Development of Polyurethane-Based Composites With Salt Clay and Industrial Wastes as Fillers: Corrosion, Mechanical Properties, and Machine Learning Insights

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dc.authorid0000-0001-9540-3475
dc.authorid0000-0001-5460-289X
dc.authorid0000-0002-3132-4468
dc.authorid0000-0002-1643-2487
dc.contributor.authorDag, Mustafa
dc.contributor.authorAydogmus, Ercan
dc.contributor.authorArslanoglu, Hasan
dc.contributor.authorYalcin, Zehra Gulten
dc.contributor.authorBarlak, Semahat
dc.date.accessioned2026-02-03T12:03:09Z
dc.date.available2026-02-03T12:03:09Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractIn this study, a polyurethane-based composite is developed by incorporating salt clay, ulexite, colemanite, and various other industrial waste materials. The effects of these fillers on the composite are evaluated and modeled using machine learning techniques. Among the tested models, random forest and neural network demonstrate the highest performance in predicting changes in compressive strength, hardness, and thermal conductivity. The dispersion of salt clay within the polyurethane matrix provides a 300%-500% increase in compressive strength and a 25%-40% improvement in hardness. Ulexite enhances compressive strength by 250%-350% and increases hardness by up to 30%, while colemanite contributes to a 400%-500% rise in compressive strength and a 35%-40% improvement in hardness. The addition of K & imath;rka clay waste and tincal further improves the composite's hardness and overall durability. Fly ash significantly increases compressive strength, although its effect on hardness is limited. The machine learning models effectively capture the relationship between input parameters and composite performance. The random forest model achieves a mean squared error (MSE) of 0.15 for compressive strength and 0.20 for hardness, while the neural network model yields the best results for thermal conductivity prediction with an MSE of 0.12. These findings highlight the potential of the developed composite for industrial applications, particularly in thermal insulation and low-load structural components. Future studies will focus on evaluating its performance under real-world conditions and further assessing its long-term durability.
dc.description.sponsorshipFimath;rat University Scientific Research Projects Unit [ADEP.25.07]
dc.description.sponsorshipThis work was supported by F & imath;rat University Scientific Research Projects Unit [ADEP.25.07].
dc.identifier.doi10.1002/vnl.22233
dc.identifier.endpage1454
dc.identifier.issn1083-5601
dc.identifier.issn1548-0585
dc.identifier.issue6
dc.identifier.scopus2-s2.0-105006788917
dc.identifier.scopusqualityQ1
dc.identifier.startpage1440
dc.identifier.urihttps://doi.org/10.1002/vnl.22233
dc.identifier.urihttps://hdl.handle.net/20.500.12428/34988
dc.identifier.volume31
dc.identifier.wosWOS:001495453800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofJournal of Vinyl & Additive Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260130
dc.subjectindustrial wastes
dc.subjectinsulation material
dc.subjectmachine learning
dc.subjectpolyurethane-based composites
dc.subjectsalt clay
dc.titleDevelopment of Polyurethane-Based Composites With Salt Clay and Industrial Wastes as Fillers: Corrosion, Mechanical Properties, and Machine Learning Insights
dc.typeArticle

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