2026-05-31T21:56:59Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/116082026-05-14T06:41:42Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220
Repositorio Institucional de la Universidad de Burgos
author
Maestro Prieto, José Alberto
author
Gil del Val, Alain
author
Bustillo Iglesias, Andrés
2026-05-12T08:14:18Z
2026-05-12T08:14:18Z
2025-09
0268-3768
https://hdl.handle.net/10259/11608
10.1007/s00170-025-16491-x
1433-3015
The tapping of metal components is a manufacturing task with great potential for automation, because the conditions affecting the industrial components are of limited variability. However, automation encounters two main problems: both the human- and the time-related costs associated with the manual classification of threads are excessive, and thread quality can vary greatly, due to tapping tool wear. In this study, the use of semi-supervised algorithms is proposed to improve the performance of machine learning–based models trained on real industrial datasets. The strategy was validated on a dataset of more than 7000 threads produced with 36 different tapping tools under the same working conditions involving nodular cast iron workpieces. Several algorithms were trained using datasets with different features and data processing. The best results were obtained with datasets using linear regression in which sinusoidal fluctuations in the raw signals were replaced by linear regressions and the slope of an 11-element rolling window was applied to extend the raw dataset. Algorithms were trained with different percentages of labeled datasets. The co-training-based algorithms almost systematically obtained the best results, yielding better results than the reference algorithms using a 100% labeled dataset. Besides, the proposed solution also achieved higher performance with 50% of labeled instances in the training dataset, drastically reducing the costs of manual labeling for that sort of industrial dataset.
eng
Atribución 4.0 Internacional
Semi-supervised learning
Fault detection
Tapping
Wear
Semi-supervised tapping wear detection in nodular cast iron workpieces under real industrial conditions
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/11608/1/Maestro-ijamt_2025.pdf
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Maestro-ijamt_2025.pdf