Data-Driven Design of Nickel-Free Superelastic Titanium Alloys
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset Establishment
2.2. Feature Construction
2.3. Feature Selection
2.4. Machine Learning
2.5. Experimental Validation
3. Results
3.1. Feature Selection and Model Establishment
3.2. New Alloy Design
- 0 ≤ Nb ≤ 25 at.%,
- 0 ≤ Mo ≤ 5 at.%,
- 0 ≤ Zr ≤ 30 at.%,
- 0 ≤ Sn ≤ 5 at.%,
- 0 ≤ Ta ≤ 5 at.%,
- Nb + Mo + Zr + Sn + Ta + Ti = 100 at.%,
- Zr < Ti,
- strain = 0.985
- T1 = 1173 K,
- t1 = 30 min.
3.3. Feature Analysis
3.4. Experimental Validation
4. Conclusions
- Machine learning models were established based on 125 data points. Among them, the CatBoost model constructed with features strain, T1, t1, E, MP3 and PE4 performed the best with an R2 of 0.83 and MAE of 0.44.
- The features included in the final feature set, such as strain, T1, and t1 were mainly related to the processing and preparation of the alloy. The reason higher cold rolling deformation and annealing temperature were advantageous for the superelastic performance of the alloy was that high-temperature annealing after large-deformation cold rolling facilitates the formation of fine grains and strong texture.
- Three alloy compositions including Ti-11Nb-26Zr-2Sn, Ti-12Nb-18Zr-2Sn and Ti-12Nb-16Zr-3Sn were designed using a Bayesian optimization algorithm which could quickly perform alloy composition optimization. After 60 iterations, it searched for alloy compositions with a superelastic strain greater than 6%.
- Due to the lack of assessment of β stability in the model, only the Ti-12Nb-18Zr-2Sn and Ti-12Nb-16Zr-3Sn alloys exhibited a superelastic performance. The superelastic performance of both alloys matched the predictions of machine learning based on actual processes. After thermal treatment process optimization, they exhibited a recovery strain of 4.65% and 3.01%, respectively.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Classification | Description | Symbol |
---|---|---|
Processing process | Cold rolling reduction | strain |
Annealing temperature | T1 | |
Annealing time | t1 | |
Empirical parameters of alloy design | Mo equivalent | Moeq |
Bo | Bo | |
Md | Md | |
Valence electron concentration | VEC | |
Phase formation parameters | Enthalpy of mixing | ΔHmix |
Entropy of mixing | ΔSmix | |
Solid solution phase formation parameter | Ω | |
Element features | Atomic number | NA |
Atomic mass | MA | |
Density | ρ | |
Period | P | |
Group | G | |
Atomic radius | RA | |
Covalent radius | RC | |
Lattice constant | a | |
Lattice constant | c | |
Physical and chemical features | Young’s modulus | E |
Melting point | MP | |
Boiling point | BP | |
Resistance | R | |
Thermal conductivity | TC | |
Thermal expansion coefficient | Cte | |
Pauling electronegativity | PE | |
Heat of fusion | Hf |
Models | Hyperparameters |
---|---|
SVR | C: 4.87, gamma: 2.03, epsilon: 4.38 × 10−4 |
CatBoost | iterations: 399, learning_rate: 0.23, l2_leaf_reg: 1.90, depth:6, subsample: 0.45, rsm: 0.95 |
KNN | n_neighbors: 6, weights: distance, algorithm: kd_tree |
BPNN | n_layers: 2, n_units_l0: 88, n_units_l1: 40, alpha: 0.60, learning_rate_init: 0.002, momentum: 0.60, max_iter: 5000 |
GPR | alpha: 0.01 |
XGBoost | lambda: 136.32, alpha: 0.02, learning_rate: 1.49, n_estimators: 1017, max_depth: 120, gamma: 0.0003, min_child_weight: 1 |
Alloys | Nb | Zr | Sn | Ti |
---|---|---|---|---|
Ⅰ | 11 | 26 | 2 | 61 |
Ⅱ | 12 | 18 | 2 | 68 |
Ⅲ | 12 | 16 | 3 | 69 |
Alloys | Nb | Zr | Sn | Ti |
---|---|---|---|---|
Ⅰ | 10.72 | 26.51 | 2.00 | 60.77 |
Ⅱ | 12.28 | 17.14 | 2.02 | 68.55 |
Ⅱ | 11.62 | 15.89 | 3.12 | 69.37 |
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Chen, H.; Ye, W.; Hui, S.; Yu, Y. Data-Driven Design of Nickel-Free Superelastic Titanium Alloys. Materials 2024, 17, 1793. https://doi.org/10.3390/ma17081793
Chen H, Ye W, Hui S, Yu Y. Data-Driven Design of Nickel-Free Superelastic Titanium Alloys. Materials. 2024; 17(8):1793. https://doi.org/10.3390/ma17081793
Chicago/Turabian StyleChen, Haodong, Wenjun Ye, Songxiao Hui, and Yang Yu. 2024. "Data-Driven Design of Nickel-Free Superelastic Titanium Alloys" Materials 17, no. 8: 1793. https://doi.org/10.3390/ma17081793