Weibull Statistic and Artificial Neural Network Analysis of the Mechanical Performances of Fibers from the Flower Agave Plant for Eco-Friendly Green Composites
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Date
2024-01
Journal Title
Journal ISSN
Volume Title
Publisher
JOURNAL OF NATURAL FIBERS
Abstract
The research conducted focused on examining the unique properties of
Agave Americana Flower Stem fiber (AAFS), particularly its behavior under
quasi-static tensile conditions. A total of 200 AAFS fibers were subjected to
tensile tests using a standard gauge length of 40 mm. Tests spanned seven
groups with quantities (N) ranging from 30 to 200. The study aimed to
understand the fibers’ mechanical traits, as tensile resistance and modulus
of elasticity, and to see how different test quantities influence these properties. A significant observation was the dispersion of the tensile characteristics
of AAFS fibers, a common trait of natural fibers. To understand this, we
applied rigorous statistical tools, including the Weibull distribution at
a 95% confidence interval and one-way ANOVA. A mathematical model
was produced utilizing data from experiments regarding the tensile behavior
of AAFS fibers. The ANN provided correlation coefficients (R2
) of 0.9897,
0.9971, 0.9993, and 0.9939 for training, validation, testing, and all datasets
respectively, which were able to accurately predict the experimental data.
The proposed model would be of tremendous assistance to engineers and
designers in obtaining green composite materials that are based on natural
fibers and thereby more durable. These methods illuminated the patterns in
our results, enriching our understanding of AAFS fiber mechanics