Analysis of the machinability of carbon fiber composite materials in function of tool wear and cutting parameters using the artificial neural network approach

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Show simple item record Feito Sánchez, Norberto Muñoz Sánchez, Ana Díaz Álvarez, Antonio Loya Lorenzo, José Antonio 2021-02-24T12:17:07Z 2021-02-24T12:17:07Z 2019-09-01
dc.identifier.bibliographicCitation Materials, (2019), 12(17), 2747.
dc.identifier.issn 1996-1944
dc.description.abstract Local delamination is the most undesirable damage associated with drilling carbonfiber reinforced composite materials (CFRPs). This defect reduces the structural integrity of thematerial, which a ects the residual strength of the assembled components. A positive correlationbetween delamination extension and thrust force during the drilling process is reported in literature.The abrasive e ect of the carbon fibers modifies the geometry of the fresh tool, which increases thethrust force and, in consequence, the induced damage in the workpiece. Using a control systembased on an artificial neural network (ANN), an analysis of the influence of the tool wear in the thrustforce during the drilling of CFRP laminate to reduce the damage is developed. The spindle speed,feed rate, and drill point angle are also included as input parameters of the study. The training andtesting of the ANN model are carried out with experimental drilling tests using uncoated carbidehelicoidal tools. The data were trained using error-back propagation-training algorithm (EBPTA).The use of the neural network rapidly provides results of the thrust force evolution in function of thetool wear and cutting parameters. The obtained results can be used by the industry as a guide tocontrol the impact of the wear of the tool in the quality of the finished workpiece.
dc.description.sponsorship The Ministry of Economy and Competitiveness of Spain, projects DPI2017-89197-C2-1-R and DPI2017-89197-C2-2-R and the Ministry of Science, Innovation and Universities, grant number [FJCI-2017-34910], funded this research.
dc.format.extent 13
dc.language.iso eng
dc.publisher MDPI
dc.rights © 2019 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 (CC BY) license (
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.subject.other Tool wear
dc.subject.other Delamination
dc.subject.other Drilling
dc.subject.other Machinability
dc.subject.other Composite
dc.subject.other CFRP
dc.subject.other Neural network
dc.title Analysis of the machinability of carbon fiber composite materials in function of tool wear and cutting parameters using the artificial neural network approach
dc.type article
dc.description.status Publicado
dc.subject.eciencia Ingeniería Industrial
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. DPI2017-89197-C2-1-R
dc.relation.projectID Gobierno de España. DPI2017-89197-C2-2-R
dc.relation.projectID Gobierno de España. FJCI-2017-34910
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 17(2747)
dc.identifier.publicationlastpage 13
dc.identifier.publicationtitle Materials
dc.identifier.publicationvolume 12
dc.identifier.uxxi AR/0000026489
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.affiliation.dpto UC3M. Departamento de Ingeniería Mecánica
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Tecnologías de Fabricación y Diseño de Componentes Mecánicos y Biomecánicos
dc.affiliation.area UC3M. Área de Ingeniería Mecánica
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