Gözü, Erman2017-09-152017-09-152017-092017-092017-09-14http://hdl.handle.net/11693/33610Cataloged from PDF version of article.Thesis (M.S.): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2017.Includes bibliographical references (leaves 50-55).Force modeling based on process input parameters is usually considered as the first step in process modeling. Predicting process forces in micromilling is dif- ficult 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, experiments are performed to investigate the in uence of micromilling process parameters on machining forces, tool edge condition, and surface texture. Built-up edge formation is observed to have a significant in uence on the process outputs in micromilling of titanium alloy Ti6Al4V. In the second part, Bayesian inference is applied to model micromilling forces. The effectiveness of employing Bayesian inference in micromilling force modeling considering special machining cases is discussed. In the third part, finite element simulation of machining processes is employed and process outputs are used to update our knowledge about force coefficients. As a result of uncertainty analysis, the mean and standard deviations of the micromilling forces can be estimated. Bayesian inference can be useful since previous evidence or expertise is insufficient, or when obtaining the related information requires costly and time-consuming machining experiments.xiv, 94 leaves : illustrations (some color), charts, graphics ; 30 cmEnglishinfo:eu-repo/semantics/openAccessMicromillingMechanistic modelingBayesian inferenceMarkov chain Monte CarloUncertainty analysisFinite element simulationThesisB156342