2026-07-19T16:19:00Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/39282024-05-13T07:56:35Zcom_10259_3830com_10259_5086com_10259_2604col_10259_3832

Pereda, María Santos Martín, José Ignacio Martín, Óscar Galán Ordax, José Manuel 2016-02-12T12:38:23Z 2016-02-12T12:38:23Z 2015-11 1362-1718 http://hdl.handle.net/10259/3928 10.1179/1362171815Y.0000000052 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 Spanish MICINN Project CSD2010-00034 (SimulPast CONSOLIDER-INGENIO 2010) and by the Junta de Castilla y León GREX251-2009 application/pdf eng Maney Publishing 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 Resistance spot welding Classification Pattern recognition Quality control Support vector machines Random forest Artificial neural networks Gestión de empresas Industrial management Direct quality prediction in resistance spot welding process: Sensitivity, specificity and predictive accuracy comparative analysis info:eu-repo/semantics/article info:eu-repo/semantics/submittedVersion info:eu-repo/semantics/openAccess