Computer-aided engineering (CAE) applications support the digital transformation of the manufacturing industry. They facilitate virtual product development and product testing via computer simulations. CAE applications generate vast quantities of heterogeneous data. Domain experts struggle to access and analyze them, because such engineering data are not sufficiently described with metadata. Moreover, data in companies are often separated into silos and difficult to find by domain experts.
In this project, Julian Ziegler identified unsolved challenges for a data and metadata management that is tailored to the CAE domain. Ziegler proposes a metadata model that addresses all challenges and provides a connected view on all CAE data, metadata, and work activities of virtual product development projects. A prototypical implementation of this metadata model is already being applied to a real-world use case of the industry partner. This shows how the metadata eases the task of domain experts to discover relevant data for their applications. Furthermore, the metadata model enables a variety of new use cases for data analytics. It couples different data silos and for instance enables a feedback loop, where computer simulations are automatically optimized by analyzing test bench data. Going further, data analyses can directly use the metadata structure to provide added value without having to access the big amounts of CAE data. Thus, process structures in development projects can be analyzed with little effort, for example to identify good or inefficient processes in development projects.
Collaboration Partner and Funding
This project is funded by the MANN+HUMMEL GmbH as part of the Graduate School of Excellence advanced Manufacturing Engineering (GSaME).