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 functionally 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, dimensional 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.
All Science Journal Classification (ASJC) codes
- General Materials Science