Identification of internal process parameters of micro milling considering machined surface topography

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buir.advisorKarpat, Yiğit
dc.contributor.authorMasrani, Abdulrzak
dc.date.accessioned2022-08-26T11:02:15Z
dc.date.available2022-08-26T11:02:15Z
dc.date.copyright2022-07
dc.date.issued2022-07
dc.date.submitted2022-08-24
dc.departmentDepartment of Mechanical Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Mechanical Engineering, İhsan Doğramacı Bilkent University, 2022.en_US
dc.descriptionIncludes bibliographical references (leaves 74-79).en_US
dc.description.abstractMicro-milling is a fast and versatile machining method that can be used to manufacture three-dimensional parts of a wide range of materials with high accuracy. Modeling of micro-milling processes is complex due to size effects, where the chip thickness becomes comparable to the cutting edge radius. Furthermore, tool runout and deflection effects on the process outputs are amplified and cannot be neglected. As the process is scaled down where micrometer accuracy is required; modeling and identifying the process parameters becomes essential to optimize or monitor the process. This study presents a systematic approach to force modeling and parameter identification of micro-milling processes. Finite element analysis of tool deflection is integrated into mechanistic modeling of micro-milling forces together with considering the trochoidal trajectory of the cutting teeth, tool runout, and chip thickness accumulation due to minimum uncut chip thickness. The internal process parameters are identified using the experimental cutting forces and machined surface topography with a novel methodology. The research results are experimentally validated by slot and side micro-milling tests on commercially pure titanium, using coated carbide micro-end-mills with diameters of 0.2 and 0.4 mm, and accurate predictions of model parameters and cutting forces are obtained. The proposed force models can be used in smart manufacturing and digital twin applications to reduce the time and costs associated with process optimization. The proposed parameter identification techniques can also help to reduce the need for advanced measurement systems.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityby Abdulrzak Masranien_US
dc.embargo.release2023-02-01
dc.format.extentxiv, 82 leaves : color illustrations, charts ; 30 cm.en_US
dc.identifier.itemidB161216
dc.identifier.urihttp://hdl.handle.net/11693/110472
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMicro-millingen_US
dc.subjectMechanistic force modelingen_US
dc.subjectFinite element analysisen_US
dc.subjectSurface topographyen_US
dc.subjectCP titaniumen_US
dc.titleIdentification of internal process parameters of micro milling considering machined surface topographyen_US
dc.title.alternativeMikro frezeleme işlemi iç parametrelerinin işlenmiş yüzey topografisi yardımıyla belirlenmesien_US
dc.typeThesisen_US
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