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Parameter Estimation Using Markov Chain Monte Carlo Method in Mechanistic Modeling of Tool Wear During Milling

[+] Author Affiliations
Farbod Akhavan Niaki, Durul Ulutan, Laine Mears

Clemson University, Greenville, SC

Paper No. MSEC2015-9357, pp. V002T04A006; 8 pages
doi:10.1115/MSEC2015-9357
From:
  • ASME 2015 International Manufacturing Science and Engineering Conference
  • Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing
  • Charlotte, North Carolina, USA, June 8–12, 2015
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5683-3
  • Copyright © 2015 by ASME

abstract

Several models have been proposed to describe the relationship between cutting parameters and machining outputs such as cutting forces and tool wear. However, these models usually cannot be generalized, due to the inherent uncertainties that exist in the process. These uncertainties may originate from machining, workpiece material composition, and measurements. A stochastic approach can be utilized to compensate for the lack of certainty in machining, particularly for tool wear evolution. The Markov Chain Monte Carlo (MCMC) method is a powerful tool for addressing uncertainties in machining parameter estimation. The Hybrid Metropolis-Gibbs algorithm has been chosen in this work to estimate the unknown parameters in a mechanistic tool wear model for end milling of difficult-to-machine alloys. The results show a good potential of the Markov Chain Monte Carlo modeling in prediction of parameters in the presence of uncertainties.

Copyright © 2015 by ASME

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