journal article·2025-05

Fitting-free mechanical response prediction in dual-phase steels by crystal plasticity theory guided deep learning

Siyu Han, Chenchong Wang, Qingquan Lai, Lingyu Wang, Wei Xu, Hongshuang Di

Acta Materialia· Volume 289
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: Microstructure and Mechanical Properties of Steels · Hydrogen embrittlement and corrosion behaviors in metals · Non-Destructive Testing Techniques

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

References (63)

Show source-provided references
1. A collaborative design platform for new alloy material development10.1016/j.aei.2021.101488 ↗
2. Recent progress in the machine learning-assisted rational design of alloys10.1007/s12613-022-2458-8 ↗
3. Prediction of ultimate tensile strength of Al-Si alloys based on multimodal fusion learning
4. Microstructure evolution dependence of work-hardening characteristic in cold deformation of a difficult-to-deform nickel-based superalloy10.1016/j.msea.2020.140280 ↗
5. The effect of tailored deformation on fatigue strength of austenitic 316L stainless steel10.1002/adem.201800554 ↗
6. Wear resistance of high c high si steel with low retained austenite content and kinetically activated bainite10.3390/met10050672 ↗
7. Tensile property prediction by feature engineering guided machine learning in reduced activation ferritic/martensitic steels10.1016/j.jnucmat.2019.151823 ↗
8. Controlling strength and ductility: Dislocation-based model of necking instability and its verification for ultrafine grain 316L steel10.1016/j.actamat.2016.01.005 ↗
9. Investigation and mean-field modelling of microstructural mechanisms driving the tensile properties of dual-phase steels10.1016/j.msea.2021.141532 ↗
10. Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications10.1016/j.actamat.2009.10.058 ↗
11. Revisited storage and dynamic recovery of dislocation density evolution law: toward a generalized Kocks–Mecking model of strain-hardening10.1002/adem.201200083 ↗
12. An extension of the Kocks-Mecking model of work hardening to include kinematic hardening and its application to solutes in ferrite10.1080/14786435.2012.704419 ↗
13. Towards the microstructure design of DP steels: A generic size-sensitive mean-field mechanical model10.1016/j.msea.2015.04.017 ↗
14. Transformation plasticity in high strength, ductile ultrafine-grained FeMn alloy processed by heavy ausforming10.1016/j.ijplas.2021.103151 ↗
15. Modeling for onset strain of deformation-induced martensite transformation in Q&P steel by a mean-field Eshelby approach10.1088/1361-651X/ab41da ↗
16. Average stress in matrix and average elastic energy of materials with misfitting inclusions10.1016/0001-6160(73)90064-3 ↗
17. Mechanism based mean-field modeling of the work-hardening behavior of dual-phase steels10.1016/j.msea.2016.11.005 ↗
18. Microstructure effects on the plastic anisotropy of a fine-structured dual-phase steel10.1016/j.promfg.2020.04.349 ↗
19. Constrained Voronoi models for interpreting surface microstructural measurements10.1016/j.mechmat.2021.103892 ↗
20. Crystal plasticity finite element analysis of gradient nanostructured TWIP steel10.1016/j.ijplas.2020.102703 ↗
21. The effects of heat treatment on the response of WE43 Mg alloy: crystal plasticity finite element simulation and SEM-DIC experiment10.1016/j.ijplas.2020.102917 ↗
22. Stress–strain relationship between ferrite and martensite in a dual-phase steel studied by in situ neutron diffraction and crystal plasticity theories10.1016/j.actamat.2012.08.054 ↗
23. A crystal plasticity finite element model embedding strain-rate sensitivities inherent to deformation mechanisms: Application to alloy AZ3110.1016/j.ijplas.2021.103031 ↗
24. A dislocation density based crystal plasticity finite element model: Application to a two-phase polycrystalline HCP/BCC composites10.1016/j.jmps.2014.01.006 ↗
25. Study of the influence of δ-ferrite on the TRIP effect of a low-density high aluminum steel by using full-field crystal plasticity modeling10.1016/j.msea.2019.138667 ↗
26. Experimental characterization and crystal plasticity modeling of dual-phase steels subjected to strain path reversals10.1016/j.mechmat.2022.104293 ↗
27. Crystal plasticity approach for predicting the Bauschinger effect in dual-phase steels10.1016/j.msea.2012.01.092 ↗
28. A minimum parameter approach to crystal plasticity modelling10.1016/j.actamat.2012.06.057 ↗
29. Experiment and non-local crystal plasticity finite element study of nanoindentation on Al-8Ce-10Mg alloy
30. Studying the micromechanical behaviors of a polycrystalline metal by artificial neural networks10.1016/j.actamat.2021.117006 ↗
31. A new ANN based crystal plasticity model for FCC materials and its application to non-monotonic strain paths10.1016/j.ijplas.2021.103059 ↗
32. Data-driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology
33. Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials10.1038/s41524-021-00571-z ↗
34. Towards an instant structure-property prediction quality control tool for additive manufactured steel using a crystal plasticity trained deep learning surrogate10.1016/j.matdes.2021.110345 ↗
35. Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning10.1016/j.cma.2019.01.005 ↗
36. A convolutional neural network based crystal plasticity finite element framework to predict localised deformation in metals10.1016/j.ijplas.2022.103374 ↗
37. Mechanical properties prediction for hot rolled alloy steel using convolutional neural network10.1109/ACCESS.2019.2909586 ↗
38. Some applications of electron back scattering diffraction (EBSD) in materials research
39. DAMASK–The Düsseldorf Advanced Material Simulation Kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale10.1016/j.commatsci.2018.04.030 ↗
40. Incorporation of deformation twinning in crystal plasticity models10.1016/S0022-5096(97)00051-3 ↗
41. Integrated experimental–simulation analysis of stress and strain partitioning in multiphase alloys10.1016/j.actamat.2014.07.071 ↗
42. Study on the plastic anisotropy of advanced high strength steel sheet: Experiments and microstructure-based crystal plasticity modeling10.1016/j.ijmecsci.2020.105569 ↗
43. Strain localization and damage in dual phase steels investigated by coupled in-situ deformation experiments and crystal plasticity simulations10.1016/j.ijplas.2014.06.004 ↗
44. Crystal plasticity based predictions of mechanical properties from heterogeneous steel microstructures10.1088/1757-899X/651/1/012032 ↗
45. Surrogate model via artificial intelligence method for accelerating screening materials and performance prediction
46. On the use of transfer modeling to design new steels with excellent rotating bending fatigue resistance even in the case of very small calibration datasets10.1016/j.actamat.2022.118103 ↗
47. Considere
48. Achieving high tensile elongation in an ultra-fine grained Al alloy via low dislocation density10.1016/j.matlet.2012.05.046 ↗
49. WANG · 2020
50. Plastic flow and fracture of a particulate metal matrix composite10.1016/1359-6454(96)00028-6 ↗
51. A micro mechanical study on failure initiation of dual phase steels under tension using single crystal plasticity model10.1016/j.ijplas.2010.12.001 ↗
52. Measuring the critical resolved shear stresses in Mg alloys by instrumented nanoindentation10.1016/j.actamat.2014.03.014 ↗
53. Multiscale modeling of cruciform dwell tests with the uncertainty-quantified parametrically homogenized constitutive model10.1016/j.actamat.2020.09.037 ↗
54. A methodology to study crystal plasticity inside a compression test sample based on image correlation and EBSD10.1016/j.matchar.2011.05.009 ↗
55. F. Ofli, F. Alam, M. Imran, Analysis of social media data using multimodal deep learning for disaster response, arXiv preprint arXiv: 2004. 11838 (2020). https://doi.org/10.48550/arXiv.2004.11838.
56. More diverse means better: Multimodal deep learning meets remote-sensing imagery classification10.1109/TGRS.2020.3016820 ↗
57. Multimodal deep learning for biomedical data fusion: a review10.1093/bib/bbab569 ↗
58. A review on methods and applications in multimodal deep learning10.1145/3545572 ↗
59. A generic and extensible model for the martensite start temperature incorporating thermodynamic data mining and deep learning framework10.1016/j.jmst.2022.04.014 ↗
60. CNN-based multimodal touchless biometric recognition system using gait and speech10.3233/JIFS-189765 ↗
61. P.V. de Freitas, P.R. Mendes, G.N. dos Santos, A.J. Busson, Á.L. Guedes, S. Colcher, R.L. Milidiú, A multimodal CNN-based tool to censure inappropriate video scenes, arXiv preprint arXiv:1911.03974 (2019). https://doi.org/10.48550/arXiv.1911.03974.
62. Building a quantitative composition-microstructure-property relationship of dual-phase steels via multimodal data mining10.1016/j.actamat.2023.118954 ↗
63. Employing deep learning in non-parametric inverse visualization of elastic–plastic mechanisms in dual-phase steels