2026-07-20T01:17:27Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/39282024-05-13T07:56:35Zcom_10259_3830com_10259_5086com_10259_2604col_10259_3832
Direct quality prediction in resistance spot welding process: Sensitivity, specificity and predictive accuracy comparative analysis
Pereda, María
Santos Martín, José Ignacio
Martín, Óscar
Galán Ordax, José Manuel
Resistance spot welding
Classification
Pattern recognition
Quality control
Support vector machines
Random forest
Artificial neural networks
In this work, several of the most popular and state-of-the-art classification methods are compared as pattern recognition tools for classification
of resistance spot welding joints. Instead of using the result of a non-destructive
testing technique as input variables, classifiers are trained directly with the
relevant welding parameters, i.e. welding current, welding time and the type of
electrode (electrode material and treatment). The algorithms are compared in
terms of accuracy and area under the receiver operating characteristic (ROC)
curve metrics, using nested cross-validation. Results show that although there is not a dominant classifier for every specificity/sensitivity requirement, support vector machines using radial kernel, boosting and random forest techniques obtain the best performance overall
2016-02-12
2016-02-12
2015-11
info:eu-repo/semantics/article
1362-1718
http://hdl.handle.net/10259/3928
10.1179/1362171815Y.0000000052
eng
Science and technology of welding and joining. 2015, V. 20, n. 8, p. 679-685
http://www.tandfonline.com/doi/full/10.1179/1362171815Y.0000000052
info:eu-repo/grantAgreement/MICINN/CSD2010-00034
info:eu-repo/grantAgreement/JCyL/GREX251-2009
info:eu-repo/semantics/openAccess
Maney Publishing