EXPERIMENTAL INVESTIGATION OF WEDM FOR MACHINING EN-31 MMC

Mechanical Engineering

The study presents an investigation of WEDM on metals difficult to machine. Accompanying the development of mechanical industry, the demands for alloy materials having high hardness, toughness and impact resistance are increasing. WEDM machines are used to cut conductive metals of any hardness or that are difficult or impossible to cut with traditional methods. The machines also specialize in cutting complex contours or fragile geometries that would be difficult to be produced using conventional cutting methods. Machine tool industry has made exponential growth in its manufacturing capabilities in last decade but still machine tools are not utilized at their full potential. This limitation is a result of the failure to run the machine tools at their optimum operating conditions. The problem of arriving at the optimum levels of the operating parameters has attracted the attention of the researchers and practicing engineers for a very long time. T he EN-31 MMC material is extensively used for aerospace application. The consistent quality of parts being machined in WEDM is difficult because the process parameters cannot be controlled effectively. Even most WEDM machine today have process control, but selecting and maintaining optimal setting is still an extremely difficult job which yet to be satisfactorily addressed. The objective is to study the effect of current, voltage, pulse on time individually on the final outcome i.e. MRR, surface roughness (Ra) and to establish the mathematical models relating the performance measures and input parameters by regression analysis. Response surface methodology (Central Composite Design) will be used to carry out the experiments. Statistical methods will be used to estimate relation between output parameters and input parameters. Optimization of input parameters will be carried out by using principal component analysis. This method is chosen because it can perform analysis of more than one factor at a same time while reducing number of experiment which indirectly reduces cost and time in finding optimal parameter.

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