Transactions of the Korean Society of Machine Tool Engineers (한국공작기계학회논문집)

The Korean Society of Manufacturing Technology Engineers (한국생산제조학회)
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A Study on the Cutting Characteristics and Detection of the Abnormal Tool State in Hard Turning

고경도강 선삭 시 절삭특성 및 공구 이상상태 검출에 관한 연구

  • 김태영 (전북대 기계항공시스템공학부, 자동차 신기술 연구소) ;
  • 신형곤 (전북대 산학협력원) ;
  • 이상진 (전북대 정밀기계공학과 대학원) ;
  • 이한교 (전북기능대학 컴퓨터응용기계학과)
  • Published : 2005.12.01
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Abstract

The cutting characteristics of hardened steel(AISI 52100) by PCBN tools is investigated with respect to cutting force, workpiece surface roughness and tool flank wear by the vision system. Hard Owning is carried out with various cutting conditions; spindle rotational speed, depth of cut and feed rate. Backpropagation neural networks(BPNs) are used for detection of tool wear. The input vectors of neural network comprise of spindle rotational speed, feed rates, vision flank wear, and thrust force signals. The output is the tool wear state which is either usable or failure. The detection of the abnormal states using BPNs achieves $96.8\%$ reliability even when the spindle rotational speed and feedrate are changed.

Keywords

References

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