Advanced Alloy Design and Additive Manufacturing: Recent Developments and Future Perspectives

The convergence of advanced computational methods and additive manufacturing (AM) technologies has revolutionized the field of materials science and metallurgy, enabling unprecedented control over alloy composition, microstructure, and properties. This comprehensive review examines the state-of-the-art developments in advanced alloy design methodologies integrated with additive manufacturing processes, with particular emphasis on machine learning-driven materials discovery, microstructural control strategies, and high-entropy alloys (HEAs). We analyze recent breakthroughs in high-throughput preparation techniques, generative machine learning models for eutectic compositionally complex alloys (ECCAs), and sustainable manufacturing approaches. The review synthesizes findings from over ۲۰۰ recent publications, highlighting how conditional variational autoencoders and artificial neural networks are accelerating materials discovery across quaternary to senary alloy systems. Key challenges including hot cracking susceptibility, microstructural anisotropy, and process-structure-property relationships are discussed alongside emerging solutions such as in-situ alloying, grain refinement strategies, and real-time process monitoring. Future perspectives encompass the integration of materials genome initiatives with additive manufacturing databases, the development of autonomous materials discovery platforms, and the advancement of sustainable metallurgical practices. This review provides a roadmap for researchers and practitioners seeking to harness the synergistic potential of computational materials science and advanced manufacturing technologies.

Keywords:

Additive manufacturing , alloy design , machine learning , high-entropy alloys , microstructure control , materials informatics , laser powder bed fusion