This thesis examines the feasibility of combining Digital Image Correlation (DIC) with laser speckle based methods to form a new hybrid deformation measurement method called Digital Laser Speckle Image Correlation (DilSIC). Consequently, this method does not require any sample preparation and allows for the measurement of displacement of micro structures in addition to large displacements. In this technique, a coherent 30mW-632nm laser beam is expanded with 40X lens and then illuminated on the target surface to produce a fine, homogenous laser speckle pattern. Images were captured before and after deformation due to external load and the whole field displacement and strain were determined by the DIC method. This technique could measure displacement less than 30-μm with high accuracy when a 120mm × 80mm area of the surface was inspected. Up to 10% strain was measured by this technique with high accuracy during the whole range. Eventually the sub-surface crack was located successfully, which is a revolutionary achievement in NDT optical methods. This method was tested in different material, with different roughness. Aluminum sheet and rubber material were used mostly. This method could broaden the capability of displacement measurement and subsurface crack detection in wide range of materials.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / à corrente o uso de inspeÃÃo nÃo-destrutiva ultrassÃnica na detecÃÃo de descontinuidades nos mais diversos materiais utilizados na indÃstria Adicionalmente informaÃÃes sobre a microestrutura do material inspecionado podem ser obtidas a partir do processamento da sÃrie temporal produzida durante a inspeÃÃo A simulaÃÃo do ensaio ultrassÃnico representa uma importante ferramenta para o entendimento e previsÃo da interaÃÃo da onda mecÃnica com o meio No entanto faz-se necessÃrio primeiramente modelar o meio que reproduza as caracterÃsticas de uma amostra objeto de estudo por onde a onda propaga Cinco meios unidimensionais compostos por domÃnios (representando grÃos) com tamanho mÃdio distinto e a mesma densidade mÃdia foram definidos neste trabalho SimulaÃÃes de propagaÃÃo de ondas ultrassÃnicas nos meios modelados foram executadas para quatro diferentes frequÃncias de ondas Concomitantemente foram capturados sinais ultrassÃnicos sobre cinco amostras de aÃo contendo diferentes tamanhos mÃdios de grÃo utilizando transdutores de 2.25 5.0 10.0 e 20.0 MHz Todos os sinais obtidos foram submetidos à detrended fluctuation analysis DFA e rescaled range analysis R/S duas tÃcnicas de anÃlise de flutuaÃÃes em sÃries temporais com vista a filtrar informaÃÃes espÃrias e avaliar influÃncia das variÃveis selecionadas (tamanho de grÃo e frequÃncia do sinal) sobre os sinais ultrassÃnicos obtidos Por fim à feita uma comparaÃÃo entre os dados simulados e experimentais e avaliaÃÃo da qualidade da simulaÃÃo / It is usual the application of ultrasonic non-destructive evaluation to detect discontinuities in different materials applied in industry Furthermore information about the microstructure of the inspected material can be obtained by signal processing of time series produced during the inspection Simulation of ultrasonic testing can be an important tool to help the understanding and predicting the interaction of mechanical wave in materials First of all computational materials modeling to reproduce the characteristics of specimens through which the wave propagates is necessary Five different one-dimensional materials composed of domains (grains) with different average size and same density were designed in this work Simulations of ultrasonic wave propagation in the modeled materials were performed at four different wave frequencies Concomitantly ultrasonic signals were acquired from five steel samples containing different average grain sizes using probes with central frequency of 225 5 10 and 20 MHz Detrended fluctuation analysis DFA and rescaled range analysis R/S two techniques for analyzing fluctuations in time series were used to process the signals in order to filter out spurious information and evaluate the influence of selected variables (grain size and frequency of the signal) on the ultrasonic signals obtained Finally simulated and experimental data and compared in order to evaluate the simulation performance

Non-destructive testing (NDT) is the process of non-invasive material examination. Within this field, ultrasonic inspection is one method of examination used to detect flaws in structural and functional industrial components, to assess their structural integrity and fitness for service. Conventional NDT ultrasonic array techniques transmit on multiple elements in parallel, according to a focal law, which facilitates beam steering, focussing and scanning within the test component. Received signals are then 'stacked' to generate images of the test component. With the advent of affordable high speed computing, novel data acquisition techniques based on sequential transmission are now able to be developed, which allow images to be generated using advanced signal processing and image reconstruction algorithms. One such data acquisition technique known as Full Matrix Capture (FMC), has received considerable research attention in recent years, largely because it allows fully focused images of test components to be generated. This project provides an improved understanding of the FMC technique and associated signal processing algorithms. It achieves this through the development of novel inspection techniques and signal processing algorithms. Collectively algorithms developed within this work were termed Sequential Phased Array (SPA). Initially comparisons were made between conventional ultrasonic techniques and the SPA algorithms in terms of image quality and speed of inspection. A novel approach was then suggested to facilitate inspection through dual-layered media, separated by a refractive interface using the SPA algorithms. The use of sparse arrays to enhance the speed of inspection using the SPA algorithms was also investigated, and the trade-off between speed of inspection against image degradation was addressed. Finally a novel approach to calibration of a FMC based system was developed, in order to provide uniform image sensitivity during inspection.

The focus of this thesis is to derive analytical tools for the design of infrared nondestructive tests in single and multi layer material bodies. This requires the predetermination of the parameters of the experiment such that the infrared image has the required resolution for defect detection. Inverse heat conduction in single and multi-layer materials is investigated to determine the required frequency of excitation in order to obtain a desired temperature at the observation point. We use analytical quadrupole representation to derive a polynomial relation to estimate the frequency of the periodic excitation as a function of the temperature amplitude at a given observation point within the body. The formula includes characteristic geometric and material parameters of the system. The polynomial formula can be an e ective design tool for quick frequency predetermination in the design of non-destructive testing experiments with infrared thermography. The convergence and accuracy of the formula is assessed by comparison with the analytical thermal quadrupole solution and experimental results. We also investigate the e ect of the nite length of the material domain in order to establish the range of applicability of a simpli ed formula based on semi-in nite domain assumption. The e ect of nite length is investigated analytically by using (i) Fourier series which accounts for transients and (ii) Time varying solution associated to the steady state solution when a purely periodic excitation is applied. These results are also compared with numerical solution obtained with commercial nite element software ANSYSTM.

This thesis presents the development and validation of a new wireless Impact Echo (IE) system for condition assessment of reinforced concrete slabs. The new IE prototype was compared with other commercially available non-destructive testing (NDT) devices used for similar purposes, namely Ground-Penetrating Radar (GPR) and Ultrasonic Pulse Echo (UPE). Monitoring and structural inspections are critical to effective management of civil infrastructure and NDTs can enhance the quality of condition assessments by providing objective visualizations of the interior of a structural element. The IE method, first developed in the 1980s, has seen few advancements in the last 20 years. The method has been standardized and used on site, but the underlying technology has become outdated. The data obtained from the transducer is difficult to interpret and requires a computer to post-process it before being usable, thus limiting the direct feedback of the method when conducting tests on-site. Because of those limitations and the test being relatively more time consuming than other alternatives, the method is lacking in usability. A new prototype IE device was designed and built by the project industry partner, FPrimeC Solutions. The methodology followed the traditional approach, but it was designed to work with today’s technology. The device is operated wirelessly via a Bluetooth connection, uses smaller-sized electronic components, and connects with a user-friendly interface on a small tablet to set-up the tests and compute the results immediately. The first part of the project focused on product development by testing iterations of the prototype and providing user feedback to improve the device and accompanying software. The second part of the project aimed to validate the new technology using a set of three large reinforced concrete slabs containing artificial defects. The studied points of interest were sound concrete, effect of boundaries and steel reinforcements, vertical cracks, presence of a hollow conduit, artificial voids and delamination. The IE results were also compared with those from commercial GPR and UPE devices. GPR was found to be the quickest method by far, although the results gathered seemed to be limited by the presence of steel reinforcement and also failed to locate certain defects. UPE was a bit slower than GPR, but was generally able to locate more accurately the artificial flaws created in the test specimens. The results showed poor definition of the flaws making it difficult sometimes to properly locate them. The UPE results also seemed to be negatively affected by the presence of reinforcement which were causing frequent abnormal values. Lastly, the IE method was used. This method was greatly improved during the first phase, but it is still a time-consuming method. The value of the data, however, has great potential when compared to the other options. It accurately located most of the flaws generated and was practically unaffected by the presence of steel reinforcing bars. Also, with further analysis of the data, it was possible to determine the depth of some of the flaws accurately. Due to the time-consuming testing phase and the longer analysis of the data required to obtain the higher quality of results, this study suggests that IE is not likely to be the best choice for a general inspection of a large area (depending on the nature of the information needed). Rather, it is suggested to first conduct a general review of the structure using a quicker method like GPR to locate the problematic areas. After that, refining the grid at key locations to test with IE should provide the best quality of data in a reasonable amount of time.

Anomaly detection generally refers to algorithmic procedures aimed at identifying relatively rare events in data sets that differ substantially from the majority of the data set to which they belong. In the context of data series generated by sensors mounted on mobile devices for non-destructive inspection and monitoring, anomalies typically identify defects to be detected, therefore defining the main task of this class of devices. In this case, a useful way of operationally defining anomalies is to look at their information content with respect to the background data, which is typically noisy and therefore easily masking the relevant events if unfiltered. In this thesis, a Kullback-Leibler (KL) Divergence filter is proposed to detect signals with relatively high information content, namely anomalies, within data series. The data is generated by using the model of a broad class of proximity sensors that apply to devices commonly used in engineering practice. This includes, for example, sensory devices mounted on mobile robotic devices for the non-destructive inspection of hazardous or other environments that may not be accessible to humans for direct inspection. The raw sensory data generated by this class of sensors is often challenging to analyze due to the prevalence of noise over the signal content that reveals the presence of relevant features, as for example damage in gas pipelines. The proposed filter is built to detect the difference of information content between the data series collected by the sensor and a baseline data series, with the advantage of not requiring the design of a threshold. Moreover, differing from the traditional filters which need the prior knowledge or distribution assumptions about the data, this KL Divergence filter is model free and suitable for all kinds of raw sensory data. Of course, it is also compatible with classical signal distribution assumptions, such as Gaussian approximation, for instance. Also, the robustness and sensitivity of the KL Divergence filter are discussed under different scenarios with various signal to noise ratios of data generated by a simulator reproducing very realistic scenarios and based on models of real sensors provided by manufacturers or widely accepted in the literature.

Concrete has been used in a number of civil engineering applications due to its interesting fresh, hardened, and durability-related properties. 28-day compressive strength is the most important hardened state property and is frequently used as an indicator of the material’s quality. However, early-age mechanical properties are a key factor nowadays to enhance construction planning. Several advanced techniques have been proposed to appraise concrete microstructure and quality, and among those electrical resistivity (ER) is one of the most commonly used since it is a non-destructive and low-cost technique. Although recent literature data have shown that ER may be significantly influenced by a variety of parameters such as the test setup, material porosity and moisture content, binder type/amount and presence of supplementary cementing materials (SCMs) along with the nature of the aggregates used in the mix, further research must be performed to clarify the influence of the raw materials (i.e. SCMs and aggregate nature) on ER using distinct setups. Therefore, this work aims to appraise the influence of the coarse aggregate nature and binder replacement/amount on the concrete ER and compressive strength predictions models through ER. Twenty-four concrete mixtures were developed with two different coarse aggregate natures (i.e. granite and limestone), two different water-to-binder ratios (w/b; i.e. 0.6 and 0.4) and incorporating two different SCMs (i.e. slag and fly-ash class F) with different replacement levels. Moreover, three distinct ER techniques (e.g. bulk, surface, and internal) and compressive strength tests were performed at different ages (i.e. 3, 7, 14, and 28 days). Results indicate that the binder type and replacement amount significantly affect ER and compressive strength. Otherwise, the coarse aggregate nature presented only trivial influence for 0.6 w/b mixes, except for 50% fly-ash replacement samples; whereas for concrete specimens with enhanced microstructure (i.e. 0.4 w/b), the aggregate nature influence was statically significant especially for the binary mixtures with high SCMs replacement levels (i.e. 70% GGBS and 50% fly-ash). Finally, all ER test setups were considered to be quite suitable and reliable NDT techniques correlating themselves very well. Yet the internal resistivity setup demonstrated to be the device which yields the lowest variability amongst them.

abstract: Non-Destructive Testing (NDT) is a branch of scientific methods and techniques used to evaluate the defects and irregularities in engineering materials. These methods conduct testing without destroying or altering material’s structure and functionality. Most of these defects are subsurface making them difficult to detect and access. SONIC INFRARED (IR) is a relatively new and emerging vibrothermography method under the category of NDT methods. This is a fast NDT inspection method that uses an ultrasonic generator to pass an ultrasonic pulse through the test specimen which results in a temperature variation in the test specimen. The temperature increase around the area of the defect is more because of frictional heating due to the vibration of the specimen. This temperature variation can be observed using a thermal camera. In this research study, the temperature variation in the composite laminate during the SONIC IR experimentation using an infrared thermal camera. These recorded data are used to determine the location, dimension and depth of defects through SONIC IR NDT method using existing defect detection algorithms. Probability of detection analysis is used to determine the probability of detection under specific experimental conditions for two different types of composite laminates. Lastly, the effect of the process parameters such as number of pulses, pulse duration and time delay between pulses of this technique on the detectability and probability of detection is studied in detail. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2019

Performing non-destructive testing (NDT) on materials is a helpful tool for maintenance and quality control because the materials are not destroyed or disturbed; ultrasound imaging is one type of NDT. Ultrasound imaging of heterogeneous materials contains many echoes from the material itself. These echoes come from changes in the acoustic impedance, i.e. changes in the relation between the density and the sound speed of the material. However, these echoes will show speckle characteristics in images, making it hard to detect any defects in the imaged material. In this work, a method of suppressing this speckle noise is proposed. The proposed method is a 2D Wiener filter, which with the help of an image of the healthy material models changes in the material when a new image is taken later. The filter models the changes of the speckle noise between images of a defected- and healty material and then supresses the speckle from the image with defects. The filter works well on the artificial images used in this work but have yet to be tested on actual data. A version of a weighted moving average filter was also looked into, but this filter did not produce usable results.

As the nation’s infrastructure continues to age, there is a growing need for methods to safely inspect critical structures, often during operation. The failure of post-tensioned anchor rods in Tainter style flood gates presented an immediate need for new inspection capabilities for U.S. Army Corps of Engineers (USACE) managed flood control gates. In response to this need, the Sensor Integration Branch (SIB) of The U.S. Army Engineer Research and Develop Center (ERDC) developed the capability to non-destructively test (NDT) both greased and grouted cylindrical steel anchor rods using higher order guided wave ultrasonic testing. Understanding the results requires a knowledge of both guided waves and digital signal processing in order to identify the possibility of a defect. In order to both facilitate rapid defect identification and expanding ease-of-use of the equipment, the research in this thesis uses a combination of the discrete wavelet transform (DWT) and gradient boosting machine learning to build a model capable of identifying the dispersive defect responses in the rods.