• DocumentCode
    2099517
  • Title

    Cutting torque estimation using probabilistic incremental program evolution algorithm

  • Author

    Kawaji, Shigeyasu ; Arao, Masaki ; Chen, Yuehui

  • Author_Institution
    Graduate Sch. of Sci. & Technol., Kumamoto Univ., Japan
  • Volume
    1
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    1
  • Abstract
    Cutting torque and thrust force should be the main output variables in the designing of drilling control systems. In the paper, a method for estimating the cutting torque in the drilling process by using a probabilistic incremental program evolution (PIPE) algorithm is proposed. The simulated and experimental results demonstrate the effectiveness of the proposed method
  • Keywords
    automatic programming; cutting; learning (artificial intelligence); machining; probability; process control; state estimation; cutting torque estimation; drilling control systems; probabilistic incremental program evolution algorithm; thrust force; Control system synthesis; Control systems; Drilling machines; Feeds; Force control; Neural networks; Servomotors; Sliding mode control; System buses; Torque control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics Society, 2001. IECON '01. The 27th Annual Conference of the IEEE
  • Conference_Location
    Denver, CO
  • Print_ISBN
    0-7803-7108-9
  • Type

    conf

  • DOI
    10.1109/IECON.2001.976443
  • Filename
    976443
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2099517