Detection and Visualization of Flaws in Ultrasonic Defectoscopy

In ultrasonic defectoscopy it is very difficult to detect flaws in materials with coarse-grain structure. Measured ultrasonic signal contains echoes from flaw and echoes from grains included in material. These echoes characterized noise have to be cancelled. A common source of noise is electronic circuitry, which is used for processing of the ultrasonic signal, and scattering at the inhomogeneities in the structure of a grainy material. For reduction of noise efficient methods for suppression of noise have to be used. In this case the most efficient method for noise reduction is method based on discrete wavelet transform. This method gives the best results for filtering of ultrasonic signal in A-scan. In this work method based on neural networks for flaw detection in B-scan is used. Proposed methods are efficient used for separation of flaw from undesirable echoes. For visualization of flaw volume construction was created. The 3D volume visualization characterizes volumetric depiction of measured material. By using 3D volume visualization of ultrasonic signal it is possible to determine character and place of flaws in ultrasonic defectoscopy.