Method for predicting molding defects, method for reducing molding defects, program for predicting molding defects, and program for reducing molding defects

Abstract

This molding failure prediction method comprises: a step (S1) for creating a flow analysis model in which an injection molding article is divided into a plurality of elements; a step (S2) for obtaining the temperature and pressure of each of the elements; a step (S3) for calculating a temperature distribution and a pressure distribution; a step (S4) for using linear expansion coefficient data, elastic modulus temperature dependent data, and PVT data and calculating an elastic modulus distribution, a volume contraction rate distribution, and a temperature load; a step (S5) for calculating a first strain generated in each element of a first structural analysis model through structural analysis; and a step (S6) for predicting a produced spot of a shrinkage and / or the amount of shrinkage by calculating a deformation amount of the first structural analysis model.

Description

[Technical Field]

[0001] The present disclosure relates to prediction of molding defects in resin molded products. [Background technology]

[0002] Injection molding is 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 thermoplastic resin is injected into the mold and 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 (shrinkage) in volume 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 become the starting point for breakage). Therefore, there is a need for improved technology to suppress sink marks and voids.

[0005] Countermeasures to prevent sink marks and voids include checking the actual molded product and changing the molding conditions, or changing the design of the molded product's gate or wall thickness, etc. However, because these require a huge amount of time and cost, in recent years, research has been conducted into whether it is possible to predict the occurrence of sink marks and voids through injection molding simulations using flow analysis software equipped with a sink mark analysis (sink mark occurrence prediction) function, and thereby optimize the product shape and molding conditions.

[0006] In Patent Document 1, the deformation amount of a resin molded product is predicted using physical property values ​​calculated using orientation parameters obtained by comparing flow direction analytical values ​​with actual measured values. In Patent Document 2, flow analysis and structural analysis are combined to predict deformation taking fiber orientation into consideration, but this is used to predict warpage and is not used to predict sink marks or voids. Furthermore, although the combination of flow analysis and structural analysis improves the prediction accuracy of deformation, it requires many experiments and measurements to obtain physical property values, resulting in enormous calculation costs. Patent Document 3 describes that, regarding shrinkage defects that occur when a molten material solidifies in a mold, it is possible to predict the location and size of shrinkage defects that will occur by appropriately setting the heat transfer coefficient depending on the part of the mold and the time. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Laid-Open No. 2004-25796 [Patent Document 2] Japanese Patent Application Laid-Open No. 2016-203584 [Patent Document 3] Japanese Patent Application Laid-Open No. 2004-38444 Summary of the Invention [Problem to be solved by the invention]

[0008] An object of the present invention is to provide a method for accurately predicting the occurrence of sink marks or voids in resin molded products. By solving this problem, it becomes possible to predict in advance at the design stage the product shape design, mold design, molding condition setting, and molding material required to obtain molded products free of sink marks or voids, thereby enabling efficient commercialization.

[0009] During molding, the thermoplastic resin injected in a molten state is cooled by the mold, which absorbs heat. As a result, the surface of the molded product that comes into contact with the mold solidifies first (see Figure 1A). Furthermore, the molten thermoplastic resin inside solidifies while shrinking due to linear shrinkage or crystallization. Whether this results in a sink mark or a void is influenced by the shrinkage force of the molten resin inside (how strong the force pulling it inward) relative to the solidification state (rigidity) of the molded product surface. If the shrinkage force of the molten resin toward the inside is strong relative to the rigidity of the molded product surface, the surface side of the molded product will sink, resulting in a sink mark (see Figure 1B). Conversely, if the shrinkage force of the molten resin toward the inside is weak, the resin inside the molded product will be pulled toward the surface, creating a cavity inside and resulting in a void (see Figure 1C).

[0010] Some conventional flow analysis software has a sink mark analysis function (sink mark occurrence prediction function). However, this software does not take into account the distribution of elastic modulus of the molten thermoplastic resin in the molded product, and performs sink mark analysis using only the elastic modulus at room temperature. Furthermore, prior art documents do not take into account the elastic modulus distribution and volumetric shrinkage distribution necessary for accurate prediction of sink marks and voids. Therefore, there may be a large discrepancy between the results of sink marks in actual products. Furthermore, conventional flow analysis software and prior art documents have not provided satisfactory results in void analysis (void occurrence prediction) when compared with actual products.

[0011] The present disclosure has been devised to solve at least one of the problems of the prior art, and aims to accurately predict the occurrence behavior of sink marks or voids. Another aim is to reduce sink marks and voids in resin molded products. Another aim is to provide a program for causing a computer to execute a method for predicting or reducing sink marks and voids in resin molded products. [Means for solving the problem]

[0012] In light of the above-described circumstances, the inventors have conducted extensive research and discovered that by performing coupled analysis in which the elastic modulus distribution and volumetric shrinkage distribution are obtained from temperature data and pressure data obtained by flow analysis and then applied to structural analysis (strain analysis), it is possible to predict the locations and amounts of sink marks and voids at a level comparable to the results obtained in actual products, and have thus completed the present disclosure.

[0013] More specifically, the present disclosure includes the following aspects.

[0014] [1] A molding defect prediction method for predicting the occurrence behavior of molding defects in an injection-molded product obtained by injection-molding a thermoplastic resin into a mold, the molding defect prediction method comprising the steps of: creating a flow analysis model by dividing the injection-molded product into a plurality of elements (S1); determining the temperature and pressure of each element in a process of molding the thermoplastic resin (S2); calculating the temperature distribution and pressure distribution in the injection-molded product from the temperatures and pressures of the plurality of elements (S3); calculating the elastic modulus distribution, volumetric shrinkage distribution, and temperature load of the flow analysis model from the temperature distribution and the pressure distribution using previously measured linear expansion coefficient data, elastic modulus temperature dependency data, and PVT data of the thermoplastic resin (S4); calculating a first strain generated in each element of a first structural analysis model by structural analysis using the temperature load, the pressure distribution, the elastic modulus distribution, and the volumetric shrinkage distribution (S5); and predicting the location of sink marks and / or the amount of sink marks by calculating the deformation amount of the first structural analysis model from the first strain (S6).

[0015] [2] The molding defect prediction method according to [1], further comprising the steps of: creating a second structural analysis model (S7) that reflects the location of sink marks and the amount of sink marks and is divided into a plurality of elements; calculating a second strain that occurs in each element of the second structural analysis model by structural analysis using temperature load, pressure distribution, elastic modulus distribution, and volumetric shrinkage distribution; and predicting the location of voids and / or the amount of voids from the second strain (S9).

[0016] [3] The molding defect prediction method according to [1] or [2], wherein the step (S2) of determining the temperature and pressure of each element is a step (S2) of determining the temperature and pressure of each element in the process in which the thermoplastic resin is injected from the gate of the mold into the cavity and the injection-molded product reaches the mold temperature.

[0017] [4] The molding defect prediction method according to any one of [1] to [3], wherein in the step (S4) of calculating the elastic modulus distribution and the volumetric shrinkage distribution of the flow analysis model, the volumetric shrinkage distribution is calculated starting from the gate seal and ending when the injection-molded product reaches the mold temperature.

[0018] [5] The method for predicting molding defects described in [4], wherein the gate seal is determined by setting molding conditions in a flow analysis of the flow analysis model so that the weight of the injection-molded product is maximized and the time to the gate seal is minimized.

[0019] [6] In the step (S8) of calculating the second strain, the elastic modulus distribution, volumetric shrinkage distribution, and temperature load are calculated starting from when the injection-molded product is cooled under pressure and the surface layer is solidified, and ending from when the injection-molded product reaches the mold temperature. [2] The molding defect prediction method according to [2].

[0020] [7] The method for predicting molding defects according to [4], wherein the gate seal is at a point where the temperature at the center of the gate reaches a flow stop temperature.

[0021] [8] The molding defect prediction method according to [7], wherein the flow stop temperature is the inflection point when cooling at a cooling rate in the range of 1°C / min to 50°C / min in specific heat measurement.

[0022] [9] A molding defect prediction method according to any one of [1] to [8], wherein in step (S2) of calculating the temperature and pressure of each element, the thermal conductivity calculated using a thermal conductivity measurement method called the AC steady-state method (ISO22007-6) is used for the calculation.

[0023]

[10] The molding defect prediction method according to [2], wherein in the step (S8) of calculating the second strain, a second temperature distribution and a second pressure distribution in the injection-molded product are calculated from the temperatures and pressures for a plurality of elements of the second structural analysis model; a second elastic modulus distribution, a second volumetric shrinkage rate distribution, and a second temperature load of the second structural analysis model are calculated from the second temperature distribution and the second pressure distribution using previously measured linear expansion coefficient data, elastic modulus temperature dependency data, and PVT data of the thermoplastic resin; and a second strain generated in each element of the second structural analysis model by structural analysis using the second temperature load, the second pressure distribution, the second elastic modulus distribution, and the second volumetric shrinkage rate distribution of the second structural analysis model.

[0024]

[11] A method for reducing molding defects, comprising: repeating the step of predicting the amount of sink marks by the molding defect prediction method described in [1] while changing one or more of the design, molding conditions, and molding materials, until the predicted amount of sink marks is reduced to a predetermined amount or less.

[0025]

[12] A method for reducing molding defects, comprising the steps of: predicting the amount of voids by the molding defect prediction method described in [2] while changing one or more of the design, molding conditions, and molding material, until the predicted amount of voids is reduced to a predetermined amount or less.

[0026]

[13] A computer-readable storage medium storing a program for causing a computer to execute the molding defect prediction method according to any one of [1] to

[10] .

[0027]

[14] A computer-readable storage medium storing a program for causing a computer to execute the molding defect reduction method according to

[11] or

[12] . [Brief explanation of the drawings]

[0028] [Figure 1]FIG. 1 is a diagram for explaining the occurrence of sink marks and voids. [Figure 2] FIG. 2 is a flowchart illustrating an example of a method for predicting a location of a sink mark and / or an amount of a sink mark according to an embodiment. [Figure 3] FIG. 3 is a flowchart illustrating an example of a method for predicting the location of void occurrence and / or the amount of voids according to the embodiment. [Figure 4] FIG. 4 shows an example of the temperature distribution obtained from the flow analysis. [Figure 5] FIG. 5 is a diagram showing an example of data on the temperature dependency of elastic modulus of a thermoplastic resin. [Figure 6] FIG. 6 is a diagram showing an example of PVT data of a thermoplastic resin. [Figure 7] FIG. 7 is a diagram showing an example of the shape of a molded product. [Figure 8] FIG. 8 is a cross-sectional view of a flow analysis model used in an example of the method according to this embodiment. [Figure 9] FIG. 9 is a diagram showing examples of temperature distribution, pressure distribution, elastic modulus distribution, and volumetric shrinkage distribution in an example according to the method of this embodiment. [Figure 10] FIG. 10 is a diagram showing sink marks in an actual molded product corresponding to FIG. [Figure 11] FIG. 11 is a diagram showing a comparison of sink marks in an actual molded product corresponding to FIG. 7, sink marks predicted by the present method, and sink marks predicted by the conventional method. [Figure 12] FIG. 12 is a diagram showing a comparison of voids in an actual molded product corresponding to FIG. 7, voids predicted by the present method, and voids predicted by a conventional method. [Figure 13] FIG. 13 is a diagram showing the relationship between the amount of sink marks predicted by the present method and the time point at which the elastic modulus is calculated, which corresponds to FIG. DETAILED DESCRIPTION OF THE INVENTION

[0029] Hereinafter, embodiments of the present disclosure will be described, but the present disclosure is not limited to the following embodiments.

[0030] (Method for predicting the occurrence of sink marks) An example of the method for predicting the behavior of sink marks according to this embodiment will be described in detail with reference to Fig. 2. This method involves performing a coupled analysis in which the results of flow analysis are applied to structural analysis. As shown in the flowchart of FIG. 2 , an example of the method for predicting the behavior of sink marks according to this embodiment includes the steps of creating a flow analysis model (S1), calculating the temperature and pressure of each element of the flow analysis model (S2), calculating the temperature distribution and pressure distribution (S3), calculating the elastic modulus distribution, the volumetric shrinkage distribution, and the temperature load (S4), calculating a first strain generated in each element of the first structural analysis model (S5), and predicting the location of sink marks and / or the amount of sink marks (S6).

[0031] (Creating a model for flow analysis (S1)) In step (S1) of creating a flow analysis model in which the injection-molded part is divided into multiple elements, the shape of the injection-molded part is divided into tiny elements to create the model necessary for running the simulation (see Figure 8 for an example). For example, the shape of the injection-molded part (which can be the design shape of the injection-molded part or the shape of the mold, etc., including conditions such as the position, number, and size of runners and gates) is input into a computer using 3D shape measurement or a CAD system. Next, the shape input into the computer is divided into multiple 3D elements using an element division preprocessor or the like to create the flow analysis model.

[0032] (Calculation of temperature and pressure for each element of the flow analysis model (S2)) In step (S2) of calculating the temperature and pressure of each element of the flow analysis model, a flow analysis (simulation) of injection molding is performed using the created flow analysis model. The flow analysis calculates the temperature and pressure of each element of the flow analysis model during the process of injecting and molding a thermoplastic resin (which may also include a cooling process). 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 a flow analysis include data that represents the relationship between pressure, volume, and temperature (hereinafter referred to as "PVT data." See Figure 6 for an example), thermal conductivity data, and specific heat data. The specific heat of a thermoplastic resin can be measured using a differential scanning calorimeter (DSC). The thermal conductivity of a thermoplastic resin can be determined using the AC steady-state method (ISO 22007-6).

[0033] Next, enter the analysis conditions for thermoplastic resin flow analysis. Molding conditions include resin (cylinder) temperature, mold temperature, injection speed, dwell pressure, and dwell time. The molding conditions also include the specification of the start and end points of the flow analysis, which can be, for example, the point at which the thermoplastic resin begins to be injected into the mold cavity. The end point of a flow analysis can be the point in time when the temperature of the injection-molded product reaches the mold temperature during the process of injecting molten thermoplastic resin into a mold cavity and cooling it under pressure. Here, "reaching mold temperature" means that the entire molded product reaches the mold temperature, or the maximum temperature in the temperature distribution inside the molded product reaches the mold temperature.

[0034] (Calculation of temperature and pressure distribution (S3)) In the step (S3) of calculating the temperature distribution and pressure distribution, the temperature distribution and pressure distribution of the injection-molded product in the mold during the process of injecting and molding the thermoplastic resin (which may also include a cooling process) can be calculated from the calculated temperature and pressure of each element. Figure 4 shows an example of the temperature distribution obtained by flow analysis.

[0035] (Calculation of elastic modulus distribution, volumetric shrinkage distribution and temperature load (S4)) In step (S4) of calculating the elastic modulus distribution, volumetric shrinkage distribution, and temperature load, the calculated temperature distribution and pressure distribution are used to determine the elastic modulus distribution, volumetric shrinkage distribution, and temperature load (temperature difference distribution) of the injection-molded product. In particular, the elastic modulus distribution, volumetric shrinkage distribution, and temperature load of the injection-molded product can be calculated from the temperature distribution and pressure distribution using a conversion program for a computer.

[0036] (Elastic modulus distribution) The elastic modulus distribution of an injection-molded product can be determined by applying previously obtained data on the temperature dependency of the elastic modulus of the molding material (thermoplastic resin) to the temperature distribution obtained by flow analysis. Figure 5 shows data on the temperature dependence of the elastic modulus of a certain thermoplastic resin. The horizontal axis represents temperature, and the vertical axis represents the elastic modulus. The temperature T on the horizontal axis represents the transition temperature of the thermoplastic resin. Here, the transition temperature is the temperature that separates the solidified and molten regions of the thermoplastic resin, and is sometimes called the no-flow temperature, solidification temperature, solidification temperature, or solid-liquid transition temperature. As shown in Figure 5, if data on the temperature dependency of the elastic modulus of a thermoplastic resin is available, the elastic modulus distribution can be obtained from the temperature distribution.

[0037] (Volumetric shrinkage distribution) The volumetric shrinkage distribution of an injection-molded product can be determined by applying previously obtained (actually measured) data ("PVT data") that shows the relationship between the pressure, volume, and temperature of the molding material (thermoplastic resin) to the temperature and pressure distribution data obtained by flow analysis. Figure 6 shows the PVT data for a thermoplastic resin. The horizontal axis is temperature (unit: °C), and the vertical axis is the inverse of density, i.e., specific volume (unit: cm 3 / g). Figure 6 shows the relationship between temperature and specific volume when the pressure (P) is 50 MPa. Assume that the relationship between temperature and specific volume has been measured in advance for several pressures other than 50 MPa. The relationship between temperature and specific volume at pressures for which there are no actual measurements can be obtained, for example by interpolation, from the relationship between temperature and specific volume at pressures for which there are actual measurements. In Figure 6, at the time when the thermoplastic resin at the gate solidifies and stops flowing (called the "gate seal time"), the temperature of the thermoplastic resin is 200°C and the specific volume is approximately 0.82 cm. 3 Similarly, when the temperature of an injection-molded thermoplastic resin reaches the mold temperature, the specific volume is approximately 0.70 cm3 when the temperature of the thermoplastic resin is 40°C. 3 / g.

[0038] The volumetric shrinkage distribution of an injection molded product is calculated as follows. First, the temperature (T1) and pressure (P1) at a certain point before shrinkage are determined from the temperature and pressure distribution data obtained by flow analysis. These are then applied to the PVT data of the thermoplastic resin obtained in advance to convert them into the volume (V1) at the point before shrinkage. In the example of gate seal time in Figure 6, T1 = 200°C, P1 = 50 MPa, and the specific volume is approximately 0.82 cm 3 / g. In other words, the volume per unit weight (1g) at this point is V1 = approximately 0.82 cm 3 is. Similarly, the temperature (T2) and pressure (P2) at a certain point after contraction are determined from the temperature and pressure distribution data obtained by flow analysis. These are then applied to the PVT data to convert them into the volume (V2) at the time of contraction. In the example shown in Figure 6, when the mold temperature is cooled to T2 = 40°C, P2 = 50 MPa, the specific volume is approximately 0.70 cm 3 / g. In other words, the volume per unit weight (1g) at this point is V2 = approximately 0.70 cm 3 is. Then, from the volumes (V1, V2) of each element before and after shrinkage, the distribution of the volumetric shrinkage rate of each element ((volume before shrinkage - volume after shrinkage) ÷ (volume before shrinkage), i.e., (V1 - V2) / V1) is calculated. In other words, when the pressure is constant at 50 MPa (P1 = P2 = 50 MPa) and cooling is performed from T1 = 200°C to T2 = 40°C, the volumetric shrinkage rate can be calculated as (V1 - V2) / V1 = (0.82 - 0.70) / 0.82 = 0.146, or 14.6%. Even if the pressure is not constant before and after shrinkage, the temperature (T1) and pressure (P1) of each element before shrinkage, and the temperature (T2) and pressure (P2) of each element after shrinkage are calculated by flow analysis. If the relationship between the temperature (T1, T2) and specific volume at the pressures (P1, P2) before and after shrinkage (PVT data) is obtained in advance, the volumetric shrinkage rate (V1-V2) / V1 of each element can be calculated. In this way, the volumetric shrinkage distribution can be calculated from the PVT data, temperature distribution, and pressure distribution.

[0039] (temperature load) The temperature load (temperature difference distribution) is found by converting the volumetric shrinkage rate of each element into a temperature difference by dividing it by the previously obtained volumetric expansion rate of the thermoplastic resin. The volumetric expansion rate is the coefficient β in the relational equation ΔV / V=βΔT, when the original volume V changes by ΔV due to a temperature rise ΔT. Note that the volumetric expansion rate is three times the linear expansion rate, so it can be obtained from the previously measured linear expansion rate instead of the volumetric expansion rate of the thermoplastic resin. Note that 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 shrinkage distribution. The latter temperature difference, calculated from the temperature data obtained in the flow analysis, is simply a calculation of "temperature only." In contrast, the former temperature load is calculated using "temperature and pressure" rather than "temperature only," since it is a value obtained by first calculating the volume from "temperature and pressure" using PVT data and then converting it back to temperature using the volumetric expansion coefficient. Therefore, the temperature load is a "temperature difference" that takes into account the effects of the pressure experienced by the resin during actual molding, unlike the difference between only the temperature at a certain point before shrinkage and the temperature at a certain point after shrinkage. Using a temperature load will yield more accurate analysis results.

[0040] (Creating a model for structural analysis) An injection-molded product is divided into multiple elements to create a model (called the "first structural analysis model" to distinguish it from the structural analysis model for predicting the behavior of voids, which will be described later) required to perform structural analysis (simulation) to predict the behavior of sink marks. However, the flow analysis model created in the step (S1) of creating the flow analysis model can also be used as the structural analysis model as is, and therefore the step of creating the first structural analysis model may generally be omitted.

[0041] (Calculation of the first strain occurring in each element of the first structural analysis model (S5)) In step (S5) of calculating the strain occurring in each element of the first structural analysis model (referred to as "first strain" to distinguish it from the strain occurring in each element of the second structural analysis model for predicting the void generation behavior described below), a structural analysis is performed using temperature load, pressure distribution, elastic modulus distribution, and volumetric shrinkage distribution to determine the distribution of strain amounts.

[0042] (Prediction of the location and / or amount of sink marks (S6)) In the step (S6) of predicting the location and / or amount of sink marks, the amount of deformation is calculated based on the distribution of the amount of strain in the first structural analysis model, and the occurrence of sink marks is predicted. Conventional analysis software such as Moldflow only allows input of the elastic modulus at room temperature (one value), which does not adequately consider changes in the elastic modulus of each part when the temperature changes, making it impossible to accurately predict the occurrence of sink marks.In contrast, the improved method disclosed herein can calculate the amount of strain in each part of the molded product using the volumetric shrinkage distribution and temperature load in addition to the elastic modulus distribution due to temperature changes in each element based on the temperature and pressure distributions obtained from flow analysis, thereby improving prediction accuracy.

[0043] (Method for predicting void generation behavior) An example of the method for predicting the behavior of void generation in this embodiment will be described in detail with reference to FIG. 3 in addition to FIG. In one example of the method for predicting the behavior of void occurrence in this embodiment, the location of sink marks and the amount of sink marks are first predicted by the method (steps (S1) to (S6)) shown in the flowchart of FIG. Furthermore, as shown in the flowchart of FIG. 3, the process includes the steps of creating a second structural analysis model of the molded product shape after the occurrence of sink marks (S7), calculating a second strain occurring in each element of the second structural analysis model (S8), and predicting the location of void occurrence and / or the void volume (S9).

[0044] (Creating a second structural analysis model of the molded product shape after the sink mark has occurred (S7)) In step (S7) of creating a second structural analysis model of the molded product shape after the occurrence of sink marks based on the prediction of sink mark occurrence, first, the shape of the injection-molded product that reflects the sink mark occurrence location and sink mark amount predicted from the first structural analysis model is input into a computer. Furthermore, the imported shape is divided into multiple 3D elements using an element division preprocessor or the like to create a second structural analysis model, which is used to predict the generation behavior of voids.

[0045] (Calculation of temperature and pressure for each element) The temperature and pressure of each element of the created second structural analysis model are calculated. Voids occur after the surface has solidified. Here, the time when the surface of the resin molded product solidifies (for example, "a certain point before shrinkage" is defined as "injection + dwelling pressure + cooling time" = 36.6 seconds) is used as the starting point. In addition, the timing when voids are completely generated is calculated by calculating the temperature and pressure of each element, with the end point being the time when the molded product reaches the mold temperature (for example, 40°C) (corresponding to "a certain point after shrinkage"). The temperature and pressure of each element of the second structural analysis model may be calculated, for example, by a new flow analysis, or may be derived from the temperature and pressure of each element of the flow analysis model.

[0046] (Calculation of temperature and pressure distribution) The temperature distribution and pressure distribution are calculated from the temperature and pressure of each element of the second structural analysis model. For example, the temperature distribution and pressure distribution of the injection molded product in the mold can be calculated from the calculated temperature and pressure of each element of the second structural analysis model. The temperature and pressure of each element of the second structural analysis model can be considered to be values ​​at the center of each element, which makes it possible to calculate the temperature and pressure distributions of the second structural analysis model (see Calculation of Temperature and Pressure Distributions (S3)).

[0047] (Calculation of elastic modulus distribution and volumetric shrinkage distribution) Using the temperature dependency data of the elastic modulus of the thermoplastic resin (see FIG. 5 for an example) and the PVT data (see FIG. 6 for an example), the elastic modulus distribution, volumetric shrinkage rate distribution, and temperature load (distribution of temperature difference) of the second structural analysis model are calculated from the temperature distribution and pressure distribution (see Calculation of Elastic Modulus Distribution and Volumetric Shrinkage Rate Distribution (S4)). In particular, by using the conversion program used in calculating the elastic modulus distribution, volumetric shrinkage distribution, and temperature load in sink mark prediction (S4), it is possible to calculate the elastic modulus distribution, volumetric shrinkage distribution, and temperature load of the second structural analysis model.

[0048] (Calculation of second strain occurring in each element of the second structural analysis model (S8)) In step (S8) of calculating the strain (referred to as "second strain") occurring in each element of the second structural analysis model, a structural analysis is performed using temperature load, pressure distribution, elastic modulus distribution, and volumetric shrinkage distribution to determine the distribution of strain.

[0049] That is, in the step (S8) of calculating the second strain, calculating a temperature distribution and a pressure distribution in the injection-molded product (referred to as a second temperature distribution and a second pressure distribution, respectively) from the temperatures and pressures of the plurality of elements of the second structural analysis model; calculating an elastic modulus distribution, a volumetric shrinkage distribution, and a temperature load (referred to as a second elastic modulus distribution, a second volumetric shrinkage distribution, and a second temperature load, respectively) of a second structural analysis model from the second temperature distribution and the second pressure distribution using previously measured linear expansion coefficient data, elastic modulus temperature dependency data, and PVT data of the thermoplastic resin; A second strain occurring in each element of the second structural analysis model can be calculated by structural analysis using the second temperature load, second pressure distribution, second elastic modulus distribution, and second volumetric shrinkage rate distribution of the second structural analysis model.

[0050] (Prediction of void occurrence location and / or void volume (S9)) In the step (S9) of predicting the location of void occurrence and / or the amount of voids, the amount of deformation is calculated based on the distribution of the amount of strain in the second structural analysis model, and the occurrence of voids is predicted.

[0051] Next, examples of the 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 prediction of sink marks and voids according to the embodiment of the present disclosure. Note that the present disclosure is not limited to these examples.

[0052] (Product shape and molding material) Figure 7 shows CAD data (Figure 7A) of a flanged cylinder (a molded product shape with a bolt-like, thick wall and ribs) according to the example, and an X-ray CT scan of the actual molded product (Figure 7B). The molding material (thermoplastic resin) used for the actual molded product was Polyplastics' Duracon (registered trademark) POM M90-44 (unfilled). 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). As shown in Figure 7A, the width of the hexagonal flange of the flanged cylinder is 20 mm and the thickness is 6 mm. The height of the cylinder is 15 mm. There is a gate on one side of the hexagonal flange. FIG. 7B shows sink marks and voids confirmed by X-ray CT.

[0053] For the flow analysis in the examples and comparative examples, Autodesk's Moldflow (registered trademark) Insight 2019.0.5 (3D solid model) build 20180921.0959_C70L71 was used. Adventure Cluster 2021 manufactured by Allied Engineering Co., Ltd. was used for the structural analysis in the examples. In addition, a conversion program created by ourselves using Visual Basic (registered trademark) was used to calculate the elastic modulus distribution and the volume shrinkage distribution (S4) in the examples. A contour measuring machine (Mitutoyo surface roughness measuring machine SURFTEST (registered trademark) EXTREME SV-3000CNC) was used to measure the amount of sink marks on the actual molded product.

[0054] (Example) The molding conditions for the actual molded product and the analysis conditions using this method are shown in Table 1. In the examples, the physical property values ​​of the thermoplastic resin are set to the same as the physical property values ​​of the thermoplastic resin in the actual molded product. FIG. 8 shows a cross-sectional view of the flow analysis model used in the example. [Table 1]

[0055] In the example, a conversion program was used to obtain the elastic modulus distribution, volumetric shrinkage distribution, and temperature load of the injection-molded product (inside and surface) from the temperature and pressure distributions obtained by flow analysis (S4 in Fig. 2), and structural analysis was performed to calculate strain and predict the location and amount of sink marks (S5 and S6 in Fig. 2). Figure 9 shows examples of temperature distribution, pressure distribution, elastic modulus distribution, and volumetric shrinkage distribution in the example. Figure 9 shows (1) the temperature distribution at the time of gate sealing, (2) the pressure distribution at the time of gate sealing, (3) the elastic modulus distribution 40 seconds after the start of injection, and (4) the volumetric shrinkage distribution 40 seconds after the start of injection.

[0056] (Comparative Example) In the comparative example relating to the conventional method, sink marks and voids were predicted from the volumetric shrinkage distribution value calculated by flow analysis using the same flow analysis model as in the example, the same thermoplastic resin property values ​​(excluding temperature dependence of elastic modulus, etc.) and the same analysis conditions as in the example. That is, in the comparative example, the coupled analysis was not performed, and sink marks and voids were predicted from the volumetric shrinkage distribution obtained from the flow analysis.

[0057] (Comparison of sink mark predictions) The locations of sink marks and the amount of sink marks in actual molded products are shown in Figures 10A and 10B, respectively. The sink marks for the actual molded product, the examples, and the comparative examples are shown in Table 2. The sink mark amount (amount of depression from the top surface) for the actual molded product was 0.59 mm. The sink mark amount for the comparative example was 0.0065 mm, which means that the difference between the results for the actual molded product and the comparative example is extremely large, at about 100 times. On the other hand, the sink mark amount in the example using the improved method of the present disclosure was 0.36 mm, which is an extremely good reproduction of the sink mark amount in the actual molded product in practical terms. The prediction accuracy in the example was significantly improved (by double digits) compared to Comparative Example 1 using the conventional method. [Table 2]

[0058] A comparison of the locations and amounts of sink marks in an actual molded product, an example using the improved method according to the present disclosure, and Comparative Example 1 using the conventional method is shown in Figure 11. In Comparative Example 1 using the conventional method, the predicted amount of sink marks is extremely small, and the prediction accuracy of sink marks is low. Furthermore, in the conventional method, we attempted to predict sink marks based on the ratio (sink mark index) between the deviation of the molten resin mass from the ideal mass and the ideal mass of the total wall thickness, but as shown in Figure 11 (Comparative Example 2), we were unable to predict the occurrence of sink marks. That is, while the conventional method cannot predict the location where sink marks will occur, the improved method of the present disclosure makes it possible to determine the location where sink marks will occur.

[0059] (Comparison of void predictions) For the example of the improved method, a second structural analysis model of the molded product shape after the occurrence of sink marks was created (S7 in Fig. 3), the second strain occurring in each element of the second structural analysis model was calculated (S8 in Fig. 3), and the location of void occurrence and the void volume were predicted (S9 in Fig. 3). In the comparative example relating to the conventional method, as described above, voids are predicted from the volumetric shrinkage distribution obtained by flow analysis.

[0060] A comparison of void occurrence in the actual molded product, the example using the improved method, and Comparative Example 3 using the conventional method is shown in Figure 12. In the comparative example, voids occurred throughout the entire interior of the molded product, which is significantly different from the locations where voids occurred in the actual molded product. In this example, after predicting sink marks on the molded product surface, a void analysis is then performed using the product shape that reflects the sink marks. As shown in Figure 12, by using the product shape that reflects the sink marks, the locations of void occurrence in the actual molded product and the analysis become closer, making it possible to accurately represent the actual void occurrence phenomenon in the coupled analysis.

[0061] In the examples, the product shape reflecting the sink marks is the shape at the point when no more sink marks occur due to volumetric shrinkage (the point when the molded product surface stops deforming into a depression), that is, the shape at the point when only voids are formed due to volumetric shrinkage. As will be described later, in the improved method (S4 in Fig. 2), the timing at which the elastic modulus distribution is calculated particularly affects the accuracy of predicting sink marks and voids.

[0062] In the improved method, the volumetric shrinkage distribution (S4 in Fig. 2) can be calculated starting from the gate seal (the point at which the thermoplastic resin at the gate solidifies and stops flowing) and ending when the injection-molded product reaches the mold temperature. The gate sealing time may be based on the actual measured value used for the actual molded product. Alternatively, it may be set so that the weight of the injection-molded product is maximized and the time until gate sealing is minimized in the flow analysis of the flow analysis model. In the flow analysis, gate sealing may be defined as the time when the temperature at the center of the gate reaches the flow stop temperature. The flow stop temperature may also be the inflection point when the material is cooled at a cooling rate in the range of 1°C / min to 50°C / min in specific heat measurement.

[0063] Figure 13 shows the time dependence of the predicted sink mark amount. The horizontal axis indicates the time (unit: seconds) from the start of injection when the elastic modulus used in the structural analysis for sink mark prediction is calculated (S4 in Figure 2). The vertical axis indicates the sink mark amount (unit: mm) predicted by the method according to the present disclosure. As mentioned above, the starting point of the volumetric shrinkage rate can be the gate seal time, which is shown on the horizontal axis at 8 seconds. Furthermore, the void prediction in the examples was performed starting from the time when the injection molded product was cooled under pressure (injection, pressure holding, cooling) and the surface layer solidified (36.6 seconds on the horizontal axis). As shown in Figure 13, the sink marks shown in Table 2 are predicted values ​​when the elastic modulus is calculated at the time when the surface solidifies after injection, pressure holding, and cooling. It can be seen that the time to calculate the elastic modulus is between the gate sealing time and the time when the surface solidifies.

[0064] (Methods to reduce sink marks) The present disclosure also includes a method for reducing sink marks that occur on the surface of an injection-molded article obtained by injection molding a thermoplastic resin into a mold. That is, while changing one or more of the design, molding conditions, and molding material, the amount of sink marks is predicted using the above-mentioned method for predicting the behavior of sink marks, and this is repeated until the predicted amount of sink marks is reduced to a predetermined amount or less.

[0065] (Methods for reducing voids) The present disclosure also includes a method for reducing voids that occur inside an injection-molded article formed by injection molding a thermoplastic resin into a mold. That is, while changing one or more of the design, molding conditions, and molding material, the void amount is predicted using the above-mentioned method for predicting void generation behavior, and this is repeated until the predicted void amount is reduced to a predetermined amount or less.

[0066] The present disclosure is not limited to the above-described embodiments, but includes various modifications in which components are added, deleted, or converted from the above-described configurations. In addition, each embodiment can be combined in various ways. In particular, the present disclosure should not be construed as being limited to the above-described embodiments or examples in terms of shape, material, or condition.

[0067] The present disclosure also includes a program for causing one or more processors to execute the method for predicting the occurrence behavior of sink marks or voids according to the present disclosure or the method for reducing sink marks or voids according to the present disclosure. The program may be provided by being recorded on a computer-readable, non-transitory storage medium.

Claims

1. A molding defect prediction method for predicting the occurrence behavior of molding defects in an injection-molded product obtained by injection molding a thermoplastic resin into a mold, comprising: A step (S1) of creating a flow analysis model in which the injection molded product is divided into a plurality of elements; A step (S2) of determining the temperature and pressure of each element in the process of molding the thermoplastic resin; A step (S3) of calculating a temperature distribution and a pressure distribution in the injection molded product from the temperatures and pressures of the plurality of elements; a step (S4) of calculating an elastic modulus distribution, a volumetric shrinkage distribution, and a temperature load of the flow analysis model from the temperature distribution and the pressure distribution using previously measured linear expansion coefficient data, elastic modulus temperature dependency data, and PVT data of the thermoplastic resin; a step (S5) of calculating a first strain generated in each element of the first structural analysis model by structural analysis using the temperature load, the pressure distribution, the elastic modulus distribution, and the volumetric shrinkage rate distribution; and (S6) predicting a location of occurrence of a sink mark and / or an amount of the sink mark by calculating a deformation amount of the first structural analysis model from the first strain.

2. A step (S7) of creating a second structural analysis model divided into a plurality of elements, reflecting the location of the sink mark and the amount of the sink mark; a step (S8) of calculating a second strain generated in each element of the second structural analysis model by structural analysis using a temperature load, a pressure distribution, an elastic modulus distribution, and a volumetric shrinkage distribution; The molding defect prediction method according to claim 1 , further comprising a step (S9) of predicting a location of void occurrence and / or a void amount from the second strain.

3. 2. The molding defect prediction method according to claim 1, wherein the step (S2) of determining the temperature and pressure of each element is a step (S2) of determining the temperature and pressure of each element during a process in which the thermoplastic resin is injected from a gate of the mold into a cavity and the injection-molded product reaches the mold temperature.

4. 2. The molding defect prediction method according to claim 1, wherein in the step (S4) of calculating the elastic modulus distribution and the volumetric shrinkage distribution of the flow analysis model, the volumetric shrinkage distribution is calculated starting from a gate seal and ending when the injection-molded product reaches the mold temperature.

5. 5. The molding defect prediction method according to claim 4, wherein the gate seal is determined by setting molding conditions in a flow analysis of the flow analysis model so that the weight of the injection-molded product is maximized and the time until the gate seal is minimized.

6. 3. The molding defect prediction method according to claim 2, wherein in the step (S8) of calculating the second strain, the elastic modulus distribution, volumetric shrinkage distribution, and temperature load are calculated starting from when the injection-molded product is cooled under pressure and the surface layer is solidified, and ending from when the injection-molded product reaches the mold temperature.

7. The molding defect prediction method according to claim 4, wherein the gate seal is at a point where the temperature at the center of the gate reaches a flow stop temperature.

8. 8. The molding defect prediction method according to claim 7, wherein the flow stop temperature is an inflection point when the specific heat is measured at a cooling rate in the range of 1°C / min to 50°C / min.

9. 2. The molding defect prediction method according to claim 1, wherein in the step (S2) of calculating the temperature and pressure of each element, the thermal conductivity calculated by a thermal conductivity measurement method called the AC steady-state method (ISO 22007-6) is used for the calculation.

10. In the step (S8) of calculating the second strain, calculating a second temperature distribution and a second pressure distribution in the injection-molded product from temperatures and pressures for a plurality of elements of the second structural analysis model; calculating a second elastic modulus distribution, a second volumetric shrinkage rate distribution, and a second temperature load of the second structural analysis model from the second temperature distribution and the second pressure distribution using previously measured linear expansion coefficient data, elastic modulus temperature dependency data, and PVT data of the thermoplastic resin; 3. The molding defect prediction method according to claim 2, further comprising: calculating the second strain generated in each element of the second structural analysis model by structural analysis using the second temperature load, the second pressure distribution, the second elastic modulus distribution, and the second volumetric shrinkage rate distribution of the second structural analysis model.

11. 10. A molding defect reduction method, comprising: repeating the process of predicting a sink mark amount by the molding defect prediction method according to claim 1 while changing one or more of a design, molding conditions, and molding materials, until the predicted sink mark amount is reduced to a predetermined amount or less.

12. A method for reducing molding defects, comprising the steps of repeating the steps of predicting the amount of voids using the molding defect prediction method described in claim 2 while changing one or more of the design, molding conditions, and molding material until the predicted amount of voids is reduced to a predetermined amount or less.

13. A computer-readable storage medium storing a program for causing a computer to execute the molding defect prediction method according to claim 1.

14. A computer-readable storage medium storing a program for causing a computer to execute the molding defect reduction method according to claim 11.

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