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Composite materials are susceptible to barely visible impact damages (BVID) due to low-velocity impact. Therefore, an automated damage detection and quantification technique is highly desirable for quick inspection of large number of composite structures. Among various different non-destructive techniques (NDTs), active thermography NDT can be used for detecting damage on aircraft structures, using an infrared camera to capture the temperature distribution on the structure after it is exposed to heat using a flash lamp. In this paper, an image analysis algorithm that analyzes the infrared image, acquired using NDTherm NT, by determining the changes in the colormap values to automate the detection and quantification of the damage size was proposed. An area of the second derivative pre-processed grayscale image acquired using NDTherm NT is scanned in the x-direction and y-direction, and for each scan region the histogram of colormap values is extracted and stored. Irregularities in the structure result in non-uniform temperature distributions, which cause the infrared image to have a wide-range of grayscale colormap values in the damaged area. Therefore, the damage region is identified by monitoring the changes in the number of detected grayscale colormap values. The proposed image analysis technique was implemented for automated damage detection on Boeing 787 skin's curved CFRP panel, with dimensions of 1.3 × 1.3 m. The proposed method detected the damage and determined the maximum damage length in the x-direction and y-direction to be 70.1 mm and 57.8 mm, respectively. Moreover, the proposed technique is suitable for feature identification applications.

Structural Health Monitoring (SHM) using Ultrasonic Guided Waves (UGWs) offers great potential in detection of minor flaws, due to the employed short wavelengths. A bottleneck in UGWs-based schemes lies in the extensive computational costs for evaluating the associated wave propagation models. Such detailed models form though a necessity to reach higher levels of SHM, e.g. localization and assessment of flaws. Reduced Order Models (ROMs) and surrogate models allow for lowering the substantial numerical costs for SHM applications, especially if they are parameterized with respect to the characteristics of different flaw configurations. Machine Learning (ML) algorithms can be trained for this purpose, however, in the case of black box ML algorithms, this comes with the drawback of the requirement for substantial data availability for the purpose of training. Such, training data, which are typically derived from full order numerical simulations, are computationally costly to obtain. To reduce the amount of training data, known information on the mechanical behavior can be harnessed and inserted into the estimation process. In the present work, a method is introduced that exploits the properties of the interaction of UGWs with flaws in the frequency domain. It can be shown that the frequency domain response is characterized by periodic features that are linked to the flaw location. An ML model based on this knowledge can be trained with less training data. The potential of this approach for damage localization in the context of SHM is illustrated in a simulated example of a composite plate. ISSN:0277-786X

This work proposes a novel technique for the localization of low-velocity impacts in composites without a-priori knowledge of the mechanical properties nor the speed of propagating waves, thus overcoming current limitations of existing impact localization methods. The proposed algorithm is based on the estimation of the power of acoustic emissions generated by impacts on a composite plate instrumented with embedded piezo-transducers. The signal power values calculated at sparse sensor locations are interpolated over the sample by using radial basis function networks. The impact coordinates on the specimen surface are estimated by a center-of-gravity method based on the interpolated power values. Experimental tests were performed by using both an instrumented impact hammer and a drop tower. The results obtained showed the validity of the presented approach, which was able to identify the impact locations with high level of accuracy.

Maintenance tasks and safety aspects represent a strategic role in the managing of the modern aircraft fleets. The demand for reliable techniques for structural health monitoring represent so a key aspect looking forward to new generation aircraft. In particular, the use of more technologically complex materials and manufacturing methods requires anyway more efficient as well as rapid application processes to improve the design strength and service life. Actually, it is necessary to rely on survey instruments, which allow for safeguarding the structural integrity of the aircraft, especially after the wide use of composite structures highly susceptible to non-detected damages as delamination of the ply. In this paper, the authors have investigated the feasibility to implement a neural network-based algorithm to predict the impact event at low frequency, typically due to the bird collision. Relying upon a numerical model, representative of a composite flat panel, the approach has been also experimentally validated. The purpose of the work is therefore the presentation of an innovative application within the Non Destructive Testing field based upon vibration measurements. The aim of the research has been the development of a Non Destructive Test which meets most of the mandatory requirements for effective health monitoring systems while, at the same time, reducing as much as possible the complexity of the data analysis algorithm and the experimental acquisition instrumentation. Future activities will be addressed to test such technique on a more complex aeronautical system.

In this study, a mechanical shaker operating at low frequencies is demonstrated to be a viable excitation source for vibrothermography. Additionally, a low-cost transduction approach based on commercially available piezoelectric materials (PZT) is investigated. These PZT transducers are assessed for their excitation efficiency to allow crack detection in metallic structures. Cracks as small as 1 mm are detected using the mechanical shaker regardless of a beam structure orientation or crack location. Although the low-cost PZT-based transduction approach had sufficient excitation power to generate vibration, localized heat generation was not observed at crack locations., SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, March 8-12, 2009, San Diego, CA, USA, Series: Proceedings of SPIE; no. 7294