3.1. Digital Twin Concept and Definition
It is a tough task to define what a Digital Twin is. The fact that Wikipedia has nine definitions for digital twins is a clear indicator of this problem. To some authors, a DT is a simulation model that mirrors physical systems and allows simulations; to others it is something that can mirror the state of a real asset, allowing its monitoring, control and alteration as is common in IoT [51
] The differences in the definition seem to be related to the focus that each author has in the DT concept. Negri et al [52
] gave a comprehensive overview of the DT definitions available in the literature from its first appearance in 2012 to 2016. However, even in recent years the definition of DT still seems to change according to the authors and their purpose.
Martin Morháč’s team [53
] with their focus on industrial process lines, defined the DT concept as the functional system of a continuous process optimization, formed by the cooperation of the physical and the digital production lines. Thus, DT continuously collects and evaluates process information in order to shorten production cycles, accelerate the introduction of new products and reduce process inefficiency.
In addition, for Jiewu Leng et al [54
] DTs are related to optimization, with a DT being a “simulation with ability of real-time control and optimization of products and production lines.” However, Bao et al [55
] saw the DT as a method or tool to be used in the simulation and modeling of the behavior and status of entities.
According to Schluse et al [56
] DTs are deeply associated with the Industry 4.0 development. For the authors, a DT is a one-to-one representation of a real-world element (such as a machine, component or part of the environment) or a real subject (person, software or system). The DT comprises the virtual representation of this element, its behavior and its communication facilities. In their work, the authors presented a simulation technology, based on the concept of “Experimentable Digital Twins,” which combine DTs with simulation technologies to bring DTs to life.
Graessler and Poehle [57
] developed a DT that assumes the employee communication and coordination tasks with the production system. The usual concept of a DT that emulates the properties and behavior of a system was adapted by the authors to act as a representative for a human employee in a cyber-physical production system (CPPS), since the property and behavior of the human DT need to be based on user feedback and recorded patterns instead of actual measured data.
According to Rovere et al [58
] the DT is the semantic, functional and simulation-ready representation of each shop floor CPS. It can define performance specifications and behavioral and functional models. Thus, it can be said, according to the authors, that the DT is a composite concept that aggregates the following elements:
CPS Prototype is a model that defines the structure and the associate semantic for a certain class of CPS.
CPS Instance is a computer-based representation of an instantiation of a CPS prototype. As the DT is an instance of a CPS prototype, it can be said that a CPS instance is a computer-based representation of its DT.
Behavioral Models are simulation models related with the semantic representation of a CPS prototype and instance. Each DT can address different behavioral models to allow multi-disciplinary simulations.
Functional Models allow the analysis of data from the shop floor. The result of the data analysis is used to enrich the DT, for example to enable predictive maintenance.
Therefore, as already referred, several definitions of DTs exist in the literature and each author adapts the definition according to their purpose. However, all the definitions have something in common: the simulation of something in a specific environment. The definition presented in the Industrial Internet Consortium White Pape [59
] seems to be general and consider all the definitions presented before. The authors stated that “a DT is a formal digital representation of some asset, process or system that captures attributes and behaviors of that entity suitable for communication, storage, interpretation or processing within a certain context.”
3.2. State of the Art in Digital Twins
Many DT applications can be found in the literature, applied to several fields such as smart cities, construction, healthcare, agriculture, cargo shipping, drilling platform, automobile, aerospace, electricity, etc. All these different applications originated the huge amount of DT definitions mentioned in the previous section.
With the increasing level of digitalization and increasing complexity of machines and products, DTs might be a kind of next generation of industrial simulation [60
According to Malakuti et al., the DTs can act in the overall product life-cycle for design, manufacturing, operating and maintenance purpose, collecting relevant data for model-based simulation and prediction [59
Tao et al. presented the benefits of the DT in the design phase [38
]. In early and conceptual design stages where design directions are set, the DT can collect relevant data for, e.g., customer satisfaction, financial or economic plans into physical models. Therefore, the DT becomes a precise, particularly virtual, representation. This facilitates the communication between designers and customers especially in the current trend of increasing visualization in the product design process. Since functionality, configurations and design parameters are defined, checked and probably updated in the design phase, these adjustments must also be tested by simulation. Without the DT, there are no real time or environment-influenced data. This makes the DT a useful tool for virtual verification and testing without a fabricated product or prototype. The DT also uses experiences from the production of similar products or previous generations in the form of historical data. This gives the DT a high prediction capacity to test whether investment and product improvement plans are profitable as expected. Furthermore, the prediction makes it easier to detect and correct design failures saving time and money in the design phase.
For modern manufacturing, the monitoring and analysis during operation is a crucial issue to ensure long-term and reliable operation of equipment. In this context, Wang et al. [61
] presented a DT reference model designed for rotating machinery fault diagnosis. To detect deviations from the standard, the DT allows the integration and interpretation of the physical knowledge and data measurements using simulation models, instead of relying solely on sensor data. Thus, the mechanism of typical failure models can be simulated in the DT to analyze the root cause of the failure and predict the evolution of the degradation process. Using these capabilities, the authors developed a pilot prototype of a rotor system to demonstrate the effectiveness of the DT model on unbalance quantification and localization for fault diagnosis. Experimental results show that the constructed rotor model can achieve accurate fault diagnosis and adaptive degradation prediction.
The DT also gives optimization opportunities in the production process. Since the DT has information about production assets and the availability of resources, it reflects the current state of machines, products and processes. Therefore, the consequences of decisions, e.g., the scheduling of manufacturing steps, can be imitated by the DT [62
], which optimizes decision support in manufacturing plans [37
] before executing any production process. The DT can also anticipate the consequences of countermeasures for detected malfunctions, misbehavior and failures before applications.
Since there is not yet a commonly agreed definition of Digital Twins, it is not surprising that there is also a lack of standards for the construction of DTs. Nevertheless, there are several approaches towards modeling and construction of DTs available in the literature that aim for supporting the system engineer in the DT construction process. In 2016, Jain and Lechevalier [63
] proposed an approach towards automated generation of virtual factory models using manufacturing configuration data in standard formats as the primary input. One of the main objectives of the approach is to reduce the needed and expensive expert knowledge for the DT construction. Ding et al. [64
] introduced a reference architecture for a version of DTs called “digital twin-based cyber-physical production system.” This architecture relies on external construction of the virtual replica of the system and focuses on adding components and data flows for enabling automated control.
Damjanovic-Behrendt and Behrendt [65
] explored the potentials of available open source tools and services, which can help developing an open source DT ex nihilo. They proposed a DT concept based on a micro-services architecture to design a flexible, open source solution for digital twins and make it accessible to a wider industrial and research audience. This remains however more of a conceptual proposal than an actual open-source tool for DT development.
Another reference architecture based on micro-services was introduced by the consortium of the research project MAYA [58
]. The focus is on the structure of a middleware for the synchronization of the CPS and its virtual representation while accessing big data. The digital twin contains functional and behavioral models that are activated when the CPS logs into the centralized support architecture. Communication between CPS and the digital representative is enabled by a WebSocket channel. If the CPS disconnects from the support architecture, the digital twin and all its components are deactivated.
Within the research project AUTOWARE, an eponymous cognitive digital automation operation system was developed [66
]. The framework includes a reference architecture, in which Digital Twins of (smart) products are included. According to the authors, AUTOWARE can support the efficient development and operation of cognitive manufacturing systems. The used digital twin is an extension of the software architecture developed in the project ReconCell [67
]. Within ReconCell, a set of components for a robot workstation for automated assembly tasks is developed. One of those components is a Digital Twin of the work cell. The DT is based on the commercial 3D simulation software VEROSIM.
Tao et al. divided the DTs of subjects participating in the manufacturing context into a hierarchical model [68
]. There are DTs for single components such as machines, material or equipment at unit level and DTs for production line, complex products or the shop floor at system level. Through interoperability and dependencies in cooperated applications and collection data over the whole life-cycle the system level DTs build up a SoS DT. Therefore, the DTs “co-evolve over the lifecycle of the product process.” According to the authors, models play an important role for DTs to interpret and predict behavior. Since different models exists in deeper unit level for each subject, all of them have to be combined and integrated by the DT at the system level. The same challenge exists for DTs at SoS level with system DT models.
As the authors of [37
] provided more extensive surveys on recent developments in the area of digital twins for manufacturing, we proceed with highlighting key enabling technologies for modeling digital twins, as presented recently by Rasheed et al. [70
]. The authors clustered those key enabling technologies into five categories and conducted a survey on the state of the art in five categories: physics-based modeling, data-driven modeling, big data cybernetics, infrastructure and platforms and human–machine interface. Technologies for physics-based modeling tackle aspects of how to translate theories developed through observation of physical phenomena into mathematical equations and how to solve them. Examples for such technologies are 3D modeling and simulation techniques such as those presented in [71
] and numerical solvers that are already integrated into numerical simulators such as the open-source software OpenFOAM that allows simulating fluid dynamics [72
]. Data-driven modeling aims for the identification of patterns in large datasets, assuming that the data already encode information on physical phenomena. Enabling technologies include data generation techniques (e.g., crowd-based data gathering [73
]), data privacy solutions [74
], machine learning algorithms [75
] and artificial intelligence approaches such as generative adversarial networks for denoising images [77
]. Such techniques allow reducing the difference between real operational situation the real system is facing and the interpreted situation that the digital twin creates by sensor data of its physical counterpart. Big data cybernetics [78
] merges control theory with big data and aims for steering the system to a given state. From control theory perspective, the behavior of the system is constantly monitored and the evolution is compared to the reference state. Big data approaches are used for increasing the understanding of the observations, which can be used for improving controller performance for the digital twin and its physical counterpart. Examples for such big data approaches are filtering techniques [79
] and reduced ordering modeling [80
], both aiming for identifying what is relevant in the dataset. Enabling technologies of digital twins in the area of infrastructure and platforms include IoT, cloud computing and 5G (see [70
] for a list of approaches and commercial services in this area).
The increasing use of DTs in the several fields previously described led to the emergence of several software solutions dedicated to the DT implementation. As what happens with the definition of DT, usually, those software solutions are designed taking into account just the functionality that is needed in some moment for specific applications and the specific chosen definition for a DT. For this reason, the main software solutions are proprietary solutions designed by each player to accomplish the need of their product. There are many commercial software solutions that implement industrial DT technology, mainly developed by big companies of the manufacturing sector. The following is a non-exhaustive list of popular vendor DT technologies:
General electric (GE) developed an advanced and functional Digital Twin that integrates analytic models for components of the power plant that measure asset health, wear and performance. This DT can be integrated into the GE developed distributed predix platform for “large-scale machine data processing, management and analytics” and IIoT applications [81
PTC Windchill is a DT developed by PTC to help manufacturers across industries understanding how their customers are using their products. This way, they can help them to improve the design and performance of those products [82
3DS is a DT developed by Dassault Systemes that allows manufacturers to make virtual products available to the market for experimentation and testing in realistic conditions before engaging in any real production [83
Microsoft Azure DT Software is an IoT service that virtually replicates the physical world by modeling the relationships among people, places and devices in a spatial intelligence graph [84
Seebo DT is a graphical interface that allows the generation of actionable insights that maximize overall equipment effectiveness, reduce unplanned downtime and uncover the root cause of issues. Dashboards allow real-time visualization of the operational health of deployed machines and display enriched alerts with predictive metrics based on key machine parameters, such as machine temperature, pressure, vibration, humidity, fatigue and wear in order to quickly identify and solve issues remotely [85
Anylogic software provides simulation capabilities in a single commercial package with special research licenses available. It is specialized in factories and production lines, with discrete-event simulation capabilities, and has libraries capable of supporting several types of fields [86
]. The tool was used for a prototypical implementation of the data-driven DT generation approach in [64
Ansys developed a DT that can be used to monitor real-time prescriptive analytics and test predictive maintenance to optimize asset performance. The DT can also provide data to be used to improve the physical product design throughout the product lifecycle [87
IBM developed a DT framework that helps companies to virtually create, test, build and monitor a product, reducing the latency in the feedback loop between design and operation. It enables identifying and fixing problems and bringing products to market more quickly [88
Factory I/O is a software developed by Real Games [89
] that allows setting up configurable 3D-simulations by plug in components of a given industrial equipment catalog. To this end, the software provides simulation aspects of digital twins, explicit synchronization between real system and virtual replica is limited to the integration of several Programmable Logic Controller (PLC) for simulating the virtual factory.
Siemens offers several services for constructing digital twins, including a machine– human interface [90
] that can be used for the construction of a digital twin for humans and a portfolio called Digital Enterprise Suite [91
], which includes, e.g., digital twins for material transport equipment.
Unlike proprietary products, open-source solutions allow the technology to be freely redistributable and modifiable, supporting manufacturers in combining older equipment with modern sensor-based machines and tools from different vendors. Moreover, open source hardware supports faster prototyping and customization, which helps manufacturers to accelerate the design and improve interoperation across actual lifecycle processes [66
]. Although the open source community works hard to develop software and hardware solutions to be applied in Industry 4.0 and smart manufacturing, only few open source DT solutions are available:
CPS Twinning is a framework for generating and executing DTs that mirror cyber-physical systems [92
]. It is a proof of concept that can be used as first approach to model some environments, but also has some limitations such as inability to generate DTs for wireless devices [93
Wrld3d is an open source platform that allows the creation of DTs in a quick and easy manner, using a comprehensive set of self-serve tools, SDKs, APIs and location intelligent services. As a dynamic 3D mapping platform, it allows to create virtual indoor and outdoor environments upon which data from sensors, systems, mobile devices and location services can be visualized within millimeter accuracy [94
Mago3D is a platform for visualizing massive and complex 3D objects including building information modeling (BIM) on a web browser. Thus, it is possible to model DTs that creates parallel “worlds” in a virtual reality with several sensors [95
i-Maintenance toolkit enables to create a DT of an industrial asset in order to obtain information on the status of all components related to the production and maintenance of the industrial process, collect, monitor and analyze life-cycle data. It is composed of a messaging system, a set of adapters to integrate sensor/actuator systems and other software components that are used as a technical foundation for the DT development [96
At the intersection between pure proprietary and real open-source DT technology, open source solutions are developed by big companies, which make them limited in scope, due to the commercial interests of the developers [97
Eclipse Ditto is a DT developed by Bosch. It enables the design of DTs in the form of IoT development patterns. It can be seen as an open source foundational layer of Bosch IoT platform [98
imodel.js is a platform for creating, accessing, leveraging and integrating infrastructure DTs. As what happens with Eclipse Ditto, it is a commercial initiative connected to the US infrastructure company Bentley. According to the developers, it was designed to be both flexible and open, so that it can be easily used and integrated with other systems [99
In the research project “Twin-Control” a DT for machine tools and process was developed. The final result based on finite element analysis (FEA) software that integrates machine structure and processes. For FEA there exist commercial (EA autodesk [100
] and Ansys [101
]) as well as open source [102
] software tools.
Summarizing the literature review, we emphasize the following statement of Lu et al. [42
]: “Though studies have reported the potential application scenarios of Digital Twin in manufacturing, we identified that current approaches to the implementation of Digital Twin in manufacturing lack a thorough understanding of Digital Twin concept, framework, and development methods, which impedes the development of genuine Digital Twin applications for smart manufacturing.” Several tools already exist for building and running digital twins, but most are specialized for constructing a DT for a specific component, losing sight of the connection and dependencies to their environment. Hence, there is a lack of methods for increasing the understanding of effects of the (mis-)behavior of a component on the overall system such as a factory or a network of factories. This aspect is related to the findings of Tao et al. [38
] in 2018, who identified gaps in the usage of DTs in control applications.