Tool Wear Monitoring in Milling Operation Using ART2 Neural Network

ART2 신경회로망을 이용한 밀링공정의 공구마모 진단

  • Yoon, Sun-Il ;
  • Ko, Tae-Jo ;
  • Kim, Hee-Sool
  • 윤선일 (영남대학교 기계공학과 대학원) ;
  • 고태조 (영남대학교 기계공학과) ;
  • 김희술 (영남대학교 기계공학과)
  • Published : 1995.12.01

Abstract

This study introduces a tool wear monitoring technology in face milling operation comprised of an unsupervised neural network. The monitoring system employs two types of sensor signal such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are calculated for te input patterns of neural network. ART2 neural network, which is capable of self organizing without supervised learning, is used for clustering of tool wear states. The experimental results show that tool wear can be effectively detected under various cutting conditions without prior knowledge of cutting processes.

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