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Arduino BlogArduino Team2 min readintermediate

Turn Arduino® UNO™ Q into your local 3D printing watchdog

Read on arduino.cc

Summary

A guide details how to build a local 3D printing watchdog using an Arduino UNO Q and a camera. It leverages Edge Impulse's FOMO-AD model for anomaly detection, pausing prints via Moonraker API without cloud connectivity.

  • The system uses an Arduino UNO Q (4GB) and a V2-style IMX219 camera for monitoring.
  • It employs Edge Impulse's FOMO-AD model to detect general anomalies, not specific print failures.
  • Training only requires images of normal prints, simplifying data collection.
  • Prints are paused via the Moonraker API (for Klipper) only after multiple anomaly detections to prevent false positives.

This project provides an affordable, privacy-preserving solution for adding advanced print monitoring to 3D printers lacking integrated smart features or for users avoiding cloud services.

6/10
machine learningembeddediotedge aiarduino3d printing

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