A Comprehensive Review of AI-Based Digital Twin Applications in Manufacturing: Integration Across Operator, Product, and Process Dimensions

Abstract

Digital twins (DTs) represent a transformative technology in manufacturing, facilitating significant advancements in monitoring, simulation, and optimization. This paper offers an extensive bibliographic review of AI-Based DT applications, categorized into three principal dimensions: operator, process, and product. The operator dimension focuses on enhancing safety and ergonomics through intelligent assistance, utilizing real-time monitoring and artificial intelligence, notably in human–robot collaboration contexts. The process application concerns itself with optimizing production flows, identifying bottlenecks, and dynamically reconfiguring systems through predictive models and real-time simulations. Lastly, the product dimension emphasizes the applications focused on the improvements in product design and quality, employing lifecycle and historical data to satisfy evolving market requirements. This categorization provides a structured framework for analyzing the specific capabilities and trends of DTs, while also identifying knowledge gaps in contemporary research. This review highlights the key challenges of technological interoperability, data integration, and high implementation costs while emphasizing how digital twins, supported by AI, can drive the transition toward sustainable, human-centered manufacturing systems in line with Industry 5.0. The findings provide valuable insights for advancing the state of the art and exploring future opportunities in digital twin applications.