Enabling technologies and tools for digital twin

By Qinglin Qi, Fei Tao, Tianliang Hu, Nabil Anwer, Ang Liu, Yongli Wei, Lihui Wang, Andrew Yeh-Ching Nee. 2019

1,279 citations recorded by the source corpus.

Abstract Digital twin is revolutionizing industry. Fired by sensor updates and history data, the sophisticated models can mirror almost every facet of a product, process or service. In the future, everything in the physical world would be replicated in the digital space through digital twin technology. As a cutting-edge technology, digital twin has received a lot of attention. However, digital twin is far from realizing their potential, which is a complex system and long-drawn process. Researchers must model all the different parts of the objects or systems. Varied types of data needed to be collected and merged. Many researchers and participators in engineering are not clear which technologies and tools should be used. 5-dimension digital twin model provides reference guidance for understanding and implementing digital twin. From the perspective of 5-dimension digital twin model, this paper tries to investigate and summarize the frequently-used enabling technologies and tools for digital twin to provide technologies and tools references for the applications of digital twin in the future.

Subfields: Industrial and Manufacturing Engineering, Information Systems, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/d8a9251c4d12737192197c26e9387a2969d285b3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. Mehmet Karamanoglu
2. Edward Elson Kosasih
3. Jie Zhang
4. Angira Sharma
5. Alexandra Melike Brintrup
6. A. Calinescu

In Information Systems

1. Edward Elson Kosasih
2. Jie Zhang
3. Angira Sharma
4. Alexandra Melike Brintrup
5. A. Calinescu

Citations in the corpus

• Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions (2020, background): “Despite being dependent on multiple technologies (explored in detail in [58]) which requires experts and resources, DT can lead to huge cost reductions for the one time investment [29].”
• A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, neutral): “Five-dimensional digital twin (Qi et al., 2021) State”
• Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects (2022, background): “In [115], the authors took a closer look at the specific frameworks and software that can be used for DT development.”