will be described later.

[0021] The mapping sample 42 is a flat plate perpendicular to the W direction, taken from the forged material 22. The mapping sample 42 may also be a rectangular parallelepiped having faces perpendicular to the L direction, W direction, and T direction.

[0022] In the following explanation, the plane perpendicular to the W direction will be referred to as the TL plane. One side of the mapping sample 42 is polished smoothly to form a mapping area 421 suitable for elemental mapping using FE-EPMA. In Figure 1, the mapping area 421 is indicated by hatching.

[0023] The matrix composition of the mapping region 421 is mapped using the element mapping function of the FE-EPMA. The mapping results are analyzed to calculate the principal component contribution ratio. A method for calculating the principal component contribution ratio will be described later. In this embodiment, the principal component contribution ratio of the TL plane will be described, but the principal component contribution ratios of the TW plane perpendicular to the L direction and the LW plane perpendicular to the T direction can also be calculated in a similar manner.

[0024] [Method of Calculating Isotropic Variable Q] Figure 2 is an explanatory diagram illustrating the change in the unit cell before and after forging. The equation for calculating the isotropic variable Q will be explained using Figures 1 and 2. Note that because the shapes of the steel ingot 21 and the forging 22 are not necessarily rectangular parallelepipeds, the outlines of the steel ingot 21 and the forging 22 are shown as virtual lines.

[0025] In the following description, a tiny cube virtually defined inside the steel ingot 21 will be referred to as an as-cast unit cell 31. Each side of the as-cast unit cell 31 is parallel to the L direction, the W direction, and the T direction, respectively. The length of one side of the as-cast unit cell 31 is denoted by D0.

[0026] The as-cast unit cell 31 contains iron and trace elements added for composition adjustment. Segregation, in which iron atoms and atoms of the trace elements are unevenly distributed, occurs within the as-cast unit cell 31. The degree of segregation differs among the L direction, W direction, and T direction.

[0027] As described above, a forged material 22 is produced by forging a steel ingot 21. In the following description, the forging ratio will be referred to as FR. The as-cast unit cell 31 inside the steel ingot 21 is deformed into a rectangular parallelepiped by forging, becoming a forged unit cell 32. The sides of the forged unit cell 32 are parallel to the L direction, W direction, and T direction, respectively. The lengths of the sides of the forged unit cell 32 in the L direction, W direction, and T direction are indicated by the symbols DL, DW, and DT.

[0028] Since the volume of the unit cell remains unchanged before and after forging, equation (1) holds. DO·DO·DO=DL·DW·DT (1)

[0029] The forging ratio FR is defined as in formula (2).

[0030]

[0031] Assuming that the dimensional change measurement sample 41 taken from the forged material 22 is composed of after-forged unit cells 32 of the same size arranged without gaps, the numbers of after-forged unit cells 32 aligned on the sides of the dimensional change measurement sample 41 in the L direction, W direction, and T direction are denoted by NL, NW, and NT, respectively. The definitions of NL, NW, and NT are shown in equations (2) to (4). NL = SL / DL (2) NW = SW / DW (3) NT = ST / DT (4)

[0032] The isotropic variable Q is defined by equation (5). From equation (5), the isotropic variable Q is a dimensionless quantity having no unit.

[0033]

[0034] Since NL, NW, and NT cannot be measured, the isotropic variable Q cannot be calculated from equation (5) alone. However, by substituting equations (1) to (4) into equation (5), equation (6) is obtained.

[0035]

[0036] From equation (6), the isotropic variable Q can be calculated from the lengths SL, SW, and ST of the three sides of the dimensional change measurement sample 41 made from the forged material 22, and the forging ratio FR. Note that, when calculating the isotropic variable Q from equation (6), it is not necessary to actually make the dimensional change measurement sample 41, and it is sufficient to simply determine the dimensions of the dimensional change measurement sample 41 virtually.

[0037] Therefore, the isotropic variable Q can be calculated much more easily than the dimensional change characteristics calculated from the actual measurement values ​​of the dimensions of the dimensional change measurement sample 41 before and after heat treatment, and the main component contribution ratio calculated through element mapping of the mapping sample 42. The fact that the isotropic variable Q has a correlation with the dimensional change characteristics and can be used as a substitute characteristic for the dimensional change characteristics will be explained in the experimental examples described later.

[0038] [Outline of Measurement and Calculation Method of Dimensional Change Characteristics] As described above, a dimensional change measurement sample 41 is taken from the forged material 22. After the dimensional change measurement sample 41 has cooled to room temperature, the length of each side before heat treatment is measured. After the heat treatment of the dimensional change measurement sample 41 is completed, the length of each side of the mapping sample 42 that has cooled to room temperature is measured.

[0039] The dimensional change rates before and after heat treatment are calculated using equation (7) for each of the L, W, and T directions of the dimensional change measurement sample 41. For one dimensional change measurement sample 41, the dimensional change rates are calculated in a total of three directions: the L, T, and W directions.

[0040] LA is the length before heat treatment. LB is the length after heat treatment.

[0041] The average value and sample standard deviation are calculated from the dimensional change ratio values ​​of the three points. The dimensional change ratios in the L direction, W direction, and T direction, as well as the average value and sample standard deviation of the dimensional change ratios in the three directions, are used as the dimensional change characteristics.

[0042] [Outline of Calculation Method of Main Component Contribution Ratio] As described above, the dimensional change measurement sample 41 is prepared from the forged material 22. One surface of the mapping sample 42 is polished smoothly to form a mapping region 421.

[0043] The elemental mapping function of the FE-EPMA acquires mapping data relating to the matrix composition of the mapping region 421. The measurement conditions of the FE-EPMA are, for example, a measurement field of view of 8 mm x 4 mm, a beam diameter of 20 μm, an acceleration voltage of 15 kV, and an irradiation current of 0.1 μA.

[0044] From the mapping data relating to the matrix composition, mapping data for the Ms point is created. The Ms point is the temperature at which the transformation from austenite, which is the crystalline structure of steel, to martensite begins.

[0045] The mapping data of the Ms point can be prepared using, for example, the calculation method of the Ms point prediction formula by Ishida (ISHIDA, K. Calculation of the effect of alloying elements on the Ms temperature in steels. Journal of Alloys and Compounds, 1995, 220.126-131.).

[0046] A mapping image can be generated based on the mapping data of the Ms point. The mapping image is then subjected to binarization processing using known image processing software, etc. A threshold value for the binarization processing can be determined, for example, by discriminant analysis.

[0047] Next, principal component analysis is performed on the binarized Ms-point mapping image to calculate the first and second principal components at each measurement location. The first principal component is the principal component vector with the largest eigenvalue, and the second principal component is the principal component vector with the second largest eigenvalue. Data is extracted from, for example, three measurement locations starting with the largest first principal component. For each measurement location, the contribution rate of the first principal component and the contribution rate of the second principal component are calculated using equations (8) and (9).

[0048] a is the magnitude of the first principal component, and b is the magnitude of the second principal component.

[0049] The significance of the first and second principal components will be briefly explained. When the ratio of the first and second principal components is close to 1, the contribution ratios of the first and second principal components are each approximately 0.5. This indicates that the temperature difference between the Ms points at the measurement point and its surrounding points is small, and that the distribution of the Ms points is isotropic. Therefore, steel products in which the contribution ratios of the first and second principal components are close to 0.5 are likely to be steel products that are less susceptible to dimensional changes due to heat treatment.

[0050] The further the ratio of the first principal component to the second principal component is from 1, the greater the temperature difference between the Ms points at the measurement point and its surrounding points, indicating anisotropy in the distribution of the Ms points. Therefore, steel materials in which the contribution ratios of the first principal component and the second principal component are far from 0.5 are likely to be steel materials that are prone to dimensional deformation due to heat treatment.

[0051] 3 is an explanatory diagram illustrating the configuration of the information processing device 10. The information processing device 10 includes a control unit 11, a main memory device 12, an auxiliary memory device 13, a communication unit 14, a display unit 15, an input unit 16, a reading unit 19, and a bus.

[0052] The control unit 11 is an arithmetic and control device that executes the program of this embodiment. The control unit 11 uses one or more central processing units (CPUs), graphics processing units (GPUs), multi-core CPUs, etc. The control unit 11 is connected to each hardware unit that constitutes the information processing device 10 via a bus.

[0053] The main memory device 12 is a storage device such as an SRAM (Static Random Access Memory), a DRAM (Dynamic Random Access Memory), a flash memory, etc. The main memory device 12 temporarily stores information required during processing performed by the control unit 11 and programs currently being executed by the control unit 11.

[0054] The auxiliary storage device 13 is a storage device such as an SRAM, a flash memory, a hard disk, or a magnetic tape, etc. The auxiliary storage device 13 stores programs to be executed by the control unit 11 and various data required for executing the programs.

[0055] The communication unit 14 is an interface for communication between the information processing device 10 and a network. The display unit 15 is, for example, a liquid crystal display panel or an organic EL (Electro-Luminescence) panel. The input unit 16 is, for example, an input device such as a keyboard, a mouse, or a microphone. The display unit 15 and the input unit 16 may be stacked to form a touch panel.

[0056] The portable recording medium 96 is, for example, a USB (Universal Serial Bus) memory, a CD-ROM (Compact Disc Read Only Memory), a magneto-optical disk medium, another optical disk medium, an SD memory card, etc. The portable recording medium 96 stores a program 97, which will be described later.

[0057] The reading unit 19 is an interface that can connect to a portable recording medium 96, such as a USB connector, a CD-ROM drive, or an SD memory reader. The semiconductor memory 98 stores a program 97 and is a memory that can be attached inside the information processing device 10.

[0058] The information processing device 10 is a general-purpose personal computer, a tablet, a mainframe computer, a virtual machine running on a mainframe computer, or a quantum computer. The information processing device 10 may be configured with hardware such as multiple personal computers or mainframe computers that perform distributed processing. The information processing device 10 may be configured with a cloud computing system. The information processing device 10 may be configured with hardware such as multiple personal computers or mainframe computers that operate in cooperation with each other.

[0059] The program 97 is recorded on a portable recording medium 96. The control unit 11 reads the program 97 via the reading unit 19 and stores it in the auxiliary storage device 13. The control unit 11 may also read out the program 97 stored in a semiconductor memory 98. Furthermore, the control unit 11 may download the program 97 from another server computer (not shown) connected via the communication unit 14 and a network (not shown) and store it in the auxiliary storage device 13.

[0060] The program 97 is installed as a control program for the information processing device 10, and is loaded into and executed by the main storage device 12. The program 97 of this embodiment is an example of a program product.

[0061] Experimental Example A steel ingot 21 was produced. The steel ingot 21 was produced by a single casting and divided into four ingots 21, which were numbered as ingot No. 1 to ingot No. 4. Ingot No. 1 and ingot No. 3 were taken from the top side of the steel ingot 21 before division. Ingot No. 2 and ingot No. 4 were taken from the bottom side of the steel ingot 21 before division.

[0062] Each steel ingot 21 was subjected to a forging process that repeatedly included hot forging, cooling, and annealing, to obtain a forged material 22. Table 1 shows the isotropic variable Q calculated using equation (6) based on assumed dimensions of the dimensional change measurement specimen 41 taken from each forged material 22. The test specimen number indicates the number assigned to the assumed dimensional change measurement specimen 41. The first number in the test specimen number indicates the steel ingot number of the forged material 22, and the letter following the hyphen indicates a code that identifies the difference in dimensions of the dimensional change measurement specimen 41.

[0063]

[0064] The correlation between the isotropic variable Q shown in Table 2 and the dimensional change characteristics and the principal component contribution ratio, which will be explained separately, will be described later.

[0065] The results of analyzing the composition of the forged material 22 are shown in Table 2.

[0066]

[0067] From each of the forgings 22, dimensional change measurement samples 41 having the dimensions shown in Table 2 were actually taken. Six dimensional change measurement samples 41 corresponding to each test piece number were taken from adjacent locations of one forging 22.

[0068] The SL, SW and ST of the dimensional change measurement sample 41 were measured.

[0069] The dimensional change measurement samples 41 were subjected to heat treatment by semi-cooling quenching from 1020°C for 10 minutes and tempering twice. "Semi-cooling" refers to the time required to reach half the temperature from 1020°C, or 510°C. The tempering temperature used in the tempering treatment was different for each of the six dimensional change measurement samples 41 assigned the same test piece number.

[0070] After the heat treatment, the SL, SW, and ST of the dimensional change measurement samples 41 were returned to room temperature and measured. The dimensional change rates in the L, T, and W directions for each dimensional change measurement sample 41 were calculated using equation (7). Furthermore, the average value ave and the sample standard deviation s of the dimensional change rates in the three directions were calculated.

[0071] Fig. 4 is a graph that shows the dimensional change that occurred during heat treatment for Test Specimen No. 1-L. Fig. 5 is a graph that shows the dimensional change that occurred during heat treatment for Test Specimen No. 1-S. Fig. 6 is a graph that shows the dimensional change that occurred during heat treatment for Test Specimen No. 2-L. ​​Fig. 7 is a graph that shows the dimensional change that occurred during heat treatment for Test Specimen No. 2-S. Fig. 8 is a graph that shows the dimensional change that occurred during heat treatment for Test Specimen No. 3-L. Fig. 9 is a graph that shows the dimensional change that occurred during heat treatment for Test Specimen No. 4-L.

[0072] The horizontal axis of the graphs in Figures 4 to 9 indicates the tempering temperature, and the vertical axis indicates the dimensional change calculated by equation (7). "As (Q)" on the horizontal axis indicates that quenching was performed by semi-cooling from 1020°C for 10 minutes. The gray area indicates the range calculated for each heat treatment condition, with ave ±3 seconds.

[0073] The dimensional change rate is shown as a positive value when the dimension, indicating expansion, increases after heat treatment, and a negative value when the dimension, indicating shrinkage, decreases. Black circles indicate the measured value of the dimensional change rate in the L direction. Black squares indicate the dimensional change rate in the W direction. Black triangles indicate the dimensional change rate in the T direction. s is the sample standard deviation calculated from the numerical values ​​of the three dimensional change rates in the L, W, and T directions at each tempering temperature.

[0074] Table 3 shows the isotropic variable Q and the sample standard deviation s when the tempering temperature is 500° C. The isotropic variable Q is the same as the value shown in Table 1.

[0075]

[0076] FIG. 10 is a graph showing the relationship between the isotropic variable Q and the sample standard deviation s of the dimensional change rate. The original data for the graph is Table 3. The horizontal axis of FIG. 10 represents the isotropic variable Q. The vertical axis represents the standard deviation of the dimensional change rate. The test specimen number is shown adjacent to each data point. FIG. 10 is an example of a graph using the isotropic variable Q on the first axis and the dimensional change characteristics on the second axis.

[0077] In FIG. 10, the larger the value on the vertical axis, the larger the difference in dimensional change rate in the L, W and T directions, indicating a material with strong anisotropy, and the smaller the value on the vertical axis, the smaller the material's anisotropy.

[0078] 4 and 5, it can be seen that Test Specimen No. 1-S had a larger sample standard deviation s than Test Specimen No. 1-L, regardless of the tempering temperature. Referring to Table 3, even though Test Specimen No. 1-S was sampled from the same forging 22, which is steel ingot No. 1, the sample standard deviation s of Test Specimen No. 1-S was 11% or more larger than the sample standard deviation s of Test Specimen No. 1-L. From the above, it can be seen that the forging 22 of steel ingot No. 1 has strong anisotropy.

[0079] Similarly, when Fig. 6 and Fig. 7 are compared, it can be seen that for both Test Specimen No. 2-L and Test Specimen No. 2-S, the difference in the specimen standard deviation s due to the tempering temperature was small. With reference to Table 3, the difference between the specimen standard deviation s of Test Specimen No. 2-S and the specimen standard deviation s of Test Specimen No. 2-L was about 5%. From the above, it can be seen that the forged material 22 of Steel Ingot No. 2 had weak anisotropy.

[0080] With reference to Table 3, in steel ingot No. 1, which has strong anisotropy, both Test Specimen No. 1-L and Test Specimen No. 1-S have small isotropic variable Q. In steel ingot No. 2, which has weak anisotropy, both Test Specimen No. 2-L and Test Specimen No. 2-S have large isotropic variable Q. With reference to Fig. 10, it can be seen that there is a negative correlation between the isotropic variable Q and the sample standard deviation s.

[0081] From the above, it can be seen that there is a correlation between the anisotropy of the forged material 22, which is the variation in dimensional deformation characteristics depending on the direction, and the isotropic variable Q defined by equation (6), and that the isotropic variable Q can be used as a proxy characteristic indicating the degree of anisotropy of the forged material 22.

[0082] Next, the calculation of the contribution ratio of the main components will be described. After the dimensional change measurement sample 41 was taken, the remaining forged material 22 was subjected to a quenching treatment by semi-cooling from 1,020°C for 10 minutes. After the quenching treatment, the material was tempered twice at 500°C to produce a quenched and tempered material.

[0083] A mapping sample 42 measuring 30 mm in the L direction, 25 mm in the W direction, and 20 mm in the T direction was taken from the quenched and tempered material. One of the TL faces was mirror-polished to create a mapping region 421.

[0084] The elemental mapping function of the FE-EPMA was used to obtain mapping data on the matrix composition of the mapping region 421. The measurement conditions for the FE-EPMA are, for example, a measurement field of view of 8 mm × 4 mm, a beam diameter of 20 μm, an acceleration voltage of 15 kV, and an irradiation current of 0.1 μA. The mapping region 421 is made larger than the measurement field of view.

[0085] Mapping data of the Ms point was created from the mapping data relating to the matrix composition. The subsequent processing may be executed by the control unit 11 or by other hardware.

[0086] Figure 11 shows an example of an Ms point mapping image, which is a visualization of the Ms point mapping data. Due to the limitations of the electronic application software used for filing, the image is in black and white, but in reality, the Ms points measured at each measurement point are color-mapped, with blue representing the low-temperature side and red representing the high-temperature side. The white areas indicate measurement points where martensitic transformation does not occur and therefore the Ms point cannot be evaluated.

[0087] Before performing the binarization process described below, it is preferable to perform an equalization process on the Ms mapping image using commercially available image processing software such as ImageJ. This normalization process involves color-coding (subtractive color conversion) for each layer in accordance with a data structure called a lookup table, which divides the distribution of countless Ms points into equally spaced layers and was created to improve efficiency by replacing Ms point data with a simple array lookup process. This lookup table can be a lookup table built into commercially available image processing software.

[0088] The Ms point mapping image is subjected to binarization processing to convert it into a black and white image. Figure 12 shows an example of the binarized image obtained by performing normalization processing and binarization processing on the image shown in Figure 11. The threshold value for the binarization processing was determined using discriminant analysis (Otsu's method).

[0089] Principal component analysis was performed on the binarized image using the Python-OpenCV library. Figure 13 shows the principal component vectors obtained by principal component analysis superimposed on Figure 11. A large arrow indicating the first principal component and a small arrow indicating the second principal component extend from a block in the binarized image. Arrows that are too small are displayed as dots.

[0090] Although the image is in black and white due to limitations of the electronic application software used for the application, in reality, the first principal component is indicated by a red arrow and the second principal component is indicated by a blue arrow in FIG.

[0091] The largest first principal component was extracted from one Ms point mapping image. The contribution ratios of the first and second principal components were calculated using equations (8) and (9) based on the magnitude (a) of each first principal component and the magnitude (b) of the second principal component with a common origin. Table 4 shows the magnitude and contribution ratios of the principal components and the isotropy variable Q.

[0092]

[0093] Fig. 14 is a graph showing the relationship between the isotropic variable Q and the contribution rate of the first principal component. The original data for the graph is Table 4. The horizontal axis of Fig. 14 represents the isotropic variable Q. The vertical axis represents the contribution rate of the first principal component. The steel ingot number is shown adjacent to each data point.

[0094] From equation (8), the value of the first principal component is greater than 0.5. As mentioned above, the closer the value of the first principal component is to 0.5, the smaller the temperature difference at the Ms point between the measurement point and its surrounding points, indicating that the distribution of the Ms points is isotropic. Therefore, steel materials with a contribution ratio of the first principal component close to 0.5 are likely to be steel materials that are less susceptible to dimensional changes due to heat treatment.

[0095] 14, it can be seen that there is a negative correlation between the isotropic variable Q and the first principal component contribution ratio. Therefore, it can be seen that the isotropic variable Q, like the first principal component contribution ratio, can be used as a proxy characteristic indicating the degree of anisotropy of the forged material 22.

[0096] 15 is a flowchart illustrating the processing flow of the program 97 for calculating the isotropic variable Q. The control unit 11 acquires the forging ratio FR (step S501). Generally, the forging ratio FR is an important control item in the steel forging process, and is recorded together with the lot number, etc.

[0097] The control unit 11 acquires the dimensions (step S502). For example, the control unit 11 accepts input of dimensions by the user. The user inputs, for example, the dimensions of a dimensional change measurement sample 41 that has actually been produced or is planned to be produced. The user may also input the dimensions of a virtual dimensional change measurement sample 41.

[0098] The control unit 11 calculates the isotropic variable Q based on equation (6) (step S503). The control unit 11 outputs the calculated isotropic variable Q. The control unit 11 then ends the process.

[0099] For example, the user inputs the dimensions of the virtual material in step S502 and checks the isotropic variables output in step S504. Based on the check results, the user determines whether to actually prepare a dimensional change measurement sample 41 and measure the dimensional change characteristics, or to proceed with the production of the product without measuring.

[0100] According to this embodiment, the isotropic variable Q is used as a substitute characteristic, thereby making it possible to easily estimate the dimensional change characteristics of the forged material 22. In other words, the use of the isotropic variable Q makes it possible to realize an evaluation method for obtaining steel with stable quality.

[0101] It is known that the properties of steel vary greatly depending on the amount of elements. For example, the graph described using Figure 10 can be created by actually measuring the dimensional deformation properties of an existing forged material 22. It is desirable to confirm in the created graph that there is a correlation between the isotropic variable Q and the dimensional deformation properties.

[0102] Thereafter, improved versions of the steel can be developed in a relatively short time by making minor adjustments to the composition and forging process and evaluating the isotropic parameter Q. Adjustments to the forging process may be made to obtain the desired isotropic parameter Q.

[0103] Recommended dimensions for parts manufactured from forging 22 may be established so that the isotropic parameter Q falls within an acceptable range.

[0104] A computer program can be deployed to be executed on a single computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

[0105] The technical features (constituent elements) described in each embodiment can be combined with each other, and by combining them, new technical features can be formed. The embodiments disclosed herein are illustrative in all respects and should be considered not to be limiting. The scope of the present invention is defined by the claims, not by the meaning described above, and is intended to include all modifications within the meaning and scope of the claims.

[0106] Independent and dependent claims may be combined with each other in any combination, regardless of the reference format. Furthermore, while the claims use a format in which a claim references two or more other claims (multiple claim format), this is not a limitation. Multiple claims that reference at least one other claim (multi-multi claim format) may also be used.

[0107] REFERENCE SIGNS LIST 10 Information processing device 11 Control unit 12 Main memory device 13 Auxiliary memory device 14 Communication unit 15 Display unit 16 Input unit 19 Reading unit 21 Steel ingot 22 Forged material 31 As-cast unit cell 32 Forged unit cell 40 Sample 41 Sample for dimensional change measurement 42 Sample for mapping 421 Mapping area 96 Portable recording medium 97 Program 98 Semiconductor memory

Claims

1. A method for evaluating steel, comprising the steps of: obtaining a forging ratio when a steel ingot is forged to obtain a forged material; obtaining dimensions of a rectangular parallelepiped sample of said forged material, one side of which is in the elongation direction during forging; and calculating a variable related to the isotropy of said sample based on said forging ratio and said dimensions.

2. The method for evaluating steel according to claim 1, wherein the variable is an isotropic variable, and the isotropic variable is calculated by the following formula (1). Q is an isotropic variable. SL is the length of the side of the sample in the stretch direction. ST is the length of one side of the sample perpendicular to the stretch direction. SW is the length of the other side of the sample perpendicular to the stretch direction. FR is the forging ratio.

3. The method of evaluating steel according to claim 2, further comprising the steps of: measuring the dimensional change characteristics of samples taken from each of a plurality of forged materials; calculating the isotropic variables for each sample; and outputting a graph based on the isotropic variables and the dimensional change characteristics, with the isotropic variables on the first axis and the dimensional change characteristics on the second axis.

4. The method for evaluating steel according to claim 3, wherein the dimensional change characteristic is a sample standard deviation of the dimensional change rate that occurred in the direction of each side when the sample was heat treated.

5. A program that causes a computer to execute the following processes: obtaining a forging ratio when a steel ingot in an as-cast state is forged; obtaining dimensions of a rectangular parallelepiped sample having one side in the elongation direction during forging for the steel ingot after forging; and calculating variables related to the isotropy of the sample based on the forging ratio and the dimensions.

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