Experimentation and Optimization of Shrinkage in Plastic Injection Molded GPPS Part

DOI

10.2991/iccasp-16.2017.18How to use a DOI?

Keywords

Moldflow plastics insight, Analysis of variance, Autodesk mold-flow insight, Signal to noise

Abstract

The objective of this research is to find out the optimized process parameters for minimum shrinkage. For this purpose, the injection molded General Purpose Polystyrene (GPPS) part which is used in refrigerators is taken. The Taguchi method is used for design of experiments. For five parameters with three levels, L27 orthogonal array in case of Taguchi method is selected. The experiments are performed in Autodesk mold-flow insight simulation software with mold temperature, melt temperature, injection time, packing pressure and cooling time as process parameters. Analysis of variance is used to find out optimized set of process parameters and percentage effect of each parameter on shrinkage. Regression analysis is carried out to predict shrinkage value using regression equation. Artificial Neural Network (ANN) is used to predict shrinkage value for optimized process setting. Thus, experimental, statistical and computational approaches are used for validation of research.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tejas Bhirud
AU  - Rajesh Metkar
PY  - 2016/12
DA  - 2016/12
TI  - Experimentation and Optimization of Shrinkage in Plastic Injection Molded GPPS Part
BT  - Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)
PB  - Atlantis Press
SP  - 107
EP  - 112
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasp-16.2017.18
DO  - 10.2991/iccasp-16.2017.18
ID  - Bhirud2016/12
ER  -