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


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.


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


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