Multi-objective optimisation of EDM process using ANN integrated with NSGA-II algorithm

Shiba Narayan Sahu and Narayan Chandra Nayak

International Journal of Manufacturing Technology and Management, 2018, vol. 32, issue 4/5, 381-395

Abstract: Simultaneous optimisation of each selected parameter in case of EDM process is difficult. As a result, modelling and optimisation of EDM process has been emerged as a prominent research area. This paper presents an artificial intelligent approach for process modelling and optimisation of A2 steel using EDM. In this investigation, appropriate manufacturing conditions, optimal MRR and TWR are focussed. Initially, process modelling of MRR and TWR of A2 steel using EDM has been performed by ANN. Then, NSGA-II has been implemented to find out the best trade-ups between the two conflicting response parameters MRR and TWR. Maximum MRR is achieved at upper bound parameter settings of Ip and Tau and lower bound parameter settings of Ton and V. Again, optimum TWR can be achieved by the lower bound parameter settings of Ip and Tau, upper bound of V, and the middle of the machining range of Ton.

Keywords: electro-discharge machining; EDM; artificial neural network; ANN; genetic algorithm; GA; multi-objective optimisation; MOO; tool wear rate; TWR; material removal rate; MRR. (search for similar items in EconPapers)
Date: 2018
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