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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01zc77st165
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dc.contributor.advisorAdams, Ryan P
dc.contributor.authorAgureev, Alexander
dc.date.accessioned2020-10-02T21:30:15Z-
dc.date.available2020-10-02T21:30:15Z-
dc.date.issued2020-10-02-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01zc77st165-
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.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.titleComputer Vision Applications in CNC Machinery
dc.typePrinceton University Senior Theses
pu.date.classyear2020
pu.departmentElectrical Engineering
pu.pdf.coverpageSeniorThesisCoverPage
pu.contributor.authorid920227230
pu.certificateNone
Appears in Collections:Electrical Engineering, 1932-2020

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