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Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer

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dc.citedby3
dc.contributor.authorDele-Afolabi T.T.en_US
dc.contributor.authorAhmadipour M.en_US
dc.contributor.authorAzmah Hanim M.A.en_US
dc.contributor.authorOyekanmi A.A.en_US
dc.contributor.authorAnsari M.N.M.en_US
dc.contributor.authorSikiru S.en_US
dc.contributor.authorKumar N.en_US
dc.contributor.authorid56225674500en_US
dc.contributor.authorid57203964708en_US
dc.contributor.authorid24723635600en_US
dc.contributor.authorid57194067040en_US
dc.contributor.authorid55489853600en_US
dc.contributor.authorid57211063469en_US
dc.contributor.authorid58832061500en_US
dc.date.accessioned2025-03-03T07:45:24Z
dc.date.available2025-03-03T07:45:24Z
dc.date.issued2024
dc.description.abstractThe impact of multi-walled carbon nanotubes (MWCNTs) on the development of the intermetallic compound (IMC) at the interface of the Sn5Sb/Cu solder joint was investigated. Reflow soldering was used to produce the samples, which were subsequently isothermally aged at different temperatures. The presence of MWCNTs in the Sn-5Sb solder alloy significantly prevented IMC formation at the interface and enhanced the shear strength, according to empirical observations, which were supported by the excellent properties of MWCNTs. An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. The AO-ELM model's input parameters included a number of significant variables, such as MWCNTs addition, aging temperature, and aging period that have an impact on the IMC thickness and the shear strength of the solder composite joints. In terms of the statistical accuracy measures, it was observed that the AO-ELM outperformed the traditional ANN and ELM models in predicting the IMC thickness and shear strength of MWCNTs-reinforced Sn5Sb/Cu composite solder joints. The novelty of the approach recommended stems from the accuracy attained by modifying hyper-parameters with AO that has been paired with the fast processing speed of ELM. ? 2023 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo172684
dc.identifier.doi10.1016/j.jallcom.2023.172684
dc.identifier.scopus2-s2.0-85175627414
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85175627414&doi=10.1016%2fj.jallcom.2023.172684&partnerID=40&md5=b129fb6bcb7b72110e360eab02a63d48
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36875
dc.identifier.volume970
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Alloys and Compounds
dc.subjectForecasting
dc.subjectKnowledge acquisition
dc.subjectLead-free solders
dc.subjectMachine learning
dc.subjectMultiwalled carbon nanotubes (MWCN)
dc.subjectOptimization
dc.subjectSoldered joints
dc.subjectSoldering
dc.subjectAquila optimizer
dc.subjectExtreme learning machine
dc.subjectIntermetallic compound layer
dc.subjectLearning machines
dc.subjectMachine modelling
dc.subjectMulti-walled-carbon-nanotubes
dc.subjectOptimizers
dc.subjectPerformance assessment
dc.subjectShears strength
dc.subjectSn-based solders
dc.subjectTin alloys
dc.titlePerformance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizeren_US
dc.typeArticleen_US
dspace.entity.typePublication
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