4.8 Article

Role of heat treatment and build orientation in the microstructure sensitive deformation characteristics of IN718 produced via SLM additive manufacturing

Journal

ADDITIVE MANUFACTURING
Volume 22, Issue -, Pages 479-496

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.addma.2018.04.032

Keywords

Microstructure; Selective laser melting; Digital image correlation; High energy X-ray diffraction; Synchrotron based microtomography

Funding

  1. DARPA [N66001-14-1-4041]
  2. US Department of Energy, Office of Science, Office of Basic Energy Sciences [DE-AC02-06CH11357]

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The benefits of additive manufacturing have been well documented, but prior to these materials being used in critical applications, the deformation mechanisms must be properly characterized. In this work, the role of heat treatment and build orientation of selective laser melting IN718 is investigated through detailed characterization. The microstructure of this material is probed through a combination of electron microscopy to identify the precipitate structure, electron backscatter diffraction to quantify the grain-level features, and synchrotron-based X-ray microcomputed tomography to detect porosity. A high degree of porosity is observed spatially near the free surface of the part, where the contour during the build process meets the interior hatch. Further, microstructure based deformation mechanisms are explored through digital image correlation relative to the grain features after monotonic and cyclic loading and in situ high-energy X-ray diffraction to identify the lattice strain evolution in these materials. Demarcations between the behaviors of the as-built versus post-processed materials are discussed; specifically, in terms of anisotropy with respect to build direction and values of the strength properties, based on the grain morphology, coherent twin formation, and precipitate structure. Lastly, the presence of dislocation sub-structures within the grains is observed to homogenize deformation within the asbuilt sample, while strain partitioning is observed during loading of the post-processed sample.

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