Magnesium (Mg), being the lightest structural metal, holds immense
potential for widespread applications in various fields. The development
of high-performance and cost-effective Mg alloys is crucial to further
advancing their commercial utilization. With the rapid advancement of
machine learning (ML) technology in recent years, the ``data-driven''
approach for alloy design has provided new perspectives and
opportunities for enhancing the performance of Mg alloys. This paper
introduces a novel regression-based Bayesian optimization active
learning model (RBOALM) for the development of high-performance
Mg-Mn-based wrought alloys. RBOALM employs active learning to
automatically explore optimal alloy compositions and process parameters
within predefined ranges, facilitating the discovery of superior alloy
combinations. This model further integrates pre-established regression
models as surrogate functions in Bayesian optimization, significantly
enhancing the precision of the design process. Leveraging RBOALM,
several new high-performance alloys have been successfully designed and
prepared. Notably, after mechanical property testing of the designed
alloys, the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional
mechanical properties, including an ultimate tensile strength of 406
MPa, a yield strength of 287 MPa, and a 23% fracture elongation.
Furthermore, the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile
strength of 211 MPa, coupled with a remarkable 41% fracture elongation.
