Brian Stephen Wong

Experimental evaluation of delaminations in composite plates by the use of Lamb waves

Abstract The fundamental S 0 mode of Lamb waves can be used in testing for delaminations in plate material. By scanning over the surface beneath which a delamination lies, it is possible to measure the size of the delamination. The amplitude of the wave decreases over a delamination. It has been found that the phase velocity does not change significantly with the depth of delamination. The rate of decrease in amplitude of an individual pulse cycle was detected to vary with the depth of the delamination, being most sensitive to delaminations near the surface of the plate. This is particularly useful when sizing for defects close to the surface, where a normal-incidence pulse-echo ultrasonic method has problems, particularly when the depth of the defect or the back-wall echoes lie within the length of the transmitted ultrasonic pulse. The rate of change in signal amplitude over the delamination also depends on the surface area of the probe in contact with the specimen surface, i.e. a small probe is more receptive to the presence of a delamination. The technique has potential for faster c-scanning of a complete plate than the usual normal-incidence pulse-echo method.

Comparison of Lamb waves and pulse echo in detection of near-surface defects in laminate plates

Abstract One of the problems faced in ultrasonic nondestructive testing (NDT) of composite laminates is near-surface delaminations which may be due to impact damage. The normal incidence pulse echo technique has difficulty in resolving echoes from this type of defect since they often lie within the length of the transmitted ultrasonic pulse. Although a high frequency probe may be used, it has its limitations and could be problematic in composite materials in which ply reflections may interfere with defect reflections. The use of an acoustic delay, together with a high frequency probe, may improve the detection. In this paper, a different approach has been developed using the fundamental Lamb wave (S 0 mode) to detect delaminations in unidirectional fibre composite materials. It has been found that the Lamb wave amplitude decreases significantly over a delamination region. The decrease in amplitude is strongly dependent upon the depth of the delamination and is most sensitive to the delaminations near to the surface of the laminate. By scanning the transducer over the surface, it is possible to measure the size and depth of this kind of delamination. This technique is comparable to the delayed pulse echo technique and can be used to complement other techniques.

Image enhancement for radiography inspection

The x-ray radiographic testing method is often used for detecting defects as a non-destructive testing method (NDT). In many cases, NDT is used for aircraft components, welds, etc. Hence, the backgrounds are always more complex than a piece of steel. Radiographic images are low contrast, dark and high noise image. It is difficult to detect defects directly. So, image enhancement is a significant part of automated radiography inspection system. Histogram equalization and median filter are the most frequently used techniques to enhance the radiographic images. In this paper, the adaptive histogram equalization and contrast limited histogram equalization are compared with histogram equalization. The adaptive wavelet thresholding is compared with median filter. Through comparative analysis, the contrast limited histogram equalization and adaptive wavelet thresholding can enhance perception of defects better.

Measurement and characterization of porosity in aluminium selective laser melting parts using X-ray CT

ABSTRACT Selective laser melting (SLM) is an additive manufacturing technique which has the capability to produce complex metal parts with almost 100% density and good mechanical properties. Despite the potential benefits of SLM technology, there are technical challenges relating to the qualification and certification of the manufactured parts that limits its application in safety-critical industries, such as aerospace. Material porosity in SLM parts is detrimental for aerospace applications since it compromises structural integrity and could result in premature structural failure of parts. This paper describes the application of the non-destructive X-ray computed tomography (XCT) method to characterize the internal structure to enhance the understanding of the process parameters on material porosity and thus provide quality control of the SLM AlSi10Mg parts. An efficient and reliable XCT image processing procedure that involves image enhancement and ring artefact removal prior to image segmentation is presented. The obtained porosity level is compared with the conventional Archimedes method, showing good agreement. The characteristics of pores, such as shapes and sizes, are also discussed.

X-ray image segmentation based on genetic algorithm and maximum fuzzy entropy

The X-ray radiographic testing method is often used for detecting defects as a non-destructive testing method (NDT). In many cases, NDT is used for aircraft components, welds, etc. Hence, the backgrounds are always more complex than a piece of steel. It is difficult to detect defects using conventional image processing methods. In this paper, we propose a genetic algorithm to find the optimal thresholds to segment X-ray images. In our algorithms, after obtaining the X-ray image, we firstly use adaptive histogram equalization technique and wavelet thresholding to improve the quality of the radiographic image. Then the image is divided into three parts, namely dark, gray and white part. The fuzzy region of their member functions can be determined by maximizing fuzzy entropy. The procedure to find the optimal combination of all the fuzzy parameters is implemented by genetic algorithm, which can overcome the computational complexity problem. The experimental results show that our proposed method gives good performance for X-ray image.

Development of an automated ultrasonic testing system

Non-Destructive Testing is necessary in areas where defects in structures emerge over time due to wear and tear and structural integrity is necessary to maintain its usability. However, manual testing results in many limitations: high training cost, long training procedure, and worse, the inconsistent test results. A prime objective of this project is to develop an automatic Non-Destructive testing system for a shaft of the wheel axle of a railway carriage. Various methods, such as the neural network, pattern recognition methods and knowledge-based system are used for the artificial intelligence problem. In this paper, a statistical pattern recognition approach, Classification Tree is applied. Before feature selection, a thorough study on the ultrasonic signals produced was carried out. Based on the analysis of the ultrasonic signals, three signal processing methods were developed to enhance the ultrasonic signals: Cross-Correlation, Zero-Phase filter and Averaging. The target of this step is to reduce the noise and make the signal character more distinguishable. Four features: 1. The Auto Regressive Model Coefficients. 2. Standard Deviation. 3. Pearson Correlation 4. Dispersion Uniformity Degree are selected. And then a Classification Tree is created and applied to recognize the peak positions and amplitudes. Searching local maximum is carried out before feature computing. This procedure reduces much computation time in the real-time testing. Based on this algorithm, a software package called SOFRA was developed to recognize the peaks, calibrate automatically and test a simulated shaft automatically. The automatic calibration procedure and the automatic shaft testing procedure are developed.

Time of flight diffraction: an alternate non-destructive testing procedure to replace traditional methods

Time-of-flight-diffraction Technique (TOFD) is considered as one of the fastest methods of Non-destructive testing (NDT) since a weld can be characterized to a certain degree with one single scan along its length with two probes. An image of the complete weld is created showing component and, more importantly, any defect information. In this paper a comprehensive review of the TOFD technique covering many aspects, e.g. accuracy, coverage, resolution, repeatability, and last not least speed where the real value of TOFD lies-despite its few inherent limitations is presented. This paper presents the results of experimental investigations carried out using various NDT techniques including TOFD on specimens such as welds with various types of defects. The results of these investigations are compared and the feasibility of using TOFD as an alternative NDT procedure to replace the traditional NDT methods of inspecting fabricated pressure vessel components are examined.

Radiographic Image Segmentation for Weld Inspection Using a Robust Algorithm

ABSTRACT The radiographic testing method is often used as a nondestructive testing method for detecting welding defects. Because of the degraded quality and the small size of the defects, X-ray films are sometimes difficult to inspect. The interpretation of such images is often affected by a human operators subjectivity. Digital image-processing techniques allow the interpretation to be automated. A key step in the automated-interpretation process is the segmentation of indications from the background. In this article, a robust method is presented to segment the radiographic image. In our algorithm, first adaptive wavelet thresholding and adaptive histogram equalization techniques are used to improve the quality of the radiographic image. Then, the radiographic image is divided into three parts, namely black, gray, and white parts using three-level thresholding based on maximum fuzzy entropy. The procedure to find the optimal thresholds is implemented by a genetic algorithm, which can overcome the computational-complexity problem. The experimental results show that our proposed method gives good performance for radiographic images.

Nondestructive evaluation of aircraft structure using lock-in thermography

This paper describes experiments conducted with the lock-in thermographic procedure. A carbon fiber reinforced composite specimen with defects of various sizes and depths below the test surface was analyzed. The detectivity of AGEMA 900 lock- in system was investigated. The experimental results show that the detectivity of lock-in thermography depends on inspection frequency, intensity of heat source, resolution of lock-in system, distance between the IR camera and the object. It was found that inspection frequency has significant effect on the phase difference produced by a certain defect. At blind frequency, the defect produces no difference or very small difference. There are 2 optimum frequencies at which the defect produces maximum positive and negative phase differences respectively. At a depth that both high frequency and low frequency thermal waves can reach, lock-in thermography is more sensitive at high frequency. Lock-in phase sensitive thermography was found to be more sensitive than conventional reflection and transmission thermography. Sight line angle, which is the angle between the surface plane of the object and the sight line of the camera, has no significant effect on detectivity of lock-in thermography.

X-ray image segmentation using wavelet method

The X-ray radiographic testing method is often used for detecting defects as a non-destructive testing method (NDT). In many cases, NDT is used for aircraft components, welds, etc. Hence, the backgrounds are always more complex than a piece of steel. It is difficult to detect defects using conventional image processing methods, in this paper, we apply wavelet method to segment X-ray images. In our algorithms, after obtaining the X-ray image, we firstly use wavelet thresholding to reduce image noise to improve the quality of the radiographic image. Then we extract defects from the radiographic image. In the process of extracting edge features, according to the wavelet multi-scale character, we integrate the coefficients of wavelet transforms on a series of scales to look for the best scale where the edges are well discriminated from noise to extract edge features. With the help of wavelet algorithm, an objective and fast computer based evaluation of defect indications is possible.