The term “digital twin” was first used by Dr. Michael Grieves, a researcher at the University of Michigan, in 2002. Grieves was working with NASA at the time, exploring ways to use digital twins to improve spacecraft design and performance. The idea of using virtual replicas to simulate and optimize real-world objects quickly caught on.
Since then, digital twin technology has expanded into various industries and applications. Today, digital twins are used in manufacturing, healthcare, smart cities, and sports to improve efficiency, enhance performance, and reduce costs.
The history of digital twin technology is still relatively short, but it is rapidly evolving and promises to shift gears on how we design, build, and operate complex systems.
In this article, we will explore the ins and outs of digital twin technology – what it is, how it works, and and its applications today.
What is Digital Twin Technology?
Digital twin technology refers to a digital replica of a physical object or system that can be tested and examined without actually interacting with it in the real world. This can be anything from a small factory machine to a huge wind turbine or even an entire city.
In simpler terms, it’s like having a virtual sandbox that allows you to test and play with physical objects or systems in a safe and risk-free environment.
A digital twin has three components:
- A physical object,
- A digital twin in software form
- Data that links the two together.
This link makes digital twins unique, as users can investigate the object or process by querying the data and communicate actions through the digital twin to its physical counterpart.
The Digital Twin Consortium adds an important phrase to the definition, “synchronized at a specified frequency and fidelity.” This means that the digital twin and the physical object are mirrored as closely as possible regarding accuracy, precision, and speed of updates.
Digital twins typically rely on IoT sensors to establish the physical connections between the object and its twin. As such, implementing IoT technology is often considered a necessary requirement for successfully deploying digital twin systems.
Integrating artificial intelligence (AI), machine learning, and software analytics is essential in processing and analyzing digital twin data.
How Does Digital Twin Technology Work?
To create a digital twin, professionals in applied mathematics or data science usually start by researching the physics and operational data of a physical object or system. Based on this information, they develop a mathematical model that simulates the original system.
After data is collected from sensors on the real-world version, developers create a virtual computer model that receives feedback. This allows the digital twin to simulate and mirror the actions of the physical system in real time. Doing so can provide valuable insights into performance and potential issues.
The complexity of digital twins can vary depending on the amount of data used to create them. This determines how accurately they can simulate their real-world counterparts.
They are versatile tools that can provide feedback on product development when used with a prototype or can even act as a prototype themselves to model the behavior of a physical version.
Digital Twins vs. Simulations: The Difference
The difference between a digital twin and simulation is the ability to provide real-time updates.
In other words, while simulations are mainly used for design and offline optimization, digital twins are more complex virtual environments that can be interacted with and updated in real-time. They are larger in scale and have more applications.
To illustrate the difference, consider a car simulation. A new driver can use a simulation to get an immersive training experience, learn about different car parts and how they operate, and navigate through different real-world scenarios while virtually driving.
However, the scenarios in the simulation are not linked to a physical car.
On the other hand, a digital twin of the car is linked to the actual physical vehicle and contains vital performance statistics, information on replaced parts, potential issues observed by sensors, previous service records, and more.
This makes the digital twin much more accurate and useful than a simulation when it comes to monitoring and optimizing the performance of the actual vehicle.
What are the Types of Digital Twins?
Digital twin technology offers various types of digital twins that can run simultaneously within a system. Each digital twin type is critical in providing a virtual representation of an object.
The most common digital twin types include the following:
Also known as parts twins, component twins represent a single piece of an entire system. They are crucial for the functioning of an asset, such as a motor in a wind turbine.
Visual representation of asset twins allows businesses to understand how different components interact with each other, generating valuable data on their performance. This data can then be used to make informed decisions, leading to improved operations and profitability.
System twins provide a more advanced level of abstraction compared to asset twins, as they showcase how various assets interact with each other as part of a larger system. In addition, system twin technology provides visibility for performance enhancements and efficiency decisions.
These digital twins offer valuable insights into the digital environment of an object. They simulate how different components, assets, and units work together in a cohesive manner. As an example, digital process twins can simulate the operation of a manufacturing facility by integrating all of its components.
Applications of Digital Twin Technology
More and more industries are embracing digital twins, which allow them to create virtual replicas of their physical systems.
This technology is used for simulation, optimization, testing, and collaboration. As a result, various industries have experienced increased efficiency, productivity, and cost savings.
Some of the sectors that are already implementing digital twin technology include the following:
The use of digital twins has been prevalent in the industrial world for years, particularly in the manufacturing sector, where they have been employed to create models of parts, products, and systems.
Nowadays, manufacturers increasingly utilize digital twins to simulate production processes and even entire smart factories, resulting in improved manufacturing efficiency.
For example, Cuby Technologies, Inc. has taken advantage of this technology to reduce waste in their prefab construction process by up to 90%. They achieve this by creating 1-to-1 kits of the required parts using digital twin technology.
Medical records, images, and genome sequencing can be used to create digital twins that allow healthcare professionals to simulate procedures and treatments in a controlled environment, avoiding risks to actual patients.
Practitioners can diagnose and treat patients more accurately by comparing the digital twin of a patient with similar profiles to identify the best therapy options. Simulations can also be run on anonymized digital twin data to determine the best treatment options.
Digital twins of patients or organs allow medical professionals to simulate procedures and specific environments beforehand, reducing the risks and costs of real-life trials.
This technology allows healthcare organizations to test-drive new interventions and engagement strategies while lowering the risks and costs of rolling out new programs.
A digital twin also enables accurate and continuous monitoring to inform data-driven decisions. For example, doctors can leverage a population-wide data set of digital twins to replicate an individual human body’s internal system to improve treatment outcomes for all.
Digital twin technology is already used to design large structures such as buildings and offshore oil rigs. Moreover, some users are extending its use to cover entire neighborhoods and cities, focusing on infrastructure. One noteworthy project is the UK’s development of a national digital twin.
Digital twins also play a significant role in smart city initiatives, which aim to digitally connect infrastructure through IoT and apply AI and analytics to the data.
The goal is to make transportation more efficient and conserve energy, among other things. For instance, complete digital twins of cities, such as those in Singapore and Shanghai, are already being used to improve energy consumption, traffic flow, and development planning.
Smart cities are quickly becoming a reality, offering a promising way to reduce pollution and enhance the well-being of residents.
Energy and Utilities
Electric companies use digital twin technology to improve the efficiency of power plants, electric grids, transmission, and consumption.
This technology is also helpful for optimizing renewable energy systems like solar installations and wind farms, which have unpredictable energy production compared to traditional fossil fuel plants.
In fact, digital twins can also be used to create virtual replicas of entire electric grids in the future.
An example of the technology’s effectiveness is GE’s wind farm, which has seen an impressive 20% increase in productivity. This is made possible by capturing real-time information from sensors on each turbine and feeding it to their digital twin replicas.
By utilizing this data, it becomes possible to design more efficient turbines and identify changes that can increase the effectiveness of each active turbine.
Digital twins have become essential components in both the product-design stage and later stages of a vehicle’s lifecycle, such as servicing. Automotive manufacturers are now using digital twins to improve the efficiency of assembly plants.
These 3D models can offer valuable information about the vehicle’s physical behavior, mechanical models, electrical models, and software.
With predictive maintenance, digital twins can alert service centers or users when they detect issues with component performance, making it a valuable tool in the automotive industry.
Moreover, digital twins are anticipated to contribute to minimizing the occurrences of vehicle recalls by enabling the analysis of each vehicle’s individual twin for potential defects.
Benefits of Digital Twin Technology
One of the most significant digital twin technology benefits is them being virtual copies of real-world objects.
This offers a substantial advantage as it can help reduce maintenance costs and save millions of dollars. Moreover, they can mitigate the risks associated with working directly on physical objects.
There are numerous advantages of using digital twins beyond just reducing maintenance costs and mitigating risks. Here are some of the additional benefits that digital twins offer:
With digital twins, businesses can test and optimize processes virtually, increasing efficiency in the real world. This helps companies avoid costly errors and streamline operations by identifying and addressing issues before implementation.
Digital twins also offer the potential to improve productivity by providing valuable insights into optimizing equipment and processes. By using real-time data to monitor and analyze digital twins, businesses can make informed decisions and enhance their overall performance.
Digital twins allow businesses to monitor and analyze equipment and processes in the now. This enables them to identify and address performance issues as they arise, leading to enhanced performance and the prevention of costly downtime.
They can help companies predict and prevent downtime by identifying and addressing issues before they become critical problems. As a result, uptime is higher, and business operations are improved, ultimately leading to increased profitability.
This technology offers significant cost savings to businesses by avoiding costly errors, reducing maintenance costs, and increasing efficiency and productivity. In this way, companies can save millions of dollars in maintenance and repair costs, ultimately contributing to their bottom line.
Challenges of Digital Twin Technology
Organizations face significant challenges when developing digital twins, but overcoming them is essential for a successful implementation.
Here are five major challenges that need to be addressed:
Data Privacy and Security
The data of a digital twin is often transmitted through multiple networks and software applications, making it vulnerable to breaches.
Securing the data at every stage of its transmission is, therefore, critical to ensure the privacy and security of the digital twin.
Integration with Legacy Systems
Integrating digital twins with legacy systems is challenging because digital twins typically originate in CAD software but are primarily utilized in product lifecycle management (PLM).
Although some CAD and PLM software vendors offer integrations, these integrations may not always be sufficient, especially for smaller vendors that lack built-in integration capabilities.
Lack of Standardization
The lack of standardization in digital twin technology makes it challenging to integrate digital twins with other systems and share data between different organizations. This can hinder seamless integration and data exchange.
Therefore, having a clear set of standards is essential to ensure smooth integration and data exchange between different systems and organizations.
High Implementation Costs
Implementing digital twin solutions requires substantial investments in technology platforms, infrastructure development, maintenance, data quality control, and security solutions.
Maintaining the digital twin infrastructure can also be steep, which could slow down its deployment due to the high fixed costs and complex infrastructure.
The complexity of digital twin technology is a significant hurdle for organizations. The data collected from multiple software applications used by manufacturers and suppliers is extensive and constantly changing, making it challenging to effectively manage, analyze, and utilize.
The sheer volume and dynamic nature of the data pose a significant obstacle for organizations looking to leverage the full potential of digital twin technology.
The bottom line, the high cost of deployment, increased demand for power and storage, integration challenges with existing systems or proprietary software, and complexity of architecture are significant challenges that could impede the growth of the digital twin market.
How is Digital Twin Technology Related to Metaverse?
Digital twins are a critical aspect of the metaverse, which merges virtual reality and other immersive technologies to create interactive digital worlds.
Another key technology in the metaverse – augmented reality, can overlay a digital twin on an object, providing more detailed maintenance data for field technicians. Digital twins can also generate data for virtual reality images, making them more realistic.
To create a digital twin, the process of reality capture is used. This involves using a laser scanner to measure an object and its surroundings, with the resulting scan files being combined into a 3D point cloud that outlines the object’s geometry. The point cloud is often enhanced with color and processed further using CAD software.
Advancements in technology have expanded reality capture beyond just laser scanners, with smartphone software and drones being used to capture data. These advancements should make developing and applying digital twins easier in various industries.
Digital Twin Technology: Key Takeaways
Digital twin technology has been a game-changer for various industries, offering virtual replicas of physical systems that can be used for simulation, optimization, testing, and collaboration.
Digital twins offer numerous benefits, including the following:
- Increased efficiency,
- Improved productivity,
- Enhanced performance,
- Reduced downtime,
- Cost savings.
The importance of digital twin technology will only continue to grow as more industries embrace it. To stay ahead of the curve, businesses can take advantage of digital twin technology to address the challenges that come with it.