https://orcid.org/0000-0002-3950-4375
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ABSTRACT
The main objective of the present study was to investigate both the effect of incorporating Jute Fibers (JF) into the high density polyethylene (HDPE) matrix and to model the water uptake behavior of the biocomposites (HDPE/%JF) using the artificial neural network (ANN) model to predict the absorption ratio as a function of immersion time. Due to the fact that even partially biocomposites have a low resistance to moisture, which degrades their mechanical properties over time, their field of application is limited as a result of this notable defect. Absorption tests were carried out by immersing the biocomposite samples in distilled water at room temperature for several days until absorption became stable. Water absorption increased with both jute filler loading and immersion time and that the uptake process was fast at the begging of the experiments to reach saturation in time immersion close to 120 h. The results of ANN predicted values are close to the parity threshold; they are in perfect agreement with those obtained experimentally. Thus, the ANN method is able to reliably predict the water absorption of HDPE/%Jute fiber biocomposites. Therefore, it should be concluded that the ANN model provides better prediction accuracy than the RSM model. Finally, the findings of this study have positive implications for future applications of HDPE/%Jute biocomposites whether in the design or maintenance phase; application engineers can easily determine the swelling coefficient of such biocomposites without experimentation, saving thus money and time.
摘要
本研究的主要目的是研究将黄麻纤维 (JF) 加入高密度聚乙烯 (HDPE) 基质中的效果, 并使用人工神经网络 (ANN) 模型模拟生物复合材料 (HDPE/%JF) 的吸水行为, 以预测作为浸泡时间函数的吸水率. 由于即使是部分生物复合材料也具有较低的防潮性, 这会随着时间的推移降低其机械性能, 因此, 由于这一显著缺陷, 其应用领域受到限制. 通过将生物复合物样品在室温下浸入蒸馏水中数天直到吸收变得稳定, 进行吸收测试. 吸水率随黄麻填料负载量和浸渍时间的增加而增加, 并且在实验开始时, 吸水过程很快, 在接近 120h 的浸渍时间内达到饱和. ANN 预测值的结果接近奇偶校验阈值; 它们与实验获得的结果完全一致. 因此, ANN 方法能够可靠地预测 HDPE/% 黄麻纤维生物复合材料的吸水率. 因此, 可以得出结论, ANN 模型比 RSM 模型提供更好的预测精度. 最后, 本研究的结果对HDPE/%黄麻生物复合材料的未来应用具有积极意义, 无论是在设计阶段还是在维护阶段; 应用工程师可以轻松确定此类生物复合材料的膨胀系数, 而无需进行实验, 从而节省了资金和时间.
Acknowledgments
The authors would gratefully like to acknowledge the financial support of DGRSDT (la Direction Générale de la Recherche Scientifique et du Développement Technologique, Algérie) for their support in this work. The authors Azzedine MAKHLOUF, Ahmed BELAADI and Messaouda BOUMAAZA are funded by research project number A11N01UN210120210001 from January 1, 2021.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Ethics approval
The work contains no libelous or unlawful statements, does not infringe on the rights of others, or contains material or instructions that might cause harm or injury.