Guided Lamb waves for identification of damage in composite structures: A review

This paper presents a new in situ Structural Health Monitoring (SHM) system able to identify the location of acoustic emission (AE) sources due to low-velocity impacts and to determine the group velocity in complex composite structures with unknown lay-up and thickness. The proposed algorithm is based on the differences of stress waves measured by six piezoelectric sensors surface bonded. The magnitude of the Continuous Wavelet Transform (CWT) squared modulus was employed for the identification of the time of arrivals (TOA) of the flexural Lamb mode ( A 0 ). Then, the coordinates of the impact location and the flexural wave velocity were obtained by solving a set of non-linear equations through a combination of global Line Search and backtracking techniques associated to a local Newton’s iterative method. To validate this algorithm, experimental tests were conducted on two different composite structures, a quasi-isotropic CFRP and a sandwich panel. The results showed that the impact source location and the group speed were predicted with reasonable accuracy (maximum error in estimation of the impact location was approximately 2% for quasi-isotropic CFRP panel and nearly 1% for sandwich plate), requiring little computational time (less than 2 s).

A new algorithm for acoustic emission localization and flexural group velocity determination in anisotropic structures

This paper investigates the development of an in situ impact detection monitoring system able to identify in real-time the acoustic emission location. The proposed algorithm is based on the differences of stress waves measured by surface-bonded piezoelectric transducers. A joint time-frequency analysis based on the magnitude of the continuous wavelet transform was used to determine the time of arrival of the wavepackets. A combination of unconstrained optimization technique associated with a local Newton's iterative method was employed to solve a set of nonlinear equations in order to assess the impact location coordinates and the wave speed. With the proposed approach, the drawbacks of a triangulation method in terms of estimating a priori the group velocity and the need to find the best time-frequency technique for the time-of-arrival determination were overcome. Moreover, this algorithm proved to be very robust since it was able to converge from almost any guess point and required little computational time. A comparison between the theoretical and experimental results carried out with piezoelectric film (PVDF) and acoustic emission transducers showed that the impact source location and the wave velocity were predicted with reasonable accuracy. In particular, the maximum error in estimation of the impact location was less than 2% and about 1% for the flexural wave velocity.

/pdf/acoustic-emission-source-localization-and-velocity-3lim0a20lx.pdf

Acoustic emission source localization and velocity determination of the fundamental mode A0 using wavelet analysis and a Newton-based optimization technique

In this work a methodology for impact identification on composite stiffened panels using piezoceramic sensors has been presented. A large number of impacts covering a wide range of energies (corresponding to small and large mass impacts) at various locations of a composite stiffened panel have been simulated using the finite element (FE) method. To predict the impact location, artificial neural networks have been established using the data generated from FE analyses. A number of sensor signal features have been examined as inputs to the neural network and the effect of noise on the predictions has been investigated. The results of the study show that the trained network is capable of locating impacts with different energies at different locations (e.g. in the bay, over/under the stringer and on the foot of the stringer) in a complicated structure such as a composite stiffened panel.

https://iopscience.iop.org/article/10.1088/0964-1726/21/10/105026/pdf

Determination of impact location on composite stiffened panels

In this work, a new methodology is presented for reconstruction of the impact force history using artificial neural networks (ANNs) and spectral components of sensor data recorded by piezoceramic sensors. A large set of data, required for training the ANNs, was generated by using an efficient nonlinear finite element (FE) model of a sensorised composite stiffened panel. Impact experiments were performed on a composite plate equipped with surface-mounted piezoceramic sensors to validate the numerical modelling approach. Using the FE model of the panel, data were generated for impacts that are likely to occur during the life-time of an aircraft, consisting of large mass (e.g. dropping tool) and small mass (e.g. debris) impacts at various locations, i.e. in the bay, on the foot of a stringer and over/under a stringer. Even though the panel undergoes large deformation during impact (nonlinear response), the established networks predict the impact force history and its peak with reasonable accuracy.

/pdf/identification-of-impact-force-for-smart-composite-stiffened-4vl0niqceb.pdf

Identification of impact force for smart composite stiffened panels

Low-velocity impact damage is a major concern in the design of structures made of advanced laminated composites, because such damage is mostly hidden inside the laminates and cannot be detected by visual inspection. It is necessary to develop the impact monitoring techniques providing on-line diagnostics of smart composite structures susceptible to impacts. In this paper, we discuss the process for impact location detection in which the generated acoustic signals are detected by PZT using the improved neural network paradigms. To improve the accuracy and reliability of a neural network based impact identification method, the Levenberg-Marquardt algorithm and the generalization methods were applied. This study concentrates not only on the determination of the location of impacts from sensor data, but also the implementation of time-frequency analysis such as the Wavelet Transform (WT) to measure the characteristic frequencies of acoustic emission waves for the determination of the occurrence and the estimation of impact damage.

성대운

Papers

신경회로망과 웨이블릿 변환을 이용한 스마트 복합적층판의 충격 모니터링 연구 = Impact monitoring of smart composite laminates using neural networks and wavelet analysis