One of the main differences, in my opinion, is in the feedback applied.

For example, if you have a 6DOF model of a collaborative robot arm, you could simulate different movements of the robot on its 3D model. The model of the arm would look and move exactly like the real-world arm. That would be a classic simulation model.

However, if the real-world robot arm would be, at some point, stopped and moved by the human to the new position and that movement would be detected by the encoders (sensors) and reflected in real-time in the 3D model via encoders’ feedback, that would be a primary novel characteristic of “Digital Twin”, which classical models do not include.

Besides, if one stops the arm in the simulation model (metaverse (VR)) and moves it to another position, that should be reflected in the real-world robot arm in real-time. This is also not included in classical models.

“Digital Twin” might therefore be understood as a simulation model connected to a real-world object, which incorporates the feedback from the sensors to reflect the interactions in real-time between:

- the environment (human or some other intervention) and real-world objects (robot arm)

- simulation model (metaverse (VR)) and real-world objects (robot arm)

Thus, digital twin should enable two-way, real-time interaction.

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My question

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explain modelling, simulation and digital twins with references

ChatGPT

Modelling, simulation, and digital twins are all concepts related to representing and understanding real-world systems in a virtual environment. While they share similarities, each term has its own distinct characteristics. Let's explore them in more detail:

For example, in engineering, a model of a bridge may be created to analyze its structural integrity under various conditions, such as different loads or environmental factors. Models can be physical (e.g., scale models) or conceptual (e.g., mathematical models) depending on the nature of the system being represented.

Simulations can be performed using computer programs or physical prototypes. They allow researchers, engineers, or decision-makers to observe and understand the behavior of complex systems without the need for costly or time-consuming real-world experiments.

Digital twins are particularly prevalent in the Internet of Things (IoT) domain, where physical objects are connected and communicate data to their virtual counterparts. By linking the physical and digital realms, digital twins enable real-time monitoring, predictive maintenance, optimization, and decision-making.

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