RT info:eu-repo/semantics/article
T1 High-accuracy classification of thread quality in tapping processes with ensembles of classifiers for imbalanced learning
A1 Diez Pastor, José Francisco
A1 Gil del Val, Alain
A1 Veiga, Fernando
A1 Bustillo Iglesias, Andrés
K1 Bagging
K1 Imbalanced datasets
K1 Threading
K1 Cutting taps
K1 Quality assessment
K1 Informática
K1 Computer science
AB 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.
PB Elsevier
SN 0263-2241
YR 2021
FD 2021-01
LK http://hdl.handle.net/10259/5474
UL http://hdl.handle.net/10259/5474
LA eng
NO 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
DS Repositorio Institucional de la Universidad de Burgos
RD 27-abr-2024