Void occurrence prediction method, void reduction method, and computer-readable recording medium

Abstract

A void occurrence prediction method comprises: using formula (I) to acquire, from a viscosity measure result, temperature dependency data on Young's modulus in a molten state; generating an analysis model of an injection molded article; acquiring a temperature distribution and a pressure distribution of the analysis model in a molding step; calculating a temperature distribution of the analysis model in a cooling process after mold release; calculating an elastic modulus distribution and a temperature load distribution of the analysis model on the basis of the temperature dependency data on Young's modulus, the temperature distribution, and the pressure distribution; performing structural analysis using the elastic modulus distribution and the temperature load distribution to calculate distortion that occurs to the analysis model; and using the distortion to predict at least one of the amount of a void and the position of the occurrence of the void. (I): E=ω1η+ω2; η: viscosity; E: Young's modulus; ω1, ω2: constant

Description

Void generation prediction method, void reduction method, and computer-readable recording medium

[0001] The present disclosure relates to a void occurrence prediction method, a void reduction method, and a computer-readable recording medium.

[0002] Injection molding has traditionally been used to manufacture parts with complex shapes using thermoplastic resins. Depending on the molding conditions or product shape, molding defects such as "sink marks" (depressions on the surface of the molded product) or "voids" (cavities inside the molded product) can occur in the resin molded product.

[0003] Sink marks and voids occur during injection molding of thermoplastic resins when the molten resin is cooled and solidified. In particular, with crystalline resins, the molecular chains, which were random immediately after filling the mold, become oriented (folded and aligned) as they crystallize, resulting in a decrease in volume (shrinkage) compared to the volume immediately after filling the mold (mold dimensions), resulting in sink marks or voids.

[0004] These molding defects can lead to a decrease in the dimensional accuracy of the product (for example, in airtight parts, dents can create gaps on the sealing surface that comes into contact with the mating member) or a decrease in strength (voids can easily cause breakage). Therefore, there is a need for improved technology to suppress sink marks and voids.

[0005] Measures to prevent sink marks and voids include checking the actual molded product and changing the molding conditions, or changing the design of the gate or wall thickness of the molded product, etc. However, because these measures require a huge amount of time and cost, in recent years, investigations have been conducted into the use of injection molding simulations using flow analysis software to predict the occurrence of sink marks and / or voids and optimize the product shape and molding conditions.

[0006] For example, in Patent Document 1, data obtained by flow analysis software is applied to strain analysis using structural analysis software to predict the occurrence of voids. In Patent Document 2, elastic modulus distribution and volumetric shrinkage distribution are obtained from temperature and pressure data obtained by flow analysis, and the occurrence of sink marks or voids is predicted by coupled analysis applied to structural analysis (strain analysis). Furthermore, Patent Document 3 discloses a method for measuring the elastic modulus of a liquid food over a wide temperature range to be used in coupled analysis.

[0007] Japanese Patent Publication No. 2009-233882 Japanese Patent No. 7366327 Japanese Patent Publication No. 2001-59806

[0008] However, existing injection molding simulation methods are unclear about how to handle temperature dependence of the elastic modulus and other parameters used in the calculations, and there is no known method for obtaining data such as elastic modulus, particularly for molten thermoplastic resins. For example, the elastic modulus measurement method described in Patent Document 3 is primarily intended for liquid foods, so it is not practical to apply this measurement method to molten thermoplastic resins at very high temperatures. For these reasons, even with flow analysis software, accurate injection molding simulations are difficult to perform, and the occurrence of voids in molded products may not be predicted with sufficient accuracy.

[0009] The purpose of the present disclosure is to provide a method for accurately predicting the behavior of voids in resin molded products produced by an injection molding process using resin materials. By solving this problem, it becomes possible to anticipate product shape design, mold design, molding condition setting, and molding material in advance at the design stage in order to obtain molded products without voids, thereby enabling efficient commercialization.

[0010] According to one aspect of the present disclosure, a void occurrence prediction method is a void occurrence prediction method for predicting the occurrence behavior of voids generated in an injection-molded product obtained by injection-molding a thermoplastic resin into a mold, and includes the steps of: acquiring temperature dependency data of Young's modulus in a molten state from viscosity measurement results of the thermoplastic resin in a molten state using equation (I) which shows the relationship between the viscosity and Young's modulus of the thermoplastic resin in a molten state; creating an analytical model by dividing the injection-molded product into a plurality of elements; acquiring temperature and pressure distributions of the analytical model in a molding process of the thermoplastic resin; calculating a temperature distribution of the analytical model in a cooling process after demolding using the temperature and pressure distributions; calculating an elastic modulus distribution and a temperature load distribution of the analytical model based on the temperature dependency data of the Young's modulus, the temperature and pressure distributions in the molding process, and the temperature distribution in the cooling process; calculating strain generated in each element of the analytical model by structural analysis using the elastic modulus distribution and the temperature load distribution in the cooling process after demolding; and predicting at least one of the location of void occurrence and the amount of voids from the strain. E = ω1η + ω2 (I) η: viscosity E: Young's modulus ω1, ω2: constants

[0011] According to another aspect of the present disclosure, the void reduction method is a void reduction method for reducing voids that occur in an injection-molded product obtained by injection molding a thermoplastic resin into a mold, in which the amount of voids is predicted using the above-mentioned void occurrence prediction method, and if the predicted amount of voids is equal to or greater than a predetermined threshold, one or more of the design, molding conditions, and molding material are changed and the amount of voids is predicted again, and this process is repeated until the predicted amount of voids is reduced to less than the predetermined threshold.

[0012] FIG. 1 is a diagram illustrating the injection molding process. FIG. 2 is a flow diagram illustrating a void occurrence prediction method according to an embodiment. FIG. 3 is a flow diagram illustrating a physical property value acquisition method according to an embodiment. FIG. 4 is a diagram illustrating a specific example of PVT data for a thermoplastic resin. FIG. 5 is a diagram illustrating a specific example of temperature dependency data for the elastic modulus of a thermoplastic resin. FIG. 6 is a diagram illustrating 3D CAD data according to an example. FIG. 7 is a cross-sectional view of an analytical model according to an example. FIG. 8 is a diagram illustrating the temperature dependency of Young's modulus in a solid state. FIG. 9 is a diagram illustrating the correlation between the temperature dependency of Young's modulus and viscosity in a molten state. FIG. 10 is a diagram illustrating the temperature dependency of Young's modulus from a molten state to a solid state. FIG. 11 is a diagram illustrating the temperature dependency of Poisson's ratio. FIG. 12 is a diagram illustrating a specific example of a cross section of a void occurrence region. FIG. 13 is a block diagram illustrating an example of the hardware configuration of an information processing device.

[0013] Before describing an embodiment of the present disclosure, a general injection molding process will be described. Fig. 1 is a diagram for explaining each step of injection molding. Fig. 1 shows a timeline of molding, focusing on one mold.

[0014] Injection molding begins by injecting resin into the mold cavity through a gate. Immediately after injection, the resin delivery speed is controlled, but when, for example, 99% of the resin has been filled to a preset amount, the control switches to resin pressure control (holding pressure). Even after the switch, injection continues while maintaining pressure.

[0015] Gate sealing is one indicator for obtaining stable injection-molded products. Gate sealing is a phenomenon in which the resin at the gate solidifies and stops flowing, and the time it takes for the resin at the gate to solidify and stop flowing is called the gate sealing time. If the dwell pressure is stopped before the gate sealing time, the molten resin will flow back through the gate into the injection molding machine, resulting in poor filling and weight loss. On the other hand, if the dwell pressure is stopped after the gate sealing time, the gate will have solidified and the resin will not flow back, resulting in a stable injection-molded product. For this reason, gate sealing time is an indicator that is always measured at the molding site.

[0016] The gate sealing time may be determined as the time at which the weight of the molded product is maximized after molding conditions are set to minimize the time until gate sealing occurs. In this case, the time until gate sealing occurs and the weight of the molded product may be obtained by experiment (weighing). Alternatively, the time until gate sealing occurs and the weight of the molded product may be obtained by simulation using flow analysis instead of experiment.

[0017] Alternatively, the gate sealing time is the time when the temperature at the center of the gate reaches the flow stop temperature T S At this time, the time when the temperature at the center of the gate reaches the flow stop temperature T S The time required to reach this value may be obtained by experiment or may be determined by simulation.

[0018] Flow stop temperature T S may be the inflection point when the resin is cooled at a cooling rate in the range of 1° C. / min to 50° C. / min in specific heat measurement.

[0019] Or, the flow stop temperature T S may be calculated by the following formula (1) using data fitting coefficients b5 and b6 of the 2-DOMAIN TAIT PVT model for PVT data showing the relationship between pressure, volume, and temperature of the resin. S =b5+b6×P...(1)

[0020] Here, the 2-DOMAIN TAIT PVT model defines the specific volume V(T,P) at temperature T and pressure P as V(T,P) = V0(T) [1-C × LN(1 + P / B(T))] + V T In particular, V0(T) is a linear expression of T, and is expressed as T V T (T, P) is the high temperature side (T>T T ) is zero, but at low temperatures (T < T T ) is an exponential function of T and P. In this model, T is calculated from the PVT data. T= b5 + b6 × P, and is fitted with a linear equation of P.

[0021] Note that b5 is the inflection point of the PVT data. That is, the slope changes at b5, and a solid-liquid phase transition occurs.

[0022] Following the dwell time, the injection-molded product is cooled within the mold (in-mold cooling) for a predetermined cooling time. After the cooling time has elapsed, the mold is opened, and the injection-molded product is removed from the mold and demolded. The mold is then closed again to form a mold cavity, and the molding process moves on to obtain the next injection-molded product. The demolded injection-molded product can be cooled outside the mold (ex-mold cooling). Ex-mold cooling can continue, for example, until the injection-molded product reaches room temperature.

[0023] Here, during the cooling process of an injection-molded product, the relationship between the occurrence of voids and the distribution of internal physical properties is unclear, and for injection-molded products in which a liquid phase and a solid phase may be mixed, it is necessary to consider both the liquid phase and the solid phase. However, there is no known method for measuring Young's modulus, which is used for structural analysis, particularly for materials such as resin materials that become liquid (molten) at high temperatures.

[0024] In this disclosure, an analysis is performed that takes into account the cooling of an injection-molded product after it is released from the mold. From the temperature data obtained, the elastic modulus distribution and volumetric shrinkage distribution are obtained. By applying this to structural analysis (strain analysis), it has been discovered that the location and amount of voids can be predicted at a level comparable to the results in an actual product. In this disclosure, the injection-molded product is cooled by air inside and outside the mold, but the cooling method is not limited to air cooling.

[0025] Hereinafter, an embodiment according to the present disclosure will be described with reference to the accompanying drawings. The embodiment described below is an example and should not be construed as being limited by this description.

[0026] 2 is a flow diagram showing a method for predicting void generation behavior according to one embodiment of the present invention, which performs coupled analysis in which the results of flow analysis are applied to structural analysis.

[0027] As shown in Figure 2, the void occurrence prediction method according to one embodiment includes the steps of acquiring physical property values ​​for analysis (step S101), creating an analytical model (step S102), calculating the temperature distribution and pressure distribution in the process of injecting and molding a thermoplastic resin (injection molding process) (step S103), calculating the temperature distribution in the cooling process after demolding (step S104), calculating the elastic modulus distribution (step S105), calculating the strain generated in each element of the analytical model (step S106), and predicting the location of void occurrence and the void amount (step S107).

[0028] [Acquisition of physical property values ​​for analysis (step S101)] As shown in FIG. 3 , the step of acquiring physical property values ​​for analysis (step S101) includes a dynamic viscoelasticity data acquisition step (step S201), a viscosity acquisition step (step S202), a temperature dependency data acquisition step of Young's modulus (step S203), a PVT data acquisition step (step S204), and a Poisson's ratio / shear modulus calculation step (step S205).

[0029] [Dynamic Viscoelasticity Data Acquisition Step (Step S201)] In the dynamic viscoelasticity data acquisition step (Step S201), the temperature dependence of Young's modulus of a thermoplastic resin in a solid state is measured. That is, data on the temperature dependence of Young's modulus of a thermoplastic resin in a solid state is acquired using, for example, a universal material testing machine or a dynamic viscoelasticity measuring device equipped with a thermostatic bath.

[0030] [Viscosity Acquisition Step (Step S202)] When acquiring data on the temperature dependence of Young's modulus using the universal testing machine or dynamic viscoelasticity measuring device, the thermoplastic resin must be in a solid state, and it is difficult to measure the Young's modulus in a high temperature range near the melting point or in a molten state. Therefore, for measuring the Young's modulus in a high temperature range near the melting point or in a molten state, it is possible to use, for example, a rotational rheometer or a melt viscoelasticity measuring device.

[0031] However, when using these devices, high shear stress occurs when the temperature of the thermoplastic resin approaches the solidification temperature, making measurement mechanically difficult. In other words, since the Young's modulus in tension is measured in the solid state and the shear modulus in the molten state are measured, the shape of the test piece changes significantly, raising concerns about the continuity between the solid and molten states, making it difficult to obtain the data itself.

[0032] From the above, in the viscosity acquisition step (step S202), the viscosity of the thermoplastic resin in a molten state is measured, and the Young's modulus of the thermoplastic resin in a molten state is calculated based on the measured viscosity.

[0033] Generally, the relationship between viscosity and Young's modulus is expressed by the following formula (2). η: Viscosity E: Young's modulus ω: Frequency

[0034] Therefore, the Young's modulus E can usually be calculated from the measurement data of the viscosity η using the following equation (3): E=ηω (3)

[0035] However, for thermoplastic resins in a molten state, the shear rate dependency of viscosity exhibits non-Newtonian properties. Therefore, even if the shear rate is converted to frequency ω, it is unclear at what shear rate the viscosity is such that continuity with the solid state is obtained, and the relationship of the above formula (2) has not been confirmed.

[0036] Therefore, when the shear modulus of a molten thermoplastic resin was measured using a rotational rheometer, the results showed that there was a proportional relationship between the viscosity measured using a capillary and the shear modulus. Because the Poisson's ratio of a molten thermoplastic resin is close to 0.5, the relationship between the shear modulus and Young's modulus can be obtained by substituting the Poisson's ratio ν = 0.5 into the following equation (4). G: Shear modulus of elasticity E: Young's modulus ν: Poisson's ratio

[0037] That is, the relationship between the shear modulus and Young's modulus is expressed by the following equation (5): E = 3G (5) Therefore, by determining the frequency ω in the above equation (3), the Young's modulus E of the thermoplastic resin in a molten state can be determined from the results of viscosity measurement.

[0038] In the present disclosure, the Young's modulus E of a thermoplastic resin in a molten state is calculated by the following equation (6), which is an improvement on the above equation (3). That is, the relationship between viscosity and Young's modulus is expressed by the above equation (3), but Young's modulus is a static value, whereas viscosity is a measurement result in a dynamic state. Therefore, when determining the actual correlation between viscosity and Young's modulus, it was found that Young's modulus E can be expressed by a linear equation related to viscosity η, as shown in equation (6): E = ω1η + ω2 (6) E: Young's modulus η: viscosity ω1, ω2: constants

[0039] In the viscosity acquisition step (step S202), the viscosity of the thermoplastic resin in a molten state is measured, and data on the temperature dependency of Young's modulus of the thermoplastic resin in a molten state is acquired by the above equation (6).

[0040] [Step S203 for Acquiring Temperature Dependence Data on Young's Modulus] In the step S203 for acquiring temperature dependence data on Young's modulus, data on the temperature dependence of Young's modulus over a wide temperature range is acquired based on the Young's moduli of the thermoplastic resin in the solid and molten states. That is, the temperature dependence data on Young's modulus in the solid state acquired in the step S201 for acquiring dynamic viscoelasticity data and the temperature dependence data in the molten state acquired in the step S202 for acquiring viscosity data are combined to acquire the temperature dependence data on Young's modulus.

[0041] In the case of crystalline resins, the temperature dependence of Young's modulus between the solid and molten states changes significantly near the crystallization temperature. Furthermore, crystallization behavior is difficult to measure because it depends on the cooling rate and the physical properties change depending on the molding conditions in injection molding. Therefore, the range between the crystallization onset temperature and the crystallization end temperature may be determined by linear interpolation between the Young's modulus in the solid state and the Young's modulus in the molten state. In this case, the crystallization onset temperature or the crystallization end temperature may be the inflection point when the resin is cooled at a cooling rate ranging from 1°C / min to 50°C / min in specific heat measurement.

[0042] [PVT Data Acquisition Step (Step S204)] In the PVT data acquisition step (Step S204), data (PVT data) representing the relationship between the pressure, volume, and temperature of the molding material (thermoplastic resin) is acquired.

[0043] Figure 4 shows specific examples of PVT data for thermoplastic resins. In Figure 4, the horizontal axis represents temperature (unit: °C), and the vertical axis represents the reciprocal of density, i.e., specific volume (unit: cm ３ / g). Figure 4 shows the relationship between temperature and specific volume when the pressure (P) is 50 MPa. It is assumed that the relationships between temperature and specific volume have been measured in advance for several pressures other than 50 MPa. The relationship between temperature and specific volume at pressures that have not been measured can be obtained, for example, by interpolation from the relationship between temperature and specific volume at pressures that have been measured.

[0044] As shown in FIG. 4, when the pressure (P) is 50 MPa, at the time when the thermoplastic resin at the gate solidifies and stops flowing (hereinafter also referred to as the "gate seal time"), the temperature of the thermoplastic resin is 200°C and the specific volume is about 0.82 cm ３ Similarly, when the temperature of the injection molded product made of thermoplastic resin reaches the mold temperature, the temperature of the thermoplastic resin is 40°C and the specific volume is about 0.70 cm ３ / g.

[0045] [Poisson's Ratio and Shear Elastic Modulus Calculation Step (Step S205)] In the Poisson's ratio and shear elastic modulus calculation step (Step S205), the Poisson's ratio and shear elastic modulus are calculated from the temperature dependency data of Young's modulus and the PVT data.

[0046] The Poisson's ratio of an injection-molded product is calculated from the Young's modulus and the bulk modulus K obtained from the PVT data by the following equation (7). ν: Poisson's ratio E: Young's modulus K: Bulk modulus

[0047] The shear modulus of elasticity of the injection molded product is calculated from the Poisson's ratio and Young's modulus using the following formula (8). G: Shear modulus of elasticity E: Young's modulus ν: Poisson's ratio

[0048] In the Poisson's ratio and shear modulus calculation step (step S205), the Poisson's ratio and shear modulus are calculated from the Young's modulus at each temperature using the above equations (7) and (8), thereby obtaining temperature dependency data of the Poisson's ratio and shear modulus.

[0049] [Creating an Analysis Model (Step S102)] In the step of creating an analysis model (step S102), the shape of the injection-molded product is divided into tiny elements, and a model to be used for performing the simulation is created. Here, the shape of the injection-molded product (which may be the design shape of the injection-molded product or the shape of the mold, etc. The shape of the mold also includes conditions such as the position, number, and size of runners and gates) is input into a computer using, for example, a three-dimensional shape measurement or CAD system. Next, the shape input into the computer is divided into multiple three-dimensional elements using an element division preprocessor or the like, and an analysis model is created.

[0050] In this embodiment, an example will be described in which the same analysis model is carried over from the flow analysis to the structural analysis in the coupled analysis of the flow analysis and the structural analysis. However, the analysis may be performed by preparing a flow analysis model and a separate structural analysis model.

[0051] [Calculation of Temperature Distribution and Pressure Distribution (Step S103)] In the step of calculating the temperature distribution and pressure distribution in the injection molding process of the thermoplastic resin (Step S103), a flow analysis (simulation) of the injection molding is performed using the created analytical model. That is, the flow analysis calculates the temperature and pressure of each element of the analytical model in the process of injecting and molding the thermoplastic resin (which may include the in-mold cooling process). This makes it possible to calculate the temperature distribution and pressure distribution of the analytical model in the injection molding process.

[0052] When performing a flow analysis, the physical property values ​​of the thermoplastic resin used in injection molding are input. The physical property values ​​used in the flow analysis include data representing the relationship between pressure, volume, and temperature (PVT data), thermal conductivity data, specific heat data, etc.

[0053] The specific heat of a thermoplastic resin can be measured, for example, by a differential scanning calorimetry (DSC). The thermal conductivity of a thermoplastic resin can be determined, for example, by the AC steady-state method (ISO 22007-6). Methods for measuring thermal conductivity include the AC steady-state method, the hot-wire method, and the hot-disk method. The AC steady-state method can determine thermal conductivity with high accuracy.

[0054] After entering the physical property values, the analysis conditions for thermoplastic resin flow analysis are entered. The molding conditions include the resin (cylinder) temperature, mold temperature, injection speed, dwell pressure, and dwell time. The mold temperature may be set to the same as the cooling water temperature or heater set temperature.

[0055] The molding conditions also include the specification of the time start and end points of the flow analysis. The start point of the flow analysis can be, for example, the time when the thermoplastic resin starts to be injected from the mold gate into the cavity. On the other hand, the end point of the flow analysis can be, for example, the time when the cooling time ends after the molten thermoplastic resin is injected into the mold cavity and before the mold is released. In particular, the end point of the flow analysis can be the time when the mold is released. The time when the mold is released in the flow analysis can be the time when a predetermined time has elapsed since the start of injection (which may be determined based on the time until mold release in the actual process).

[0056] [Calculation of Temperature Distribution in Cooling Process After Demolding (Step S104)] In the step of calculating the temperature distribution in the cooling process after demolding (outside-mold cooling) (step S104), the temperature distribution of each element of the analytical model in the cooling process after demolding (outside-mold cooling) is calculated using the temperature distribution calculated in step S103. This step S104 can be achieved by analysis using structural analysis software.

[0057] The time starting point for an outside-mold cooling analysis can be, for example, the time of mold release. For an analysis of the inside of the mold, the mold itself is modeled in addition to the injection-molded product, and a finite element model of the mold is required to consider the interaction between the injection-molded product and the mold. On the other hand, for an analysis of the outside of the mold, a model of the mold itself is not required, so the model and boundary conditions of the injection-molded product must be significantly changed, making it extremely difficult to set the analysis conditions.

[0058] The time end point of the out-of-mold cooling analysis can be, for example, the time when the injection-molded product reaches room temperature outside the mold. Here, the time when the injection-molded product reaches room temperature outside the mold can be specified, for example, according to the time from the start of injection in an actual process until the injection-molded product reaches room temperature outside the mold. Alternatively, for example, the time when the temperature of the injection-molded product reaches room temperature in the out-of-mold cooling analysis can be determined. Generally, the time when a predetermined time has elapsed since the start of injection can be set as the end point of the out-of-mold cooling analysis.

[0059] [Calculation of Elastic Modulus Distribution (Step S105)] In the step of calculating the elastic modulus distribution (Step S105), the elastic modulus distribution, volumetric shrinkage distribution, and temperature load distribution of the injection-molded product are obtained using the calculated temperature distribution (temperature distribution in the process of molding the thermoplastic resin and temperature distribution in the cooling process after demolding) and pressure distribution (pressure distribution in the process of molding the thermoplastic resin). In particular, the elastic modulus distribution, volumetric shrinkage distribution, and temperature load distribution of the injection-molded product can be calculated based on the temperature distribution and pressure distribution using a conversion program for a computer.

[0060] (Elastic Modulus Distribution) The elastic modulus distribution of the injection-molded product can be obtained by applying the temperature dependency data of the elastic modulus (including Young's modulus, Poisson's ratio, and shear modulus) obtained in step S101 to the temperature distribution obtained in step S103.

[0061] Figure 5 shows specific examples of data on the temperature dependence of the elastic modulus of a thermoplastic resin. In Figure 5, the horizontal axis represents temperature, and the vertical axis represents the elastic modulus (e.g., Young's modulus). The horizontal axis also represents the transition temperature (T) of the thermoplastic resin. The transition temperature is the temperature that separates the solidified region from the molten region, and is also called the no-flow temperature, solidification temperature, solidification temperature, or solid-liquid transition temperature.

[0062] If the temperature dependency data of the elastic modulus as shown in FIG. 5 is obtained, the elastic modulus distribution of the analytical model can be obtained based on the temperature distribution of the analytical model. The elastic modulus can include three types: Young's modulus, Poisson's ratio, and shear modulus. That is, the respective elastic modulus distributions can be obtained from the temperature dependency data of Young's modulus, Poisson's ratio, and shear modulus calculated in step S101. Using these three types of elastic modulus distributions enables more accurate analysis.

[0063] (Volumetric Shrinkage Distribution) The volumetric shrinkage distribution of an injection-molded product can be calculated as follows. First, the temperature (T1) and pressure (P1) at a certain point before shrinkage are determined from the temperature and pressure distributions obtained by flow analysis. These are then applied to the PVT data of the thermoplastic resin to convert them into the volume (V1) at the point before shrinkage.

[0064] For example, in the PVT data shown in FIG. 4, in the example of the gate seal time, T = 200°C, P = 50 MPa, and the specific volume is about 0.82 cm 3 / g. In other words, the volume per unit weight (1 g) at this point is V = approximately 0.82 cm 3 is.

[0065] Similarly, the temperature (T2) and pressure (P2) at a certain point after shrinkage can be determined from the temperature and pressure distributions obtained by flow analysis. These are then applied to the PVT data to convert them into the volume (V2) at the time after shrinkage. Note that this certain point after shrinkage can be determined as a point after a predetermined time has elapsed since the start of injection (for example, it may be determined based on the time it takes for the temperature of the injection-molded product to reach the mold temperature in an actual process).

[0066] For example, in the PVT data shown in FIG. 4, when the temperature is cooled to the mold temperature, T = 40°C, P = 50 MPa, and the specific volume is about 0.70 cm 3 / g. In other words, the volume per unit weight (1 g) at this point is V = approximately 0.70 cm 3 is.

[0067] The distribution of the volumetric shrinkage rate ((V1-V2) / V1) of each element in the analytical model can then be calculated from the volume (V1, V2) of each element before and after shrinkage. For example, the volumetric shrinkage rate of an element cooled from T1=200°C to T2=40°C under a constant pressure of 50 MPa (P1=P2=50 MPa) can be calculated as (V1-V2) / V1=(0.82-0.70) / 0.82=0.146, or 14.6%. Even if the pressure before and after shrinkage is not constant, the temperature (T1, T2) and pressure (P1, P2) of each element before and after shrinkage have been calculated by flow analysis, and the volumetric shrinkage rate (V1-V2) / V1 of each element can be calculated using the PVT data.

[0068] In this way, the volumetric shrinkage distribution in the analytical model can be calculated from the temperature distribution, pressure distribution and PVT data.

[0069] (Temperature Load Distribution) Next, the temperature load (temperature difference) distribution is calculated by dividing the volumetric shrinkage rate of each element by the volumetric expansion rate of the thermoplastic resin, which has been previously obtained, and converting the result into a temperature difference. The volumetric expansion rate is the coefficient β in the relational expression ΔV / V = βΔT, where the original volume V changes by ΔV due to a temperature rise ΔT. Generally, the volumetric expansion rate is calculated from PVT data. However, if the thermoplastic resin is isotropic, the volumetric expansion rate is three times the linear expansion rate. Therefore, instead of the volumetric expansion rate calculated from the PVT data, a volumetric expansion rate calculated from a previously measured linear expansion rate may be used. The volumetric expansion rate and the linear expansion rate (hereinafter collectively referred to as "thermal expansion rate") may be temperature-dependent.

[0070] The temperature load refers to the temperature difference before and after shrinkage and is distinct from the difference between the "temperature at a certain point before shrinkage" and the "temperature at a certain point after shrinkage" used in the process of calculating the volumetric shrinkage distribution. In other words, the temperature difference calculated from the temperature distribution used to calculate the volumetric shrinkage distribution is simply a calculation of temperature alone. In contrast, the temperature load is calculated using temperature and pressure, not just temperature, because it is calculated by using PVT data to calculate volume from temperature and pressure, and then converting the volume back to temperature using the volumetric expansion coefficient. Therefore, the temperature load is different from a simple temperature difference between points before and after shrinkage in that it is a temperature difference that takes into account the effect of pressure on thermoplastic resins during actual molding. Therefore, using a temperature load can yield more accurate analysis results.

[0071] [Calculation of Distortion of Analytical Model (Step S106)] In the step of calculating the distortion occurring in each element of the analytical model (Step S106), a structural analysis is performed using the elastic modulus distribution (Young's modulus distribution, Poisson's ratio distribution, and shear modulus distribution) during the cooling process after demolding, and the volumetric shrinkage distribution or the temperature load distribution (at least one of the volumetric shrinkage distribution and the temperature load distribution), and the distortion occurring in each element of the analytical model (distribution of the amount of distortion in the analytical model) is calculated.

[0072] When calculating the temperature distribution after demolding (step S104), it is necessary to calculate the initial temperature at the time of demolding, so the initial temperature is calculated based on the temperature distribution in the mold before demolding. This initial temperature may also be used when calculating the distortion generated in each element of the analysis model.

[0073] The start point in time for calculating the strain can be, for example, the time when the mold is released after pressure-holding and cooling. The end point in time for calculating the strain can be, for example, the time when the temperature of the injection-molded product becomes equal to room temperature. Alternatively, the end point in time for calculating the strain can be the time when a predetermined time has elapsed since the start of injection (for example, this may be determined based on the time when the temperature of the injection-molded product becomes room temperature in the actual process).

[0074] [Prediction of void occurrence location and void amount (step S107)] In the step of predicting void occurrence location and void amount (step S107), the amount of deformation is calculated based on the distribution of strain amounts in the analytical model, and the occurrence of voids is predicted.

[0075] Specifically, the occurrence of sink marks is predicted from the distribution of the amount of strain, and the location and amount of voids are predicted by analyzing the shape of the sink marks. Alternatively, without predicting the occurrence of sink marks, it is possible to compare the amount of strain in each element of the analytical model with a threshold value for void occurrence determined in advance by actual measurement for each resin material, and predict that voids will occur in elements where the amount of strain is equal to or greater than the threshold value.

[0076] Conventional analysis software does not consider the temperature dependence of elastic modulus (Young's modulus, Poisson's ratio, and shear modulus) and linear expansion coefficient, and therefore only allows input of elastic constants (single values) at room temperature. This results in insufficient consideration of changes in elastic modulus in each part of an injection-molded product as the temperature changes, limiting the accuracy of void occurrence predictions. In contrast, the void occurrence prediction method disclosed herein calculates the elastic modulus distribution (Young's modulus distribution, Poisson's ratio distribution, and shear modulus distribution) of each element of an analytical model due to temperature changes, as well as the volumetric shrinkage distribution and temperature load distribution, based on the temperature and pressure distributions obtained by flow analysis. These distributions can then be used to calculate the strain of each part of an injection-molded product, thereby improving the accuracy of void occurrence predictions.

[0077] Furthermore, in the void occurrence prediction method disclosed herein, the temperature distribution and pressure distribution of each element of the analytical model during the cooling process outside the mold are calculated (step S104), thereby predicting the occurrence of voids taking into account cooling outside the mold, thereby further improving prediction accuracy.

[0078] Next, examples of the void generation prediction method according to the present embodiment (hereinafter also referred to simply as the "improved method") and comparative examples of the conventional method will be shown to specifically explain the void prediction according to the present disclosure. Note that the technology of the present disclosure is not limited to these examples.

[0079] (Product Shape and Molding Material) Figure 6 shows three-dimensional CAD data for an example. This three-dimensional CAD data is shape data for a thick-walled, ribbed, bolt-shaped injection-molded product. As shown in Figure 6, the width of the top surface of the hexagonal flange portion is 20 mm and the thickness is 6 mm. The height of the cylinder is 15 mm. A gate is located on one side of the hexagonal flange portion.

[0080] The molding material (thermoplastic resin) of the actual molded product was DURACON® POM M90-44 (unfilled) manufactured by Polyplastics Co., Ltd. The specific heat of the molding material was measured using a differential scanning calorimeter (DSC), and the thermal conductivity was determined using a thermal conductivity measurement method known as the AC steady-state method (ISO 22007-6).

[0081] For the flow analysis in the examples and comparative examples, Autodesk's Moldflow® Insight 2019.0.5 (3D solid model) Build 20180921.0959_C70L71 (hereinafter referred to as "Moldflow") was used. For the external mold cooling analysis and structural analysis in the examples, Allied Engineering's ADVENTURECluster 2021 (hereinafter referred to as "ADVC") was used. For the calculation of the elastic modulus distribution and volumetric shrinkage distribution in the examples (step S105), a conversion program created by myself using Visual Basic® was used.

[0082] (Example) The molding conditions for the actual molded product were set as follows: barrel temperature 200°C, cooling water temperature 40°C, injection speed 10 mm / s, dwell pressure 60 MPa, dwell time 15 seconds, and cycle time 36 seconds, and test samples were obtained under these molding conditions. Also, a cross-sectional view of the analysis model used in the example is shown in Figure 7.

[0083] The Young's modulus of the thermoplastic resin in the solid state was measured using an RSA III manufactured by Rheometric Scientific. Specifically, a three-point bending test jig was used to measure the Young's modulus of a test piece having a width of 12.86 mm and a thickness of 1.57 mm at a frequency of 1.0 Hz over a temperature range of -40°C to 140°C, thereby obtaining data on the temperature dependence of the Young's modulus. The measurement results are shown in Figure 8.

[0084] The shear modulus of the thermoplastic resin in a molten state was measured using a rotational rheometer, DISCOVERY HR-3, manufactured by TI Instruments, Inc. Specifically, the thermoplastic resin was placed between parallel plates, and the shear modulus was measured at an angular frequency of 600 rad / s to obtain data on the temperature dependence of the shear modulus.

[0085] The viscosity of the thermoplastic resin in a molten state was measured using a Capillograph 1D manufactured by Toyo Seiki Seisaku-sho. Specifically, the viscosity at each temperature was determined using an orifice with an orifice length of 20 mm and an orifice diameter of 1 mm, and the viscosity was fitted to the CROSS-WLF equation shown in the following equations (9) to (11). Then, the fitted coefficients and the viscosity at a shear rate of 400 / s were calculated to obtain data on the temperature dependency of viscosity. η: Viscosity η0: Zero shear viscosity γ: Shear rate τ * : Reference shear stress T: Temperature T * : Glass transition temperature P: Pressure A1, A2, D1, D2, D3: Parameters

[0086] The correlation between Young's modulus, obtained by tripling the shear modulus in the molten state, and viscosity is shown in Figure 9. From Figure 9, it can be seen that Young's modulus and viscosity in the molten state are in a linear relationship. From this linear relationship, the parameters ω1 and ω2 in the above equation (6) were calculated using least squares approximation. ω1 is 1.05 × 10 -3 , ω2 is -8.81 × 10 -2 was asked.

[0087] The Young's modulus between the solid state and the molten state was calculated by linear interpolation from the temperature dependency data of Young's modulus in the solid state shown in Fig. 8 and the temperature dependency data of Young's modulus in the molten state obtained using the above formula (6). Fig. 10 shows the temperature dependency data of Young's modulus at temperatures from the solid state to the molten state of a thermoplastic resin.

[0088] The Poisson's ratio was calculated from the temperature dependency data of Young's modulus and the PVT data. Specifically, the Poisson's ratio was calculated from the temperature dependency data of Young's modulus shown in Figure 10 and the bulk modulus obtained from the PVT data according to the above formula (7), thereby obtaining the temperature dependency data of the Poisson's ratio. The calculation results are shown in Figure 11.

[0089] Flow stop temperature T S Regarding the actual measured value of 8 seconds, in the example, analysis of injection, pressure holding and cooling in the mold was carried out and calculation was carried out according to formula (1), resulting in a value of 7.9 seconds.

[0090] In the examples, the elastic modulus distribution (Young's modulus distribution, Poisson's ratio distribution, and shear modulus distribution), volumetric shrinkage distribution, and temperature load distribution of the injection-molded product (inside and surface) were obtained by a conversion program from the temperature distribution and pressure distribution obtained by the analysis of injection, pressure holding, and in-mold cooling, and the temperature distribution obtained by the analysis of cooling outside the mold. Then, a structural analysis was performed using the elastic modulus distribution, volumetric shrinkage distribution, and temperature load distribution, and the distortion of the analytical model was calculated to predict the location of voids and the amount of voids.

[0091] (Comparative Example) In a comparative example using a conventional method, the Young's modulus and Poisson's ratio in the solid state temperature range shown in Figure 8 were uniformly used for the same analytical model as in the example, but other than that, the same physical property values ​​and analytical conditions as in the example were used, and the location of void occurrence and the amount of voids were predicted from the volumetric shrinkage distribution calculated by flow analysis. As described above, the conventional method does not take into account the elastic modulus distribution in the molten state (Young's modulus distribution, Poisson's ratio distribution, and shear elastic modulus distribution), and therefore, as described below, the comparative example resulted in inferior analysis accuracy compared to the example.

[0092] (Comparison of Void Prediction) FIG. 12 shows the actual void measurement values ​​obtained by X-ray CT of the actual molded product, the predicted void results for the example, and the predicted void results for the comparative example.

[0093] In the comparative example using the conventional method, voids occurred in some parts of the analytical model, resulting in a large discrepancy with the actual measured values ​​of voids in the actual molded product. In contrast, in the example using the improved method, the predicted locations of void occurrence approached the locations of void occurrence in the actual molded product, indicating that the actual occurrence of voids was predicted with high accuracy.

[0094] (Method for Reducing Voids) The present disclosure also includes a method for reducing voids generated inside an injection-molded product obtained by injection molding a thermoplastic resin into a mold. That is, the amount of voids is predicted by the above-described void occurrence prediction method, and if the predicted amount of voids is equal to or greater than a predetermined threshold, one or more of the design, molding conditions, and molding material are changed and the amount of voids is predicted again, and this process is repeated until the predicted amount of voids is reduced to less than the predetermined threshold.

[0095] The present disclosure is not limited to the above-described embodiment, but includes various modifications in which components are added, deleted, or converted from the above-described configuration. Furthermore, the above-described embodiments can be combined in various ways. In particular, the present disclosure should not be interpreted as being limited to the above-described embodiment or example in terms of shape, material, or conditions.

[0096] The void occurrence prediction method according to the present disclosure can be executed by an information processing device. Fig. 13 is a block diagram showing an example of the hardware configuration of an information processing device 100 that executes the void occurrence prediction method. As shown in Fig. 13, the information processing device 100 includes a processor 101, a main memory device 102, an auxiliary memory device 103, an I / O (Input / Output) interface 104, and a network interface (hereinafter abbreviated as "NW interface") 105.

[0097] The processor 101 includes, for example, a central processing unit (CPU), a field programmable gate array (FPGA), or a digital signal processor (DSP), and controls the entire information processing device 100 and executes various types of arithmetic processing.

[0098] The main storage device 102 includes, for example, a random access memory (RAM) or a read only memory (ROM), and stores information used in the arithmetic processing executed by the processor 101 .

[0099] The auxiliary storage device 103 includes, for example, a hard disk drive (HDD) or a solid state drive (SSD), and stores various programs and data.

[0100] The I / O interface 104 is an interface through which a user inputs information and outputs information to a user, and may include, for example, a keyboard, a display, a touch panel, a microphone, or a speaker.

[0101] The NW interface 105 is an interface for connecting to a network via wire or wirelessly.

[0102] The information processing device 100 acquires physical property values ​​for analysis via the I / O interface 104 and the NW interface 105. The processor 101 then executes a program stored in the auxiliary storage device 103 while using the main storage device 102 to create an analytical model and perform flow analysis and structural analysis. Furthermore, the processor 101 calculates the elastic modulus distribution, volumetric shrinkage distribution, and temperature load distribution in the analytical model from the results of the flow analysis and structural analysis, and calculates the strain generated in each element of the analytical model. The processor 101 then predicts the location of voids and the amount of voids based on the calculated strain.

[0103] The processes executed by the information processing device 100 can also be written as a computer-executable program. In this case, the program can be stored on a computer-readable, non-transitory recording medium and installed on the computer. Examples of such recording media include portable recording media such as CD-ROMs, DVDs, and USB memory, as well as semiconductor memories such as flash memories.

Claims

1. A void generation prediction method for predicting the generation behavior of voids occurring in an injection molded product obtained by injection molding a thermoplastic resin into a mold, the method comprising: - obtaining temperature-dependent data of Young's modulus in a molten state from a viscosity measurement result of the thermoplastic resin in a molten state using Equation (I) showing the relationship between the viscosity and Young's modulus of the thermoplastic resin in a molten state; - creating an analysis model in which the injection molded product is divided into a plurality of elements; 4. The step of obtaining the temperature dependence data of the Young's modulus obtains the bulk modulus from the temperature dependence data of the Young's modulus and the PVT data, and further obtains the temperature dependence data of the Poisson's ratio of the injection molded product from the bulk modulus using Equation (II). The void generation prediction method according to claim 1 or 2. ν: Poisson's ratio E: Young's modulus K: Bulk modulus - obtaining a temperature distribution and a pressure distribution of the analysis model in the molding process of the thermoplastic resin; - calculating a temperature distribution of the analysis model in the cooling process after demolding using the temperature distribution and the pressure distribution; - calculating an elastic modulus distribution and a temperature load distribution of the analysis model based on the temperature-dependent data of Young's modulus, the temperature distribution and the pressure distribution in the molding process, and the temperature distribution in the cooling process; - calculating the strain generated in each element of the analysis model by structural analysis using the elastic modulus distribution and the temperature load distribution in the cooling process after demolding; - predicting at least one of the void generation location and the void amount from the strain. - Equation (I): E = ω1η + ω2 - η: Viscosity - E: Young's modulus - ω1, ω2: Constants 2. The step of obtaining the temperature-dependent data of Young's modulus is to obtain the temperature-dependent data of Young's modulus by linearly interpolating the Young's modulus in the solid state and the Young's modulus in the molten state between the crystallization start temperature and the crystallization end temperature. The void generation prediction method according to claim 1 3. The crystallization start temperature or the crystallization end temperature is a inflection point when the thermoplastic resin is cooled at a cooling rate included in the range from 1 °C / min to 50 °C / min in specific heat measurement. The void generation prediction method according to claim 2 5. The step of calculating the elastic modulus distribution and the temperature load distribution is to calculate the elastic modulus distribution and the temperature load distribution with the gate seal time as the temporal starting point and the time point when a predetermined time has elapsed from the start of injection as the temporal ending point. The void generation prediction method according to claim 1 6. The gate seal time is determined as the time at which the weight of the molded product becomes maximum after setting the molding conditions so that the time until gate seal occurs is minimized in the flow analysis of the analysis model according to claim 5. The void generation prediction method according to claim 5.

7. The gate seal time is determined as the time at which the temperature at the center of the gate reaches the flow stop temperature according to claim 5. The void generation prediction method according to claim 5.

8. The flow stop temperature is obtained by the formula (III) using the data fitting coefficients b5 and b6 of the 2 - DOMAIN TAIT PVT model for the PVT data showing the relationship between the pressure, volume, and temperature of the resin, according to the void generation prediction method of claim 7. T S = b5 + b6 × P... (III) T S : Flow stop temperature P: Pressure 9. The step of calculating the strain uses the elastic modulus distribution and the temperature load distribution considering temperature dependence, with the time when the injection molded product is demolded after pressure holding and cooling as the temporal starting point and the time when a predetermined time has elapsed since the start of injection as the temporal ending point, to calculate the strain according to claim 1. The void generation prediction method according to claim 1.

10. The step of obtaining the temperature distribution and the pressure distribution uses the thermal conductivity obtained by a thermal conductivity measurement method using the alternating current steady state method (ISO22007-6) in the calculation according to claim 1. The void generation prediction method according to claim 1.

11. A void reduction method for reducing voids generated in an injection molded product obtained by injecting a thermoplastic resin into a mold, predicting the void amount by the void generation prediction method according to claim 1, and when the predicted void amount is equal to or more than a predetermined threshold value, changing one or more of the design, molding conditions, and molding material and predicting the void amount again until the predicted void amount is reduced to less than the predetermined threshold value. Repeating until. The void reduction method.

12. A computer-readable recording medium storing a void generation prediction program for causing a computer to execute the void generation prediction method according to claim 1.

13. A computer-readable recording medium storing a void reduction program for causing a computer to execute the void reduction method according to claim 11.

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