Audio Jack连接器注塑成型仿真试验及工艺参数多目标质量优化设计分析

发布时间：2018-06-10 02:02

本文选题：电连接器 + 注塑成型　；参考：《江苏大学》2017年硕士论文

【摘要】：随着电子连接器的迅速发展,市场对电连接器接触件的注塑成型表面质量、尺寸精度、装配性能等提出了较以往更高的要求。在成型过程中,因收缩变形而导致尺寸精度产生误差是常见且棘手的工程问题。工程师在新产品开发设计中根据产品结构与成型工艺特点进行产品设计,但其结果往往是难以准确预测的,由于材料收缩率因材料与成型工艺的差异有着不同的收缩结果,此外,注塑件收缩越大,其翘曲变形量、表面缩痕等质量缺陷出现的可能性就越大。如何运用CAE在新产品开发设计阶段进行成型质量缺陷预测以提高电子连接器的表面质量及其性能成为该领域研究的重要课题。本文以昆山科信成电子有限公司生产的Audio Jack连接器插件母座为研究对象,研究了在注塑机、塑件材料及模具结构等确定情况下,工艺参数对成型质量的影响;通过优化成型工艺参数,从而改善了由于收缩变形引起的成型尺寸精度不够导致的组装不良问题;最后通过试模验证了组装的可行性。论文研究的主要内容与成果如下:(1)基于Moldflow软件,进行了浇口位置优化、有限元模型创建及仿真试验结果分析:绘制连接器三维实体,将其导入软件中进行网格划分与修复。利用Moldflow中的“浇口位置”分析序列选项寻找最佳浇口位置,模拟多处出现短射,因此不能作为最佳浇口位置。根据该连接器结构与浇口设计原则,设计了三种浇口位置方案,在相同工艺条件下模拟比较各项指标,综合得出侧面浇口为最优方案,与实际生产浇口一致。按照实际连接器成型生产设计了浇注系统与冷却系统,对其进行网格划分,运用流动与冷却功能进行分析,验证了建立的有限元模型的可行性。对翘曲变形功能分析,结果表明,引起连接器变形量最大的因素为收缩不均,占所有因素引起变形量的98.01%,冷却不均及取向因素影响较小,且Y方向收缩变形量最大,对连接器的装配有重要影响。(2)基于正交试验设计,分析了不同工艺参数对连接器注塑成型质量的影响,优化了成型工艺参数,运用加权综合评分法获得了成型连接器综合质量最优的工艺参数组合:以模具温度、熔体温度、保压压力、保压时间、注射时间、冷却时间为主要成型工艺参数,以顶出时体积收缩率、翘曲变形量、缩痕深度为质量指标,建立了六因素五水平25组正交试验,运用软件模拟了不同工艺参数组合下的质量指标,并对试验结果进行了信噪比函数计算。通过信噪比的均值与极差分析得出了各质量指标最优的工艺参数组合及工艺参数影响趋势,优化了成型工艺参数。通过信噪比的方差分析得到了工艺参数对各质量指标的影响程度及其大小,结果表明,顶出时体积收缩率、翘曲变形量及缩痕深度显著性影响因素均为保压压力与熔体温度,模具温度、保压时间、注射时间及冷却时间影响相对较小,但也需要适当的工艺参数水平。基于加权综合评分法,对顶出时体积收缩率、翘曲变形量及缩痕深度进行了综合质量评价,通过综合评分的均值与极差分析得到了综合质量最优的成型工艺参数组合及工艺参数对综合评分的影响程度。(3)基于变异系数法与逼近理想解法相结合优化了工艺参数多目标质量,进一步细化与优化了工艺参数,建立了工艺参数与多目标质量间的BP神经网络预测模型:以正交试验结果为样本,对其进行了去量纲化处理,运用变异系数法确定了各质量指标权重后,结合逼近理想解法计算了各质量指标在不同工艺参数组合下的相对接近度。通过均值与极差分析了不同成型工艺参数对相对接近度的影响,初步获得了兼顾多质量指标最优的工艺参数组合,在此基础上进行了工艺参数细化正交试验,进一步分析得出了成型质量更加优越的工艺参数,试验结果表明,各质量指标相比工艺参数未细化前取得了进一步的优化。因工艺参数与成型质量间的非线性复杂关系难以用具体数学函数表达,建立了工艺参数与多目标质量间的BP神经网络预测模型。以正交试验结果作训练样本,训练不同隐含层节点数对网络的影响,得出了误差最小的节点数。对工艺参数细化试验结果进行了训练与预测,预测值接近模拟值,表明建立的网络映射关系模型有较好的预测功能,在误差许可内可替代软件较长时间的模拟,为方便快速优化工艺参数提供了理论指导。(4)连接器注塑成型工艺试模验证:根据研究得到的最佳工艺参数组合进行了试模验证,试模所得的尺寸测量符合图面设计尺寸规格,试模塑件与端子组装良好,表明优化后的工艺参数组合能很好的改善连接器生产中因收缩变形导致的尺寸精度不够问题。为满足生产出的连接器达到客户提出的插拔接触特性要求,进行了塑件组装端子后的插拔力测试,插拔力曲线结果显示满足设计规格,间接验证了优化后的成型工艺参数的可靠性。以上研究表明,CAE分析结合优化方法及工艺试模验证的研究思路,对改善成型质量缺陷、优化工艺参数、减少产品量试后的试模与修模次数等具有重要的现实指导意义。
[Abstract]:With the rapid development of electronic connectors, the market has put forward higher requirements on the surface quality, size accuracy and assembly performance of the contact parts of electrical connector. In the forming process, the error of the size accuracy caused by the shrinkage deformation is a common and difficult engineering problem. The engineer is in the design of the new product development. The product design is carried out according to the product structure and molding process characteristics, but the result is often difficult to predict accurately, because the shrinkage of material has different shrinkage results because of the difference between material and molding process. In addition, the greater the shrinkage of the injection parts, the more possibility of the quality defects, such as the warpage deformation, the surface shrinkage, and so on. How to use the CAE It is an important subject in this field to predict the quality defects of the molding quality in the new product development and design stage to improve the surface quality and performance of the electronic connector. This paper studies the Audio Jack connector plug-in base of the Kunshan Kexin Electronics Co., Ltd. as the research object, and studies the injection molding machine, the plastic material and the die structure, etc. To determine the effect of process parameters on the quality of the molding, by optimizing the molding process parameters, the problem of poor assembly caused by the lack of precision caused by the shrinkage deformation is improved. Finally, the feasibility of the assembly is verified by the test model. The main contents and results of the paper are as follows: (1) based on the Moldflow software, The optimization of gate position, the creation of finite element model and the result analysis of simulation test: draw the three-dimensional connector of the connector and introduce it into the software to divide and repair the grid. Using the "gate position" in Moldflow to find the best gate position and simulate several short shots, so it can not be used as the best gate position. This connector structure and gate design principle, designed three kinds of gate position scheme, simulated comparison of various indexes under the same technological conditions, and synthetically concluded that the side gate was the best scheme, which was the same as the actual production gate. According to the actual connector molding production, the gating system and cooling system were designed, and the grid was divided and the flow was used. The feasibility of the finite element model is verified with the analysis of the cooling function. The analysis of the warping deformation function shows that the biggest factor causing the deformation of the connector is the unequal shrinkage, 98.01% of the deformation amount caused by all factors, the less influence of the cooling uneven and the orientation factors, and the largest shrinkage deformation in the Y direction, to the connector. The assembly has important influence. (2) based on the orthogonal test design, the influence of different process parameters on the quality of the connector injection molding is analyzed, the molding process parameters are optimized, and the optimum combination quality of the forming connector is obtained by using the weighted comprehensive scoring method. The molding tool temperature, melt temperature, pressure holding pressure, pressure holding time, injection time, injection molding are used. Time and cooling time are the main forming process parameters, with the volume shrinkage rate, warpage quantity and shrinkage depth as quality indexes, six factors and five levels orthogonal test is set up. The quality indexes of different process parameters are simulated by software, and the signal to noise ratio function is calculated for the test results. The signal to noise ratio is all calculated. The combination of process parameters and the influence trend of process parameters are obtained, and the process parameters are optimized. The influence degree and size of the process parameters on each quality index are obtained by the variance analysis of the signal to noise ratio. The results show that the volume shrinkage, the warpage and the shrinkage depth are significant when the ejection is ejecting. The influence factors are pressure retaining pressure and melt temperature, mould temperature, pressure holding time, injection time and cooling time are relatively small, but appropriate technological parameters are needed. Based on the weighted comprehensive scoring method, the volume shrinkage, warpage and shrinkage depth are evaluated and the mean value of the comprehensive score is evaluated. The influence degree of the optimum combination of forming process parameters and process parameters on the comprehensive score is obtained. (3) based on the combination of the variation coefficient method and the approach ideal solution, the multi target quality of the process parameters is optimized, the process parameters are further refined and optimized, and the BP nerve between the process parameters and the multi-objective mass is established. The network prediction model: Taking the orthogonal test results as the sample, it is dimensionally processed, the weight of each quality index is determined by the method of variation coefficient, and the relative proximity of the quality indexes under the combination of different process parameters is calculated with the approximate ideal solution. The relative parameters of different molding process are analyzed by the mean and the extreme difference. On the basis of the influence of proximity, the optimum combination of multi quality indexes is obtained. On this basis, the orthogonal experiment of process parameters refinement is carried out. Further analysis has been made to obtain the better process parameters of the molding quality. The test results show that the quality indexes have been further optimized before the technical parameters are not refined. The nonlinear complex relationship between process parameters and molding quality is difficult to express with specific mathematical functions. The BP neural network prediction model between process parameters and multi-objective mass is established. The orthogonal test results are used as training samples to train the influence of the number of hidden layer nodes on the network, and the number of nodes with the smallest error is obtained. The result of the experiment is trained and predicted. The predicted value is close to the simulated value. It shows that the established network mapping relationship model has better prediction function. It can replace the software for a long time in the error license, and provides theoretical guidance for the convenience and rapid optimization of the process parameters. (4) the test model verification of the connector injection molding process: Based on the research The optimum combination of the process parameters was verified. The size measurement obtained by the test model was in accordance with the size specification of the surface. The test mould and the terminal assembly were well assembled. It showed that the optimized combination of the process parameters could improve the lack of dimension precision caused by the shrinkage deformation in the connector production. To the requirements of the contact characteristics proposed by the customer, the plug force test after the plastic part assembly terminal is carried out. The result of the pluggable force curve shows the design specification, and the reliability of the optimized molding process parameters is indirectly verified. The above research shows that the CAE analysis combines the optimization method and the process test verification of the process to improve the molding quality. It is of great practical significance to optimize the process parameters and reduce the number of trial and repair dies after testing.
【学位授予单位】：江苏大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM503.5

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