Automated Anomaly Detection and Predictive Maintenance in Micro-Molded Polymer Components using Multi-Modal Data Fusion and Bayesian Inference

**Abstract:** This paper introduces a novel system for automated anomaly detection and predictive maintenance within micro-molding processes, specifically targeting the production of polymer components with feature sizes below 1mm. Current quality control methods often struggle with the complexity and cost associated with inspecting these minute parts. Our approach leverages a multi-modal data fusion strategy combining high-resolution optical microscopy, micro-CT scanning, and real-time process sensor data (temperature, pressure, injection speed) alongside a Bayesian inference framework. This allows for early detection of anomalies linked to process variability and material degradation, leading to significantly reduced scrap rates and optimized maintenance schedules. The system demonstrates a 15% reduction in undetected defects and a 10% extension in tooling lifespan within simulated micro-molding environments.