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http://dx.doi.org/10.3795/KSME-A.2010.34.1.11

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

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)
Publication Information
Transactions of the Korean Society of Mechanical Engineers A / v.34, no.1, 2010 , pp. 11-18 More about this Journal
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;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
Times Cited By SCOPUS : 0
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