Case Study: Visual AI Detects Crusher Liner Wear and Prevents Production Risks

At a ferroalloy operation site, Razor Labs deployed DataMind AI to monitor material size trends using Visual AI. A camera was installed just after the jaw crusher, providing real-time monitoring of the output stream.
Shortly after deployment, the system detected that oversized boulders were exiting the crusher – a clear sign that something was wrong. While the site expected consistent sizing post-crushing, trend data showed multiple outliers exceeding safe thresholds. The system also extended the lifespan of belts, conveyor drive systems, and pulleys, which would have otherwise suffered accelerated wear due to excessive load.

Without intervention, this issue could have led to mill overload trips,
increased wear on downstream equipment, reduced grinding efficiency, and higher energy consumption.
Thanks to automated alerts and early detection from DataMind AITM, the site avoided major production disruptions and asset damage — all with minimal manual intervention. The system proved to be a reliable,
cost-saving layer of operational intelligence.