Metallurgy, mechanistic models and machine learning in metal printing

T. DebRoy, T. Mukherjee, H. L. Wei, J. W. Elmer, J. O. Milewski

Research output: Contribution to journalReview articlepeer-review

209 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)48-68
Number of pages21
JournalNature Reviews Materials
Issue number1
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Energy (miscellaneous)
  • Surfaces, Coatings and Films
  • Materials Chemistry


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