High-throughput studies and machine learning for design of β titanium alloys with optimum properties

用于设计具有最佳性能β钛合金的高通量研究和机器学习

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.

基于实验数据构建了钛基合金杨氏模量、硬度和热加工能力的机器学习模型。首先,从扩散偶的成分变化和纳米压痕数据中重新高通量评估互扩散和力学性能数据。然后,通过所构建的模型在交互循环中筛选出一种具有单一BCC相的Ti-(22±0.5)%Nb-(30±0.5)%Zr-(4±0.5)%Cr (摩尔分数)(TNZC)合金。并且,该合金具有相对较低的杨氏模量((58±4) GPa)、高纳米硬度((3.4±0.2) GPa)、高显微硬度(HV (520±5))、高压缩屈服强度((1220±18) MPa)、大于30%的大塑性应变以及优异的干磨损和湿磨损性能。结果表明,机器学习与高通量分析方法相结合可以作为一个强大的工具来加速设计具有优异性能的多元钛合金,同时也表明TNZC合金是生物医学应用中一种具有吸引力的候选材料。