Indian Journal of Engineering & Materials Sciences

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Multifactor optimization for development of hybrid aluminium matrix composites


Affiliations
1 Department of Technical Education and Industrial Training (IT-wing), Punjab-160036, India
2 Department of Production Engineering Guru Nanak Dev Engineering College, Ludhiana-141006, India
3 Department of Mechanical Engineering, National Institute of Technical Teachers Training and Research, Chandigarh-160019, India
4 Department of Mechanical Engineering, Beant College of Engineering and Technology, Gurdaspur-143521,, India

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.
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  • Multifactor optimization for development of hybrid aluminium matrix composites

Abstract Views: 150  | 

Authors

Swarndeep Singh
Department of Technical Education and Industrial Training (IT-wing), Punjab-160036, India
Rupinder Singh
Department of Production Engineering Guru Nanak Dev Engineering College, Ludhiana-141006, India
Rupinder Singh
Department of Mechanical Engineering, National Institute of Technical Teachers Training and Research, Chandigarh-160019, India
Simranpreet Singh Gill
Department of Mechanical Engineering, Beant College of Engineering and Technology, Gurdaspur-143521,, India

Abstract


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.