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

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.

Keywords

Tool Condition Monitoring;Shaving;Beta Probability Distribution;Method of Moments

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