eeworldonline.com | designworldonline.com 4 • 2020 DESIGN WORLD — EE NETWORK 17is more repeatable. For example, if your
current process produces 1,000 defects, RPA
will make 10 times as many defects, but also
produce 10 times as many good parts. This is
great for throughput but not very effective for
improving quality.
Once manufacturers started shifting
paradigms to become more data-led in their
production systems (by using historians, PLCs,
etc.), it enabled them to draw on that data
to inform the expert analysis of the process.
This results in improved control limits and a
reduction in the variance and in the number
of quality defects. It does however make the
operators ́ job more difficult, as they need to
manually maintain the process within these
finite control bounds.
The next level of improvement is to
make these adaptive changes as quickly
as possible, with an understanding of the
system from start to finish, to improve
production. This final step brings us closer
to the goal of autonomous manufacturing.
Production systems need to be flexible to
tolerate the variance in upstream processes,
without compromising on the quality of the
final output.
The future of industry 4.0 is definitely
more flexible. We ́re at a point where
artificial intelligence (AI) systems are able to
correct at the highest rate possible, which
is ahead of real-time, to produce the best
quality at the lowest cost, without the need
for a human expert.
THE STATE OF AUTONOMOUS
MANUFACTURING TODAY
The journey to autonomous manufacturing
is complicated. It’s not as trivial as simply
turning on a solution. In the final step
described previously, the AI solution needs to
offer guidance, making corrective suggestions
to the production team to improve quality.
This helps to reduce the manufacturing risk
because the quality result is assured despite a
large variance in input material. Total system
efficiency is improved because less scrap
means greater production capacity, as well as
the production of better-quality parts.
Most plants today draw data from their
production systems and send it to their
engineering or production teams. Operators
are left to follow their own inquisitiveness
as they look through the data to devise an
optimization or system improvement. There
isn ́t a holistic view or use of data from the
start to the end of a process to achieve an
overall system improvement.
These data led investigations are
also limited by the complexity of the
manufacturing system. These systems-of-
processes are often too complex to express in
the terms of classical engineering descriptions
of their processes. An engineering model that
would be able to handle material from the
start of the process through to the finished
goods is too complex to express analytically
or interrogate with traditional methods. In
addition, the traceability of the componentthrough a complex process is difficult
to achieve. Unless the system achieves
a rigorous sampling and tracking of the
component flow, people alone are not able to
join the data from step A through to step Z.
The solution to this complexity is a
system that allows manufacturers to express
the relationship between the start of a
process and the end of the process, without
having to enforce the rigorous traceability
that would typically be required. This system
requires looking at the process with a slightly
different view. It ́s important to understand
the quality result from each step in the
process to make a final quality improvement
at the end of the process.EXISTING SYSTEMS CAN BE USED
The paradigm of autonomous manufacturing
is specifically designed to work with existing
processes. The only change is that the
manufacturing system becomes more
data-led, by using production data and
quality data to make the prescribed process
changes that result in improved quality. The
journey to autonomous manufacturing is,
in fact, predicated on having an existing
production system - although it can also
work in greenfield spaces. Autonomous
manufacturing only requires enough data
to describe the process in order to make a
substantial impact on the system.PAGE TITLE WILL GO HERE
The future of industry 4.
is more flexible. We ́re at
a point where artificial
intelligence (AI) systems
are able to correct at the
highest rate possible, which
is ahead of real-time, to
produce the best quality at
the lowest cost, without the
need for a human expert.