2026-04-29T12:40:56Zhttps://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 c64c3885-d555-4b37-9378-32af8bc6c908 500 Santos Martín, José Ignacio 526 500 0000-0002-6653-043X Martín, Óscar b8e82c82-cd88-4bf9-880d-f98b7f75ec71 500 Galán Ordax, José Manuel 188 500 0000-0003-3360-7602 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 THUMBNAIL Pereda-STWJ_2015.pdf.jpg Pereda-STWJ_2015.pdf.jpg IM Thumbnail image/jpeg 4537 https://riubu.ubu.es/bitstream/10259/3928/4/Pereda-STWJ_2015.pdf.jpg 42f7d07c8be60c2be55eb7038b873104 MD5 4 ORIGINAL Pereda-STWJ_2015.pdf Pereda-STWJ_2015.pdf application/pdf 498862 https://riubu.ubu.es/bitstream/10259/3928/1/Pereda-STWJ_2015.pdf 70453d37abc08b2bb1a8b1b4eb59cdb7 MD5 1 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/3928/2/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 2 TEXT Pereda-STWJ_2015.pdf.txt Pereda-STWJ_2015.pdf.txt Extracted text text/plain 35167 https://riubu.ubu.es/bitstream/10259/3928/3/Pereda-STWJ_2015.pdf.txt 8a05a44e3acbdcaaa00d3a92ff37dafa MD5 3 10259/3928 oai:riubu.ubu.es:10259/3928 2024-05-13 09:56:35.35 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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