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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01x059cb30c
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dc.contributor.advisorAdams, Ryan P-
dc.contributor.authorAgureev, Alexander-
dc.date.accessioned2020-08-13T12:27:41Z-
dc.date.available2020-08-13T12:27:41Z-
dc.date.created2020-05-
dc.date.issued2020-08-13-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01x059cb30c-
dc.description.abstractThis paper discusses the practical implications of introducing a vision based system for use in CNC machinery for the specific purpose of workpiece coordinate determination. Workpiece coordinate determination is an essential part of many mass production processes based on CNC machinery. However, the current processes used for this purpose are costly and require a great deal of human input. The system proposed in this paper aims to tackle those issues and provide a robust, quick, and accurate alternative approach. The results that were achieved are overall satisfactory for some applications that require a relatively low level of precision. On top of that a novel deep learning based method was proposed. If developed further it could be turned into an industry grade product.en_US
dc.format.mimetypeapplication/pdf-
dc.language.isoenen_US
dc.titleLICENSEen_US
dc.titleComputer Vision Applications in CNC Machineryen_US
dc.titleLICENSEen_US
dc.typePrinceton University Senior Theses-
pu.date.classyear2020en_US
pu.departmentElectrical Engineeringen_US
pu.pdf.coverpageSeniorThesisCoverPage-
pu.contributor.authorid920227230-
Appears in Collections:Electrical Engineering, 1932-2020

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