Finding efficient frontier of process parameters for plastic injection molding

Subject Areas : Mathematical Optimization

Wu-Lin Chen ¹ , Chin-Yin Huang ² , Ching-Ya Huang ³

1 - Department of Computer Science and Information Management, Providence University, Taichung, Republic of China (Taiwan)
2 - Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Republic of China (Taiwan)
3 - Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Republic of China (Taiwan)

Received: 2020-10-01 Accepted : 2020-10-01 Published : 2013-12-01

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

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.