The following data files include in-situ thermographic measurement results and various additional experiment design data associated with laser-scanned single tracks and multi-track pads on bare (no-powder) nickel superalloy In718 for the 2022 Additive Manufacturing Benchmark (AM-Bench) test series. These data are associated with the AMB2022-03 series of modeling challenges described here: https://www.nist.gov/system/files/documents/2022/05/26/AMB2022-03%20Measurement%20and%20Challenge%20Descriptions_1.01.pdf. However, these data may also be used in future AM-Bench challenges. These laser-scanning tests and thermographic measurements were performed on the NIST Additive Manufacturing Metrology Testbed (AMMT, https://www.nist.gov/el/ammt-temps).Information on the directory structure and file formats are provided in the README.txt file. Note that this dataset will be periodically updated, and additional data will be added as it is made available. Future publications will also provide more in-depth description of the data in this dataset, as will links to available analysis code and scripts. Refer to the Version number below, and updates described in this Description and the README.txt file.

The following optical micrograph data files are provided in support of the AM Bench 2018 modeling challenges associated with bare plate single track laser scans performed on the NIST Additive Manufacturing Metrology Testbed. These measurements were used in the AMB2018-02 set of challenges associated with track melt pool geometry (CHAL-AMB2018-02-MP). Description of the associated measurements and modeling challenges are provided on the AMB2018-02 challenge description webpage ().

The following optical micrograph data files are provided in support of the AM Bench 2018 modeling challenges associated with bare plate single track laser scans performed on the NIST Additive Manufacturing Metrology Testbed. These measurements were used in the AMB2018-02 set of challenges associated with track melt pool geometry (CHAL-AMB2018-02-MP). Description of the associated measurements and modeling challenges are provided on the AMB2018-02 challenge description webpage ().

The Additive Manufacturing Benchmark Test Series (AM-Bench) is developing a continuing series of controlled benchmark tests, in conjunction with a conference series, with two initial goals: 1) to allow modelers 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. This dataset provides the files to allow the participants in the study (modelers) to understand the experiments and measurements and the files to facilitate the model development. Types of files include .stl files, process videos, and material information.

The Additive Manufacturing Benchmark Test Series (AM-Bench) is developing a continuing series of controlled benchmark tests, in conjunction with a conference series, with two initial goals: 1) to allow modelers 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. This dataset provides the files to allow the participants in the study (modelers) to understand the experiments and measurements and the files to facilitate the model development. Types of files include .stl files, process videos, and material information.

The following data files include microstructure measurement results associated with the 2022 Additive Manufacturing Benchmark test series (AM-Bench 2022) AMB2022-01 benchmark on laser powder bed fusion (LPBF) 3D builds of nickel-based superalloy IN718 test objects. The AM builds were performed on the NIST Additive Manufacturing Metrology Testbed (AMMT) and the microstructure measurements were conducted using scanning electron microscopy (SEM), transmission electron microscopy (TEM), ultra-small-angle X-ray scattering (USAXS), small-angle X-ray scattering (SAXS), wide-angle X-ray scattering (WAXS), and automated serial sectioning. Detailed descriptions of the build process parameters, scan pattern, heat treatment, and descriptions of all of the AMB2022-01 measurements are provided on the AMB2022-01 challenge description webpage (https://www.nist.gov/ambench/amb2022-01-benchmark-measurements-and-challenge-problems).Due to the time-sensitive nature of the AM Bench challenge problems, those measurements and analyses were prioritized. The challenges that this data publication address are:Microstructure (CHAL-AMB2022-01-MS): Histograms of direction-specific grain sizes from specified regions within as-built and heat-treated samples.Phase Evolution (CHAL-AMB2022-01-PE): Formation and evolution of phases and phase fractions, including major precipitates, as a function of time for heat treatments of IN718 from a 2.5 mm leg.The data provided for CHAL-AMB2022-01-PE are preliminary since an additional phase in the as-build material has not yet been positively identified. These data will be updated shortly. Also, additional datasets that are not required for the challenges will be added soon. For updates, please check back here or at www.nist.gov/ambench.

This dataset includes files from the experiment titled 'OverhangPartX4' pertaining to a three-dimensional (3D) additive manufacturing (AM) build performed on the Additive Manufacturing Metrology Testbed (AMMT) by Ho Yeung and Brandon Lane on June 28, 2019. The files include the input command files, materials data, in-situ process monitoring data, and metadata. This data is one of a set of 'AMMT Process Monitoring Datasets', as part of the Metrology for Real-Time Monitoring of Additive Manufacturing project at the National Institute of Standards and Technology (NIST). Ex-situ part characterization data, including X-ray computed tomography measurements, will be provided as they are made available. Readers should refer to the AMMT datasets web page for updates (https://www.nist.gov/el/ammt-temps/datasets).

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. For the laser pads, the laser scan patterns and most of the laser parameters match those used for the 2.5 mm legs from the AMB2022-01 3D builds. The laser tracks and pads were cross sectioned at different locations and examined using scanning electron microscopy (SEM) electron backscatter diffraction (EBSD) and energy dispersive spectroscopy (EDS). Descriptions and measurement data for all of the other AMB2022-03 measurements may be found on the AM Bench website at www.nist.gov/ambench.

The following data files include residual elastic strain, residual stress, and part deflection results associated with the 2022 Additive Manufacturing Benchmark test series (AM Bench 2022) AMB2022-01 benchmark on laser powder bed fusion (LPBF) 3D builds of nickel-based superalloy IN718 test objects. The AM builds were performed on the NIST Additive Manufacturing Metrology Testbed (AMMT). The residual elastic strains were measured using synchrotron X-ray diffraction at the Cornell High Energy Synchrotron Source (CHESS) and neutron diffraction at the Oak Ridge National Laboratory (ORNL) High Flux Isotope Reactor (HFIR). Residual stresses were characterized using the contour method by UC Davis and Hill Engineering. Part deflection after partial cutting of the build part off the build plate was measured at NIST. Detailed descriptions of the build process parameters, scan pattern, heat treatment, and descriptions of all of the AMB2022-01 measurements are provided on the AMB2022-01 challenge description webpage (https://www.nist.gov/ambench/amb2022-01-benchmark-measurements-and-challenge-problems). Due to the time-sensitive nature of the AM Bench challenge problems, those measurements and analyses were prioritized. The challenges that this data publication address are:Residual elastic strain (CHAL-AMB2022-01-RS): Residual elastic strain components at select locations internal to the bridge structure, corresponding to synchrotron X-ray diffraction measurements. Part deflection (CHAL-AMB2022-01-PD): Deflection of the as-built (no heat treatment) bridge structure after it is partially separated from the build plate.Additional datasets that are not required for the challenges will be added soon. For updates, please check back here or at www.nist.gov/ambench.

This dataset includes files from the experiment titled OverhangPartX16 pertaining to a three-dimensional (3D) additive manufacturing (AM) build performed on the Additive Manufacturing Metrology Testbed (AMMT) by Ho Yeung and Brandon Lane on July 3, 2019. Users should refer to the included User Notes file: OverhangX16_In-situData_UserNotes.pdf, which describes unique attributes of this experiment and dataset from previous, similar AMMT datasets. Users should refer to the data description article https://doi.org/10.6028/jres.125.027 for in-depth discussion of the file types and data structure, which are similar for this dataset. The files included in this dataset include the input command files and in-situ process monitoring data. Experiment and measurement metadata may be obtained from the previous dataset: https://doi.org/10.18434/M32233.This data is one of a set of 'AMMT Process Monitoring Datasets', as part of the Metrology for Real-Time Monitoring of Additive Manufacturing project at the National Institute of Standards and Technology (NIST). Ex-situ part characterization data, including X-ray computed tomography measurements, will be provided as they are made available. Readers should refer to the AMMT datasets web page for updates (https://www.nist.gov/el/ammt-temps/datasets).