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Published on: January 16, 2019
Experimental data from service-like creep-fatigue experiments on grade P92 steel
Nadja Sonntag1, Maria Jürgens1, Patrick Uhlemann1
1Bundesanstalt für Materialforschung und -prüfung (BAM), Division 5.2 Metallic High Temperature Materials, 12205 Berlin, Germany.
This study presents valuable mechanical data from complex creep-fatigue tests on P92 steel at 620°C. The findings aid in understanding material behavior under service-like conditions and improving lifetime modeling.
Area of Science:
- Materials Science
- Mechanical Engineering
- Metallurgy
Background:
- High-temperature steels like P92 are crucial for power generation components.
- Understanding creep-fatigue behavior under service-like conditions is vital for component integrity.
- Standard tests may not fully capture complex loading experienced in real-world applications.
Purpose of the Study:
- To present experimental mechanical data from novel service-like creep-fatigue tests on P92 steel.
- To provide a dataset for analyzing cyclic softening and stress-strain hysteresis.
- To support the development of advanced lifetime models for components under creep-fatigue loading.
Main Methods:
- Isothermal creep-fatigue experiments conducted at 620°C with a 0.2% strain amplitude.
- Three types of tests were performed: standard relaxation fatigue (RF), strain-controlled service-like relaxation (SLR), and partly stress-controlled service-like creep (SLC).
- Data collected include cyclic deformation (stresses) and full hysteresis loops for each fatigue cycle.
Main Results:
- Detailed cyclic deformation and hysteresis data were obtained from three distinct creep-fatigue test conditions.
- The datasets capture the material's response to combined stress- and strain-controlled dwell times.
- The data allow for the approximation of cyclic softening and detailed analysis of stress-strain hystereses.
Conclusions:
- The presented data from non-standard service-like tests are valuable for material characterization.
- These datasets can inform the design of future complex experiments and analyses.
- The findings contribute essential input for advanced parametric lifetime modeling and model calibration.
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