journal article·2024-01

Interpretable machine learning for microstructure-dependent models of fatigue indicator parameters

Cooper K. Hansen, Gary F. Whelan, Jacob D. Hochhalter

Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Hydrogen embrittlement and corrosion behaviors in metals · Metallurgy and Material Forming · High Temperature Alloys and Creep

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (50)

Show source-provided references
1. A perspective on trends in multiscale plasticity10.1016/j.ijplas.2010.02.008 ↗
2. Coupling in situ experiments and modeling – Opportunities for data fusion, machine learning, and discovery of emergent behavior10.1016/j.cossms.2019.100797 ↗
3. Exploring new links between crystal plasticity models and high-energy X-ray diffraction microscopy10.1016/j.cossms.2019.07.002 ↗
4. A method to generate conformal finite-element meshes from 3D measurements of microstructurally small fatigue-crack propagation10.1111/ffe.12449 ↗
5. DREAM. 3D: a digital representation environment for the analysis of microstructure in 3D10.1186/2193-9772-3-5 ↗
6. An energy-based microstructure model to account for fatigue scatter in polycrystals10.1016/j.jmps.2010.12.014 ↗
7. Examining metrics for fatigue life predictions of additively manufactured IN718 via crystal plasticity modeling including the role of simulation volume and microstructural constraints10.1016/j.msea.2020.139312 ↗
8. Recent developments in assessing microstructure-sensitive early stage fatigue of polycrystals10.1016/j.cossms.2014.03.001 ↗
9. A critical plane approach to multiaxial fatigue damage including out-of-phase loading10.1111/j.1460-2695.1988.tb01169.x ↗
10. Predicting fatigue crack initiation from coupled microstructure and corrosion morphology effects10.1016/j.engfracmech.2019.106661 ↗
11. Microstructure-sensitive extreme value probabilities for high cycle fatigue of Ni-base superalloy IN10010.1016/j.ijplas.2009.08.001 ↗
12. A digital twin feasibility study (Part I): Non-deterministic predictions of fatigue life in aluminum alloy 7075-T651 using a microstructure-based multi-scale model10.1016/j.engfracmech.2020.106888 ↗
13. A digital twin feasibility study (Part II): Non-deterministic predictions of fatigue life using in-situ diagnostics and prognostics10.1016/j.engfracmech.2020.106903 ↗
14. Influence of microstructure variability on short crack behavior through postulated micromechanical short crack driving force metrics10.1016/j.engfracmech.2015.03.001 ↗
15. A geometric approach to modeling microstructurally small fatigue crack formation: II. Physically based modeling of microstructure-dependent slip localization and actuation of the crack nucleation mechanism in AA 7075-T65110.1088/0965-0393/18/4/045004 ↗
16. Data-driven materials investigations: the next frontier in understanding and predicting fatigue behavior10.1007/s11837-018-2894-0 ↗
17. A data analytics approach to discovering unique microstructural configurations susceptible to fatigue10.1007/s11837-018-2881-5 ↗
18. Materials fatigue prediction using graph neural networks on microstructure representations10.1038/s41598-023-39400-2 ↗
19. Machine learning-enabled identification of micromechanical stress and strain hotspots predicted via dislocation density-based crystal plasticity simulations10.1016/j.ijplas.2023.103646 ↗
20. Rozemberczki · 2022
21. Molnar · 2020
22. Castelluccio · 2012 · 278
23. Hennessey · 2015
24. 3D reconstruction of microstructure in a commercial purity aluminum10.1016/j.scriptamat.2006.02.017 ↗
25. A framework for automated analysis and simulation of 3D polycrystalline microstructures.: Part 1: Statistical characterization10.1016/j.actamat.2007.11.041 ↗
26. A framework for automated analysis and simulation of 3D polycrystalline microstructures. Part 2: Synthetic structure generation10.1016/j.actamat.2007.11.040 ↗
27. Generation of statistically representative synthetic three-dimensional microstructures10.1016/j.scriptamat.2017.11.034 ↗
28. Statistical volume element method for predicting microstructure–constitutive property relations10.1016/j.cma.2008.01.008 ↗
29. Material spatial randomness: From statistical to representative volume element10.1016/j.probengmech.2005.07.007 ↗
30. Microstructure-sensitive computational modeling of fatigue crack formation10.1016/j.ijfatigue.2010.01.003 ↗
31. Whelan · 2020
32. Uncertainty quantification in ICME workflows for fatigue critical computational modeling10.1016/j.engfracmech.2019.106673 ↗
33. McGinty · 2001 · 407
34. Continuous distributions of dislocations: a new application of the methods of non-Riemannian geometry
35. Elastic-Plastic Deformation at Finite Strains10.1115/1.3564580 ↗
36. Microstructure-sensitive modeling of polycrystalline IN 10010.1016/j.ijplas.2008.01.001 ↗
37. Microstructure-sensitive probabilistic modeling of HCF crack initiation and early crack growth in Ni-base superalloy IN100 notched components10.1016/j.ijfatigue.2011.09.014 ↗
38. Stopka · 2019
39. Microstructure-sensitive computational multiaxial fatigue of Al 7075-T6 and duplex Ti-6Al-4V10.1016/j.ijfatigue.2019.105460 ↗
40. “Cube slip” in near-[111] oriented specimens of a single-crystal nickel-base superalloy10.1016/S1359-6462(98)00446-1 ↗
41. Hiplot: A comprehensive and easy-to-use web service boosting publication-ready biomedical data visualization
42. A Matlab toolbox to analyze slip transfer through grain boundaries
43. Crystal plasticity finite element modeling of grain size and morphology effects on yield strength and extreme value fatigue response10.1016/j.jmrt.2022.06.075 ↗
44. Grain boundaries and interfaces in slip transfer10.1016/j.cossms.2014.05.003 ↗
45. Effect of annealing twins on crack initiation under high cycle fatigue conditions10.1007/s10853-012-7021-y ↗
46. Bomarito · 2022
47. Randall DL, Townsend TS, Hochhalter JD, Bomarito GF. Bingo: a customizable framework for symbolic regression with genetic programming. In: Proceedings of the genetic and evolutionary computation conference companion. 2022, p. 2282–8.10.1145/3520304.3534031 ↗
48. Development of interpretable, data-driven plasticity models with symbolic regression10.1016/j.compstruc.2021.106557 ↗
49. La Cava W, Moore JH. Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods. In: Proceedings of the genetic and evolutionary computation conference. 2017, p. 961–8.10.1145/3071178.3071215 ↗
50. AI Feynman: A physics-inspired method for symbolic regression10.1126/sciadv.aay2631 ↗