Improving roughness quality of end milling Al 7075-T6 alloy with Taguchi based multiobjective quantum behaved particle swarm optimisation algorithm

Wen-Jong Chen, C. C. Hsu, Y. L. Yang

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The purpose of this study was to determine the optimal surface roughness for an end milled Al 7075-T6 alloy by using the Taguchi method and multiobjective quantum behaved particle swarm optimisation (MOQPSO). First, the Taguchi orthogonal array L27(35) and analysis of variance (ANOVA) were used to determine the factors crucial to surface roughness: the feedrate, spindle speed and cutting depth. Response surface methodology (RSM) was then used to construct prediction models for the surface roughness characteristics Ra, Rmax and Rz. Finally, an MOQPSO algorithm was used to solve the multiobjective optimisation problem. The results show that the surface roughness quality generated using this algorithm is superior to that produced in nonoptimal conditions, Taguchi method and traditional multiobjective particle swarm optimisation. Therefore, the methods proposed in this study enhance machining quality and can be widely applied to other metal materials to improve machining efficiency.

Original languageEnglish
Pages (from-to)S2647-S2653
JournalMaterials Research Innovations
Volume18
DOIs
Publication statusPublished - 2014 Jan 1

Fingerprint

Particle swarm optimization (PSO)
surface roughness
roughness
Surface roughness
Taguchi methods
optimization
Multiobjective optimization
machining
Machining
analysis of variance
spindles
Analysis of variance (ANOVA)
Metals
methodology
predictions
metals

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

Cite this

@article{9bf3ef01725140b38d75577acc8c1b1a,
title = "Improving roughness quality of end milling Al 7075-T6 alloy with Taguchi based multiobjective quantum behaved particle swarm optimisation algorithm",
abstract = "The purpose of this study was to determine the optimal surface roughness for an end milled Al 7075-T6 alloy by using the Taguchi method and multiobjective quantum behaved particle swarm optimisation (MOQPSO). First, the Taguchi orthogonal array L27(35) and analysis of variance (ANOVA) were used to determine the factors crucial to surface roughness: the feedrate, spindle speed and cutting depth. Response surface methodology (RSM) was then used to construct prediction models for the surface roughness characteristics Ra, Rmax and Rz. Finally, an MOQPSO algorithm was used to solve the multiobjective optimisation problem. The results show that the surface roughness quality generated using this algorithm is superior to that produced in nonoptimal conditions, Taguchi method and traditional multiobjective particle swarm optimisation. Therefore, the methods proposed in this study enhance machining quality and can be widely applied to other metal materials to improve machining efficiency.",
author = "Wen-Jong Chen and Hsu, {C. C.} and Yang, {Y. L.}",
year = "2014",
month = "1",
day = "1",
doi = "10.1179/1432891714Z.000000000562",
language = "English",
volume = "18",
pages = "S2647--S2653",
journal = "Materials Research Innovations",
issn = "1432-8917",
publisher = "Maney Publishing",

}

TY - JOUR

T1 - Improving roughness quality of end milling Al 7075-T6 alloy with Taguchi based multiobjective quantum behaved particle swarm optimisation algorithm

AU - Chen, Wen-Jong

AU - Hsu, C. C.

AU - Yang, Y. L.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - The purpose of this study was to determine the optimal surface roughness for an end milled Al 7075-T6 alloy by using the Taguchi method and multiobjective quantum behaved particle swarm optimisation (MOQPSO). First, the Taguchi orthogonal array L27(35) and analysis of variance (ANOVA) were used to determine the factors crucial to surface roughness: the feedrate, spindle speed and cutting depth. Response surface methodology (RSM) was then used to construct prediction models for the surface roughness characteristics Ra, Rmax and Rz. Finally, an MOQPSO algorithm was used to solve the multiobjective optimisation problem. The results show that the surface roughness quality generated using this algorithm is superior to that produced in nonoptimal conditions, Taguchi method and traditional multiobjective particle swarm optimisation. Therefore, the methods proposed in this study enhance machining quality and can be widely applied to other metal materials to improve machining efficiency.

AB - The purpose of this study was to determine the optimal surface roughness for an end milled Al 7075-T6 alloy by using the Taguchi method and multiobjective quantum behaved particle swarm optimisation (MOQPSO). First, the Taguchi orthogonal array L27(35) and analysis of variance (ANOVA) were used to determine the factors crucial to surface roughness: the feedrate, spindle speed and cutting depth. Response surface methodology (RSM) was then used to construct prediction models for the surface roughness characteristics Ra, Rmax and Rz. Finally, an MOQPSO algorithm was used to solve the multiobjective optimisation problem. The results show that the surface roughness quality generated using this algorithm is superior to that produced in nonoptimal conditions, Taguchi method and traditional multiobjective particle swarm optimisation. Therefore, the methods proposed in this study enhance machining quality and can be widely applied to other metal materials to improve machining efficiency.

UR - http://www.scopus.com/inward/record.url?scp=84920666000&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84920666000&partnerID=8YFLogxK

U2 - 10.1179/1432891714Z.000000000562

DO - 10.1179/1432891714Z.000000000562

M3 - Article

VL - 18

SP - S2647-S2653

JO - Materials Research Innovations

JF - Materials Research Innovations

SN - 1432-8917

ER -