A titanium alloys design method based on high-throughput experiments and machine learning
Chengpeng Zhu, Chao Li, Di Wu, Wan Ye, Shuangxi Shi, Hui Ming, Xiaoyong Zhang, Kechao Zhou
Abstract
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 α 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 °C and aging at 550 °C for 6 h. During deformation, Primary globular primary α phases were elongated, and secondary acicular α phases resisted the dislocation slipping, which provides good plasticity and strength for the alloys, respectively.