JPH10138310A - Method for determining optimal molding conditions for injection molding machine - Google Patents

Abstract

(57) [Summary] Prior to designing and manufacturing a mold for injection molding, an injection in which the deformation amount and the surface roughness amount of the outer shape of a resin molded product manufactured by the mold are within desired values. The optimum molding conditions set for the molding machine are derived. Kind Code: A1 A deformation amount and a degree of deformation of a molded article finite element model data for CAE analysis.
(Steps S5 and S7), and until the deformation amount and the degree of the deformation are within desired values, the product is redesigned by the CAD system (Step S1) and the CAE analysis (Steps S3 and S4) is performed. After determining the molding conditions 63A, the mold is designed and manufactured (steps S9 and S1).
0). Therefore, it is possible to determine the optimum molding condition 63A that can solve the problem (deformation amount) generated in the final resin molded product before designing the mold.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

TECHNICAL FIELD The present invention relates to, for example, CAD
The present invention relates to a method for determining an optimum molding condition of an injection molding machine capable of producing a resin molded product faithful to a resin molded product model created by a system.

[0002]

2. Description of the Related Art Conventionally, when manufacturing a resin molded product by injection molding of a thermoplastic resin, based on a product design drawing,
The mold to be mounted on the injection molding machine is manufactured based on the experience and know-how of a skilled worker, the mold is mounted on the injection molding machine, and a test shot is performed. The process of correcting the mold based on the measurement result and further performing the test punching has been repeated until a desired resin molded product is obtained.

[0003] Therefore, a great deal of labor and time have been required to produce a desired mold for obtaining a desired resin molded product.

In order to solve this problem, recently, a computer assisted engineering (CAE) analysis method capable of performing various simulations without actually manufacturing a mold has been proposed.

[0005] This CAE analysis method firstly applies to the molded article model data corresponding to the final resin molded article created by the computer assisted design (CAD) system.
A supplementary condition such as a gate and a runner is added to create finite element model data of a molded article composed of finite elements for CAE analysis. Next, CAE analysis is performed to simulate the flow and solidification state of the molten metal in the mold during injection molding using the created molded article finite element model data, and the mold shape and molding conditions (injection molding Temperature and pressure profiles to be set on the machine).

[0006]

However, a mold designed and manufactured according to the mold shape determined by using the CAE analysis method is mounted on an actual injection molding machine, and the molding conditions are similarly determined by the CAE analysis method. When the actual injection molding of a thermoplastic resin is performed, the outer shape of the completed resin molded product can be almost the desired shape, but the unevenness of the surface of the completed resin molded product, that is, the surface roughness, Because the amount of deformation (defective molding) such as deformation is not taken into account, the amount of deformation often exceeds the specification.

In order to set the amount of deformation to a desired value within the specifications, the redesign and manufacture of the mold including the incidental conditions of the gate, the runner, etc. based on the experience and know-how of the above-mentioned expert, and the molding condition It is necessary to make changes, and after all, as long as several types of dies are not mounted on the injection molding machine and test punching is repeated, a desired resin molded product that satisfies both the deformation amount and the deformation amount of the external shape is The problem that the mold and the molding conditions of the injection molding machine to obtain cannot be determined has not been solved yet.

[0008] The determination of the molding conditions of the mold and the injection molding machine is required every time a new molded resin product is designed and manufactured.

The present invention has been made in view of the above problems, and the amount of deformation and the amount of deformation of a resin molded product are within specifications (desired values) before designing and manufacturing a mold. It is an object of the present invention to provide a method for determining an optimum molding condition of an injection molding machine, which makes it possible to determine an optimum molding condition to be satisfied.

[0010]

According to the present invention, for example, as shown in FIG. 2, a first step of creating molded article model data 70 corresponding to a resin molded article as a product by a CAD system (step S1). After adding additional conditions such as gates and runners to the created molded article model data (step S2), molded article finite element model data 70B composed of CAE analysis finite elements is created (step S3). CAE is calculated from the finite element model data
An analysis is performed to derive molding conditions (step S4), a deformation amount of the product outer shape is calculated (step S5), and molded product model data is created by the CAD system until the deformation amount falls within a desired amount. A second process of obtaining desired molded product model data 70B and molding condition data 63A of a CAE analysis result by repeating the first process to the operation of calculating the deformation amount, and a molded product finite element for the CAE analysis. A third step of predicting the degree of deformation at each node position of the finite element constituting the model data (step S7), and, when the degree of deformation exceeds a desired value, the desired value A fourth step (step S8) of repeating the second and subsequent steps until the value becomes within
When the degree of the deformation is within the desired value, after designing and manufacturing a mold based on the data obtained by adding the incidental condition to the molded article model data, the mold is inserted into an injection molding machine. And a fifth step (steps S9 to S11) of performing injection molding by determining the molding condition data finally obtained in the second step as optimal molding conditions.

According to the present invention, the deformation amount and the degree of deformation (degree) are predicted with respect to the molded article finite element model data for CAE analysis, and the deformation amount and the deformation degree are determined to be within desired values. Until the product redesign and CAE analysis are performed by CAD, the optimum molding conditions are determined. After that, the mold is designed and manufactured. For this reason,
It is possible to determine the optimum molding conditions that can solve the problem (deformation amount) generated in the final resin molded product before designing the mold.

In this case, the degree of deformation is determined by a multiple regression equation in which the explanatory variables are a thickness value, a pressure integral value, a pressurizing time, and a resin inflow temperature value at each node position forming each of the finite elements. You can ask.

[0013]

An embodiment of the present invention will be described below with reference to the drawings.

FIG. 1 shows the configuration of an injection molding system 10 to which an embodiment of the present invention is applied.

The injection molding system 10 is basically a CAD system 12 composed of computers.
And a CAE system 14, a cause analysis system 16, and an injection molding machine 18 for actually manufacturing the resin molded product 60.

The injection molding machine 18 injects the thermoplastic resin stored in the hopper 25 into the cavity 20 by pushing a mold 21 having a cavity 20 and a screw 23 in a cylinder 22 toward the mold 21. And an injection ram 24.

The injection molding machine 18 includes a hydraulic pressure sensor 31 for detecting a hydraulic pressure related to the hydraulic control of the injection ram 24, a position sensor 32 for detecting the amount of movement of the screw 23, and a nozzle pressure sensor provided at the resin injection port. 33, a resin temperature sensor 34 also provided at the resin inlet, a cavity pressure sensor 35 for measuring the pressure and the resin temperature in the cavity 20 formed by the mold 21, and a resin temperature sensor 37, respectively. Mold opening sensor 3 for detecting opening and closing of mold 21
6, a mold surface temperature sensor 38, a mold cooling water temperature sensor 39, and a product removal temperature sensor 40 are attached.

The output signals of these sensors 31 to 40 are
The signal is supplied to the computer 43 constituting the cause analysis system 16 through each amplifier 41 of the interface box 42, and the AD converter 4 in the computer 43 is provided.
4 changes to a digital signal (digital data). This digital signal is stored in an R storage unit under the control of the CPU 45 which functions as each unit for judgment, control and calculation.
The data is stored in the AM 46 and, if necessary, stored in the hard disk 47 as a non-volatile storage means.

The CPU 45 has a ROM 5 as storage means for storing system programs, control programs, and the like.
0, a keyboard 51 as input means for characters and symbols, and a CRT display 52 as display means are connected. The CPU 45 includes a three-dimensional shape measuring device 62 for measuring a three-dimensional shape (outer shape) of the resin molded product 60 as a product manufactured by the injection molding machine 18, and a surface irregularity of the resin molded product 60. Is connected to a surface roughness meter 61 for measuring the amount of deformation (defective molding) such as surface roughness.

Cause analysis system 16 and CAE system 1
4 and the CAD system 12, in this embodiment, C
It is configured such that data and the like can be mutually communicated via the AE system 14. Further, molding conditions (also referred to as molding condition data) 63 determined by the CAE system 14 are set in the molding machine control panel 64.

Various set values actually set on the molding machine control panel 64 are taken into the cause analysis system 16 as molding machine control data. The injection start signal and the mold closing signal are transmitted from the interface box 42 by the molding machine control panel 64.
At the same time, actual control (oil pressure control, injection control, temperature control) of the injection molding machine 18 by the molding machine control panel 64 is executed.

Next, the operation of the injection molding system 10 to which the above-described embodiment is applied will be described in detail with reference to a flowchart shown in FIG.

In the injection molding system 10, first, molded article model data 70, which is CAD data corresponding to a resin molded article 60 as a product, is created by the CAD system 12 (step S1). In this case, for example, the molded article model data 70 of the resin bumper of the automobile is created based on the design drawing, and the molded article model data 70 created by the CAD system 12 is supplied to the CAE system 14. The reference numeral of the desired resin molded product (resin molded product without deformation and deformation) corresponding to the molded product model data 70 is represented by 60A (see the shape indicated by the solid line in FIG. 3).

Next, on the CAE system 14, additional condition data of a mold such as a gate and a runner is automatically or manually added to the molded product model data 70 (step S2).

Next, an analytical model is formed on the CAE system 14 with respect to the molded article model data 70A to which the additional condition data such as the gate and the runner is added (step S3). In this case, molded article model data 7 with incidental conditions
0A is automatically decomposed into three-dimensional finite elements and CAE
Molded article finite element model data 70B including finite elements for analysis is created.

Next, in the CAE system 14, the molded article finite element model data 70B and the specifications and characteristics of the thermoplastic resin such as viscosity, specific heat, and PVT (pressure / volume / temperature) values supplied from the storage means 73 are provided. Material data 74 representing
CA based on the performance (specification) data (molding machine data) 76 of the injection molding machine 18 supplied from the storage means 75
An E-analysis simulation is performed to calculate molding conditions (for example, molding conditions including a pressure profile, a resin temperature profile, a mold temperature profile, etc., and hereinafter, also referred to as optimal molding condition data 63A) for calculating an optimal deformation amount. (Step S4).

The optimum molding condition data 63A is based on the CAE
Storage means 8 such as a hard disk constituting the system 14
1 and is converted into molding condition data 63 as setting data for the injection molding machine 18 through a molding machine control parameter conversion program 82 constituting the CAE system 14 and supplied to the molding machine control panel 64. In this case, by the molding machine control parameter conversion program 82, for example,
Although the optimum molding condition data 63A is defined by a pressure value (physical quantity) in [MPa] unit, this value is converted into what percentage (relative quantity) of the maximum pressure value of the injection molding machine 18. And supplied to the molding machine control panel 64. It should be noted that the molding machine control parameter conversion program 82 for executing the conversion into the molding condition data 63 is installed in the cause analysis system 16 and is used by utilizing the cause analysis system 16.
May be supplied to the molding machine control panel 64.

After the optimal molding condition calculation process in step S4, the molded product finite element model data 70B of the analysis result when the optimal molding condition data 63A is calculated is used.
CA estimates the shrinkage of the molded product depending on the product shape, mold structure, and molding process, and the estimated value of the warpage deformation caused by this shrinkage.
The calculation is performed by the E system 14 (step S5).

Next, it is evaluated whether the calculated deformation amount (deformation amount of the outer shape) is within a predetermined desired deformation amount (step S).
6). As shown in FIG. 3, this evaluation is performed by calculating (simulating) the resin molded product 60B on the desired resin molded product 60A by the CAE system 14.
The CAE system 1 determines whether or not the deformation amount (the shape indicated by the two-dot chain line in FIG. 3), that is, the dimensional difference is within an allowable range.
4 or by the cause analysis system 16. In this embodiment, the determination is made by the cause analysis system 16.

If the result of the evaluation in step S6 is negative, the process returns to step S1, where the CAD system 12 changes the design of the product, and the steps S2 to S2 are repeated until the determination in step S6 becomes positive. The processing of S6 is repeated.

If the determination in step S6 is affirmative, that is, if the predicted deformation amount is within the desired amount, the finite element for CAE analysis created in step S3 is used. Molded product finite element model data 7
Each node position (node point) of each finite element constituting 0B
Is estimated based on the molding defect prediction program (step S7). In this embodiment, the molding failure prediction program is installed in the cause analysis system 16 in advance, and is executed by the cause analysis system 16.

FIG. 4 shows a detailed flowchart of the subroutine processing in step S7 for estimating the degree of deformation (the degree of surface roughness, the degree of surface irregularities) using the molding failure prediction program.

Here, first, the molded article finite element model data 70B composed of CAE analysis finite elements created in the step S3 is read from the CAE system 14 (step S7a).

Next, the thickness t of each node constituting each finite element constituting the read molded article finite element model data 70B is calculated (step S7b).

In this case, each finite element (the code is denoted by Ei) has the thickness value ti as an attribute value for each finite element Ei, but does not have it for each node. Therefore,
For example, as schematically shown in FIG. 5, when a node of Ei = E1 among the finite elements Ei is defined as a node 90, the thickness value ti = t1 of the node 90 shares this node 90. Finite elements Ej (j = 1, 2,...,
N, in the example of FIG. 5, j = 1, 2,..., 6) is obtained as an arithmetic average of the respective thickness values tj having as attribute values {see equation (1)}.

Ti = (1 / N) Σtj (1) In the example of FIG. 5, the thickness t1 of the node 90 is t1 =
(T1 + t2 +... + T6) {t1 on the left side is the thickness value of the node 90, while t1 on the right side is the thickness value of the finite element E1}. Although the finite element Ei in the example of FIG. 5 is a finite element having a triangular surface, the present invention is not limited to this, and an orthogonal difference finite element can be similarly applied.

Next, in the data of the CAE analysis result in step S4 when the determination in step S6 is established,
The predetermined molding condition for each finite element Ei is read (step S7c). In this embodiment, the predetermined molding conditions are a pressure profile and a resin temperature profile.

FIG. 6 shows the pressure P for a certain finite element Ei.
FIG. 7 shows a profile of the temperature Tp with respect to the certain finite element Ei. In FIG. 6, the horizontal axis is time T (seconds), and the vertical axis is pressure Pp (kgf / cm ² ). In FIG. 7, the horizontal axis represents time T (second), and the vertical axis represents temperature Tp (° C.).
It is. 6 and FIG. 7, the time point when the time T is T = 0 is shown in FIG.
The middle point shows the moment when the molten resin is supplied to the mounting position of the nozzle pressure sensor 33. Also, the time T is T ≒ 21
At the time when the molten resin arrives at a certain finite element Ei.

Next, a failure prediction (use) parameter for each node of the finite element Ei is calculated (step S7d).
In this embodiment, the failure prediction parameters are shown in FIG.
The pressurization time (integration time) PT during which the pressure Pp is applied as indicated by the hatched portion, the integral value SP (SP = ∫PpdT) of the pressure Pp within the pressurization time PT, and the inflow which is the peak temperature in FIG. The temperature FT and the above-mentioned thickness value ti. Note that, as described above, the inflow temperature FT is a temperature Tp (T 時 21) at the time when the molten thermoplastic resin reaches the node position related to the finite element Ei (before the temperature profile in FIG. 7). Edge temperature). In addition, in the temperature profile of FIG. 7, the trailing edge means the point of time when the mold is opened.

Next, a failure prediction formula is calculated from a molding failure database obtained in advance (step S7e). The failure prediction formula uses the degree of deformation (surface irregularity) as Rd, and uses this as an objective variable. The thickness value ti, pressure integrated value SPi, inflow temperature FTi, and pressurization at each node position forming each finite element Ei. When the time PTi is an explanatory variable and the constant term is e, it has been found that it is given by a regression equation (defective prediction equation) of the following equation (2).

Rd = a · ti + b · SPi + c · FTi + d · PTi + e (2) where a to d are partial regression coefficients (multiple regression coefficients), and e
Is a constant term.

In the equation (2), the degree of deformation Rd is
In the deform quality judgment table shown in FIG. 8 and the explanatory diagram of the deform amount shown in FIG. 9, how much the irregularities (this is defined as the depth) Dp (mm) at a certain width Wd (mm) is different from the reference plane 91. Means the value of
For example, in FIG. 8, when the width Wd is Wd = 100 and the depth Dp is Dp = 0.05, the degree of deformation Rd is defined as Rd = 1. Also, for example, when the width Wd is Wd = 20 and the depth Dp is Dp =
When it is 0.02, the degree of deformation Rd is Rd
= 1. When the value of the degree of deformation Rd is equal to or less than 1 (Rd <1), it is determined that the deformation amount is in a pass area where the amount of deformation is within a desired value, and a value exceeding 1 (Rd> 1). , It is determined that the deform amount is in the rejected area.

The molding failure database is basically created in advance according to the following procedure. That is,
When the deformation amount predicted value in step S6 becomes equal to or less than a desired value, the optimum molding conditions calculated in step S4 are set in the injection molding machine 18, and at this time, step S4 is performed.
The mold is manufactured based on the molded article model data 70A to which the additional conditions for the mold such as the gate and the runner obtained in 2 are added. The produced mold is mounted on the injection molding machine 18,
Actually, the test molding is performed under the optimum molding condition 63A, and the resin molded product 60 having the result of the trial molding is manufactured. The degree of deformation Rd of the resin molded product 60 is measured by a surface roughness meter 61. The measured deformation degree Rd, the thickness value ti (the above calculated value) at the node position of the finite element Ei corresponding to the position where the deformed degree Rd was measured, and the pressure integration value SPi (the above calculated value) ), An inflow temperature FTi (calculated value), and a pressurization time PTi (calculated value) are created as a molding failure database.

Until the significance of the regression equation of the equation (2) becomes within a desired value, the molding failure database is used to perform test hitting by changing the optimum molding conditions 63A in various ways, and to form data (constituting the correspondence table). Deformation degree Rd, wall thickness t
i, pressure integrated value SPi, inflow temperature FTi, pressurization time PT
i) is increased. It should be noted that the molding failure database is updated when necessary as described later.

In the combination data of the molding failure database, the degree of deformation Rd is used as a target variable, and the thickness value ti at that time, the pressure integration value SPi as the molding condition, the inflow temperature FTi, and the pressurization time PTi are By performing multiple regression analysis with
The regression equation (failure prediction equation) in which ｅe is determined is obtained. The multiple regression coefficient of the obtained regression equation was very close to the value 1, confirming that the fit of the regression equation was good.

Therefore, the parameters ti, for each node, of the CAE analysis result calculated in steps S7b and S7d
The occurrence of a failure is predicted by substituting SPi, FTi, and PTi into the failure prediction equation of the equation (2) in which the multiple regression coefficient is obtained (step S7f). The smaller the value of the degree of deformation Rd is smaller than the value 1, the lower the probability of occurrence of a defect.

Next, an intermediate file for a screen display program is created for the degree of deformation Rd of each node calculated in step S7f, and the result of the prediction of the occurrence of a defect is displayed on the CRT display 52 under the intermediate file. It is displayed (step S7g). As shown in FIG. 10, the display on the CRT display 52 is based on the value of the degree of deformation Rd on the resin molded product model 60M which is a product shape drawn based on the incidental molded product model data 70A. For example, a portion where the degree of deformation Rd exceeds 1 is red (R), a portion where the value of the degree of deformation Rd is 1 to 0.9 is yellow (Y),
Portions where the value of the degree of deformation Rd is 0.9 or less are displayed in green (G). Thus, for example, by determining the red (R) portion as a defective portion, it is possible to easily visually predict a defective occurrence portion.

Next, the process returns to the main routine again to evaluate the predicted molding failure value, in this case, the value of the degree of deformation Rd (step S8). Specifically, the value Rd of the degree of deformation calculated by the above equation (2) at each node position of each finite element Ei constituting the molded article finite element model data 70B for CAE analysis is a desired value. Judge whether the value is 1 or less, and if the degree of deformation Rd
If there is a finite element Ei whose value exceeds the value 1,
Again, the process returns to step S2, and the plan of the gate runner or the like, which is an incidental condition, is changed. In this case, after the position of the runner 69 schematically shown in FIG. 10 is moved by a predetermined amount, for example, in the direction of the arrow (the direction of the defective part), the CAE analysis of steps S3 to S8 is performed until the determination of step S8 is satisfied. Is repeated.

When the value of the degree of deformation Rd is a desired value 1
If the following conditions are satisfied, the mold is designed based on the molded article model data 70A to which the additional condition data such as the gate and the runner created in step S2 is added (step S9). (See FIG. 1) (step S10).

Next, the mold 2 prepared in step S10
1 is mounted on the injection molding machine 18, while Step S4
The profile (eg, pressure profile, resin temperature profile, etc.) of the injection time, injection pressure, holding pressure, resin temperature, etc. finally obtained in The data transformed into the molding condition data 63 as setting data is set in the molding machine control panel 64, and a trial shot (molding trial) is performed (step S11).

A resin molded product 6 as a result of the trial hitting
0 (see FIG. 1), the surface roughness and the amount of deformation are measured by a surface roughness meter 61 and a three-dimensional shape measuring device 62, respectively, and it is checked whether those values are within a desired value (design value). (Step S12).

If a failure has occurred, the molding failure data is added to the previously stored molding failure data (step S13), and storage means 83 such as a hard disk is used.
To be stored. Further, by performing a multiple regression analysis using the new molding failure data as well, (2)
Try to improve the accuracy of the regression equation shown in the equation.

If no molding failure has occurred in the determination in step S12, a molding trial was performed.
The product is driven based on the mold 21 and the optimum molding condition data 63A.

As described above, according to the above-described embodiment,
After designing the molded article model data 70 by the CAD system 12 and attaching additional conditions such as gate runners thereto,
The AE system 14 creates molded article finite element model data 70B including the finite elements Ei for CAE analysis, and CAE analyzes the molded article finite element model data 70B to derive the optimal molding conditions 63A. Then, the amount of deformation of the outer shape of the molded product based on the result of the CAE analysis when the optimum molding condition 63A is derived is calculated, and the CAD system 12 controls the molded product model data 70 until the predicted deformation becomes a value within the desired amount. The process from the design to the CAE analysis is repeated. Next, in order to verify the optimum molding condition 63A, each finite element constituting the molded product finite element model data 70B for the CAE analysis is determined by the failure prediction formula of the above-mentioned equation (2) using the optimum molding condition 63A as a parameter. The value of the degree of deformation Rd at each node position is calculated.

When the value of the degree of deformation Rd is larger than a desired value, it is determined that the product has a desired shape but the molding conditions are not the optimum molding conditions. Until the value of the degree Rd of the foam is within a desired value, the incidental conditions such as the gate runner in the mold are changed and the CAE analysis is performed, and the optimal molding condition 63A for the new molded article finite element model data 70B is determined. Derive.

The forming conditions derived when the value of the degree of deformation Rd falls within a desired value are used as the optimum forming condition data 63A, and the mold 21 is designed and manufactured.

Therefore, according to the above embodiment,
By preparing a regression equation only once in advance, when designing and manufacturing a new resin molded product, the outer shape (deformation amount) of the resin molded product 60 is required before designing and manufacturing the mold 21. Thus, the effect is obtained that the optimum molding condition data 63A in which both the deform amount and the desired value become desired values can be derived by the CAE analysis simulation.

It should be noted that the present invention is not limited to the above-described embodiment, but can adopt various configurations without departing from the gist of the present invention.

[0059]

As described above, according to the present invention, a molding defect is accurately predicted from the result of a CAE analysis simulation of a resin molded product before a mold is designed. Using the CAE system to logically derive and determine the optimal molding conditions of an injection molding machine that can prevent the occurrence of defects in actual resin molded products by taking measures against failures on the CAD system and CAE system The effect that can be done is achieved.

More specifically, the amount of deformation and the degree of deformation are estimated with respect to the molded article finite element model data for CAE analysis, and the amount of deformation and the degree of deformation are set to desired values. Until it is within the range, the remolding of the product using the CAD system and the analysis simulation by the CAE system are performed to determine the optimal molding conditions, and then the mold is designed and manufactured. For this reason, the effect that the optimal molding conditions which can solve the problem (deformation amount) generated in the final resin molded product before designing the mold can be determined is achieved.

In this case, the degree of the deformation is determined by using the degree of the deformation as an objective variable, and the explanatory variable is defined as a thickness value, a pressure integrated value, a pressure time, an inflow time and a pressure value at each node position forming each of the finite elements. It can be obtained by a multiple regression equation using a temperature value.

Incidentally, to explain just in case, the derivation of the optimal molding conditions by CAE analysis simulation has been conventionally performed, but the optimal molding conditions derived by the conventional technology include the deformation of the entire product outer shape. Only the amount was taken into consideration, and the amount of deformation related to surface irregularities was not taken into account, and in that sense, it could not be said to be the optimum molding conditions. After all, in the CAE simulation according to the conventional technology, every time a resin product having a new shape is designed, it is necessary to prototype a mold and determine the optimum molding conditions by trial punching using an injection molding machine. According to the above, when a resin molded article having a new shape is to be manufactured after the regression equation is determined once, the amount of deformation and the amount of deformation are considered in the stage of CAE analysis. The special effect is eliminated in most cases, and therefore test hitting is also eliminated in most cases.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of an injection molding system to which an embodiment of the present invention is applied.

FIG. 2 is a flowchart of a main routine used for explaining the operation of the example in FIG. 1;

FIG. 3 is a perspective view for explaining a predicted deformation amount of a CAE analysis result.

FIG. 4 is a flowchart of a subroutine used for explaining processing by a molding defect prediction program in the flowchart of FIG. 2;

FIG. 5 is a diagram used for explaining calculation of a thickness value with respect to a node position of a finite element;

FIG. 6 is a characteristic diagram showing a molding pressure profile for a certain node position.

FIG. 7 is a characteristic diagram showing a molding temperature profile for a certain node position.

FIG. 8 is a diagram showing a deform quality determination table.

FIG. 9 is a diagram used to explain the degree of deformation.

FIG. 10 is a perspective view showing the degree of deformation on a resin molded product.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 10 ... Injection molding system 12 ... CAD system 14 ... CAE system 16 ... Cause analysis system 18 ... Injection molding machine 20 ... Cavity 21 ... Mold 60 ... Resin molded article 61 ... Surface roughness meter 62 ... 3D shape measuring machine

 ──────────────────────────────────────────────────の Continued on the front page (72) Inventor Ryoji Kotake 1-10-1 Shinsayama, Sayama City, Saitama Prefecture Honda Engineering Co., Ltd.

Claims (2)

[Claims] 1. A first step of creating molded article model data corresponding to a resin molded article as a product by a CAD system, and after adding incidental conditions such as gates and runners to the created molded article model data. , Creates molded article finite element model data composed of finite elements for CAE analysis, conducts CAE analysis based on the molded article finite element model data, derives molding conditions, and calculates the deformation amount of the product outer shape. The process from the first step of creating the molded article model data by the CAD system to the work of calculating the deformation amount is repeated until the value is within the desired amount, and the molding conditions of the desired molded article model data and the CAE analysis result A second step of obtaining data; and a degree of deformation at each node position of a finite element constituting the molded article finite element model data for the CAE analysis. A third step of predicting have, when the degree of the deforming exceeds a desired value,
A fourth step of repeating the second and subsequent steps until the value falls within the desired value; and if the degree of deformation is within the desired value, the incidental condition is added to the molded article model data. After designing and manufacturing a mold based on the data added, the mold is mounted on an injection molding machine, and the molding condition data finally obtained in the second process is determined as the optimal molding condition, and the injection is performed. 5. A method for determining optimum molding conditions for an injection molding machine, comprising: a fifth step of performing molding. 2. The method according to claim 1, wherein the prediction of the degree of deformation in the third step is:
On the basis of the molded article finite element model data for the CAE analysis and the optimal molding conditions, the thickness value, pressure integrated value, pressurized time, and inflow temperature value at each node position forming each finite element are explanatory variables. And performing a multiple regression analysis with the degree of surface roughness actually measured for the resin molded product manufactured by the injection molding machine as a target variable, and predicting the obtained regression equation to optimize the injection molding machine. Method for determining molding conditions.