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Multifactor optimization for development of hybrid aluminium matrix composites
Indian Journal of Engineering & Materials Sciences Pub Date : 2020-08-03
Swarndeep Singh, Rupinder Singh, Simranpreet Singh Gill

The present study aims to multi factor optimization for preparation of aluminum matrix composites (AMC) by reinforcement of SiC/ Al2O3/ Al2O3+ SiC particles having dual particle size (DPS) and triplicate particle size (TPS) based upon signal to noise (S/N) ratio analysis. In this work the amalgamation of fused deposition modelling (FDM) and vacuum moulding (V-process) assisted stir casting (SC) has been employed for the development of AMC. The process parameters under investigation are: particle size (DPS/ TPS); reinforcement type (Al2O3/ SiC/ Al2O3+ SiC); vacuum pressure (VP) (300-400 mm of Hg); moulding sand grit size (American foundry society (AFS) No. 50-70); vibration time (VT) (4-6 sec) and reinforcement proportion/composition (5/7.5/10 by wt.%). The S/N ratio based upon the wear performance (pin-on disc tester), micro hardness (HV) and dimensional accuracy/deviation (Δt) has been evaluated by using Minitab-17 software which further acts as input for multifactor optimization. The best parametric setting proposed for multi objective/factor optimization is: DPS of Al2O3+ SiC reinforcement at 350 mm of Hg VP with 50 AFS No. sand grain size, 4sec VT and 10% composition/proportion. The results of analysis of variance (ANOVA) highlight that particle size (with 18.49% contribution) and reinforcement type (with 42.13% contribution) have significant influence on multi factor optimization for the development of AMC. Confirmatory experiments have been performed which shows that the proposed amalgamation of FDM and V-process assisted SC can be successfully applied for enhancing the performance of AMC. Finally the X-chart and R-chart have been plotted at the proposed settings, which highlights that amalgamation process is controlled and useful for mass/ batch production.

中文翻译:

混合铝基复合材料开发的多因素优化

本研究旨在通过增强SiC / Al 2 O 3 / Al 2 O 3 +具有双重粒径(DPS)和三重粒径(TPS)的SiC颗粒来制备铝基复合材料(AMC)的多因素优化。信噪比(S / N)分析。在这项工作中,融合沉积模型(FDM)和真空成型(V-process)辅助搅拌铸造(SC)的合并已用于AMC的开发。研究中的工艺参数为:粒径(DPS / TPS);增强型(Al 2 O 3 / SiC / Al 2 O 3+ SiC); 真空压力(VP)(300-400毫米汞柱); 型砂粒度(美国铸造学会(AFS)第50-70号); 振动时间(VT)(4-6秒)和增强比例/组成(5 / 7.5 / 10 wt。%)。通过使用Minitab-17软件评估了基于磨损性能(针式圆盘测试仪),显微硬度(HV)和尺寸精度/偏差(Δt)的信噪比,该软件进一步用作多因素优化的输入。为多目标/因素优化建议的最佳参数设置是:Al 2 O 3的DPS+ SiC增强剂在350 mm Hg VP下具有50 AFS号砂粒尺寸,4sec VT和10%的组成/比例。方差分析(ANOVA)的结果表明,粒径(占18.49%)和增强类型(占42.13%)对AMC发展的多因素优化具有重要影响。已经进行了验证性实验,表明所提出的FDM与V-过程辅助SC的合并可以成功地用于增强AMC的性能。最后,在建议的设置下绘制了X图表和R图表,这突出说明了合并过程是可控的,可用于批量生产。
更新日期:2020-08-03
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