Sensitivity Analysis and Optimization of Machining Parameters Based on Surface Roughness Prediction for AA6061

Yahya, Elssawi, Ding, Guo Fu and Qin, Sheng-feng (2014) Sensitivity Analysis and Optimization of Machining Parameters Based on Surface Roughness Prediction for AA6061. In: Applied Mechanics and Materials. Trans Tech Publications, pp. 181-188. ISBN 9783038351795

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Abstract

Surface roughness is strongly affected by machining parameters. In the past few decades, many researchers have established the relationship between the surface roughness and machining parameters, but less attention has been paid to tool shape and geometry. In addition, the number of tool flutes was ignored, which affects in vibrations and machining system. Therefore, this study first-time includes the tool flutes in addition to cutting speed, depth of cut and feed rate as independent variables. Firstly, a set of machining experiments were conducted using AA6061 as a work piece material to provide original data. Response Surface Model (RSM) adopted to establish the relationship model of surface roughness and machining parameters using Minitab 16. Based on analysis of variance (ANOVA), the results show cutter flutes has higher significant followed by feed rate, depth of cut and cutting speed which has less significant. Finally, machining parameters were optimized to desired surface roughness, and optimization prediction error has limited values between-0.02 and 0.02μm.

Item Type:	Book Section
Uncontrolled Keywords:	Machining Parameters, Optimization, Response Surface Method (RSM), Sensitivity Analysis, Surface Roughness (SR)
Subjects:	H900 Others in Engineering
Department:	Faculties > Arts, Design and Social Sciences > Design
Depositing User:	Becky Skoyles
Date Deposited:	12 Dec 2018 15:49
Last Modified:	11 Oct 2019 15:00
URI:	http://nrl.northumbria.ac.uk/id/eprint/37218

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