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Título : Direct quality prediction in resistance spot welding process: Sensitivity, specificity and predictive accuracy comparative analysis
Autor : Pereda, María
Santos Martín, José Ignacio
Martín, Óscar
Galán Ordax, José Manuel
Publicado en: Science and technology of welding and joining. 2015, V. 20, n. 8, p. 679-685
Editorial : Maney Publishing
Fecha de publicación : nov-2015
ISSN : 1362-1718
DOI: 10.1179/1362171815Y.0000000052
Resumen : 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
Palabras clave: Resistance spot welding
Classification
Pattern recognition
Quality control
Support vector machines
Random forest
Artificial neural networks
Materia: Empresas-Gestión
Industrial management
URI : http://hdl.handle.net/10259/3928
Versión del editor: http://www.tandfonline.com/doi/full/10.1179/1362171815Y.0000000052
Aparece en las colecciones: Artículos Organización de Empresas

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