IIUM Repository

Surface roughness optimization in end milling using the multi objective genetic algorithm approach

Al Hazza, Muataz Hazza Faizi and Adesta, Erry Yulian Triblas and Riza, Muhammad and Mohammad Yuhan, Suprianto (2012) Surface roughness optimization in end milling using the multi objective genetic algorithm approach. Advanced Materials Research, 576. pp. 103-106. ISSN 1022-6680

[img] PDF - Published Version
Restricted to Registered users only

Download (269kB) | Request a copy
[img] PDF (SCOPUS) - Supplemental Material
Restricted to Repository staff only

Download (157kB) | Request a copy


In finishing end milling, not only good accuracy but also good roughness levels must be achieved. Therefore, determining the optimum cutting levels to achieve the minimum surface roughness is important for it is economical and mechanical issues. This paper presents the optimization of machining parameters in end milling processes by integrating the genetic algorithm (GA) with the statistical approach. Two objectives have been considered, minimum arithmetic mean roughness (Ra) and minimum Root-mean-square roughness (Rq). The mathematical models for the surface roughness parameters have been developed, in terms of cutting speed, feed rate, and axial depth of cut by using Response Methodology Method (RSM). Due to complexity of this machining optimization problem, a multi objective genetic algorithm (MOGA) has been applied to resolve the problem, and the results have been analyzed.

Item Type: Article (Journal)
Additional Information: 6852/55357
Uncontrolled Keywords: arithmetic mean roughness, Root-mean-square roughness, RSM, genetic algorithm
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: 04 Apr 2017 10:06
Last Modified: 04 Apr 2017 10:06
URI: http://irep.iium.edu.my/id/eprint/55357

Actions (login required)

View Item View Item


Downloads per month over past year