Manufacturing / Steel

POSCO DX: Unloading steel coils and controlling cranes

Company
POSCO DX
Country
South Korea
Adoption stage
Limited operation
Source published
Date basis
The date the source was published. It can differ from the date adoption started.
How the source was checked
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The work problem

On industrial sites it was hard to collect field data and test AI models, because doing so could disrupt operations or raise safety and security concerns. Equipment and sensors are strongly affected by the physical environment, including inertia, acceleration and noise. Handling irregular products that differ in size and packaging with a crane was especially difficult.

Technology and data

POSCO DX built an AI model that reproduces a real factory inside a virtual environment. The company said it established a physical AI development scheme that trains and validates AI before it goes into the field. It put the conditions the equipment would meet on the real site into the virtual space and ran many simulations so the AI could learn the best motion for the equipment. The simulation was built with Isaac Sim on the NVIDIA Omniverse platform. The company also built a dedicated optical laboratory at its Pangyo office. It is a space that artificially reproduces the illumination, temperature and movement of a real site to test sensor response and the precision that follows from the specification. Sensor data validated there was fed back into the AI simulation to narrow the gap between the virtual and the real site.

Results

POSCO DX is applying physical AI first to cranes that move irregular products. In the first half of this year it developed an AI model that automates unloading coil products delivered by trailer with a crane, and completed a virtual commissioning run. The company said it is now applying this on site and plans to spread it horizontally. It said it expects simulation in a virtual environment to sharply cut the time and cost of developing AI models and putting them into the field. Yoon Il-yong, head of the AI Technology Development Center, said the goal is to turn manufacturing equipment into autonomous physical systems on the basis of converging AI with IT and OT technology.

Limits and open questions

The source gives no figure for the time or cost saved, or for the success rate of the unloading work. Coil unloading automation is described as having reached virtual commissioning, so rollout on site is still at the planning stage.

Sources

Compiled from public sources. These are not results from ATF Works customers.

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