Keeping track of requirements from eliciting data to making decision needs an effective path from data to decision [43]. Visualization science helps to create this path by extracting insights from flood of data. Model helps to shape the transformation of data to visualization. Defect Detection and Prevention model was created to assess quality assurance activities. We selected DDP and started enhancing user interactivity with requirements visualization over basic DDP with implementing a visual requirements analytics framework. By applying GQM table to our framework, we added six visualization features to the existing visual requirements visualization approaches. We applied this framework to technical and non-technical stakeholder scenarios to gain the operational insights of requirements-driven risk mitigation in practice. The combination of the first and second scenarios' result presented the multiple stakeholders scenario result which was a small number of strategies from kept tradespase with common mitigations that must deploy to the system.

Visualization and exploration of nematic liquid crystal (NLC) data is a challenging task due to the multidimensional and multivariate nature of the data. Simulation study of an NLC consists of multiple timesteps, where each timestep computes scalar, vector, and tensor parameters on a geometrical mesh. Scientists developing an understanding of liquid crystal interaction and physics require tools and techniques for effective exploration, visualization, and analysis of these data sets. Traditionally, scientists have used a combination of different tools and techniques like 2D plots, histograms, cut views, etc. for data visualization and analysis. However, such an environment does not provide the required insight into NLC datasets. This thesis addresses two areas of the study of NLC data---understanding of the tensor order field (the Q-tensor) and defect detection in this field. Tensor field understanding is enhanced by using a new glyph (NLCGlyph) based on a new design metric which is closely related to the underlying physical properties of an NLC, described using the Q-tensor. A new defect detection algorithm for 3D unstructured grids based on the orientation change of the director is developed. This method has been used successfully in detecting defects for both structured and unstructured models with varying grid complexity.

This dissertation presents an automated detection algorithm that identifies severe external defects on the surfaces of barked hardwood logs and stems. The defects detected are at least 0.5 inch in height and at least 3 inches in diameter, which are severe, medium to large in size, and have external surface rises. Hundreds of real log defect samples were measured, photographed, and categorized to summarize the main defect features and to build a defect knowledge base. Three-dimensional laser-scanned range data capture the external log shapes and portray bark pattern, defective knobs, and depressions. The log data are extremely noisy, have missing data, and include severe outliers induced by loose bark that dangles from the log trunk. Because the circle model is nonlinear and presents both additive and non-additive errors, a new robust generalized M-estimator has been developed that is different from the ones proposed in the statistical literature for linear regression. Circle fitting is performed by standardizing the residuals via scale estimates calculated by means of projection statistics and incorporated in the Huber objective function to bound the influence of the outliers in the estimates. The projection statistics are based on 2-D radial-vector coordinates instead of the row vectors of the Jacobian matrix as proposed in the statistical literature dealing with linear regression. This approach proves effective in that it makes the GM-estimator to be influence bounded and thereby, robust against outliers. Severe defects are identified through the analysis of 3-D log data using decision rules obtained from analyzing the knowledge base. Contour curves are generated from radial distances, which are determined by robust 2-D circle fitting to the log-data cross sections. The algorithm detected 63 from a total of 68 severe defects. There were 10 non-defective regions falsely identified as defects. When these were calculated as areas, the algorithm locates 97.6% of the defect area, and falsely identifies 1.5% of the total clear area as defective. / Ph. D.

This research study examined the potential application of a color-based machine vision system under development at Virginia Tech for lumber processing in the furniture rough mill. The evaluation was done by conducting a yield study using 134 red oak boards. ROMI-RIP, a rip-first simulation program by Thomas (1995), was used to simulate yields for both the manually digitized lumber data and the machine vision scanned lumber data. The color-based machine vision system was evaluated by comparing the optimum yield obtainable when using lumber data derived from the automatic scanning system to: (1) observed yield from an existing state-of-the-art rip-first rough mill and (2) the optimum yield from manually digitized lumber data. Overall, the color-based machine vision system resulted in about 17 percent lower yield than was measured in the rough mill and 20 percent lower than the optimum, based on manually digitized lumber data. An analysis of the yield percentage point difference between the machine vision-based yields and optimal yields indicates: (1) approximately 11.5 yield points were lost due to errors in defect detection accuracy, (2) 7.3 yield points were lost due to errors in the machine vision material handling system, and (3) 1.3 yield points were lost due to data digitization and truncation errors. Since material handling, data digitization, and truncation problems are solvable with current technologies, future research should focus on developing systems that can improve the accuracy of feature recognition in lumber. / Master of Science

Railroad tracks require consistent and periodic monitoring to ensure safety and reliability. Unmanned Aerial Vehicles (UAVs) have great potential because they are not constrained to the track, allowing trains to continue running while the UAV is inspecting. Also, they can be quickly deployed without human intervention. For these reasons, the first steps towards creating a track-monitoring UAV system have been completed. This thesis focuses on the design of algorithms to be deployed on a UAV for the purpose of monitoring the health of railroad tracks. Before designing the algorithms, the first steps were to design a rough physical structure of the final product. A small multirotor or fixed-wing UAV will be used with a gimbaled camera mounted on the belly. The camera will take images of the tracks while the onboard computer processes the images. The computer will locate the tracks in the image as well as perform defect detection on those tracks. Algorithms were implemented once a rough physical structure of the product was completed. These algorithms detect and follow rails through a video feed and detect defects in the rails. The rail following algorithm is based on a custom-designed masking technique that locates rails in images. A defect detection algorithm was also created. This algorithm detect defects by analyzing gradient data on the rail surface. / Master of Science

Additive Manufacturing (AM) is a fast-growing technology in manufacturing industries. Applications of AM are spread across a wide range of fields. The aerospace industry is one of the industries that use AM because of its ability to produce light-weighted components and design freedom. Since the aerospace industry is conservative, quality control and quality assurance are essential. The quality of the welding is one of the factors that determine the quality of the AM components, hence, detecting faults in the welding is crucial. In this thesis, an automated system for detecting the faults in the welding process is presented. For this, three methods are proposed to find the anomalies in the process. The process videos that contain weld melt-pool behaviour are used in the methods. The three methods are 1) Autoencoder method, 2) Variational Autoencoder method, and 3) Image Classification method. Methods 1 and 2 are implemented using Convolutional-Long Short Term Memory (LSTM) networks to capture anomalies that occur over a span of time. For this, instead of a single image, a sequence of images is used as input to track abnormal behaviour by identifying the dependencies among the images. The method training to detect anomalies is unsupervised. Method 3 is implemented using Convolutional Neural Networks, and it takes a single image as input and predicts the process image as stable or unstable. The method learning is supervised. The results show that among the three models, the Variational Autoencoder model performed best in our case for detecting the anomalies. In addition, it is observed that in methods 1 and 2, the sequence length and frames retrieved per second from process videos has effect on model performance. Furthermore, it is observed that considering the time dependencies in our case is very beneficial as the difference between the anomalous and the non anomalous process is very small

The railroad industry is pivotal in the United States to ensure that the supply chain does not shut down for the American people. Non-Destruction Evaluation (NDE) approaches are preferred and performed on the railways to ensure the safety of the population that is exposed to the railway industry. When damage occurs on the rail base, there is an increased risk derailment of the train cars. Due to the nature of the railroad industry, there are challenges with developing a quick and reliable inspection method, along with the improvement of current NDE methods. The load, speed, and cycles of trains have increased the load that track sections endure over time. Some railways that were originally built in the early 20th century are still utilized today, designed for trains that are not nearly as heavy or fast as used today. Defects and damage on the railways lead to the need of development of an NDE approach utilizing Line Scan Thermography approaches. One of the most common defects that are formed are on the rail base is known as “base nicks” and “half-moon cracks”, these types of defects can occur over time. This research aims to study the feasibility of applying this NDE technique to detect defects that can occur on a rail base, both internal and external. For this research, a heat source up to 6000 W and tested velocities up to 447.1 mm/s (1.0 mph) are used to study the effects of line scanning thermography on various samples. In total, 10 samples are employed to test for feasibility: each one having a unique set of defects. Some defects fabricated on these samples are internal, such as bottom drilled holes (BDH) and side drilled holes (SDH); some of these samples are fabricated from actual rail samples. From tests conducted for internal defects, it can be concluded that defects with diameters of 6.35 mm (0.25”) can be detected at a remaining thickness from the observation surface of 6.35 mm. Along with internal defects, there are also external defects employed on the samples; these defects include simulated base nicks, fractures, and half-moon cracks. For surface defects tests from this research, it is found that the anomalies can be detected visually. The results from the experimental studies provide insight and limitations of LST for the possibility of a future commercial application.

This doctoral thesis deals with analysis of existing area defect detection methods in solar cells and with concept of its innovation and of the development of faster detection method. Results of measurement is analyzing in practical and theoretical part. The most important is LBIC (Light Beam Induced Current) method innovated of different wavelength light source usage and Electroluminescence method. On the bases of this knowledge is created Fast LBIC method and then is created catalog of defects in monocrystalline silicon solar cells.

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections.

Mechanical waves provide information about the stiffness and the condition of a medium; thus, changes in medium conditions can be inferred from changes in wave velocity and attenuation. Non-destructive testing (NDT) methods based on ultrasonic waves are often more economical, practical and faster than destructive testing. Multichannel analysis of surface waves (MASW) is a well-established surface wave method used for determination of the shear-wave profile of layered medium. The MASW test configuration is also applicable to assess the condition of concrete elements using appropriate frequency range. Both attenuation and dispersion of ultrasonic waves can be evaluated by this technique. In ultrasonic testing, the characterization of a medium requires the precise measurement of its response to ultrasonic pulses to infer the presence of defects and boundary conditions. However, any ultrasonic transducer attached to a surface affects the measured response; especially at high frequencies. On the other hand, ultrasonic transducers available for engineering application are mostly used to measure wave velocities (travel time method). Therefore, these transducers do not have a flat response in the required frequency range. Moreover, in the case of full-waveform methods, the recorded signals should be normalized with respect to the transfer functions of the transducers to obtain the real response of the tested specimen. The main objective of this research is to establish a comprehensive methodology based on surface wave characteristics (velocity, attenuation and dispersion) for condition assessment of cemented materials with irregular defects. To achieve the major objective, the MASW test configuration is implemented in the ultrasonic frequency range. The measured signals are subjected to various signal processing techniques to extract accurate information. In addition, a calibration procedure is conducted to determine the frequency response functions (FRF) of the piezoelectric accelerometers outside their nominal frequency range. This calibration is performed using a high-frequency laser vibrometer. This research includes three main studies. The first study introduces the calibration approach to measure the FRFs of the accelerometers outside of their flat frequency range. The calibrated accelerometers are then used to perform MASW tests on a cemented-sand medium. The original signals and the corrected ones by eliminating the effect of the FRFs are used to determine material damping of the medium. Although, the damping ratios obtained from different accelerometers are not same, the values from the corrected signals are found closer to the characteristic damping value compared to those from the uncorrected signals. The second study investigates the sensitivity of Rayleigh wave velocity, attenuation coefficient, material damping and dispersion in phase velocity to evaluate the sensitivity of these characteristics to the damage quantity in a medium. The soft cemented-sand medium is preferred as the test specimen so that well-defined shaped defects could be created in the medium. MASW test configuration is implemented on the medium for different cases of defect depth. The recorded signals are processed using different signal processing techniques including Fourier and wavelet transforms and empirical mode decomposition to determine the surface wave characteristics accurately. A new index, ‘dispersion index’, is introduced which quantifies the defect based on the dispersive behaviour. All surface wave characteristics are found capable of reflecting the damage quantity of the test medium at different sensitivity levels. In the final study, the condition assessment of six lab-scale concrete beams with different void percent is performed. The beam specimens involving Styrofoam pellets with different ratios are tested under ultrasonic and mechanical equipment. The assessment produce established in the second study with well-defined defects is pursed for the beams with irregular defects. Among the characteristics, attenuation, P and R-wave velocities and dispersion index are found as the promising characteristics for quantifying the defect volume.