Void occurrence prediction method, void reduction method, void occurrence prediction program, and void reduction program

Abstract

To accurately predict a behavior of voids occurring in a resin molded product produced by an injection molding process using resin materials.SOLUTION: A void occurrence prediction method includes: obtaining temperature dependency data of Young's modulus in a molten state from viscosity measurement results using formula (I); creating an analytical model of an injection molded product; obtaining a temperature distribution and a pressure distribution of the analytical model during a molding process; calculating a temperature distribution of the analytical model during a cooling process after demolding; calculating an elastic modulus distribution and a temperature load distribution of the analytical model based on the temperature dependency data of Young's modulus and the temperature distribution and pressure distribution; calculating distortion occurring in the analytical model by structural analysis using the elastic modulus distribution and the temperature load distribution; and predicting at least one of a void occurrence location and a void volume from the distortion. E=ω1η+ω2 ... (I), where η: viscosity, E: Young's modulus, ω1,ω2: constant.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present disclosure relates to a void generation prediction method, a void reduction method, a void generation prediction program, and a void reduction program.

Background Art

[0002] Conventionally, injection molding has sometimes been used in the production of 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)" may occur in the resin molded product.

[0003] Sink marks and voids occur in the process of cooling and solidifying the thermoplastic resin injected in a molten state in the injection molding of thermoplastic resins. Particularly in the case of crystalline resins, the molecular chains that were in a random state immediately after mold filling are oriented (folded and aligned) by crystallization, and as a result, sink marks or voids are generated due to a decrease (shrinkage) in volume compared to the volume (mold dimensions) immediately after mold filling.

[0004] When these molding defects occur, a decrease in the dimensional accuracy of the product (for example, in parts for airtight applications, a gap may form in the sealing surface that contacts the mating member due to the depression) or a decrease in strength (fracture is likely to occur starting from the void) may occur. Therefore, technological improvements regarding the suppression of sink marks and voids are required.

[0005] As measures for suppressing sink marks and voids, checking the actually molded product and changing the molding conditions, or changing the design of the gate or wall thickness of the molded product, etc. can be mentioned. However, since the time and cost involved in these measures are enormous, in recent years, it has been studied to predict the occurrence of sink marks and / or voids by injection molding simulation using flow analysis software and optimize the product shape and molding conditions.

[0006] For example, in Patent Document 1, the data obtained by fluid analysis software is applied to strain analysis by structural analysis software, and the generation of voids is predicted. Further, in Patent Document 2, from the temperature data and pressure data obtained by fluid analysis, an elastic modulus distribution and a volume shrinkage rate distribution are obtained, and the generation of sink marks or voids is predicted by a coupled analysis applied to structural analysis (strain analysis). Furthermore, Patent Document 3 shows a method for measuring the elastic modulus of liquid foods in a wide temperature range used for coupled analysis.

Prior Art Documents

Patent Documents

[0007]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0008] However, in existing injection molding simulation methods, the handling of temperature dependencies such as the elastic modulus used in calculations is unclear, and in particular, the method for obtaining data such as the elastic modulus for a molten thermoplastic resin is also unknown. That is, for example, the method for measuring the elastic modulus shown in Patent Document 3 is mainly a measurement method for liquid foods, so it is not practical to apply this measurement method to a molten thermoplastic resin at a very high temperature. For these reasons, it is difficult to perform accurate injection molding simulations even using fluid analysis software, and the generation of voids in molded products may not be predicted with sufficient accuracy.

[0009] An object of the present disclosure is to provide a method for accurately predicting the behavior of void generation in a resin molded product by an injection molding process using a resin material. By solving this problem, it becomes possible to assume in advance the product shape design, mold design, molding conditions setting, and molding material for obtaining a molded product without voids at the design stage, and productization can be efficiently performed.

Means for Solving the Problem

[0010] According to one aspect of the present disclosure, a void generation prediction method is a void generation prediction method for predicting the behavior of void generation in an injection molded product obtained by injection molding a thermoplastic resin into a mold, and using an equation (I) showing the relationship between the viscosity and Young's modulus of the thermoplastic resin in a molten state, obtaining temperature-dependent data of Young's modulus in the molten state from the viscosity measurement result of the thermoplastic resin in the molten state; creating an analysis model in which the injection molded product is divided into a plurality of elements; obtaining the temperature distribution and pressure distribution of the analysis model in the molding process of the thermoplastic resin; calculating the temperature distribution of the analysis model in the cooling process after mold release using the temperature distribution and the pressure distribution; calculating the elastic modulus distribution and temperature load distribution of the analysis model based on the temperature-dependent data of Young's modulus, the temperature distribution and 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 temperature load distribution in the cooling process after mold release; and predicting at least one of the void generation location and void amount from the strain. E = ω1η + ω2 ··· (I) η: Viscosity E: Young's modulus ω1, ω2: Constants

[0011] Also, according to another aspect of the present disclosure, a void reduction method is a void reduction method for reducing voids generated in an injection molded product formed by injection molding a thermoplastic resin into a mold. The void amount is predicted by the above void generation prediction method. When the predicted void amount is equal to or greater than a predetermined threshold, one or more of the design, molding conditions, and molding material are changed, and the void amount is predicted again until the predicted void amount is reduced to less than the predetermined threshold.

[0012] Also, according to still another aspect of the present disclosure, a void generation prediction program causes a computer to execute the above void generation prediction method.

[0013] Also, according to still another aspect of the present disclosure, a void reduction program causes a computer to execute the above void reduction method.

Brief Description of Drawings

[0014]

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DETAILED DESCRIPTION OF THE INVENTION

[0015] Before describing one embodiment according to the present disclosure, first, a general injection molding process will be described. FIG. 1 is a diagram for explaining each step of injection molding. In FIG. 1, the time series of molding is shown by focusing on one mold.

[0016] Injection molding starts with injecting resin through a gate into a mold cavity. Immediately after injection, the delivery rate of the resin is controlled. For example, when 99% of a preset amount of resin is filled, the control of the resin pressure (holding pressure) is switched. Injection continues while maintaining the holding pressure after the switch.

[0017] Here, gate seal is one of the indexes for obtaining a stable injection molded product. Gate seal is a phenomenon in which the resin at the gate part solidifies and the flow stops. The time when the resin at the gate part solidifies and the flow stops in this way is called the gate seal time. If the holding pressure is stopped before the gate seal time, the molten resin will flow back to the injection molding machine side through the gate, resulting in filling defects and weight loss. On the other hand, if the holding pressure is stopped after the gate seal time, since the gate is solidified, the resin will not flow back, and a stable injection molded product can be obtained. For this reason, the gate seal time is an index that must be measured at the molding site.

[0018] The gate seal time may be determined as the time when the weight of the molded product is maximized after setting the molding conditions so that the time until the gate seal occurs is minimized. At this time, the time until the gate seal occurs and the weight of the molded product may be obtained by experiment (weighing). Further, the time until the gate seal occurs and the weight of the molded product may be obtained by simulation using flow analysis instead of experiment.

[0019] Alternatively, the gate seal time may be determined as the time until 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 may be obtained by experiment or may be determined by simulation.

[0020] The flow stop temperature T S may be the inflection point when the resin is cooled at a cooling rate in the range from 1 °C / min to 50 °C / min in specific heat measurement.

[0021] Alternatively, the flow stop temperature T S may be obtained by the following formula (1) 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 (Pressure), volume (Volume) and temperature (Temperature) of the resin. T S = B5 + B6 × P ···(1)

[0022] Here, in the 2-DOMAIN TAIT PVT model, it is assumed that the specific volume V(T,P) at temperature T and pressure P is expressed as V(T,P)=V0(T)[1 - C × LN(1 + P / B(T))]+V T (T,P). In particular, V0(T) is a linear expression of T, and for a predetermined temperature T T with respect to V T (T,P) is zero on the high temperature side (T > T T ), but is zero on the low temperature side (T < T T) becomes an exponential function of T and P. And in this model, T is fitted with a linear equation of P as T T = B5 + B6×P.

[0023] Note that B5 is the inflection point of the PVT data. That is, the slope changes with B5 as the boundary, and solid-liquid phase transition occurs.

[0024] Subsequent to the pressure holding, the injection molded product is cooled in the mold (in-mold cooling) for a predetermined cooling time. When the cooling time has elapsed, the mold is opened, and the injection molded product is taken out of the mold and demolded. Thereafter, the mold is closed again to form a mold cavity, and the process proceeds to molding for obtaining the next injection molded product. The demolded injection molded product can be cooled outside the mold (out-of-mold cooling). The out-of-mold cooling can continue, for example, until the injection molded product reaches room temperature.

[0025] Here, in the cooling process of the injection molded product, the relationship between the generation of voids and the internal physical property distribution is not clear, and for an injection molded product in which the liquid phase and the solid phase may coexist, it is necessary to consider both the liquid phase and the solid phase. However, in particular, for a material that becomes a liquid phase (molten state) at a high temperature such as a resin material, a means for measuring the Young's modulus used in structural analysis is not known.

[0026] In the present disclosure, an analysis considering the cooling after demolding the injection molded product from the mold is performed. From the temperature data obtained thereby, an elastic modulus distribution and a volume shrinkage rate distribution are obtained, and it has been found that the location and amount of void generation can be predicted at a level comparable to the results in an actual product by a coupled analysis applied to structural analysis (strain analysis). Note that in the present disclosure, although the injection molded product is described as being cooled by air cooling inside and outside the mold, the cooling means is not limited to air cooling.

[0027] Hereinafter, an embodiment according to the present disclosure will be described with reference to the accompanying drawings. The embodiments described below are illustrative and are not to be construed as being limited by this description.

[0028] [Method for Predicting Void Generation Behavior] FIG. 2 is a flowchart showing a method for predicting void generation behavior according to an embodiment. This method performs coupled analysis that applies the results of fluid analysis to structural analysis.

[0029] As shown in FIG. 2, the void generation prediction method according to an embodiment includes obtaining physical property values for analysis (step S101), creating an analysis 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 analysis model (step S106), and predicting the void generation location and void amount (step S107).

[0030] [Obtaining Physical Property Values for Analysis (Step S101)] The step of obtaining physical property values for analysis (step S101) includes, as shown in FIG. 3, a dynamic viscoelastic data acquisition step (step S201), a viscosity acquisition step (step S202), a temperature dependence 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).

[0031] [Dynamic Viscoelastic Data Acquisition Step (Step S201)] In the dynamic viscoelastic data acquisition step (step S201), the temperature dependence of the Young's modulus of the thermoplastic resin in the solid state is measured. That is, for example, using a universal material testing machine or a dynamic viscoelasticity measuring device equipped with a constant temperature bath, the temperature dependence data of the Young's modulus of the thermoplastic resin in the solid state is obtained.

[0032] [Viscosity Acquisition Step (Step S202)] When obtaining temperature-dependent data of Young's modulus using the above universal material testing machine or dynamic viscoelasticity measuring device, it is required that the thermoplastic resin be in a solid state, and it is difficult to measure the thermoplastic resin in a high-temperature region near the melting point or in a molten state. Therefore, for measuring Young's modulus in a high-temperature region near the melting point or in a molten state, it is conceivable to use, for example, a rotational rheometer or a melt viscoelasticity measuring device.

[0033] However, when using these devices, when the temperature of the thermoplastic resin approaches the solidification temperature, a high shear stress is generated, making it mechanically difficult to measure. That is, since the Young's modulus in the tensile state can be obtained by measurement in the solid state and the shear modulus can be obtained by measurement in the molten state, the form of the test piece changes greatly, so the continuity between the solid state and the molten state is a concern, and it is difficult to obtain the data itself.

[0034] From the above, in the viscosity acquisition step (step S202), the viscosity of the thermoplastic resin in the molten state is measured, and the Young's modulus of the thermoplastic resin in the molten state is calculated based on the measured viscosity.

[0035] Generally, there is a relationship of the following formula (2) between viscosity and Young's modulus. TIFF2025112831000002.tif13170η: Viscosity E: Young's modulus ω: Frequency

[0036] Therefore, usually, Young's modulus E can be calculated from the measurement data of viscosity η by the following formula (3). E = ηω ···(3)

[0037] However, regarding the thermoplastic resin in the molten state, since the shear rate dependence of viscosity shows non-Newtonian properties, even if the shear rate is converted to the frequency ω, it is unclear which viscosity at which shear rate is the viscosity that can obtain continuity with the solid state, and the relationship of the above formula (2) has not been confirmed.

[0038] Therefore, when the shear modulus of a thermoplastic resin in a molten state was determined using a rotational rheometer, it was found that the viscosity measured using a capillary and the shear modulus had a proportional relationship. Since the Poisson's ratio of a thermoplastic resin in a molten state is close to 0.5, substituting Poisson's ratio ν = 0.5 into the following equation (4) gives the relationship between the shear modulus and Young's modulus. TIFF2025112831000003.tif14170G: Shear modulus E: Young's modulus ν: Poisson's ratio

[0039] That is, the following relationship of equation (5) holds between the shear modulus and Young's modulus. E = 3G ···(5) Therefore, if the frequency ω in the above equation (3) is determined, Young's modulus E of the thermoplastic resin in a molten state can be determined from the result of viscosity measurement.

[0040] In the present disclosure, Young's modulus E of the thermoplastic resin in a molten state is calculated by the following equation (6) obtained by improving the above equation (3). That is, the relationship between viscosity and Young's modulus is shown by the above equation (3), but since Young's modulus is a value in a static state while viscosity is a measurement result in a dynamic state, when obtaining the actual correlation between viscosity and Young's modulus, it was found that Young's modulus E is represented by a linear equation with respect to viscosity η as shown in equation (6). E = ω1η + ω2···(6) E: Young's modulus η: Viscosity ω1, ω2: Constants

[0041] In the viscosity acquisition step (step S202), the viscosity of the thermoplastic resin in a molten state is measured, and temperature-dependent data of Young's modulus in the molten state of the thermoplastic resin is obtained by the above equation (6).

[0042] [Step for obtaining temperature-dependent data of Young's modulus (step S203)] In the Young's modulus temperature dependence data acquisition step (step S203), based on the Young's modulus of the thermoplastic resin in the solid state and the molten state, temperature dependence data of the Young's modulus in a wide temperature range is acquired. That is, the temperature dependence data in the solid state obtained in the dynamic viscoelasticity data acquisition step (step S201) and the temperature dependence data in the molten state obtained in the viscosity acquisition step (step S202) are combined to acquire the temperature dependence data of the Young's modulus.

[0043] Here, in the case of a crystalline resin, the temperature dependence of the Young's modulus between the solid state and the molten state changes greatly near the crystallization temperature. And in addition to the crystallization behavior depending on the cooling rate, the physical property values change depending on the molding conditions in injection molding, so it is difficult to measure the crystallization behavior. Therefore, between the crystallization start temperature and the crystallization end temperature, it may be obtained by linearly interpolating the Young's modulus in the solid state and the Young's modulus in the molten state. At this time, the crystallization start temperature or the crystallization end temperature may be the inflection point when the resin is cooled at a cooling rate included in the range from 1 °C / min to 50 °C / min in specific heat measurement.

[0044] [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.

[0045] Figure 4 shows a specific example of the PVT data of the thermoplastic resin. In Figure 4, the horizontal axis is the temperature (unit: °C), and the vertical axis is the reciprocal of the density, that is, the specific volume (unit: cm 3 / g). What is shown in Figure 4 is the relationship between the temperature and the specific volume when the pressure (P) is 50 MPa. Assume that the relationships between the temperature and the specific volume for some pressures other than 50 MPa have also been measured in advance. The relationship between the temperature and the specific volume at pressures that have not been measured can be obtained, for example, by interpolation from the relationships between the temperature and the specific volume at the measured pressures.

[0046] As shown in Fig. 4, when the pressure (P) is 50 MPa, at the time when the thermoplastic resin in the gate part solidifies and the time required for the flow to stop (hereinafter also referred to as "gate seal time"), the temperature of the thermoplastic resin is 200 °C and the specific volume is about 0.82 cm 3 / g. 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.

[0047] [Poisson's Ratio and Shear Modulus Calculation Step (Step S205)] In the Poisson's ratio and shear modulus calculation step (Step S205), the Poisson's ratio and shear modulus are calculated from the temperature dependence data of Young's modulus and PVT data.

[0048] The Poisson's ratio of the injection molded product is calculated by the following formula (7) from Young's modulus and the bulk modulus K obtained from PVT data. TIFF2025112831000004.tif14170 ν: Poisson's ratio E: Young's modulus K: Bulk modulus

[0049] Also, the shear modulus of the injection molded product is calculated by the following formula (8) from the Poisson's ratio and Young's modulus. TIFF2025112831000005.tif14170 G: Shear modulus E: Young's modulus ν: Poisson's ratio

[0050] In the Poisson's ratio and shear modulus calculation step (Step S205), the temperature dependence data of the Poisson's ratio and shear modulus are obtained by calculating the Poisson's ratio and shear modulus from Young's modulus at each temperature according to the above formulas (7) and (8).

[0051] [Creation of Analytical Model (Step S102)] In the step of creating an analysis model (step S102), the shape of the injection molded product is divided into minute elements, and a model used for executing simulation is created. Here, for example, the shape of the injection molded product (which can be the designed 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 the runner and gate) is input into the computer by a three-dimensional shape measurement or a CAD system, etc. Next, the shape input into the computer is divided into a plurality of three-dimensional elements by an element division preprocessor or the like, and an analysis model is created.

[0052] In the present 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 executed by preparing a flow analysis model and a separate structural analysis model.

[0053] [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 injection molding is executed using the created analysis model. That is, by the flow analysis, the temperature and pressure of each element of the analysis model in the process of injecting and molding the thermoplastic resin (which may include the in-mold cooling process) are calculated. Thereby, the temperature distribution and pressure distribution of the analysis model in the injection molding process can be calculated.

[0054] When executing the flow analysis, physical property values of the thermoplastic resin used for injection molding are input. The physical property values used for the flow analysis include data representing the relationship between pressure, volume, and temperature (PVT data), thermal conductivity data, specific heat data, and the like.

[0055] Incidentally, the specific heat of a thermoplastic resin can be measured, for example, by a differential scanning calorimeter (DSC: Differential Scanning Calorimetry). Further, the thermal conductivity of a thermoplastic resin can be determined, for example, by an alternating current steady state method (ISO 22007-6). Examples of measurement methods for thermal conductivity include the alternating current steady state method, the hot wire method, and the hot disk method. According to the alternating current steady state method, the thermal conductivity can be determined with good accuracy.

[0056] After inputting the physical property values, analysis conditions for performing a flow analysis of the thermoplastic resin are input. The molding conditions include the resin (cylinder) temperature, mold temperature, injection speed, holding pressure, and holding time. Note that the mold temperature may be equal to the cooling water temperature or the heater set temperature.

[0057] In addition, the molding conditions include specifying the temporal 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 gate of the mold into the cavity. On the other hand, the end point of the flow analysis can be, for example, the time before the cooling time ends and before demolding after the molten thermoplastic resin is injected into the mold cavity. In particular, the end point of the flow analysis may be the demolding time. The demolding time in the flow analysis can be the time when a predetermined time (which may be determined based on the time until demolding in the actual process) has elapsed since the start of injection.

[0058] [Calculation of temperature distribution in the cooling process after demolding (step S104)] In the step (step S104) of calculating the temperature distribution in the cooling process after demolding (external mold cooling), the temperature distribution of each element of the analysis model in the cooling process after demolding (external mold cooling) is calculated using the temperature distribution calculated in step S103. This step S104 can be realized by an analysis using structural analysis software.

[0059] The temporal starting point of the mold external cooling analysis can be, for example, the demolding time point. In the case of the analysis regarding the inside of the mold, in addition to the injection molded product, it is necessary to use the finite element model of the mold in order to model the mold itself and consider the interaction between the injection molded product and the mold. On the other hand, in the case of the analysis regarding the outside of the mold, since the model of the mold itself is not necessary, it is necessary to greatly change the model of the injection molded product and the boundary conditions, making it extremely difficult to set the analysis conditions.

[0060] The temporal ending point of the mold external cooling analysis can be, for example, the time point when the injection molded product reaches room temperature outside the mold. Here, the time point when the injection molded product reaches room temperature outside the mold may be specified according to, for example, the time from the start of injection to when the injection molded product reaches room temperature outside the mold in the actual process. Alternatively, for example, the time point when the temperature of the injection molded product becomes room temperature in the mold external cooling analysis may be obtained. Generally, the time point when a predetermined time has elapsed from the start of injection can be set as the ending point of the mold external cooling analysis.

[0061] [Calculation of Elastic Modulus Distribution (Step S105)] In the step of calculating the elastic modulus distribution (Step S105), 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), the elastic modulus distribution, volume shrinkage rate distribution, and temperature load distribution of the injection molded product are obtained. In particular, based on the temperature distribution and pressure distribution, by using a conversion program for a computer, the elastic modulus distribution, volume shrinkage rate distribution, and temperature load distribution of the injection molded product can be calculated.

[0062] (Elastic Modulus Distribution) The elastic modulus distribution of the injection molded product can be obtained by applying the temperature-dependent data of the elastic modulus (including Young's modulus, Poisson's ratio, and shear elastic modulus) obtained in Step S101 to the temperature distribution obtained in Step S103.

[0063] FIG. 5 shows a specific example of temperature-dependent data of the elastic modulus of a thermoplastic resin. In FIG. 5, the horizontal axis represents temperature, and the vertical axis represents the elastic modulus (e.g., Young's modulus). The transition temperature (T) of the thermoplastic resin is shown on the horizontal axis. The transition temperature is the temperature that separates the solidified region and the molten region, and is also called the no-flow temperature, solidification temperature, curing temperature, or solid-liquid transition temperature.

[0064] If temperature-dependent data of the elastic modulus as shown in FIG. 5 is obtained, the elastic modulus distribution in the analysis model can be obtained based on the temperature distribution of the analysis model. Note that the elastic modulus can include three types: Young's modulus, Poisson's ratio, and shear modulus. That is, the elastic modulus distribution can be obtained from the temperature-dependent data of Young's modulus, Poisson's ratio, and shear modulus calculated in step S101, respectively. By using these three types of elastic modulus distributions, more accurate analysis becomes possible.

[0065] (Volume shrinkage rate distribution) The volume shrinkage rate distribution of the injection molded product can be calculated as follows. First, from the temperature distribution and pressure distribution obtained by the flow analysis, the temperature (T1) and pressure (P1) at a certain point before shrinkage are obtained. Then, by applying these to the PVT data of the thermoplastic resin, it is converted into the volume (V1) at the point before shrinkage.

[0066] For example, in the PVT data shown in FIG. 4, in the example of the gate seal time, T1 = 200 °C, P1 = 50 MPa, and the specific volume is about 0.82 cm 3 / g. That is, the volume per unit weight (1 g) at this point is V1 = about 0.82 cm 3 .

[0067] Similarly, from the temperature distribution and pressure distribution obtained by the flow analysis, the temperature (T2) and pressure (P2) at a certain point after shrinkage are determined. Then, by applying these to the PVT data, they are converted into the volume (V2) at the time after shrinkage. Note that a certain point after shrinkage can be the point in time when a predetermined time (for example, it may be determined based on the time when the temperature of the injection molded product becomes the mold temperature in the actual process) has elapsed since the start of injection.

[0068] For example, in the PVT data shown in FIG. 4, in the example at the time when it is cooled to the mold temperature, T2 = 40°C, P2 = 50 MPa, and the specific volume is about 0.70 cm 3 / g. That is, the volume per unit weight (1 g) at this time is V2 = about 0.70 cm 3 is.

[0069] And from the volumes (V1, V2) of each element before and after shrinkage, the distribution of the volume shrinkage rate ((V1 - V2) / V1) of each element of the analysis model is determined. For example, for an element where the pressure is constant at 50 MPa (P1 = P2 = 50 MPa) and is cooled from T1 = 200°C to T2 = 40°C, the volume shrinkage rate is (V1 - V2) / V1 = (0.82 - 0.70) / 0.82 = 0.146, that is, it can be calculated as 14.6%. At this time, even if the pressure before and after shrinkage is not constant, since the temperatures (T1, T2) and pressures (P1, P2) of each element before and after shrinkage are calculated by the flow analysis, the volume shrinkage rate (V1 - V2) / V1 of each element can be calculated by using the PVT data.

[0070] In this way, the volume shrinkage rate distribution in the analysis model can be calculated from the temperature distribution, pressure distribution, and PVT data.

[0071] (Temperature load distribution) Next, the temperature load (temperature difference) distribution is obtained by dividing the volume shrinkage rate of each element by the volume expansion rate of the thermoplastic resin obtained in advance and converting it into a temperature difference. The volume expansion rate is the coefficient β in the relational expression ΔV / V = βΔT when the original volume V changes by ΔV due to a temperature rise ΔT. Generally, the volume expansion rate is obtained from PVT data. However, when the thermoplastic resin is isotropic, since the volume expansion rate is three times the linear expansion rate, instead of the volume expansion rate obtained from PVT data, the volume expansion rate obtained from the linearly expansion rate measured in advance may be used. The volume expansion rate and the linear expansion rate (hereinafter collectively referred to as "thermal expansion rate") may have temperature dependence.

[0072] Note that the temperature load means the temperature difference before and after shrinkage, and is distinguished 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 obtaining the volume shrinkage rate distribution. That is, the temperature difference obtained from the temperature distribution used when obtaining the volume shrinkage rate distribution is simply the calculation of only the temperature. In contrast, since the temperature load is a value obtained by obtaining the volume from the temperature and pressure using PVT data and then returning the volume to the temperature using the volume expansion rate, it is calculated using not only the temperature but also the temperature and pressure. Therefore, the temperature load is different from the simple temperature difference between the points before and after shrinkage in that it is a temperature difference considering the influence of the pressure received by the thermoplastic resin in actual molding, and more accurate analysis results can be obtained by using the temperature load.

[0073] [Calculation of distortion of analysis model (step S106)] In the step of calculating the distortion generated in each element of the analysis 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) in the cooling process after mold release and the volume shrinkage rate distribution or the temperature load distribution (at least one of the volume shrinkage rate distribution and the temperature load distribution), and the distortion generated in each element of the analysis model (the distribution of the amount of distortion in the analysis model) is calculated.

[0074] In addition, when obtaining the temperature distribution after mold release (step S104), since it is necessary to obtain the initial temperature at the time of mold release, the initial temperature is obtained based on the temperature distribution in the mold before mold release. And this initial temperature may also be used when calculating the strain generated in each element of the analysis model.

[0075] The temporal starting point of the strain calculation can be, for example, the time point of mold release after pressure holding and cooling. Also, the temporal ending point of the strain calculation can be, for example, the time point when the temperature of the injection molded product becomes equal to room temperature. Alternatively, the temporal ending point of the strain calculation can be the time point after a predetermined time (for example, determined based on the time when the temperature of the injection molded product becomes room temperature in the actual process) has elapsed since the start of injection.

[0076] [Prediction of void generation location and void amount (step S107)] In the step of predicting the void generation location and void amount (step S107), the deformation amount is calculated based on the strain amount distribution in the analysis model, and the generation of voids is predicted.

[0077] Specifically, the occurrence of sink marks is predicted from the strain amount distribution, and by analyzing the shape where sink marks occur, the void generation location and void amount are predicted. Or, without predicting the occurrence of sink marks, for example, the strain amount in each element of the analysis model is compared with the void generation threshold value obtained in advance by actual measurement for each resin material, and it is also possible to predict that voids will occur in the elements where the strain amount is equal to or greater than the threshold value.

[0078] In conventional analysis software, since the temperature dependencies of the elastic modulus (Young's modulus, Poisson's ratio, and shear modulus) and the coefficient of linear expansion are not considered, only the elastic constants at room temperature (single values) can be input. Therefore, there is insufficient consideration of the change in the elastic modulus in each part of the injection molded product when the temperature changes, and there is a certain limit to improving the prediction accuracy of void generation. In contrast, in the void generation prediction method according to the present disclosure, based on the temperature distribution and pressure distribution obtained by the flow analysis, in addition to the elastic modulus distribution (Young's modulus distribution, Poisson's ratio distribution, and shear modulus distribution) due to the temperature change of each element of the analysis model, the volume shrinkage rate distribution and the temperature load distribution are calculated. And since the amount of strain in each part of the injection molded product can be calculated using these distributions, the prediction accuracy of void generation can be improved.

[0079] Furthermore, in the void generation prediction method according to the present disclosure, by calculating the temperature distribution and pressure distribution of each element of the analysis model during the cooling process outside the mold (step S104), the prediction of void generation considering the cooling outside the mold is performed, so the prediction accuracy is further improved.

[0080] Next, an example according to the void generation prediction method (hereinafter also simply referred to as the "improved method") of the present embodiment and a comparative example according to the conventional method are shown, and the prediction of voids according to the present disclosure will be specifically described. Note that the technology of the present disclosure is not limited to these examples.

[0081] (Product shape and molding material) FIG. 6 is a diagram showing 3D CAD data according to the example. This 3D CAD data is the shape data of a bolt-shaped injection molded product having a thick wall and ribs. As shown in FIG. 6, the width of the top surface of the hexagonal flange portion is 20 mm and the thickness is 6 mm. Also, the height of the cylinder is 15 mm. There is a gate on one side of the hexagonal flange portion.

[0082] The molding material (thermoplastic resin) of the actual molded product is DuPont (registered trademark) POM M90-44 (unfilled) made of polyplastics. The specific heat of the molding material was measured by a differential scanning calorimeter (DSC), and the thermal conductivity was determined by a thermal conductivity measurement method called the alternating current steady-state method (ISO22007-6).

[0083] For the flow analysis in the examples and comparative examples, Moldflow (registered trademark) Insight2019.0.5 (3D solid model) build 20180921.0959_C70L71 (hereinafter referred to as "Moldflow") manufactured by Autodesk was used. Also, for the external mold cooling analysis and structural analysis in the examples, ADVENTURECluster2021 (hereinafter referred to as "ADVC") manufactured by Allied Engineering Co., Ltd. was used. Also, for calculating the elastic modulus distribution and volume shrinkage rate distribution (step S105) in the examples, a self-made conversion program using Visual Basic (registered trademark) was used.

[0084] (Example) The molding conditions of the actual molded product were set as barrel temperature 200°C, cooling water temperature 40°C, injection speed 10 mm / s, holding pressure 60 MPa, holding 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 Fig. 7.

[0085] For measuring the Young's modulus of the thermoplastic resin in the solid state, RSAIII manufactured by RHEOMETRIC SCIENTIFIC was used. Specifically, a three-point bending test jig was used for a test piece with a width of 12.86 mm and a thickness of 1.57 mm, and the Young's modulus in the range from -40°C to 140°C was measured at a frequency of 1.0 Hz to obtain the temperature dependence data of the Young's modulus. The measurement results are shown in Fig. 8.

[0086] To measure the shear modulus of the thermoplastic resin in the molten state, a rotational rheometer DISCOVERY HR-3 manufactured by TI Instruments was used. Specifically, the thermoplastic resin was placed between parallel plates, and the temperature dependence data of the shear modulus was obtained by measuring the shear modulus at an angular frequency of 600 rad / s.

[0087] To measure the viscosity of the thermoplastic resin in the molten state, a capillary rheograph 1D manufactured by Toyo Seiki was used. Specifically, the viscosities at various temperatures were determined using an orifice with an orifice length of 20 mm and an orifice diameter of 1 mm, and were fitted to the CROSS-WLF equations shown in the following equations (9) to (11). Then, the temperature dependence data of the viscosity was obtained by calculating the fitted coefficients and the viscosity at a shear rate of 400 / s. TIFF2025112831000006.tif47170η: 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

[0088] The correlation between the Young's modulus obtained by multiplying the shear modulus in the molten state by 3 and the viscosity is shown in Fig. 9. From Fig. 9, it can be seen that the Young's modulus and the viscosity in the molten state are linearly related. From this linear relationship, the parameters ω1 and ω2 in the above equation (6) were determined by the least squares approximation. ω1 is 1.05×10 -3 , ω2 is -8.81×10 -2 and was determined.

[0089] From the temperature dependence data of the Young's modulus in the solid state shown in Fig. 8 and the temperature dependence data of the Young's modulus in the molten state obtained using the above equation (6), the Young's modulus between the solid state and the molten state was determined by linear interpolation. Fig. 10 shows the temperature dependence data of the Young's modulus at the temperatures from the solid state to the molten state of the thermoplastic resin.

[0090] The Poisson's ratio was calculated from the temperature dependence data of the Young's modulus and the PVT data. Specifically, the temperature dependence data of the Poisson's ratio was obtained by calculating the Poisson's ratio according to the above formula (7) from the temperature dependence data of the Young's modulus shown in FIG. 10 and the bulk modulus obtained from the PVT data. The calculation results are shown in FIG. 11.

[0091] Flow stop temperature T S Regarding this, for the measured value of 8 seconds, in the example, the injection, pressure holding, and cooling analysis in the mold were performed, and when calculated according to formula (1), it was calculated to be 7.9 seconds.

[0092] In the example, from the temperature distribution and pressure distribution by the analysis of injection, pressure holding, and cooling in the mold, and the temperature distribution by the external mold cooling analysis, the elastic modulus distribution (Young's modulus distribution, Poisson's ratio distribution, and shear modulus distribution), volume shrinkage rate distribution, and temperature load distribution of the injection molded product (inside and surface) were obtained by a conversion program. Then, a structural analysis was performed using the elastic modulus distribution, volume shrinkage rate distribution, and temperature load distribution, and the strain of the analysis model was calculated to predict the location of void generation and the amount of voids.

[0093] (Comparative Example) In the comparative example using the conventional method, for the same analysis model as in the example, the Young's modulus and Poisson's ratio in the temperature range in the solid state shown in FIG. 8 were uniformly used, but other physical property values and analysis conditions were the same as in the example, and the location of void generation and the amount of voids were predicted from the volume shrinkage rate distribution calculated by the flow analysis. As described above, in the conventional method, since the elastic modulus distribution (Young's modulus distribution, Poisson's ratio distribution, and shear modulus distribution) in the molten state is not considered, as described below, the analysis accuracy in the comparative example was inferior to that in the example.

[0094] (Comparison of Void Prediction) FIG. 12 shows the measured value of voids by X-ray CT of the actual molded product, the prediction result of voids by the example, and the prediction result of voids by the comparative example, respectively.

[0095] In the comparative example according to the conventional method, voids are generated in part of the internal analytical model, resulting in a large deviation from the measured void values in the actual molded product. In contrast, in the example according to the improved method, the predicted void generation locations are closer to the void generation locations in the actual molded product, indicating that the actual void generation is accurately predicted.

[0096] (Method for reducing voids) The present disclosure also includes a method for reducing voids generated inside an injection molded product formed by injecting a thermoplastic resin into a mold. That is, the void amount is predicted by the above-described void generation prediction method, and when the predicted void amount is equal to or greater than a predetermined threshold value, one or more of the design, molding conditions, and molding material are changed and the void amount is predicted again until the predicted void amount is reduced to less than the predetermined threshold value.

[0097] The present disclosure is not limited to the above-described embodiment, and includes various modified examples in which components are added, deleted, or converted with respect to the above-described configuration. Also, the above-described embodiments can be combined in various ways. In particular, the present disclosure should not be construed as being limited to the above-described embodiment or example with respect to shape, material, or conditions.

[0098] The void generation prediction method according to the present disclosure can be executed by an information processing apparatus. FIG. 13 is a block diagram showing a hardware configuration example of an information processing apparatus 100 that executes the void generation prediction method. As shown in FIG. 13, the information processing apparatus 100 includes a processor 101, a main storage device 102, an auxiliary storage device 103, an I / O (Input / Output) interface 104, and a network interface (hereinafter abbreviated as "NW interface") 105.

[0099] The processor 101 has, for example, a CPU (Central Processing Unit), an FPGA (Field Programmable Gate Array), or a DSP (Digital Signal Processor), etc., and comprehensively controls the entire information processing apparatus 100 while executing various arithmetic processes.

[0100] The main memory device 102 has, for example, a RAM (Random Access Memory) or a ROM (Read Only Memory), etc., and stores information used for the arithmetic processes executed by the processor 101.

[0101] The auxiliary storage device 103 has, for example, an HDD (Hard Disk Drive) or an SSD (Solid State Drive), etc., and stores various programs and data.

[0102] The I / O interface 104 is an interface through which a user inputs information or outputs information to the user. The I / O interface 104 may include, for example, a keyboard, a display, a touch panel, a microphone, or a speaker, etc.

[0103] The NW interface 105 is an interface for connecting to a network by wire or wirelessly.

[0104] The information processing apparatus 100 acquires physical property values for analysis via the I / O interface 104 and the NW interface 105. Then, the processor 101 executes a program stored in the auxiliary storage device 103 while using the main memory device 102 to create an analysis model and execute a fluid analysis and a structural analysis. Further, the processor 101 calculates an elastic modulus distribution, a volume shrinkage rate distribution, and a temperature load distribution in the analysis model from the results of the fluid analysis and the structural analysis, and calculates the strain generated in each element of the analysis model. Then, the processor 101 predicts the void generation location and the void amount based on the calculated strain.

[0105] Furthermore, the processing executed by the above-described information processing apparatus 100 can also be described as a program executable by a computer. In this case, it is also possible to store this program in a computer-readable and non-transitory recording medium and introduce it into the computer. Examples of such recording media include portable recording media such as CD-ROMs, DVD disks, USB memories, and semiconductor memories such as flash memories.

Explanation of Signs

[0106] 101 Processor 102 Main Memory Device 103 Auxiliary Storage Device 104 I / O Interface 105 NW Interface

Claims

1. A void generation prediction method for predicting the void generation behavior in injection-molded products formed by injection molding of thermoplastic resin into a mold, Using equation (I), which shows the relationship between the viscosity and Young's modulus of a molten thermoplastic resin, the step of obtaining temperature dependence data of the Young's modulus in the molten state from the viscosity measurement results of the molten thermoplastic resin, The steps include creating an analytical model by dividing the injection-molded product into multiple elements, A step of obtaining the temperature distribution and pressure distribution of the analytical model in the molding process of thermoplastic resin, The steps include: calculating the temperature distribution of the analytical model during the cooling process after demolding using the temperature distribution and pressure distribution; A step of calculating the elastic modulus distribution and temperature load distribution of the analytical model based on the temperature dependence data of Young's modulus, the temperature distribution and pressure distribution in the molding process, and the temperature distribution in the cooling process. The steps include: calculating the strain generated in each element of the analysis model by performing a structural analysis using the elastic modulus distribution and temperature load distribution during the cooling process after demolding; A step of predicting at least one of the location and amount of voids from the aforementioned strain, A method for predicting void formation, including the following. E=ω 1 the+oh 2 ・・・(I) η: Viscosity E: Young's modulus ω 1 ω 2 : fixed number

2. The step of obtaining the temperature dependence data of the Young's modulus is: The temperature dependence of Young's modulus can be determined by linear interpolation between 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 method for predicting void formation according to claim 1.

3. The crystallization start temperature or crystallization end temperature is, In specific heat measurement, this is the inflection point when cooling thermoplastic resins at cooling rates ranging from 1°C / min to 50°C / min. The method for predicting void formation according to claim 2.

4. The step of obtaining the temperature dependence data of the Young's modulus is: The bulk modulus is determined from the temperature dependence data of Young's modulus and the PVT data, and then the temperature dependence data of the Poisson's ratio of the injection-molded product is further obtained from the bulk modulus using equation (II). A method for predicting void formation according to claim 1 or 2. [Math 1] ν: Poisson's ratio E: Young's modulus K: Bulk modulus

5. The steps for calculating the elastic modulus distribution and temperature load distribution are as follows: The elastic modulus distribution and temperature load distribution are calculated using the gate sealing time as the starting point in time and the time after a predetermined period of time has elapsed since the start of injection as the ending point in time. The method for predicting void formation according to claim 1.

6. The gate sealing time is In the flow analysis of the aforementioned analytical model, the molding conditions are set so as to minimize the time until gate sealing occurs, and the time at which the weight of the molded product is maximized is determined. The method for predicting void formation according to claim 5.

7. The gate sealing time is This is determined as the time until the temperature at the center of the gate reaches the flow stop temperature. The method for predicting void formation according to claim 5.

8. The aforementioned flow stop temperature is, For PVT data showing the relationship between resin pressure, volume, and temperature, the data fitting coefficient b of the 2-DOMAIN TAIT PVT model is applied. 5 and b 6 Using this, it can be obtained by equation (III). The method for predicting void formation according to claim 7. T S =b 5 +b 6 ×P ・・・(III) T S Flow stopping temperature P: Pressure

9. The step of calculating the aforementioned strain is: The time when the injection-molded product has been released after holding pressure cooling is taken as the starting point in time, and the time when a predetermined amount of time has elapsed since the start of injection is taken as the ending point in time, and the strain is calculated using the elastic modulus distribution and temperature load distribution, taking into account the temperature dependence. The method for predicting void formation according to claim 1.

10. The step of obtaining the temperature distribution and pressure distribution is: The thermal conductivity obtained using the AC steady-state method (ISO 22007-6) is used in the calculation. The method for predicting void formation according to claim 1.

11. A void reduction method for reducing voids that occur in injection-molded products formed by injection molding thermoplastic resin into a mold, The void amount is predicted using the void generation prediction method described in claim 1. If the predicted void amount is above a predetermined threshold, one or more of the design, molding conditions, and molding material are changed, and the void amount is predicted again. This process is repeated until the predicted void amount is reduced to below the predetermined threshold. Void reduction method.

12. A void generation prediction program that causes a computer to execute the void generation prediction method described in claim 1.

13. A void reduction program that causes a computer to execute the void reduction method described in claim 11.

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