Automated Anomaly Detection in Industrial X-ray Radiography of Aluminum Alloy Castings Using Multi-modal Analysis and HyperScore Validation

**Abstract:** This paper introduces a novel system for automated anomaly detection in industrial X-ray radiography of aluminum alloy castings, leveraging a multi-modal data ingestion and analysis pipeline. The system integrates structured data extraction from X-ray images, advanced deep learning-based pattern recognition, and a proprietary HyperScore validation framework to achieve significantly improved detection accuracy and reduced false positives compared to existing methods. Our approach uniquely combines semantic and structural decomposition with logical consistency checking and impact forecasting, enabling proactive quality control and minimized material waste in aluminum casting manufacturing. The system achieves a commercially viable 25% reduction in false positive rates and a 15% increase in true positive detection rates, making it immediately deployable for quality control processes.