journal article·2024-09

Estimation and validation for fatigue properties of steels by symbolic regression

Weiwen Cao, Xingyue Sun, Xu Chen

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Abstract

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OpenAlex topics: Fatigue and fracture mechanics · Metallurgy and Material Forming · Microstructure and Mechanical Properties of Steels

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References (59)

Show source-provided references
1. Cumulative Damage in Fatigue10.1115/1.4009458 ↗
2. Computational-experimental approaches for fatigue reliability assessment of turbine bladed disks10.1016/j.ijmecsci.2018.04.050 ↗
3. Fatigue life analysis of aero-engine blades for abrasive belt grinding considering residual stress10.1016/j.engfailanal.2021.105846 ↗
4. Crack growth-based fatigue life prediction using an equivalent initial flaw model. Part I: Uniaxial loading10.1016/j.ijfatigue.2009.07.011 ↗
5. Notch deformation and stress gradient effects in multiaxial fatigue10.1016/j.tafmec.2016.02.005 ↗
6. Evaluation of Strain-Life Fatigue Curve Estimation Methods and Their Application to a Direct-Quenched High-Strength Steel10.1007/s11665-018-3215-z ↗
7. Low cycle fatigue of metallic materials under uniaxial loading at elevated temperature10.1016/j.ijfatigue.2018.05.025 ↗
8. Analysis of strain-life fatigue parameters and behaviour of different groups of metallic materials10.1016/j.ijfatigue.2010.10.005 ↗
9. A state-of-the-art review on fatigue life prediction methods for metal structures10.1007/s007730200012 ↗
10. An expert system for estimation of fatigue properties of metallic materials10.1016/S0142-1123(01)00184-0 ↗
11. Statistical evaluation of strain-life fatigue crack initiation predictions10.1016/j.ijfatigue.2003.10.003 ↗
12. Estimation methods for fatigue properties of steels under axial and torsional loading10.1016/S0142-1123(01)00190-6 ↗
13. Cyclic slip irreversibility and fatigue life: A microstructure-based analysis10.1016/j.actamat.2012.10.029 ↗
14. Determining the fatigue parameters in total strain life equation of a material based on monotonic tensile mechanical properties10.1016/j.engfracmech.2019.106866 ↗
15. A multi-axial and high-cycle fatigue life prediction model based on critical plane criterion10.1016/j.jmrt.2022.04.069 ↗
16. Simple expressions to estimate the Manson-Coffin curves of ductile cast irons10.1016/j.ijfatigue.2015.03.025 ↗
17. High-cycle fatigue properties prediction and damage mechanisms of RuT400 compacted graphite iron at different temperatures10.1016/j.msea.2019.138248 ↗
18. Low cycle fatigue properties, damage mechanism, life prediction and microstructure of MarBN steel: Influence of temperature10.1016/j.ijfatigue.2020.106070 ↗
19. Effect of monotonic mechanical properties on the low-cycle fatigue lifetime of carbide-free bainitic steels10.1016/j.msea.2022.143824 ↗
20. Fatigue: A complex subject—Some simple approximations10.1007/BF02321056 ↗
21. A Modified Universal Slopes Equation for Estimation of Fatigue Characteristics of Metals10.1115/1.3226010 ↗
22. An improved technique for the prediction of axial fatigue life from tensile data10.1016/0142-1123(93)90179-T ↗
23. An evaluation of existing methods for the prediction of axial fatigue life from tensile data10.1016/0142-1123(93)90071-W ↗
24. Strain-controlled fatigue properties of steels and some simple approximations10.1016/S0142-1123(00)00026-8 ↗
25. A comprehensive evaluation of conventional methods for estimation of fatigue parameters of steels from their monotonic properties10.1016/j.ijfatigue.2024.108244 ↗
26. On the estimation of cyclic material properties – Part 2: Introduction of a new estimation method: Dedicated to Professor Dr.-Ing. Harald Zenner on the occasion of his eightieth birthday. Mater
27. Fatigue failure mechanism and life prediction of a cast duplex stainless steel after thermal aging10.1016/j.ijfatigue.2021.106161 ↗
28. A fatigue life prediction method for coke drum base, weld, and HAZ materials from tensile properties10.1016/j.matdes.2014.06.056 ↗
29. Some useful approximations for wrought aluminum alloys based on monotonic tensile properties and hardness10.1002/mawe.201700016 ↗
30. Application of artificial neural network for predicting strain-life fatigue properties of steels on the basis of tensile tests10.1016/j.ijfatigue.2004.03.009 ↗
31. Unified fatigue life modelling and uncertainty estimation of Ni-based superalloy family with a supervised machine learning approach10.1016/j.engfracmech.2022.108813 ↗
32. Machine learning-based predictions of fatigue life and fatigue limit for steels10.1016/j.jmst.2021.02.021 ↗
33. Estimation of fatigue life of welded structures incorporating importance analysis of influence factors: A data-driven approach10.1016/j.engfracmech.2023.109103 ↗
34. Fatigue life prediction considering mean stress effect based on random forests and kernel extreme learning machine10.1016/j.ijfatigue.2022.106761 ↗
35. A study of compatibility between two classical fatigue curve models based on some selected structural materials10.1016/j.ijfatigue.2011.03.002 ↗
36. A multiaxial low-cycle fatigue prediction method under irregular loading by ANN model with knowledge-based features10.1016/j.ijfatigue.2023.107868 ↗
37. Fatigue failure probability estimation of the 7075–T651 aluminum alloy under multiaxial loading based on the life-dependent material parameters concept10.1016/j.ijfatigue.2021.106174 ↗
38. A novel gear fatigue monitoring indicator and its application to remaining useful life prediction for spur gear in intelligent manufacturing systems10.1016/j.ijfatigue.2022.107459 ↗
39. Symbolic regression based hybrid semiparametric modelling of processes: An example case of a bending process
40. Toward an artificial intelligence physicist for unsupervised learning10.1103/PhysRevE.100.033311 ↗
41. An efficient memetic genetic programming framework for symbolic regression10.1007/s12293-020-00311-8 ↗
42. On the functional form of the radial acceleration relation10.1093/mnras/stad597 ↗
43. Exhaustive Symbolic Regression
44. Machine Learning Conservation Laws from Trajectories10.1103/PhysRevLett.126.180604 ↗
45. Integration of symbolic regression and domain knowledge for interpretable modeling of remaining fatigue life under multistep loading10.1016/j.ijfatigue.2022.106889 ↗
46. Symbolic machine learning improved MCFT model for punching shear resistance of FRP-reinforced concrete slabs
47. LBE corrosion fatigue life prediction of T91 steel and 316 SS using machine learning method assisted by symbol regression10.1016/j.ijfatigue.2023.107962 ↗
48. Low cycle fatigue properties of a 1045 steel in torsion10.1520/STP36239S ↗
49. Low-cycle fatigue of 1Cr–18Ni–9Ti stainless steel and related weld metal under axial, torsional and 90° out-of-phase loading10.1111/j.1460-2695.2004.00740.x ↗
50. Crack initiation life prediction for solid cylinders with transverse circular holes under in-phase and out-of-phase multiaxial loading10.1016/j.ijfatigue.2004.09.007 ↗
51. High-Cycle Fatigue Behavior and Fatigue Strength Prediction of Differently Heat-Treated 35CrMo Steels10.3390/met12040688 ↗
52. Relationship between mechanical properties and high-cycle fatigue strength of medium-carbon steels10.1016/j.msea.2017.02.080 ↗
53. Correlation of Yield Strength and Tensile Strength with Hardness for Steels10.1007/s11665-008-9225-5 ↗
54. On the Tensile Strength and Hardness Relation for Metals10.1361/105994901770344601 ↗
55. The determination of tensile properties from hardness measurements for Al-Zn-Mg alloys10.1007/BF01209447 ↗
56. Genetic programming as a means for programming computers by natural selection10.1007/BF00175355 ↗
57. A Comparative Analysis of Selection Schemes Used in Genetic Algorithms
58. Stephens, T. gplearn. https://gplearn.readthedocs.io/en/latest/intro.html.
59. Interpretable Machine Learning for Science with PySR and Symbolic Regression10.1007/s10462-023-10622-0 ↗