Optimization of Spark Gap in Powder Mixed Wire Electric Discharge Machining through Genetic Algorithm Approach

Abstract

Focus of industry is continuously shifting toward miniaturized components which cannot be fabricated through conventional manufacturing methods, which has led to the development of newer methods of manufacturing. Powder mixed wire electric discharge machining (pwEDM) is a route of machining which modifies the conventional wire Electric Discharge Machining (w-EDM). In pwEDM, powder is added to the dielectric fluid which influences the properties of the finally fabricated part. Spark gap is an important parameter in pwEDM which must be closely controlled. The spark gap should ideally be equal to the wire diameter but in practice it is always slightly more. This slight deviation in spark gap results in inaccuracy in the fabricated part and as such makes the pwEDM process unsuitable for precise manufacturing. This study is focused on reducing the average deviation in spark gap through control of the input process parameters such as pulse on time, pulse off time, servo voltage (SV), wire feed (WF), and powder concentration. The experiments were conducted as per Taguchi L27 array and the spark gap was measured using an inverted microscope. The data collected through Taguchi Array was used to develop a mathematical model through regression analysis. The mathematical model, this developed, was optimized through genetic algorithm. After optimization, the average deviation in the spark gap corresponding to a particular level of parameters was found to be 23.185 µm, which was minimum for the given set of conditions. Such optimization is useful for precise machining of parts using pwEDM.