A linear axis testbed at the Center for Intelligent Maintenance Systems (IMS Center) at the University of Cincinnati was run to failure (the detection of backlash) over one year with periodic data collected from an inertial measurement unit (IMU) on the carriage, two triaxial accelerometers on the ball nut, and the controller.

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. This repository includes Python code for analyzing and managing images and videos of the printing process during extrusion of single filaments. The zip file contains the state of the code when the associated paper was submitted. The link to the GitHub page goes to version 1.0.0, which is the same as the code attached here. From there, you can also access the current state of the code.Associated with: L. Friedrich, R. Gunther, J.

These images, videos, and tables show experimental data, where single lines of viscoelastic inks were extruded into moving viscoelastic support baths. Lines were printed at varying angles relative to the camera, such that videos and images captured the side of horizontal lines, cross-sections of horizontal lines, and the side of vertical lines. Metadata including pressure graphs, programmed speeds, toolpaths, and rheology data are also included.

One additively manufactured (AM) laser powder bed fusion (PBF-L) Inconel 625 mesoscale tensile specimen (gauge dimensions approximately 0.2mm x 0.2 mm x 1mm) was extracted from build AMB2022-CBM-B1 specimen TH1 and tested at room temperature using a quasistatic strain rate of 0.001/s to failure.  Microstructure was measured using x-ray computed tomography (XRCT) and scanning electron microscopy (SEM) techniques on the specimen gauge section or adjacent material.  Large-area electron backscatter diffraction was used to measure crystallographic texture and grain size/morphology of the entire

Additively manufactured (AM) laser powder bed fusion (PBF-L) Inconel 625 blocks were built with two different scan strategies: XY and X-only.  96 tensile specimens were extracted from blocks at different tensile axis orientations with respect to the build direction to yield the following conditions: XY scan strategy (0, 30, 45, 60, and 90 degree orientation w.r.t. build direction) and X-only scan strategy (0, 60, 90 degree orientation w.r.t.

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.

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 tool uses techniques from ASTM E3200 for evaluating manufacturing investments from the perspective of environmentally sustainable manufacturing by pairing economic methods of investment analysis with environmental aspect of manufacturing. The economic techniques used include net present value, internal rate of return, payback period, and hurdle rate. These four techniques are deterministic, meaning that they deal with known values that are certain. The tool also conducts a sensitivity analysis using Monte Carlo techniques.

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.

NIST developed a reference material consisting of synthetic fragments of the SARS-CoV-2 virus RNA, which is the target of molecular diagnostic tests. These RNA fragments can assist in the development and validation of RT-qPCR assays for the detection SARS-CoV-2. The RNA fragments are characterized for concentration using digital PCR methods, may be used to assess limits of detection for SARS-CoV-2 assays, and may calibrate other in-house or commercial SARS-CoV-2 controls. This dataset includes data used for RTGM 10169 SARs-Cov-2 Research Grade Test Material validation.