September 2022 - 9

feature | digital transformation
IN THE AGE of readily available and inexpensive compute
resources, many advances play a significant role in
commerce. These include high-bandwidth data-transfer
technologies and easy access to vast data storage, combined
with advanced image-processing algorithms and
robust imaging hardware development. Today's manufacturing
industry leverages these advances to improve
product quality and reduce waste. As shop-floor assets,
contemporary machine-vision (MV) systems generate
valuable, unparalleled data for beneficial insights into
the production process.
What is a machine vision system and how does it add
value to an industrial process?
Fast and accurate MV systems enable 100% product
inspection with low false-failure rates for real-time quality
control. As more industries adopt artificial intelligence
to gain competitive advantage, MV systems also leverage
deep learning (DL) to expand the scope of inspection
types. These inspection types are extremely challenging
to solve using rules-based techniques. MV systems can
also output data that can then be fed into centralized
data analytics and visualization tools to derive rich
insights related to product quality, yield maximization,
and process improvement. In other applications, this
data can be used to build and validate models enabling
predictive maintenance, a principal trend of the modern
manufacturing process.
MV systems implement multidisciplinary technolRobot
" eyes "
is one of many
applications in
which machinevision
can play a significant
role in production
and reliability.
ogies, such as digital imaging, lighting, optics, image
processing, and programmable logic controllers (PLCs)
to analyze digital images of a product. This qualitative
analysis is based on predefined thresholds for the inspection
metrics. The acquired images are processed using
computer-vision algorithms. These systems can range
from smart cameras that fit in the palm to large inspection
cells with multiple inspection stations, including
electromechanical actuators, conveyors, and robots for
part handling. Large systems perform extremely complex
inspections and may use imaging-sensor technologies
from across the spectrum.
Other tasks of MV systems include:
outputting more complex part orientation data
for robot guidance
performing complex part measurements
reading 1D/2D machine-readable codes.
Machine-vision systems automate various industrial
processes such as quality control, part handling and
tracking, and process control.
As one of the techniques to measure quality, the data
generated from these systems are typically used to
automate separation and isolation of non-compliant
product before it leaves the plant. The inspection data
can be further analyzed in tandem with the noncompliant
products to identify process deficiencies such
as failing machinery, improper process-parameter setup,
or foreign-material introduction. Additionally, this
analysis can be essential for improving waste-reduction
methods. Manual inspections are ambiguous, slow, and
inaccurate, whereas extremely powerful MV systems
typically run in near-real time and quickly identify and
correct process issues.
Defect detection is the most common MV application.
These MV systems implement rules-based imageprocessing
algorithms for relatively simple flaws but
may also use deep learning techniques to detect highly
subjective defects or those with weak definitions. Application
examples include finding anomalies on automotive
parts, missing paint/primer on oriented strand
board (OSB) plywood, incomplete thermal seals, and
font-agnostic OCR.
MV systems excel at performing real-time part measurements
with high accuracy and repeatability. Known
as MV gauging systems, these capture an image using
specialized lenses designed to minimize distortions and
then place virtual calipers to measure various features
extremely quickly. These systems can be installed inline
to measure critical features of every part being produced.
One class of MV systems is designed to serve as a
robot's " eyes " to guide parts. Typically, a vision sensor
images a bin full of parts; the vision algorithms identify
the candidate parts in the visible layer of the bins and
determine their location and orientation in space. This
location and orientation data is then transferred to the
robot to be efficiently picked up and placed in a specific
location. Examples include product orientation on a
conveyor, part transfer and singulation from bin to conveyor,
and part alignment. This solution is also implemented
for automated assembly and machining.
MV systems can also provide vision-based feedback to
control processes. The MV-generated measurement data

September 2022

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