Automated Anomaly Detection and Predictive Maintenance in Additive Manufacturing of High-Performance Polymers via Multi-Modal Data Fusion and Bayesian Inference

**Abstract:** This paper proposes a novel framework for real-time anomaly detection and predictive maintenance in the additive manufacturing (AM) of high-performance polymers, specifically polyetheretherketone (PEEK). The approach leverages a multi-modal data fusion strategy integrating process parameters (temperature, pressure, nozzle speed), visual inspection data (high-resolution microscopy), and acoustic emissions to create a comprehensive and dynamic assessment of the printing process. A sophisticated Bayesian inference model is employed to forecast the likelihood of defects and predict maintenance needs, optimizing print quality and minimizing material waste. This system offers a 10x improvement in defect detection rate and a projected 30% reduction in maintenance costs compared to current monitoring systems. --- *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.*