Digital twins are virtual copies of physical devices that data scientists and engineers can use to run simulations before real devices are built and deployed. Digital twins can also ingest IoT (Internet of Things) data in real time for analysis and performance optimization, with or without artificial intelligence.
Increasingly complex objects are acquiring the ability to generate and transmit data (#IoT). Having a digital equivalent therefore gives the possibility to optimize physical deployments, create several hypothetical scenarios, and apply artificial intelligence on this data to better understand how the systems work.
A digital twin is therefore a computer program that takes data from a physical object or system as input and produces predictions or simulations about how that physical object or system will be affected by changes in various parameters.
Digital twins offer five key benefits:
- Accelerate risk assessment and production time. Digital twins can help manufacturers test and validate their products virtually before they exist in the real world. Engineers can use them to identify process failures.
- Benefit from predictive maintenance. Plants can use digital twins to proactively monitor equipment and systems to plan maintenance before failures occur, improving production efficiency.
- Real-time remote monitoring by users.
- Better team cooperation. Automated processes and 24/7 access to system information allow technicians to spend more time collaborating.
- Make better financial decisions. By integrating financial data, companies can use digital twins to make smarter and faster regulatory decisions.
There are several categories of digital twins. IBM offers a classification system based not on the industry vertical, but on the complexity of what is being matched. Here's a look at the wide range of what digital twins can do for you:
- Component twins (or parts) that simulate the smallest possible example of a component in action.
- Asset twins simulate two or more components working together and allow you to study their interactions.
- System twins allow you to see how multiple assets work together, such as an entire production line.
- Process twins provide a high-level view of systems working together, allowing you to understand how the entire plant might operate.
It should be noted that adding more ingredients to the mix adds complexity. For example, mixing and matching components from different manufacturers can be difficult because you will need the intellectual property of each.
Manufacturing quality improvement - i.e. understanding how to get it right the first time, predictive maintenance, monitoring your production life cycle, etc.
Let's take a concrete example from the automotive industry, Ford.
Ford is developing seven digital twins for each vehicle model it produces.
Each twin covers a different aspect of production, from design to construction and operation.
They also use digital models for the manufacturing process, production facilities and customer experience.
For their production facilities, the digital twin accurately detects energy losses and identifies areas where energy can be conserved. It also analyzes the overall performance of the production line.
The digital twin is constantly learning, being able to anticipate and predict what can/will happen.
Without being too wrong, we can clearly talk about metavers as the future of the digital twin. If we listen to the Meta advertisement broadcasted in a loop at the end of 2022, it tells us about agricultural simulations to save water, traffic to make cities more fluid, etc.
If we think about it, each person could have a digital twin in the future. Indeed, we could for example test vaccines in a much more efficient way, predict the arrival of a cancer according to different parameters such as diet, genetics and be able to avoid it, etc.
In short, we are only at the beginning...
