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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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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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/147314

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Title: Analysis of the Machinability of Carbon Fiber Composite Materials in Function of Tool Wear and Cutting Parameters Using the Artificial Neural Network Approach
Author: Feito-Sánchez, Norberto Muñoz-Sánchez, A. Diaz-Alvarez, A. Loya, J.A.
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería Mecánica y de Materiales - Departament d'Enginyeria Mecànica i de Materials
Issued date:
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 ...[+]
Subjects: Delamination , Tool wear , Drilling , Machinability , Composite , CFRP , Neural network
Copyrigths: Reconocimiento (by)
Source:
Materials. (eissn: 1996-1944 )
DOI: 10.3390/ma12172747
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/ma12172747
Thanks:
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.[+]
Type: Artículo

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