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.

Python scripts and Python+Qt graphical user interface for calculating Feldman-Cousins confidence intervals for low-count Poisson processes in the presence of a known background and for Gaussian processes with a physical lower limit of 0.

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.

Round 9 Test DatasetThis is the test data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

Round 9 Holdout DatasetThis is the holdout data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

Round 10 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of object detection AIs trained on the COCO dataset. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 144 AI models using a small set of model architectures.

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.

The development of large residual elastic strains and stresses during laser powder-bed fusion (LPBF) additive manufacturing is one of the most significant barriers to widespread adoption. Accurate modeling of these strains and stresses is broadly recognized as an effective tool for mitigating these challenges, but rigorous validation data are needed. This data publication includes measurement data from diffraction-based characterizations of residual elastic strains in as-built (not heat treated) artifacts manufactured as part the the 2018 Additive Manufacturing Benchmark Series (AM Bench).