A digital twin is a digital representation of a process, service, or product from a physical world into the digital world. Digital twining is not a single product, but is a concept of multiple technologies like 3D simulation, Internet of Things (IoT), big data, cloud computing and artificial intelligence (AI). The equivalence between the digital twin and the physical pair enables analysis of data and system monitoring. This leads to a proper understanding of the physical system behavior like its dynamics and complexity. One can easily compare the design choices on the digital twin and do test and validation before deployment on the real system. Figure 1 illustrates an example of digital twin for an industrial cell, adopted from www.machining4.eu.
Fig. 1. An example of digital twin provided for an industrial cell, adopted from www.fontys.nl/robotica
Although both digital twins and simulation technology enable execution of virtual simulations for a physical asset, they are quite different. A digital twin is more powerful rather than a traditional simulation technique. Since digital twins are powered by industrial internet of things (IoT) platforms, they receive real data from the physical system very fast and then process it quickly. This leads to an integrated and closed-loop digital twin that enables the user to virtually observe how the real product is operating (www.siemens.com).
A digital twin integrates all data produced or associated with the physical process or system it represents in the digital world. That is why, it is defined as a bridge between the physical and digital worlds. The data used in digital twins are generally collected from the IoT devices in the form of sensors, HMIs and other embedded devices. As such, the stored data integrates high-level information that represents the behavior of assets in the digital world. Although digital twin and simulation share a common sense with an amateur similar is basically different from simulation. Indeed, digital twins:
- include not only models (dynamic, data-driven, finite elements), but also include real-time data from smart components that use sensors
- could be applied to either individual assets (a system, a component, or a system of systems) or a network of entities
- keep the history and monitor the lifetime performance of the asset
- continuously updating the models while the physical asset is operating
- always provide a reliable digital representation of the current state of the asset
Digital twins and lead time reduction
Time is money, that is why the lead time reduction is very important in manufacturing production systems. The main benefits of using digital twins for quick response manufacturing can be listed as follows (mathworks.com):
- Optimizing fleet or system behavior to reduce the lead time
- Calculating control set points and parameters to provide a proper supervisor for the entire system
- Detecting faults and predicting future behavior or events
All these three aspects lead to the lead time reduction, given by the digital twining.
Need some help?
The Interreg project QRM4.0 supports production companies in improving their lead times by providing practical advice and granting financial support to companies that want to take steps to implement digital tools on their shop floor. Would you like to know more? Contact email@example.com