Parametrically upscaled crack nucleation model (PUCNM) for fatigue nucleation in titanium alloys containing micro-texture regions (MTR)
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- @Article{SHEN:2023:actamat,
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author = "Jinlei Shen and Vasisht Venkatesh and Ryan Noraas and
Somnath Ghosh",
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title = "Parametrically upscaled crack nucleation model
({PUCNM)} for fatigue nucleation in titanium alloys
containing micro-texture regions ({MTR)}",
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journal = "Acta Materialia",
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volume = "252",
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pages = "118929",
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year = "2023",
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ISSN = "1359-6454",
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DOI = "doi:10.1016/j.actamat.2023.118929",
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URL = "https://www.sciencedirect.com/science/article/pii/S1359645423002604",
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keywords = "genetic algorithms, genetic programming, Micro-texture
region (MTR), Parametrically upscaled crack nucleation
model (PUCNM), Dual-phase titanium alloy, Dwell fatigue
crack nucleation, Machine learning",
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abstract = "Micro-texture regions (MTRs), delineated as the
clusters of grains with similar crystallographic
orientations in the polycrystalline microstructure,
play a significant role in fatigue crack nucleation and
life of structures of Ti alloys. This paper develops a
parametrically upscaled constitutive and crack
nucleation modeling (PUCM/PUCNM) platform for
predicting structural-scale fatigue crack nucleation in
alpha/beta Ti-6Al-4V alloys, whose polycrystalline
microstructures contain MTRs. The PUCM/PUCNM platform
bridges micro and macro scales through
thermodynamically consistent incorporation of
representative aggregated microstructural parameters
(RAMPs) in macroscopic constitutive relations. A novel
RAMP kMTR?c that captures both the MTR size and
contrast in the overall texture is proposed to
quantitatively represent the MTR intensity in the
microstructure. Geometric analysis and comparison with
experimental data establish the effectiveness of kMTR?c
in characterizing the MTR distributions. The impact of
MTR characteristics on fatigue crack nucleation is
evaluated through the support vector regression
(SVR)-aided Sobol analysis, based on data from crystal
plasticity FE simulations of polycrystalline
microstructure volumes with a grain-scale crack
nucleation model. The PUCNM is uniquely suitable for
the incorporation of the MTR RAMP kMTR?c in its
probabilistic framework. A novel functional form is
derived using the genetic programming-based symbolic
regression (GPSR). The PUCM/PUCNM tool is used to
simulate an engine blade under dwell loading
conditions. Results exhibit the reduction of nucleation
life with a higher level of MTR intensity, despite the
same overall textures",
- }
Genetic Programming entries for
Jinlei Shen
Vasisht Venkatesh
Ryan Noraas
Somnath Ghosh
Citations