A titanium alloys design method based on high-throughput experiments and machine learning

In this work, the effect of Mo and Cr on microstructure and mechanical properties of newly titanium alloys (Ti-3Al-2Nb-1.2V-1Zr-1Sn-xCr-yMo) was investigated, and a composition-microstructure-properties relationship was established by diffusion multiple. The microstructure characterization (volume fraction, size of a phases) for alloys with different molybdenum equivalent (Mo[q]) was predicted by machine learning (BP neural network), and the result shows a good agreement between the predicted results and experimental values. Combining diffusion multiple and BP neural network, a Ti alloy (Ti-3Al-2Nb-1.2V-1Zr-1Sn-4Cr-4Mo) with outstanding mechanical properties was successfully designed. The mechanical test result shows that excellent balance of strength (YS-1200 MPa) and plasticity (El-12%) can be achieved after the solution treatment at 750 degrees C and aging at 550 degrees C for 6 h. During deformation, Primary globular primary a phases were elongated, and secondary acicular a phases resisted the dislocation slipping, which provides good plasticity and strength for the alloys, respectively. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

By investigating the effects of Mo and Cr on the microstructure and mechanical properties of titanium alloys, a Ti alloy with outstanding mechanical properties was successfully designed. Machine learning was utilized to predict the microstructure characteristics of different alloys, achieving optimization of mechanical performance.