Abstract Product quality for plastic injection molding process is highly related with the settings for its process parameters. Additionally, the product quality is not simply based on a single quality index, but multiple interrelated quality indices. To find the settings for the process parameters such that the multiple quality indices can be simultaneously optimized is becoming a research issue and is now known as finding the efficient frontier of the process parameters. This study considers three quality indices in the plastic injection molding: war page, shrinkage, and volumetric shrinkage at ejection. A digital camera thin cover is taken as an investigation example to show the method of finding the efficient frontier. Solidworks and Moldflow are utilized to create the part’s geometry and to simulate the injection molding process, respectively. Nine process parameters are considered in this research: injection time, injection pressure, packing time, packing pressure, cooling time, cooling temperature, mold open time, melt temperature, and mold temperature. Taguchi’s orthogonal array L27 is applied to run the experiments, and analysis of variance is then used to find the significant process factors with the significant level 0.05. In the example case, four process factors are found significant. The four significant factors are further used to generate 34 experiments by complete experimental design. Each of the experiments is run in Moldflow. The collected experimental data with three quality indices and four process factors are further used to generate three multiple regression equations for the three quality indices, respectively. Then, the three multiple regression equations are applied to generate 1,225 theoretical datasets. Finally, data envelopment analysis is adopted to find the efficient frontier of the 1,225 theoretical datasets. The found datasets on the efficient frontier are with the optimal quality. The process parameters of the efficient frontier are further validated by Moldflow. This study demonstrates that the developed procedure has proved a useful optimization procedure that can be applied in practice to the injection molding process.73810

Keywords: Injection molding; Taguchi’s orthogonal array; Mutiple regression analysis; Data envelopment analysis; Optimization

Introduction

Along with the rapid progress of production techniques for high-tech products, better and better quality of products is required for the survival in the current market. Besides pro- viding various functions, the trend of the design for plastic products is light, thin, short, and small. Therefore, the setting of process parameters for plastic products has a re- markable influence on their quality (Huang and Tai   2001).

Injection molding is one of the most important  tech- niques for polymer processing (to manufacture plastic products)  because  of  its  high  speed  for  molding  and  its

1Department of Computer Science and Information Management, Providence University, Taichung, Republic of China (Taiwan)

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capability of manufacturing complex geometric shapes of products. Besides, injection molding is capable of mass pro- duction, so it is widely used for many products, especially for electronic products, such as computers and communi- cation products. Injection molding is usually adopted to produce thin parts or thin covers for these    products.

Currently, there are two categories for the setting of process parameters for injection  molding:  one  is based on the technicians’ previous experience and the  other takes advantage of mold flow analysis softwares, such as Moldflow (used in this study) to find the initial values for process parameters (by  running  various simulations on these moldflow analyses). However, no method can quickly find the reasonable combination of process pa- rameters. In addition, trial and error is required for both