Improve wire EDM performance at different machining parameters - ANFIS modeling

Ibrahem Maher; Liew Hui Ling; Ahmed A.D. Sarhan; M. Hamdi

doi:10.1016/j.ifacol.2015.05.109

Improve wire EDM performance at different machining parameters - ANFIS modeling

Ibrahem Maher^*
, Liew Hui Ling
, Ahmed A.D. Sarhan
, M. Hamdi

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

49 Scopus citations

Abstract

This study presents an experimental investigation of wire electric discharge machining (WEDM) for improving the process performance. The effects of the machining parameters were investigated on the machining performance. Adaptive neuro-fuzzy inference system (ANFIS) was applied to determine the effect of significant parameters on WEDM performance. In addition, ANFIS was used to predict the cutting speed, surface roughness and heat affected zone in WEDM. The predicted cutting speed, surface roughness, and heat affected zone were compared with measured data, and the average prediction error for cutting speed, surface roughness, and heat affected zone were 3.41, 3.89, and 4.1 respectively.

Original language	English
Pages (from-to)	105-110
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	28
Issue number	1
DOIs	https://doi.org/10.1016/j.ifacol.2015.05.109
State	Published - 1 Feb 2015
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Keywords

ANFIS
Cutting speed
Heat-affected zone
Surface roughness
WEDM

ASJC Scopus subject areas

Control and Systems Engineering

UN SDGs

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

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10.1016/j.ifacol.2015.05.109

Cite this

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title = "Improve wire EDM performance at different machining parameters - ANFIS modeling",

abstract = "This study presents an experimental investigation of wire electric discharge machining (WEDM) for improving the process performance. The effects of the machining parameters were investigated on the machining performance. Adaptive neuro-fuzzy inference system (ANFIS) was applied to determine the effect of significant parameters on WEDM performance. In addition, ANFIS was used to predict the cutting speed, surface roughness and heat affected zone in WEDM. The predicted cutting speed, surface roughness, and heat affected zone were compared with measured data, and the average prediction error for cutting speed, surface roughness, and heat affected zone were 3.41, 3.89, and 4.1 respectively.",

keywords = "ANFIS, Cutting speed, Heat-affected zone, Surface roughness, WEDM",

author = "Ibrahem Maher and \{Hui Ling\}, Liew and Sarhan, \{Ahmed A.D.\} and M. Hamdi",

year = "2015",

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doi = "10.1016/j.ifacol.2015.05.109",

language = "English",

volume = "28",

pages = "105--110",

journal = "IFAC-PapersOnLine",

issn = "2405-8971",

publisher = "Elsevier B.V.",

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T1 - Improve wire EDM performance at different machining parameters - ANFIS modeling

AU - Maher, Ibrahem

AU - Hui Ling, Liew

AU - Sarhan, Ahmed A.D.

AU - Hamdi, M.

PY - 2015/2/1

Y1 - 2015/2/1

N2 - This study presents an experimental investigation of wire electric discharge machining (WEDM) for improving the process performance. The effects of the machining parameters were investigated on the machining performance. Adaptive neuro-fuzzy inference system (ANFIS) was applied to determine the effect of significant parameters on WEDM performance. In addition, ANFIS was used to predict the cutting speed, surface roughness and heat affected zone in WEDM. The predicted cutting speed, surface roughness, and heat affected zone were compared with measured data, and the average prediction error for cutting speed, surface roughness, and heat affected zone were 3.41, 3.89, and 4.1 respectively.

AB - This study presents an experimental investigation of wire electric discharge machining (WEDM) for improving the process performance. The effects of the machining parameters were investigated on the machining performance. Adaptive neuro-fuzzy inference system (ANFIS) was applied to determine the effect of significant parameters on WEDM performance. In addition, ANFIS was used to predict the cutting speed, surface roughness and heat affected zone in WEDM. The predicted cutting speed, surface roughness, and heat affected zone were compared with measured data, and the average prediction error for cutting speed, surface roughness, and heat affected zone were 3.41, 3.89, and 4.1 respectively.

KW - ANFIS

KW - Cutting speed

KW - Heat-affected zone

KW - Surface roughness

KW - WEDM

UR - https://www.scopus.com/pages/publications/84954124378

U2 - 10.1016/j.ifacol.2015.05.109

DO - 10.1016/j.ifacol.2015.05.109

M3 - Conference article

AN - SCOPUS:84954124378

SN - 2405-8971

VL - 28

SP - 105

EP - 110

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 1

ER -

Improve wire EDM performance at different machining parameters - ANFIS modeling

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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