Title: SmartMill Guardian - CNC Machine Predictive Health & Vibration Diagnostics Edge System

Problem statement:

High-precision CNC milling centers and industrial machining tools frequently suffer from sudden spindle bearing degradation and thermal runaway. A single unplanned spindle seizure costs manufacturing facilities upwards of $40,000 per incident in damaged workpieces, emergency tooling, and shop-floor downtime. Traditional plant maintenance relies on manual spot checks every 4 to 8 hours or cloud-only IoT gateways that suffer from 2–5 second network latencies—far too slow to trigger an emergency feed-hold before catastrophic mechanical failure. Furthermore, retrofitting older factory machinery with proprietary PLC hardware (e.g., Siemens S7 racks) is prohibitively expensive and requires disruptive rewiring.

Solution:

Designed and deployed a deterministic edge monitoring unit powered by a Raspberry Pi 4B running bare-metal FreeRTOS and Embedded C (C99):

High-Rate Vibration FFT: Interfaces with an ADXL345 3-axis MEMS accelerometer via hardware SPI at 1 kHz ODR. An on-chip CMSIS-DSP Fast Fourier Transform (FFT) paired with a lightweight µTensor INT8 model identifies bearing defect frequencies (BPFO/BPFI harmonics) with 94% accuracy.

Thermal Profiling & PLC Bridging: Continuously monitors bearing temperatures using an MLX90614 infrared sensor over I²C while polling spindle speed, drive current, and status words from existing Siemens S7-1200 PLCs over RS-485 Modbus RTU.

Deterministic Emergency Interlock: Features a 4-channel opto-isolated relay interface capable of triggering a CNC machine feed-hold or spindle stop in <5 ms upon anomaly detection—eliminating cloud latency.

Cloud & SCADA Telemetry: Securely streams time-series vibration metrics and alerts via TLS 1.3 MQTT to AWS IoT Core, populating live shop-floor InfluxDB and Grafana dashboards for condition-based maintenance scheduling.