Graph neural networks enable design of high-performance aluminum alloys for laser powder bed fusion

The trade-off between printability and mechanical properties is a major challenge in additive manufacturing (AM) of high-performance aluminum (Al) alloys. Here we propose a new machine learning-driven alloy design framework integrating graph attention networks with a physical-metallurgy knowledge graph, which synergistically incorporates complex factors including the mechanistic information related to processing parameters and thermodynamic information related to alloy compositions. By further incorporating a genetic algorithm, a novel Al alloy KG-AMAA has been developed as the optimization of relative density, as confirmed by the robustness of printing parameters in fabricating crack-free samples. The new alloy KG-AMAA exhibits superior strength and ductility compared with most existing AM-ed Al alloys. Microstructural characterization revealed that the coexistence of multiple precipitate systems in the designed alloy, such as the primary precipitate Al3Sc, and the eutectic phases AlSc2Si2 and Si, which collectively contributed to the enhanced printability, together with Al2Cu strengthening phases, established exceptional strength-ductility synergy. This novel machine learning paradigm, integrating physical metallurgy knowledge, offers innovative approaches and great potential for alloy design for additive manufacturing.