SECTOR FOCUS INDUSTRIAL ELECTRONICS
one type of machine, it’s relatively
easy to retrain and adapt and reuse;
you can take AI and repeatedly reuse it
instantly making data scientists more
productive,” Cresci said.
Data scientists can be called on
to make sense of data outputs during
testing. This analysis can then be
the basis for a neural network and
AI learning. At United Technologies,
a helicopter sub-system was tested
using a digital twin as the sub-system.
A data scientist was able to examine
the data and identify what was normal
behaviour, what was due to wear and
what was a potentially failing spindle.
Once these behaviours were labelled,
AI was able to learn and repeat with
increased accuracy.
Graphics processing units (GPUs)
enable the compute-intensive
operations in real time, which is
bringing AI to mainstream use,
continued Cresci. The company’s
CUDA (Compute Uni ed Device
Architecture) is a parallel computing
platform and application programming
interface (API) model for GPUs. AI is
maths – essentially, statistical linear
algebra, explained Cresci, which will
be too slow without a GPU. Company
scientists can write algorithms to run
on NVIDIA libraries and use Jetson,
the industrialised supercomputer on
a module, to train large data servers,
with smaller Jetsons at the edge for
local decisions.
AI can examine the data and
identify an anomaly and continue to
learn and identify errors. A system
specialist can label that anomaly,
which is used in the next level of
training when something is wrong,
which is easily identi ed using
NVIDIA’s hardware, compute
systems, software infrastructure and
tools, such as Tensor ow, proposed
Cresci.
“In my opinion, this is one of the
biggest bene ts of AI – an ability to
be repeatable and learn in a new
environment very quickly,” he said.
There are several tools that can guide
operators through a remote factory and
provide information via tags connected
to IT and product lifecycle management
(PLM) sources.
“We see the digital twin as the key
to optimising the performance from the
cell to the factory in a global network,”
commented Thomas Maurer, Senior
Director, Strategic Communications,
Siemens Digital Industries Software.
All of its factories operate digital twins.
Its Intosite is a ‘classic’ digital
twin tool, used for visual planning,
simulation and monitoring. Maurer
quipped that it is GoogleEarth for
manufacturing. Operators can be
in an of ce and managing a global
network of factories. It oversees
order ful lment and stock levels for
the environment in which the plant is
operating at that time.
As demonstrated at Hannover
Messe, the connected factory and the
sensor data it produces is becoming
more relevant. Information can be
gathered from sensors for worrying
conditions and to understand why
a system is varying from its normal
course. For example, in a PCB factory,
a re ow oven can be monitored for
the correct temperature and speed
to ensure the soldering and machine
quality are to the expected levels.
For overall equipment effectiveness
(OEE) and data analysis, the
MindSphere application platform
enables developers to create apps
from information from multiple sources
to navigate and contextualise the
equipment in a particular factory. It is
also possible, added Maurer, to reach
down to an individual machine and
interrogate individual products.
It connects machines and can
turn them into machines with
sensors: “essentially connected
to change the machine into an
edge device,” Maurer said. Using
condition monitoring algorithms
for comparison with historical
data, it adds intelligence.
It can initiate activity when
routed through Teamcenter, the
company’s enterprise lifecycle
management tool. It supports
managers in planning, initiating extra
shifts, changing resources and assess
the product on the production line
itself.
Maurer explained its role of Action
on Insight as part of the closed loop of
the digital twin. “It can ask: Under this
workload, we are seeing this condition
– is this normal or do we need to look
at it?”
By comparing against the baseline
data, the digital twin can also ‘Marty
McFly’ a production line. Maurer
explained: “By being able to look at
future capacity requirements, based
on the factory’s performance, it is
possible to identify where capacity
is expected, what material will be
needed and where to meet this future
demand. Then it can go ‘Back to the
Future’ and make adjustments”.
The importance of operating in real
time is vital when products are built to
order. Designers used to be focused
on validating a design, now they use
simulation and AI to drive generative
design.
The idea of generative design,
using AI, marks a paradigm shift in
design for Maurer. Most companies
can optimise the topology but using
computational uid dynamics (CFD) to
effect what the design is going to look
like creates an exciting prospect.
“CFD, machine learning and
an interactive design process can
optimise design-based simulation,”
he asserted. He conceded that
investment in machine learning is
still needed but today electronics,
mechanical and physical engineers are
all using simulation to drive design.
Generative design will be the
convergence of simulation and
design, and allow engineers to
concentrate on the functional
part of the design. “Design
is going to be model-based,
using functional design
tools to develop functions,
and physical design, using
intelligence to feedback and
see what a design does,”
predicted Maurer.
“CFD, machine
learning and
an interactive
design process
can optimise
design-based
simulation.”
Thomas Maurer
Below: Data
scientists can run
algorithms to run on
Nvidia libraries and
use Jetson to train
large data servers
with smaller Jetsons
at the edge for local
decisions
www.newelectronics.co.uk 10 September 2019 21
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