Predicting remaining useful life of high speed milling cutters based on artificial neural network

Amit Kumar Jain, Bhupesh Kumar Lad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

Precise Remaining Useful Life (RUL) prediction of cutting tools is crucial for reliable operation and to reduce the maintenance cost. This paper proposes Artificial Neural Network (ANN) based approach for accurate RUL prediction of high speed milling cutters. Developed ANN model uses time and statistical features, selected through stepwise regression feature subset selection technique, as input. By doing this, the strong correlation model is achieved and the performance of cutting tool prognosis is enhanced. An examination is carried out in this work on functioning of distinctive models established with same data. Developed ANN model demonstrates improved performance over conventional Multi-Regression Model (MRM) and Radial Basis Functional Network (RBFN).
Original languageEnglish
Title of host publication2015 International Conference on Robotics, Automation, Control and Embedded Systems (RACE)
PublisherIEEE
Number of pages5
ISBN (Electronic)9788192597430
DOIs
Publication statusPublished - 30 Apr 2015

Keywords

  • artificial neural networks
  • force
  • milling
  • prognostics and health management
  • predictive models
  • mathematical model
  • monitoring

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