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Machine learning assisted optimization design and microstructure-property evolution mechanisms of 600 °C high temperature burn resistant titanium alloys
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DOI：10.1016/j.matdes.2025.115381.png)
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Abstract
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• A machine learning and genetic algorithm integrated approach was employed to design a novel titanium alloy with superior oxidation resistance, demonstrating a 42% reduction in the parabolic oxidation rate constant compared to the conventional burn resistant Ti-alloys. • The designed alloy exhibits outstanding tensile strength at both room temperature and 600 °C, attributed to a dual-phase synergistic strengthening mechanism. • The alloy shows significantly enhanced burn-resistant performance compared to conventional titanium alloys, with superior anti-ignition capability at temperatures above 600 °C. • A universal alloy design framework was established, which is applicable to other titanium-based systems and validated by close agreement between simulation and experimental results.
Keywords:
600 °C high temperature burn resistant titanium alloy
Machine learning
Material optimization design
Microstructure and properties
Strengthening mechanism
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