digital manufacturing simulation models

Digital manufacturing simulation models: the differences and use areas


...Published 2018-02-26

There are many terms that are thrown around when it comes to Digital manufacturing and in particular, simulation modeling. This article will discuss the differences between Agent-based modeling, Discrete events simulation modeling, and Continuous modeling.

Digital Manufacturing

In short digital manufacturing is the concept of creating a duplicate (“Digital-twin”) of the system within a manufacturing environment. Then the proposed actions or solutions can be safely tested within the virtual world before spending large amounts of capital money to roll out the solution.

Different simulation model approaches

Agent-based simulation modeling

Now that we know what Digital Manufacturing is, we can look at the different types of simulation models that are out there. In short Agent-based modeling (or ABS) is the simulation of individual agents (on a micro level) and how they interact with each other and their environment (on a macro level) [1]. A typical example would be a logistics model, as seen in Figure 1, where the roads, entry and exit points (onto the roads), how the lanes work and vehicles passing each other are created as rules and the basic frame within which the model will be run. Then the agents themselves are created which could be cars, buses and trucks and each of these have their unique behaviors. These agents are then pushed into the system and left to see how they react to each other and the roads on which they drive. The key thing here is that the “main” logic resides within the entities and they make the decisions. Typical application fields are biology, ecology, and social science.

digital manufacturing ABS systemFigure 1: ABS logistics System

Discrete event simulation modeling

Discrete event simulation modeling (or DES) on the other hand is a modeling approach where the entire system is modeled in detail and the logic is encapsulated within the framework of the system. The entities are for all intensive purposes dumb and just move through the system, the system then decides what to do with them [2]. Discrete in this case means that the models time frame does not run every second, it only runs events jumping from one event to the other. If there is a 10-hour plant shut down in your model, you as the viewer of the model won’t witness this time gap, the model will just jump past it to the next event [3]. Table 1 shows a comparison of DES models and ABS models, from the “Journal of Simulation” [2]:

digital manufacturing simulation model tableTable 1: Attributes that define the model type 

These days most DES software comes standard with object orientated building blocks. This means that pure DES models can be built, hybrid DES/ABS models are built (with smart logic/rules in the entities as well as the system) and a pure ABS can be built from these software packages. It then depends on the modeling approach used to define the problem and build the model.

Continuous simulation modeling

The last approach is continuous. In contrast to DES, continuous is used in systems where the variables can change continuously [4]. As an example: a normal bank queuing problem can be modeled with a DES because the number of people in the system at any point in time can only be discrete values. Good examples of continuous are any type of flow, like the volume in a tanker measured against time as the water is being flushed out of the system. Just like the previous comparison between ABS and DES, we find that in modern-day software models are hybrids of continuous and DES. A fast-moving bottle filling factory line would be an example of this. The entities themselves represent discrete units entering and exiting the system at discrete moments in time, however, the line pushes so many bottles through per second that the DES model is beginning to look more like a continuous model.

Hybrid 3D modeling in digital manufacturingFigure 2: Hybrid 3D model [5]

References

[1] Agent-based-models.com. (2017). Agent-Based Modeling: An Introduction | Agent-Based Models. [online] Available at: http://www.agent-based-models.com/blog/2010/03/30/agent-based-modeling/ [Accessed 30 Oct. 2010].

[2] Siebers, P., Macal, C., Garnett, J., Buxton, D. and Pidd, M. (2017). Discrete-event simulation is dead, long live agent-based simulation!. [online] SpringerLink. Available at: https://link.springer.com/article/10.1057/jos.2010.14 [Accessed 27 Nov. 2017].

[3] Matloff, N. (2008). Introduction to Discrete-Event Simulation and the SimPy Language. [ebook] p.3. Available at: http://heather.cs.ucdavis.edu/~matloff/156/PLN/DESimIntro.pdf [Accessed 27 Nov. 2017].

[4] Agents.fel.cvut.cz. (2018). Agent-based Computing for Intelligent Transport Systems. [online] Available at: http://agents.fel.cvut.cz/projects/agents4its# [Accessed 5 Feb. 2018].

[5] Flickr. (2018). Tecnomatix 12 Plant Simulation 3D Visualization. [online] Available at: https://www.flickr.com/photos/31274959@N08/15718572982 [Accessed 5 Feb. 2018].


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