Digital Twins: the next frontier of VR technology

A digital twin is a digital model of a real-world physical product, system, or process. Originating from the United States’ National Aeronautical and Space Administration in 2010, as part of an attempt to improve the physical-model simulation of spacecraft, the term describes a counterpart for simulation, integration, testing, monitoring and maintenance.

According to the dictionary, a digital twin is “the digital replica of an object, process, or system”. In automobile manufacturing, the first digital twins were created by digitising or copying real vehicles piece by piece, right down to the tiniest bolt – hence the term ‘twin’.

With technological advancements, digital twins are now created before their physical other. Their name is misleading because, in fact, the digital twin is a compilation of all the digital and physical models of the physical object that does not yet exist! A physical copy will only take shape once the models reach a   certain level of maturity.

Lifecycle modeling

The digital twin has been intended from its initial introduction to be the underlying premise for product lifecycle management and exists throughout the entire lifecycle (create, build, operate/support and dispose) of the physical entity it represents. Since information is granular, the digital twin representation is determined by the value-based use cases it is created to implement.

The digital twin can and does often exist before there is a physical entity. The use of a digital twin in the creation phase allows the intended entity’s entire lifecycle to be modelled and simulated. A digital twin of an existing entity may be used in real-time and regularly synchronized with the corresponding physical system.

Digital twins are the result of continual improvement in the creation of product design and engineering activities. Product drawings and engineering specifications have progressed from hand-made drafting to computer-aided drafting and design to model-based systems engineering and strict link to signal from the physical counterpart.

The digital twin concept, which has been known by different names, was subsequently called the “digital twin” by John Vickers of NASA in a  2010 Roadmap Report. The digital twin concept consists of three distinct parts:

•        The physical object or process and its physical environment;

•        The digital representation of the object or process; and

•        The communication channel between the physical and virtual representations.

The connections between the physical version and the digital version include information flows and data that includes physical sensor flows between the physical and virtual objects and environments. The communication connection is referred to as the digital thread.

The International Council of Systems Engineers (INCOSE) maintains in its Systems Engineering Book of Knowledge (SEBoK) that a digital twin is a related yet distinct concept to digital engineering. The digital twin is a high-fidelity model of the system which can be used to emulate the actual system.

Digital components

The digital twin comprises three primary elements: the physical object or process, its digital representation, and the communication channel, often referred to as the digital thread.

Digital twins are commonly divided into sub-types that sometimes include digital twin prototype (DTP), digital twin instance (DTI) and digital twin aggregate (DTA).

•        The DTP consists of the designs, analyses and processes that realise a physical product. The DTP exists before there is a physical product.

•        The DTI is the digital twin of each individual instance of the product once it is manufactured. The DTI is linked with its physical counterpart for the remainder of the physical counterpart’s life.

•        The DTA is the aggregation of DTIs whose data and information can be used for interrogation about the physical product, prognostics and learning.

The specific information contained in the digital twins is driven by use cases. The digital twin is a logical construct, meaning that the actual data and information may be contained in other applications in physical and virtual versions.

Prominent features

One of the main characteristics of digital twin technology is its connectivity. The recent development of the Internet of Things (IoT) brings forward numerous new technologies. The development of IoT also brings forward the development of digital twin technology.

First and foremost, the technology enables connectivity between the physical component and its digital counterpart. The basis of digital twins is based on this connection; without it, digital twin technology would not exist.

This connectivity is created by sensors on the physical product which obtain data and integrate and communicate this data through various integration technologies. Digital twin technology enables increased connectivity between organizations, products, and customers.

For example, connectivity between partners and customers in a supply chain can be increased by enabling members of this supply chain to check the digital twin of a product or asset. These partners can then check the status of this product by simply checking the digital twin.

‘Servitisation’ is the process of organisation that keeps on adding value to the core corporate offerings through services. In the case of engines, the manufacturing of the engine is the core offering of this organisation: they then add value by providing a service of checking the engine and offering maintenance.

Digital twins can be further characterised as a digital technology that is both the consequence and an enabler of the homogenisation of data. Due to the fact  that any type of information or content can now be stored and transmitted in the same digital form, it can be used to create a virtual representation of the product (in the form of a digital twin), thus decoupling the information from its physical form.

Therefore, the homogenisation of data and the decoupling of the information from its physical artifact have allowed digital twins to come into existence.  However, digital twins also enable increasingly more information on physical products to be stored digitally and become decoupled from the product itself.

Homogenous data

As data is increasingly digitised, it can be transmitted, stored and computed in fast and low-cost ways. According to Moore’s law, computing power will continue to increase exponentially over the coming years, while the cost of computing decreases significantly.

This would, therefore, lead to lower marginal costs of developing digital twins and make it comparatively much cheaper to test, predict and solve problems on virtual representations – rather than testing on physical models and waiting for physical products to break   before intervening.

Another consequence of the homogenisation and decoupling of information is that the user experience converges. As information from physical objects is digitised, a single artifact can have multiple new affordances. Digital twin technology allows detailed information about a physical object to be shared with a larger number of agents, unconstrained by physical location or time.

In the past, factory managers had their office overlooking the factory so that they could get a feel for what was happening on the factory floor. With the digital twin, not only the factory manager, but everyone associated with factory production could have that same virtual window to not only a single factory, but to all the factories across the globe.

Smart tracing

The digital twin is also reprogrammable in an automatic manner, through the sensors on the physical product, artificial intelligence technologies and predictive analytics. A consequence of this reprogrammable nature is the emergence  of functionalities.

If we take the example of an engine again, digital twins can be used to collect data about the performance of the engine and if needed adjust the engine, creating a newer version of the product.

Also, ‘servitisation’ can be seen as a consequence of the reprogrammable nature as well. Manufactures can be responsible for observing the digital twin, making adjustments, or reprogramming the digital twin when needed and they can offer this as an extra service.

Another characteristic observed is the fact that digital twin technologies leave behind digital traces. These traces can be used by engineers in instances where a machine malfunctions to go back and check the traces of the digital twin, to diagnose where the problem occurred.

These diagnoses can also be used by the manufacturer of these machines, to improve their designs so that these same malfunctions will occur less often in the future.



Industrial applications

Digital twins offer several benefits to the industry that include extend the life of assets and equipment, uncover operational inefficiencies, help deploy preventive maintenance, reduce maintenance costs.

It also enables better response to episodes of downtime and improves situational awareness. The physical manufacturing objects are virtualised and represented as digital avatars seamlessly and closely integrated in both the physical and cyber spaces. Physical objects and twin  models interact in a mutually beneficial manner.

The digital twin is disrupting the entire product lifecycle management (PLM), from design to manufacturing to service and operations. Nowadays, PLM is very time-consuming in terms of efficiency, manufacturing, intelligence, service phases and sustainability in product design.

A digital twin can merge the product physical and virtual space. The digital twin enables companies to have a digital footprint of all of their products, from design to development and throughout the entire product life cycle.

In the manufacturing process, the digital twin is like a virtual replica of the near-time occurrences in the factory. Thousands of sensors are being placed throughout the physical manufacturing process, all collecting data from different dimensions, such as environmental conditions, behavioural characteristics of the machine  and work that is being performed.

All this data is continuously communicating and collected by the digital twin. Due to IoT, digital twins have become more affordable and could drive the future of the manufacturing industry. A benefit for engineers lies in real-world usage of products that are virtually being designed by the digital twin.

Advanced ways of product and asset maintenance and management come within reach as there is a digital twin of the real ‘thing’ with real-time capabilities.

Business potential

Digital twins offer a great amount of business potential by predicting the future instead of analysing the past of the manufacturing process. The representation of reality created by digital twins allows manufacturers to evolve towards ex-ante business practices.

As there is an increasing digitalisation in the stages of a manufacturing process, opportunities are opening up to achieve a higher productivity. This starts with modularity and leading to higher effectiveness in the production system. Autonomy enables the production system to respond to unexpected events in an efficient and intelligent way.

Geographic digital twins have been popularised in urban planning practice, given the increasing appetite for digital technology in the ‘Smart Cities’ movement. These digital twins are often proposed in the form of interactive platforms to capture and display real-time 3D and 4D spatial data in order to model urban environments (cities) and the data feeds within them

Visualisation technologies such as augmented reality (AR) systems are being used as both collaborative tools for design and planning in the built environment integrating data feeds from embedded sensors in cities and API services to form digital twins.

For example, AR can be used to create augmented reality maps, buildings, and data feeds projected onto tabletops for collaborative viewing by built environment professionals.

In the built environment, partly through the adoption of building information modeling (BIM) processes, planning, design, construction, and operation and maintenance activities are increasingly being digitised, and digital twins of built assets are seen as a logical extension.

Some examples

One of the earliest examples of a working ‘digital twin’ was achieved in 1996 during construction of the Heathrow Express facilities at Heathrow Airport in London. The consultants connected movement sensors in the coffer dam and bore holes to the digital object-model to display movements in the model. A digital grouting object was made to monitor the effects of pumping grout into the earth to stabilise ground movements.

Digital twins have also been proposed as a method to reduce the need for visual inspections of buildings and infrastructure after earthquakes by using unmanned vehicles to gather data to be added to a virtual model of the affected area.

The concept of digital twin in the healthcare industry was originally proposed and first used in product or equipment prognostics. With a digital twin, lives can be improved in terms of medical health, sports and education by taking a more data-driven approach to healthcare.

The biggest benefit of the digital twin on the healthcare industry is the fact that healthcare can be tailored to anticipate on the responses of individual patients. Previously, ‘healthy’ was seen as the absence of disease indications. Now, ‘healthy’ patients can be compared to the rest of the population in order to really define healthy.

Advanced implementation

Industry has been quick to realize the many advantages that the technology offers and have increasingly adopted it in designing and production. A specific example of digital twin technology in the automotive industry is where automotive engineers use digital twin technology in combination with the firm’s analytical tool in order to analyze how a specific car is driven.

In doing so, they can suggest incorporating new features in the car that can reduce car accidents on the road, which was previously not possible in such a short time frame.

Digital twins can be built for not just individual vehicles but also the whole mobility system, where humans (drivers, passengers, pedestrians), vehicles (connected and automated vehicles), and traffic (networks and infrastructures) can seek guidance from their digital twins deployed on edge/cloud servers to actuate real-time decisions.

The Emirates Team New Zealand (sailing), for instance, uses digital twin for testing boat designs without physical prototypes. A digital twin of sailing environments, boats, and crew members enables Emirates Team New Zealand to test boat designs without actually building them.

Beer manufacturer Anheuser-Busch InBev utilises a brewing and supply chain digital twin for adaptive production. This enables the company to adjust inputs based on active conditions and can automatically compensate for production bottlenecks – for instance, when vats are full.

Space Force, a branch of the US armed forces, is creating a digital twin of space, including replicas of extra-terrestrial bodies and satellites. SpaceX applies digital twins for space-related simulations and optimisation to maximise safety and reliability during transport.



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