Steel quenching simulation method, information processing method, program, and information processing device

Abstract

To provide a quenching simulation method for steel with high accuracy.SOLUTION: Provided is a quenching simulation method for steel, comprising: a calculation section 1 calculating a distribution of temperatures by a heat conduction analysis using a finite element method after inputting each of steel phases and physical properties at each of steel temperatures to separate a shape model; a calculation section 2 calculating distribution of strain or stress by an elastoplastic analysis; a calculation section 3 calculating distribution of phase fraction through calculation of a basic equation representing phase transformation; and a calculation section 4 calculating distribution of cooling speed from time change of temperature of each of elements, the method comprising a step of carrying out coupled analysis using phase transformation parameters of the steel calculated from the cooling speed calculated with the calculation section 4 on a procedure to calculate the phase fraction in the calculation section 3.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a steel quenching simulation method, an information processing method, a program, and an information processing device. [Background technology]

[0002] Traditionally, computer-based simulation methods have been used to predict the state of steel after quenching. In particular, in the quenching of die-casting dies, the steel is thick, and the effects of heat treatment on the surface and interior are likely to differ. This has led to a demand for simulation methods that can predict steel temperature, stress, and phase fraction as distributed values ​​between the surface and interior. Quenching simulation differs from general structural analysis in that it deals with steel phase transformations. Steel phase transformations, such as martensitic and bainite transformations, are characterized by the material composition, the microscopic carbon diffusion state, or temperature and time, which determine the phases and transformation temperatures that appear. For this reason, the finite element method (FEM) has traditionally employed a method in which multiple material property data corresponding to each phase are stored for each element, and the phase fractions that appear are then calculated. High-precision simulations require computational and data processing methods for accurately predicting phase fractions. In Non-Patent Document 1, in a method for performing coupled analysis of temperature, strain amount, stress, and phase fraction, a fundamental equation for calculating phase fraction that includes carbon concentration distribution dependency is introduced to calculate the change in the martensitic transformation start temperature. Patent Document 1 also uses a quenching simulation method that takes phase transformation into account from the cooling rate. This proposal is excellent in that it determines the phase fraction from the continuous cooling transformation (CCT) of the steel and uses it in heat conduction analysis. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2023-112708 A (page 9, Figures 1-4) [Non-patent literature]

[0004] [Non-Patent Document 1] Shigeru Yamanaka et al., "Effect of Transformation Plasticity on Deformation after Carburizing and Quenching," Journal of Materials Science, Vol. 48, No. 7, 1999, pp. 733-739 Summary of the Invention [Problem to be solved by the invention]

[0005] The simulation method disclosed in the above-mentioned Non-Patent Document 1 has the advantage of being able to consider the dependence on carbon concentration in the phase fraction calculation, but it requires the collection of basic data that shows the relationship between the carbon concentration distribution and the phase fraction. It is difficult to investigate the carbon concentration data in detail for a die-casting die, which is a major problem in performing a highly valid simulation. The simulation method disclosed in the aforementioned Patent Document 1 is advantageous in that it reflects the dependency on cooling rate in phase fraction calculations and can determine physical quantities after quenching with a small amount of calculation. However, the main calculation section relies on heat conduction analysis and does not handle calculations of strain or stress. To predict phenomena such as quench cracking due to stress generation, it is necessary to perform a coupled analysis in which the calculation results of each calculation section are exchanged at each step to determine stress. Furthermore, in methods for determining phase fractions from CCT, it is difficult to represent changes in cooling rate due to, for example, switching cooling methods, which creates the constraint of treating the cooling rate at each time as a constant value. An object of the present invention is to provide a highly accurate steel quenching simulation method that accurately predicts the phase fraction of steel by determining values ​​at each position and each time for a cooling rate, for which data collection is relatively easy, and using the values ​​in a numerical calculation of the phase fraction, and then performs a coupled analysis including an elastic-plastic analysis. [Means for solving the problem]

[0006] The inventors have investigated the problem of collecting basic data for performing highly valid simulations, and have found that by determining the phase transformation parameters of steel from the cooling rate, highly accurate prediction of phase fractions can be achieved by collecting data relatively easily, thereby arriving at the present invention. That is, the present invention is a method for simulating the quenching of steel, comprising: calculation unit 1 which inputs the physical property values ​​of each phase of steel and each temperature as material data, divides a geometric model, and determines the temperature distribution by heat conduction analysis using the finite element method; calculation unit 2 which determines the strain or stress distribution by elastic-plastic analysis; calculation unit 3 which calculates the phase fraction distribution by calculating the fundamental equation representing phase transformation; and calculation unit 4 which determines the cooling rate distribution from the change in temperature of each element over time, and which performs a coupled analysis using the phase transformation parameters of the steel calculated from the cooling rate determined by calculation unit 4 in the procedure for determining the phase fraction in calculation unit 3. Preferably, the method is a method for simulating the hardening of steel for outputting thermal stress or thermal deformation to a die-casting die. [Effects of the Invention]

[0007] The method for simulating quenching of steel according to the present invention is advantageous in that it can calculate the phase fraction using the cooling rate, which is relatively easy to handle, as a variable, and therefore has the effect of being able to accurately predict the distribution of phase fractions. Therefore, a quenching simulation using this method has the effect of being able to predict the state of steel after quenching with high precision. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a conceptual diagram of coupled analysis according to the present invention. [Figure 2] FIG. 1 is a diagram showing an example of a continuous cooling transformation diagram (CCT) of steel. [Figure 3] FIG. 1 is a diagram showing an example of the relationship between the cooling rate and the phase transformation parameters. [Figure 4] FIG. 10 is a diagram illustrating an example of a dilatometry curve. [Figure 5] FIG. 2 is a diagram showing an example of the distribution of cooling rates in Example 1. [Figure 6] FIG. 2 is a diagram showing an example of the distribution of phase fractions in Example 1. [Figure 7] FIG. 10 is a diagram showing the distribution of cooling rates in Example 2. [Figure 8] FIG. 10 is a diagram showing the distribution of the bainite phase fraction in Example 2, calculated using the phase transformation parameters of No. 3 in Table 3. [Figure 9] FIG. 10 is a diagram showing the distribution of the bainite phase fraction of a comparative example calculated using the phase transformation parameters of No. 4 in Table 3. [Figure 10] 1 is a graph illustrating the difference in bainite phase fraction between Example 2 and the comparative example. [Figure 11] FIG. 1 is an explanatory diagram illustrating a configuration of an information processing device. [Figure 12] 10 is a flowchart illustrating the flow of processing of a program. [Figure 13] 10 is a flowchart illustrating a process flow of a phase fraction calculation subroutine. DETAILED DESCRIPTION OF THE INVENTION

[0009] [Embodiment 1] As described above, an important feature of the present invention is that the cooling rate is defined as a variable to be used in the numerical calculation, the phase transformation parameters are calculated, the phase fractions are determined, and a coupled analysis is performed. The method for simulating quenching of steel according to the present invention will be described in detail below.

[0010] Figure 1 is a conceptual diagram of the coupled analysis according to the present invention. The simulation program used was a combination of a commercially available numerical analysis program using the finite element method, which has a GUI and pre- and post-processing functions, and a program created in accordance with the present invention. In the coupled analysis shown in Figure 1, a commercially available program was used as is for calculation units 1 and 2, which have functions for handling the transfer of variables between calculation units, while a program created in accordance with the present invention was used for calculation units 3 and 4.

[0011] To perform the numerical calculations, the physical properties of each steel phase and at each temperature were input and used as material data. The steel of the present invention is an alloy steel, such as SKD61 (JIS G4404 hot work die steel). The steel phases considered were austenite, ferrite, cementite, pearlite, bainite, and martensite, which are known as common steel structures. In particular, austenite, bainite, and martensite, which are the main structures in quenching, were treated as the main phases. While these structures are typically distinguished by differences in microscopic crystal structure and composition, for the purposes of the numerical calculations, each structure was defined as a separate phase. Furthermore, the composite state was quantitatively expressed by phase fraction. Regarding physical properties, each phase was treated as having its own individual physical property value. Furthermore, the physical properties of the composite state were calculated by weighting the physical property values ​​of each phase based on the phase fraction. To perform the coupled analysis shown in Figure 1, the physical property values ​​used were material data corresponding to the calculations in each calculation section. That is, for calculation section 1, thermophysical properties such as density, thermal conductivity, and specific heat were input; for calculation section 2, mechanical properties such as Young's modulus, Poisson's ratio, and deformation resistance were input; and for calculation section 3, phase transformation parameters for defining the amount of transformation were input.

[0012] As mentioned above, the division of the geometric model and the numerical analysis by the finite element method were performed using a commercially available program. As with the general method of numerical analysis by the finite element method, the element division was performed with a reasonable number of divisions, taking into account the accuracy of the calculation and the computing power of the computer.

[0013] Calculation unit 1, which determines the temperature distribution through heat conduction analysis, handles the above-mentioned material data as input data for numerical calculations, as well as boundary conditions that represent the cooling or heating state of the steel and the transformation latent heat determined by calculation unit 3. The basic equation used is a general heat conduction equation for heat conduction analysis. The output data from calculation unit 1 includes the temperature and thermal strain at each position and time in the steel. The boundary conditions used to represent the cooling or heating state of the steel were temperature boundary conditions and heat transfer boundary conditions. For example, when using heat transfer boundary conditions, the quenching process, such as air cooling, oil cooling, and water cooling, was expressed by defining the ambient temperature and heat transfer coefficient.

[0014] Calculation section 2, which determines the amount of strain or stress distribution through elastic-plastic analysis, handles the above-mentioned material data as input data for numerical calculations, as well as the constraint conditions representing the steel fixing method, the temperature and thermal strain determined in calculation section 1, and the transformation strain and transformation plastic strain determined in calculation section 3. The fundamental equations used are general constitutive equations for elastic-plastic analysis. The output data from calculation section 2 includes stress, elastic strain, and plastic strain at each position and time in the steel. In calculation section 2, it is assumed that the steel is placed stationary in a heat treatment furnace, and minimum constraints are imposed to prevent translation or rotation of the steel, allowing it to deform in any direction. The calculation unit 2 can handle deformation and processing heat generated when a load is applied by an external processing device as a model. The present invention is targeted at quenching, and has not considered plastic processing by external processing devices in detail, but it is believed that the present invention can be applied to such cases by collecting the necessary material data.

[0015] Calculation unit 3 calculates the phase fraction distribution by calculating the fundamental equations representing phase transformations. In addition to the material data described above, the input data for the numerical calculations used were the temperature determined by calculation unit 1, the stress determined by calculation unit 2, and the cooling rate determined by calculation unit 4. The fundamental equations used were, for example, the Koistinen-Marburger equation or the Johnson-Mehl-Avrami-Kolmogorov equation. The output data from calculation unit 3 included the phase fraction, transformation latent heat, transformation strain, and transformation plastic strain at each position and time in the steel. The fundamental equations that describe phase transformations must be selected appropriately depending on the combination of parent and child phases. For the martensitic transformation from austenite to martensite, the Koistinen-Marburger equation shown in equation (1) was used, and for the bainite transformation from austenite to bainite, the Johnson-Mehl-Avrami-Kolmogorov equation shown in equation (2) was used. In equation (1), f1 represents the phase fraction, T represents the temperature, and a and TM represent the phase transformation parameters. In equation (2), f2 represents the phase fraction, t represents the time, and b and n represent the phase transformation parameters. f1=1-a*exp(T-TM) Equation (1) f2=1-exp(-b*t^n)...Equation (2)

[0016] Calculation unit 4, which calculates the cooling rate distribution from the time change in temperature of each element, uses the temperatures calculated by calculation unit 1 as input data for performing numerical calculations. The basic equation used is the cooling rate calculation formula shown in equation (3), for example. The cooling rate was treated as output data from calculation unit 4. In equation (3), CR represents the cooling rate, ΔT represents the temperature rise per step, and Δt represents the time width per step. CR=ΔT / Δt Equation (3)

[0017] It is preferable to use the CR calculated by equation (3) as is. However, if Δt fluctuates significantly in the numerical calculation, for example due to the generation of transformation latent heat, the CR may fluctuate significantly unintentionally. This fluctuation is not only due to actual phenomena, but also due to convenience in data processing of the numerical calculation. When fluctuations occur, smoothing processing may be performed in the calculation to stabilize the calculation. One method of smoothing is to take a moving average of the CR at each time.

[0018] The aforementioned calculation unit 3 calculates the phase fraction from time, temperature, and phase transformation parameters. Here, time is a variable common to the coupled analysis, and temperature is a variable calculated from calculation unit 1. The phase transformation parameters are variables calculated from the stress calculated from calculation unit 2, the cooling rate calculated from calculation unit 4, and the initial values. A method for determining the phase transformation parameters of formulas (1) and (2) using the cooling rate determined by calculation unit 4, which is a feature of the present invention, will be described.

[0019] Figure 2 shows the CCT. The horizontal axis represents the time elapsed since the start of cooling, and the vertical axis represents the temperature. The three curves extending from the high temperature point at time 0 are cooling curves that represent the relationship between time and temperature when the cooling rate is constant. The curves other than the cooling curves are transformation curves, and represent the transformation start temperature or transformation end temperature.

[0020] In Figure 2, the line marked with the symbol Ms is a transformation curve that indicates the conditions under which martensitic transformation begins when the temperature of the steel is decreased. In the following explanation, the transformation curve marked with the symbol Ms may be referred to as the MS point transformation curve. Here, the MS point refers to the temperature at which the transformation from austenite to martensite begins.

[0021] Similarly, the line marked with the symbol Bs is a transformation curve that indicates the conditions under which the bainite transformation begins when the temperature of the steel is decreased. In the following explanation, the transformation curve marked with the symbol Bs may be referred to as the BS point transformation curve. Here, the BS point refers to the temperature at which the transformation from austenite to bainite begins.

[0022] Regarding the method for calculating the phase transformation parameters of Equation (1), when we look at the transformation curve of the MS point in Figure 2, we can see that the MS point decreases on the long-term side, i.e., on the slow cooling side. This corresponds to the change of TM, one of the phase transformation parameters in Equation (1), to a lower value.

[0023] Figure 3 is a graph showing the relationship between the cooling rate and TM based on the relationship in Figure 2. The horizontal axis represents the cooling rate, and the vertical axis represents TM. By expressing this relationship in the form of a table or an approximate formula in the calculation unit 3, the phase transformation parameter TM can be obtained from the cooling rate.

[0024] Next, regarding how to calculate the phase transformation parameters in equation (2), Figure 4 shows an example of a dilatometry curve that includes bainite transformation at a certain cooling rate. The horizontal axis represents temperature, and the vertical axis represents the amount of deformation of the steel. The amount of deformation is calculated from a combination of the linear expansion coefficients of austenite, bainite, and martensite, as well as the transformation strain that occurs when austenite transforms to bainite and when austenite transforms to martensite. Because the magnitude of the transformation strain is determined by the phase fraction, Figure 4 is also a graph that shows the change in phase fraction.

[0025] By regarding the cooling rate CR in equation (3) as the derivative of temperature with respect to time (dT / dt), and differentiating and rearranging equation (2), equation (4) can be obtained. df2 / dT=1 / CR*n*b^(1 / n)*[(-ln(1-f2))^((n-1) / n)]*(1-f2)...Equation (4)

[0026] Equation (4) expresses the change in phase fraction f2 in terms of cooling rate, temperature, phase fraction, and phase transformation parameters. The left-hand side of equation (4), df2 / dT, can be converted to the slope shown in Figure 4 using the linear expansion coefficient and transformation strain. That is, by determining the cooling rate, temperature, and phase fraction for any point in Figure 4 and substituting them into equation (4), the corresponding phase transformation parameters n and b can be determined. Figure 4 shows graph shapes that vary depending on the cooling rate, so the relationship between CR, n, and b can be determined for each corresponding cooling rate. By expressing this relationship in table form or approximate formula form in calculation section 3, the phase transformation parameters n and b can be determined from the cooling rate.

[0027] When determining the phase transformation parameters using the above method, in order to ensure calculation accuracy, it is advisable to select the point with the smallest gradient (negative value) when selecting any point from Figure 4. Furthermore, among the phase transformation parameters, it is easier to obtain high calculation accuracy by keeping n constant, for example, at an easy-to-handle value such as n = 2.

[0028] The steel quenching simulation method of the present invention is particularly suitable for die-casting die products, as it determines the distribution of cooling rates and calculates strain and stress. Die-cast products are often used with a thickness of 100 mm or more. It is known that the larger the size, the more different the cooling rates between the surface and core. In such cases, the steel quenching simulation method of the present invention is appropriate. [Example]

[0029] The following examples further illustrate the calculation of martensitic transformation according to the present invention. A 1 / 4 scale 3D model with a symmetric boundary was created, with a square plate shape measuring 500 mm on each side (250 mm on the computer due to symmetry) and 100 mm thick. Element division was performed, and the material data for each phase and the physical properties of the alloy steel at each temperature were entered. A finite element analysis was performed. The temperature boundary conditions were entered as a time-temperature combination on the top surface of the square (the +Z side of the XYZ coordinate system) such that the temperature change per unit time was -0.2°C / s. The bottom surface of the square (the -Z side of the XYZ coordinate system) was entered as a time-temperature combination such that the temperature change per unit time was -0.1°C / s. The cooling rates for the top surface and bottom surface were adjusted to CR = -0.2°C / s and CR = -0.1°C / s, respectively. The fundamental equations used to describe the phase transformation were Equation (1) for martensitic transformation and Equation (2) for bainitic transformation. In the example, the phase transformation parameters were determined depending on the cooling rate using a program having calculation units 1, 2, 3, and 4, and the steel quenching simulation method of the present invention was performed. In the comparative example, the phase transformation parameters were set to constant values ​​regardless of the cooling rate using an existing program having calculation units 1, 2, and 3. Of the input data, the phase transformation parameters used in the calculation of martensitic transformation are shown in Table 1.

[0030] [Table 1]

[0031] Figure 5 shows the distribution of cooling rates determined by the present invention. It can be seen that the cooling rates on the top and bottom surfaces are different.

[0032] Figure 6 shows the relationship between the temperature and the phase fraction at the center points of the top surface and the bottom surface, as determined by the steel quenching simulation method of the present invention. Focusing on the transformation start temperature, it can be seen that in the comparative example, the transformation starts at the same temperature regardless of position, whereas in the present invention, the transformation starts at different temperatures between the surface and the core depending on the distribution of the cooling rate as shown in Figure 5. In other words, it can be seen that the present invention can more accurately predict the phase fraction of steel as a value depending on the cooling rate. [Example]

[0033] In the following examples, calculation of bainite transformation will be described. The difference from Example 1 is the cooling rate, but the other calculation conditions are the same as those of Example 1. Table 2 shows the cooling conditions of Example 1 and Example 2 side by side.

[0034] [Table 2]

[0035] As shown in Table 2, the cooling rate in Example 2 is half that of Example 1, and bainite transformation occurs during cooling. Therefore, Equation (2) was used as the fundamental equation for expressing the phase transformation. The phase transformation parameters used in the calculation of the bainite transformation are shown in Table 3.

[0036] [Table 3]

[0037] Fig. 7 is a diagram showing the distribution of cooling rates in Example 2. The cooling rates in Fig. 7 are data calculated by calculation unit 4 in Fig. 1. It can be seen that the cooling rates have different values ​​on the top and bottom surfaces.

[0038] Fig. 8 is a diagram showing the distribution of the bainite phase fraction for Example 2, calculated using the phase transformation parameter No. 3 in Table 3. Fig. 9 is a diagram showing the distribution of the bainite phase fraction for Comparative Example, calculated using the phase transformation parameter No. 4 in Table 3. Both Fig. 8 and Fig. 9 show the distribution of the bainite phase fraction at the end of quenching in gray scale. White areas indicate areas with a high bainite phase fraction, and areas closer to black indicate areas with a low bainite phase fraction.

[0039] As can be seen from Fig. 8, in Example 2, a layer with a relatively high bainite phase fraction exists near the top surface, and a layer with a low bainite phase fraction exists inside that. The bainite phase fraction is high in approximately the lower half of the specimen. As can be seen from Fig. 9, in the comparative example, the bainite phase fraction increases almost monotonically from the top surface to the bottom surface.

[0040] FIG. 10 is a graph illustrating the difference in bainite phase fraction between Example 2 and the Comparative Example. The horizontal axis indicates the position along the center of the surface shown in FIGS. 8 and 9. The left end of the horizontal axis indicates the intersection point between the top surface and the center of the surface, and the right end of the horizontal axis indicates the intersection point between the bottom surface and the center of the surface. The vertical axis indicates the bainite phase fraction. The bainite phase fraction increases upward along the vertical axis. The solid line indicates the distribution of bainite phase fraction for Example 2 shown as No. 3 in Table 3. The dashed line indicates the distribution of bainite phase fraction for the Comparative Example shown as No. 4 in Table 3.

[0041] The graph in Fig. 10 shows the change in bainite phase fraction along the plane center with higher resolution than Figs. 8 and 9, which are displayed in grayscale. According to Fig. 10, the change in bainite phase fraction with position is more drastic in Example 2 than in the Comparative Example. The graph in Example 2 matches the phenomenon actually observed, and it is clear that the bainite phase fraction can be predicted with high accuracy according to this embodiment.

[0042] This embodiment makes it possible to perform coupled analysis that incorporates the distribution of cooling rates in steel. Therefore, this analysis method can be applied to applications where it is essential to design a heat treatment method that takes into account the distribution of cooling rates, such as the thermal stress and thermal deformation that occurs during heat treatment of die-casting dies.

[0043] According to this embodiment, in the procedure for calculating the phase fraction in the calculation unit 3, the phase transformation parameters calculated from the cooling rate obtained in the calculation unit 4 are used, thereby realizing coupled analysis.

[0044] The phase transformation parameters shown in No. 1 of Table 1 and No. 3 of Table 3 are examples. The phase transformation parameters are determined appropriately based on, for example, data actually measured for each component of the steel material.

[0045] [Embodiment 2] This embodiment relates to hardware and software used in the simulation method described in embodiment 1. Explanation of parts common to embodiment 1 will be omitted.

[0046] 11 is an explanatory diagram illustrating the configuration of an 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.

[0047] 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.

[0048] 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.

[0049] The auxiliary storage device 13 is a storage device such as an SRAM, a flash memory, a hard disk, or a magnetic tape. The auxiliary storage device 13 stores programs to be executed by the control unit 11, a heat conduction analysis simulation software 31, an elastic-plastic analysis simulation software 32, and various data required for executing each software. The heat conduction analysis simulation software 31 and the elastic-plastic analysis simulation software 32 may be stored in an external mass storage device connected to the information processing device 10.

[0050] The communication unit 14 is an interface for communicating 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.

[0051] 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.

[0052] 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 installed inside the information processing device 10.

[0053] 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.

[0054] 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.

[0055] The program 97 is installed as a control program for the information processing device 10, and is executed by being loaded into the main storage device 12. The program 97 of this embodiment is an example of a program product.

[0056] The heat conduction analysis simulation software 31 is software that uses a numerical analysis method such as finite element analysis to simulate the heat conduction behavior when the temperature of the object to be analyzed differs from the ambient temperature. The heat conduction analysis simulation software 31 of this embodiment includes a so-called mesh creation function that divides the object to be analyzed into a plurality of finite elements. Note that the mesh creation function may be realized by a separate program that operates in cooperation with the heat conduction analysis simulation software 31 and the elastic-plastic analysis simulation software 32. The heat conduction analysis simulation software 31 realizes the function of the calculation unit 1 in FIG. 1.

[0057] The elastic-plastic analysis simulation software 32 is software that uses numerical analysis methods such as finite element analysis to simulate the elastic deformation behavior and plastic deformation behavior of an object to be analyzed when the temperature is not uniform. The elastic-plastic analysis simulation software 32 realizes the functions of the calculation unit 2 in FIG. 1.

[0058] In the following description, an example will be given in which the element division of the analysis object by the heat conduction analysis simulation software 31 is commonly used in subsequent processing. Each divided element will be referred to as a finite element.

[0059] Since the heat conduction analysis simulation software 31 and the elasto-plastic analysis simulation software 32 are both publicly known, detailed explanations of the processes will be omitted. Information input to and output from each simulation software will be described later.

[0060] The heat conduction analysis simulation software 31 and the elastic-plastic analysis simulation software 32 may be provided in the form of SaaS (Software as a Service) by a large scale computer connected to the information processing device 10 via a network or a cloud service.

[0061] Fig. 12 is a flowchart explaining the flow of program processing. The program in Fig. 12 is a program that simulates the state when a steel analysis object heated to the quenching temperature is cooled to room temperature.

[0062] The control unit 11 acquires the calculation conditions (step S501). The calculation conditions include information about the shape of the object to be analyzed, information about the physical property values ​​of the object to be analyzed, and information about the initial state of the analysis. The calculation conditions are supplied, for example, by a text file in which data is recorded according to a predetermined format. The calculation conditions may be input by the user via the input unit 16 each time the program is executed.

[0063] Information about the shape of the object to be analyzed is expressed, for example, by a 3D-CAD (3-dimensional computer-aided design) model. If the object to be analyzed has a simple shape, such as a square plate, information about the shape of the object to be analyzed may be expressed by several parameters, such as a code indicating the shape, the length of one side, and the thickness.

[0064] The information on the physical properties of the object of analysis includes the thermophysical and mechanical properties of each phase of the steel that constitutes the object of analysis at each temperature. The thermophysical properties include density, thermal conductivity, and specific heat. The mechanical properties include Young's modulus, Poisson's ratio, and deformation resistance during plastic deformation.

[0065] The information about the initial state is, for example, the initial temperature and boundary conditions of the object to be analyzed. The initial temperature is, for example, the quenching temperature, that is, the temperature immediately before heating ends and cooling begins. When calculating the state after quenching, the initial temperature of the object to be analyzed is uniform. Initial conditions may also be input in which the temperature of the object to be analyzed varies from part to part.

[0066] The boundary condition is, for example, the ambient temperature of the object to be analyzed. The ambient temperature is, for example, room temperature. When analyzing a situation in which the object to be analyzed is held by a jig or a stand, the temperature at the contact point between the object to be analyzed and the jig or the stand is also the ambient temperature of the object to be analyzed.

[0067] The control unit 11 performs a heat conduction analysis (step S502). Specifically, the control unit 11 inputs input parameters to the heat conduction analysis simulation software 31 and acquires output parameters indicating the heat conduction analysis results after the time width Δt has elapsed. Table 4 shows example items of the input parameters and output parameters of the heat conduction analysis simulation software 31. The output parameters of the heat conduction analysis simulation software 31 are examples of the heat conduction analysis results.

[0068] [Table 4]

[0069] The control unit 11 calculates the cooling rate distribution (step S503). Specifically, the control unit 11 calculates, for each finite element, ΔT, which is the difference between the latest temperature and the temperature at the time of the previous iterative calculation. The control unit 11 calculates the cooling rate CR using equation (3). The control unit 11 realizes the function of the calculation unit 4 in FIG. 1 through step S503.

[0070] The control unit 11 performs an elastic-plastic analysis (step S504). Specifically, the control unit 11 inputs input parameters to the elastic-plastic analysis simulation software 32 and acquires output parameters at the same time. Table 5 shows example items of input parameters and output parameters of the elastic-plastic analysis simulation software 32. The output parameters of the elastic-plastic analysis simulation software 32 are examples of the elastic-plastic analysis results.

[0071] [Table 5]

[0072] The control unit 11 starts a phase fraction calculation subroutine (step S505). The phase fraction calculation subroutine is a subroutine that calculates the phase fraction distribution in the object to be analyzed. The processing flow of the phase fraction calculation subroutine will be described later. The control unit 11 realizes the function of the calculation unit 3 in FIG. 1 by the phase fraction calculation subroutine.

[0073] The control unit 11 determines whether or not to end the iterative calculation process (step S506). The control unit 11 determines to end the iterative calculation process when the cooling of the object to be analyzed is completed. Specifically, the control unit 11 determines to end the process when, for example, the temperature of the object to be analyzed obtained in step S502 falls below room temperature. The control unit 11 may also determine to end the process when ΔT, which is the difference between the latest temperature obtained in step S502 and the temperature at the time of the previous iterative calculation, falls below a predetermined threshold. In either case, the temperature used for the determination is a statistical value such as the average value or mode of the temperatures of the finite elements that make up the object to be analyzed.

[0074] The control unit 11 may determine to terminate the processing when the calculation time, which is the product of the time width Δt and the number of times the loop from step S502 to step S506 is executed, exceeds a predetermined value. The control unit 11 may also determine to terminate the processing when the number of times step S506 is executed exceeds a predetermined value.

[0075] If it is determined not to end (NO in step S506), the control unit 11 returns to step S502 and acquires output parameters indicating the heat conduction analysis results after the time width Δt has elapsed since the previous step S502. As described above, for each loop from step S502 to step S506, the state change of the calculation object with the elapse of time for the time width Δt is calculated.

[0076] If it is determined to end (YES in step S506), the control unit 11 outputs the calculated data in a format such as that shown in FIGS. 5 and 6 (step S507). The control unit 11 may store the calculated data in the auxiliary storage device 13. The control unit 11 may also accept a user's designation of an output format and output the data in the designated format. Thereafter, the control unit 11 ends the processing.

[0077] The control unit 11 may not use the element division performed by the heat conduction analysis simulation software 31 started in step S502 in subsequent processing. For example, the control unit 11 may perform new element division in the elasto-plastic analysis simulation software 32 started in step S504. By performing element division appropriate for each of the heat conduction analysis and the elasto-plastic analysis, highly accurate calculations can be performed.

[0078] 13 is a flowchart illustrating the processing flow of the phase fraction calculation subroutine. The phase fraction calculation subroutine is a subroutine for calculating the phase fraction distribution in the object to be analyzed. The control unit 11 extracts information about one finite element (step S521). Table 6 shows example items of the extracted information.

[0079] [Table 6]

[0080] The control unit 11 calculates the phase transformation parameters related to the bainite transformation based on No. 3 in Table 3 (step S522). The control unit 11 calculates the bainite phase fraction f2 based on equation (2) (step S523). Note that if the bainite transformation has not started, the bainite phase fraction f2 calculated in step S523 will be the same value as the value in the previous loop.

[0081] The control unit 11 calculates the phase transformation parameters related to the martensitic transformation based on No. 1 in Table 1 (step S524). The control unit 11 calculates the phase fraction f1 of martensite based on the formula (1) (step S525). Note that if the martensitic transformation has not started, the phase fraction f1 of martensite calculated in step S525 will be the same value as the value in the previous loop.

[0082] The control unit 11 determines whether or not the processing of all finite elements constituting the analysis object has been completed (step S526). If it is determined that the processing has not been completed (NO in step S526), ​​the control unit 11 returns to step S521. If it is determined that the processing has been completed (YES in step S526), ​​the control unit 11 ends the processing.

[0083] According to this embodiment, it is possible to provide a program that can execute the coupled analysis described in the first embodiment.

[0084] In step S502 of the program described using Fig. 12, the control unit 11 calculates the temperature and thermal strain of each finite element for each time interval Δt, as shown in Table 4. The control unit 11 may further calculate and output thermal stress or thermal deformation based on the calculated temperature and thermal strain and the physical property values ​​of the object to be analyzed.

[0085] The program of this embodiment is intended for use in a range in which martensitic transformation and bainite transformation can occur. If use under a wider range of conditions is intended, the program may be configured to calculate phase transformation parameters and phase fractions for, for example, austenite-to-ferrite transformation and austenite-to-pearlite transformation in the loop from step S521 to step S526 of the phase fraction calculation subroutine described with reference to FIG. 13 .

[0086] The program is an example of a program product. The program may be provided on a recording medium or may be distributed from an external computer. 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.

[0087] 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 not be considered as limiting. The scope of the present invention is defined by the claims, not by the above meaning, and is intended to include all modifications within the meaning and scope of the claims.

[0088] Independent and dependent claims may be combined with each other in any combination, regardless of the reference format. Furthermore, while the claims may be written in 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 (multiple multiple claim format) may also be written. [Explanation of symbols]

[0089] 10. Information processing equipment 11 Control section 12 Main storage 13 Auxiliary storage device 14 Communications Department 15 Display 16 Input section 19 Reading unit 31 Heat conduction analysis simulation software 32 Elastic-plastic analysis simulation software 96 Portable recording media 97 Programs 98 Semiconductor Memory

Claims

1. The system comprises a calculation unit 1 that inputs the physical property values ​​of each phase of steel and each temperature as material data, divides the shape model, and calculates the temperature distribution by heat conduction analysis using the finite element method; a calculation unit 2 that calculates the strain or stress distribution by elastic-plastic analysis; a calculation unit 3 that calculates the phase fraction distribution by calculating the fundamental equations that represent phase transformation; and a calculation unit 4 that calculates the cooling rate distribution from the change in temperature of each element over time. a step of calculating a phase fraction in the calculation unit (3) by performing a coupled analysis using a phase transformation parameter of the steel calculated from the cooling rate obtained in the calculation unit (4).

2. The method for simulating quenching of steel according to claim 1, for outputting thermal stress or thermal deformation for a die-casting die.

3. Obtain information about the shape of the object to be analyzed, Acquire physical property values ​​for each phase of the steel constituting the analysis object and at each temperature; acquiring information about an initial state of the object to be analyzed after heating; At the time after the time width Δt has elapsed obtaining a thermal conduction analysis result of the object to be analyzed; obtaining an elastic-plastic analysis result of the object to be analyzed based on the heat conduction analysis result and the physical property value; calculating a cooling rate distribution of the object to be analyzed based on the results of the heat conduction analysis; Calculating the phase fraction distribution of the object to be analyzed based on the results of the heat conduction analysis, the results of the elastic-plastic analysis, and the cooling rate distribution. The process is repeated to calculate the state of the object to be analyzed at the end of cooling. An information processing method in which processing is performed by a computer.

4. the physical properties include density, thermal conductivity, specific heat, Young's modulus, Poisson's ratio, and deformation resistance; The information about the initial state includes the temperature of the object to be analyzed and the ambient temperature. The information processing method according to claim 3 .

5. the cooling rate distribution is a cooling rate calculated for each of a plurality of finite elements obtained by dividing the object to be analyzed, The phase fraction distribution is the phase fraction calculated for each of the finite elements. The information processing method according to claim 3 .

6. calculating phase transformation parameters using the calculated cooling rates for the finite elements; Calculating the phase fraction of the finite element using the phase transformation parameters The information processing method according to claim 5 .

7. The phase fractions calculated for each of the finite elements are the bainite phase fraction and the martensite phase fraction. The information processing method according to claim 5 .

8. Obtain information about the shape of the object to be analyzed, Acquire physical property values ​​for each phase of the steel constituting the analysis object and at each temperature; acquiring information about an initial state of the object to be analyzed after heating; At the time after the time width Δt has elapsed obtaining a thermal conduction analysis result of the object to be analyzed; obtaining an elastic-plastic analysis result of the object to be analyzed based on the heat conduction analysis result and the physical property value; calculating a cooling rate distribution of the object to be analyzed based on the results of the heat conduction analysis; Calculating the phase fraction distribution of the object to be analyzed based on the results of the heat conduction analysis, the results of the elastic-plastic analysis, and the cooling rate distribution. The process is repeated to calculate the state of the object to be analyzed at the end of cooling. A program that causes a computer to perform a process.

9. An information processing device including a control unit, The control unit Obtain information about the shape of the object to be analyzed, Acquire physical property values ​​for each phase of the steel constituting the analysis object and at each temperature; acquiring information about an initial state of the object to be analyzed after heating; At the time after the time width Δt has elapsed obtaining a thermal conduction analysis result of the object to be analyzed; obtaining an elastic-plastic analysis result of the object to be analyzed based on the heat conduction analysis result and the physical property value; calculating a cooling rate distribution of the object to be analyzed based on the results of the heat conduction analysis; Calculating the phase fraction distribution of the object to be analyzed based on the results of the heat conduction analysis, the results of the elastic-plastic analysis, and the cooling rate distribution. The process is repeated to calculate the state of the object to be analyzed at the end of cooling. Information processing device.

Images