Surface roughness prediction in high speed end milling using adaptive neuro-fuzzy inference system

One of the significant characteristics in machining process is final quality of surface. The best measurement for this quality is the surface roughness. Therefore, estimating the surface roughness before the machining is a serious matter. The aim of this research is to estimate and simulate the aver...

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Main Authors:	Al Hazza, Muataz Hazza Faizi, Seder, Amin M. F., Adesta, Erry Yulian Triblas, Taufik, Muhammad, Idris, Abdul Hadi
Format:	Article
Language:	English
Published:	Trans Tech Publications, Switzerland 2015
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/42930/ http://irep.iium.edu.my/42930/ http://irep.iium.edu.my/42930/ http://irep.iium.edu.my/42930/1/42930_-_Surface_roughness_prediction_in_high_speed_end_milling_using.pdf

id	iium-42930
recordtype	eprints
spelling	iium-429302017-11-08T06:10:02Z http://irep.iium.edu.my/42930/ Surface roughness prediction in high speed end milling using adaptive neuro-fuzzy inference system Al Hazza, Muataz Hazza Faizi Seder, Amin M. F. Adesta, Erry Yulian Triblas Taufik, Muhammad Idris, Abdul Hadi T Technology (General) One of the significant characteristics in machining process is final quality of surface. The best measurement for this quality is the surface roughness. Therefore, estimating the surface roughness before the machining is a serious matter. The aim of this research is to estimate and simulate the average surface roughness (Ra) in high speed end milling. An experimental work was conducted to measure the surface roughness. A set of experimental runs based on box behnken design was conducted to machine carbon steel using coated carbide inserts. Moreover, the Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used as one of the unconventional methods to develop a model that can predict the surface roughness. The adaptive-network-based fuzzy inference system (ANFIS) was found to be capable of high accuracy predictions for surface roughness within the range of the research boundaries. Trans Tech Publications, Switzerland 2015 Article PeerReviewed application/pdf en http://irep.iium.edu.my/42930/1/42930_-_Surface_roughness_prediction_in_high_speed_end_milling_using.pdf Al Hazza, Muataz Hazza Faizi and Seder, Amin M. F. and Adesta, Erry Yulian Triblas and Taufik, Muhammad and Idris, Abdul Hadi (2015) Surface roughness prediction in high speed end milling using adaptive neuro-fuzzy inference system. Advanced Materials Research, 1115. pp. 122-125. ISSN 1022-6680 http://www.ttp.net/1022-6680.html 10.4028/www.scientific.net/AMR.1115.122
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Al Hazza, Muataz Hazza Faizi Seder, Amin M. F. Adesta, Erry Yulian Triblas Taufik, Muhammad Idris, Abdul Hadi Surface roughness prediction in high speed end milling using adaptive neuro-fuzzy inference system
description	One of the significant characteristics in machining process is final quality of surface. The best measurement for this quality is the surface roughness. Therefore, estimating the surface roughness before the machining is a serious matter. The aim of this research is to estimate and simulate the average surface roughness (Ra) in high speed end milling. An experimental work was conducted to measure the surface roughness. A set of experimental runs based on box behnken design was conducted to machine carbon steel using coated carbide inserts. Moreover, the Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used as one of the unconventional methods to develop a model that can predict the surface roughness. The adaptive-network-based fuzzy inference system (ANFIS) was found to be capable of high accuracy predictions for surface roughness within the range of the research boundaries.
format	Article
author	Al Hazza, Muataz Hazza Faizi Seder, Amin M. F. Adesta, Erry Yulian Triblas Taufik, Muhammad Idris, Abdul Hadi
author_facet	Al Hazza, Muataz Hazza Faizi Seder, Amin M. F. Adesta, Erry Yulian Triblas Taufik, Muhammad Idris, Abdul Hadi
author_sort	Al Hazza, Muataz Hazza Faizi
title	Surface roughness prediction in high speed end milling using adaptive neuro-fuzzy inference system
title_short	Surface roughness prediction in high speed end milling using adaptive neuro-fuzzy inference system
title_full	Surface roughness prediction in high speed end milling using adaptive neuro-fuzzy inference system
title_fullStr	Surface roughness prediction in high speed end milling using adaptive neuro-fuzzy inference system
title_full_unstemmed	Surface roughness prediction in high speed end milling using adaptive neuro-fuzzy inference system
title_sort	surface roughness prediction in high speed end milling using adaptive neuro-fuzzy inference system
publisher	Trans Tech Publications, Switzerland
publishDate	2015
url	http://irep.iium.edu.my/42930/ http://irep.iium.edu.my/42930/ http://irep.iium.edu.my/42930/ http://irep.iium.edu.my/42930/1/42930_-_Surface_roughness_prediction_in_high_speed_end_milling_using.pdf
first_indexed	2023-09-18T21:01:10Z
last_indexed	2023-09-18T21:01:10Z
_version_	1777410633210265600

Surface roughness prediction in high speed end milling using adaptive neuro-fuzzy inference system

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