Digital twins are digital or virtual representations of a physical object or process. These digital representations are fed with real data acquired by sensors placed in the real component or process, which allow the simulation of behaviours, and can predict how the object or process will work.
In the industrial environment we can find product or process digital twins. Digital product twins represent virtually all the physical parts of a product: electronics, mechanics, and software. This virtual representation allows different aspects of the manufacturing process and the product’s use to be simulated. In this way, these aspects can be validated and tested, before manufacturing.
Digital process twins are a virtual reproduction of the production lines where all the stages of the process are integrated. If the process is real, data can be obtained from it to simulate certain behaviours, or if production is not yet in operation because the product/process is in the design phase, data and production situations can be simulated before starting up.
Digital twins can be very complex or very simple, depending on the process or product that are to be represented and depending on the amount of data available for the simulation.
How does a digital twin work?
To develop and implant digital twins, it is necessary to collect the information that is generated in the physical environment. This information is collected by sensors and other devices that collect real data on the status of the process or product.
The digital twin receives the information from the sensors in real time and mimics what happens in reality. Consequently, it can virtually replicate potential problems that could arise during the development or operation of a process or machine. For this reason, it is very useful for the improvement of behaviours and the efficiency of the machines.
What are the advantages of digital twins?
Digital twins have numerous advantages since they improve the behaviour of processes and products, which generally means improving their efficiency.
They allow you to anticipate potential problems that may arise in the future. This reduces product defects and shortens manufacturing time among other things.
- Improvement and optimisation of production processes through real information.
- Reduce unplanned downtime due to potential errors.
- They reduce accidents since they allow simulating all kinds of situations and potential problem areas.
- Reduction of maintenance costs by performing preventive maintenance tasks.
- Opportunities for continuous improvement through simulations, identifying failures and inefficiencies.
Applying digital twins across industry
Digital twins can be applied in all types of industry and for different applications. Here are some examples of the sectors and applications in which digital twins can be used.
Manufacturing
These digital models allow complete processes to be simulated, and in turn optimised. It can be applied directly to products in both the design and manufacturing process. They allow predictions about the operation of a product or production process before its start-up, anticipating possible errors and so saving costs.
Energy
Models based on real data can be developed to predict behaviours that optimize activities for power generation. Such twins allow the performance of power plants to be modelled, representing their status and operation. They can also help to identify energy demand, reduce the costs of implementing new plants and improve decision-making in the energy storage stages.
Automotive
Digital twins can simulate behaviours and prevent possible failures both in production and in the subsequent operation of the vehicle. This saves costs and reduces errors in the production stages. It also allows potential bottlenecks to be identified and development times reduced. Lastly, they can help improve the quality of the final vehicle.
To conclude, digital twins allow virtual tests to be run much more cost-effectively than if they had to be performed in the real world. They allow you to test new technologies, reduce failures and optimise all kinds of processes and products.
(Original article from Atria Innovation)
