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Optimization of warpage on plastic part by using genetic algorithm (GA)

机译:遗传算法(GA)优化塑料部件翘曲

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This study was concentrated on the effects of parameters processing on the molded part towards the warpage issues by implementing Computer-Aided Engineering (CAE) which is Autodesk Moldflow Insight (AMI) software for the simulation of the experiment. Details properties of 80 Tonne Nessei NEX 1000 injection molding machine has been used in this study. Acrylonitrile Butadiene Styrene (ABS) was used as the thermoplastic material to mold the front panel housing. The variable parameters of the process are melt temperature, cooling time, packing pressure and packing time. Experimental data are set off by Design of Experiment (DOE) based on face centred Center Composite Design (CCD). The prediction model of warpage will be used in Genetic Algorithm (GA) for optimization of variables to minimize the warpage. The result shows that GA has reduced the warpage value by 36.15%.
机译:本研究集中在通过实施计算机辅助工程(CAE)对翘曲问题对翘曲问题的参数处理的影响,该计算机辅助工程(CAE)是用于模拟实验的Autodesk Moldflow Insight(AMI)软件。本研究已经使用了80吨Nessei Nex 1000注塑机的特性。丙烯腈丁二烯苯乙烯(ABS)用作热塑性材料以模制前面板壳体。该过程的可变参数是熔融温度,冷却时间,包装压力和包装时间。基于面中心的中心复合设计(CCD)的实验(DOE)设计出现实验数据。翘曲预测模型将以遗传算法(GA)用于优化变量以最小化翘曲。结果表明,GA使翘曲值降低了36.15%。

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