- In today's hypercompetitive environment,
there's pressure to develop and bring to market
products faster, to identify efficiencies
and how they are built, and to incorporate
innovations in design and features.
This is particularly true of highly sophisticated
machinery and other complex systems,
including buildings and cities.
In the new product introduction, or NPI, process,
the luxury of spending months and even years
designing an improved or new system
seldom exists anymore.
Being able to relatively quickly create a accurate,
functioning virtual replica of a future system
and test it under all manner of scenarios
is highly desired and beneficial.
This is the role of a digital twin in the design phase.
In the past few decades, the tools available
to a design engineer have improved greatly.
Initially, system design was done
by a highly skilled hand and then supported
by protractors, compasses, and stencils.
However, beginning in the late 1950s,
electronic support for design arrived.
The productivity of two-dimensional
or 2D drawing quickly increased.
In the 1960s, support for three-dimensional,
or 3D models was introduced.
Capabilities in electronically supported design
closely aligned with the evolution of microchips
and computers.
In the 1970s, the notion of comprehensive
computer aided design, or CAD, was introduced.
In 1982, AutoDesk began selling AutoCAD,
the first 2D CAD software made for personal computers
instead of mainframes and mini-computers.
This revolutionized and democratized
all manner of system and product design.
In the years that have followed,
CAD software has become sophisticated and powerful.
Remarkable design and collaboration
can now be achieved as a result of high performance
processors, including those focused on graphics,
the availability of big data, fast global networks,
massive storage, cloud technology,
and artificial intelligence.
Today, a design engineer can render
a detailed 3D model, manipulate all or part
of the model, and view from any perspective.
Its operational behavior can be simulated
and better yet, conceivably every exhaustive variant
of environmental factors can be emulated.
By extending 3D CAD with several additional capabilities,
we get the tools for designing a digital twin.
The software that powers a digital twin today
is typically a collaboration platform.
Multiple stakeholders in the design process
can engage as necessary.
For example, the use of model-based systems engineering,
or MBSE, takes the emphasis off the traditional
hierarchy complexity of documents and drawings.
It does this by testing and validating
system characteristics early with other engineers
and stakeholders, enabling faster feedback
on requirements and design decisions.
Another important characteristic of a digital twin
in modeling is the use of data to inform design.
In particular, historical data can be fed
into the model to simulate how it performs
under the new design.
Hypothetical data is also essential
for predicting behavior later, in the physical system.
Simulations can also be used to design
not just the product, but the production line
for manufacturing the product.
The most state-of-the-art design processes
are now supported by what's called generative design.
In this approach, designers provide parameters,
such as required materials, manufacturing methods,
and cost constraints.
The software then explores multiple permutations
to generate several design alternatives.
Simulations then test and learn from each alternative
in order to determine what design works
and what doesn't.
We're beginning to see digital twins
in the design phase used to form
higher quality 3D printing, or additive manufacturing, too.
Simulating a 3D manufacturing process
can result in better outcomes as the impact
on complex design and material choices
can be known and refined in advance.
So in summary, a digital twin in the design phase
is used to more accurately predict the performance
of a to be built product or system.
It serves to verify and validate design choices.
It provides design engineers with the confidence
that the physical end result will meet expectations,
avoid failure, and generally reduce risk.
Finally, it's no surprise that when recently asked
in a poll, organizations said that the biggest benefit
of using a digital twin in the design phase
was to ensure product quality.