Why digital available material data are important

Material data is key information that is needed in many areas of production, for example for simulations, to define manufacturing parameters, to reduce scrap, or to optimize production times. Until now, much of this data has been shared in non-machine-readable formats such as PDFs or printed tables. This means a lot of manual work, delays, and risks due to transmission errors and information loss.

In the PMD-X-MAPRO project, the partners are developing digital solutions that make material data available even before physical delivery and enable it to be exchanged bidirectionally (i.e., in both directions) along the supply chain. This should make production processes more efficient and reliable. In addition, the availability of trustworthy and machine-interpretable data makes it easier to comply with legal information and documentation requirements, such as digital product passports or CO2 footprints. The feasibility is being demonstrated on multi-stage manufactured steel components.

Context as a success factor

Making material data compatible with industrial data spaces is more complex than simply converting file formats. Material knowledge must be modeled in such a way that computers can not only read the data, but also understand its meaning: What materials are involved? What are their properties? How were they manufactured and how were they tested? To present all this information in a coherent and structured way ontologies are used in the MAPRO project. This allows data from different sources, such as ERP systems, laboratory tests, or simulations, to be merged into a comprehensive digital picture. Such semantic models also enable AI systems to interpret data in context and incorporate it into decision-making processes. In modern data platforms, ontologies are, in a sense, the operating system of the digital world: they connect data, processes, and applications with their real-world counterparts and create the basis for end-to-end workflows and intelligent assistance systems.

Digital twins and data spaces – how the exchange works

At the heart of the project is the creation of digital material twins. These are digital representations of real materials that contain all important properties and contextual information. These twins are embedded in data space-compatible structures so that they can be exchanged between partners.

A key aspect of this is bidirectional communication: partners can not only receive data, but also feed it back, enabling them to work together on the database. For example, measurement data from production can flow directly into the digital twin and improve it. The goal is to create a material data space in which data can be shared securely without companies losing control over sensitive information.

Using the example of a camshaft, test data, simulation results, and supplier information for three different requirements are brought together in a semantic framework: cross-company verification of material properties, automated data provision for digital product passports, and determination of the CO2 footprint of products. This reduces the manual effort required for data transfer and validation for industrial companies. At the same time, it creates a consistent database on which AI-supported development and decision-making processes can run.

Competencies for digital material data spaces

As consortium leader, Fraunhofer IESE contributes comprehensive expertise in the field of software-based and data-driven systems as well as in the digital transformation of industrial processes. With Eclipse BaSyx, the institute has developed Industry 4.0 middleware that is used by numerous manufacturing companies to implement digital twins with administration shells. In addition, IESE operates the AAS Dataspace for Everybody, where companies can set up federated data spaces for their supply chains using client models. By drawing on these established standards and open-source technologies, connectivity to existing initiatives such as Catena-X and other Manufacturing-X implementations is ensured.

In this project, Fraunhofer IWM is developing the semantic basis for the material data space and encoding the domain knowledge of materials science in formal ontologies. These ontologies describe the relationships between materials, manufacturing processes, testing methods, and resulting properties, and link them to the digital material twins in the data space. This creates a kind of “semantic operating layer” for material data: Companies can connect their data sources to a common knowledge model, interpret material information unambiguously, and integrate it automatically into simulations or AI models. In this way, material knowledge is not only made available digitally, but also contextualized, interoperable, and immediately usable for data-driven industrial processes.

Other partners create interfaces to corporate IT (e.g., PLM/ERP systems), implement adaptive material models in industry, or integrate aspects such as sustainability and recycling data directly into the material twins.

Funding information
The PMD-X-MAPRO Cross-Company Material Data and Material Simulation in Production project is being funded for a period of one year by the German Federal Ministry of Research, Technology, and Space (BMFTR) as part of the BMFTR's MaterialDigital platform until the end of June 2026.