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

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.

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Abstract

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

Item Type:	Conference or Workshop Item (Invited Papers)
Additional Information:	6852/46494
Uncontrolled Keywords:	SVM, ELM, ANN, Ra, high speed hard turning
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Manufacturing and Materials Engineering
Depositing User:	Dr Muataz Hazza Alhazza
Date Deposited:	21 Dec 2015 16:27
Last Modified:	30 Mar 2017 11:18
URI:	http://irep.iium.edu.my/id/eprint/46494

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