EU project metaFacturing: Digitalization in the production of metal parts
The EU-funded Horizon Europe project “Data and Metadata for advanced Digitalization of Manufacturing Industrial Lines” (metaFacturing) aims to significantly advance digitalization in the production of metal parts – particularly in casting and welding.
In particular, this should reduce waste and costs. At the same time, operation is simplified. This is achieved by developing a digital twin based on efficient process simulation, data standardization and data consolidation.
This approach brings together all available process data – such as material data, process measurements, quality control and product validation. This creates automatic feedback for process control. In addition, the process model is to be used for training employees in production, so that the training of skilled workers also benefits.
Demonstrator projects
Two demonstrator projects are planned for implementation:
Casting of metal parts: Decision support for process improvement with the aim of increasing product quality.
Welding of structural parts: Improving efficiency and quality by combining data from process planning, the ongoing process and quality analysis. The results are fed back into the process by reconfiguring sensors and actuators.
Strong international consortium
The project consortium comprises 13 international partners from five countries. Industry and application partners are working closely with research institutions. Thanks to the involvement of local companies such as Fill GmbH, Fronius GmbH and Nemak, Upper Austria as a business location also benefits directly from the results.
Contribution of RISC Software GmbH
RISC Software GmbH contributes its expertise in the areas of mathematical hybrid modeling, data analysis and data engineering. This includes data pre-processing and integration in particular.
RISC Software GmbH is also responsible for managing the WP3 work package, which is concerned with defining the data processing strategy. It is also responsible for data cleansing and integration to ensure seamless collaboration between different systems. Another important aspect is the investigation and expansion of AI algorithms and technologies. This improves the automatic conversion of process information so that system settings can be optimized.
