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Neuro-Fuzzy Process Control System for Sinking EDM

✍ Scribed by A. Behrens; J. Ginzel

Publisher: Society of Manufacturing Engineers
Year: 2003
Tongue: English
Weight: 512 KB
Volume: 5
Category: Article
ISSN: 1526-6125
DOI: 10.1016/s1526-6125(03)70038-3

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✦ Synopsis

Electrical discharge machining (EDM) stands for highly accurate and very sophisticated metal shaping. The physical process takes advantage of electrical field effects between a tool-electrode and a workpiece-electrode. Material is removed by sequences of electrical discharges. The electrically efficient gap width, which is determined among other parameters by the electric conductivity of the gap and the geometrical distance of the electrodes, varies from spark to spark. This leads to a highly nonlinear control problem. Various optimization control algorithms have been developed to improve the performance of EDM sinking machines.

Soft computing technologies like fuzzy logic and neural networks have gained much popularity in this field. This paper introduces a process control system consisting of a fuzzy gapwidth controller adapted by a neural network. By combining a neural network with a fuzzy controller in this way, a learning process control system is achieved. Experimental results show the working efficiency of this neuro-fuzzy system.

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