Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorMoia, D. F. G.
dc.creatorThomazella, I. H.
dc.creatorAguiar, P. R.
dc.creatorBianchi, E. C.
dc.creatorMartins, C. H. R.
dc.creatorMarchi, Marcelo
dc.date2015-10-21T21:08:36Z
dc.date2016-10-25T21:09:13Z
dc.date2015-10-21T21:08:36Z
dc.date2016-10-25T21:09:13Z
dc.date2015-03-01
dc.date.accessioned2017-04-06T09:10:02Z
dc.date.available2017-04-06T09:10:02Z
dc.identifierJournal Of The Brazilian Society Of Mechanical Sciences And Engineering, v. 37, n. 2, p. 627-640, 2015.
dc.identifier1678-5878
dc.identifierhttp://hdl.handle.net/11449/129458
dc.identifierhttp://acervodigital.unesp.br/handle/11449/129458
dc.identifierhttp://dx.doi.org/10.1007/s40430-014-0191-6
dc.identifierWOS:000350399200017
dc.identifierhttp://link.springer.com/article/10.1007%2Fs40430-014-0191-6
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/940013
dc.descriptionThe grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as "sharp''(with cutting capacity) or "dull''(with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.
dc.languageeng
dc.publisherSpringer
dc.relationJournal Of The Brazilian Society Of Mechanical Sciences And Engineering
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDressing
dc.subjectGrinding
dc.subjectTool condition monitoring
dc.subjectAcoustic emission
dc.subjectNeural network
dc.titleTool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks
dc.typeOtro


Este ítem pertenece a la siguiente institución