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dc.contributor.advisorLin, Yuyieng
dc.contributor.authorHarby, Donald, 1967-eng
dc.date.issued2007eng
dc.date.submitted2007 Falleng
dc.descriptionThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.eng
dc.descriptionTitle from title screen of research.pdf file (viewed on February 13, 2008)eng
dc.descriptionVita.eng
dc.descriptionThesis (Ph. D.) University of Missouri-Columbia 2007.eng
dc.description.abstractOver the years machine tools for manufacturing have developed into two classes. One is the general purpose machine tools, and the other is designed for machining large quantities of a specific part or process. These specialized machine tools are very effective, however they have disadvantages. One is that if the product design is changed, the machine tool may have to be redesigned and rebuilt. Another problem is the custom built machines usually have long lead-times for their design and build. A solution is to produce a reconfigurable or modular machine tool (MMT). Then custom machine tools could be designed and built quickly from a library of modular components. Using a library of standard steel components leads to a very interesting discrete structural optimization (DSO) problem. A branch and bound discrete topology and sizing optimization method was applied to this problem. A hybrid approach of first using a particle swarm optimization (PSO) method and then switching to a fast gradient based method proved effective. A neural network (NN) based FEA approximation was developed to evaluate the constraints to the optimization. The results were compared to a more conventional material density topology optimization method. The method was successfully tested on a large scale 3D MMT component.eng
dc.description.bibrefIncludes bibliographical references.eng
dc.identifier.merlinb6201156xeng
dc.identifier.oclc192005881eng
dc.identifier.urihttps://doi.org/10.32469/10355/4871eng
dc.identifier.urihttps://hdl.handle.net/10355/4871
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subject.lcshMachine-tools -- Designeng
dc.subject.lcshEngineering designeng
dc.subject.lcshComputer-aided designeng
dc.subject.lcshModularity (Engineering)eng
dc.titleParametric and optimal design of modular machine toolseng
dc.typeThesiseng
thesis.degree.disciplineMechanical and aerospace engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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