Publication:
Prediction of the NiTi shape memory alloy composition with the best corrosion resistance for dental applications utilizing artificial intelligence

dc.contributor.coauthorN/A
dc.contributor.departmentDepartment of Mechanical Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorCanadinç, Demircan
dc.contributor.kuauthorNazarahari, Alireza
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2024-11-09T23:52:08Z
dc.date.issued2021
dc.description.abstractThis paper presents an artificial intelligence (AI) framework proposed to predict the optimum composition of the NiTi shape memory alloy (SMA) to be used in dental applications. A multilayer feed forward neural network (MLFFNN) was adopted for machine learning (ML) model to train the readily available experimental data in literature on the Ni ion release from a variety of NiTi compositions into artificial saliva (AS) solutions to predict the NiTi SMA composition to exhibit the lowest amount of Ni ion release into oral cavity. As a result, 51.5 at.% Ni - balance Ti composition was predicted to be the optimum NiTi SMA composition to release the lowest amount of Ni ions into the oral cavity, which was supported by the validation experiments utilizing static immersion experiments carried out in AS and the post-mortem inductively coupled plasma mass spectrometer (ICP-MS) analysis of the immersion fluids. The findings of the work presented herein not only demonstrate that the proposed AI framework successfully predicts the most biocompatible NiTi SMA for dental applications, but also open a venue for the utility of the current AI framework in the design of other medical alloys and SMAs for a variety of applications.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.openaccessNO
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipKoc University Graduate School of Sciences and Engineering The authors thank the Koc University Graduate School of Sciences and Engineering for the financial support provided for A. Nazarahari. The help of Ms. Defne Kudu.g, Ms. Gamze Kecibas and Ms. Selin Sur with data mining is gratefully acknowledged.
dc.description.volume258
dc.identifier.doi10.1016/j.matchemphys.2020.123974
dc.identifier.eissn1879-3312
dc.identifier.issn0254-0584
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-85096179675
dc.identifier.urihttps://doi.org/10.1016/j.matchemphys.2020.123974
dc.identifier.urihttps://hdl.handle.net/20.500.14288/14810
dc.identifier.wos594996200001
dc.keywordsArtificial intelligence
dc.keywordsMachine learning
dc.keywordsNiTi
dc.keywordsShape memory alloy
dc.keywordsBiocompatibility
dc.language.isoeng
dc.publisherElsevier Science Sa
dc.relation.ispartofMaterials Chemistry and Physics
dc.subjectMaterials science
dc.titlePrediction of the NiTi shape memory alloy composition with the best corrosion resistance for dental applications utilizing artificial intelligence
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorNazarahari, Alireza
local.contributor.kuauthorCanadinç, Demircan
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Mechanical Engineering
local.publication.orgunit2Graduate School of Sciences and Engineering
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