Finding efficient frontier of process parameters for plastic injection molding

Authors

  • Chin-Yin Huang Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Republic of China (Taiwan)
  • Ching-Ya Huang Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Republic of China (Taiwan)
  • Wu-Lin Chen Department of Computer Science and Information Management, Providence University, Taichung, Republic of China (Taiwan)
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.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

Optimization of Plastic Injection Molding Process by Combination of Artificial Neural Network and Genetic Algorithm

Injection molding is one of the most important and common plastic formation methods. Combination of modeling tools and optimization algorithms can be used in order to determine optimum process conditions for the injection molding of a special part. Because of the complication of the injection molding process and multiplicity of parameters and their interactive effects on one another, analytical...

full text

Precision Injection Molding PRECISION PROCESS CONTROL OF INJECTION MOLDING

1 INTRODUCTION The technical requirements of precision injection molded components demand a heightened level of process performance, and with corresponding process monitoring and control technologies. To obtain the desired critical to quality attributes (CTQs) of the molded products, the injection molding process must be consciously designed such that the key process variables (KPVs) are observ...

full text

Experimental Study and Numerical Simulation of Injection Molding Process for Special-Shaped Plastic Part

Taking the special-shaped plastic part as the research object, numeric simulation and experimental study of the injection molding process are completed based on the synthetical application of numeric simulation technology, orthogonal experiment method, Moldflow, injection machine and CMM. Optimal feeding system and molding process parameters are obtained and qualified products are produced. The...

full text

A neural network-based approach for dynamic quality prediction in a plastic injection molding process

This paper presents an innovative neural network-based quality prediction system for a plastic injection molding process. A self-organizing map plus a back-propagation neural network (SOM-BPNN) model is proposed for creating a dynamic quality predictor. Three SOM-based dynamic extraction parameters with six manufacturing process parameters and one level of product quality were dedicated to trai...

full text

Process parameter optimization for MIMO plastic injection molding via soft computing

Determining optimal process parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding (PIM) industry. Previously, production engineers used either trial-and-error method or Taguchi’s parameter design method to determine optimal process parameter settings for PIM. However, these methods are unsuitable in present PIM because the increa...

full text

optimization of plastic injection molding process by combination of artificial neural network and genetic algorithm

injection molding is one of the most important and common plastic formation methods. combination of modeling tools and optimization algorithms can be used in order to determine optimum process conditions for the injection molding of a special part. because of the complication of the injection molding process and multiplicity of parameters and their interactive effects on one another, analytical...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 9  issue 1

pages  -

publication date 2013-12-01

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023