Digital twins are increasingly being used to
help inform wider business decisions as
well as fundamentally changing business
models. And, when implemented in an IIoT (Industrial
Internet of Things) environment across a digital
thread, digital twins can share data between
different systems, providing a clearer and more
precise picture of performance on a larger scale,
such as entire factories and supply chains.
Where simulation can provide the means to
understand what may happen in the real world, a
digital twin allows engineers and operations technicians
to compare what may happen concurrent
with what is happening in real time. The digital
transformation of production processes or assets
in the field can enhance product design, optimize
asset performance, and provide insights into building
the next generation of systems and factories.
However, this is very much dependent on the
quality of the data, and the level of intelligence in
smart edge devices and sensors.
Digital twins are components of larger IIoT ecosystems
based on a multi-tiered architecture that
ranges from enterprise-level cloud network infrastructure
to edge computing services and platforms,
and culminates at the far edge of intelligent
devices and IIoT endpoints. Therefore, digital twins
are dependent on the digital thread supported by
this multi-tiered architecture.
Key differences
The underlying simulation technology used for
product testing and validation (computer aided
engineering—CAE) is shared with the virtual modeling
of physical production systems and assets in
the field to implement a digital twin. Although
both applications share the ability to execute virtual
simulations, they are not the same. While the
simulation capability of today’s integrated design/
test platforms is very powerful and has significantly
accelerated the product development process,
the capabilities of a digital twin extend well
beyond product development. In both applications
the simulation is executed using a virtual model,
but the model becomes a digital twin only after
the product is produced.
When implementing a digital twin, users
quickly understand that it needs to function
within an IIoT environment with a working digital
thread across the product/production lifecycle.
A digital twin operating in this environment can
receive real-world data quickly and process it,
allowing the product designer or manufacturing
engineer to virtually see how the real product,
equipment, or asset is operating.
While virtual simulation—for both product
testing and digital twins—uses virtual models to
replicate product functions and production processes,
there are some key differences in application
of the two instances. CAE simulation applications
usually determine if a product meets design
criteria for fit, form, and function. Conversely,
in the implementation of a digital twin, a virtual
environment is created where engineers can study
multiple simulations backed up with real-time data
and two-way flow of data between the digital twin
models and the sensors and intelligent end devices
that collect the data from the asset(s). The result
is more accurate predictive/prescriptive analytics
that drives the optimization and enhanced operational
understanding of products and assets.
A product test/validation simulation typically
uses CAE applications such as finite element
analysis, multi-physics, and computational fluid
dynamics to create a simulation model into which
the designer can introduce and test various design
elements. The resulting computer aided design
(CAD) model is basically static until the designer
introduces new elements.
The virtual aspect of a digital twin can begin
with the creation of CAD. However, the real value
of a digital twin is realized when the virtual models
receive real-time data from its physical world
counterpart. At this point the digital twin simulation
becomes active and the model changes as
real-world data is received. The dynamic nature of
the digital twin, based on constantly changing data,
gives it the ability to mature through the product
lifecycle, as well as drive business decisions based
on the maturing and improvement of the product.
Recommendations
Adoption of digital twins is currently gaining
momentum across a variety of industries, especially
as suppliers that offer comprehensive closed loop
digital twin platforms and technologies have
emerged. Embarking on a digital twin journey can
look very daunting initially, given the advanced simulation
technology involved, the operational and
infrastructure requirements, and the clear definition
of use cases you want to focus on. These use
cases can involve the entire enterprise landscape,
including product development, manufacturing
processes, and a business model development
process. Companies should research and assess
suppliers that can offer a comprehensive and integrated
digital twin that includes proven simulation
technology, IIoT connectivity, and accompanying
infrastructure architecture.
