In laminated composite structures, delamination is one of the most common defects. The delamination affects the vibration characteristics of laminates, and thus these indicators can be used to detect the potentially catastrophic failures and measures the health characteristics of laminates. In this study, particle swarm optimization (PSO) and artificial neural network (ANN) are used to optimize and predict the influences of location and size of delamination on the dynamic behavior of composite plates. The classical laminated plate theory
In the process of structural damage detection using continuous wavelet transform (CWT), the perturbation or damage is located by identifying the defects locally in the input signal data. In this work the damage identification procedure using continuous wavelet transform is developed. This method is studied numerically using a simple beam model. The influence of reduced spatial sampling using fundamental mode shape is investigated in detail. The method is also investigated to ascertain the smallest level of damage identified using strain energy mode shape data.
This paper presents a dynamic characterization of the delaminated composite plate structure. The governing Equation of the delaminated composite plate developed using finite element method (FEM) formulation grounded on a classical laminated plate theory. The FEM formulation designed for the delaminated composite plate under bending validated by comparing the natural frequencies evaluated using the present FEM formulation through MATLAB coding and experimental results. Various parametric studies have investigated. Simulation study conducted for different interfaces with different delamination location problems solved by the FEM. It shows that percentage of natural frequency decrease in the delaminated composite plate when compared to the intact composite plate. The significant variation in the natural frequency depends on the delamination location and delamination interface. This study is useful for the designers to tailor the structures with various interface delamination present in the arrangements. Artificial neural network algorithm implemented to study the effect of delamination in laminated composite plate structures
The objective of this work focuses on performance comparison of different wavelet families based on vibration damage detection in a Timoshenko beam structure. Module 1 considers only rotational damaged data only. Module 2 considers the difference data between undamaged and damaged beam structure. Wavelet properties are important factor for wavelet selection process to applicable area. The different types of continuous wavelet transform like daubechies, symlets, Coiflets, Gaussian, dmeyer, morlet, biorsplines, reverse bior are used. The rectangular beam is modeled numerically. The modal analysis is performed in a Timoshenko beam with two ends clamped boundary condition. The natural frequency and mode shapes of different damaged conditions are obtained. Damage is created by diminishing the young's modulus value percentage of single or multiple element, Rotational mode shape data's are effective in damage detection up to damage case 2, and Difference data of rotational mode shape data's are effective all damage cases. Gaussian white noise SNR (Sound Noise ratio) 24 added with difference data, noise affected the damage detection algorithm performances in least damage case 6 and triple damage case 8. Damage localize by sym3, dmey, bior6.8 wavelets are effective comparing to all other wavelet families. The absolute wavelet coefficients are used for damage localization and the maximum absolute wavelet coefficient are intended for identifying the damage severity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.