AI-Enhanced Digital Twins in Predictive Smart Manufacturing

Abstract

The integration of Artificial Intelligence (AI) with Digital Twin (DT) technology has emerged as a transformative approach for advancing predictive capabilities in smart manufacturing environments. AI-enhanced digital twins leverage real-time data streams, physics-based models, and machine learning algorithms to create high-fidelity virtual replicas of physical assets, processes, and systems. This paper presents a comprehensive examination of AI-augmented DT frameworks for predictive maintenance, process optimization, and adaptive control in manufacturing. Through an extensive literature survey, we identify prevailing architectures, data integration strategies, and AI methodologies driving predictive smart manufacturing. A mixed-method research design—incorporating case studies, simulation experiments, and field deployments—was employed to evaluate the performance gains and implementation challenges of AI-enhanced DTs. Results demonstrate up to a 30% reduction in unplanned downtime, a 20% improvement in overall equipment effectiveness (OEE), and significant enhancements in supply chain resilience. The findings underscore the critical roles of data quality, semantic interoperability, and adaptive learning models in realizing robust predictive frameworks. We conclude with a discussion of scalability considerations, cybersecurity implications, and future research directions, offering a roadmap for practitioners and researchers aiming to harness AI-driven digital twins for next-generation smart manufacturing.