Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorPapa, João Paulo
dc.creatorNakamura, Rodrigo Y.M.
dc.creatorDe Albuquerque, Victor Hugo C.
dc.creatorFalcão, Alexandre X.
dc.creatorTavares, João Manuel R.S.
dc.date2014-05-27T11:28:17Z
dc.date2016-10-25T18:43:16Z
dc.date2014-05-27T11:28:17Z
dc.date2016-10-25T18:43:16Z
dc.date2013-02-01
dc.date.accessioned2017-04-06T02:11:42Z
dc.date.available2017-04-06T02:11:42Z
dc.identifierExpert Systems with Applications, v. 40, n. 2, p. 590-597, 2013.
dc.identifier0957-4174
dc.identifierhttp://hdl.handle.net/11449/74468
dc.identifierhttp://acervodigital.unesp.br/handle/11449/74468
dc.identifier10.1016/j.eswa.2012.07.062
dc.identifierWOS:000310945000020
dc.identifier2-s2.0-84867677551
dc.identifierhttp://dx.doi.org/10.1016/j.eswa.2012.07.062
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/895233
dc.descriptionThe automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. © 2012 Elsevier Ltd. All rights reserved.
dc.languageeng
dc.relationExpert Systems with Applications
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectComputer classifiers
dc.subjectComputer methods
dc.subjectGray and malleable cast irons
dc.subjectMaterial characterization
dc.subjectNodular
dc.subjectOtsu's method
dc.subjectBinarize
dc.subjectComplex methods
dc.subjectComputer techniques
dc.subjectGraphite particles
dc.subjectIndustrial materials
dc.subjectMachine learning classification
dc.subjectMalleable cast iron
dc.subjectMaterial characterizations
dc.subjectMechanical properties of materials
dc.subjectMetallographic images
dc.subjectOptimum-path forests
dc.subjectForestry
dc.subjectGraphite
dc.subjectIndustry
dc.subjectMalleable iron castings
dc.subjectMechanical properties
dc.subjectMetallography
dc.subjectCharacterization
dc.subjectCastings
dc.subjectClassifiers
dc.subjectComputers
dc.subjectIron
dc.subjectMechanical Properties
dc.titleComputer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials
dc.typeOtro


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