Siemens has developed a showcase to demonstrate how the industrial
metaverse can help manufacturers collaborate and work in the future.
In collaboration with FREYR,
a Norwegian clean battery producer,
Siemens developed an industrial metaverse experience based on their first
FREYR Gigafactory.
The process began with importing physical and digital data sources to the
Siemens Xcelerator platform and leveraging IoT twin maker from AWS and
the NVIDIA Omniverse to create this Xcelerator.
Industrial Metaverse Experience. There are many ways for FREYR to use this
industrial metaverse experience.
In the first example,
FREYR executives use the industrial metaverse to sell a plant to
prospective investors,
giving them confidence in the proposed design.
Through the ability to interact with a fully functional operational model
of the plant,
users can browse the location and status of plants across the globe at
different levels of construction.
Operationally key KPIs and information are displayed for each plant.
For example,
when the Giga Arctic factory in Norway is selected,
it is easy to see greater detail about the plant,
including geographic information and the surrounding terrain.
This is a fly through of the plant starting in the material Bay slurry
mixing.
This is the most critical process area.
Each of these assets is connected to the digital twin,
so all aspects of the equipment can be simulated.
Key information,
including critical KPIs,
real time series,
data issues,
service history and requests are available for each piece of equipment.
This includes the ability to look at OEE information to understand
current operational performance,
availability and quality,
as well as to understand the overall performance or make predictions
about future performance.
The Quality Prediction Hub allows production managers,
quality and manufacturing engineers and operators to better understand
the complex relationship between production machine parameters and the
resulting product quality.
With real time input of actual manufacturing parameters,
we can apply machine learning techniques to automatically predict
resulting product quality characteristics before a defect and quality
loss occurs.
Mixing is a critical process,
so leveraging the digital twin to optimize all aspects is essential.
This high fidelity simulation of the laminar and particle flow of the
mixing process is leveraged to optimize the stirring process.
In the Industrial Metaverse showcase,
we can engage with any asset to understand the operational performance or
what process it may be performing.
This robot,
like any asset,
includes many data sources as part of its digital twin.
This can be used to dive into the performance of the main control drives,
identifying anomalies and service requests or schedule maintenance.
This showcase demonstrates how,
in the Industrial metaverse,
users will be able to make the best engineering decisions because of
precise, accurate UpToDate information.
For example,
a service engineer may want to understand the detailed digital design
information for this robot,
including the bill of materials and detailed 3D design information.
In the industrial metaverse,
this can be explored not only from a mechanical perspective,
but also electrical and software,
even down to the tolerances of the machine.
This is an emergency backup generator the Energy Manager dashboard allows
quick access to generator metrics,
the overall share of the generator to the whole factory consumption,
as well as telemetry information about its condition.
The flow charts depict the energy flow across the factory and from the
share of supply to the individual departments.
It is clear where most of the resources in the plant are being consumed.
The Energy media reports enable users to better understand what resources
over time are being used by equipment in financial terms or CO2
equivalents. Passing through the aging area onto where the battery packs
will be assembled.
This area was added by Siemens for demonstration purposes as FREYR has
not yet finalized this area.
The digital twin drives the precise orchestration of each robot,
each cell,
and also how they interact in harmony across the entire line.
Digital twin behavioral models are not restricted to just machines and
processes, but can also be used to optimize and validate human
interactions and processes.
And these can be used to simulate or optimize for safety or performance.
Using true physicsbased,
human models to optimize tasks and working environments enables use for
training and documentation.
Siemens is excited about the potential for the industrial metaverse to
improve how we design,
collaborate and manufacture products.
Another use case looks at leveraging the industrial metaverse from a
plant operational perspective.
A plant manager arrives to work to find that the previous shift performed
below its targets.
He quickly looks at the key events and reported issues that were recorded
or triggered.
But the first challenge is to work out how to get the weekly production
targets back on track.
Optimize my plant unlocks the digital twin of the factory and allows
users to predict the performance of their production systems easily.
The predicted KPIs for the next couple of weeks are based on the current
production forecast.
An issue is found on assembly line one.
A robot is failing and it requires urgent maintenance.
We quickly simulate that situation to predict the impact this will have
on the overall performance of the factory.
We see here a 9% impact on overall.
Throughput a couple of simulation,
scenarios are evaluated.
The first is temporarily moving that area to a 24/7 setup.
The second is to temporarily increase the speed of the two other lines to
evaluate if running at high speed will be worth the tradeoff in energy
and quality.
The simulation can predict the upcoming production plan and compare our
alternatives. With the baseline now set to our degraded situation due to
maintenance, we can quantify the performance of the two other scenarios.
Modifying shifts is really outperforming.
The other option,
increased speed,
is not having a significant impact due to the fact the lines were not
fully utilized previously.
Now let's investigate the issue with the robot that is affecting
production. Start by looking at recorded events and triggered issues.
Robot limits have been periodically exceeded and a report that the robot
is jerky at certain times.
You can quickly pull up time sync sensor data with multiple recorded
camera feeds for the robot at the time of the concern.
Clearly there is an issue.
Next we can pull up more detailed information about the health of the
robot. As this robot is used in many locations and the same issue has
been recorded elsewhere.
As this user is not an expert,
they start a collaborative Microsoft team meeting around the open ticket.
Experts from around the globe and even the robot manufacturer are
conferenced in to investigate the issue further.
An executable digital twin model is leveraged to compare how the robot
performed last week and now against its current state to identify problem
area. The team then looks at detailed design information,
narrowing the issue down to a faulty bearing.
The issue ticket is closed and the meeting recording is saved as
resolution. An urgent service ticket request is then opened.
A service operator on the shop floor receives the request with the
location of the robot and its level of importance.
After understanding the request in more detail,
the service operator is given step by step instructions on how to carry
out the repair via their AR headset.
Siemens continues to work with customers and partners to develop and
validate use cases where the industrial metaverse can drive true business
value. All.