Data driven models for prognostics of high speed milling cutters

Amit Kumar Jain*, Bhupesh Kumar Lad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

Effectiveness of tool condition monitoring strategy depends on accuracy in failure prediction (prognostics) of cutting tools. Data driven approaches are generally used for prognostics of cutting tools. Various prognostics models have been proposed in the literature. Performance of these models in terms of accuracy and applicability are found to be the major constraints for use in real industrial applications. Moreover, application of these models is mainly limited to wear prediction. Extension of such models for remaining life prediction is not explored adequately in the literature. The main contribution of this paper is the development of accurate and applicable data driven models for tool wear estimation and remaining useful life prediction of high speed Computer Numerical Control (CNC) milling machine cutters. These models are developed and validated based on experimental data. Proposed models have demonstrated better results in terms of predicting cutter wear as compared to those mentioned in the literature. It also helps in predicting remaining useful life of cutters under following two industrial cases: - Case I: When only online monitoring data are available. - Case II: When incidental (or planned) offline inspection data are also available.
Original languageEnglish
Pages (from-to)03-012
Number of pages10
JournalInternational Journal of Performability Engineering
Volume12
Issue number1
DOIs
Publication statusPublished - Jan 2016

Keywords

  • condition monitoring
  • cutting tool
  • neural network
  • prognostics
  • tool wear

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