Delamination detection in composite laminates using improved surrogate-assisted optimization

2021 ◽  
Vol 277 ◽  
pp. 114622
Author(s):  
Huawei Tong ◽  
Jingwen Pan ◽  
Hemant Kumar Singh ◽  
Weili Luo ◽  
Zhifang Zhang ◽  
...  
Keyword(s):  
Composite Laminates ◽  
Delamination Detection

Delamination Detection in Woven Composite Laminates with Embedded Optical Fibers

2001 ◽  
Vol 3 (7) ◽  
pp. 492-496 ◽  
Author(s):  
P. S. Uskokovic ◽  
I. Balac ◽  
L. Brajovic ◽  
M. Simic ◽  
S. Putic ◽  
...  
Keyword(s):  
Optical Fibers ◽  
Composite Laminates ◽  
Woven Composite ◽  
Delamination Detection ◽  
Woven Composite Laminates

Comparison of Inverse Algorithms for Delamination Detection in Composite Laminates

2012 ◽  
Author(s):  
Zhifang Zhang ◽  
Obinna Kenneth Ihesiulor ◽  
Krishna Shankar ◽  
Tapabrata Ray
Keyword(s):  
Theoretical Model ◽  
Composite Laminates ◽  
Graphical Method ◽  
Vibration Monitoring ◽  
Validation Data ◽  
Frequency Measurements ◽  
Delamination Detection ◽  
Measured Frequency ◽  
Surrogate Based Optimization ◽  
Graphical Technique

Delamination is a frequent and potentially serious damage that can occur in laminated polymer composites due to the poor inter-laminar fracture toughness of the matrix. Vibration based detection methods employ changes caused by loss of stiffness in dynamic parameters such as frequencies and mode shapes to detect and assess damage. Because it is a whole field method, and can be applied instantaneously and remotely, vibration monitoring using frequency measurements offers great potential for implementation in online structural health monitoring systems. However, one of the disadvantages of using frequency measurements is that while the presence of damage is easily identified through a shift in measured frequency, the determination of the location and the severity of the damage is not easy to accomplish. To determine the location and severity of damage from measured changes in frequency, it is necessary to solve the inverse problem, which requires the solution of a set of non-linear simultaneous equations. In this paper, we have compared the performance of three different inverse algorithms for delamination detection in the fibre-reinforced composite laminates: direct of solution using a graphical method, artificial neural network (ANN) and surrogate-based optimization. In particular, the graphical method which was earlier proposed for problems of two variables has been extended to solution of three variables, the interface, location along the beam length and size of delamination in laminated composite beams. The three inverse algorithms have been compared using numerical validation data generated from the theoretical model of delaminated beam with and without artificial errors. All three algorithms can predict the delamination parameters accurately using the validation data directly generated from theoretical model. However, if artificial errors are introduced in the numerical data to simulate uncertainties in measurement of frequencies, ANN does not fare as well as the the other two methods as it is more sensitive to the artificial discrepancies. Also, ANN requires the network to be retrained if the measured frequency modes do not match the input modes in the existing network. The graphical technique and the surrogate based optimization performed equally well in the validations. However, the graphical technique is only applicable to no more than three variables, while the surrogate-based optimization algorithm can be applied to inverse problems with several unknown parameters such as in the case of delaminations in composite plates.

A two-step method for delamination detection in composite laminates using experience-based learning algorithm

2021 ◽  
pp. 147592172110181
Author(s):  
Tongyi Zheng ◽  
Weili Luo ◽  
Huawei Tong ◽  
Xing Liang
Keyword(s):  
Composite Laminates ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Optimization Method ◽  
Mode Shapes ◽  
Artificial Noise ◽  
Detection Accuracy ◽  
Step Method ◽  
Delamination Detection ◽  
Experience Based Learning

Delamination in composite laminates reduces the structural stiffness and thus causes changes in the vibration responses of the laminates. Therefore, it is feasible to employ dynamic characteristics (such as natural frequencies and mode shapes) for delamination detection by using an optimization method. In the present study, a two-step method is proposed for the delamination detection in composite laminates using an experience-based learning algorithm. In the first step, one-dimensional equivalent through-thickness beam elements are employed to model the composite laminated beam and potential delamination locations are identified. In the second step, a typical three-dimensional finite mesh is utilized for the beam’s modeling and the detailed delamination information (including the delamination location, size, and interface layer) is detected. This two-step method combines the advantages of the two different modeling techniques and is able to significantly reduce the computational cost without reducing detection accuracy. The proposed method is applied for an eight-layer quasi-isotropic symmetric (0/-45/45/90)s composited beam with different delamination situations to verify its effectiveness and robustness. The performance of the two-step method is demonstrated by comparing with the one-step method and other three state-of-the-art algorithms (CMFOA, PSO, and SSA). Moreover, the influence of artificial noise on the accuracy of the detection performance is also investigated. Both numerical and experimental results confirm the superiority of the proposed method for delamination detection in composite laminates especially for the prediction of delamination interface.

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