Democratizing Production-Ready Edge-Based AI Solutions for Frontline Steel SMEs
This co-presentation will showcase several edge-based AI smart asset health and process control optimization use cases that span across rolling mill and reheating furnaces.
Key performance indicators achieved include reducing overall unplanned maintenance hours and a real-time reduction in total fuel/gas consumption. Gerdau will also touch on value-added implementation ownership lessons learned from deployed AI/ML applications.
There will be further takeaways on how to accurately calculate the true total cost of ownership for steel producers that seek to democratize AI/ML solutions internally and empower their frontline workers to become citizen data scientists without having to write complex code or acquire a Ph.D. in data science.
AJ Alexander, SORBOTICS, and Leonardo Rosa Lemos, Gerdau Long Steel North America
- Data Acquisition
- Anomaly Detection
- Virtual Sensor
- Process Control Optimization
- Value-added and Lessons Learned