Digital production twin in single and small series production
- 01.11.2018 to 31.10.2020
- Organizational Unit:
- Chair of Production Engineering, Business Development
- German Research Foundation DFG
The overriding goal of the research project is to realize the potential that industry 4.0 offers the manufacturing industry in the area of single-part and small-series production. In contrast to series production, individual and small series production is characterised by low repetition rates of the manufacturing processes, which make it considerably more difficult to master the production processes and to realise economies of scale. The low repetition frequency of the manufacturing processes also impairs the correct recording of production data in practice, as the required viewing range often varies. In order to successfully meet the complexity and the challenges mentioned above, it is essential for individual and small batch production companies to improve their own production processes and thus ensure their long-term competitiveness.
The linking of the physical and digital worlds in the context of industry 4.0 enables companies in individual and small series production to make their production processes more efficient and resource-saving. This is achieved through the systematic recording and processing of data and the networking of production. In order to control and allocate the data generated during the manufacturing process of a product, it is necessary to record a sufficiently precise digital image of the production in order to be able to analyse the manufacturing process in a target-oriented manner on the basis of this image. The research project therefore focuses on the necessary prerequisites for the realization of a digital twin in the manufacturing or production process. This so-called digital production twin can be seen as a kind of "flight recorder" of the production processes and represents the connection between process parameters and a virtual, simulated process image. As a result of the project, the relevant data and its dependency patterns are shown in the form of cause-effect relationships of different data networks. Finally, the data and cause-effect relationships are integrated into a digital production twin model.