FUSION Issue 3-2026 DIGITAL - Flipbook - Page 31
Sensors scan
physical
object
Virtual model
updates in real time
Where it is already working
The aerospace sector has led adoption,
for understandable reasons. The cost of a
defect in an aerospace composite structure is
enormous, and the regulatory requirement for
traceability is stringent. Airbus has connected
over 12,000 aircraft to its Skywise platform,
where real-time sensor data feeds virtual twins
that monitor structural health and predict
maintenance requirements. At its composite
manufacturing facilities, digital twins monitor
parameters including speed, pressure,
temperature, and humidity at draping stations
to detect quality issues before they propagate
through the production process.
The EU-funded GENEX project, which
concluded its 42-month research programme
earlier this year, demonstrated an end-to-end
digital framework for composite aerostructures
that synchronised physical manufacturing
with real-time virtual models across design,
production, structural health monitoring,
repair, and lifecycle management. The results
pointed clearly toward predictive maintenance
and production consistency improvements
that would be relevant well beyond aerospace.
In automotive manufacturing, digital twins of
entire factory environments are now being
used to plan and validate production changes
before implementation. The value is not
primarily in avoiding catastrophic failures; it is
in the accumulation of marginal gains: fewer
unplanned stoppages, faster changeovers,
tighter process tolerances, and reduced
material waste.
The composites opportunity
For composite manufacturers, the technology
addresses several of the most persistent
production challenges simultaneously.
Process consistency is the obvious starting
point. Composite manufacturing involves
a large number of interacting variables, and
Insights fed back
into production
“ Digital twin technology
has crossed from
large-scale aerospace
programmes into
broader manufacturing.
The question is
whether businesses are
ready for it. ”
maintaining consistent quality across shifts,
across operators, and across production runs
has always relied heavily on skilled human
judgement. Digital twins do not replace that
judgement; they support it with data that no
individual operator could hold in their head
across a full production environment.
model of what conditions produce the best
results is not a theoretical future state. The
sensor technology and data infrastructure
required to build it exist now. What has been
missing in most finishing operations is the
framework to connect those inputs into
something actionable.
Material traceability is another area of direct
relevance. As supply chain scrutiny increases
and customer quality requirements tighten,
the ability to demonstrate exactly what
happened to every component at every stage
of production is becoming a competitive
differentiator. A digital twin that logs process
data in real time creates that audit trail as a
by-product of normal operation, rather than as
a separate documentation burden.
As the technology matures and the cost of
implementation falls, finishing operations
that invest in this kind of process intelligence
will have a measurable advantage in
consistency, waste reduction, and the ability
to demonstrate quality to customers who are
increasingly asking for it.
Tooling and cure cycle optimisation represent
a third significant opportunity. Autoclave and
out-of-autoclave processes involve energyintensive cycles that are often conservatively
specified to guarantee quality across a range
of conditions. Digital twin modelling allows
manufacturers to test variations in cure cycles
virtually and identify opportunities to reduce
energy consumption or cycle time without
compromising part quality.
What it means for
finishing and coatings
The paintshop and finishing environment
presents a particularly interesting case.
Coating application quality is highly sensitive
to environmental conditions: temperature,
humidity, surface preparation standard, and
equipment calibration all interact in ways that
experienced operators understand intuitively
but that have historically been difficult to
monitor systematically at scale.
A digital twin of a spray booth environment
that monitors conditions in real time,
correlates those conditions with application
outcomes, and builds a continuously improving
The practical starting point
For most manufacturers outside the large
aerospace and automotive primes, a full
factory digital twin is not where the journey
starts. The more realistic and more immediately
valuable entry point is a process-level twin:
a virtual model of a single critical process,
whether a resin infusion line, an autoclave
cycle, or a spray booth, that captures real-time
data and begins building the kind of process
intelligence that supports better decisions.
The investment required has fallen significantly.
Cloud-based simulation platforms have
reduced the infrastructure cost. Sensor
hardware is more accessible than it has ever
been. And the workforce skills required to
operate these systems, while not trivial,
are increasingly available through training
programmes specifically designed for
manufacturing environments.
The manufacturers beginning this journey
now, even at a single-process level, will
be considerably further ahead when their
customers start asking for the kind of process
traceability and quality assurance that digital
twins make possible. In aerospace and premium
automotive supply chains, that question is
already being asked.
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