JP7146377B2 - Material structure prediction method and material structure prediction device - Google Patents

Description

The present invention relates to a material structure prediction method and the like for predicting the material structure of an Al (aluminum) alloy after heat treatment.

2. Description of the Related Art In recent years, techniques have been proposed for predicting changes in the properties of a metal material as each manufacturing process progresses, for example, in order to examine the manufacturing conditions of a metal material manufactured by an industrial process. As an example, Patent Literatures 1 and 2 disclose a method of predicting (calculating) the material structure of a heat-treated Al alloy (a heat-treated Al alloy).

Japanese Patent Application Laid-Open No. 2002-224721 Japanese Patent Application Laid-Open No. 2017-20098

However, as described below, there is room for improvement in methods for predicting the material structure of Al alloys after heat treatment. An object of one aspect of the present invention is to predict the material structure of the Al alloy more accurately than in the past.

In order to solve the above problems, a material structure prediction method according to an aspect of the present invention is a material structure prediction method for predicting a material structure of an aluminum alloy after heat treatment, wherein at an initial time before the heat treatment, the above In the aluminum alloy, (i) information indicating the solid solution amount of the precipitated alloy component, and (ii) the dispersion state of the second phase particles of the precipitated alloy component in each of the crystal grains and grain boundaries of the aluminum alloy. An initial structure information acquisition step of acquiring information indicating the heat treatment condition information acquisition step of acquiring heat treatment condition information that is information indicating the conditions of the heat treatment, and based on the heat treatment condition information, the heat treatment is performed from the initial time. At each time until the final time of completion, a temperature setting step of setting the temperature to be applied to the aluminum alloy, and at each time, based on the temperature, (i) the second Considering the precipitation of the phase particles and (ii) the precipitation of the second phase particles at the grain boundaries of the aluminum alloy, a solid solution amount calculation step of calculating the solid solution amount, and at each time, the above Based on the solid solution amount and the temperature, (i) first dispersion information indicating the dispersion state within the crystal grains and (ii) second dispersion information indicating the dispersion state at the crystal grain boundaries are separately obtained. and a distributed state calculation step of calculating the

Further, in order to solve the above problems, a material structure prediction device according to one aspect of the present invention is a material structure prediction device for predicting a material structure of an aluminum alloy after heat treatment, wherein at an initial time before the heat treatment , of the aluminum alloy, (i) information indicating the solid solution amount of the precipitated alloy component, and (ii) second phase particles of the precipitated alloy component in the grains and grain boundaries of the aluminum alloy, respectively. An initial structure information acquisition unit that acquires information indicating the state of dispersion, a heat treatment condition information acquisition unit that acquires heat treatment condition information that is information indicating the conditions of the heat treatment, and based on the heat treatment condition information, from the initial time to the above A temperature setting unit that sets the temperature applied to the aluminum alloy at each time until the final time when the heat treatment is completed, and at each time, based on the temperature, (i) the above Considering the precipitation of the second phase particles and (ii) the precipitation of the second phase particles at the grain boundaries of the aluminum alloy, the solid solution amount calculation unit for calculating the solid solution amount, and at each time , based on the solid solution amount and the temperature, (i) first dispersion information indicating the dispersion state within the crystal grains, and (ii) second dispersion information indicating the dispersion state at the crystal grain boundaries; and a distributed state calculation unit that calculates separately.

According to the material structure prediction method according to one aspect of the present invention, it is possible to predict the material structure of an Al alloy after heat treatment more accurately than in the past. Further, the material structure prediction device according to one aspect of the present invention also has the same effect.

2 is a functional block diagram showing the configuration of main parts of the information processing apparatus according to Embodiment 1; FIG. 2 is a diagram illustrating a flow of heat treatment calculation processing in the information processing apparatus of FIG. 1; FIG. It is a figure which shows each calculated value in each measured value in a 1st example, and an Example. 4(a) to 4(c) are diagrams showing the relationship between each measured value in FIG. 3 and each calculated value in the example. (a) and (b) are diagrams showing actual measured values in the second example and calculated values in the example, respectively. FIG. 10 is a diagram showing each actual measurement value, each calculated value in an example, and each calculated value in a comparative example in a third example;

[Embodiment 1]
The information processing device 1 (material structure prediction device) of the first embodiment includes information indicating an initial material structure (hereinafter referred to as an initial structure) before heat treatment of an Al alloy (eg, an Al alloy ingot), and in addition to the Al alloy Based on the information indicating the heat treatment conditions obtained, temporal changes in the material structure of the Al alloy are predicted. First, prior to describing the specific configuration of the information processing apparatus 1, the problems of the prior art will be described.

(Problem of conventional technology)
For example, an Al alloy plate material is manufactured through the steps of casting→homogenization→hot rolling→cold rolling (including intermediate annealing and final annealing if necessary). Also, the Al extruded material is manufactured by a process of extrusion→tempering (solution treatment, aging, annealing, and drawing).

In any manufacturing process, it is important to control the material quality of the final product (plate material and extruded material). For example, in the case of an automobile body sheet material, if the material is not suitable, deterioration of surface properties and deterioration of solution heat treatment properties may occur. In addition, in the case of extruded materials, grain loss or the like may occur if the material is not suitable. Furthermore, it is important to control the material quality of the Al alloy also in intermediate steps (intermediate steps) in the manufacturing process. This is because if the material of the Al alloy in the intermediate process is inappropriate, it may adversely affect the material of the final product.

Conventionally, the conditions (manufacturing conditions) for controlling the material quality of the Al alloy in each step have generally been examined based on experiments (prototypes). However, in recent years, the manufacturing process has become complicated due to the demand for higher quality of final products. For this reason, it is not easy to determine the conditions for controlling the material of the Al alloy with high accuracy in the entire process based on experiments.

In addition, variations in materials within one lot in the final product also pose a problem. For example, in a plate material manufacturing process, variations occur in the heating rate and the cooling rate during homogenization. This causes temperature variations within the material. Further, in the case of automobile body sheet materials, the recrystallized structure during hot rolling and the dispersion of solution heat treatment properties pose problems. In order to deal with such variations in material properties, it is necessary to control Mg ₂ Si, which is the main second phase particle in ingot homogenization.

Mg ₂ Si tends to precipitate at Al alloy grain boundaries. Also, precipitation of Mg ₂ Si at grain boundaries competes with precipitation of precipitates within grains. The priority of the competition varies depending on the thermal history (temporal change in temperature applied to the Al alloy). Therefore, changes in the state of dispersion (distribution) of the second phase particles occur, for example due to temperature variations within the material. As a result, the material quality of the Al alloy fluctuates due to the change in the dispersed state of the second phase particles.

Also, when controlling the material structure of the Al alloy after heat treatment, variations in industrial production (fluctuations in thermal history such as heating rate) are unavoidable. In addition, it is not realistic to verify such variations in industrial production by experiments. Therefore, it is necessary to predict the material structure by simulation (desktop study).

Patent Literature 1 discloses a predictive model of material structure in each manufacturing process of Al alloy. However, the technique of Patent Document 1 can only predict rough changes in the material structure, and cannot predict detailed changes in the material structure. For example, the technique of Patent Document 1 cannot predict the number (quantity) and size of second phase particles in an Al alloy.

The number and size of the second phase particles in the Al alloy after heat treatment are particularly important information (indicators) in controlling the recrystallization phenomenon and strength of the material structure in downstream processes (e.g. hot rolling process and extrusion process). is. Therefore, a technique that can predict the number and size of second phase particles in Al alloys after heat treatment is desired.

Therefore, Patent Document 2 proposes a predictive model of material structure that can predict the number and size of second phase particles. The prediction model of Patent Document 2 can handle the inside of the crystal grains of Al alloys. For example, in a 3000-series Al alloy (Al--Mn alloy), the second phase particles are precipitated (formed) almost exclusively within the crystal grains. Therefore, the prediction model of Patent Document 2 is suitable for predicting the material structure of 3000 series Al alloys.

However, in some alloy systems, second-phase particles precipitate not only within grains but also at grain boundaries. For example, in a 6000-series Al alloy (Al--Mg--Si alloy), some _second phase particles (eg, Mg.sub.2Si) tend to precipitate at grain boundaries. However, the predictive model of Patent Document 2 cannot deal with the grain boundaries of Al alloys. Therefore, the prediction model of Patent Document 2 is not sufficient to predict the material structure of, for example, 6000 series Al alloys.

In view of the above points, the information processing device 1 of Embodiment 1 can change the material structure after heat treatment even for an alloy system (eg, a 6000 series Al alloy) in which the second phase particles are precipitated at the grain boundary. It is intended to predict. According to the information processing device 1, changes in the second phase particles (more specifically, changes in the number and size of the second phase particles) can be predicted separately for each of the crystal grain interior and the crystal grain boundary. Therefore, important information for controlling the recrystallization phenomenon and strength of the material structure in the downstream process can be presented to the user.

(Configuration of information processing device 1)
FIG. 1 is a functional block diagram showing the configuration of main parts of an information processing apparatus 1. As shown in FIG. The information processing device 1 includes a material texture prediction unit 10 , an input unit 70 , a display unit 71 and a storage unit 80 . For example, the information processing device 1 can cause a control section (not shown) that controls each section of the information processing device 1 to function as the material texture prediction section 10 . The storage unit 80 stores various programs executed by the material texture prediction unit 10 and data used by the programs.

The material structure prediction unit 10 includes a data acquisition unit 11 (initial structure information acquisition unit, heat treatment condition information acquisition unit), a temperature setting unit 12, a solid solution amount calculation unit 13 (solid solution amount calculation unit), and a nucleation rate calculation unit. 14 (dispersion state calculator), a size variation calculator 15 (dispersion state calculator), and a data recording unit 16 . The operation of each functional unit in the material texture prediction unit 10 will be described later.

The input unit 70 receives user input (user input). The display unit 71 may display various data (in particular, various calculation results of the material structure prediction unit 10) as graphs. Moreover, the display unit 71 may display an input screen for user input. Since the display unit 71 can provide a GUI (Graphic User Interface) to the user, user convenience is improved. When the information processing apparatus 1 includes a touch panel, the input section 70 and the display section 71 can be realized as an integrated member.

(Processing flow of heat treatment calculation)
FIG. 2 is a flowchart illustrating the flow of heat treatment calculation processes S1 to S13 in the information processing apparatus 1. As shown in FIG. The manufacturing process assumed in the heat treatment calculation may involve heat treatment of the Al alloy. As an example, the heat treatment may include homogenization of the Al alloy ingot.

In the heat treatment calculation of FIG. 2, the existence state of the second phase particles in the material at each time is expressed as a PSD (PSD: Particle Size Distribution) function. The PSD function is also called the particle size distribution function (see also US Pat. A PSD function is a function that indicates the correspondence between particle size (eg, particle radius) and the number of particles having that particle size. As an example, the PSD function may be represented as a histogram showing the particle size range (horizontal axis: class) and the number of particles (vertical axis: frequency) included in the particle size range.

More specifically, the PSD function in Embodiment 1 is expressed as data (function) indicating the correspondence relationship between the size of the second phase particles (eg, the radius of the second phase particles) and the number of the second phase particles. be done. The information processing device 1 calculates temporal changes in the PSD function (more specifically, the PSD function at each time) based on the flowchart of FIG.

(First step: reading initial organization information)
Information (data set) indicating an initial structure (hereinafter referred to as initial structure information) is preset in the information processing apparatus 1 (eg, storage unit 80) by user input. The data acquisition unit 11 (initial tissue information acquisition unit) reads initial tissue information from the storage unit 80 (S1). S1 may be referred to as an initial tissue information acquisition step.

The initial structure information includes (i) information indicating alloy components, (ii) second phase particles of main elements (precipitated alloy components) of the Al alloy at time t = 0 (initial time in heat treatment calculation) described below. (that is, PSD function), and (iii) information indicating the solid solution amount of the main element (solid solution element amount).

The initial structure information was obtained by the user's separate evaluation of the grain interior and the grain boundary. That is, the initial structure information is treated as separate information for the inside of the grain and the grain boundary. More specifically, in the initial structure information, information indicating the state of dispersion in each of grains and grain boundaries is handled as separate information. Hereinafter, the information indicating the dispersion state within the crystal grain is also referred to as first dispersion information. Information indicating the state of dispersion at grain boundaries is also referred to as second dispersion information. The first distributed information and the second distributed information are different information.

(Second step: reading heat treatment condition information)
Information indicating heat treatment conditions for the Al alloy (hereinafter referred to as heat treatment condition information) is also preset in the information processing device 1 (eg, the storage unit 80) by user input. The data acquisition unit 11 (heat treatment condition information acquisition unit) reads the heat treatment condition information from the storage unit 80 (S2). S2 is also called a heat treatment condition information acquisition step.

The heat treatment condition information includes information indicating the temperature T applied to the Al alloy at time t. Hereinafter, temperature T at time t is also expressed as temperature T(t). The heat treatment condition information may be defined as a function of time t or as a data set corresponding to time t.

Note that the initial time in the heat treatment calculation is t=0. t=0 is the time before the heat treatment (before heat treatment is applied to the Al alloy). Also, let t=tf be the final time in the heat treatment calculation. The final time tf is the time when the heat treatment is completed. Information indicating the final time tf is included in the heat treatment condition information.

The heat treatment condition information may include information indicating the holding temperature Th1 and the holding time th1 in the homogenization process. In this case, assuming that the time at which the holding (warming) step in the homogenization process is started is t=ts1, the temperature setting unit 12 described below sets T(t)=Th1 where ts1≦t≦ts1+th1. you can

Similarly, the heat treatment condition information may include information indicating the holding temperature Th2 and the holding time th2 in the holding step before hot rolling. In this case, if the time at which the holding step in the homogenization process is started is t=ts2, the temperature setting unit 12 may set T(t)=Th2 where ts2≦t≦ts2+th2.

The heat treatment condition information may further include information indicating the heating rate and the cooling rate. The temperature setting unit 12 sets the temperature T(t) as a function of a predetermined format (eg, linear function).

(Third step: setting temperature T(t))
The temperature setting unit 12 acquires heat treatment condition information from the data acquisition unit 11 . First, the temperature setting unit 12 sets the time t to t=0 (initial time) (S3). Subsequently, the temperature setting unit 12 sets the step width (minute time) Δt of time (S4). As an example, the temperature setting unit 12 may set Δt based on the heat treatment condition information (for example, based on the holding times th1 and th2 described above). The value of Δt in the process of heat treatment calculation may be constant for all loop counts of heat treatment calculation, or may be changed for each loop count.

Based on the heat treatment condition information, the temperature setting unit 12 sets the temperature T(t) at each time (every Δt) from time t=0 (initial time) to t=tf (final time) (S5 ). S5 is also called a temperature setting step. Specifically, the temperature setting unit 12 sets the temperature T(t) at time t (current time) based on the heat treatment condition information.

As an example, when t=0, the temperature setting unit 12 sets the temperature T(t)=T(0) (temperature T at the initial time). According to the temperature setting unit 12, the thermal history of the Al alloy (a form of time change of the temperature T given to the Al alloy) can be represented on the calculation model.

(Fourth step: calculation of solid solution element amount C)
The solid-solution element amount calculation unit 13 calculates the solid-solution element amount C (the solid-solution amount of the precipitated alloy component) in the Al alloy at the temperature T=T(t) (the temperature set by the temperature setting unit 12). (S6). S6 is also called a solid solution amount calculation step.

Specifically, the solid solution element amount calculation unit 13 uses the following formula (1),

The solid solution element amount C is calculated by

In equation (1), _C0 is the solute addition amount (the addition amount of the precipitated alloy component, which is the solute in the second phase particles), and _Cβ is the solute equilibrium concentration in the second phase particles ( is the equilibrium concentration of the precipitated alloy component, which is the solute of f _pIG is the volume fraction of second phase grains in grains and f _pGB is the volume fraction of second phase grains at grain boundaries. The values of C ₀ and C _β in equation (1) are preset in the information processing device 1 .

As shown in the formula (1), the information processing device 1 (solid-solution element amount calculation unit 13) calculates “precipitation of second phase particles in grains” (a term represented as “−f _pIG C _β ”) In addition to , the amount of solute elements C is calculated taking into account the “precipitation of the second phase particles at the grain boundaries” (the term expressed as “−f _pGB C _β ”).

in short. As described above, the information processing apparatus 1 calculates the solid solution element amount C in consideration of the phenomenon that "the second phase particles also precipitate at the grain boundaries." The calculation model of Embodiment 1 (hereinafter referred to as the calculation model) differs from the conventional calculation model in that it calculates the solid solution element amount C in consideration of the phenomenon (that is, using formula (1)). They have different technical ideas. According to the calculation model of the present invention, it is possible to calculate the solid solution element amount C in consideration of the above phenomenon by a simple calculation (calculation using formula (1)).

(Fifth step: calculation of nucleation rate J in grains and nucleation rate J _gb at grain boundaries)
The nucleation rate calculator 14 calculates the nucleation rate J in the crystal grain (the nucleation rate of the second phase particles in the crystal grain) at the temperature T=T(t) (S7). Specifically, the nucleation rate calculator 14 uses the solid solution element amount C calculated by the solid solution element amount calculator 13 to calculate the following equations (2) to (7),

to calculate the nucleation rate J in the grain (see, in particular, formula (2)).

In the formulas after formula (2), k is the Boltzmann constant. In Equation (3), D is the diffusion coefficient and a is the lattice constant of the parent phase. In equation (4), V _β is the molar volume of the precipitated layer and γ (gamma) is the energy of the particle/matrix interface. In equation (5), r _c is the critical radius of nucleation. In Equation (6), ΔG is the volume free energy of the precipitation phase. In equation (7), R is the gas constant, and C _α is the solute equilibrium concentration in the matrix (equilibrium concentration of precipitated alloy components). These values in equations (2) to (7) are preset in the information processing apparatus 1. FIG.

Subsequently, the nucleation rate calculator 14 calculates the grain boundary nucleation rate J _gb (the nucleation rate of the second phase grains at the grain boundary) at the temperature T=T(t) (S8). Specifically, the nucleation rate calculator 14 uses the solid solution element amount C calculated by the solid solution element amount calculator 13 to calculate the following equations (8) to (11),

to calculate the nucleation rate J in the grain (see, in particular, equation (8)).

In Equation (10), γ _αα is the interfacial energy of Al grain boundaries. In formula (11), B is the grain size of the Al crystal, and δ is the thickness of the grain boundary. These values in equations (10) to (11) are set in the information processing apparatus 1 in advance.

As shown in equation (8), nucleation at grain boundaries is considered to be heterogeneous nucleation, unlike nucleation within grains. In addition, the volume ratio Bv (the volume ratio between the Al crystal grains at the grain boundaries and the Al crystal grains within the grains) is used in the formula (8). By using Bv , the ratio of the number of nucleation site candidates between grain boundaries and grain interiors can be considered.

Subsequently, the nucleation rate calculator 14 uses the nucleation rate J in the crystal grain to calculate the number of second phase particles newly formed (precipitated) in the crystal grain during the minute time Δt. do. Hereinafter, the number of second phase grains newly formed in the crystal grain during the minute time Δt is expressed as ΔN. Specifically, the nucleation rate calculator 14 calculates ΔN as ΔN=J _gb ×Δt.

Further, the nucleation rate calculator 14 uses the nucleation rate _Jgb of the grain boundary to calculate the number of second phase grains newly formed in the minute time Δt at the grain boundary. Hereinafter, the number of second phase grains newly formed at the grain boundary during the minute time Δt is expressed as ΔN _gb . Specifically, the nucleation rate calculator 14 calculates ΔN _gb as ΔN _gb =J _gb ×Δt.

The radius (size) of the second-phase grains newly formed in the grains and the radius of the second-phase grains newly formed in the grain boundaries are both r _c (critical radius of nucleation ).

As described above, the first distributed information and the second distributed information are treated as separate information. More specifically, in the information processing device 1, the PSD function is handled as different information for the inside of the crystal grain and the crystal grain boundary. Therefore, the nucleation rate calculator 14 uses the above calculation results to individually update the PSD functions inside the grain and at the grain boundary.

Hereinafter, the PSD function in the crystal grain is referred to as PSD1 (first PSD function, first particle size distribution function). PSD1 is an example of first distributed information. Also, the PSD function at the grain boundary is referred to as PSD2 (second PSD function, second grain size distribution function). PSD2 is an example of second distributed information.

Specifically, the nucleation rate calculator 14 adds “ΔN second phase particles having a radius of r _c ” to the data of PSD1. More specifically, the nucleation rate calculator 14 calculates the number (frequency) of the second phase particles belonging to the class corresponding to the radius _rc (the size range of the second phase particles including the radius _rc ) in PSD1. , counts up by ΔN.

In addition, the nucleation rate calculator 14 adds “ΔN _gb second phase particles having a radius r _c ” to the PSD2 data. In PSD2, the number of second phase particles belonging to the class corresponding to radius r _c is counted up by ΔN _gb .

Thus, the fifth step (S7 and S8) may be understood as a step of individually updating (calculating) PSD1 and _PSD2 for one radius rc. A fifth step may be referred to as a nucleation calculation step. The nucleation calculation step can be expressed as a step of calculating ΔN and ΔN _gb separately.

(Sixth step: calculation of size change V of second phase particles)
The size change V of the second phase particles present in the material structure is expressed by the following formulas (12) to (13),

can be expressed as a function of the particle radius (the radius of the second phase particles) r by (see in particular equation (12)). Equation (12) is an equation that expresses the size growth of the second phase grains. Note that R in Equation (13) indicates a gas constant, as in Equation (7).

The size change amount calculator 15 calculates the size change of the second phase grains in each of the crystal grains and grain boundaries (S9). S9 is also called a size change calculation step. The size change calculation step can be expressed as a step of separately calculating (i) the size change of the second phase grains within the crystal grains and (ii) the size change of the second phase grains at the grain boundaries.

First, the size change amount calculation unit 15 uses formulas (12) to (13) for a predetermined radius r (which may be a value included in one of a plurality of classes in PSD1), A size change V of the second phase grains within the grain is calculated. The numerical value (physical property value) of the diffusion coefficient D described in various handbooks is the body diffusion coefficient (diffusion coefficient indicating the behavior of body diffusion). Therefore, by using the numerical value of the volume diffusion coefficient, it is possible to appropriately calculate the growth rate (in other words, size change V) of the second phase particles in the crystal grains.

On the other hand, at grain boundaries, grain boundary diffusion and intragranular diffusion occur simultaneously. For this reason, in calculations at grain boundaries, the use of either the diffusion coefficient that indicates the behavior of grain boundary diffusion or the diffusion coefficient that indicates the behavior of intragranular diffusion will cause the growth rate of the second phase grains to become too large or too small. likely to be calculated.

Therefore, when calculating the size change V of the second phase grains at the grain boundary, the size change amount calculation unit 15 replaces the “diffusion coefficient D” (body diffusion coefficient) in the above equation (12) with the following: Formula (14),

is replaced by the “diffusion factor D _GBapp ” represented by . The diffusion coefficient _DGBapp is the diffusion coefficient at grain boundaries. More specifically, the diffusion coefficient _DGBapp is a collector plate mechanism described in, for example, Masato Enomoto, Phase Transformation of Metals, pp. 116-119 (Uchida Rokakuho, published on October 25, 2000). Diffusion coefficient with theory applied. Note that _DGB in Equation (14) is a diffusion coefficient that indicates the behavior of solute atoms that form the second phase particles when diffusing at grain boundaries .

Hereinafter, to distinguish from the size change V of the second phase grains within the crystal grains, the size change of the second phase grains at the grain boundaries is also expressed as _Vgb . The size variation calculator 15 uses the diffusion coefficient _DGBapp to calculate the size variation _Vgb of the second phase grains at the grain boundaries with respect to the predetermined radius r. In this way, in the size change calculation step, the size changes V and _Vgb are separately calculated using different diffusion coefficients in the crystal grains and grain boundaries.

The change Δr in the grain size (particle radius) that occurs within a minute time Δt in the grain is expressed as Δr= VΔt . The size change amount calculator 15 calculates Δr with respect to the predetermined radius r using the formula.

The size variation calculation unit 15 performs the above calculation for the radius r corresponding to all classes of PSD1. Thereby, the PSD1 (dispersion state of the second phase particles in the crystal grains) at the time t can be calculated for all the sizes of the second phase particles in the crystal grains. Therefore, by calculating the PSD1 each time the time t is updated (see S11 described later), the temporal change of the PSD1 can be derived.

In addition, the change _Δr gb in the grain size (grain radius) that occurs during the minute time Δt at the grain boundary is expressed as Δ r gb ₌ V _gb Δt. The size change amount calculator 15 calculates Δ r _gb with respect to the predetermined radius r using the formula. The size variation calculation unit 15 performs the above calculation for the radius r corresponding to all classes of PSD2. Thereby, the PSD2 (dispersion state of the second phase particles at the grain boundaries) at the time t can be calculated for all the sizes of the second phase particles at the grain boundaries. Therefore, by calculating PSD2 every time the time t is updated, the temporal change of PSD2 can be derived.

As described above, according to the fifth step and the sixth step (S7 to S9), PSD1 (first dispersion information) and PSD2 ( second distributed information) can be calculated separately. Therefore, the fifth step and the sixth step are also generically referred to as the distributed state calculation step. More specifically, the dispersed state calculation step separately calculates PSD1 and PSD2 based on the calculation result in the nucleation calculation step and the calculation result in the size change calculation step.

According to the above configuration, the first dispersion information (PSD1) and the second dispersion information (PSD2) can include information about the number and size of the second phase particles at each time. Therefore, the dispersion state of the second phase particles in each of the crystal grains and the crystal grain boundaries can be expressed more specifically.

(Seventh step: recording of intermediate calculation results)
The data recording unit 16 acquires the solid solution element amount C, PSD1, and PSD2 at time t as intermediate calculation results (calculation results at current time t). Then, the data recording section 16 records these data in the storage section 80 (S10).

From the PSD function, as secondary information, the average particle size, number density, and inter-particle distance can be derived. Therefore, based on PSD1 and PSD2, the information processing device 1 can also derive the average size, number density, and inter-particle distance for each of the second-phase particles in the crystal grains and the crystal grain boundaries at time t. .

(Eighth step: update of time and determination of presence/absence of iterative calculation)
The temperature setting unit 12 updates the time t using Δt set in S4. Specifically, the temperature setting unit 12 sets t=t+Δt and counts up the time t by Δt (S11). S11 is a process for expressing progress of time in the heat treatment.

Subsequently, the temperature setting unit 12 determines whether or not the time t updated in S8 has reached the final time tf. Specifically, the temperature setting unit 12 determines whether or not t=tf (S12). If t<tf (NO in S12), return to S5 and repeat the above-described processes S5 to S11. On the other hand, if t=tf (YES in S12), proceed to S13.

(Ninth step: Recording final calculation results)
The data recording unit 16 acquires the solid solution element amount C, PSD1, and PSD2 at the final time tf as the final calculation result (calculation result at the final time tf). Then, the data recording section 16 records these data in the storage section 80 (S13). The final calculation result can be used as information indicating the material structure of the Al alloy after heat treatment (hereinafter referred to as final structure information).

The final structure information can be used as information for designing the entire manufacturing process combining rolling, extrusion, forging, heat treatment, and the like. When predicting the material structure in each step of the manufacturing process, the material structure prediction method of Embodiment 1 (the method for predicting the material structure of the Al alloy after heat treatment) can be combined with the material property prediction method in other steps. . This makes it possible to predict the material structure (that is, predict the material) over the entire manufacturing process.

As described above, according to the present calculation model, using the solid solution/precipitation calculation, (i) the solid solution amount of the main element (precipitated alloy component), and (ii) the precipitate of the precipitated alloy component (second The size and amount of two-phase particles) can be calculated. In particular, calculations can be performed by treating grain interiors and grain boundaries separately.

Note that in the calculation model, crystallized substances present in the Al alloy before heat treatment (eg, Al alloy ingot before homogenization treatment) can be excluded from the solid solution/precipitation calculation targets. For the main elements to be calculated in the material structure prediction method, an element that is considered to have a large effect on the characteristics (material) of the final product should be selected.

(Effect of information processing device 1)
As described above, in the conventional technology, the material structure after heat treatment is appropriately adjusted for an alloy system (eg, 6000 series Al alloy) in which second phase particles are precipitated simultaneously in the crystal grains and at the grain boundaries. could not be predicted.

On the other hand, according to the calculation model of the present invention, it is possible to perform calculations by separately handling the inside of grains and the grain boundaries, so it is possible to appropriately predict the material structure after heat treatment even for the above alloy system. That is, the material structure of the Al alloy after heat treatment can be predicted more accurately than conventionally.

Furthermore, since the material structure of the Al alloy can be predicted by highly accurate simulation, the number of experiments (prototypes) for examining manufacturing conditions can be reduced. Therefore, it is also possible to effectively reduce the cost required for the examination.

Further, in the information processing apparatus 1, the size and amount of precipitates (second phase particles) present in the grains and grain boundaries can be calculated by the material structure calculation after the homogenization process. Then, from the size and amount, the deterrent force during recrystallization can be predicted. The restraining force is information that serves as a guideline for setting (controlling) manufacturing conditions in downstream processes (eg, hot rolling process and extrusion process).

Further, in the information processing apparatus 1, it is also possible to predict whether re-dissolution of the target particles according to the solution treatment will be realized by calculating the material structure after the solution treatment. In addition, it is also possible to select optimum solution treatment conditions for realizing re-solution based on the prediction results.

〔Example〕
In order to confirm the usefulness of the calculation model, the inventors of the present application (hereinafter referred to as the inventors) compare the calculation results obtained by the calculation model (hereinafter referred to as the calculated values in the examples) with the actual measured values. compared.

(first example)
The inventors prepared an AA (Aluminum Association) standard 6106 alloy ingot having a thickness of 490 mm and a width of 1600 mm by DC (Direct Chill) casting, and used it as a research material. Then, the ingot was heat-treated. Then, a small piece (sample) was cut from the 1/4 thickness portion of the heat-treated ingot where the material structure is stable. After that, the inventors actually measured the Mg solid-solution amount (solid-solution element amount) and the average particle diameter (average value of particle diameter) of precipitates (second phase particles) in the small pieces. Mean grain diameter measurements were made for each grain intragranular and grain boundary.

Specifically, the measured value of the Mg solid-solution amount was calculated as a value obtained by subtracting the amount of precipitated Mg (amount of precipitated Mg) from the Mg content in the ingot. In order to calculate the amount of precipitated Mg, a solution was prepared by dissolving the precipitate extracted from the small pieces by the hot phenol dissolution method with a mixed solution of hydrochloric acid and hydrofluoric acid. The amount of precipitated Mg was calculated by performing ICP (Inductively Coupled Plasma) analysis on a solution obtained by diluting the solution. Thus, the method for measuring the solid solution amount of Mg is the same as in Patent Document 2.

Also, the average particle diameter was measured as follows. First, a small piece sample cut out from the Al alloy ingot was mirror-polished. Subsequently, SEM (Scanning Electron Microscope) observation was performed on the resulting polished surface, and 10 photographs (observation photographs) were taken at respective magnifications of 100 times, 500 times, 1000 times, and 5000 times. I took pictures one by one. Then, the second phase particles were observed using a total of 40 photographs obtained. Subsequently, using the shape feature extraction function of image analysis software WinROOF V5.7 (Mitani Shoji Co., Ltd.) for these photographs, the equivalent circle diameter (average particle diameter) of the second phase particles was calculated.

FIG. 3 is a table showing some examples of measured values and calculated values. As shown in FIG. 1 to No. Measured values were obtained for nine cases of nine. Subsequently, the inventors obtained calculated values using the present calculation model for the nine cases.

In FIG. 1 to No. 9, the Mg addition amount (solute addition amount) of the investigation material and the heat treatment conditions (homogenization treatment conditions and holding conditions before hot rolling) of the investigation material are shown. No. 1 to No. 9 differ in at least one of the amount of Mg added, the homogenization treatment conditions, and the holding conditions before hot rolling.

(a) to (c) of FIG. 4 are graphs showing comparison results between the measured values and the calculated values in FIG. (a) of FIG. 1 to No. 9 is a graph in which measured values are plotted on the horizontal axis and calculated values are plotted on the vertical axis for "Mg solid solution amount" shown in Fig. 9; As shown in FIG. 4(a), it was confirmed that each calculated value of the solid solution amount of Mg substantially coincided with the measured value.

(b) and (c) of FIG. 4 correspond to No. 1 of FIG. 1 to No. 9 is a graph in which the measured values are plotted on the horizontal axis and the calculated values on the vertical axis for "average particle diameter of precipitates in grains" and "average particle diameter of precipitates at grain boundaries" shown in 9. FIG. As shown in Figs. 4(b) and 4(c), it was confirmed that the calculated values for the average particle diameter of precipitates in grains and grain boundaries substantially agreed with the measured values. As described above, it was confirmed that the calculation model can predict the material structure of the Al alloy after heat treatment with high accuracy.

(Second example)
Subsequently, the inventors used No. 1 in FIG. 7, the PSD function was actually measured in each of grain interiors and grain boundaries. Furthermore, the inventors found that the No. 7, calculated values of the PSD function in each of the grain interior and the grain boundary were obtained by calculation using the present calculation model.

(a) and (b) of FIG. 5 are graphs comparing the measured and calculated values of these PSD functions, respectively. In FIG. 5(a), the measured and calculated values of the PSD function in grains are plotted. Also, in (b) of FIG. 5, the measured values and calculated values of the PSD function at the grain boundary are plotted.

As shown in FIGS. 5(a) and 5(b), it was confirmed that each calculated value of the PSD function in grain interiors and grain boundaries substantially agrees with the measured values. The second example also confirms that the calculation model can predict the material structure of the Al alloy after heat treatment with high accuracy.

(Third example)
Subsequently, the inventors performed calculations using the calculation model of Patent Document 2 (conventional calculation model). Then, the calculation results obtained by the calculation model of Patent Document 2 (hereinafter, calculated values in the comparative example) were compared with the measured values and the calculated values in the example.

FIG. 6 is a table showing some examples of (i) measured values, (ii) calculated values in a comparative example, and (iii) calculated values in an example. As shown in FIG. 1 to No. Measured values were obtained in the same manner as in the first example for the two cases of No. 2. Subsequently, the inventors obtained (i) calculated values in the comparative example and (ii) calculated values in the example for the two cases.

As shown in FIG. 1 to No. 2, the calculated value of the "Mg solid solution amount" in the example was closer to the measured value than the calculated value in the comparative example. In particular, No. In 2, the calculated value in the comparative example deviated greatly from the measured value. On the other hand, the No. 2, the calculated value in the example substantially matched the measured value.

Also, No. 1 to No. 2, the calculated value of the "average particle diameter of precipitates in grains" in the example was closer to the actual measurement than the calculated value in the comparative example. In particular, No. In 1, the calculated value in the comparative example deviated greatly from the measured value. On the other hand, the No. In No. 1, the calculated values in the example substantially matched the measured values.

As shown in FIG. 6, the calculation model of Patent Document 2 cannot calculate the “average particle diameter of precipitates at grain boundaries”. This is because, as described above, the calculation model of Patent Document 2 cannot calculate the "average particle radius of precipitates at grain boundaries" in the first place. In other words, the calculation model of Patent Document 2 cannot take into account "the influence of precipitation of second-phase particles at grain boundaries on precipitation of second-phase particles within grains". This is one of the reasons why the "Mg solid solution amount" and the "average grain diameter of precipitates in grains" could not be calculated with high accuracy using the calculation model of Patent Document 2.

On the other hand, the present calculation model can calculate the "average particle radius of precipitates at grain boundaries". Therefore, in the present calculation model, the "average particle diameter of precipitates at grain boundaries" can be calculated as twice the "average particle diameter of precipitates at grain boundaries". No. 1 to No. 2, the calculated value of the "average particle diameter of precipitates at the grain boundary" in the example was close to the measured value.

Further, according to the calculation model of the present invention, it is possible to consider "the effect of the precipitation of the second phase particles at the grain boundaries on the precipitation of the second phase particles inside the grains". As a result, the "Mg solid solution amount" and the "average grain diameter of precipitates in grains" can be calculated with higher accuracy than before. The third example also confirms that the material structure prediction method of the first embodiment can predict the material structure of the Al alloy after heat treatment with higher accuracy than the conventional method.

[Embodiment 2]
In the first embodiment, it has been explained that the calculation model can be used to predict the material structure of the Al alloy after heat treatment with high accuracy. However, the metal material to which the calculation model is applied is not limited to Al (Al alloy). For example, the computational model may be applied to predict the material structure of a given metal product after heat treatment. As an example, the computational model may be applied to predict the material structure of iron (Fe) or copper (Cu).

(Advantages when applying this calculation model to Al alloy)
(1) Iron-based alloys (iron-based alloys) can have different crystal structures in the temperature range in which they exist as solids. For example, an iron-based alloy can have (i) a body-centered cubic structure at low temperatures and (ii) a face-centered cubic structure at high temperatures, respectively, as crystal structures. Thus, allotropic transformations can occur in iron-based alloys. In iron-based alloys, allotropic transformations can be used to design microstructure control processes to reduce the effects of the precipitation state of the precipitates (precipitated alloy constituents).

On the other hand, an Al alloy has only a face-centered cubic crystal structure in the temperature range in which it exists as a solid. That is, Al alloy does not undergo allotropic transformation. Therefore, in Al alloys, it is more difficult than in iron-based alloys to design a microstructure control process to reduce the influence of the precipitation state of precipitates. Therefore, predicting the precipitation state in an Al alloy using the present calculation model is particularly useful in controlling the structure of the Al alloy.

(2) Depending on the size of the precipitate, the precipitate may (i) act as a nucleus for recrystallization or (ii) act as an inhibitor of grain growth. Precipitates in iron-based alloys are thermally stable. Therefore, in iron-based alloys, if the precipitation state of precipitates is confirmed experimentally, it is possible to predict the influence of the precipitates from the confirmation results.

In contrast, Al alloy precipitates (eg, Mg ₂ Si) are thermally unstable. Therefore, the form of the precipitate (eg, precipitation ⇔ solid solution) can change in a complicated manner even in a general manufacturing process. Therefore, even if the precipitation state of the precipitates is confirmed experimentally, it is difficult to predict the influence of the precipitates from the confirmation results. According to the calculation model of the present invention, it is possible to predict the influence of precipitates by predicting with high accuracy the state of precipitation of Al alloy during the manufacturing process. From this point of view as well, the calculation model is particularly suitable for predicting the material structure of Al alloys.

(3) As an example, in a heat treatment type Al alloy manufacturing process (eg, a manufacturing process for automobile body sheet materials), a treatment (solution treatment) is performed to reheat the material to redissolve precipitates. The conditions of solution treatment (eg, heating temperature and heating time) need to be set (changed) according to the precipitation state (eg, size and number) of precipitates. In addition, the precipitation state of precipitates greatly affects the properties of products (eg, strength and workability). According to the calculation model of the present invention, the precipitation state in the Al alloy can be predicted with high accuracy. Therefore, as described above, it is possible to facilitate the selection of optimum solution treatment conditions.

[Example of realization by software]
The control block of the information processing apparatus 1 (particularly, the material structure prediction unit 10) may be implemented by a logic circuit (hardware) formed in an integrated circuit (IC chip) or the like, or may be implemented by software.

In the latter case, the information processing apparatus 1 is provided with a computer that executes instructions of a program, which is software that implements each function. This computer includes, for example, one or more processors, and a computer-readable recording medium storing the program. In the computer, the processor reads the program from the recording medium and executes it, thereby achieving the object of the present invention. As the processor, for example, a CPU (Central Processing Unit) can be used. As the recording medium, a "non-temporary tangible medium" such as a ROM (Read Only Memory), a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like can be used. In addition, a RAM (Random Access Memory) for developing the above program may be further provided. Also, the program may be supplied to the computer via any transmission medium (communication network, broadcast wave, etc.) capable of transmitting the program. Note that one aspect of the present invention can also be implemented in the form of a data signal embedded in a carrier wave in which the program is embodied by electronic transmission.

〔summary〕
A material structure prediction method according to aspect 1 of the present invention is a material structure prediction method for predicting a material structure of an aluminum alloy (Al alloy) after heat treatment, wherein at an initial time (t = 0) before the heat treatment, the above In the aluminum alloy, (i) information indicating the solid solution amount (solid solution element amount C) of the precipitated alloy component, and (ii) the amount of the precipitated alloy component in each of the crystal grains and grain boundaries of the aluminum alloy. An initial structure information acquisition step (example: S1) for acquiring information indicating the dispersed state of the second phase particles, and a heat treatment condition information acquisition step (example: S2) for acquiring heat treatment condition information that is information indicating the conditions of the heat treatment. Then, based on the heat treatment condition information, the temperature (T=T(t)) to be applied to the aluminum alloy is set at each time from the initial time to the final time (t=tf) at which the heat treatment is completed. In a temperature setting step (eg, S5), at each time, (i) the precipitation of the second phase particles in the grains of the aluminum alloy, and (ii) the grains of the aluminum alloy, based on the temperature. Considering the precipitation of the second phase particles in the field, a solid solution amount calculation step (eg, S6) for calculating the solid solution amount, and at each time, based on the solid solution amount and the temperature, (i) first dispersion information (PSD1) indicating the dispersion state within the crystal grains, and (ii) second dispersion information (PSD2) indicating the dispersion state at the crystal grain boundaries are separately calculated. and a state calculation step (eg, S7 to S9).

As described above, in the conventional calculation model, it was originally impossible to calculate the precipitation of the second phase particles at the grain boundaries. Therefore, it has not been possible to appropriately predict the material structure after heat treatment for Al alloys (eg, 6000-series Al alloys) in which second-phase particles precipitate simultaneously in grains and grain boundaries.

On the other hand, according to the above configuration (that is, according to the present calculation model), the first dispersion information and the second dispersion information can be calculated separately (that is, the inside of the grain and the grain boundary can be calculated separately can handle and perform calculations). In addition, in calculating the amount of solid solution, precipitation of the second phase particles at the grain boundaries can be further taken into consideration in addition to the precipitation of the second phase particles inside the crystal grains.

Therefore, unlike conventional calculation models, it is possible to appropriately predict the material structure after heat treatment even for Al alloys in which second phase particles precipitate simultaneously in grains and grain boundaries. That is, it becomes possible to predict the material structure of the Al alloy after heat treatment more accurately than before.

In the material structure prediction method according to aspect 2 of the present invention, in aspect 1, the solid solution amount calculating step is the following formula (1),
C=C ₀ −f _pIG C _β −f _pGB C _β (1)
C: the solid solution amount C ₀ : the added amount of the precipitated alloy component, which is the solute in the second phase particles,
C _β : solute equilibrium concentration in the second phase particles;
f _pIG : volume fraction of the second phase grains in the grains;
f _pGB : the volume fraction of the second phase grains at the grain boundary;
may further include a step of calculating the solid solution amount.

According to the above configuration, a simple calculation (calculation using formula (1)) can be used to determine the amount of solid solution in consideration of precipitation of second phase particles in grains and precipitation of second phase particles at grain boundaries. can be calculated.

A material structure prediction method according to aspect 3 of the present invention is the aspect 1 or 2, wherein the dispersion state calculation step includes, at each time, based on the solid solution amount and the temperature, (i) in the crystal grain and (ii) a nucleation calculation step (eg, S7 S8), (i) the size change of the second phase grains within the crystal grains, and (ii) the size change of the second phase grains at the grain boundaries, are calculated separately. (S9), and the dispersion state calculation step calculates the first dispersion information and the second dispersion based on the calculation result in the nucleation calculation step and the calculation result in the size change calculation step. The step of separately calculating the information may be further included.

According to the above configuration, the first dispersion information and the second dispersion information can include information about the number and size of the second phase particles at each time. Therefore, the dispersion state of the second phase particles in each of the crystal grains and the crystal grain boundaries can be expressed more specifically.

In the material structure prediction method according to aspect 4 of the present invention, in aspect 3, the size change calculation step uses different diffusion coefficients in the crystal grain and the crystal grain boundary to determine: (i) in the crystal grain and (ii) the step of separately calculating the size change of the second phase grains at the grain boundaries.

As described above, grain boundary diffusion and intragranular diffusion occur simultaneously at grain boundaries. Therefore, according to the above configuration, a diffusion coefficient different from the diffusion coefficient D (body diffusion coefficient) in the crystal grain (eg, the diffusion coefficient D _GBapp in the above equation (14)) is used to obtain the first diffusion coefficient at the grain boundary. The size change of the biphasic particles can be calculated. Therefore, the size change of the second phase grains at the grain boundaries can be better calculated.

A material structure prediction method according to Aspect 5 of the present invention is a method according to any one of Aspects 1 to 4, wherein the first dispersion information is the size of the second phase particles in the crystal grains and the size of the second phase particles. and the second dispersion information is the correspondence between the size of the second phase grains at the grain boundary and the number of the second phase grains It is preferably represented by a second particle size distribution function that indicates the relationship.

According to the above configuration, the first dispersion information and the second dispersion information can each be represented by separate PSD functions (particle size distribution functions).

In the material structure prediction method according to aspect 6 of the present invention, in any one of aspects 1 to 5, it is preferable that the heat treatment includes homogenization treatment for the aluminum alloy ingot.

According to the above configuration, it is possible to predict the material structure of an aluminum alloy ingot that has been subjected to a homogenization treatment (a typical example of heat treatment). Therefore, for example, the user can be presented with information that serves as a guideline for setting manufacturing conditions in the downstream process.

A material structure prediction device (1) according to aspect 7 of the present invention is a material structure prediction device for predicting a material structure of an aluminum alloy after heat treatment, the aluminum alloy at an initial time before the heat treatment, (i ) information indicating the solid solution amount of the precipitated alloy component, and (ii) information indicating the dispersion state of the second phase particles of the precipitated alloy component in each of the crystal grains and grain boundaries of the aluminum alloy. An initial structure information acquisition unit (data acquisition unit 11), a heat treatment condition information acquisition unit (data acquisition unit 11) for acquiring heat treatment condition information that is information indicating the conditions of the heat treatment, and based on the heat treatment condition information, the above A temperature setting unit (12) for setting the temperature applied to the aluminum alloy at each time from the initial time to the final time when the heat treatment is completed, and at each time, based on the temperature, (i) the aluminum Solid solution amount calculation for calculating the solid solution amount in consideration of precipitation of the second phase particles in the grains of the alloy and (ii) precipitation of the second phase particles at the grain boundaries of the aluminum alloy (i) first dispersion information indicating the dispersion state in the crystal grain, and (ii) based on the solid solution amount and the temperature at each time. a second dispersion information indicating the dispersion state at the grain boundary;

According to the above configuration, the material structure prediction device according to this aspect has the same effects as the material structure calculation method according to the first aspect.

[Additional notes]
The present invention is not limited to the above-described embodiments, but can be modified in various ways within the scope of the claims, and can be obtained by appropriately combining technical means disclosed in different embodiments. is also included in the technical scope of the present invention.

[another expression of the invention]
Note that one embodiment of the present invention can also be expressed as follows.

A material structure prediction method according to an aspect of the present invention includes (i) the initial solid solution amount of alloying elements in an Al alloy, (ii) the size and amount of second phase particles, and (iii) the applied heat history (increase A material structure prediction method for predicting the dispersion state of second phase particles and the change in the amount of solid solution elements in an Al alloy using the temperature rate, holding temperature, holding time and cooling rate), Second-phase grains and changes in second-phase grains at grain boundaries are separately predicted.

In addition, the material structure prediction method according to an aspect of the present invention reflects (considers) the influence of nucleation that occurs during heat treatment on the parameters of the size and amount of the second phase particles in consideration of the distribution state. The change in the size of the two-phase particles and the rate of solid solution re-dissolution are calculated to predict the change in the amount of the second-phase particles and solute elements.

Further, the material structure prediction method according to one aspect of the present invention expresses the dispersed state of the second phase particles as a function of size and amount.

Further, a material structure prediction method according to an aspect of the present invention predicts changes in second phase particles and in the amount of solid solution elements due to homogenization treatment of an aluminum alloy ingot.

1 Information processing device (material structure prediction device)
11 data acquisition unit (initial structure information acquisition unit, heat treatment condition information acquisition unit)
12 temperature setting unit 13 solid solution element amount calculation unit (solid solution amount calculation unit)
14 Nucleation rate calculation unit (dispersion state calculation unit)
15 size variation calculation unit (dispersion state calculation unit)
PSD1 PSD function in crystal grain (first particle size distribution function, first dispersion information)
PSD2 PSD function at grain boundaries (second particle size distribution function, second dispersion information)
C: Solid solution amount (solid solution amount of precipitated alloy components)
t time tf final time T, T(t) temperature

Claims (6)

A material structure prediction method for predicting a material structure of an aluminum alloy after heat treatment, comprising:
(i) information indicating the solid solution amount of the precipitated alloy component of the aluminum alloy at the initial time before the heat treatment, and (ii) the precipitated alloy in each of the crystal grains and grain boundaries of the aluminum alloy an initial structure information acquisition step of acquiring information indicating the number and size of the dispersed state of the second phase particles of the component;
A heat treatment condition information acquisition step of acquiring heat treatment condition information that is information indicating the conditions of the heat treatment;
a temperature setting step of setting the temperature applied to the aluminum alloy at each time from the initial time to the final time when the heat treatment is completed, based on the heat treatment condition information;
At each time, based on the temperature, (i) precipitation of the second phase particles in the grains of the aluminum alloy, and (ii) precipitation of the second phase particles at the grain boundaries of the aluminum alloy. , Considering the solid solution amount calculation step of calculating the solid solution amount,
At each time, based on the solid-solution amount and the temperature, (i) first dispersion information indicating the dispersion state within the crystal grains, and (ii) second dispersion information indicating the dispersion state at the crystal grain boundaries. and a distributed state calculation step of individually calculating the distributed information ,
The solid solution amount calculation step is performed by the following formula (1),
C=C ₀ −f _pIG C _β −f _pGB C _β (1)
C: the solid solution amount,
C ₀ : the added amount of the precipitated alloy component, which is the solute in the second phase particles;
C _β : solute equilibrium concentration in the second phase particles;
f _pIG : volume fraction of the second phase grains in the grains;
f _pGB : the volume fraction of the second phase grains at the grain boundary;
By calculating the above solid solution amount,
In the dispersion state calculation step, at each time, based on the solid solution amount and the temperature,
Nuclei for separately calculating (i) the number of the second phase particles generated by nucleation in the grains and (ii) the number of the second phase particles generated by nucleation at the grain boundaries a generation calculation step;
(i) the size change of the second phase grains within the crystal grains; and (ii) the size change of the second phase grains at the grain boundaries. and
The dispersion state calculation step separately calculates the first dispersion information and the second dispersion information based on the calculation result in the nucleation calculation step and the calculation result in the size change calculation step.
A material structure prediction method characterized by: In the nucleation calculation step, (i) the number of the second phase particles generated by nucleation in the crystal grain, and 2. The material structure prediction method according to claim 1, further comprising: (ii) individually calculating the number of said second phase grains generated by nucleation at said grain boundaries. In the size change calculation step, using different diffusion coefficients in the crystal grain and the grain boundary, (i) the size change of the second phase grain in the crystal grain, and (ii) the grain boundary 3. The material structure prediction method according to claim 1 or 2 , further comprising the step of separately calculating the size change of the second phase particles in the . The first dispersion information is expressed by a first particle size distribution function indicating a correspondence relationship between the size of the second phase particles and the number of the second phase particles in the crystal grains,
The second dispersion information is expressed by a second particle size distribution function that indicates a correspondence relationship between the size of the second phase particles and the number of the second phase particles at the grain boundaries. Item 4. The material structure prediction method according to any one of Items 1 to 3 . 5. The material structure prediction method according to any one of claims 1 to 4 , wherein the heat treatment includes a homogenization treatment for the aluminum alloy ingot. A material structure prediction device for predicting a material structure of an aluminum alloy after heat treatment,
(i) information indicating the solid solution amount of the precipitated alloy component of the aluminum alloy at the initial time before the heat treatment, and (ii) the precipitated alloy in each of the crystal grains and grain boundaries of the aluminum alloy an initial structure information acquisition unit that acquires information indicating the number and size of the dispersed state of the second phase particles of the component;
a heat treatment condition information acquisition unit that acquires heat treatment condition information that is information indicating the conditions of the heat treatment;
a temperature setting unit that sets the temperature to be applied to the aluminum alloy at each time from the initial time to the final time when the heat treatment is completed, based on the heat treatment condition information;
At each time, based on the temperature, (i) precipitation of the second phase particles in the grains of the aluminum alloy, and (ii) precipitation of the second phase particles at the grain boundaries of the aluminum alloy. , Considering the solid solution amount calculation unit for calculating the solid solution amount,
At each time, based on the solid-solution amount and the temperature, (i) first dispersion information indicating the dispersion state within the crystal grains, and (ii) second dispersion information indicating the dispersion state at the crystal grain boundaries. and a dispersion state calculation unit that individually calculates the dispersion information ,
The solid solution amount calculation unit uses the following formula (1),
C=C ₀ −f _pIG C _β −f _pGB C _β (1)
C: the solid solution amount,
C ₀ : the added amount of the precipitated alloy component, which is the solute in the second phase particles;
C _β : solute equilibrium concentration in the second phase particles;
f _pIG : volume fraction of the second phase grains in the grains;
f _pGB : the volume fraction of the second phase grains at the grain boundary;
By calculating the above solid solution amount,
The dispersion state calculation unit, at each time, based on the solid solution amount and the temperature,
Nuclei for separately calculating (i) the number of the second phase particles generated by nucleation in the grains and (ii) the number of the second phase particles generated by nucleation at the grain boundaries a generation calculation step;
performing a size change calculation step of separately calculating (i) the size change of the second phase grains in the crystal grains and (ii) the size change of the second phase grains at the grain boundaries; ,
The dispersion state calculation unit separately calculates the first dispersion information and the second dispersion information based on the calculation result in the nucleation calculation step and the calculation result in the size change calculation step.
A material structure prediction device characterized by:

Images