journal article·2022-11Open access

Very high cycle fatigue assessment at elevated temperature of 100 μm thin structures made of high-strength steel X5CrNiCuNb16-4

Florian Himmelbauer, Gerhard Winter, Benjamin Seisenbacher, Florian Grün, Constantin Kiesling

Abstract

Many components and structures are exposed to very high number of cycles and challenging environmental conditions during operation. This study contributes to a better understanding of the very high cycle fatigue (VHCF) properties of high-strength steel X5CrNiCuNb16-4 at room temperature (RT) and 350 °C. For this purpose, conventional specimens and thin-walled structures are extensively examined with novel high-frequency fatigue testing techniques at elevated temperature. Tests with unnotched specimens at 350 °C show a 21.7% reduction in fatigue strength for 107 cycles and a different failure mechanism compared to RT. In contrast, no temperature influence is observed for mildly notched specimens and even a higher local fatigue strength is found for sharply notched specimens at 350 °C. The decrease in fatigue strength for 109 cycles is more pronounced at 350 °C (−10%) than at RT (−5%), and it is proven that notched specimens adequately represent the VHCF behaviour of structures. The transferability of specimen results to components and structures is given great attention. A new proposal for the VHCF strength assessment of structures with high stress gradients is presented, which is based on specimen results, an extended material-mechanical support factor and a VHCF reduction factor. The prediction model gives conservative fatigue strength estimates for 109 cycles with a maximum deviation of 5.8%. This demonstrates that even complex shaped structures can be successfully evaluated with suitable specimens and methods.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Fatigue and fracture mechanics · Fire effects on concrete materials · High Temperature Alloys and Creep

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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