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Metallurgy, mechanistic models and machine learning in metal printing


Additive manufacturing enables the printing of metallic parts, such as customized implants for patients, durable single-crystal parts for use in harsh environments, and the printing of parts with site-specific chemical compositions and properties from 3D designs. However, the selection of alloys, printing processes and process variables results in an exceptional diversity of microstructures, properties and defects that affect the serviceability of the printed parts. Control of these attributes using the rich knowledge base of metallurgy remains a challenge because of the complexity of the printing process. Transforming 3D designs created in the virtual world into high-quality products in the physical world needs a new methodology not commonly used in traditional manufacturing. Rapidly developing powerful digital tools such as mechanistic models and machine learning, when combined with the knowledge base of metallurgy, have the potential to shape the future of metal printing. Starting from product design to process planning and process monitoring and control, these tools can help improve microstructure and properties, mitigate defects, automate part inspection and accelerate part qualification. Here, we examine advances in metal printing focusing on metallurgy, as well as the use of mechanistic models and machine learning and the role they play in the expansion of the additive manufacturing of metals.

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Fig. 1: Schematics of three metal printing processes.
Fig. 2: Contributions of metallurgy, mechanistic models and machine learning in the various steps of metal printing.
Fig. 3: Properties of printed metallic components.
Fig. 4: Results from various types of mechanistic models for metal printing.
Fig. 5: Applications of machine learning in metal printing.


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All authors contributed in researching previous works, discussions of the contents of the manuscript, writing portions of the text and reviewing and editing the manuscript.

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DebRoy, T., Mukherjee, T., Wei, H.L. et al. Metallurgy, mechanistic models and machine learning in metal printing. Nat Rev Mater 6, 48–68 (2021).

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