A study by the Aerospace Technology Institute has revealed the challenges and opportunities surrounding the use of Digital Twins.
Digital Twins are virtual models of a product, process or service that can be used to predict their properties and behaviours.
Throughout the lifecycle of the product, process or service, data from the physical world is collected, processed and compared against the Digital Twin, continually verifying the model and enabling diagnosis and pre-emptive action to be taken in the physical world.
Digital Twins can also be used to run detailed simulations on how something will react in any given environment – such as in extreme high and low temperatures or dry and wet conditions, for example.
Early adopters are establishing the requirements to deliver this capability, according to the Aerospace Technology Institute (ATI), digitising internal processes before establishing how data can be incorporated from suppliers and external sources.
Digital Twins: Opportunities
A Digital Twin is a dynamic object, increasing in sophistication and fidelity throughout the life of a product, process or service. Establishing how data is collected, joined and analysed requires an overarching architecture.
A Digital Twin is enabled by, and can support, other critical digital technologies, including the Internet of Things and big data analytics.
For example, aggregating data, identifying outliers and linking this back to in-service components or early-life failed parts can help deliver improved route cause analysis to determine specific recalls as opposed to whole batch products.
Digital Twins: Challenges
Products and process involve multiple stakeholders. But consistency and accuracy of data are fundamental to the validity of a Digital Twin.
Overcoming this requires greater co-ordination of operating practices and standards, and data architecture flexible enough to support future requirements.