JP4829672B2 - Molding condition setting method, program, and injection molding machine - Google Patents

Description

  The present invention relates to a method for setting molding conditions when a resin product is manufactured by an injection molding method, an injection compression molding method, an injection press molding method, or the like.

  Technology for searching for optimum conditions by combining CAE technology and CAO technology in the selection of injection molding conditions for plastic parts has been put into practical use and has achieved certain results. However, there are always variations in material characteristics and environmental conditions in the manufacturing and use processes, and the performance obtained by such optimum design cannot always be realized.

  Therefore, it is conceivable to obtain an optimal solution for the target performance by using a quality engineering method for setting a robust molding condition in consideration of variations in design variables. However, the amount of calculation is enormous, and when a general computer device is used, it takes an enormous amount of time, so that practicality is lost.

JP 2005-7859 A

  The present invention has been made in view of the above circumstances, can maintain the performance against variations in various conditions in the manufacturing and use process, and can reduce the calculation load, and is a highly practical molding. It aims at providing the setting method of conditions.

In order to achieve the above object, a molding condition setting method according to the present invention is a molding condition setting method for achieving an objective performance at an optimum value using a combination of an injection molding analysis and a computer-aided optimization method. , Sampling the design variables in advance and performing injection molding analysis to obtain a response surface approximation formula that approximates the objective performance evaluation function at each sampling point; and, based on the response surface approximation formula, And an optimization step for obtaining an optimum solution of the target performance using a quality engineering method for setting a robust molding condition in consideration.

In the present invention , based on the response surface approximation formula that approximates the objective performance evaluation function, an optimization process for obtaining an optimal solution for the objective performance is performed, so there is no need to perform injection molding analysis each time in the optimization process, The amount of computation is greatly reduced and thus the time to obtain the result is shortened.

Method of setting molding conditions according to the present invention, obtained in advance the range of the design variables of the form defect occurs, and wherein the excluding from the scope of the optimization process.

In the present invention, it is possible to avoid the problem of performing an optimization within a range of design variables in which molding defects occur to generate an inappropriate solution. Molding defects include 1) process defects that hinder molding operations, 2) quality defects that impair quality, and 3) those that belong to both. For example, “short shot” that ends without completely filling the cavity with resin. 3). “Yake”, “weld”, “air trap”, “sink”, “warp”, etc., which are poor appearance of the product, are 2).

The pre-Symbol objective performance may be characterized in that the mold clamping force.
Alternatively, the pre-Symbol objective performance, it is also possible to molding cycle.

A program according to the present invention is a program for executing each of the above steps.
The method for producing a molded product according to the present invention is a method for producing a molded product, characterized in that injection molding is performed under molding conditions obtained by executing the above steps.

A storage medium according to the present invention is a storage medium containing a program for executing the above steps.
An injection molding machine according to the present invention has an injection molding machine main body and a control unit that controls the injection molding machine main body according to molding conditions obtained by executing the above steps. It is.

According to the present invention, it is possible to maintain performance against variations in various conditions in the manufacturing / use process, and to provide a practical setting method for molding conditions with reduced calculation load. it can.

Embodiments of the present invention will be described below with reference to the drawings.
In this embodiment, a robust molding condition (design variable) that takes into account variations that optimize the target performance is obtained by a combination of numerical analysis for calculating the injection molding process and a computer-aided optimization technique.

Embodiments of the present invention will be described below with reference to the drawings.
FIG. 1 shows a molding condition setting device (computer device) 10 for carrying out the molding condition setting method according to the present invention. The calculation / control unit 11 performs various calculations and data processing, and the calculation / control unit. A first storage unit 12 such as a RAM for storing an injection molding analysis and optimization program executed by the computer 11; a second storage unit 13 for storing data in the calculation / control unit 11; Are provided, and an input unit 14 for displaying various outputs to the operator.

  The steps executed by the molding condition setting device will be described with reference to the flowchart of FIG. First, in Step 1, an analytical shape model for analyzing the resin flow in the injection molding process is created. As the resin product, for example, a rectangular (aspect ratio = 4/5) flat plate member as shown in FIG. In this shape model, a rectangular opening is formed at one corner of the flat plate to make the resin flow non-uniform. The product thickness distribution is shown in FIG. In the mold, a resin injection port is disposed at a substantially central portion of the flat plate and a substantially central portion of the side end portion close to the opening. Resin from the injection machine is supplied to each injection port via a hot runner, a tapered sprue, or further via a cold runner.

  Next, in step 2, an evaluation function for evaluating the target performance is set. In this embodiment, multipurpose optimization is performed by setting a mold clamping force and a molding cycle, for example. Since the mold clamping force is an index indicating the apparatus scale of the injection molding machine, and the molding cycle is an index indicating the production efficiency of the apparatus, it is economically advantageous that both are small. Next, in step 3, for example, the injection time (it), the mold temperature (mot), and the resin temperature (met) are set as design variables.

  Next, in step 4, sampling calculation is performed using injection molding analysis software. That is, sampling points are appropriately set in a multi-dimensional space having design variables as coordinate axes, for example, as shown in FIG. 4, and an evaluation function is calculated at each point, and whether or not molding defects occur Determine. In this embodiment, it is determined that a short shot occurs when the resin temperature reaches the solidification point before completion of injection. If other molding defects are targeted, the determination criterion corresponding to each is adopted.

In step 5, it is determined whether or not a molding defect has occurred at the sampling point.
If no molding defect is observed at any sampling point, a response surface approximation formula for the evaluation function is created in step 6 in the entire region within the constraint condition range. In step 7, an optimization process is performed by setting design variables in consideration of variations in design variables. Then, it is determined in step 8 whether or not the censoring condition has been reached. If it is determined that the censoring condition has been reached, the solution at that time is set as the optimum solution in step 9 and the censoring condition has not been reached in step 8. If it is determined, the design variable is changed according to the algorithm of the optimization software, and the steps 7 to 8 are repeated until the abort condition is reached.

  On the other hand, if there is a molding defect at the sampling point in step 5, the design space is divided in step 10. This is to divide the portion from which the section space where the molding defect has occurred is removed so that each becomes a continuous rectangular space with respect to each design variable. FIG. 5 illustrates this process for the case of two design variables. In this case, if a molding defect occurs in the range X in the rectangular design range, the region from which the range X is removed is divided into two regions, a range A and a range B. On the other hand, FIG. 6 explains this process for the case of three design variables. The number of divisions increases as the number of variables and generation space increases and as the generation region is inside the space.

  Hereinafter, as in the case of FIG. 6, description of the process will be continued by taking as an example a case where the design range is divided into three ranges A, B, and C. First, in step 11, a response surface approximation formula of the evaluation function is created for the range A, and using this, an optimization process is performed in step 12 by design variable setting in consideration of variations in design variables. Then, it is determined in step 13 whether or not the abort condition has been reached. If it is determined that the abort condition has not been reached, the design variable is changed in accordance with the algorithm of the optimization software. The optimal solution is generated for the range A by repeating the 13 steps until the abort condition is reached.

  Next, in step 15, the same process (step 11 to step 14) as in range A is performed for range B, and an optimal solution is generated for range B. Similarly, in step 16, the same process (steps 11 to step) is performed. 14) is performed for the range C, and an optimal solution is generated for the range C. Next, in step 17, the optimum solutions for the ranges A, B, and C are compared, an optimum solution is selected from these, and this is set as the overall optimum solution.

The molding conditions of the difference which shape | mold the resin of the shape and dimension shown in FIG. 3 using Sumitomo Noblen AZ564 (brand name: Sumitomo Chemical Co., Ltd.) which is polypropylene resin as a material were calculated | required. The melt flow rate (MFR) measured by the method specified in JIS-K7210 is 30 [g / 10 min, 230 ° C], and the specific gravity is 0.9.
For numerical analysis for calculating the injection molding process, Moldflow Plastics Insight version 5.0 (trade name: manufactured by Moldflow Corporation), which is an injection molding analysis software, was used.
Three variables were selected as design variables: injection time (it), mold temperature (mot), and resin temperature (met). Table 1 shows the initial conditions and constraints regarding the design variables.

Random variables were set for each design variable, and three variables were set. The variation was a normal distribution with a standard deviation of 0.1 s for it and a standard deviation of 0.5 ° C for mot and met. Design variables were considered variation using Taylor linear approximation. The reliability constraint conditions were a reliability of 3σ (99.7%) or more for a mold clamping force of 5000 t or less, and a reliability of 3σ (99.7%) or more for a molding cycle of 100 s or less.
As an evaluation function, a linear sum of average value of mold clamping force (MCF), standard deviation of mold clamping force (SCF), average value of molding cycle (MCT), and standard deviation of molding cycle (SCT) was used.
(MCF + 3SCF) / MCF0 + (MCT + 3SCT) / MCT0
The molding cycle was the sum of the injection time (design variable: it), the cooling time (response value), and the product removal time (fixed value: 10 seconds), and the cooling time was the maximum value of the solidification time (excluding the runner part). MCF0 and MCT0 are average values of the clamping force and the molding cycle in the initial conditions, respectively.

  In addition to the evaluation function used in this example, the weight of plastic parts, the amount of warp, etc. are used as evaluation functions, and numerical indexes such as filling balance, weld position, molding defects such as Paris, sink marks, and burns are evaluated. It can be set to a function or constraint.

Further, in this embodiment, the short shot molding conditions are eliminated and the optimization is performed. However, the present invention may be applied to the case where the molding conditions in which warpage, welds, air traps, sink marks, burns, etc. occur are excluded.
For warping, for example, internal stress is calculated for each node of the analysis model, the amount of warping of the product is calculated, and the molding conditions when the warping amount has finished a preset tolerance may be excluded. it can.

  For welds, for example, the flow front merging angle is calculated for each node of the analysis model, the occurrence of welds is determined based on this, and the molding conditions when welds occur in specific areas are excluded. Or, as disclosed in JP-A-2005-007860, a value obtained by multiplying the number of welds (number of nodes) in a specific region by a weighting factor set for each region is used as an evaluation value. Molding conditions when the evaluation value exceeds a preset allowable value can be excluded.

  For air traps, for example, the node surrounded by the flow front or the node surrounded by the mold wall and flow front is the air trap generation point, and the molding conditions when an air trap occurs in a specific area are excluded It can be. Alternatively, the value obtained by multiplying the number of air traps (number of nodes) in a specific area by the weighting factor set for each area is taken as the evaluation value, and the evaluation value exceeds the preset allowable value In some cases, the molding conditions can be excluded.

With respect to sink marks, for example, a portion where the shrinkage rate is locally large can be regarded as a point where sink marks have occurred, and molding conditions at that time can be excluded. For burns, for example, by the above method, the air trap generation point is obtained, and the molding condition when the size of the area where the air trap occurs exceeds a preset allowable value is set as the molding condition for burning. Can be eliminated.
In this embodiment, the robustness is considered for the evaluation functions for all the target performances. However, the evaluation functions considering the robustness may be set for only some evaluation functions as necessary.

As an optimization method, Adaptive Simulated Annealing (ASA), response surface is Radial Basis
Function (RBF) approximation model, Six Sigma Robust Design (DFSS) was used as a quality engineering technique.

When creating the response surface approximation formula, a total of 64 analyzes of 4 levels were sampled for each design variable. For the sampling level, the injection time was 1 s, 4 s, 7 s, 10 s, the mold temperature was 30 ° C, 43.3 ° C, 56.7 ° C, 70.0 ° C, and the resin temperature was 180 ° C, 200 ° C, 220 ° C, 240 ° C. . The result of the division is as shown in FIG. Therefore, in this embodiment, the process jumps from step 5 to step 10 in the flowchart shown in FIG.
Regarding the range C in FIG. 6, in this embodiment, since the data is one level of the mold temperature of 70 ° C., if the variation is taken into consideration as it is, the short shot is generated. Therefore, this process is excluded from the search area for the sake of simplicity.

Table 2 shows the robust optimization results in each range. The overall evaluation function shows the lowest optimization result in range A.

Table 3 shows the results of robust design in the entire design range. As a comparative example, an optimization result in which an injection molding analysis is executed each time without using the response surface method is also shown. Table 4 shows the constraint conditions of the comparative example.

  By this method, the value of the overall evaluation function was almost the same as the optimization result (about 1.8) without using RSM. The optimization time was reduced to approximately 0.032 times (64 analysis / 2028 analysis).

In the above embodiment, the range of 1-level data such as region C is excluded from the search region, but the following method may be used.
1) A sampling point is added in a range wider than the divided ranges by the variation of the design variable and the like, and whether or not a short shot has occurred is confirmed. Finally, optimization is performed within a range where no short shot occurs.
2) When creating the response surface approximation formula, a range wider than the design range by the variation of the design variable is set as the sampling area in advance. Then, after performing the region division, optimization is performed in a narrow range corresponding to variations in design variables.
These methods can be similarly applied to the edge portion of the design range.

  The molding condition setting method of the present invention can be used, for example, when a designer designs an apparatus for molding a specific resin product, and further used for controlling an injection molding apparatus in operation. Can do. FIG. 7 shows, for example, the configuration of an apparatus for controlling an injection molding apparatus so as to optimize a plurality of target performances. An injection molding apparatus 20 includes a mold, an accessory device thereof, and a molten resin injection apparatus. And the like, a storage unit 22 for storing the optimum solution obtained by the method of the present invention, and a control unit 23 for controlling the molding machine body 21 based on the optimum solution and an operator's instruction. Have.

  For example, the operator inputs data indicating the clamping force and the priority of the molding cycle, and the control unit 23 selects the molding conditions according to the instruction from the optimum solution representing the molding conditions, and each part of the molding machine main body 21 is selected. To control. Thereby, the driving | running on the molding conditions according to the situation can be performed without bothering manpower. In addition, you may make it perform the calculation process which calculates | requires the optimal solution showing molding conditions in the control part 23. FIG.

It is a figure which shows the structure of the computer apparatus which performs the setting method of the molding conditions of this invention. It is a flowchart explaining the setting method of the molding conditions of this invention. (A) is a figure for demonstrating the shape of the product of embodiment of this invention, (b) is a figure which shows thickness distribution of the cavity which molds a product. It is a figure for demonstrating 1 process of the setting method of the molding conditions of this invention. It is a graph explaining the effect | action of this invention. It is a graph explaining the effect of this invention. It is a figure showing typically the injection molding machine of an embodiment of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Injection molding machine 11 Injection molding machine main body 12 Memory | storage part 13 Control part

Claims (7)

In the method of setting molding conditions to optimize the target performance using a combination of injection molding analysis and computer-aided optimization methods,
Sampling the design variables in advance and performing injection molding analysis to obtain a response surface approximation formula that approximates the objective performance evaluation function at each sampling point;
Based on the response surface approximation formula, an optimization step for obtaining an optimal solution of the target performance using a quality engineering method for setting a robust molding condition in consideration of variations in design variables;
I have a,
A method for setting molding conditions, characterized in that a range of design variables in which molding defects occur is obtained in advance and is excluded from the range of the optimization step. 2. The molding condition setting method according to claim 1, wherein the target performance is a clamping force. 2. The molding condition setting method according to claim 1, wherein the target performance is a molding cycle. The program which performs each process in any one of Claim 1 thru | or 3 . A method for producing a molded article, wherein injection molding is performed under molding conditions obtained by executing the steps according to any one of claims 1 to 3 . A storage medium containing a program for executing each step according to any one of claims 1 to 3 . An injection molding machine main body and a control unit that controls the injection molding machine main body according to molding conditions obtained by executing each step according to any one of claims 1 to 3. Molding machine.

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