Using soft computing methods as an effective tool in predicting surface roughness

The aim of this research is to compare between two different approaches in predicting and modeling the surface roughness in high speed hard turning: regression analysis approach and soft computing approach. Three different soft computing techniques have been applied: Support vector machine (SVM,) Ex...

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Main Authors:	Al Hazza, Muataz Hazza Faizi, Adesta, Erry Yulian Triblas, Seder, Amin M. F.
Format:	Conference or Workshop Item
Language:	English English
Published:	The Institute of Electrical and Electronics Engineers, Inc. 2016
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/46494/ http://irep.iium.edu.my/46494/ http://irep.iium.edu.my/46494/ http://irep.iium.edu.my/46494/1/46494_using_soft_computing.pdf http://irep.iium.edu.my/46494/4/46494-Using%20soft%20computing%20methods%20as%20an%20effective%20tool%20in%20predicting%20surface%20roughness_SCOPUS.pdf

id	iium-46494
recordtype	eprints
spelling	iium-464942017-03-30T03:18:43Z http://irep.iium.edu.my/46494/ Using soft computing methods as an effective tool in predicting surface roughness Al Hazza, Muataz Hazza Faizi Adesta, Erry Yulian Triblas Seder, Amin M. F. T Technology (General) The aim of this research is to compare between two different approaches in predicting and modeling the surface roughness in high speed hard turning: regression analysis approach and soft computing approach. Three different soft computing techniques have been applied: Support vector machine (SVM,) Extreme learning machine (ELM) and Artificial neural network (ANN). A set of sparse experimental have been conducted in turning hardened steel (AISI 4340) by using mixed ceramic tools made up of aluminum oxide and titanium carbide as cutting tool. Design for experiment (DoE) 8.0 software and JMP Software have been used to design the experiment and to analyses the results statistically. Full Factorial Design (FFD) has been applied for the experiment design. The experimental work was conducted under dry cutting conditions with three cutting parameters: cutting speed, feed rate, and negative rake angle with a constant depth of cut. The results show a better and more accurate estimation for the soft computing methods The Institute of Electrical and Electronics Engineers, Inc. 2016 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/46494/1/46494_using_soft_computing.pdf application/pdf en http://irep.iium.edu.my/46494/4/46494-Using%20soft%20computing%20methods%20as%20an%20effective%20tool%20in%20predicting%20surface%20roughness_SCOPUS.pdf Al Hazza, Muataz Hazza Faizi and Adesta, Erry Yulian Triblas and Seder, Amin M. F. (2016) Using soft computing methods as an effective tool in predicting surface roughness. In: 2015 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT 2015), 8th-10th Dec. 2015, Kuala Lumpur. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7478710&filter%3DAND%28p_IS_Number%3A7478698%29 10.1109/ACSAT.2015.17
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General)
spellingShingle	T Technology (General) Al Hazza, Muataz Hazza Faizi Adesta, Erry Yulian Triblas Seder, Amin M. F. Using soft computing methods as an effective tool in predicting surface roughness
description	The aim of this research is to compare between two different approaches in predicting and modeling the surface roughness in high speed hard turning: regression analysis approach and soft computing approach. Three different soft computing techniques have been applied: Support vector machine (SVM,) Extreme learning machine (ELM) and Artificial neural network (ANN). A set of sparse experimental have been conducted in turning hardened steel (AISI 4340) by using mixed ceramic tools made up of aluminum oxide and titanium carbide as cutting tool. Design for experiment (DoE) 8.0 software and JMP Software have been used to design the experiment and to analyses the results statistically. Full Factorial Design (FFD) has been applied for the experiment design. The experimental work was conducted under dry cutting conditions with three cutting parameters: cutting speed, feed rate, and negative rake angle with a constant depth of cut. The results show a better and more accurate estimation for the soft computing methods
format	Conference or Workshop Item
author	Al Hazza, Muataz Hazza Faizi Adesta, Erry Yulian Triblas Seder, Amin M. F.
author_facet	Al Hazza, Muataz Hazza Faizi Adesta, Erry Yulian Triblas Seder, Amin M. F.
author_sort	Al Hazza, Muataz Hazza Faizi
title	Using soft computing methods as an effective tool in predicting surface roughness
title_short	Using soft computing methods as an effective tool in predicting surface roughness
title_full	Using soft computing methods as an effective tool in predicting surface roughness
title_fullStr	Using soft computing methods as an effective tool in predicting surface roughness
title_full_unstemmed	Using soft computing methods as an effective tool in predicting surface roughness
title_sort	using soft computing methods as an effective tool in predicting surface roughness
publisher	The Institute of Electrical and Electronics Engineers, Inc.
publishDate	2016
url	http://irep.iium.edu.my/46494/ http://irep.iium.edu.my/46494/ http://irep.iium.edu.my/46494/ http://irep.iium.edu.my/46494/1/46494_using_soft_computing.pdf http://irep.iium.edu.my/46494/4/46494-Using%20soft%20computing%20methods%20as%20an%20effective%20tool%20in%20predicting%20surface%20roughness_SCOPUS.pdf
first_indexed	2023-09-18T21:06:12Z
last_indexed	2023-09-18T21:06:12Z
_version_	1777410950313279488

Using soft computing methods as an effective tool in predicting surface roughness

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