2024-03-29T15:36:11Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/54742023-01-14T23:42:06Zcom_10259_5377com_10259_5086com_10259_2604com_10259_4219col_10259_5378col_10259_4220
Diez Pastor, José Francisco
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0000-0001-5013-7505
Gil del Val, Alain
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Veiga, Fernando
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Bustillo Iglesias, Andrés
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2020-09-22T10:26:06Z
2020-09-22T10:26:06Z
2021-01
0263-2241
http://hdl.handle.net/10259/5474
10.1016/j.measurement.2020.108328
Industrial threading processes that use cutting taps are in high demand. However, industrial conditions differ markedly from laboratory conditions. In this study, a machine-learning solution is presented for the correct classification of threads, based on industrial requirements, to avoid expensive manual measurement of quality indicators. First, quality states are categorized. Second, process inputs are extracted from the torque signals including statistical parameters. Third, different machine-learning algorithms are tested: from base classifiers, such as decision trees and multilayer perceptrons, to complex ensembles of classifiers especially designed for imbalanced datasets, such as boosting and bagging decision-tree ensembles combined with SMOTE and under-sampling balancing techniques. Ensembles demonstrated the lowest sensitivity to window sizes, the highest accuracy for smaller window sizes, and the greatest learning ability with small datasets. Fourth, the combination of models with both high Recall and high Precision resulted in a reliable industrial tool, tested on an extensive experimental dataset.
Projects TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Governmentand project BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y Le ́on, both co-financed through European-Union FEDER funds and project IT-2005/00201 of the INNOTEK Program of the Basque Government
application/pdf
eng
Elsevier
Measurement. 2021, V. 168, 108328
https://doi.org/10.1016/j.measurement.2020.108328
info:eu-repo/grantAgreement/MINECO/TIN2015-67534-P
info:eu-repo/grantAgreement/JCyL/BU085P17
info:eu-repo/grantAgreement/GV/IT-2005-00201
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
Bagging
Imbalanced datasets
Threading
Cutting taps
Quality assessment
Informática
Computer science
High-accuracy classification of thread quality in tapping processes with ensembles of classifiers for imbalanced learning
info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
Measurement
168
108328
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