Uniaxial and multiaxial cyclic deformation behavior prediction of Z2CN18.10 austenitic stainless steel based on Transformer deep learning method
Bibliographic record
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: Microstructure and Mechanical Properties of Steels · Metallurgy and Material Forming · Fatigue and fracture mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (31)
Show source-provided references
1. Ratcheting of cyclically hardening and softening materials: I. Uniaxial behaviour10.1016/0749-6419(94)90033-7 ↗
2. Ratcheting of cyclically hardening and softening materials: II. Multiaxial behavior10.1016/0749-6419(94)90034-5 ↗
3. A new method of analyzing stresses and strains in work hardening plastic solids10.1115/1.4011389 ↗
4. Armstrong PJ, Frederick CO. A mathematical representation of the multiaxial bauschinger effect. CEGB Report No. RD/B/N 731, 1966.
5. Time-independent constitutive theories for cyclic plasticity10.1016/0749-6419(86)90010-0 ↗
6. Kinematic hardening rules with critical state of dynamic recovery, part I: Formulation and basic features for ratchetting behavior10.1016/0749-6419(93)90042-O ↗
7. Kinematic hardening rules with critical state of dynamic recovery, part II: Application to experiments of ratchetting behavior10.1016/0749-6419(93)90043-P ↗
8. Modified kinematic hardening rule for multiaxial ratcheting prediction10.1016/j.ijplas.2003.05.005 ↗
9. On the Ohno-Wang kinematic hardening rules for multiaxial ratcheting modeling of medium carbon steel10.1016/j.ijplas.2004.05.005 ↗
10. A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions10.1016/j.ijfatigue.2021.106236 ↗
11. A novel approach based on the elastoplastic fatigue damage and machine learning models for life prediction of aerospace alloy parts fabricated by additive manufacturing10.1016/j.ijfatigue.2020.106089 ↗
12. A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures10.1016/j.engfracmech.2021.108130 ↗
13. Fatigue life prediction considering mean stress effect based on random forests and kernel extreme learning machine10.1016/j.ijfatigue.2022.106761 ↗
14. A novel deep learning approach of multiaxial fatigue life-prediction with a self-attention mechanism characterizing the effects of loading history and varying temperature10.1016/j.ijfatigue.2022.106851 ↗
15. A novel method of multiaxial fatigue life prediction based on deep learning10.1016/j.ijfatigue.2021.106356 ↗
16. Machine learning-based predictions of fatigue life for multi-principal element alloys10.1016/j.scriptamat.2022.115214 ↗
17. High cycle fatigue life prediction of laser additive manufactured stainless steel: A machine learning approach10.1016/j.ijfatigue.2019.105194 ↗
18. Machine-learning-based investigation into the effect of defect/inclusion on fatigue behavior in steels10.1016/j.ijfatigue.2021.106597 ↗
19. Machine learning-based genetic feature identification and fatigue life prediction10.1111/ffe.13532 ↗
20. A deep learning framework for constitutive modeling based on temporal convolutional network10.1016/j.jcp.2021.110784 ↗
21. FEA-net: a physics-guided data-driven model for efficient mechanical response prediction10.1016/j.cma.2020.112892 ↗
22. Deep learning for plasticity and thermo-viscoplasticity10.1016/j.ijplas.2020.102852 ↗
23. Deep learning predicts path-dependent plasticity10.1073/pnas.1911815116 ↗
24. On the potential of recurrent neural networks for modeling path dependent plasticity10.1016/j.jmps.2020.103972 ↗
25. Predicting stress-strain curves using transfer learning: Knowledge transfer across polymer composites10.1016/j.matdes.2022.110700 ↗
26. Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning10.1016/j.ijplas.2021.103046 ↗
27. A convolutional neural network based crystal plasticity finite element framework to predict localised deformation in metals10.1016/j.ijplas.2022.103374 ↗
28. Fast stress evaluation of the top coat of thermal barrier coatings under CaO–MgO–Al2O3–SiO2 penetration based on image recognition and an artificial neural network10.1016/j.ceramint.2021.03.145 ↗
29. ASTM E2207-02. Standard practice for strain-controlled axial-torsional fatigue testing with thin-walled tubular specimens. Annual Book of ASTM Standards, 2002.
30. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. Attention is All you Need. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA., 2017.
31. Deep neural networks for YouTube recommendations10.1145/2959100.2959190 ↗