Volume 47 Issue 3
Jun.  2026
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NING Zhen, ZHU Yanlin, WU Die, LIAO Zhehan, LIU Yihang. High-throughput CALPHAD-guided multi-objective design of TC4 titanium alloy[J]. IRON STEEL VANADIUM TITANIUM, 2026, 47(3): 54-62. doi: 10.7513/j.issn.1004-7638.2026.03.006
Citation: NING Zhen, ZHU Yanlin, WU Die, LIAO Zhehan, LIU Yihang. High-throughput CALPHAD-guided multi-objective design of TC4 titanium alloy[J]. IRON STEEL VANADIUM TITANIUM, 2026, 47(3): 54-62. doi: 10.7513/j.issn.1004-7638.2026.03.006

High-throughput CALPHAD-guided multi-objective design of TC4 titanium alloy

doi: 10.7513/j.issn.1004-7638.2026.03.006
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  • Received Date: 2025-10-13
  • Accepted Date: 2026-02-09
  • Rev Recd Date: 2025-12-30
  • Publish Date: 2026-06-29
  • To address the pressing need for TC4 (Ti-6Al-4V) titanium alloys that combine high strength and toughness for aerospace, deep-sea, and biomedical applications, we established a high-throughput CALPHAD workflow based on Thermo-Calc/TC-Python. A total of 6,241 candidate compositions were rapidly screened for phase equilibria, solid solubility, and mechanical properties under the constraints of yield strength > 950  MPa, Laves-phase onset temperature <850  K, and Ti3Al-phase onset temperature < 800  K. The results show that Al content is the primary factor in increasing the α-phase volume fraction and solid-solution strengthening; V and Fe, as β stabilizers, markedly modulate the α/β phase fraction ratio. Although O strongly contributes to solid-solution strengthening, it indirectly reduces α-phase fraction by enhancing the solubility of V in the β phase. A multi-objective analysis identifies an optimal compositional window of Al 5.8–6.3 wt. %, V 3.6–4.2 wt. %, Fe ≤ 0.18 wt. %, and O 0.09–0.15 wt. %, which simultaneously meets the strength requirement and suppresses the formation of brittle phases. Compared with traditional empirical design, this numerical strategy narrows the candidate compositional space to <2 %, significantly lowering experimental trial-and-error costs and providing a solid data foundation for subsequent multi-objective optimization of targeting strength, toughness, and workability.
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