The digital twin phenomenon is entering different industries with great success; to quote the International Council of Systems Engineers (INCOSE), on what is a digital twin:
Another view of what is a digital twin comes from the U.S. Department of Defense (DoD) Digital Engineering Strategy, stating in 2018 that a digital twin is:
Today the use of this groundbreaking technology is being applied to healthcare, smart cities, automotive, manufacturing, and retail to name a few. The following text presents more details, history, types, use cases, benefits, challenges, and Internet of Everything Corporation's contribution to digital twin tech.
The first steps into this groundbreaking technology were foreseen in 1991 in David Gelernter's book Mirror Worlds. It wasn't until 2002 when Michael Grieves introduced the digital twin concept and model at the Society of Manufacturing Engineers conference for a Product Lifecycle Management (PLM) center, referencing it as a “Conceptual Ideal for PLM.” Its final consolidation as a digital twin had to wait until 2010. John Vickers of NASA on a Roadmap Report used this terminology to use it in their technology roadmaps and proposals for sustainable space exploration.
In a later publication, Origins of the Digital Twin Concept, uploaded to ResearchGate in 2016, by Grieves, authored by Grieves and Vickers, Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems (Excerpt), the definition of what is a digital twin is as follows:
By 2017 DT reached Gartners Top 10 Technology Trends, indicating that, Within three to five years, billions of things will be represented by digital twins, a dynamic software model of a physical thing or system.
The development of DT technology is based on two types, as mentioned earlier: Digital Twin Prototype (DTP) and Digital Twin Instance (DTI). In addition, there are Digital Twin Environment (DTE) and Digital Twin Aggregate (DTA). Let's look at these in more detail.
A DTP describes the prototypical physical artifact, consisting of the designs, analyses, and processes that realize and exist before a physical product. In other words, it contains the informational sets necessary to describe and produce a physical version that duplicates or twins the virtual version. Some of the informational sets consists of the following components:
The DTI describes a specific corresponding physical product that an individual digital twin remains linked to throughout the life of that physical product once it's manufactured. For it to function it requires some of the following information sets:
These are just some of the information sets required, note that these are conditioned to the use cases.
A DTA is simply the aggregation of all the DTIs and as such, it may not be an independent data structure. Its function works within a computing construct accessing all DTIs and querying them either ad-hoc or proactively:
In this case, the DT is designed to be an integrated, multi-domain physics application space for operating on a variety of purposes. A couple of examples the DTE can focus on include:
Digital twin use cases can be implemented in various industries. We already mentioned how NASA began to use it for sustainable space exploration. Currently, urban construction, i.e., smart cities use digital twin tech plus 3D and 4D models to learn about improvement possibilities and possible failure scenarios.
Building information modeling (BIM) has an exceptional extension in digital twins to digitize construction and urban planning processes, planning, design, construction, operation, and maintenance activities. An example of DT implementation is the partnership between the University of Cambridge and the UK's Department for Business, Energy, and Industry Strategy to solidify the concept of the digital twin in The Gemini Paper:
Digital twin use cases in healthcare are applied to surgery training, people movement within hospitals, tracking where Healthcare-Associated Infections (HAIs) may exist, and potential infection areas. In a wider scope that touches many industries, equipment, and machinery digital twins provide insights to optimize maintenance.
Retail's DT use offers customers the possibility to engage with products before purchase. In the automotive industry, Daimler allows customers to test-drive the vehicle via their customer twin. Another use case for the auto industry is gathering and analyzing operational data from a vehicle in order to assess its status in real-time and inform product improvements.
As you can see, the potential of digital twin use cases is limitless and can be used throughout the value chain of practically all industries.
Looking at specific digital twin benefits that can be implemented into, e.g., manufacturing, healthcare, automotive, construction, and oil and gas among others, are the following:
Not surprisingly, the technology faces shared challenges in parallel with embedded tech, AI, and IoT. The most common are data standardization, data management, data security, and barriers to its implementation and legacy system transformation. Other challenges are updating old IT infrastructure, connectivity, privacy, sensitive data security, and the lack of a standardized modeling approach.
As for economic challenges likely to hamper DT's market growth include deployment's high cost and increased demand for power and storage. The technical obstacles are subject to existing systems or proprietary software integration and its architectural complexity.
Implement digital twin solutions is costly, and the investment can be significant considering the required:
Therefore there are high fixed costs and complex infrastructure to successfully deploy digital twin tech into workflows. A situation predicting digital twin's market deployment slow down.
It is impossible to predict where digital twin use will be in five years, but Internet of Everything Corporation (IoE Corp) strives to accelerate its deployment. Our technology is meticulously designed as a stepping stone into the fourth industrial revolution's data-driven industry. Digital twins require constant real-time data flows that are verifiable and transmitted through secure paths.
Adding to the obstacles mentioned, cost-effective data management is essential and its quality is paramount. Internet of Everything Corporation's contribution to digital twin tech purposes to accelerate DT to reach its full potential, deploying a decentralized software blockchain-secured platform. Using existing hardware computing power on-premises allows data generation at the source to be managed at the edge level.
Investing in this breakthrough approach relieves you from centralized data management costs, ensuring real-time data to information cost-effectively. Sustainability comes inherent via our sustainable computing approach to embedded systems and occurrence AI analytics instead of full-block AI analysis.
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Are you ready to step into Industry 4.0 ahead of the curve and achieve the promise of technology to provide better living standards for societies and reach the UN's SDGs? Digital twin tech is vital for this to happen and we know how to help. Apply to the Planet Partner Program here.
