Effective elastic properties of biocomposites using 3D computational homogenization and X-ray microcomputed tomography
2021 (English) In: Composite structures, ISSN 0263-8223, E-ISSN 1879-1085, Vol. 273, article id 114302Article in journal (Refereed) Published
Abstract [en]
A 3D computational homogenization method based on X-ray microcomputed tomography (μCT) was proposed and implemented to investigate how the fiber weight fraction, orthotropy and orientation distribution affect the effective elastic properties of regenerated cellulose fiber-polylactic acid (PLA) biocomposites. Three-dimensional microstructures reconstructed by means of the X-ray μCT were used as the representative volume elements (RVEs) and incorporated into the finite element solver within the computational homogenization framework. The present method used Euclidean bipartite matching technique so as to eliminate the generation of artificial periodic boundaries and use the in-situ solution domains. In addition, a reconstruction algorithm enabled finding the volume and surface descriptions for each individual fiber in a semi-automatic manner, aiming at reducing the time and labor required for fiber labeling. A case study was presented, through which the method was compared and validated with the experimental investigations. The present study is thus believed to give a precise picture of microstructural heterogeneities for biocomposites of complex fiber networks and to provide an insight into the influences of the individual fibers and their networks on the effective elastic properties.
Place, publisher, year, edition, pages Elsevier, 2021. Vol. 273, article id 114302
Keywords [en]
computational homogenization, biocomposites, fiber, X-ray microcomputed tomography, reconstruction
National Category
Composite Science and Engineering
Research subject Polymeric Composite Materials
Identifiers URN: urn:nbn:se:ltu:diva-86260 DOI: 10.1016/j.compstruct.2021.114302 ISI: 000685078700007 Scopus ID: 2-s2.0-85109619598 OAI: oai:DiVA.org:ltu-86260 DiVA, id: diva2:1577408
Note Validerad;2021;Nivå 2;2021-08-02 (beamah);
Forskningsfinansiär: Aalto University; Academy of Finland BESIMAL (334197)
2021-07-022021-07-022021-08-30 Bibliographically approved