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Estimation of Cutting Force Model Coefficients to Track Wear in Milling Using Bayesian Analysis

[+] Author Affiliations
Mehdi Nouri, Barry K. Fussell, Robert B. Jerard, Ernst Linder

University of New Hampshire, Durham, NH

Lei Gao

George Mason University, Fairfax, VA

Paper No. MSEC2012-7259, pp. 737-743; 7 pages
  • ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing
  • ASME 2012 International Manufacturing Science and Engineering Conference
  • Notre Dame, Indiana, USA, June 4–8, 2012
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5499-0
  • Copyright © 2012 by ASME


Our previous research has demonstrated the feasibility of monitoring tool wear during milling by continuously updating the coefficients of a cutting force model. The method requires a robust method for on-line model coefficient estimation. Estimation using Least Square Regression (LSR) is easily implemented but requires that the data points come from different cutting conditions which is not always possible. In this paper, a method for coefficient estimation and wear tracking based on Bayesian updating is described. The Bayesian method has the advantage that it can be used when the cutting conditions are constant.

Copyright © 2012 by ASME
Topics: Wear , Cutting , Milling



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