Stochastic Tool Wear Prediction for Sustainable Manufacturing

Peng Wang, Robert X. Gao

Research output: Contribution to journalConference articlepeer-review

19 Scopus citations


To provide scientific support for decision-making in critical applications such as maintenance scheduling and inventory management, tool wear monitoring and service life prediction are of significance to achieving sustainable manufacturing. Past research typically assumed time-invariant machining settings in modeling wear progression, hence is limited in accurately tracking varying wear rates. This paper presents a stochastic joint-state-and-parameter model with machining setting as a parameter that affects the state evolution or tool wear propagation. The model is embedded in a particle filter for recursive wear state prediction. Effectiveness of this method is verified through experimental data measured on a CNC milling machine.

Original languageEnglish
Pages (from-to)236-241
Number of pages6
JournalProcedia CIRP
StatePublished - 2016
Event23rd CIRP Conference on Life Cycle Engineering, LCE 2016 - Berlin, Germany
Duration: May 22 2016May 24 2016

Bibliographical note

Publisher Copyright:
© 2016 The Authors.


  • Tool wear prediction
  • particle filter
  • stochastic modeling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


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