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The purpose of this paper is to assess the quality of adhesively bonded joints using an alternative artificial neural networks (ANN) approach.

Following the necessary surface pre-treatment and bonding process, the coupons were investigated for possible defects using C-scan ultrasonic inspection. Afterwards, the damage severity factor (DSF) theory was applied in order to quantify the existing damage state. A series of G _IC mechanical tests was then conducted so as to assess the fracture toughness behavior of the bonded samples. Finally, the data derived both from the NDT tests (DSF) and the mechanical tests (fracture toughness energy) were combined and used to train the ANN which was developed within the present work.

Using the developed neural network (NN) the bonding quality, in terms not only of defects but also of fracture toughness behavior, can be accessed through NDT testing, minimizing the need for mechanical tests only in the initial material characterization phase.

The innovation of the paper stands on the feasibility of an alternative approach for assessing the quality of adhesively bonded joints using and ANNs, thus minimizing the necessary testing effort.

The purpose of this paper is to develop a multi-scale modeling approach for simulating the tensile behavior of corroded aluminum alloy 2024 T3.

The approach combines two FE models: a model of a three-dimensional representative unit cell representing a pit and a model of the tensile specimen. The models lie at the micro- and macro-scales, respectively. The local homogenized mechanical behavior of the corroded material is simulated for different pit configurations. Then, the behavior of the pits is introduced into different areas (elements) of the tensile specimen and final analyses are performed to simulate the mechanical behavior of the corroded material. The approach has been applied to six different exposure periods of the exfoliation corrosion test.

The numerical results show that the presence of pits and exfoliated areas reduces the yield strength of the material. The comparison of predicted elongation to fracture with the experimental of each exposure period value allows for the indirect assessment of the effect of hydrogen embrittlement.

Since the characteristics of corrosion damage evolution with exposure time are constant for the specific material, the model could be applied for the simulation of the mechanical behavior of any corroded structural part (e.g. a mechanically fastened panel) made from the aluminum 2024 T3 alloy.

The purpose of this paper is to present the first results of tests where cracks lie in the tension field of a shear forced aluminium panel. The paper's main focus is on the crack propagation behavior and possible 3D‐effects caused by the bending of the plate. A simplified numerical approach is presented to confirm the observed phenomena.

Experiments have been performed to investigate the influence of buckling on accidental damages. A simplified numerical approach is presented and compared to the experimental results.

It can be shown that the crack propagates due to buckling of the plate. The principal stress of the neutral axis of the plate has significant influence on the crack propagation.

Investigations of stability problems and damage tolerance behavior of metallic structures have been realized but mainly separately. This paper shows that cracks propagate due to buckling and that both phenomena influence one another considering accidental damages. The paper presents the first experimental and numerical results of cracked aluminium panels subjected to cyclic shear load.

The purpose of this paper is to study the feasibility on the use of alternative parameters for representing acoustic emission (AE) and acousto-ultrasonic (AU) signals, using a wavelet-based approach and the computation of Chebyshev moments.

Two tests were performed, one on AE artificial signals generated on a CFRP plate and one on an AU setup used for actively detecting impact damage. The waveforms were represented using a data reduction technique based on the Daubechies wavelet and an image processing technique using Chebyshev moments approximation, to get 32 descriptors for each waveform.

The use of such descriptors allowed in the AE case to verify that the moments are similar when the waveforms are similar; in the AU setup the correlation coefficient of the descriptors with respect to a reference data set was found to be linked to the delimitation size.

Such a data reduction while retaining all the useful information will be positive for wireless sensor networks, where power consumption during data transmission is key. With having to send only a reliable set of descriptors and not an entire waveform, the power consumption is believed to be reduced.

This paper is a preliminary study that fulfils a need for a more reliable data reduction for ultrasonic transient signals, such as those used in AE and AU.