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 3. AI and CV Transform Quality Automation on the Factory Floor


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AI AND CV TRANSFORM QUALITY AUTOMATION ON THE FACTORY FLOOR

Published Date May 27, 2021 Author Stephanie Vozza



If you’ve ever purchased a defective product, you know how frustrating it can
be. You probably wondered, “How can this happen?” For decades, quality control
has been an unsophisticated process, typically involving QC specialists
performing spot checks of products on a factory line or warehouse floor. Due to
human error, faulty units make it out the door and into the hands of customers.
The result? Costly waste, warranty claims, and dings on the company’s
reputation.

“Checking components is not an easy job,” explains Serhan Can, Director of AI at
Relimetrics, a computer vision (CV) and machine learning (ML) software provider.
“Human operators get tired and start missing defects. Plus, there’s a huge time
pressure on manufacturers and logistics companies that need to make and ship
goods. The quality control process needs to take as little time as possible.”

While machines can do repetitive work like inspecting parts, their capabilities
haven’t yet been used to the fullest due to complexity and expense. But today,
new tools can simplify the process.


EVOLUTION FROM QC TO QA

Moving from quality control (QC) to quality automation (QA) can help companies
get to zero defects, but the transition hasn’t happened overnight. Over time the
technology has matured and the latest iterations leverage AI to detect anomalies
and adapt to production variabilities in real time.

To understand the impact these new quality control systems can have and where
your company falls on the automation progression, consider this QA maturity
model with five distinct levels, defined by Relimetrics and ABI Research:

 * Level 1: Humans collect and assess data.
 * Level 2: Cameras collect data and humans assess it.
 * Level 3: Cameras collect data, traditional CV software identifies issues, and
   humans resolve problems.
 * Level 4: Cameras integrated with AI-based machine vision software collect
   data and identify issues, and humans resolve problems including false
   detections by the AI.
 * Level 5: QA is fully digitized and automated, with human involvement only in
   marginal cases. Cameras integrated with AI-based machine vision software
   identify problems and instruct the Programmable Logic Controller (PLC) to
   scrap the item or send it to a rework station.

Each step lessens reliance on humans. “AI automation offers a greater degree of
accuracy than people performing the tedious work,” says Dr. Kemal Levi, Founder
and CEO of Relimetrics. “Most manufacturers are in the beginning of their
journey. The majority are operating at Level 3, and very few are operating at
Level 5.”

“Rather than humans writing the software, our software writes itself. The
product enables people who have no experience in deep learning to be able to
train deep-learning models, automating the whole process.” @relimetrics.

Over the next five years, the rate of QA automation is expected to rapidly
accelerate with CV playing a central role. Part of a fast adoption is the
availability of new tools. In the past, sophisticated AI technology required
high-level coding skills, but today’s systems, such as Relimetrics AI-based QA
Automation Solution for Electronics Assembly (RELI-QA), can be self-deployed
without any programming or deep learning expertise required.

“Rather than humans writing the software, our software writes itself,” says Can.
“The product enables people who have no experience in deep learning to be able
to train deep-learning models, automating the whole process.”


QUALITY AUTOMATION IN ACTION

Relimetrics recently helped HPE hardware manufacturer Foxconn get to Level 5 on
the maturity model. Using RELI-QA, it automated the QC process for Foxconn’s
production of complex HPE servers, which can come with up to 16 memory models,
each having 16, 32, 64, or 128 gigabytes. The memory configuration is one of 20
different variables. In addition, the pace of production is high.

“The challenge was that every server is manufactured according to a specific end
user’s need, and each server coming down the line is different,” says Can. “It’s
a very complex case for a human operator. Checking for defects can take a person
up to five minutes per server.”

With RELI-QA, auditing time is reduced to about 30 seconds. In addition to time
savings, the QA process reduced the number of defective HPE servers that reached
customers by 25%. And the overall production performance went from sigma 2.1 to
sigma 4.2.


DL AND CV: THE PATH TO LEVEL 5

Powered by Intel® processor-based architecture, RELI-QA uses high-definition
cameras at the edge. The solution analyzes and inspects products as they travel
through a production line or warehouse. The video stream is transferred to an
embedded or attached IT system, where data is compared to the Manufacturing
Execution System (MES) defined during the build process. The Intel® Distribution
of OpenVINO™ Toolkit optimizes the inference time of the models. If a defect or
imperfection is detected, an alert is sent in real time, fully digitizing the QA
inspection process.

“The beauty of this technique is that it learns from images,” says George Sakr,
PhD, Relimetrics’ deep-learning expert. “It looks at images, extracts the
important features, recognizes what differentiates the defected image from a
non-defective image, and learns by example. This capability is why AI is leading
the transition.”

AI-driven QA creates an ecosystem with feedback loops that provide insights that
manufacturers and logistics providers can use to improve efficiency and
operations—helping them ship products to customers defect-free.

And in the supply chain, QA automation assures traceability. In the case of a
product recall, for example, a company can take quick action and identify
affected items as opposed to disposing of an entire batch, generating
significant savings.

Closing the loop in production is critical for Industry 4.0, and real-time
feedback is how it can happen.

“Instead of waiting until the end of the production to assess whether products
have been properly manufactured, any issues can be identified and corrected at
the time of manufacturing before they end up in the customers’ hands,” says
Levi. “Continuous feedback gives us hope for more efficient processes and higher
profitability for the future.”


ABOUT THE AUTHOR

Stephanie Vozza is a B2B writer who specializes in retail, technology and
finance. In 2006, she launched her own eCommerce brand and sold it five years
later to FranklinCovey Products. Stephanie has written articles, white papers
and ebooks for companies that include Staples, Mastercard, Epson, Oracle and
HPE. She's also a regular contributor to Fast Company and Forbes.com.

More Content by Stephanie Vozza
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