Time Of Flight Diffraction Images Research Articles

Enhancing ultrasonic time-of-flight diffraction measurement through an adaptive deconvolution method

Deconvolution is generally applied to improve the temporal resolution of ultrasonic signals. However, using this process in the time-of-flight diffraction (TOFD) measurement of small and shallow defects is challenging because TOFD signals are dispersive in space–frequency distribution. Particularly, determining the reference signal for deconvolution remains a critical barrier. To this end, an adaptive deconvolution method is proposed in this study. Using wavelet transform, we firstly decompose the TOFD signals into sub-band signals to standardise the space–frequency distribution. Then, sub-band signals with strong coherences are adaptively selected on the basis of coherence coefficient metric. Upon the opted sub-band signals, a lateral wave can be readily used as the reference signal, and TOFD signals can be reconstructed with established Wiener filtering and spectral extrapolation methods. The feasibility of the proposed method is validated with the TOFD measurement of a small side-drilled hole near the surface. Results show that the proposed method effectively separates overlapping TOFD signals and improves the axial resolution of a TOFD image.

Non-contacted Permanent Magnetic Absorbed Wall-climbing Robot for Ultrasonic Weld Inspection of Spherical Tank

A novel wall-climbing robot for the onsite weld inspecting of spherical tank with Time of Flight Diffraction (TOFD) method has been developed, in order to liberate operators from dangerous and heavy working environment. Patented non-contacted permanent magnetic absorbed technology was adopted to realize reliable and flexible all-position moving along the weld seam on the surface of spherical tank. To ensure stable coupling for TOFD test, a surface adaptive probe holder which can provide constant contact force, have been particularly designed. Equipped with a visual sensing based weld seam tracking unit and industrial PC station, the robot could perform automatic flaw detection remotely even in the darkness environment. Onsite ultrasonic weld inspections have been carried out on a 4000m3spherical tank with 28mm in thickness. It is verified that the robot could accomplish tasks in any position and the acquired TOFD images satisfy the requirements of engineering evaluation.

Hybrid Ultrasonic TOFD Imaging of Weld Flaws Using Wavelet Transforms and Image Registration

Ultrasonic time of flight diffraction (TOFD) is an effective weld crack inspection technique. Due to the intensity of diffraction wave is rather weak compared with the lateral wave and the bottom echo wave, thus the signal-to-ratio (SNR) of TOFD image is low. A new dichotomous method is comprised of two steps that contains wavelet shrinkage and image registration is proposed in this paper to reduce the noise and improve the resolution of TOFD images as well. In order to evaluate the reliability of our proposed method in this paper, we have established the experiment system, and sampled a number of TOFD data with random distribution of noise characteristics. We adopted one-dimension wavelet transform and two-dimension wavelet transform in the very beginning of the first step of the proposed algorithm respectively. The SNR of the result obtained in this step is improved significantly compared with the classic algorithms. Next, the image registration is applied. After the registered images have been added to form a new one, then it comes to the final result that shows not only the SNR but also the definition of the image is enhanced effectively.

Sparse Representations to Replace TOFD Images in Non-destructive Testing of Materials

This paper describes a proposed method for the selection of relevant samples of ultrasonic signals during automatic material inspection. Instead of the well-known time of flight diffraction (TOFD) images, data are stored as a sparse matrix in which the elements only indicate whether a defect has been detected. This technique avoids storage of useless signals received during probe displacement in cases of low and high signal-to-noise ratios that correspond to coarse-grained and fine-grained materials, respectively. The approach is based on comparing the positions of maximum amplitudes, which are randomly located when signals only consist of noise but are in the same signal range when a defect is detected. The matrix elements are then applied as inputs to a self organizing map by neural networks to produce a normalized sparse matrix as the output, with a constant number of elements. This approach will be beneficial to enable the use of selected data in intelligent systems requiring a fixed number of inputs to characterize defects.

Optimization of the Cascade Feed Forward Back Propagation network for defect classification in ultrasonic images

Ultrasonic Time of Flight Diffraction (TOFD) is now a well established NDE technique finding wide applications in the industry for inspection during manufacture, pre-service and also inservice. While conventionally interpretations of UT images are done by the inspector, a need has always been felt for automated evaluation and interpretation especially when large inspection volumes are involved. Apart from enhancing the speed of inspection, automated evaluation and interpretation provides better reliability of inspection. A number of approaches based on signal analysis coupled with artificial neural networks (ANN) are being tried internationally and limited success has also been obtained. This paper focuses on the development of a semi automatic toolbox for reliable and fast flaw classification in TOFD images using ANN. TOFD images are first acquired and statistical parameters such as mean, standard deviation, energy, skewness and kurtosis are calculated for the region of interest in the images. The classification of the flawed region like Crack, Lack of Fusion, Lack of Penetration, Porosity and Slag Inclusion was materialized using different ANN approaches which made use of these statistical parameters as their input. The process of optimization of a network involves comparison of classification accuracy and the sensitivity of the selected networks. The Cascade Feed Forward Back Propagation (CFBP) network with log sigmoidal activation function proved to be the optimized network model for the data set considered in this study.

Comparative Study of Diverse Techniques for Flaw Segmentation in TOFD Images of Austenitic Stainless Steel Weld

Background/Objectives: Grain structure of Austenitic Stainless Steel (ASS) weld causes difficulty in defect detection while inspecting using Time of Flight Diffraction (TOFD) method. This study aims at comparing segmentation methods to overcome this difficulty based on defect characterization. Methods/Statistical Analysis: This study makes use of TOFD and Radiographic images of ASS weld pads fabricated with defined linear and volumetric defects. The Region-Based Level Set algorithm and Discontinuity Based Segmentation algorithm were explored for achieving flaw segmentation and quantitative characterization and validation of the result with that of standard radiographic results. Findings: The efficiency of the algorithms was analyzed by comparing and validating the size of defect with that of standard radiographic results in the form of error percentage. The consistency of error percentage in defect sizing (up to 11%) achieved by Region- Based Level Set algorithm for all the test images given in the database indicate that, this algorithm is the best as compared to Discontinuity Based Segmentation algorithm(error percentage up to 47%) for defect segmentation and characterization in TOFD images. Application/Improvements: The segmentation algorithm enables automation of measurement process and enhanced detection and characterization of defects at the initial stage. Further, it reduces human fatigue caused by operator while defect detection as the volume of data increases. The future direction is to fully automate the system in order to save time for interpretation and to modify the algorithm to segment images with multiple defects.

Mathematical morphology for TOFD image analysis and automatic crack detection

The aim of this work is to automate the interpretation of ultrasonic images during the non-destructive testing (NDT) technique called time-of-flight diffraction (TOFD) to aid in decision making.In this paper, the mathematical morphology approach is used to extract relevant pixels corresponding to the presence of a discontinuity, and a pattern recognition technique is used to characterize the discontinuity. The watershed technique is exploited to determine the region of interest and image background is removed using an erosion process, thereby improving the detection of connected shapes present in the image. Remaining shapes, are finally reduced to curves using a skeletonization technique. In the case of crack defects, the curve formed by such pixels has a parabolic form that can be automatically detected using the randomized Hough transform.

Noise Reduction Method for Ultrasonic TOFD Image Based on Image Registration

Influenced by random noises from inhomogeneous material scattering and fluctuation of detected electric signals, the signal-to-noise ratio (SNR) of ultrasonic time-of-flight-diffraction (TOFD) image decreases significantly. For the noise reduction of TOFD images, several D-scanned TOFD images with different distribution of noise characteristics are obtained through repeating detection and slightly and randomly changing the probe’s initial position each time. The registered images then are processed by shift-and-add (SAA) technique to reduce the noise level of the TOFD images. Besides, correlation image registration algorithm based on optimization method was established to avoid the shift of TOFD images due to slight change of probe’s initial position. Noises in the registered images show stochastic behavior at the same position. In order to verify reliability of the algorithm, an experimental TOFD detection system for weld defects has been designed to acquire and experiment with TOFD images. The experiment results have been evaluated in terms of cross correlation coefficient, SNR and standard variance of images. The results show that the proposed method could effectively enhance SNR of TOFD images and improve the ability to identify weld defects of materials.

A New Defect Detection Method of Ultrasonic TOFD Image for Lateral Waves Suppression

TOFD (Time of flight diffraction) is suitable for the weld defects in the detection and has been widely used in the vessel of pressure. To widen the range of detection in TOFD and the removal of the background wave, a new lateral wave suppression method for TOFD image is proposed. Firstly, ultrasound TOFT image signal of the model is established. Secondly, a new searching clustering algorithm is proposed. Then, it is used to lateral wave suppression. The results show that the lateral wave can be eliminated effectively even if lateral waves are shaking seriously.

Texture Analysis for Flaw Detection in Ultrasonic Images

In this paper, we present two approaches for flaw detection in TOFD (Time of Flight Diffraction) images based on texture features. Texture is one of the most important features used in recognizing patterns in an image. The paper describes texture features by two methods: Multiresolution analysis such as wavelet transforms and Gabor filters bank. The two-dimensional wavelet transform is used to decompose the input image into a multiresolution framework. The textural statistical parameters are used to allow the choice of the decomposition channel. The Gabor filter is a Gaussian kernel function modulated by a sinusoidal plane wave. All Gabor filters can be generated from one mother wavelet by dilation and rotation. These filters represent an appropriate choice for tasks requiring simultaneous measurement in both space and frequency domains. The most relevant features are optimized by Principal Components Analysis (PCA) and used as input data on a Fuzzy C-Mean clustering classifier. We use two classes: ‘defects’ or ‘no defects’. The proposed approach is tested on the TOFD image achieved in an industrial field.

Image processing and wavelets transform for sizing of weld defects using ultrasonic TOFD images

Ultrasonic time-of-flight diffraction (TOFD) is rapidly gaining prominence as a reliable nondestructive testing technique for weld inspection in steel structures, providing highly accurate positioning and sizing of flaws. A number of signal and image processing tools have been specifically developed for use with TOFD data and adapted to function autonomously without the need for continuous intervention, configuring automatically according to the nature of the data and the data acquisition settings. This paper presents the results of several innovative procedures based on Multiresolution analysis such as wavelet transforms and texture analysis for detecting edges of planar defects. The approach is based on the decomposition by packets of wavelets of the image while taking into account of the underimage content textural after each level of decomposition. The reconstruction of the image is done by eliminating the underimages of poor textural information and at the end the segmented image is got by a binarisation operation done on the image rebuilt histogram. The automation of sizing and positioning of weld flaws in TOFD data as an essential stage of a comprehensive TOFD inspection and interpretation aid is developed and implemented. Key words: Ultrasonic Time-Of-Flight Diffraction., Texture Analysis, Wavelet Transform.

Method for crack tip recognition in an ultrasonic time of flight diffraction image

An aluminum sample containing artificial internal crack is inspected by using the method of ultrasonic time of flight diffraction(TOFD),and the low resolution B-scan image with hyperbolic feature is processed in order to improve testing precision.According to the inspection manner of ultrasonic TOFD,a synthetic aperture focusing technique(SAFT)algo- rithmic model for B-scan image reconstruction is founded.By using the model,ultrasonic TOFD B-scan image can be en- hanced.The time resolution of the B-scan image is improved by the pre-processing of searching for gray peak value,and the lateral resolution of the image is enhanced by suppression of the cutter by the following processing of SAFT.A B-scan im- age processing method that based on searching for gray peak value and SAFT is therefore proposed.A processing window is adopted in order to minimize the execution time of the algo- rithm,and proper width of the window is chosen.The result shows that the method can effectively enhance the target in the image,and the location of upper and lower crack tip in the sample can be measured rapidly and accurately accordingly.