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.
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