Thirupathi Suresh, Gopalan Venkatachalam, Mallichetty Elango
School of Mechanical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India.
Centre for Advanced Materials and Innovative Technologies, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India.
Sci Rep. 2025 Jul 2;15(1):22757. doi: 10.1038/s41598-025-06740-0.
Void development is one of the main problems faced by natural fiber polymer composites since it severely affects their physical and mechanical properties. It limits these composites for use in construction, aerospace, automotive and marine uses. Hence, this study takes on this issue by incorporating various nanosized Multi-Walled Carbon Nanotubes (MWCNT), hexagonal Boron Nitride (h-BN) and Alumina (AlO) nanofillers in epoxy-based Borassus flabellifer fiber (BFF) composites fabricated using the hand layup technique. The results show that increasing volume fraction of nanofiller gives way to decreasing fiber volume fraction, together with increase in composite density and void content. MWCNT-filled composites have the highest void content percentage among the different nanofillers investigated because of their lower theoretical density, which is inversely proportional to void content percentage. This research investigates the effects of the type of nanofiller, BFF mesh size and weight percent of the fiber upon the void content in fiber-reinforced composites. Design of Experiments approach is utilized to analyse the effect of these parameters and ANN model, employing advanced hyperparameter optimization strategy developed in Python, is used to elaborate upon specifics of the characteristics of void formation. Quantitative analysis of void content and particle distribution, analysed through SEM imaging and microstructural characterization through optical microscopy, further confirms these results, providing detailed information about void formation and filler dispersion. The optimized combination (1 wt% fiber content, 75 µm fiber mesh size and 1 wt% h-BN nanofiller) yielded 1.90% lowest void content after experimentation. This research provides fundamental understanding of the void mechanisms concerning bio-nano composites and presents an optimal predictor model that minimizes voids. This contributes and builds toward the directions of advancing materials for high-performance applications.
孔隙形成是天然纤维聚合物复合材料面临的主要问题之一，因为它严重影响其物理和机械性能。这限制了这些复合材料在建筑、航空航天、汽车和船舶领域的应用。因此，本研究通过在采用手糊工艺制备的环氧基扇叶棕榈纤维（BFF）复合材料中加入各种纳米级多壁碳纳米管（MWCNT）、六方氮化硼（h-BN）和氧化铝（AlO）纳米填料来解决这一问题。结果表明，纳米填料体积分数的增加导致纤维体积分数的降低，同时复合材料密度和孔隙率增加。在研究的不同纳米填料中，MWCNT填充的复合材料孔隙率最高，因为其理论密度较低，这与孔隙率百分比成反比。本研究调查了纳米填料类型、BFF筛网尺寸和纤维重量百分比对纤维增强复合材料孔隙率的影响。采用实验设计方法分析这些参数的影响，并使用基于Python开发的先进超参数优化策略的人工神经网络（ANN）模型来详细阐述孔隙形成特性的具体情况。通过扫描电子显微镜（SEM）成像分析孔隙率和颗粒分布，并通过光学显微镜进行微观结构表征，进一步证实了这些结果，提供了有关孔隙形成和填料分散的详细信息。经过实验，优化组合（1 wt%纤维含量、75 µm纤维筛网尺寸和1 wt% h-BN纳米填料）产生了1.90%的最低孔隙率。本研究提供了对生物纳米复合材料孔隙机制的基本理解，并提出了一个使孔隙最小化的最优预测模型。这有助于并推动了高性能应用材料的发展方向。
