2024-03-29T08:56:33Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/39282021-11-10T09:38:22Zcom_10259_3830com_10259_5086com_10259_2604col_10259_3832
Pereda, María
Santos Martín, José Ignacio
Martín, Óscar
Galán Ordax, José Manuel
2015-11
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
application/pdf
http://hdl.handle.net/10259/3928
eng
Maney Publishing
Direct quality prediction in resistance spot welding process: Sensitivity, specificity and predictive accuracy comparative analysis
info:eu-repo/semantics/article
TEXT
RIUBU. Repositorio Institucional de la Universidad de Burgos
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