Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/47728
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Meyveci A. | - |
dc.contributor.author | Karacan I. | - |
dc.contributor.author | Durmuş H. | - |
dc.contributor.author | Çaligülü U. | - |
dc.date.accessioned | 2023-01-09T21:29:47Z | - |
dc.date.available | 2023-01-09T21:29:47Z | - |
dc.date.issued | 2012 | - |
dc.identifier.issn | 0025-5300 | - |
dc.identifier.uri | https://doi.org/10.3139/120.110290 | - |
dc.identifier.uri | https://hdl.handle.net/11499/47728 | - |
dc.description.abstract | In this study, the effect of aging heat treatment on the hardness of AA 2024 and AA 6063 aluminum alloys was investigated by experimental and an Artificial Neural Network (ANN). AA 2024 and AA 6063 aluminum alloys were solution treated at two different temperatures of 490° C and 520° C. Then both samples were cooled to room temperature. After this process, the samples were aged at three different temperatures (140° C, 180° C, 220° C) for ten different periods of time (2, 4, 6, 8, 10, 12, 14, 16, 18, and 20 h.). The experimental results were trained in an ANNs program, and the results were compared with experimental values. It is observed that the experimental results coincided with the ANNs results. © Carl Hanser Verlag, München. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Carl Hanser Verlag | en_US |
dc.relation.ispartof | Materialpruefung/Materials Testing | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Aluminum | en_US |
dc.subject | Deep neural networks | en_US |
dc.subject | Hardness | en_US |
dc.subject | Neural networks | en_US |
dc.subject | AA 2024 | en_US |
dc.subject | AA 6063 | en_US |
dc.subject | Aging heat treatment | en_US |
dc.subject | Experimental values | en_US |
dc.subject | Hardness prediction | en_US |
dc.subject | Aluminum alloys | en_US |
dc.title | Artificial Neural Network (ANN) approach to hardness prediction of aged aluminium 2024 and 6063 Alloys | en_US |
dc.type | Article | en_US |
dc.identifier.volume | 54 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.startpage | 36 | en_US |
dc.identifier.endpage | 40 | en_US |
dc.identifier.doi | 10.3139/120.110290 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 35315946400 | - |
dc.authorscopusid | 57213864487 | - |
dc.authorscopusid | 55970321700 | - |
dc.authorscopusid | 24166286400 | - |
dc.identifier.scopus | 2-s2.0-84855875927 | en_US |
dc.identifier.scopusquality | Q3 | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
CORE Recommender
SCOPUSTM
Citations
12
checked on Nov 23, 2024
Page view(s)
16
checked on Aug 24, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.