Z. Sharif-Khodaei

Determination of impact location on composite stiffened panels

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.

Identification of impact force for smart 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.

Impact damage detection in composite plates using a self-diagnostic electro-mechanical impedance-based structural health monitoring system

In this work, application of the electro-mechanical impedance (EMI) method in structural health monitoring as a damage detection technique has been investigated. A damage metric based on the real and imaginary parts of the impedance measures is introduced. Numerical and experimental tests are carried out to investigate the applicability of the method for various types of damage, such as debonding between the transducers and the plate, faulty sensors and impact damage in composite plates. The effect of several parameters, such as environmental effects, frequency sweep, severity of damage, location of damage, etc., on the damage metric has been reported.

Impact Detection Using Artificial Neural Networks

In this work, a number of impacts on a composite stiffened panel fitted with piezoceramic sensors were simulated with the finite element (FE) method. During impacts, the contact force history and strains at the sensors were recorded. These data were used to train, validate and test two artificial neural networks (ANN) for the prediction of the impact position and the peak of the impact force. The performance of the network for location detection has been promising but the other network should be further improved to provide acceptable predictions about the peak force.

Electro-Mechanical Impedance Technique for Structural Health Monitoring of Composite Panels

Numerical modelling of EMI for damage detection has been presented in this paper. The PZT model is validated against the published experimental result for free disk and tied to the structure. The numerical modelling of the PZT patch will result in the admittance measure of the structure. The imaginary part of the admittance measure is used for developing a self-diagnostic sensor system. The real part of the admittance measure was used to develop a damage detection algorithm. Damage detection using EMI method was successfully applied to a simple composite disk and a stiffened panel. The EMI method is suitable for short range damage detection in structural parts with limited or no access.

SMART Platform for Structural Health Monitoring of Sensorised Stiffened Composite Panels

A SMART Platform is developed based on sensor readings for Structural Health Monitoring of a stiffened composite panel. The platforms main function is divided into three categories: Passive sensing, Active sensing and Optimal sensor positioning. The platform has self-diagnostic capabilities, i.e. prior to its application the health of the sensors and their connection will be checked to avoid any false alarm. Passive sensing results in impact location and force magnitude detection. Active sensing is performed for damage detection. It results in detecting the damage location and severity. Finally the optimal sensor location can be provided given the number of sensors and probability of detection value. This platform is the first step in applying the developed SHM methodologies to real size structures in service load conditions.

Lamb-Wave Based Technique for Impact Damage Detection in Composite Stiffened Panels

The propagation characteristic of Lamb waves activated by Piezoelectric actuators and collected by sensors in a stiffened panel has been investigated. A network of actuators is used to scan the structure before and after the presence of damage. A diagnostic imaging algorithm has been developed based on the probability of damage at each point of the structure measured by the signal reading of sensors in the benchmark and damaged structure. A damage localization image is then reconstructed by superimposing the image obtained from each sensor-actuator path. Three-dimensional finite element model with a transducer network is modeled. Damage is introduced as a small softening area in the stiffened panel. Applying the imaging algorithm, the damage location was predicted with good accuracy. This method proves to be suitable for stiffened panels, where the complicated geometry and boundary reflections make the signal processing more complicated.

Lamb-Wave Based Damage Detection in Anisotropic Composite Plates

Damage detection in anisotropic composite plates based on Lamb wave technique has been investigated. A network of transducers is used to detect barely visible damage caused by impact. A CFRP composite plate has been impacted and tested to verify the proposed damage detection algorithms. The difference in the propagational properties of Lamb waves in the pristine state and the damage state is used through data fusion and imaging algorithms to detect, locate and characterise the damage. The influence of directionality of the velocity on the validity of the detection algorithm is examined and some results are presented.

Lamb-Wave Based Technique for Multi-Site Damage Detection

The propagation characteristic of Lamb waves activated by Piezoelectric actuators and collected by sensors in a stiffened panel has been investigated. A network of actuators is used to scan the structure before and after the presence of damage. A diagnostic imaging algorithm has been developed based on the probability of damage at each point of the structure measured by the signal reading of sensors in the baseline and damaged structure. A damage localization image is then reconstructed by superimposing the image obtained from each sensor-actuator path. Three-dimensional finite element model with a transducer network is modelled. Damage is introduced as a small softening area in the panel. Applying the imaging algorithm, the damage location was predicted with good accuracy. The validity of the algorithm was tested for multiple damages.

Influence of Adhesive Layer on Actuation of Lamb Wave Signals

In this work, Lamb wave generation and propagation have been modelled in composite plates. Actuation and acquisition of signals when the PZT transducers are tied to the structure or bonded with an adhesive layer are investigated. The effect of adhesive thickness and actuation frequency of Lamb wave have been examined.