40,383 research outputs found
One-step deposition of nano-to-micron-scalable, high-quality digital image correlation patterns for high-strain in-situ multi-microscopy testing
Digital Image Correlation (DIC) is of vital importance in the field of
experimental mechanics, yet, producing suitable DIC patterns for demanding
in-situ mechanical tests remains challenging, especially for ultra-fine
patterns, despite the large number of patterning techniques in the literature.
Therefore, we propose a simple, flexible, one-step technique (only requiring a
conventional deposition machine) to obtain scalable, high-quality, robust DIC
patterns, suitable for a range of microscopic techniques, by deposition of a
low melting temperature solder alloy in so-called 'island growth' mode, without
elevating the substrate temperature. Proof of principle is shown by
(near-)room-temperature deposition of InSn patterns, yielding highly dense,
homogeneous DIC patterns over large areas with a feature size that can be tuned
from as small as 10nm to 2um and with control over the feature shape and
density by changing the deposition parameters. Pattern optimization, in terms
of feature size, density, and contrast, is demonstrated for imaging with atomic
force microscopy, scanning electron microscopy (SEM), optical microscopy and
profilometry. Moreover, the performance of the InSn DIC patterns and their
robustness to large deformations is validated in two challenging case studies
of in-situ micro-mechanical testing: (i) self-adaptive isogeometric digital
height correlation of optical surface height profiles of a coarse, bimodal InSn
pattern providing microscopic 3D deformation fields (illustrated for
delamination of aluminum interconnects on a polyimide substrate) and (ii) DIC
on SEM images of a much finer InSn pattern allowing quantification of high
strains near fracture locations (illustrated for rupture of a Fe foil). As
such, the high controllability, performance and scalability of the DIC patterns
offers a promising step towards more routine DIC-based in-situ micro-mechanical
testing.Comment: Accepted for publication in Strai
Deep learning approach to scalable imaging through scattering media
We propose a deep learning technique to exploit “deep speckle correlations”. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media.Published versio
Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media
Imaging through scattering is an important yet challenging problem. Tremendous progress has been made by exploiting the deterministic input–output “transmission matrix” for a fixed medium. However, this “one-to-one” mapping is highly susceptible to speckle decorrelations – small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical “one-to-all” deep learning (DL) technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable DL approach for imaging through scattering media.National Science Foundation (NSF) (1711156); Directorate for Engineering (ENG). (1711156 - National Science Foundation (NSF); Directorate for Engineering (ENG))First author draf
- …