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dc.contributor.advisorMorkos, Beshoy
dc.contributor.authorPiazza, Andrea
dc.date.accessioned2018-07-09T17:40:06Z
dc.date.available2018-07-09T17:40:06Z
dc.date.created2018-07
dc.date.issued2018-06
dc.date.submittedJuly 2018
dc.identifier.urihttp://hdl.handle.net/11141/2519
dc.descriptionThesis (M.S.) - Florida Institute of Technology, 2018en_US
dc.description.abstractAdditive manufacturing (AM) is becoming a valuable option in medium to large scale manufacturing operations, due to the increasing technological advances and popularization of different techniques. Product development assembly costs can be greatly reduced with additive manufacturing, producing directly consumer-usable parts without the need of always rising labor costs. Entry level companies can bridge the gap between Tier 1 suppliers and Research & Development (R&D) businesses by utilizing the lower capital and reoccurring costs of modern additive manufacturing techniques, thereby avoiding the necessity for retooling and specialized machinery. The following study both explores the advantages and quantifies the cost factors, such as manufacturing, assembly costs, and material considerations, when assemblies and/or single components are replaced with an additively manufactured part, in mass produced applications. Manufacturing cost models are analyzed to show feasibility of changeover to an additive manufactured part utilizing three generic products (water pump, GoPro Mount, and stapler), and results have been subsequently compared to real world quotes and analyzed for accuracy. The results show a high correlation between manufacturing volume and part cost in two of the analyzed models (RM2003 and AS), while the HD model showed that its numerous significant cost driving variables were production volume, part volume, and material cost. An analysis of the simulated data showed that major costs drivers (over 90% of total costs per part) for low production volumes were machine costs, while for high production volume, it was the material costs. Comparison with real-world cost data revealed an average error across the models of 38.6%, which returns the conclusion that more detailed and advanced models needs to be created to better simulate AM costs, allowing for higher utilization of AM processes in large scale manufacturing.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.rightsCC BY 4.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/legalcodeen_US
dc.titleCost Model Evaluation for Large Scale Additive Manufacturingen_US
dc.typeThesisen_US
dc.date.updated2018-06-25T20:35:29Z
thesis.degree.nameMaster of Science in Mechanical Engineeringen_US
thesis.degree.levelMastersen_US
thesis.degree.disciplineMechanical Engineeringen_US
thesis.degree.departmentMechanical and Aerospace Engineeringen_US
thesis.degree.grantorFlorida Institute of Technologyen_US
dc.type.materialtext


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