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General Information
    • ISSN: 1793-8198
    • Frequency: Quarterly
    • DOI: 10.18178/IJMMM
    • Editor-in-Chief: Prof. K. M. Gupta, Prof. Ian McAndrew
    • Executive Editor: Ms. Cherry L. Chen
    • Abstracting/Indexing: EI (INSPEC, IET), Chemical Abstracts Services (CAS), Engineering & Technology Digital Library,  ProQuest, Crossref, Ulrich's Periodicals Directory, DOAJ, and Electronic Journals Library .
    • E-mail ijmmm@ejournal.net
Editor-in-chief
Prof. Ian McAndrew
Embry Riddle Aeronautical University, UK.
It is my honor to be the editor-in-chief of IJMMM. I will do my best to help develop this journal better.

IJMMM 2015 Vol.3(1): 36-39 ISSN: 1793-8198
DOI: 10.7763/IJMMM.2015.V3.162

An Improved Mathematical Model to Predict Surface Roughness Using Hybrid Method

Mohd Fadzil Faisae Ab. Rashid
Abstract—Surface roughness is one of the most important requirements in machining process. In order to obtain needed surface roughness, the proper setting of cutting parameters is crucial before the process take place. Therefore, an accurate mathematical model to predict surface roughness is totally needed. This research presents a hybrid method which combine conventional multiple regression analysis and genetic algorithm to improve the accuracy of mathematical model to predict surface roughness. In experiment, three independent variables: spindle speed, feed rate and depth of cut were manipulated in collecting data. Full factorials cut were performed using FANUC CNC Milling α-Τ14ιE. The results show that the proposed hybrid method capable to improve accuracy of model with 23% and 28% of reduction in error.

Index Terms—Surface roughness, linear regression, genetic algorithm.

Mohd Fadzil Faisae Ab. Rashid is with the Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia (e-mail: ffaisae@ump.edu.my).

[PDF]

Cite: Mohd Fadzil Faisae Ab. Rashid, "An Improved Mathematical Model to Predict Surface Roughness Using Hybrid Method," International Journal of Materials, Mechanics and Manufacturing vol. 3, no. 1, pp. 36-39, 2015.

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