PCB Tool Anomaly Detection Using Phase-Aware Image Analysis

A machine learning system acts as an intermediary between the lamination tool and the technician. The system automatically analyzes image data from multiple cameras, processes shaft rotation counts, and generates detailed anomaly reports with root cause identification. This intermediary handles the complex analysis work that would otherwise require significant technician time and expertise, thereby improving tool availability while managing system complexity through automated intelligence.

The patent replaces the manual mechanical process of technician investigation with an automated machine learning-based analysis system. The CNN-based system automatically processes image data from cameras, correlates it with shaft rotation counts, and generates comprehensive root cause analysis reports. This substitution eliminates information loss by automatically capturing and analyzing all relevant data without relying on manual observation, while managing complexity through the use of trained machine learning models.

The system performs preliminary analysis by automatically processing image data and shaft rotation counts to identify potential root causes before a technician needs to intervene. The machine learning models are pre-trained with expert knowledge embedded in the training data, enabling them to quickly analyze new anomalies and provide accurate root cause identification. This preliminary automated action significantly reduces the time required for troubleshooting while maintaining high accuracy through the use of trained models that incorporate expert knowledge.