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CORRELATING MICROSTRUCTURAL DEVELOPMENT AND FAILURE MECHANISMS TO PHOTOSTIMULATED LUMINESCENCE SPECTROSCOPY AND ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY IN THERMAL BARRIER COATINGS
Title
CORRELATING MICROSTRUCTURAL DEVELOPMENT AND FAILURE MECHANISMS TO PHOTOSTIMULATED LUMINESCENCE SPECTROSCOPY AND ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY IN THERMAL BARRIER COATINGS.
Creator
Jayaraj, Balaji, Sohn, Yongho, University of Central Florida
Abstract / Description
Thermal barrier coatings (TBCs) are widely used for thermal protection of hot section components in turbines for propulsion and power generation. Applications of TBCs based on a clearer understanding of failure mechanisms can help increase the performance and life-cycle cost of advanced gas turbine engines. Development and refinement of robust non-destructive evaluation techniques can also enhance the reliability, availability and maintainability of hot section components in gas turbines...
Show moreThermal barrier coatings (TBCs) are widely used for thermal protection of hot section components in turbines for propulsion and power generation. Applications of TBCs based on a clearer understanding of failure mechanisms can help increase the performance and life-cycle cost of advanced gas turbine engines. Development and refinement of robust non-destructive evaluation techniques can also enhance the reliability, availability and maintainability of hot section components in gas turbines engines. In this work, degradation of TBCs was non-destructively examined by photostimulated luminescence spectroscopy (PSLS) and electrochemical impedance spectroscopy (EIS) as a function of furnace thermal cycling carried out in air with 10-minute heat-up, 0.67, 9.6 and 49.6 -hour dwell duration at 1121°C (2050°F), and 10-minute forced-air quench. TBCs examined in this study consisted of either electron beam physical vapor deposited and air plasma sprayed yttria-stabilized zirconia (YSZ) on a variety of bond coat / superalloy substrates including bond coats of NiCoCrAlY and (Ni,Pt)Al, and superalloys of CMSX-4, Rene'N5, Haynes 230 and MAR-M-509. Detailed microstructural characterization by scanning electron microscopy and energy dispersive spectroscopy was carried out to document the degradation and failure characteristics of TBC failure, and correlate results of PSLS and EIS. Mechanisms of microstructural damage initiation and progression varied as a function of TBC architecture and thermal cycling dwell time, and included undulation of the interface between the thermally grown oxide (TGO) and bond coats, internal oxidation of the bond coats, and formation of Ni/Co-rich TGO. These microstructural observations were correlated to the evolution in compressive residual stress in the TGO scale determined by PSLS shift. Correlations include stress-relief and corresponding luminescence shift towards stress-free luminescence associated with subcritical cracking of the TGO scale and stress-relaxation associated with gradual shift in the luminescence towards stress-free luminescence is related to the undulation of TGO/bondcoat interface (e.g., rumpling and ratcheting). Microstructural changes in TBCs such as YSZ sintering, TGO growth, and subcritical damages within the YSZ and TGO scale were also correlated to the changes in electrochemical resistance and capacitance of the YSZ and TGO, respectively. With thermal exposure the YSZ/TGO resistance and capacitance increased and decreased as result of sintering and TGO growth. With progressive thermal cycling damages in the TGO was related to the TGO capacitance showing a continuous increase and at failure TGO capacitance abruptly increased with the exposure of bondcoat. Further correlations among the microstructural development, PSLS and EIS are documented and discussed, particularly as a function of dwell time used during furnace thermal cycling test, with due respect for changes in failure characteristics and mechanisms for various types of TBCs.
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Date Issued
2011
Identifier
CFE0003635, ucf:48882
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003635
Analytical study of computer vision-based pavement crack quantification using machine learning techniques
Title
Analytical study of computer vision-based pavement crack quantification using machine learning techniques.
Creator
Mokhtari, Soroush, Yun, Hae-Bum, Nam, Boo Hyun, Catbas, Necati, Shah, Mubarak, Xanthopoulos, Petros, University of Central Florida
Abstract / Description
Image-based techniques are a promising non-destructive approach for road pavement condition evaluation. The main objective of this study is to extract, quantify and evaluate important surface defects, such as cracks, using an automated computer vision-based system to provide a better understanding of the pavement deterioration process. To achieve this objective, an automated crack-recognition software was developed, employing a series of image processing algorithms of crack extraction, crack...
Show moreImage-based techniques are a promising non-destructive approach for road pavement condition evaluation. The main objective of this study is to extract, quantify and evaluate important surface defects, such as cracks, using an automated computer vision-based system to provide a better understanding of the pavement deterioration process. To achieve this objective, an automated crack-recognition software was developed, employing a series of image processing algorithms of crack extraction, crack grouping, and crack detection. Bottom-hat morphological technique was used to remove the random background of pavement images and extract cracks, selectively based on their shapes, sizes, and intensities using a relatively small number of user-defined parameters. A technical challenge with crack extraction algorithms, including the Bottom-hat transform, is that extracted crack pixels are usually fragmented along crack paths. For de-fragmenting those crack pixels, a novel crack-grouping algorithm is proposed as an image segmentation method, so called MorphLink-C. Statistical validation of this method using flexible pavement images indicated that MorphLink-C not only improves crack-detection accuracy but also reduces crack detection time.Crack characterization was performed by analysing imagerial features of the extracted crack image components. A comprehensive statistical analysis was conducted using filter feature subset selection (FSS) methods, including Fischer score, Gini index, information gain, ReliefF, mRmR, and FCBF to understand the statistical characteristics of cracks in different deterioration stages. Statistical significance of crack features was ranked based on their relevancy and redundancy. The statistical method used in this study can be employed to avoid subjective crack rating based on human visual inspection. Moreover, the statistical information can be used as fundamental data to justify rehabilitation policies in pavement maintenance.Finally, the application of four classification algorithms, including Artificial Neural Network (ANN), Decision Tree (DT), k-Nearest Neighbours (kNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is investigated for the crack detection framework. The classifiers were evaluated in the following five criteria: 1) prediction performance, 2) computation time, 3) stability of results for highly imbalanced datasets in which, the number of crack objects are significantly smaller than the number of non-crack objects, 4) stability of the classifiers performance for pavements in different deterioration stages, and 5) interpretability of results and clarity of the procedure. Comparison results indicate the advantages of white-box classification methods for computer vision based pavement evaluation. Although black-box methods, such as ANN provide superior classification performance, white-box methods, such as ANFIS, provide useful information about the logic of classification and the effect of feature values on detection results. Such information can provide further insight for the image-based pavement crack detection application.
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Date Issued
2015
Identifier
CFE0005671, ucf:50186
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005671
Investigation of infrared thermography for subsurface damage detection of concrete structures
Title
Investigation of infrared thermography for subsurface damage detection of concrete structures.
Creator
Hiasa, Shuhei, Catbas, Necati, Tatari, Omer, Nam, Boo Hyun, Zaurin, Ricardo, Xanthopoulos, Petros, University of Central Florida
Abstract / Description
Deterioration of road infrastructure arises from aging and various other factors. Consequently, inspection and maintenance have been a serious worldwide problem. In the United States, degradation of concrete bridge decks is a widespread problem among several bridge components. In order to prevent the impending degradation of bridges, periodic inspection and proper maintenance are indispensable. However, the transportation system faces unprecedented challenges because the number of aging...
Show moreDeterioration of road infrastructure arises from aging and various other factors. Consequently, inspection and maintenance have been a serious worldwide problem. In the United States, degradation of concrete bridge decks is a widespread problem among several bridge components. In order to prevent the impending degradation of bridges, periodic inspection and proper maintenance are indispensable. However, the transportation system faces unprecedented challenges because the number of aging bridges is increasing under limited resources, both in terms of budget and personnel. Therefore, innovative technologies and processes that enable bridge owners to inspect and evaluate bridge conditions more effectively and efficiently with less human and monetary resources are desired. Traditionally, qualified engineers and inspectors implemented hammer sounding and/or chain drag, and visual inspection for concrete bridge deck evaluations, but these methods require substantial field labor, experience, and lane closures for bridge deck inspections. Under these circumstances, Non-Destructive Evaluation (NDE) techniques such as computer vision-based crack detection, impact echo (IE), ground-penetrating radar (GPR) and infrared thermography (IRT) have been developed to inspect and monitor aging and deteriorating structures rapidly and effectively. However, no single method can detect all kinds of defects in concrete structures as well as the traditional inspection combination of visual and sounding inspections; hence, there is still no international standard NDE methods for concrete bridges, although significant progress has been made up to the present.This research presents the potential to reduce a burden of bridge inspections, especially for bridge decks, in place of traditional chain drag and hammer sounding methods by IRT with the combination of computer vision-based technology. However, there were still several challenges and uncertainties in using IRT for bridge inspections. This study revealed those challenges and uncertainties, and explored those solutions, proper methods and ideal conditions for applying IRT in order to enhance the usability, reliability and accuracy of IRT for concrete bridge inspections. Throughout the study, detailed investigations of IRT are presented. Firstly, three different types of infrared (IR) cameras were compared under active IRT conditions in the laboratory to examine the effect of photography angle on IRT along with the specifications of cameras. The results showed that when IR images are taken from a certain angle, each camera shows different temperature readings. However, since each IR camera can capture temperature differences between sound and delaminated areas, they have a potential to detect delaminated areas under a given condition in spite of camera specifications even when they are utilized from a certain angle. Furthermore, a more objective data analysis method than just comparing IR images was explored to assess IR data. Secondly, coupled structural mechanics and heat transfer models of concrete blocks with artificial delaminations used for a field test were developed and analyzed to explore sensitive parameters for effective utilization of IRT. After these finite element (FE) models were validated, critical parameters and factors of delamination detectability such as the size of delamination (area, thickness and volume), ambient temperature and sun loading condition (different season), and the depth of delamination from the surface were explored. This study presents that the area of delamination is much more influential in the detectability of IRT than thickness and volume. It is also found that there is no significant difference depending on the season when IRT is employed. Then, FE model simulations were used to obtain the temperature differences between sound and delaminated areas in order to process IR data. By using this method, delaminated areas of concrete slabs could be detected more objectively than by judging the color contrast of IR images. However, it was also found that the boundary condition affects the accuracy of this method, and the effect varies depending on the data collection time. Even though there are some limitations, integrated use of FE model simulation with IRT showed that the combination can be reduce other pre-tests on bridges, reduce the need to have access to the bridge and also can help automate the IRT data analysis process for concrete bridge deck inspections. After that, the favorable time windows for concrete bridge deck inspections by IRT were explored through field experiment and FE model simulations. Based on the numerical simulations and experimental IRT results, higher temperature differences in the day were observed from both results around noontime and nighttime, although IRT is affected by sun loading during the daytime heating cycle resulting in possible misdetections. Furthermore, the numerical simulations show that the maximum effect occurs at night during the nighttime cooling cycle, and the temperature difference decreases gradually from that time to a few hours after sunrise of the next day. Thus, it can be concluded that the nighttime application of IRT is the most suitable time window for bridge decks. Furthermore, three IR cameras with different specifications were compared to explore several factors affecting the utilization of IRT in regards to subsurface damage detection in concrete structures, specifically when the IRT is utilized for high-speed bridge deck inspections at normal driving speeds under field laboratory conditions. The results show that IRT can detect up to 2.54 cm delamination from the concrete surface at any time period. This study revealed two important factors of camera specifications for high-speed inspection by IRT as shorter integration time and higher pixel resolution.Finally, a real bridge was scanned by three different types of IR cameras and the results were compared with other NDE technologies that were implemented by other researchers on the same bridge. When compared at fully documented locations with 8 concrete cores, a high-end IR camera with cooled detector distinguished sound and delaminated areas accurately. Furthermore, indicated location and shape of delaminations by three IR cameras were compared to other NDE methods from past research, and the result revealed that the cooled camera showed almost identical shapes to other NDE methods including chain drag. It should be noted that the data were collected at normal driving speed without any lane closures, making it a more practical and faster method than other NDE technologies. It was also presented that the factor most likely to affect high-speed application is integration time of IR camera as well as the conclusion of the field laboratory test.The notable contribution of this study for the improvement of IRT is that this study revealed the preferable conditions for IRT, specifically for high-speed scanning of concrete bridge decks. This study shows that IRT implementation under normal driving speeds has high potential to evaluate concrete bridge decks accurately without any lane closures much more quickly than other NDE methods, if a cooled camera equipped with higher pixel resolution is used during nighttime. Despite some limitations of IRT, the data collection speed is a great advantage for periodic bridge inspections compared to other NDE methods. Moreover, there is a high possibility to reduce inspection time, labor and budget drastically if high-speed bridge deck scanning by the combination of IRT and computer vision-based technology becomes a standard bridge deck inspection method. Therefore, the author recommends combined application of the high-speed scanning combination and other NDE methods to optimize bridge deck inspections.
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Date Issued
2016
Identifier
CFE0006323, ucf:51575
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006323