Performance of predicting surface quality model using softcomputing, a comparative study of results

dc.creatorCorrea Valencia, Maritza
dc.creatorFlores, Víctor
dc.creatorQuinonez, Alma Yadira
dc.date.accessioned2019-11-28T20:43:34Z
dc.date.accessioned2022-09-22T18:24:57Z
dc.date.available2019-11-28T20:43:34Z
dc.date.available2022-09-22T18:24:57Z
dc.date.created2019-11-28T20:43:34Z
dc.date.issued2017-05-27
dc.identifierFlores V., Correa M., Quiñonez Y. (2017) Performance of Predicting Surface Quality Model Using Softcomputing, a Comparative Study of Results. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science, vol 10337. Springer, Cham
dc.identifier978-3-319-59740-9 (en línea)
dc.identifier9783319597393 (impreso)
dc.identifier1611-3349 (en línea)
dc.identifier0302-9743 (impresa)
dc.identifierhttp://hdl.handle.net/10614/11616
dc.identifierhttps://link.springer.com/chapter/10.1007/978-3-319-59773-7_51
dc.identifierhttps://link.springer.com/content/pdf/10.1007%2F978-3-319-59773-7.pdf
dc.identifierhttps://doi.org/10.1007/978-3-319-59740-9_23
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3450526
dc.description.abstractThis paper describes a comparative study of performance of two models predicting surface quality in high-speed milling (HSM) processes using two different machining centers. The models were created with experimental data obtained from two machine-tools with different characteristics, but using the same experimental model. In both cases, work pieces (probes) of the same material were machined (steel and aluminum probes) with cutting parameters and characteristics proper of production processes in industries such as aeronautics and automotive. The main objective of this study was to compare surface quality prediction models created in two machining centers to establish differences in outcomes and the possible causes of these differences. In addition, this paper deals with the validation of each model concerning surface quality obtained, along with comparing the quality of the models with other predictive surface quality models based on similar techniques
dc.languageeng
dc.publisherSpringer, Cham
dc.relationLecture Notes in Computer Science. 10338. Theoretical Computer Science and General Issues. 10338
dc.relationNatural and Artificial Computation for Biomedicine and Neuroscience : International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Corunna, Spain, June 19-23, 2017, Proceedings, Part I. Páginas 233-242
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightsDerechos Reservados - Universidad Autónoma de Occidente
dc.sourceinstname:Universidad Autónoma de Occidente
dc.sourcereponame:Repositorio Institucional UAO
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dc.subjectCenter kernel alignment
dc.subjectFeature selection
dc.subjectFeature selection
dc.subjectHuman motion
dc.subjectKinematics
dc.subjectMotion capture data
dc.subjectPrincipal component analysis
dc.subjectRelevance
dc.titlePerformance of predicting surface quality model using softcomputing, a comparative study of results
dc.typeCapítulo - Parte de Libro


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