Comparative analysis of artificial neural networks and adaptive neuro-fuzzy inference system for biocomposite material synthesis and property prediction

Basit Öğe Kaydı
dc.authorid0000-0002-1643-2487
dc.authorid0000-0003-2746-9477
dc.authorid0000-0002-3132-4468
dc.contributor.authorAydin, Muhammet
dc.contributor.authorAydogmus, Ercan
dc.contributor.authorArslanoglu, Hasan
dc.date.accessioned2026-02-03T12:02:41Z
dc.date.available2026-02-03T12:02:41Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractBiocomposite materials (BMs) are becoming increasingly prevalent in modern applications. Estimating their production values involves various techniques, depending on the proportions of materials used. Among these techniques, artificial neural networks (ANN), fuzzy logic, statistical methods, and the adaptive neural fuzzy inference system are prominent. In this study, polyester biocomposites have been synthesized experimentally by adjusting the quantities of methyl ethyl ketone peroxide (MEKP), cobalt octoate (Co Oc) metal catalyst, marble factory waste, modified castor oil (MCO), and polyester raw material (UP) in specific ratios. The testing and analysis of these materials are conducted to determine parameters such as bulk density (BD), thermal conductivity coefficient (TCC), and activation energy (Ea). Subsequently, input and output values of the BMs are obtained, and ANN and adaptive neuro-fuzzy inference system (ANFIS) methods are employed for assessment. Both networks are trained and modeled using experimental data to construct their respective architectures. Validation of the models has been performed using data separate from the training set. A comparison between the actual values and those predicted by the network architectures revealed that the ANN method yielded outcomes with an average error of 0.3849 %, outperforming ANFIS. The findings showed that while ANFIS produced superior predictions for the Ea output value, the ANN structure fared better in predicting output values the BD and TCC.
dc.description.sponsorshipFimath;rat University Scientific Research Projects Unit [ADEP.25.34]
dc.description.sponsorshipThe financial support of this study was provided by F & imath;rat University Scientific Research Projects Unit (ADEP.25.34) .
dc.identifier.doi10.1016/j.matchemphys.2025.131150
dc.identifier.issn0254-0584
dc.identifier.issn1879-3312
dc.identifier.scopus2-s2.0-105008173945
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.matchemphys.2025.131150
dc.identifier.urihttps://hdl.handle.net/20.500.12428/34820
dc.identifier.volume344
dc.identifier.wosWOS:001514185100002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Science Sa
dc.relation.ispartofMaterials Chemistry and Physics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260130
dc.subjectPolyester biocomposite
dc.subjectBulk density
dc.subjectThermal conductivity
dc.subjectActivation energy
dc.subjectArtificial neural networks
dc.subjectAdaptive neuro-fuzzy inference system
dc.titleComparative analysis of artificial neural networks and adaptive neuro-fuzzy inference system for biocomposite material synthesis and property prediction
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu