[Report] "Farewell to Wrist Pain from Scraping Slag" POSCO's 'Managers' Embrace AI [AI Era, Jobs Are Changing]

Full Implementation of AI-Based KR Automation System
Introduction of Quantitative Metrics such as 'Slag Removal Rate'
Enhancing Quality and Safety

Editor's NoteThe use of artificial intelligence (AI) in industrial sites, including manufacturing, is expected to become the biggest issue in the industry this year. AI, which has already been deployed in manufacturing sites, has proven to be a driving force in improving productivity. Its scope will expand in earnest this year. However, concerns about job losses lie beneath the surface of this AI-driven production innovation. This is because the concept of the so-called 'Dark Factory,' where production takes place without human intervention, is gradually becoming a reality, making full automation possible. However, The Asia Business Daily’s investigation into job changes at manufacturing sites where AI has been implemented reveals a more complex picture. Repetitive and hazardous tasks are being handled by machines, while decision-making, management, and responsibility remain firmly in the hands of humans, creating a clear division of roles. In other words, AI is not pushing people out but is beginning to redefine jobs. The AI era presents not only challenges of productivity and technological competition but also the task of job transition. The Asia Business Daily visited industrial sites at the start of the new year to observe firsthand how AI is changing jobs.

'Pretreatment (KR) Control Room' at the No. 3 Steelmaking Plant at POSCO Pohang Steelworks in Pohang, Gyeongbuk. The characteristic roar and intense heat of a steel mill were nowhere to be found; instead, dozens of monitors, reminiscent of an IT company’s control center, filled the silence. On the split screens, a massive skimmer was meticulously scraping slag-the residue floating on molten iron. This was the very site where, in the past, skilled workers would visually check the furnace flames and maneuver a joystick.

At the main console in the control room, artificial intelligence (AI) was analyzing on-site data in real time. The control screen dynamically displayed the converter number, the composition of the molten iron, the location and operational status of the pretreatment equipment, and the movement trajectory of the skimmer.

Last month, at the POSCO Pohang Steelworks 3rd Steelmaking Plant 'Pre-treatment Control Room' in Pohang, Gyeongbuk, a worker was observing the slag removal operation performed by artificial intelligence (AI). POSCO

AI Adoption Frees Workers from Shoulder and Wrist Pain

The pretreatment process, which marks the beginning of the steelmaking process, involves removing impurities from molten iron and adjusting the sulfur (S) content, a key factor in quality. This is known as the 'slag removal' operation. Although it appears simple, adjusting the tilt of the ladle (the vessel for molten iron) to remove only the slag in front of the high-temperature metal requires intense concentration.

Shin Seungmin, a supervisor in the steelmaking department, said his "shoulders feel much lighter" since the introduction of AI. Previously, he had to operate the joystick more than 400 times per task. On days with high production volumes, his wrists and shoulders would ache.

However, since AI was introduced to all pretreatment facilities at the No. 3 Steelmaking Plant in May, Supervisor Shin has been able to monitor the overall quality of the process instead of operating the joystick. He said, "As the physical burden has decreased, I can now focus on new tasks such as equipment management and generating ideas for system improvements, further enhancing the work environment."

From 'Intuition' to 'Metrics'... World’s First Quantitative 'Slag Removal Rate' Introduced

During the preliminary processing stage, artificial intelligence (AI) is directly judging and removing slag. POSCO

The core of this innovation lies in the combination of thermal imaging cameras and high-precision angle data. Kwon Ohyung, assistant manager of the steelmaking department, explained, "In the process of automating the expertise of highly skilled workers, we began recognizing slag distribution with cameras and started using the objective metric of 'slag removal rate'." In other words, AI was applied to the traditionally intuition-based slag removal operation to create a measurable indicator. The proportion of slag removed and the iron (Fe) loss rate are now compiled as real-time data, making this the first case of introducing quantitative metrics to a traditionally skill-dependent process. This allows for precise quality management by adjusting the slag removal rate according to the steel grade. The time required for slag removal has also been reduced by about 3-5% compared to before.

AI has also solved the most technically challenging step: tilting the ladle. To effectively remove slag, the ladle must be tilted at just the right angle; if the angle is off, molten iron can overflow, leading to major accidents. To prevent this, POSCO implemented high-resolution cameras and a 'redundant decision algorithm.' If any abnormality is detected, the AI automatically halts operations-serving as a double safety mechanism.

Technology That Protects, Not Replaces, People

Since last year, the adoption of AI in domestic manufacturing sites has begun in earnest. Along with the implementation of autonomous manufacturing systems, there have been concerns that on-site workers may no longer have a place. However, POSCO has demonstrated that coexistence is possible by combining the expertise of skilled workers with AI. The realization is that while AI handles data collection and learning, it is the skilled workers on site who generate that data.

The roles of on-site employees have not disappeared but evolved. Instead of simple repetitive work, training has focused on developing capabilities as 'automation system managers.' Workers participated directly from the development stage.

On the shop floor, the transfer of systems incorporating the know-how of highly skilled workers is already actively taking place. Assistant Manager Kwon said, "We educated workers about the structure of the automated pretreatment system and how to respond in emergencies, focusing on the role of automation system managers. From the moment development was completed, we invested significant time and effort in preparing user manuals with both developers and workers."

Yoon Junggyun, head of the steelmaking department at POSCO, said, "In the early days of system implementation, there were many shortcomings because we couldn’t fully replicate the workers’ meticulous operating methods. However, as on-site workers began participating in regular meetings, we developed an automation system with capabilities similar to those of the workers." He added, "As AI expands into areas such as robotics and pretreatment automation, efforts to ensure worker safety remain just as important."