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A general approach to calculate the stiffness tensor of short-fiber composites using the fabric tensor determined by X-ray computed tomography

TitoloA general approach to calculate the stiffness tensor of short-fiber composites using the fabric tensor determined by X-ray computed tomography
Tipo di pubblicazioneArticolo su Rivista peer-reviewed
Anno di Pubblicazione2022
AutoriDe Pascalis, F., Lionetto F., Maffezzoli A., and Nacucchi Michele
RivistaPolymer Composites
ISSN02728397
Parole chiaveComposite matrices, Computerized tomography, Fabric tensors, Fibers, General method, Method validations, Micro-structures, Microstructure, Reinforced plastics, Short fibre composites, stiffness, Stiffness tensor, Symmetry properties, Tensors, Thermoplastic composite, X-ray computed tomography
Abstract

In this work, a general method to predict the stiffness tensor of short-fiber composites as a function of the fabric tensor (i.e. the tensor describing the architectural anisotropy of the micro-structure), determined by X-ray computed tomography, is presented. The proposed method does not depend on the type of fabric tensor used to characterize the symmetry properties of the composite. The experimental data used for method validation were obtained from thermoplastic composite matrices reinforced with short carbon and basalt fibers. The components of the stiffness tensor were calculated using the Mori-Tanaka model and the Halpin-Tsai equation for the stiffness of aligned short-fiber composites, but other micromechanics models could be used as well. The statistical alignment of the fibers in each portion of the sample was measured by determining the principal axes of the fabric tensor. The average properties calculated on sub-volumes provided moduli values in very good agreement with those determined experimentally. This approach is very general and easy to implement and does not need any numerical analysis tools. © 2022 Society of Plastics Engineers.

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85142151380&doi=10.1002%2fpc.27143&partnerID=40&md5=c57da13c2d2b0fb6ac56c216a33039a2
DOI10.1002/pc.27143
Citation KeyDePascalis2022