Milling Process Monitoring Based on Intelligent Real-Time Parameter Identification for Unmanned Manufacturing
| dc.contributor.author | Araghizad, Arash Ebrahimi | |
| dc.contributor.author | Tehranizadeh, Faraz | |
| dc.contributor.author | Pashmforoush, Farzad | |
| dc.contributor.author | Budak, Erhan | |
| dc.contributor.other | Mechatronics Engineering | |
| dc.contributor.other | 05. Faculty of Engineering and Natural Sciences | |
| dc.contributor.other | 01. Kadir Has University | |
| dc.date.accessioned | 2024-06-23T21:39:26Z | |
| dc.date.available | 2024-06-23T21:39:26Z | |
| dc.date.issued | 2024 | |
| dc.description | Pashmforoush, Farzad/0000-0002-2219-5158; Ebrahimi Araghizad, Arash/0000-0003-4117-1773 | 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. (c) 2024 CIRP. Published by Elsevier Ltd. All rights reserved. | en_US |
| dc.description.sponsorship | Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, (219M487); Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK | en_US |
| dc.description.sponsorship | TUBITAK [219M487] | en_US |
| dc.description.sponsorship | The authors greatly appreciate the support of TUBITAK (219M487) . | en_US |
| dc.identifier.citationcount | 0 | |
| dc.identifier.doi | 10.1016/j.cirp.2024.04.083 | |
| dc.identifier.issn | 0007-8506 | |
| dc.identifier.issn | 1726-0604 | |
| dc.identifier.scopus | 2-s2.0-85194108205 | |
| dc.identifier.uri | https://doi.org/10.1016/j.cirp.2024.04.083 | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.relation.ispartof | CIRP Annals | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Milling | en_US |
| dc.subject | Monitoring | en_US |
| dc.subject | Machine learning | 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 | |
| gdc.author.id | Pashmforoush, Farzad/0000-0002-2219-5158 | |
| gdc.author.id | Ebrahimi Araghizad, Arash/0000-0003-4117-1773 | |
| gdc.author.institutional | Tehranizadeh, Faraz | |
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| gdc.author.wosid | Ebrahimi Araghizad, Arash/KHW-0682-2024 | |
| gdc.author.wosid | Budak, Erhan/AAB-7226-2020 | |
| gdc.author.wosid | Tehranizadeh, Faraz/HGE-9338-2022 | |
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| gdc.description.department | Kadir Has University | en_US |
| gdc.description.departmenttemp | [Araghizad, Arash Ebrahimi; Pashmforoush, Farzad; Budak, Erhan] Sabanci Univ, Mfg Res Lab, Istanbul, Turkiye; [Tehranizadeh, Faraz] Kadir Has Univ, Fac Engn & Nat Sci, Istanbul, Turkiye | en_US |
| gdc.description.endpage | 328 | en_US |
| gdc.description.issue | 1 | en_US |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | Q1 | |
| gdc.description.startpage | 325 | en_US |
| gdc.description.volume | 73 | en_US |
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