Počet záznamů: 1
Thermomechanical and isothermal fatigue properties of MAR-M247 superalloy
- 1.0585632 - ÚFM 2025 RIV NL eng J - Článek v odborném periodiku
Šulák, Ivo - Obrtlík, Karel
Thermomechanical and isothermal fatigue properties of MAR-M247 superalloy.
Theoretical and Applied Fracture Mechanics. Roč. 131, Jun (2024), č. článku 104443. ISSN 0167-8442. E-ISSN 1872-7638
Institucionální podpora: RVO:68081723
Klíčová slova: Thermomechanical fatigue * Nickel-based superalloy * Damage mechanisms * Lifetime prediction model
Obor OECD: Materials engineering
Impakt faktor: 5.3, rok: 2022
Způsob publikování: Omezený přístup
https://www.sciencedirect.com/science/article/pii/S0167844224001927?via%3Dihub
Thermomechanical low-cycle fatigue (TMF) tests were performed on polycrystalline cast nickel-based superalloy
MAR-M247 under in-phase (IP) and out-of phase (OP) loading in the temperature range of 500–900 ◦C. A
constant heating and cooling rate of 5 ◦C.s− 1 was selected. Furthermore, isothermal low-cycle fatigue (ILCF) tests
with the same cycle period as TMF cycle were carried out at the maximal temperature of TMF cycle (900 ◦C). All
tests ran under strain control and fully reversed cycle on solid cylindrical specimens in laboratory air. High
resolution scanning electron microscopy and transmission electron microscopy were utilized to characterize the
damage mechanism occurring during TMF and ILCF loading. The cyclic deformation and lifetime behaviour as
well as damage mechanisms dependent on loading regime were examined. Results show that the TMF loading
has a detrimental effect on the lifetime of MAR-M247. Lifetime reduction is most significant under IP loading.
The fracture surfaces are characterized by typical striation fields, where the largest spacing between striations
was observed for IP loading with prevailing intercrystalline cracking followed by OP and isothermal ILCF loading
with transcrystalline crack propagation. To improve discussion of obtained results, experimental data were
compared with damage and life prediction models.
Trvalý link: https://hdl.handle.net/11104/0353317
Počet záznamů: 1