Multi-response optimization of process parameters in AWJ machining of hybrid GFRP composite by grey relational method

D. Deepak, J. Paulo Davim

Research output: Contribution to journalConference article

Abstract

In recent days, AWJ is emerged as one of the best tools for machining of fiber reinforced polymer. In this study, multi-response optimization of process parameters is carried out using grey relational method to reduce the surface taper and surface roughness while AWJ machining of graphite laced GFRP. The grey relational grades are analysed using ANOVA to determine the influence of process parameters. The feed rate showed highest influence (24.26 %) on the response followed by interaction effect of the operating pressure and feed rate (23.54 %) as-well-as operating pressure and standoff distance (16.12 %). Whereas the contribution of operating pressure and standoff distance on response is 11.50% and 8.20 % respectively. Delamination is not observed on the surfaces machined at optimum combination of process parameters.

Original languageEnglish
Pages (from-to)1211-1221
Number of pages11
JournalProcedia Manufacturing
Volume35
DOIs
Publication statusPublished - 01-01-2019
Externally publishedYes
Event2nd International Conference on Sustainable Materials Processing and Manufacturing, SMPM 2019 - Sun City, South Africa
Duration: 08-03-201910-03-2019

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Machining
Composite materials
Analysis of variance (ANOVA)
Delamination
Graphite
Surface roughness
Fibers
Polymers

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

Cite this

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title = "Multi-response optimization of process parameters in AWJ machining of hybrid GFRP composite by grey relational method",
abstract = "In recent days, AWJ is emerged as one of the best tools for machining of fiber reinforced polymer. In this study, multi-response optimization of process parameters is carried out using grey relational method to reduce the surface taper and surface roughness while AWJ machining of graphite laced GFRP. The grey relational grades are analysed using ANOVA to determine the influence of process parameters. The feed rate showed highest influence (24.26 {\%}) on the response followed by interaction effect of the operating pressure and feed rate (23.54 {\%}) as-well-as operating pressure and standoff distance (16.12 {\%}). Whereas the contribution of operating pressure and standoff distance on response is 11.50{\%} and 8.20 {\%} respectively. Delamination is not observed on the surfaces machined at optimum combination of process parameters.",
author = "D. Deepak and {Paulo Davim}, J.",
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Multi-response optimization of process parameters in AWJ machining of hybrid GFRP composite by grey relational method. / Deepak, D.; Paulo Davim, J.

In: Procedia Manufacturing, Vol. 35, 01.01.2019, p. 1211-1221.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Multi-response optimization of process parameters in AWJ machining of hybrid GFRP composite by grey relational method

AU - Deepak, D.

AU - Paulo Davim, J.

PY - 2019/1/1

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N2 - In recent days, AWJ is emerged as one of the best tools for machining of fiber reinforced polymer. In this study, multi-response optimization of process parameters is carried out using grey relational method to reduce the surface taper and surface roughness while AWJ machining of graphite laced GFRP. The grey relational grades are analysed using ANOVA to determine the influence of process parameters. The feed rate showed highest influence (24.26 %) on the response followed by interaction effect of the operating pressure and feed rate (23.54 %) as-well-as operating pressure and standoff distance (16.12 %). Whereas the contribution of operating pressure and standoff distance on response is 11.50% and 8.20 % respectively. Delamination is not observed on the surfaces machined at optimum combination of process parameters.

AB - In recent days, AWJ is emerged as one of the best tools for machining of fiber reinforced polymer. In this study, multi-response optimization of process parameters is carried out using grey relational method to reduce the surface taper and surface roughness while AWJ machining of graphite laced GFRP. The grey relational grades are analysed using ANOVA to determine the influence of process parameters. The feed rate showed highest influence (24.26 %) on the response followed by interaction effect of the operating pressure and feed rate (23.54 %) as-well-as operating pressure and standoff distance (16.12 %). Whereas the contribution of operating pressure and standoff distance on response is 11.50% and 8.20 % respectively. Delamination is not observed on the surfaces machined at optimum combination of process parameters.

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