We're here in Orlando, Florida, at the Siemens Spotlight on Innovation.
The concept of a digital twin is becoming increasingly more important in manufacturing.
Today, we're speaking with Dr. Norbert Gaus of Siemens.
He's one of the world's experts on the digital twin concept.
Tell us about your role at Siemens.
I'm with Siemens Corporate Technology.
My responsibility there is all technologies around digitization and automation.
What does that encompass?
That encompasses technology areas like the digital twin like simulation, like cybersecurity,
artificial intelligence, IoT, and many, many other areas, software systems, so all technologies
that bring together the world of IoT and digitalization.
Dr. Gaus, when we talk about a digital twin, please tell us what that is.
A simple answer is, the digital twin is a digital representation of a physical product
in all its aspects.
Now, the last part of the sentence really is the critical one because it means it covers
the whole lifecycle of a product.
It describes the product in designing it, designing it from a mechanical point of view,
describing it from embedded software, from a flow mechanics point of view, and in many,
many other aspects, electrical aspects.
A digital twin also describes how to manufacture the product.
It describes how the product behaves during operation and in service and in maintenance.
It covers the whole product lifecycle.
Why use a digital twin?
If we have a digital twin that really represents the physical product in the digital world,
then we can, where we want, perform tasks in the digital world, allowing us to do things
faster.
For example, simulation instead of timely and costly prototyping.
We can do things much more often until we find the optimal solution.
We will spend less resources, both monetary resources but also manpower.
The digital twin can be used during all phases of the product development lifecycle, the
evaluation, and then the ultimate even use of that product?
Yes, in all phases.
It's essentially a simulation then based on the most accurate data that you can possibly
pull together.
It's a bit more than a simulation.
It's a kind of a simulation of many different models instead of just one.
For the purpose, you then pick the ones you really need for this application.
Tell us about those models.
Typically, the lifecycle starts in designing a product or in designing a plant.
This is where you pursue many different avenues.
You design products.
You build CAD models.
You simulate those CAD models.
You pick alternatives.
You try to optimize.
You try to get an idea about the embedded software that has to run on the model, on
the physical product.
You model this without really implementing and coding it in detail.
You simulate in all the different aspects, so it's models in the CAD area.
It's simulation models for software.
It's computational flow dynamics.
It's simulating electrical circuits.
It's a very broad set of models in the design space.
Then, in the next phase of, let me call it, a life of a product when it goes into manufacturing,
for example, or installation if it's a larger plant, then you simulate different things.
You simulate or you design how to manufacture the product, how well it's built for manufacturability.
That allows you already there in the digital space to build a feedback loop designed for
manufacturability by simulating how it's manufactured.
Once you have designed the manufacturing, you can engineer it.
You can, for example, automatically feedforward this information and automatically generate
the PLC code, for example, so it's a feedforward loop and a feedback loop you already have
in these two phases.
During the manufacturing process then, you try to, of course, optimize the manufacturing.
All of a sudden, you need a pretty accurate model of the manufacturing site itself so
that you can optimize this part of the lifecycle.
The next phase of a lifecycle then is the longest one, which is operations.
In operations, then you also want to optimally operate the system.
You want to, for example, optimize the efficiency of a gas turbine or minimize the emissions
of a gas turbine.
You want to minimize downtime of a large motor, and so on and so forth.
During the operation, you need a model that is less complex.
You cannot afford hours of CPU times if you want to make adaptions and optimizations in
real time.
You need to reduce the order of the model, significantly reducing the complexity.
At the same time, for the critical components you want to optimize, the model has to be
still pretty accurate.
You reduce the order of the complexity of the model so that you can do inline and real-time
kind of real-time simulation to optimize operations.
Then there's another important phase.
This is service and maintenance.
These are typically data-driven models.
Artificial intelligence, when it comes to predictive maintenance, in a way is nothing
else than just another kind of a model, driven and defined by data, but it's a model.
This is the last part of the model in our lifecycle.
Important is that that it's really a combination of a feedforward loop, but you can automate
some of the steps increasingly more so.
It's also a feedback loop from service into manufacturing, from service into designing
the next product generation, from the factory back into design, and so on and so forth.
With so many components, how do you ensure that, at each step of the process, of the
lifecycle, that the digital twin is accurately, completely accurately, representing the physical
object itself and the characteristics, the behavior, and the implications that flow from
that?
As a company, we started this journey 15 years ago; more than 10 years ago, I have to say,
and invested a lot to ensure exactly the consistency between the very different representations
to find the hooks and handles between the mechanical design and the flow design where
embedded software comes in and what it does, and so on and so forth, really finding the
hooks and handles and implementing them.
Another very important component is the products we sell.
They have a lifetime, depending on the vertical market, of between 10 and 30 years.
Now from the consumer side, we are all used to replace the devices we have in this field
every two or three years.
We have to ensure that what we sell from a digital twin perspective is still an accurate
representation also after 10, 15, and 20 years, so we really also need to manage the lifecycle.
We have to be able to update software and represent this in the digital twin.
We have to ensure that the as-built digital twin is where the as-built is changing.
You can imagine, if you build a larger plant, what is on the drawings in the design that
there will be some changes when we build this large plant; that the changes are being fed
back into the plants.
These processes have been established and that has been a major part of our investment.
It's not only bringing the capabilities of defining those models into our company.
The big step really then has to be to integrate this as a real toolbox and suite.
Can you elaborate more on the role of artificial intelligence in the building, the use, and
the benefit of digital twins?
The artificial intelligence is a technology that, together with the digital twin, I think
stands out when it comes to digitalization.
A digital twin stands out because digitalization means to having a digital representation of
the physical product.
Then it's artificial intelligence because digitization really is about data from products
in the fields, in the factory, from wherever, and then generating value from these data.
That's why these two technologies stand out.
They do come together.
Actually, they have always come together because remember what I said at the beginning of our
discussion is, when it comes to maintenance, especially predictive maintenance, preventive
maintenance, you do build models based on data.
Now, these are also models and part of the digital twin so, in a way, artificial intelligence
actually has always been part of the digital twin.
While I know that for most people this was a different world, and it was really a separated
world, now it does come together.
It also comes together in other areas of the digital twin.
We use artificial intelligence for model order reduction, as one example.
While model order reduction has always been a known technology, in some cases we need
higher complexity and nonlinear models where it's very difficult to, more or less, linearize
and use traditional technologies, so we use a neural network to generate a model that
pretty accurately represents a motor, for example.
That's very interesting.
That's the way we do it.
You're using AI to help you create the model that's then used in the digital twin.
Exactly.
We feed the network with the very complex design model, for example, out of an X with,
let me say, one billion degrees of freedom.
We push it through a model to cut it down to maybe a hundred degrees of freedom, and
then make sure that we, from those 100 states, we still would be able to represent, to recalculate
the critical states.
That we do with a neural network with deep learning, helping us to, where we cannot,
for example, measure critical states of large motors, simulate this in real time and still
know how the motor behaves.
We also use artificial intelligence in generative design.
Now, some ways of generative design is not new but, with artificial intelligence, what
we try to do is--and not only mechanical design; also designing electrical circuits--first,
we widen the design space in which we look.
But if you widen the design space, you get many more design options.
Now, typically, all these options have to be simulated and these simulations are very
time-consuming.
We are not using artificial intelligence to simulate, but we use artificial intelligence
to preselect options that have the highest chance, for example, in a finite element simulation
to converge.
We first widen the design space with artificial intelligence in exploring it and then we also
use AI to reduce the amount of simulations we need, which I think has huge potential.
We do this in a few areas already and I'm convinced that it really can help us to be
much broader.
What are the best applications for digital twins, looking at it from the customer perspective?
Actually, I don't think there is a best application.
I'm really convinced that digitalization is about the whole lifecycle depending on the
vertical industries.
Of course, in some of the vertical industries, not in all phases, you really need a digital
twin.
Some products will all always just work and operate and you will not need real-time inline
simulation.
In essence, every market, every product owner has to understand that he or she needs some
kind of digital representation throughout the whole lifecycle to speed up time to market,
to reduce cost, to being able to have a much broader portfolio and offering to the market,
to use the data that is coming from the field, one way or another, from sensors, from service
reports, from many data sources, which today, in in a lot of cases, are not really being
utilized, not really understanding there is a lot of value in these data and this value
needs to be feedback into the future product lines.
Again, I don't think there is a priority.
There are priorities, at the same time--now, this may sound like a contradiction--depending
in which market you are, the majority of your product lines, and so on and so forth.
But in terms of a goal, you have to cover the whole product lifecycle.
What advice do you have for business leaders who are listening to this discussion and saying,
"Yeah, this sounds very interesting, but what should I do?"
The most critical decision is how to get started on this journey.
There is not a blueprint in which product, in which lifecycle phase a company should
start, but it needs a careful analysis of, what are the dynamics of our markets; where
do we as a company want to differentiate against our competitors; where do we differentiate
today; where do we want to differentiate in the in the future for what reason; and how
can digitalization, which is, basically, in essence, a digital twin, really helped us?
As you define where you want to differentiate, these should be the areas where you get started.
When you say differentiate, so that the core components of, say, your business or your
strategy.
Yes.
We all have competitors in this world, and we all need to find the core markets where
we want to be a leader and what's important to them.
This is where we need to differentiate or want to differentiate as our product strategy.
There are other parts of a corporate strategy but, from a portfolio strategy point of view,
and where we want to differentiate on the portfolio, I think this will only be possible
by being also leading in the digital part of the portfolio.
Fantastic.
Dr. Norbert Gaus, thank you so much.
You're very welcome.
It was a pleasure.