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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Microstructure and corrosion behavior of friction-surfaced 304L austenitic stainless steels
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Multiphysics modeling of particle spattering and induced defect formation mechanism in Inconel 718 laser powder bed fusion
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Effects of weave path parameters on the geometry of wire arc additive manufactured features
Here, the present work investigates the effect of process parameters on the geometry of wire arc additive manufactured parts. The geometric accuracy of features produced with a weaving strategy is compared to what can be accomplished with a typical overlapping bead strategy. In this work, single-layer and multi-layer geometries were deposited under varying process and path parameters. The wavelength, amplitude, and torch speed of the weaving path were varied, while the power, wire feed speed, and contact tip to work distance remained constant. The geometric deposition efficiencies of several samples produced with a weave strategy are directly compared to samples generated with two parallel overlapping beads with torch speeds defined to match the deposition rate of the weave samples. Feature geometries were characterized using optical microscopy and laser scanning data. The results indicate that implementing a weave path strategy can improve the geometric accuracy of wire arc additive features, thus increasing the effective volumetric deposition rate of the process. It is shown that the most consistent improvements resulted from the combination of low wavelength and high amplitude, which correlate to wider and taller printed layers.
Application of hybrid laser powder bed fusion additive manufacturing to microwave radio frequency quarter wave cavity resonators
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Integrating robotic wire arc additive manufacturing and machining: hybrid WAAM machining
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Dissimilar material joining of densified superwood to aluminum by adhesive bonding
Abstract Superwood is a densified wood product that shows promise as a lightweight and renewable alternative for metallic materials. In order for this high-performance new material to be used in multi-material products, it must be able to be joined with other major materials. For example, joining superwood to aluminum would provide a key enabling technology for its use in automotive components since aluminum is presently a major lightweight material for such applications. In this paper, a methacrylate-based adhesive has been identified to provide high lap shear strength (7.5 MPa) for aluminum-to-superwood joints. The aluminum-to-superwood samples were prepared with different amounts of pre-polishing to create openings to the pores in the superwood so adhesive could penetrate into them and create a mechanical interlock, in addition to the hydrogen/chemical bonding at the surface between the methyl methacrylate (MMA) in methacrylate-based adhesive and the cellulose in superwood. For aluminum samples, a thin layer (typically a few nanometers) of oxide film on the surfaces provides hydrogen/chemical bond to MMA structure in the adhesive layer. The failure strength of the superwood-to-aluminum joint sample is about 50% higher than that of natural wood to natural wood joint sample and comparable to that of aluminum-to-aluminum joint sample.
A novel approach to voice of customer extraction using GPT-3.5 Turbo: linking advanced NLP and Lean Six Sigma 4.0
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Improving operations through a lean AI paradigm: a view to an AI-aided lean manufacturing via versatile convolutional neural network
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Rapid thermal modeling of wire arc additive manufacturing process using a mesh-free spectral graph theory approach
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Laser direct fabrication and characterization of 3d lattice structures using continuous carbon fiber thermoset composites
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The optimization landscape of hybrid quantum–classical algorithms: From quantum control to NISQ applications
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A linear programming formulation for eco-driving over road slopes
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Integrating production scheduling and process control using latent variable dynamic models
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Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology
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Δ -PINNs: Physics-informed neural networks on complex geometries
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Optimal test and sensor selection for active fault diagnosis using integer programming
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Variable structure Human Intention Estimator with mobility and vision constraints as model selection criteria
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