Prediction of Grain Density for TC4 Titanium Alloy Castings Using Random Forest Regression
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References (34)
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1. Classification and applications of titanium and its alloys: A review10.1016/j.jacomc.2024.100019 ↗
2. A novel nano-particle strengthened titanium alloy with exceptional specific strength10.1038/s41598-019-48139-8 ↗
3. Hierarchically ordered coherent interfaces-driven ultrahigh specific-strength and toughness in a nano-martensite titanium alloy10.1016/j.actamat.2023.119540 ↗
4. Numerical simulation for the investment casting process of a large-size titanium alloy thin-wall casing10.1016/j.pnsc.2018.06.005 ↗
5. Effect of pouring time on microstructure and mechanical properties of centrifugal cast Ti-46Al alloy tubes10.1007/s41230-025-3171-9 ↗
6. Influence of Casting Temperature and Mold Preheating Temperature on Centrifugal Casting by Numerical Simulation10.1007/s11665-022-07608-4 ↗
7. Parameter Optimization of Al-5Mg-3Zn-1Cu Basin-Shaped Centrifugal Casting: Simulation and Experimental Verification10.1007/s40962-022-00822-9 ↗
8. Effect of grain size and volume fraction of eutectic structure on mechanical properties and corrosion behavior of as-cast Zn–Mg binary alloys10.1016/j.jmrt.2021.12.101 ↗
9. Effect of forging temperature on the microstructure, subsequent aging precipitation behavior, and mechanical properties of Mg-Gd-Y-Zr-Ag alloy10.1016/j.jmst.2023.09.024 ↗
10. An Overview of the Effect of Grain Size on Mechanical Properties of Magnesium and Its Alloys10.2320/matertrans.MT-MF2022005 ↗
11. Hard-soft-hard layered structure for enhanced mechanical properties in corrugated rolled AZ31 magnesium alloy sheets10.1016/j.jmrt.2025.01.239 ↗
12. Numerical Investigation on the Evolution Behavior of Solidification Structure in Titanium Alloy During Vacuum Arc Remelting Process10.1007/s11663-024-03316-7 ↗
13. Static solid cooling (SSC) method for directional solidification and single crystal production: A CAFE simulation approach10.1016/j.jmrt.2024.12.006 ↗
14. Coupling Simulation and Mechanism Analysis of Grain Formation and Growth in Horizontal Centrifugal Casting10.1007/s40962-024-01470-x ↗
15. Quantitative prediction of microstructure and mechanical properties of additively manufactured titanium alloys using machine learning10.1016/j.jallcom.2026.186754 ↗
16. In situ conductometry for studying the homogenization of Al-Mg-Si alloys and predicting extrudate grain structure through machine learning10.1016/j.matdes.2024.113070 ↗
17. Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model10.1038/s41598-022-14731-8 ↗
18. A physics-informed machine learning method for predicting grain structure characteristics in directed energy deposition10.1016/j.commatsci.2021.110958 ↗
19. Computational, Data-Driven, and Physics-Informed Machine Learning Approaches for Microstructure Modeling in Metal
20. Predicting grain growth kinetic in steels using machine learning and XAI for mechanical properties10.1371/journal.pone.0341053 ↗
21. Turbine blade investment casting: a review of process mechanisms, modeling, and intelligent manufacturing10.1016/j.pmatsci.2026.101748 ↗
22. A rapid and effective method for alloy materials design via sample data transfer machine learning10.1038/s41524-023-00979-9 ↗
23. Numerical Simulation and Machine Learning Prediction of the Direct Chill Casting Process of Large-Scale Aluminum Ingots10.3390/ma17061409 ↗
24. Physics-informed machine learning framework for grain size prediction in laser-processed 316L stainless steel: integration of experimental microstructure and surrogate data10.1080/00084433.2025.2593051 ↗
25. Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption10.1016/j.enbuild.2017.04.038 ↗
26. Grinsztajn L, Oyallon E, Varoquaux G. Why do tree-based models still outperform deep learning on typical tabular data? Proceedings of the 36th International Conference on Neural Information Processing Systems, Red Hook, NY, USA: Curran Associates Inc.; 2022, p. 507–520.10.52202/068431-0037 ↗
27. A 3D Cellular Automaton algorithm for the prediction of dendritic grain growth10.1016/S1359-6454(96)00303-5 ↗
28. A coupled finite element-cellular automaton model for the prediction of dendritic grain structures in solidification processes10.1016/0956-7151(94)90302-6 ↗
29. Three-dimensional probabilistic simulation of solidification grain structures: Application to superalloy precision castings10.1007/BF02657334 ↗
30. Multiscale modelling of microstructure formation during vacuum arc remelting of titanium 6-410.1023/B:JMSC.0000048731.42495.1c ↗
31. Thermal Stress Field and Microstructure Evolution of Ti-6Al-4V Fabricated by Laser Engineered Net Shaping10.1007/s12540-023-01504-9 ↗
32. Determination and controlling of grain structure of metals after laser incidence: Theoretical approach10.1038/srep41527 ↗
33. From local explanations to global understanding with explainable AI for trees10.1038/s42256-019-0138-9 ↗
34. Kurz W, Fisher DJ, Rappaz M. Fundamentals of Solidification 5th Edition: vol. 103. Trans Tech Publications Ltd; 2023. https://doi.org/10.4028/b-v7b35q.10.4028/b-UK5DlH ↗