A Digital Twin is defined as a virtual representation of an object or system. The Digital Twin’s lifecycle is from updating real-time data and then uses simulation, machine learning, and reasoning to assist in decision-making. For example, a typical machine in a workshop[1]. A machine is fitted with sensors that monitor the machine’s working and then produces data about its’ working. This data is then forwarded to a processing system. The processing system makes a digital copy with similar attributes and applies the input. Once it gains the information, it studies performance issues. It generates possible improvements to create valuable insights, then used back to the original machine for progress and better results.

The application and advantages of using of Digital Twin are also covered with used cases of Kaeser’s product to service transformation and Boeing’s future predictive models. The growth of this industry is expected to expand at a compound annual growth rate (CAGR) of $45.6 billion from 2021 to 2026.

Two Classifications of Digital Twin

The Digital Twin can be classified into four levels: Parts Twins, Asset/Product Twins, System Twins, and Process Twins.

Parts Twinning: In this process, the virtual representations of the individual factors give the capability to understand the physical, mechanical, and electrical characteristics.

Product Twinning: The Product twinning system integrates the parts as they work together to make a product in digital form.

System Twinning: The system twinning allows the working engineers to operate and maintain
entire lines of distant products that work together to achieve a result at a system position.

Process Twinning: The Process twinning empowers the optimization of the operations involved in enhancing the raw stuff or product of finished goods [3]. For example-Suppose, we have to make a digital twin of the working of a car factory. There are several units with various parts for making the vehicle in a car-making factory. The twinning of the entire factory working is cared by the Process twinning, which twins the whole working of the factory. There are various units in the car factory like Engine making unit, Body House, Painting department, etc. The Twin of every team separately would be done under the System Twinning. Moreover, the machines running in each unit would be twinned and tagged under Product twinning. Further, the smaller parts of the machines would be twinned under the Parts Twinning. In this way, the entire procedure of twinning is done, and the whole project is covered in tiny bits accounting for a huge amount of work.

Applications of Digital Twins

Large machines, including jet machines, locomotive engines, and power-generating turbines, benefit extensively from digital twins, especially for helping to establish timeframes for regularly required maintenance. For instance, large buildings or coastal drilling platforms, large physical structures can be enhanced through digital twins, particularly during their design. Also helpful in designing the systems handling within those structures [4].

Since digital twins are meant to reflect a product’s entire lifecycle, it’s not surprising that digital twins have become universal in all stages of manufacturing, guiding productions from the blueprint to the completed product and all steps in between. Civil engineers and others involved in urban planning exercises are significantly backed by digital twins, which can show 3D spatial data in real time and incorporate augmented reality systems into constructed environments [5].

Kaeser’s Digital Twin

Kaeser, a German company that is one of the leading manufacturing companies of compressed air products provider, has applied Digital Twins that enable the company to switch from vending a product to vending a service. Digital Twin system constantly provides
Kaeser with operating data on the equipment during its lifecycle, personalizing the air consumption rate. Monitoring has permitted Kaeser to charge customers on the ground of air consumption rather than at a fixed rate.

As a result, the company has managed to reduce commodity costs by 30% and onboarded nearly half of all significant dealers using digital twins. This digital transformation also helped them in making more profits over time [6].

Boeing’s Digital Twin

At Boeing, digital twins are used to planning aircraft. A digital twin is created for a new airplane, after which simulations are run that predict the version of various airline elements over the product’s lifecycle [7]. As a result, Boeing engineers can forecast when products are expected to fail. The company has achieved a 40% enhancement rate in the first-time quality of parts by using a digital twin. The company plans to digitize all of its engineering and development systems in the future and share this information with its supply chain.

Market of Digital Twins

The global digital twin demand size was valued at USD 5.04 billion in 2020. It’s expected to expand at a compound annual growth rate (CAGR) of 45.6 billion from 2021 to 2026 [8]. The demand witnessed a slight recession in 2020 owing to the shutting down of manufacturing places, production factories, and other plants in the first half of the cycle triggered by the spread of the COVID-19 pandemic. However, owing to the varied operations of digital twins, the demand is strengthened for exponential growth[9].

Benefits of using Digital Twin

The use of digital twins enables further effective research and design of products, with an abundance of data created about likely performance issues. That information can lead to
insights that help companies make needed product advancements before starting a product’s production [10]. Even after a new product has gone into effect, digital twins can help reflect and watch production systems to achieve and maintain peak effectiveness throughout the entire manufacturing process. Required updates and timely modifications can also be done with the Digital Twin. Future issues can also be determined using the behavior of the input [11].

Designing

A Digital Twin is made up by gathering data and creating models of exact characteristics to test it. A digital twin requires data about an object or process to create a virtual miniature that can define the behaviors or states of the real-world item or procedure. This data may relate to a product’s lifecycle and include design specifications, product processes, or engineering information[12]. It can also include product information, including outfit, accouterments, corridor, styles, and quality control. Once the data has been gathered, it can be used to produce computational analytical models to show operating effects, predict states such as exhaustion, and determine behaviors[13]. These models can define behavior based on engineering simulations, physics, chemistry, statistics, engine knowledge, artificial intelligence, business logic, or intentions. These models can be displayed via 3D representations and augmented reality modeling to help humans understand the findings. The findings from digital twins can be linked to produce an overview, similar as by taking the results of equipment twins and putting them into a production line twin, which can again inform a manufactory-scale digital twin. Using linked digital twins in this way makes it possible to enable intelligent artificial operations for real-world operational developments and advancements [14].

Conclusion

An essential change to existing operating models is happening. A digital reinvention is being in asset-intensive industries that are altering operating models in a disruptive way, challenging an integrated physical plus digital view of assets, equipment, installations, and procedures [15]. Digital twins are a major part of this realignment. The future of digital twins is nearly indefinite because increasing quantities of cognitive power are constantly being devoted to their use. So Digital Twins are constantly getting new expertise and capabilities, which means they can continue to bring about the insights required to make products better and processes more productive [16]. The information generated by the real product can lead to insights that help companies make required product advancements before starting a product’s production. The segmentation of twinning a body is sublime, making it step-wise and easier to identify the upcoming issues and enhance them accordingly.

As more companies use digital twins to make products, they can start assembling up entire ecosystems. Products can react in a virtual climate, giving real- time data to help evolve future internet of effects (IoT) products and more.