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13:30
20 mins
Fatigue life prediction of metallic materials using the Tanaka-Mura-Wu model
Siqi Li, Rong Liu, Xijia Wu, Zhong Zhang
Session: Session 14: Fatigue crack growth and life prediction methods IV
Session starts: Wednesday 28 June, 13:30
Presentation starts: 13:30
Room: Theatre café: parallel
Siqi Li (Carleton University)
Rong Liu (Carleton University)
Xijia Wu (National Research Council Canada)
Zhong Zhang (National Research Council Canada)
Abstract:
In this research, the recently developed Tanaka-Mura-Wu (TMW) model is applied to common engineering materials including nickel-based superalloys Haynes 282 and Inconel 617, aluminum alloys 7075-T6 and 2024-T3, alloy steels SAE 4340 and SAE 1020, and titanium alloy Ti-6Al-4V, as well as a high entropy alloy (HEA) CoCrFeMnNi over the full fatigue range comprised of low cycle fatigue (LCF) and high cycle fatigue (HCF). The TMW model is derived from the mechanism of dislocation dipole pileup, which is presented with a plastic strain-based expression and a stress-based expression for fatigue crack nucleation. The plastic strain-based equation can provide class-A predictions for the LCF life with the plastic strain above 0.001. The stress-based equation is more suitable for HCF life prediction where macroscopic plasticity is discernible, but the lattice resistance needs to be calibrated to one S N point close to the fatigue endurance limit. The TMW model is shown to be applicable to a wide range of common engineering alloys for fatigue life prediction within a scatter factor of 2 as compared with the experimental data and/or Coffin-Manson-Basquin relations. By virtue of the physics of failure, it offers a greater applicability for crystalline materials. A relationship of fatigue life versus the total strain is established with the use of the Ramberg Osgood equation. The TMW model describes the full range fatigue life in terms of material’s elastic modulus, Poisson’s ratio, surface energy and the Burgers vector. Thus, it establishes a physics-based baseline for characterizing the effects of other contributing factors such as microstructure and surface roughness, which contributes to the uncertainty in the fatigue scatter.