Optimizing casting process using a combination of small data machine learning and phase-field simulations
NPJ Computational Materials
; London Vol. 11, Iss. 1, (2025): 27.
DOI:10.1038/s41524-025-01524-6
PDF
CiteCite
Copy URL
https://www.proquest.com/scholarly-journals/optimizing-casting-process-using-combination/docview/3165586688/se-2?accountid=208611
PrintAll OptionsReferences (75)
- 6.
Synchronously enhancing the strength, toughness, and stress corrosion resistance of high-end aluminum alloys via interpretable machine learning
Jiang, L; Fu, H; Zhang, Z; Zhang, H; Zhang, X; Feng, X; Xu, X; Mao, M; Xie, J. Acta Mater Vol. 270, .- Times cited 20 on ProQuest
- 7.
Interpretable predicting creep rupture life of superalloys: enhanced by domain-specific knowledge
Yin, J; Rao, Z; Wu, D. Adv. Sci Vol. 11, Iss. 11, .- Times cited 5 on ProQuest
- 8.
Development of phase-field modeling in materials science in China: A review
Zhao, Y; Xing, H; Zhang, L; Huang, H; Sun, D; Dong, X. Acta Metall Sin-Engl Vol. 36, .- Times cited 30 on ProQuest
- 10.
Application of the thermodynamic extremal principle to diffusion-controlled phase transformations in Fe-CX alloys: modeling and applications
Kuang, W; Wang, H; Li, X; Zhang, J; Zhou, Q; Zhao, Y. Acta Mater Vol. 159, .- Times cited 53 on ProQuest
- 11.
Machine-learning microstructure for inverse material design
Pei, Z; Rozman, K; Doğan, Ö.N; Wen, Y; Gao, N; Holm, E. Adv Sci Vol. 8, .- Times cited 3 on ProQuest
- 12.
A conditioned Latin hypercube method for sampling in the presence of ancillary information
B Minasny; AB McBratney. Comput Geosci Vol. 32, Iss. 9, (2006): 1378-1388.- Times cited 4 on ProQuest
- 13.
Application of machine learning for the classification of corrosion behavior in different environments for material selection of stainless steels
Hakimian, S; Pourrahimi, S; Bouzid, A.-H; Hof, L. Comput Mater Sci Vol. 228, .- Times cited 6 on ProQuest
- 14.
Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs
Malkov, Y; Yashunin, D. IEEE Trans. Pattern Anal. Mach. Intell Vol. 42, Iss. 4, .- Times cited 16 on ProQuest
- 15.
Predicting the hardness of high-entropy alloys based on compositions
Guo, Q; Pan, Y; Hou, H; Zhao, Y. Int. J. Refract. Met. H Vol. 112, .- Times cited 11 on ProQuest
- 16.
A systematic approach to model and optimize qualities of castings produced by squeeze casting process
Zhou, D; Su, X; Yang, C. Int. J. Met Vol. 17, Iss. 3, .- Times cited 4 on ProQuest
- 17.
Model selection and evaluation for machine learning: deep learning in materials processing
Kopper, A; Karkare, R; Paffenroth, R; Apelian, D. Integr. Mater. Manuf. Innov Vol. 9, .- Times cited 3 on ProQuest
- 18.
Predict the phase formation of high-entropy alloys by compositions
Guo, Q; Xu, X; Pei, X; Duan, Z; Liaw, P; Hou, H; Zhao, Y. J. Mater. Res. Technol Vol. 22, .- Times cited 28 on ProQuest
- 19.
Role of interfacial energy anisotropy in dendrite orientation in Al-Zn alloys: A phase field study
Zhao, Y; Liu, K; Hou, H; Chen, L.-Q. Mater. Des Vol. 216, .- Times cited 14 on ProQuest
- 20.
Cooperative effect of strength and ductility processed by thermomechanical treatment for Cu-Al-Ni alloy
Tian, X; Zhao, Y; Gu, T. Mater Sci Eng A Vol. 849, .- Times cited 8 on ProQuest