{"title":"Investigation of nano-hBN/ natural fibers reinforced epoxy composites for thermal and electrical applications using GRA and ANFIS optimization methods","authors":"Ramraji Kirubakaran , Dinesh Ramesh Salunke , Shenbaga Velu Pitchumani , Venkatachalam Gopalan , Aravindh Sampath","doi":"10.1016/j.polymertesting.2024.108561","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":20628,"journal":{"name":"Polymer Testing","volume":"139 ","pages":"Article 108561"},"PeriodicalIF":5.0000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0142941824002381/pdfft?md5=f1cd18b818134fed1d25dd54a9aafcb7&pid=1-s2.0-S0142941824002381-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Polymer Testing","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0142941824002381","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0
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.
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.