Electrical Discharge Machining technological law is very complex, and many factors influence machining process. From the point of improving process efficiency and process quality, this paper analyses the characteristic in the process course of EDM first. Based on the analyses, a fuzzy neural control system for EDM was achieved. Utilizing the characteristic of fuzzy controller and combining with...

Elman Networks is a one of the dynamic recurrent neural networks. In this research it is used for the prediction of surface roughness in Electrical Discharge Machining (EDM). Training of the models was performed with data from series of EDM experiments on SKD 11 (AISI D2) Tool steel; in the development of predictive models, machining parameters of discharge current, pulse duration and duty cycl...

This paper presents an investigation on the effect of machining parameters on the surface roughness (centre line average roughness: Ra) and the optimal combination of process parameter for minimum Ra in wire electrical discharge machining (WEDM) of EN 31 steel using artificial bee colony (ABC) algorithm. The experimental studies are conducted by varying discharge current, voltage, pulse on time...

The wire electrical discharge machining (WEDM) experiments has been conducted using air and water mixture as a dielectric medium, is called environmental friendly near dry WEDM. In this technology, the dielectric fluid (Deionized water) is replaced by liquid-air to minimize the environmental impact in the machining zone and operator health. The effect of discharge current (I), Pulse width (TON)...