Optimization of Mechanical Properties of Cordia dichotoma/Alumina Nanoparticle Reinforced Epoxy Nanocomposites Under Alkali Treatment Using ANN Techniques
J. Environ. Nanotechnol., Volume 14, No 2 (June 2025) pp. 525-540
Abstract
The growing interest in natural fiber- and nano-filler-reinforced composites is driven by their excellent strength-to-weight ratio, recyclability, and environmental sustainability. This study aims to optimize key parameters influencing the mechanical performance of hybrid nanocomposites reinforced with Cordia dichotoma (CD) fibers and nano alumina (nAl₂O₃). The composites were fabricated using the conventional hand lay-up technique by varying the content of CD fiber (wt%), fiber length (mm), nAl₂O₃ content (wt%), and CD fiber alkali treatment duration. ASTM standards assess mechanical properties. The Taguchi optimization method was employed to identify optimal process parameters. Maximum tensile strength was achieved with 10 wt% Cordia dichotoma fiber (10 mm), 4 wt% nAl₂O₃, and 12 hours of NaOH treatment. Optimal flexural strength was obtained under similar conditions, with an 8-hour treatment, while the best impact strength was observed with a 15 mm fiber length, 6 wt% nAl₂O₃, and 12-hour alkali treatment. Experimental results closely aligned with artificial neural network (ANN) predictions, with deviations within 3-4%, confirming the model's predictive accuracy. ANOVA analysis revealed that fiber content, filler content, and alkali treatment contributed 4%, 1%, and 0.1%, respectively, to the variation in properties, with fiber content yielding approximately 90% influence. The close agreement between experimental data and ANN forecasts highlights the potential of ANN-based models for predicting mechanical behavior and optimizing composite design. This approach provides valuable insights for enhancing reliability and predicting service life in various industrial applications.
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Reference
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