Work

Digital Machining - Tool Failure Predictor

CNC
IoT
ML
Manufacturing

Developed a digital machining pipeline using CNC sensor data (vibration, sound, AE) combined with machine learning models to predict tool failure and optimize cutting efficiency. The system integrated real-time edge data acquisition, anomaly detection, and cloud-based monitoring.

A close-up of a CNC machine tool during high-speed operation with sensor overlay

This project focused on applying AI to digital machining.

We integrated vibration/accelerometer, acoustic emission, and spindle current signals from CNC machines to build a predictive model. Using Python and edge deployment via Azure IoT, the solution performs on-device anomaly detection and sends alerts to the edge/cloud dashboard.

The result: improved uptime, early detection of tool breakage, and smarter process monitoring — all without interrupting production.