Machinery Update July / August 2020
The July / August 2020 issue of Machinery Update.
The July / August 2020 issue of Machinery Update.
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www.machineryupdate.co.uk JULY/AUGUST <strong>2020</strong> MACHINERY UPDATE 53<br />
phase is finalised. Indeed, it<br />
must be static and can no longer<br />
change to make it versioncontrolled<br />
for validation.<br />
Syntegon Technology is<br />
currently working on such<br />
a project to implement Deep<br />
Learning algorithms for<br />
the inspection of syringe<br />
stopper edges on its AIM 5023<br />
inspection machine.<br />
The pharmaceutical industry<br />
is known for its conservative<br />
approach to innovation.<br />
This is mainly due to the very<br />
strict regulatory guidelines for<br />
process validation – overall<br />
a highly positive attribute<br />
since the manufactured<br />
products have a direct<br />
impact on the health and<br />
safety of patients. “Such an<br />
ambitious project needs a<br />
lot of experience regarding<br />
software implementation and<br />
process validation to push<br />
the concept beyond the finish<br />
line – a combination not every<br />
machine manufacturer can<br />
offer,” says Dr Jose Zanardi<br />
who is responsible for<br />
inspection development and<br />
applications at Syntegon.<br />
BESPOKE APPROACH<br />
Typically, a ‘one size fits all’<br />
approach will not work in Deep<br />
Learning projects for visual<br />
inspection. Instead, the first<br />
step should consist in a preassessment<br />
based on a large<br />
number of diverse images<br />
from reference samples.<br />
“In our example this could<br />
be images of good units with<br />
bubbles, different stopper<br />
positions, products and fill<br />
volumes for body inspection,<br />
as well as different types<br />
of particles intrinsic to the<br />
process,” says Zanardi.<br />
Based on the available<br />
image data, offline verification<br />
studies provide the basis for the<br />
integration of Deep Learning<br />
models into the existing<br />
software. In the second<br />
step, a customer-specific<br />
project should be defined<br />
with parameters such as<br />
product, existing machinery,<br />
expectations and timeline.<br />
“We believe that this<br />
technology has the potential<br />
to achieve detection rates<br />
close to 99% in the future<br />
while reducing false reject<br />
rates dramatically by half<br />
or more,” says Zanardi.<br />
He is confident that the Deep<br />
Learning application can<br />
be implemented in a GMP<br />
environment – and will obtain<br />
regulatory endorsement for<br />
both the qualification strategy<br />
and implementation.<br />
T 01332 626262<br />
W www.syntegon.com<br />
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