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Sashanka Akurati (1), Anton Jansson (2), Jacob L. Jones (2,3), Dipankar Ghosha (1)
Materialia, 16, May 2021. DOI: 10.1016/j.mtla.2021.101054
Ice-templated composites; Compressive response; X-ray nano computed tomography; Kink band; Delamination; Brittle fracture
The ice-templating technique enables the fabrication of multilayered ceramic-based composite materials. Very little is known on the inelastic deformation mechanisms that evolve in this class of composite materials under compressive loading conditions and cause macroscopic failure. The current investigation is motivated by a recent study by the authors, which revealed that the uniaxial compressive response of ice-templated ceramic–polymer composites is strongly dependent on the loading direction relative to the layer orientation. The current investigation reveals that the inelastic deformation mechanisms in ice-templated alumina–epoxy composites are strongly influenced by the compressive loading orientation relative to the growth direction of ice crystals. The deformation mechanisms were investigated for the loading directions of 0° (parallel to the growth direction), 45° (to the growth direction), and 90° (to the growth direction). For 0°, kink band formation and longitudinal splitting were observed to be the primary strength limiting mechanisms. Kink band formation could be the primary strength limiting factor and responsible for the catastrophic-type compressive failure response. For the loading directions of 45° and 90°, interface delamination and fracture within the lamella walls and across the alumina–epoxy interfaces were the main deformation mechanisms. These mechanisms significantly reduced the compressive strength but attributed progressive-type failure behavior in ice-templated composites. The knowledge of the inelastic deformation mechanisms in ice-templated ceramic–polymer composites under compressive loading is vital for an improved understanding of structure–mechanical property relationships and hierarchical materials design.
Dragonfly was used to perform image processing of X-ray tomography images reconstructed CT images. Plus, Dragonfly’s Deep Learning network was implemented to reveal the cracks present within the ceramic lamella walls of the deformed composite specimens
(1) Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA 23529, United States.
(2) Analytical Instrumentation Facility, North Carolina State University, Raleigh, NC 27695, United States.
(3)Department of Materials Science and Engineering, North Carolina State University, Raleigh, NC 27695, United States.
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