Milling process monitoring based on intelligent real-time parameter identification for unmanned manufacturing
dc.authorscopusid | 59141874400 | |
dc.authorscopusid | 57195759159 | |
dc.authorscopusid | 45161611900 | |
dc.authorscopusid | 7004303301 | |
dc.contributor.author | Tehranizadeh, Faraz | |
dc.contributor.author | Tehranizadeh,F. | |
dc.contributor.author | Pashmforoush,F. | |
dc.contributor.author | Budak E., (1), | |
dc.date.accessioned | 2024-06-23T21:39:26Z | |
dc.date.available | 2024-06-23T21:39:26Z | |
dc.date.issued | 2024 | |
dc.department | Kadir Has University | en_US |
dc.department-temp | Ebrahimi Araghizad A., Manufacturing Research Laboratory, Sabanci University, Istanbul, Turkey; Tehranizadeh F., Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Turkey; Pashmforoush F., Manufacturing Research Laboratory, Sabanci University, Istanbul, Turkey; Budak E., (1), Manufacturing Research Laboratory, Sabanci University, Istanbul, Turkey | en_US |
dc.description.abstract | This study addresses the critical need for intelligent process monitoring in unmanned manufacturing through real-time fault detection. The proposed hybrid approach, which is focused on overcoming the limitations of existing methods, utilizes machine learning (ML) for precise parameter identification in real-time to detect deviations. The ML system is developed using extensive data obtained from simulations based on enhanced force models also achieved through ML. Demonstrating over 96 % accuracy in real-time predictions, the method proves applicable for diverse unmanned manufacturing applications, including monitoring and process optimization, emphasizing its adaptability for industrial implementation using CNC controller signals. © 2024 CIRP | en_US |
dc.description.sponsorship | Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, (219M487); Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK | en_US |
dc.identifier.citation | 0 | |
dc.identifier.doi | 10.1016/j.cirp.2024.04.083 | |
dc.identifier.issn | 0007-8506 | |
dc.identifier.scopus | 2-s2.0-85194108205 | |
dc.identifier.scopusquality | Q1 | |
dc.identifier.uri | https://doi.org/10.1016/j.cirp.2024.04.083 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12469/5880 | |
dc.identifier.wosquality | Q2 | |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Inc. | en_US |
dc.relation.ispartof | CIRP Annals | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Milling | en_US |
dc.subject | Monitoring | en_US |
dc.title | Milling process monitoring based on intelligent real-time parameter identification for unmanned manufacturing | en_US |
dc.type | Article | en_US |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | db49445c-e704-4e9e-8c2b-75a770ea52ad | |
relation.isAuthorOfPublication.latestForDiscovery | db49445c-e704-4e9e-8c2b-75a770ea52ad |