Automated Anomaly Detection and Predictive Maintenance in Composite Material Manufacturing using Multi-Modal Data Fusion and Bayesian Optimization

**Abstract:** This paper introduces a novel framework for enhancing anomaly detection and predictive maintenance in composite material manufacturing processes. We leverage a robust multi-modal data ingestion and normalization layer, a semantic and structural decomposition module, and a multi-layered evaluation pipeline incorporating logical consistency verification, code and formula validation, and impact forecasting. A meta-self-evaluation loop dynamically optimizes the evaluation process, leading to a highly accurate and robust anomaly detection system with demonstrable improvements over existing statistical process control (SPC) methods. This system is immediately commercializable, addressing the critical need for enhanced process control and reduced downtime in the rapidly expanding composite materials industry. --- *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.*