Ahmad Osman

Improvement of X-ray castings inspection reliability by using Dempster-Shafer data fusion theory

The aim of this work is to improve the classification of defects in X-ray inspection by developing a new method based on Dempster-Shafer data fusion theory where measured features on the detected objects are considered as information sources. From the histogram of features values on a learning database of manually classified objects, an automatic procedure is proposed to define a set of mass functions for each feature. The spatial repartition of features is divided into regions of confidence with corresponding mass functions. A smooth transition between regions is ensured by using fuzzy membership functions. The whole process is carried out without any expert intervention. Validation takes place on a testing database. Data fusion leads to a significant improvement of classification performances with respect to the actual system.

An automated data processing method dedicated to 3D ultrasonic non destructive testing of composite pieces

State-of the art Non Destructive Testing using ultrasound is based on evaluation of C-scan images, which is done mainly visually. The development of the new Sampling Phased Array technique SPA by IZFP Fraunhofer provides a fast three-dimensional reconstruction of inner object structures. This new inspection technique is to be complemented with fully or semi-automated evaluation of ultrasonic data, providing maximum support to the operator. We present in this contribution a processing method for SPA ultrasonic data, where the main focus of this paper will be on speckle noise reduction. The evaluation method is applied on carbon fibre composite where it demonstrates robust and successful performance in recognition of defects.

An Infrared-Induced Terahertz Imaging Modality for Foreign Insert Detection in A Glass Fiber-Skinned Lightweight Honeycomb Composite Panel

In this paper, terahertz time-domain spectroscopy (THz-TDS) is used for the first time to detect fabricated defects in a glass fiber-skinned lightweight honeycomb composite panel. A novel amplitude polynomial regression (APR) algorithm is proposed as a preprocessing method. This method segments the amplitude–frequency curves to simulate the heating and the cooling monotonic behavior as in infrared thermography. Then, the method of empirical orthogonal function (EOF) imaging is applied on the APR preprocessed data as a postprocessing algorithm. Signal-to-noise ratio analysis is performed to verify the image improvement of the proposed APR-EOF modality from a quantitative point of view. Finally, the experimental results and the physical analysis show that THz is more suitable with respect to the detection of defects in glass fiber lightweight honeycomb composites.

Automatic classification of 3D segmented CT data using data fusion and support vector machine

The three dimensional X-ray computed tomography (3D-CT) has proved its successful usage as inspection method in non destructive testing. The generated 3D volume using high efficiency reconstruction algorithms contains all the inner structures of the inspected part. Segmentation of this volume reveals suspicious regions which need to be classified into defects or false alarms. This paper deals with the classification step using data fusion theory and support vector machine. Results achieved are very promising and prove the effectiveness of the data fusion theory as a method to build stronger classifier.

Speeding up 3D SAFT for ultrasonic NDT by sparse deconvolution

In this paper we propose to pre-process ultrasonic measurements (A-scans) in Non-Destructive Testing (NDT) by sparse deconvolution before post-processing the data with the Synthetic Aperture Focusing Technique (SAFT). Compared to state-of-the-art SAFT post-processing of raw A-scan measurements, pre-processing by sparse deconvolution can improve NDT in the following ways: First, the temporal resolution of signal reflections is increased. Second, because the A-scans appear as a sparse signal of spikes, it is possible to formulate the time-domain SAFT algorithm in a new fashion that is both faster compared to conventional SAFT and the deconvolved input data can be focussed better leading to a higher resolution. Since sparse deconvolution could be implemented directly into the ultrasonic probe hardware/software measurement setup, this approach can significantly speed up measurements in time-critical environments. We test the proposed scheme on CIVA simulation data as well as measurements and show B- and C-images of raw SAFT vs. Orthogonal Matching Pursuit (OMP) + SAFT and Basis Pursuit Denoising (BPDN) + SAFT.

Eddy current pulsed thermography for ballistic impact evaluation in basalt-carbon hybrid composite panels

In this paper, eddy current pulsed thermography was used to evaluate ballistic impact damages in basalt-carbon hybrid fiber-reinforced polymer composite laminates for the first time, to our knowledge. In particular, different hybrid structures including intercalated stacking and sandwich-like sequences were used. Pulsed phase thermography, wavelet transform, principle component thermography, and partial least-squares thermography were used to process the thermographic data. Ultrasound C-scan testing and X-ray computed tomography were also performed for comparative purposes. Finite element analysis was used for validation. Finally, an analytical and comparative study was conducted based on signal-to-noise ratio analysis.

Enhanced Infrared Image Processing for Impacted Carbon/Glass Fiber-Reinforced Composite Evaluation

In this paper, an infrared pre-processing modality is presented. Different from a signal smoothing modality which only uses a polynomial fitting as the pre-processing method, the presented modality instead takes into account the low-order derivatives to pre-process the raw thermal data prior to applying the advanced post-processing techniques such as principal component thermography and pulsed phase thermography. Different cases were studied involving several defects in CFRPs and GFRPs for pulsed thermography and vibrothermography. Ultrasonic testing and signal-to-noise ratio analysis are used for the validation of the thermographic results. Finally, a verification that the presented modality can enhance the thermal image performance effectively is provided.

Nondestructive evaluation using eddy current pulsed thermographic imaging of basalt-carbon hybrid fiber-reinforced composite laminates subjected to low-velocity impact loadings

In this paper, eddy current pulsed thermography in transmission mode was used to detect the damages caused by low-velocity impacts in carbon fiber-reinforced polymer and basalt-carbon hybrid fiber-reinforced polymer laminates. In particular, different hybrid structures including intercalated stacking and sandwich-like structures were used. The impact energy of 12.5 was used for the evaluation of the impact damage level. Ultrasonic phased-array C-scan was performed for comparative purposes. In addition, the advantages and disadvantages of the two structures were identified and discussed.

Infrared and Terahertz time-domain imaging for evaluation of impacted thick homogeneous particleboards of sugarcane bagasse

In this paper, both infrared thermography (IRT) and terahertz time-domain imaging (THz-TDI) were used to evaluate impacted homogeneous particleboards of sugarcane bagasse (SCB), which have the thickness of 2 cm. In this study, the THz data were processed by applying the well-known image processing algorithms used in IRT. The comparative results show that IRT can provide some finer details in the detection of shallow thermal imprints; however, it is difficult to reveal deeper (typically ~1 cm) information. On the contrary, THz-TDI may explore the entire thickness of the samples, although – for this specific study – its resolution is lower. In addition, THz-TDI has the advantage to detect resin-rich and fibre-rich regions. .

Sparse Signal Recovery for ultrasonic detection and reconstruction of shadowed flaws

In this paper we propose a method to improve the detection of shadowed flaws in ultrasonic non-destructive testing (NDT). The B-scans are expressed in the context of Sparse Signal Recovery (SSR), where the shadowing effect is incorporated during the reconstruction: Whenever a new defect is found, its shadow on all other dictionary atoms is determined and the dictionary is updated accordingly. We develop models for determining the affected dictionary entries as well as for the intensity of the attenuation due to the shadow. Using Orthogonal Matching Pursuit (OMP), we demonstrate that the proposed method significantly improves the reconstruction amplitudes, i.e., the detection reliability, compared to conventional detection without incorporation of shadowing.