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A low cost three axes commercial microEDM machine has been developed indigenously in order to implement precision micromachining. The developed microEDM consists of various components such as machining chamber, X-Y table, tool feed and tool holder assembly, dielectric circulation system, power supply and automatic driving devices to control the spark gap distance. The setup is capable of performing the 8µm movement in Z-axis.The developed 3 axes machine and the commercial available machine are investigated with the most important process parameters such as Current, Pulse on time and Pulse off time in order to study the Material Removal Rate (MRR) and Tool Wear Rate (TWR). The material chosen for the current research is Stainless Steel 316L, which has high corrosion resistance and the electrode was tungsten 300µm. In most of the experiments, the developed machine has performed with 6.52% higher MRR and 26.10% Lower TWR. This is due to the effective spark gap circuit developed with the use of hall current sensor.The mathematical model of MRR and TWR are obtained by correlating the process parameter using Response Surface Methodology (RSM).The chosen objectives are contradictory in nature, we employ a non-dominated Genetic algorithm to find the optimal process parameters. The developed 3 axes microEDM has yield a 10.14% and 33.12% better performance on optimum maximum MRR and minimum TWR respectively when compared to the commercial machine.

Electrical discharge machines (EDM) are widely employed in machining components containing complex profiles of hard-to-cut and machining materials. However, the fabrication-of-tool time for the EDM process is excessively high in the traditional machining method, which significantly affects the machining rate. Therefore, in this paper, a powder metallurgy (PM) technique is employed to fabricate the tool electrode using copper (Cu), titanium carbide (TiC), and zirconium silicate (ZrSiO4) for different combinations. An L18 orthogonal array (OA) is planned using the following input parameters: three types of tools (Cu, Cu90, Cu80), peak current (PC) [A], pulse on time (PT) [µs], and gap voltage (GV) [V]. The performance of EDM is evaluated through the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). The process parameters are optimized using two different techniques: the technique for order of preference by similarity to the ideal solution (TOPSIS) and grey relational analysis (GRA). TOPSIS and GRA optimization techniques produce the same optimal parametric solution for less TWR, SR, and higher MRR with the combination of the Cu90 tool, E8 APC, 15 µs pulse PT, and 75 V GV. Based on the ANOVA table of TOPSIS, pulse on time plays a major role, contributing 46.8 % of the machining performance; peak current shows the most significant contribution of 39.3 % of the machining performance using GRA values. Furthermore, the scanning electron microscope (SEM) image analyses are carried out on the machined workpiece surface to understand the effect of tools on machining quality.

The process of energy saving mechanisms is been utilized by proper planning to transmit both critical clip and non-real infor-mation by reading overheads there by reducing throughput and bandwidth in large scale critical clip networks. The existing bundle sched-uling mythologies used were based on the First -in First-out (FIFO) manner in such cases the critical information at particular instant cannot be processed quickly but proposed system is designed in such a way it consists of three tier precedence structure .the critical in-formation bundles are placed in the higher status prioritizing queue and processed immediately and sent to the destination node where oth-er information bundles are given less precedence on the basis of position for non critical information bundles. The proposed scheme, ener-gy efficient in reducing the number of transmission by using merger technique. The lowest precedence bundles are processed after the higher precedence bundles. The proposed algorithm proves its uniqueness based on end -to-end delay than Energy efficient wake up scheduling MAC (EEWS) and Traffic Adaptive MAC protocol (TAMAC)