Exploring the integration of digital twin and additive manufacturing technologies

Nursultan Jyeniskhan, Kemel Shomenov, Md. Hazrat Ali, Essam Shehab

International Journal of Lightweight Materials and Manufacture, 2024

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.1016/j.ijlmm.2024.06.004
Data confidence 85 high
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Abstract

This paper offers a comprehensive overview of recent advancements in digital twin technology applied to additive manufacturing (AM), focusing on recent research trends, methodologies, and the integration of machine learning. By identifying emerging developments and addressing challenges, it serves as a roadmap for future research. Specifically, it examines various AM types, evolving trends, and methodologies within digital twin frameworks, highlighting the role of machine learning in enhancing AM processes. Ultimately, the paper aims to underscore the significance of digital twin technology in advancing smart manufacturing practices. A total of 133 papers were identified for analysis through IEEExplore, ScienceDirect, Web of Science, and Google Scholar and web resource. Approximately 74% of the papers are journals and 21% are conferences and proceedings. Moreover, 78% of the journal papers were Q1 journals. The paper identifies the potential benefits of digital twins at different levels, the existing problems associated with implementing digital twin in additive manufacturing, recent advancements, the existing approaches, and the framework. This review provides a comprehensive overview of the current landscape of research in digital twin technology for additive manufacturing, utilizing the latest resources to identify cutting-edge developments and methodologies. Through an exploration of potential benefits and implementation challenges, the review offers valuable insights to researchers and practitioners in the field. Additionally, it contributes to the discourse by offering a nuanced discussion on future research directions, paving the way for further advancements.