CN111079338A - Method for optimizing injection molding process of automobile rearview mirror shell - Google Patents
Method for optimizing injection molding process of automobile rearview mirror shell Download PDFInfo
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Abstract
The invention relates to an injection molding process optimization method of an automobile rearview mirror shell, which comprises the following steps: (1) the numerical simulation method comprises the following steps: carrying out digital modeling and injection molding numerical simulation by using a digital modeling technology; (2) guiding the model into a double-cavity design stage, adopting a double-cavity mold, wherein a pouring system consists of a main runner and a sub-runner, and a cooling system is automatically set by Moldflow; (3) orthogonal experimental design: selecting factors influencing warping, and designing a test; (4) carrying out variance analysis on the test result; (5) setting a BP neural network by combining a genetic algorithm to carry out function extremum optimization; (6) obtaining an optimal process parameter combination; the injection molding process numerical simulation is carried out by combining the orthogonal test with the Moldflow software, the influence rule of the process parameters on the warping deformation is determined by using variance analysis, and the process parameters are optimized by combining a BP neural network with a genetic algorithm, so that the injection molding process optimization method capable of guiding the production practice is obtained, the design efficiency is improved, the mold testing time is shortened, and the production cost is reduced.
Description
Technical Field
The invention relates to a process optimization method, in particular to an injection molding process optimization method for an automobile rearview mirror shell, and belongs to the technical field of injection molding process optimization.
Background
In the injection molding process of the injection molding part, injection molding process parameters such as mold temperature, melt temperature, pressure holding pressure, pressure holding time and the like have important influence on the molding quality of the injection molding part. The injection molding process parameters have a plurality of complex nonlinear influences on the injection molding quality, and how to establish an optimized model with high approximation degree to economically and quickly obtain high-quality injection molded products becomes one of the main challenges of injection molding process optimization and quality control. Much research has been conducted on this. Numerical simulation techniques can guide process adjustments to reach valuable conclusions, but are computationally expensive and require trial and error adjustments. The test design method can reduce the blindness of repeated test, can obtain a better process combination in a test range with less test times, and is beneficial to optimizing the forming process parameters by analyzing the influence of process conditions on the product quality through range analysis or variance analysis, but is difficult to realize global optimization.
The appearance surface of the automobile rearview mirror shell is a streamline smooth curved surface, has the characteristics of complex shape, high assembly size precision requirement, strict surface quality requirement and the like, and in injection molding, the deformation of a product can be reduced, the mechanical property of the product is increased, the apparent quality of the product is improved, the size precision of the product is improved only by comprehensively optimizing a pouring system, a cooling system and a molding process, so that the research on the process optimization has certain guiding significance.
With the development of artificial intelligence technology, the intelligent optimization technology of the injection molding process is mature day by day. The artificial neural network is used for training a prediction model, and the process parameters are optimized by combining a genetic algorithm, a particle swarm algorithm, sequential approximation optimization and the like, so that huge calculation amount and calculation time caused by numerical solution are reduced, and the optimization design process is accelerated. The integration application of the numerical simulation technology, the orthogonal test method and the intelligent algorithm can qualitatively and quantitatively analyze and solve the problem of process parameter optimization. Have begun to be applied in the optimization of process parameters.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the optimization method of the injection molding process of the automobile rearview mirror shell, and by adopting the method, the design efficiency can be improved, the mold testing time can be shortened, and the production cost can be reduced. The specific implementation scheme is that the injection molding process numerical simulation is carried out by combining an orthogonal test with Moldflow software, the influence rule of the process parameters on the warping deformation is determined by using variance analysis, and the optimal combination of the injection molding process parameters of the automobile rearview mirror capable of guiding the production practice is obtained by using a BP neural network and combining a genetic algorithm to optimize the process parameters. The invention has the advantages of improving the design efficiency, reducing the time of die testing and reducing the production cost.
In order to achieve the purpose, the invention adopts the technical scheme that:
an optimization method for an injection molding process of an automobile rearview mirror shell comprises the following steps:
(1) the numerical simulation method comprises the following steps: the method comprises the steps of carrying out digital modeling and injection molding numerical simulation on a rearview mirror shell by using a digital modeling technology, using Moldflow for simulation in an injection molding process, converting a shell model into triangular units by adopting double-layer surface analysis, dividing and repairing a model grid of the rearview mirror shell, wherein the model grid comprises 57556 curved surface triangles, the average aspect ratio is 1.74, the free edges, the crossed edges and the units with incorrect alignment are all 0, no crossed units and no overlapped units exist, the matching percentage is 93%, and the grid analysis shows that no other defects exist, so that the method is suitable for double-layer surface analysis;
(2) after grid division, guiding the model into a double-cavity design stage, wherein the design comprises the steps of gate position analysis, and building a pouring system and a cooling system; optimizing the position of the gate by analyzing the matching of the gate, comprehensively considering the higher requirement on the appearance quality of the rearview mirror shell, and finally determining the position of the gate in the groove of the rearview mirror shell to avoid the defect of gate trace; a double-cavity mold is adopted, a pouring system consists of a main runner and a sub-runner, and a cooling system is automatically set by Moldflow;
(3) the attribute of the material is obtained by the query of the Moldflow 2018 Chinese version softwareThe parameters provide reference for setting the process parameters of the simulation experiment; selecting the mold temperature, the melt temperature, the pressure maintaining time, the pressure maintaining pressure and the injection speed as potential factors influencing the warping, and analyzing the warping deformation of the rearview mirror shell under different process conditions by selecting filling, pressure maintaining and warping; selecting 3 levels for each parameter, and adopting Tiankou L for the parameters and the levels27(35) A design method, wherein a Minitab software is used for making a Taguchi orthogonal table and carrying out a simulation test;
(4) and (3) carrying out variance analysis on the test results: firstly, decomposing the deviation square sum of the test results, and analyzing whether the test result fluctuation value is caused by different levels of parameters; comparing the mean square sum with MS to judge whether each parameter has obvious influence on the index;
(5) setting a BP neural network by combining a genetic algorithm to carry out function extremum optimization: firstly, training a BP neural network by taking a process parameter horizontal value as input and warping as output, and predicting warping by using the trained BP neural network with high goodness of fit; taking the prediction result after BPNN training as an individual fitness value of a genetic algorithm, searching a global optimal value of warping and corresponding production conditions through selection, intersection and variation operations, and finally obtaining the maximum warping deformation;
(6) obtaining an optimal process parameter combination:
the heating temperature of the die is 50-80 ℃, and the optimal temperature is 73.9386 ℃;
the melting temperature of the ABS material is 220-260 ℃, and the optimal melting temperature is 249.6096 ℃;
the ABS material is put into an injection molding machine for injection time of 1.2s-5s, and the optimal injection time is 1.8089 s;
the pressure maintaining time is 15-25s, the pressure maintaining pressure is 60-90Mpa, the optimal pressure maintaining time is 19.8883s, and the pressure maintaining pressure is 79.1498 Mpa.
The invention has the beneficial effects that:
the optimization method adopts orthogonal test and Moldflow software to carry out numerical simulation of the injection molding process, determines the influence rule of the process parameters on the warping deformation by variance analysis, and optimizes the process parameters by BP neural network and genetic algorithm to obtain the optimal combination of the injection molding process parameters of the automobile rearview mirror capable of guiding production practice. The invention has the advantages of improving the design efficiency, reducing the time of die testing and reducing the production cost.
Drawings
FIG. 1 is a repair-qualified rearview mirror housing grid model;
FIG. 2 shows a runner system and a cooling system;
fig. 3 is a cloud diagram of warp deformation of a process parameter product after optimization of the BP-GA hybrid algorithm (maximum warp 0.9497).
Detailed Description
The present invention is further illustrated in detail by the following examples, which are provided only for illustrating the present invention and are not intended to limit the scope of the present invention.
Example 1
(1) The rearview mirror shell is subjected to digital modeling and injection molding numerical simulation by utilizing a digital modeling technology, so that the cost waste caused by multiple tests can be greatly reduced. Moldflow is used for simulation of an injection molding process, a shell model is converted into a triangular unit by adopting double-layer surface analysis, the rearview mirror shell model is divided into grids and repaired to comprise 57556 curved surface triangles, the average aspect ratio is 1.74, the free edges, the crossed edges and the units with incorrect alignment are all 0, no crossed units and overlapped units exist, the matching percentage is 93%, and the grid analysis shows that no other defects exist, so that the rearview mirror shell model is suitable for double-layer surface analysis (as shown in figure 1);
(2) after grid division, the model is led into a double-cavity design stage, and the design comprises pouring gate position analysis, pouring system and cooling system establishment. And the position of the gate is optimized by analyzing the matching of the gate, the requirement on the appearance quality of the rearview mirror shell is comprehensively considered to be higher, and the position of the gate is finally determined in the groove of the rearview mirror shell to avoid the defect of gate mark. A double-cavity mold is adopted, a pouring system consists of a main runner and a sub-runner, and a cooling system is automatically arranged by Moldflow (as shown in figure 2);
(3) the properties of the material are obtained by searching through Moldflow 2018 Chinese version software, and the parameters provide references for the setting of process parameters of the simulation experiment. Selecting the temperature of the mould, the temperature of the melt and the pressure maintaining timeThe pressure maintaining pressure and the injection speed are potential factors influencing the warping, and the warping deformation of the rearview mirror shell under different process conditions is analyzed by selecting filling, pressure maintaining and warping. In addition, each parameter of the test is selected from 3 levels, the parameters and the levels are shown in the table 1, and a Taokou L is adopted27(35) Designing method, using Minitab software to make Taguchi orthogonal table, and making simulation test. The simulation is shown in table 2;
(4) and carrying out variance analysis on the test result. The sum of squares of the deviations of the test results is first decomposed to analyze whether the test results fluctuate due to different levels of the parameters. And comparing the mean square sum with the MS to judge whether each parameter has obvious influence on the index. The result shows that the P value of each parameter is less than 0.01, which indicates that the method has a stronger judgment result, namely that each parameter has a significant influence on the warping under 99% confidence. The influence degree of each parameter on the warping of the rearview mirror shell is the maximum influence degree of the dwell time, and the data fluctuation caused by the horizontal change of the parameter accounts for 51.81 percent of the sum of squares of the total deviation. The parameters are arranged from large to small as follows: dwell time > melt temperature > injection time > dwell pressure > mold temperature (as in table 3);
(5) setting a BP neural network by combining a genetic algorithm to carry out function extremum optimization, firstly training the BP neural network by taking a process parameter horizontal value as input and warping as output, and predicting the warping by using the trained BP neural network with high fitting goodness. And (3) taking the prediction result after BPNN training as an individual fitness value of a genetic algorithm, and searching a global optimal value of warping and corresponding production conditions through selection, intersection and mutation operations. The maximum warpage of about 0.9497mm was obtained. The error between the predicted value and the simulation value is less than 1 percent, and the coincidence degree of the predicted value and the simulation value is better. Meanwhile, the warping is reduced from 0.9648mm to 0.9497 mm.
(6) The optimum process parameter combination (see table 4) was obtained, and under this process condition, the product warpage cloud is shown in fig. 3.
TABLE 1 Process parameters and levels
TABLE 2L27(35) Taguchi orthogonality chart and warping results
TABLE 3 warped ANOVA Table
TABLE 4 rearview mirror injection molding process parameters and warpage values optimized by genetic algorithm
Claims (5)
1. An optimization method for an injection molding process of an automobile rearview mirror shell is characterized by comprising the following steps: the method comprises the following steps:
(1) the numerical simulation method comprises the following steps: the method comprises the steps of carrying out digital modeling and injection molding numerical simulation on a rearview mirror shell by using a digital modeling technology, using Moldflow for simulation in an injection molding process, converting a shell model into triangular units by adopting double-layer surface analysis, dividing and repairing a model grid of the rearview mirror shell, wherein the model grid comprises 57556 curved surface triangles, the average aspect ratio is 1.74, the free edges, the crossed edges and the units with incorrect alignment are all 0, no crossed units and no overlapped units exist, the matching percentage is 93%, and the grid analysis shows that no other defects exist, so that the method is suitable for double-layer surface analysis;
(2) after grid division, guiding the model into a double-cavity design stage, wherein the design comprises the steps of gate position analysis, and building a pouring system and a cooling system; optimizing the position of the gate by analyzing the matching of the gate, comprehensively considering the higher requirement on the appearance quality of the rearview mirror shell, and finally determining the position of the gate in the groove of the rearview mirror shell to avoid the defect of gate trace; a double-cavity mold is adopted, a pouring system consists of a main runner and a sub-runner, and a cooling system is automatically set by Moldflow;
(3) the material attribute is obtained by searching through Moldflow 2018 Chinese version software, and the parameter provides reference for the setting of the simulation experiment process parameter; selecting the mold temperature, the melt temperature, the pressure maintaining time, the pressure maintaining pressure and the injection speed as potential factors influencing the warping, and analyzing the warping deformation of the rearview mirror shell under different process conditions by selecting filling, pressure maintaining and warping; selecting 3 levels for each parameter, and adopting Tiankou L for the parameters and the levels27(35) A design method, wherein a Minitab software is used for making a Taguchi orthogonal table and carrying out a simulation test;
(4) and (3) carrying out variance analysis on the test results: firstly, decomposing the deviation square sum of the test results, and analyzing whether the test result fluctuation value is caused by different levels of parameters; comparing the mean square sum with MS to judge whether each parameter has obvious influence on the index;
(5) setting a BP neural network by combining a genetic algorithm to carry out function extremum optimization: firstly, training a BP neural network by taking a process parameter horizontal value as input and warping as output, and predicting warping by using the trained BP neural network with high goodness of fit; taking the prediction result after BPNN training as an individual fitness value of a genetic algorithm, searching a global optimal value of warping and corresponding production conditions through selection, intersection and variation operations, and finally obtaining the maximum warping deformation;
(6) obtaining an optimal process parameter combination:
heating the die at 50-80 deg.c;
the melting temperature of the ABS material is 220-260 ℃;
the ABS material is put into an injection molding machine for injection time of 1.2s-5 s;
the pressure maintaining time is 15-25s, and the pressure maintaining pressure is 60-90 Mpa.
2. The method for optimizing the injection molding process of the automobile rearview mirror casing according to claim 1, wherein the method comprises the following steps: the mold temperature was 73.9386 ℃.
3. The method for optimizing the injection molding process of the automobile rearview mirror casing according to claim 1, wherein the method comprises the following steps: the melting temperature of the ABS material was 249.6096 ℃.
4. The method for optimizing the injection molding process of the automobile rearview mirror casing according to claim 1, wherein the method comprises the following steps: the injection time of ABS material put into the injection molding machine is 1.8089 s.
5. The method for optimizing the injection molding process of the automobile rearview mirror casing according to claim 1, wherein the method comprises the following steps: the dwell time was 19.8883s, and the dwell pressure was 79.1498 Mpa.
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Cited By (10)
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| CN111823530A (en) * | 2020-06-29 | 2020-10-27 | 广东海洋大学 | Injection molding process of beacon light shell based on orthogonal test and BPNN-GA algorithm |
| CN111975453A (en) * | 2020-07-08 | 2020-11-24 | 温州大学 | Numerical simulation driven machining process cutter state monitoring method |
| CN112102896A (en) * | 2020-08-07 | 2020-12-18 | 上海交通大学 | Alloy component optimization method and equipment for improving fluidity of cast high-temperature alloy |
| CN112115635A (en) * | 2020-07-30 | 2020-12-22 | 南京邮电大学 | Injection molding process optimization method based on deep learning |
| CN113119425A (en) * | 2021-03-22 | 2021-07-16 | 广东工业大学 | Injection molding product quality prediction method based on improved support vector machine |
| CN113642160A (en) * | 2021-07-26 | 2021-11-12 | 南京工业大学 | Aluminum alloy engine cylinder body casting process design optimization method based on BP neural network and fish swarm algorithm |
| CN114290631A (en) * | 2021-12-28 | 2022-04-08 | 武汉燎原模塑有限公司 | Injection molding test process and inspection method for automobile bumper |
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| CN111975453A (en) * | 2020-07-08 | 2020-11-24 | 温州大学 | Numerical simulation driven machining process cutter state monitoring method |
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| CN112102896A (en) * | 2020-08-07 | 2020-12-18 | 上海交通大学 | Alloy component optimization method and equipment for improving fluidity of cast high-temperature alloy |
| CN112102896B (en) * | 2020-08-07 | 2022-12-20 | 上海交通大学 | Alloy component optimization method and equipment for improving fluidity of cast high-temperature alloy |
| WO2022148247A1 (en) * | 2021-01-11 | 2022-07-14 | 重庆平伟汽车零部件有限公司 | Plastic part forming method using hot runner mold driven by electric servo |
| CN113119425A (en) * | 2021-03-22 | 2021-07-16 | 广东工业大学 | Injection molding product quality prediction method based on improved support vector machine |
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Application publication date: 20200428 |