Simulation study of the localization of a near-surface crack using an air-coupled ultrasonic sensor array

The importance of Non-Destructive Testing (NDT) to check the integrity of materials in different fields of industry has increased significantly in recent years. Actually, industry demands NDT methods that allow fast (preferably non-contact) detection and localization of early-stage defects with easy-to-interpret results, so that even a non-expert field worker can carry out the testing. The main challenge is to combine as many of these requirements in one single technique. The concept of acoustic cameras, developed for low frequency NDT, meets most of the above mentioned requirements. These cameras make use of an array of microphones to visualize noise sources by estimating the Direction Of Arrival (DOA) of the impinging sound waves. Until now, however, because of limitations in frequency range and lack of integrated nonlinear post-processing, acoustic camera systems have never been used for the localization of incipient damage. The goal of the current paper is to numerically investigate the capabilities of locating incipient damage by measuring the nonlinear airborne emission of the defect using a non-contact ultrasonic sensor array. We will consider a simple case of a sample with a single near-surface crack and prove that after efficient excitation of the defect sample, the nonlinear defect responses can be detected by a uniform linear sensor array. These responses are then used to determine the location of the defect by means of three different DOA algorithms. The results obtained in this study can be considered as a first step towards the development of a nonlinear ultrasonic camera system, comprising the ultrasonic sensor array as hardware and nonlinear post-processing and source localization software.status: publishe

Large plate monitoring using guided ultrasonic waves

Areas of stress concentration around welded structures are likely to lead to fatigue cracks and corrosion pitting during the life time of technical machinery. Performing periodical non-destructive testing of the critical area is crucial for the maintenance of structural integrity and the prevention of unforeseen shutdowns of the system. Low frequency guided ultrasonic waves can propagate along thin structures and allow for the efficient testing of large components. Structural damage can be localized using a distributed array of guided ultrasonic wave sensors. Guided waves might be employed to overcome the accessibility problem for stiffened plate structures where access to some parts of the inspected structure is not possible. The transmission and reflection of the A0 Lamb wave mode for a variation of the stiffener geometry and excitation frequency was investigated numerically and verified experimentally. The dispersive behaviour of the guided waves has been studied to ascertain a frequency thickness product that provides limited pulse distortion. The limitations of the plate geometry as well as the excitation and monitoring locations were discussed. The radial spreading of the incident, transmitted and reflected waves from a stiffener has been investigated. The efficient quantification of the transmitted and reflected waves from the stiffener for a wide range of angles has been obtained from a single Finite Element model containing two parallel lines of nodes in front of and past the stiffener. The research outcomes have shown the dependency of the scattered wave on the incident angle and stiffener dimensions. Reasonably good A0 wave mode transmission was obtained from the oblique wave propagation (up to an angle of 45o) across realistic stiffener geometries. The choice of an optimum excitation frequency, which can ensure maximum transmission across the stiffener for specific plate geometry, was recommended. The ability for defect detection in inaccessible areas has been investigated numerically and validated experimentally. The possibility of detecting and characterizing the reflection of a guided wave pulse (A0 mode) from a through-thickness notch located behind the stiffener has been discussed. Two different approaches, based on the access to the sides of the stiffener on the plate, were employed. The limitations of the detectable defect size and location behind the stiffener have been investigated. The energy of the transmitted wave across the stiffener was adequate to detect simulated damage behind the stiffener. The evaluation has shown that defect detection in inaccessible areas behind stiffeners is achievable if the signal-to-noise ratio is high enough. In experimental measurements the noise level was of similar magnitude to the observed reflections at the defect. Thus, there is necessity to enhance the signal-to-noise ratio in experimental measurements

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Guided wave propagation and scattering in anisotropic composite structures

Carbon fibre reinforced polymer (CFRP) laminates are widely used for aerospace applications as they reduce the weight of structures whilst maintaining mechanical strength. Composites have highly anisotropic material properties and high in-plane strength but poor interlaminar strength, making them vulnerable to barely visible impact damage (BVID) caused by low velocity impacts. Composite damage is multi-modal, consisting of fibre breakage, matrix cracking, and delaminations, with delaminations causing the most significant strength reduction. Guided ultrasonic waves, often generated using a sparse network of sensors bonded to a structure, provide a promising structural health monitoring (SHM) technique for composites. Guided waves propagate along a structure, with energy throughout the entire thickness, making them ideal for rapid, long-range inspection of large areas. In anisotropic materials wave energy is focused along the high stiffness (fibre) directions, resulting in higher amplitude and wave speed in these directions. Waves launched away from the fibre direction are steered towards the fibres. These anisotropic effects could lead to inaccuracies in damage localization if not accounted for. Propagation of the fundamental, flexural (A0) guided wave mode was investigated in an undamaged unidirectional CFRP panel. Anisotropic effects including the directionality of wave velocities, skew angles, and beam spreading were quantified through both finite element simulations and experiments, achieving good agreement with predictions obtained from dispersion curves. Scattering of the A0 mode at an artificial delamination was studied for a quasiisotropic CFRP plate layup. Wave-trapping on top of the delamination, and strong forward scattering at the delamination exit was found. Significantly different scattering behaviour was observed to that of a magnet target, often used to develop SHM systems. Scattering around both damage targets was found to be directionally dependent, with higher amplitudes in the fibre directions of the outermost laminae. Implications for the SHM of composites were discussed

Master of Science

thesisNondestructive evaluation (NDE) is a means of assessing the reliability and integrity of a structural component and provides such information as the presence, location, extent, and type of damage in the component. Structural health monitoring (SHM) is a subfield of NDE, and focuses on a continuous monitoring of a structure while in use. SHM has been applied to structures such as bridges, buildings, pipelines, and airplanes with the goal of detecting the presence of damage as a means of determining whether a structure is in need of maintenance. SHM can be posed as a modeling problem, where an accurate model allows for a more reliable prediction of structural behavior. More reliable predictions make it easier to determine if something is out of the ordinary with the structure. Structural models can be designed using analytical or empirical approaches. Most SHM applications use purely analytical models based on finite element analysis and fundamental wave propagation equations to construct behavioral predictions. Purely empirical models exist, but are less common. These often utilize pattern recognition algorithms to recognize features that indicate damage. This thesis uses a method related to the k-means algorithm known as dictionary learning to train a wave propagation model from full wavefield data. These data are gathered from thin metal plates that exhibit complex wavefields dominated by multipath interference. We evaluate our model for its ability to detect damage in structures on which the model was not trained. These structures are similar to the training structure, but variable in material type and thickness. This evaluation will demonstrate how well learned dictionaries can both detect damage in a complex wavefield with multipath interference, and how well the learned model generalizes to structures with slight variations in properties. The damage detection and generalization results achieved using this empirical model are compared to similar results using both an analytical model and a support vector machine model