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Warpage and Shrinkage Analysis and Optimization of Rapid Tooling Molded thin Wall Component Using Modified Particle Swarm Algorithm

机译:改进粒子群算法快速工具模制薄壁部件的翘曲和优化

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摘要

This paper presents a systematic methodology to determine optimal injection molding conditions for minimum warpage and shrinkage in a thin wall relay part using modified particle swarm optimization algorithm (MPSO). Polybutylene terephthalate (PBT) and polyethylene terephthalate (PET) were injected in a thin wall relay component for different processing parameters: melt temperature, packing pressure and packing time. Further, Taguchi’s L9 (32) orthogonal array is used for conducting simulation analysis to consider the interaction effects of the above parameters. A predictive mathematical model for shrinkage and warpage is developed in terms of the above process parameters using regression analysis. ANOVA analysis is performed to establish statistical significance within the injection molding parameters. The analytical model is further optimized using a newly developed MPSO algorithm and the process parameters values are predicted for minimizing shrinkage and warpage. The predicted values of shrinkage and warpage using MPSO algorithm are improved by approximately 30% as compared to the initial simulation values and comparable to previous literature results.
机译:本文提出了一种系统方法,用于确定使用修改的粒子群优化算法(MPSO)的薄壁继电器部分中最小翘曲和收缩的最佳注射成型条件。对苯二甲酸丁酯(PBT)和聚对苯二甲酸乙二醇酯(PET)注射在薄壁继电器部件中,用于不同的加工参数:熔融温度,包装压力和包装时间。此外,Taguchi的L9(32)正交阵列用于进行模拟分析以考虑上述参数的相互作用效应。使用回归分析的上述工艺参数,开发了用于收缩和翘曲的预测数学模型。进行ANOVA分析以在注塑参数内建立统计学意义。通过新开发的MPSO算法进一步优化分析模型,并预测过程参数值以最小化收缩和翘曲。与初始模拟值相比,使用MPSO算法的收缩和翘曲的预测值提高了大约30%,并且与先前的文献结果相当。

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