• DocumentCode
    2428308
  • Title

    Monitoring of tool wear using artificial neural networks

  • Author

    Venkatesh, Kurapati ; Zhou, MengChu ; Caudill, Reggie

  • Author_Institution
    Center for Manuf. Syst., New Jersey Inst. of Technol., Newark, NJ, USA
  • Volume
    3
  • fYear
    1994
  • fDate
    29 June-1 July 1994
  • Firstpage
    2565
  • Abstract
    An online scheme for tool wear monitoring using artificial neural networks (ANNs) has been proposed. Motivated by the fact that the tool wear at a given instance of time depends on the tool wear value at previous instance of time, memory was included in ANN. With this aim, an ANN without memory, an ANN with one phase memory, and an ANN with two are investigated in this study. The advantages and unique characteristics of the proposed tool wear modeling scheme with earlier methods of tool wear estimation are discussed.
  • Keywords
    computerised monitoring; machine tools; manufacturing data processing; neural nets; real-time systems; wear; machine tools; neural networks; online scheme; tool wear monitoring; Adaptive control; Artificial neural networks; Condition monitoring; Machine tools; Machining; Manufacturing systems; Surface finishing; Switches; Training data; Wearable sensors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference, 1994
  • Print_ISBN
    0-7803-1783-1
  • Type

    conf

  • DOI
    10.1109/ACC.1994.735022
  • Filename
    735022