By
@Gerafftes
The software automatically monitors 3D prints on the Flashforge Guider 2S using its built-in camera. AI regularly analyzes camera images to detect common print failures such as spaghetti or detached parts. If a failure is detected multiple times, the software can automatically pause the print and optionally send a notification.
@Gerafftes
on
3D print Spaghetti ai
1h 42m 32s logged
The monitoring loop now takes exactly one Guider 2S camera snapshot per minute and can switch on the printer light when the image is too dark.
I connected the app to Home Assistant’s Core API and configured light.cab_guider_ii_series_light as the automatic camera light. The app measures the configured ROI, and when its 75th-percentile luminance falls below 45/255, it turns the light on for the next scheduled snapshot. It does not immediately retry the dark frame, so the one-image-per-minute limit remains true.
The most important decision was keeping the one-image limit even when the first frame is dark. The tricky part was that changing the app’s default interval did not change the existing Home Assistant option, so I updated the stored option from 5 to 60 seconds, restarted the app, and verified that version 0.1.3 is running with poll_interval_seconds: 60.
This milestone proves that the new lighting and cadence configuration is released on main, and all 16 automated tests pass. The printer is currently off, so the camera/light behavior is not yet live-validated. Shadow mode, phone notifications, and printer decisions remain disabled.
Next: turn the printer on, verify the app-container camera connection and dark-frame light response, then complete two full shadow-mode prints before testing the phone notification and controlled pause/continue flow.
@Gerafftes
on
3D print Spaghetti ai
4h 46m 4s logged
The Spaghetti AI detector is now running as a Home Assistant app and watching the Guider 2S camera in safe shadow mode.
I first created and verified a protected Home Assistant backup. Then I installed the CPU-only ONNX detector, connected its status to MQTT, and checked the live service: it currently reports normal, a score of 0.0, and no error.
The most important decision was keeping detection separate from printer control. Shadow-mode detections can be recorded for evaluation, but the software cannot pause or abort the active print. Phone notifications and the Home Assistant decision automations also remain disabled.
The difficult part was fitting the model into a lightweight Home Assistant app. I limited ONNX Runtime to one CPU thread and disabled its CPU memory arena instead of deploying the much larger full Obico server.
This milestone proves that the monitoring pipeline is installed and running. It does not yet prove that spaghetti detection is accurate enough for real printer decisions.
Next: complete two full shadow-mode prints, review every detection, and only then test the phone notification and a controlled pause/continue decision.
