Damage Detection in a Composite Plate Using Modal Analysis and Artificial Intelligence

Nasiri, Mahdi; Mahjoob, M. J.; Aghakasiri, A.

doi:10.1007/s10443-011-9231-x

Cited by 19 publications

(12 citation statements)

References 11 publications

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“…A structure can be considered as a dynamic system with distributed stiffness, mass and damping [15]. Defects emerged in the structure will lead to the changes in the structural physical properties, altering the dynamic properties of the structure.…”

Section: Introductionmentioning

confidence: 99%

Defect detection of flip-chip solder joints using modal analysis

Liu¹,

Shi²,

Wang³

et al. 2012

Microelectronics Reliability

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Section: Introductionmentioning

confidence: 99%

Defect detection of flip-chip solder joints using modal analysis

Liu¹,

Shi²,

Wang³

et al. 2012

Microelectronics Reliability

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“…6–9 Attention has been given to damage identification in metals, composites and sandwich structures analyzing the variation of the modal parameters. 10–18 The change in natural frequency is the most commonly used parameter for damage detection. Nasiri et al.…”

Section: Introductionmentioning

confidence: 99%

“…Nasiri et al. 12 used the frequency response functions of the damaged composite plate to feed an intelligent classification model to automatically detect and localize delamination damage. Radzieński et al.…”

Section: Introductionmentioning

confidence: 99%

Damage quantification in foam core sandwich composites via finite element model updating and artificial neural networks

Mardanshahi

Izadi

2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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The main idea of this paper is to propose a nondestructive evaluation (NDE) system for two types of damages, core cracking and skin/core debonding, in fiberglass/foam core sandwich structures based on the inverse eigensensitivity-based finite element model updating using the modal test results, and the artificial neural networks. First, the modal testing was conducted on the fabricated fiberglass/foam core sandwich specimens, in the intact and damaged states, and the natural frequencies were extracted. Finite element modeling and inverse eigensensitivity-based model updating of the intact and damaged sandwich structures were conducted and the parameters of the models were identified. Afterward, the updated finite element models were employed to generate a large dataset of the first five harmonic frequencies of the damaged sandwich structures with different damage sizes and locations. This dataset was adopted to train the machine learning models for detection, localization, and size estimation of the core cracking and skin/core debonding damages. A multilayer perceptron neural network classification model was used for detection of types of damages and also a multilayer perceptron neural network regression model was fitted to the dataset for automatically estimation of the locations and dimensions of damages. This intelligent system of damage quantification was also used to make predictions on real damaged specimens not seen by the system. The results indicated that the extracted natural frequencies from the accurate finite element model, in coordination with the experimental data, and using the artificial neural networks can provide an effective system for nondestructive evaluation of foam core sandwich structures.

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“…Experiments were then conducted to validate the developed model. Nasiri [12] their study was to applied the Genetic algorithm with fem delamination modeling for the quantitative identification of delamination in composite plates. Damage assessment methods based on frequency changes require frequency measurements to be taken both on the undamaged and damaged composite plate structures.…”

Section: Introductionmentioning

confidence: 99%

Delamination Modeling and Assessment for Composite Laminated Plates using Particle Swarm Optimization (PSO)

Hadi¹

2016

IJCA

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This work reports on the finite element method and particle swarm optimization method (PSO) for modeling and identification of delamination in composite material. A first order shear deformation using nine nodded isoparametric quadratic element with a simple multiple delamination model is used to develop the finite element analysis procedure. Delamination is the most common failure mechanism in composite structures. These damage causes changes in the physical properties by reducing the stiffness of the structure which leads to changes in modal parameters such as the frequencies, mode shapes, and modal damping factors. Identify the presence of damage and assess its size and location from a few lower frequency modes in modal test is the main objective of this study. Particle Swarm Optimization delamination detection based on the utilization of the changes in modal parameters as the objective function successfully identify the presence, location, and relative area size in composite laminated plates.

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Damage Detection in a Composite Plate Using Modal Analysis and Artificial Intelligence

Cited by 19 publications

References 11 publications

Defect detection of flip-chip solder joints using modal analysis

Defect detection of flip-chip solder joints using modal analysis

Damage quantification in foam core sandwich composites via finite element model updating and artificial neural networks

Delamination Modeling and Assessment for Composite Laminated Plates using Particle Swarm Optimization (PSO)

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