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).

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.

Synthetic training and test datasets for experiments on deep generative modeling of noise time series. Consists of data for the following noise types: 1) band-limited thermal noise, i.e., bandpass filtered white Gaussian noise, 2) power law noise, including fractional Gaussian noise (FGN), fractional Brownian motion (FBM), and fractionally differenced white noise (FDWN), 3) generalized shot noise, 4) impulsive noise, including Bernoulli-Gaussian (BG) and symmetric alpha stable (SAS) distributions.

The following data files include microstructure measurement results associated with the 2022 Additive Manufacturing Benchmark test series (AM Bench 2022) AMB2022-03 set of benchmarks. These AMB2022-03 benchmarks explore a range of individual and overlapping melt pool behaviors using individual laser tracks and 2D arrays of laser tracks (pads) on solid metal IN718 plates. For the individual laser tracks, a range of laser parameters was used, with variations in laser power, speed, and spot diameter.

This data publication contains the mass spectrometry chemical characterization of microplastic and nanoplastic chemical analysis. The data from this study includes mass spectra of pure, mixed, and weathered microplastics and nanoplastics at high and low fragmentation, extracted ion chronograms, Kendrick mass defect plots, code, and the derived and processed data. The data analysis code (MATLAB 2022a*) used for unsupervised learning of cluster and compositional relationships is also included.

This module for ns-3 implements the classes required to model a network architecture based on the O-RAN Alliance's specifications. These models include a Radio Access Network (RAN) Intelligent Controller (RIC) that is functionally equivalent to O-RAN's Near-Real Time (Near-RT) RIC, and reporting modules that attach to simulation nodes and serve as communication endpoints with the RIC in a similar fashion as the E2 Terminators in O-RAN.

Our work towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) aims to provide plots, tools, methods, and strategies to extract insights out of various machine learning (ML) and Artificial Intelligence (AI) data.Included in this software is the MIg analyZeR (mizr) R software package that produces various plots.

Digital light processing (DLP) vat photopolymerization (VP) additive manufacturing (AM) uses patterned UV light to selectively cure a liquid photopolymer into a solid layer. Subsequent layers are printed on to preceding layers to eventually form a desired 3 dimensional (3D) part. This data set characterizes the 3D geometry of a single layer of voxels (volume pixels) printed with photomasks assigned random intensity levels at every pixel. The masks are computer generated, then printed onto a glass cover slide.

This dataset contains a comparison of packet loss counts vs handovers using four different methods: baseline, heuristic, distance, and machine learning, as well as the data used to train a machine learning model. This data was generated as a result of the work described in the paper, "O-RAN with Machine Learning in ns-3," by the authors Wesley Garey, Tanguy Ropitault, Richard Rouil, Evan Black, and Weichao Gao from the 2023 Workshop on ns-3 (WNS3 2023), that was June 28-29, 2023, in Arlington, VA, USA, and published by ACM, New York, NY, USA.

Videos and .stl files from Synergistic Fire Resistance of Nanobrick Wall Coated 3D Printed Photopolymer Lattices.The videos are from the fire tests of 3D printed photopolymer parts, from which time series still images were acquired. In these tests, printed parts (with or without a nanocoating) are subjected to direct blowtorch exposure until part failure (defined as when the part shatters). Most videos include time after torch removal while the part continues to burn.The .stl files are the files used to 3D print the parts studied in this work.