A hybrid intelligent method for modelling the EDM process

Kesheng Wang; Hirpa L. Gelgele; Yi Wang; Qingfeng Yuan; Minglung Fang

doi:10.1016/S0890-6955(03)00102-0

A hybrid intelligent method for modelling the EDM process

Kesheng Wang, Hirpa L. Gelgele, Yi Wang, Qingfeng Yuan, Minglung Fang

Research output: Contribution to journal › Article › peer-review

Abstract

This paper discusses the development and application of a hybrid artificial neural network and genetic algorism methodology to modelling and optimisation of electro-discharge machining. The hybridisation approach is aimed not only at exploiting the strong capabilities of the two tools, but also at solving manufacturing problems that are not amenable for modelling using traditional methods. Based on an experimental data, the model was tested with satisfactory results. The developed methodology with the model is highly beneficial to manufacturing industries, such as aerospace, automobile and tool making industries. © 2003 Elsevier Science Ltd. All rights reserved.

Original language	English
Pages (from-to)	995-999
Number of pages	4
Journal	International Journal of Machine Tools and Manufacture
Volume	43
Issue number	10
DOIs	https://doi.org/10.1016/S0890-6955(03)00102-0
Publication status	Published - Aug 2003

Keywords

Artificial neural networks
Computational intelligence
Electro-discharge machining
Genetic algorithms
Hybrid systems
Modelling
Optimisation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/S0890-6955(03)00102-0

http://www.sciencedirect.com/science/article/pii/S0890695503001020

Cite this

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title = "A hybrid intelligent method for modelling the EDM process",

abstract = "This paper discusses the development and application of a hybrid artificial neural network and genetic algorism methodology to modelling and optimisation of electro-discharge machining. The hybridisation approach is aimed not only at exploiting the strong capabilities of the two tools, but also at solving manufacturing problems that are not amenable for modelling using traditional methods. Based on an experimental data, the model was tested with satisfactory results. The developed methodology with the model is highly beneficial to manufacturing industries, such as aerospace, automobile and tool making industries. {\textcopyright} 2003 Elsevier Science Ltd. All rights reserved.",

keywords = "Artificial neural networks, Computational intelligence, Electro-discharge machining, Genetic algorithms, Hybrid systems, Modelling, Optimisation",

author = "Kesheng Wang and Gelgele, {Hirpa L.} and Yi Wang and Qingfeng Yuan and Minglung Fang",

year = "2003",

month = aug,

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journal = "International Journal of Machine Tools and Manufacture",

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TY - JOUR

T1 - A hybrid intelligent method for modelling the EDM process

AU - Wang, Kesheng

AU - Gelgele, Hirpa L.

AU - Wang, Yi

AU - Yuan, Qingfeng

AU - Fang, Minglung

PY - 2003/8

Y1 - 2003/8

N2 - This paper discusses the development and application of a hybrid artificial neural network and genetic algorism methodology to modelling and optimisation of electro-discharge machining. The hybridisation approach is aimed not only at exploiting the strong capabilities of the two tools, but also at solving manufacturing problems that are not amenable for modelling using traditional methods. Based on an experimental data, the model was tested with satisfactory results. The developed methodology with the model is highly beneficial to manufacturing industries, such as aerospace, automobile and tool making industries. © 2003 Elsevier Science Ltd. All rights reserved.

AB - This paper discusses the development and application of a hybrid artificial neural network and genetic algorism methodology to modelling and optimisation of electro-discharge machining. The hybridisation approach is aimed not only at exploiting the strong capabilities of the two tools, but also at solving manufacturing problems that are not amenable for modelling using traditional methods. Based on an experimental data, the model was tested with satisfactory results. The developed methodology with the model is highly beneficial to manufacturing industries, such as aerospace, automobile and tool making industries. © 2003 Elsevier Science Ltd. All rights reserved.

KW - Artificial neural networks

KW - Computational intelligence

KW - Electro-discharge machining

KW - Genetic algorithms

KW - Hybrid systems

KW - Modelling

KW - Optimisation

U2 - 10.1016/S0890-6955(03)00102-0

DO - 10.1016/S0890-6955(03)00102-0

M3 - Article

SN - 0890-6955

VL - 43

SP - 995

EP - 999

JO - International Journal of Machine Tools and Manufacture

JF - International Journal of Machine Tools and Manufacture

IS - 10

ER -

A hybrid intelligent method for modelling the EDM process

Abstract

Keywords

UN SDGs

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