Investigation of nano-hBN/ natural fibers reinforced epoxy composites for thermal and electrical applications using GRA and ANFIS optimization methods

IF 5 2区 材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Polymer Testing Pub Date : 2024-09-05 DOI:10.1016/j.polymertesting.2024.108561
Ramraji Kirubakaran , Dinesh Ramesh Salunke , Shenbaga Velu Pitchumani , Venkatachalam Gopalan , Aravindh Sampath
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Abstract

Polymer composites reinforced with natural fibers have made great strides in industrial appliance use owing to the fibers exceptional composite properties, low environmental impact, and long lifespan. Five natural fibers-banana, sugar cane, coir, wood, and rice husk—are employed as short fibers in this experiment. The electrical and thermal properties of hybrid filler polymer (HFP) composites are also examined in relation to the thermal conductivity nano hBN filler weight ratio. HFP composites are prepared using the Taguchi design to select nano filler ratios and fifteen tests. Experimental results demonstrate that HFP composites with the maximum h-BN content are the most electrically and thermally robust. HFP composite material has the highest thermal conductivity and electrical resistance of 1.01 W/m-K and 346.91 Giga-Ohms respectively, with 5 % nano hBN and 2 % RH (sample 6). Nano h-BN fillers positively increase the thermal conductivity and electrical resistance of the composite structures. An improvement in thermal conductivity and electrical resistance is evident for the sample 6 composite, which increased by 16.09 % and 154.05 %, respectively, compared to the S1 interlaced multiphase hybrid polymer composite. Sample 4, containing rice husk fiber, achieves the minimal dielectric constant of 0.94, whereas sample 12, containing banana fiber, achieves a dielectric constant of 0.98. ANOVA is used to determine how such variables affect output variables. Performance measures are determined using the adaptive neuro-fuzzy inference system model with a hybrid grey base. Using the adaptive network-based fuzzy inference system, the input-output relation is modeled. After comparing experimental and ANFIS-anticipated data, the latter accurately predicted HFP composite behaviour. The combination of h-BN and natural fiber composites holds significant potential for various electrical and thermal applications due to their exceptional overall properties.

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使用 GRA 和 ANFIS 优化方法研究用于热和电气应用的纳米-hBN/天然纤维增强环氧树脂复合材料
由于天然纤维具有优异的复合性能、对环境影响小、使用寿命长等特点,用天然纤维增强的聚合物复合材料在工业设备的应用中取得了长足的进步。本实验采用了香蕉、甘蔗、椰子纤维、木材和稻壳五种天然纤维作为短纤维。本实验还研究了杂化填料聚合物(HFP)复合材料的电性能和热性能与导热纳米 hBN 填料重量比的关系。在制备 HFP 复合材料时,采用了田口设计法来选择纳米填料的比例,并进行了 15 次测试。实验结果表明,h-BN 含量最大的 HFP 复合材料具有最强的导电性和热稳定性。在纳米 hBN 含量为 5%、相对湿度为 2%的情况下,HFP 复合材料的热导率和电阻率最高,分别为 1.01 W/m-K 和 346.91 Giga-Ohms(样品 6)。纳米 h-BN 填料能积极提高复合材料结构的导热性和电阻。与 S1 交错多相杂化聚合物复合材料相比,样品 6 复合材料的热导率和电阻率分别提高了 16.09% 和 154.05%。含有稻壳纤维的样品 4 实现了 0.94 的最小介电常数,而含有香蕉纤维的样品 12 则实现了 0.98 的介电常数。方差分析用于确定这些变量如何影响输出变量。使用混合灰色基的自适应神经模糊推理系统模型确定性能指标。使用基于自适应网络的模糊推理系统对输入输出关系进行建模。在对实验数据和 ANFIS 预测数据进行比较后,后者准确地预测了 HFP 复合材料的行为。由于 h-BN 和天然纤维复合材料具有优异的综合性能,因此它们的组合在各种电气和热应用中具有巨大的潜力。
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来源期刊
Polymer Testing
Polymer Testing 工程技术-材料科学:表征与测试
CiteScore
10.70
自引率
5.90%
发文量
328
审稿时长
44 days
期刊介绍: Polymer Testing focuses on the testing, analysis and characterization of polymer materials, including both synthetic and natural or biobased polymers. Novel testing methods and the testing of novel polymeric materials in bulk, solution and dispersion is covered. In addition, we welcome the submission of the testing of polymeric materials for a wide range of applications and industrial products as well as nanoscale characterization. The scope includes but is not limited to the following main topics: Novel testing methods and Chemical analysis • mechanical, thermal, electrical, chemical, imaging, spectroscopy, scattering and rheology Physical properties and behaviour of novel polymer systems • nanoscale properties, morphology, transport properties Degradation and recycling of polymeric materials when combined with novel testing or characterization methods • degradation, biodegradation, ageing and fire retardancy Modelling and Simulation work will be only considered when it is linked to new or previously published experimental results.
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