Mapping microstructure to mechanical property by disentangling strengthening mechanism with deep learning
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OpenAlex topics: Machine Learning in Materials Science · Image Processing Techniques and Applications · Non-Destructive Testing Techniques
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References (61)
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1. Development of strong, oxidation and corrosion resistant nickel-based superalloys: critical review of challenges, progress and prospects10.1080/09506608.2018.1516713 ↗
2. Predicting dwell fatigue life in titanium alloys using modelling and experiment10.1038/s41467-020-19470-w ↗
3. A defect-resistant Co-Ni superalloy for 3D printing10.1038/s41467-020-18775-0 ↗
4. Evolution analysis of γ’precipitate coarsening in Co-based superalloys using kinetic theory and machine learning10.1016/j.actamat.2022.118101 ↗
5. A novel Re-free Ni-based single-crystal superalloy with enhanced creep resistance and microstructure stability10.1016/j.actamat.2022.118336 ↗
6. Microstructural and mechanical properties of precipitation-strengthened Al-Mg-Zr-Sc-Er-Y-Si alloys10.1016/j.actamat.2023.119167 ↗
7. Microstructural evolution and strain-hardening in TWIP Ti alloys10.1016/j.actamat.2019.11.009 ↗
8. Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders10.1016/j.actamat.2023.119204 ↗
9. A machine learning method to quantitatively predict alpha phase morphology in additively manufactured Ti-6Al-4V10.1038/s41524-023-01152-y ↗
10. Accelerated prediction of vickers hardness of Co-and Ni-based superalloys from microstructure and composition using advanced image processing techniques and machine learning10.1016/j.actamat.2020.06.042 ↗
11. Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques10.1016/j.pmatsci.2018.01.005 ↗
12. Characterization and reconstruction of 3D stochastic microstructures via supervised learning10.1111/jmi.12441 ↗
13. Machine learning based quantitative characterization of microstructures10.1016/j.actamat.2023.119106 ↗
14. Designing ultra-strong and ductile hierarchical titanium alloys via interstitial solute-mediated multi-morphologic α-nanoprecipitates10.1016/j.actamat.2023.119082 ↗
15. Integrated multi-scale modeling of variant selection during stress-induced martensite formation in metastable β Ti-alloys10.1016/j.actamat.2022.118342 ↗
16. Building a quantitative composition-microstructure–property relationship of dual-phase steels via multimodal data mining10.1016/j.actamat.2023.118954 ↗
17. 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 ↗
18. A descriptor-based design methodology for developing heterogeneous microstructural materials system10.1115/1.4026649 ↗
19. 3D-quantification of the distribution of continuous fibres in unidirectionally reinforced composites10.1016/j.compositesa.2008.10.014 ↗
20. Stochastic microstructure characterization and reconstruction via supervised learning10.1016/j.actamat.2015.09.044 ↗
21. Statistical model for characterizing random microstructure of inclusion–matrix composites10.1007/s10853-006-1117-1 ↗
22. Two-point cluster function for continuum percolation10.1063/1.454440 ↗
23. Lineal-path function for random heterogeneous materials10.1103/PhysRevA.45.922 ↗
24. Chord-length distribution function for two-phase random media10.1103/PhysRevE.47.2950 ↗
25. Stochastic reconstruction using multiple correlation functions with different-phase-neighbor-based pixel selection10.1103/PhysRevE.90.023306 ↗
26. Modeling heterogeneous materials via two-point correlation functions. II. Algorithmic details and applications10.1103/PhysRevE.77.031135 ↗
27. Employing deep learning in non-parametric inverse visualization of elastic–plastic mechanisms in dual-phase steels
28. Data-driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology
29. Machine learning-guided plasticity model in refractory high-entropy alloys
30. A deep learning approach for complex microstructure inference10.1038/s41467-021-26565-5 ↗
31. Recent advances and applications of deep learning methods in materials science10.1038/s41524-022-00734-6 ↗
32. Thermal conductivity prediction of UO2-BeO composite fuels and related decisive features discovery via convolutional neural network10.1016/j.actamat.2022.118352 ↗
33. Predicting structure zone diagrams for thin film synthesis by generative machine learning10.1038/s43246-020-0017-2 ↗
34. Establishing structure–property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches10.1016/j.actamat.2018.12.045 ↗
35. Material structure–property linkages using three-dimensional convolutional neural networks10.1016/j.actamat.2017.11.053 ↗
36. Machine-learning microstructure for inverse material design10.1002/advs.202101207 ↗
37. Material microstructure design using VAE-regression with a multimodal prior
38. Exploration of optimal microstructure and mechanical properties in continuous microstructure space using a variational autoencoder10.1016/j.matdes.2021.109544 ↗
39. Characterization of porous membranes using artificial neural networks10.1016/j.actamat.2023.118922 ↗
40. Design of architectured composite materials with an efficient, adaptive artificial neural network-based generative design method10.1016/j.actamat.2021.117548 ↗
41. Establishing the interfacial microstructure-behavior relations in composites via stochastic morphology reconstruction and deep learning10.1016/j.actamat.2022.118582 ↗
42. Addressing materials’ microstructure diversity using transfer learning10.1038/s41524-022-00703-z ↗
43. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning10.1038/s41467-019-13297-w ↗
44. Microstructure cluster analysis with transfer learning and unsupervised learning10.1007/s40192-018-0116-9 ↗
45. Design of nanostructure in solid electrolyte interphase for enhancing the mechanical durability of lithium metal anode by deep-learning approach
46. Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics10.1016/j.actamat.2017.09.004 ↗
47. Microstructure and mechanical properties of Inconel 625 superalloy10.1016/S0022-3115(00)00723-6 ↗
48. Laser aided direct metal deposition of Inconel 625 superalloy: Microstructural evolution and thermal stability10.1016/j.msea.2009.01.009 ↗
49. The performance of alloy 625 in long-term intermediate temperature applications10.1016/0308-0161(94)90140-6 ↗
50. Microstructure segmentation with deep learning encoders pre-trained on a large microscopy dataset10.1038/s41524-022-00878-5 ↗
51. Kingma · 2014
52. Simple and effective VAE training with calibrated decoders
53. W. Liu, et al., Towards visually explaining variational autoencoders, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 8642–8651.10.1109/CVPR42600.2020.00867 ↗
54. Automatic chemical design using a data-driven continuous representation of molecules10.1021/acscentsci.7b00572 ↗
55. Robustness of random forests for regression10.1080/10485252.2012.715161 ↗
56. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778.10.1109/CVPR.2016.90 ↗
57. Efficientnet: Rethinking model scaling for convolutional neural networks
58. Six decades of the Hall–Petch effect–a survey of grain-size strengthening studies on pure metals10.1080/09506608.2016.1191808 ↗
59. Effect of grain refinement and twin structure on the strength and ductility of inconel 625 alloy10.1016/j.msea.2021.141739 ↗
60. B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba, Learning deep features for discriminative localization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2921–2929.10.1109/CVPR.2016.319 ↗
61. R.R. Selvaraju, et al., Grad-cam: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 618–626.10.1109/ICCV.2017.74 ↗