Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.]

Surface roughness is often used as a measure to identify surface integrity of machined parts. The objective of this study was to optimise part surface roughness by investigating the effects of cutting speed, feed rate, depth of cut and tool nose radius on the surface roughness of Aluminium 6061. A f...

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Main Authors: V. Chowdary, Boppana, Jahoor,, Riaz, Ali,, Fahraz, Trishel, Gokool
Format: Article
Language:English
Published: Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2019
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Online Access:http://ir.uitm.edu.my/id/eprint/36424/1/36424.pdf
http://ir.uitm.edu.my/id/eprint/36424/
https://jmeche.uitm.edu.my/
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spelling my.uitm.ir.364242020-11-09T07:49:47Z http://ir.uitm.edu.my/id/eprint/36424/ Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.] V. Chowdary, Boppana Jahoor,, Riaz Ali,, Fahraz Trishel, Gokool TJ Mechanical engineering and machinery Surface roughness is often used as a measure to identify surface integrity of machined parts. The objective of this study was to optimise part surface roughness by investigating the effects of cutting speed, feed rate, depth of cut and tool nose radius on the surface roughness of Aluminium 6061. A five-level L25 Taguchi orthogonal array was modified to accommodate a four-level process parameter. The optimization was conducted on the prediction model generated by use of Response Surface Methodology (RSM) together with Analysis of Variance (ANOVA), and confirmation test validated the predicted values obtained from the Genetic Algorithm (GA). The best combination of parameters for minimum surface roughness was found to be a cutting speed of 250 m/min, feed rate of 0.03 mm/rev, depth of cut of 0.2 mm and tool nose radius of 0.503 mm. The study proves the efficacy of the GA approach in optimisation of machining parameters for improved surface roughness. Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2019 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/36424/1/36424.pdf V. Chowdary, Boppana and Jahoor,, Riaz and Ali,, Fahraz and Trishel, Gokool (2019) Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.]. Journal of Mechanical Engineering (JMechE), 16 (2). pp. 77-91. ISSN 1823-5514 ; 2550-164X https://jmeche.uitm.edu.my/
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
V. Chowdary, Boppana
Jahoor,, Riaz
Ali,, Fahraz
Trishel, Gokool
Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.]
description Surface roughness is often used as a measure to identify surface integrity of machined parts. The objective of this study was to optimise part surface roughness by investigating the effects of cutting speed, feed rate, depth of cut and tool nose radius on the surface roughness of Aluminium 6061. A five-level L25 Taguchi orthogonal array was modified to accommodate a four-level process parameter. The optimization was conducted on the prediction model generated by use of Response Surface Methodology (RSM) together with Analysis of Variance (ANOVA), and confirmation test validated the predicted values obtained from the Genetic Algorithm (GA). The best combination of parameters for minimum surface roughness was found to be a cutting speed of 250 m/min, feed rate of 0.03 mm/rev, depth of cut of 0.2 mm and tool nose radius of 0.503 mm. The study proves the efficacy of the GA approach in optimisation of machining parameters for improved surface roughness.
format Article
author V. Chowdary, Boppana
Jahoor,, Riaz
Ali,, Fahraz
Trishel, Gokool
author_facet V. Chowdary, Boppana
Jahoor,, Riaz
Ali,, Fahraz
Trishel, Gokool
author_sort V. Chowdary, Boppana
title Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.]
title_short Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.]
title_full Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.]
title_fullStr Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.]
title_full_unstemmed Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.]
title_sort optimisation of surface roughness when cnc turning of al-6061: application of taguchi design of experiments and genetic algorithm / boppana v. chowdary...[et al.]
publisher Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM)
publishDate 2019
url http://ir.uitm.edu.my/id/eprint/36424/1/36424.pdf
http://ir.uitm.edu.my/id/eprint/36424/
https://jmeche.uitm.edu.my/
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score 13.214268