Friction stir extrusion: Parametrical optimization for improved Al-Si aluminum tube production

dc.contributor.authorAkbari, Mostafa
dc.contributor.authorAsadi, Parviz
dc.contributor.authorBedir, Fevzi
dc.contributor.authorChoupani, Naghdali
dc.date.accessioned2025-10-29T11:27:18Z
dc.date.issued2025
dc.departmentFakülteler, Mühendislik Fakültesi, Makine Mühendisliği Bölümü
dc.description.abstractFriction Stir Extrusion (FSE) was employed to convert cylindrical LM13 ingots into pipes, utilizing three distinct designs of rotating tool heads. This study examined the influence of process variables, consisting of tool rotational speed and plunging speed, on key properties of the resulting products. The properties investigated encompassed the size of Si precipitates, microhardness, wear resistance, and ultimate compressive strength (UCS). To effectively establish the relationships between the process input variables and the resulting mechanical and microstructural characteristics of the produced pipes, an artificial neural network (ANN) was used. This established correlation was integrated into a hybrid multi-objective optimization framework to identify the optimal process parameters. The investigation determined the ideal configuration: a plunging rate of 31 mm/min, a rotational rate of 653 rpm, and tool design number 3. (c) 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).
dc.description.sponsorshipTUBITAK 2221 program [TUBITAK 2221, 2023/6]
dc.description.sponsorshipParviz Asadi acknowledges the TUBITAK 2221 program for visiting scientists on sabbatical leave (2023/6) to visit Gebze Technical University, Turkiye.
dc.identifier.doi10.1016/j.ijlmm.2024.11.003
dc.identifier.endpage193
dc.identifier.issn2589-7225
dc.identifier.issn2588-8404
dc.identifier.issue2
dc.identifier.orcid0000-0001-7872-6408
dc.identifier.scopus2-s2.0-85217901126
dc.identifier.scopusqualityQ1
dc.identifier.startpage182
dc.identifier.urihttps://doi.org/10.1016/j.ijlmm.2024.11.003
dc.identifier.urihttps://hdl.handle.net/20.500.14854/10664
dc.identifier.volume8
dc.identifier.wosWOS:001508601500001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherKeai Publishing Ltd
dc.relation.ispartofInternational Journal of Lightweight Materials and Manufacture
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20251020
dc.subjectFSE
dc.subjectTool design
dc.subjectArtificial neural network
dc.subjectAl-Si cast alloy
dc.subjectCompression strength
dc.subjectWear rate
dc.titleFriction stir extrusion: Parametrical optimization for improved Al-Si aluminum tube production
dc.typeArticle

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