Modern advanced manufacturing technologies have the potential to revolutionize the way we fabricate components in the future. However, to be widely adopted by industry, adjustments to current industry methodologies and standards for part certification and qualification are needed to capture the increased complexity surrounding those manufacturing processes. Toward this goal, there is a growing research focus on developing new methodologies for certification and qualification that synthesize modeling and simulation, in-situ monitoring, and ex-situ characterization. In this context, thermal imaging has an important role to play as it can provide direct measurements of the manufactured components or of the machine itself. In this presentation, we will illustrate how thermal imaging is used to advance the field of advanced manufacturing by providing a series of examples on additive and hybrid manufacturing systems from the ORNL Manufacturing Demonstration Facility.
A multidisciplinary research conducted at the Oak Ridge National Laboratory aims at understanding the molecular
controls of partitioning, transport and fate of carbon fixed by photosynthesis in plants and its correlation
with other measured plant system properties. Ultimately, we intend to develop a modeling framework to assess,
correlate and predict as to which spatiotemporal changes in system dynamics are key to predicting emergent
properties of system. Within this research, this paper relates to the quantitative morphological imaging of the
main structures forming a plant (stem, roots, and leaves), their internal sub-structures, and changes occurring
overtime.
At the Spallation Neutron Source (SNS), an accelerator-based neutron source located at the Oak Ridge National
Laboratory (Tennessee, USA), the production of neutrons is obtained by accelerating protons against a mercury
target. This self-cooling target, however, suffers rapid heat deposition by the beam pulse leading to large
pressure changes and thus to cavitations that may be damaging to the container. In order to locally compensate
for pressure increases, a small-bubble population is added to the mercury flow using gas bubblers. The geometry
of the bubblers being unknown, we are testing several bubblers' configurations and are using machine vision
techniques to characterize their efficiency by quantitative measurement of the created bubble population. In this
paper we thoroughly detail the experimental setup and the image processing techniques used to quantitatively
assess the bubble population. To support this approach we are comparing our preliminary results for different
bubblers and operating modes, and discuss potential improvements.
In this paper, we describe a new 3D light propagation model aimed at understanding the effects of various
physiological properties on subcutaneous vein imaging. In particular, we build upon the well known MCML
(Monte Carlo Multi Layer) code and present a tissue model that improves upon the current state-of-the-art by:
incorporating physiological variation, such as melanin concentration, fat content, and layer thickness; including
veins of varying depth and diameter; using curved surfaces from real arm shapes; and modeling the vessel wall
interface. We describe our model, present results from the Monte Carlo modeling, and compare these results
with those obtained with other Monte Carlo methods.
The first and perhaps most important phase of a surgical procedure is the insertion of an intravenous (IV)
catheter. Currently, this is performed manually by trained personnel. In some visions of future operating rooms,
however, this process is to be replaced by an automated system. We previously presented work for localizing
near-surface veins via near-infrared (NIR) imaging in combination with structured light ranging for surface
mapping and robotic guidance. In this paper, we describe experiments to determine the best NIR wavelengths
to optimize vein contrast for physiological differences such as skin tone and/or the presence of hair on the arm
or wrist surface. For illumination, we employ an array of NIR LEDs comprising six different wavelength centers
from 740nm to 910nm. We capture imagery of each subject under every possible combination of illuminants and
determine the optimal combination of wavelengths for a given subject to maximize vein contrast using linear discriminant analysis.
KEYWORDS: Veins, Near infrared, 3D modeling, 3D image processing, Cameras, Skin, 3D acquisition, Light emitting diodes, Image processing, Structured light
Vein localization and catheter insertion constitute the first and perhaps most important phase of many medical procedures. Currently, catheterization is performed manually by trained personnel. This process can prove problematic, however, depending upon various physiological factors of the patient. We present in this paper initial work for localizing surface veins via near-infrared (NIR) imaging and structured light ranging. The eventual goal of the system is to serve as the guidance for a fully automatic (i.e., robotic) catheterization device. Our proposed system is based upon near-infrared (NIR) imaging, which has previously been shown effective in enhancing the visibility of surface veins. We locate the vein regions in the 2D NIR images using standard image processing techniques. We employ a NIR line-generating LED module to implement structured light ranging and construct a 3D topographic map of the arm surface. The located veins are mapped to the arm surface to provide a camera-registered representation of the arm and veins. We describe the techniques in detail and provide example imagery and 3D surface renderings.
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