JP7545907B2 - Method, device, program, and recording medium for predicting fiber orientation state of fiber-reinforced plastic molded products - Google Patents

Description

This disclosure relates to a method and device for predicting the fiber orientation state of a fiber-reinforced plastic molded product, a program for causing a computer to execute the method, and a computer-readable recording medium on which the program is recorded.

Conventionally, in order to facilitate product design, etc., of injection-molded fiber-reinforced resins, the flow and solidification behavior of fiber-reinforced resins in a mold has been analyzed using CAE (Computer-Aided-Engineering) (see, for example, Patent Documents 1 and 2).

In fiber-reinforced resins, if anisotropy occurs in the orientation of the fibers, anisotropy also occurs in the amount of shrinkage and elastic modulus of the molded product, which in turn affects the dimensional accuracy and strength of the molded product. Therefore, accurately predicting the fiber orientation in flowing resin is important in predicting the dimensional accuracy and strength of injection-molded products (see, for example, Patent Document 3).

JP 2005-241443 A JP 2015-189117 A JP 2014-226871 A

In conventional CAE analysis, the fiber orientation behavior in flowing resin, i.e., the fiber direction and its distribution, is calculated based on the resin flow velocity obtained by flow analysis.

Meanwhile, actual measurements of fiber orientation in injection molded products show that anisotropy tends to be lower in the outermost layer compared to the flowing part. However, when calculations are performed using conventional methods, the effect of shear flow becomes greater in the outermost layer, resulting in fibers being strongly aligned in the direction of resin flow, and reducing the accuracy of fiber orientation predictions.

This disclosure provides a method and device for predicting the fiber orientation state of fiber-reinforced plastic molded products that can improve the accuracy of fiber orientation prediction in a simple manner, a program for causing a computer to execute the method, and a computer-readable recording medium on which the program is recorded.

In order to solve the above problems, the fiber orientation prediction method for fiber-reinforced plastic molded products disclosed herein is a method for predicting the orientation state of fibers contained in injection molded products of fiber-reinforced plastic by computer simulation, and includes an analytical model creation process for dividing the shape data of the cavity of a mold into multiple microelements to create an analytical model, a flow analysis process for calculating the flow rate of the resin contained in the fiber-reinforced resin when the fiber-reinforced resin is injected into the cavity, and a fiber orientation tensor calculation process for calculating a fiber orientation tensor that indicates the orientation state of the fibers based on the flow rate of the resin, and is characterized in that in the fiber orientation tensor calculation process, when the region in the resin from the surface that contacts the cavity to a predetermined depth is defined as the outermost layer portion, and the region deeper than the outermost layer portion is defined as the inner portion, the fiber orientation tensor of the outermost layer portion is calculated based on the fiber orientation tensor of the inner portion.

The orientation of the fibers contained in injection molded products made of fiber-reinforced resin is affected by the flow speed of the resin during injection molding. During injection molding, the flow speed of the resin in the inner part increases the deeper it is from the surface that contacts the cavity. Therefore, in the part of the inner part closer to the outermost layer, the flow speed decreases, and due to the velocity gradient that occurs in the inner part, the closer to the outermost layer the fibers are oriented in a direction parallel to the flow direction of the resin. With conventional methods that reflect this velocity gradient, the calculation results show that the fibers in the outermost layer are strongly oriented parallel to the flow direction of the resin.

However, when the fiber orientation in the outermost layer is actually measured, the anisotropy of the fiber orientation is lower than the results calculated using the conventional method. In other words, the conventional method has low accuracy in predicting the fiber orientation in the outermost layer.

During injection molding, a fountain-like flow of resin called fountain flow (hereinafter also referred to as "FF") occurs in the flow front of the resin from the center of the inner part toward the cavity surface. The resin in the center of the inner part is pressed against the cavity surface by the FF and solidifies due to the temperature difference between the mold and the resin, forming the outermost layer. Then, it is considered that the fibers in the inner part of the flow front part are guided by the flow of the FF while maintaining their fiber orientation, flow to the cavity surface, and solidify together with the resin to form the outermost layer part. In fact, the inventors of the present application found that the fiber orientation state of the outermost layer part is correlated with the fiber orientation state of the inner part. On the other hand, in order to express the FF in the flow front part by calculation, detailed mesh division is required in the flow direction and the depth (thickness) direction, and very complicated calculations are required. Therefore, according to this configuration, the fiber orientation state of the outermost layer part can be predicted with high accuracy by calculating the fiber orientation tensor of the outermost layer part based on the fiber orientation tensor of the inner part. In addition, the fiber orientation state of the molded product can be accurately predicted using a simple method.

Preferably, in the fiber orientation tensor calculation process, the fiber orientation tensor of the outermost layer is calculated as the average value of the fiber orientation tensor in the flow front portion of the inner layer.

The flow rate of the resin in the inner part varies depending on the depth from the surface of the cavity, and the fiber orientation also varies depending on the depth. If we assume that the fibers oriented in multiple directions in the inner part of the flow front flow to the cavity surface while maintaining their orientation, it is considered that the outermost layer contains fibers with the above-mentioned multiple types of orientation while maintaining their orientation. In fact, the inventors of the present application found that the fiber orientation tensor in the outermost layer is equivalent to the average value of the fiber orientation tensor in the inner part. Therefore, according to this configuration, since the fiber orientation tensor in the outermost layer is calculated as the average value of the fiber orientation tensor in the inner part, the fiber orientation state of the outermost layer, and therefore of the molded product, can be accurately predicted by a simple method.

Preferably, the inner portion comprises a surface layer portion in contact with the outermost surface layer portion and a center portion inside the surface layer portion, and the average value is at least the average value of the fiber orientation tensor of the surface layer portion and the fiber orientation tensor of the center portion.

The fiber orientation state may differ between the surface layer and the center. By using the average value of the fiber orientation tensor at least in the surface layer and the center, it is possible to improve the prediction accuracy of the fiber orientation state of the outermost layer, and ultimately of the molded product.

Preferably, the average value is the average value of the fiber orientation tensor obtained for each of the layers obtained by dividing the inner portion into 7 to 18 layers in the depth direction.

This configuration reduces the amount of calculations while improving the accuracy of prediction of the fiber orientation state of the outermost layer, and ultimately of the molded product.

Preferably, the predetermined depth in the outermost layer is 0.2 mm or less.

This configuration improves the accuracy of predicting the fiber orientation state of the outermost layer, and ultimately of the molded product.

Preferably, the infinitesimal elements of the analytical model are 2.5-dimensional thin-walled shell elements.

With this configuration, the cavity shape is divided into thin plate-like shell elements, reducing the amount of calculations required and enabling accurate prediction of the fiber orientation state of the molded product in a simple and quick manner.

The fiber orientation prediction device for fiber-reinforced resin molded products disclosed herein is a device that predicts the orientation state of fibers contained in injection molded products of fiber-reinforced resin by computer simulation, and includes an analytical model creation means that divides the shape data of the cavity of a mold into multiple microelements to create an analytical model, a flow analysis means that calculates the flow rate of the resin contained in the fiber-reinforced resin when the fiber-reinforced resin is injected into the cavity, and a fiber orientation tensor calculation means that calculates a fiber orientation tensor that indicates the orientation state of the fibers based on the flow rate of the resin, and is characterized in that the fiber orientation tensor calculation means calculates the fiber orientation tensor of the outermost layer based on the fiber orientation tensor of the inner layer, when the region in the resin from the surface that contacts the cavity to a predetermined depth is defined as the outermost layer, and the region deeper than the outermost layer is defined as the inner layer.

According to this configuration, the fiber orientation tensor of the outermost layer is calculated based on the fiber orientation tensor of the inner layer, so the fiber orientation state of the outermost layer can be predicted with high accuracy. In this way, the fiber orientation state of the molded product can be predicted with high accuracy using a simple method.

Preferably, the fiber orientation tensor of the outermost layer is calculated as the average value of the fiber orientation tensor at the flow front portion of the inner layer.

With this configuration, the fiber orientation tensor of the outermost layer is calculated as the average value of the fiber orientation tensors of the inner layer, making it possible to accurately predict the fiber orientation state of the outermost layer, and ultimately the molded product, in a simple manner.

Preferably, the inner portion comprises a surface layer portion in contact with the outermost surface layer portion and a center portion inside the surface layer portion, and the average value is at least the average value of the fiber orientation tensor of the surface layer portion and the fiber orientation tensor of the center portion.

The fiber orientation state may differ between the surface layer and the center. By using the average value of the fiber orientation tensor at least in the surface layer and the center, it is possible to improve the prediction accuracy of the fiber orientation state of the outermost layer, and ultimately of the molded product.

Preferably, the average value is the average value of the fiber orientation tensor obtained for each of 7 to 18 layers obtained by dividing the inner portion in the depth direction.

This configuration reduces the amount of calculations while improving the accuracy of prediction of the fiber orientation state of the outermost layer, and ultimately of the molded product.

Preferably, the predetermined depth in the outermost layer is 0.2 mm or less.

This configuration improves the accuracy of predicting the fiber orientation state of the outermost layer, and ultimately of the molded product.

Preferably, the infinitesimal elements of the analytical model are 2.5-dimensional thin-walled shell elements.

With this configuration, the cavity shape is divided into thin plate-like shell elements, reducing the amount of calculations required and enabling accurate prediction of the fiber orientation state of the molded product in a simple and quick manner.

The program disclosed herein is a program for causing a computer to execute the above-mentioned method for predicting the fiber orientation state of a fiber-reinforced plastic molded product.

The recording medium disclosed herein is a computer-readable recording medium on which the above-mentioned program is recorded.

As described above, according to the present disclosure, the fiber orientation tensor of the outermost layer portion is calculated based on the fiber orientation tensor of the inner portion, so that the fiber orientation state of the outermost layer portion can be predicted with high accuracy. In this way, the fiber orientation state of the molded product can be predicted with high accuracy using a simple method.

FIG. 1 is a diagram showing an example of the configuration of a fiber orientation prediction device according to the present disclosure. 1 is a flowchart for explaining a fiber orientation state prediction method according to the present disclosure. 1A to 1C are diagrams for explaining the relationship between two types of resin flows and fiber orientation. 13A and 13B are diagrams for explaining the relationship between the resin flow and the fiber orientation in a tip region including a flow front portion. FIG. 13 is a diagram showing a test piece divided into nine layers in the thickness direction. X-ray-CT images of layer L2 (surface layer) and layer L5 (center portion) in FIG. 5. FIG. 1 is a diagram for explaining the definition of fiber orientation tensor. 6 is a histogram of the relative fiber amount for each angle in layer L2 (surface layer) of FIG. 5 . 6 is a histogram of relative fiber content by angle in layer L5 (center) of FIG. 5 . 6 is a graph showing the measured values of the fiber orientation tensor in each layer of FIG. 5 . FIG. 1 is a diagram showing a comparison between the mechanism of an actual phenomenon and the mechanism of a conventional method for predicting fiber orientation using CAE. 1 is a graph showing the relationship between the measured fiber orientation tensor value of the outermost layer portion and the average value of the fiber orientation tensor value of the flow portion. FIG. 2 shows an analytical model of a test specimen. Graph showing the measured fiber orientation tensor values for a 1.5 mm thick test piece and the predicted values for the examples and comparative examples. Graph showing the measured fiber orientation tensor values for a 2.0 mm thick test piece and the predicted values for the examples and comparative examples. Graph showing the measured fiber orientation tensor values for a 2.5 mm thick test piece and the predicted values for the examples and comparative examples. Graph showing the measured fiber orientation tensor values for a 3.0 mm thick test piece and the predicted values for the examples and comparative examples.

Embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. The following description of the preferred embodiments is merely exemplary in nature and is in no way intended to limit the present disclosure, its applications, or its uses.

<Fiber orientation prediction device>
1 shows an example of the configuration of a fiber orientation state prediction device 100 (hereinafter also referred to as the "prediction device 100") for a fiber reinforced plastic molded product according to the present disclosure. The prediction device 100 is a device that predicts the orientation state of fibers contained in a fiber reinforced plastic injection molded product by computer simulation, and is a CAE (Computer Aided Engineering) system that basically includes a computer 110.

The prediction device 100 includes a storage unit 120 and a processor 130. The prediction device 100 also includes a display unit 140, such as a display, an input unit 150, such as a keyboard, and a reading unit 160 for acquiring information stored in various recording media 170. The storage unit 120 and/or the recording media 170 store information such as programs for arithmetic processing and various analysis data. The processor 130 performs various arithmetic processing based on the information stored in the storage unit 120, information input via the input unit 150, and information acquired from the recording media 170 via the reading unit 160.

The prediction device 100 creates an analytical model by dividing shape data, such as 3D CAD data that defines the cavity of the mold that serves as the flow path for the fiber-reinforced resin, into multiple microelements using the analytical model creation means 131. As the analytical model creation means 131 that creates the microelements, for example, CAE preprocessors such as 3D TIMON (registered trademark)-Pre/Post by Toray Engineering D Solutions Co., Ltd., FEMAP (registered trademark) by NST Corporation, Patran (registered trademark) by MSC Software Co., Ltd., and Hypermesh (registered trademark) by Altair, Inc. can be used.

The analysis condition setting means 132 sets the analysis conditions based on material property data related to the type and composition of resin and fiber, etc., and boundary condition data describing molding conditions such as injection speed and resin temperature. Then, the flow analysis means 133 executes a flow analysis to analyze the behavior of the resin when the material is injected into the cavity. The flow analysis provides various data including information on the flow speed of the resin for each minute element. In this specification, the term "flow speed" is synonymous with "flow speed" and is used as a concept that includes both the speed and direction of the flow. For the analysis condition setting means 132 and the flow analysis means 133, injection molding CAE software such as 3D TIMON (registered trademark) manufactured by Toray Engineering D Solutions Co., Ltd. can be used.

The fiber orientation tensor calculation means 134 calculates a fiber orientation tensor that indicates the orientation state of the fibers based on the resin flow velocity calculated by the flow analysis means 133. As the fiber orientation tensor calculation means 134, for example, a fiber orientation analysis solver such as 3D TIMON (registered trademark)-FIBER manufactured by Toray Engineering D Solutions Co., Ltd. can be used.

<Injection molded products>
The injection molded product to be analyzed is not particularly limited, but examples thereof include automobile parts, rockets, aircraft parts, sporting goods, etc. Preferably, a plate-shaped injection molded product such as an interior or exterior member of a vehicle is used.

The resin is not particularly limited, and may be any well-known resin, specifically, for example, polypropylene resin, polycarbonate resin, polyester resin, polyamide (PA) resin, etc. These resins may be used alone or in a mixture of two or more. The fibers are not particularly limited, and may be any well-known fiber, specifically, for example, glass fiber, carbon fiber, cellulose nanofiber, etc. These fibers may be used alone or in a mixture of two or more. The fiber diameter, fiber length, etc. are not particularly limited, and may be generally used conditions. The fiber content in the molded product is not particularly limited, and may be a general content, for example, 1% by mass or more and 50% by mass or less, preferably 5% by mass or more and 30% by mass or less. In addition, the molded product may contain about 5% by mass of fillers, pigments, dyes, impact modifiers, UV absorbers, and other additives from the viewpoint of improving moldability, strength, design, functionality, etc. These additives may be added alone or in multiple types.

The minute elements in the above-mentioned analysis model are not particularly limited, and well-known elements such as solid elements and shell elements can be used. However, when analyzing a plate-shaped injection molded product, it is preferable to use 2.5-dimensional thin shell elements. By dividing the shape data into thin plate-shaped shell elements, the amount of calculations can be reduced, and fiber orientation can be predicted easily and quickly with high accuracy.

<Method for predicting fiber orientation state>
2 is a flowchart showing a procedure for carrying out a method for predicting a fiber orientation state of a fiber-reinforced plastic molded product according to the present disclosure (hereinafter, also referred to as a "prediction method") The prediction method includes an analysis model creation step S1, an analysis condition setting step S2, a flow analysis step S3, and a fiber orientation tensor calculation step S4.

First, in the analytical model creation step S1, as described above, the analytical model creation means 131 divides the shape data of the mold cavity created using 3D CAD or the like into infinitesimal elements for numerical analysis to create an analytical model.

Next, in the analysis condition setting process S2, analysis conditions such as material property data and boundary condition data are set. Then, in the flow analysis process S3, flow analysis is performed and the flow velocity of the resin is calculated.

Next, in the fiber orientation tensor calculation step S4, the fiber orientation tensor is calculated based on the resin flow velocity obtained from the flow analysis. Then, the flow analysis S3 and the fiber orientation tensor calculation step S4 are repeated until filling of the fiber-reinforced resin is completed (step S5).

In the fiber orientation tensor calculation process S4, the fiber orientation state is predicted using, for example, the Advani-Tucker equation shown in the following equation (1) (see Patent Document 3).

In equation (1), the fiber orientation tensor is indicated by the subscript a.

[Fiber orientation tensor calculation process]
The fiber orientation tensor calculation step S4 will be described in detail below with reference to Fig. 2 to Fig. 11. In the following description, a plate-shaped molded product is used as the injection molded product, and 2.5-dimensional thin shell elements are used as the infinitesimal elements. However, the prediction method according to the present disclosure can also be applied to cases where an analytical model is created using molded products other than plate-shaped products and other infinitesimal elements.

Figure 3 shows the two main types of resin flows in the cavity, parallel flow and shear flow, and the resulting orientation of fiber G1.

In the parallel flow, there is no speed difference or speed gradient between the resin flow velocities _V1 and _V2 acting on both ends of the fiber G1, and the flow velocity is uniform throughout the fiber, so the fiber G1 flows without rotating. In other words, the orientation of the fiber G1 is in the same direction as the upstream.

On the other hand, a shear flow is a flow when there is a speed difference/speed gradient between the resin flow velocities _V1 , _V2 acting on both ends of the fiber G1. In this case, the end of the fiber G1 with the larger absolute value of the acting flow velocity (the end where _V1 acts) moves faster in the flowing resin than the end with the smaller absolute value of the acting flow velocity (the other end where _V2 acts), so the fiber G1 rotates and eventually flows in a state parallel to the resin flow direction. In other words, the orientation of the fiber G1 is parallel to the resin flow.

Next, FIG. 4 is a schematic diagram showing the tip region 210 including the flow front portion 211 of the resin 200 flowing within the cavity C of the mold M, and includes an oblique view from above, a cross-sectional view taken along line A-A in the oblique view, and a cross-sectional view taken along line B-B in the oblique view (line B-B is also shown as a dashed line in the oblique view). The resin 200 is assumed to flow overall in the direction indicated by the symbol 200A (hereinafter also referred to as "flow direction 200A").

In general, when molten resin (e.g., about 200°C) flows through a plate-shaped cavity C, the flow of resin in the center of the thickness direction shows a form of a jet flow called a fountain flow (FF) that turns toward the surface of the cavity C at the flow front part 211. The resin 200 that contacts the surface of the cavity C by the FF is quenched by the mold M (e.g., about 40°C), and solidification progresses. Thus, a solidified outermost layer part 217 is formed in the region of the resin 200 from the surface that contacts the cavity C to a predetermined thickness (predetermined depth). Then, the flow of the resin 200 continues in the flow part 212 (inner part) in the region deeper than the outermost layer part 217. The thickness of the outermost layer part 217 is not particularly limited, but from the viewpoint of improving prediction accuracy, it is preferable that it is, for example, 0.2 mm or less.

Next, consider the orientation of the fibers in the flow section 212. As shown in the cross-sectional view of line A-A, the flow section 212 comprises a surface layer 215 in contact with the outermost layer 217, and a central section 213 inside the surface layer 215. The thickness of the central section 213 is not particularly limited, but for example, the central section 213 is preferably within ±0.3 mm (thickness 0.6 mm or less) from the center of the thickness direction of the resin 200 (corresponding to line B-B in FIG. 4), and more preferably within ±0.2 mm (thickness 0.4 mm or less). The surface layer 215 can be the region between the central section 213 and the outermost layer 217.

Since heat is lost from the flowing resin 200 to the mold M and the atmosphere, the temperature drops and the viscosity rises on the surface layer 215 side compared to the center 213 side, resulting in a tendency for the flow rate to be faster on the center 213 side and slower on the surface layer 215 side. This generates a shear flow in the thickness direction, where the flow rate decreases from the center 213 side toward the surface layer 215 side. In the surface layer 215, where the influence of this shear flow is stronger, the fibers G11 rotate so as to be parallel to the flow direction of the resin 200. On the other hand, the fibers G12 in the center 213 are hardly influenced by the shear flow in the thickness direction.

Next, the cross-sectional view of line B-B shows a cross-section perpendicular to the thickness direction of the central portion 213. When the resin 200 flows radially from the gate (not shown), the area of the resin 200 increases from the gate side toward the flow front portion 211 side. As described above, the resin temperature decreases and the viscosity increases due to heat loss to the mold M and the atmosphere. Then, the flow rate of the resin 200 decreases from the gate side toward the flow front portion 211 side, and the flow rate difference becomes a velocity gradient, causing a shear flow in the flow direction 200A. The fiber G12 in the central portion 213 is affected by the shear flow in the flow direction 200A and rotates from the gate side toward the flow front portion 211 side. Then, as it approaches the flow front portion 211, the flow rate of the resin acting on both ends of the fiber G12 becomes the same, and the fiber G12 is oriented perpendicular to the flow of the resin 200 in the central portion 213.

In this way, the fibers in the resin 200 rotate while undergoing complex three-dimensional movements due to the influence of the shear flow in the thickness direction and flow direction 200A. As a result, the fibers in the surface layer 215 tend to be oriented parallel to the flow direction of the resin 200, while the fibers in the center 213 tend to be oriented perpendicular to the flow direction of the resin 200.

As shown in Figure 5, a test piece TP manufactured under the same conditions and with the same materials as the test piece TP of the reference example described later was divided into nine parts in the thickness direction, and the fiber orientation state of each layer, indicated by symbols L1 to L9 in Figure 5, was observed using an X-ray CT device (Shimadzu Corporation SMX-1000 Plus, resolution 5 μm). Figure 6 shows X-ray CT images of layer L2 (surface layer 215) and layer L5 (center portion 213). As shown in Figure 6, it can be seen that in layer L2, there are many fibers oriented parallel to the resin flow direction, and in layer L5, there are many fibers oriented perpendicular to the resin flow direction.

To predict the fiber orientation state using CAE analysis, the fiber orientation state needs to be quantified. Figure 7 shows the definition of the fiber orientation tensor, which indicates the fiber orientation state. In the fiber orientation tensor, a state in which all fibers are oriented parallel to the resin flow direction is set to 1, and a state in which all fibers are oriented perpendicular to the resin flow direction is set to 0.

The fiber orientation tensor was calculated for each layer, L1 to L9, using the following procedure.

First, the number of fibers was counted at every 10° angle from 0° to 180° with respect to the resin flow direction by image analysis of X-ray CT images taken for each layer, and a histogram of the relative fiber amount (%) against the angle was created. As shown in Figure 6, the fiber angle is 90° for the direction parallel to the resin flow direction, and 0° (180°) for the direction perpendicular to the resin flow direction. Figures 8 and 9 show histograms for layers L2 and L5, respectively. Note that the proportion of 0° (=180°) is omitted in Figures 8 and 9. As shown in Figure 8, layer L2 has more fibers parallel to the resin flow direction. Also, as shown in Figure 9, layer L5 has more fibers perpendicular to the resin flow direction.

Next, the fiber orientation tensor of each layer was calculated based on the histogram created for each layer. The results are shown in Figure 10. As shown in Figure 10, in layers L2 to L4 and layers L6 to L8 of the surface layer 215, fibers oriented parallel to the resin flow direction are dominant, and the fiber orientation tensor is close to 1. In addition, when comparing the fiber orientation tensors of layers L2 to L4 and layers L6 to L8, the fiber orientation tensor approaches 1, especially as it approaches the outermost layer 217. On the other hand, in layer L5 of the center 213, fibers oriented perpendicular to the resin flow direction are dominant, and the fiber orientation tensor approaches 0. The fiber orientation tensor of the outermost layer 217 tends to be intermediate between the fiber orientation tensor of the surface layer 215 (layers L2 to L4 and layers L6 to L8) and the fiber orientation tensor of the center 213 (layer L5). That is, in the surface layer 215, the fibers oriented parallel to the resin flow direction increase as one approaches the outermost layer 217, and the anisotropy increases. However, in the outermost layer 217, the anisotropy of the fiber orientation decreases, becoming closer to a random orientation.

Next, the mechanism of the actual phenomenon in FIG. 10 and the mechanism of the conventional fiber orientation prediction method using CAE analysis will be explained with reference to FIG. 11. FIG. 11 is a diagram equivalent to the cross-sectional view of line A-A in FIG. 4, with the upper half showing the mechanism of the actual phenomenon and the lower half showing the mechanism of the conventional fiber orientation prediction method using CAE analysis.

As shown in the lower half of Figure 11, according to conventional CAE analysis, the orientation of fibers is calculated based on the velocity gradient of the shear flow that occurs in the flowing resin. In a prediction method that assumes this velocity gradient, the orientation of fibers changes from a direction perpendicular to the resin flow direction toward the surface layer 215 from the center 213. The fiber orientation is calculated so that the fiber orientation in the outermost layer 217 is even stronger in the direction parallel to the resin flow direction than in the surface layer 215, that is, so that the anisotropy is high (see the comparative examples (before CAE improvement) in Figures 14 to 17 described later).

However, as shown in FIG. 10, in the actual phenomenon, the fiber orientation tensor of the outermost layer 217 is an intermediate value between the fiber orientation tensors of the central portion 213 and the surface layer 215, and the fiber orientation in the outermost layer 217 is weak, i.e., the anisotropy is reduced.

As shown in the upper half of Figure 11, in the actual phenomenon, this is thought to be due to the fact that the fibers that have flowed through the flow section 212, i.e., the central section 213 and the surface section 215, reach the flow front area 211, and are then guided to the FF and flow onto the surface of the cavity C. The fibers guided to the FF maintain their orientation in the flow section 212, and are deposited on the surface of the cavity C, where they are incorporated into the outermost layer section 217 and fixed there.

In the conventional CAE analysis shown in the lower half of Figure 11, the inflow of fibers due to FF is not taken into account, which causes a decrease in the prediction accuracy of the fiber orientation in the outermost layer 217. In addition, to faithfully reproduce the actual phenomenon, FF would have to be reproduced, but this would result in the problem of complicated calculations.

As a result of investigations aimed at improving the accuracy of fiber orientation prediction by CAE analysis using a simple configuration, the inventors of the present application found that the fiber orientation state of the outermost layer 217 correlates with the fiber orientation state of the flow portion 212. In other words, the inventors of the present application found that the fiber orientation tensor of layers L1 and L9 (outermost layer 217) in Figure 10 correlates with the average value of the fiber orientation tensor of layers L2 to L8 (flow portion 212).

Specifically, for 16 types of test pieces TP having the same shape as the reference example described below, but differing in thickness and fiber content in the resin, X-ray CT images were taken using the same method as described above with reference to Figures 5 to 10, and the fiber orientation tensor of each layer was calculated. Figure 12 is a graph showing the relationship between the actual fiber orientation tensor value of the outermost layer 217 obtained from the X-ray CT images of each test piece TP and the average value of the fiber orientation tensor of the center portion 213 and the surface layer 215. As shown in Figure 12, there is a sufficient correlation between the two, and it was found that the fiber orientation tensor of the outermost layer 217 is equivalent to the average value of the fiber orientation tensor of the flow portion 212.

From the results of FIG. 12, in the prediction method according to the present disclosure, in the fiber orientation tensor calculation step S4, the fiber orientation tensor of the outermost layer 217 is calculated based on the fiber orientation tensor of the flow portion 212. Specifically, for example, as shown in the following formula (2), the fiber orientation tensor of the outermost layer 217 is calculated as the average value of the fiber orientation tensor of the flow portion 212 or a value reflecting the average value.

Fiber orientation tensor of the outermost layer = 1/n (Σ[k=1→n] fiber orientation tensor of the flow part (k)) × b
+ Outermost layer conventionally calculated orientation tensor × (1-b) ... (2)
In formula (2), n is the number of divisions of the flow portion in the thickness direction (= “number of divisions of the cavity in the thickness direction” −2), and b is a weighting coefficient (user setting: greater than 0 and less than 1, default: 1).

The above configuration allows FF to be reproduced with a simple structure, improving the accuracy of predicting the fiber orientation of the outermost layer 217, and ultimately making it possible to predict the fiber orientation state of the molded product easily and with high accuracy.

The average value of the fiber orientation tensor in the flow section 212 is preferably the average value of at least the fiber orientation tensor in the surface layer section 215 and the fiber orientation tensor in the central section 213. By adopting the average value of the fiber orientation tensors in the surface layer section 215 and the central section 213, which have different fiber orientation states as described above, as the average value, the prediction accuracy of the fiber orientation in the outermost layer section 217, and in turn, the molded product, can be further improved.

In the tests shown in Figures 5 to 12, the cavity C was divided into nine in the thickness direction, and the average value of the fiber orientation tensor in the seven layers L2 to L8 was compared with the measured fiber orientation tensor of the outermost layer 217. In the prediction method according to the present disclosure, it is desirable to divide the cavity C into layers preferably 5 to 30, more preferably 9 to 20, in the thickness direction and perform calculations. In detail, the cavity C is divided into the two outermost layers corresponding to the outermost layer 217 in the thickness direction, and the flow portion 212 is preferably divided into layers preferably 3 to 28, more preferably 7 to 18. In other words, in the above formula (2), n is preferably 3 ≦ n ≦ 28, more preferably 7 ≦ n ≦ 18. Then, the average value of the fiber orientation tensor obtained for each layer of the flow portion 212 may be used as the fiber orientation tensor of the outermost layer 217. This makes it possible to improve the prediction accuracy of the fiber orientation while reducing the amount of calculation.

An example of the specific procedure of the fiber orientation tensor calculation step S4 is shown in FIG. 2. As shown in FIG. 2, first, the flow velocity of the resin in the tip region 210 is obtained from the results of the flow analysis (step S41). In addition, in the center portion 213 and the surface layer portion 215, for example, the fiber orientation tensor of the fiber in the element immediately before the flow front portion 211, i.e., immediately upstream of the flow front portion 211, is obtained (step S42). Then, based on the fiber orientation tensor in the element immediately upstream and the flow velocity of the resin in the surrounding elements, the fiber orientation tensor of the fiber in the flow front portion 211 is calculated (step S43). Then, based on the fiber orientation tensor of the fiber in the flow front portion 211, the fiber orientation tensor of the outermost layer portion 217 is calculated. In this way, the fiber orientation tensor of the outermost layer portion 217 and the flow portion 212 is calculated.

<Program and its recording medium>
Each step of the fiber orientation prediction method described above is programmed. That is, the program according to this embodiment is a program for making a computer execute the procedure of each step described above. This program can be executed by the processor 130 in a state stored in the storage unit 120. The program is not limited to being stored in the storage unit 120, but can be recorded in various well-known computer-readable recording media 170 such as optical disk media and magnetic tape media. The program can be executed by loading such a recording medium into the reading unit 160 and reading out the program.

Next, we will explain the specific implementation examples.

Figure 13 shows a schematic diagram of the analytical model of the test piece TP used in the CAE analysis of the examples and comparative examples. The molded product was a plate of 375 mm x 120 mm, with four thicknesses t of 1.5 mm, 2.0 mm, 2.5 mm, and 3.0 mm. The 3D CAD data of the above shape was divided into 20 layers in the thickness direction, and each layer was divided into 2.5-dimensional thin-walled shell elements of a square shape (5 mm x 5 mm) to create an analytical model. Note that each element shown in Figure 13 is shown schematically and does not accurately reflect the actual element size.

The analysis conditions were as follows. The resin used was short glass fiber reinforced polypropylene resin R-350G (fiber content in the molded product: 30% by mass) manufactured by Prime Polymer. Flow analysis was performed when the resin was injected from the resin injection point (gate) P1 with an injection time of 2.5 seconds, a resin temperature of 240°C, and a mold temperature of 50°C. 3D TIMON (registered trademark, manufactured by Toray Engineering D Solutions Co., Ltd.) was used for the flow analysis.

Next, the fiber orientation tensor of each layer at the position indicated by symbol P2 was calculated using the resin flow velocity information obtained from the flow analysis. In the comparative example, the fiber orientation tensor was calculated using 3D TIMON (registered trademark)-FIBER (manufactured by Toray Engineering D Solutions Co., Ltd.). In the example, the fiber orientation tensor was calculated using an improved analysis solver in which the logic of the above-mentioned equation (2) was incorporated into the above-mentioned 3D TIMON (registered trademark)-FIBER to calculate the fiber orientation tensor of the outermost layer 217.

Furthermore, as a reference example, a test piece TP of the same shape and material as the test piece TP used in the CAE analysis was actually manufactured by injection molding under the same conditions as the CAE analysis. The test piece TP of the reference example was then divided into nine layers in the thickness direction, and an image was taken at the position indicated by the symbol P2 using an X-ray CT device (SMX-1000 Plus, manufactured by Shimadzu Corporation). From the obtained X-ray CT images, histograms similar to those in Figures 8 and 9 were created. The fiber orientation tensor of each layer was then calculated.

Figures 14 to 17 show the fiber orientation tensors of test pieces TP with thicknesses t of 1.5 mm, 2.0 mm, 2.5 mm, and 3.0 mm, respectively. In the comparative example, the fibers in the outermost layer 217 are calculated to be more strongly oriented in a direction parallel to the resin flow than the fibers in the surface layer 215 for all thicknesses of the test pieces TP in Figures 14 to 17. On the other hand, the calculation results of the working example show that the fibers in the outermost layer 217 tend to be oriented in all directions, i.e., randomly, compared to the fibers in the surface layer 215, resulting in a calculation result with reduced anisotropy, which is close to the tendency of the measured values in the reference example. In addition, even when the thickness of the test piece TP was changed, the calculation results were improved in the working example compared to the comparative example, and the reproducibility of the prediction method according to the present disclosure for thickness was confirmed.

This disclosure is extremely useful because it provides a method and apparatus for predicting the fiber orientation state of fiber-reinforced plastic molded products that can improve the accuracy of fiber orientation prediction in a simple manner, a program for causing a computer to execute the method, and a computer-readable recording medium on which the program is recorded.

100 Fiber orientation state prediction device, prediction device 120 Storage unit 130 Processor 131 Analysis model creation means 132 Analysis condition setting means 133 Flow analysis means 134 Fiber orientation tensor calculation means 170 Recording medium 200 Resin 210 Tip region 211 Flow front portion 212 Flow portion (inner portion)
213 central portion 215 surface layer portion 217 outermost surface layer portion C cavity M mold S1 analysis model creation process S2 analysis condition setting process S3 flow analysis process S4 fiber orientation tensor calculation process

Claims (14)

A method for predicting the orientation state of fibers contained in a fiber-reinforced resin injection molded product by computer simulation, comprising:
an analytical model creation step of dividing shape data of a cavity of a mold into a plurality of minute elements to create an analytical model;
A flow analysis step of calculating a flow velocity of the resin contained in the fiber reinforced resin when the fiber reinforced resin is injected into the cavity;
A fiber orientation tensor calculation process for calculating a fiber orientation tensor indicating an orientation state of the fibers based on the flow velocity of the resin,
A method for predicting the fiber orientation state of a fiber-reinforced plastic molded product, characterized in that in the fiber orientation tensor calculation process, when a region in the resin from the surface that contacts the cavity to a predetermined depth is defined as an outermost layer portion, and a region deeper than the outermost layer portion is defined as an inner portion, the fiber orientation tensor of the outermost layer portion is calculated based on the fiber orientation tensor of the inner portion. In claim 1,
A method for predicting the fiber orientation state of a fiber-reinforced plastic molded product, characterized in that in the fiber orientation tensor calculation process, the fiber orientation tensor of the outermost layer is calculated as the average value of the fiber orientation tensors in the flow front portion of the inner part. In claim 2,
The inner portion includes a surface layer portion in contact with the outermost surface layer portion and a center portion on the inner side of the surface layer portion,
A method for predicting the fiber orientation state of a fiber-reinforced plastic molded product, characterized in that the average value is an average value of at least the fiber orientation tensor of the surface layer portion and the fiber orientation tensor of the central portion. In claim 2 or 3,
A method for predicting the fiber orientation state of a fiber-reinforced plastic molded product, characterized in that the average value is the average value of the fiber orientation tensor obtained for each of the layers, the inner portion being divided into 7 to 18 layers in the depth direction. In any one of claims 1 to 4,
The method for predicting the fiber orientation state of a fiber-reinforced plastic molded product, wherein the predetermined depth in the outermost layer portion is 0.2 mm or less. In any one of claims 1 to 5,
A method for predicting the fiber orientation state of a fiber-reinforced plastic molded product, characterized in that the microelements of the analytical model are 2.5-dimensional thin-walled shell elements. An apparatus for predicting the orientation state of fibers contained in a fiber-reinforced resin injection molded product by computer simulation,
an analytical model creating means for creating an analytical model by dividing shape data of a cavity of a mold into a plurality of infinitesimal elements;
A flow analysis means for calculating a flow velocity of a resin contained in the fiber reinforced resin when the fiber reinforced resin is injected into the cavity;
and a fiber orientation tensor calculation means for calculating a fiber orientation tensor indicating an orientation state of the fibers based on the flow velocity of the resin,
The fiber orientation tensor calculation means calculates the fiber orientation tensor of the outermost layer based on the fiber orientation tensor of the inner layer, when the region in the resin from the surface in contact with the cavity to a predetermined depth is defined as the outermost layer, and the region deeper than the outermost layer is defined as the inner layer. This is a fiber orientation state prediction device for a fiber-reinforced plastic molded product, characterized in that In claim 7,
A fiber orientation tensor prediction device for a fiber-reinforced plastic molded product, characterized in that the fiber orientation tensor of the outermost layer portion is calculated as an average value of the fiber orientation tensors in the flow front portion of the inner portion. In claim 8,
The inner portion includes a surface layer portion in contact with the outermost surface layer portion and a center portion on the inner side of the surface layer portion,
A fiber orientation state prediction device for a fiber-reinforced plastic molded product, characterized in that the average value is an average value of at least the fiber orientation tensor of the surface layer portion and the fiber orientation tensor of the central portion. In claim 8 or claim 9,
A fiber orientation state prediction device for a fiber-reinforced plastic molded product, characterized in that the average value is the average value of fiber orientation tensors obtained for each of 7 to 18 layers obtained by dividing the inner portion in the depth direction. In any one of claims 7 to 10,
The fiber orientation state prediction device for fiber reinforced resin, characterized in that the predetermined depth in the outermost layer portion is 0.2 mm or less. In any one of claims 7 to 11,
A device for predicting the fiber orientation state of a fiber-reinforced plastic molded product, characterized in that the microelements of the analytical model are 2.5-dimensional thin-walled shell elements. A program for causing a computer to execute the method for predicting the fiber orientation state of a fiber-reinforced plastic molded product described in any one of claims 1 to 6. A computer-readable recording medium having the program described in claim 13 recorded thereon.

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