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Software to Process 3D Scanner Data NIST -- National Ins

titute of Standards and Technology, data.nist.gov (Standard Reference / public data, DCAT/NERDm). {"@context":["https://data.nist.gov/od/dm/nerdm-pub-context.jsonld",{"@base":"ark:/88434/mds115hkhm"}],"_schema":"https://data.nist.gov/od/dm/nerdm-schema/v0.7#","_extensionSchemas":["https://data.nist.gov/od/dm/nerdm-schema/pub/v0.7#/definitions/PublicDataResource"],"@type":["nrdp:PublicDataResource"],"@id":"ark:/88434/mds115hkhm","doi":"doi:10.18434/T4/1502473","title":"Software to Process 3D Scanner Data","contactPoint":{"fn":"Prem Rachakonda","hasEmail":"mailto:prem.rachakonda@nist.gov"},"modified":"2018-03-12","ediid":"6CE523B140F0303DE053245706810DEE1929","landingPage":"https://data.nist.gov/od/id/6CE523B140F0303DE053245706810DEE1929","description":["This software was developed per the ASTM E3125-17 documentary standard for 3D imaging systems. 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The datasets can be used by users (e.g., software developers, data scientists) who work on robot health management (including accuracy) but have limited or no access to robots that can capture real data. The datasets can support the:","- Development of robot health monitoring algorithms and tools\n- Research of technologies and tools to support robot monitoring, diagnostics, prognostics, and health management (collectively called PHM)\n- Validation and verification of the industrial PHM implementation. For example, the verification of a robot's TCP accuracy after the work cell has been reconfigured, or whenever a manufacturer wants to determine if the robot arm has experienced a degradation.","For data collection, a trajectory is programmed for the Universal Robot (UR5) approaching and stopping at randomly-selected locations in its workspace. The robot moves along this preprogrammed trajectory during different conditions of temperature, payload, and speed. The TCP (x,y,z) of the robot are measured by a 7-D measurement system developed at NIST. Differences are calculated between the measured positions from the 7-D measurement system and the nominal positions calculated by the nominal robot kinematic parameters. The results are recorded within the dataset. Controller level sensing data are also collected from each joint (direct output from the controller of the UR5), to understand the influences of position degradation from temperature, payload, and speed. Controller-level data can be used for the root cause analysis of the robot performance degradation, by providing joint positions, velocities, currents, accelerations, torques, and temperatures. For example, the cold-start temperatures of the six joints were approximately 25 degrees Celsius. After two hours of operation, the joint temperatures increased to approximately 35 degrees Celsius. Control variables are listed in the header file in the data set (UR5TestResult_header.xlsx).","If you'd like to comment on this data and/or offer recommendations on future datasets, please email guixiu.qiao@nist.gov."],"keyword":["Manufacturing","Robotics in manufacturing","Sensing and perception","accuracy degradation","PHM"],"theme":["Manufacturing:Robotics in manufacturing"],"topic":[],"references":[{"@type":["npg:Article"],"@id":"#ref:10.1016/j.jmsy.2018.04.004","refType":"IsCitedBy","location":"https://doi.org/10.1016/j.jmsy.2018.04.004","_extensionSchemas":["https://data.nist.gov/od/dm/nerdm-schema/bib/v0.7#/definitions/DCiteReference"],"title":"Quick health assessment for industrial robot health degradation and the supporting advanced sensing development","issued":"2018-07","citation":"Qiao, G., & Weiss, B. A. (2018). Quick health assessment for industrial robot health degradation and the supporting advanced sensing development. 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Eng. Chem. Res. Abstract: Calculation of thermodynamic phase equilibrium is error-prone and can fail both near the critical point and at very low temperatures due to the limited precision available in double precision arithmetic. Most importantly, these calculations frequently represent a computational bottleneck. In this work we extend the \"super-ancillary\" equation approach developed for reference multiparameter equations of state to classical cubic equations of state (van der Waals, Redlich-Kwong-Soave, Peng-Robinson). Iterative calculations in double precision are replaced by non-iterative evaluation of pre-built Chebyshev expansions constructed with extended precision arithmetic. Exact solutions for the equation of state constants are given. The Chebyshev expansions are shown to reproduce the equation of state values to within nearly double precision (aside from in the very near vicinity of the critical point) and are more than 40 times faster to evaluate than the VLE calculations from the fastest computational library. In this way we further expand the domains in which iterative calculations for pure fluid phase equilibria may be rendered obsolete. A C++ header implementing these expansions (and with no external dependencies) is provided as supplemental information. 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J. (2008). The Spectrum of Th‐Ar Hollow Cathode Lamps in the 691–5804 nm region: Establishing Wavelength Standards for the Calibration of Infrared Spectrographs. The Astrophysical Journal Supplement Series, 178(2), 374–381. doi:10.1086/590111\n","_extensionSchemas":["https://data.nist.gov/od/dm/nerdm-schema/bib/v0.7#/definitions/DCiteReference"]},{"@type":["deo:BibliographicReference"],"@id":"#ref:10.1086/590111","refType":"IsReferencedBy","location":"https://dx.doi.org/10.1086/590111","_extensionSchemas":["https://data.nist.gov/od/dm/nerdm-schema/bib/v0.7#/definitions/DCiteReference"]}],"keyword":["hollow cathode lamps","infra red","lamps","spectra"],"landingPage":"https://www.nist.gov/pml/spectrum-th-ar-hollow-cathode-lamps","title":"NIST The Spectrum of Th-Ar Hollow Cathode Lamps - SRD 161","theme":["Standards:Reference data","Physics:Spectroscopy"],"programCode":["006:052"],"description":["This atlas presents observations of the infra-red (IR) spectrum of a low current Th-Ar hollow cathode lamp with the 2-m Fourier transform spectrometer (FTS) at NIST. 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