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The IUP Journal of Mechanical Engineering
Fabrication of PRAl-SiC-MMC Using Melt-Stirring Technique and Analysis of EDM Parameters
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The paper explains the melt-stirring technique for fabrication of Particulate Reinforced Al/SiC Metal Matrix Composites (PRAl-SiC-MMC). The MMCs of different size bars are prepared varying the weight fractions of SiC reinforced particles and mesh size such as 5 wt%, 10 wt%, 15 wt%, 20 wt% and 220 mesh, 300 mesh and 400 mesh. The stirring is performed at 200 rev/min constant rotating speed by graphite impeller for 10 min. A series of machining tests are performed on Electrical Discharge Machining (EDM). The effect of different parameters of EDM on machining response characteristics, such as Tool Wear Rate (TWR) (g/min), Metal Removal Rate (MRR) (g/min), average surface roughness heights Ra (m) and Rz (m), are investigated through various graphs. The experiments are performed by changing the parametric setting values such as pulse peak current (IP: 2, 6, 10, and 14 amp), pulse on time (Ton: 30, 50, 70 and 90 s), pulse off time (Toff: 3, 5, 7 and 9 s) and gap voltage (Vg: 25, 30, 35 and 40 Volts). The investigated results are plotted in various graphs and their effects on various response characteristics explained.

 
 

Advance Particulate Reinforced Al/SiC Metal Matrix Composites (PRAl-SiC-MMC) are gradually becoming very important material in manufacturing industries, e.g., aerospace, automotive and automobile industries, due to their superior properties such as light weight, low density, high strength to weight ratio, high hardness, high temperature and thermal shock resistance, superior wear and corrosive resistance, high specific modulus, high fatigue strength. MMCs have very light weight, high strength, and stiffness and exhibit greater resistance to corrosion, oxidation and wear. Fatigue resistance is an especially important property of Al/SiC-MMC, which is essential for automotive application. These properties are not achievable with light weight monolithic titanium, magnesium and aluminum alloys. Particulate MMCs have nearly isotropic proper ties when compared to long fiber reinforced composites. MMC is an engineered combination of metal as matrix and hard particles as reinforcement to tailored properties. Stir casting is one of the important fabrication methods that can be applied to fabricate Al/SiC-MMC (Surappa, 1997). This liquid metallurgy technique is the most economical of all the available routes for MMC production (Surappa, 1997) and allows very large sized components to be fabricated. The cost of preparing composites material using a casting method is about one-third to half of competitive methods, and for high volume production, it is projected that the cost falls to one-tenth (Skibo et al., 1988). Among the nonconventional methods, Electrical Discharge Machining (EDM) is the most widely and successfully applied process in machining of hard metals or those that would be very difficult to machine with traditional techniques. The material is removed from the workpiece by the thermal erosion process, i.e., by a series of recurring electrical discharges between a cutting tool acting as an electrode and a conductive workpiece in the presence of a dielectric fluid. This discharge occurs in a voltage gap between the electrode and workpiece. Heat from the discharge vaporizes minute particles of workpiece material, which are then washed from the gap by the continuously flushing dielectric fluid (Puertas and Luis, 2003). The application of composite electrodes obtained a higher MRR over Cu metal electrodes and the recast layer was thinner, and fewer cracks were present on the machined surface, as explained by Tsai (2003). The regression models (Petropoulos et al., 2004) and Taguchi methods (George et al., 2004) are used for modeling and analyzing the influence of process parameters of EDM on machining response characteristics (Mahdavinejad, 2008). Again, Mahdavinejad (2009) stated that the test results obtained from the electric discharge machining of WC-Co confirmed the capability of the system of predictive controller model based on neural network with 32.8% efficiency increasing in stock removal rate.

 
 

Mechanical Engineering Journal, PRAl-SiC-MMC, Melt stirring technique, Tool Wear Rate (TWR), Metal Removal Rate (MRR).