Use a digital twin to design, develop, manage and optimise the performance of a product or entire system.
Simulation digital twins can add value to your organisation and product in multiple levels stating from the design phase, new competitive digital advantages to the product, increased performance and expected lifetime, new insight of the product in service and business opportunities. Below the concept of digital twins and how to approach some of these is outlined.
Digital development & optimisation
During the design phase of a product, creating a digital twin prototype (DTP) allows representing the ideal version of the coming product using 3D models and simulations.
The digital representation of what once was only an idea, knowing the performance of a possible product, leaving room for new improvement ideas before expensive manufacture and testing.
This simulation driven design results in significant cost reduction compared to traditional product design iterations.
Digital twins in service
With a true simulation digital twin you can obtain the accuracy of high-fidelity simulations for multiple physical disciplines combined with the speed and dynamic response of system modelling in real time from the DTP while utilising live sensor data. That both gives you the benefit of viewing your developments throughout the design phase or see the performance enhancements for new system changes and access new value insight on various performance parameters. Specific added value are very case specific but ranges from training of operational staff, what-if-scenarios, predictive maintenance and new business opportunities such as maintenance services.
Interacting with the product through the digital twin
With technologies such as AR and MR (augmented and mixed reality) the end user can interact with the physical product through the digital twin.
By use of a digital twin it is possible to access real sensor data and simulated data of otherwise inaccessible information from physical sensors e.g. in extreme harsh environments for physical sensors.
Having the extra layer of information from simulated data improves the insight in the current state, performance and processes of/in the product for a more solid decision making.
Predictive maintenance for optimal performance
Having a digital twin updated with real-time conditions of the physical product, allows performing data analysis and running what-if scenarios for predictive maintenance. This means that it is possible to detect early fault before getting critical, which may prevent suboptimal operating conditions, and thus ensure safe working conditions, low operating cost, and maximum efficiency.
This can also be used for time-to-failure prediction, allowing early planning of future required actions, therefore eliminating or reducing unexpected down-time for maintenance and planned revision.