Uncertainty analysis of force coefficients during micromilling of titanium alloy

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dc.citation.epage855en_US
dc.citation.issueNumber1-4en_US
dc.citation.spage839en_US
dc.citation.volumeNumber93en_US
dc.contributor.authorGözü, E.en_US
dc.contributor.authorKarpat, Y.en_US
dc.date.accessioned2018-04-12T10:38:02Z
dc.date.available2018-04-12T10:38:02Z
dc.date.issued2017en_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.departmentInstitute of Materials Science and Nanotechnology (UNAM)en_US
dc.departmentDepartment of Mechanical Engineeringen_US
dc.description.abstractPredicting 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.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T10:38:02Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2017en
dc.identifier.doi10.1007/s00170-017-0567-8en_US
dc.identifier.issn0268-3768
dc.identifier.urihttp://hdl.handle.net/11693/36379
dc.language.isoEnglishen_US
dc.publisherSpringeren_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s00170-017-0567-8en_US
dc.source.titleInternational Journal of Advanced Manufacturing Technologyen_US
dc.subjectMicromillingen_US
dc.subjectMechanistic modelingen_US
dc.subjectBayesian inferenceen_US
dc.subjectMarkov chain Monte Carloen_US
dc.subjectUncertainty analysisen_US
dc.titleUncertainty analysis of force coefficients during micromilling of titanium alloyen_US
dc.typeArticleen_US

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