Optimisation of the electrical discharge machining process using a GA-based neural network

Abstract

In this paper, the optimisation of the EDM process parameters from the rough cutting stage to the finish cutting stage has been reported. A trained neural network was used to establish the relationship between the process parameters and machining performance. Genetic algorithms with properly defined objective functions were then adapted to the neural network to determine the optimal process parameters. Examples with specifications intentionally assigned the same values as those recorded in the database or selected arbitrarily have been fed into the developed GA-based neural network in order to verify the optimisation ability throughout the machining process. Accordingly, the optimised results indicate that the GA-based neural network can be successfully used to generate optimal process parameters from the rough cutting stage to the finish cutting stage.

Modelling of Electro-Discharge Machining of Difficult-to-Machine Materials: An Overview

“…In different workOptimization of the EDM parameters, from the rough cutting to the finish cutting stage, has been done by Su et.al. [27].…”

Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II

“…Several researchers carried out various investigations for improving the process performance [2][3][4][5][6][7]. Proper selection of machining parameters for the best process performance is still a challenging job.…”

“…Optimization of the EDM parameters, from the rough cutting to the finish cutting stage, has been done by Su et al [5]. A trained neural network was used to establish the relationship between the process parameters and machining performance.…”

Simulation analysis of bubble motion under ultrasonic assisted Electrical Discharge Machining

“…On the other hand, the movement of the flow field of EDM gap is so complex that it is difficult to obtain the analytical solution directly by theoretical calculations. Moreover, the bubble volume generated by the inter electrode gap in the process of EDM is effected differently by some parameters, such as the electrode discharge voltage, discharge current, pulse width, pulse interval, electrode machining time and the conditions of liquid [3][4]. Those parameters make EDM exhibit various stability and efficiency.…”