Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10363
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dc.contributor.authorGüdelek, M. Uğur-
dc.contributor.authorSerin, Gökberk-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorÜnver, Hakkı Özgür-
dc.date.accessioned2023-04-16T10:01:18Z-
dc.date.available2023-04-16T10:01:18Z-
dc.date.issued2022-
dc.identifier.issn0954-4089-
dc.identifier.issn2041-3009-
dc.identifier.urihttps://doi.org/10.1177/09544089221142161-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/10363-
dc.descriptionArticle; Early Accessen_US
dc.description.abstractTool wear is a fundamental aspect of the machining process. Therefore, tool condition monitoring is of paramount importance to ensure part quality and avoid catastrophic damage. Tool wear has a direct correlation with the vibration emanating from the process; however, accurate prediction of tool wear indirectly from the vibration level is difficult because machining parameters such as cutting speed, depth of cut, and feed rate may vary continuously during an operation, depending on tool diameter, geometry, and material. These affect vibration levels as much as wear progress, which demands advanced intelligence that can adapt to variations in cutting conditions. This paper proposes a wavelet long-short term memory (WLSTM)-deep multilayer perceptron (DMLP)-based model, which utilizes the continuous wavelet transform for preprocessing of raw data, long-short term memory (LSTM) for extracting temporal information, and DMLP for regression of the tool wear. First, the model is evaluated by comparing it with other LSTM studies developed using the PHM 2010 dataset in the literature. Afterward, its industrial viability and adaptability performance to variations in cutting speed and tool diameter are assessed with several training scenarios. The results revealed auspicious performance in the proposed architecture's potential in predicting tool wear under operational variability.en_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofProceedings of the Institution of Mechanical Engineers Part E-Journal of Process Mechanical Engineeringen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTool condition monitoringen_US
dc.subjectdeep learningen_US
dc.subjectwavelet long-short term memoryen_US
dc.subjectdeep multilayer perceptronen_US
dc.subjectcontinuous wavelet transformen_US
dc.subjectNeural-Networksen_US
dc.subjectWearen_US
dc.subjectPredictionen_US
dc.titleAn industrially viable wavelet long-short term memory-deep multilayer perceptron-based approach to tool condition monitoring considering operational variabilityen_US
dc.typeArticleen_US
dc.departmentTOBB ETÜen_US
dc.identifier.wosWOS:000893732700001en_US
dc.identifier.scopus2-s2.0-85144399325en_US
dc.institutionauthor-
dc.identifier.doi10.1177/09544089221142161-
dc.authorscopusid57202719011-
dc.authorscopusid57202305094-
dc.authorscopusid6505999525-
dc.authorscopusid6603873269-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ3-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
crisitem.author.dept02.7. Department of Mechanical Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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