Hard turning optimization using neural network modeling and swarm intelligence

Yiǧit Karpat, Tuǧrul Özel

Research output: Contribution to journalConference articlepeer-review

26 Scopus citations

Abstract

In this paper, multi-objective optimization of hard turning has been reported. A neural network model was developed in order to model the surface roughness and tool wear characteristics of hard turning when CBN tools are used. Objective is to obtain optimum process parameters, which satisfies given limit, tool wear and surface roughness values and maximizes the productivity at the same time. A recently developed optimization algorithm called particle swarm optimization is used to find optimum process parameters. Accordingly, the results indicate that a system where neural network is used to model and predict process outputs and particle swarm optimization is used to obtain optimum process parameters can be successfully applied to multi-objective optimization of hard turning.

Original languageEnglish (US)
Pages (from-to)179-186
Number of pages8
JournalTransactions of the North American Manufacturing Research Institute of SME
Volume33
StatePublished - 2005
EventNorth American Manufacturing Research Conference, NAMRC 33 - New York, NY, United States
Duration: May 24 2005May 27 2005

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Keywords

  • Hard turning
  • Neural networks
  • Optimization
  • Swarm intelligence

Fingerprint

Dive into the research topics of 'Hard turning optimization using neural network modeling and swarm intelligence'. Together they form a unique fingerprint.

Cite this