The field of additive manufacturing and process monitoring is moving towards the integration of machine learning (ML) and advanced data analysis techniques to improve efficiency, quality, and productivity. Researchers are focusing on developing novel frameworks and methodologies to address challenges such as redundancy, anomaly detection, and data fusion. Notable papers in this area include: Redundancy Analysis and Mitigation for Machine Learning-Based Process Monitoring of Additive Manufacturing, which proposes a comprehensive framework to reduce redundancy and improve model performance. Enhanced Semi-Supervised Stamping Process Monitoring with Physically-Informed Feature Extraction, which introduces a semi-supervised anomaly detection framework to capture process anomalies effectively. Generative Multimodal Multiscale Data Fusion for Digital Twins in Aerosol Jet Electronics Printing, which presents a novel generative modeling methodology for data fusion in aerosol jet printing. Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review, which provides a functional classification of generative ML approaches and highlights their potential for manufacturing control.