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).

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).

This dataset includes the files pertaining to a 3D additive manufacturing build performed on the Additive Manufacturing Metrology Testbed (AMMT) by Ho Yeung on July 8, 2018. The files include the input command files and in-situ process monitoring data, and metadata. This data is the first of the AMMT Process Monitoring Reference Datasets (https://www.nist.gov/el/ammt-temps/datasets), as part of the Metrology for Real-Time Monitoring of Additive Manufacturing project (https://www.nist.gov/programs-projects/metrology-real-time-monitoring-additive-manufacturing).Details on the experiment design, data formats and processing, and file structures can be found in the data description article: https://doi.org/10.6028/jres.124.033

This dataset includes files generated from post-build X-ray computed tomography (XCT) measurements of the sixteen parts built as part of the "Overhang Part X16" in-situ process monitoring dataset (available at https://doi.org/10.18434/mds2-2309). The "Overhang Part X16" dataset was 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. The files in this dataset include XCT image sequences for each part, and stereolithography files (.STL) of the surface data extracted from XCT, measured by Maxwell Praniewicz at the Precision Machining Research Consortium at Georgia Institute of Technology, Atlanta, GA. Details on the dataset can be found in the included data description document "DataDescription_OverhangPartX16_XCT.pdf"

This dataset includes files generated from post-build X-ray computed tomography (XCT) measurements of the sixteen parts built as part of the "Overhang Part X16" in-situ process monitoring dataset (available at https://doi.org/10.18434/mds2-2309). The "Overhang Part X16" dataset was 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. The files in this dataset include XCT image sequences for each part, and stereolithography files (.STL) of the surface data extracted from XCT, measured by Maxwell Praniewicz at the Precision Machining Research Consortium at Georgia Institute of Technology, Atlanta, GA. Details on the dataset can be found in the included data description document "DataDescription_OverhangPartX16_XCT.pdf"

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.

This document details the data registration process for the previously published datasets from Additive Manufacturing Metrology Testbed (AMMT) parts, "Overhang Part X4," generated at the National Institute of Standards and Technology (NIST). The two datasets —one for process monitoring and the other for XCT inspection—covering four overhang parts, along with their descriptions were published in 2020. The published data have been well-received by the community, advancing the understanding of laser powder bed fusion additive manufacturing (AM). In the last four years, the NIST team encountered numerous questions regarding the published datasets, as the raw data were not easily interpretable for mining process-structure relationships. To support a wider range of research efforts across multiple disciplines, the NIST team conducted additional data analysis, resulting in a fully registered and well-documented dataset for publication. This document provides a detailed overview of the data processing pipeline and the multi-modal data registration techniques employed, including preprocessing, feature extraction, and data alignment. The final registered dataset consists solely of numerical values, fully aligned with the machine coordinate system. Key features of the registered data include process parameters, laser power, in-situ melt pool characteristics, in-situ layerwise optical intensity, and ex-situ XCT voxel values. Additionally, this document provides uncertainty analysis for each feature to help users better select data for their applications and evaluate their results. It can also serve as a framework for processing similar datasets collected on the same testbed in future research.

This document details the data registration process for the previously published datasets from Additive Manufacturing Metrology Testbed (AMMT) parts, "Overhang Part X4," generated at the National Institute of Standards and Technology (NIST). The two datasets —one for process monitoring and the other for XCT inspection—covering four overhang parts, along with their descriptions were published in 2020. The published data have been well-received by the community, advancing the understanding of laser powder bed fusion additive manufacturing (AM). In the last four years, the NIST team encountered numerous questions regarding the published datasets, as the raw data were not easily interpretable for mining process-structure relationships. To support a wider range of research efforts across multiple disciplines, the NIST team conducted additional data analysis, resulting in a fully registered and well-documented dataset for publication. This document provides a detailed overview of the data processing pipeline and the multi-modal data registration techniques employed, including preprocessing, feature extraction, and data alignment. The final registered dataset consists solely of numerical values, fully aligned with the machine coordinate system. Key features of the registered data include process parameters, laser power, in-situ melt pool characteristics, in-situ layerwise optical intensity, and ex-situ XCT voxel values. Additionally, this document provides uncertainty analysis for each feature to help users better select data for their applications and evaluate their results. It can also serve as a framework for processing similar datasets collected on the same testbed in future research.

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.