Additive manufacturing of alloys with programmable microstructure and properties

Additive manufacturing of alloys with programmable microstructure and properties

Nature Communications

10.1038/s41467-023-42326-y

http://dx.doi.org/10.1038/s41467-023-42326-y

Shubo Gao, Zhi Li, Steven Van Petegem, Junyu Ge, Sneha Goel, Joseph Vimal Vas, Vladimir Luzin, Zhiheng Hu, Hang Li Seet, Dario Ferreira Sanchez, Helena Van Swygenhoven, Huajian Gao, Matteo Seita

additive manufacturing, Microstructure

30 October 2023

Gao, S., Li, Z., Van Petegem, S., Ge, J., Goel, S., Vas, J. V., Luzin, V., Hu, Z., Seet, H. L., Sanchez, D. F., Van Swygenhoven, H., Gao, H., & Seita, M. (2023). Additive manufacturing of alloys with programmable microstructure and properties. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-42326-y

In metallurgy, mechanical deformation is essential to engineer the microstructure of metals and to tailor their mechanical properties. However, this practice is inapplicable to near-net-shape metal parts produced by additive manufacturing (AM), since it would irremediably compromise their carefully designed geometries. In this work, we show how to circumvent this limitation by controlling the dislocation density and thermal stability of a steel alloy produced by laser powder bed fusion (LPBF) technology. We show that by manipulating the alloy’s solidification structure, we can ‘program’ recrystallization upon heat treatment without using mechanical deformation. When employed site-specifically, our strategy enables designing and creating complex microstructure architectures that combine recrystallized and non-recrystallized regions with different microstructural features and properties. We show how this heterogeneity may be conducive to materials with superior performance compared to those with monolithic microstructure. Our work inspires the design of high-performance metal parts with artificially engineered microstructures by AM.

Attribution 4.0 International (CC BY 4.0)

This research / project is supported by the Agency for Science, Technology and Research - Advanced Models for Additive Manufacturing (AM2)
Grant Reference no. : M22L2b0111

This research / project is supported by the National Research Foundation - NRF Fellowship Programme
Grant Reference no. : NRF-NRFF2018-05

This research is supported by core funding from: Science and Engineering Research Council
Grant Reference no. : 142 68 00088

https://oar.a-star.edu.sg/communities-collections/articles/20173

2041-1723

Institute of High Performance Computing 

http://dx.doi.org/10.1038/s41467-023-42326-y