JP2019181801A - Shrinkage rate prediction device, shrinkage rate prediction model learning device, and program - Google Patents

Abstract

To be able to predict accurately a shrinkage rate due to cooling during molding of a molded product.SOLUTION: A molding analysis unit 82 acquires molding history information calculated from a resin molding analysis corresponding to a predetermined molding condition of a molded article model to be predicted obtained by injection molding of a resin material under a predetermined molding condition. An eigenvalue analysis unit 84 performs eigenvalue analysis on a fiber orientation tensor at an end of molding included in the molding history information. A shrinkage rate prediction unit 86 predicts a shrinkage rate of the molded article based on a prediction model learned in advance for predicting the shrinkage rate due to cooling during molding of the molded article from the molding history information of the molded article.SELECTED DRAWING: Figure 5

Description

  The present invention relates to a shrinkage rate prediction device, a shrinkage rate prediction model learning device, and a program, and in particular, a shrinkage rate prediction device, a shrinkage rate prediction model learning device for predicting a shrinkage rate due to cooling during molding of a molded product, And the program.

  2. Description of the Related Art Conventionally, a technique is known in which a resin flow is simulated so that shrinkage strain of a thermosetting resin can be predicted in consideration of a curing reaction, a temperature change, and a pressure change (Patent Document 1).

  In addition, for a molded product with a basic shape such as a flat plate, the shrinkage strain is obtained as a function of the flow length from the gate, plate thickness, molding temperature, holding pressure, and holding time by experiments and analysis in advance. A technique for predicting shrinkage strain is known (Patent Document 2).

  Also, by using the resin flow direction measured using a flat plate in advance and the shrinkage rate in the direction perpendicular to it, it is possible to predict the difference (anisotropy) in the shrinkage rate depending on the direction by combining with the resin flow simulation. A technique is known (Patent Document 3).

  In addition to the resin flow direction and the direction perpendicular to the resin flow direction, a technique for measuring the shrinkage rate in the plate thickness direction and predicting the shrinkage rate from an empirical formula using them is known (Patent Document 4).

  We also know technology for measuring the relationship between the birefringence, which represents the strength of molecular orientation, and the shrinkage rate in advance for flat molded products, etc., and combining the relationship with the resin flow simulation to predict the anisotropy of the shrinkage rate. (Patent Document 5).

  In addition, a technique is known in which shrinkage rate prediction is performed in consideration of a difference in crystallinity in a molded product using PVT characteristics at an arbitrary crystallinity (Patent Document 6).

  In addition, a technique is known in which PVT characteristics are expressed as a function of sheet thickness and mold temperature, and the prediction accuracy of volume shrinkage rate is improved using this (Patent Document 7).

  Further, a technique that proposes a correction formula for taking into consideration the cooling rate dependency based on the volume shrinkage calculated from the PVT characteristics is known (Patent Document 8).

JP 2012-101448 Japanese Patent Laid-Open No. 2002-49650 Japanese Patent Laid-Open No. 2003-103565 JP-A-8-230008 JP 2007-83602 Japanese Patent Laid-Open No. 9-262887 Japanese Unexamined Patent Publication No. 2000-313035 JP 2008-23974

  However, the technique described in Patent Document 1 cannot be directly applied to the shrinkage rate prediction of a thermoplastic resin.

  In the technique described in Patent Document 2, since the fiber orientation is not considered, the anisotropy of the shrinkage due to the orientation cannot be predicted.

  The anisotropy handled by the technique described in Patent Document 3 is based on the flow direction of the resin and cannot represent anisotropy due to fiber orientation.

  The anisotropy handled by the technique described in Patent Document 4 is based on the resin flow direction, as in Patent Document 3, and cannot represent anisotropy due to fiber orientation.

  Further, since the molecular orientation and the fiber orientation are different, even the technique described in Patent Document 5 cannot express anisotropy due to the fiber orientation.

  In addition, since the degree of crystallinity also changes due to the difference in cooling rate, the technique described in Patent Document 6 is one of prediction techniques that considers the cooling rate dependency. In addition, since the cooling rate changes depending on the plate thickness and the mold temperature, the technique described in Patent Document 7 is also one of the prediction techniques considering the cooling rate dependency.

  As described above, in the prior art, there are several proposals for predicting shrinkage rate depending on the cooling rate, but no proposal for predicting shrinkage rate anisotropy based on fiber orientation. Regarding the anisotropy prediction of the shrinkage rate, only the prediction of the anisotropy based on the flow direction and the prediction of the anisotropy due to the molecular orientation can be seen. In the above-mentioned Patent Document 8, there is a description that the orientation analysis is performed and the distribution of shrinkage strain in each direction, that is, the anisotropy is considered based on the result, but a specific method is shown. Absent. The relationship between the orientation degree of the filler and the shrinkage strain is not a simple relationship (for example, if the orientation degree is doubled, the shrinkage strain becomes 1/2). It is difficult to consider anisotropy from a simple description alone.

  The present invention has been made in view of the above circumstances, and provides a shrinkage rate prediction device, a shrinkage rate prediction model learning device, and a program capable of accurately predicting a shrinkage rate due to cooling during molding of a molded product. For the purpose.

  In order to achieve the above object, the shrinkage rate predicting apparatus according to the first invention corresponds to the predetermined molding condition of a molded article model to be predicted obtained by injection molding of a resin material under a predetermined molding condition. Based on a molding history acquisition unit that acquires molding history information calculated from resin molding analysis, and a pre-learned prediction model for predicting the shrinkage rate due to cooling during molding of the molded product from the molding history information of the molded product A shrinkage rate prediction unit that predicts the shrinkage rate of the molded product with respect to the molding history information acquired by the molding history acquisition unit.

  According to a second aspect of the present invention, there is provided a program for calculating a molding calculated from a resin molding analysis corresponding to a predetermined molding condition of a predicted molding model obtained by injection molding of a resin material under a predetermined molding condition. Based on a molding history acquisition unit that acquires history information, and a molding history acquisition unit that predicts a shrinkage rate due to cooling during molding of the molded product from the molding history information of the molded product. It is a program for functioning as a shrinkage rate prediction unit for predicting the shrinkage rate of the molded product with respect to the obtained molding history information.

  According to the first invention and the second invention, the resin corresponding to the predetermined molding condition of the molded article model to be predicted obtained by the molding history obtaining unit by injection molding of the resin material under the predetermined molding condition. Obtain molding history information calculated from molding analysis. Then, the shrinkage rate prediction unit is acquired by the molding history acquisition unit based on a previously learned prediction model for predicting the shrinkage rate due to cooling during molding of the molded product from the molding history information of the molded product. The shrinkage rate of the molded product with respect to the molding history information is predicted.

  Thus, based on the prediction model for predicting the shrinkage rate due to cooling during molding of the molded product from the molding history information of the molded product, the shrinkage rate of the molded product with respect to the acquired molding history information is calculated. By predicting, the shrinkage rate due to cooling during the molding of the molded product can be accurately predicted.

  The shrinkage rate prediction model learning device according to the third invention is calculated from a resin molding analysis corresponding to the predetermined molding condition of an actual molded product obtained by injection molding of a resin material under a predetermined molding condition. Based on the molding history acquisition unit that acquires molding history information, the shrinkage rate due to cooling during molding measured for the actual molded product, and the molding history information acquired by the molding history acquisition unit. And a learning unit that learns a prediction model for predicting the shrinkage rate of the molded product from the molding history information.

  According to a fourth aspect of the present invention, there is provided a program for calculating a molding history calculated from a resin molding analysis corresponding to a predetermined molding condition of an actual molded product obtained by injection molding of a resin material under a predetermined molding condition. A molding history acquisition unit that acquires information, and a molding history of the molded product based on the shrinkage rate due to cooling during molding and the molding history information acquired by the molding history acquisition unit, measured for the actual molded product It is a program for functioning as a learning unit for learning a prediction model for predicting the shrinkage rate of the molded product from information.

  According to the third invention and the fourth invention, the molding history acquisition unit is a resin molding corresponding to the predetermined molding condition of an actual molded product obtained by injection molding of the resin material under the predetermined molding condition. Acquire molding history information calculated from the analysis. Then, the learning unit measures the molding from the molding history information of the molded product based on the shrinkage rate due to cooling during molding and the molding history information acquired by the molding history acquisition unit, measured for the actual molded product. Learn predictive models for predicting product shrinkage.

  As described above, the actual shrinkage obtained by injection molding of the resin material under the predetermined molding conditions was measured, and the shrinkage ratio due to cooling during molding and the resin molding analysis of the molded article corresponding to the predetermined molding conditions. The accuracy of shrinkage due to cooling during molding of a molded product is learned by learning a prediction model for predicting the shrinkage rate of the molded product from the molding history information of the molded product based on the molding history information calculated from Can be predicted well.

  As described above, according to the shrinkage rate prediction device, the shrinkage rate prediction model learning device, and the program of the present invention, it is possible to accurately predict the shrinkage rate due to cooling during molding of a molded product. It is done.

It is a block diagram showing a contraction rate prediction model learning device and a contraction rate prediction device according to an embodiment of the present invention. It is a functional block diagram which shows the shrinkage | contraction rate prediction model learning apparatus which concerns on embodiment of this invention. It is a figure for demonstrating the method to measure the shrinkage rate of a molded article. It is a figure which shows the example of the result of having measured the shrinkage rate of the molded article. It is a functional block diagram which shows the shrinkage | contraction rate prediction apparatus which concerns on embodiment of this invention. It is a flowchart which shows the content of the contraction rate prediction model learning process routine of the contraction rate prediction model learning apparatus which concerns on embodiment of this invention. It is a flowchart which shows the content of the shrinkage rate prediction process routine of the shrinkage rate prediction apparatus which concerns on embodiment of this invention. It is a figure which shows the comparison result of the predicted value of shrinkage | contraction rate, and an actual value.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

<Outline of Embodiment of the Present Invention>
In the embodiment of the present invention, since the prediction formula is created using the actually measured shrinkage rate, it is possible to predict the shrinkage rate with high accuracy.

  On the other hand, even if the actual measurement value of the shrinkage rate is used, unless an appropriate molding history parameter is used, the accuracy of fitting to the actual measurement value is lowered, and it is difficult to predict the shrinkage rate with high accuracy. Therefore, in the embodiment of the present invention, the molding history parameter is selected based on the following concept.

  The linear shrinkage rate obtained from the resin molding analysis is calculated based on the PVT characteristics, reflecting the temperature history and pressure history during molding. Therefore, it is an appropriate method for calculating the shrinkage rate. However, since the PVT characteristic data is measured under slow cooling conditions, there is a decrease in accuracy in predicting the rapid cooling state in actual molding. In addition, since the linear shrinkage rate is calculated on the assumption that the volume shrinkage rate is isotropic, the ratio of each direction is unknown in anisotropic materials such as fiber reinforced resin, and the shrinkage rate in each direction cannot be obtained. . However, since there are no major factors that reduce the accuracy other than the above two points, it is considered that the actual shrinkage rate can be expressed by correcting the linear shrinkage rate obtained from the analysis.

  Therefore, the linear contraction rate is used as a reference parameter to be corrected.

  Next, the correction considering rapid cooling was performed according to the following policy. That is, correction of the absolute value of the contraction rate is performed using the actually measured value. Therefore, it is only necessary that the parameter representing the rapid cooling can express the difference in relative cooling rate for each part in the molded product. For this purpose, in the embodiment of the present invention, the temperature change immediately after filling is used as a parameter. In addition to this, any parameter can be used as long as it can represent the difference in cooling rate for each part. However, the higher the resin temperature, the easier the difference between the parts, so the temperature change immediately after filling is preferable.

  In addition, in order to correct the anisotropy, that is, to correct the linear shrinkage obtained by assuming isotropic property, the fiber orientation tensor was used as a parameter in order to obtain the shrinkage in each direction. Since this tensor is an amount representing the fiber orientation state obtained as a result of fiber orientation analysis, it is optimal for representing anisotropy based on fiber orientation. In applying this tensor, eigenvalue analysis was performed to determine eigenvalues in each principal axis direction, that is, the degree of orientation. In the general coordinate system, a shear component of strain may also occur during contraction, but in the main axis coordinate system, since the direction of each coordinate axis is the principal axis direction of the fiber orientation, it is assumed that no shear component occurs in the contraction strain. It was thought that the normal component of, ie the contraction rate in the main axis direction, can be expressed by the degree of orientation in the main axis direction.

  From the above, the shrinkage rate in each principal axis direction (this can be derived by coordinate transformation of strain from the actual shrinkage rate) is obtained assuming the corresponding orientation degree in the principal axis direction, the temperature change immediately after filling, and isotropic properties. The prediction formula was determined so that it could be expressed by three parameters of the shrinkage rate. Further, it is difficult to think that the degree of orientation and the contribution of temperature change that play a role in correcting the linear shrinkage rate are linear. Therefore, in the prediction formula, their contribution is expressed by a polynomial using the degree of orientation and temperature change, and the shrinkage rate prediction performance is improved.

  Also, in each part of the molded product, the resin shrinkage analysis calculates the degree of orientation in the principal axis direction, the temperature change immediately after filling and the isotropic property, and calculates the linear shrinkage rate. Since the shrinkage rate is calculated, the shrinkage rate distribution in the molded product can be predicted with high accuracy.

<Configuration of Shrinkage Rate Prediction Model Learning Device of Embodiment of the Present Invention>
As shown in FIG. 1, a contraction rate prediction model learning device 10 according to an embodiment of the present invention includes a CPU 12, a ROM 14, a RAM 16, an HDD 18, a communication interface 21, and a bus 22 for connecting them together. Yes.

  The CPU 12 executes various programs. The ROM 14 stores various programs, parameters, and the like. The RAM 16 is used as a work area when the CPU 12 executes various programs. The HDD 18 as a recording medium stores various programs and various data including a program for executing a contraction rate prediction model learning processing routine described later.

  When the contraction rate prediction model learning device 10 according to the present embodiment is expressed as a functional block along a program for executing a contraction rate prediction model learning processing routine, it is as shown in FIG. The contraction rate prediction model learning device 10 includes an input unit 20, a calculation unit 30, and an output unit 50.

  The input unit 20 is measured for an actual molded product obtained by injection molding of the resin material under the predetermined molding condition for each of the predetermined molding conditions, and a shrinkage rate due to cooling during molding, shape data, The predetermined molding conditions and information indicating the material characteristics of the resin are received as inputs.

  The shrinkage rate due to cooling during the molding of the actual molded product that is input is, for example, a measurement of the change in the spacing between the marking lines applied to the mold.

  Here, an example of the result of measuring the shrinkage rate for glass fiber (20 wt%) reinforced polypropylene is shown.

  For example, as shown in FIG. 3A, a flat plate with a marking line is formed by standard molding conditions (resin temperature: 230 ° C., mold temperature: 70 ° C., filling time: 0.75 sec, holding pressure: 20 MPa, holding pressure time. : 7 seconds, cooling time: 20 seconds). Further, injection molding is performed under molding conditions in which conditions other than the cooling time are changed to mold a flat plate.

  The distance between the marking lines and the plate thickness in the x and y directions are measured for each square (FIG. 3B) surrounded by the marking lines, and the shrinkage rate in each direction is obtained. As an example of the measurement result, FIG. 4 shows the measurement result of the shrinkage in the x direction at A-A ′ in FIG.

  In addition, the shrinkage | contraction rate by cooling during the shaping | molding of the actual molded article input may be measured by processing the change of the embossed pattern given to the metal mold | die by the image correlation method.

  The calculation unit 30 includes a shaping analysis unit 32, an eigenvalue analysis unit 34, and a prediction model learning unit 36. The molding analysis unit 32 is an example of a molding history acquisition unit.

  The molding analysis unit 32 performs resin molding analysis of a finite element of a molded product model obtained by modeling the actual molded product corresponding to a predetermined molding condition, and calculates molding history information for each finite element. The molding history information represents a history that the resin material receives during molding, and includes a temperature change of the resin, a fiber orientation tensor at the end of molding, and a linear shrinkage rate.

For example, a resin model analysis corresponding to each molding condition is performed by a resin molding analysis software 3DTIMON (Toray Engineering (R)) using a shape model obtained by dividing a flat molded product by finite elements as a molded product model. Thereby, the distribution of the fiber orientation tensor D, the linear shrinkage ε ₀ , and the temperature change ΔT immediately after filling in the flat plate for each molding condition is obtained.

The eigenvalue analysis unit 34 performs eigenvalue analysis on the fiber orientation tensor at the end of molding obtained for each finite element of the molded product model, and performs three principal axis directions (eigenvectors) and orientations in the respective axial directions. Degree (eigenvalue) λ _i (i = 1,2,3) is obtained.

  The predictive model learning unit 36 measures the actual shrinkage obtained by injection molding of the resin material under the predetermined molding conditions, the shrinkage rate due to cooling during molding, and the molding model corresponding to the predetermined molding conditions. Based on the molding history information calculated from the resin molding analysis, a prediction model for predicting the shrinkage rate of the molded product from the molding history information of the molded product is learned.

  Here, the principle of learning the coefficient of the prediction formula as the prediction model will be described.

As described in the outline of the embodiment of the present invention, it is assumed that the contraction rate ε _{i in} each principal axis direction can be expressed by the equation (1) by using a polynomial with λ _i , ΔT, and ε ₀ . Here, a _k (k = 0 to 25) is a coefficient. Also, assuming that no shear component of strain occurs during shrinkage in the main axis coordinate system, the shrinkage rate ε _X (X = x, y, z) in the overall coordinate system defined for the entire part is the coordinate transformation of the strain. Is expressed by equation (2).

(1)
here,


(2)

Furthermore, the shrinkage rate E _X (X = x, y, z) measured in the unit of the grid in the molded product is expressed by the equation (3) using ε _{X of} each finite element constituting the grid. Assuming that the volume average is obtained, λ _i , ΔT and ε ₀ calculated in each element unit from the resin molding analysis and E _X actually measured in the grid unit are related by the equations (1) to (3). The coefficient a _k is obtained by multiple regression analysis.

(3)

Where l ₁ represents the direction cosine between the main axis 1 and the x axis, l ₂ represents the direction cosine between the main axis 2 and the x axis, and l ₃ represents the difference between the main axis 3 and the x axis. M ₁ represents the direction cosine between the main axis 1 and the y axis, m ₂ represents the direction cosine between the main axis 2 and the y axis, and m ₃ represents the main cosine 3 represents the direction cosine between the y-axis, n ₁ represents the direction cosine between the main axis 1 and the z-axis, n ₂ represents the direction cosine between the main axis 2 and the z-axis, and n ₃ represents , Represents the direction cosine between the main axis 3 and the z-axis. V _k represents the volume of the finite element k.

In accordance with the principle described above, the prediction model learning unit 36 matches the shrinkage rate actually measured for the square with the shrinkage rate calculated for the square according to the above equation (3) for each square unit. As described above, the coefficient a _k (k = 0 to 25) is learned by multiple regression analysis, and the learning result is output by the output unit 50.

Here, the shrinkage rate calculated for the square by the above equation (3) is the above-described prediction formula for each finite element included in the square and the temperature change of the resin obtained for the finite element. , And the linear shrinkage rate, the three principal axis directions (eigenvectors) obtained from the eigenvalue analysis of the fiber orientation tensor at the end of molding for the finite element, and the degree of orientation (eigenvalue) λ _i (i = 1, 2,3) and calculated.

<Configuration of Shrinkage Rate Prediction Device of Embodiment of the Present Invention>
As shown in FIG. 1, the shrinkage rate prediction apparatus 60 according to the embodiment of the present invention includes a CPU 12, a ROM 14, a RAM 16, an HDD 18, a communication interface 21, and a bus 22 for connecting them to each other. .

  The CPU 12 executes various programs. The ROM 14 stores various programs, parameters, and the like. The RAM 16 is used as a work area when the CPU 12 executes various programs. The HDD 18 as a recording medium stores various programs and various data including a program for executing a shrinkage rate prediction processing routine described later.

  When the contraction rate prediction device 60 in the present embodiment is represented by a functional block along a program for executing a contraction rate prediction processing routine, it is as shown in FIG. The contraction rate prediction device 60 includes an input unit 70, a calculation unit 80, and an output unit 90.

  The input unit 70 receives, as inputs, shape data of a predicted molded product obtained by injection molding of a resin material under predetermined molding conditions, information indicating the predetermined molding conditions and resin material characteristics.

  The calculation unit 80 includes a molding analysis unit 82, an eigenvalue analysis unit 84, and a shrinkage rate prediction unit 86. The molding analysis unit 82 is an example of a molding history acquisition unit.

  The molding analysis unit 82 performs injection molding of the resin material under the predetermined molding conditions in accordance with the predetermined molding conditions based on the input shape data, predetermined molding conditions, and information indicating the material characteristics of the resin. The resin molding analysis of the finite element of the molding model to be predicted obtained by the above is performed, and the molding history information is calculated for each finite element. The molding history information represents a history that the resin material receives during molding, and includes a temperature change of the resin, a fiber orientation tensor at the end of molding, and a linear shrinkage rate. The molding history information may further include resin pressure change, shear rate change, viscosity change, and the like.

The eigenvalue analysis unit 84 performs eigenvalue analysis on the fiber orientation tensor at the end of molding obtained for each finite element of the molding model to be predicted, and performs three principal axis directions (eigenvectors) and each axis. The degree of orientation (eigenvalue) λ _i (i = 1, 2, 3) is obtained.

The shrinkage rate prediction unit 86 includes, for each finite element of the prediction target molded product model, a prediction formula as a prediction model learned by the shrinkage rate prediction model learning device 10, and a temperature change of the resin obtained for the finite element, And the linear shrinkage rate, and the three principal axis directions (eigenvectors) and the degree of orientation (eigenvalues) λ _i (i = 1,2) obtained from the eigenvalue analysis of the fiber orientation tensor at the end of molding for the finite element. 3), the shrinkage rate distribution of the prediction target molded product is predicted by predicting the shrinkage rate of the finite element with respect to the molding history information according to the above formulas (1) to (2).

  Specifically, for each finite element of the molding model to be predicted, in the principal axis coordinate system obtained by eigenvalue analysis of the fiber orientation tensor, the above (1) ) To determine each component of contraction strain in the main axis coordinate system, and to determine each component of contraction strain in the global coordinate system according to the above equation (2) based on each component of contraction strain in the main axis coordinate system. The shrinkage rate of the finite element is predicted based on each component of shrinkage strain in the global coordinate system.

The output unit 90 outputs the shrinkage rate predicted for each finite element of the prediction target molded article model as a prediction result of the shrinkage rate distribution. Then, for example, warpage deformation of the molded product is predicted from the shrinkage rate distribution of the predicted molded product model. When warping deformation is predicted, if the shear components (γ _yz , γ _zx , γ _xy ) of the shrinkage strain in the global coordinate system are required, they can be obtained from the equation (4). In addition, the prediction result of the shrinkage rate distribution of the molding model to be predicted may be used for deformation prediction other than warp deformation.

(4)

<Operation of shrinkage prediction model learning device>
Next, the operation of the contraction rate prediction model learning device 10 according to the embodiment of the present invention will be described.

  The shrinkage rate due to cooling during molding, shape data, the predetermined molding conditions, and the material characteristics of the resin, measured for the actual molded product obtained by injection molding of the resin material under the predetermined molding conditions by the input unit 20 6 is received as an input, the contraction rate prediction model learning apparatus 10 executes a contraction rate prediction model learning process routine shown in FIG.

  First, in step S100, the molding analysis unit 32 performs resin molding analysis of a finite element of a molded product model obtained by modeling the actual molded product corresponding to a predetermined molding condition, and a molding history for each finite element. Calculate information.

In step S102, the eigenvalue analysis unit 34 performs eigenvalue analysis on the fiber orientation tensor at the end of molding obtained for each finite element of the molded product model, and performs three principal axis directions (eigenvectors) and each. Obtain the degree of axial orientation (eigenvalue) λ _i (i = 1,2,3).

  In step S <b> 104, the prediction model learning unit 36 corresponds to the shrinkage rate due to cooling during molding and the predetermined molding conditions measured for an actual molded product obtained by injection molding of the resin material under the predetermined molding conditions. Based on the molding history information calculated from the resin molding analysis of the molded product model and the eigenvalue analysis result, a prediction model for predicting the shrinkage rate of the molded product from the molding history information of the molded product is learned, and the output unit 50 To terminate the shrinkage rate prediction model learning process routine.

<Operation of shrinkage rate prediction device>
Next, the operation of the contraction rate prediction apparatus 60 according to the embodiment of the present invention will be described.

  When the input unit 70 receives, as input, shape data of a molded article to be predicted obtained by injection molding of a resin material under a predetermined molding condition, and information indicating the predetermined molding condition and the material characteristics of the resin, the shrinkage rate prediction is performed. The contraction rate prediction processing routine shown in FIG.

  First, in step S110, the molding analysis unit 82 corresponds to the predetermined molding condition based on the input shape data, the predetermined molding condition, and information indicating the material characteristics of the resin. Resin molding analysis of a finite element of a molding model to be predicted obtained by injection molding of a resin material is performed, and molding history information is calculated for each finite element.

In step S112, the eigenvalue analysis unit 84 performs eigenvalue analysis on the fiber orientation tensor at the end of molding obtained for the finite element for each finite element of the molding model to be predicted, and performs three principal axis directions (eigenvectors). ) And the degree of orientation (eigenvalue) λ _i (i = 1, 2, 3) in each axial direction.

In step S <b> 114, the shrinkage rate prediction unit 86 calculates a prediction formula as a prediction model learned by the shrinkage rate prediction model learning device 10 for each finite element of the prediction target molded product model, and the resin obtained for the finite element. The three principal axis directions (eigenvectors) obtained from the eigenvalue analysis of the fiber orientation tensor at the end of molding of the finite element and the degree of orientation (eigenvalue) λ _i (i = 1, 2, 3) According to the above formulas (1) to (2), by predicting the shrinkage rate of the finite element for the molding history information, the shrinkage rate distribution of the molding to be predicted Is output by the output unit 90, and the contraction rate prediction processing routine is terminated.

  As described above, according to the shrinkage rate prediction apparatus according to the embodiment of the present invention, based on the prediction model for predicting the shrinkage rate due to cooling during molding of the molded product from the molding history information of the molded product, By predicting the shrinkage rate of the molded product with respect to the acquired molding history information, the shrinkage rate due to cooling during molding of the molded product can be accurately predicted.

Here, using a _k obtained by shrinkage prediction model learning device, the (1) ~ (3) E X calculated from equation was examined whether the measured values can extent reproduced. The result is shown in FIG. The correlation coefficient between them is 0.92, and it can be seen that the measured shrinkage rate can be almost predicted using the equations (1) to (3).

  Further, according to the shrinkage rate prediction model learning device according to the embodiment of the present invention, the shrinkage rate due to cooling during molding, measured for an actual molded product obtained by injection molding of a resin material under predetermined molding conditions. And a prediction model for predicting the shrinkage rate of the molded product from the molding history information of the molded product based on the molding history information calculated from the resin molding analysis of the molded product model corresponding to a predetermined molding condition. By doing so, it is possible to accurately predict the shrinkage rate due to cooling during the molding of the molded product.

  Further, since the shrinkage rate calculated by general resin molding analysis software is a linear shrinkage rate, it is isotropic and gives an accurate value only in the case of slow cooling. In the embodiment of the present invention, in order to correct the linear shrinkage rate by the fiber orientation tensor calculated from the actually measured shrinkage rate and the resin molding analysis and the temperature change after filling, it is anisotropic and is used for rapid cooling during actual molding. Corresponding shrinkage can be predicted.

  Further, the warp deformation of the resin molded product appears as a result of contraction of each part in the molded product as a whole. According to the embodiment of the present invention, the shrinkage ratio distribution of a molded product can be predicted with high accuracy. As a result, the accuracy of warping deformation analysis of the molded product can be improved.

  Note that the present invention is not limited to the above-described embodiment, and various modifications and applications are possible without departing from the gist of the present invention.

  For example, in the above-described embodiment, the case where the prediction formula of the shrinkage rate is learned by the linear multiple regression analysis from the actually measured shrinkage rate and the calculated molding history information is described as an example. Instead, the prediction model of the contraction rate may be learned using nonlinear multiple regression analysis using a neural network method or the like.

  Also, the case where the contraction rate prediction model learning device and the contraction rate prediction device are configured as separate devices has been described as an example, but the present invention is not limited to this, and the contraction rate prediction model learning device and the contraction rate prediction device May be configured as one apparatus.

  Moreover, you may apply this invention with respect to molded articles other than a flat plate. The program of the present invention may be provided by being stored in a storage medium.

DESCRIPTION OF SYMBOLS 10 Shrinkage rate prediction model learning apparatus 20, 70 Input part 30, 80 Calculation part 32, 82 Molding analysis part 34, 84 Eigenvalue analysis part 36 Prediction model learning part 50, 90 Output part 60 Shrinkage rate prediction apparatus 86 Shrinkage rate prediction part

Claims (9)

A molding history acquisition unit that acquires molding history information calculated from a resin molding analysis corresponding to the predetermined molding condition of a prediction target molded product model obtained by injection molding of a resin material under a predetermined molding condition;
The molding with respect to the molding history information acquired by the molding history acquisition unit based on a previously learned prediction model for predicting the shrinkage rate due to cooling during molding of the molded product from the molding history information of the molded product. A shrinkage rate prediction unit for predicting the shrinkage rate of the product,
A shrinkage rate prediction apparatus including:   The shrinkage rate prediction apparatus according to claim 1, wherein the molding history information represents a history received by the resin material during molding, and includes a temperature change of the resin, a fiber orientation tensor at the end of molding, and a linear shrinkage rate. The molding history acquisition unit acquires molding history information calculated from a resin molding analysis of the element corresponding to the predetermined molding condition for each element of the molded product model,
The shrinkage rate prediction unit predicts the shrinkage rate of the element with respect to the molding history information acquired by the molding history acquisition unit based on the prediction model for each element of the molded product model. The shrinkage rate prediction apparatus according to claim 1, wherein the shrinkage rate distribution of a molded product is predicted. The molding history information represents the history that the resin material undergoes during molding, and includes the temperature change of the resin, the fiber orientation tensor at the end of molding, and the linear shrinkage rate,
The shrinkage rate prediction unit assumes that no shear component occurs during shrinkage due to cooling in the principal axis coordinate system obtained by eigenvalue analysis of the fiber orientation tensor for each element of the molded product model. Each component of the contraction strain in the main axis coordinate system, and each component of the contraction strain in the global coordinate system based on each component of the contraction strain in the main axis coordinate system. The shrinkage rate predicting apparatus according to claim 3, wherein the shrinkage rate of the element is predicted based on each component of shrinkage strain at. A molding history acquisition unit that acquires molding history information calculated from a resin molding analysis corresponding to the predetermined molding condition of an actual molded product obtained by injection molding of a resin material under a predetermined molding condition;
The shrinkage rate of the molded product is predicted from the molding history information of the molded product based on the shrinkage rate due to cooling during molding and the molding history information acquired by the molding history acquisition unit measured for the actual molded product. A contraction rate prediction model learning device including: a learning unit that learns a prediction model for performing the operation.   The shrinkage rate prediction model learning device according to claim 5, wherein the forming history information represents a history received by the resin material during forming, and includes a temperature change of the resin, a fiber orientation tensor at the end of forming, and a linear shrinkage rate. .   By measuring the change in the interval between the marking lines applied to the molded product by the mold, or by processing the change in the embossed pattern applied to the molded product by the mold using the image correlation method, the actual molded product shrinks. The contraction rate prediction model learning device according to claim 5 or 6, wherein the rate is measured. Computer
A molding history acquisition unit that acquires molding history information calculated from a resin molding analysis corresponding to the predetermined molding condition of a molding model to be predicted obtained by injection molding of a resin material under a predetermined molding condition, and molding The molded product with respect to the molding history information acquired by the molding history acquisition unit based on a previously learned prediction model for predicting the shrinkage rate due to cooling during molding of the molded product from the molding history information of the product. A program for functioning as a contraction rate predicting unit for predicting the contraction rate of. Computer
A molding history acquisition unit that acquires molding history information calculated from a resin molding analysis corresponding to the predetermined molding condition of an actual molded product obtained by injection molding of a resin material under a predetermined molding condition, and the actual In order to predict the shrinkage rate of the molded product from the molding history information of the molded product based on the shrinkage rate due to cooling during molding and the molding history information acquired by the molding history acquisition unit. A program for functioning as a learning unit for learning prediction models.