Damage classification in carbon fibre composites using acoustic emission: A comparison of three techniques (original) (raw)
Related papers
Volume 1: 22nd Biennial Conference on Mechanical Vibration and Noise, Parts A and B, 2009
Recent publications show that there is an increasing interest in the aircraft industry in monitoring the actual condition of a structure in real time and while the structure is in service. It is hoped that this Structural Health Monitoring (SHM) could make some regular inspections unnecessary and allow maintenance only when required. This is particularly important for CFRP structures for which aircraft manufacturers are increasingly interested. For these new composite structures where the incidence of fatigue is low, this technique could potentially be economical and improve the safety of the structures.
Applied Sciences, 2018
Acoustic emission is a part of structural health monitoring (SHM) and prognostic health management (PHM). This approach is mainly based on the activity rate and acoustic emission (AE) features, which are sensitive to the severity of the damage mechanism. A major issue in the use of AE technique is to associate each AE signal with a specific damage mechanism. This approach often uses classification algorithms to gather signals into classes as a function of parameters values measured on the signals. Each class is then linked to a specific damage mechanism. Nevertheless, each recorded signal depends on the source mechanism features but the stress waves resulting from the microstructural changes depend on the propagation and acquisition (attenuation, damping, surface interactions, sensor characteristics and coupling). There is no universal classification between several damage mechanisms. The aim of this study is the assessment of the influence of the type of sensors and of the propagat...
Feasibility and limitations of damage identification in composite materials using acoustic emission
Composites Part A: Applied Science and Manufacturing, 2015
One of the current challenges in health monitoring of composite materials is the use of acoustic emission to identify damage modes. Many classification procedures have been reported in the literature but none of them clearly state limitations to their applicability, making it difficult to quantify them in different testing conditions. In the present paper, a method is described to characterize energy attenuation and how it affects AE signals features based solely on AE signals recorded during mechanical tests. Limitations to damage identification based on AE signals features can therefore be defined. The method is demonstrated on AE signals recorded during tensile tests on four different layups of carbon fiber reinforced polymer composites using signals frequency centroids to describe AE sources.
Damage in carbon fibre composites: The discrimination of acoustic emission signals using frequency
This work considers the use of frequency content as a discriminating factor for acoustic emission (AE) signals from damage mechanisms in carbon fibre composite materials. Using a broadband conical transducer as an artificial source, investigations were made into the effects of source frequency (relaxation time), specimen geometry and sensor response on the frequency content of the recorded signals. It was shown that source frequency had an effect on the frequency content of the recorded signals, however, the specimen geometry and sensor response were shown to have a more significant effect. Additionally, AE signals were recorded from real damage mechanisms in tensile and beam buckling coupon specimens. The peak frequency content was used to examine signals resulting from the different damage modes identified. It was shown that some level of discrimination could be achieved and observations were in general agreement with previous research studies. However it was shown that great care is required when using peak frequency content as a discriminating factor because geometry and sensor response can have a distorting effect on the results.
Acoustic emission (AE) is widely used to characterize damage occurring in composite materials: however, the discrimination between AE signatures due to different damage mechanisms is still an open issue. In this work, the various failure mechanisms in bidirectional glass/epoxy laminates subjected to uni-axial tension are identified using AE monitoring. AE data recorded during the tensile testing of a single layer specimen are used to identify matrix cracking and fiber failure. In contrast, delamination signals are characterized using a two layer specimen with a pre induced defect, produced by artificially inserting a 10 mm wide Teflon tape in the middle portion of the two layers. Twelve-layer GFRP laminates were also tested as a reference for the comparison of results. The procedure leading to signal discrimination involves a number of steps. First, Fuzzy C-means clustering associated with principal component analysis are used to discriminate between failure mechanisms, whilst parametric studies using AE count rate and cumulative counts allowed damage discrimination at various stages of loading. The two above methods led to AE waveform selection: on the selected waveforms, Fast Fourier Transform (FFT) enabled calculating the frequency content of each damage mechanism. Continuous wavelet transform allowed identifying frequency range and time history for failure modes in each signal, whilst noise content associated with the different failure modes is calculated and removed by discrete wavelet transform. Short Time FFT (STFFT) finally highlighted the possible failure mechanism associated with each signal.
A pattern recognition system based on acoustic signals for fault detection on composite materials
European Journal of Mechanics - A/Solids, 2017
The use of composite materials in industry applications is constantly growing. However, fault detection and prediction on these materials is not as simple as in traditional materials. Thus, the development of a methodology for fault detection is strictly necessary to ensure the integrity of a structure. This paper proposes a pattern recognition system that implements an Artificial Neural Network classifier to detect and classify damage on composite beams. Classifiers were trained and tested using acoustic signals emitted by healthy and damaged beams after an impulsive load was applied to them. Singular Value Decomposition was used to filter the acoustic signals whereas Principal Component Analysis was implemented to extract relevant information from the filtered signal. The extracted information was used as inputs to the classifier that was able to predict four different levels of damage on glass fiber and carbon fiber beams with more than 97% accuracy. These results suggest that the proposed methodology can be further investigated to develop a robust system for automatic detection of damage on composite structures.
Lecture Notes in Mechanical Engineering
Advanced non-destructive monitoring scheme is necessary for modern-day lightweight composite structures used in aerospace industry, due to their susceptibility to barely visible damages from minor impact loads. Acoustic emission (AE) based monitoring of these structures has received significant attention in the past few years primarily due to their possibility of use in operating structures under service loads. However, localization and characterization of damages using AE is still an open area of research. The exploration of the space of signal features collected by a distributed sensor network and its reliable mapping to damage metrics (such as location, nature, intensity) is still far from conclusive. This problem becomes more critical for composite structures with complex features/geometry where the localized effects of discontinuity in geometric or mechanical properties do not make it appropriate to rely on simple signal features (such as time difference of arrival, peak amplitude, etc.) to identify damage. In this work, the AE signal features (which are spatially and temporally correlated) have been mapped to the damage properties empirically with a training dataset using metamodeling techniques. This is used in the online monitoring phase to infer the probabilistic description of the acoustic emission source within a hierarchical Bayesian inference framework. The methodology is tested on a carbon fibre composite panel with stiffeners that is subjected to impact and dynamic fatigue loading. The study presents a generalized machine learning-based automated AE damage detection methodology which both localizes and characterizes damage under varying operational loads.
Characterisation of Damage in Composite Structures using Acoustic Emission
Journal of Physics: Conference Series, 2011
Detection and characterisation of damage in composite structures during inservice loading is highly desirable. Acoustic emission (AE) monitoring of composite components offers a highly sensitive method for detecting matrix cracking and delamination damage mechanisms in composites. AE relies on the detection of stress waves that are released during damage propagation and using an array of sensors, damage location may be determined. A methodology for damage characterisation based on measuring the amplitude ratio (MAR) of the two primary lamb wave modes; symmetric (inplane) and asymmetric (outofplane) that propagate in plate like structures has been developed. This paper presents the findings of a series of tensile tests in composite coupons with large central ply blocks. The specimens were monitored using AE sensors throughout loading and once significant AE signals were observed the loading process was stopped. The specimens were removed and subjected to xray inspection to assess for any damage. The onset of damage was successfully detected using AE and was identified as being matrix cracking using the MAR methodology. The results were validated with xray inspection and a strong correlation was observed between the number of significant AE signals recorded and the number of identified matrix cracks.
Mechanical Systems and Signal Processing, 2007
Detecting and locating damage in structural components and joints that have high feature densities and complex geometry is a difficult problem in the field of structural health monitoring (SHM). Active propagation of diagnostic waves is one approach that is used to detect damage. But small cracks and damage are difficult to detect because they have a small effect on the propagating waves as compared to the effects the complex geometry itself which causes dispersion and reflection of waves. Another limitation of active wave propagation is that pre-damage data is required for every sensor-actuator combination, and a large number of sensors might be needed to detect small cracks on large structures. Overall, the problem of detecting damage in complex geometries is not well investigated in the field of SHM. Nevertheless, the problem is important because damage often initiates at joints and locations where section properties change.
Acoustic emission and signal processing for fault detection and location in composite materials
2015
The renewable energy industry is in a constant improvement in order to compete and cover any evolving opportunity presented. Nowadays one of those remarkable competitive advantages is focused on maintenance management and terms as operating and maintenance costs, availability, reliability, safety, lifetime, etc. The objectives of this paper are focused on the blades of a wind turbine. A structural health monitoring study is presented, that starts with the collection and analysis of data coming from different nondestructive tests. Signals from acoustic emissions are studied by a novel signal processing approach to detect cracks on the surface of the blades. The case study proposes a new localization method using macro-fibre composite sensors and actuators. The monitoring system uses three sensors strategically located on the blade section. Among the main difficulties involved in this first approach, the modal separation of the wave is taken into account for its importance when drawin...