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Register a new userMetals by micro-scale additive manufacturing: comparison of microstructure and mechanical properties
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Many emerging applications in microscale engineering rely on the fabrication of three-dimensional architectures in inorganic materials. Small-scale additive manufacturing (AM) aspires to provide flexible and facile access to these geometries. Yet, the synthesis of device-grade inorganic materials is still a key challenge towards the implementation of AM in microfabrication. Here, we present a comprehensive overview of the microstructural and mechanical properties of metals fabricated by most state-of-the-art AM methods that offer a spatial resolution $leq$10$mu$m. Standardized sets of samples were studied by cross-sectional electron microscopy, nanoindentation and microcompression. We show that current microscale AM techniques synthesize metals with a wide range of microstructures and elastic and plastic properties, including materials of dense and crystalline microstructure with excellent mechanical properties that compare well to those of thin-film nanocrystalline materials. The large variation in materials performance can be related to the individual microstructure, which in turn is coupled to the various physico-chemical principles exploited by the different printing methods. The study provides practical guidelines for users of small-scale additive methods and establishes a baseline for the future optimization of the properties of printed metallic objects $-$ a significant step towards the potential establishment of AM techniques in microfabrication.
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Wire-feed laser additive manufacturing is an emerging fabrication technique capable of highly automated large-scale volume production that can reduce both material waste and overall cost while improving product lead times. Quality assurance is necessary for implementation into critical structural applications. However, the large number of process variables along with the cost associated with traditional trial and error methods makes this difficult. This study investigates a comprehensive quality framework based on learning from experimental data that will enable improved quality control along with consistent microstructural features of the part. Specifically, a comprehensive experimental data across multiple process variables and output characteristics in terms of overall bead quality, geometric shape (i.g. bead height, width, fusion zone depth, etc.), and microstructural features are collected. The predicted process-geometry-microstructure relations are then captured by virtue of data-driven machine learning models. The properties of printed beads are visualized based on an extensive range of processing space within a 3-dimensional contour map. The insights and impacts of process variables on bead morphology, geometric and microstructural features are comprehensively investigated for quality improvement during manufacturing processes.
A thick electrode with high areal capacity has been developed as a strategy for high-energy-density lithium-ion batteries, but thick electrodes have difficulties in manufacturing and limitations in ion transport. Here, we reported a new manufacturing approach for ultra-thick electrode with aligned structure, called structure electrode additive manufacturing or SEAM, which aligns active materials to the through-thicknesses direction of electrodes using shear flow and a designed printing path. The ultra-thick electrodes with high loading of active materials, low tortuous structure, and good structure stability resulting from a simple and scalable SEAM lead to rapid ion transport and fast electrolyte infusion, delivering a higher areal capacity than slurry-casted thick electrodes. SEAM shows strengths in design flexibility and scalability, which allows the production of practical high energy/power density structure electrodes.
Evaluating the mechanical response of fiber-reinforced composites can be extremely time consuming and expensive. Machine learning (ML) techniques offer a means for faster predictions via models trained on existing input-output pairs and have exhibited success in composite research. This paper explores a fully convolutional neural network modified from StressNet, which was originally for lin-ear elastic materials and extended here for a non-linear finite element (FE) simulation to predict the stress field in 2D slices of segmented tomography images of a fiber-reinforced polymer specimen. The network was trained and evaluated on data generated from the FE simulations of the exact microstructure. The testing results show that the trained network accurately captures the characteristics of the stress distribution, especially on fibers, solely from the segmented microstructure images. The trained model can make predictions within seconds in a single forward pass on an ordinary laptop, given the input microstructure, compared to 92.5 hours to run the full FE simulation on a high-performance computing cluster. These results show promise in using ML techniques to conduct fast structural analysis for fiber-reinforced composites and suggest a corollary that the trained model can be used to identify the location of potential damage sites in fiber-reinforced polymers.
An investigation on the additive manufacturing and the experimental testing of 3D models of tensegrity prisms and columns is presented. An electron beam melting facility (Arcam EBM S12) is employed to 3D print structures composed of tensegrity prisms endowed with rigid bases and temporary supports, which are made out of the titanium alloy Ti6Al4V. The temporary supports are removed after the additive manufacturing phase, when Spectra cross-strings are added to the 3D printed models, and a suitable state of internal prestress is applied to the structure. The experimental part of the study shows that the examined structures feature sitffening-type elastic response under large or moderately large axial strains induced by compressive loading. Such a geometrically nonlinear behavior confirms previous theoretical results available in the literature, and paves the way to the use of tensegrity prisms and columns as innovative mechanical metamaterials and smart devices.
Atom probe tomography (APT), electron probe microanalysis (EPMA) and nanoindentation were used to characterise the oxygen-rich layer on an in-service jet engine compressor disc, manufactured from the titanium alloy TIMETAL 834. Oxygen ingress was quantified and related to changes in mechanical properties through nanoindentation studies. The relationship between oxygen concentration, microstructure, crystal orientation and hardness has been explored through correlative hardness mapping, EPMA and electron backscatter diffraction (EBSD). The role of microstructure on oxygen ingress has been studied and oxygen ingress along a potential alpha/ beta interface was directly observed on the nanoscale using APT.
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