Aerospace & Defense Technology - June 2024 - 32

Tech Briefs
puting tasks, " Redding said. " This is significant
because we can apply random
weights very efficiently in the optical
domain simply by scattering light off of
a rough surface, or, as we show in this
paper, scattering light off non-uniformities
in an optical fiber. "
In traditional, digital electronics-based
computers, there wouldn't be much
advantage to doing this because every
multiplication operation is just as expensive,
in terms of time and energy, whether
multiplying by a random number or
by a value carefully selected through
training.
" This implies that in the optical
domain, we may want to design our neural
network architectures differently to
take advantage of the unique features of
optics - some things are easier to do in
optics and some things are harder, therefore
simply porting the same neural network
architecture that was optimized for
digital electronics implementations may
not be the ideal solution in the optical
domain, " Redding said.
A more subtle feature of NRL's fiber
platform is performing convolutions,
similar to a convolutional neural network
(CNN), a rarity for an optical
computing platform. Convolutions
are very powerful for tasks like image
processing, which led to the widespread
use of CNNs within the Department
of Defense image processing
applications.
" The Navy payoff is implementing
machine learning algorithms faster,
reducing the delay before we arrive at an
answer, " said Joseph Murray, Ph.D., a
Research Physicist from the NRL Optical
Sciences Division. " We are also exploring
applying these algorithms directly
on analog data without requiring intermediate
digitization and storage, which
could have a significant benefit when
processing high bandwidth data that is
difficult to record and analyze in realtime,
such as: high-resolution image
data or RF data for electronic warfare
applications. "
This research was performed by Brandon
Redding, Joseph Murray and a team
of research physicists for the Naval
Research Laboratory. For more information,
download the Technical Support
Package (free white paper) at
mobilityengineeringtech.com/tsp
under the Electronics category.
A New Approach to Assessing the Quality of
Aerospace Components
A sensing technology that can assess the quality of components in fields such as aerospace could
transform UK industry.
University of Bristol, Bristol, UK
I
n a study, published in the Journal Waves
in Random and Complex Media, researchers
from the University of Bristol have derived
a formula that can inform the design
boundaries for a given component's geometry
and material microstructure.
A commercially viable sensing technology
and associated imaging algorithm to
assess the quality of such components currently
does not exist. If the additive manufacturing
(3D printing) of metallic components
could satisfy the safety and quality
standards in industries there could be significant
commercial advantages in the
manufacturing sector.
The key breakthrough is the use of ultrasonic
array sensors, which are essentially
the same as those used in medical imaging
in, for example, creating images of babies
in the womb. However, these new laser
based versions would not require the sensor
to be in contact with the material.
Author Professor Anthony Mulholland,
Head of the School of Engineering
Maths and Technology, explained:
" There is a potential sensing method
32
using a laser based ultrasonic array and
we are using mathematical modeling to
inform the design of this equipment
ahead of its in situ deployment. "
The team built a mathematical model
that incorporated the physics of ultrasonic
waves propagating through a layered
(as additively manufactured) metallic
material, which took into account the
variability one gets between each manufactured
component.
The mathematical formula is made up of
the design parameters associated with the
ultrasonic laser and the nature of the particular
material. The output is a measure of
how much information will be produced
by the sensor to enable the mechanical
integrity of the component to be assessed.
The input parameters can then be varied to
maximize this information content.
It is hoped their discovery will accelerate
the design and deployment of this
proposed solution to this manufacturing
opportunity.
Professor Mulholland added: " We
can then work with our industry partmobilityengineeringtech.com
ners
to produce a means of assessing
the mechanical integrity of these safety
critical components at the manufacturing
stage.
" This could then lead to radically new
designs (by taking full advantage of 3D
printing), quicker and more cost effective
production processes, and significant
commercial and economic advantage
to UK manufacturing. "
Now the team plan to use the findings
to help their experimental collaborators
who are designing and building the laser
based ultrasonic arrays.
These sensors will then be deployed
in situ by robotic arms in a controlled
additive manufacturing environment.
They will maximize the information
content in the data produced by the
sensor and create bespoke imaging
algorithms to generate tomographic
images of the interior of components
supplied by their industry partners.
Destructive means will then be employed
to assess the quality of the tomographic
images produced.
Aerospace & Defense Technology, June 2024

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Aerospace & Defense Technology - June 2024

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