DefinePK

Abstract

There is an increasing demand for industrial products, not only with the increased number of functions and also there will be a requirement of product in reduced size. Hence, it is essential to develop a product with maximum functions and minimum size. Micromachining technology gives the best solution to develop the product with maximum number of functions and also the size of the product will be in micrometers range. Micromachining technology uses various machining techniques to launch miniaturized products more efficiently and well ahead of their competitors in the market. One of the machining techniques involved in micromachining is Micro Electrical discharge machining (Micro – EDM). Micro-EDM uses the same working principle as EDM which produces repetitive discharges of electrical sparks between the gap of tool (electrode) and the work piece. AISI 1040 steel is a high carbon steel which provides high yield strength and also it is employed in making spring materials, cutting saws, blades and in micro level applications it is used in manufacture of micro grippers, micro actuators. The wear rate is also less compared to the copper and graphite electrodes employed previously in EDM machining. In this present work, optimization of micro electrical discharge machining parameters using Taguchi’s approach is proposed for AISI 1040 steel because of its higher hardness and also economically feasible to produce dies at cheaper cost. Experimentation was planned as per Taguchi’s L9 orthogonal array. Each experiment was performed under different machining conditions of gap voltage, capacitance, feed, and threshold. Two responses namely material removal rate and surface roughness were considered for each experiment. The optimum machining parameter combination is obtained by using the analysis of signal to noise (S/N) ratio. The level of importance of the machining parameters on the material removal rate and surface roughness is determined by using analysis of variance (ANOVA).The highly effective parameters on both the MRR and surface roughness are found as gap voltage and capacitance. The variation of the MRR and surface roughness with machining parameters is optimized by using Taguchi technique and gray relational analysis technique with the experimental values.