Transactions of the Korean Society of Mechanical Engineers A (대한기계학회논문집A)

The Korean Society of Mechanical Engineers (대한기계학회)
권호 표지
DOI QR코드

DOI QR Code

Feature Analysis Based on Beta Distribution Model for Shaving Tool Condition Monitoring

세이빙공구 상태 감시를 위한 베타분포모델에 기반한 특징 해석

  • Choe, Deok-Ki (Department of Precision Mechanical Engineering, Gangneung-Wonju National University) ;
  • Kim, Seong-Jun (Department of Industrial, Information, and Management Engineering, Gangneung-Wonju National University) ;
  • Oh, Young-Tak (Department of Mechanical Engineering, Ansan College of Technology)
  • 최덕기 (강릉원주대학교 정밀기계공학과) ;
  • 김성준 (강릉원주대학교 산업정보경영공학과) ;
  • 오영탁 (안산공과대학 기계과)
  • Published : 2010.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

Tool condition monitoring (TCM) is crucial for improvement of productivity in manufacturing process. However, TCM techniques have not been applied to monitor tool failure in an industrial gear shaving application. Therefore, this work studied a statistical TCM method for monitoring gear shaving tool condition. The method modeled the vibration signal of the shaving process using beta probability distribution in order to extract the effective features for TCM. Modeling includes rectifying for converting a bi-modal distribution into a unimodal distribution, estimating the parameters of beta probability distribution based on method of moments. The performance of features obtained from the proposed method was evaluated and discussed.

공구상태 감시기술은 지능형 생산시스템 구축을 위하여 중요한 요소 기술이다. 다양한 생산 공정 분야에 걸쳐 연구가 진행되었지만 기어 세이빙 공정에서 공구파손을 검출하는 연구가 발표된 바가 없다. 본 연구에서는 기어 세이빙 공정 중에 세이빙 공구의 상태를 검출하기 위하여 베타확률분포를 활용하는 통계적 기법을 제안하였다. 신뢰성 있는 공구상태 감시를 위하여 선행되어야 할 특징값 추출을 위하여 공정 중에 발생하는 진동 신호를 베타확률분포로 모델링하였다. 신호의 양봉 분포를 단봉 분포로 변환한 후 모멘트법을 사용하여 베타확률분포의 파라미터들을 추정함으로써 특징값들을 추출하였다. 특징값들의 유효성을 평가 결과, 베타분포 모델의 파라미터 중 모드가 우수한 세이빙 공구상태 감시 성능을 갖고 있음을 확인하였다.

Keywords

References

  1. Dong-Sik Gu, Jeong-Hwan Lee, Bo-Suk Yang and Byeong-Keun Choi, 2008, “Application of Envelop Analysis and Wavelet Transform for Detection of Gear Failure,” Trans. of the KSME A, Vol. 32, No. 11, pp.905-910 https://doi.org/10.3795/KSME-A.2008.32.11.905
  2. Dong-Hee Kwon, Myung-Chang Kang and Jeong-Suk Kim, 2006, “A Study on the Tool Life Prediction of Micro Endmill Using Cutting Force Signal,” Proceedings of the KSME 2006 Spring Annual Meeting, pp. 3041-3046.
  3. Braun, S., 1986, Mechanical Signature Analysis: Theory and Application, Academic Press
  4. Bhujanga Rao, V., 1999, “Kurtosis as a Metric in the Assessment of Gear Damage,” The Shock and Vibration Digest, Vol.31, No.6, pp.443-448 https://doi.org/10.1177/058310249903100601
  5. Stewart, R.M., 1977, "Some Useful Data Analysis Techniques for Gearbox Diagnostics," Institute of Sound and Vibration Research Paper, MHM/R/10/77
  6. Stewart, R.M., 1977, "Some Useful Data Analysis Techniques for Gearbox Diagnostics," Institute of Sound and Vibration Research Paper, MHM/R/10/77105950, presented at the 47th M. F. P. G. Meeting, Virginia Beach, VA, April 13-15, pp.1-10
  7. McClintic, Katherine, Lebold, Mitchell, Maynard, Kenneth, Byington, Carl, and Campbell, Robert, 2000, “Residual and Difference Feature Analysis with Transitional Gearbox Data,” Proceedings of the 54th M. F. P. G. Meeting, Virginia Beach, VA, May 1-4, pp.635-645.
  8. United States Cutting Tool Institute, 1989, Metal Cutting Tool Handbook, Industrial Press, Inc.
  9. Oguamanam, D. C. D., Martin, H. R. and Huissoon, J. P., 1995, “On the Application of the Beta Distribution to Gear Damage Analysis,” Applied Acoustics, Vol. 45, Issue 3, pp.247-261 https://doi.org/10.1016/0003-682X(95)00001-P