As power-generating technologies attempt to increase design temperatures and boost economic efficiency, advanced manufacturing technologies have the potential to modularize component production and decrease capital costs. In this work, a multi-objective, parametric optimization of grade 316H stainless steel (SS-316H) produced via laser powder bed fusion was performed to assess the printability of the higher-carbon system. For a geometrically complex part comprising 11 unique regions, a hierarchical optimization framework using in situ process monitoring and high-throughput X-ray computed tomography (XCT) was applied to assess spatially resolved porosity distributions for 54 unique sets of processing parameters. Within the processing window that produced minimized porosity, destructive characterization of low-porosity samples revealed a grain morphological transformation from refined chevron-shaped grains to epitaxial columnar grains. This transition in grain texture and size resulted in significant mechanical anisotropy, warranting a final parametric filtering threshold linked to the grain structure within the minimized porosity region observed via XCT analysis. The final processing window that minimized low porosity and anisotropy used input parameters in a range of 52–71 J/mm3 equivalent volumetric energy density. The multi-objective parameter optimization process outlined here is critical for designing materials with simultaneously low defect density and minimized microstructural anisotropy.

An additively manufactured 316L stainless steel pressure limiting structure (PLS) has been designed as an end cap for an irradiation capsule to be inserted into the high flux isotope reactor (HFIR) at Oak Ridge National Laboratory. The PLS was printed using a laser powder bed fusion printer and includes a thin cylindrical rupture wall, a shield, and internal supports to facilitate printing and ensure mechanical integrity. Its overall dimensions are 9 mm tall and 10 mm in diameter. The pressure at which a 160-mu m thick rupture wall fails is 5270 +/- 120 psi (36.34 +/- 0.83 MPa) for a monolithically printed cap and 4,764 +/- 36 psi (32.85 +/- 0.25 MPa) for a welded cap. Because these pressures are well below that at which the capsule housing begins to plastically deform (similar to 6250 to 6500 psi or similar to 43.1 to 44.8 MPa), the PLS maintains the capsule at a safe operating pressure while it is in the reactor. The printing of all capsule components was fast (<1 h/capsule), reproducible, and customizable. Applications for this type of compact PLS likely extend beyond reactor science and include aerospace/defense, automotive, petrochemical, and space.

Three high–intermetallic volume Nb–Si–Cr–(Mo) alloys were designed using CALPHAD modeling with the goal of identifying high–specific strength, oxidation-resistant alloys that can be additively manufactured using powder bed fusion. The silicides Nb5Si3 and Nb9Si2Cr3 were targeted as the primary strengthening phases, and the addition of Cr promoted the NbCr2 phase. These alloys were cast and surface-processed with electron beam welding at different speeds to simulate additive manufacturing, and the phases and microstructures of both cast and welded regions were characterized. The weld processing was found to produce fine-grained microstructures in each alloy with fine-scale intermetallics uniformly distributed among a body-centered cubic Nb matrix. Microstructural refinement and hardness were found to increase with weld velocity; one alloy reached its highest hardness of approximately 16 GPa before the brittleness at higher velocities became detrimental. One alloy was found to be qualitatively the least brittle while also attaining a hardness of 13 GPa and was therefore identified as a good candidate for additive manufacturing.

Droplet-on-demand liquid metal jetting (DOD-LMJ) is a new method for additive manufacturing of bulk structural alloys. Here, we report on the microstructure, tensile, and fatigue properties of an Al-7Si-0.4Mg (A356) alloy fabricated with LMJ. Liquid metal droplets were shielded by high-purity Ar gas shroud during deposition. Atom probe tomography revealed that a few nanometers thick (Al-Mg-Si)-O oxide film formed on the droplets despite Ar gas shielding. Tensile tests on peak-aged LMJ A356 alloy showed that yield strength was isotropic (250 MPa), but ductility was lower in the build direction (6.1 +/- 1.4 %) compared to the transverse direction (9.4 +/- 1.0 %). Lower ductility in the build direction was attributed to delamination of metal-oxide interfaces at layer boundaries. The ductility and yield strength of LMJ A356 were similar to cast A356 and laser powder bed fused (LPBF) A357 alloys, indicating the limited impact of oxide film on tensile properties. The oxide film severely impacted the fatigue properties. Fatigue resistance of LMJ A356 was limited by fatigue crack initiation at lack-of-fusion defects and fatigue crack propagation along layer boundaries by delamination of the metaloxide interface. The fatigue strength of LMJ A356 at 60 MPa was lower than cast A356 and LPBF A357 alloys in the peak-aged condition. This research underscores the need for managing droplet oxidation during LMJ additive manufacturing of structural alloys.

Creep deformation and cavitation were investigated at 300 ºC in both tension and compression for an additively manufactured Al-8.6Cu-0.5Mn-0.9Zr (wt%) alloy in the as-fabricated state and after various aging treatments (aging at 300 °C/200h or 350 °C/24h and overaging at 400 °C/200h). Creep mechanisms at 300oC were determined by relating the measured creep response to corresponding microstructural and X-ray computed tomography observations. In compression, alloys in the as-fabricated and two aging conditions exhibited similarly high creep resistance. Overaging (400 °C/200h) led to substantial coarsening of intragranular θ-Al2Cu precipitates and an expected drop in their Orowan strengthening contribution. In tension, minimum strain rates comparable to those in compression were obtained at any given stress; however, upon accumulation of some plastic strain in the matrix, creep cavities started to form, leading to accelerated tertiary stage creep deformation and rupture. Cavitation occurred exclusively along melt pool boundaries due to locally enhanced diffusion enabled by (ⅰ) large grain-boundary area in adjacent fine-grained zones and (ⅱ) localization of creep strain in nearby heat-affected zones. Although cavity growth was initially diffusion-controlled, its rate was determined by matrix creep rate, consistent with constrained cavity growth mechanisms. This study reveals how microstructural complexities induced by the additive manufacturing process affect the creep and cavitation behavior of Al-Cu-Mn-Zr alloys. The underlying creep and cavitation mechanisms uncovered in this study point to pathways that improve the high-temperature properties of additively manufactured alloys.

AA7050 via Electron Backscatter Diffraction (EBSD), TEM, optical microscopy, and Scanning Electron Microscopy (SEM). The microstructural characterization revealed refined constituent particles and grains throughout the as-deposited AA7050 microstructure. Furthermore, quasi-static tensile experiments were conducted in both the build and transverse directions, in order to quantify the orientation influence on tensile properties of the as-deposited AA7050 build. Spatially dependent tensile properties were observed in the material due to heat input variation coarsening of secondary phases towards the initial layers of the AFS-D build. Post-mortem analysis revealed that voids nucleated and coalesced from the overgrowth of the strengthening precipitates present in the material, resulting in fracture.

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.