Machine Learning Design of Tungsten Alloys With Strength–Ductility Synergy

Abstract

In the artificial intelligence (AI) era, knowledge-based machine learning (ML) models can accelerate the design and modification of existing materials toward target properties. Therefore, an integrated framework combining ML with solid solution softening knowledge was introduced to design multielement tungsten alloys with strength–ductility synergy. The chemical short-range ordered (CSRO) body-centered cubic (BCC) phases, B32, B2, C11_b, and D0₃, are chosen as training dataset generated from high-throughput first-principle calculations. After meticulous feature engineering and hyperparameter optimization, AdaBoost and Gradient Boosting Regression algorithms are identified as the ML models for describing the thermodynamical and mechanical properties of multielement tungsten alloys. Spanning from binary to quaternary tungsten-based systems, W–Ta–Re is predicted by ML models and displays promising feature in overcoming the inherent strength-ductility trade-off. To validate the ML prediction, a W–10Ta–10Re alloy is synthesized via mechanical alloying and spark plasma sintering. TEM characterization confirms the formation of a uniform solid solution with a strong CSRO tendency. An exceptional compressive strength of 3586.7 MPa and a high work hardening rate are obtained, which surpass current tungsten alloy systems. This study not only establishes a reliable ML pathway for the design of high-performance tungsten alloys but also provides insights into the role of CSRO.

Keywords

chemical short-range ordering / high-throughput DFT calculations / machine learning / strength–ductility synergy / tungsten alloys

References

[1] S. Zhao, J. Li, J. Wang, T. Lookman, and R. Yuan, “Closed-Loop Inverse Design of High Entropy Alloys Using Symbolic Regression-Oriented Optimization,” Materials Today 88 (2025): 263–271, https://doi.org/10.1016/j.mattod.2025.06.033.

[2] Z. Rao, P. Y. Tung, R. Xie, et al., “Machine Learning–Enabled High-Entropy Alloy Discovery,” Science (1979) 378, no. 6615 (2022): 78–85, https://doi.org/10.1126/science.abo4940.

[3] C. Wen, Y. Zhang, C. Wang, et al., “Machine Learning Assisted Design of High Entropy Alloys With Desired Property,” Acta Materialia 170 (2019): 109–117, https://doi.org/10.1016/j.actamat.2019.03.010.

[4] J. Yin, Q. Lei, X. Li, et al., “A Novel Neural Network-Based Alloy Design Strategy: Gated Recurrent Unit Machine Learning Modeling Integrated With Orthogonal Experiment Design and Data Augmentation,” Acta Materialia 243 (2023): 118420, https://doi.org/10.1016/j.actamat.2022.118420.

[5] Y. Hu, G. Zhou, X. Yuan, et al., “An Artificial Neural Network-Based Model for Roping Prediction in Aluminum Alloy Sheet,” Acta Materialia 245 (2023): 118605, https://doi.org/10.1016/j.actamat.2022.118605.

[6] D. Marchand, A. Jain, A. Glensk, and W. A. Curtin, “Machine Learning for Metallurgy I. A Neural-Network Potential for Al–Cu,” Physical Review Materials 4, no. 10 (2020): 103601, https://doi.org/10.1103/physrevmaterials.4.103601.

[7] A. Merchant, S. Batzner, S. S. Schoenholz, M. Aykol, G. Cheon, and E. D. Cubuk, “Scaling Deep Learning for Materials Discovery,” Nature 624, no. 7990 (2023): 80–85, https://doi.org/10.1038/s41586-023-06735-9.

[8] T. Jin, I. Park, T. Park, J. Park, and J. H. Shim, “Accelerated Crystal Structure Prediction of Multi-Elements Random Alloy Using Expandable Features,” Scientific Reports 11, no. 1 (2021): 5194, https://doi.org/10.1038/s41598-021-84544-8.

[9] C. Ren, Z. Z. Fang, M. Koopman, B. Butler, J. Paramore, and S. Middlemas, “Methods for Improving Ductility of Tungsten – A Review,” International Journal of Refractory Metals and Hard Materials 75 (2018), https://doi.org/10.1016/J.IJRMHM.2018.04.012.

[10] B. G. Butler, J. D. Paramore, J. P. Ligda, et al., “Mechanisms of Deformation and Ductility in Tungsten – A Review,” International Journal of Refractory Metals and Hard Materials 75 (2018): 248–261, https://doi.org/10.1016/j.ijrmhm.2018.04.021.

[11] A. Talignani, R. Seede, A. Whitt, et al., “A Review on Additive Manufacturing of Refractory Tungsten and Tungsten Alloys,” Additive Manufacturing 58 (2022): 103009, https://doi.org/10.1016/j.addma.2022.103009.

[12] T. Fu, K. Cui, Y. Zhang, et al., “Oxidation Protection of Tungsten Alloys for Nuclear Fusion Applications: A Comprehensive Review,” Journal of Alloys and Compounds 884 (2021): 161057, https://doi.org/10.1016/j.jallcom.2021.161057.

[13] A. Argon, Strengthening Mechanisms in Crystal Plasticity (Oxford University Press, 2007), https://doi.org/10.1093/acprof:oso/9780198516002.001.0001.

[14] S. I. Rao, C. Woodward, B. Akdim, O. N. Senkov, and D. Miracle, “Theory of Solid Solution Strengthening of BCC Chemically Complex Alloys,” Acta Materialia 209 (2021): 116758, https://doi.org/10.1016/j.actamat.2021.116758.

[15] X. F. Xie, Z. M. Xie, R. Liu, et al., “Hierarchical Microstructures Enabled Excellent Low-Temperature Strength-Ductility Synergy in Bulk Pure Tungsten,” Acta Materialia 228 (2022): 117765, https://doi.org/10.1016/j.actamat.2022.117765.

[16] D. D. Caillard, “A TEM in Situ Study of the Softening of Tungsten by Rhenium,” Acta Materialia 194 (2020): 249–256, https://doi.org/10.1016/j.actamat.2020.04.039.

[17] Y. H. Li, H. B. Zhou, L. Liang, et al., “Transition From Ductilizing to Hardening in Tungsten: The Dependence on Rhenium Distribution,” Acta Materialia 181 (2019): 110–123, https://doi.org/10.1016/j.actamat.2019.09.035.

[18] H. X. Xue, X. C. Cai, B. R. Sun, et al., “Bulk Nanocrystalline W-Ti Alloys With Exceptional Mechanical Properties and Thermal Stability,” Journal of Materials Science & Technology 114 (2022): 16–28, https://doi.org/10.1016/j.jmst.2021.11.015.

[19] W. D. Klopp, Review of Ductilizing of Group VIA Elements by Rhenium and Other Solutes (National Aeronautics and Space Administration, 1968), https://www.osti.gov/biblio/5785668.

[20] R. P. Goodman, I. A. T. Schaap, C. F. Tardin, et al., “Chemistry: Rapid Chiral Assembly of Rigid DNA Building Blocks for Molecular Nanofabrication,” Science (1979) 310, no. 5754 (2005): 1661–1665, https://doi.org/10.1126/science.1120367.

[21] A. Luo, D. L. Jacobson, and K. S. Shin, “Solution Softening Mechanism of Iridium and Rhenium in Tungsten at Room Temperature,” International Journal of Refractory Metals and Hard Materials 10, no. 2 (1991): 107–114, https://doi.org/10.1016/0263-4368(91)90028-m.

[22] Y. J. Hu, M. R. Fellinger, B. G. Bulter, et al., “Solute-Induced Solid-Solution Softening and Hardening in Bcc Tungsten,” Acta Materialia 141 (2017): 304, https://doi.org/10.1016/j.actamat.2017.09.019.

[23] J. Liu, Y. Huang, W. Liu, Y. Wang, Y. Zhang, and Y. Ma, “Effect of Tantalum Concentration on the Microstructure and Mechanical Properties of Novel W–Ta–Re Alloy,” Vacuum 207 (2023): 111627, https://doi.org/10.1016/j.vacuum.2022.111627.

[24] Y. Zhang, Y. Huang, W. Liu, Y. Ma, Y. Wang, and C. Liang, “Effect of Tantalum Addition Method on Microstructure and Mechanical Properties of the 90(W–20Ta)–Ni–Fe Alloy,” International Journal of Refractory Metals and Hard Materials 105 (2022): 105848, https://doi.org/10.1016/j.ijrmhm.2022.105848.

[25] Y. Wang, Y. Huang, W. Liu, et al., “In-situ Formation of Ni3Ta Nanophase for Strengthening Tungsten Alloy,” Journal of Alloys and Compounds 941 (2023): 168927, https://doi.org/10.1016/j.jallcom.2023.168927.

[26] Y. Zhang, Y. Huang, W. Liu, Y. Ma, J. Liu, and C. Liang, “Effect of Tungsten Tantalum Pre-Alloying on the Sintering Structure of 90(W–Ta)–Ni–Fe Alloy,” Journal of Alloys and Compounds 891 (2022): 161881, https://doi.org/10.1016/j.jallcom.2021.161881.

[27] X. Duan, Y. Huang, W. Liu, Q. Cai, W. Liu, and Y. Ma, “Effect of Ta on the Microstructure and Mechanical Properties of W–Ta Alloys Prepared by Arc Melting,” Materials Characterization 188 (2022): 111823, https://doi.org/10.1016/j.matchar.2022.111823.

[28] H. Cui, N. Liu, L. Luo, Y. Xu, J. Cheng, and Y. Wu, “A Prospect of Using Ternary W–5 wt%V–5 Wt%Ta Alloy Manufactured by Mechanical Alloying and Spark Plasma Sintering as Plasma-Facing Material,” Journal of Alloys and Compounds 903 (2022): 163899, https://doi.org/10.1016/j.jallcom.2022.163899.

[29] O. El Atwani, H. T. Vo, M. A. Tunes, et al., “A Quinary Wtacrvhf Nanocrystalline Refractory High-Entropy Alloy Withholding Extreme Irradiation Environments,” Nature Communications 14, no. 1 (2023): 2516, https://doi.org/10.1038/s41467-023-38000-y.

[30] J. M. Cowley, “Short-Range Order and Long-Range Order Parameters,” Physical Review 138, no. 5A (1965): A1384–A1389, https://doi.org/10.1103/physrev.138.a1384.

[31] J. M. Cowley, “An Approximate Theory of Order in Alloys,” Physical Review 77, no. 5 (1950): 669–675, https://doi.org/10.1103/physrev.77.669.

[32] D. Raabe, Phase Transformations in Materials (Wiley, 2001), https://onlinelibrary.wiley.com/doi/book/10.1002/352760264X.

[33] J. Brodie, J. Wang, J. P. Couzinié, et al., “Stability of the B2 Phase in Refractory High Entropy Alloys Containing Aluminum,” Acta Materialia 268 (2024): 119745, https://doi.org/10.1016/j.actamat.2024.119745.

[34] L. La Rosa, J. Brodie, M. Ghazisaeidi, and F. Maresca, “Atomistic Simulations Reveal Slip Selection in B2-type Intermetallic Alloys,” Acta Materialia 301 (2025): 121561, https://doi.org/10.1016/j.actamat.2025.121561.

[35] R. Feng, Y. Rao, C. Liu, et al., “Enhancing Fatigue Life by Ductile-Transformable Multicomponent B2 Precipitates in a High-Entropy Alloy,” Nature Communications 12, no. 1 (2021): 3588, https://doi.org/10.1038/s41467-021-23689-6.

[36] H. Liu, S. Tang, Y. Ma, W. Liu, and C. Liang, “Short-Range Ordering Governs Brittleness and Ductility in W-Ta Solid Solution: Insights From Pugh’s Shear-to-Bulk Modulus ratio,” Scripta Materialia 204 (2021): 114136, https://doi.org/10.1016/j.scriptamat.2021.114136.

[37] J. Ding, H. Liu, Y. Ma, W. Liu, and C. Liang, “Effect of Order-Disorder Transition and Lattice Distortion on the Mechanical Properties of W–X (X = V, Nb, Ta) Solid Solutions,” Journal of Alloys and Compounds 1008 (2024): 176833, https://doi.org/10.1016/j.jallcom.2024.176833.

[38] C. D. Woodgate and J. B. Staunton, “Short-Range Order and Compositional Phase Stability in Refractory High-Entropy Alloys via First-Principles Theory and Atomistic Modeling: Nbmota, NbMoTaW, and VNbMoTaW,” Physical Review Materials 7, no. 1 (2023): 013801, https://doi.org/10.1103/physrevmaterials.7.013801.

[39] X. Wu, “Chemical Short-Range Orders in High-/Medium-Entropy Alloys,” Journal of Materials Science and Technology (2023), https://doi.org/10.1016/j.jmst.2022.10.070.

[40] C. G. Schön, “On Short-Range Order Strengthening and Its Role in High-Entropy Alloys,” Scripta Materialia 196 (2021): 113754, https://doi.org/10.1016/j.scriptamat.2021.113754.

[41] Y. Sohail, C. Zhang, D. Xue, et al., “Machine-Learning Design of Ductile FeNiCoAlTa Alloys With High Strength,” Nature 643, no. 8070 (2025): 119–124, https://doi.org/10.1038/s41586-025-09160-2.

[42] F. Wang, M. Song, M. N. Elkot, et al., “Shearing Brittle Intermetallics Enhances Cryogenic Strength and Ductility of Steels,” Science (1979) 384, no. 6699 (2024): 1017–1022, https://doi.org/10.1126/science.ado2919.

[43] J. Huang, W. Li, J. He, et al., “Dual Heterogeneous Structure Facilitating an Excellent Strength-Ductility Combination in an Additively Manufactured Multi-Principal-Element Alloy,” Materials Research Letters 10, no. 9 (2022): 575–584, https://doi.org/10.1080/21663831.2022.2067790.

[44] W. Zhao, Z. Chen, Y. Shang, et al., “A Physics-Informed Machine Learning Framework for Accelerated Discovery of Single-Phase B2 Multi-Principal Element Intermetallics,” npj Computational Materials 11, no. 1 (2025): 278, https://doi.org/10.1038/s41524-025-01775-3.

[45] Y. Zhang, C. Wen, C. Wang, et al., “Phase Prediction in High Entropy Alloys With a Rational Selection of Materials Descriptors and Machine Learning Models,” Acta Materialia 185 (2020): 528–539, https://doi.org/10.1016/j.actamat.2019.11.067.

[46] C. Wen, C. Wang, Y. Zhang, et al., “Modeling Solid Solution Strengthening in High Entropy Alloys Using Machine Learning,” Acta Materialia 212 (2021): 116917, https://doi.org/10.1016/j.actamat.2021.116917.

[47] X. Huang, L. Zheng, H. Xu, and H. Fu, “Predicting and Understanding the Ductility of BCC High Entropy Alloys via Knowledge-Integrated Machine Learning,” Materials & Design 239 (2024): 112797, https://doi.org/10.1016/j.matdes.2024.112797.

[48] G. Kresse and J. Hafner, “Ab Initio Molecular Dynamics for Liquid Metals,” Physical Review B: Condensed Matter 47, no. 1 (1993): 558–561, https://doi.org/10.1103/physrevb.47.558.

[49] G. Kresse and D. Joubert, “From Ultrasoft Pseudopotentials to the Projector Augmented-Wave Method,” Physical Review B: Condensed Matter 59, no. 3 (1999): 1758–1775, https://doi.org/10.1103/physrevb.59.1758.

[50] J. P. Perdew, J. A. Chevary, S. H. Vosko, et al., “Atoms, Molecules, Solids, and Surfaces: Applications of the Generalized Gradient Approximation for Exchange and Correlation,” Physical Review B: Condensed Matter 46, no. 11 (1992): 6671–6687, https://doi.org/10.1103/physrevb.46.6671.

[51] H. J. Monkhorst and J. D. Pack, “Special Points for Brillouin-Zone Integrations,” Physical Review B: Condensed Matter 13, no. 12 (1976): 5188–5192, https://doi.org/10.1103/physrevb.13.5188.

[52] R. Yu, J. Zhu, and H. Q. Ye, “Calculations of Single-Crystal Elastic Constants Made Simple,” Computer Physics Communications 181, no. 3 (2010): 671–675, https://doi.org/10.1016/j.cpc.2009.11.017.

[53] C. Liang, R. C. Longo, F. Kong, et al., “Ab Initio Study on Surface Segregation and Anisotropy of Ni-Rich LiNi 1–2 Y Co Y Mn Y O 2 (NCM) (Y ≤ 0.1) Cathodes,” ACS Applied Materials & Interfaces 10, no. 7 (2018): 6673–6680, https://doi.org/10.1021/acsami.7b17424.

[54] R. Gaillac, P. Pullumbi, and F. X. Coudert, “ELATE: An Open-Source Online Application for Analysis and Visualization of Elastic Tensors,” Journal of Physics: Condensed Matter 28, no. 27 (2016): 275201, https://doi.org/10.1088/0953-8984/28/27/275201.

[55] O. H. Nielsen and R. M. Martin, “Quantum-Mechanical Theory of Stress and Force,” Physical Review B: Condensed Matter 32, no. 6 (1985): 3780–3791, https://doi.org/10.1103/physrevb.32.3780.

[56] O. H. Nielsen and R. M. Martin, “Erratum: Quantum-Mechanical Theory of Stress and Force,” Physical Review B: Condensed Matter 35, no. 17 (1987): 9308, https://doi.org/10.1103/physrevb.35.9308.

[57] F. Wang, H. Liu, S. Tang, Y. Ma, W. Liu, and C. Liang, “Effects of Order-Disorder Transition on Phase Relationship, Elastic Strength, and Mechanical Anisotropy of Al–Li Alloys,” Materialia 24 (2022): 101483, https://doi.org/10.1016/j.mtla.2022.101483.

[58] H. Zhou, F. Wang, S. Tang, Y. Ma, W. Liu, and C. Liang, “Unraveling the Leverage Effect in Phases Stability and Mechanical Properties of Al–Zn–Mg–Cu Alloy,” Journal of Alloys and Compounds 982 (2024): 173763, https://doi.org/10.1016/j.jallcom.2024.173763.

[59] H. Luo, W. Liu, and C. Liang, “Decoupling Atomic Diffusion Degeneracy in L1 2 Intermetallics,” Materials Research Letters 12, no. 9 (2024): 644–652, https://doi.org/10.1080/21663831.2024.2367645.

[60] S. Guo, “Phase Selection Rules for Cast High Entropy Alloys: An Overview,” Materials Science and Technology 31, no. 10 (2015): 1223–1230, https://doi.org/10.1179/1743284715y.0000000018.

[61] V. I. I. Karl Pearson, “Note on Regression and Inheritance in the Case of Two Parents,” Proceedings of the Royal Society of London 58, no. 347–352 (1895): 240–242, https://doi.org/10.1098/rspl.1895.0041.

[62] L. Peterson, “K-Nearest Neighbor,” Scholarpedia 4, no. 2 (2009): 1883, https://doi.org/10.4249/scholarpedia.1883.

[63] A. J. Smola, B. Schölkopf, and S. Schölkopf, A Tutorial on Support Vector Regression (Kluwer Academic Publishers, 2004), https://link.springer.com/article/10.1023/B:STCO.0000035301.49549.88.

[64] X. Su, X. Yan, and C. L. Tsai, “Linear Regression,” Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 3 (2012): 275–294, https://doi.org/10.1002/wics.1198.

[65] G. C. McDonald, “Ridge Regression,” WIREs Computational Statistics 1 (2009): 93–100, https://doi.org/10.1002/wics.14.

[66] B. de Ville, “Decision Trees, Wiley Interdiscip,” Reviews: Computational Statistics 5, no. 6 (2013): 448–455, https://doi.org/10.1002/wics.1278.

[67] S. J. Rigatti, “Random Forest,” Journal of Insurance Medicine 47 (2017): 31–39, https://doi.org/10.17849/insm-47-01-31-39.1.

[68] D. P. Solomatine and D. L. Shrestha, “AdaBoost.RT: A Boosting Algorithm for Regression Problems,” in IEEE International Conference on Neural Networks - Conference Proceedings, Vol. 2, (2004), 1163–1168.

[69] C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, “A Comparative Analysis of Gradient Boosting Algorithms,” Artificial Intelligence Review 54, no. 3 (2021): 1937–1967, https://doi.org/10.1007/s10462-020-09896-5.

[70] Y. H. Zhang, E. Ma, J. Sun, and W. Z. Han, “Unveiling the Intrinsic Rhenium Effect in Tungsten,” Acta Materialia 264 (2024): 119586, https://doi.org/10.1016/j.actamat.2023.119586.

[71] H. Chen, L. Ye, Y. Han, C. Chen, and J. Fan, “Additive Manufacturing of W–Fe Composites Using Laser Metal Deposition: Microstructure, Phase Transformation, and Mechanical Properties,” Materials Science and Engineering: A 811 (2021): 141036, https://doi.org/10.1016/j.msea.2021.141036.

[72] X. Lan, H. Zhang, Z. B. Li, and G. H. Zhang, “Preparation of Fine-Grained Mo–W Solid Solution Alloys With Excellent Performances,” Materials Characterization 191 (2022): 112140, https://doi.org/10.1016/j.matchar.2022.112140.

[73] A. Fantin, A. M. Manzoni, H. Springer, R. D. Kamachali, and R. Maaß, “Local Lattice Distortions and Chemical Short-Range Order in MoNbTaW,” Materials Research Letters 12, no. 5 (2024): 346–354, https://doi.org/10.1080/21663831.2024.2326014.

[74] E. C. O’Quinn, K. E. Sickafus, R. C. Ewing, et al., “Predicting Short-Range Order and Correlated Phenomena in Disordered Crystalline Materials,” Science Advances 6, no. 35 (2020): eabc2758, https://doi.org/10.1126/sciadv.abc2758.

[75] R. Zhang, S. Zhao, J. Ding, et al., “Short-Range Order and Its Impact on the CrCoNi Medium-Entropy Alloy,” Nature 581, no. 7808 (2020): 283–287, https://doi.org/10.1038/s41586-020-2275-z.

[76] X. Chen, Q. Wang, Z. Cheng, et al., “Direct Observation of Chemical Short-Range Order in a Medium-Entropy Alloy,” Nature 592, no. 7856 (2021): 712–716, https://doi.org/10.1038/s41586-021-03428-z.

[77] A. Fantin, G. O. Lepore, A. M. Manzoni, et al., “Short-Range Chemical Order and Local Lattice Distortion in a Compositionally Complex Alloy,” Acta Materialia 193 (2020): 329–337, https://doi.org/10.1016/j.actamat.2020.04.034.

[78] J. Wang, A. Barooni, and M. Ghazisaeidi, “Stability of the B2 Phase Among Refractory Metals,” Acta Materialia 279 (2024): 120323, https://doi.org/10.1016/j.actamat.2024.120323.

[79] W. Liu, Y. Huang, Y. Wang, W. Liu, and Y. Ma, “W–Ta–Nb Solid Solutions Prepared by Vacuum Arc Melting: Microstructure Evolution and Mechanical Properties,” Vacuum 212 (2023): 111959, https://doi.org/10.1016/j.vacuum.2023.111959.

[80] J. Li, Y. Wu, L. Xue, and Z. Wei, “Laser Powder Bed Fusion In-Situ Alloying of Refractory WTa Alloy and Its Microstructure and Mechanical Properties,” Additive Manufacturing 67 (2023): 103493, https://doi.org/10.1016/j.addma.2023.103493.

[81] Z. Xiong, W. Ma, Z. Deng, and D. Dong, “Strong yet Ductile Self-Passivating W–10Cr Alloy by Laser Powder Bed Fusion,” International Journal of Refractory Metals and Hard Materials 116 (2023): 106365, https://doi.org/10.1016/j.ijrmhm.2023.106365.

[82] L. Huang, L. Jiang, T. D. Topping, et al., “In Situ Oxide Dispersion Strengthened Tungsten Alloys With High Compressive Strength and High Strain-to-Failure,” Acta Materialia 122 (2017): 19–31, https://doi.org/10.1016/j.actamat.2016.09.034.

[83] C. Wang, X. Ling, Y. Cui, F. Bai, and C. Liu, “High Compression Strength Pure Tungsten Fabricated by Plasma Arc Additive Manufacturing,” International Journal of Refractory Metals and Hard Materials 116 (2023): 106368, https://doi.org/10.1016/j.ijrmhm.2023.106368.

[84] Y. Yuan, Y. Han, K. Xu, et al., “Revealing the Solidification Microstructure Evolution and Strengthening Mechanisms of Additive-Manufactured W–FeCrCoNi Alloy: Experiment and Simulation,” Journal of Materials Science & Technology 204 (2025): 302–313, https://doi.org/10.1016/j.jmst.2024.03.075.

[85] Z. Que, X. Li, L. Zhang, et al., “Preparation, Microstructure, and Mechanical Properties of HfN in-situ Doped W–Re Alloys by Nitridation-Denitridation Method,” International Journal of Refractory Metals and Hard Materials 128 (2025): 107044, https://doi.org/10.1016/j.ijrmhm.2025.107044.

[86] J. Sun, L. Zhang, Y. Huang, et al., “Strain Hardening Rate and Strain Rate Sensitivity Behavior of bcc/fcc-Dual-Phase Tungsten Heavy Alloy,” International Journal of Refractory Metals and Hard Materials 116 (2023): 106363, https://doi.org/10.1016/j.ijrmhm.2023.106363.

[87] A. Panchal, K. V. Reddy, P. A. Azeem, T. K. Nandy, and A. K. Singh, “On the Flow and Work Hardening Behavior of Tungsten Heavy Alloy 92W–5.5Ni–2.5Fe,” International Journal of Refractory Metals and Hard Materials 88 (2020): 105203, https://doi.org/10.1016/j.ijrmhm.2020.105203.

[88] S. Poluru, A. Jaimin, N. Kotkunde, S. K. Singh, A. Panchal, and P. Gnanasambandam, “Flow Stress and Work Hardening Behavior of 92W–5Co–3Ni Alloy at Different Strain Rates and Elevated Temperatures,” Journal of Materials Engineering and Performance 34, no. 12 (2025): 11566–11580, https://doi.org/10.1007/s11665-024-09965-8.

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