Application of response surface methodology and enhanced non- Dominated sorting genetic algorithm for optimisation of grinding process

Dayananda Pai, Shrikantha Rao, Rio D'Souza

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Optimisation of grinding process during grinding of A16061-SiC composites is investigated in this study. Stir cast A16061-SiC composites with varying volume percentage of SiC reinforcement were ground on a conventional grinding machine with diamond grit grinding wheel. Three grinding variables were studied for simultaneous optimization of material removal rate and surface roughness. Initially, the response surface models for grinding process parameters were developed using response surface methodology. Further, the developed models were optimized using enhanced elitist non-dominated sorting genetic algorithm (enhanced NSGA-II), a time saving algorithm in comparison to conventional NSGA-II. The suitable grinding conditions for multi-objective optimization of the grinding process were obtained from enhanced NSGA-II. Finally the confirmation tests were performed to validate the results obtained from response surface methodology and enhanced NSGA-II. It is observed that, experimental results and the results obtained from enhanced NSGA-II are in close conformance. Hence it is concluded that the developed algorithm can effectively be used for optimization of grinding process.

Original languageEnglish
Pages (from-to)1199-1208
Number of pages10
JournalProcedia Engineering
Volume64
DOIs
Publication statusPublished - 2013

Fingerprint

Sorting
Genetic algorithms
Grinding machines
Grinding wheels
Composite materials
Multiobjective optimization
Diamonds
Reinforcement
Surface roughness

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

@article{3c22fae9893a4ade888abd18ee5d94c4,
title = "Application of response surface methodology and enhanced non- Dominated sorting genetic algorithm for optimisation of grinding process",
abstract = "Optimisation of grinding process during grinding of A16061-SiC composites is investigated in this study. Stir cast A16061-SiC composites with varying volume percentage of SiC reinforcement were ground on a conventional grinding machine with diamond grit grinding wheel. Three grinding variables were studied for simultaneous optimization of material removal rate and surface roughness. Initially, the response surface models for grinding process parameters were developed using response surface methodology. Further, the developed models were optimized using enhanced elitist non-dominated sorting genetic algorithm (enhanced NSGA-II), a time saving algorithm in comparison to conventional NSGA-II. The suitable grinding conditions for multi-objective optimization of the grinding process were obtained from enhanced NSGA-II. Finally the confirmation tests were performed to validate the results obtained from response surface methodology and enhanced NSGA-II. It is observed that, experimental results and the results obtained from enhanced NSGA-II are in close conformance. Hence it is concluded that the developed algorithm can effectively be used for optimization of grinding process.",
author = "Dayananda Pai and Shrikantha Rao and Rio D'Souza",
year = "2013",
doi = "10.1016/j.proeng.2013.09.199",
language = "English",
volume = "64",
pages = "1199--1208",
journal = "Procedia Engineering",
issn = "1877-7058",
publisher = "Elsevier BV",

}

Application of response surface methodology and enhanced non- Dominated sorting genetic algorithm for optimisation of grinding process. / Pai, Dayananda; Rao, Shrikantha; D'Souza, Rio.

In: Procedia Engineering, Vol. 64, 2013, p. 1199-1208.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Application of response surface methodology and enhanced non- Dominated sorting genetic algorithm for optimisation of grinding process

AU - Pai, Dayananda

AU - Rao, Shrikantha

AU - D'Souza, Rio

PY - 2013

Y1 - 2013

N2 - Optimisation of grinding process during grinding of A16061-SiC composites is investigated in this study. Stir cast A16061-SiC composites with varying volume percentage of SiC reinforcement were ground on a conventional grinding machine with diamond grit grinding wheel. Three grinding variables were studied for simultaneous optimization of material removal rate and surface roughness. Initially, the response surface models for grinding process parameters were developed using response surface methodology. Further, the developed models were optimized using enhanced elitist non-dominated sorting genetic algorithm (enhanced NSGA-II), a time saving algorithm in comparison to conventional NSGA-II. The suitable grinding conditions for multi-objective optimization of the grinding process were obtained from enhanced NSGA-II. Finally the confirmation tests were performed to validate the results obtained from response surface methodology and enhanced NSGA-II. It is observed that, experimental results and the results obtained from enhanced NSGA-II are in close conformance. Hence it is concluded that the developed algorithm can effectively be used for optimization of grinding process.

AB - Optimisation of grinding process during grinding of A16061-SiC composites is investigated in this study. Stir cast A16061-SiC composites with varying volume percentage of SiC reinforcement were ground on a conventional grinding machine with diamond grit grinding wheel. Three grinding variables were studied for simultaneous optimization of material removal rate and surface roughness. Initially, the response surface models for grinding process parameters were developed using response surface methodology. Further, the developed models were optimized using enhanced elitist non-dominated sorting genetic algorithm (enhanced NSGA-II), a time saving algorithm in comparison to conventional NSGA-II. The suitable grinding conditions for multi-objective optimization of the grinding process were obtained from enhanced NSGA-II. Finally the confirmation tests were performed to validate the results obtained from response surface methodology and enhanced NSGA-II. It is observed that, experimental results and the results obtained from enhanced NSGA-II are in close conformance. Hence it is concluded that the developed algorithm can effectively be used for optimization of grinding process.

UR - http://www.scopus.com/inward/record.url?scp=84891766763&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84891766763&partnerID=8YFLogxK

U2 - 10.1016/j.proeng.2013.09.199

DO - 10.1016/j.proeng.2013.09.199

M3 - Article

VL - 64

SP - 1199

EP - 1208

JO - Procedia Engineering

JF - Procedia Engineering

SN - 1877-7058

ER -