2026-04-08T17:55:43Zhttps://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

Peralta Abadía, José Joaquín CUESTA ZABALAJAUREGUI, MIKEL Larrinaga, Felix 2024-01-19T12:14:21Z 2024-01-19T12:14:21Z 2023 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=172533 https://hdl.handle.net/20.500.11984/6115 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. eng Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2024 The Authors tool wear deep learning Industry 4.0 tool condition monitoring ensemble learning A meta-learning strategy based on deep ensemble learning for tool condition monitoring of machining processes http://purl.org/coar/resource_type/c_c94f