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Optimisation of warpage on thin shell plastic part using response surface methodology (RSM) and glowworm swarm optimisation (GSO)

机译:响应面法(RSM)和萤火虫群优化薄壳塑料部件优化薄壳塑料部件(GSO)

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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.
机译:在制造各种部件时,塑料注塑是广泛使用的。注塑工艺参数发挥了重要作用,影响了产品的质量和生产力。在最小化诸如人工神经网络,遗传算法,萤火虫群优化和混合方法的诸如人工神经网络,遗传算法,萤石群优化和混合方法时存在许多方法。本文提出了一种用于确定注射成型工艺中的翘曲和收缩的系统方法,特别是在薄壳塑料部件中。为了识别加工参数对翘曲和收缩值的影响,应用了响应面方法。在TheS学习中,选择电子夜灯的一部分作为模型。首先,使用实验设计来确定针对不同厚度值的翘曲上的注射参数。用于分析翘曲的软件是Autodesk Moldflow Insight(AMI)2012。

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