FT654
www.foodtechnology.co.nz 35
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
/www.foodtechnology.co.nz
