Speaker
Description
In the ongoing digital transformation of the steel industry, Artificial Intelligence is more and more
integrated into the automation of the production processes. However, the true potential of AI
technologies can only be realized if preconditions to data quality are given, and domain expertise is
effectively digitized.
This presentation explores the application of AI technologies—such as machine learning, deep
learning, and expert systems—in areas like predictive maintenance, process control, and defect
detection. A special focus is placed on the unique characteristics of steel production data, including its
complexity, heterogeneity, and real-time processing requirements. Additionally, we discuss the
importance of capturing and structuring expert knowledge in digital form to enhance AI-driven
decision-making and ensure long-term knowledge retention. By combining high-quality data with
digitized know-how, steel manufacturers can achieve tangible benefits such as reduced downtime,
improved product quality, enhanced sustainability, and continuous improvement of the production
processes along the entire production chain. This paper will highlight best practices for transforming
raw data and human expertise into actionable insights, demonstrating how AI can drive measurable
benefits in steel production automation.