MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN END MILLING OF ALUMINIUM USING LEAST SQUARE APPROXIMATION METHOD AND RESPONSE SURFACE METHODOLOGY

Okokpujie, Imhade P. and Ajayi, O. O. and Afolalu, Sunday A. and Abioye, Abiodun A. and Salawu, Enesi Y. and Udo, Mfon .O and Okonkwo , Ugochukwu C. and Orodu, Kale B. and Ikumapayi, O. M. (2018) MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN END MILLING OF ALUMINIUM USING LEAST SQUARE APPROXIMATION METHOD AND RESPONSE SURFACE METHODOLOGY. International Journal of Mechanical Engineering and Technology (IJMET), 9 (1). pp. 587-600. ISSN 0976-6359

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Abstract

In end milling, accurate setting of process parameters is extremely important to obtained enhanced surface roughness (SR). Due to a recent innovation in mechanization made it possible to produce high quality manufacturing products. The perceptions of quality in mechanical products are their physical look that is the surface roughness (SR). The aim of this research work is to develop mathematical expression (M.E) and mathematical model using least square approximation method and Response Surface Methodology (RMS) to predict the SR for end milling of Al 6061 alloy. The process parameters that were selected as predictors for the SR are Spindle speed (V), axial depth of cut (a), feed rate (f) and radial depth of cut (d). 30 samples of Al 6061 alloy were carried out using SIEG 3/10/0010 CNC machines and each of the experimental result was measured using Mitutoyo surface roughness tester and Presso-firm. The minimum SR of 0.5 μm were obtained at a spindle speed of 2034.608 rpm, feed rate of 100 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.5 mm. Analysis of variances shows that the most influential parameters was feed rate. After the predicted SR has been obtained by using the two methods, average percentage deviation was calculated, the result obtained using least square approximation method (that is the mathematical expression) show the accuracy of 99% and Response Surface Methodology (RSM) mathematical model shows accuracy of 99.6% which is viable and appropriate in prediction of SR. When either of these models are applied this will enhance the rate of production.

Item Type:	Article
Uncontrolled Keywords:	End milling; Minimum Quantity Lubrication (MQL); Response Surface Methodology; Surface Roughness (SR); Optimization.
Subjects:	T Technology > T Technology (General) T Technology > TJ Mechanical engineering and machinery
Divisions:	Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User:	Mrs Hannah Akinwumi
Date Deposited:	20 Aug 2018 09:11
Last Modified:	20 Aug 2018 09:11
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/11386

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