Kinematic and dynamic data from a robotic assembly of aeronautical threaded fasteners

Industrial screwing is one of several industry branches' most common manufacturing processes. Good quality and structured data from these operations have increased demand with the popularization of data-driven techniques for manufacturing automation. The dataset presented in this paper comprise...

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Autores principales: Lahr, Gustavo Jose Giardini, Silva, Thiago Henrique Segreto, Moreira, Guilherme Ribeiro, Boaventura, Thiago, Caurin, Glauco Augusto de Paula, Ajoudani, Arash
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Data Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630597/
https://www.ncbi.nlm.nih.gov/pubmed/38020438
http://dx.doi.org/10.1016/j.dib.2023.109674
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author Lahr, Gustavo Jose Giardini
Silva, Thiago Henrique Segreto
Moreira, Guilherme Ribeiro
Boaventura, Thiago
Caurin, Glauco Augusto de Paula
Ajoudani, Arash
author_facet Lahr, Gustavo Jose Giardini
Silva, Thiago Henrique Segreto
Moreira, Guilherme Ribeiro
Boaventura, Thiago
Caurin, Glauco Augusto de Paula
Ajoudani, Arash
author_sort Lahr, Gustavo Jose Giardini
collection PubMed
description Industrial screwing is one of several industry branches' most common manufacturing processes. Good quality and structured data from these operations have increased demand with the popularization of data-driven techniques for manufacturing automation. The dataset presented in this paper comprises screwing experiments with aeronautical nuts performed by an industrial robot Kuka KR-16 in a lab setting. The data comprises force, torque, linear and angular displacements, and velocities in time-series format. The dataset contains three different experiment results: mounted, jammed, and not mounted, which can be used as labels for classification techniques.
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spelling pubmed-106305972023-10-14 Kinematic and dynamic data from a robotic assembly of aeronautical threaded fasteners Lahr, Gustavo Jose Giardini Silva, Thiago Henrique Segreto Moreira, Guilherme Ribeiro Boaventura, Thiago Caurin, Glauco Augusto de Paula Ajoudani, Arash Data Brief Data Article Industrial screwing is one of several industry branches' most common manufacturing processes. Good quality and structured data from these operations have increased demand with the popularization of data-driven techniques for manufacturing automation. The dataset presented in this paper comprises screwing experiments with aeronautical nuts performed by an industrial robot Kuka KR-16 in a lab setting. The data comprises force, torque, linear and angular displacements, and velocities in time-series format. The dataset contains three different experiment results: mounted, jammed, and not mounted, which can be used as labels for classification techniques. Elsevier 2023-10-14 /pmc/articles/PMC10630597/ /pubmed/38020438 http://dx.doi.org/10.1016/j.dib.2023.109674 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Lahr, Gustavo Jose Giardini
Silva, Thiago Henrique Segreto
Moreira, Guilherme Ribeiro
Boaventura, Thiago
Caurin, Glauco Augusto de Paula
Ajoudani, Arash
Kinematic and dynamic data from a robotic assembly of aeronautical threaded fasteners
title Kinematic and dynamic data from a robotic assembly of aeronautical threaded fasteners
title_full Kinematic and dynamic data from a robotic assembly of aeronautical threaded fasteners
title_fullStr Kinematic and dynamic data from a robotic assembly of aeronautical threaded fasteners
title_full_unstemmed Kinematic and dynamic data from a robotic assembly of aeronautical threaded fasteners
title_short Kinematic and dynamic data from a robotic assembly of aeronautical threaded fasteners
title_sort kinematic and dynamic data from a robotic assembly of aeronautical threaded fasteners
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630597/
https://www.ncbi.nlm.nih.gov/pubmed/38020438
http://dx.doi.org/10.1016/j.dib.2023.109674
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