Control of grain structure, phases, and defects in additive manufacturing of high-performance metallic components

The properties and serviceability of 3D-printed metal parts depend on a variety of attributes. These include the chemical composition, phases, morphology, spatial distributions of grain size and shape, crystallographic texture, and various defects. Control of these attributes remains an exciting opportunity and a major challenge because of the many process variants and parameters that need to be optimized. The desired attributes of industrially relevant common additive manufacturing alloys such as steels, nickel, titanium, aluminum, and copper alloys, and func-tionally graded materials vary widely and require alloy-specific strategies for their control. The recent reviews address the valuable processing-microstructure-property relations but do not focus on their control strategies. Here we seek to unify the disjointed literature and critically review recent advances in controlling grain structure, phases, and defects. The emerging use of digital tools such as mechanistic models and data-driven techniques such as machine learning, dimen-sional analysis, and statistical methods in controlling part attributes is emphasized. Finally, we identify opportunities for high-impact research in metal printing and present an outlook for the future based on existing evidence.

Automated summary

The properties and serviceability of 3D-printed metal parts depend on various attributes such as chemical composition, phases, morphology, grain size and shape, texture, and defects. Controlling these attributes is challenging due to numerous process variants and parameters. This article critically reviews recent advances in controlling grain structure, phases, and defects, emphasizing the use of digital tools and data-driven techniques. Opportunities for high-impact research in metal printing are identified, and an outlook for the future is provided.