2026-05-06T17:41:40Zhttps://riubu.ubu.es/oai/request

oai: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 156 500 0000-0001-5013-7505 Gil del Val, Alain b9267df2-032f-4657-ad30-3030a2859229 500 Veiga, Fernando 51d02213-23c9-4099-9d6e-4863e4763870 500 Bustillo Iglesias, Andrés 81 500 0000-0003-2855-7532 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 TEXT Diez-measurement-2021.pdf.txt Diez-measurement-2021.pdf.txt Extracted text text/plain 71532 https://riubu.ubu.es/bitstream/10259/5474/5/Diez-measurement-2021.pdf.txt b14d5140bfc9653f54580632f8adef9d MD5 5 THUMBNAIL Diez-measurement-2021.pdf.jpg Diez-measurement-2021.pdf.jpg IM Thumbnail image/jpeg 4364 https://riubu.ubu.es/bitstream/10259/5474/4/Diez-measurement-2021.pdf.jpg f7a1ba3088b67b62e1a81585d02f364a MD5 4 Diez-measurement_2021.pdf.jpg Diez-measurement_2021.pdf.jpg IM Thumbnail image/jpeg 4364 https://riubu.ubu.es/bitstream/10259/5474/6/Diez-measurement_2021.pdf.jpg f7a1ba3088b67b62e1a81585d02f364a MD5 6 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/5474/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 805 https://riubu.ubu.es/bitstream/10259/5474/2/license_rdf 4460e5956bc1d1639be9ae6146a50347 MD5 2 ORIGINAL Diez-measurement_2021.pdf Diez-measurement_2021.pdf application/pdf 1767375 https://riubu.ubu.es/bitstream/10259/5474/1/Diez-measurement_2021.pdf 0c83e7d68d2c31696bde792d82350b7c MD5 1 10259/5474 oai:riubu.ubu.es:10259/5474 2023-01-15 00:42:06.654 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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