A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications

By Barbara Rita Barricelli, Elena Casiraghi, Daniela Fogli. IEEE Access, 2019

1,050 citations recorded by the source corpus.

When, in 1956, Artificial Intelligence (AI) was officially declared a research field, no one would have ever predicted the huge influence and impact its description, prediction, and prescription capabilities were going to have on our daily lives. In parallel to continuous advances in AI, the past decade has seen the spread of broadband and ubiquitous connectivity, (embedded) sensors collecting descriptive high dimensional data, and improvements in big data processing techniques and cloud computing. The joint usage of such technologies has led to the creation of digital twins, artificial intelligent virtual replicas of physical systems. Digital Twin (DT) technology is nowadays being developed and commercialized to optimize several manufacturing and aviation processes, while in the healthcare and medicine fields this technology is still at its early development stage. This paper presents the results of a study focused on the analysis of the state-of-the-art definitions of DT, the investigation of the main characteristics that a DT should possess, and the exploration of the domains in which DT applications are currently being developed. The design implications derived from the study are then presented: they focus on socio-technical design aspects and DT lifecycle. Open issues and challenges that require to be addressed in the future are finally discussed.

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

View Paper: https://www.semanticscholar.org/paper/02bb3f2a500793aa3f8eb7395dc096d68c1cafdb

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

In Safety, Risk, Reliability and Quality

Citations in the corpus

Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects (2022, background): “Barricelli et al. [6] propose a study on the DT’s deﬁni-tions, characteristics, applications, and design implications.”
Digital twin in healthcare: Recent updates and challenges (2023, background): “defects, damages and failures) through the closed-loop optimisation between DT and surrounding environment.(13) Generally speaking, the technologies required for DT can be divided into two categories, one is a statistical model driven by data, and the other is a mechanical model that integrates mult”
The Road Towards 6G: A Comprehensive Survey (2021, background): “…IoT devices, the deployment of the zero-touch and self-management approaches to the devices and processes, lack of existing models and methodologies in the area of AI to virtualize the physical object, security and privacy, and many others that are thoroughly addressed in [164], [165], and [169].”
Digital twin paradigm: A systematic literature review (2021, background): “Regardless of the context domain, the DT has a series of functions in each phase of life cycle (Barricelli et al., 2019) that can be summarized in: Accelerating the product development speed; Identifying customers’ needs; Performance optimization and validation; Remote commissioning and diagnostics.”
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, methodology): “A digital twin model of Apollo 13 was created on earth to allow engineers on the ground to test possible solutions for a rescue mission in space (Barricelli et al., 2019; Nguyen et al., 2021).”