Architecture of a Human-Digital Twin as Common Interface for Operator 4.0 Applications

Löcklin, Andreas; Jung, Tobias; Jazdi, Nasser; Ruppert, Tamás; Weyrich, Michael

doi:10.1016/j.procir.2021.11.077

Cited by 27 publications

(6 citation statements)

References 38 publications

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“…The HDT they defined includes both first principles models which are based on fundamental understanding and statistical models, which are both modeled using various attributes from an individual or a human class [24]. Recently, HDT are widely used in various domains, including precision medicine [6][25] [26][27] [28], fitness management [29], industrial production [30][31] [32][33][1] [34][35] [36].…”

Section: The Emergence and Vision Of Human Digital Twinmentioning

confidence: 99%

“…Regarding the HDT model design, the HDT model is more complicitly designed than DT due to the consideration of the entire human lifecycle and the distinctive characteristics of humans in the design of HDT. Firstly, the behaviors allowed by HDT model must be based on human prototypes in the physical world, so it can not only perform professional behaviors as DT in work, but also do what humans can do in daily life, and have continuous learning capabilities according to changes in the environment and themselves [30]. Secondly, HDT designers can assign one or more behaviors to the HDT model as required, and need to provide extensible interfaces for unassigned behaviors [18].…”

Section: The Emergence and Vision Of Human Digital Twinmentioning

confidence: 99%

“…The HDT model can adapt by learning current data from the physical world and the historical data stored in the digital world, making it more suitable for the current scene. It is like a process of continuous human learning and growth, through the learning of different behaviors, from "general" to "professional" [30].…”

Section: The Emergence and Vision Of Human Digital Twinmentioning

confidence: 99%

“…Pires et al [3] discussed the significance and challenges of HDT. Locklin et al [30] analyzed the feasibility of HDT in the industry. They argued that HDT could adjust its relationship with the physical world at a lower cost, as natural as employee job transfers.…”

Section: Industry Domainmentioning

confidence: 99%

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Human Digital Twin: A Survey

Lin

Chen

Ali

et al. 2022

Preprint

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Digital twin has recently attracted growing attention, leading to intensive research and applications. Along with this, a new research area, dubbed as "human digital twin" (HDT), has emerged. Similar to the conception of digital twin, HDT is referred to as the replica of a physical-world human in the digital world. Nevertheless, HDT is much more complicated and delicate compared to digital twins of any physical systems and processes, due to humans' dynamic and evolutionary nature, including physical, behavioral, social, physiological, psychological, cognitive, and biological dimensions. Studies on HDT are limited, and the research is still in its infancy. In this paper, we first examine the inception, development, and application of the digital twin concept, providing a context within which we formally define and characterize HDT based on the similarities and differences between digital twin and HDT. Then we conduct an extensive literature review on HDT research, analyzing underpinning technologies and establishing typical frameworks in which the core HDT functions or components are organized. Built upon the findings from the above work, we propose a generic architecture for the HDT system and describe the core function blocks and corresponding technologies. Following this, we present the state of the art of HDT technologies and applications in the healthcare, industry, and daily life domain. Finally, we discuss various issues related to the development of HDT and point out the trends and challenges of future HDT research and development.

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Section: The Emergence and Vision Of Human Digital Twinmentioning

confidence: 99%

Section: The Emergence and Vision Of Human Digital Twinmentioning

confidence: 99%

Section: The Emergence and Vision Of Human Digital Twinmentioning

confidence: 99%

Section: Industry Domainmentioning

confidence: 99%

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Human Digital Twin: A Survey

Lin

Chen

Ali

et al. 2022

Preprint

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“…Additionally, DTs can assist with process monitoring, virtual commissioning, predictive maintenance, lifecycle management, process diagnosis, and production responses [ 18 – 23 ]. DTs also provide information interfaces between humans and machines to improve operators’ capability and productivity in manufacturing [ 24 – 26 ]. Moreover, DTs can address the difficulties of productivity and quality control in industrial processes and allow organizations to act on a digital model for testing, and optimization [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

A digital twin ecosystem for additive manufacturing using a real-time development platform

Pantelidakis

Mykoniatis

Liu

et al. 2022

Int J Adv Manuf Technol

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Additive manufacturing is often used in rapid prototyping and manufacturing, allowing the creation of lighter, more complex designs that are difficult or too expensive to build using traditional manufacturing methods. This work considers the implementation of a novel digital twin ecosystem that can be used for testing, process monitoring, and remote management of an additive manufacturing–fused deposition modeling machine in a simulated virtual environment. The digital twin ecosystem is comprised of two approaches. One approach is data-driven by an open-source 3D printer web controller application that is used to capture its status and key parameters. The other approach is data-driven by externally mounted sensors to approximate the actual behavior of the 3D printer and achieve accurate synchronization between the physical and virtual 3D printers. We evaluate the sensor-data-driven approach against the web controller approach, which is considered to be the ground truth. We achieve near-real-time synchronization between the physical machine and its digital counterpart and have validated the digital twin in terms of position, temperature, and run duration. Our digital twin ecosystem is cost-efficient, reliable, replicable, and hence can be utilized to provide legacy equipment with digital twin capabilities, collect historical data, and generate analytics.

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