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Abstract Additive manufacturing (AM) is revolutionizing the aerospace, transportation, energy, and biomedical fields due to its capacity for rapid production of geometrically complex components. The advancement of alloys for AM is critical to further boost those applications. The first-generation alloys for AM largely rely on conventional commercial alloys that were originally developed under the assumption of near-equilibrium solidification. However, some of these materials are incompatible with the non-equilibrium metallurgical behavior of AM, which may face issues like high crack susceptibility. Thus, the second-generation alloy design for AM builds upon conventional alloy systems by inoculation treatments with alloying elements (e.g., zirconium) or ceramic reinforcements (e.g., titanium carbide) to achieve better material printability and properties. Meanwhile, the limitations of commercial materials underscore the need for developing novel alloys specifically tailored for AM. The third-generation empirical approach for material design adopts a knowledge-driven strategy by leveraging established metallurgical principles and empirical correlations to guide targeted composition optimization. Such trial-and-error strategies for discovering new materials face substantial bottlenecks like long development cycles and high costs. Hence, it has propelled research toward the fourth-generation paradigm—data-driven artificial intelligence (AI) assisted materials design for AM, which is an effective and innovative material solution guided by AM-specific metallurgical features. Advances in AI and robotics will shift the future paradigm of AM-specific alloy design toward an autonomous AI-Lab, which leverages an intelligent AM agent and automated high-throughput AM printing and testing for materials discovery.
Zhao et al. (Fri,) studied this question.