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. Yes. 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.
