Application of High Resolution Inversion of Ultrasonic Data to the Imaging of Multi-Layered Composite Structures

Ultrasonic imaging has evolved from its early application which utilized only amplitude C-scans to more complex techniques which make extensive use of digital signal processing. Techniques, such as one-and two-dimensional deconvolution processing and synthetic aperture focussing techniques (SAFT), are becoming more widely accepted for conventional applications. In general, each of these techniques aims to improve the interpretability of the ultrasonic image by increasing the resolution in one or more dimensions

Rapid detection and quantification of features such as damage or flaws in composite and metallic structures

An apparatus, system, and method for non-destructible evaluation (NDE) of a material use thermography to rapidly detect and/or generally locate a feature such as, for example, damage or a defect in the material. The apparatus, system, and method also use ultrasound to specifically locate the feature in the material for quantification and/or evaluation either by an operator or by an external device suited for such purpose. Accordingly, the apparatus, system and method are particularly useful for NDE in applications such as the analysis of the structure of an aircraft, for example, in which the scale of the material to be analyzed is large, thus requiring the rapid NDE afforded by thermography, and in which quantification and/or evaluation of a feature must be performed with precision, thus requiring the relatively high-resolution NDE afforded by ultrasound

Automated analysis of non destructive evaluation data

Interpretation of NDE data can be unreliable and difficult due to the complex interaction between the instrument, object under inspection and noise and uncertainties about the system or data. A common method of reducing the complexity and volume of data is to use thresholds. However, many of these methods are based on making subjective assessments from the data or assumptions about the system which can be source of error. Reducing data whilst retaining important information is difficult and normally compromises have to be made. This thesis has developed methods that are based on sound mathematical and scientific principles and require the minimum use of assumptions and subjective choices. Optimisation has been shown to reduce data acquired from a multilayer composite panel and hence show the ply layers. The problem can be ill-posed. It is possible to obtain a solution close to optimum and obtain confidence on the result. Important factors are: the size of the search space, representation of the data and any assumptions and choices made. Further work is required in the use of model based optimisation to measure layer thicknesses from a metal laminate panel. A number of important factors that must be addressed have been identified. Two novel approaches to removing features from Transient Eddy-Current (TEC) data have been shown to improve the visibility of defects. The best approach to take depends on the available knowledge of the system. Principal Value Decomposition (PVD) has been shown to remove layer interface reflections from ultrasonic data. However, PVD is not suited to all problems such as the TEC data described. PVD is best suited in the later stages of data reduction. This thesis has demonstrated new methods and a roadmap for solving multivariate problems, these methods may be applied to a wide range of data and problems