Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/19184
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorCapiluppi, S-
dc.contributor.advisorSwift, S-
dc.contributor.authorBorland, Toby David-
dc.date.accessioned2019-09-25T15:44:52Z-
dc.date.available2019-09-25T15:44:52Z-
dc.date.issued2019-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/19184-
dc.descriptionThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University Londonen_US
dc.description.abstractEnterprise efficiency is limited by data exchange. A product designer might specify the geometry of a product with a Computer Aided Design program, an engineer might re-use that geometry data to calculate physical properties of the product using a Finite Element Analysis program. These different domains place different requirements on the product representation. Representations of product data required for different tasks is dependent on the vendor software associated with those tasks, sharing data between different vendor programs is limited by incompatibility of the vendor formats used. In the case of Computer Aided Design where the virtual form of an object is modelled, no standard data format captures complete model data. Common data standards transfer model surface geometry without capturing the topological elements from which these geometries are constructed. There are prescriptive data representations to allow these features to be specified in a neutral format, but little incentive for vendors to adopt these schemes. Recent efforts instead focus on identifying similar feature elements between different vendor CAD programs, however this approach relies on onerous manual identification requiring frequent revision. This research develops methods to automate the task of mapping relationships between different data format representations. Two independent matching techniques identify similar CAD feature functions between heterogeneous programs. Text similarity and object geometry matching techniques are combined to match the data formats associated with CAD programs. An efficient search for matching function parameters is performed using a genetic algorithm that incorporates semantic data matching and geometry data matching. A greedy semantic matching algorithm is developed that compares with the Doc2vec short text matching technique over the API dataset tested. A SVD geometric surface registration technique is developed that requires fewer calculations than an equivalent Iterative Closest Point method.en_US
dc.language.isoenen_US
dc.publisherBrunel University Londonen_US
dc.relation.urihttps://bura.brunel.ac.uk/bitstream/2438/19184/1/FulltextThesis.pdf-
dc.subjectSemantic matchingen_US
dc.subjectGenetic algorithmsen_US
dc.subjectShape registrationen_US
dc.subjectSVD transformen_US
dc.titleAn automated method mapping parametric features between computer aided design softwareen_US
dc.typeThesisen_US
Appears in Collections:Computer Science
Dept of Computer Science Theses

Files in This Item:
File Description SizeFormat 
FulltextThesis.pdf5.47 MBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.