In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. Using OpenFOAM, we simulated the extrusion of filaments and droplets into a moving bath. OpenFOAM is an open source computational fluid dynamics solver. This repository contains the following Python tools: - Tools for generating input files for OpenFOAM v1912 or OpenFOAM v8 tailored to a conical or cylindrical nozzle extruding a filament into a static support bath. - Tools for monitoring the status of OpenFOAM simulations and aborting them if they are too slow.

A code repository and accompanying data for incorporating imperfect theory into machine learning for improved prediction and explainability. Specifically, it focuses on the case study of the dimensions of a polymer chain in different solvent qualities. Jupyter Notebooks for quickly testing concepts and reproducing figures, as well as source code that computes the mean squared error as a function of dataset size for various machine learning models are included.For additional details on the data, please refer to the README.md associated with the data.

SolDet is an object-oriented package for solitonic feature detection in absorption images of Bose-Einstein condensate. with wider use for cold atom image analysis. Featured with classifier, object detector, and Mexican hat metric methods. Technical details are explained in https://arxiv.org/abs/2111.04881.

Temperature profiles of acrylonitrile butadiene styrene (ABS) filament during material extrusion additive manufacturing. Printing temperature and velocities cover the full "printable" range of the ABS filament and are corrected for reflected infrared photons. The profile of the active printing layer and two sub-layers are included. See the associated publication for the full experimental details.

This challenge is to predict the macroscopic stress-strain response of compression samples across a range of temperatures taken from the base leg of the IN625 AMB2018-01 build in both the build direction (Z-axis) and a transverse-build direction (Y-axis). The specific temperatures of interest are 298 K, 523 K, and 773 K. The calibration data provided in this dataset corresponds to the build direction compression tests done at 298 K and 773 K.

This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models.

Lipid nanoparticles (LNPs) were prepared as described (https://doi.org/10.1038/s42003-021-02441-2) using the lipids DLin-KC2-DMA, DSPC, cholesterol, and PEG-DMG2000 at mol ratios of 50:10:38.5:1.5. Four sample types were prepared: LNPs in the presence and absence of RNA, and with LNPs ejected into pH 4 and pH 7.4 buffer after microfluidic assembly. To prepare samples for imaging, 3 ?L of LNP formulation was applied to holey carbon grids (Quantifoil, R3.5/1, 200 mesh copper).

Macro-scale tensile data on additively manufactured (AM) IN625 in the as-built microstructural condition at room temperature and at different strain rates are provided in this data publication. The specimens are machined in accordance with ASTM-E8 subsize specimen with a gauge width of 6 mm and a gauge thickness of 4 mm. Three specimens are tested at a nominal strain rate of 0.001 1/s and 2 specimens are tested at a nominal strain rate of 0.01 1/s on a servo hydraulic material testing machine using a constant crosshead displacement rate of 0.03175 mm/s and 0.3175 mm/s, respectively.

This repository contains scripts and supporting information for a set of demonstrations related to the 2022 AM-Bench challenge series.In particular, the data used in this demonstration comes from the 2018 AM-Bench challenge documented at https://www.nist.gov/ambench/amb2018-02-description.The script queries the AM-Bench 2018 repository located at https://ambench.nist.gov/, then processes the returned XML-based results, does some image processing, and generates plots of melt pool dep

Abstract (from the manuscript): High cycle fatigue life of laser-powder bed fusion (L-PBF) parts depends on several factors; as-built surfaces, when present, are a particular concern. This work measures as-built L-PBF surfaces with X-ray computed tomography, and uses rotating beam fatigue (RBF) testing to measure high cycle fatigue life. Surfaces with different, but consistent, characteristics are achieved by build in vertical specimens and changing only the number of contour passes.