A machine learning approach to tool wear behavior operational zones

Paul J.A. Lever, Michael M. Marefat, Tanti Ruwani

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

The range of permitted temperature and stress produced during a machining process is related to the metallurgical properties for each tool material and can be empirically determined. For each combination of tool and workpiece material, particular constants are approximated to prescribe the relationship between the temperature-stress combination and the feed rate-speed combination. Using this concept an operational zone for each tool-workpiece combination can be defined. This paper proposes a machine learning algorithm to determine this operational zone. Instead of relying totally on empirical testing, a learning algorithm is used to incrementally define the operational zone with the related parameters described above. Once determined, the operational zone is then used to enhance machining control.

Original languageEnglish (US)
Pages (from-to)264-273
Number of pages10
JournalIEEE Transactions on Industry Applications
Volume33
Issue number1
DOIs
StatePublished - Dec 1 1997

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Keywords

  • Knowledged-based control
  • Machine learning
  • Process operational zones

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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