Quenching simulation method for steel, 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] Conventionally, 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 inside are likely to differ, so a simulation method that predicts the temperature, stress, and phase fraction of steel as values ​​with distributions on the surface and inside has been required. Quenching simulation has a feature different from general structural analysis in that it deals with phase transformation of steel. As represented by martensitic transformation and bainite transformation, the phases that appear and the transformation temperatures are determined by the material composition, the microscopic carbon diffusion state, or the temperature and time. For this reason, in the finite element method, a method has been adopted in which one element has multiple material property data corresponding to each phase, and which phase appears is calculated as the phase fraction. In order to perform a highly accurate simulation, calculation and data processing methods for accurately predicting the phase fraction are required. In Non-Patent Document 1, in a method of performing coupled analysis of temperature, strain amount, stress, and phase fraction, a fundamental equation for calculating phase fraction including carbon concentration distribution dependency is introduced to calculate the change in the martensitic transformation start temperature. Patent Document 1 uses a quenching simulation method that takes into account phase transformation from the cooling rate. This proposal is excellent in that the phase fraction is obtained from the continuous cooling transformation (CCT) of steel and used 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," Materials, 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 is advantageous in that it can take into account the dependence on carbon concentration in the phase fraction calculation, but it requires the collection of basic data that shows the relationship between the distribution of carbon concentration 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 simulation with high validity. The simulation method disclosed in the above-mentioned Patent Document 1 is advantageous in that it reflects the dependency on the cooling rate in the phase fraction calculation and can obtain physical quantities after quenching with a small amount of calculation. However, the main calculation part is based on heat conduction analysis and does not handle calculation of strain or stress. In predicting 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 part are transferred at each step to obtain stress. In addition, in the method of obtaining the phase fraction from the CCT, it is difficult to express the change in the cooling rate due to, for example, switching of the cooling method, and there is a restriction that the cooling rate at each time is treated as a constant value. An object of the present invention is to provide a highly accurate steel quenching simulation method that obtains values ​​for cooling rates, for which data can be collected relatively easily, at each position and each time in the steel, and uses the values ​​in numerical calculations of the phase fractions to accurately predict the phase fractions in the steel, thereby performing coupled analysis including 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, it is possible to predict the phase fraction with high accuracy by collecting data relatively easily, thereby arriving at the present invention. That is, the present invention is a method for simulating quenching of steel, which comprises a calculation unit 1 that inputs physical property values ​​for each phase of steel and at each temperature as material data, divides a geometric model, and determines the temperature distribution by heat conduction analysis using the finite element method, a calculation unit 2 that determines the strain or stress distribution by elastic-plastic analysis, a calculation unit 3 that calculates the phase fraction distribution by calculating a fundamental equation representing phase transformation, and a calculation unit 4 that determines the cooling rate distribution from the change in temperature of each element over time, and which performs a coupled analysis in the procedure of determining the phase fraction in the calculation unit 3, using phase transformation parameters of steel calculated from the cooling rate determined in the calculation unit 4. Preferably, the present invention is a method for simulating hardening of steel for outputting thermal stress or thermal deformation to a die casting die. Effect of the Invention

[0007] The method for simulating quenching of steel according to the present invention is advantageous in that it is possible to calculate the phase fraction using a 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 the phase fraction. A quenching simulation using this method has the effect of being able to predict the state of steel after quenching with high accuracy. [Brief description of the drawings]

[0008] [Figure 1] FIG. 2 is a conceptual diagram of a coupled analysis according to the present invention. [Diagram 2] FIG. 1 is a diagram showing an example of a continuous cooling transformation diagram (CCT) of steel. [Diagram 3] FIG. 2 is a diagram showing an example of the relationship between the cooling rate and phase transformation parameters. [Figure 4] FIG. 1 is a diagram showing an example of a dilatometry curve. [Diagram 5] FIG. 4 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 a phase fraction distribution in Example 1. [Figure 7] FIG. 13 is a diagram showing the distribution of cooling rates in Example 2. [Figure 8] FIG. 13 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. 13 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 a difference in bainite phase fraction between Example 2 and a comparative example. [Figure 11] FIG. 2 is an explanatory diagram illustrating a configuration of an information processing device. [Figure 12] 11 is a flowchart illustrating a process flow of a program. [Figure 13] 10 is a flowchart illustrating a process flow of a phase fraction calculation subroutine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

[0010] Fig. 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-post functions, and a program created in the present invention. In the coupled analysis shown in Fig. 1, a commercially available program was used as is for calculation unit 1 and calculation unit 2, which have functions for handling the transfer of variables between calculation units, and a program created in the present invention was used for calculation units 3 and 4.

[0011] In order to perform the numerical calculation, the physical property values ​​of each phase of the steel and at each temperature were input and used as material data. The steel of the present invention is an alloy steel, for example, typified by SKD61 (JIS G4404 hot die steel). The phases of the steel were austenite, ferrite, cementite, pearlite, bainite, and martensite, which are known as structures of general steel. In particular, austenite, bainite, and martensite, which are the main structures in quenching, were treated as the main phases. The metallurgical distinction between these structures is originally made by differences in microscopic crystal structure and composition, but in the numerical calculation, each structure was defined as a different type of phase. In addition, the composite state is quantitatively expressed by the phase fraction. Regarding the physical property values, each phase was treated as having an individual physical property value. In addition, the physical property values ​​of the composite state were obtained by a weighted average of the physical property values ​​of each phase by the phase fraction. The physical property values ​​used as material data corresponded to the calculations in each calculation section in order to perform the coupled analysis shown in Figure 1. That is, for calculation section 1, thermal 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 described above, the division of the geometric model and the numerical analysis by the finite element method were performed using a commercially available program. 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, as in the case of a general method of numerical analysis by the finite element method.

[0013] Calculation unit 1, which finds the temperature distribution through heat conduction analysis, handles the above-mentioned material data as well as boundary conditions that represent the cooling or heating state of the steel and the transformation latent heat found in calculation unit 3 as input data for performing numerical calculations. A general heat conduction equation for heat conduction analysis is used as the fundamental equation. The output data from calculation unit 1 includes the temperature and thermal strain at each position and time of the steel. The boundary conditions used to represent the cooling or heating state of 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 represented by defining the ambient temperature and heat transfer coefficient.

[0014] Calculation unit 2, which determines the amount of strain or stress distribution through elastic-plastic analysis, handles the above-mentioned material data as input data for performing numerical calculations, as well as the constraint conditions representing the fixing method of the steel, the temperature and thermal strain determined in calculation unit 1, and the transformation strain and transformation plastic strain determined in calculation unit 3. A general constitutive equation for performing elastic-plastic analysis was used as the fundamental equation. The output data from calculation unit 2 included the stress, elastic strain, and plastic strain at each position on the steel and at each time. In calculation section 2, it is assumed that the steel is placed stationary inside a heat treatment furnace, and minimum constraints are imposed to prevent the steel from translating or rotating, allowing it to deform in any direction. The calculation unit 2 can handle the 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 the details of plastic processing by an external processing device, but it is believed that the present invention can be applied to such cases by collecting the necessary material data.

[0015] Calculation unit 3, which calculates the distribution of phase fractions by calculating the fundamental equations representing phase transformations, handles the temperature calculated in calculation unit 1, the stress calculated in calculation unit 2, and the cooling rate calculated in calculation unit 4 as input data for performing numerical calculations, in addition to the material data described above. For example, the Koistinen-Marburger equation and the Johnson-Mehl-Avrami-Kolmogorov equation are used as fundamental equations. The output data from calculation unit 3 includes the phase fraction, transformation latent heat, transformation strain, and transformation plastic strain at each position and time in the steel. The fundamental equations that represent 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] In calculation unit 4, which determines the cooling rate distribution from the change in temperature of each element over time, the temperatures determined in calculation unit 1 are used as input data for performing numerical calculations. For example, the cooling rate calculation formula shown in equation (3) is used as the fundamental equation. The cooling rate is used as output data from calculation unit 4. In equation (3), CR represents the cooling rate, ΔT represents the amount of 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 formula (3) as it is. However, for example, when Δt varies greatly in the numerical calculation due to the generation of transformation latent heat, the CR may vary greatly unintentionally. This variation includes not only that based on the actual phenomenon, but also that due to the convenience of data processing in the numerical calculation. When a variation occurs, a calculation process for smoothing may be performed in order to stabilize the calculation. For example, the smoothing process can be performed by taking a moving average of the CR at each time.

[0018] The aforementioned calculation unit 3 calculates the phase fraction from the 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) based on 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 shows the time elapsed after the start of cooling, and the vertical axis shows the temperature. The three curves extending from the high temperature point at time 0 are cooling curves that show the relationship between time and temperature when the cooling rate is constant. The curves other than the cooling curves are transformation curves, and show the transformation start temperature or transformation finish temperature.

[0020] In Fig. 2, the line marked with the symbol Ms is a transformation curve showing the condition at which martensitic transformation starts when the temperature of the steel is decreased. In the following description, the transformation curve marked with the symbol Ms may be referred to as the MS point transformation curve. Here, the MS point means the temperature at which the transformation from austenite to martensite starts.

[0021] Similarly, the line marked with the symbol Bs is a transformation curve that indicates the condition at which the bainite transformation starts when the temperature of the steel is decreased. In the following description, 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 starts.

[0022] Regarding the method for calculating the phase transformation parameters of formula (1), when we look at the transformation curve of the MS point in Figure 2, we can see that the MS point is lower on the long-term side, i.e., on the slow cooling side. This corresponds to the TM of the phase transformation parameters of formula (1) changing 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 section 3, the phase transformation parameter TM can be obtained from the cooling rate.

[0024] Next, regarding how to calculate the phase transformation parameters of equation (2), Figure 4 is an example of a dilatometry curve including 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 composite of the linear expansion coefficients of austenite, bainite, and martensite, the transformation strain that occurs when austenite transforms into bainite, and the transformation strain that occurs when austenite transforms into martensite. Since 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) is 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. df2 / dT on the left side of equation (4) can be converted to the slope in Figure 4 from 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 obtained. Since Figure 4 shows a graph shape that differs depending on the cooling rate, the relationship between CR, n, and b can be obtained for each corresponding cooling rate. By expressing this relationship in the form of a table or an approximate formula in calculation section 3, the phase transformation parameters n and b can be obtained from the cooling rate.

[0027] When determining the phase transformation parameters by the above method, from the viewpoint of ensuring calculation accuracy, it is advisable to select a point with the smallest gradient (negative value) when selecting any point from Fig. 4. In addition, among the phase transformation parameters, it is easier to obtain high calculation accuracy by making n a constant value that is easy to handle, such as n=2.

[0028] The method for simulating the quenching of steel according to the present invention is particularly suitable for die-casting die products, since 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 rate is between the surface and the core. In such cases, the steel quenching simulation method of the present invention is appropriate. EXAMPLES

[0029] The following examples further illustrate the calculation of martensitic transformation according to the invention. As a 1 / 4 3D model with a symmetrical boundary, a square plate-shaped model with a side of 500 mm (250 mm on the computer due to symmetry) and a thickness of 100 mm was created, element division was performed, and the physical properties of each phase of the alloy steel and each temperature were input as material data, and a finite element analysis was performed. For the boundary conditions, a combination of time and temperature was input as a temperature boundary so that the temperature change per hour was -0.2°C / s on the top surface of the square (the surface on the +Z side of the XYZ coordinate system). In addition, a combination of time and temperature was input so that the temperature change per hour was -0.1°C / s on the bottom surface of the square (the surface on the -Z side of the XYZ coordinate system). In other words, the cooling rate of the top surface was adjusted to CR = -0.2°C / s, and the cooling rate of the bottom surface was adjusted to CR = -0.1°C / s. For the fundamental equations expressing phase transformation, equation (1) was used for martensitic transformation, and equation (2) was used for bainitic transformation. In the working example, the phase transformation parameters were calculated according to the cooling rate using a program having calculation units 1, 2, 3, and 4, and the quenching simulation method of the present invention was carried out. In the comparative example, the quenching simulation method was carried out in the case where the phase transformation parameters were constant values ​​regardless of the cooling rate, using an existing program having calculation units 1, 2, and 3. Among the input data, the phase transformation parameters used in the calculation of martensitic transformation are shown in Table 1.

[0030] [Table 1]

[0031] Fig. 5 shows the distribution of cooling rates obtained by the present invention. It can be seen that the cooling rates have different values ​​on the top and bottom surfaces.

[0032] Fig. 6 shows the relationship between the temperature and the phase fraction at the center point of the top surface and the center point of the bottom surface, which was 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 Fig. 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. EXAMPLES

[0033] In the following examples, calculation of bainite transformation will be described. The difference from Example 1 is the cooling rate, and 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 in 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 show data calculated by calculation unit 4 in Fig. 1. It can be seen that the cooling rates have different values ​​on the upper and lower surfaces.

[0038] Fig. 8 is a diagram showing the distribution of the bainite phase fraction of 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 of a 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 grayscale. White parts indicate areas with a high bainite phase fraction, and the closer to black the area is, the lower the bainite phase fraction is.

[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 about the lower half of the portion. 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 FIG. 8 and FIG. 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 vertical axis indicates that the bainite phase fraction is higher at the top. The solid line indicates the distribution of the bainite phase fraction for Example 2 shown as No. 3 in Table 3. The dashed line indicates the distribution of the 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 face center with higher resolution than Fig. 8 and Fig. 9, which are shown in gray scale. 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 of Example 2 matches the phenomenon actually observed, and it is clear that the bainite phase fraction can be predicted with high accuracy by this embodiment.

[0042] According to this embodiment, it is possible to execute a coupled analysis incorporating the distribution of the cooling rate in the steel material. 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 the cooling rate, such as thermal stress and thermal deformation during heat treatment of a die-casting die.

[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 a coupled analysis.

[0044] The phase transformation parameters shown in No. 1 of Table 1 and No. 3 of Table 3 are merely examples. The phase transformation parameters are appropriately determined 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. Description 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 storage device 12, an auxiliary storage 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 CPUs (Central Processing Units), GPUs (Graphics Processing Units), multi-core CPUs, etc. The control unit 11 is connected to each hardware part constituting 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 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 the 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 piece of software. The heat conduction analysis simulation software 31 and the elastic-plastic analysis simulation software 32 may be stored in an external large-capacity 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 Universal Serial Bus (USB) memory, a Compact Disc Read only memory (CD-ROM), 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 to which a portable recording medium 96, such as a USB connector, a CD-ROM drive, or an SD memory reader, can be connected. The semiconductor memory 98 stores a program 97 and is a memory that can be attached 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 a plurality of personal computers or a mainframe computer 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 a plurality of personal computers or a mainframe computer that operate in cooperation with each other.

[0054] The program 97 is recorded in 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 the present 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 analysis object is different from the surrounding temperature. The heat conduction analysis simulation software 31 of this embodiment includes a so-called mesh creation function that divides the analysis object into a plurality of finite elements. The mesh creation function may be realized by another 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 a numerical analysis method such as finite element analysis to simulate the elastic deformation behavior and the plastic deformation behavior of an object to be analyzed when the temperature is not uniform. The elastic-plastic analysis simulation software 32 realizes the function of the calculation unit 2 in FIG. 1.

[0058] In the following description, an example will be described 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 is referred to as a finite element.

[0059] Since the heat conduction analysis simulation software 31 and the elastic-plastic analysis simulation software 32 are both known, detailed processing will not be described. 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 mainframe computer connected to the information processing device 10 via a network, a cloud service, or the like.

[0061] Fig. 12 is a flowchart explaining the flow of the program processing. The program in Fig. 12 is a program for simulating 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 on the shape of the object to be analyzed, information on the physical property values ​​of the object to be analyzed, and information on 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 analysis object is expressed, for example, by a 3D-CAD (3 Dimensional Computer Aided Design) model. When the analysis object has a simple shape, such as a square plate, information about the shape of the analysis object 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 thermal properties and mechanical properties of each phase of the steel that constitutes the object of analysis at each temperature. The thermal properties include, for example, density, thermal conductivity, and specific heat. The mechanical properties include, for example, Young's modulus, Poisson's ratio, and deformation resistance during plastic deformation.

[0065] The information on 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 starts. When calculating the state after the quenching process ends, the initial temperature of the object to be analyzed is uniform. Initial conditions in which the temperature of the object to be analyzed varies from part to part may also be input.

[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 state in which the object to be analyzed is held on a jig or a stand, the temperature of the contact part 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 obtains output parameters indicating the heat conduction analysis results after the time width Δt has elapsed. Table 4 shows examples 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 by the formula (3). The control unit 11 realizes the function of the calculation unit 4 in FIG. 1 by 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 obtains output parameters at the same time. Table 5 shows example items of the 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 for calculating a phase fraction distribution in an analysis object. The process 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 judges whether or not to end the repetitive calculation process (step S506). The control unit 11 judges to end the repetitive calculation process when the cooling of the analysis object is completed. Specifically, the control unit 11 judges to end the process when the temperature of the calculation object acquired in step S502 falls below room temperature, for example. The control unit 11 may also judge to end the process when ΔT, which is the difference between the latest temperature acquired in step S502 and the temperature at the time of the previous repetitive calculation, falls below a predetermined threshold. In either case, the temperature used for the judgment is a statistical value such as the average value or the mode of the temperatures of the finite elements constituting the analysis object.

[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 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 result 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 time width Δt is calculated.

[0076] When 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 Fig. 5 and Fig. 6 (step S507). The control unit 11 may store the calculated data in the auxiliary storage device 13. The control unit 11 may accept a user's designation of an output format and output the data in the designated format. Thereafter, the control unit 11 ends the process.

[0077] The control unit 11 may not use the element division by the heat conduction analysis simulation software 31 started in step S502 in the subsequent processing. For example, the control unit 11 may execute new element division in the elastic-plastic analysis simulation software 32 started in step S504. By executing element division suitable for each of the heat conduction analysis and the elastic-plastic analysis, it is possible to execute calculations with high accuracy.

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

[0079] [Table 6]

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

[0081] The control unit 11 calculates the phase transformation parameters related to the martensitic transformation based on No. 1 of Table 1 (step S524). The control unit 11 calculates the phase fraction f1 of martensite based on the formula (1) (step S525). If the martensitic transformation has not started, the phase fraction f1 of martensite calculated in step S525 will be the same value as that 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 capable of executing the coupled analysis described in the first embodiment.

[0084] In step S502 of the program described with reference to 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 to be used in a range where martensitic transformation and bainite transformation can occur. If it is intended to be used under a wider range of conditions, 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.

[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 (constitutive 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 scope and meaning equivalent to the claims.

[0088] Independent claims and dependent claims described in the claims may be combined with each other in any combination, regardless of the reference format. Furthermore, the claims may be written in a format in which a claim references two or more other claims (multiple claim format), but this is not limited to this format. The claims may also be written in a format in which a multiple claim references at least one other claim (multiple multiple claim format). [Explanation of symbols]

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

Claims

1. The system comprises a 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 obtains the temperature distribution by heat conduction analysis using the finite element method, a calculation unit 2 which obtains the strain or stress distribution by elastic-plastic analysis, a calculation unit 3 which calculates the phase fraction distribution by calculating the fundamental equation representing phase transformation, and a calculation unit 4 which obtains the cooling rate distribution from the change in temperature of each element over time. A method for simulating quenching of steel, characterized in that in the procedure of calculating a phase fraction in the calculation unit 3, a coupled analysis is performed using phase transformation parameters of the steel calculated from the cooling rate obtained in the calculation unit 4.

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

3. Obtaining information about the shape of the object to be analyzed; Acquire physical property values ​​for each phase and each temperature of the steel constituting the analysis object; Obtaining information regarding an initial state of the object to be analyzed after heating; At the time after the time span Δt has elapsed Obtaining a thermal conduction analysis result of the analysis object; obtaining an elastic-plastic analysis result of the analysis object based on the heat conduction analysis result and the physical property value; Calculating a cooling rate distribution of the analysis object based on the heat conduction analysis result; Calculating a 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 atmospheric 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 analysis object, The phase fraction distribution is a 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 phase fraction of bainite and the phase fraction of martensite. The information processing method according to claim 5.

8. Obtaining information about the shape of the object to be analyzed; Acquire physical property values ​​for each phase and each temperature of the steel constituting the analysis object; Obtaining information regarding an initial state of the object to be analyzed after heating; At the time after the time span Δt has elapsed Obtaining a thermal conduction analysis result of the analysis object; obtaining an elastic-plastic analysis result of the analysis object based on the heat conduction analysis result and the physical property value; Calculating a cooling rate distribution of the analysis object based on the heat conduction analysis result; Calculating a 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 carry out processing.

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

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