ABSTRACT
Natural fibers continue to attract the attention of researchers because of their use in reinforced polymer composites. They allow industrial designers to find solutions to aging infrastructure problems more than 50 years after their use in aerospace, automotive, construction, consumer products, etc. These fibers are economical and low density. In fact, in addition to their specific properties, such as non-abrasiveness and biodegradability, they are an ecological material with low environmental impact. Washingtonia Filifera (WF) fiber, among others, is attracting more and more researchers to replace certain fibers such as synthetic or glass fibers, being widely used in the world. This study aims to determine the mechanical parameters of WF fibers with a gauge length (GL = 50 mm) in quasi-static tension. Tensile tests were carried out on 150 fibers in five-test series to determine the influence of their variability on the tensile stress, strain at break and Young’s modulus of plant fibers. Due to the dispersion of the results of the mechanical tensile properties of WF fibers, which is a characteristic of natural fibers, a statistical study is necessary. Thus, statistical tools such as the two and three-parameter Weibull distribution at 95% confidence level (CI) and the one-way analysis of variance ANOVA were carried out to study this dispersion.
摘要
天然纤维因其在增强聚合物复合材料中的应用而继续引起研究人员的注意. 在航空航天、汽车、建筑、消费品等领域使用了50多年后,它们使工业设计师能够找到解决老化基础设施问题的方法. 这些纤维既经济又低密度. 事实上, 除了它们的特殊性质, 如非研磨性和生物降解性,它们是一种对环境影响较小的生态材料. Washingtonia Filifera (WF) 纤维等正在吸引越来越多的研究人员来替代某些纤维, 如合成纤维或玻璃纤维,这些纤维在世界上被广泛使用. 本研究旨在确定准静态拉伸下标距长度 (GL=50 mm) 的WF纤维的力学参数. 对五个试验系列中的150根纤维进行了拉伸试验, 以确定它们的可变性对植物纤维的拉伸应力、断裂应变和杨氏模量的影响. 由于WF纤维的机械拉伸性能 (这是天然纤维的-个特征) 的结果分散, 因此有必要进行统计研究. 因此, 采用统计工具, 如95%置信水平下的二参数和三参数威布尔分布 (CI) 以及单向方差分析 (ANOVA) 来研究这种离散度.
Nomenclature
σ0: Characteristic strength
ε0: Characteristic strain
E0: Characteristic Young’s modulus
mσ, mε, mE: Weibull modulus
AD: Adjusted Anderson-Darling goodness-of-fit.
ANOVA: Analysis of Variance
LS: Least squares
LM: Maximum likelihood
CDF: Cumulative Distribution Function
CI: Confidence interval
P: Probability
SS: Sum of squares
MS: Mean of squares
BG: Between groups
WG: Within groups
Acknowledgments
The authors gratefully acknowledge (la Direction Générale de la Recherche Scientifique et du Développement Technologique, Algérie) DGRSDT for their support in this work.
Disclosure statement
No potential conflict of interest was reported by the author(s).