Digitizing the Drill Rig: A Step Towards a Smarter Mining Ecosystem-PPT Presentation

PPT Presentation

Pdf Summary

The project at TU Bergakademie Freiberg’s Research and Educational Mine demonstrates how an existing Sandvik DE110 drill rig can be digitized and connected to a smart mining ecosystem without structural modifications.<br /><br />A data acquisition system measures drilling length and penetration rate, rig vibration, drill-rod speed, hydraulic pressures and temperature, flushing-water pressure and flow, and power use. Data is collected for live operator displays and transmitted from the mine to storage and analysis platforms, including Mine.io. The system is designed to withstand demanding underground conditions, such as high humidity and acidic mine water.<br /><br />The project aims to give operators detailed, real-time information, improve drilling efficiency, help new operators learn more quickly, assess rock conditions and drill-bit wear, and support predictive maintenance. AI-based material evaluation is intended to identify transitions between ore veins and host rock and detect rock-quality features such as fracture zones. Machine-learning tools, including AutoML and deep learning, are used to identify equipment degradation, detect anomalies, forecast bit wear, and recommend maintenance.<br /><br />Initial reported benefits include more consistent penetration rates, improved core quality, larger core sections, fewer core breaks, and faster operator learning. Overall, the pilot shows that legacy mining equipment can be equipped with sensors and integrated into a digital mine, enabling real-time monitoring and data-driven operational improvements.

Keywords

Sandvik DE110

digital mining

sensor integration

real-time monitoring

predictive maintenance

machine learning

drill rig

Mine.io

drilling efficiency

underground mining