This Photopolymer AM-Bench 2022 Challenge is to accurately model the relationship between photopatterned print fidelity and cure depth to exposure time with four resins, which serve to orthogonally probe the relationship between resin reactivity and viscosity. The data sets included here are broken down into three categories and are as follows: (1) resin characterization: Fourier transform infrared spectroscopy and rheometry, (2) light engine characterization: photomask dimensions, beam profilometry and radiometry, and (3) cure depth and profile: laser scanning confocal microscopy.

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).

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.

This resource is the implementation in XML Schema [1] of a data model that describes the Additive Manufacturing Benchmark 2022 series data. It provides a robust set of metadata for the build processes and their resulting specimens and for measurements made on these in the context of the AM Bench 2022 project.The schema was designed to support typical science questions which users of a database with metadata about the AM Bench results might wish to pose.

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 data repository provides a central location for a body of work using one build of nickel-based alloy 718 (IN718) material and resulted in three different studies. The IN718 parts were manufactured by laser powder bed fusion using a range of laser energy densities (manipulation of processing variables) and orientations with respect to the build direction. The influence of processing variables on resulting grain structures, pore structures, and mechanical properties were studied in the as-built (not heat treated) condition.

Contains associated data for the manuscript "Layered Security Guidance for Data Asset Management in Additive Manufacturing" by Fahad Milaat and Joshua Lubell. The associated data is the source of the OSCAL representations in Section 4 of the manuscript.

These measurements were performed as part of the 2018 Additive Manufacturing Benchmark Test Series (AM-Bench), specifically supporting the melt pool geometry challenge (CHAL-AMB2018-02-MP) and cooling rate challenge (CHAL-AMB2018-02-CR).

These measurements were performed as part of the 2018 Additive Manufacturing Benchmark Test Series (AM-Bench). This dataset and the associated experiments are part of a continuing series of controlled benchmark tests, in conjunction with a conference series, with two initial goals, 1) to allow modelers of Additive Manufacturing processes to test their simulations against rigorous, highly controlled additive manufacturing benchmark test data, and 2) to encourage additive manufacturing practitioners to develop novel mitigation strategies for challenging build scenarios.