Digital material twin for the shape memory alloy Nitinol

• Applicant: Professorin Dr.-Ing. Katrin Schulz
• Subject Area: Computer-Aided Design of Materials and Simulation of Materials Behaviour from Atomic to Microscopic Scale ; Mechanical Properties of Metallic Materials and their Microstructural Origins
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 554862650

Shape memory alloys such as Nitinol are essential for high-tech applications, for example in medical technology, aerospace and robotics. However, their complex microstructural behavior, in particular the influence of dislocations, grain size, texture and interfaces on the macroscopic properties, is still not fully understood. The goal of the project is to develop a digital material data twin for Nitinol that systematically combines experimental data, physics-based simulation models and machine learning. In collaboration with Admedes GmbH, high-quality multimodal experimental data sets are generated for the digital material data twin. The aim is to precisely link microstructure and macroscopic material properties, particularly for medical technology applications. Inverse modeling is used: instead of deriving the material parameters of a model from experimentally determined material behavior, the inverse approach is used to specifically determine the optimal microstructure properties and process parameters from the desired macroscopic properties. The scientific focus here is on the investigation of dislocations, interfaces and material heterogeneities as well as their influence on phase transformation and superelasticity. Multi-scale models enable a comprehensive insight into the deformation mechanisms and functional fatigue under cyclic loading. The combination of experimental and simulative data in a combined database according to the data fusion approach should ensure a robust model parameterization with quantified uncertainties. The continuously updatable material data twin thus improves the accuracy of predictions and allows a better risk assessment for industrial applications. In close cooperation with Admedes GmbH, the approach is being validated using data sets in real production processes, including the prototype process of the “shape-setting” of a medical stent.

• DFG Programme: Research Grants (Transfer Project)
• Application Partner: ADMEDES GmbH
• Co-Investigator: Dr.-Ing. Marek Fassin