Swarm-intelligent neural network system (SINNS) based multi-objective optimization of hard turning

Yiǧit Karpat, Tuǧrul Özel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

In this paper, particle swarm optimization, which is a recently developed evolutionary algorithm, is used to optimize machining parameters in hard turning processes where multiple conflicting objectives are present. The relationships between machining parameters and the performance measures of interest are obtained by using experimental data and swarm intelligent neural network systems (SINNS). The results showed that particle swarm optimization is an effective method for solving multi-objective optimization problems, and an integrated system of neural networks and swarm intelligence can be used in solving complex machining optimization problems.

Original languageEnglish (US)
Title of host publicationTransactions of the North American Manufacturing Research Institute of SME 2006 - Papers Presented at NAMRC 34
Pages611-618
Number of pages8
StatePublished - 2006
Event34th North American Manufacturing Research Conference - Milwaukee, WI, United States
Duration: May 23 2006May 26 2006

Publication series

NameTransactions of the North American Manufacturing Research Institute of SME
Volume34

Other

Other34th North American Manufacturing Research Conference
Country/TerritoryUnited States
CityMilwaukee, WI
Period5/23/065/26/06

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Keywords

  • Multi-objective optimization
  • Neural networks and hard turning
  • Particle swarm optimization

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