Allowing people to break away from monotony and make their own choices is known to increase engagement
and motivation. A similar principle can be applied to machines. John Young, APAC sales director at industrial
parts supplier EU Automation, shares three ways manufacturers can benefit from machine learning technology.
Machine learning means machines
do not have to be programmed
to perform exact tasks on a
repetitive basis, they collect data and use
it to make informed decisions about their
next move. This allows them to correct
any errors and improve their operational
parameters. There are three key areas
where manufacturers can benefit from this
technology.
INDUSTRIAL MAINTENANCE
According to McKinsey, artificial intelligence
can generate a ten per cent reduction in
maintenance costs, up to a 20 per cent
reduction in downtime and a 25 per cent
reduction in inspection costs. Machine
learning is a significant player in this positive
impact of artificial intelligence.
In traditional predictive maintenance,
engineers program the thresholds for
a component’s normal operation into a
supervisory control and data acquisition
(SCADA) system. When the component
deviates from normal operation, the system
alerts an engineer of the developing fault.
The problem with this approach is the lack of
flexibility. It does not take into consideration
variations in plant activity or the context of
manufacturing processes. For example, a
system may detect a sudden increase in a
component’s operating temperature and
interpret this as a developing fault, when in
fact it is due to the machine being sterilised.
Machine learning technology means
predictive maintenance systems do not have
to be programmed with normal operating
thresholds. They use data from the factory
floor and IT systems to monitor operational
patterns and make informed decisions about
what is normal and abnormal activity.
QUALITY ASSURANCE
There are two main ways machine learning
can improve quality assurance (QA). Firstly,
it enables assembly robots to continuously
monitor and optimise their processes.
Secondly, machine learning increases the
capabilities of machine vision systems. Like
with predictive maintenance, traditional
machine vision systems for QA lack flexibility.
For example, if a product is presented to a
system in a lower illumination than usual, the
system may interpret this as a quality defect.
Machine vision systems with machine
learning capabilities use algorithms to
optimise the camera and illumination settings
for the object being inspected and for the
environment it is operating in. They can
also detect and localise objects without any
operator input.
COLLABORATIVE ROBOTS
Collaborative robots work alongside
humans but are only able to do this thanks
to machine learning technology. Because
the environment they work in is dynamic,
they must be able to adapt to a large variety
of circumstances, from things as simple
as somebody blocking their route, to more
complex situations like a new piece of
equipment being introduced onto the factory
floor.
This adaptability is important for ensuring the
work is done quickly and to a high standard,
as well as ensuring the safety of human
staff. If robots perform the same actions
repeatedly, regardless of their surrounding
environment, they can cause injuries.
Siemens’ DexNet 2.0 robotic system
demonstrates the value of machine learning
capabilities in manufacturing facilities.
Training a robot to pick up an object without
dropping it requires complex programming.
The DexNet 2.0 uses a 3D sensor and
machine learning to process information on
the shape and appearance of an object and
decide how to pick it up. As a result, it can
pick up objects that it has never seen before.
Manufacturers should continue enabling
human workers to have their own ideas
and make their own decisions. However,
they should also extend this liberty to their
machines, to increase productivity, product
quality and overall equipment effectiveness.
Luckily, you don’t need a state-of-the art
system to introduce machine learning
technology into your manufacturing plant.
Older systems can be retrofitted with smart
technology to help you make the most of the
capabilities that this technology offers.
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