Title: AI-based modelling and optimisation of rotating magnetic force assisted powder mixed EDM

Authors: Ram Sajeevan; Avanish Kumar Dubey; Ajit Kumar Singh; Pankaj Kumar Shrivastava

Addresses: Mechanical Engineering Department, Motilal Nehru National Institute of Technology, Allahabad, Prayagraj (U.P.), India ' Mechanical Engineering Department, Motilal Nehru National Institute of Technology, Allahabad, Prayagraj (U.P.), India ' Mechanical Engineering Department, Motilal Nehru National Institute of Technology, Allahabad, Prayagraj (U.P.), India ' Mechanical Engineering Department, AKS University, Satna (M.P.), India

Abstract: Though widely used to process newly developed materials, electrical discharge machining (EDM) possesses poor machining efficiency. Modifications/hybridisation of EDM can improve its performance with respect to different quality parameters. Mixing powder in dielectric and applying magnetic field near machining zone are few of EDM modifications. The present paper investigates the performance of EDM with above mentioned modifications applied simultaneously. The innovative setup was designed and fabricated to perform rotary magnetic field assisted powder mixed EDM (MFPMEDM). The experiments were conducted on aluminium titanium di-boride metal matrix composite to evaluate MRR by varying different process parameters including powder concentration under the influence of magnetic field. The performance of both EDM and MFPMEDM has been compared and latter has been found much better in terms of process efficiency. Further, hybrid evolutionary optimisation algorithm has been used for modelling and optimisation of MRR.

Keywords: artificial neural network; ANN; genetic algorithm; GA; magnetic field assisted EDM; material removal rate; MRR; powder mixed EDM.

DOI: 10.1504/IJAT.2022.125270

International Journal of Abrasive Technology, 2022 Vol.11 No.1, pp.17 - 32

Received: 06 Feb 2021
Accepted: 22 Dec 2021
Published online: 05 Sep 2022 *

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