A digital twin is more than a CAD model or a one-off simulation: it is a living virtual counterpart of a physical asset, kept in sync by a constant stream of sensor data. This bidirectional link lets engineers test changes, predict failures and optimise operation in the virtual world before — or instead of — touching the real machine.
Working principle
Three elements define a twin: the physical asset with IoT sensors, the virtual model (physics-based and/or data-driven), and the data connection that links them. Telemetry (vibration, temperature, load) streams to the model, which updates its state; analytics and simulation then generate predictions and optimised set-points that flow back to control the asset, closing the loop.
| Level | Capability | Data link |
|---|---|---|
| Digital model | Static design replica | Manual |
| Digital shadow | Mirrors current state | One-way (asset→model) |
| Digital twin | Predicts & controls | Bidirectional |
| Cognitive twin | Self-optimising w/ AI | Bidirectional + learning |
Key trade-offThe hard part is model fidelity vs. real-time speed: high-fidelity physics is too slow for live control, so twins blend reduced-order models with machine-learning surrogates.
Applications
- Predictive maintenance — forecast bearing/tool wear before failure
- Process optimisation and what-if testing without downtime
- Virtual commissioning of new lines and robot cells
- Whole-factory twins for throughput and energy optimisation
References & further reading
- Grieves & Vickers, “Digital Twin: Mitigating Unpredictable Behavior in Complex Systems,” 2017.
- Tao et al., “Digital Twin in Industry: State-of-the-Art,” IEEE Trans. Industrial Informatics, 2019.
- Kritzinger et al., “Digital Twin in manufacturing: A categorical literature review,” IFAC, 2018.