Crack growth analysis for rice husk reinforced polypropylene composite using equivalent initial flaw size concept

Crack growth that takes place in natural fibre polymer composite formations is dependent on several factors, whereby primary crack size is a key aspect that influences uncertainty of the crack growth. The nucleation stage is strongly affected by the fracture collapse of structures, unavoidably affec...

Full description

Saved in:
Bibliographic Details
Main Authors: Mohamed, Sity Ainy Nor, Zainudin, Edi Syams, Salit, Mohd Sapuan, Md. Deros, Mohd Azaman, Tajul Arifin, Ahmad Mubarak
Format: Article
Published: North Carolina University 2021
Online Access:http://psasir.upm.edu.my/id/eprint/96559/
https://ojs.cnr.ncsu.edu/index.php/BioRes/article/view/BioRes_16_3_4963_Nor_Mohamed_Crack_Growth_Analysis_Rice_Husk/8576
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Crack growth that takes place in natural fibre polymer composite formations is dependent on several factors, whereby primary crack size is a key aspect that influences uncertainty of the crack growth. The nucleation stage is strongly affected by the fracture collapse of structures, unavoidably affecting the accuracy of the estimation of total fatigue life. In this research, fatigue crack was examined using rice husk/polypropylene composite specimens across stress loads ranging from 80 to 90% for ultimate tensile strength at the stress ratios R=0.1, 0.3, and 0.5. Consequently, the propagation rate of the crack was dependent on the stress ratio. Crack resistance showed a drop in the propagation rate of the crack rate with an increase in the R value. This effect produced more fibres/matrix fracture at high stress ratio, in comparison to the low stress ratio, which was verified further through scanning electron microscopy. Moreover, the S-N curve method was proposed, as it facilitates the deterministic total fatigue life discovery in a highly favorable manner via equivalent crack size approach. A strong consensus was observed between the model of prediction and the outcomes of the experiment.