Optimal Selection of Machining Conditions in the Electrojet Drilling Process Using Hybrid NN-DF-GA Approach

Abstract

This article presents a hybrid neural network, desirability function, and genetic algorithm (NN-DF-GA) approach for optimal selection of the input process parameters for optimizing the multiresponse parameters of the electrojet drilling (EJD) process. EJD is a promising nontraditional machining technique that is used for machining microholes (<1 mm in diameter) in difficult-to-machine materials. The proposed approach first uses a back propagation neural network to formulate a fitness function for predicting the response parameters of the process. From the network output, the desirability method obtains a composite fitness function for further use in the genetic algorithm. The genetic algorithm predicts the optimal input parametric combinations and simultaneously optimizes the multiresponse characteristics of the process. Simulated results confirm the feasibility of this approach and show a good agreement with experimental results for a wide range of machining conditions.

Keywords:

• Artificial intelligence
• Desirability function
• Electrochemical machining
• Electrojet drilling
• Evolutionary techniques
• Genetic algorithm
• Hybrid modelling
• Material removal rate
• Micro-hole drilling
• Multiresponse optimization
• Neural network
• Nickel base super alloy
• Non-traditional machining
• Roundness error