The Surface Response to Excitation (SuRE) method is a guided-wave based Structural Health Monitoring (SHM) technique. Up to date, no analytical model has been developed and validated for the SuRE method. This paper experimentally and analytically investigates the delamination between two plates using the SuRE method in conjunction with the COMSOL Multiphysics software. Simulation results are validated by experimental results. The results showed that the findings from the analytical approach correspond with the experimental results and can be effectively used for studying delamination. This approach can be utilized for different types of structures with similar conditions.
Composite is one of the most widely used industrial materials because of high strength, low weight, and high corrosion resistance properties. Different parts of composite structures are normally joined using adhesives or fasteners that are prone to defects and damages. A reliable method for prediction of the defect location is needed for an efficient structural health monitoring (SHM) process. Heterodyne effect is recently utilized for damage detection in the bonding zone of composite structures where debonding is expected to change the linear characteristics of the system into nonlinear characteristics. This paper briefly introduces this novel defect locating approach in composite plates using the heterodyne effect. For the first time, an Artificial Neural Network methodology is utilized with heterodyne effect method to find the defect location in composite plates. The main objective of this article is to develop a neural network based methodology for prediction of damage location, particularly for the bond inspection of composite plates.
Lamb wave method detects the defects from the propagation characteristics of the created brief harmonic signals.
Generally, the defects are detected by analyzing the delays and amplitudes of the received waves. The envelopes of the
sensory signals may be used to calculate the delays and amplitudes of the received signals. Sometimes, similar envelopes
could be observed at different test conditions. Use of the time-frequency spectra of the s-transformation is proposed for
distinction of the problems when the envelopes of the monitored signals are very similar. In the study, a beam was
compressed from different points with a hydraulic crimping tool. In separate tests, the cross sectional area at the middle
of the beam was reduced by opening slots. The envelopes and time-frequency spectra of the sensory signals were
calculated by using the s-transformation. The difference of the time-frequency spectra successfully distinguished the test
condition when the envelopes were very similar.
During the interpretation of the Lamb wave data, the main concern is often the arrival times of wave groups. Group
arrival times determine the distance of the source or the reflector. The inspection of sensory signal envelopes is
satisfactory to identify and localize defects. The S-transformation is proposed for isolating wave forms at their
excitation frequency and obtaining their envelopes. To further minimize the storage and computational costs, reduction
of the data size by down sampling (skipping 5 data points for each saved one) and compression via calculating the
wavelet transformation three times are proposed. The data was reduced to 1/30th of its original size, while the
reconstructed wavelet transformation had a less than 1% average error with respect to the down sized envelope signal.
In this study, the feasibility of monitoring the structural integrity of welded thick aluminum plates was experimentally
tested using two widely used SHM methods: impedance and Lamb wave analyses. The test structure was fabricated
from two 1/4 inch thick aluminum plates welded together, and various structural defects, such as holes and cuts, were
applied. At each of these damage steps, data were collected for both the impedance and Lamb wave techniques. Results
consistently revealed the impedance method to be sensitive to damage in and through the weld. The envelopes of the
Lamb wave signals were calculated using the S-transformation of the time histories. There was significant change to the
curves when different defects were added to the plate. Both of the SHM methods studied detected each of the cuts and
holes acting to reduce the overall strength of the structure. Each technique also detected the hole damage on the opposite
side of the weld as the sensor(s) used for damage detection. The study further verified that surface waves move across
welds allowing SHM methods to detect the defects even if the sensors are located on neighboring plates or geometries.
The detection and microscopic characterization of hidden corrosion has recently been a focus of several advanced NDE research efforts. A variety of approaches have been suggested, with laser ultrasonic (LU), scanning acoustic microscopy (SAM), thermography,and x-ray systems being four of the most promising NDE techniques. In this effort, a side-by-side comparison of each of these four techniques was conducted with the goal of assessing the detailed microscopic features of engineered and realistic hidden pitting corrosion reference samples. The reference samples included laser-etched cutouts and electro-chemically created surface pits ranging in size for 250 μm to 5 mm in surface extent, and depths of 25 μm to 1 mm. The effects of material loss/topography, corrosion-byproduct, and paint thickness levels were all addressed. Variations in measurement sensitivity, detectivity, and spatial resolution were studied, with particular attention being focused on the ability of the NDE technique to not only detect the hidden corrosion, but to provide any additional information regarding the microscopic nature of the corrosion area, its roughness, material loss levels, and pitting sharpness. In all cases, the NDE techniques provided an 'image' of the hidden corrosion areas, with some capability for assessing the internal structures of the pits from the measured signal levels or brightness levels of the measured image fields.
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