2026-04-10T13:27:15Zhttps://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

00925njm 22002777a 4500 dc Peralta Abadía, José Joaquín author CUESTA ZABALAJAUREGUI, MIKEL author Larrinaga, Felix author 2023 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. https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=172533 https://hdl.handle.net/20.500.11984/6115 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