2021
Díez-Pastor, José Francisco; Val, Alain Gil; Veiga, Fernando; Bustillo, Andrés
High-accuracy classification of thread quality in tapping processes with ensembles of classifiers for imbalanced learning Journal Article
In: Measurement, vol. 168, no. 108328, 2021, ISSN: 0263-2241.
Abstract | Links | BibTeX | Tags: Bagging, Cutting taps, Imbalanced datasets, Quality assessment, SELECTED, Threading
@article{Díez-Pastor2021,
title = {High-accuracy classification of thread quality in tapping processes with ensembles of classifiers for imbalanced learning},
author = {José Francisco Díez-Pastor and Alain Gil Val and Fernando Veiga and Andrés Bustillo},
url = {https://www.sciencedirect.com/science/article/pii/S0263224120308654},
doi = {https://doi.org/10.1016/j.measurement.2020.108328},
issn = {0263-2241},
year = {2021},
date = {2021-01-15},
journal = {Measurement},
volume = {168},
number = {108328},
abstract = {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.},
keywords = {Bagging, Cutting taps, Imbalanced datasets, Quality assessment, SELECTED, Threading},
pubstate = {published},
tppubtype = {article}
}
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.
2016
Bustillo, Andres; Lacalle, Luis López N; Fernández-Valdivielso, Asier; Santos, Pedro
Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components Journal Article
In: Journal of Computational Design and Engineering, vol. 3, no. 4, pp. 337 - 348, 2016, ISSN: 2288-4300.
Abstract | Links | BibTeX | Tags: Ensembles, Forming taps, Regression trees, Roll taps, Roll-tap wear, Rotation forest, Threading
@article{BUSTILLO2016337,
title = {Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components},
author = {Andres Bustillo and Luis López N Lacalle and Asier Fernández-Valdivielso and Pedro Santos},
url = {http://www.sciencedirect.com/science/article/pii/S2288430016300306},
doi = {https://doi.org/10.1016/j.jcde.2016.06.002},
issn = {2288-4300},
year = {2016},
date = {2016-10-01},
journal = {Journal of Computational Design and Engineering},
volume = {3},
number = {4},
pages = {337 - 348},
abstract = {An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.},
keywords = {Ensembles, Forming taps, Regression trees, Roll taps, Roll-tap wear, Rotation forest, Threading},
pubstate = {published},
tppubtype = {article}
}
An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.