Damage Detection in Composites By Artificial Neural Networks Trained By Using in Situ Distributed Strains

From Springer Nature via Jisc Publications RouterHistory: received 2020-06-26, rev-recd 2020-06-26, registration 2020-07-21, accepted 2020-07-21, pub-electronic 2020-08-07, online 2020-08-07, pub-print 2020-10Publication status: PublishedFunder: Università degli Studi della Campania Luigi VanvitelliAbstract: In this paper, a passive structural health monitoring (SHM) method capable of detecting the presence of damage in carbon fibre/epoxy composite plates is developed. The method requires the measurement of strains from the considered structure, which are used to set up, train, and test artificial neural networks (ANNs). At the end of the training phase, the networks find correlations between the given strains, which represent the ‘fingerprint’ of the structure under investigation. Changes in the distribution of these strains is captured by assessing differences in the previously identified strain correlations. If any cause generates damage that alters the strain distribution, this is considered as a reason for further detailed structural inspection. The novelty of the strain algorithm comes from its independence from both the choice of material and the loading condition. It does not require the prior knowledge of material properties based on stress-strain relationships and, as the strain correlations represent the structure and its mechanical behaviour, they are valid for the full range of operating loads. An implementation of such approach is herein presented based on the usage of a distributed optical fibre sensor that allows to obtain strain measurement with an incredibly high resolution

Damage detection and monitoring in composites using piezoelectric sensors

International audienceComposite materials have become more and more attractive for use in structural applications over recent years, but assessment of damage remains a challenge since this is barely visible, especially when subjected to relatively low energy impact events. The use of piezoelectric transducers for structural health monitoring (SHM) of composites is increasing due to their ability to be embedded without disturbing the structure, low cost, small size, durability, and low power consumption. There is a wealth of research supporting their use for passive and active SHM, yet few studies combine the two. In this work, a multi-layered carbon fibre/epoxy composite pipe is subjected to multiplecycles of mechanical loading/unloading in a three point bending configuration. The specimen is instrumented with eight piezoelectric wafer active sensors (PWAS), used as passive receivers of acoustic emission signals during loading. It is possible to track the location of damage as the test progresses, by triangulation of AE signals. Active monitoring of the specimen is performed using piezoelectric sensors successively as transmitters and receivers of guided waves, in a pitch-catch configuration. Signals are recorded between successive loadings of the specimen to assess the state of damage at each stage, and compare against the ‘pristine’ condition. Cross-comparison of tuningcurves obtained from the pristine condition and test data show attenuation in amplitude of the L(0,2) mode. A damage index is proposed based on this amplitude reduction

Damage identification in composites through acoustic emission monitoring

International audienceIn this work, three carbon/glass hybrid composite tubes are instrumented with eight piezoelectric wafer active sensors (PWAS), used as passive receivers of acoustic emission (AE) signals. A three point bending quasi-static loading is conducted, either in a single cycle until failure or incrementally through multiple loading/unloading cycles. In the first instance, AE signal features such as maximum amplitude, peak frequency, signal duration, and energy are used to distinguish between different damage mechanisms such as matrix cracks, delamination, and fibre breakage. The group velocity of the longitudinal modes – L(0,1) and L(0,2) – is obtained experimentally in a pitch-catch configuration between the PWAS network using the time of flight (ToF). The time of arrival (ToA) method is then used to calculate damage source locations from the received AE signals. To involve more signal features for data classification, an unsupervised clustering algorithm is applied to the datasets. Optimisation of the number of clusters is completed by maximising the robustness and minimising the uncertainty of the final result. It is found that the temporal evolution of clusters indicate the ability to distinguish between the initiation and growth of damage, as well as identifying non-damage related signals caused by extraneous noise or related to the test set-up

Integration of distributed optical fibres and piezoelectric sensors for SHM of composite tubes

International audienceThe aim of the study is to detect, localise, and attempt to classify damage mechanisms in a carbon fibre/epoxy composite tube from an early stage of loading until final failure. Parametric analysis is completed on acoustic emission (AE) data to attempt damage classification, while experimental wave velocities are used to estimate damage locations. This is complemented by damage index calculations based on the amplitude reduction observed in guided wave signals. The application of an unsupervised clustering algorithm enables observation of the evolution of damage, as separated into groups. When overlaid with the cumulated energy obtained from AE signals, these “damage profiles” for each cluster of data can be linked more closely to the known damage processes