Optimization of Material Removal Rate in Micro-EDM Using Artificial Neural Network and Genetic Algorithms

Abstract

The present work reports on the development of modeling and optimization for micro-electric discharge machining (μ-EDM) process. Artificial neural network (ANN) is used for analyzing the material removal of µ-EDM to establish the parameter optimization model. A feed forward neural network with back propagation algorithm is trained to optimize the number of neurons and number of hidden layers to predict a better material removal rate. A neural network model is developed using MATLAB programming, and the trained neural network is simulated. When experimental and network model results are compared for the performance considered, it is observed that the developed model is within the limits of the agreeable error. Then, genetic algorithms (GAs) have been employed to determine optimum process parameters for any desired output value of machining characteristics. This well-trained neural network model is shown to be effective in estimating the MRR and is improved using optimized machining parameters.

Keywords:

• Artificial neural network (ANN)
• Genetic algorithms
• Micro-electric discharge machining (µ-EDM)
• Optimization