Perfection becomes a virtual reality in the age of the digital twin
Imagine being able to foresee system performance accurately over its whole lifespan and handle problems even before they occur! That would put you ahead of the digital disruption, right? Welcome to digital twins—computer systems that predict asset performance using input from real-world data. Only big and sophisticated assets with virtual reality are justified by their utilization due to time and expense.
Three astronauts’ lives were significantly in danger when an oxygen tank on board the Apollo-13 spacecraft burst on April 13, 1970. Despite having no room for mistake, NASA mission controllers fiddled with the 15 training simulators and rapidly developed foolproof procedures that successfully returned all three men to earth. Even if the phrase “digital twin” wasn’t in use at the time, this was possibly its first use. That’s roughly how helpful even the most basic digital twins were fifty years ago. Simulators are not digital twins by themselves, thus the term “rudimentary.” They were getting near to digital twins as they experimented to mimic the conditions in space.
Moving forward to the present, Deloitte used digital twins to reduce the time between changeovers for its industrial clients by 20%. Boeing used digital twins to increase the quality of their first parts by 40%. A single wind turbine might generate an additional $100 million during its lifespan thanks to GE’s digital wind farm, which is expected to increase power output by up to 20%.
Digital twins initially gained popularity in 2017 when Gartner, Inc., a technology consulting and research organization, listed it as one of the top ten key technology trends. In 2018, the company issued a similar grade. This is why it is expected that by 2026, the worldwide market for digital twins would have increased from $3.1 billion to $48.2 billion.
How do digital twins work?
A virtual or digital model, representation, or replica of a real item is known as a “digital twin.” The physical asset’s twin represents it throughout its full existence.
- Data inputs in real-time from the actual asset.
- Analyzes the data and improve asset performance using machine learning, simulation, and reasoning.
Product, system, or process are all examples of assets. A manufacturing process, vehicle, aircraft, cargo ship, turbine, engine, city, bridge, building, offshore oil platform, person, or another object, for example, might be considered an asset.
Digital twins are essentially computer algorithms that anticipate asset performance using real-world data inputs. These data are collected by Internet of Things (IoT) sensors, which are also used to forecast failure and other issues.
A physical prototype of the asset might also serve as the foundation for the digital twin, which would be used to optimize its performance before being produced. Alternately, the prototype might be the twin itself. Digital twins examine numerous processes and obtain real-time data, in contrast to simulations. This enables them to conduct countless simulations to examine various problems from various angles. Instead of real-time data, predictive twins employ archived data from other physical assets.
Other than component and partial twins, others manage the data of various entities that collaborate. The level that digital twins represent determines how they are classified:
- Individual components are duplicated via Component Twins. Part Twins are those of somewhat less significant parts.
- Asset Twins stand for several interconnected elements.
- System / Unit Twins simulate the transfers of various assets.
- Digital replicas of two or more systems are known as process twins.
Applications, Drawbacks, and Pros
Sophisticated systems are necessary to create, use, and manage assets as they become increasingly complex. It makes sense to be aware of the continuously changing expectations of your customers.
Benefits are tied to the information they gather and assess:
- By predicting asset performance and giving information on how customers utilize assets after purchase, manufacturers can be ahead of digital disruption. The latter aids in comprehending shifting consumer expectations.
- Boost the effectiveness of the product-making processes that are already in place.
- Eliminate unused features, goods, or parts by analyzing data on how customers use assets after buying them. Both money and time are saved.
- Before manufacturing starts, do research and development on potential performance scenarios to enable better product design.
- Improve processes and redefine erroneous presumptions in product development.
- Make it easier to decide which method to use as items reach the end of their useful lives. The materials that manufacturers may profitably extract from such items might also be decided by the manufacturers.
- By allowing professionals to test a potential remedy on an asset before applying it, maintenance may be made simpler.
- Increase traceability by establishing a digital link across several systems.
- From a distance, repair the device.
The following already employ digital twins:
- Power Production The maintenance of turbines, jet engines, and locomotive engines can be better planned by device operators.
- Manufacture operations provide better goods because the process is simplified from design to manufacturing. Keep in mind that digital twins depict the whole lifespan of the system or product.
- Because they are a complex systemic configuration of many interconnected systems, automobiles are a perfect application for digital twins. The performance of automobiles and the effectiveness of production methods are improved through virtual models.
- Large structures must strictly abide by engineering regulations, such as skyscrapers, bridges, offshore drilling platforms, and the like.
- Digital twins are used by health services to virtually model people to collect and analyze data on health variables.
- Augmented reality and real-time, three or even four-dimensional spatial information are both used in town planning. This makes it possible to forecast the effects of suggested adjustments better. The fourth dimension is quite ambiguous. It is understood to refer to time or anything perpendicular to a cube.
Digital twins should not be used on all assets. They could occasionally be a technical overkill, as Gartner cautions. They also raise issues with cost, privacy, security, and integration.
It is only economically feasible for big and sophisticated systems, processes, and products, such as the following:
- Systems Engineering
- Manufacturing Processes
- Power Generation & Distribution Equipment
- Aircraft & Automobile Production
- Railcar Design
- Large Constructions
Asset-intensive industries, in particular, but all industries, depend on the health and capability of their assets as well as the performance parameters of their processes. As more resources are allocated to enhancing digital twins’ cognitive capabilities, their analytical ability will grow, thus enhancing their value in production.
Edited by Prakriti Arora