dc.contributorUniversidade Estadual Paulista (UNESP)
dc.contributorUNITAU - TAUBATÉ University
dc.date.accessioned2022-04-29T08:48:13Z
dc.date.accessioned2022-12-20T03:21:50Z
dc.date.available2022-04-29T08:48:13Z
dc.date.available2022-12-20T03:21:50Z
dc.date.created2022-04-29T08:48:13Z
dc.date.issued2010-06-01
dc.identifierStrojniski Vestnik/Journal of Mechanical Engineering, v. 56, n. 4, p. 284-288, 2010.
dc.identifier0039-2480
dc.identifierhttp://hdl.handle.net/11449/231922
dc.identifier2-s2.0-77952748102
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5412056
dc.description.abstractThis paper describes a method to identify morphological attributes that classify wear particles in relation to the wear process from which they originate and permit the automatic identification without human expertise. The method is based on the use of Multi Layer Perceptron (MLP) for analysis of specific types of microscopic wear particles. The classification of the wear particles was performed according to their morphological attributes of size and aspect ratio, among others. © 2010 Journal of Mechanical Engineering.
dc.languageeng
dc.relationStrojniski Vestnik/Journal of Mechanical Engineering
dc.sourceScopus
dc.subjectArtificial neural network
dc.subjectExpert system
dc.subjectWear particles analysis
dc.titleWear particle classifier system based on an artificial neural network
dc.typeActas de congresos


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