Uncertainty analysis of cutting force coefficients during micromilling of titanium alloy

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buir.advisorKarpat, Yiğit
dc.contributor.authorGözü, Erman
dc.date.accessioned2017-09-15T11:27:53Z
dc.date.available2017-09-15T11:27:53Z
dc.date.copyright2017-09
dc.date.issued2017-09
dc.date.submitted2017-09-14
dc.departmentDepartment of Industrial Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2017.en_US
dc.descriptionIncludes bibliographical references (leaves 50-55).en_US
dc.description.abstractForce 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.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityby Erman Gözü.en_US
dc.embargo.release2018-09-13
dc.format.extentxiv, 94 leaves : illustrations (some color), charts, graphics ; 30 cmen_US
dc.identifier.itemidB156342
dc.identifier.urihttp://hdl.handle.net/11693/33610
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMicromillingen_US
dc.subjectMechanistic modelingen_US
dc.subjectBayesian inferenceen_US
dc.subjectMarkov chain Monte Carloen_US
dc.subjectUncertainty analysisen_US
dc.subjectFinite element simulationen_US
dc.titleUncertainty analysis of cutting force coefficients during micromilling of titanium alloyen_US
dc.title.alternativeMikro frezeleme kuvvetlerinin belirsizlik analizi ve olasılıksal modellemesien_US
dc.typeThesisen_US

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