Effect of delamination on the flexural stiffness of composite laminates

Tracy, John J.; Pardoen, Gerard C.

doi:10.1016/0263-8231(88)90018-3

Cited by 19 publications

(8 citation statements)

References 1 publication

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“…A significant reduction of natural frequencies has been observed as a result of delamination growth. 127,128 The reduction of natural frequencies leads to a reduction in the stiffness values which thereafter affects the structural integrity. Delamination behaviour study is vital for composite design and application.…”

Section: Damage Detection In Composites Using Annmentioning

confidence: 99%

Damage detection in composites using non-destructive testing aided by ANN technique: A review

Saha

Roy

Topdar

2023

Journal of Thermoplastic Composite Materials

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Damages are inevitable in structures and effective damage detection techniques are important for maintaining their health. Many weight-sensitive engineering applications use composite materials, especially fiber-reinforced laminates. Common damages of these materials include delamination, fiber breakage, fiber pull-out, etc. Various non-destructive testing (NDT) techniques are reported in the literature for damage detection in composites, such as ultrasonic testing, vibration-based techniques, acoustic emission technique, optical NDT and imagining techniques. However, due to the complex properties of composite materials, conventional techniques for analyzing NDT data are difficult to implement. In this context, artificial neural network (ANN) technique is a promising alternative for analyzing NDT data for damage detection. In this study, an attempt is made to explore the state-of-the-art of damage detection in composites using NDT aided by ANN. The work discusses the pros and cons of different methods and is expected to help in identifying the appropriate method for damage detection in composites.

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Section: Damage Detection In Composites Using Annmentioning

confidence: 99%

Damage detection in composites using non-destructive testing aided by ANN technique: A review

Saha

Roy

Topdar

2023

Journal of Thermoplastic Composite Materials

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“…Solving the deflection functions in equations ( 7) and ( 8) give 20 unknown coefficients. 30 A Matlab program is coded to solve the simultaneous equations and generate the modal frequencies for various delamination positions, sizes, and locations. The first eight flexural modal frequencies for different delamination signatures ([Z, X, a]) are computed.…”

Section: Modeling Of the Laminated Composite Beammentioning

confidence: 99%

Validation of algorithms for delamination detection in composite structures using experimental data

Ihesiulor

Shankar

Zhang

et al. 2013

Journal of Composite Materials

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This research work deals with aspects concerned with delamination detection in composite structures as revealed by an approach based on vibration measurements. Variations in vibration characteristics generated in composite laminates indicate the existence of delaminations because degradation due to delamination causes reduction in flexural stiffness and strength of the material and as a result vibration parameters like natural frequency responses are changed. Hence, it is possible to monitor the variation in natural frequencies to identify the presence of delamination, and assess its size and location for online structural health monitoring (SHM). The approach to this paper, therefore, typically depends on undertaking the analysis of structural models implemented by finite element analysis (FEA). The numerical solutions using FE models known as the simulator computes the natural frequencies for the delaminated and undelaminated specimens of composite laminates. However, these FE models are computationally expensive, and surrogate (approximation) models are introduced to curtail the computational expense. The simulator is employed to solve the inverse problem using algorithms based on computational intelligence concepts. An artificial neural network (ANN) model is developed to also solve the inverse problem for delamination detection directly and to provide surrogate models integrated with optimization algorithms (the gradient-based local search and non-dominated sorting genetic algorithm-II) to contain the computationally expensive simulations by FEA. This approach is termed as surrogate assisted optimization and it is seen that the engagement of surrogate models in lieu of the FE models in the optimization loop greatly enhances the accuracy of delamination detection results within an affordable computational cost and provides control over handling different variables. Meanwhile, to aid with the building of effective surrogate models using substantial number of training datasets, K-means clustering algorithm is harnessed and this effectively reduces the large training datasets usually required for ANN training. This paper demonstrated that ANN and optimization algorithms with surrogates show immense potentialities for use in delamination damage detection scenarios. Prediction errors of the algorithms were quantified and they were shown to be satisfactory when applied to previously experimental data. The algorithms in their inverse formulations are capable of predicting accurately delamination parameters. Hence, these algorithms should be employed for application in the domain of SHM where their small computational requirements could be exploited for online damage detection.

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“…where u i and w i are axial and transverse deflections, P i is the axial forces, and ω is the angular frequency. This set of coupled equations has 20 unknowns and 20 boundary and conditions [45]. Using MATLAB, the simultaneous solution produced the modal frequencies for various delamination sizes and locations.…”

Section: Classical-beam Theory and Finite Element Simulationmentioning

confidence: 99%

Modal analysis using fiber optic sensors and neural networks for prediction of composite beam delamination

Watkins

Sanders

Akhavan

et al. 2002

Smart Mater. Struct.

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Extrinsic Fabry-Perot interferometric (EFPI) fiber optic sensors and a neural network provided a health-monitoring capability for laminated glass/epoxy composite beams. The EFPI sensors experimentally determined the first five modal frequencies of the cantilever beams. The feedforward backpropagation neural network used these modal frequencies to predict the size and location of delaminations in the composite beams. Beam modal frequencies shift as a function of delamination size and location. Five beams with prescribed delaminations, as well as a 'healthy' beam with no delaminations, were excited by a surface-mounted piezoelectric actuator at frequencies up to 1 kHz. All beams had an eight-ply symmetric glass/epoxy composite design, were fabricated simultaneously, and had length and width dimensions of 26.04 and 2.33 cm, respectively. The beams with flaws had different delamination sizes ranging from 1.27-6.35 cm long prescribed in the mid-plane, i.e. between the fourth and fifth plies. The neural network was trained using classical-beam theory and tested using the experimental EFPI data. The delamination size and location predictions resulting from the neural network simulation had an average error of 5.9 and 4.7%, respectively. Also, analytical classical-beam theory, finite element methods, and ceramic piezoelectric sensors validated the EFPI modal frequency measurements.

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Effect of delamination on the flexural stiffness of composite laminates

Cited by 19 publications

References 1 publication

Damage detection in composites using non-destructive testing aided by ANN technique: A review

Damage detection in composites using non-destructive testing aided by ANN technique: A review

Validation of algorithms for delamination detection in composite structures using experimental data

Modal analysis using fiber optic sensors and neural networks for prediction of composite beam delamination

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