A Study on the Wear Detection of Drill State for Prediction Monitoring System

예측감시 시스템에 의한 드릴의 마멸검출에 관한 연구

  • Published : 2002.03.01


Out of all metal-cutting process, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. There are two systems, Basic system and Online system, to detect the drill wear. Basic system comprised of spindle rotational speed, feed rates, thrust torque and flank wear measured by tool microscope. Outline system comprised of spindle rotational speed feed rates, AE signal, flank wear area measured by computer vision, On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. The output was the drill wear state which was either usable or failure. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.


  1. Annals of the CIRP v.35 Tool Wear and Breakage Using A Process Model Koren, Y.;Ulsoy, A. G.;Danai, K. https://doi.org/10.1016/S0007-8506(07)61889-7
  2. Wear v.115 Detection of Tool Flank Wear Using Acoustic Signature Analysis Sadat, A. B.;Raman, S. https://doi.org/10.1016/0043-1648(87)90216-X
  3. 한국 공작기계학회지 v.7 no.6 ADI재의 드릴가공시 가공조건에 따른 절삭저항 및 AE신호 특성 유경곤;전태욱;박홍식
  4. ASME Journal of Engineering For Industry v.116 Self-Organizing Neural Network Application to Drill Wear Classification Govekar E.;Grabec, I. https://doi.org/10.1115/1.2901935
  5. Trans of the ASME, Journal of Engineering ofr Industry v.112 Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring S. Rangwala;D. A. Domfeld https://doi.org/10.1115/1.2899578
  6. Prediction of Tool wear using Neural Networks Sandeep S. Jaiswal
  7. ASME Journal of ma-nufacturing Science and Engineering v.199 A Method of Using Neural networks and Inverse Kinematics for machine Tools Error Estimation and Correction J. Mou
  8. 한국정밀공학회지 v.16 no.6 신경회로망을 이용한 드릴공정에 서의 칩 배출 상태 감시 김화영;안중환
  9. Drilling Process Evaluation by Presicting Drilled Hole Quality and Drill Bit Wear With On-Line Acoustic Emission Signals Kuang-Jen Wang
  10. Journal of Physics. E, Scientific Instruments v.20 Sensors in Industrial Metrology Jones, B. E. https://doi.org/10.1088/0022-3735/20/9/007
  11. 한국공작기계학회 v.10 no.2 고강력 열연강판의 드릴 가공시 공구마멸에 관한 연구 신형곤;김성일;김태영
  12. Mechanical Systems and Signal Processing v.12 Intelligent Detection of Drill Wear T.I. Liu;W.Y. Chen https://doi.org/10.1006/mssp.1998.0165
  13. In Fundamental Issues In Machining v.43 A Preliminary Investigation Into the Prediction of Drill Wear Using Acoustic Emission S. Chandrashekhar;R. H. Osuri;S. Chatter Jee
  14. Annals of the CIRP v.39 no.1 Prediction of Tool Fracture in Drilling E. Brinksmeier https://doi.org/10.1016/S0007-8506(07)61011-7
  15. 한국공작기계학회지 v.9 no.3 유리섬유 강화 폴리에스터의 드릴가공특성 김성일