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Optimization of Machining Process in Particulate Reinforced Aluminium Matrix Composite


Affiliations
1 Dept. of Mechanical Engg., Bannari Amman Institute of Technology, Sathyamangalam-638401, Erode District, India
2 Dept. of Mechanical Engg., Coimbatore Institute of Technology, Coimbatore-641014, India
     

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In the present study A356/SiCp metal matrix composite is machined using electro chemical machining. Silicon Carbide with an average particle size 0/40 microns is tried in three different proportions, namely 5%, 10% and 15% by weight. Taguchi's L27 orthogonal array is chosen to design the experiments and two repetitions for every combination are made to study the effect of various parameters chosen. Material removal rate is determined for different combinations of applied voltage, electrolyte concentration, feed rate and percentage of silicon carbide. Optimum factors are identified from the signal-to-noise ratio. Result from Pareto ANOVA, which is a method to analyze data, showed that feed rate is the most influencing parameter followed by electrolyte concentration in maximizing the material removal rate. A regression model is also developed and found that the experimental data closely agree with the prediction.
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  • Optimization of Machining Process in Particulate Reinforced Aluminium Matrix Composite

Abstract Views: 230  |  PDF Views: 0

Authors

K. L. Senthil Kumar
Dept. of Mechanical Engg., Bannari Amman Institute of Technology, Sathyamangalam-638401, Erode District, India
R. Sivasubramanian
Dept. of Mechanical Engg., Coimbatore Institute of Technology, Coimbatore-641014, India

Abstract


In the present study A356/SiCp metal matrix composite is machined using electro chemical machining. Silicon Carbide with an average particle size 0/40 microns is tried in three different proportions, namely 5%, 10% and 15% by weight. Taguchi's L27 orthogonal array is chosen to design the experiments and two repetitions for every combination are made to study the effect of various parameters chosen. Material removal rate is determined for different combinations of applied voltage, electrolyte concentration, feed rate and percentage of silicon carbide. Optimum factors are identified from the signal-to-noise ratio. Result from Pareto ANOVA, which is a method to analyze data, showed that feed rate is the most influencing parameter followed by electrolyte concentration in maximizing the material removal rate. A regression model is also developed and found that the experimental data closely agree with the prediction.