The effect of filler type (tungsten carbide, zinc oxide) and content on the mechanical and wear behavior of jute/flax reinforced epoxy hybrid composites: Experimental and artificial neural network analysis

被引:0
|
作者
Demir, Mehmet Emin [1 ]
机构
[1] Batman Univ, Besiri Organized Ind Zone Vocat Sch, Batman, Turkiye
关键词
ANN; jute/flax; mechanical properties; WC; wear; ZnO; DRY SLIDING WEAR; GLASS-FIBER; THERMAL-PROPERTIES; TRIBOLOGICAL PROPERTIES; ZNO; FRICTION; CARBON; JUTE; TENSILE; NANOPARTICLES;
D O I
10.1002/pc.29879
中图分类号
TB33 [复合材料];
学科分类号
摘要
The application of natural fiber-reinforced composite (NFRC) is growing rapidly in various industrial sectors, such as automotive, packaging, and construction materials, because of its affordability and excellent mechanical properties. This study investigates the effects of tungsten carbide (WC) and zinc oxide (ZnO) filler content on the mechanical and wear performance of jute/flax-reinforced hybrid composites. WC filler enhanced the tensile stress of composites more effectively than ZnO filler. Adding 1% WC and 1.5% ZnO enhanced the tensile strength of the unfilled composite by 41% and 24%, respectively. Wear tests conducted under various parameters revealed that increasing the load reduced the coefficient of friction (COF) but increased the wear rate and mass loss for both neat and filled composites. The lowest COF values were observed in WC-filled composites. It was observed that ZnO and WC-filled composites exhibited less matrix deformation, pitting, and porosity compared to neat composites. Impact resistance improved with the addition of ZnO and WC fillers, with the impact strength of 1.5% WC-filled hybrid composites increasing from 4.5 to 10.3 J/m2. The experimental wear rate and COF closely matched the predicted values acquired from the model, demonstrating the success of the artificial neural network (ANN) model in precisely estimating COF and wear rate values.
引用
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页数:20
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