Additive Manufacturing Fleet Monitoring for Build Anomaly Correction

The patent replaces manual expert diagnosis with an automated machine learning system that analyzes sensor data from additive manufacturing processes. The system uses trained models to automatically identify failed builds and determine root causes, substituting human expert analysis with computational algorithms that process manufacturing data in real-time.

The system enables self-diagnosis of additive manufacturing failures through automated analysis of sensor data and comparison against learned patterns from historical builds. The machine learning models autonomously identify issues without requiring external expert intervention, allowing the system to self-diagnose problems and determine corrective actions.

The system performs preliminary analysis by continuously monitoring sensor data during builds and comparing it against patterns learned from historical data. The machine learning models are pre-trained on extensive datasets of successful and failed builds, enabling the system to proactively identify potential issues and determine root causes before builds complete, rather than requiring post-build analysis.

The patent replaces complex manual root cause analysis with automated machine learning algorithms that process sensor data and identify causal factors. The system uses trained models to automatically determine root causes of failures, substituting the complex human analytical process with computational methods that can rapidly evaluate multiple potential causes simultaneously.

The system implements continuous feedback by monitoring sensor data during additive manufacturing builds and automatically comparing it against expected patterns from historical data. When deviations are detected, the machine learning models identify potential issues and can trigger alerts or automatic corrective actions, creating a closed-loop system that continuously improves build reliability through real-time feedback and adjustment.

The patent replaces manual quality assurance and reliability monitoring with automated machine learning systems that analyze sensor data and predict build outcomes. The system uses trained models to assess build quality in real-time, substituting human inspection and manual quality control processes with automated computational analysis that provides consistent, scalable reliability monitoring.

The system performs preliminary processing by pre-training machine learning models on extensive historical datasets before deployment. Once trained, the models can rapidly analyze new sensor data with minimal computational overhead, as the heavy lifting of pattern recognition has already been performed during the training phase. This allows high-precision monitoring with reduced real-time computational energy consumption.

The system implements selective data processing by focusing computational resources on the most critical sensor data and features that have been identified as most predictive of build outcomes during training. Rather than processing all sensor data equally, the system prioritizes analysis of key parameters and features that contribute most to monitoring precision, reducing overall computational load while maintaining high accuracy.