Machine-learning assisted compositional optimization of 2xxx series aluminum alloys towards tensile strength

High-strength 2xxx series aluminum alloys (Al-Cu system) have been favored by the aerospace and railway transportation industries. Traditionally, developing new materials with targeted properties is guided by extensive experiments and expert experience, causing the development process to be dismayingly slow and expensive. Here, a Kriging model-based efficient global optimization(EGO) lgorithm is applied to search for new 2xxx series aluminum alloys with high tensile strength in a huge search space. After four iterations, the alloy’s ultimate tensile strength increased by 60 MPa, which is higher than that of the best alloy in the initial data set. This study demonstrates the feasibility of using machine-learning to search for 2xxx alloys with good mechanical performance.

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