Metals manufacturers face immense challenges in avoiding production losses due to unplanned equipment downtimes and quality variances. To overcome these challenges, there is an immediate need to leverage the existing equipment and sensor data and draw intelligent insights that are actionable at plant-scale.
Time Series AI automatically detects operational anomalies stemming from the production data. By directly interacting with the AI platform, maintenance and reliability engineers are able to quickly diagnose, prioritize and resolve critical issues in the mill. Like a million eyes monitoring your plant, the AI provides your plant team with a holistic view of day-to-day operations, including timely alerts of asset conditions, workflow integration and reporting.
Avoid lost production from unplanned downtime
Minimize scrap and rework due to quality issues
Achieve plant-scale operational visibility
Unplanned downtime in hot mills and casters has a major impact on throughput and yield
Analyze data from multiple sources to identify patterns and provide alerts several days before a pinch roll fails or a mold breakout happens
Moving from reactive to proactive maintenance helps achieve better outcomes in planned maintenance schedule
A malfunctioning motor in HRT introduces surface defects onto passing steel plates
Analyze data from hundreds of motor-rollers to identify earliest onset of anomalous behaviour and fix the errant motor before defects are introduced
Focus human attention on emerging problems and achieve faster resolution, avoiding quality-impacting failures
Inconsistent rolling forces in tandem cold rolling finishing mill could introduce surface defects in the sheet metal
Automatically detect abnormal passes and associated causal factors, fix the issue by recalibrating mill parameters
Timely resolution of abnormalities prevents product degradation and helps maximize yield and uptime of the mill
Chief Information Officer at Ternium
Digitalization Supervisor at North American Stainless
