Finite element analysis of bending tests for fatigue cracking assessment

This study investigates material fatigue failure and fractures, mainly focusing on surface cracks and their implications in engineering applications. It reviews existing research to underscore the significance of understanding fatigue behaviour for maintaining structural reliability. The problem st...

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Bibliographic Details
Main Authors: Muhamad Husnain, Mohd Noh, Lee, J. Q., Mohd Akramin, Mohd Romlay, Mohd Shamil, Shaari, Takahashi, Akiyuki
Format: Article
Language:English
Published: Springer Berlin Heidelberg 2025
Subjects:
Online Access:https://umpir.ump.edu.my/id/eprint/44991/
Description
Summary:This study investigates material fatigue failure and fractures, mainly focusing on surface cracks and their implications in engineering applications. It reviews existing research to underscore the significance of understanding fatigue behaviour for maintaining structural reliability. The problem statement emphasises the challenges posed by material failure and the necessity for practical analysis to extend the product lifespan. The study aims to predict fatigue crack propagation in aluminium alloys under bending tests by using finite element analysis and fatigue crack growth (FCG) models. It utilises the S-version finite element method (S-FEM). It compares different FCG models to select the best model and to improve its accuracy towards actual experiment results. Specific study limits have been employed in this research to provide a particular investigational scope. The study examines four FCG models: Paris, Walker, Frost & Pook, and Huang & Moan. The specimens used in this analysis are aluminium alloy 7075-T6 and aluminium alloy AISi10Mg. The bending models used in this research are the three-point bending and four-point bending models. The result is examined based on the objectives of this study by using data collected from the simulation and experiment. Subsequently, a conclusion is drawn from the study’s findings which found that the Frost and Pook model demonstrated the most effective FCG prediction among the evaluated models.