Sensitivity analysis and optimization of machining parameters based on surface roughness prediction for AA6061

Elssa Wi Yahya, Guo Fu Ding, Sheng Feng Qin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationAdvanced Materials, Mechanics and Industrial Engineering
PublisherTrans Tech Publications
Pages181-188
Number of pages8
ISBN (Print)9783038351795
DOIs
Publication statusPublished - Jul 2014
Event4th International Conference on Mechanics, Simulation and Control, ICMSC 2014 - Moscow, Russian Federation
Duration: 21 Jun 201422 Jun 2014

Publication series

NameApplied Mechanics and Materials
Volume598
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Conference

Conference4th International Conference on Mechanics, Simulation and Control, ICMSC 2014
CountryRussian Federation
CityMoscow
Period21/06/1422/06/14

Fingerprint

Dive into the research topics of 'Sensitivity analysis and optimization of machining parameters based on surface roughness prediction for AA6061'. Together they form a unique fingerprint.

Cite this