Machine Learning Assisted Prediction of Microstructures and Young's Modulus of Biomedical Multi-Component β-Ti Alloys

Recently, the development of beta-titanium (Ti) alloys with a low Young's modulus as human implants has been the trend of research in biomedical materials. However, designing beta-titanium alloys by conventional experimental methods is too costly and inefficient. Therefore, it is necessary to propose a method that can efficiently and reliably predict the microstructures and the mechanical properties of biomedical titanium alloys. In this study, a machine learning prediction method is proposed to accelerate the design of biomedical multi-component beta-Ti alloys with low moduli. Prediction models of microstructures and Young's moduli were built at first. The performances of the models were improved by introducing new experimental data. With the help of the models, a Ti-13Nb-12Ta-10Zr-4Sn (wt.%) alloy with a single beta-phase microstructure and Young's modulus of 69.91 GPa is successfully developed. This approach could also be used to design other advanced materials.

Automated summary

A machine learning prediction method is proposed to efficiently and reliably predict the microstructures and mechanical properties of biomedical titanium alloys. Using this method, a low modulus beta-Ti alloy was successfully designed.