Damage detection of carbon fibre reinforced polymer cantilever beam using modal analysis of structural vibration - Loo, Kai Xen; Md Rezali, Khairil Anas; Mohamed Yusuf, Mohd Zuhri; Mohammad Hassan, Azmi (Society of Vibration and Acoustics Malaysia, 2023)
Research in structural health monitoring is becoming intense because it is used for detecting and preventing failures and damages in structures or systems. The characteristics for detection of failures or damages depends on many factors such as methods of detection, types of material and structures. Carbon fiber composite has become common nowadays, used in many mechanical structures. Carbon fiber composite is light, and strong (high tensile strength per weight) but it is also brittle, thus preventing it from fail before it occurs is needed to be done to make sure it is always in good condition. The aim of this project was to investigate the effect of damage location, severity, and number of damages on the natural frequencies of the carbon fiber composite cantilever beam. The natural frequencies were acquired using vibration modal analysis on the carbon fiber composite cantilever beam. A model of carbon fiber beam was developed in ANSYS with the material defined by the results from a tensile test. The model was then optimized and validated by comparing the natural frequencies of the beam predicted by ANSYS with those acquired experimentally. Once validated, simulations were conducted with artificial damages created in ANSYS based on three common damage parameters which were damage location, severity of damage, and number of damages. It was found that natural frequencies of the beam structure reduced with increasing severity of damage, increasing location distance from the free end of the cantilever beam to the damage position, and increasing the number of damages. It was concluded based on the study that the damage in cantilever beam can be detected through modal analysis of structural vibration including classifying the number of damages, location, and severity. These findings will help industry in planning maintenance, reduce downtime and improve product quality.