Response surface methodology and artificial neural network-based models for predicting performance of wire electrical discharge machining of inconel 718 alloy

Lalwani, V., Sharma, P., Pruncu, C.I. et al. (1 more author) (2020) Response surface methodology and artificial neural network-based models for predicting performance of wire electrical discharge machining of inconel 718 alloy. Journal of Manufacturing and Materials Processing, 4 (2). 44.

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2020 The Authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/
Keywords: response surface method (RSM); artificial neural network (ANN); wire electrical discharge machining (WEDM); kerf width (Kf); surface roughness (Ra); material removal rate (MRR); NSGA-II
Dates:
  • Accepted: 1 May 2020
  • Published: 6 May 2020
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Materials Science and Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 29 Jun 2020 13:16
Last Modified: 29 Jun 2020 13:16
Status: Published
Publisher: MDPI AG
Refereed: Yes
Identification Number: https://doi.org/10.3390/jmmp4020044

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