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.
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ASME 2015 International Manufacturing Science and Engineering Conference
June 8–12, 2015
Charlotte, North Carolina, USA
Conference Sponsors:
- Manufacturing Engineering Division
ISBN:
978-0-7918-5683-3
PROCEEDINGS PAPER
Parameter Estimation Using Markov Chain Monte Carlo Method in Mechanistic Modeling of Tool Wear During Milling Available to Purchase
Farbod Akhavan Niaki,
Farbod Akhavan Niaki
Clemson University, Greenville, SC
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Durul Ulutan,
Durul Ulutan
Clemson University, Greenville, SC
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Laine Mears
Laine Mears
Clemson University, Greenville, SC
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Farbod Akhavan Niaki
Clemson University, Greenville, SC
Durul Ulutan
Clemson University, Greenville, SC
Laine Mears
Clemson University, Greenville, SC
Paper No:
MSEC2015-9357, V002T04A006; 8 pages
Published Online:
September 25, 2015
Citation
Niaki, FA, Ulutan, D, & Mears, L. "Parameter Estimation Using Markov Chain Monte Carlo Method in Mechanistic Modeling of Tool Wear During Milling." Proceedings of the 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. V002T04A006. ASME. https://doi.org/10.1115/MSEC2015-9357
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