Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/99492
Full metadata record
DC FieldValueLanguage
dc.creatorCarlos Alberto Conceição António
dc.creatorPaulo Davim
dc.creatorVitor Lapa
dc.date.accessioned2019-02-05T21:06:31Z-
dc.date.available2019-02-05T21:06:31Z-
dc.date.issued2006
dc.identifier.othersigarra:55890
dc.identifier.urihttps://repositorio-aberto.up.pt/handle/10216/99492-
dc.description.abstractIn this paper an Artificial Neural Network (ANN) aiming the efficient modeling of a set ofmachining conditions in the orthogonal cutting of composite materials is presented. Theexperimental procedure considers process parameters as cutting speed and feed rate, the typeof insert of the tool and the type of workpiece material in order to obtain a set of results usedfor ANN learning. The supervised learning of the ANN is based on a genetic algorithm withan elitist strategy. Input, hidden and output layers model the topology of the ANN. Theweights of the synapses, the bias for the hidden and output nodes and the number of neuralnodes of the hidden layer are used as design variables. Sigmoid activation functions are usedin hidden and output layers. The square error between experimental and numerical results isused to monitoring the learning process aiming to obtain the completeness of modeling of themachining process.
dc.language.isoeng
dc.relation.ispartofMechanics and materials in design
dc.rightsrestrictedAccess
dc.subjectCiências Tecnológicas
dc.subjectTechnological sciences
dc.titleArtificial Neural Network Based on Genetic Learning for Machining of Composite Materials
dc.typeArtigo em Livro de Atas de Conferência Internacional
dc.contributor.uportoFaculdade de Engenharia
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

Files in This Item:
File Description SizeFormat 
55890.pdf
  Restricted Access
Artificial Neural Network Based on Genetic Learning for Machining of Composite Materials, artigo completo em CD69.68 kBAdobe PDF    Request a copy from the Author(s)


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