Non-destructive evaluation of components throughout the engineering industry allows the health of components and structures to be analysed from the surface. Ultrasonic guided wave testing is one of many non-destructive evaluation methods used to locate and characterise the severity of inhomogeneities within large thin walled, plate-like structures. As a result of this, guided wave testing has been adopted by the automotive, aerospace, pipeline, pressure vessel and infrastructure industry. In-situ guided wave testing is the permanent integration of a guided wave testing system with a component to constantly monitor its structural integrity. Guided wave testing works with an array of surface bonded or imbedded transducers to actuate and sense Lamb waves. The component is interrogated with the fundamental anti-symmetric Lamb wave mode, which scatter off inhomogeneities. The scattered Lamb waves are received by sensing transducers within the array, these signals are analysed to determine the location and severity of a defect within the component.
The beamforming method is a defect location method used throughout guided wave testing to constantly search for defects and their locations. Using the scatter signals received by sensing transducers the beamforming method is able to determine the location of the defect. A number of user defined parameters are required for the beamforming method to be applied. Such parameters are; Number of transducers, location of transducers, location of the defect with respect to transducers and interrogation pulse properties.
Experimentally, the beamforming method can return significantly inaccurate results due to poor transducer array design or wrongly specified interrogation pulse properties. Using knowledge of the effects of varying user defined parameters on location accuracy, the beamforming method can be used efficiently to locate defects with minimal error.
The aim of this thesis is to investigate the effects of each user defined parameter on the accuracy of the defect location determined by the beamforming model. These effects have been investigated using a simplified non-experimental beamforming program which has been developed in Python over the course of this thesis and allows the user to vary such parameters.
The parameter study has been conducted through a number of carefully designed beamforming configurations. Each configuration has been run and location accuracy data has been recorded and analysed. From this analysis conclusions can be drawn on the effects of these parameters on location accuracy.