RT info:eu-repo/semantics/article
T1 Comparative study of classification algorithms for quality assessment of resistance spot welding joints from preand post-welding inputs
A1 Martín, Óscar
A1 Ahedo García, Virginia
A1 Santos Martín, José Ignacio
A1 Galán Ordax, José Manuel
K1 Resistance spot welding
K1 Quality control
K1 Welding parameters
K1 Ultrasonic testing
K1 Tree ensembles
K1 Stacking
K1 Welding
K1 Acoustics
K1 Classification algorithms
K1 Electrodes
K1 Steel
K1 Boosting
K1 Testing
K1 Materiales
K1 Materials
K1 Ingeniería mecánica
K1 Mechanical engineering
K1 Ensayos (Tecnología)
K1 Testing
AB Resistance spot welding (RSW) is a widespread manufacturing process in the automotive industry. There are different approaches for assessing the quality level of RSW joints. Multi-input-single-output methods, which take as inputs either the intrinsic parameters of the welding process or ultrasonic nondestructive testing variables, are commonly used. This work demonstrates that the combined use of both types of inputs can significantly improve the already competitive approach based exclusively on ultrasonic analyses. The use of stacking of tree ensemble models as classifiers dominates the classification results in terms of accuracy, F-measure and area under the receiver operating characteristic curve metrics. Through variable importance analyses, the results show that although the welding process parameters are less relevant than the ultrasonic testing variables, some of the former provide marginal information not fully captured by the latter.
PB Institute of Electrical and Electronics Engineers (IEEE)
YR 2022
FD 2022-01
LK http://hdl.handle.net/10259/6386
UL http://hdl.handle.net/10259/6386
LA eng
NO This work was supported in part by the Spanish Ministry of Science, Innovation and Universities (Excellence Networks) under Grant HAR2017-90883-REDC and Grant RED2018-102518-T, and in part by the Junta de Castilla y León Consejería de Educación under Grant BDNS 425389. The work of Virginia Ahedo was supported by the European Social Fund.
DS Repositorio Institucional de la Universidad de Burgos
RD 24-nov-2024