Dataset from healthy and defective spot welds in refill friction stir spot welding using acoustic emission

The dataset presented in this paper deals with real-time measurements carried out during the welding of 78 spot welds including heathy and defective states. These measurements are composed of acoustic emission signals and welding parameters. Acoustic emission signals were captured by three different...

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Autores principales: Dahmene, Fethi, Yaacoubi, Slah, El Mountassir, Mahjoub, Porot, Gaëlle, Masmoudi, Mohamed, Nennig, Pascal, Suhuddin, Uceu Fuad Hasan, Fernandez dos Santos, Jorge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Data Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9747647/
https://www.ncbi.nlm.nih.gov/pubmed/36533284
http://dx.doi.org/10.1016/j.dib.2022.108750
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author Dahmene, Fethi
Yaacoubi, Slah
El Mountassir, Mahjoub
Porot, Gaëlle
Masmoudi, Mohamed
Nennig, Pascal
Suhuddin, Uceu Fuad Hasan
Fernandez dos Santos, Jorge
author_facet Dahmene, Fethi
Yaacoubi, Slah
El Mountassir, Mahjoub
Porot, Gaëlle
Masmoudi, Mohamed
Nennig, Pascal
Suhuddin, Uceu Fuad Hasan
Fernandez dos Santos, Jorge
author_sort Dahmene, Fethi
collection PubMed
description The dataset presented in this paper deals with real-time measurements carried out during the welding of 78 spot welds including heathy and defective states. These measurements are composed of acoustic emission signals and welding parameters. Acoustic emission signals were captured by three different piezoelectric sensors, which are connected to a Vallen AMSY5 system through preamplifiers. Welding parameters where digitized using the M-SCOPE software. Both measurements can be used for the establishment of an automatic criterion able to detect defective spot welds in Refill Friction Stir Spot Welding.
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spelling pubmed-97476472022-12-15 Dataset from healthy and defective spot welds in refill friction stir spot welding using acoustic emission Dahmene, Fethi Yaacoubi, Slah El Mountassir, Mahjoub Porot, Gaëlle Masmoudi, Mohamed Nennig, Pascal Suhuddin, Uceu Fuad Hasan Fernandez dos Santos, Jorge Data Brief Data Article The dataset presented in this paper deals with real-time measurements carried out during the welding of 78 spot welds including heathy and defective states. These measurements are composed of acoustic emission signals and welding parameters. Acoustic emission signals were captured by three different piezoelectric sensors, which are connected to a Vallen AMSY5 system through preamplifiers. Welding parameters where digitized using the M-SCOPE software. Both measurements can be used for the establishment of an automatic criterion able to detect defective spot welds in Refill Friction Stir Spot Welding. Elsevier 2022-11-15 /pmc/articles/PMC9747647/ /pubmed/36533284 http://dx.doi.org/10.1016/j.dib.2022.108750 Text en © 2022 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
Dahmene, Fethi
Yaacoubi, Slah
El Mountassir, Mahjoub
Porot, Gaëlle
Masmoudi, Mohamed
Nennig, Pascal
Suhuddin, Uceu Fuad Hasan
Fernandez dos Santos, Jorge
Dataset from healthy and defective spot welds in refill friction stir spot welding using acoustic emission
title Dataset from healthy and defective spot welds in refill friction stir spot welding using acoustic emission
title_full Dataset from healthy and defective spot welds in refill friction stir spot welding using acoustic emission
title_fullStr Dataset from healthy and defective spot welds in refill friction stir spot welding using acoustic emission
title_full_unstemmed Dataset from healthy and defective spot welds in refill friction stir spot welding using acoustic emission
title_short Dataset from healthy and defective spot welds in refill friction stir spot welding using acoustic emission
title_sort dataset from healthy and defective spot welds in refill friction stir spot welding using acoustic emission
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9747647/
https://www.ncbi.nlm.nih.gov/pubmed/36533284
http://dx.doi.org/10.1016/j.dib.2022.108750
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