Digital Twin-Driven Intelligent Manufacturing of Internet of Things, Artificial Intelligent and Machine Learning Integration
International Symposium for Production Research, ISPR 2025, İstanbul, Turkey, 9 - 11 October 2025, pp.236-249, (Full Text)
- Publication Type: Conference Paper / Full Text
- Doi Number: 10.1007/978-3-032-22784-3_22
- City: İstanbul
- Country: Turkey
- Page Numbers: pp.236-249
- Keywords: Digital Twin, Intelligent Manufacturing, Internet of Things, Machine Learning, Predictive Maintenance, Quality Control
- Yıldız Technical University Affiliated: Yes
Abstract
This study provides a comprehensive review of how digital twin (DT), internet of things (IoT) and machine learning (ML) technologies are integrated within manufacturing, particularly focusing on predictive maintenance, production monitoring and quality control. This review explores the application of various ML techniques—including random forest, long short-term memory (LSTM) networks and hybrid models—in detecting anomalies, diagnosing faults and predicting the remaining useful life (RUL) of equipment. To ground the discussion in real-world practice, case studies from diverse industrial sectors such as automotive, aerospace, energy and process industries were analyzed, illustrating the tangible benefits that technology integration brings. Emerging research areas like edge AI, federated learning, blockchain-supported IoT security, explainable AI (XAI) and the development of industrial metaverse environments were also mentioned. These advancements point toward future directions for smarter and more secure manufacturing systems. The findings suggest that combining DT, IoT and ML significantly boosts productivity, efficiency, and decision-making in manufacturing.