Even bad weather is okay...AI to diagnose aggregate quality immediately

[Anchor]
Artificial intelligence technology has been developed to identify aggregate conditions in real time and help improve quality based on diagnosis results.

In particular, it is expected to be useful at construction sites as it is not affected by inclement weather such as heavy dust or heavy rain.

This is reporter Park Na-yeon.

[Reporter]
a construction site littered with dust, large and small.

The work of carrying, breaking, and splitting aggregates is in full swing.

Aggregates are expensive and time-consuming to transport because the production process is complicated and needs to be moved according to the process.

In addition, since it is mainly used for construction or road pavement, precision that divides it into a certain size is required.

[Kim Byung-hak / Senior Researcher at Daekyung Technology Commercialization Headquarters, Korea Institute of Production Technology: Poor aggregate products can lead to poor cement mixtures and consequently poor buildings, which can cause major disasters, so strict quality inspections are required.]

However, domestic researchers have developed artificial intelligence technology that diagnoses aggregate quality in real time and helps solve problems.

By linking electro-optical sensors and production facilities to artificial intelligence technology through communication, the aggregate production process can be implemented in one device without separate movement.

In addition, it is equipped with a vision sensor to shoot in poor weather conditions such as dust, rainfall, and illuminance changes.

[Song Chang-heon / Senior Researcher at Daekyung Technology Commercialization Headquarters, Korea Institute of Production Technology: Based on empirical tests that have lasted for more than a month, we confirmed that if a problem occurs, the crushing device can adjust the control value and produce optimal aggregate.]

This study is significant in that it is the first in Korea to achieve process automation in an outdoor environment.

The researchers plan to continue follow-up research with participating companies with the aim of commercialization next year.

I'm Park Nayeon of YTN Science.

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video: Hwang Yu-min