Summary and conclusions

Part quality for plastic injection molding is often evalu- ated by multiple interrelated quality indices, and each quality index is highly related with process parameters. This paper proposes a method of finding the complete efficient frontier of process parameters with only a few times of experiments when multiple quality indices are considered for plastic injection molding. The thin front cover of a digital camera is provided as the example of executing the method. Based on the literature, nine process parameters are considered in this research. The experimental design with the Taguchi orthogonal L27 is used to run the experiment on  Moldflow.  ANOVA  is then executed to find significant parameters to affect the part’s quality indices, and the results show that four out of nine parameters are significant with the  significant level 0.05. In order to set up the complete efficient fron- tier of DEA analysis, more data are required, and the re- gression equations are used to  create  them.  To  have good  accuracy  of  the  multiple  regressed  equations, the

complete  experimental  design  with  34   times  (only four

significant process parameters are considered) of experi- ments is again executed on Moldflow. The multiple re- gression equations are then set up and are  used  to produce the dataset for DEA analysis. The  results  of DEA analysis shows that the  five  combinations  are on the efficient frontier.

To show the efficiency  of these  combinations suggested in this paper, DEA analysis is again conducted on them as well as the results of the experiments of 34  times used for

establishing multiple regression equations. The results show that only one combination is not as efficient mainly because of the error of the regressed equations at this combination. Hence, the method proposed here is believed indeed can find the efficient frontier of process parameters with only a few times of experiments.

The classic DEA method, CCR, is  used  in  this paper; in the future, some other DEA methods, such as BCC, can be used, and  the  performance  of  each  method can be compared. Another  possible  future  research  topic is to evaluate the performance of Moldflow  analysis.

摘要

注塑成型的产品质量与工艺参数紧密相关。此外产品质量不只取决于一种质量标准，而是与多种相互关联的质量标准有关。要找到工艺参数的设置，使得多质量指标可以同时得到优化正在成为一个研究问题，被称为寻找工艺参数的有效边界。本研究考虑塑料注塑成型的三个质量指标：翘曲，收缩，和注射时的体积收缩。本文以数码相机薄盖为例子来显示寻找有效边界的方法。利用SolidWorks和Moldflow描绘零件的几何图形，并模拟注射成型过程。在本研究中考虑了九个工艺参数：注射时间、注射压力，包压时间，包压压力，冷却时间，冷却温度，开模时间，熔化温度，模具温度。将田口正交表L27应用于实验运行，然后使用方差分析。寻找达到0.05水平的重要工艺因素。这四个重要因素进一步用于生成3、4实验，完成实验设计。每个实验都用Moldflow进行分析。收集到的实验数据有3个质量指标和4个过程因素，进一步用于生成3个多元回归方程对应的三个质量指标。然后,将3个多元回归方程应用于产生1225的理论数据。最后,采用数据包络分析发现1225的理论有效的边界数据，发现数据集有效边界上的最优质量。进一步通过Moldflow分析验证有效边界的工艺参数。这项研究表明，开发的程序被证明是一个有用的优化程序,可以在实践中应用于注塑过程。