Time-of-flight diffraction ultrasonics

About: Time-of-flight diffraction ultrasonics is a research topic. Over the lifetime, 544 publications have been published within this topic receiving 3189 citations.

Combining fuzzy logic and neural networks in classification of weld defects using ultrasonic time-of-flight diffraction

Abstract: The ultrasonic Time-of-Flight Diffraction (TOFD) technique is gaining rapid prominence in non-destructive testing due its high accuracy in detection, positioning and sizing of weld flaws in steel structures. Until lately, TOFD was used reliably only in fast-track inspections due its portability in automatic scanning and data acquisition. However, data processing and interpretation of TOFD data requires expert knowledge and accuracy largely depends on the operator experience. Hence, results suffer errors as interpretation is often carried out offline, especially when dealing with large volumes of data. A fully comprehensive automatic detection and interpretation can be achieved using advanced image and signal processing and artificial intelligence techniques, thus reducing time, cost and errors in the detection, interpretation and classification of flaws in steel structures. This paper presents current research using advanced methods for automatic interpretation and classification of weld defects in TOFD data. In the classification stage three different classification techniques are employed and compared: anartificialneuralnetwork-basedclassifier.afuzzv logic-based classifier and a hybrid neural-fuzzy classifier. A neural classifier can learn from data, but the output does not lend itself naturally to interpretation. A fuzzy classifier on the other hand consists of interpretable linguistic rules, but they cannot learn. A neural-fuzzy classifier is based on a three-layer feed-forward neural network and combines the merits of both neural and fuzzy classifiers while overcoming their drawbacks and limitations. The developed neural-fuzzy classifier exhibits high levels of accuracy, consistency and reliability, with acceptably low computational time and is a promising new development in the field of semi-automatic weld inspection.

Ultrasonic TOFD flaw sizing and imaging in thin plates using embedded signal identification technique (ESIT)

Abstract: The ultrasonic Time-of-Flight Diffraction (TOFD) technique is a well developed technique for sizing defects in thick sections (thickness >10 mm). Attempt has been made here to extend this technique for thin sections (6-10mm). An automated defect sizing algorithm using the Embedded Signal Identification Technique (ESIT) was developed for separating partially superimposed signals often encountered in thin sections and the results were compared with the manual sizing method. Both EDM notches and more realistic fatigue cracks in thin section were used to evaluate the proposed technique.

Enhancement of ultrasonic images for sizing of defects by time-of-flight diffraction

Abstract: A signal processing technique is presented for significantly sharpening the resolution of ultrasonic images, similar to those acquired in the nondestructive evaluation of girth welds in oil/gas pipelines. This enhancement allows a much improved estimate of the exact size of any detected anomaly in the weld, such that fracture mechanics can be used to gauge the probability of weld failure. The algorithm is based on the synthetic aperture focusing technique (SAFT), combined with a variation of Wiener filtering and autoregressive spectral extrapolation. An analytical model of the transducer is used to construct an appropriate reference spectrum for the deconvolution operation, and accounts for the dependence of a beam's frequency spectrum on the position of a flaw relative to the transmitter. Experimental results are used to provide an estimate of the improvement in flaw sizing accuracy.

Deconvolution method for recovering the photon time-of-flight distribution from time-resolved measurements

Abstract: A nonparametric deconvolution algorithm for recovering the photon time-of-flight distribution (TOFD) from time-resolved (TR) measurements is described. The algorithm combines wavelet denoising and a two-stage deconvolution method based on generalized singular value decomposition and Tikhonov regularization. The efficacy of the algorithm was tested on simulated and experimental TR data and the results show that it can recover the photon TOFD with high fidelity. Combined with the microscopic Beer-Lambert law, the algorithm enables accurate quantification of absorption changes from arbitrary time-of-flight windows, thereby optimizing the depth sensitivity provided by TR measurements.

Automatic detection of defects in industrial ultrasound images using a neural network

Abstract: Time-of-flight diffraction (TOFD) is a relatively new method of ultrasonic inspection and is well suited to semi- automation using methods such as robotic scanning, computer conditioned data acquisition and signal and image enhancement. However very little work has been documented on the full computer understanding of such scans. Instead, most work has been directed at aiding the manual interpretation process to determine defect characteristics. This paper describes the application of image processing and neural networks (ANNs) to the task of completely automating the decision making process involved in the interpretation of TOFD scans. Local area analysis is used to derive a feature vector which contains 2D information on defect/component and non-defect areas. These vectors are then classified using an ANN trained with the backpropagation algorithm. The labelled image is then further segmented using binary shape analysis to discriminate between component echoes, or defect signals. Time-of-flight correction techniques may be then used in order to determine the location of defects within a scanned weld.