No abstract

No abstract

a b s t r a c tMicro-machining technology is effectively used in modern manufacturing industries. This paper investigates the influence of three different input parameters such as voltage, capacitance and feed rate of micro-wire electrical discharge machining (micro-WEDM) performances of material removal rate (MRR), Kerf width (KW) and surface roughness (SR) using response surface methodology with central composite design (CCD). The experiments are carried out on titanium alloy (Tie6Ale4V). The machining characteristics are significantly influenced by the electrical and non-electrical parameters in micro-WEDM process. Analysis of variance (ANOVA) was performed to find out the significant influence of each factor. The model developed can use a genetic algorithm (GA) to determine the optimal machining conditions using multi-objective optimization technique. The optimal machining performance of material removal rate, Kerf width and surface roughness are 0.01802 mm 3 /min, 101.5 mm and 0.789 mm, respectively, using this optimal machining conditions viz. voltage 100 V, capacitance 10 nF and feed rate 15 mm/s.

No abstract

No abstract

Recent advancement in manufacturing industries has given rise to miniaturized and light weight products with increasing high engineering applications. The miniaturized products demand innovative manufacturing methods. Microelectric discharge machining (micro-EDM) is one of the most powerful technologies capable of fabricating microstructures/parts. Experiments have been conducted on the machining of EN24 die steel with different electrodes such as tungsten, copper, copper tungsten, and silver tungsten in micro-EDM setup. The process performance was estimated based on material removal rate (MRR), circularity, overcut, and heat-affected zones (HAZ) of micromachined holes. Further, the outcome of various machining parameters such as gap voltage, capacitance, threshold, and feed rate of the electrode has also been investigated. The results revealed that the Cu as electrode achieved maximum MRR followed by AgW, CuW, and W, respectively.

Abstract. The next generation wireless systems will consist of heterogeneous networks from cellular, Wi-Fi, WiMAX to other emerging access technologies. Vertical handoff occurs when a mobile terminal decides to switch between networks. One of the challenging problems during vertical handoff is the selection of an optimal network that maximizes end users satisfaction. This paper presents an intelligent vertical handoff decision algorithm that selects the target network based on the traffic class of the mobile user. The algorithm uses two modules, to estimate the handoff requirement and to select the optimal network. These modules utilize Fuzzy Logic and Genetic algorithm to make an intelligent vertical handoff decision.

No abstract