AUTOMATION
common approach is
Design of Experiments
(DOE), a statistical
method that builds a
mathematical model of a
system by simultaneously
investigating the
effects of various
WWW.MADEIN.IE « MARCH 2021 « 27
Sir Martin Donnelly, president of
Boeing Europe and managing director
of Boeing in the UK and Ireland, said
the project shows how industry can
successfully partner with government
and academia to spur UK innovation.
“We are proud to see this project
move forward because of what it
promises aviation and manufacturing,
and because of what it represents for
the UK’s innovation ecosystem,”
Donnelly said. “We helped found the
AMRC two decades ago, Intellegens
was one of the companies we invested
in as part of the ATI Boeing
Accelerator and we have longstanding
research partnerships with
Cambridge University and the
University of Sheffield. We are excited
to see what comes from this
continued collaboration and how we
might replicate this formula in other
ways within the UK and beyond.”
Aerospace components have to
withstand certain loads and
temperature resistances, and some
materials are limited in what they can
offer. There is also simultaneous push
for lower weight and higher
temperature resistance for better fuel
efficiency, bringing new or previously
impractical-to-machine metals into
the aerospace material mix.
One of the main drawbacks of AM is
the limited material selection
currently available and the design of
new materials, particularly in the
aerospace industry, requires expensive
and extensive testing and certification
cycles which can take longer than a
year to complete and cost as much as
£1 million to undertake. Project
MEDAL aims to accelerate this
process, using Machine Learning (ML)
to rapidly optimise AM processing
parameters for new metal alloys,
making the development process
more time and cost efficient.
Pellegrini said experimental design
techniques are extremely important to
develop new products and processes
in a cost-effective and confident
manner. The most common approach
is Design of Experiments (DOE), a
statistical method that builds a
mathematical model of a system by
simultaneously investigating the
effects of various factors.
“DOE is a more
efficient, systematic
way of choosing and
carrying out
experiments
compared to the
Change One Separate
variable at a Time
(COST) approach.
However, the high
number of experiments
required to obtain a reliable
covering of the search space means
that DOE can still be a lengthy and
costly process, which can be
improved,” explained Pellegrini.
“The machine learning solution in
this project can significantly reduce
the need for many experimental cycles
by around 80%. The software platform
will be able to suggest the most
important experiments needed to
optimise AM processing parameters,
in order to manufacture parts that
meet specific target properties. The
platform will make the development
process for AM metal alloys more
time and cost efficient. This will in
turn accelerate the production of
more lightweight and integrated
aerospace components, leading to
more efficient aircrafts and improved
environmental impact.”
Intellegens will produce a software
platform with an underlying machine
learning algorithm based on its
Alchemite platform. It has already
been used successfully to overcome
material design problems in a
University of Cambridge research
project with a leading OEM where a
new alloy was designed, developed
and verified in 18 months rather than
the expected 20-year timeline, saving
about $10m.
Ian Brooks, AM technical fellow at
University of Sheffield North West,
said by harnessing two key
technologies - artificial intelligence
and additive manufacturing - Project
MEDAL hopes to unlock big benefits
aligned to the Aerospace Technology
Institute’s strategic themes on
aerostructures, propulsion and power,
and systems.
“It targets future integrated
structures by accelerating
development of new metal alloys and
optimising an AM process to create
lightweight components; its key
driver is to protect the
environment by reducing
material usage and
waste; and it looks to
minimise fuel
consumption
through
lightweighting of
components for flight
controls and
potentially landing gear
systems,” said Brooks.
While this new method is
“The most
factors. ”
being developed with aerospace in
mind, the team believes it will have
applications for other sectors too.
Brooks said: “The opportunity for this
project is to provide end users with a
validated, economically viable
method of developing their own
powder and parameter combinations.
Research findings from this project
and the project output will have
applications for other sectors
including automotive, space,
construction, oil and gas, offshore
renewables and agriculture.” MADE
/WWW.MADEIN.IE