The replica of a physical device can be considered an edge-of-IoT system connected to the physical one and exchanging data with it. Under this definition, there are three levels of integrity for the cyber-physical system coupling – namely, model’, ‘digital-shadow’, and ‘digital-twin’, sometimes wrongly regarded as synonyms [15, 16]. The
1.2 The digital twin 15
(a) (b)
(c)
Figure 1.1: Data flow with a digital model (a), a digital shadow (b), and a digital twin (c).
differentiation is based on the type of data exchange between the physical and the ‘cyber’
parts of the cyber-physical system. The system is called a digital model if the digital representation of the physical entity is not connected to the existing device and the data flow is manual during modelling, as depicted in Figure 3.12a. In a digital model, system data flow neither from physical object to digital one norvice versa. If data flow from the physical object to the digital one and not in the opposite direction, as illustrated in Figure 3.12b, the system is called a digital shadow. In systems of this sort, any change to the physical device affects the simulations performed in the digital system, while the simulation results do not have any influence over the physical device.
Finally, in a digital-twin set-up, as shown in Figure 3.12e, the digital and physical sub-systems are fully integrated, so the real-time exchange of data keeps them updated and synchronised, and the simulations are performed in a time span comparable with true physical time. The subsystem updating takes place via instantaneous data flow from the physical entity to its digital twin andvice versa. For various purposes, including prod-uct design, energy-efficiency enhancements to industrial devices, a reconfigurable man-ufacturing system (RMS) approach, and assessment of the remaining useful life (RUL) of industrial equipment [17], digital-twin technology is vital. It affords monitoring and maintenance services, and it aids in management, optimisation, and safety work for pro-duction lines and products [18].
1.2.1 Digital-twin applications
Although the digital twin (DT) has a wide range of applications, the discussion here con-tinues with a brief explanation that focuses on the last two of those listed above: RMS development and RUL assessments.
1.2.2 Digital twins for reconfigurable manufacturing systems
In a flexible (or smart) production line, specific sets of changes in the process or materials cause various other products to be produced. Reconfigurable manufacturing systems were originally introduced in a paper by Koren et al. [19] in which the authors suggested that high-frequency change in the competitive global market demands quick and cost-effective responses. Even highly controllable production lines suffer from flexibility constraints, as many industrial robots are mapped with characteristics of complex functions and the manufacturing devices are restricted by inflexible programming [20]. Traditional pro-duction lines can only accommodate tiny variations in the product [21]. On the other hand, customisation of products is rapidly leading factories toward flexible production lines on which reconfiguration of hardware–software elements is performed easily. This is challenging: reconfiguration of elements should consider multidimensional optimisa-tion variables such as business and marketing concerns, the timing/schedules for delivery of products, the sequence of processes, environmental impact, etc. To achieve manufac-turing that is smart enough to cope with such a landscape, a digital twin of the process should be available for investigation of whether or not the planned changes to the pro-duction line truly are going to yield the desired product. Also, they can inform checking for mistakes in the practical reconfiguration and making sure all the requirements are met [22]. This technology provides a powerful simulation tool for examining any variations in the configuration of a manufacturing system [23].
Zhang et al. [20] have proposed a digital-twin virtual entity (DTVE) model for a reconfig-urable DT-based manufacturing system (RDTMS). Their model covers five dimensions:
a geometric model (GM), physical model (PM), capability model (CM), behaviour model (BM), and rule model (RM). The first,GM =f(Shape, Size, Location, Rotation, etc.), represents the need to perform 3D virtual visualisation of the process for monitoring and management purposes [24]. The physical one,P M =f(Speed, M ass, F riction, Abrasion, etc.), is necessary for assessing the functionality of various configurations by predicting the results, evaluating performance, and planning the optimisation of various alternatives [25, 26]. With the CM, in turn, the goal is to identify the complementary devices that should be connected as functional interfaces. Therefore, it is essential to know the capa-bilities of each entity in the physical layer in this model [27]: what it can do and what can be done [20],CM=f(Cando, Isdoing, Canbedone, Isbeingdone, etc.).
Each of a production line’s devices has a function to perform in completing the production of a product. Some of these elements – e.g., robots and machining tools under computer control – apply patterns of logical behaviour and should be dealt with as independent units to reduce the inflexible programming [20]. Consequently, the behaviour logic of all entities in the physical manufacturing system should be determined through the BM [28],BM =f(T ake, M otoron, W ait, F ault). In the final stage, some rules derived by
1.2 The digital twin 17
experts or mined from Big Data would be expected, to ensure the safety of all the entities’
operations (the interfaces’ rules should be compatible, the larger system must comply with certain standards, etc.).
1.2.3 Digital twins in gauging remaining service life
The digital-twin concept was proposed for public use in a NASA document on technol-ogy roadmaps, Technoltechnol-ogy Area 11 [29]. This document describes a DT collecting sensor data from a vehicle’s on-board Integrated Vehicle Health Management (IVHM) system, the historical data available from maintenance services, etc. obtained via text-mining and other data-mining. Combining all of this information, the digital twin would instanta-neously forecast the mission success probability, the health of the vehicle/system, and its remaining useful life. The DT’s on-board systems would be capable of performance degradation or mitigating damages in the system by suggesting changes in operation pro-file that increase both likelihood of operation accomplishment and the lifespan [30, 29].
Therefore, digital-twin technology has been considered a framework for predictive main-tenance strategy [31].
1.2.4 Digital twins’ challenges
Several challenges stand in the way of a highly capable digital-twin service. such as lack of extensive enough IT infrastructure, accuracy issues with the data generated, matters of the data’s trustworthiness, and security issues related to the algorithms and technologies used in the DT, as mentioned by [15].
The IT infrastructure should be capable of executing very computationally expensive al-gorithms for either carrying out simulations or obtaining a reliable result from the corre-sponding Big Data. The way to handle expensive computations is to use multiple graphics processing units (GPUs) to provide the essential computation resources. The alternative to GPUs is on-demand use of cloud computing, taking advantage of online resources pro-vided by Google, Amazon, Nvidia, etc. These resources are accompanied by such trade-offs as the cost of GPUs being higher than that of on-demand cloud resources whereas GPUs allow higher-speed operation.
On the other hand, the speed and accuracy of simulations are inversely proportional; i.e., the higher the accuracy, the lower the speed. Therefore, the only way to maintain their accuracy while increasing the computation speed without simplifying the algorithms is to develop new algorithms, ones that do not demand as many computation operations and are better suited to parallel computing.
Another challenge in the shift toward DTs is to convince companies, other organisations, and users as to the gains with this technology. These parties might have concerns related to the reliability of the simulations, machine-learning algorithms, and system performance.
One important means of increasing trust is to assure the managers that the data are se-cure and privacy is preserved. As they might deal with sensitive data of their customers, protection of digital-twin data is a matter of great importance.