Aerospace & Defense Technology - December 2024 - 30

Tech Briefs
diminishing the practical advantages of
using simulations in the first place.
So, team member Hudson Liu, an APL
Student Program to Inspire, Relate, and
Enrich (ASPIRE) intern and high school
student at Gilman School in Baltimore,
developed the final puzzle piece: a
machine learning model that would
greatly reduce the need to run costly
simulations.
Liu combined several existing machine
learning model types to create what the
team refers to as a diffusion probabilistic
field model. Somewhat like the familiar
Stable Diffusion platform that creates
images from text, Liu's model generates
images based on two key LPBF parameters:
the cooling rate and the temperature
gradient, which quantifies how temperature
changes over distance, particularly
from the point of laser impact and the
surrounding solid metal.
The model was trained on data from
more than 400 simulations conducted
at APL, with the microstructure model
validated against experimental microscopy
measurements of the LPBF material
provided by Maureen Williams and
Lyle Levine at the National Institute of
Standards and Technology in Gaithersburg,
Maryland.
The result is a model that can effectively
approximate simulations with
enough accuracy to dramatically accelerate
the process of developing additively
manufactured materials using
LPBF.
" The key benefit of using a model is its
speed. Our model can approximate in
seconds or minutes what would take
hours in a simulation, " Liu said. " This
allows researchers to quickly explore a
wide range of parameters and at much
lower cost. "
Originally developed with internal
funding, this work is directly applicable
to NASA's Space Technology Research
Institute (STRI), and the team is working
to implement it soon toward that project's
goals.
" NASA wants validated models that
can help them predict what will happen
in a build, and how the subsequent
part will perform, without
expensive experimentation, " Ma said.
" So this approach is valuable, particularly
when you think about doing
additive manufacturing on the Moon
or in space, where experimentation
becomes so expensive that it's effectively
impossible. "
This work serves as a link in a chain in
which computational scientists on the
STRI team are working to predict the
properties of full-size additively manufactured
aerospace components directly
from the characteristics and processing
conditions of the raw materials.
The team is already at work scaling
up what their models can do. In addition
to predicting microstructures of
larger components and the results of
more laser passes, they intend to train
models using video data, which will
enable them to predict 3D as well as 2D
microstructures.
The team's work was published in a
special issue of Metallography, Microstructure,
and Analysis. Ma presented
results from the paper at the Integrated
Computational Materials and Engineering
for Defense Summit and at
APL's Accelerating Materials Discovery
for National Security Symposium in
September. She is also chairing sessions
at the Materials Science and Technology
technical meeting and exhibition in
October.
This effort is part of a larger portfolio of
work at APL focused on using artificial
intelligence to accelerate the discovery of
novel materials for extreme environments.
This article was written by Ajai Raj,
Science and Technology Writer, The
Johns Hopkins University Applied Physics
Laboratory. For more information,
contact Amanda Zreibec, Amanda.
Zrebiec@jhuapl.edu.
Aviation Safety Expert Unites Industry to Safeguard
Aviation Supply Chain
A new aviation supply chain integrity coalition has offered 13 recommended actions to prevent the circulation
of non-serialized aircraft parts throughout the global aviation industry.
Embry-Riddle Aeronautical University, Daytona Beach, FL
I
n the summer of 2023, a receiving clerk
in the procurement department of TAP
Air Portugal, a Lisbon-based airline,
made a curious discovery: A $65 engine
part that should have appeared brandnew
showed signs of significant wear.
The clerk checked the documentation
from the London-based parts supplier
and noticed that the submitted documentation
was also suspicious.
Using his safety training, the employee
immediately reported the anomaly to
30
TAP Air Portugal management, which
raised the issue with the jet engine's manufacturer.
Little did the procurement
clerk know at the time, but this escalation
led to one of the biggest investigations in
the history of the aviation supply chain,
as reported by Reuters and the British
Broadcasting Corporation in 2023.
The discovery also marshaled an
immediate response from regulators
worldwide. Nearly all major airlines were
affected when they were forced to temmobilityengineeringtech.com
porarily
ground aircraft on which the
spare par ts in quest ion had been
installed. Though the number of engines
impacted was relatively small compared
to the size of the global fleet, the incident
underscored the need for additional
safeguards in the Maintenance, Repair
and Overhaul (MRO) industry.
According to the Honorable Robert L.
Sumwalt, Executive Director for the Boeing
Center for Aviation & Aerospace
Safety at Embry Riddle Aeronautical
Aerospace & Defense Technology, December 2024

http://www.mobilityengineeringtech.com

Aerospace & Defense Technology - December 2024

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