Analytical Treatment of Polar Backscattering from Porous Composites

Polar backscattering from a fiber-reinforced composite which contains regions of porosity, consists of several components. Roughly speaking these components can be attributed to effects of finite beam width, to the structuring of the material and to the existence of porosity. The backscatter amplitude strongly depends on the polar and the azimuthal angles, which together define the position of the transducer. It has been shown experimentally that the backscatter amplitude shows a steep peak when the incident beam is normal to the fiber direction [l]–[4]

Analysis of Bond-Stiffness Deterioration due to Distributed Disbonds

This paper is concerned with the characterization of residual adhesive strength. The deterioration of adhesive strength depends on damage mechanisms in thin layers at the interfaces of adherends and adhesives. In this work adhesive-bond damage mechanisms, particularly distributions of disbonds, are modeled and related to nonlinear changes of the gross stiffness of the adhesive layer. In turn, the changes in adhesive-layer stiffness affect coefficients of reflection and transmission for reflection and transmission of ultrasonic signals by the adhesive-bond layer. Hence the reflection and transmission measurements can be used to characterize adhesive bond damage

Measurements of Thin-Film Elastic Properties by Line-Focus Acoustic Microscopy

Quantitative acoustic microscopy has been used to measure the velocity of leaky surface acoustic waves (SAWs) [1,2]. This technique measures a V(z) curve, which is a record of the voltage output V of the transducer as a function of the distance z between the acoustic lens and the specimen. Line-focus acoustic microscopy (LFAM) allows the measurement of the SAW velocity in specified directions. In earlier papers, LFAM has been used to determine the elastic constants of isotropic thin films [3,4] and anisotropic thin films [5–7]. The directional variation of the SAW velocity of a thin-layer/anisotropic substrate configuration may be quite different from that of the bare substrate. It follows that this variation can be used to determine the elastic properties of thin films

Crack Sizing Using a Neural network Classifier Trained with Data Obtained form Finite Element Models

Ultrasonic inspection of riveted joints carried out by human operator is cumbersome and time consuming. An automated signal classification system would provide better reliability and accuracy in the determination of crack size and orientation. In this paper, we discuss a neural network designed for use in ultrasonic signal classification. The network can give classification results in a short time which makes possible real time ultrasonic inspection. An automated crack sizing system was presented earlier for similar applications [1] and the present paper is an extension of that work. The latest improvement is the use of numerically obtained ultrasonic data to train the neural network classifier (NNC)

Ultrasonic Evaluation of Damage in Concrete Bridge Deck Pavements

Interest in repair, maintenance and characterization of infrastructure in the United States has reached unprecedented highs in recent years. The interest in this area is motivated by the aging and associated degradation of our built environment. There is also a perception that the complexity of the problems encountered in this area are a suitable venue for application of new technology from academic and defense related research. However, while applying established technological solutions to the challenges encountered in infrastructure research, basic questions are encountered regarding the nature of the materials used in roads, bridges and other structures. In spite of the enormous experience base with many of the materials used in infrastructure, often the behavior of the materials has only been understood in terms of the gross behavior in large scale measurements. The materials often show a large variation in their properties between samples as well as a large spatial variation in properties in a single sample. Perhaps the most important material, concrete, may also be the most variable material used in infrastructure

Lamb Wave Tomography Using Laser-Based Ultrasonics

Lamb waves are widely used for the nondestructive evaluation of plate structures. By using Lamb wave attenuation, velocity and mode conversion, information about the sizes and positions of existing defects can be obtained. Lamb waves have also been used for C-scan imaging of plates. In C-scan imaging, the measurement has to be performed at each point on the sample to characterize the material at that point. Recently, computed tomography techniques using Lamb waves and surface acoustic waves have been proposed and investigated [1–3]. The computed tomographic technique provides faster image reconstruction and the ability to image an area from outside the area. This is often desired when the defected area is not directly accessible

A Non-Collinear Mixing Technique to Measure the Acoustic Nonlinearity Parameter of Adhesive Bond from Only One Side of the Sample

The acoustic nonlinearity parameter (ANLP) of a material is often positively correlated with the damage in the material. Therefore, the ability to nondestructively measure the ANLP may enable the nondestructive characterization of the material’s remaining strength. In this work, we developed a non-collinear mixing technique to measure the ANLP of adhesive bonds. One of the most significant features of the new method is that it requires only one-side access to the adhesive bond being measured, which significantly increases it utility in field measurements. Specifically, the test sample considered in this study consists of two aluminum plates adhesively joined together through a commercial adhesive tape. The non-collinear wave mixing technique consists of generating a longitudinal wave and a shear wave by piezoelectric transmitters attached to the same surface of the sample under test. These waves are introduced into the sample in such an angle that they will mix at the adhesive bond region. Mixing of these two waves generates a shear wave that propagates back towards the surface where the two waves were generated. This mixing wave is then recorded by a shear wave receiver placed on the same surface where the longitudinal and shear wave transmitters are located. It was shown that amplitude of this mixing wave is proportional to the ANLP of the adhesive bond. To demonstrate the effectiveness of the newly developed technique, a freshly made adhesive sample was first measured using the non-collinear mixing technique to obtain the ANLP of the adhesive bond. This sample is then placed inside a thermal chamber for aging to change its ANLP. The sample was taken out the thermal chamber periodically to measure its ANLP. The measured results clearly show that the ANLP varies with aging time. Initially, the ANLP decreases with aging time, possibly due to further curing. Afterward, the ANLP begins to increase with aging time, likely due to aging induced damage in the polymer adhesive. To verify that the signals received from the shear wave receiver are indeed the mixing wave, the finite element method was used to simulate the wave motion in the test sample. The simulation results clearly show that the signals recorded by the shear wave receiver are indeed the desired mixing wave, whose amplitude is proportional to the ANLP of the adhesive bond

A model for the ultrasonic detection of surface-breaking cracks by the scanning laser-source technique

A model for the Scanning Laser Source (SLS) technique is presented. The SLS is a novel laser based inspection method for the ultrasonic detection of small surface-breaking cracks. The generated ultrasonic signal is monitored as a line-focused laser is scanned over the defect. Characteristic changes in the amplitude and the frequency content are observed. The modelling approach is based on the decomposition of the field generated by the laser in a cracked two-dimensional half-space, by virtue of linear superposition, into the incident and the scattered fields. The incident field is that generated by laser illumination of a defect-free half-space. A thermoelastic model has been used which takes account of the effect of thermal diffusion, as well as the finite width and duration of the laser source. The scattered field incorporates the interactions of the incident field with the surface-breaking crack. It has been analyzed numerically by a direct frequency domain boundary element method. A comparison with an experiment for a large defect shows that the model captures the observed phenomena

An Artificial Intelligence Technique to Characterizae Surface-Breaking Cracks

A neural network with an analog output is presented to determine the angle of inclination of a surface-breaking crack from ultrasonic backscattering data. A neural network which was trained by the use of synthetic data set to estimate the depth of a crack, assuming that the inclined crack angle is known, was presented earlier[1,2]. In this study, a neural network estimates the angle of inclination of the surface-breaking crack, assuming that the depth of the crack is 2.0mm, by utilizing the waveforms of backscattered signals from the crack. The plate with a surface-breaking crack is immersed in water and the crack is insonified from the opposite side of the plate. The angle of incidence with the normal to the insonified face of the plate is taken to be 18.9°. The neural network is a feed-forward three layered network. The training algorithm is an error back-propagation algorithm which has been discussed in Refs. [3,4]. The theoretical data obtained by the boundary element method are used for the training. The performance of the trained network is tested by synthetic and experimental data