dc.contributor.author | Arriandiaga Laresgoiti, Ander | |
dc.contributor.author | Portillo Pérez, Eva | |
dc.contributor.author | Sánchez Galíndez, José Antonio | |
dc.contributor.author | Cabanes Axpe, Itziar | |
dc.contributor.author | Pombo Rodilla, Iñigo | |
dc.date.accessioned | 2016-01-29T11:41:55Z | |
dc.date.available | 2016-01-29T11:41:55Z | |
dc.date.issued | 2014-05 | |
dc.identifier.citation | Sensors 14(5) : 8756-8778 (2014) | es |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10810/17066 | |
dc.description.abstract | Grinding 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.sponsorship | Thanks 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.iso | eng | es |
dc.publisher | MDPI | es |
dc.rights | info:eu-repo/semantics/openAccess | es |
dc.subject | virtual sensor | es |
dc.subject | grinding process | es |
dc.subject | wheel wear | es |
dc.subject | surface roughness | es |
dc.subject | artificial neural networks | es |
dc.subject | networks | es |
dc.subject | system | es |
dc.subject | backpropagation | es |
dc.subject | operations | es |
dc.title | Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process | es |
dc.type | info:eu-repo/semantics/article | es |
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.publisherversion | http://www.mdpi.com/1424-8220/14/5/8756 | es |
dc.identifier.doi | 10.3390/s140508756 | |
dc.departamentoes | Ingeniería mecánica | es_ES |
dc.departamentoes | Ingeniería de sistemas y automática | es_ES |
dc.departamentoeu | Ingeniaritza mekanikoa | es_ES |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | es_ES |
dc.subject.categoria | BIOCHEMISTRY AND MOLECULAR BIOLOGY | |
dc.subject.categoria | PHYSICS, ATOMIC, MOLECULAR AND CHEMICAL | |
dc.subject.categoria | ELECTRICAL AND ELECTRONIC ENGINEERING | |
dc.subject.categoria | CHEMISTRY, ANALYTICAL | |