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Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method

Taguchi's design of experiment is utilized to optimize the process parameters in turning operation with dry environment. Three parameters, cutting speed (v), feed (f), and depth of cut (d), with three different levels are taken for the responses like material removal rate (MRR) and surface roug...

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Autores principales: Parida, Arun Kumar, Routara, Bharat Chandra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4897259/
https://www.ncbi.nlm.nih.gov/pubmed/27437503
http://dx.doi.org/10.1155/2014/905828
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author Parida, Arun Kumar
Routara, Bharat Chandra
author_facet Parida, Arun Kumar
Routara, Bharat Chandra
author_sort Parida, Arun Kumar
collection PubMed
description Taguchi's design of experiment is utilized to optimize the process parameters in turning operation with dry environment. Three parameters, cutting speed (v), feed (f), and depth of cut (d), with three different levels are taken for the responses like material removal rate (MRR) and surface roughness (R (a)). The machining is conducted with Taguchi L(9) orthogonal array, and based on the S/N analysis, the optimal process parameters for surface roughness and MRR are calculated separately. Considering the larger-the-better approach, optimal process parameters for material removal rate are cutting speed at level 3, feed at level 2, and depth of cut at level 3, that is, v (3)-f (2)-d (3). Similarly for surface roughness, considering smaller-the-better approach, the optimal process parameters are cutting speed at level 1, feed at level 1, and depth of cut at level 3, that is, v (1)-f (1)-d (3). Results of the main effects plot indicate that depth of cut is the most influencing parameter for MRR but cutting speed is the most influencing parameter for surface roughness and feed is found to be the least influencing parameter for both the responses. The confirmation test is conducted for both MRR and surface roughness separately. Finally, an attempt has been made to optimize the multiresponses using technique for order preference by similarity to ideal solution (TOPSIS) with Taguchi approach.
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spelling pubmed-48972592016-07-19 Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method Parida, Arun Kumar Routara, Bharat Chandra Int Sch Res Notices Research Article Taguchi's design of experiment is utilized to optimize the process parameters in turning operation with dry environment. Three parameters, cutting speed (v), feed (f), and depth of cut (d), with three different levels are taken for the responses like material removal rate (MRR) and surface roughness (R (a)). The machining is conducted with Taguchi L(9) orthogonal array, and based on the S/N analysis, the optimal process parameters for surface roughness and MRR are calculated separately. Considering the larger-the-better approach, optimal process parameters for material removal rate are cutting speed at level 3, feed at level 2, and depth of cut at level 3, that is, v (3)-f (2)-d (3). Similarly for surface roughness, considering smaller-the-better approach, the optimal process parameters are cutting speed at level 1, feed at level 1, and depth of cut at level 3, that is, v (1)-f (1)-d (3). Results of the main effects plot indicate that depth of cut is the most influencing parameter for MRR but cutting speed is the most influencing parameter for surface roughness and feed is found to be the least influencing parameter for both the responses. The confirmation test is conducted for both MRR and surface roughness separately. Finally, an attempt has been made to optimize the multiresponses using technique for order preference by similarity to ideal solution (TOPSIS) with Taguchi approach. Hindawi Publishing Corporation 2014-10-29 /pmc/articles/PMC4897259/ /pubmed/27437503 http://dx.doi.org/10.1155/2014/905828 Text en Copyright © 2014 A. K. Parida and B. C. Routara. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Parida, Arun Kumar
Routara, Bharat Chandra
Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method
title Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method
title_full Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method
title_fullStr Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method
title_full_unstemmed Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method
title_short Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method
title_sort multiresponse optimization of process parameters in turning of gfrp using topsis method
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4897259/
https://www.ncbi.nlm.nih.gov/pubmed/27437503
http://dx.doi.org/10.1155/2014/905828
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