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dc.contributor.authorHe, K.en_US
dc.contributor.authorZhang, Q.en_US
dc.contributor.authorHong, Y.en_US
dc.description.abstractIn order to monitor the quality of parts in printing, the methodology to monitor the geometric quality of the printed parts in fused deposition modeling process is researched. A non-contact measurement method based on machine vision technology is adopted to obtain the precise complete geometric information. An image acquisition system is established to capture the image of each layer of the part in building and image processing technology is used to obtain the geometric profile information. With the above information, statistical process control method is applied to monitor the geometric quality of the parts during the printing process. Firstly, a border signature method is applied to transform complex geometry into a simple distance-angle function to get the profile deviation data. Secondly, monitoring of the profile deviation data based on profile monitoring method is studied and applied to achieve the goal of layer-to-layer monitoring. In the research, quantile-quantile plot method is used to transform the profile deviation point cloud data monitoring problem into a linear profile relationship monitoring problem and EWMA control charts are established to monitor the parameters of the linear relationship to detect shifts occurred in the Fused Deposition Modeling process. Finally, laboratory experiments are conducted to demonstrate the effectiveness of the proposed approach. © 2018, The Author(s).en_US
dc.publisherSpringer New York LLC
dc.rightsCreative Commons Attribution 4.0 International
dc.subjectFused deposition modeling
dc.subjectMachine vision
dc.subjectProfile monitoring
dc.subjectQuality control
dc.subjectStatistical process control
dc.titleProfile monitoring based quality control method for fused deposition modeling processen_US
dc.typeArticle - Refereed
dc.description.notesAcknowledgements The work by Hong was supported in part by the United States National Science Foundation Grant CMMI-1634867 to Virginia Tech.
dc.title.serialJournal of Intelligent Manufacturing

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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International