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dc.contributor.authorThomas, Dylanen
dc.date.accessioned2006-08-22 13:47:41 (GMT)
dc.date.available2006-08-22 13:47:41 (GMT)
dc.date.issued2001en
dc.date.submitted2001en
dc.identifier.urihttp://hdl.handle.net/10012/801
dc.description.abstractThis thesis presents a numerical method of predicting both static and dynamic denting phenomena in automotive body panels. The finite element method is used as a predictive tool to assess panel performance prior to production of tooling. A custom software package has been developed to transform existing finite element forming models into ready-to-run finite element denting models, minimising the effort required to perform dent simulations. Over 50 multi-step finite element models were performed. Each of these models simulated the forming, springback and subsequent denting of either 1. 05mm thick AA5754, or0. 81mm, 0. 93mm or 1. 00mm thick AA6111 aluminum sheet. Experimental validation of dent predictions using this method has shown that the trends in both static and dynamic dent resistance have been captured quite well. These validation studies demonstrated the sensitivity of the results to various parameters such as panel thickness, pre-strain, curvature and thickness, as well as numerical formulation parameters. It has been determined that it is particularly important to use forming data within the denting models for accurate results to be obtained.en
dc.formatapplication/pdfen
dc.format.extent16191365 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.rightsCopyright: 2001, Thomas, Dylan. All rights reserved.en
dc.subjectMechanical Engineeringen
dc.subjectdent resistanceen
dc.subjectpanelen
dc.subjectFEMen
dc.subjectautomotiveen
dc.subjectaluminumen
dc.subjectremeshingen
dc.titleNumerical Prediction of Panel Dent Resistance Incorporating Panel Forming Strainsen
dc.typeMaster Thesisen
dc.pendingfalseen
uws-etd.degree.departmentMechanical Engineeringen
uws-etd.degreeMaster of Applied Scienceen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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