Neural network approach for surface roughness prediction in surface grinding

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorAguiar, Paulo R.
dc.creatorCruz, Carlos E. D.
dc.creatorPaula, Wallace C. F.
dc.creatorBianchi, Eduardo C.
dc.creatorThomazella, Rogério
dc.creatorDotto, Fábio R. L.
dc.date2014-05-27T11:22:43Z
dc.date2016-10-25T18:24:55Z
dc.date2014-05-27T11:22:43Z
dc.date2016-10-25T18:24:55Z
dc.date2007-12-01
dc.date.accessioned2017-04-06T01:28:51Z
dc.date.available2017-04-06T01:28:51Z
dc.identifierProceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2007, p. 96-101.
dc.identifierhttp://hdl.handle.net/11449/70158
dc.identifierhttp://acervodigital.unesp.br/handle/11449/70158
dc.identifierWOS:000246292900018
dc.identifier2-s2.0-38349113851
dc.identifierhttp://www.actapress.com/Abstract.aspx?paperId=29434
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/891302
dc.descriptionSeveral systems are currently tested in order to obtain a feasible and safe method for automation and control of grinding process. This work aims to predict the surface roughness of the parts of SAE 1020 steel ground in a surface grinding machine. Acoustic emission and electrical power signals were acquired by a commercial data acquisition system. The former from a fixed sensor placed near the workpiece and the latter from the electric induction motor that drives the grinding wheel. Both signals were digitally processed through known statistics, which with the depth of cut composed three data sets implemented to the artificial neural networks. The neural network through its mathematical logical system interpreted the signals and successful predicted the workpiece roughness. The results from the neural networks were compared to the roughness values taken from the worpieces, showing high efficiency and applicability on monitoring and controlling the grinding process. Also, a comparison among the three data sets was carried out.
dc.languageeng
dc.relationProceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2007
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAcoustic emission
dc.subjectElectric power
dc.subjectNeural network
dc.subjectSurface finishing
dc.subjectSurface grinding
dc.subjectSurface roughness
dc.subjectAcoustic emission testing
dc.subjectElectric power systems
dc.subjectFinishing
dc.subjectGrinding (machining)
dc.subjectNeural networks
dc.titleNeural network approach for surface roughness prediction in surface grinding
dc.typeOtro


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