Additively manufactured metamaterials such as lattices offer unique physical properties such as high specific strengths and stiffnesses. However, additively manufactured parts, including lattices, exhibit a higher variability in their mechanical properties than wrought materials, placing more stringent demands on inspection, part quality verification, and product qualification. Previous research on anomaly detection has primarily focused on using in-situ monitoring of the additive manufacturing process or post-process (ex-situ) x-ray computed tomography. In this work, we show that convolutional neural networks (CNN), a machine learning algorithm, can directly predict the energy required to compressively deform gyroid and octet truss metamaterials using only optical images. Using the tiled nature of engineered lattices, the relatively small data set (43 to 48 lattices) can be augmented by systematically subdividing the original image into many smaller sub-images. During testing of the CNN, the prediction from these sub-images can be combined using an ensemble-like technique to predict the deformation work of the entire lattice. This approach provides a fast and inexpensive screening tool for predicting properties of 3D printed lattices. Importantly, this artificial intelligence strategy goes beyond ‘inspection’, since it accurately estimates product performance metrics, not just the existence of defects.
Residual strain in electrodeposited Li films may affect safety and performance in Li metal battery anodes, so it is important to understand how to detect residual strain in electrodeposited Li and the conditions under which it arises. To explore this Li films, electrodeposited onto Cu metal substrates, were prepared under an applied pressure of either 10 or 1000 kPa and subsequently tested for the presence or absence of residual strain via sin(ψ) analysis. X-ray diffraction (XRD) analysis of Li films required preparation and examination within an inert environment; hence, a Be-dome sample holder was employed during XRD characterization. Results show that the Li film grown under 1000 kPa displayed a detectable presence of in-plane compressive strain (-0.066%), whereas the Li film grown under 10 kPa displayed no detectable in-plane strain. The underlying Cu substrate revealed an in-plane residual strain near zero. Texture analysis via pole figure determination was also performed for both Li and Cu and revealed a mild fiber texture for Li metal and a strong bi-axial texture of the Cu substrate. Experimental details concerning sample preparation, alignment, and analysis of the particularly air-sensitive Li films have also been detailed. This work shows that Li metal exhibits residual strain when electrodeposited under compressive stress and that XRD can be used to quantify that strain.
The high-cycle fatigue life of nanocrystalline and ultrafine-grained Ni-Fe was examined for five distinct grain sizes ranging from approximately 50–600 nm. The fatigue properties were strongly dependent on grain size, with the endurance limit changing by a factor of 4 over this narrow range of grain size. The dataset suggests a breakdown in fatigue improvement for the smallest grain sizes <100 nm, likely associated with a transition to grain coarsening as a dominant rate-limiting mechanism. The dataset also is used to explore fatigue prediction from monotonic tensile properties, suggesting that a characteristic flow strength is more meaningful than the widely-utilized ultimate tensile strength.
Nanocrystalline metals are promising radiation tolerant materials due to their large interfacial volume fraction, but irradiation-induced grain growth can eventually degrade any improvement in radiation tolerance. Therefore, methods to limit grain growth and simultaneously improve the radiation tolerance of nanocrystalline metals are needed. Amorphous intergranular films are unique grain boundary structures that are predicted to have improved sink efficiencies due to their increased thickness and amorphous structure, while also improving grain size stability. In this study, ball milled nanocrystalline Cu-Zr alloys are heat treated to either have only ordered grain boundaries or to contain amorphous intergranular films distributed within the grain boundary network, and are then subjected to in situ transmission electron microscopy irradiation and ex situ irradiation. Differences in defect density and grain growth due to grain boundary complexion type are then investigated. When amorphous intergranular films are incorporated within the material, fewer and smaller defect clusters are observed while grain growth is also limited, leading to nanocrystalline alloys with improved radiation tolerance.
Recent work in metal additive manufacturing (AM) suggests that mechanical properties may vary with feature size; however, these studies do not provide a statistically robust description of this phenomenon, nor do they provide a clear causal mechanism. Because of the huge design freedom afforded by 3D printing, AM parts typically contain a range of feature sizes, with particular interest in smaller features, so the size effect must be well understood in order to make informed design decisions. This work investigates the effect of feature size on the stochastic mechanical performance of laser powder bed fusion tensile specimens. A high-throughput tensile testing method was used to characterize the effect of specimen size on strength, elastic modulus and elongation in a statistically meaningful way. The effective yield strength, ultimate tensile strength and modulus decreased strongly with decreasing specimen size: all three properties were reduced by nearly a factor of two as feature dimensions were scaled down from 6.25 mm to 0.4 mm. Hardness and microstructural observations indicate that this size dependence was not due to an intrinsic change in material properties, but instead the effects of surface roughness on the geometry of the specimens. Finite element analysis using explicit representations of surface topography shows the critical role surface features play in creating stress concentrations that trigger deformation and subsequent fracture. The experimental and finite element results provide the tools needed to make corrections in the design process to more accurately predict the performance of AM components.
Ductile rupture or tearing usually involves structural degradation from the nucleation and growth of voids and their coalescence into cracks. Although some materials contain preexisting pores, the first step in failure is often the formation of voids. Because this step can govern both the failure strain and the fracture mechanism, it is critical to understand the mechanisms of void nucleation and the enabling microstructural configurations which give rise to nucleation. To understand the role of dislocations during void nucleation, the present study presents ex-situ cross-sectional observations of interrupted deformation experiments revealing incipient, subsurface voids in a copper material containing copper oxide inclusions. The local microstructural state was evaluated using electron backscatter diffraction (EBSD), electron channeling contrast (ECC), transmission electron microscopy (TEM), and transmission kikuchi diffraction (TKD). Surprisingly, before substantial growth and coalescence had occurred, the deformation process had resulted in the nucleation of a high density of nanoscale (≈50 nm) voids in the deeply deformed neck region where strains were on the order of 1.5. Such a proliferation of nucleation sites immediately suggests that the rupture process is limited by void growth, not nucleation. With regard to void growth, analysis of more than 20 microscale voids suggests that dislocation boundaries facilitate the growth process. The present observations call into question prior assumptions on the role of dislocation pile-ups and provide new context for the formulation of revised ductile rupture models. While the focus of this study is on damage accumulation in a highly ductile metal containing small, well-dispersed particles, these results are also applicable to understanding void nucleation in engineering alloys.