Sustainable Plastics - May/June 2021 - 12

quality assurance
Reliable quality
inspection of plastics
with Autonomous
Machine Vision
The S70 Gen.2 takes images
with diff erent light intensity
and direction and fuses them
into a single reflection-less
image Inspekto photos
For traditional machine vision solutions, inspecting shiny
surfaces can be challenging due to refl ections on the surface,
which distort images and lead to inaccurate defect detection.
Because this issue is common in plastics, it often prevents
plastics manufacturers from using automated quality assurance
(QA) methods. According to Yonatan Hyatt, CTO at Inspekto,
Autonomous Machine Vision systems can help.
M
achine vision
systems are the
backbone of
quality assurance
and are commonly used to inspect
the results of all plastics
processing methods, including
injection moulding, rotational
moulding, extrusion, compression
and more. These systems
can identify products with manufacturing
defects using cameras
that analyse the product from
diff erent angles. The data is then
processed by integrated software
that detects faulty products,
which are then removed
either manually or automatically
from the production line.
However, there are several
shortcomings to the use of
traditional machine vision systems
to inspect plastics, says
Hyatt. " One common issue is
the inspection of highly reflective
surfaces. Light reflections
create a certain visual noise
that is seen by the system's
cameras as a physical feature
of the inspected part. The system
might in turn classify these
features as defects, generating
false rejects. Similarly, reflections
can also cause certain
areas of the inspected product
to appear hidden, thus preventing
inspection. This means that
for plastic packaging or other
glossy items, the systems' inability
to cope with reflections
can be a serious pain point, " he
explained.
On top of this, the fact that
12
May/June 2021
the same production line can
create items of diff erent colours
and shapes is problematic for
traditional machine vision solutions,
only capable of inspecting
one product at a time. This
is especially common in plastic
injection moulding, where frequent
mould changes are made
in order to be able manufacture
a variety of products.
To solve this issue, human
workers need to perform quality
assurance, alone or alongside
machine vision systems, to meticulously
inspect each product
and reject the faulty ones. This
process can be exhaustive and
too repetitive for humans, leading
to errors. Faulty products
could be sent to companies and
rejected further on, causing a
disruption in the logistics chain
and amplifying waste.
Autonomous
Machine Vision
Autonomous Machine Vision
(AMV) is a new approach to
quality assurance, designed to
be more than automatic, but
autonomous, from determining
the ideal number of samples the
system needs to learn the characteristics
of an item, to self-adjusting
the camera and lighting
settings to obtain the best
image possible of the product
to be inspected. The system's
algorithm autonomously optimises
the camera and illumination
settings to take the best
possible image of the object to
inspect, and then detects and
locates the object without any
input from the operator.
The only AMV system currently
on the market, the Inspekto
S70, has just been relaunched
with new and improved settings
that allow it to take clear and
informative images of even the
shiniest objects, so that they
can be reliably inspected.
" The S70 Gen.2 is equipped
with 5000K LEDs that are controlled
by pulse-width modulation
and arranged in several
distinct segments. This allows
the system to autonomously
control the direction of the illumination
and take several images
with varying light direction
and intensities, " said Hyatt. " The
images are then fused to create
a single reflection-less HDR image.
This patent pending technology
makes the system ideal
for the inspection of highly reflective
plastics. The innovative
illumination system is designed
to avoid the human eye from
seeing any flickering or light
variation, so workers nearby
will not be disturbed by sudden
light changes. "
As AMV systems are flexible
and applicable to a huge variety
of use cases, they can also allow
multiple products to be inspected
at the same location on
the production line, contrary to
what traditional systems are capable
of. This makes them ideal
for applications where the same
production line manufactures a
variety of items, such as in plastic
injection moulding.
Autonomous
Machine Vision AI
At the core of Inspekto's systems
are three independent
and synergetic AI-based engines
driving a proprietary electro-optic
system, a technology
called Autonomous Machine
Vision AI (AMV-AI). The first
engine is in charge of adapting
the operating parameters
of the electro-optical system
in real-time to take clear and
highly informative images of the
object to inspect even in changing
environmental conditions.
The second is the detection and
alignment AI module, which allows
the system to recognise
the parts it sees in the live video
stream from the camera and
identify the best moment to acquire
an image to perform the
last task - inspection. Lastly, the
inspection AI engine inspects
the part by comparing it with
the sample images memorised
during set up, without involving
any experts or AI-training in the
process.
Due to the fact that the system
is highly cost-eff ective,
manufacturers can choose to
implement not just one, but
several inspection systems
along the production line to en

Sustainable Plastics - May/June 2021

Table of Contents for the Digital Edition of Sustainable Plastics - May/June 2021

Contents
Sustainable Plastics - May/June 2021 - Cover1
Sustainable Plastics - May/June 2021 - Cover2
Sustainable Plastics - May/June 2021 - Contents
Sustainable Plastics - May/June 2021 - 4
Sustainable Plastics - May/June 2021 - 5
Sustainable Plastics - May/June 2021 - 6
Sustainable Plastics - May/June 2021 - 7
Sustainable Plastics - May/June 2021 - 8
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Sustainable Plastics - May/June 2021 - 34
Sustainable Plastics - May/June 2021 - Cover3
Sustainable Plastics - May/June 2021 - Cover4
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