Resistance spot welding insights: A dataset integrating process parameters, infrared, and surface imaging

The evaluation of the Resistance Spot Welding (RSW) that guarantees satisfactory performance of mechanical characteristics without altering physical properties can be reached by modeling the input parameters such as current, welding time, and applied force from which each unit has been built and correlating with digital images of the surface and infrared images that allows to identify variations on the parameters that modify the quality of the welding spot [1]. With this, mechanical and surface characteristics can be detected without the need for a mechanical test that modifies the structure of the unit. The database serves as a comprehensive record of the welding spot process, including the monitor of crucial input parameters such as current and force. The constructions and documentation of the testing platform through the instrumentation of a resistance welding will assess the variability of the input parameters and their impact on the output in surface and thermographic imaging, welding nugget diameter and it's mechanical strength. Additionally, it documents characteristics of the material used as thickness and material type and its output as the mechanical resistance and nugget diameter, along with its corresponding classification. Thus, the database not only captures the details of the welding process, but it also provides a valuable resource for analyzing and evaluating the performance of the welding operation.

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Publication Details

Journal
Data in Brief
Published
2025-02-07
DOI
https://doi.org/10.1016/j.dib.2025.111373
Citations
6
Primary Topic
Advanced Welding Techniques Analysis
Type
article
Field-Weighted Citation Impact
4.28

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article

Resistance spot welding insights: A dataset integrating process parameters, infrared, and surface imaging

Edgar Alejandro Rivas Araiza, Juan Carlos Jáuregui-Correa, Jesús Carlos Pedraza‐Ortega, Lucas Domı́nguez et al.
6 citations
Data in Brief
Advanced Welding Techniques Analysis
4.28
article

Resistance spot welding insights: A dataset integrating process parameters, infrared, and surface imaging

Edgar Alejandro Rivas Araiza, Juan Carlos Jáuregui-Correa, Jesús Carlos Pedraza‐Ortega, Lucas Domı́nguez, Jose L. Gonzalez-Cordoba, András Takács
article en
6 citations

Abstract

The evaluation of the Resistance Spot Welding (RSW) that guarantees satisfactory performance of mechanical characteristics without altering physical properties can be reached by modeling the input parameters such as current, welding time, and applied force from which each unit has been built and correlating with digital images of the surface and infrared images that allows to identify variations on the parameters that modify the quality of the welding spot [1]. With this, mechanical and surface characteristics can be detected without the need for a mechanical test that modifies the structure of the unit. The database serves as a comprehensive record of the welding spot process, including the monitor of crucial input parameters such as current and force. The constructions and documentation of the testing platform through the instrumentation of a resistance welding will assess the variability of the input parameters and their impact on the output in surface and thermographic imaging, welding nugget diameter and it's mechanical strength. Additionally, it documents characteristics of the material used as thickness and material type and its output as the mechanical resistance and nugget diameter, along with its corresponding classification. Thus, the database not only captures the details of the welding process, but it also provides a valuable resource for analyzing and evaluating the performance of the welding operation.

Data in BriefVol. 59
Autonomous University of Queretaro (MX)
Universidad Autónoma de Querétaro, Consejo Nacional de Ciencia y Tecnología
Openalex Percentile: Top 6%
Advanced Welding Techniques Analysis
4.28
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Resistance spot welding insights: A dataset integrating process parameters, infrared, and surface imaging — Edgar Alejandro Rivas Araiza, Juan Carlos Jáuregui-Correa, et al. · Data in Brief (2025) | TGRS Research Map | TGRS