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

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