Tool condition monitoring in milling based on cutting forces by a neural network

Basit Öğe Kaydı
dc.contributor.authorSaglam, H
dc.contributor.authorUnuvar, A
dc.date.accessioned2020-03-26T16:46:16Z
dc.date.available2020-03-26T16:46:16Z
dc.date.issued2003
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractAutomated machining systems require reliable online monitoring processes. The application of a multilayered neural network for tool condition monitoring in face milling is introduced and evaluated against cutting force data. The work uses the back-propagation algorithm for training neural network of 5 x 10 x 2 architecture. An artificial neural network was used for feature selection in order to estimate flank wear (Vb) and surface roughness (Ra) during the milling operation. The relationship of cutting parameters with Vb and Ra was established. The sensor selection using statistical methods based on the experimental data helps in determining the average effect of each factor on the performance of the neural network model. This model, including cutting speed, feed rate, depth of cut and two cutting force components (feed force and vertical Z-axis force), presents a close estimation of Vb and Ra. Therefore, the neural network with parallel computation ability provides a possibility for setting up intelligent sensor systems.en_US
dc.identifier.doi10.1080/0020754031000073017en_US
dc.identifier.endpage1532en_US
dc.identifier.issn0020-7543en_US
dc.identifier.issue7en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1519en_US
dc.identifier.urihttps://dx.doi.org/10.1080/0020754031000073017
dc.identifier.urihttps://hdl.handle.net/20.500.12395/18612
dc.identifier.volume41en_US
dc.identifier.wosWOS:000182329300009en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTAYLOR & FRANCIS LTDen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF PRODUCTION RESEARCHen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleTool condition monitoring in milling based on cutting forces by a neural networken_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
saglam2003.pdf
Boyut:
208.34 KB
Biçim:
Adobe Portable Document Format
Açıklama:
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu