High-throughput studies and machine learning for design of β titanium alloys with optimum properties
(1. Guangdong Provincial Key Laboratory of Metal Toughening Technology and Application, National Engineering Research Center of Powder Metallurgy of Titanium & Rare Metals, Institute of New Materials, Guangdong Academy of Sciences, Guangzhou 510650, China;
2. Institute of Advanced Wear & Corrosion Resistant and Functional Materials, Jinan University, Guangzhou 510632, China;
3. Institute of High Performance Computing, Agency for Science, Technology and Research, 138632, Singapore;
4. State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, China)
2. Institute of Advanced Wear & Corrosion Resistant and Functional Materials, Jinan University, Guangzhou 510632, China;
3. Institute of High Performance Computing, Agency for Science, Technology and Research, 138632, Singapore;
4. State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, China)
Abstract: Based on experimental data, machine learning (ML) models for Young’s modulus, hardness, and hot-working ability of Ti-based alloys were constructed. In the models, the interdiffusion and mechanical property data were high- throughput re-evaluated from composition variations and nanoindentation data of diffusion couples. Then, the Ti-(22±0.5)at.%Nb-(30±0.5)at.%Zr-(4±0.5)at.%Cr (TNZC) alloy with a single body-centered cubic (BCC) phase was screened in an interactive loop. The experimental results exhibited a relatively low Young’s modulus of (58±4) GPa, high nanohardness of (3.4±0.2) GPa, high microhardness of HV (520±5), high compressive yield strength of (1220±18) MPa, large plastic strain greater than 30%, and superior dry- and wet-wear resistance. This work demonstrates that ML combined with high-throughput analytic approaches can offer a powerful tool to accelerate the design of multicomponent Ti alloys with desired properties. Moreover, it is indicated that TNZC alloy is an attractive candidate for biomedical applications.
Key words: high-throughput; machine learning; Ti-based alloys; diffusion couple; mechanical properties; wear behavior