High-resolution synchrotron x-ray computed tomography (CT) in situ pull-out tests with stepwise increased loading were performed to investigate the force transfer between a shape memory alloy (SMA) wire and the surrounding epoxy polymer matrix. The advancing interfacial failure was observed. The stochastic surface structure of the SMA wire was utilized to determine the axial and radial strains introduced into the SMA wire during the test by performing digital volume correlation on the reconstructed surface data. The global and local strain of the embedded SMA wire volume could be correlated with the force of the first interfacial failure. Using image segmentation on the cross-sections derived from the reconstructed CT volume data, the growth of the delamination along the observed length of the embedded SMA wire for increasing load levels was measured. In addition, the advancing interfacial failure was correlated with changes in the cross-sectional area of the SMA wire due to transverse contraction. The local surface strain characteristics of an embedded SMA wire during CT of an in situ pull-out test were compared to a non-embedded SMA wire loaded in situ. It was found that the polymer matrix exerts an external stress on the SMA wire, constraining its radial strain. Thereby, the study reveals that interfacial failure is not only a shear-stress-induced failure, but shear strain and even more normal strain due to transverse contraction of the SMA wire plays an important role too.
The Helmholtz-Zentrum Hereon is operating imaging beamlines for X-ray tomography (P05 IBL, P07 HEMS) for academic and industrial users at the synchrotron radiation source PETRA III at DESY in Hamburg, Germany. The high X-ray flux density and coherence of synchrotron radiation enables high-resolution in situ/operando/vivo tomography experiments and provides phase contrast, respectively. Large amounts of 3D/4D data are collected that are difficult to process and analyze. Here, we report on the application of machine learning for image segmentation including a guided interactive framework, multimodal data analysis (virtual histology), image enhancement (denoising), and self-supervised learning for phase retrieval.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.