4.4 Article

The ballistic and quasi-static puncture resistance of 3D fabrics impregnated with novel shear thickening fluids and modeling quasi-static behavior using artificial intelligence

期刊

JOURNAL OF COMPOSITE MATERIALS
卷 57, 期 21, 页码 3417-3432

出版社

SAGE PUBLICATIONS LTD
DOI: 10.1177/00219983231184941

关键词

3D fabric; Chemical modification; Shear thickening fluid; Artificial intelligence; Energy dissipation; Ballistic impact

向作者/读者索取更多资源

The present study focuses on chemically modifying polyethylene glycol (PEG) to enhance shear thickening fluids (STFs) and improve the ballistic impact and quasi-static resistance performance of 3D E-glass fabrics. Two agents, oxalic acid and glutaric acid, were used to modify the carrier fluid (PEG 200), and the modified PEGs were characterized using FTIR analysis. Rheological analysis showed that the modified STFs (G/STF and O/STF) had higher peak viscosity compared to pure STF (P/STF). The improved viscosity resulted in enhanced ballistic resistance and quasi-static performance of the modified STF-treated fabrics.
The present study deals with the chemical modification of polyethylene glycol (PEG) based on shear thickening fluids (STFs) and their application to improve the ballistic impact and quasi-static resistance performance of 3D E-glass fabrics. The carrier fluid (PEG 200) was modified with two different agents, oxalic acid and glutaric acid. The modified PEGs were then characterized by FTIR analysis. The rheological analysis of modified STF using glutaric (G/STF) and oxalic acid (O/STF) showed an improvement in peak viscosity by 10.33 and 3.28 times compared to pure STF (P/STF), respectively. Moreover, PEG modification resulted in higher chain length and a higher number of hydrophilic functional groups, representing superior media-particle interaction through abundant H-bonding. As a result of improved viscosity, the ballistic resistance and quasi-static performance of modified STF-treated fabrics were enhanced compared to that of P/STF-treated fabrics. A two-step artificial intelligence regression analysis was performed to predict quasi-static puncture resistance at different puncture speeds. The results showed a strong correlation between the load-deformation behavior and the loading speed.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据