Automated Structural Integrity Assessment of Additively Manufactured Titanium Alloys via Multi-Modal Fusion and HyperScore-Driven Anomaly Detection

**Abstract:** This paper introduces a novel framework for the automated assessment of structural integrity in additively manufactured (AM) titanium alloys. Leveraging multi-modal data fusion – integrating non-destructive testing (NDT) modalities (ultrasonic testing, X-ray computed tomography) with high-resolution microscopy and process parameters – we present a system capable of accurately predicting the onset of defects and their impact on fatigue life. The core innovation lies in a hyper-scoring algorithm (HyperScore) that fuses these disparate data streams and prioritizes anomalies based on their likelihood of triggering catastrophic failure. This system, integrating advanced algorithms for image processing, machine learning, and automated theorem proving, offers a significant advance over existing visual inspection and numerical modeling methods, enabling accelerated qualification and improved reliability of AM components. The potential for significant reductions in material waste, improved design confidence, and accelerated manufacturing throughput positions this technology for rapid commercialization within the aerospace, medical device, and automotive industries. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*