3D Printer Health Diagnosis Using Sensor Data and Machine Learning

The patent applies preliminary action by collecting sensor data (acceleration, sound, temperature, vibration) during the 3D printing process before failures occur. The system performs real-time analysis of this data to detect early signs of component degradation, enabling diagnosis before actual failures stop production. This allows the printing process to continue uninterrupted while maintaining component health monitoring.

The patent replaces manual visual inspection and offline measurement with automated sensor-based monitoring systems. Acceleration sensors, acoustic emission sensors, temperature sensors, and vibration sensors automatically collect and analyze data, substituting human operators and simple inspection tools with an integrated electronic monitoring system that provides continuous real-time diagnosis without interrupting production.

The patent implements feedback by continuously collecting sensor data during printing operations and using machine learning algorithms to analyze patterns indicating component health status. The system provides objective, data-driven diagnosis feedback that replaces subjective operator judgment. The accumulated data creates a knowledge base that improves diagnostic accuracy over time, ensuring consistent and reliable failure detection regardless of operator experience levels.

The system performs self-diagnosis by automatically analyzing its own operational data through embedded sensors and processing units. The 3D printer monitors its own component health status without requiring external inspection, enabling autonomous detection of anomalies and predictive maintenance scheduling based on actual component conditions rather than manual assessment.

The patent enables continuous useful action by performing diagnosis operations concurrently with the 3D printing process. Sensor data is collected and analyzed in real-time without interrupting material deposition or layer formation. This continuous monitoring approach maintains production flow while simultaneously detecting component degradation and predicting failures before they occur, eliminating the need for post-printing inspection stoppages.

The system performs preliminary diagnosis actions by detecting early signs of component failure through sensor monitoring during normal operation. By identifying anomalies in acceleration patterns, acoustic emissions, temperature variations, or vibration characteristics before they lead to actual failures, the system enables preventive maintenance scheduling that avoids urgent process stoppages and material waste associated with reactive repairs.