This work discusses the Bayesian parameter inference method for a mechanistic force model for machining. Bayesian inference methods have gained popularity recently owing to their intuitiveness and ease with which empirical knowledge may be combined with experimental data considering the uncertainty. The first part of the paper discusses Bayesian parameter inference and Markov Chain Monte Carlo (MCMC) methods. MCMC method effectiveness has been further analyzed by (1) changing the number of particles in MCMC estimation and (2) changing the MCMC move step size. The second part of the paper discusses two example applications as nonlinear mechanistic force model coefficient identification. The Bayesian inference scheme performs prediction of the cutting force coefficients from the training data. Using these coefficients and input parameters to the model, the cutting force is predicted. This prediction is validated using experimental data, and it is demonstrated that with very few parameter updates the predicted force converges with the measured cutting force. The paper is concluded with the discussion of future work.
The International Journal of Advanced Manufacturing Technology – Springer Journals
Published: Jan 27, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
All the latest content is available, no embargo periods.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud