Digital twin technology definition

Digital twin technology is actually an electronic picture of a product, fashion, or gadget that extends its lifecycle and is actually improved along with real-time information. It works with simulation, gizmo mastering, as well as thinking to enrich decision-making. This modern technology can incorporate the web of points , synthetic intellect, and also software application analytics to bolster its own output.

It is a computer programme that analyses real world data to create simulations that can forecast how a product or process will perform.

It is a virtual model of a process, product or service, that allows analysis of data and monitoring of systems to head off any problems.

It is a virtual depiction of a physical product, process, or system that allows businesses to replicate and assess the systems real-time performance. As a result, it can allow firms to improve the design of new products, services, and business models, optimise the performance of existing systems, and save costs.

Digital twin technology is considered a game-changer for several industries such as manufacturing, shipping, healthcare and beyond. By giving a digital representation of a physical thing or system, it lets companies to improve their performance, build new goods and services, and cut expenses.

Digital twin technology characteristics

Digital twin technology offers several critical qualities, including connectivity. Connectivity is vital for digital twin technology as data gathering is only possible with the correct sensors attached to the right portions of any physical object.

Another property of digital twin technology is that it is a computer software that uses real-world data to build simulations that can forecast how a product or process will perform. These programmes can incorporate the Internet of Things (Industry 4.0), artificial intelligence, and software analytics to enhance the output.

Digital twin technology is often described as consisting of a physical entity, a virtual counterpart, and the data connections in between. It is increasingly being examined as a means of boosting the performance of physical entities through using computational approaches, which are enabled through the virtual equivalent.

DTs are defined as machines or computer-based models that replicate, emulate, mirror, or make a twin out of physical phenomena, processes, or traits of human beings. A DT is a living, developing and intelligent model that is the virtual entity of a physical entity or process.

Digital twin technology types

Digital twin technology can be separated into numerous forms, depending on the level of product magnification. Some frequent categories include component, asset, system, and process twins.

Component twins are digital replicas of individual components or parts, such as motors, sensors, switches, and valves.

Asset twins are digital replicas of complete assets, such as a building or a machine, and can be used to monitor and optimise the operation of the actual asset.

System twins are digital representations of complete systems, such as a manufacturing process or supply chain, and can be used to optimise the performance of the entire system.

Process twins are digital representations of business processes, such as customer service or logistics, and can be used to optimise the performance of the business process.

Digital twin can also be broken down into three main types, which highlight the different occasions when the procedure might be used: Digital Twin Prototype (DTP) – This is conducted before a real product is built, Digital Twin Instance (DTI) – This is done once a product is built in order to execute tests on multiple usage scenarios.

Product, Production, and Performance digital twins are often used to mimic distinct stages of product lifecycle. The combining and integration of the three digital twins as they evolve together is known as the digital thread.

Digital twin technology use cases

Digital twin technology has a wide range of use cases in numerous industries.

One of the major use cases of digital twins in aerospace is product development and prototyping. 3D visualisation enables designers, engineers, and other stakeholders to better collaborate and assess design and manufacturing choices for complex systems.

In the automobile business, digital twins are utilised to generate the virtual model of a linked vehicle. Vehicle businesses employ the technologies to build the ideal automotive product even before production starts. They simulate and study the production phase and the problems that might develop once the car enters the street.

Digital twin technology businesses utilise machine learning algorithms to process vast amounts of sensor data and detect data trends. Artificial intelligence and machine learning (AI/ML) deliver data insights on performance optimization, maintenance, and efficiency.

A typical digital twin use case is engine development. For the aviation industry, the build and performance of an engine require the highest care from engineers. Computer twin technology allows engineers to operate a digital copy of the physical engine and test its limits in various simulated weather conditions and other settings. This technology is also employed in oil and gas business.

In Service Lifecycle Management, digital twin is used to access data about the product usage, enabling technicians to have a clear view into exactly how the product was used, when, and can often tell even by who.