Fatigue crack initiation and crack growth in defect-afflicted high entropy nitride coatings

• Applicant: Professorin Dr.-Ing. Martina Zimmermann
• Subject Area: Mechanical Properties of Metallic Materials and their Microstructural Origins ; Coating and Surface Technology
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 566332027

High entropy nitride coatings can be classified as super hard while damage tolerant (tough, ductile) coating systems and a common understanding of the deformation and damage mechanisms under static mechanical loading was the focus of a preceding project. However, to open up new application scenarios for this particular type of coatings crack initiation and crack growth mechanisms under cyclic mechanical loading have to be the subject of fundamental research. Hence, the overall target of the project is the investigation of the fatigue crack initiation and early crack growth behavior of defect-afflicted high entropy nitride coatings, accompanied by the application and adaption of the Kitagwa-Takahashi-diagram and the √Area concept regarding the influence of size, type and position of the coating defects. The coatings will be produced by means of vacuum arc vaporization, where the defect population can be manipulate by utilizing particular filter technologies, resulting in highly defect-afflicted coatings and coatings with low defect density alike. Fatigue crack initiation, early crack growth, the formation of crack networks and a likely change in the residual stresses within the coating and their interrelation will be analyzed for the coatings with different defect population. Residual stresses will be macroscopically measured by way of substrate bending method acc. to Stoney et al. while local residual stresses will be determined applying the FIB-DIC method acc. to National Physical Laboratories. For the fatigue life prediction concept acc. to the Kitagwa-Takahashi-diagram in combination with the √Area concept both “naturally” process-induced defects as well as artificially by means if FIB-cutting introduced defects will be considered alike. ML algorithms will be applied to integrate the different defect classes and residual stress states in the fatigue life prediction concept in order to define a critical strain amplitude for different defect conditions. In the project the critical strain amplitude refers to the strain amplitude, where even in the high to very high cycle range no damage evolution at the surface has developed.

• DFG Programme: Research Grants