However, the digital twin shop-floor is a complex and specialized work to be built, especially the models including geometry, rule, behavior, dynamics models. With the help of services, these models do not have to be created by manufacturer themselves.
For physical equipment and pervasive rules, their models which have been established by other manufacturers, can be bought to use in the form of services. Current manufacturer only needs to create the special models, which is only suitable for themselves. Besides, during the operation of the shop-floor, some services, such as data processing, shop-floor management, etc., need to invoke from the services system of digital twin shop-floor.
A main cause of Digital Twin confusion is the variety of focused areas within different disciplines. In order to encourage further contribution in this field of study, the establishment of a common definition is necessary. Additionally reference models, which fulfil the domain specific requirements of the focused areas, must be developed.
A first step towards a common definition of digital twin is based on the differentiation between particular levels of integration. The development of the digital twin is still in early stages, since many of current reports mainly consists of concept papers without concrete case-studies.
However, some applied case-studies already exist – especially at the lower levels of integration. A main focus of recent reports concerning the digital twin in manufacturing is dealing with production planning and control as it is a main data-sink within a production system that ties everything together.
With a mid-level time-horizon, simulation is often used in order to exploit the models at their best. However, the digital twin can also be used in domains with higher time-frequencies as e.g. process control and condition based maintenance, without using time intense simulation, but using other data driven approaches. There is a further research need for case studies industrial environments in order to evaluate the potential applications of digital twins.
Digital twin has provided a promising opportunity to implement smart manufacturing and advanced industrial networks by integrating the digital and physical worlds in manufacturing. The service-oriented architecture may expand the functions of digital twin. Digital Twin services can have high potential application in design, manufacturing and product condition assessments.
Combined with the services and the digital twin, how the various components of digital twin are encapsulated to services and used in the form of services specifies, are specified. At present, reports need much more works to improve and enrich the methods of digital twin modelling and services.
With new information technologies developed and applied continuously, developing digital twins to start new paradigm of shopfloor becomes imperative. To support the further convergence in shop-floor, digital twin shop floor model provides evolved models with high fidelity, continuous interactions between physical and virtual spaces and fused data converging those two spaces.
Here we provide a summary of digital twin impacts and a guideline for the future work. The main contributions are concluded as follows: 1) The concept and operation mechanism of digital twin shop floor are explored. 2) two-way high fidelity connection between physical and virtual spaces, 3) service management and precious service-demand matching,
The Digital Twin in its origin describes mirroring a product, while the state of the art allows processes, manufacturing, power generation etc. to be subjects of virtual space reproduction “Twinning” in order to gain the very same benefits.
A central aspect of the digital twin is the ability to provide different information in a consistent format. Digital Twins are more than just pure data, they include algorithms, which describe their real counterpart and decide about action in the production system based on this processed data.
Manufacturing digital twin can be viewed as consisting of a virtual representation of a production system capable of running on different simulation disciplines characterized by links between the virtual and real system. Sensed data and connected smart devices, along with mathematical models and real time data elaboration are also major components of digital twin.
Due to the multiple existing solutions and concepts of digital twin across industries a diverse and incomplete understanding of this concept exist. Considering definitions of a Digital Twin in any context, Digital Twins is identified, as digital counterparts of physical objects.
Within these definitions, the terms Digital Model, Digital Shadow and Digital Twin are often used synonymously. However, the given definitions differ in the level of data integration between the physical and digital counterpart. Some digital representations are modelled manually and are not connected with any physical object in existence, while others are fully integrated with real-time data exchange.
Therefore, we could offer classification of Digital Twins into subcategories, according to their level of data integration. Digital Model is a digital representation of an existing or planned physical object that does not use any form of automated data exchange between the physical object and the digital object.
The digital representation might include a more or less comprehensive description of the physical object to include, but are not limited to simulation models of planned factories, mathematical models of new products, or any other models of a physical object, which do not use any form of automatic data integration.
Digital data of existing physical systems might still be in use for the development of such models, but all data exchange is done in a manual way. A change in state of the physical object has no direct effect on the digital object and vice versa.
Based on the definition of a Digital Model, if there exists an automated one-way data flow between the state of an existing physical object and a digital object, one might refer to such a combination as Digital Shadow. A change in state of the physical object leads to a change of state in the digital object, but not vice versa.
Digital Twin can be placed in a subcategory where, the data flows between an existing physical object and a digital object are fully integrated in both directions,. In such a combination, The digital object might also act as controlling instance of the physical object. There might also be other objects, physical or digital, which induce changes of state in the digital object. A change in state of the physical object directly leads to a change in state of the digital object and vice versa.
Due to its tremendous potential for disruptive development of industry, digital twin is receiving more and more attention from the industry. Digital twin reflects two-way dynamic mapping of physical objects and virtual models. By building digital twin system that integrates the manufacturing process, the innovation and efficiency from product design, production planning to manufacturing implementation, can be effectively enhanced.
The application approach of digital twin in factory design is different with the other reported applications. Digital twin should mirror the designed factory that will be physical in the future. Because the uncertainty exists in each design stages, the design changes before it was constructed. In order to offer an elaborate simulation and help the designer to make decision.
Digital twin mirrors not only the final physical factory but also the virtual factory corresponding to each design version. For this reason, another problem arises because digital twin has to change with the change of design, whereby establishing a digital twin model can take a considerable amount of time and a lot of digital twin models will result huge modeling time and workload.
To solve this problem, a modular approach for building flexible digital twin is useful. The modular approach is building reusable and parameterized modules corresponding to physical entities in advance. When physical entities changed, the modules conduct corresponding changes driven by parameters and integrate as a digital twin model of physical factory. Through the modular approach, the modeling time is greatly reduced.
When the factory design changes, the digital twin can conduct the corresponding change highlighting the flexible abilities of digital twin.
The performance degradation of physical equipment is inevitable. When the equipment malfunction, it results in high maintenance costs and postponement of tasks.
Tools are necessary to monitor the equipment condition, predict and diagnose equipment faults and component lifetime. In digital twin driven condition tools, virtual models of physical equipment are linked with the real state of the equipment. The operation status of the equipment, and the condition status of the components, are captured in real time.
A high-fidelity digital mirror for the equipment provides access to the equipment even out of physical proximity. Interaction of digital twin can reduce the disturbances from the external environment, improving accuracy. In such a process, the models are accessed through services. Moreover, when the failures occur, repair services are invoked to repair, or replace the broken-down equipment.
- Digital Twin utilization in factory design discovers design flaws, reduces build duration of model improves application to changeable factory design
- Digital Twin reflects two-way dynamic mapping of physical objects and virtual models to integrate/implement manufacturing process
- Digital Twin includes consideration of all pieces of equipment and production line on shop floor/factory
- Digital Twin Fusion data is core driver including data from physical entities, virtual models and services
- Digital Twin driven design turns expected design product plans into digital representation based on existing physical products
- Digital Twin is description of component, product, system or process by set of well-aligned, descriptive and executable models
- Digital Twin is semantically linked collection of digital artifacts include design/operational data and behaviour descriptions
- Digital Twin changes with real system along the whole life cycle and integrates the currently available and commonly required data
- Digital Twin creation/deployment of simulation models sets with defined purpose and validity range to add new models for intended/future function
- Digital Twin provides anchor for all lifecycle phases and support of transformation to the next phase