CN112140413B - Method and system for predicting die sinking shrinkage rate of plastic part - Google Patents
Method and system for predicting die sinking shrinkage rate of plastic part Download PDFInfo
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- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
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Abstract
The invention provides a method and a system for predicting the die sinking shrinkage of a plastic part, which are suitable for the manufacturing process of various thermoplastic material parts. By optimally combining different parameters in the 3D structure data, the material data and the processing technology data of the workpiece, a more accurate shrinkage rate calculation method is obtained. And further, the calculation model is optimized by considering the influence of the process of cooling the material to the ambient temperature during demolding on the product, so that the shrinkage rate of the whole process of the product is predicted more accurately, accurate reference can be provided for the design of a mold, and the production of the plastic product with higher precision is facilitated.
Description
Technical Field
The invention belongs to the technical field of injection molding, and particularly relates to a method and a system for predicting die sinking shrinkage.
Background
The plastic has the characteristics of strong plasticity, light weight, excellent chemical stability and the like, and is widely applied to national economy. Injection molding is an important manufacturing method of plastic products, by which plastic products of various shapes, sizes, and precisions can be manufactured, thereby satisfying the demands of various fields, such as manufacturing various industrial parts, structural members, housings, and the like by injection molding.
In the injection molding process, a completely molten plastic material needs to be injected into a mold cavity at a certain temperature, and a molded product is obtained after cooling and solidification. However, during the process from the molten viscous state to the cooling solidification and setting, the plastic part shrinks, so that the difference between the size of the plastic part and the size of the cavity of the mold is formed. Therefore, the influence caused by the shrinkage deformation of the material needs to be considered during the mold design, otherwise, a part meeting the requirement cannot be manufactured, which means that the accurate prediction of the mold opening shrinkage rate is a key index related to the production success of the part.
The method for determining the die sinking shrinkage rate in the prior art comprises the following steps: 1. an experimental trial and error method for designing an experimental soft mold in advance and carrying out an experiment is adopted, but the method for calculating the mold opening shrinkage rate by opening the experimental soft mold and a molded part involves the defects of high cost and long project period; 2. the size of the die is corrected, the shrinkage rate is adapted through multiple local corrections of the die, but the defects of high cost and long project period caused by the multiple die correction times and the multiple trial-manufacture times are also involved; 3. the empirical method for obtaining the shrinkage rate through a large amount of project application data has the advantages that although similar parts have high referential property, the parts have large structure difference in practical application, and the referential property of the empirical method is low when different parts are faced; 4. the shrinkage rate recommended by the material manufacturer is obviously not suitable for parts with any structures, which adds more difficulty to the later material adjustment.
Therefore, although some methods in the prior art hope to obtain the shrinkage parameter, the methods generally have the defects of high cost, long time period or lack of universality, and the method has very limited help for improving the preparation precision of plastic parts.
Disclosure of Invention
In view of the above disadvantages in the prior art, the present invention provides a method for accurately predicting mold opening shrinkage of a plastic part, and specifically, the method includes the following steps:
s1, acquiring 3D data, material data and processing parameters of the workpiece, wherein the acquisition of the processing parameters comprises determining an analysis sequence;
s2, calculating a plurality of result outputs under different analysis sequences;
s3, determining result output which has larger influence on shrinkage prediction from the plurality of result outputs;
s4, calculating the weight value and the corresponding simulation shrinkage rate of each result output determined in the step S3;
s5, calculating the die sinking shrinkage rate according to the following formula:
where i is the serial number of the result output, n is the number of the result outputs determined in step S3, and fiOutputting the corresponding weight value, s, for the ith resultiAnd outputting the corresponding simulation shrinkage rate for the ith result.
Further, in step S4, the method for calculating the weight value includes the following steps:
s41, determining a plurality of results with the closest simulation shrinkage rates to be output;
s42, outputting results with similar simulation shrinkage rate results and giving the same weight value;
s43, the weight values corresponding to the other result outputs determined in the step S3 are endowed with corresponding weight values according to the influence on the results.
Preferably, in the mesh preprocessing process of the 3D data of the workpiece, the cell type of the neutral plane cell or the double-sided cell is selected. The material data includes viscosity data, PVT data, mechanical property data, crystallographic morphology data, filling data, stress-strain data, and thermal property data. The processing parameters comprise melt temperature, mold temperature, filling time, filling volume, holding pressure and cooling time. The analysis sequence comprises the following two analysis sequences: cooling + filling + pressure holding + shrinking, and cooling + filling + pressure holding + warping.
Further, the prediction method further comprises the following steps:
s6, calculating the die release linear shrinkage rate according to the following formula:
wherein epsilon is the linear shrinkage of the mold release, alpha is the linear thermal expansion coefficient of the material, and T0Is the initial temperature before demolding, TrIs the ambient temperature after cooling.
Further, the prediction method further comprises the following steps:
s7, adding the die sinking shrinkage rate obtained by calculation in the step S5 and the die-releasing linear shrinkage rate, and taking the result of the addition as a predicted die sinking shrinkage rate value.
Meanwhile, the invention also provides a system for predicting the mold opening shrinkage of the plastic part, which comprises a processor, a memory and a shrinkage calculation unit, wherein the shrinkage calculation unit further comprises:
the data acquisition unit is used for acquiring 3D data, material data and processing technological parameters of a workpiece, wherein the acquisition of the processing technological parameters comprises the determination of an analysis sequence;
the computing unit is used for computing a plurality of result outputs under different analysis sequences;
a result output determination unit configured to determine a result output having a large influence on the shrinkage prediction from among the plurality of result outputs;
the weight calculation unit is used for calculating the weight value of each result output determined by the result output determination unit and the corresponding simulation shrinkage rate;
the shrinkage rate obtaining unit is used for calculating the die sinking shrinkage rate according to the following formula:
wherein i is the resultThe number of outputs, n being the number of result outputs determined by the result output determining unit, fiOutputting the corresponding weight value, s, for the ith resultiAnd outputting the corresponding simulation shrinkage rate for the ith result.
Further, the prediction system further comprises a die swell calculation unit for calculating the die swell according to the following formula:
wherein epsilon is the linear shrinkage of the mold release, alpha is the linear thermal expansion coefficient of the material, and T0Is the initial temperature before demolding, TrIs the ambient temperature after cooling.
Further, the prediction system further comprises a mold opening shrinkage prediction unit, which is used for adding the mold opening shrinkage calculated by the shrinkage acquisition unit and the mold release linear shrinkage, and taking the added result as a mold opening shrinkage prediction value.
Correspondingly, the invention also provides equipment for predicting the mold opening shrinkage of the plastic part, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor. The processor implements the steps of the prediction method described above when executing the computer program.
Accordingly, the present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program, when executed by a processor, implements the steps of the prediction method described above.
Compared with the prior art, the technical scheme of the invention obtains a more accurate mould opening shrinkage calculation method by optimally combining different parameters in the 3D structure data, the material data and the processing technology data of the workpiece. Further, the calculation model is optimized by considering the process of cooling the material to the ambient temperature during demolding and the influence of the subsequent technological process on the workpiece, so that the method, the system and the equipment capable of accurately predicting the whole process shrinkage rate of the workpiece are provided.
The foregoing description is only an overview of the technical solutions of the present application, and in order to make the technical solutions of the present application more clear and clear, and to implement the technical solutions according to the content of the description, the following detailed description is made with reference to the preferred embodiments of the present application and the accompanying drawings.
Drawings
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings;
FIG. 1 is a schematic view of a main implementation flow of the method for predicting the mold opening shrinkage rate of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Example one
The invention provides a method for accurately predicting the mold opening shrinkage of a plastic part by specifically combining and selecting parameters or processes and optimizing a relevant model. The main implementation flow of the method is shown in the attached figure 1 of the specification, and mainly comprises a parameter combination optimization step in the figure and a step of further optimizing a prediction model through a special data processing method. There are some existing commercial software in the prior art to assist in predicting die sinking shrinkage, such as commercial die flow analysis software. In the following embodiments, the prediction method of the present invention is described by taking commercial model flow analysis software as an example, but it is obvious that the concept of the present invention is that the selection and combination of parameters in the prediction method and the optimization calculation of the prediction model are a general method with universality, and are not limited to the specific software.
The method comprises the steps of firstly selecting and processing data required by predicting the die sinking shrinkage rate. Referring to fig. 1, the prediction method requires three types of data, namely 3D data representing the structure of a workpiece, material data representing the material of the workpiece, and actual processing data representing the manufacturing process of the workpiece.
Regarding the 3D data of the finished part, the type of the finite element unit needs to be selected in the process of grid preprocessing. In the present embodiment, the cell type of the neutral plane cell is preferably set to the following theory: 1. the thickness t of the part is much smaller than other dimensions of the part, for example: t < < L (long), W (wide) and H (high); 2. after the normal line perpendicular to the neutral plane is deformed, the normal line is still a straight line and is perpendicular to the neutral plane; 3. the stress and the strain are small enough; 4. the normal stress in the normal direction is sufficiently small and negligible compared to the normal stress in other directions. The number of meshes produced is smaller with the choice of neutral plane elements compared to other types, but with a completely satisfactory precision in terms of structural expression, in particular in terms of the thickness direction, for example for large-area sheet-like parts. Therefore, the neutral surface unit is used for carrying out the grid pre-processing of the 3D data of the workpiece, and the calculation efficiency can be greatly improved on the basis of ensuring the precision. It will be appreciated by those skilled in the art that other cell types may be selected for different product configurations, for example, according to the following: 1. if the large sheet-shaped structural part is a neutral surface unit, the neutral surface unit is preferably selected; 2. in some cases, a double-sided unit can be selected, and if the double-sided unit is selected, the unit matching rate needs to reach more than 90%, which has certain limitation on the structures of some parts. In addition, special processing such as elimination for a part of the structure of the object, for example, a small structure with a very thin thickness (generally less than 0.5mm) or a very thick thickness (generally more than 5mm) can be performed as needed to improve the accuracy and computational efficiency of the prediction.
With respect to material data, there are many different parameters that reflect the material of the part, but it has been found experimentally that not all parameters are necessary, some of which have a significant impact on the predicted result relative to others. According to experiments, the following 7 parameters are found to be necessary for predicting the mold opening shrinkage: viscosity data, PVT data, mechanical property data, crystallographic morphology data, filling data, stress-strain data, thermal property data. The "filling data" specifically refers to the type of the filler in the material and the proportion of the filler, and the "crystal morphology data" is only used for the crystalline material, but has a great influence on the prediction error, which is clearly shown by the experiment on the polypropylene PP material in the following of the present embodiment, and other parameters besides the "crystal morphology data" are also necessary for predicting the shrinkage. Therefore, the parameter data is used as complete material data representing the material for predicting the subsequent shrinkage rate.
Table 1 shows the crystallographic morphology data used in the experiments for the polypropylene PP material of the present example, and table 2 shows the difference between the results obtained using the crystallographic morphology data and without using the crystallographic morphology data according to the process of the present invention (described in detail later).
TABLE 1 crystalline morphology data schematic of PP materials
TABLE 2 influence of crystallization data of PP materials on shrinkage
| Ordinal number of results | Name of result | Taking into account the crystalline morphology of the material | Irrespective of the crystalline form of the material |
| 1 | Isotropic shrinkage | 0.786％ | 0.832％ |
| 2 | Parallel shrinkage (before warping) | 0.421％ | 0.616％ |
| 3 | Vertical shrinkage (before warping) | 1.046％ | 1.192％ |
| 4 | Average linear shrinkage | 0.768％ | 0.823％ |
| 5 | Parallel shrinkage | 0.668％ | 0.577％ |
| 6 | Vertical shrinkage rate | 0.965％ | 1.18％ |
| 7 | Shrinkage in X direction | 0.566％ | 0.589％ |
| 8 | Shrinkage in Y direction | 0.501％ | 0.533％ |
| 9 | Shrinkage in Z direction | 0.484％ | 0.515％ |
| 10 | Global deformation shrinkage compensation value | 0.777％ | 0.827％ |
| Weighted average shrinkage results | 0.755％ | 0.824％ |
As can be seen from the last row of table 2, the crystal morphology data has a large influence on the average shrinkage results, considering that the results of the crystal morphology data are closer to the actual situation. Therefore, crystalline morphology data is indispensable in material data, which can produce effects on errors as high as 8-10%.
Regarding the processing parameters, the necessary parameters are also selected experimentally, and particularly with experiments on polypropylene PP materials such as shown in the following table, these parameters are all selected to be complete.
TABLE 3 typical PP processing parameters
Specifically, in order to ensure as close as possible to the actual processing conditions, melt temperature, mold temperature, filling time, filling volume, holding pressure, and cooling time are all necessary process parameters. The range of each parameter is determined according to the actual material of the product, and in this embodiment, the specific parameter values are shown in the table. In the mould opening shrinkage prediction process, different analysis sequences are selected to greatly influence the subsequent result, and different shrinkage results are obtained and output by different analysis sequences. Therefore, the choice of analytical sequence should ensure that all the required shrinkage results output for the overall shrinkage prediction calculation can be obtained. In this embodiment, the results output required for the shrinkage prediction calculation can be shown in table 4 by experimental summary, some of the shrinkage results output are shrinkage calculation outputs, and some are warp calculation outputs, and both the shrinkage calculation outputs and the warp calculation outputs are necessary for accurately predicting the mold-opening shrinkage. The two analytical sequences selected in table 3 therefore need to be able to ensure that the resulting output data, described later, which is critical for shrinkage prediction, can be included in the analytical results of the sequences. The order of the two analysis sequences in table 3 is not changed, the initial stage of the calculation is input with the cooling conditions as boundary conditions, and the subsequent filling, pressure holding, warping or shrinking is based on the cooling boundary conditions. Besides the process parameters in the table, information such as water paths and flow channels can be further extracted from the mould data and added to the processing process parameters, so that the accuracy of shrinkage prediction is further improved.
The selected and processed data form a specific parameter combination, specific boundary conditions are determined by combining actual process procedures such as material crystallinity and post shrinkage of a workpiece along with temperature change (a variable mold temperature cooling method), then the parameter combination and the boundary conditions are input into a shrinkage rate calculation unit for operation, specifically, the shrinkage rate calculation unit comprises commercial mold flow analysis software, a weight calculation unit and a shrinkage rate acquisition unit, and results in various different ele and nod formats corresponding to different analysis sequences are obtained through operation and output. In these results, if only one or two shrinkage values are considered to represent the shrinkage of the whole process, it is not accurate. Therefore, the invention proposes to compare the result outputs, omit the result outputs which have no direct influence on the shrinkage prediction, and keep the result outputs which have influence on the overall shrinkage prediction. In this example, 10 results, which have the greatest influence on the accuracy of the predicted die-sinking shrinkage ratio, were selected for output, as shown in table 4. Those skilled in the art will appreciate that other numbers of result outputs may be selected for subsequent calculations if the requirements are met.
TABLE 4 data of important results from different analysis sequences
In the above result output, although different result outputs have direct influence on the shrinkage factor prediction result, each result output has different calculation contribution to the final die sinking average shrinkage factor, and the statistical result output related to the shrinkage factor needs to be adjusted, and the corresponding weight value is calculated by the weight calculation unit, so that the shrinkage factor obtaining unit can obtain a more accurate predicted value of the die sinking shrinkage factor. Taking the items of this embodiment as an example, the data results corresponding to the above 10 result outputs are processed to calculate the simulation shrinkage result value corresponding to each of the result outputs, and the results are shown in table 5. Then, the weight calculation unit processes the results to determine the most similar outputs among the simulated shrinkage result values, for example, in table 5, the results with ordinal numbers 1, 4 and 10 are the most similar, and the same weight value is given to the output results with similar shrinkage results. This process may use an order relationship analysis to rank the importance of each result output, for example, the importance is ranked by the following analysis: the result outputs of ordinal numbers 1, 4, 10, 3, 6, 5, 7, 8, 9, and 2. For the result output with high importance degree, the assigned weight value is also higher, for example, each weight value follows the 'Telfiby method' to be graded according to the importance of the index, and the higher the importance is, the higher the weight is. Table 5 lists the weight values corresponding to the 10 outcome outputs selected in this embodiment.
TABLE 5 weight calculation of different shrinkage results output
Based on the data statistics, the simulation shrinkage factor corresponding to each result output is weighted and averaged by the shrinkage factor obtaining unit, and the predicted value of the mold opening shrinkage factor is calculated according to the following formula:
wherein f isiWeights, s, output for different resultsiAnd outputting corresponding simulation shrinkage rate results for different results. In the above embodiment, since 10 result outputs are finally selected, the value range of i is 1 to 10. The shrinkage rate calculation method adopting the 10 result outputs and the same weight values in the table 5 can be also suitable for other projects, and has universality for prediction of the shrinkage rate of the thermoplastic injection molding material.
The above results only take into account the shrinkage of the material from the molten state of the molding to the solidification by cooling, and do not take into account the shrinkage of the material when it is cooled to ambient temperature when it is removed from the mold. In order to further improve the manufacturing precision of the product, the shrinkage prediction method of the invention also calculates the shrinkage of the material at the stage. Specifically, considering the effect of expansion with heat and contraction with cold of the workpiece, the thermal expansion coefficient alpha of the introduced material, and the initial temperature of the workpiece when the workpiece is cooled to be demoulded are about 25-30 ℃ and the ambient temperature. The change of the product in the process can be expressed by epsilon and is called the off-mold linear shrinkage rate, and is calculated by the following formula:
wherein epsilon is the linear shrinkage of the mold release, alpha is the linear thermal expansion coefficient of the material, and T0Is the initial temperature before demolding, TrIs the ambient temperature after cooling. For example, the temperature T at which the part is released0At 30 deg.C, cooling to ambient temperature TrAt 26 ℃, the linear thermal expansion coefficient of the material is: 7.907e-05 then the value of the off-mode linear shrinkage at this stage is (30-26) x 7.907e-05 ═ 3.2e-04, i.e. 0.032%.
Thus, the final predicted open mold shrinkage value in this example will be the sum of the average shrinkage and the off-mold linear shrinkage, i.e.
Wherein f isiWeights, s, output for different resultsiOutputting corresponding simulation shrinkage rate results for different results, wherein the value range of i is 1 to the number of the selected results, epsilon is the die release linear shrinkage rate, alpha is the linear thermal expansion coefficient of the material, and T is the linear thermal expansion coefficient of the material0Is the initial temperature before demolding, TrIs the ambient temperature after cooling.
Example two
In this embodiment, a system for predicting mold opening shrinkage of a plastic part is provided, including a processor, a memory, and a shrinkage calculation unit, where the shrinkage calculation unit further includes:
the data acquisition unit is used for acquiring 3D data, material data and processing parameters of the workpiece, wherein the acquisition of the processing parameters comprises determination of an analysis sequence. The above-mentioned parameter data is obtained in the same manner as the parameter data in the first embodiment.
And the calculating unit is used for calculating a plurality of result outputs under different analysis sequences.
A result output determination unit configured to determine a result output having a large influence on the shrinkage prediction from among the plurality of result outputs. For example, referring to table 5 in example one, 10 results outputs with significant impact on the results are illustrated.
And the weight calculation unit is used for calculating the weight value of each result output determined by the result output determination unit and the corresponding simulation shrinkage rate. See table 5 in example one for specific examples.
A shrinkage rate obtaining unit for calculating the mold opening shrinkage rate according to the following formula:
wherein i is the serial number of the result output, n is the number of the result output determined by the result output determining unit, fiOutputting the corresponding weight value, s, for the ith resultiAnd outputting the corresponding simulation shrinkage rate for the ith result.
Further, in order to improve the manufacturing precision of the part, the shrinkage prediction system of the invention also calculates the shrinkage of the material in the stage that the part is taken out from the die, stands in the ambient temperature and is cooled until the shrinkage reaches the balance. The prediction system thus further comprises an off-mode linear shrinkage calculation unit for calculating an off-mode linear shrinkage according to the following formula:
wherein epsilon is the linear shrinkage of the mold release, alpha is the linear thermal expansion coefficient of the material, and T0Is the initial temperature before demolding, TrIs the ambient temperature after cooling.
Further, in order to comprehensively consider the calculation results of the shrinkage rate obtaining unit and the off-mold linear shrinkage rate calculating unit, the prediction system further includes an open mold shrinkage rate prediction unit, configured to calculate an open mold shrinkage rate prediction value according to the following formula:
EXAMPLE III
In this embodiment, an apparatus for predicting mold opening shrinkage of plastic parts is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor implements the method steps in the first embodiment when executing the computer program, or implements the functions of the units of the system in the second embodiment when executing the computer program.
Accordingly, this embodiment also provides a computer-readable storage medium, which stores a computer program, and the computer program implements the method steps in the first embodiment when executed by a processor, or implements the functions of the units of the system in the second embodiment when executed by a processor.
The method for predicting the die sinking shrinkage of the plastic part is suitable for manufacturing various thermoplastic materials and various structural plastic parts suitable for selecting the neutral surface unit grids or the double-surface unit grids, and obtains a more accurate average shrinkage calculation method by optimally combining different parameters in 3D structural data, material data and processing process data of the plastic part. Furthermore, the influence of the process of cooling to the ambient temperature when the material is separated from the mold on the workpiece is considered, and the calculation model is optimized, so that the method for accurately predicting the shrinkage rate of the whole process of the workpiece is provided, accurate reference can be provided for the mold design, and the plastic workpiece with higher precision can be produced.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (13)
1. A method for predicting the mold opening shrinkage of a plastic part is characterized by comprising the following steps:
s1, acquiring 3D data, material data and processing parameters of the workpiece, wherein the acquisition of the processing parameters comprises determining an analysis sequence;
s2, calculating a plurality of result outputs under different analysis sequences;
s3, determining result output which has larger influence on shrinkage prediction from the plurality of result outputs;
s4, calculating the weight value and the corresponding simulation shrinkage rate of each result output determined in the step S3;
s5, calculating the die sinking shrinkage rate according to the following formula:
where i is the serial number of the result output, n is the number of the result outputs determined in step S3, and fiOutputting the corresponding weight value, s, for the ith resultiAnd outputting the corresponding simulation shrinkage rate for the ith result.
2. The method for predicting mold opening shrinkage of a plastic part according to claim 1, wherein:
in step S4, the method for calculating the weight value includes the following steps:
s41, determining a plurality of results with the closest simulation shrinkage rates to be output;
s42, outputting results with similar simulation shrinkage rate results and giving the same weight value;
s43, the weight values corresponding to the other result outputs determined in the step S3 are endowed with corresponding weight values according to the influence on the results.
3. The method for predicting mold opening shrinkage of a plastic part according to claim 1, wherein:
and selecting the cell type of a neutral surface cell or a double-surface cell in the grid preprocessing process of the 3D data of the workpiece.
4. The method for predicting mold opening shrinkage of a plastic part according to claim 1, wherein:
the material data includes viscosity data, PVT data, mechanical property data, crystallographic morphology data, filling data, stress-strain data, and thermal property data.
5. The method for predicting mold opening shrinkage of a plastic part according to claim 1, wherein:
the processing parameters comprise melt temperature, mold temperature, filling time, filling volume, holding pressure and cooling time.
6. The method for predicting mold opening shrinkage of a plastic part according to claim 1, wherein:
the analysis sequence comprises the following two analysis sequences: cooling + filling + pressure holding + shrinking, and cooling + filling + pressure holding + warping.
7. The method for predicting mold opening shrinkage of a plastic part according to any one of claims 1 to 6, further comprising the steps of:
s6, calculating the die release linear shrinkage rate according to the following formula:
wherein epsilon is the linear shrinkage of the mold release, alpha is the linear thermal expansion coefficient of the material, and T0Is the initial temperature before demolding, TrIs the ambient temperature after cooling.
8. The method for predicting mold opening shrinkage of a plastic part according to claim 7, further comprising the steps of:
and S7, adding the mold opening shrinkage rate obtained by calculation in the step S5 and the mold release linear shrinkage rate obtained by calculation in the step S6, and taking the result of the addition as a predicted value of the mold opening shrinkage rate of the prediction method.
9. A system for predicting mold opening shrinkage of a plastic part comprises a processor, a memory and a shrinkage calculation unit, wherein the shrinkage calculation unit further comprises:
the data acquisition unit is used for acquiring 3D data, material data and processing technological parameters of a workpiece, wherein the acquisition of the processing technological parameters comprises the determination of an analysis sequence;
the computing unit is used for computing a plurality of result outputs under different analysis sequences;
a result output determination unit configured to determine a result output having a large influence on the shrinkage prediction from among the plurality of result outputs;
the weight calculation unit is used for calculating the weight value of each result output determined by the result output determination unit and the corresponding simulation shrinkage rate;
the shrinkage rate obtaining unit is used for calculating the die sinking shrinkage rate according to the following formula:
wherein i is the serial number of the result output, n is the number of the result output determined by the result output determining unit, fiOutputting the corresponding weight value, s, for the ith resultiAnd outputting the corresponding simulation shrinkage rate for the ith result.
10. The system for predicting mold opening shrinkage of plastic parts according to claim 9, further comprising a mold release linear shrinkage calculation unit for calculating the mold release linear shrinkage according to the following formula:
wherein epsilon is the linear shrinkage of the mold release, alpha is the linear thermal expansion coefficient of the material, and T0Is the initial temperature before demolding, TrIs the ambient temperature after cooling.
11. The system for predicting mold opening shrinkage of a plastic part according to claim 10, further comprising a mold opening shrinkage predicting unit configured to add the mold opening shrinkage calculated by the shrinkage obtaining unit to the off-mold linear shrinkage, and to use a result of the addition as a predicted mold opening shrinkage.
12. An apparatus for predicting mold opening shrinkage of plastic parts, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 8 when executing the computer program.
13. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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| WO2019210056A1 (en) * | 2018-04-26 | 2019-10-31 | Markforged, Inc. | System and method for minimizing deviations in 3d printed and sintered parts |
| CN112140413B (en) * | 2020-09-02 | 2022-04-08 | 金发科技股份有限公司 | Method and system for predicting die sinking shrinkage rate of plastic part |
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| WO2006064885A1 (en) * | 2004-12-17 | 2006-06-22 | Mitsubishi Heavy Industries Plastic Technology Co., Ltd. | Injection molding simulation apparatus and method of injection molding simulation |
| CN101859137A (en) * | 2010-05-28 | 2010-10-13 | 武汉理工大学 | Speed chain intelligent optimization system during tread extruding process and optimization method thereof |
| CN106055787A (en) * | 2016-05-30 | 2016-10-26 | 广西科技大学 | Automotive trim panel injection moulding technology based on BP (Back Propagation) neural network |
| CN109543239A (en) * | 2018-10-29 | 2019-03-29 | 西安空间无线电技术研究所 | LTCC shrinkage pre-judging method based on nerual network technique |
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