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Ph.D.
(Engineering & Technology)
EXPLORATORY INQUISITION OF AL-SIC COMPOSITE
MATERIAL THROUGH DRY ELECTRO DISCHARGE MACHINING

Ph.D. Scholar : Patel Chirag Prahaladbhai
Research Supervisor : Dr. M. G. Bhatt

Regi. No.: 11146051004
Abstract :
Aluminium-based composite is widely used in aerospace, automobile industries, etc. due
to its lighter weight, but a trouble is, it is easily worn-out and life of the components is
reduced. To overcome these problems, Al-SiC (Aluminium silicon carbide) composite
material has been developed with good wear-resistance, hardness and lighter in weight.
SiC have good mechanical properties like wear resistance, hardness, tougher and high
melting point. SiC weight fractions (20%, 25%, 30%, and 35 % respectively) have been
added to the aluminum matrix to improve the mechanical properties of the composite
material. Al-SiC composite material properties have been checked for microstructure
examination, compressive strength, and hardness. Developed Al-SiC composite material
has been machined using Dry Electro Discharge Machining (EDM) process because it is
used in an eco-friendly environment, good surface finishing for harder components. A
Special Dry EDM machining unit attachment has been developed on present oil-based
EDM Machine. Experiments have been carried out using compressed air as a dielectric
fluid and copper tubular electrode with varying input parameters like SiC%, discharge
current, gap voltage, and pulse-on time, spindle rotational speed and compressed air
pressure to check the effect on output parameters like; Material removal rate, Tool wear
rate and Surface Roughness. Regression models for Material Removal Rate, Tool Wear
Rate and Surface Roughness have been developed by conducting a designed experiment
based on the Central Composite Design. Genetic Algorithm (GA) based multi-objective
optimization for maximization of Material Removal Rate and minimization of Tool Wear
Rate and Surface Roughness has been done by using the developed regression models.
Optimization results have been used for identifying the machining conditions. For
verification of the regression models and the optimization results, conduct experiments
using the same machined conditions. Finally, a comparison of the process performance
in dry EDM and oil EDM has been made. It was experimentally found that although the
Material Removal Rate is lower in dry EDM, surface finish and tool wear rate are much
better than in oil EDM.

Keywords: Al-SiC Composite Material with Weight fraction of SiC, Dry Electrical Discharge
Machining, Input Machining Parameters, Response Parameters, Multi-objective GA.

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