Analysis method, analysis device, and analysis program of resin molding, and recording medium

Abstract

To provide an analysis method, an analysis device and an analysis program of resin molding, and a recording medium, which improves prediction accuracy by fully considering crystallization behavior of a resin in a molding process.SOLUTION: A resin molding analysis method for analyzing resin molding by computer simulation includes: a physical property information acquisition step; a resin characteristic calculation step; a cooling speed calculation step; a crystallinity calculation step of calculating crystallinity of a resin; and a physical property information correction step of correcting resin physical property information, on the basis of the crystallinity. The physical property information acquisition step of the next time step tn+1 acquires the corrected physical property information as resin physical property information. The crystallinity calculation step calculates the crystallinity by considering the crystallinity behavior of the resin previously obtained by high speed differential scanning calorimetry.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present disclosure relates to a method for analyzing resin molding, an analysis apparatus, an analysis program, and a recording medium.

Background Art

[0002] Conventionally, for the purpose of improving the accuracy, efficiency, and cost reduction of product design and the like in resin molded products, the behavior of the resin in the mold has been analyzed using CAE (Computer-Aided-Engineering) (see, for example, Patent Document 1).

[0003] Patent Document 1 discloses a method for simulating the behavior of molten crystalline resin. In this method, when calculating resin properties such as temperature, pressure, shear viscosity, and specific volume at a certain time, simulation is performed on the premise of physical property information such as specific heat and PVT properties, and when simulating the behavior of the resin at the next time after a minute time has elapsed from a certain time, the resin properties are calculated by correcting the specific heat and PVT properties as physical property information. Specifically, for example, the unsteady PVT properties at a certain time are obtained from the cooling rate, pressure, and temperature at that time, and the steady PVT properties as the physical property information set as the premise of the calculation at that time are corrected to the unsteady PVT properties.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Patent Document 1 describes functionalizing property changes dependent on the cooling rate based on the relationship between the temperature of a crystalline resin and the specific heat obtained by measurement using differential scanning calorimetry (DSC) at a cooling rate of 2°C / min to 50°C / min. However, in ordinary DSC, it is substantially impossible to reproduce measurements in a high-speed cooling environment of, for example, 3°C / s or more, particularly 10°C / s or more, which is achieved during actual molding processing. Also, in ordinary DSC, it is substantially impossible to measure the crystallization behavior during the isothermal process, particularly in the low-temperature range of a general molding processing temperature range (e.g., 25°C to 300°C).

[0006] As described above, in the prior art, since the crystallization behavior of the resin during the molding process, particularly in the low-temperature range of the cooling rate and the molding processing temperature range during actual molding processing, cannot be experimentally observed, there has been a problem that it is difficult to sufficiently consider the crystallization behavior of the resin during the molding process in CAE analysis.

[0007] Therefore, in the present disclosure, in a resin molding analysis method, an analysis apparatus, an analysis program, and a recording medium, an object is to improve the prediction accuracy by sufficiently considering the crystallization behavior of the resin during the molding process.

Means for Solving the Problems

[0008] To solve the above problems, one aspect of the resin molding analysis method disclosed herein is a method for analyzing resin molding by computer simulation, comprising a physical property information acquisition step of acquiring physical property information of the resin used for calculation at time step t n ; a resin property calculation step of calculating resin properties including the temperature of the resin at the time step t based on the physical property information; a cooling rate calculation step of calculating the cooling rate of the resin at the time step t n based on the temperature; and a crystallinity calculation step of calculating the crystallinity of the resin at the time step t based on the temperature and the cooling rate n ; and a crystallinity calculation step of calculating the crystallinity of the resin at the time step t based on the temperature and the cooling rate n . A physical property information correction step of correcting the physical property information of the resin based on the degree of crystallinity; In the physical property information acquisition step at the next time step t n+1 the corrected physical property information is acquired as the physical property information of the resin; In the degree of crystallinity calculation step, the degree of crystallinity is calculated in consideration of the crystallization behavior of the resin obtained in advance by high-speed differential scanning calorimetry. It is characterized by the above.

[0009] A fast scanning calorimeter (Fast Scanning Calorimetry, hereinafter also referred to as "FSC") can perform non-isothermal heat measurement at a heating and cooling rate of 3°C / s or more, preferably 10°C / s or more, and can set the sample temperature to a desired temperature at a heating and cooling rate of 100°C / s or more, preferably 1000°C / s or more and 10000°C or less and hold it at the desired temperature for isothermal heat measurement. That is, in FSC, it is possible to perform measurement that reproduces the high-speed cooling environment (for example, 3°C / s or more, particularly 10°C / s or more) achieved during actual molding processing, and to measure the crystallization behavior during the isothermal process in the general molding processing temperature range (for example, 25°C to 300°C, and in the case of polypropylene, for example, 40°C to 120°C).

[0010] In this configuration, in the degree of crystallinity calculation step, the degree of crystallinity is calculated in consideration of the crystallization behavior of the resin obtained by FSC measurement. As a result, it becomes possible to perform more accurate analysis of the crystallization behavior of the resin based on the actual phenomenon, and thus the prediction accuracy of the analysis is improved.

[0011] Preferably, the physical property information includes the shear viscosity of the resin, In the resin property calculation step, the shear rate of the resin is calculated as the resin property based on the shear viscosity, In the physical property information correction step, the shear viscosity is corrected to the shear viscosity calculated by the following formula (1), |η| = X c |η c |+(1 - X c )|η m | ···(1) (However, in Formula (1), η is the shear viscosity, η c is the shear viscosity in the solid state, η m is the shear viscosity in the molten state, X c is the degree of crystallinity.) In the resin property calculation step for the next time step t n+1 , the shear rate is calculated based on the corrected shear viscosity.

[0012] For example, conventional flow analysis regarding resin injection molding performs flow calculations using resin shear rate data. At this time, in the cooling process, it is assumed that the flow stops uniformly at the timing when the set temperature is reached, and the shear viscosity, which is a prerequisite for calculating the shear rate, is set to a constant value below that temperature. However, for example, the cooling rate of the resin varies depending on the molding conditions, so the flow stop temperature also changes. Then, in the conventional flow analysis where the flow stop temperature is uniformly determined, the prediction accuracy of the shear rate is insufficient, and sufficient prediction accuracy cannot be ensured, for example, in predicting the flow length etc.

[0013] In this configuration, using the degree of crystallinity calculated in consideration of the crystallization behavior of the resin obtained in advance by high-speed differential scanning calorimetry, based on the above Formula (1), the shear viscosity used in the calculation for the next time step t n+1 is calculated. Thereby, based on the degree of crystallinity, it is possible to reproduce the actual phenomenon where a semi-crystalline state in which the molten resin and the solid resin are mixed exists. And through the correction of the shear viscosity, since the flow stop temperature can be substantially set variably according to the cooling rate, the prediction accuracy of the shear rate is improved, and the prediction accuracy of the flow length etc. is improved.

[0014] Preferably, the physical property information includes the specific volume of the resin, in the resin property calculation step, based on the specific volume, the pressure of the resin is calculated as the resin property, in the physical property information correction step, the specific volume is corrected to the specific volume calculated by the following Formula (2), |v| = X c |v c | + (1 - X c)|v m | ···(2) (However, in formula (2), v is the specific volume, v c is the specific volume of the crystalline part, v m is the specific volume of the amorphous part, X c is the degree of crystallinity.) In the resin property calculation step of the next time step t n+1 , based on the corrected specific volume, the pressure is calculated.

[0015] In conventional analysis, calculations are performed using the pressure data of the resin. At this time, the specific volume on which the pressure calculation is based is calculated with the temperature and pressure of the resin as input information and the Tait equation or the like as the governing equation. However, in the case of a crystalline resin, when the temperature of the resin reaches a certain temperature as it decreases during the molding process, crystallization starts and the rate of decrease in specific volume increases. The temperature at which this crystallization starts varies depending on the cooling rate of the resin. Since the above-mentioned Tait equation is a model in which one specific volume V is obtained from the pressure P and the temperature T, the dependence of the specific volume on the cooling rate in the crystallization behavior of the resin cannot be considered. That is, in the current general CAE analysis, the change in resin physical properties based on the crystallization behavior of the resin during the molding process cannot be considered, and there is room for improvement from the perspective of improving prediction accuracy.

[0016] In this configuration, using the degree of crystallinity calculated in consideration of the crystallization behavior of the resin obtained in advance by high-speed differential scanning calorimetry, based on the above formula (2), the specific volume used in the calculation at the next time step t n+1 is calculated. As a result, based on the degree of crystallinity, the actual phenomenon in which a semi-crystalline state in which the molten resin and the solid resin are mixed exists can be reproduced. And through the correction of the specific volume, an analysis that more accurately considers the dependence of the specific volume on the cooling rate based on the actual phenomenon becomes possible, so the prediction accuracy of the analysis is improved.

[0017] Preferably, the physical property information includes the thermal conductivity of the resin, in the resin property calculation step, based on the thermal conductivity, the temperature of the resin is calculated as the resin property, In the physical property information correction step, the thermal conductivity is corrected to the thermal conductivity calculated by the following formula (3), |λ| = X c |λ c |+(1 - X c )|λ m | ···(3) (However, in formula (3), λ is the thermal conductivity, λ c is the thermal conductivity in the solid state, λ m is the thermal conductivity in the molten state, and X c is the crystallinity.) In the resin property calculation step of the next time step t n+1 , the temperature is calculated based on the corrected thermal conductivity.

[0018] Conventional analysis performs calculations using the temperature data of the resin. At this time, regarding the thermal conductivity of the resin that is the premise of temperature calculation, in the cooling process, for example, it is assumed that when the resin changes from the molten state to the solid state at the timing of reaching an arbitrarily set temperature, etc., and it is set to change uniformly from the thermal conductivity in the molten state to the thermal conductivity in the solid state at that timing. However, in the actual molding process, there are cases where the resin is in a state where the molten state and the solid state are mixed, and it is considered that the thermal conductivity changes continuously depending on the crystallinity of the resin. Then, in the conventional analysis where the thermal conductivity is set to change uniformly from the value in the molten state to the value in the solid state, the prediction accuracy of the temperature is insufficient, and sufficient prediction accuracy of the analysis cannot be ensured.

[0019] In this configuration, using the crystallinity calculated in consideration of the crystallization behavior of the resin obtained in advance by high-speed differential scanning calorimetry, based on the above formula (3), the thermal conductivity used for the calculation at the next time step t n+1 is calculated. Thereby, based on the crystallinity, it is possible to reproduce the actual phenomenon in which a semi-crystalline state in which the molten resin and the solid resin coexist exists. And since the crystallinity dependence of the thermal conductivity can be considered in accordance with the actual phenomenon, the prediction accuracy of the temperature is improved, and the prediction accuracy of the analysis is improved.

[0020] The crystallinity is the relative crystallinity, The change with time φ(t) of the relative crystallinity is calculated using the Nakamura model represented by the following formula (4),

[0021] [Number]

[0022] (However, in formula (4), φ(t) is the change with time of the relative crystallinity, K(T) is a value related to the rate constant of crystal growth, and n is the Avrami exponent.) The K(T) and the n are determined in consideration of the crystallization behavior obtained by the high-speed differential scanning calorimetry which is preferable.

[0023] According to this configuration, since the value K(T) related to the rate constant of crystal growth and the Avrami exponent n are determined in consideration of the crystallization behavior obtained by FSC measurement, the prediction accuracy of the analysis is improved.

[0024] The crystallization behavior obtained by the high-speed differential scanning calorimetry is obtained by at least one of isothermal measurement at an arbitrary temperature within the molding processing temperature range and non-isothermal measurement in a temperature range including the arbitrary temperature which is preferable.

[0025] According to this configuration, the prediction accuracy of the analysis is improved.

[0026] The crystallinity is the absolute crystallinity, and the crystallinity calculation step includes a relative crystallinity calculation step of calculating the change with time of the relative crystallinity of the resin, and an absolute crystallinity calculation step of calculating the change with time of the absolute crystallinity of the resin based on the change with time of the relative crystallinity which is preferable.

[0027] According to this configuration, since the influence of the cooling rate can be considered more accurately, the prediction accuracy of the analysis is improved.

[0028] Preferably, the change in the absolute crystallinity Φ(t) over time is represented by the following formula (5):

[0029] [Number]

[0030] (However, in formula (5), φ is the relative crystallinity, and f c (T) is the degree of crystallinity reached during isothermal or non-isothermal crystallization at temperature T (the crystallization peak temperature), and f 1 ~f 6 are coefficients and input parameters that depend on the material.) The time derivative of φ(t) in formula (5) represents the rate of change of the relative crystallinity over time (also referred to as the "rate of progress of the relative crystallinity"). By integrating over time the product of the time derivative of φ(t) and the degree of crystallinity reached during isothermal or non-isothermal crystallization at temperature T at that time t (indicating how much crystallinity can be reached at that temperature T based on the melting enthalpy of the crystal shown in the literature values), the absolute crystallinity at time t and temperature T can be calculated. According to this configuration, the influence of the cooling rate can be considered more accurately, so the prediction accuracy of the analysis is improved.

[0031] Preferably, the property information includes the temperature of the resin, In the resin property calculation step, based on the temperature of the resin as the property information and the modified heat conduction equation represented by the following formula (D1), the temperature of the resin is calculated as the resin property,

[0032] [Number]

[0033] (However, in formula (D1), ρ is the density, Cv is the specific heat, T is the temperature, λ is the thermal conductivity, η is the viscosity, γ dot is the shear rate, φ is the relative crystallinity, ΔHc is the crystallization heat generation term, {φ(t n ) - φ(t n-1 )} is the degree of progress of the relative crystallinity at time step t n and ΔHc (T) is the crystallization enthalpy when isothermally crystallized at temperature T, h 1 ~h 6 is a coefficient and an input parameter depending on the material.) In the physical property information correction step, the temperature of the resin as the physical property information is corrected to the temperature of the resin calculated as the resin property, in the resin property calculation step of the next time step t n+1 the temperature of the resin as the resin property is calculated based on the corrected temperature.

[0034] In Equation (D1), a crystallization heat generation term ΔHc considering crystallization heat generation is added to the conventional heat conduction equation. And the crystallization heat generation term ΔHc is expressed as the product of the crystallization enthalpy ΔH c (T) and the progress degree of relative crystallinity {φ(t n ) - φ(t n-1 ))}. According to this configuration, since the crystallization behavior of the resin can be considered in calculating the temporal change of the resin temperature, the prediction accuracy of the analysis is improved.

[0035] Preferably, the specific heat Cv is given by the following Equation (D2).

[0036] Cv = g 1 T 5 + g 2 T 4 + g 3 T 3 + g 4 T 2 + g 5 T + g 6 ···(D2) (However, in Equation (D2), g 1 ~g 6 are coefficients and input parameters depending on the material.) According to this configuration, it is advantageous for improving the prediction accuracy of the analysis.

[0037] One aspect of the resin molding analysis apparatus disclosed herein is An apparatus for analyzing resin molding by computer simulation, comprising: Time step t n A physical property information acquisition unit that acquires the physical property information of the resin used for the calculation of; Based on the physical property information, a resin property calculation unit that calculates resin properties including the temperature of the resin at the time step t n ; Based on the temperature, a cooling rate calculation unit that calculates the cooling rate of the resin at the time step t n ; Based on the temperature and the cooling rate, a crystallinity calculation unit that calculates the crystallinity of the resin at the time step t n ; A physical property information correction unit that corrects the physical property information of the resin based on the crystallinity, and the physical property information acquisition unit acquires the corrected physical property information as the physical property information of the resin at the next time step t n+1 ; the crystallinity calculation unit calculates the crystallinity in consideration of the crystallization behavior of the resin obtained in advance by high-speed differential scanning calorimetry .

[0038] According to this configuration, since it is possible to perform analysis that more accurately considers the crystallization behavior of the resin based on the actual phenomenon, the prediction accuracy of the analysis is improved.

[0039] One aspect of the resin molding analysis program disclosed herein is a program for analyzing resin molding by computer simulation, which causes a computer to perform at least procedure A for acquiring the physical property information of the resin used for the calculation of time step t ; n procedure B for calculating resin properties including the temperature of the resin at the time step t based on the physical property information ; n procedure C for calculating the cooling rate of the resin at the time step t based on the temperature ; nProcedure C for calculating the cooling rate of the resin in Based on the temperature and the cooling rate, for the time step t n Procedure D for calculating the crystallinity of the resin in Based on the crystallinity, it causes to execute Procedure E for correcting the physical property information of the resin, In the next time step t n+1 In Procedure A, obtain the corrected physical property information as the physical property information of the resin, In Procedure D, calculate the crystallinity in consideration of the crystallization behavior of the resin obtained in advance by high-speed differential scanning calorimetry It is characterized by this.

[0040] According to this configuration, since it is possible to perform an analysis that more accurately considers the crystallization behavior of the resin based on the actual phenomenon, the prediction accuracy of the analysis is improved.

[0041] One aspect of the recording medium disclosed herein is A computer-readable recording medium on which the above-described resin molding analysis program is recorded.

Advantages of the Invention

[0042] As described above, according to the present disclosure, since it is possible to perform an analysis that more accurately considers the crystallization behavior of the resin based on the actual phenomenon, the prediction accuracy of the analysis is improved.

Brief Description of the Drawings

[0043]

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Mode for Carrying Out the Invention

[0044] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. The following description of the preferred embodiments is merely illustrative in nature and is in no way intended to limit the present disclosure, its applications, or its uses.

[0045] (Embodiment 1) <Molding of Resin and Resin Molded Article> The technology of the present disclosure can be applied to all molding methods using a mold made of a resin material as a raw material. Specific examples of the molding method include injection molding, transfer molding, plunger molding, press molding, blow molding, vacuum molding, pressure air molding, and the like.

[0046] In this specification, the terms "resin" and "resin material" mean a resin composition containing a resin raw material and, if necessary, any optional additive.

[0047] - Injection Molding of Resin - As an example of a forming method, the outline of injection molding will be described. FIG. 1 is a diagram for explaining each step in the injection molding of resin and a graph showing an example of the pressure and temperature history of the resin.

[0048] As shown in FIG. 1, the injection molding of resin includes an injection step S51, a holding pressure step S52, and a cooling step S53.

[0049] First, a molten resin material heated to temperature T is injected into the cavity formed by clamping the molds M1 and M2 (injection step S51). 1 When the resin is filled throughout the cavity, in order to reduce the excessive shrinkage amount of the resin due to temperature drop, additional resin filling injection is started (time A1). Then, a pressure exceeding atmospheric pressure P is applied to the resin in the cavity. And while adjusting the additional resin filling amount, the resin pressure is maintained around the set predetermined pressure P (holding pressure step S52).

[0050] When a certain amount of additional resin has been filled for a certain time or a certain amount, the filling of additional resin is stopped (time A2). As a result, the pressure of the resin in the cavity gradually decreases from P and eventually becomes atmospheric pressure P (time A3). The molded product is cooled in the mold, then demolded by mold opening, and air-cooled (cooling step S53). 0 1

[0051] 1 0

[0052] The molded product obtained in this way is not shown in FIG. 1, but after being cooled to normal temperature T, it becomes a product through post-processing such as deburring. 0

[0053] ​​​​​​In the injection step S51, molecular orientation of the polymer chains occurs due to the shear flow of the resin. In the holding pressure step S52, crystallization proceeds as the temperature decreases in the molecular orientation state, and volume shrinkage due to crystallization occurs. In the mold cooling of the cooling step S53, although the shrinkage of the resin progresses, residual stress is generated in the molded product due to mold restraint. In the air cooling of the cooling step S53, the residual stress is released by demolding, and deformation of the molded product occurs.

[0054] Note that, not limited to injection molding, it is generally known that there are two types, primary crystallization and secondary crystallization, in the process of resin crystallization. In this specification, "primary crystallization" means crystallization in which spherulites composed of a laminated structure (lamellar structure) of crystal layers formed by folding of polymer chains and amorphous layers sandwiched between adjacent crystal layers are formed. Further, "secondary crystallization" refers to further thickening growth of the crystal layers and completion of the lamellar structure in the lamellar structure generated by primary crystallization.

[0055] Note that, as a result of intensive research, the inventors of the present application have found that the crystallization of the resin proceeding from the holding pressure step S52 to the mold cooling of the cooling step S53 in injection molding is mainly primary crystallization in a non-isothermal process, but in addition, secondary crystallization in a non-isothermal process also proceeds. Further, crystallization proceeds further in the air cooling of the cooling step S53, and the crystallization is mainly secondary crystallization in an isothermal process.

[0056] -Resin molded product- The resin molded product is not particularly limited as long as it is a resin molded product manufactured by the above-described various molding methods. Specific examples of the resin molded product include, for example, parts for automobiles, parts for rockets, aircraft, etc., and sports goods. Preferably, a plate-shaped molded product such as an interior or exterior member of a vehicle is mentioned. Note that the resin molded product may be an insert molded product.

[0057] As the resin raw material, well-known crystalline resins can be targeted, and specifically, for example, polypropylene resin, polyethylene resin, polyacetal resin, polyamide (PA) resin, etc. can be mentioned. These resins can be used singly or in combination of two or more.

[0058] The resin may contain additives such as reinforcing fibers, fillers, pigments, dyes, impact modifiers, UV absorbers, etc. The reinforcing fibers are not particularly limited, and well-known fibers can be used. Specifically, for example, glass fibers, carbon fibers, cellulose nanofibers, etc. can be mentioned. These fibers can be used singly or in combination of two or more. The fiber diameter, fiber length, content, etc. of the reinforcing fibers, and the specifications, content, etc. of other additives are not particularly limited and can be set to generally used conditions. These additives can be added singly or in combination of multiple types.

[0059] <Resin Molding Analysis Device> Fig. 2 shows a configuration example of a resin molding analysis device 100 (hereinafter also referred to as "analysis device 100") according to the present embodiment. The analysis device 100 is a CAE (Computer Aided Engineering) system having a computer 110 as a basic configuration. The analysis device 100 is a device that analyzes resin molding using the finite element method by computer simulation. Note that the analysis device 100 is merely an example of the resin molding analysis device according to the present disclosure, and the configuration of the analysis device is not limited to this example. Hereinafter, the case where the analysis device 100 is applied to the flow analysis of resin injection molding will be described as an example.

[0060] The analysis device 100 includes a storage unit 120 composed of, for example, a ROM, a RAM, a hard disk, etc., and a processor 130 composed of, for example, a CPU, etc. Further, the analysis device 100 includes a display unit 140 composed of, for example, a display, an input unit 150 composed of a keyboard, etc., and a reading unit 160 for acquiring information stored in various recording media 170. Information such as programs for arithmetic processing and various analysis data is stored in the storage unit 120 and / or the recording media 170. The processor 130 performs various arithmetic processes based on the information stored in the storage unit 120, the information input via the input unit 150, and the information acquired from the recording media 170 via the reading unit 160. Note that this analysis device 100 is configured to be communicable with an external device via an interface (not shown).

[0061] Based on the shape data such as 3D CAD data that defines the cavity of the mold, the analysis device 100 divides it into a plurality of minute elements by the model creation unit 131 to create a flow analysis model. Note that the flow analysis model is a finite element model used for the flow analysis described later.

[0062] Although not shown in FIG. 2, the analysis device 100 may further include a structural analysis unit that analyzes warpage deformation and the like of the resin molded product based on the material property data obtained as a result of the flow analysis. In that case, the model creation unit 131 can also create a structural analysis model that is a finite element model used for structural analysis. As the flow analysis model and the structural analysis model, the same model or different models may be used.

[0063] As the model creation unit 131, commercially available automatic mesh generation software or the like can be used. Specifically, for example, 3D TIMON (registered trademark)-Pre / Post manufactured by Toray Engineering D Solutions Co., Ltd., FEMAP (registered trademark) manufactured by NST Co., Ltd., Patran (registered trademark) manufactured by MSC Software Corporation, CAE preprocessors such as Hyper mesh (registered trademark) manufactured by Altair can be used. Note that the shape and size of the elements are not particularly limited and are appropriately set according to the product specifications, material composition, calculation efficiency, and calculation accuracy levels. Also, a finite element model of the mold may be created and used for analysis. When creating a finite element model of the mold, for example, a finite element model of only the surface that forms the cavity of the mold or a part of the mold including the surface may be created.

[0064] Analysis conditions are set based on the material condition data regarding the type, composition, additives, various physical property values, etc. of the resin, and the boundary condition data describing the molding conditions such as the injection speed and the resin temperature at the time of injection.

[0065] The flow analysis unit 133 performs a flow analysis to analyze the behavior of the resin in the injection process S51, the holding pressure process S52, and the cooling process S53 using the above-described flow analysis model. Then, the flow analysis unit 133 calculates various data including resin characteristics such as temperature information (change in temperature over time), pressure information (change in pressure over time), and shear rate information (change in shear rate over time) for each element and for each minute time interval through the flow analysis. The calculated various data are stored in the storage unit 120. As the flow analysis unit 133, for example, injection molding CAE software such as 3D TIMON (registered trademark) manufactured by Toray Engineering D Solutions Co., Ltd. can be used. Note that the minute time interval is the analysis unit time for the flow analysis and the structural analysis, and is not particularly limited, and is appropriately set according to the product specifications, material composition, calculation efficiency, and calculation accuracy levels.

[0066] The flow analysis unit 133 includes a physical property information acquisition unit 133k, a pressure calculation unit 133a, a temperature calculation unit 133b, and a shear rate calculation unit 133c as resin characteristic calculation units, a cooling rate calculation unit 133d, a crystallinity calculation unit 133f, and a physical property information correction unit 133q. Although details will be described later, each unit has the following functions.

[0067] The physical property information acquisition unit 133k acquires the physical property information of the resin used for the calculation at the time step t n In addition, the physical property information acquisition unit 133k acquires the corrected physical property information as the physical property information of the resin at the next time step t n+1

[0068] Note that the physical property information of the resin is information including the shear viscosity, specific volume, thermal conductivity, etc. of the resin.

[0069] The pressure calculation unit 133a calculates the pressure of the resin for each element at the time step t n based on physical property information such as specific volume.

[0070] The temperature calculation unit 133b calculates the temperature of the resin for each element at the time step t n based on physical property information such as thermal conductivity.

[0071] The shear rate calculation unit 133c calculates the shear rate of the resin for each element at the time step t based on physical property information such as shear viscosity. n

[0072] The cooling rate calculation unit 133d calculates the cooling rate of the resin for each element at the time step t based on the temperature. The cooling rate is the time change rate of the temperature. At the time steps t n and t n n+1 if the resin temperatures at these time steps are T n and T n+1 respectively, and the minute time is Δt, the cooling rate ΔT / Δt = (T n+1 - Tn ) / Δt.

[0073] The crystallinity calculation unit 133f calculates the crystallinity of the resin for each element at the time step t based on the temperature and the cooling rate. At this time, the crystallinity calculation unit 133f calculates the crystallinity in consideration of the crystallization behavior of the resin obtained in advance by high-speed differential scanning calorimetry. n

[0074] The physical property information correction unit 133q corrects the physical property information of the resin based on the crystallinity.

[0075] Information such as the cooling rate, crystallinity, shear viscosity, specific volume, and thermal conductivity obtained in the process of the flow analysis is also stored in the storage unit 120.

[0076] When the analysis device 100 includes a structural analysis unit, the structural analysis unit uses the above-described structural analysis model and performs a structural analysis of the resin, that is, a shrinkage behavior calculation, based on the temperature information and pressure information of the resin calculated by the flow analysis unit 133, calculates the shrinkage amount of the resin, and finally calculates the deformed shape and deformation amount of the molded product. As the structural analysis unit, for example, solvers such as Abaqus manufactured by Dassault Systèmes, 3D TIMON (registered trademark) - WARP manufactured by Toray Engineering D Solutions Co., Ltd. can be used. Information obtained by the structural analysis is also stored in the storage unit 120. ​​​

[0077] <Analysis Method for Resin Molding> FIG. 3 is a flowchart showing an example of an analysis method for resin molding according to the present embodiment (hereinafter also referred to as "this analysis method"), and shows an example when applied to the flow analysis of resin injection molding. The analysis method for resin molding according to the present embodiment is a method for analyzing resin molding using various molding methods described above by using the finite element method through computer simulation. Hereinafter, the case where this analysis method is applied to the flow analysis of resin injection molding will be described as an example. This analysis method is performed, for example, using the above-described analysis apparatus 100. When the analysis apparatus 100 includes a structural analysis unit, structural analysis can be performed using resin characteristic data and the like obtained from the flow analysis as input information. The structural analysis method is not particularly limited, and generally known methods can be adopted.

[0078] As shown in FIG. 3, this analysis method includes, for example, a model creation step S1, an analysis condition setting step S2, a time step setting step S3, a physical property information acquisition step S4, a pressure calculation step S5, a shear rate calculation step S6, and a temperature calculation step S7 as resin property calculation steps, a cooling rate calculation step S8, a crystallinity calculation step S9, a physical property information correction step S10, and a determination step S11. The outline of each step is as follows.

[0079] First, in the model creation step S1, as described above, the model creation unit 131 divides the shape data of the cavity of the mold created using 3D CAD or the like into minute elements for numerical analysis to create a flow analysis model. Next, in the analysis condition setting step S2, analysis conditions such as material condition data and boundary condition data as initial data are set. Then, in the time step setting step S3, the time information is advanced by a minute time Δt from the previous time step t n-1 to set the time step t n for calculation.

[0080] In the physical property information acquisition step S4, the physical property information of the resin used for the calculation at the time step t n is acquired.

[0081] In the pressure calculation step S5, the pressure of the resin for each element is calculated using the initial flow rate (injection speed) of the resin, the kinematic viscosity coefficient of the resin material, boundary conditions, specific volume, etc. as input information, and the Navier-Stokes equation, the continuity equation, etc. as governing equations.

[0082] Also, in the shear rate calculation step S6, the shear rate of the resin for each element is calculated using information such as the shear viscosity and temperature of the resin as input information, and the Navier-Stokes equation, the continuity equation, etc. as governing equations.

[0083] In the temperature calculation step S7, for example, in addition to information on the shear rate of the resin, information such as the initial temperature (injection temperature) of the resin, the specific heat, density, and thermal conductivity of the resin material is used as input information, and the heat conduction equation, etc. is used as the governing equation to calculate the temperature of the resin for each element.

[0084] In the cooling rate calculation step S8, based on the temperature T at the time step t n and the temperature T at the previous time step t n the cooling rate ΔT / Δt of the resin for each element at the time step t n-1 is calculated. n-1 Based on the temperature T at the time step t n and the temperature T at the previous time step t

[0085] In the crystallinity calculation step S9, based on the temperature and the cooling rate, the crystallinity of the resin at the time step t n is calculated. In the physical property information correction step S10, based on the crystallinity of the resin calculated in the crystallinity calculation step S9, the physical property information of the resin used in the calculation at the time step t n is corrected.

[0086] Then, in the determination step S11, it is determined whether the time step t n is greater than or equal to the time step t end set for the end of the analysis. If the time step t n is less than t end (NO), the process returns to the time step setting step S3, and steps S3 to S11 are repeated.

[0087] And for the next time step tn+1 In the physical property information acquisition step S4, at time step t n the physical property information corrected in the physical property information correction step S10 at time step t n+1 is obtained as the physical property information of the resin used for the calculation at time step t

[0088] Also, in the determination step S11, at time step t n is t end If it is t or more (YES), the flow analysis is terminated

[0089] This analysis method is particularly characterized by the crystallinity calculation step S9 and the physical property information correction step S10. Details will be described below

[0090] [Crystallinity calculation step] In the crystallinity calculation step S9, based on the temperature and cooling rate at time step t n that is, based on the change in temperature over time, the crystallinity of the resin is calculated. Also, depending on the model formula used, the crystallinity information of the resin is calculated based on the change in temperature over time and the change in pressure over time. At this time, the crystallization behavior of the resin obtained in advance by FSC measurement, preferably the crystallization behavior of the resin within the molding processing temperature range, is considered

[0091] In this specification, the crystallinity X c is used in the sense that it includes both the relative crystallinity φ and the absolute crystallinity Φ (also referred to as "achieved crystallinity") shown in FIG. 4

[0092] The melting enthalpy of the crystal of the resin material is known information as a literature value. However, in most cases, the resin material of the molded product does not substantially reach the crystal having the melting enthalpy of the literature value after the molding process. This is because the crystallization behavior greatly depends on the molding conditions and the like. The absolute crystallinity indicates how far the crystallization has progressed based on the melting enthalpy of the crystal shown in the literature value. On the other hand, the relative crystallinity indicates how far the crystallization has progressed based on the absolute crystallinity that can be reached according to the molding conditions, that is, the achieved crystallinity

[0093] The degree of crystallinity can be determined, for example, by measuring the heat of fusion of crystals using DSC. At this time, the "relative degree of crystallinity" means the degree of crystallinity when the total enthalpy change amount H(∞) of the crystallization exothermic peak is set to 1. The absolute degree of crystallinity means the degree of crystallinity when the enthalpy of fusion (literature value) ΔH f of the crystal is set to 1.

[0094] The degree of crystallinity calculated in the crystallinity calculation step S9 may be either the relative degree of crystallinity or the absolute degree of crystallinity. Since the absolute degree of crystallinity has a temperature dependence, from the viewpoint of improving the prediction accuracy, it is preferable to use the absolute degree of crystallinity as the degree of crystallinity. As will be described later in Embodiment 2, a model for calculating the absolute degree of crystallinity (also referred to as an "absolute degree of crystallinity model") can be created using a model for calculating the relative degree of crystallinity (also referred to as a "relative degree of crystallinity model"). Therefore, in this embodiment, the case of using the relative degree of crystallinity model will be described.

[0095] Specifically, the relative degree of crystallinity φ(t) at a certain time t can be described using the Nakamura model represented by the following formula (4).

[0096]

Equation

[0097] In formula (4), K(T) is a value related to the rate constant k(T) of crystal growth. For example, K(T) = k(T) 1 / n is. Also, n is the Avrami exponent related to the shape (dimension) of the crystal (also referred to as the "Ozawa exponent" in the Ozawa plot). k(T) or K(T) and n can be determined using a kinetics model of crystallization such as the Avrami plot (isothermal crystallization measurement), the Ozawa plot (non-isothermal crystallization measurement), the reinterpreted Ozawa plot (non-isothermal crystallization measurement), and the Hoffman-Lauritzen theory.

[0098] Specifically, for example, the Avrami plot is represented by the following formula (5).

[0099] φ(t) = 1 - exp[-kt n ···(5) When Equation (5) is transformed, the following Equation (5A) is obtained.

[0100] log[-ln(1 - φ)] = logk + nlogt ···(5A) That is, when plotted with the horizontal axis as time (logt) and the vertical axis as log[-ln(1 - φ)], the slope of the straight line corresponds to the Avrami exponent n, and the intercept corresponds to the rate constant logk.

[0101] Using FSC, the temperature of the sample in the molten state is rapidly decreased to the measurement temperature, and when the measurement temperature is reached, the temperature is kept constant, and the change in heat flow over time as shown in the upper diagram of FIG. 5, for example, is measured. Then, the change in crystallinity over time in the isothermal process at the desired measurement temperature is calculated. For the data on the change in crystallinity over time at each temperature, a graph is plotted as shown in the lower diagram of FIG. 5, and the Avrami exponent n and the rate constant k are determined based on Equation (5A).

[0102] FIG. 6 is an example of the results of calculating the Avrami exponent n and the rate constant k based on the isothermal crystallization measurement results by FSC and the Avrami plot. In FIG. 6, digital micrographs of the samples after the isothermal crystallization measurement by FSC at 50°C and 100°C are shown above the graph.

[0103] From the results of FIG. 6, the Avrami exponent n and the rate constant k can be approximated by a fifth-degree polynomial of temperature T as shown in the following Equations (6) and (7).

[0104] n(T) = a 1 T 5 + a 2 T 4 + a 3 T 3 + a 4 T 2 + a 5 T + a 6 ···(6) k(T) = b 1 T 5 + b 2 T4 +b 3 T 3 +b 4 T 2 +b 5 T + b 6 ···(7) In equations (6) and (7), a 1 ~a 6 and b 1 ~b 6 are coefficients that depend on the material.

[0105] Substituting the above equations (6) and (7) into the Nakamura model of the above equation (4) to calculate the relative crystallinity φ(t) gives the result as shown in Figure 7. In Figure 7, the solid line represents the measured results from the non-isothermal measurement of FSC, and the dashed line represents the calculated results obtained using the Nakamura model. It can be seen from Figure 7 that the relative crystallinity model using the Nakamura model can reproduce the cooling rate dependence of the crystallization behavior with high accuracy.

[0106] Note that the determination of the Avrami exponent n and the rate constant k may also use the Ozawa plot represented by the following equation (8).

[0107]

Equation

[0108] When using the Ozawa plot, measure the crystallization behavior in the non-isothermal process when the sample is cooled at a constant cooling rate using FSC. Extract the value of log[-ln(1 - φ)] for each cooling rate at a certain temperature, and plot with the horizontal axis as logβ and the vertical axis as log[-ln(1 - φ)]. The slope of the regression line for each temperature of the graph thus obtained is the Ozawa exponent n, and the intercept is the rate constant logX(T).

[0109] Furthermore, the reinterpreted Ozawa plot (A. Toda: Thermochimica Acta, 707, 179086 (2022), ibid. 713, 179244, (2022)) represented by the following equations (9) and (10) may also be used.

[0110] φ(T,β i ) = 1 - exp[-φ 0 ···(9)

[0111]

Math

[0112] The primary crystallization behavior in the non-isothermal process at a certain scanning speed β can be evaluated by the re-interpreted Ozawa plot shown in equations (9) and (10) based on the Kolmogorov-Johnson-Mehl-Avrami model. Here, φ(T,β i ) is the relative crystallinity at the cooling rate β i and temperature T, and φ in equation (10) 0 represents the case of heterogeneous nucleation, where N 0 is the initial nucleus density, g is the geometric coefficient, G is the linear growth rate, and n is the Ozawa index.

[0113] When using the re-interpreted Ozawa plot, the K(T) term of the Nakamura model is represented by the following equation (11).

[0114]

Math

[0115] β peak (T) is determined from the relationship between the cooling rate and the exothermic peak temperature and contains information on the growth rate G. By obtaining the slope of the regression line of the plotted data with the horizontal axis as log(β peak (T)) and the vertical axis as log[1 - exp(-φ 0 )], the Ozawa index at each β i can be calculated.

[0116] For the actual sample in Reference Example 1 described later, the Ozawa plot and the Ozawa index n calculated using the re-interpreted Ozawa plot are shown in Figure 8. As shown in Figure 8, when using the re-interpreted Ozawa plot, the same number of n as the number of measurement data is obtained, so the reliability of the data is improved compared to the Ozawa plot.

[0117] Figure 9 shows the heat flow curve (actual measurement) obtained by non-isothermal measurement using FSC for the actual sample of Reference Example 1 described later, and the simulation result of the heat flow curve obtained by differentiating the relative crystallinity calculated using the relative crystallinity model of Equation (4) under the conditions of Example 1 with respect to temperature. In the crystallinity calculation step, the Ozawa plot was reinterpreted to obtain the n and K(T) terms. As shown in Figure 9, it can be seen that the predicted values are in good agreement with the actual measured values.

[0118] Also, although details are omitted, as a method for giving the K(T) term of the Nakamura model, the Hoffman-Lauritzen theory represented by the following Equation (12) may be used.

[0119]

Number

[0120] In Equation (12), Tg is the glass transition temperature, and T m is the melting point. By using Equation (12), the pressure dependence of the melting point and the glass transition temperature, which are temperatures related to the temperature at which crystallization starts, can be considered.

[0121] [Physical Property Information Correction Step] In the physical property information correction step S10, as described above, based on the crystallinity of the resin calculated in the crystallinity calculation step S9, the physical property information of the resin used in the calculation of the time step t n is corrected.

[0122] Examples of the physical property information corrected at this time include shear viscosity, specific volume, thermal conductivity, etc.

[0123] -Correction of Shear Viscosity- When the resin property calculation step is the shear rate calculation step S6, shear viscosity is mentioned as the physical property information that is a prerequisite for calculating the shear rate.

[0124] In this case, in the physical property information correction step S10, at the time step t nThe shear viscosity used in the calculation is corrected to the shear viscosity calculated by the following formula (1).

[0125] |η| = X c |η c |+(1 - X c )|η m | ···(1) (However, in formula (1), η is the shear viscosity, η c is the shear viscosity in the solid state, η m is the shear viscosity in the molten state, and X c is the degree of crystallinity.) And in the shear rate calculation step at the next time step t n+1 , the shear rate is calculated based on the shear viscosity corrected by formula (1).

[0126] Conventional flow analysis performs flow calculations using shear rate data of resin. At this time, in the cooling process, it is assumed that the flow stops uniformly at the timing when the set temperature is reached, and the shear viscosity that is a prerequisite for shear rate calculation is set to a constant value below that temperature. However, for example, the cooling rate of the resin varies depending on the molding conditions, so the flow stop temperature also changes. Then, in the conventional flow analysis that uniformly determines the flow stop temperature, the prediction accuracy of the shear rate is insufficient, and for example, sufficient prediction accuracy cannot be ensured in the prediction of the flow length or the like.

[0127] In this configuration, using the degree of crystallinity calculated in consideration of the crystallization behavior of the resin obtained in advance by FSC measurement, based on the above formula (1), the shear viscosity used in the calculation at the next time step t n+1 is calculated. As a result, based on the degree of crystallinity, the actual phenomenon in which a semi-crystalline state in which the molten resin and the solid resin coexist can be reproduced. And through the correction of the shear viscosity, the flow stop temperature can be substantially set variably according to the cooling rate, so the prediction accuracy of the shear rate is improved, and the prediction accuracy of the flow length and the like is improved.

[0128] -Correction of specific volume- When the resin property calculation process is the pressure calculation process S5, the specific volume is cited as the physical property information that is a prerequisite for calculating the pressure.

[0129] In this case, in the physical property information correction process S10, the specific volume used in the calculation of the time step t n is corrected to the specific volume calculated by the following formula (2).

[0130] |v| = X c |v c | + (1 - X c )|v m | ···(2) (However, in formula (2), v is the specific volume, v c is the specific volume of the crystalline part, v m is the specific volume of the amorphous part, X c is the degree of crystallinity.) And in the pressure calculation process S5 of the next time step t n+1 , the pressure is calculated based on the corrected specific volume corrected by formula (2).

[0131] Formula (2) can also be described as the following formula (13).

[0132]

Number

[0133] (However, in formula (13), T is the temperature, p is the pressure, t is the time, v is the specific volume, v c is the specific volume of the crystalline part, v m is the specific volume of the amorphous part, Xc is the degree of crystallinity, m 1 ~m 3 , c 1 ~c 3 are coefficients.) In conventional flow analysis, flow calculations are performed using resin pressure data. At this time, the specific volume that is the premise of pressure calculation is calculated with the temperature and pressure of the resin as input information and the Tait equation or the like as the governing equation. However, in the case of crystalline resins, when the temperature of the resin drops to a certain temperature during the molding process, crystallization starts and the rate of decrease in specific volume increases. The temperature at which this crystallization starts changes depending on the cooling rate of the resin. Since the above-mentioned Tait equation is a model in which one specific volume V is obtained from the pressure P and the temperature T, the dependence of the specific volume on the cooling rate in the crystallization behavior of the resin cannot be considered. That is, in the current general CAE analysis, changes in resin physical properties based on the crystallization behavior of the resin during the molding process cannot be considered, and there is room for improvement from the viewpoint of improving prediction accuracy.

[0134] In this configuration, using the crystallinity calculated in consideration of the crystallization behavior of the resin obtained in advance by high-speed differential scanning calorimetry, based on the above formula (2), for the next time step t n+1 the specific volume used in the calculation at is calculated. As a result, based on the crystallinity, it is possible to reproduce the actual phenomenon in which a semi-crystalline state in which molten resin and solid resin coexist exists. Then, through the correction of the specific volume, a flow analysis that more accurately considers the dependence of the specific volume on the cooling rate based on the actual phenomenon becomes possible, and the prediction accuracy of the flow analysis is improved.

[0135] Hereinafter, the derivation methods of v c and v m will be described.

[0136] First, using a capillary rheometer (known piston method), the temperature of the molten sample is lowered at a constant cooling rate at pressure p 0 and the PVT characteristics are measured. An example of the PVT characteristics is shown in Fig. 10. The PVT characteristics indicated by the symbol × in Fig. 10 are the PVT characteristics of the resin material in Reference Example 1 described later when the temperature is lowered at p 0 = 10 MPa and a cooling rate of 3 °C / min.

[0137] The PVT property decreases linearly in the liquid phase but decreases rapidly when the temperature drops below the temperature at which crystallization of the resin material begins. The amorphous part can be considered basically as the liquid phase, and the specific volume v of the amorphous part at pressure P 0 can be modeled by a straight line including the dashed line obtained as a regression equation of the data in the high-temperature part of the PVT property (i in Figure 10). m (T, p 0 )

[0138] Next, after finishing the measurement of the PVT property, the sample is taken out and the crystallinity X c is measured by DSC, XRD, etc. (ii in Figure 10).

[0139] Furthermore, the specific volume v of the crystalline part at pressure p 0 is calculated from Equation (13) (Equation (2)). Specifically, the specific volume v of the amorphous part c (T, p 0 ) has been obtained as a regression equation of the data in the high-temperature part of the PVT property as described above. Also, the crystallinity X m (T, p 0 ) has also been obtained by measurement. Furthermore, the specific volume v(T, p c ) of the resin material at pressure p 0 is known information from the results of PVT property measurement by the piston method. Substituting the values of these v 0 (T, p m ), X 0 and v(T, p c ) into Equation (13), v 0 (T, p c ) can be obtained (iii in Figure 10). 0 )

[0140] The above PVT property measurement and operations (i) to (iii) are repeated by changing the pressure p 0 . Finally, v c (T, p), v m (T, p) are modeled.

[0141] The specific volume v of the amorphous part m(T, p) has a linear relationship with each of the temperature T and the pressure p, and it was found that it can be modeled by a plane as shown in FIG. 11, that is, the above-mentioned formula (13). m 1 , m 2 and m 3 are coefficients that depend on the material. For the material whose PVT characteristics were measured in FIG. 10, m 1 = 0.701786, m 2 = -1.1009, m 3 = 1166.315, and a very good correlation with the coefficient of determination R 2 = 0.99 is obtained (see Table 2 described later).

[0142] The specific volume v c (T, p) has a linear relationship with each of the temperature T and the pressure p, and it was found that it can be modeled by a plane as shown in FIG. 12, that is, the above-mentioned formula (12). c 1 , c 2 and c 3 are coefficients that depend on the material. For the material whose PVT characteristics were measured in FIG. 10, c 1 = 0.207416, c 2 = -0.72424, c3 = 1008.499, and a good correlation with the coefficient of determination R 2 = 0.92 is obtained (see Table 2 described later).

[0143] Using the formula (1) of the specific volume model thus obtained, the reproducibility of the cooling temperature dependence of the PVT characteristics was verified.

[0144] FIG. 13 shows the simulation results of the PVT characteristics of Comparative Example 1 and Example 1 described later. In the crystallinity calculation step, n and k(t) were obtained using the Avrami plot, and the relative crystallinity was calculated as the crystallinity. It can be seen that when the two-domain Tait model of Comparative Example 1 is used, only the PVT characteristics at the cooling rate (3 °C / min) used in the experiment can be simulated. On the other hand, when the new model of formula (1) is used, it was found that the cooling rate dependence of the PVT characteristics in which crystallization occurs at a lower temperature as the cooling rate increases can be reproduced.

[0145] -Correction of Thermal Conductivity- When the resin property calculation process is the temperature calculation process S7, the thermal conductivity is cited as the physical property information that is a prerequisite for calculating the temperature.

[0146] In this case, in the physical property information correction process S10, the thermal conductivity used in the calculation of the time step t n is corrected to the thermal conductivity calculated by the following formula (3).

[0147] |λ| = X c |λ c | + (1 - X c ) |λ m | ···(3) (However, in formula (3), λ is the thermal conductivity, λ c is the thermal conductivity in the solid state, λ m is the thermal conductivity in the molten state, and X c is the crystallinity.) Then, in the temperature calculation process of the next time step t n+1 , the temperature is calculated based on the thermal conductivity corrected by formula (3).

[0148] Conventional flow analysis performs flow calculations using the temperature data of the resin. At this time, regarding the thermal conductivity of the resin that is a prerequisite for temperature calculation, in the cooling process, for example, it is assumed that when the resin changes from the molten state to the solid state at the timing of reaching an arbitrarily set temperature, etc., and it is set to change uniformly from the thermal conductivity in the molten state to the thermal conductivity in the solid state at that timing. However, in the actual molding process, there are cases where the resin is in a state where the molten state and the solid state are mixed, and it is considered that the thermal conductivity changes continuously depending on the crystallinity of the resin. Then, in the conventional flow analysis where the thermal conductivity is set to change uniformly from the value in the molten state to the value in the solid state, the prediction accuracy of the temperature is insufficient, and sufficient prediction accuracy of the flow analysis cannot be ensured.

[0149] In this configuration, using the crystallinity calculated in consideration of the crystallization behavior of the resin obtained in advance by high-speed differential scanning calorimetry, based on the above formula (3), for the next time step t n+1Calculate the thermal conductivity used in the calculation. As a result, based on the crystallinity, it is possible to reproduce the actual phenomenon in which a semi-crystalline state in which a molten resin and a solid resin coexist exists. Thus, since the crystallinity dependence of the thermal conductivity can be considered in accordance with the actual phenomenon, the prediction accuracy of the temperature is improved, and the prediction accuracy of the flow analysis is improved.

[0150] <Regarding FSC measurement> As described above, in this configuration, in the crystallinity calculation step S9, the change over time of the crystallinity is calculated in consideration of the crystallization behavior of the resin obtained by FSC measurement, preferably the crystallization behavior of the resin within the molding processing temperature range. As a result, since it is possible to perform a flow analysis that more accurately considers the cooling rate dependence of the PVT curve based on the actual phenomenon, the prediction accuracy of the flow analysis is improved.

[0151] Note that for the isothermal measurement at an arbitrary temperature (measurement temperature) within the molding processing temperature range by FSC, it is preferable to use a temperature profile in which the sample temperature is cooled to the measurement temperature at a cooling rate of preferably 100 ° C / s or more, more preferably 1000 ° C / s or more and 10000 ° C / s or less, and held at the measurement temperature for a predetermined time.

[0152] Further, for the non-isothermal measurement in the temperature range including the above arbitrary temperature (measurement temperature), it is preferable to use a temperature profile in which the sample temperature is lowered at a cooling rate of preferably 1 ° C / s or more and 1000 ° C / s or less, more preferably 1 ° C / s or more and 320 ° C / s or less, preferably at a constant cooling rate.

[0153] For the measurement of the absolute (reached) crystallinity by reheating the sample after the isothermal measurement or the non-isothermal measurement, it is preferable to use a temperature profile in which the sample temperature is heated at a heating rate of preferably 100 ° C / s or more and 10000 ° C / s or less, more preferably 500 ° C / s or more and 2000 ° C / s or less, preferably at a constant heating rate.

[0154] According to the above configuration, the crystallization behavior can be accurately observed.

[0155] <Resin molding analysis program and its recording medium> At least a part of each step of the above analysis method is programmed as an analysis program for resin molding using the above various molding methods. That is, the analysis program for resin molding according to the present embodiment causes a computer to execute, among the procedures of the above steps, at least procedure A of the physical property information acquisition step S4, procedure B of the resin property calculation step, procedure C of the cooling rate calculation step S8, procedure D of the crystallinity calculation step S9, and procedure E of the physical property information correction step S10. Note that the program may be configured to cause the computer to execute the procedures of the entire analysis step, that is, in addition to the above procedures A to E, the procedures of other steps. This analysis program is stored in, for example, the storage unit 120 and can be executed by the processor 130. Further, the analysis program is not limited to being stored in the storage unit 120, and can be recorded on various well-known computer-readable recording media such as optical disk media and magnetic tape media. Then, by mounting such a recording medium on the reading unit 160 and reading out the analysis program, the program can be executed.

[0156] (Embodiment 2) Hereinafter, other embodiments according to the present disclosure will be described in detail. In the description of these embodiments, the same parts as those in Embodiment 1 are denoted by the same reference numerals, and detailed description thereof will be omitted.

[0157] In Embodiment 1, the case where the relative crystallinity φ is used as the crystallinity X c was described. In Embodiment 2, the crystallinity calculation step S9 in the case where the absolute crystallinity Φ is used as the crystallinity X c will be described.

[0158] First, the time-dependent change φ(t) of the relative crystallinity is calculated by the method of Embodiment 1 above (relative crystallinity calculation step).

[0159] Then, based on the relative crystallinity φ(t), the time-dependent change Φ(t) of the absolute crystallinity of the resin is calculated (absolute crystallinity calculation step).

[0160] For example, the change in absolute crystallinity Φ(t) over time can be described by the following formula (5).

[0161] [Number]

[0162] In formula (5), the time derivative of φ(t) is the progress rate of relative crystallinity (the rate of change of the change over time), and f c (T) is the degree of crystallinity reached upon isothermal crystallization or non-isothermal crystallization at temperature T (crystallization peak temperature), and f 1 ~f 6 represents coefficients that depend on the material.

[0163] Figure 14 shows the absolute crystallinity measured using FSC for the actual sample of Reference Example 1 described later. The ○ in Figure 14 represents the isothermal measurement results, and the ● represents the non-isothermal measurement results. The isothermal measurement results were fitted using the KJMA model shown by the following formula (14), and the values of the degree of crystallinity reached in the primary crystallization were plotted. The horizontal axis is the crystallization peak temperature. As shown in Figure 14, it can be seen that the absolute crystallinity tends to be higher in the non-isothermal measurement results than in the isothermal measurement results. This indicates that under the conditions of the non-isothermal process from the pressure holding step S52 to the mold cooling in the cooling step S53 during the molding process, the degree of crystallinity reached at a certain time and temperature is higher than that in the isothermal process.

[0164] [Number]

[0165] (However, in formula (14), φ 1 (t) is the primary absolute crystallinity at time t, ΔH f 1st is the degree of crystallinity reached in the primary crystallization, and t 1 / 2 indicates the half-crystallization time.) That is, the absolute crystallinity obtained by isothermal measurement is considered to be the achieved crystallinity at the completion of primary crystallization. And the results in Fig. 14 are considered to indicate that in the non-isothermal process, in addition to primary crystallization, secondary crystallization is also progressing.

[0166] When fitting is performed for each of the data of the isothermal measurement and non-isothermal measurement in Fig. 14, it can be approximated by a fifth-degree polynomial. The approximation formula is the above f c (T) (the obtained values of f 1 ~f 6 are shown in Table 1 described later).

[0167] Fig. 15 shows the result of calculating the absolute crystallinity based on the relative crystallinity obtained by the calculation of Equation (5) and Fig. 9, f c (T) obtained by isothermal measurement and f c (T) obtained by non-isothermal measurement based on the results in Fig. 14. The isothermal measurement is shown by a broken line, and the non-isothermal measurement is shown by a solid line. Also, the × marks indicate the actually measured values by FSC.

[0168] As shown in Fig. 15, it can be seen from the actually measured values of the × marks that the absolute crystallinity has a cooling rate dependence. When using f c (T) obtained by isothermal measurement, it is difficult to reproduce the cooling rate dependence of the absolute crystallinity. On the other hand, when using f c (T) obtained by non-isothermal measurement, it was found that the cooling rate dependence of the absolute crystallinity can be reproduced.

[0169] Fig. 16 shows the result of predicting the PVT characteristics by applying the calculation result of the absolute crystallinity based on the non-isothermal measurement in Fig. 15 as the crystallinity X c to Equation (13) (Equation (2)). Note that the Tait model in Fig. 16 is the simulation result of Comparative Example 1 described later. As shown in Fig. 16, when using the new model of Equation (1), it was suggested that the cooling rate dependence of the PVT characteristics can be reproduced, and further improvement in the prediction accuracy can be expected by using the absolute crystallinity.

[0170] (Experimental Example 1) Regarding the correction of the specific volume in the crystallinity calculation step S9 and the physical property information correction step S10, Experimental Example 1 specifically implemented will be described.

[0171] <Reference Example 1> [Materials] As a test sample, isotactic polypropylene (manufactured by San Allomer Co., M w = 20.6×10 4 , M w / M n = 7.5, MFR (230 °C, load: 2.16 kg (JIS K 7210-1:2014)): 31 g / 10 min, stereoregularity ( 13 C-NMR): 97.8 mmmm) was used. The melting enthalpy (literature value) of the crystals of this material is 206.75 J / g (B. Wunderlich: Thermal Analysis of Polymeric Materials, Springer, Berlin (2005)).

[0172] [PVT Property Measurement] Using a commercially available capillary rheometer, the temperature of the molten sample was decreased from 230 °C to 70 °C at a constant cooling rate of 3 °C / min under pressures P 0 (10 MPa, 20 MPa, 30 MPa, 40 MPa), and the PVT properties were measured.

[0173] [Differential Scanning Calorimetry] The sample at 70 °C after the PVT property measurement was taken out, and the crystallinity X c was measured by DSC. Specifically, the solid sample after the PVT property measurement was cut using a microtome and made into a thin slice with a thickness of about 200 μm. This thin slice sample was punched to prepare a measurement sample with a diameter of 4.5 mm, a thickness of 200 μm, and a mass of 3.3 ± 0.1 mg. This measurement sample was subjected to DSC, and the melting heat was measured to calculate the crystallinity.

[0174] [High-Speed Differential Scanning Calorimetry] Using a high-speed differential scanning calorimeter (manufactured by Mettler-Toledo, Flash DSC1), the crystallization behavior of the resin material was evaluated.

[0175] -Non-isothermal measurement- After heating the sample to 230 °C, it was cooled at a constant rate in the range of a cooling rate of 1 to 100 °C / s. Then, if necessary, the melting enthalpy J / g was calculated from the melting curve when reheating at a heating rate of 2000 °C / s, and the absolute (attained) crystallinity was determined by dividing by the melting enthalpy of the above literature value.

[0176] -Isothermal measurement- After heating the sample to 230 °C, it was cooled at a constant rate at a cooling rate of 1000 °C / s and held isothermally for 0.015 to 500 s in the temperature range of 0 to 110 °C. Then, if necessary, the melting enthalpy J / g was calculated from the melting curve when reheating at a heating rate of 1000 °C / s, and the absolute (attained) crystallinity was determined by dividing by the melting enthalpy of the above literature value.

[0177] -Estimation of mass- The size of the solidified sample after cooling was approximately 30 to 150 μm square and about 10 to 20 μm thick. The mass m of the sample was estimated by the following formula (15) based on the specific heat C of the liquid phase of the sample measured by ordinary DSC p and the heat flow dq / dt of the liquid phase of the sample measured by FSC. For example, the mass of the 100 °C isothermal crystallization could be estimated to be 55.2 ng.

[0178]

Equation

[0179] <Example 1> The PVT characteristics were predicted using the above formulas (4), (5), (13) (formula (2)).

[0180] Table 1 shows the calculation conditions and the values of the calculation parameters used in the crystallinity calculation step.

[0181]

Table 1

[0182] Table 2 shows the calculation conditions and the values of the calculation parameters used in the specific volume calculation process.

[0183] [Table 2]

[0184] <Comparative Example 1> The PVT characteristics were predicted using the two-domain Tait model represented by the following formula (16).

[0185] [Equation]

[0186] Table 3 shows the values of the calculation parameters used for the calculation of the specific volume.

[0187] [Table 3]

[0188] (Experimental Example 2) Regarding this analysis method (flow analysis), Experimental Example 2 was conducted to verify the prediction accuracy of the resin flow length and pressure.

[0189] <Example 2 and Comparative Example 2> -Analysis Conditions- To evaluate the fluidity of the resin, a mold 300 equipped with a spiral flow-shaped cavity 310 shown in FIGS. 17 and 18 was used to perform the analysis of Example 2 and Comparative Example 2.

[0190] As shown in FIG. 17, the resin is injected from the resin inlet 301 and injected into the cavity 310 through the hot runner 302 and the gate 303.

[0191] The detailed shape of the cavity 310 is as follows.

[0192] Width: 20 mm, plate thickness: 1 mm, pressure sensors A, B, C, and D are provided at positions 60 mm, 90 mm, 180 mm, and 210 mm respectively from 303.

[0193] Analysis was performed using general-purpose thermal fluid analysis software.

[0194] As the fluid flow analysis model, a layered solid mesh model divided into 9 parts in the plate thickness direction was adopted.

[0195] The inflow conditions and boundary conditions are shown in Table 4.

[0196]

Table 4

[0197] - Material data - As material data, the relative crystallinity (used for modifying the shear viscosity and thermal conductivity) was calculated using the Nakamura model of Equation (4). For the K(T) term (kinetic crystallization model) in Equation (4), the Hoffman-Lauritzen theory of Equation (12) was used. The values of each parameter (coefficient) are as shown in Table 5.

[0198]

Table 5

[0199] Also, the absolute crystallinity (used for modifying the specific volume) was calculated using the relative crystallinity in Table 5 and Equation (5). The values of each parameter (coefficient) are as shown in Table 5. - Modification of shear viscosity - Regarding the above Equation (1) for modifying the shear viscosity, the shear viscosity η m was fitted with the Cross-WLF model equation shown in the following Equation (17). Each coefficient with a numerical value is an input parameter depending on the material.

[0200]

Number

[0201] Also, the viscosity coefficient η in the crystalline state c was fitted with the following formula (18). Each coefficient with a numerical value described is an input parameter depending on the material.

[0202]

Number

[0203] -Correction of thermal conductivity and specific volume- Regarding the above formula (3) for correcting the thermal conductivity and the above formula (13) (formula (2)) for correcting the specific volume, the numerical values of each coefficient, which are input parameters depending on the material, are shown in Table 6.

[0204]

Table 6

[0205] -Regarding the shear viscosity, thermal conductivity, and specific volume of Example 2- Regarding the shear viscosity, based on the crystallization behavior, the temperature range and gradient in which the shear viscosity increases were estimated.

[0206] Regarding the thermal conductivity, based on the crystallization behavior, the temperature range and gradient in which the thermal conductivity increases were estimated.

[0207] Regarding the specific volume, based on the crystallization behavior, the temperature range and gradient in which the specific volume decreases were estimated (the same as in Example 1).

[0208] -Regarding the shear viscosity, thermal conductivity, and specific volume of Comparative Example 2- Regarding the shear viscosity, the flow stop temperature was arbitrarily set (130 °C), and below that temperature, the shear viscosity was set to 1.0×10 9 [Pa·s].

[0209] Regarding the thermal conductivity, the timing of the change from melting to solid state was arbitrarily set (100 °C), with the thermal conductivity being 0.15 [W / mK] during melting and 0.3 [W / mK] during solid state.

[0210] The specific volume was calculated based on the pressure and temperature (similar to Comparative Example 1).

[0211] <Reference Example 2> Actual molding of Reference Example 2 was performed using the solid molds in the shapes of FIGS. 17 and 18.

[0212] <Results> The flow length of the resin is shown in FIG. 18. In Reference Example 2, the flow length varies significantly depending on the injection speed. In Comparative Example 2, this difference cannot be reproduced, while in Example 2, it can be reproduced.

[0213] The in-mold pressure detected by each pressure sensor is shown in FIG. 19. In Reference Example 2, it can be seen that the difference in injection speed has a significant impact on both the magnitude of the in-mold pressure and the timing when the in-mold pressure increases. In Comparative Example 2, regardless of the difference in injection speed, the in-mold pressure increases in the same manner between 0 seconds and 4 seconds, and this difference cannot be reproduced. In contrast, in Example 2, the tendency of Reference Example 2 can be well reproduced regarding both the magnitude of the in-mold pressure and the timing when the in-mold pressure increases.

[0214] As shown in FIG. 20, when the cooling rate is slow, crystallization starts at a higher temperature. On the other hand, when the cooling rate is fast, crystallization starts at a lower temperature. In actual crystallization behavior, there is a semi-crystalline state in which resins in both states are mixed between the solid state and the molten state. By calculating the degree of crystallinity, this semi-crystalline state can be considered, and the temperature range in which this semi-crystalline state occurs can also be considered.

[0215] FIGS. 21 to 23 are examples of the shear viscosity calculated by Equation (1), the specific volume calculated by Equation (2), and the thermal conductivity calculated by Equation (3), respectively, used in Example 2. It can be seen that the difference in the cooling rate can be well reproduced.

[0216] (Embodiment 3) As the governing equation used in the temperature calculation step S7 in the above-described Embodiments 1 and 2, a modified heat conduction equation represented by the following formula (D1) may be used.

[0217]

Equation

[0218] (However, in formula (D1), ρ is density, Cv is specific heat, T is temperature, λ is thermal conductivity, η is viscosity, γ dot is shear rate, φ is relative crystallinity, ΔHc is crystallization heat generation term, {φ(t n ) - φ(t n-1 )} is the progress degree of relative crystallinity at time step t n , ΔH c (T) is the crystallization enthalpy [J / g] when isothermally crystallized at temperature T, h 1 ~h 6 are coefficients and are input parameters depending on the material.) The temperature of the resin is calculated in the temperature calculation step S7 with the temperature information of the previous time step as one of the input information for each time step. That is, the physical property information obtained in the physical property information acquisition step S4 at time step t n includes the temperature T n-1 of the resin calculated in the temperature calculation step S7 at the previous time step t n-1 .

[0219] And in the temperature calculation step S7 at time step t n , based on the temperature T n-1 of the resin as physical property information and the modified heat conduction equation represented by formula (D1), the temperature T n of the resin at time step t n is calculated as the resin property.

[0220] In the physical property information correction step S10, the temperature T n-1 of the resin as physical property information is corrected to the temperature T n of the resin calculated as the resin property.

[0221] At the next time step tn+1 In the physical property information acquisition step S4, as the physical property information, the corrected temperature T n is acquired.

[0222] Then, in the temperature calculation step S7 at the next time step t n+1 , based on the corrected temperature T n , the temperature T of the resin as the resin property n+1 is calculated.

[0223] <Heat of crystallization> For example, as shown in FIG. 4, when the temperature of the molten resin is lowered by DSC or the like and the change in heat flow is measured, the heat flow increases with crystallization. This phenomenon is due to the phase transition caused by the crystallization of the resin, generating latent heat (also referred to as "heat of crystallization"), and this latent heat appears as heat flow.

[0224] In Equation (D1), a crystallization heat generation term ΔHc considering the heat of crystallization is added to the conventional heat conduction equation. And the crystallization heat generation term ΔHc is expressed as the product of the crystallization enthalpy ΔH c (T) at the isothermal crystallization at temperature T and the progress degree of relative crystallinity {φ(t n ) - φ(t n-1 ).

[0225] Note that the crystallization enthalpy ΔH c (T) is modeled based on actual tests. Specifically, using DSC, FSC, etc., the material is isothermally crystallized at a constant temperature, and the crystallization enthalpy for each temperature is calculated from the obtained heat flow change data. Then, as shown in FIG. 24, with the horizontal axis being temperature and the vertical axis being crystallization enthalpy, it is plotted, and the total amount of heat generation during crystallization for each temperature is fitted with a fifth-order polynomial. The coefficients h 1 ~h 6 of the thus obtained fifth-order polynomial are input parameters depending on the material and can be set for each material based on, for example, actual tests. For example, in the example of FIG. 24, as the coefficients h 1 ~h 6 , the values shown in Table 7 were obtained.

[0226]

Table 7

[0227] Thus, by multiplying the crystallization enthalpy ΔH c (T) by the degree of progress of relative crystallinity {φ(t n ) - φ(t n-1 )}, the amount of heat generated by crystallization per time step can be expressed. And by using Equation (D1) as the governing equation, the temporal change in temperature can be simulated considering the amount of heat generated by crystallization, so the prediction accuracy of the analysis is improved.

[0228] Actually, the crystallization enthalpy was calculated based on Equation (D1) using the values of the coefficients h 1 ~h 6 in Table 7, and the change in heat flow with respect to temperature was calculated for each of the cooling rates of 5 °C / s, 10 °C / s, 20 °C / s, and 50 °C / s. The results are shown in Figure 25. As shown in Figure 25, the calculation results can generally reproduce the measured results of each cooling rate measured using DSC, and it was found that the cooling rate dependence of the heat generation by crystallization can be accurately reproduced by using Equation (D1).

[0229] <Specific heat> The specific heat Cv in Equation (D1) is not particularly limited and may be a constant value or the like, but may be given, for example, as in the following Equation (D2).

[0230] Cv = g 1 T 5 + g 2 T 4 + g 3 T 3 + g 4 T 2 + g 5 T + g 6 ···(D2) (However, in Equation (D2), g 1 ~g 6 are coefficients and are input parameters that depend on the material.) Formula (D2) is modeled based on actual tests. Specifically, when the temperature of the resin is decreased using DSC or the like and the change in specific heat is measured, data as shown in Fig. 26 is obtained. For the data on the change in specific heat, for example, latent heat is excluded and fitting is performed with a fifth-degree polynomial. The coefficients g 1 ~g 6 are input parameters that depend on the material and can be set for each material based on actual tests, for example.

[0231] According to this configuration, since the change over time in the resin temperature can be simulated considering the temperature change in specific heat, it is advantageous for improving the prediction accuracy of the analysis.

Industrial Applicability

[0232] This disclosure is extremely useful because in an analysis method, analysis apparatus, analysis program, and recording medium for resin molding, the crystallization behavior of the resin during the molding process can be sufficiently considered to improve the prediction accuracy.

Explanation of Signs

[0233] 100 Analysis apparatus for resin molding 131 Model creation unit 133 Flow analysis unit 133k Physical property information acquisition unit 133a Pressure calculation unit 133b Temperature calculation unit 133c Shear rate calculation unit 133d Cooling rate calculation unit 133f Crystallinity calculation unit 133q Physical property information correction unit 170 Recording medium S4 Physical property information acquisition step S5 Pressure calculation step S6 Shear rate calculation step S7 Temperature calculation step S8 Cooling rate calculation step S9 Crystallinity calculation step S10 Physical property information correction step

Claims

1. A method for analyzing resin molding by computer simulation, comprising the steps of: Time step t ｎ A physical property information acquisition step of acquiring physical property information of the resin to be used in the calculation of Based on the physical property information, the time step t ｎ a resin characteristic calculation step of calculating resin characteristics including a temperature of the resin; Based on the temperature, the time step t ｎ A cooling rate calculation step of calculating a cooling rate of the resin in the Based on the temperature and the cooling rate, the time step t ｎ A crystallinity calculation step of calculating the crystallinity of the resin in the above step; and a physical property information correcting step of correcting physical property information of the resin based on the crystallinity, Next time step t ｎ＋１ In the physical property information acquisition step, the corrected physical property information is acquired as physical property information of the resin; In the crystallization degree calculation step, the crystallization degree is calculated in consideration of the crystallization behavior of the resin previously obtained by high speed differential scanning calorimetry. A resin molding analysis method comprising:

2. In claim 1, The physical property information includes a shear viscosity of the resin, In the resin characteristic calculation step, a shear rate of the resin is calculated as the resin characteristic based on the shear viscosity, In the physical property information correcting step, the shear viscosity is corrected to a shear viscosity calculated by the following formula (1), |η|=X ｃ |the ｃ |+(1-+) ｃ )|the ｍ |・・・(1) (In formula (1), η is the shear viscosity, η ｃ is the shear viscosity in the solid state, η ｍ is the shear viscosity of the molten state, X ｃ is the degree of crystallinity.) The next time step t ｎ＋１ In the resin characteristic calculation step, the shear rate is calculated based on the corrected shear viscosity. A resin molding analysis method comprising:

3. In claim 1 or 2, The physical property information includes a specific volume of the resin, In the resin characteristic calculation step, a pressure of the resin is calculated as the resin characteristic based on the specific volume; In the physical property information correcting step, the specific volume is corrected to a specific volume calculated by the following formula (2), |v|=X ｃ |v ｃ |+(1-X ｃ )|v ｍ | ・・・(2) (In formula (2), v is the specific volume, v ｃ is the specific volume of the crystalline part, v ｍ is the specific volume of the amorphous part, X ｃ is the degree of crystallinity.) The next time step t ｎ＋１ In the resin characteristic calculation step, the pressure is calculated based on the corrected specific volume. A resin molding analysis method comprising:

4. In claim 1 or 2, the physical property information includes a thermal conductivity of the resin; In the resin characteristic calculation step, a temperature of the resin is calculated as the resin characteristic based on the thermal conductivity; In the physical property information correcting step, the thermal conductivity is corrected to a thermal conductivity calculated by the following formula (3), |λ|=X ｃ |l ｃ |+(1-+) ｃ )|l ｍ |・・・(3) (In formula (3), λ is thermal conductivity, λ ｃ is the thermal conductivity of the solid state, λ ｍ is the thermal conductivity of the molten state, X ｃ is the degree of crystallinity.) The next time step t ｎ＋１ In the resin characteristic calculation step, the temperature is calculated based on the corrected thermal conductivity. A resin molding analysis method comprising:

5. In claim 1 or 2, The crystallinity is a relative crystallinity, The change in the relative crystallinity over time φ(t) is calculated using the Nakamura model represented by the following formula (4): [0010] (In formula (4), φ(t) is the change in relative crystallinity over time, K(T) is a value related to the rate constant of crystal growth, and n is the Avrami index.) The K(T) and n are determined taking into consideration the crystallization behavior obtained by the high-speed differential scanning calorimetry. A resin molding analysis method comprising:

6. In claim 1, The crystallization behavior obtained by the high speed differential scanning calorimetry is obtained by at least one of isothermal measurement at an arbitrary temperature within the molding processing temperature range and non-isothermal measurement in a temperature range including the arbitrary temperature. A resin molding analysis method comprising:

7. In claim 1, The crystallinity is the absolute crystallinity, The crystallinity calculation step includes: A relative crystallinity calculation step of calculating a change in the relative crystallinity of the resin over time; and an absolute crystallinity calculation step of calculating a change in absolute crystallinity of the resin over time based on the change in relative crystallinity over time. A resin molding analysis method comprising:

8. In claim 7, The change in absolute crystallinity over time Φ(t) is represented by the following formula (5): [0025] (In formula (5), φ is the relative crystallinity, f ｃ (T) is the degree of crystallinity achieved when isothermal or non-isothermal crystallization is performed at temperature T (crystallization peak temperature), f １ ~f ６ are coefficients and material-dependent input parameters.) A resin molding analysis method comprising:

9. In claim 1 or 2, The physical property information includes a temperature of the resin, In the resin characteristic calculation step, a temperature of the resin is calculated as the resin characteristic based on a temperature of the resin as the physical property information and a modified heat conduction equation represented by the following formula (D1): [0030] (In the formula (D1), ρ is density, Cv is specific heat, T is temperature, λ is thermal conductivity, η is viscosity, γ is shear rate, φ is relative crystallinity, ΔHc is crystallization heat term, {φ(t ｎ ) -φ(t ｎ－１ )) is the time step t ｎ Relative crystallinity development in ΔH ｃ (T) is the crystallization enthalpy when crystallized isothermally at temperature T, h １ ~h ６ are coefficients and material-dependent input parameters.) In the physical property information correcting step, the temperature of the resin as the physical property information is corrected to a temperature of the resin calculated as the resin characteristic, The next time step t ｎ＋１ In the resin characteristic calculation step, a temperature of the resin is calculated as the resin characteristic based on the corrected temperature. A resin molding analysis method comprising:

10. In claim 9, The specific heat Cv is given by the following formula (D2): Cv=g １ T ５ +g ２ T ４ +g ３ T ３ +g ４ T ２ +g ５ T+g ６ ・・・(D2) (In the formula (D2), g １ ~g ６ are coefficients and material-dependent input parameters.) A resin molding analysis method comprising:

11. An apparatus for analyzing resin molding by computer simulation, Time step t ｎ A physical property information acquisition unit that acquires physical property information of a resin to be used in the calculation of Based on the physical property information, the time step t ｎ a resin characteristic calculation unit for calculating resin characteristics including a temperature of the resin; Based on the temperature, the time step t ｎ A cooling rate calculation unit that calculates a cooling rate of the resin in the Based on the temperature and the cooling rate, the time step t ｎ A crystallinity calculation unit that calculates the crystallinity of the resin in a physical property information correcting unit that corrects physical property information of the resin based on the crystallinity, The physical property information acquisition unit is configured to ｎ＋１ The corrected physical property information is acquired as physical property information of the resin, The crystallinity calculation unit calculates the crystallinity in consideration of the crystallization behavior of the resin previously obtained by high speed differential scanning calorimetry. A resin molding analysis device characterized by:

12. A program for analyzing resin molding by computer simulation, At a minimum, the computer Time step t ｎ A step A of acquiring physical property information of a resin to be used in the calculation of Based on the physical property information, the time step t ｎ A step B of calculating resin properties including a temperature of the resin; Based on the temperature, the time step t ｎ A step C of calculating a cooling rate of the resin in Based on the temperature and the cooling rate, the time step t ｎ A step D of calculating the crystallinity of the resin in and a procedure E of correcting physical property information of the resin based on the degree of crystallinity, Next time step t ｎ＋１ In the step A, the corrected physical property information is acquired as physical property information of the resin; In the step D, the crystallization degree is calculated taking into consideration the crystallization behavior of the resin previously obtained by high speed differential scanning calorimetry. A resin molding analysis program characterized by:

13. A computer-readable recording medium having recorded thereon the resin molding analysis program according to claim 12.

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