2026-04-08T17:53:35Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/61152024-10-15T06:15:24Zcom_20.500.11984_1143col_20.500.11984_1148

A meta-learning strategy based on deep ensemble learning for tool condition monitoring of machining processes Peralta Abadía, José Joaquín CUESTA ZABALAJAUREGUI, MIKEL Larrinaga, Felix tool wear deep learning Industry 4.0 tool condition monitoring ensemble learning For Industry 4.0, tool condition monitoring (TCM) of machining processes aims to increase process efficiency and quality and lower tool maintenance costs. To this end, TCM systems monitor variables of interest, such as tool wear. In this paper, a novel meta-learning strategy based on ensemble learning and deep learning (DL) is proposed for tool wear monitoring and is compared with state-of-the-art DL models selected from recent literature, using open-access datasets as input validating its implementation in an industrial scenario. As a result of this study, a novel meta-learning strategy for tool wear monitoring with minimum error is proposed and validated. 2024-01-19T12:14:21Z 2024-01-19T12:14:21Z 2023 http://purl.org/coar/resource_type/c_c94f https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=172533 https://hdl.handle.net/20.500.11984/6115 eng Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2024 The Authors Elsevier