Ease Acoustic Software |WORK| Cracking
Energy is released within a material for two different transition events when the deformation of the material changes from pure elastic deformation to a combination of elastic and plastic deformation and at the point of crack extension associated with fracture. This energy is detectable by the piezoelectric sensors of the acoustic emission system. The amount of energy released by a fracture is generally far greater than the amount accompanying plastic deformation. However, both instances occur for growing cracks. The tip of a crack is the site for very large stresses. Before the crack extends, a region or zone of plastic deformation is achieved in the vicinity of the crack tip. This plastic region can be approximated, using Von Mises criterion, to determine the boundaries of the plastic zone. For the thin-walled structures of this research, the plastic zone covered a very small region near the crack tip, while the major portion of the structure underwent purely elastic deformation.
Ease Acoustic Software Cracking
Borrowing an idea from the distributed point source method  for approximating wave sources in a material, consider that each molecular change is a point source of infinitesimally small diameter, which releases a strain wave into the surrounding area. These point sources could be placed close together, forming a wave front with a specific geometric shape. By the superposition principle overlapping waves will start to cancel one another as the distance between the point sources becomes smaller. As the number of point sources increases to infinity and the distance between points approaches zero, the geometric shape of the wave becomes continuous and smooth. Waves will travel outward with this smooth shape in a direction normal to the boundary of the shape. This idea is illustrated in Figure 3, using a straight line as an example. This idea was originally used for generating wave shapes by piezoelectric actuators. However, this idea may also be applied to a collection of point sources generated by the crescent shape of the new plastic region formed during crack growth rather than a series of actuators. The wider region of the crescent shape, near the horizontal axis in Figure 2(b), contains more energy than at the sharp, pointed tips of the new plastic zone. Thus acoustic emission sensors ahead of the tip of the growing crack will detect strain waves of higher magnitude of energy when compared to sensors detecting the same wave above or behind the direction of a growing crack (see Figure 4). For example, in the figure, energy from a strain wave received by sensor (b) would be greater than the energy of the same wave received by sensor (a). Based on the direction of the growing crack, a wedge shape of intensity or magnitude of energy can be drawn, protruding outward from the crack tip. In other words, the detected wave energy increases as approaches 0. This allows for a line-of-sight principle to be applied to triangulation methods to compare detections at multiple sensors resulting from the same wave. This effect is observed in a following experiment using aluminum material to confirm the notion of directional strain waves propagating from a crack tip during crack extension.
The nervous system of humans consists of a network of passive sensors capable of detecting changes within the body. If a change is detected, the system reacts by sending a signal to the brain for further analysis of the situation. More intense signals are generated for larger anomalies that identify the specific location of the anomaly. A similar idea for a passively scanning SHM system for an aircraft has been studied for this paper. That is, as a crack grows in a structural component, the amount of energy released as strain waves is linked to the size of the crack propagation. For large crack growth, more energy is released, and thus more intense strain waves are detected by an acoustic emission system.
The first series of experiments focused on determining the extension of a crack over a short period of time using an acoustic emission system. In the case of stable crack growth, further extension will cease after a specific crack length is obtained. The crack will not extend further until a certain load condition is applied. These small crack extensions consist of rapid increasing bursts that are close to instantaneous. The purpose of these experiments was to use the detections of an acoustic emission system for a known crack extension to train an artificial neural network to link certain detections to specific crack length growths. The trained ANN could later be used to determine the length of a crack from acoustic emission measurements.
Figure 6 contains drawings that detail the dimensions of the two different test panels used in the experiments. The panel, shown in Figure 6(a), was subjected to a uniaxial tensile load to initiate crack extension in order to measure the magnitude of an increment of crack growth. An initial crack was cut into the panel from one of the side edges in the test region, and then the panel was statically loaded with an MTS Sintech 5/G machine through a pin and clevis setup as illustrated in Figure 7. The loading was gradually increased, until crack extension occurred. The crack length was measured at specific load intervals by an observer, using digital calipers. These measured crack lengths were used to create a learning dataset for an artificial neural network. Likewise, they were used to compare the crack extension calculated with a neural network relative to the actual measured values. The acoustic emission sensors, located as shown in Figure 7, continuously monitored for any crack growth during the increasing-load process. The recorded acoustic emission signals were later used for analysis with an artificial neural network. Only two sensors were used for this test since crack growth size was desired and not the position of the crack (see Figure 7(b)). The sensors were placed at similar positions away from the crack tip to avoid any effects of plastic zone deformation as well as confirm that the sensors were functioning properly.
A neural network analysis program could not be added to the Physical Acoustics software  used to measure the strain waves in the test samples. Therefore, the measured strain wave data were exported and post-processed. A dataset was created with the acoustic emission software, the measured elapsed time, and the wave characteristics for analysis, described in the following paragraph. The commercial software, NeuralWorks , was used to create the neural network to generate the datasets. A MATLAB  program was created to simulate receiving the strain waves over time. This was performed to recreate the experiment in a controlled program, allowing ease of data manipulation. During the performance of the actual experiment software constraints did not allow the ability to link a neural network analysis feature to the acoustic emission software. Thus, MATLAB programming was used to post-process the experimental data using ANNs.
The crack extension calculated by an artificial neural network (ANN), using the measured acoustic emission strain wave data, was compared with the actual measured crack extension for both training and testing datasets. The abilities of the ANN were assessed using an RMS error in comparison to the actual and values predicted by the ANNs. The concept of plastic zone interference on the release of strain waves in the material was examined as well, leading to possible future research. The neural networks created for this research were capable of detecting the actual crack extension of the test set.
The proof of concept was examined in this experiment. Due to limited data and knowledge of the wave properties, this process will need to be explored in future studies. Further testing and research will be performed by locating the position of actual crack propagation. Since energy is released at the crack tips, these will be the positions located by the neural network, allowing the entire crack to be determined as the distance between the two close crack tips. This network will then be coupled with a crack severity neural network to determine the ability of neural networks to assess damage detected by an acoustic emission system. Future study will involve combining the severity artificial neural network and a new neural network to determine the location of the crack tip.
A novel method of implementing artificial neural networks and acoustic emission sensors to form a structural health monitoring system for metallic structures was presented. Flat aluminum panels, similar in thickness to those found in many aerospace structures, were subjected to increasing static loading during laboratory tests. As the load increased, a crack in the panel increased in size, releasing strain waves into the material. These waves were then detected by acoustic emission sensors, and artificial neural networks were implemented to analyze the strain waves. From a feed-forward neural network, the crack length could be approximated with reasonable precision. Plastic zone influence on strain waves released was also observed during the analysis of the experimental data. Through a second experiment, sensors placed behind the crack front were found to detect waves with smaller amplitudes than the sensors placed in other locations. Future study will involve using this knowledge to train, or teach, an artificial neural network to determine the location of a growing crack based on this difference in amplitude. These two artificial neural networks, coupled with acoustic emission sensors, form the initial stages of the development of a structural health monitoring system for aerospace systems with the capability of determining damage severity and locations within structures.
The aim of this study was to investigate the applicability of acoustic emission (AE) technique to evaluate delamination crack in glass/epoxy composite laminates under quasi-static and fatigue loading. To this aim, double cantilever beam specimens were subjected to mode I quasi-static and fatigue loading conditions and the generated AE signals were recorded during the tests. By analyzing the mechanical and AE results, an analytical correlation between the AE energy with the released strain energy and the crack growth was established. It was found that there is a 3rd degree polynomial correlation between the crack growth and the cumulative AE energy. Using this correlation the delamination crack growth was predicted under both the static and fatigue loading conditions. The predicted crack growth values was were in a good agreement with the visually recorded data during the tests. The results indicated that the proposed AE-based method has good applicability to evaluate the delamination crack growth under quasi-static and fatigue loading conditions, especially when the crack is embedded within the structure and could not be seen visually.