Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion

Sultan Binsaeid, Shihab Asfour, Sohyung Cho, Arzu Onar

Research output: Contribution to journalArticle

42 Scopus citations

Abstract

In a fully automated manufacturing environment, instant detection of the cutting tool condition is essential for the improved productivity and cost effectiveness. This paper studies a tool condition monitoring system (TCM) via machine learning (ML) and machine ensemble (ME) approach to investigate the effectiveness of multisensor fusion technique when machining 4340 steel with multilayer coated and multiflute carbide end mill cutter. In this study, 135 different features are extracted from multiple sensor signals of force, vibration, acoustic emission and spindle power in the time and frequency domain by using data acquisition and signal processing module. Then, a correlation-based feature selection technique (CFS) evaluates the significance of these features along with machining parameters collected from machining experiments. Next, an optimal feature subset is computed for various assorted combinations of sensors. Finally, machine ensemble methods based on majority voting and stacked generalization are studied for the selected features to classify not only flank wear but also breakage and chipping. It has been found in this paper that the stacked generalization ensemble can ensure the highest accuracy in tool condition monitoring. In addition, it has been shown that the support vector machine (SVM) outperforms other ML algorithms in most cases tested.

Original languageEnglish (US)
Pages (from-to)4728-4738
Number of pages11
JournalJournal of Materials Processing Technology
Volume209
Issue number10
DOIs
StatePublished - Jun 1 2009

Keywords

  • Feature selection
  • Machine ensemble
  • Multisensor fusion
  • Tool condition monitoring

ASJC Scopus subject areas

  • Computer Science Applications
  • Modeling and Simulation
  • Ceramics and Composites
  • Metals and Alloys
  • Industrial and Manufacturing Engineering

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