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5 TRENDS SHAPING TECHNOLOGY ADOPTION

May 22, 2024

System integrator Gray Solutions focuses on AI, robotics, machine vision, edge
computing and digital twins
Anna Townshend
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Gray Solutions is leaning heavily into artificial intelligence (AI) to aid
vision solutions and digital-twin models. The focus is on systems and machines
that have the data-collection capabilities required to meet demands of the
newest technology. Greg Powers, vice president of cool stuff at Gray Solutions,
discusses five technology trends impacting system integrators.

1. Digital twins and AI make equipment easier to deploy in the field. Gray
Solutions uses digital twins for a variety of design and integration practices,
such as process optimization, experimentation with what-if scenarios, material
flows optimization, virtual testing/commissioning and proof of concepts (POCs).



“First, we use modeling/simulation to validate the model,” Powers says. “Once we
have confirmed the concept model, we can then ingest the model with real data.
From there, we can do some experiments to optimize the model. After running with
real data then we have a digital twin of that process. This same model then can
be used for commissioning. Gray Solutions uses modeling for most projects we
work on from greenfield to brownfield projects. This model will be updated at
every stage in the project all the way through commissioning.”

First and foremost, a digital-twin platform needs the necessary data to
represent the physical model of the production process, Powers says. He also
highlights the following important factors for selecting a digital-twin
platform:

 * the platform's capabilities for data ingestion, storage, processing and
   analysis
 * system scalability and flexibility to change when business processes change
 * integration of existing systems.

2. Artificial intelligence is already enhancing machine vision.

In addition to leveraging simulation/modeling software for the design process of
a facility, Gray Solutions has been working with original equipment
manufacturers (OEMs) to design machinery to be more easily deployed in a
facility. “These tools can be used to merge both physical and virtual processes
withing the facilities,” Powers says. “As we incorporate more data in our
modeling software, then we can use AI to help assess data and provide some
predictions. As for machine integration using AI, this is still in early
adoption. It’s still difficult for customers to allow control of equipment based
on AI. The exception to this would be in AI vision.”



Gray Solutions has focused largely on the use of AI with advanced vision systems
for quality inspections. “Visual-inspection-automation (VIA) software goes
beyond the capabilities of traditional machine vision in detecting anomalies and
defects, even when products have natural variations. Using proven vision AI
technology, manufacturers can scale production, reduce waste and adapt to
workforce changes, while achieving even higher levels of quality control,”
Powers says. “Machine-vision tasks now require highly complex analysis beyond
the scope of traditional vision systems. As a result, the demand for
high-quality inspection tasks in the food manufacturing space has grown
exponentially.  The increasing need to ensure the quality of manufactured food
products has exceeded the scope of traditional vision-system techniques, which
were widely incapable of performing human-like inspection tasks. Vison systems
have made great strides with AI, and, with more companies supplying AI tools,
the market has exploded.”

3. Generative AI changes the game for industrial control, but not immediately.
Large language models (LLMs) are computational, deep-learning models that form
the foundation of generative AI. Early models could predict text patterns, and
now they incorporate image, video and audio input and output.

“To build these models, significant costs have been invested in training these
models.  More training the better the model is,” Powers says. “As we put more
focus on smart equipment and smart processes, the expectations are that
generative AI will be embedded in these designs. Currently, we are building AI
processes that provide recommendations or predictions that get passed to
operators. Eventually when the model is trained well enough then we will see
more AI doing things like the controlling of the equipment.”



The customer market does not have fully trust in AI yet, Powers says. “Most
important is the data this is being used to train the model,” he explains. “For
customers to adopt AI, they must have clean and structured data to help train
the model. If I were a customer looking at an AI solution in the future, I would
put heavy emphasis of data collection, cleansing of data and data structures.

“Our focus along with equipment providers is to make sure systems/machines have
capabilities in data collection required to meet these demands in AI. This means
defining structured data, collection tools, data cleansing and integration. This
will ensure that manufacturers are ready with the data required to train the
model for AI,” Powers says.

Powers believes AI will have a huge impact on manufacturing in the future, maybe
slower than other areas, but it will impact machine builders and system
integrators. “I am optimistic that in the near future AI will not just be able
to assist with running the equipment, but eventually AI will be running the
equipment lines,” he says. “AI will be able to react very quickly to line
changes—upstream and downstream events—through data collection where a machine
operator might not be able to assemble/correlate all this data.”



4. Robotics is an important part of process automation. “Gray Solutions develop
many robotic solutions in process automation,” says Powers. “This would include
quality inspections, palletizing, machine tending and material movements using
autonomous mobile robots (AMRs).”

5. Edge computing is necessary for manufacturing to make the most of AI and
machine learning. “Having edge devices process and analyze data in real time at
a local level vs. in the cloud has given AI early detection of problems. In
addition, data can be fed directly into the model without having to aggregate
the data,” Powers says.





About the Author


ANNA TOWNSHEND | MANAGING EDITOR

Anna Townshend has been a writer and journalist for 20 years. Previously, she
was the editor of Marina Dock Age and International Dredging Review, until she
joined Endeavor Business Media in June 2020. She is the managing editor of
Control Design and Plant Services.

Email
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