Fraunhofer Institute for Mechanics of Materials IWMWith its wide variety of alloys, copper is one of the most important materials for electromobility and the energy transition. Despite its economic and political significance, however, key data on material properties, process parameters, and the carbon footprint along the value chain have not been sufficiently or consistently available to date, because information is not transferred at the interfaces between smelting, semi-finished product manufacturing, and subsequent process stages, or because standards for data exchange often do not exist.
The KupferDigital2 project addresses this gap by providing a seamless, ontology-based material data space capable of semantically mapping the entire life cycle of copper—from smelting through various copper semi-finished products and connectors, up to and including recycling. Building on this foundation, additional tools are being developed for companies in the copper industry that will enable them to promptly address future regulatory requirements as well as economic and environmental goals—such as the Digital Product Passport, CO₂ accounting, or AI-supported material development.
Material Data Repository for Increasing the Efficiency and Sustainability of the Copper Life Cycle
Ongoing research project
Project description
Market Demand and Challenges
Copper is the key material for electric transportation, renewable energy, and digital infrastructure. Consequently, demand is rising sharply due to the expansion of electric transportation and, more recently, the development of AI infrastructures. With a recycling rate of 40%, the German copper industry is also highly advanced. Nevertheless, data exchange throughout the lifecycle of semi-finished copper products and finished goods still takes place bilaterally, in non-interoperable formats, and without the necessary end-to-end traceability. At the same time, regulatory requirements are increasing: the Digital Product Passport (DPP), the Supply Chain Due Diligence Act (LkSG), the EU Green Deal, and the Product Carbon Footprint (PCF) reporting requirement all demand seamless, automated documentation across all domain boundaries and process steps. Without the digitization of materials research, manufacturing processes, and the associated data flows, European manufacturers therefore risk falling behind in international competition.
Project Approach
KupferDigital2 is developing an ontology-based material data space capable of semantically capturing the entire copper life cycle—from extraction or recycling through semi-finished product manufacturing and component production to the in-service behavior of systems or components—and linking this data in a machine-processable manner. At its core are digital twins for key manufacturing processes, which are based on machine learning, physics-based mean-field modeling, and finite element simulation. This makes it possible to predict, in real time, the effects of changes in process parameters or materials on subsequent process steps and product properties. In addition, digital workflows are being implemented for CO₂ accounting, the Digital Product Passport, and the socioeconomic supply chain assessment. Their input and output variables are available in the same data space. Furthermore, all solutions are based on open standards (PMDco, IOF, EMMO) and are interoperable with Catena-X and the MaterialDigital platform via the DSMS (Dataspace Management System), for example.
Contribution to Addressing the Challenges
The project enables companies—not only those in the copper industry, by the way—to meet regulatory requirements (DPP, PCF, LkSG) efficiently and in a semi-automated manner, thereby continuously and sustainably replacing resource-intensive processes such as manual documentation and similar tasks. Thanks to digital twins, experimental and numerical analyses can be performed multiple times in a short period, thereby significantly shortening material development cycles, optimizing production processes based on real data, and specifically designing products for durability and sustainability.
Fraunhofer IWM’s work packages in the project:
Digital Twins
Fraunhofer IWM is developing a concept for executable digital twins (aDZ) and their interlinking. The IWM is creating aDZ for several key processes in the production of copper semi-finished products and component manufacturing, thereby demonstrating how process chains can be consistently represented semantically with the help of aDZ. This makes it possible—whether for a single process step or an entire process chain—to make predictions, for example, regarding microstructure development or anisotropy resulting from a specifically selected forming process. Furthermore, the data obtained is used to create a so-called “virtual materials laboratory,” which digitally reveals the relationships between process control, rolling texture, and anisotropic component properties.
Data Space Infrastructure
Fraunhofer IWM is providing a fully functional data space infrastructure for the project. This system enables the interoperable integration of decentralized, heterogeneous simulation and experimental data, ensures connectivity to external data spaces (PMD, Catena-X, SCALE.sdm), and allows experts—even those without IT knowledge—to deploy digital twins in a decentralized manner.
Knowledge Modeling and Life Cycle Assessment
Fraunhofer IWM contributes to the project’s semantic knowledge base by developing knowledge graphs for selected reference processes in material and process simulation. In addition to mapping the respective reference process, these graphs also enable the consistent linking of subprocesses.
Based on the project results, Fraunhofer IWM can offer industrial companies the following research and development services:
1. The development and provision of executable digital twins for forming and manufacturing processes
Fraunhofer IWM develops customized, executable digital twins for production processes such as rolling and forming.
2. ML-based process-property analyses and predictions with a focus on “semi-finished product development”
Fraunhofer IWM trains neural networks based on real manufacturing data that map the relationship between process parameters and resulting material properties, and makes these available to its partners in the form of executable digital twins.
3. Physically-based mean-field modeling of entire process chains
Fraunhofer IWM simulates manufacturing steps and chains—from cold rolling through annealing to aging—using a mean-field approach to predict microstructural development and the resulting mechanical behavior.
4. Virtual laboratory for predicting texture-induced anisotropic sheet properties during cold rolling
Based on experimentally measured EBSD data and numerically determined texture information, Fraunhofer IWM calculates the direction-dependent mechanical properties and characteristic values of semi-finished copper products.
5. AI-Supported Identification of Material Model Parameters
Using machine learning-based optimization methods, Fraunhofer IWM determines the material parameters of complex material models with minimal experimental effort.
6. Development and Operation of Ontology-Based Material Data Spaces
Fraunhofer IWM supports companies in establishing semantic data spaces for material and process data, including integration with Catena-X, PMD, and other data ecosystems.
7. Semantization and integration of simulation and experimental data
Fraunhofer IWM converts heterogeneous data from FEM solvers, experiments, or process control systems into interoperable, machine-readable data models and formats.
8. Consulting on regulatory compliance based on digital material information
Fraunhofer IWM advises companies on both technological and methodological aspects so that they can use their existing material and process information in the short to long term for the semi-automated creation of digital product passports and carbon footprint calculations.
