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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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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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dc.contributor.author Feito-Sánchez, Norberto es_ES
dc.contributor.author Muñoz-Sánchez, A. es_ES
dc.contributor.author Diaz-Alvarez, A. es_ES
dc.contributor.author Loya, J.A. es_ES
dc.date.accessioned 2020-07-02T06:50:44Z
dc.date.available 2020-07-02T06:50:44Z
dc.date.issued 2019-08-27 es_ES
dc.identifier.uri http://hdl.handle.net/10251/147314
dc.description.abstract [EN] Local delamination is the most undesirable damage associated with drilling carbon fiber reinforced composite materials (CFRPs). This defect reduces the structural integrity of the material, which affects the residual strength of the assembled components. A positive correlation between delamination extension and thrust force during the drilling process is reported in literature. The abrasive effect of the carbon fibers modifies the geometry of the fresh tool, which increases the thrust force and, in consequence, the induced damage in the workpiece. Using a control system based on an artificial neural network (ANN), an analysis of the influence of the tool wear in the thrust force 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 and testing of the ANN model are carried out with experimental drilling tests using uncoated carbide helicoidal 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 the tool wear and cutting parameters. The obtained results can be used by the industry as a guide to control the impact of the wear of the tool in the quality of the finished workpiece. es_ES
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. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Materials es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Delamination es_ES
dc.subject Tool wear es_ES
dc.subject Drilling es_ES
dc.subject Machinability es_ES
dc.subject Composite es_ES
dc.subject CFRP es_ES
dc.subject Neural network es_ES
dc.subject.classification INGENIERIA DE LOS PROCESOS DE FABRICACION es_ES
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 es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/ma12172747 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-89197-C2-1-R/ES/TALADRADO DE COMPONENTES HIBRIDOS CFRPS%2FTI Y TOLERANCIA AL DAÑO DEBIDO A MECANIZADO DURANTE EL COMPORTAMIENTO EN SERVICIO DE UNIONES ESTRUCTURALES AERONAUTICAS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-89197-C2-2-R/ES/TALADRADO DE COMPONENTES HIBRIDOS CFRPS%2FTI Y TOLERANCIA AL DAÑO DEBIDO A MECANIZADO DURANTE EL COMPORTAMIENTO EN SERVICIO DE UNIONES ESTRUCTURALES AERONAUTICAS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//FJCI-2017-34910/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Mecánica y de Materiales - Departament d'Enginyeria Mecànica i de Materials es_ES
dc.description.bibliographicCitation Feito-Sánchez, N.; Muñoz-Sánchez, A.; Diaz-Alvarez, A.; Loya, J. (2019). Analysis of the Machinability of Carbon Fiber Composite Materials in Function of Tool Wear and Cutting Parameters Using the Artificial Neural Network Approach. Materials. 12(17):1-13. https://doi.org/10.3390/ma12172747 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/ma12172747 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 13 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 12 es_ES
dc.description.issue 17 es_ES
dc.identifier.eissn 1996-1944 es_ES
dc.identifier.pmid 31461912 es_ES
dc.identifier.pmcid PMC6747800 es_ES
dc.relation.pasarela S\400995 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
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