• DocumentCode
    301567
  • Title

    Model based fault detection in milling by classification of estimated cutting parameters

  • Author

    Konrad ; Isermann, H. ; Heintz, N.

  • Author_Institution
    Inst. of Autom. Control, Tech. Univ. of Darmstadt, Germany
  • Volume
    3
  • fYear
    1995
  • fDate
    22-25 Oct 1995
  • Firstpage
    2193
  • Abstract
    This paper describes a new method of fault detection in milling. The presented detection algorithm is based on the estimation of particular model parameters using measured cutting force signals. By classifying the resulting patterns of the estimated parameters, the conditions of the cutting teeth can be determined
  • Keywords
    cutting; fault diagnosis; machine tools; machining; neural nets; parameter estimation; pattern classification; cutting; machining; milling; model based fault detection; neural networks; parameter estimation; pattern classification; Fault detection; Fault diagnosis; Feeds; Force measurement; Milling; Parameter estimation; Particle measurements; Signal generators; Signal processing; Teeth;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-2559-1
  • Type

    conf

  • DOI
    10.1109/ICSMC.1995.538106
  • Filename
    538106
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=301567