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,

• author = "Jinlei Shen and Vasisht Venkatesh and Ryan Noraas and Somnath Ghosh",
• title = "Parametrically upscaled crack nucleation model ({PUCNM)} for fatigue nucleation in titanium alloys containing micro-texture regions ({MTR)}",
• journal = "Acta Materialia",
• volume = "252",
• pages = "118929",
• year = "2023",
• ISSN = "1359-6454",
• DOI = "doi:10.1016/j.actamat.2023.118929",
• URL = "https://www.sciencedirect.com/science/article/pii/S1359645423002604",
• 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",
• 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

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