Multi-response optimization of turning parameters for machining glass fiber–reinforced plastic composite rod

Glass fiber–reinforced plastics are extensively applied in engineering fields as a potential replacement to conventional steels, owing to its corrosive resistance property and high specific strength. But machining is complicated due to its anisotropic properties and non-homogeneous structure. In mac...

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Main Authors: P Raveendran, P Marimuthu
Format: Article
Language:English
Published: SAGE Publishing 2015-12-01
Series:Advances in Mechanical Engineering
Online Access:https://doi.org/10.1177/1687814015620109
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spelling doaj-2d3e0e22d23e4c2489985c797f1eb7d32020-11-25T03:32:43ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402015-12-01710.1177/168781401562010910.1177_1687814015620109Multi-response optimization of turning parameters for machining glass fiber–reinforced plastic composite rodP Raveendran0P Marimuthu1Department of Mechanical Engineering, Mahath Amma Institute of Engineering & Technology, Ariyur, IndiaDepartment of Mechanical Engineering, Syed Ammal Engineering College, Ramanathapuram, IndiaGlass fiber–reinforced plastics are extensively applied in engineering fields as a potential replacement to conventional steels, owing to its corrosive resistance property and high specific strength. But machining is complicated due to its anisotropic properties and non-homogeneous structure. In machining processes, minimum surface roughness and less tool wear are important factors influencing the quality of the surface, tool life, and productivity. Thus, the selection of tool and optimizing machining parameters are essential for perfect finishing. The tool used in this study is TiCN/TiN coated. The cutting parameters applied are cutting speed, feed, and depth of cut. As a dynamic approach, the multiple response optimization is carried out using grey relational analysis and desirability function analysis for simultaneous evaluation. These two methods are considered in optimization, as both are multiple criteria evaluation and not much complicated. Analysis of variance is employed to classify the significant parameters affecting the response.https://doi.org/10.1177/1687814015620109
collection DOAJ
language English
format Article
sources DOAJ
author P Raveendran
P Marimuthu
spellingShingle P Raveendran
P Marimuthu
Multi-response optimization of turning parameters for machining glass fiber–reinforced plastic composite rod
Advances in Mechanical Engineering
author_facet P Raveendran
P Marimuthu
author_sort P Raveendran
title Multi-response optimization of turning parameters for machining glass fiber–reinforced plastic composite rod
title_short Multi-response optimization of turning parameters for machining glass fiber–reinforced plastic composite rod
title_full Multi-response optimization of turning parameters for machining glass fiber–reinforced plastic composite rod
title_fullStr Multi-response optimization of turning parameters for machining glass fiber–reinforced plastic composite rod
title_full_unstemmed Multi-response optimization of turning parameters for machining glass fiber–reinforced plastic composite rod
title_sort multi-response optimization of turning parameters for machining glass fiber–reinforced plastic composite rod
publisher SAGE Publishing
series Advances in Mechanical Engineering
issn 1687-8140
publishDate 2015-12-01
description Glass fiber–reinforced plastics are extensively applied in engineering fields as a potential replacement to conventional steels, owing to its corrosive resistance property and high specific strength. But machining is complicated due to its anisotropic properties and non-homogeneous structure. In machining processes, minimum surface roughness and less tool wear are important factors influencing the quality of the surface, tool life, and productivity. Thus, the selection of tool and optimizing machining parameters are essential for perfect finishing. The tool used in this study is TiCN/TiN coated. The cutting parameters applied are cutting speed, feed, and depth of cut. As a dynamic approach, the multiple response optimization is carried out using grey relational analysis and desirability function analysis for simultaneous evaluation. These two methods are considered in optimization, as both are multiple criteria evaluation and not much complicated. Analysis of variance is employed to classify the significant parameters affecting the response.
url https://doi.org/10.1177/1687814015620109
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AT pmarimuthu multiresponseoptimizationofturningparametersformachiningglassfiberreinforcedplasticcompositerod
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