RUJA: Repositorio Institucional de Producción Científica

 

Applied computer vision for composite material manufacturing by optimizing the impregnation velocity: An experimental approach

dc.contributor.authorAlmazán-Lázaro, Juan Antonio
dc.contributor.authorLópez-Alba, Elías
dc.contributor.authorDíaz-Garrido, Francisco Alberto
dc.date.accessioned2024-09-26T11:56:27Z
dc.date.available2024-09-26T11:56:27Z
dc.date.issued2021-11-30
dc.descriptionT1 Q2 (15/50 Engineering, Manufacturing, IF 2022 = 6.2)es_ES
dc.description.abstractThe application of the cutting edge industrial solutions in composite manufacturing are optimizing the processes, quality control and resources usage. Computer vision is currently used in many quality control stages, although its potential advantages have not been applied in the process control. In this paper, computer vision is used to control the impregnation velocity in VARI (Vacuum Assisted Resin Infusion) process. As it is known, there is a relationship between the impregnation velocity and the final mechanical properties in LCM (Liquid Composite Manufacturing) processes. Constant and optimum flow front velocity mean optimum mechanical properties, although the nature of the process makes it difficult to keep these conditions. Then, a methodology has been proposed to identify and use the optimum velocity during the manufacturing process. Firstly, the flow front recognition algorithm was calibrated to be used in different reinforcement and fluid systems. Then, tensile and impact specimens have been manufactured and tested at different controlled and uncontrolled velocities. As a result, the tensile modulus has been increased up to 12.6%, as the tensile strength has increased up to 8.7%. Similarly, the maximum reaction force during the impact test has been increased up to 6.5%, as the damaged area has been reduced by 8.8%. For stitched laminates, force results increase up to 3.2%, as the damaged area has been reduced up to 31% when the optimum velocity is used. The experimental results have demonstrated the advantage of using mechanisms to control the impregnation process to achieve improved mechanical properties of composite materials.es_ES
dc.description.sponsorshipAirbus Defence and Space S.A.U.es_ES
dc.identifier.issn1526-6125es_ES
dc.identifier.otherhttps://doi.org/10.1016/j.jmapro.2021.11.063es_ES
dc.identifier.urihttps://hdl.handle.net/10953/3240
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.ispartofJournal of Manufacturing Processes 2022, 74, 52-62es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectComputer visiones_ES
dc.subjectOptimizationes_ES
dc.subjectManufacturinges_ES
dc.subjectControles_ES
dc.subjectMechanicses_ES
dc.subjectCompositees_ES
dc.subjectMaterialses_ES
dc.titleApplied computer vision for composite material manufacturing by optimizing the impregnation velocity: An experimental approaches_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
Almazan_JMP_2022.pdf
Tamaño:
2.54 MB
Formato:
Adobe Portable Document Format
Descripción:

Bloque de licencias

Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
1.98 KB
Formato:
Item-specific license agreed upon to submission
Descripción:

Colecciones