journal article·2024-09

Accelerating the solving of mechanical equilibrium caused by lattice misfit through deep learning method

Chen-Xi Guo, Hui-Ying Yang, Rui-Jie Zhang

Advances in Manufacturing· Volume 12· Issue 3
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Abstract

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Research context

OpenAlex topics: Solidification and crystal growth phenomena · Aluminum Alloy Microstructure Properties · Metallurgical Processes and Thermodynamics

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References (31)

Show source-provided references
1. Ma CZ, Zhang RJ, Li ZX et al (2022) Solidification shrinkage and shrinkage-induced melt convection and their relations with solute segregation in binary alloys. Comp Mater Sci 215:111815. https://doi.org/10.1016/j.commatsci.2022.11181510.1016/j.commatsci.2022.111815 ↗
2. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–44410.1038/nature14539 ↗
3. Warmuzek M, Zelawski M, Jalocha T (2021) Application of the convolutional neural network for recognition of the metal alloys microstructure constituents based on their morphological characteristics. Comp Mater Sci 199:110722. https://doi.org/10.1016/j.commatsci.2021.11072210.1016/j.commatsci.2021.110722 ↗
4. Li Y, Zhou X, Colnaghi T et al (2021) Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys. NPJ Comput Mater 7:8. https://doi.org/10.1038/s41524-020-00472-710.1038/s41524-020-00472-7 ↗
5. Ma B, Ban X, Huang H et al (2018) Deep learning-based image segmentation for Al-La alloy microscopic images. Symmetry 10(4):107–11910.3390/sym10040107 ↗
6. Yang K, Cao Y, Zhang Y et al (2021) Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks. Patterns 2(5):100243. https://doi.org/10.1016/j.patter.2021.10024310.1016/j.patter.2021.100243 ↗
7. Zapiain DMD, Stewart JA, Dingreville R (2021) Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods. NPJ Comput Mater 7:3. https://doi.org/10.1038/s41524-020-00471-810.1038/s41524-020-00471-8 ↗
8. Yang ZJ, Yabansu YC, Jha D et al (2019) Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches. Acta Mater 166:335–34510.1016/j.actamat.2018.12.045 ↗
9. Buades A, Coll B, Morel JM (2005) A review of image denoising algorithms, with a new one. Multiscale Model Sim 4(2):490–53010.1137/040616024 ↗
10. Chan T, Esedoglu S, Park F et al (2005) Recent developments in total variation image restoration. Math Models Comput Vis 17(2):17–31
11. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Lect Notes Comput Sc 9351:234–24110.1007/978-3-319-24574-4_28 ↗
12. Kim JW, Cherukara MJ, Tripathi A et al (2021) Inversion of coherent surface scattering images via deep learning network. Appl Phys Lett 119(19):191601. https://doi.org/10.1063/5.006390310.1063/5.0063903 ↗
13. Deshpande S, Lengiewicz J, Bordas SP (2022) Probabilistic deep learning for real time large deformation simulations. Comput Methods Appl Mech 398:115307. https://doi.org/10.1016/j.cma.2022.11530710.1016/j.cma.2022.115307 ↗
14. Peivaste I, Siboni NH, Alahyarizadeh G et al (2022) Machine-learning-based surrogate modeling of microstructure evolution using phase-field. Comput Mater Sci 214:111750. https://doi.org/10.1016/j.commatsci.2022.11175010.1016/j.commatsci.2022.111750 ↗
15. Mianroodi JR, Siboni NH, Raabe D (2021) Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials. NPJ Comput Mater 7:99. https://doi.org/10.1038/s41524-021-00571-z10.1038/s41524-021-00571-z ↗
16. Raissi M, Perdikaris P, Karniadakis GE (2019) Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 378:686–70710.1016/j.jcp.2018.10.045 ↗
17. Yonekura K, Maruoka K, Tyou K et al (2023) Super-resolving 2D stress tensor field conserving equilibrium constraints using physics-informed U-Net. Finite Elem Anal Des 213:103852. https://doi.org/10.1016/j.finel.2022.10385210.1016/j.finel.2022.103852 ↗
18. Zhu Q, Liu Z, Yan J (2021) Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks. Comput Mech 67:619–63510.1007/s00466-020-01952-9 ↗
19. Chen LQ (2002) Phase-field models for microstructure evolution. Annu Rev Mater Res 32(1):113–14010.1146/annurev.matsci.32.112001.132041 ↗
20. Kim SG (2007) A phase-field model with antitrapping current for multicomponent alloys with arbitrary thermodynamic properties. Acta Mater 55:4391–439910.1016/j.actamat.2007.04.004 ↗
21. Kim SG, Kim WT, Suzuki T (1999) Phase-field model for binary alloys. Phys Rev E 60(6):7186–719710.1103/PhysRevE.60.7186 ↗
22. Hu SY, Murray J, Weiland H et al (2007) Thermodynamic description and growth kinetics of stoichiometric precipitates in the phase-field approach. Calphad 31(2):303–31210.1016/j.calphad.2006.08.005 ↗
23. Hwang JY, Banerjee R, Doty HW et al (2009) The effect of Mg on the structure and properties of Type 319 aluminum casting alloys. Acta Mater 57(4):1308–131710.1016/j.actamat.2008.11.021 ↗
24. Loginova I, Ågren J, Amberg G (2004) On the formation of Widmanstätten ferrite in binary Fe-C phase-field approach. Acta Mater 52(13):4055–406310.1016/j.actamat.2004.05.033 ↗
25. Weakley-Bollin SC, Donlon W, Wolverton C et al (2004) Modeling the age-hardening behavior of Al-Si-Cu alloys. Metall Mater Trans A 35(8):2407–241810.1007/s11661-006-0221-9 ↗
26. Zhu JZ, Chen LQ, Shen J (2001) Morphological evolution during phase separation and coarsening with strong inhomogeneous elasticity. Model Simul Mater Sci Eng 9(6):499–51110.1088/0965-0393/9/6/303 ↗
27. Sharma S, Sharma S, Athaiya A (2017) Activation functions in neural networks. Towards Data Sci 6(12):310–316
28. Chen H, Wang YH, Fan CH (2021) A convolutional autoencoder-based approach with batch normalization for energy disaggregation. J Supercomput 77:2961–297810.1007/s11227-020-03375-y ↗
29. Balasundaram S, Prasad SC (2020) Robust twin support vector regression based on Huber loss function. Neural Comput Appl 32:11285–1130910.1007/s00521-019-04625-8 ↗
30. Sun RY (2020) Optimization for deep learning: an overview. J Oper Res Soc China 8(2):249–29410.1007/s40305-020-00309-6 ↗
31. Park H, Stefanski L (1998) Relative-error prediction. Stat Probabil Lett 40(3):227–23610.1016/S0167-7152(98)00088-1 ↗