Effect of the layer architectures on the tribological performance of high-performance, additive manufactured sliding layers

• Applicant: Professor Dr.-Ing. Leyu Lin
• Subject Area: Polymeric and Biogenic Materials and Derived Composites
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 516013239

Fused filament fabrication (FFF) is one of the most widely used additive manufacturing techniques for fabricating thermoplastic-based components. It offers significant advantages over conventional manufacturing methods, including the elimination of expensive tooling and the ability to fabricate customized parts with complex geometries. Moreover, this innovative processing technique allows for the design and production of highly efficient components tailored to specific load paths. The results of the current project demonstrate that sliding layers with excellent tribological properties can be successfully manufactured using FFF. Notably, experimental results reveal that these sliding layers exhibit superior tribological performance under severe load conditions. Based on this finding, the key scientific objective of this renewal proposal is derived, which is the targeted training of the infill patterns and densities (infill < 100%) of the sliding layers toward the goal of optimized tribological performance. To achieve this, sliding layers with specially designed infill patterns and densities will be printed onto the metallic substrate, which correlate with a higher local contact pressure of the tribosystem compared to that of sliding layers with 100% infill density under the same normal force. By integrating experimental investigations with virtual optimization of infill patterns and densities using artificial neural networks (ANNs), a deeper understanding of the relationship between infill design and the friction and wear properties of sliding layers will be gained. This knowledge can serve as a guideline for manufacturing material-efficient sliding layers in practical applications.

• DFG Programme: Research Grants
• Co-Investigator: Professor Dr.-Ing. Alois K. Schlarb