Tool life prediction using Bayesian updating. Part 1: Milling tool life model using a discrete grid method |
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| Authors: | Jaydeep M Karandikar Ali E Abbas Tony L Schmitz |
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| Affiliation: | 1. Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA;2. Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA;3. Department of Mechanical Engineering and Engineering Science, University of North Carolina at Charlotte, Charlotte, NC, USA |
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| Abstract: | According to the Taylor tool life equation, tool life reduces with increasing cutting speed following a power law. Additional factors can also be added, such as the feed rate, in Taylor-type models. Although these models are posed as deterministic equations, there is inherent uncertainty in the empirical constants and tool life is generally considered a stochastic process. In this work, Bayesian inference is applied to estimate model constants for both milling and turning operations while considering uncertainty. |
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| Keywords: | Tool wear Taylor tool life Bayesian updating Discrete grid Markov Chain Monte Carlo Uncertainty |
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