Enhancing Manufacturing with AI, IOT, and Machine Learning: A Focus on Predictive Maintenance
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Predictive maintenance has emerged as a critical application of artificial intelligence (AI), graph neural networks (GNNs), and machine learning in modern manufacturing. This paper explores how these technologies enable real-time monitoring, failure prediction, and anomaly detection through Internet of Things (IoT)-enabled systems. We review key methodologies including digital twins, edge AI, and federated learning, and analyze their impact on reducing unplanned downtime and optimizing operational efficiency. Case studies demonstrate tangible benefits such as energy savings, cost reductions, and enhanced system reliability. Challenges related to data quality, privacy, and integration are also discussed, along with future directions for scalable and explainable predictive maintenance frameworks.