Optimisation of Warpage on Thin Shell Plastic Part Using Response Surface Methodology (RSM) and Glowworm Swarm Optimisation (GSO)

被引:0
|
作者
Asyirah, B. N. [1 ,2 ]
Shayfull, Z. [1 ,2 ]
Nasir, S. M. [1 ,2 ,3 ]
Fathullah, M. [1 ,2 ]
Hazwan, M. H. M. [1 ,2 ]
机构
[1] Univ Malaysia Perlis, Sch Mfg Engn, Kampus Tetap Pauh Putra, Arau 02600, Perlis, Malaysia
[2] Univ Malaysia Perlis, Green Design & Mfg Res Grp, Ctr Excellence Geopolymer & Green Technol CEGeoGT, Kangar 01000, Perlis, Malaysia
[3] Univ Malaysia Perlis, CDS, Kangar 01000, Perlis, Malaysia
关键词
D O I
10.1063/1.5002281
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In manufacturing a variety of parts, plastic injection moulding is widely use. The injection moulding process parameters have played important role that affects the product's quality and productivity. There are many approaches in minimising the warpage ans shrinkage such as artificial neural network, genetic algorithm, glowworm swarm optimisation and hybrid approaches are addressed. In this paper, a systematic methodology for determining a warpage and shrinkage in injection moulding process especially in thin shell plastic parts are presented. To identify the effects of the machining parameters on the warpage and shrinkage value, response surface methodology is applied. In thos study, a part of electronic night lamp are chosen as the model. Firstly, experimental design were used to determine the injection parameters on warpage for different thickness value. The software used to analyse the warpage is Autodesk Moldflow Insight (AMI) 2012.
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页数:11
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