Research project AI-Mod
Learning method for the automatic conversion of a modelled value stream into a simulation capable value stream
Digital Innovation / Technology
Building simulation models for a value stream in a factory is a task that can take a lot of time. Although it is quick to build a first simple image (a model) of a value stream with the help of an app, it can take a long time. But for this model to be simulated, it must meet a number of requirements: the right building blocks (e.g. machines or buffers) must be inserted into the image in the correct sequence, all necessary connections between the blocks must be correctly and completely defined, and the blocks must be provided with a range of data (e.g. information on components or processing times).
The idea of the project is to train a learning procedure in a way that it supports the modeler in fulfilling this requirement and points out where possible missing blocks, edges or data are. The application of the learning procedure is based on the assumption that the transformation of the first simple model into a complete simulation model can be considered like a game: the learning procedure suggests certain moves (e.g. adding an edge or changing data). If this results in a model that can be better interpreted by the simulation software, there is a positive feedback, otherwise there is a negative feedback. In this way, the algorithm learns what changes to models are required to create simulation models.
This way of linking learning methods and simulation, the use of learning methods directly for modeling support, is a new application of AI.
The project consortium includes the following partners:
- SimPlan AG (Consortium leader)
- Hochschule RheinMain – Arbeitsgruppe LAVIS (Learning and Visual Systems)
The research project AI-Mod is funded by the state of Hesse within the Distr@l program. The funding started in October 2020 and will last 2 years.