Using Neural Networks to Generate Design Similarity Measures

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dc.contributor.advisorHerrmann, Jeffrey W.en_US
dc.contributor.authorBalasubramanian, Sundaren_US
dc.contributor.authorHerrmann, Jeffrey W.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T10:07:14Z
dc.date.available2007-05-23T10:07:14Z
dc.date.issued1999en_US
dc.description.abstractThis paper describes a neural network-based design similarity measure for a variant fixture planning approach. The goal is to retrieve, for a new product design, a useful fixture from a given set of existing designs and their fixtures. However, since calculating each fixture feasibility and then determining the necessary modifications for infeasible fixtures would require too much effort, the approach searches quickly for the most promising fixtures. The proposed approach uses a design similarity measure to find existing designs that are likely to have useful fixtures. The use of neural networks to generate design similarity measures is explored.This paper describes the back-propagation algorithm for network learning and highlights some of the implementation details involved. The neural network-based design similarity measure is compared against other measures that are based on a single design attribute.en_US
dc.format.extent93591 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/6022
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1999-38en_US
dc.subjectneural networksen_US
dc.subjectcomputer integrated manufacturing CIMen_US
dc.subjectmanufacturingen_US
dc.subjectvariant fixture planningen_US
dc.subjectdesign similarityen_US
dc.subjectSystems Integration Methodologyen_US
dc.titleUsing Neural Networks to Generate Design Similarity Measuresen_US
dc.typeTechnical Reporten_US

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