Uncertainty analysis of force coefficients during micromilling of titanium alloy
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Predicting process forces in micromilling is difficult due to complex interaction between the cutting edge and the work material, size effect, and process dynamics. This study describes the application of Bayesian inference to identify force coefficients in the micromilling process. The Metropolis-Hastings (MH) algorithm Markov chain Monte Carlo (MCMC) approach has been used to identify probability distributions of cutting, edge, and ploughing force coefficients based on experimental measurements and a mechanistic model of micromilling. The Bayesian inference scheme allows for predicting the upper and lower limits of micromilling forces, providing useful information about stability boundary calculations and robust process optimization. In the first part of the paper, micromilling experiments are performed to investigate the influence of micromilling process parameters on machining forces, tool edge condition, and surface texture. Under the experimental conditions used in this study, built-up edge formation is observed to have a significant influence on the process outputs in micromilling of titanium alloy Ti6Al4V. In the second part, Bayesian inference was explained in detail and applied to model micromilling force prediction. The force predictions are validated with the experimental measurements. The paper concludes with a discussion of the effectiveness of employing Bayesian inference in micromilling force modeling considering special machining cases.
KeywordsMicromilling Mechanistic modeling Bayesian inference Markov chain Monte Carlo Uncertainty analysis
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- 12.Niaki FA, Ulutan D, Mears L (2016) Parameter inference under uncertainty in end-milling γ-strengthened difficult-to-machine alloy. J Manuf Sci Eng 138 / 061014-1Google Scholar
- 13.Mehta P, Kuttolamadom M, Mears L (2017) Mechanistic force model for machining process—theory and application of Bayesian inference. Int J Adv Manuf Technol. doi: 10.1007/s00170-017-0064-0
- 14.Cao Z, Li H (2015) Investigation of machining stability in micro milling considering the parameter uncertainty. Adv Mech Eng:1–8. doi: 10.1177/1687814015575982
- 16.Jaffery SI, Driver N, Mativenga PT (2010) Analysis of process parameters in the micromachining of Ti-6Al-4V alloy. Proceedings of the 36th international MATADOR conference. Springer, London, 2010.Google Scholar
- 17.Hitchcock DB A history of the Metropolis–Hastings algorithm, The American StatisticianGoogle Scholar
- 19.Hoff PD (2009) A first course in Bayesian statistical methods. Springer Science & Business MediaGoogle Scholar
- 22.Characterization of areal surface texture. R. Leach Editor. Springer ISBN 978-3-642-36457-0Google Scholar
- 23.Wang Z, Kovvuri V, Araujo A, Bacci M, Hung WNP, Bukkapatnam STS (2016) Built-up-edge effects on surface deterioration in micro milling processes. J Manuf Process. doi: 10.1016/j.jmapro.2016.03.016
- 24.Duncan GS, Kurdi M, Schmitz T, Snyder J Uncertainty propagation for selected analytical milling stability limit analyses. Trans NAMRI/SME 34:17–24Google Scholar