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    Development of Polyurethane-Based Composites With Salt Clay and Industrial Wastes as Fillers: Corrosion, Mechanical Properties, and Machine Learning Insights
    (Wiley, 2025) Dag, Mustafa; Aydogmus, Ercan; Arslanoglu, Hasan; Yalcin, Zehra Gulten; Barlak, Semahat
    In 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.
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    Öğe
    Valorization of Industrial Waste in Polymer Composites: Enhancing Mechanical and Thermal Properties for Insulation Applications Using Machine Learning Analysis
    (Wiley, 2026) Dag, Mustafa; Aydogmus, Ercan; Yalcin, Zehra Gulten; Arslanoglu, Hasan
    This study investigates the incorporation of industrial waste materials into polyurethane-based composites and evaluates their mechanical, thermal, and microstructural properties. The polyurethane matrix was synthesized from methylene diphenyl diisocyanate (MDI) and polyether polyol, into which various waste fillers, including ulexite, colemanite, tincal, and K & imath;rka clay, were introduced in different proportions. Mechanical testing revealed that specific wastes significantly enhance compressive strength, with ulexite- and clay-reinforced composites achieving improvements of 42.19% and 43.54%, respectively, compared to the pure polymer. The ulexite-clay composite exhibited the highest mechanical strength (38.67 kN), whereas tincal-containing samples demonstrated the weakest performance. Shore A hardness values generally decreased with waste incorporation, indicating that filler addition reduces polymer rigidity. Thermal conductivity results showed property variations within +/- 25%, where ulexite increased conductivity while K & imath;rka clay reduced it, thereby improving thermal insulation potential. Microstructural analysis using scanning electron microscopy (SEM) confirmed heterogeneous morphologies with dense filler distribution that intensified with increasing filler ratios. Fourier transform infrared spectroscopy (FTIR) indicated both physical and chemical interactions between the polymer matrix and boron-containing fillers, highlighting the complex interfacial bonding mechanisms. To complement the experimental analyses, machine learning (ML) models were applied to predict composite performance based on waste type and ratio. Among the tested algorithms, Random Forest (RF) demonstrated the highest predictive accuracy (R-2 > 0.90), confirming its suitability for modeling composite properties. The integration of ML provided quantitative insights into the role of individual and combined waste fillers, aligning closely with experimental observations. This research demonstrates that the controlled selection and optimization of waste fillers can enhance the performance of polyurethane composites, promote recycling of industrial byproducts, and support the development of sustainable materials for applications such as thermal insulation and structural components.