Digital Twins: Basics and Applications

102 Pages·2022·3.15 MB·English

by  Zhihan Lv, Elena Fersman

Zhihan Lv
Elena Fersman   Editors
Digital Twins:
Basics and
Applications
Digital Twins: Basics and Applications
·
Zhihan Lv Elena Fersman
Editors
Digital Twins: Basics
and Applications
Editors
Zhihan Lv Elena Fersman
Department of Game Design Ericsson Artificial Intelligence Research
Faculty of Arts Institute
Uppsala University Ericsson, Sweden
Visby, Sweden
Department of Machine Design
Qingdao Institute of Bioenergy Royal Institute of Technology
and Bioprocess Technology Stockholm, Stockholms Län, Sweden
Chinese Academy of Sciences
Qingdao, China
ISBN 978-3-031-11400-7 ISBN 978-3-031-11401-4 (eBook)
https://doi.org/10.1007/978-3-031-11401-4
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature
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Contents
Digital Twins Architecture .......................................... 1
Carlos Henrique dos Santos and José Arnaldo Barra Montevechi
Digital Twins for Physical Asset Lifecycle Management ................ 13
Daniel N. Wilke
Digital Twins and Additive Manufacturing ............................ 27
Li Zhang, Wei Zhou, and Xiaoqi Chen
Agricultural Digital Twins ........................................... 37
Yuhang Zhao, Zheyu Jiang, Liang Qiao, Jinkang Guo, Shanchen Pang,
and Zhihan Lv
The Application of Digital Twins in the Field of Fashion ................ 45
Victor Kuzmichev and Jiaqi Yan
Digital Twins Collaboration in Industrial Manufacturing ............... 59
Radhya Sahal, Saeed H. Alsamhi, and Kenneth N. Brown
Social Media Perspectives on Digital Twins and the Digital Twins
Maturity Model .................................................... 73
Jim Scheibmeir and Yashwant Malaiya
v
Digital Twins Architecture
Carlos Henrique dos Santos and José Arnaldo Barra Montevechi
Abstract This chapter presents an overview of the digital twin (DT) building archi-
tecture. In general, we can understand the DT as a decision support system composed
of four main components: (i) Physical system, (ii) virtual system, (iii) systems data,
and (iv) communication interface. The physical system is composed of people,
machines, and processes and represents the main focus of DT. On the other hand,
the virtual system is composed of one or more highly synchronized virtual models,
which are capable of mirroring physical behaviors and, through analysis tools and
techniques, provides optimized decisions. Systems data correspond to both physical
and virtual data. The virtual model mirrors the physical through its data, that is,
information collected over time, while the virtual model returns information to the
physical systems through actions and decision guidelines. Finally, we highlight the
communication interface as a link that allows the integration between physical and
virtual environments. In this case, the entire structure that allows the exchange of data
and communication between the systems has a fundamental role regarding the DT’s
correct functioning. In this chapter, the reader will comprehensively understand the
role and the main characteristics of each DT component and subcomponent. From an
approach focused on conceptualization followed by practical examples, the advan-
tages and limitations associated with current DT practices are highlighted, providing
a solid basis on the main DT architectures for practitioners and researchers.
1 Why to Talk About Digital Twins?
Before talking about the pillars and components that constitute the DTs, it is impor-
tant to understand the context in which they were created and developed. By under-
standing their origin, main characteristics, and the importance of DTs for current
decision systems, we also understand the reason to talk about this topic and the
importance of dealing with the DT’s architecture.
B
C. H. dos Santos ( ) · J. A. B. Montevechi
Production Engineering and Management Institute, Federal University of Itajubá, Itajubá, Minas
Gerais, Brazil
e-mail: [e-mail]
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 1
Z. Lv and E. Fersman (eds.), Digital Twins: Basics and Applications,
https://doi.org/10.1007/978-3-031-11401-4_2
2 C.H.dosSantosandJ.A.B.Montevechi
Although several works indicate different origins of the term “digital twin,” the
most widespread by researchers is that this concept was proposed in 2010 by Mike
Shafto (and other authors), and it was initially referred to intelligent virtual copies
of equipment belonging to the United States Aerospace Agency, NASA. At the
time, DTs were based on computer models capable of evaluating and recommending
changes to physical systems to optimize them (Shafto et al. 2010). However, if we
consider the technological advances in the last decade, we noted that the DT concept
has undergone several changes and evolutions since it was proposed.
The so-called Industry 4.0, an allusion to what would be the fourth industrial
revolution (Uriarte et al. 2018), and its wave of technological developments allowed
the rise of DTs, popularizing the concept and its application in the most diverse
sectors and with different objectives (Semeraro et al. 2021). In recent years, we
noticed the adoption of DTs to support decisions in production systems of goods and
services, covering the areas of manufacturing, health, logistics, and services. In this
case, both approaches are reasonable, DTs of products and processes, with different
objectives and characteristics (Wright and Davidson 2020).
Based on the virtualization of physical systems, one of the main pillars of the
new industrial era and a fundamental part of the cyber-physical systems, DTs are
described by different authors and from different points of view. Boschert and Rosen
(2016) simplify by describing the DT as a virtual representation that mirrors the
behavior of a component, product, or system and allows its evaluation throughout its
lifecycle. Wright and Davidson (2020) add that this mirroring is carried out through
the connection of virtual models with physical systems from their data and integration
systems. Regardless of their definition, DTs have become indispensable for decision-
making and, according to Tao and Zhang (2017), their adoption by decision-makers
is an inevitable trend.
Finally, in terms of publications involving DTs, it is possible to note their impor-
tance in recent years from a simple exploratory research. Considering publications
between 2010 and 2021 listed in the Scopus® scientific base, there are about 5815
scientific works involving the keyword “digital twin,” among which about 43% of the
publications were in 2021, as shown in Fig. 1. We observe a sharp growth of works
in recent years, a fact that motivates us to develop more practical and theoretical
knowledge about the main topics related to DTs.
2 The Main Digital Twin’s Components
As previously highlighted, there are different descriptions and classifications
regarding DTs, a fact that makes it difficult to understand their building and operation
architecture. In part, the differences in the components necessary to compose the DT
are due to its scope of action; in other words, it is expected that DTs with different
objectives and areas of activity will also have differences in the components that
compose them.
DigitalTwinsArchitecture 3
Fig. 1 Scientific papers about DTs listed in Scopus® from 2010 to 2021
Alam and Saddik (2017) report, in a simplified way, that the DT is based on
two modules: (i) Physical module, composed of the main systems (processes and/or
products) and communication subsystems capable of collecting information and data
from these physical systems during their operation; and (ii) digital module, composed
of computer models capable of processing such information and optimizing the
decision-making.
Rodiˇc (2017) also reports the DT as a system based on two main components:
(i) Digital shadow: that refers to physical systems (such as products and processes)
and their respective data, which are collected and organized to compose a kind of
“shadow” of the physical environment; and (ii) digital master: composed of computer
models capable of capturing the digital shadow and mirroring the behavior of physical
systems to assist decisions.
In more detail, Zhuang et al. (2018) describe three main levels of the architecture of
a DT: (i) Element: composed of geometric models and virtual representations faithful
to physical systems; (ii) behavior: composed of mechanisms capable of representing
physical behaviors through dynamic computational models (including movements,
flows, etc.); and finally, (iii) rule: where there is integration between physical and
virtual systems to allow synchronism between both.
Moreover, Tao et al. (2018) report that a DT is based on three components: (i)
Physical entities: composed of the physical space that we intend to represent virtually
(i.e., machines and processes); (ii) virtual models: composed of computer systems
capable of mirroring the physical entities and which can simulate, monitor, analyze,
predict and control them; and (iii) connection data: which allow the integration and
synchronization between the physical and virtual environments.
Despite the different descriptions present in the literature, based on the main
scientific works in the area, including those previously presented and also other
4 C.H.dosSantosandJ.A.B.Montevechi
Fig. 2 General architecture of DTs
relevant articles, such as the proposed by Tao and Zhang (2017) and dos Santos et al.
(2021), we can consider the DT as a system of four main components: (i) Physical
system (PS), (ii) virtual system (VS), (iii) systems data (SD), and (iv) communication
interface (CI). Figure 2 illustrates the overall architecture of DTs with these four
components and the following topics detail each one.
2.1 Physical System (PS)
We can describe the PS as all elements that belong to the physical environment
and which we intend to mirror virtually. Furthermore, when we consider the main
function of DTs, that is, to virtually represent the physical behavior to better decisions,
it is clear that optimizing the PS is the real objective of creating them. Thus, some
considerations are important regarding the characteristics of the PS.
First of all, it is important to emphasize that the DT contemplates both products
and processes. In the case of products, Wright and Davidson (2020) highlight that the
design and prototyping stages represent the main applications of DTs. Therefore, we
can consider the DT as a key tool in the development of new products and, according
to Lo et al. (2021), it can assist at all stages from product design, development,
testing, and, finally, commercialization. We can observe several works in the literature
adopting such an approach, as those proposed by Dong et al. (2021) and Huang et al.
(2022).
On the other hand, concerning the DTs of processes, Tao and Zhang (2017) reveal
that the focus is on converging physical and virtual environments to solve existing
problems in addition to allowing better management practices. In this case, DTs
can be used in the planning, operation, and post-operation phases of the processes.
DigitalTwinsArchitecture 5
Furthermore, the main objectives associated with the adoption of process DTs in this
context are related to decision support in production planning, process evaluation
and control, resource allocation, and routing (dos Santos et al. 2021).
2.2 Virtual System (VS)
The VS is probably the most important component of DT considering visual char-
acteristics. Usually, the VS may be described as a DT synonym, but we already
know that this statement is wrong since there are other components necessary for the
design of a DT. In this case, we can simplify the VS as a set of all the computational
resources used to virtually represent the behavior of the PS over time.
Regardless of whether it is a product or process DT, the software and hardware
options available are wide and allow the design of virtual models with different levels
of detail and functionality. In this case, as highlighted by Zhuang et al. (2018), it is
important to ensure that the VS can capture both the visual characteristics of the
PS (such as geometric specifications, dimensions, colors, and other details), as well
as the behavioral characteristics (such as actions, movements, state changes, flows,
etc.).
Firstly, we highlight the widely used and well-known commercial packages,
which include softwares such as CATIA®, SolidWorks®, and AutoCAD® for visual
representation and FlexSim®, Tecnomatrix®, AnyLogic®, Simio®, Arena®,3DVIA
Composer®, and Unity 3D® for behavioral representation. In addition, there is a
significant portion of virtual models that are based on proprietary codes and computer
programs, developed especially for a given application (Tao and Zhang 2017; dos
Santos et al. 2021).
Zhuang et al. (2018) report that the main functions of the DT are to evaluate,
predict, simulate, validate, and optimize the PS, and, in this case, it is important
to ensure that the VS is capable of performing the desired functions. Finally, it is
important to highlight the role of new technological developments and, in this context,
functionalities linked to virtual reality and augmented reality have been highlighted
as important complements to VS (Tao and Zhang 2017).
2.3 Systems Data (SD)
According to Alam and Saddik (2017), what differentiates a simple virtual model
from a DT is its ability to capture the dynamic behavior of physical systems over
time. This capability is due, in part, to the exchange of data and information between
the physical and virtual environments, and, therefore, the SD has a fundamental role
in the building and operation architecture of the DTs. Thus, the SD can be seen as
the entire data and information structure of both PS and VS. In the case of the PS, it