WO2022048101A1 - 一种塑料制件开模收缩率的预测方法及系统 - Google Patents
一种塑料制件开模收缩率的预测方法及系统 Download PDFInfo
- Publication number
- WO2022048101A1 WO2022048101A1 PCT/CN2021/073550 CN2021073550W WO2022048101A1 WO 2022048101 A1 WO2022048101 A1 WO 2022048101A1 CN 2021073550 W CN2021073550 W CN 2021073550W WO 2022048101 A1 WO2022048101 A1 WO 2022048101A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- shrinkage rate
- mold opening
- data
- result
- mold
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 64
- 239000004033 plastic Substances 0.000 title claims abstract description 30
- 229920003023 plastic Polymers 0.000 title claims abstract description 30
- 239000000463 material Substances 0.000 claims abstract description 48
- 238000004364 calculation method Methods 0.000 claims abstract description 36
- 238000012545 processing Methods 0.000 claims abstract description 22
- 238000001816 cooling Methods 0.000 claims abstract description 19
- 238000004458 analytical method Methods 0.000 claims description 24
- 238000004088 simulation Methods 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 12
- 238000005516 engineering process Methods 0.000 claims description 12
- 239000013078 crystal Substances 0.000 claims description 9
- 230000007935 neutral effect Effects 0.000 claims description 9
- 238000003860 storage Methods 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 abstract description 23
- 238000004519 manufacturing process Methods 0.000 abstract description 8
- 238000013461 design Methods 0.000 abstract description 3
- 229920001169 thermoplastic Polymers 0.000 abstract description 2
- 239000004416 thermosoftening plastic Substances 0.000 abstract description 2
- 239000004743 Polypropylene Substances 0.000 description 9
- 238000002474 experimental method Methods 0.000 description 7
- 238000001746 injection moulding Methods 0.000 description 5
- 238000005457 optimization Methods 0.000 description 4
- 238000002360 preparation method Methods 0.000 description 4
- 239000000243 solution Substances 0.000 description 4
- -1 polypropylene Polymers 0.000 description 3
- 229920001155 polypropylene Polymers 0.000 description 3
- 238000012937 correction Methods 0.000 description 2
- 238000004836 empirical method Methods 0.000 description 2
- 239000000945 filler Substances 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000007711 solidification Methods 0.000 description 2
- 230000008023 solidification Effects 0.000 description 2
- 238000000547 structure data Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000001595 contractor effect Effects 0.000 description 1
- 239000002178 crystalline material Substances 0.000 description 1
- 238000013278 delphi method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000012778 molding material Substances 0.000 description 1
- 238000000465 moulding Methods 0.000 description 1
- 238000012856 packing Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000012815 thermoplastic material Substances 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C33/00—Moulds or cores; Details thereof or accessories therefor
- B29C33/38—Moulds or cores; Details thereof or accessories therefor characterised by the material or the manufacturing process
- B29C33/3835—Designing moulds, e.g. using CAD-CAM
Definitions
- the invention belongs to the technical field of injection molding, and particularly relates to a method and system for predicting mold opening shrinkage.
- Plastics are widely used in the national economy due to their strong plasticity, light weight and excellent chemical stability.
- Injection molding is an important preparation method for plastic products. By this method, plastic products of various shapes, sizes and precisions can be prepared, so it can meet the needs of various fields, such as the preparation of various industrial parts and structures by injection molding. parts, casings, etc.
- the fully molten plastic material needs to be injected into the mold cavity at a certain temperature, and then cooled and solidified to obtain a molded product.
- the plastic part will shrink, thus forming a difference with the size of the mold cavity. Therefore, the influence of the above-mentioned shrinkage and deformation of the material needs to be considered in the mold design, otherwise the parts that meet the requirements cannot be produced, which means that the accurate prediction of the mold opening shrinkage rate is a key indicator related to the success of the production of the parts.
- the methods for determining the mold opening shrinkage rate in the prior art are as follows: 1.
- the experimental trial-and-error method of pre-designing the experimental soft mold and carrying out the experiment, but the method of calculating the mold opening shrinkage rate by opening the experimental soft mold and forming the part involves high cost. And the shortcomings of long project cycle; 2.
- the mold size is corrected, and the shrinkage rate is adapted to the mold by local corrections for many times, but it also involves the number of mold corrections and the number of mold trial production, which will cause high costs and long project cycle shortcomings; 3.
- similar parts can be referred to, the structure of the parts is very different in practical applications. When facing different parts, the empirical method is very useful for reference. Low; 4.
- the material manufacturer recommends the shrinkage rate. Obviously, the recommended shrinkage rate is not suitable for any structural parts, which adds more difficulties to the later material adjustment.
- the present invention proposes a method for accurately predicting the mold opening shrinkage rate of plastic parts. Specifically, the method includes the following steps:
- step S4 calculating the weight value of each result output determined in step S3 and the corresponding simulated shrinkage rate
- the method for calculating the weight value includes the following steps:
- step S43 The weight values corresponding to other result outputs determined in step S3 are given corresponding weight values according to the influence on the result.
- the unit type of the neutral plane unit or the double-sided unit is selected.
- the material data includes viscosity data, PVT data, mechanical property data, crystal morphology data, filling data, stress-strain data, and thermal property data.
- the processing parameters include melt temperature, mold temperature, filling time, filling volume, holding pressure and cooling time.
- the analysis sequence includes the following two analysis sequences: cool+fill+pack+shrink, and cool+pack+pack+warp.
- the prediction method also includes the following steps:
- ⁇ is the linear shrinkage rate of mold release
- ⁇ is the linear thermal expansion coefficient of the material
- T 0 is the initial temperature before mold release
- Tr is the ambient temperature after cooling.
- the prediction method also includes the following steps:
- step S7 Add the mold opening shrinkage rate calculated in step S5 and the mold release linear shrinkage rate, and use the added result as a mold opening shrinkage rate prediction value.
- the present invention also proposes a system for predicting the mold opening shrinkage rate of plastic parts, including a processor, a memory, and a shrinkage rate calculation unit, and the shrinkage rate calculation unit further includes:
- a data acquisition unit configured to acquire 3D data of the workpiece, material data and processing technology parameters, wherein acquiring the processing technology parameters includes determining an analysis sequence
- a result output determination unit configured to determine a result output that has a greater impact on the shrinkage rate prediction from the plurality of result outputs
- a weight calculation unit 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 acquisition unit calculates the mold opening shrinkage rate according to the following formula: where i is the serial number of the result output, n is the number of result outputs determined by the result output determination unit, f i is the weight value corresponding to the i-th result output, and s i is the simulation shrinkage rate corresponding to the i-th result output .
- the prediction system also includes a mold release linear shrinkage rate calculation unit, which is used to calculate the mold release linear shrinkage rate according to the following formula:
- ⁇ is the linear shrinkage rate of mold release
- ⁇ is the linear thermal expansion coefficient of the material
- T 0 is the initial temperature before mold release
- Tr is the ambient temperature after cooling.
- the prediction system further includes a mold opening shrinkage rate prediction unit, configured to add the mold opening shrinkage rate calculated by the shrinkage rate obtaining unit and the mold release linear shrinkage rate, and use the added result as Predicted value of mold opening shrinkage.
- the present invention also provides a device for predicting the mold opening shrinkage rate of plastic parts, which includes a memory, a processor, and a computer program stored in the memory and running on the processor.
- a device for predicting the mold opening shrinkage rate of plastic parts which includes a memory, a processor, and a computer program stored in the memory and running on the processor.
- the processor executes the computer program, each step in the above prediction method is implemented.
- the present invention also provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium.
- the computer program when executed by the processor, implements the steps in the prediction method described above.
- the technical solution of the present invention obtains a more accurate calculation method of mold opening shrinkage rate by optimizing the combination of different parameters in the 3D structure data, material data and processing technology data of the workpiece.
- the calculation model is further optimized by considering the process of cooling the material to the ambient temperature when the material is released from the mold and the influence of the subsequent process on the part, so as to propose a method, system and equipment that can accurately predict the shrinkage rate of the part in the entire process.
- FIG. 1 is a schematic diagram of the main implementation flow of the mold opening shrinkage rate prediction method of the present invention.
- the present invention proposes a method for accurately predicting the mold opening shrinkage rate of plastic parts through specific combination and selection of parameters or processes and optimization of related models.
- the main implementation flow of the method is shown in FIG. 1 of the description, which mainly includes the parameter combination optimization step in the figure, and the further optimization of the prediction model through a special data processing method.
- the prediction method of the present invention is described by taking commercial mold flow analysis software as an example, but obviously, the concept of the present invention is the selection and combination of parameters in the prediction method, and the optimization calculation of the prediction model, which is a kind of A general and general approach, not limited to the specific software mentioned above.
- the present invention first selects and processes the data required for predicting the mold opening shrinkage rate.
- the prediction method requires three types of data, namely the 3D data of the workpiece reflecting the structure of the workpiece, the material data reflecting the material of the workpiece, and the actual processing data reflecting the preparation process of the workpiece.
- the finite element element type needs to be selected during the pre-processing of the mesh.
- the unit type of the neutral plane unit is preferred, and the unit type is set as follows: 1.
- the thickness t of the workpiece is much smaller than other dimensions of the workpiece, for example: t ⁇ L (length), W (width) and H (height); 2.
- the normal line perpendicular to the neutral plane is still a straight line and perpendicular to the neutral plane after deformation; 3.
- the stress and strain are small enough; 4.
- the positive line in the normal direction Compared with the normal stress in other directions, the forward stress is small enough to be ignored.
- the neutral plane unit After selecting the neutral plane element, the number of meshes generated is smaller than other types, but for large-area sheet-like parts, for example, the expression of the structure, especially the thickness direction, has the required accuracy. Therefore, using the neutral plane unit to perform the mesh preprocessing of the 3D data of the workpiece can greatly improve the computing efficiency on the basis of ensuring the accuracy.
- the selection can be based on the following basis: 1. If the workpiece is a large sheet-like structure, the "neutral plane unit" is preferred; 2. In some cases, double-sided units can be selected. If double-sided units are selected, the unit matching rate needs to reach more than 90%, which has certain limitations on some parts structures.
- Table 1 shows the crystal morphology data used in the experiment of polypropylene PP material in this example
- Table 2 shows the method according to the present invention (described in detail later) with and without crystal morphology data. difference between the results.
- crystallographic morphology data is indispensable in material data, which can have an impact on errors of up to 8-10%.
- the necessary parameters are also selected according to the experiments. Specifically, the experiments for polypropylene PP materials are shown in the following table. These parameters are selected only when they are complete.
- melt temperature, mold temperature, filling time, filling volume, holding pressure and cooling time are all necessary process parameters.
- the range of each of the above parameters is determined according to the actual workpiece material.
- the specific parameter values are shown in the table.
- the selection of different analysis sequences has a great impact on the subsequent results, and different analysis sequences will obtain different output of shrinkage rate results. Therefore, the selection of the analysis sequence should ensure that all required shrinkage result outputs are obtained for the overall shrinkage prediction calculation.
- the result output required for the shrinkage rate prediction calculation can be seen in Table 4.
- shrinkage operation outputs Some of these shrinkage rate result outputs are shrinkage operation outputs, and some are warpage operation outputs. Whether it is shrinkage operation output It is also necessary for the warpage calculation output to accurately predict the mold opening shrinkage rate. Therefore, the two analysis sequences selected in Table 3 need to be able to ensure that the result output data that is critical to shrinkage prediction described later can be included in the analysis results of the sequences. The order of the two analysis sequences in Table 3 is unchanged. The initial stage of the operation is based on the cooling condition as the boundary input, and the subsequent filling, packing, warping or shrinkage is based on the cooling boundary condition. In addition to the process parameters in the above table, information such as water channels and runners can be further extracted from the mold data and added to the processing process parameters to further increase the accuracy of shrinkage prediction.
- the above selected and processed data are formed into a specific combination of parameters, combined with the crystallinity of the material, and the actual process such as the post-shrinkage of the part with temperature changes (variable mold temperature cooling method) to determine specific boundary conditions, and then the The parameter combination and boundary conditions are input to the shrinkage rate calculation unit for operation processing.
- the shrinkage rate calculation unit includes a commercial mold flow analysis software, a weight calculation unit, and a shrinkage rate acquisition unit. Through the calculation process, various different analysis sequences corresponding to different types are obtained. The result output in ele and nod format. In these output results, it is inaccurate to consider only one or two of the shrinkage values to represent the shrinkage of the entire process.
- the present invention proposes to compare and process these result outputs, ignoring those that have no direct impact on the shrinkage rate prediction, and retain the result outputs that have an impact on the overall shrinkage rate prediction.
- 10 kinds of results are selected which have the greatest influence on the precision of predicting the mold opening shrinkage rate, as shown in Table 4.
- Those skilled in the art can understand that other numbers of result outputs can also be selected for subsequent calculation if the requirements are met.
- each result output has a different contribution to the calculation of the final average shrinkage rate of mold opening.
- the result output is adjusted, and the corresponding weight value is calculated by the weight calculation unit, so that the shrinkage rate acquisition unit can obtain a more accurate mold opening shrinkage rate prediction value.
- the data results corresponding to the above 10 kinds of result outputs are processed, and the simulated shrinkage rate result value corresponding to each of the result outputs is calculated.
- the results are shown in Table 5. Then, it is processed by the weight calculation unit to determine multiple results outputs that are the closest between the simulation shrinkage result values.
- the results with ordinal numbers of 1, 4, and 10 are the closest, and for the results with the closest shrinkage results output give the same weight value.
- the order relation analysis method can be used to sort the importance of each result output.
- the order of importance is as follows: the result output with ordinal numbers 1, 4, and 10, the result output with ordinal numbers 3 and 6, and the ordinal number.
- the assigned weight value will also be relatively high.
- each weight value follows the "Delphi method" to score and determine the weight according to the importance of the indicator. The higher the importance, the greater the weight.
- Table 5 lists the weight values corresponding to the 10 selected result outputs in this embodiment.
- the simulation shrinkage rate corresponding to each result output is weighted and averaged by the shrinkage rate acquisition unit, and the predicted value of the mold opening shrinkage rate is calculated according to the following formula:
- f i is the weight of different results output
- si is the simulation shrinkage rate corresponding to different results output.
- the value of i ranges from 1 to 10.
- the shrinkage calculation method using the above 10 result outputs and the same weight value in Table 5 can also be applied to other items, and the prediction of the model shrinkage rate of thermoplastic injection molding materials is universal.
- the shrinkage rate prediction method of the present invention also calculates the shrinkage rate of the material at this stage.
- the thermal expansion coefficient ⁇ of the introduced material the initial temperature when the product is cooled to reach demoulding, and the ambient temperature are about 25-30 °C.
- the change of the part in this process can be represented by ⁇ , and is called the linear shrinkage rate of mold release, which is calculated by the following formula:
- ⁇ is the linear shrinkage rate of mold release
- ⁇ is the linear thermal expansion coefficient of the material
- T 0 is the initial temperature before mold release
- Tr is the ambient temperature after cooling.
- T 0 is 30°C
- T r is 26°C.
- the final mold opening shrinkage prediction value in this example will be the sum of the above average shrinkage and mold release linear shrinkage, namely
- f i is the weight of different result outputs
- s i is the simulation shrinkage rate corresponding to different result outputs
- the value range of i is from 1 to the number of selected result outputs
- ⁇ is the mold release linear shrinkage rate
- ⁇ is the material Linear thermal expansion coefficient
- T 0 is the initial temperature before mold release
- Tr is the ambient temperature after cooling.
- a prediction system for mold opening shrinkage rate of plastic parts which includes a processor, a memory, and a shrinkage rate calculation unit, and the shrinkage rate calculation unit further includes:
- the data acquisition unit is used for acquiring the 3D data of the workpiece, the material data and the processing technology parameters, wherein the obtaining of the processing technology parameters includes determining an analysis sequence.
- the above parameter data is obtained in the same manner as the parameter data obtained in the first embodiment.
- a computing unit for computing multiple result outputs under different analysis sequences.
- a result output determination unit configured to determine, from the plurality of result outputs, a result output that has a greater influence on the shrinkage rate prediction. For example, see Table 5 in Embodiment 1, which exemplifies 10 kinds of result outputs that have an important impact on the results.
- 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. For a specific example, see Table 5 in Embodiment 1.
- the shrinkage rate acquisition unit calculates the mold opening shrinkage rate according to the following formula: where i is the serial number of the result output, n is the number of result outputs determined by the result output determination unit, f i is the weight value corresponding to the i-th result output, and s i is the simulation shrinkage rate corresponding to the i-th result output .
- the shrinkage rate prediction system of the present invention also calculates the shrinkage rate of the material in the stage when the parts are taken out from the mold and left to cool at ambient temperature until the shrinkage reaches equilibrium. Therefore, the prediction system further includes a mold release linear shrinkage rate calculation unit, which is used to calculate the mold release linear shrinkage rate according to the following formula:
- ⁇ is the linear shrinkage rate of mold release
- ⁇ is the linear thermal expansion coefficient of the material
- T 0 is the initial temperature before mold release
- Tr is the ambient temperature after cooling.
- the prediction system also includes a mold opening shrinkage rate prediction unit for calculating the mold opening shrinkage rate prediction value according to the following formula:
- a device for predicting the mold opening shrinkage rate of a plastic part 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 the processor implements the functions of each unit of the system in the second embodiment when the processor executes the computer program.
- this embodiment also proposes a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, implements the method steps in the above-mentioned first embodiment, or the computer When the program is executed by the processor, the functions of each unit of the system in the second embodiment above are realized.
- the method for predicting the mold opening shrinkage rate of plastic parts proposed by the invention is suitable for the production process of various thermoplastic materials and various structural plastic parts suitable for selecting neutral plane unit grids or double-sided unit grids. Different parameters in 3D structure data, material data and processing technology data are optimized and combined to obtain a more accurate calculation method of average shrinkage rate. Further, by considering the influence of the process of cooling the material to the ambient temperature when the material is released from the mold, the calculation model is optimized, and a method that can accurately predict the shrinkage rate of the whole process of the workpiece is proposed, which can provide an accurate reference for mold design. Helps to produce higher precision plastic parts.
Landscapes
- Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Mechanical Engineering (AREA)
- Injection Moulding Of Plastics Or The Like (AREA)
Abstract
一种塑料制件开模收缩率的预测方法及系统，适用于各种热塑性材料制件的制作过程。通过对制件3D结构数据、材料数据和加工工艺数据中的不同参数进行优化组合，得到更加精确的收缩率计算方法。进一步通过考虑材料离模时冷却至环境温度的过程对制件的影响来优化计算模型，从而更精确地预测制件整个制程的收缩率，以此能够为模具设计提供准确的参考，有助于生产精度更高的塑料制件。
Description
本发明属于注塑成型技术领域，特别涉及一种开模收缩率的预测方法及系统。
塑料由于具有可塑性强、重量轻、化学稳定性优良等特点，在国民经济中有着广泛的应用。注塑成型是塑料制品的一种重要的制备方法，通过该方法可以制备各种形状、尺寸、精度的塑料制品，因而能够满足各种不同领域的需求，例如通过注塑成型制备各种工业配件、结构件、壳体等等。
注塑成型过程中，在一定温度下需要将完全熔融的塑料材料注射到模具型腔中，再经过冷却固化后得到成型的制品。然而，在从熔融粘流态到冷却固化定型的过程中，塑料制件会产生收缩，从而形成与模具型腔尺寸之间的差异。因此，在模具设计时就需要考虑到上述材料收缩变形带来的影响，否则无法制作符合要求的制件，这就意味着精确预测开模收缩率是关系到制件生产成功的关键指标。
现有技术中确定开模收缩率的方法有：1、预先设计实验软模并进行实验的实验试错法，但是通过开实验软模以及成型零件后推算出开模收缩率的方法涉及成本高和项目周期长的缺点；2、模具尺寸修偏，通过模具多次局部修正来适应收缩率，但是也涉及模具修正次数、模具试制次数多会造成成本高和项目周期长的缺点；3、通过大量项目应用数据得出收缩率的经验法，虽然类似的制件可参考性较高，但在实际应用中制件结构差异很大，在面对不同的制件时，经验法可参考性很低；4、材料厂商推荐收缩率，显 然推荐的收缩率并不适用于任何结构的制件，这给后期材料调整增加了更多的困难。
由此可见，现有技术中虽然有一些方法希望获得收缩率参数，但是普遍存在成本高、时间周期长或者缺乏普适性的缺点，对提高塑料制件制备精度的帮助非常有限。
发明内容
针对上述现有技术中的缺点，本发明提出了一种可以精确预测塑料制件开模收缩率的方法，具体的，所述方法包括如下步骤：
S1、获取制件3D数据、材料数据以及加工工艺参数，其中，获取所述加工工艺参数包括确定分析序列；
S2、计算不同分析序列下的多个结果输出；
S3、从所述多个结果输出中确定对收缩率预测影响较大的结果输出；
S4、计算步骤S3所确定的每一个结果输出的权重值和对应的仿真收缩率；
S5、根据如下公式计算所述开模收缩率：
其中i为结果输出的序号，n为步骤S3所确定出的结果输出的个数，f
i为第i个结果输出对应的权重值，s
i为第i个结果输出对应的仿真收缩率。
进一步，所述步骤S4中，计算权重值的方法包括如下步骤：
S41、确定仿真收缩率最为接近的多个结果输出；
S42、对于上述仿真收缩率结果接近的结果输出给出相同的权重值；
S43、步骤S3确定的其它结果输出对应的权重值则依照对结果的影响赋予相应的权重值。
优选地，所述制件3D数据的网格前处理过程中，选择中性面单元或者双面单元的单元类型。所述材料数据包括粘度数据、PVT数据、机械属性数据、结晶形态学数据、填充数据、应力-应变数据和热属性数据。所述加 工工艺参数包括熔料温度、模具温度、充填时间、充填体积、保压压力和冷却时间。所述分析序列包括如下两个分析序列：冷却+充填+保压+收缩，和冷却+充填+保压+翘曲。
进一步，所述预测方法还包括如下步骤：
S6、根据如下公式计算离模线性收缩率：
其中，ε为离模线性收缩率，α为材料线性热膨胀系数，T
0为离模前的初始温度，T
r为冷却后的环境温度。
进一步，所述预测方法还包括如下步骤：
S7、将所述步骤S5计算获得的所述开模收缩率与所述离模线性收缩率相加，并将相加的结果作为开模收缩率预测值。
同时，本发明还提出一种塑料制件开模收缩率的预测系统，包括处理器、存储器以及收缩率计算单元，所述收缩率计算单元进一步包括：
数据获取单元，用于获取制件3D数据、材料数据以及加工工艺参数，其中，获取所述加工工艺参数包括确定分析序列；
计算单元，用于计算不同分析序列下的多个结果输出；
结果输出确定单元，用于从所述多个结果输出中确定对收缩率预测影响较大的结果输出；
权重计算单元，用于计算结果输出确定单元所确定的每一个结果输出的权重值和对应的仿真收缩率；
收缩率获取单元，根据如下公式计算所述开模收缩率：
其中i为结果输出的序号，n为结果输出确定单元所确定出的结果输出的个数，f
i为第i个结果输出对应的权重值，s
i为第i个结果输出对应的仿真收缩率。
进一步，所述预测系统还包括离模线性收缩率计算单元，用于根据如下公式计算离模线性收缩率：
其中，ε为离模线性收缩率，α为材料线性热膨胀系数，T
0为离模前的初始温度，T
r为冷却后的环境温度。
进一步，所述预测系统还包括开模收缩率预测单元，用于将所述收缩率获取单元计算的所述开模收缩率与所述离模线性收缩率相加，并将相加的结果作为开模收缩率预测值。
相应地，本发明还提出一种塑料制件开模收缩率的预测设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序。该处理器执行所述计算机程序时实现上述预测方法中的各步骤。
相应地，本发明还提出一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序。该计算机程序被处理器执行时实现上述预测方法中的各步骤。
与现有技术相比，本发明的技术方案通过对制件3D结构数据、材料数据和加工工艺数据中的不同参数进行优化组合，得到更加精确的开模收缩率计算方法。进一步通过考虑材料离模时冷却至环境温度的过程以及后续工艺过程对制件的影响来优化计算模型，从而提出一种能够精确预测制件整个制程收缩率的方法、系统和设备。
上述说明仅是本申请技术方案的概述，为了能够更清楚了解本申请的技术手段，并可依照说明书的内容予以实施，以下以本申请的较佳实施例并配合附图详细说明如后。
下面结合附图对本发明的具体实施方式作进一步详细的说明；
图1是本发明开模收缩率预测方法主要实施流程示意图。
为了使本技术领域的人员更好地理解本发明方案，下面将结合本发明 实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分的实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都应当属于本发明保护的范围。
实施例一
本发明通过对参数或流程进行特定的组合和选用以及对相关模型进行优化处理，提出一种可精确预测塑料制件开模收缩率的方法。该方法的主要实施流程参见说明书附图1所示，主要包括图中参数组合优化步骤，和通过特殊的数据处理方法进一步优化预测模型的步骤。现有技术中存在一些现有的商业软件来辅助预测开模收缩率，例如商业模流分析软件。在以下实施例中，以商业模流分析软件为例阐述本发明的预测方法，但是显然的，本发明的构思为预测方法中参数的选定与组合，以及预测模型的优化计算，是一种通用的具有普适性的方法，而不是限定于上述特定的软件中。
本发明首先对预测开模收缩率所需要的数据进行选择和处理。参见图1所示，预测方法需要三种类型的数据，分别为体现制件结构的制件3D数据，体现制件材质的材料数据，和体现制件制备过程的实际加工工艺数据。
关于制件3D数据，在网格前处理的过程中，需要对有限元单元类型进行选择。在本实施例中，将中性面单元的单元类型作为优选，该单元类型进行以下理论的设定：1.制件厚度t远小于制件其他尺寸，例如：t<<L(长)、W(宽)和H(高)；2.垂直于中性面的法线在变形后，仍为直线且垂直于中性面；3.应力、应变足够小；4.法线方向上的正向应力与其他方向正向应力相比足够小，可不计。选择中性面单元后，生成的网格数量相对于其它类型更少，但对于例如大面积的片状制件来说，在结构表达特别是厚度方向的表达上具有完全符合要求的精度。因此，使用中性面单元来进行制件3D数据的网格前处理，能够在保证精度的基础上大大提高运算效率。本领域技术人员可以理解，针对制件结构的不同也可以选用其它的单元类型， 例如可以根据以下依据进行选择：1.若是大型的片状结构制件优先选用“中性面单元”；2.一些情况下可以选择双面单元，如果选用双面单元则单元匹配率需达到90％以上，这对某些制件结构存在一定的局限性。此外，还可以根据需要，进行例如针对制件部分结构，例如厚度很薄(一般小于0.5mm)或是厚度很厚(一般大于5mm)的小结构，进行消除的特殊处理，以提高预测的准确度和计算效率。
关于材料数据，有很多不同的参数可以反映制件的材质，但经过实验发现并不是所有的参数都是必要的，这其中，有些参数相对于其它参数来说对于预测的结果影响很大。根据实验发现，如下7个参数是预测开模收缩率所必须的：粘度数据、PVT数据、机械属性数据、结晶形态学数据、填充数据、应力-应变数据、热属性数据。其中，“填充数据”具体指材料中填充物的类型以及填充物的比例，“结晶形态学数据”仅针对结晶性材料时使用，但是其对预测误差有很大的影响，这通过本实施例下文中针对聚丙烯PP材料的实验可以清楚地看出，而“结晶形态学数据”以外的其它参数也是预测收缩率所必须的。因此，将上述参数数据做为体现材质的完整的材料数据用来后续收缩率的预测。
表1展示了本实施例中聚丙烯PP材料实验中所使用的结晶形态学数据，表2展示了根据本发明的方法(后文将详细描述)使用结晶形态学数据和不使用结晶形态学数据结果之间的差别。
表1 PP材料结晶形态学数据示意
表2 PP材料结晶数据对收缩率的影响
| 结果序数 | 结果名称 | 考虑材料结晶形态 | 不考虑材料结晶形态 |
| 1 | 各向同性收缩率 | 0.786％ | 0.832％ |
| 2 | 平行收缩率(翘曲前) | 0.421％ | 0.616％ |
| 3 | 垂直收缩率(翘曲前) | 1.046％ | 1.192％ |
| 4 | 平均线性收缩率 | 0.768％ | 0.823％ |
| 5 | 平行收缩率 | 0.668％ | 0.577％ |
| 6 | 垂直收缩率 | 0.965％ | 1.18％ |
| 7 | X方向收缩率 | 0.566％ | 0.589％ |
| 8 | Y方向收缩率 | 0.501％ | 0.533％ |
| 9 | Z方向收缩率 | 0.484％ | 0.515％ |
| 10 | 整体变形收缩补偿值 | 0.777％ | 0.827％ |
| 加权平均收缩率结果 | 0.755％ | 0.824％ |
通过表2最后一行的可以看到，结晶形态学数据对平均收缩率结果的影响很大，考虑结晶形态学数据的结果更接近实际情况。因此，结晶形态学数据是材料数据中不可或缺的，其能够产生对误差的影响高达8-10％。
关于加工工艺参数，同样根据实验来选择必要的参数，具体以针对聚丙烯PP材料的实验为例如下表格所示，这些参数都选择才是完备的。
表3 PP典型加工工艺参数
具体地，为了保证尽可能的贴近实际加工工艺的情况，熔料温度、模具温度、充填时间、充填体积、保压压力和冷却时间都是必要的工艺参数。上述每个参数的范围根据实际制件材料进行确定，在本实施例中，具体参数数值如表格所示。开模收缩率预测过程中，选择不同的分析序列对后续结果影响很大，不同的分析序列将获得不同的收缩率结果输出。因此，分析序列的选择应当保证能够获得对于整体收缩率预测计算来说所有需要的 收缩率结果输出。在本实施例中，通过实验总结，收缩率预测计算所需要的结果输出可以参见表4所示，这些收缩率结果输出中有些是收缩运算输出，有些是翘曲运算输出，无论是收缩运算输出还是翘曲运算输出对于准确预测开模收缩率都是必要的。因此表3中所选择的两个分析序列需要能够保证后文所述的对收缩率预测起关键影响的结果输出数据都能够被包括在序列的分析结果中。表3中两个分析序列的顺序是不变的，运算初始阶段是以冷却条件为边界输入，后续充填、保压、翘曲或者是收缩均是基于冷却边界条件。除了上表中的工艺参数，还可以进一步从模具数据中提取水路、流道等信息，增加到加工工艺参数中，以进一步增加收缩率预测的准确性。
将上述选择并处理好的数据形成特定的参数组合，结合对材料的结晶性，以及制件随温度变化的后收缩(可变模温冷却方法)等实际工艺制程确定特定的边界条件，然后将参数组合和边界条件输入到收缩率计算单元进行运算处理，具体地，收缩率计算单元包括商业模流分析软件、权重计算单元以及收缩率获取单元，通过运算处理获得不同分析序列对应的多种不同的ele、nod格式的结果输出。在这些结果输出中，如果只考虑其中某一个或者两个收缩率值来代表整个制程的收缩率是不准确的。因此，本发明提出将这些结果输出进行对比处理，忽略掉那些对收缩率预测没有直接影响的结果输出，而保留对整体收缩率预测有影响的结果输出。在本实施例中，选择其中对预测开模收缩率精度影响最大的10种结果输出，参见表4所示。本领域技术人员可以理解，在满足需求的情况下也可以选择其它数量的结果输出进行后续计算。
表4 不同分析序列选择出的重要结果数据
上述结果输出中，不同的结果输出虽然都对收缩率预测结果有直接影响，但是每一个结果输出对最终开模平均收缩率的计算贡献是不一样的，需要对所统计的与收缩率相关的结果输出进行调整，并通过权重计算单元推算出相应的权重值，才能够由收缩率获取单元获得更精确的开模收缩率预测值。以本实施例项目为例，对上述10种结果输出分别对应的数据结果进行处理，推算出每一个所述结果输出所对应的仿真收缩率结果值，结果参见表5所示。然后，由权重计算单元处理，确定仿真收缩率结果值之间最为接近的多个结果输出，例如在表5中，序数为1、4、10的结果最为接近，对于收缩率结果接近的结果输出则给出相同的权重值。这个过程可以采用序关系分析法对每个结果输出的重要程度进行排序，例如，经过分析按照重要程度排序如下：序数为1、4、10的结果输出、序数为3、6的结果输出、序数为5的结果输出、序数为7、8的结果输出、序数为9的结果输出、以及序数为2的结果输出。对于重要程度高的结果输出，赋予的权重值也会比较高，例如每个权重值遵循“特尔斐法”根据指标的重要性来打分定权，重要性越高权重越大。表5列出了本实施例中所选择的10个结果输出所对应的权重值。
表5 不同收缩率结果输出的权重计算
| 结果序数 | 结果名称 | 权重 | 仿真收缩率结果值(％) |
| 1 | 各向同性收缩率 | f 1＝0.18 | 0.786％ |
| 2 | 平行收缩率(翘曲前) | f 2＝0.04 | 0.421％ |
| 3 | 垂直收缩率(翘曲前) | f 3＝0.09 | 1.046％ |
| 4 | 平均线性收缩率 | f 4＝0.18 | 0.768％ |
| 5 | 平行收缩率 | f 5＝0.06 | 0.668％ |
| 6 | 垂直收缩率 | f 6＝0.1 | 0.965％ |
| 7 | X方向收缩率 | f 7＝0.06 | 0.566％ |
| 8 | Y方向收缩率 | f 8＝0.06 | 0.501％ |
| 9 | Z方向收缩率 | f 9＝0.05 | 0.484％ |
| 10 | 整体变形收缩补偿值 | f 10＝0.18 | 0.777％ |
基于以上数据统计，再通过收缩率获取单元对各个结果输出所对应的仿真收缩率进行加权平均，按照如下公式计算开模收缩率的预测值：
其中，f
i为不同结果输出的权重，s
i为不同结果输出对应的仿真收缩率结果。在上述实施例中，由于最终选择了10个结果输出，因而i的取值范围为1至10。采用上述10个结果输出以及表5中相同权重值的收缩率计算方法也可以适用其它项目，对于热塑性注塑成型材料楷模收缩率的预测具有普适性。
上述结果仅考虑到材料在成型熔融状态到冷却固化时的收缩，未考虑材料离模时冷却至环境温度时的收缩，然而，零件从模具中取出后静置在环境温度中冷却直至收缩达到平衡是每一个制件都经历的制程，因此，该过程中制件的变化也应在模具设计时就考虑到。为了进一步提高制件制作精度，本发明的收缩率预测方法中还对该阶段材料的收缩率进行计算。具体地，考虑制件的热胀冷缩效应，引入材料的热膨胀系数α、制件冷却达到脱模时的初始温度，约为25～30℃以及环境温度。该过程制件的变化可以用ε来表示，并称为离模线性收缩率，通过以下公式计算获得：
其中，ε为离模线性收缩率，α为材料线性热膨胀系数，T
0为离模前的初始温度，T
r为冷却后的环境温度。例如，制件离模时温度T
0为30℃，冷却至环境温度T
r为26℃，材料的线性热膨胀系数为：7.907e-05那么该阶段 的离模线性收缩率值为(30-26)×7.907e-05＝3.2e-04，即0.032％。
因此，本实施例中最终的开模收缩率预测值将是上述平均收缩率和离模线性收缩率的和，即
其中，f
i为不同结果输出的权重，s
i为不同结果输出对应的仿真收缩率结果，i的取值范围为1到所选择结果输出的数量，ε为离模线性收缩率，α为材料线性热膨胀系数，T
0为离模前的初始温度，T
r为冷却后的环境温度。
实施例二
在本实施例中，提出一种塑料制件开模收缩率的预测系统，包括处理器、存储器以及收缩率计算单元，所述收缩率计算单元进一步包括：
数据获取单元，用于获取制件3D数据、材料数据以及加工工艺参数，其中，获取所述加工工艺参数包括确定分析序列。上述参数数据的获取与实施例一中的参数数据的获取方式相同。
计算单元，用于计算不同分析序列下的多个结果输出。
结果输出确定单元，用于从所述多个结果输出中确定对收缩率预测影响较大的结果输出。例如参见实施例一中的表5，示例出了10种对结果有重要影响的结果输出。
权重计算单元，用于计算结果输出确定单元所确定的每一个结果输出的权重值和对应的仿真收缩率。具体的示例参见实施例一中的表5。
收缩率获取单元，根据如下公式计算开模收缩率：
其中i为结果输出的序号，n为结果输出确定单元所确定出的结果输出的个数，f
i为第i个结果输出对应的权重值，s
i为第i个结果输出对应的仿真收缩率。
进一步，为了提高制件制作精度，本发明的收缩率预测系统还对零件从模具中取出后静置在环境温度中冷却直至收缩达到平衡这个阶段中材料的收缩率进行计算。因而所述预测系统还包括离模线性收缩率计算单元， 用于根据如下公式计算离模线性收缩率：
其中，ε为离模线性收缩率，α为材料线性热膨胀系数，T
0为离模前的初始温度，T
r为冷却后的环境温度。
进一步，为了综合考虑上述收缩率获取单元和离模线性收缩率计算单元的计算结果，所述预测系统还包括开模收缩率预测单元，用于根据如下公式计算开模收缩率预测值：
实施例三
在本实施例中，提出一种塑料制件开模收缩率的预测设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序。该处理器执行所述计算机程序时实现上述实施例一中的方法步骤，或者，该处理器执行所述计算机程序时实现上述实施例二中系统各个单元的功能。
相应地，本实施例还提出一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，该计算机程序被处理器执行时实现上述实施例一中的方法步骤，或者，该计算机程序被处理器执行时实现上述实施例二中系统各个单元的功能。
本发明提出的塑料制件开模收缩率的预测方法，适用各种热塑性材料、各种适合选用中性面单元网格或双面单元网格的结构塑料制件的制作过程，通过对制件3D结构数据、材料数据和加工工艺数据中的不同参数进行优化组合，得到更精确的平均收缩率计算方法。进一步通过考虑材料离模时冷却至环境温度的过程对制件的影响，优化计算模型，从而提出一种能够精确预测制件整个制程收缩率的方法，以此能够为模具设计提供准确的参考，有助于生产精度更高的塑料制件。
以上所述仅为本发明的较佳实施例，并不用以限制本发明，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。
Claims (13)
- 一种塑料制件开模收缩率的预测方法，其特征在于包括如下步骤：S1、获取制件3D数据、材料数据以及加工工艺参数，其中，获取所述加工工艺参数包括确定分析序列；S2、计算不同分析序列下的多个结果输出；S3、从所述多个结果输出中确定对收缩率预测影响较大的结果输出；S4、计算步骤S3所确定的每一个结果输出的权重值和对应的仿真收缩率；S5、根据如下公式计算所述开模收缩率：其中i为结果输出的序号，n为步骤S3所确定出的结果输出的个数，f i为第i个结果输出对应的权重值，s i为第i个结果输出对应的仿真收缩率。
- 根据权利要求1所述的塑料制件开模收缩率的预测方法，其特征在于：所述步骤S4中，计算权重值的方法包括如下步骤：S41、确定仿真收缩率最为接近的多个结果输出；S42、对于上述仿真收缩率结果接近的结果输出给出相同的权重值；S43、步骤S3确定的其它结果输出对应的权重值则依照对结果的影响赋予相应的权重值。
- 根据权利要求1所述的塑料制件开模收缩率的预测方法，其特征在于：所述制件3D数据的网格前处理过程中，选择中性面单元或双面单元的单元类型。
- 根据权利要求1所述的塑料制件开模收缩率的预测方法，其特征在于：所述材料数据包括粘度数据、PVT数据、机械属性数据、结晶形态学 数据、填充数据、应力-应变数据和热属性数据。
- 根据权利要求1所述的塑料制件开模收缩率的预测方法，其特征在于：所述加工工艺参数包括熔料温度、模具温度、充填时间、充填体积、保压压力和冷却时间。
- 根据权利要求1所述的塑料制件开模收缩率的预测方法，其特征在于：所述分析序列包括如下两个分析序列：冷却+充填+保压+收缩，和冷却+充填+保压+翘曲。
- 根据权利要求1-6任一项所述的塑料制件开模收缩率的预测方法，其特征在于还包括如下步骤：S6、根据如下公式计算离模线性收缩率：
其中，ε为离模线性收缩率，α为材料线性热膨胀系数，T 0为离模前的初始温度，T r为冷却后的环境温度。 - 根据权利要求7所述的塑料制件开模收缩率的预测方法，其特征在于还包括如下步骤：S7、将所述步骤S5中计算获得的所述开模收缩率与所述步骤S6计算获得的离模线性收缩率相加，将所述相加的结果作为所述预测方法的开模收缩率预测值。
- 一种塑料制件开模收缩率的预测系统，包括处理器、存储器以及收缩率计算单元，所述收缩率计算单元进一步包括：数据获取单元，用于获取制件3D数据、材料数据以及加工工艺参数，其中，获取所述加工工艺参数包括确定分析序列；计算单元，用于计算不同分析序列下的多个结果输出；结果输出确定单元，用于从所述多个结果输出中确定对收缩率预测影响较大的结果输出；权重计算单元，用于计算结果输出确定单元所确定的每一个结果输出的权重值和对应的仿真收缩率；收缩率获取单元，根据如下公式计算所述开模收缩率：其中i为结果输出的序号，n为结果输出确定单元所确定出的结果输出的个数，f i为第i个结果输出对应的权重值，s i为第i个结果输出对应的仿真收缩率。
- 根据权利要求9所述的塑料制件开模收缩率的预测系统，还包括离模线性收缩率计算单元，用于根据如下公式计算离模线性收缩率：
其中，ε为离模线性收缩率，α为材料线性热膨胀系数，T 0为离模前的初始温度，T r为冷却后的环境温度。 - 根据权利要求10所述的塑料制件开模收缩率的预测系统，其特征在于还包括开模收缩率预测单元，用于将所述收缩率获取单元计算的所述开模收缩率与所述离模线性收缩率相加，并将所述相加的结果作为开模收缩率预测值。
- 一种塑料制件开模收缩率的预测设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至8任一项所述方法的步骤。
- 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至8任一项所述方法的步骤。
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010912220.7 | 2020-09-02 | ||
| CN202010912220.7A CN112140413B (zh) | 2020-09-02 | 2020-09-02 | 一种塑料制件开模收缩率的预测方法及系统 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2022048101A1 true WO2022048101A1 (zh) | 2022-03-10 |
Family
ID=73889192
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/CN2021/073550 WO2022048101A1 (zh) | 2020-09-02 | 2021-01-25 | 一种塑料制件开模收缩率的预测方法及系统 |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN112140413B (zh) |
| WO (1) | WO2022048101A1 (zh) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114792059A (zh) * | 2022-05-06 | 2022-07-26 | 北京航空航天大学 | 冻结部分计算的复合材料热压罐工艺仿真系统及仿真方法 |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112140413B (zh) * | 2020-09-02 | 2022-04-08 | 金发科技股份有限公司 | 一种塑料制件开模收缩率的预测方法及系统 |
| CN114474473B (zh) * | 2022-02-08 | 2023-04-07 | 苏州博之顺材料科技有限公司 | 一种改性塑料的生产方法和系统 |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009271781A (ja) * | 2008-05-08 | 2009-11-19 | Canon Inc | 成形品の変形形状の予測方法とその装置、変形形状の予測プログラムとその記憶媒体 |
| CN104809306A (zh) * | 2015-05-13 | 2015-07-29 | 梧州学院 | 基于多质量指标的注塑成型工艺参数优化方法 |
| CN109895290A (zh) * | 2017-12-11 | 2019-06-18 | 上海新安汽车隔音毡有限公司 | 一种零件开模方法及模具 |
| WO2019210056A1 (en) * | 2018-04-26 | 2019-10-31 | Markforged, Inc. | System and method for minimizing deviations in 3d printed and sintered parts |
| CN112140413A (zh) * | 2020-09-02 | 2020-12-29 | 金发科技股份有限公司 | 一种塑料制件开模收缩率的预测方法及系统 |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040083083A1 (en) * | 2002-10-28 | 2004-04-29 | Necip Doganaksoy | Systems and methods for designing a new material that best matches an desired set of properties |
| JP4716484B2 (ja) * | 2004-12-17 | 2011-07-06 | 三菱重工プラスチックテクノロジー株式会社 | 射出成形シミュレーション装置及び射出成形シミュレーション方法 |
| CN101859137B (zh) * | 2010-05-28 | 2012-11-07 | 武汉理工大学 | 胎面挤出过程速度链智能优化系统及其优化方法 |
| CN106055787A (zh) * | 2016-05-30 | 2016-10-26 | 广西科技大学 | 一种基于bp神经网络的汽车内饰面板注塑成型工艺 |
| CN109543239B (zh) * | 2018-10-29 | 2023-06-09 | 西安空间无线电技术研究所 | 基于神经网络技术的ltcc收缩率预判方法 |
| CN110640982A (zh) * | 2019-08-26 | 2020-01-03 | 江苏师范大学 | 一种薄壁注塑件的注塑工艺参数多目标优化方法 |
-
2020
- 2020-09-02 CN CN202010912220.7A patent/CN112140413B/zh active Active
-
2021
- 2021-01-25 WO PCT/CN2021/073550 patent/WO2022048101A1/zh active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009271781A (ja) * | 2008-05-08 | 2009-11-19 | Canon Inc | 成形品の変形形状の予測方法とその装置、変形形状の予測プログラムとその記憶媒体 |
| CN104809306A (zh) * | 2015-05-13 | 2015-07-29 | 梧州学院 | 基于多质量指标的注塑成型工艺参数优化方法 |
| CN109895290A (zh) * | 2017-12-11 | 2019-06-18 | 上海新安汽车隔音毡有限公司 | 一种零件开模方法及模具 |
| WO2019210056A1 (en) * | 2018-04-26 | 2019-10-31 | Markforged, Inc. | System and method for minimizing deviations in 3d printed and sintered parts |
| CN112140413A (zh) * | 2020-09-02 | 2020-12-29 | 金发科技股份有限公司 | 一种塑料制件开模收缩率的预测方法及系统 |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114792059A (zh) * | 2022-05-06 | 2022-07-26 | 北京航空航天大学 | 冻结部分计算的复合材料热压罐工艺仿真系统及仿真方法 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112140413A (zh) | 2020-12-29 |
| CN112140413B (zh) | 2022-04-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2022048101A1 (zh) | 一种塑料制件开模收缩率的预测方法及系统 | |
| CN1084661C (zh) | 金属模具的设计方法及装置 | |
| US11860590B2 (en) | Model-free optimization method of process parameters of injection molding | |
| JP5241310B2 (ja) | 成形品の変形形状の予測方法とその装置、変形形状の予測プログラムとその記憶媒体 | |
| JP6509293B2 (ja) | 樹脂成形体の破壊箇所の予測方法、及び樹脂成形体の製造方法 | |
| CN101691058A (zh) | 树脂成形品的气孔生成预测方法 | |
| AU2012357709A1 (en) | A method for simulating bilaterally non-symmetrical injection moulding process | |
| JP4378011B2 (ja) | 金型設計装置と金型形状の設計方法 | |
| Liu et al. | Numerical simulations of residual stresses and warpage in injection molding | |
| JP5349859B2 (ja) | 成形品形状の予測方法、成形品の製造方法、成形品形状の予測プログラムとその記憶媒体 | |
| CN116252447A (zh) | 齿轮注塑成型调控方法 | |
| US6845289B2 (en) | Hybrid model and method for determining manufacturing properties of an injection-molded part | |
| Guo et al. | Warpage of injection-molded thermoplastics parts: Numerical simulation and experimental validation | |
| Fan et al. | Modeling and material data issues of injection molding simulation | |
| JP2019064177A (ja) | 樹脂成形金型設計方法、金型設計システム、金型設計プログラム及び金型設計プログラムを記憶したコンピュータ読取り可能な記憶媒体 | |
| Lin | Optimum gate design of freeform injection mould using the abductive network | |
| Monkova et al. | Application of simulation for product quality enhancement | |
| Koziey et al. | Computer simulation of thermoforming | |
| JPH08230008A (ja) | 射出成形品のそり変形予測方法及びその装置 | |
| JPH09277260A (ja) | 金型の設計方法および装置 | |
| JP2010214906A (ja) | 結晶性樹脂の射出成形シミュレーション方法 | |
| CN114506103B (zh) | 一种塑料小模数齿轮注塑成型模具型腔反演设计方法 | |
| JP2005280033A (ja) | 射出成形品の変形解析方法 | |
| JP5899004B2 (ja) | 成形品そり変形予測装置、成形品そり変形予測方法及び成形品そり変形予測プログラム | |
| JP2002219739A (ja) | 射出成形品の変形量予測方法 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 21863172 Country of ref document: EP Kind code of ref document: A1 |
|
| NENP | Non-entry into the national phase |
Ref country code: DE |
|
| 122 | Ep: pct application non-entry in european phase |
Ref document number: 21863172 Country of ref document: EP Kind code of ref document: A1 |
|
| 32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 26/07/2023) |