Tool breakage detection using support vector machine learning in a milling process

Sohyung Cho, Shihab Asfour, Arzu Onar, Nandita Kaundinya

Research output: Contribution to journalArticlepeer-review

106 Scopus citations

Abstract

In this paper, an intelligent tool breakage detection system which uses a support vector machine (SVM) learning algorithm is proposed to provide the ability to recognize process abnormalities and initiate corrective action during a manufacturing process, specifically in a milling process. The system utilizes multiple sensors to record cutting forces and power consumptions. Attention is focused on training the proposed system for performance improvement and detecting tool breakage. Performance of the developed system is compared to the results from an alternative detection system based on a multiple linear regression model. It is expected that the proposed system will reduce machine downtime, which in turn will lead to reduced production costs and increased customer satisfaction.

Original languageEnglish (US)
Pages (from-to)241-249
Number of pages9
JournalInternational Journal of Machine Tools and Manufacture
Volume45
Issue number3
DOIs
StatePublished - Mar 2005

Keywords

  • Multiple sensors
  • Support vector machine
  • Tool breakage detection

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Mechanical Engineering

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