Funding tool: Partnerships
Project-ID: FTI25-P-012
Project start: 01. Oktober 2026
Project end: 30. September 2028
Runtime: 24 months / not yet started
Funding amount: € 180.000,00

Brief summary:
Materials design in tribology currently relies on sequential, resource-intensive "forward screening" methods across multiple scales (e.g., DFT, MD, FEM), which severely limits the rapid discovery of high-performance materials. The proposed RTI-Partnership Tribo.Drive shifts this paradigm toward a data-driven, AI-assisted "inverse design" methodology. By leveraging high-fidelity simulations to generate robust training data for Machine Learning surrogate models, Tribo.Drive enables the autonomous generation of optimized tribological materials and surface architectures. These digital workflows are tightly integrated with Advanced Manufacturing (AM) technologies to ensure immediate physical realizability. To execute this highly interdisciplinary vision, the consortium unites 20 key regional players, encompassing university and non-university research institutions, large industrial enterprises, SMEs, the business agency ecoplus, and specialized associations like AM-Austria and the Austrian Tribology Society (ÖTG). The overarching goal is to sustainably anchor Artificial Intelligence and Advanced Material Design capabilities within the local industrial ecosystem, thereby strengthening the resilience of regional value chains. Crucially, Tribo.Drive establishes a long-lasting collaborative community that aligns academic research with direct industrial needs. Acting as an active incubator, this network is targeted to operate beyond the funding period and generate four major subsequent research initiatives: three national projects (e.g., via FFG) and one collaborative EU-level project.

Keywords:
Tribology; advanced materials; advanced manufacturing; additive manufacturing; AI-supported material and surface design; inverse design; FAIR data; ontology