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dc.contributor.authorArriandiaga Laresgoiti, Ander
dc.contributor.authorPortillo Pérez, Eva ORCID
dc.contributor.authorSánchez Galíndez, José Antonio ORCID
dc.contributor.authorCabanes Axpe, Itziar
dc.contributor.authorPombo Rodilla, Iñigo
dc.date.accessioned2016-01-29T11:41:55Z
dc.date.available2016-01-29T11:41:55Z
dc.date.issued2014-05
dc.identifier.citationSensors 14(5) : 8756-8778 (2014)es
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10810/17066
dc.description.abstractGrinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.es
dc.description.sponsorshipThanks are given to the Spanish Ministry of Economy and Competitiveness for their support of the Research Project. Integration of numerical models and experimental techniques for improving the added value in grinding of precision parts. (DPI2010-21652-C02-01). This work was also supported in part by the Regional Government of the Basque Country through the Departamento de Educacion, Universidades e Investigacion (Project IT719-13) and UPV/EHU under grant UFI11/28.es
dc.language.isoenges
dc.publisherMDPIes
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.subjectvirtual sensores
dc.subjectgrinding processes
dc.subjectwheel weares
dc.subjectsurface roughnesses
dc.subjectartificial neural networkses
dc.subjectnetworkses
dc.subjectsystemes
dc.subjectbackpropagationes
dc.subjectoperationses
dc.titleVirtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Processes
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).es
dc.relation.publisherversionhttp://www.mdpi.com/1424-8220/14/5/8756es
dc.identifier.doi10.3390/s140508756
dc.departamentoesIngeniería mecánicaes_ES
dc.departamentoesIngeniería de sistemas y automáticaes_ES
dc.departamentoeuIngeniaritza mekanikoaes_ES
dc.departamentoeuSistemen ingeniaritza eta automatikaes_ES
dc.subject.categoriaBIOCHEMISTRY AND MOLECULAR BIOLOGY
dc.subject.categoriaPHYSICS, ATOMIC, MOLECULAR AND CHEMICAL
dc.subject.categoriaELECTRICAL AND ELECTRONIC ENGINEERING
dc.subject.categoriaCHEMISTRY, ANALYTICAL


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