Relevant bibliographies by topics / Detecting defects

Academic literature on the topic 'Detecting defects'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Detecting defects"

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Peng, Peiran, Ying Wang, Can Hao, Zhizhong Zhu, Tong Liu, and Weihu Zhou. "Automatic Fabric Defect Detection Method Using PRAN-Net." Applied Sciences 10, no. 23 (November 26, 2020): 8434. http://dx.doi.org/10.3390/app10238434.

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Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.
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Zhao, Weidong, Hancheng Huang, Dan Li, Feng Chen, and Wei Cheng. "Pointer Defect Detection Based on Transfer Learning and Improved Cascade-RCNN." Sensors 20, no. 17 (September 1, 2020): 4939. http://dx.doi.org/10.3390/s20174939.

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To meet the practical needs of detecting various defects on the pointer surface and solve the difficulty of detecting some defects on the pointer surface, this paper proposes a transfer learning and improved Cascade-RCNN deep neural network (TICNET) algorithm for detecting pointer defects. Firstly, the convolutional layers of ResNet-50 are reconstructed by deformable convolution, which enhances the learning of pointer surface defects by feature extraction network. Furthermore, the problems of missing detection caused by internal differences and weak features are effectively solved. Secondly, the idea of online hard example mining (OHEM) is used to improve the Cascade-RCNN detection network, which achieve accurate classification of defects. Finally, based on the fact that common pointer defect dataset and pointer defect dataset established in this paper have the same low-level visual characteristics. The network is pre-trained on the common defect dataset, and weights are transferred to the defect dataset established in this paper, which reduces the training difficulty caused by too few data. The experimental results show that the proposed method achieves a 0.933 detection rate and a 0.873 mean average precision when the threshold of intersection over union is 0.5, and it realizes high precision detection of pointer surface defects.
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Shan, Ning, Xia Liu, and Yong Zhong Ma. "Experiment Research on an Optical Fiber F-p Ultrasound Sensor for Detecting Internal Defects of Metal Materials." Advanced Materials Research 549 (July 2012): 593–96. http://dx.doi.org/10.4028/www.scientific.net/amr.549.593.

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Metal materials have been used in aero industry widely because of its excellent characteristics. So its internal defects are very important. Ultrasound detection technology for detecting metal materials internal defects is related to piezoelectric ultrasonic sensor. This has a few of disadvantages. So the double wavelength optical fiber F-P ultrasound sensing system is designed in this paper. The ultrasound detecting experiment devices for internal defects of metal materials is established based on the optical fiber F-P sensing system. Experimental research of detecting the internal defects is developed. The experimental results show this sensor can detect the ultrasound signals effectively. And it’s proved that this method can be effective used in the internal defect of metal materials.
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Yin, Xiaokang, Zhuoyong Gu, Wei Wang, Xiaorui Zhang, Xin'an Yuan, Wei Li, and Guoming Chen. "Detection of Outer Wall Defects on Steel Pipe Using an Encircling Rotating Electromagnetic Field Eddy Current (RoFEC) Technique." Strojniški vestnik - Journal of Mechanical Engineering 68, no. 1 (January 15, 2022): 27–38. http://dx.doi.org/10.5545/sv-jme.2021.7288.

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In recent years, the rotating electromagnetic field eddy current (RoFEC) testing technique has attracted widespread attention due to its various advantages for inspecting tubular structures. However, most of the related work was focused on detecting inner wall defects on metal pipes using feed-through probes, which are often not applicable for outer wall defect detection. This work pushes forward the encircling RoFEC technique and demonstrates its feasibility for detecting outer wall defects. First, the basic principle of the encircling RoFEC technique is introduced. A three-dimensional finite element (FE) model was built in COMSOL to analyse the distribution of the rotating electromagnetic field and study the interaction between the defects and eddy currents. The axial component of the resultant magnetic field due to defects was selected as a characteristic signal and obtained from the FE models to study the factors, including pipe tilt, defect circumferential location, defect orientation and defect size, that influence the detection performance. An encircling RoFEC system using a probe with six excitation windings and a single bobbin pickup coil was constructed and used to inspect a steel pipe with one axial and one circumferential defect. The obtained voltage signal due to defects can form a Lissajous pattern in the impedance plane and be used for defect evaluation. The results showed that the encircling RoFEC technique can detect outer wall defects of both orientations and determine the circumferential location of the defect.
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Odgaard, P. F., J. Stoustrup, and P. Andersen. "Detection of Surface Defects on Compact Discs." Journal of Control Science and Engineering 2007 (2007): 1–10. http://dx.doi.org/10.1155/2007/36319.

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Online detection of surface defects on optical discs is of high importance for the accommodation schemes handling these defects. These surface defects introduce defect components to the position measurements of focus and radial tracking positions. The respective controllers will accordingly try to suppress these defect components resulting in a wrong positioning of the optical disc drive. In this paper, two novel schemes for detecting these surface defects are introduced and compared. Both methods, which are an extended threshold scheme and a wavelet packet-based scheme, improve the detection compared with a standard threshold scheme. The extended threshold scheme detects the four tested defects with a maximal detection delay of 3 samples while the wavelet packet-based scheme has a maximal detection delay of 6 samples. Simulations of focus and radial positions in the presence of a surface defect are performed in order to inspect the importance and consequences of the size of the detection delay, from which it can be seen that focus and radial position errors increase significantly due to the defect as the detection delay increases.
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Amini, Amin, Jamil Kanfoud, and Tat-Hean Gan. "An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS." Sensors 21, no. 18 (September 13, 2021): 6141. http://dx.doi.org/10.3390/s21186141.

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With the advancement of miniaturization in electronics and the ubiquity of micro-electro-mechanical systems (MEMS) in different applications including computing, sensing and medical apparatus, the importance of increasing production yields and ensuring the quality standard of products has become an important focus in manufacturing. Hence, the need for high-accuracy and automatic defect detection in the early phases of MEMS production has been recognized. This not only eliminates human interaction in the defect detection process, but also saves raw material and labor required. This research developed an automated defects recognition (ADR) system using a unique plenoptic camera capable of detecting surface defects of MEMS wafers using a machine-learning approach. The developed algorithm could be applied at any stage of the production process detecting defects at both entire MEMS wafer and single component scale. The developed system showed an F1 score of 0.81 U on average for true positive defect detection, with a processing time of 18 s for each image based on 6 validation sample images including 371 labels.
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Yang, Ya-xun, Wen-hao Chai, De-chuang Liu, Wei-de Zhang, Jia-cheng Lu, and Zhi-kui Yang. "An Impact-Echo Experimental Approach for Detecting Concrete Structural Faults." Advances in Civil Engineering 2021 (December 20, 2021): 1–8. http://dx.doi.org/10.1155/2021/8141015.

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For the current problem of detection of grouting defects in posttensioned prestressed concrete members, the paper takes a single-layer arrangement of prestressed pipes as the object of study. The influence law of the main factors such as pipe material, defect size, defect critical surface location, and prestressing reinforcement location on the results of the impact-echo method for detecting concrete grouting defects was studied. Firstly, the ABAQUS finite element software was used to simulate these factors to obtain the influence law on the detection results, and a modal test was conducted to verify them. The results show that the impact-echo method can effectively test the location of defects and the degree of burial depth, and the pipe material influences the test results, and the impact of corrugated metal pipe is smaller and more accurate than the PVC pipe. In addition, the greater the plate thickness frequency drift rate, the larger the transverse size of the defect, so the plate thickness frequency drift rate and the measured defect depth are combined to quantitatively determine the depth of the defect.
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Sakurada, Yuichi, Mai Takashima, Toshiyuki Yasuhara, Yoshinao Iwamoto, Makoto Matsuo, and Naoto Ohtake. "Detecting Method of Bulk Defects in DLC Films Using Light Scattering." Key Engineering Materials 523-524 (November 2012): 793–98. http://dx.doi.org/10.4028/www.scientific.net/kem.523-524.793.

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Diamond-like carbon (DLC) film has various micro-size defects like pinhole, void and particle. When DLC film is exposed to white light, light is scattered in all direction at defects in DLC film. In this paper, defects in DLC film are detected by observing scattering light from defects under dark-field microscope. DLC film has wavelength dependence of transmittance. Therefore, using its wavelength dependence allows to separate surface and inside defects of DLC film. This paper describes development of bulk defects detecting system using optical filtering and scattering light detecting. Bulk defects of DLC films were successfully separated into surface defects and inside defects. This detecting method of defect is nondestructive and easy, and applicable to DLC films as well as other coating films.
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Yang, Shihao, Dongmei Jiao, Tongkun Wang, and Yan He. "Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN." Sensors 22, no. 10 (May 21, 2022): 3907. http://dx.doi.org/10.3390/s22103907.

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With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, and large internal differences of defects, which affect the detection precision. To solve these problems, we propose a new feature pyramid network based on Faster RCNN-FPN. It can fuse features across levels and directions to improve small object detection and localization, and increase object detection precision. The method has proven its effectiveness through cross-validation experiments. On a tire crown bubble defect dataset, the mAP [0.5:0.95] increased by 2.08% and the AP0.5 increased by 2.4% over the original network. The results show that the improved network significantly improves detecting tire crown bubble defects.
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Ahmad, Rais, and Tribikram Kundu. "Structural Health Monitoring of Steel Pipes under Different Boundary Conditions and Choice of Signal Processing Techniques." Advances in Civil Engineering 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/813281.

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Guided wave technique is an efficient method for monitoring structural integrity by detecting and forecasting possible damages in distributed pipe networks. Efficient detection depends on appropriate selection of guided wave modes as well as signal processing techniques. Fourier analysis and wavelet analysis are two popular signal processing techniques that provide a flexible set of tools for solving various fundamental problems in science and engineering. In this paper, effective ways of using Fourier and Wavelet analyses on guided wave signals for detecting defects in steel pipes are discussed for different boundary conditions. This research investigates the effectiveness of Fourier transforms and Wavelet analysis in detecting defects in steel pipes. Cylindrical Guided waves are generated by piezo-electric transducers and propagated through the pipe wall boundaries in a pitch-catch system. Fourier transforms of received signals give information regarding the propagating guided wave modes which helps in detecting defects by selecting appropriate modes that are affected by the presence of defects. Continuous wavelet coefficients are found to be sensitive to defects. Several types of mother wavelet functions such as Daubechies, Symlet, and Meyer have been used for the continuous wavelet transform to investigate the most suitable wavelet function for defect detection. This research also investigates the effect of different boundary conditions on wavelet transforms for different mother wavelet functions.
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Dissertations / Theses on the topic "Detecting defects"

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Palmquist, Jonathan. "Detecting defects on cheese using hyperspectral image analysis." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172695.

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Defects such as mold and bacterial stains can appear on cheese. Manually detecting defects is a time-consuming, cost-ineffective, and ergonomically unsatisfactory process for a dairy because the quality technicians must inspect each cheese before packaging. Instead, dairies would prefer an automatic detection system, but it is unclear whether reliable options are available. One potential approach is hyperspectral image analysis, which can interpret and classify chemical information from a sample. We collected hyperspectral images from a dairy using a short-wave infrared (SWIR) camera and compared three prediction models: a PLS-discriminant analysis with the software Breeze from the analysis company Prediktera and two classifiers based on a convolutional neural network and a support vector machine, both coded in Python. We found mold and lactobacilli stains using all methods, but dirt was more challenging to detect. Also, the methods had issues with false positives of lactobacilli stains. To improve accuracy, we recommend collecting more data, especially samples with lactobacilli stains and dirt, and using more non-defect cheese for validation. To find smaller defects, we propose that future work should test a visible and near-infrared (VNIR) camera with higher resolution. Though PLS-discriminant analysis did not achieve the highest accuracy, it was not far off and had the most time-effective predictions. Since Breeze already integrates PLS-discriminant analysis, it should remain in focus, but for higher accuracy Prediktera should continue to explore other methods such as neural networks.
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Baghalian, Amin. "Detecting Structural Defects Using Novel Smart Sensory and Sensor-less Approaches." FIU Digital Commons, 2017. https://digitalcommons.fiu.edu/etd/3560.

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Monitoring the mechanical integrity of critical structures is extremely important, as mechanical defects can potentially have adverse impacts on their safe operability throughout their service life. Structural defects can be detected by using active structural health monitoring (SHM) approaches, in which a given structure is excited with harmonic mechanical waves generated by actuators. The response of the structure is then collected using sensor(s) and is analyzed for possible defects, with various active SHM approaches available for analyzing the response of a structure to single- or multi-frequency harmonic excitations. In order to identify the appropriate excitation frequency, however, the majority of such methods require a priori knowledge of the characteristics of the defects under consideration. This makes the whole enterprise of detecting structural defects logically circular, as there is usually limited a priori information about the characteristics and the locations of defects that are yet to be detected. Furthermore, the majority of SHM techniques rely on sensors for response collection, with the very same sensors also prone to structural damage. The Surface Response to Excitation (SuRE) method is a broadband frequency method that has high sensitivity to different types of defects, but it requires a baseline. In this study, initially, theoretical justification was provided for the validity of the SuRE method and it was implemented for detection of internal and external defects in pipes. Then, the Comprehensive Heterodyne Effect Based Inspection (CHEBI) method was developed based on the SuRE method to eliminate the need for any baseline. Unlike traditional approaches, the CHEBI method requires no a priori knowledge of defect characteristics for the selection of the excitation frequency. In addition, the proposed heterodyne effect-based approach constitutes the very first sensor-less smart monitoring technique, in which the emergence of mechanical defect(s) triggers an audible alarm in the structure with the defect. Finally, a novel compact phased array (CPA) method was developed for locating defects using only three transducers. The CPA approach provides an image of most probable defected areas in the structure in three steps. The techniques developed in this study were used to detect and/or locate different types of mechanical damages in structures with various geometries.
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Storozhenko, V. A., A. V. Myagkiy, and R. P. Orel. "Filtering of interference of inhomogeneous regular structure in thermal non-destructive control of cellular structures." Thesis, Eskisehir technical university, 2021. https://openarchive.nure.ua/handle/document/18954.

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Honeycomb constructions are the most widely used materials in contemporary aviation and space technology. They are the basis for the housings of practically all products of this sector, where reliability of all parts should meet the in-creased requirements. Special attention is paid to the quality of composite materials and to the absence of defects such as the places of adhesion failure (exfoliation) between the skin and the honeycomb filler. Therefore, increase in the efficiency and reliability of thermal flaw detection, based on in-depth analysis of the processes of detecting defects and development of the principles of optimization of both the procedure of control and subsequent processing of the obtained information, is an important and relevant task.
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Rainer, Alexander. "Detecting critical defects : towards standards for conducting NDE on cast iron trunk mains." Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/844891/.

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Every day, water networks across the developed world are relied on by billions of people to provide them with a fresh supply of water. Many of these networks are comprised of pipes made from grey cast iron and may have been in service for up to 150 years. Despite their age, some parts of these networks continue to operate with little degradation, whereas in other areas they degrade rapidly: more recently laid pipes are being outlived by their forerunners. In such networks, it is the trunk mains (pipes between 12-60” [300 mm to 1500 mm] in diameter) that are of great concern, since they pose the greatest risk of failure and are already bursting more frequently. Accurate NDE is required to enable the mains in poor health with the highest risk of failure to be identified and replaced before they burst. A review of the published literature has shown that whilst there are many NDE techniques to choose from, many are not practical for application to the mains. The review process also highlighted the kinds of defects present in grey cast iron and an initial stress analysis using strength models and material data published in the literature has suggested defect sizes approaching 5 mm must be able to be detected to prevent catastrophic pipe failure. Ultrasonic inspection has been investigated and shown to work effectively on uncorroded cast iron. Speed of sound values between 4100 – 4600 m s-1 have been observed across several pipes. A speed of sound of 2950 ± 80 m s-1 has been measured for graphitic corrosion, however, inspection on corroded main has not been possible. A complementary magnetic technique, with the potential to scan pipe rapidly in order to identify mains in need of further investigation, as well as providing supplementary condition data, has been trialled and shown to detect corrosion layers up to 6 mm thick. A methodology using a 3D scanner to accurately determine the “ground truth” pipe condition has been developed. This methodology proved to be successful and provided corrosion measurements that were in-keeping with those obtained through standard pit depth measurements. Further, the data showed that traditional pit depth measurements do not always find the deepest external corrosion pits, particularly where the surrounding geometry is complicated. This methodology was used in a live comparison exercise of two, commercially available techniques. This comparison highlighted problems with the surface preparation required by some techniques, which can be quite damaging, and with some proprietary post-processing algorithms – the raw data can be more useful. From this assessment process, it has been possible to specify very detailed schedule for the testing of new NDE techniques in the future.
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Poudel, Anish. "AN INTELLIGENT SYSTEMS APPROACH FOR DETECTING DEFECTS IN AIRCRAFT COMPOSITES BY USING AIR-COUPLED ULTRASONIC TESTING." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/594.

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Circular air-coupled ultrasonic testing (ACUT) setup for the inspection of commercial carbon-carbon composite aircraft brake disks was developed in Intelligent Measurement and Evaluation Laboratory (IMEL) at Southern Illinois University Carbondale (SIUC). The developed test setup utilizes Airstar single channel air-coupled equipment and has only manual A-scan and B-scan capability. The developed ACUT technique is unique compared to the commercial C-scan ultrasonic systems and is proficient, fast, economically feasible, and easy to implement method particularly for the inspection of carbon-carbon (C/C) composites aircraft brake disks. Prior to conducting air-coupled measurements, wobble analysis was carried out. This was important because significant wobbling in the test setup can lead to the interference of the reflected and the incident beam which would result to inaccurate ultrasonic measurements. The measured deviation due to wobbling, surface profile of the disk, design, and experimental error were relatively small. Therefore, these errors were neglected while performing ACUT measurements. For ACUT measurements, several through-transmitted amplitude signals were recorded within the C/C brake disks manually. The images were then reconstructed using Matlab based on the through-transmitted amplitude signals. Finally, a comparison was drawn between the reconstructed images and the C-scan images of the C/C brake disks obtained from the commercial Airstar C-scan ACUT system. Like commercial C-scan ACUT image results, reconstructed images were also able to detect all defects in the commercial C/C brake disks which served for the system verification and validation. In addition, defect, non-defect, and suspected areas within the C/C brake disks were quantified with air-coupled measurement. For this, light microscopy was conducted for every sample made from each C/C brake disks at lower magnification of 10X. It was concluded that it is very difficult to assess the crack or delamination situation based on a 2D micrograph of one depth. Also, it was concluded that an internal porosity and micro-cracks may not be only factors that can be related to defects. Finally, an intelligent systems approach, specifically, fuzzy logic and artificial neural network (ANN) methodologies were implemented for the automatic defect detection in commercial C/C aircraft brake disks by using air-coupled ultrasonic results. For this, a multi-layer perceptron (MLP) with two hidden layers and a scaled conjugate gradient back-propagation (BP) learning algorithm was used for the ANN training. The network training process was performed in an off-line mode using the ANN toolbox in Matlab. The network training was repeated until a steady state was reached, where there was no further change in the synaptic weights. The ANN provided plausible results in detecting the defect areas for different C/C brake disks. It was also demonstrated that the system was able to learn the rules without knowing any algorithm for automatic defect detection.
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Hassan, Syed Karimuddin and Syed Muhammad. "Defect Detection in SRS using Requirement Defect Taxonomy." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5253.

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Context: Defects occurred in the SRS may cause problems in project due to implementation of poor requirements which require extra time, effort, resources and budget to complete it. Reading techniques i.e., checklist based reading (CBR) helps to guide reviewers in identifying defects in software requirement specification (SRS) during individual requirement inspections. Checklists contain potential defects/problems to look for, but often lack clear definitions with examples of the problem, and also their abstractions are different. Therefore, there is a need for identifying existing defects and classifiers and to create a consolidated version of taxonomy. Objectives: We developed taxonomy for requirement defects that are in requirement specifications and compared it with the checklist based approach. The main objective was to investigate and compare the effectiveness and efficiency of inspection techniques (checklist and taxonomy) with M.Sc. software engineering students and industry practitioners by performing a both controlled student and industry experiment. Methods: Literature review, controlled student experiment and controlled industry experiment were the research methods utilized to fulfill the objectives of this study. INSPEC and Google scholar database was used to find the articles from the literature. Controlled student experiment was conducted with the M.Sc. software engineering students and controlled industry experiment was performed with the industry practitioners to evaluate the effectiveness and efficiency of the two treatments that are checklist and taxonomy. Results: An extensive literature review helped us to identify several types of defects with their definitions and examples. In this study, we studied various defect classifiers, checklists, requirement defects and inspection techniques and then built taxonomy for requirement defects. We evaluated whether the taxonomy performed better with respect to checklist using controlled experiments with students and practitioners. Moreover, the results of student experiment (p= 0.90 for effectiveness and p=0.10 for efficiency) and practitioner experiment (p=1.0 for effectiveness and p=0.70 for efficiency) did not show significant values with respect to effectiveness and efficiency. But because of less number of practitioners it is not possible to apply a statistical test since we also have used standard formulas to calculate effectiveness and efficiency. 2 out of the 3 reviewers using taxonomy found more defect types compared to 3 reviewers using checklist. 10-15% more defects have been found by reviewers using taxonomy. 2 out of the 3 reviewers using taxonomy are more productive (measuring in hours) compared to reviewers of checklist. Although the results are quite better than the student experiment but it is hard to claim that reviewers using taxonomy are more effective and efficient than the reviewers using checklist because of less subjects in number. The results of the post experiment questionnaire revealed that the taxonomy is easy to use and easy to understand but hard to remember while inspecting SRS than the checklist technique. Conclusions: Previously researchers created taxonomies for their own purpose or on industry demand. These taxonomies lack clear and understandable definitions. To overcome this problem, we built taxonomy with requirement defects which consists of definitions and examples. No claims are made based on student experiment because of insignificant values with respect to effectiveness and efficiency. Although the controlled industry experiment results showed that taxonomy performed slightly better than the checklist in efficiency i.e., in defect detection rate and effectiveness i.e., number of defect found. From this we can conclude that taxonomy helps guiding the reviewers to indentify defects from SRS but not quite much so it is recommended to perform a further study with practitioners in a large scale for effective results.
skarimuddin@yahoo.com, hassanshah357@gmail.com
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Klíma, Jakub. "Automatické vyhodnocování termovizních snímků fotovoltaických panelů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-242855.

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This thesis deals with diagnostics of photovoltaic panels especially with infrared diagnostics. There are described defects which we can examine using thermovision and also this thesis explains the cause of their formation. Practical part deals with the design of the program that automatically detects defects on infrared images.
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Rogers, Stuart Craig. "Defect Detection Microscopy." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2256.

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The automotive industry's search for stronger lighter materials has been hampered in its desire to make greater use of Magnesium alloys by their poor formability below 150°C. One current challenge is to identify the complex structure and deformation mechanisms at work and determine which of these are primary contributors to the nucleation of defects. Orientation Imaging Microscopy has been the most accessible tool for microstructural analysis over the past 15 years. However, using OIM to analyze defect nucleation sites requires prior knowledge of where the defects will occur because once the defects nucleate the majority of microstructural information is destroyed. This thesis seeks to contribute to the early detection of nucleation sites via three mechanisms: 1. Detection of cracks that have already nucleated, 2. Detection of surface topography changes that may indicate imminent nucleation and 3. Beam control strategies for efficiently finding areas of interest in a scan. Successive in-situ OIM scans of a consistent sample region while strain is increased, while using the three techniques developed in this thesis, will be employed in future work to provide a powerful defect analysis tool. By analyzing retrieved EBSD patterns we are able to locate defect / crack sites via shadowing on the EBSD patterns. Furthermore, topographical features (and potentially regions of surface roughening) can be detected via changes in intensity metrics and image quality. Topographical gradients are currently only detectable in line with the beam incidence. It is therefore suggested that the tensile specimens to be examined are orientated such that the resulting shear bands occur preferentially to this direction. The ability to refine the scan around these areas of interest has been demonstrated via an off-line adaptive scan routine that is implemented via the custom scan tool. A first attempt at a defect detection framework has been outlined and coded into MATLAB. These tools offer a first step to accessing the information about defect nucleation that researchers are currently seeking.
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Ngan, Yuk-tung Henry, and 顏旭東. "Patterned Jacquard fabric defect detection." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30070880.

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Auger, Marc. "Detection of laser-welding defects using neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2002. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ65599.pdf.

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Books on the topic "Detecting defects"

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Manning, David G. Detecting defects and deterioration in highway structures. Washington, D.C: Transportation Research Board, National Research Council, 1985.

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Alexander, A. Michel. Application of artificial neural networks to ultrasonic pulse echo system for detecting microcracks in concrete. Vicksburg, Miss: U.S. Army Engineer Waterways Experiment Station, 1998.

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Simon, Léa M. Fault detection: Theory, methods and systems. New York: Nova Science Publishers, 2011.

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Miller, Ann K. Engineering quality software: Defect detection and prevention. Reading, Mass: Addison-Wesley, 1992.

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Dahoo, Pierre Richard, Philippe Pougnet, and Abdelkhalak El Hami. Nanometer-Scale Defect Detection Using Polarized Light. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119329633.

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Goldstein, Robert V., and Gerard A. Maugin, eds. Surface Waves in Anisotropic and Laminated Bodies and Defects Detection. Dordrecht: Springer Netherlands, 2005. http://dx.doi.org/10.1007/1-4020-2387-1.

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Carlson, N. M. Ultrasonic sensing of GMAW: Laser/EMAT defect detection system. Idaho Falls, ID: E.G. & G Idaho Inc., 1992.

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Lu, Yicheng. Real time defect detection in welds by ultrasonic means. Uxbridge: Brunel University, 1992.

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tutkimuskeskus, Valtion teknillinen, ed. Detection of knots in logs using x-ray imaging. Espoo, Finland: VTT, Technical Research Centre of Finland, 1996.

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Starr, James W. Volumetric leak detection in large underground storage tanks. Cincinnati, Ohio: Risk Reduction Engineering Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 1991.

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Book chapters on the topic "Detecting defects"

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Gajdoš, Petr, and Jan Platoš. "Detecting Defects of Steel Slabs Using Symbolic Regression." In Advances in Intelligent Systems and Computing, 369–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32922-7_38.

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Yang, Peng. "Software Defects Detecting Method Based on Data Mining." In Advances in Computer Science, Environment, Ecoinformatics, and Education, 272–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23324-1_44.

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Mortin, K. V., D. G. Privezentsev, and A. L. Zhiznyakov. "A System for Detecting and Detecting Defects in Sheet Metal on Grayscale Images." In Lecture Notes in Electrical Engineering, 427–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94202-1_40.

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Boyadjian, Quentin, Nicolas Vanderesse, Matthew Toews, and Philippe Bocher. "Detecting Defects in Materials Using Deep Convolutional Neural Networks." In Lecture Notes in Computer Science, 293–306. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50347-5_26.

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Yang, Lu, Yanhua Zhang, and Gang Zhang. "Study on Ultrasonic Detecting Technology for Drill Collar Defects." In Electrical, Information Engineering and Mechatronics 2011, 1225–32. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2467-2_145.

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Raja, Babar Nasim Khan, Saeed Miramini, Colin Duffield, and Lihai Zhang. "Infrared Thermography for Detecting Subsurface Defects of Concrete Structures." In Lecture Notes in Civil Engineering, 1165–76. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8079-6_109.

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Qiu, Yang, Zhijie Ai, Ye Lin, Zhezhuang Xu, and Xinxiang Liu. "Detecting Defects of Wooden Boards by Improved YOLOv4-tiny Algorithm." In Lecture Notes in Electrical Engineering, 519–27. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6320-8_53.

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Shan, Chun, Shiyou Sun, Jingfeng Xue, Changzhen Hu, and Hongjin Zhu. "A Detecting Method of Array Bounds Defects Based on Symbolic Execution." In Network and System Security, 373–85. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64701-2_27.

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Hu, Wei, Hongyu Qi, Zhenbing Zhao, and Leilei Xu. "A Method for Detecting Surface Defects in Insulators Based on RPCA." In Lecture Notes in Computer Science, 163–73. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71589-6_15.

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Saltanovs, Rodion, and Alexander Krainyukov. "Machine Vision Using for Detecting Defects in the Flow of Goods." In Lecture Notes in Networks and Systems, 389–97. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68476-1_36.

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Conference papers on the topic "Detecting defects"

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Tran, Dat, LeRoy Winemberg, Darrell Carder, Xijiang Lin, Joe LeBritton, and Bruce Swanson. "Detecting and diagnosing open defects." In 2010 IEEE International Test Conference (ITC). IEEE, 2010. http://dx.doi.org/10.1109/test.2010.5699303.

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Bison, P., M. Ceseri, F. Clarelli, and G. Inglese. "Detecting hidden defects from real data." In 2014 Quantitative InfraRed Thermography. QIRT Council, 2014. http://dx.doi.org/10.21611/qirt.2014.052.

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Travassos, Guilherme, Forrest Shull, Michael Fredericks, and Victor R. Basili. "Detecting defects in object-oriented designs." In the 14th ACM SIGPLAN conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/320384.320389.

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Vasilic, Suzana, and Zeljko Hocenski. "The Edge Detecting Methods in Ceramic Tiles Defects Detection." In 2006 IEEE International Symposium on Industrial Electronics. IEEE, 2006. http://dx.doi.org/10.1109/isie.2006.295640.

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Vertiy, A. A. "Complex for detecting defects in radiotransparent objects." In 16th International Conference on Infrared and Millimeter Waves. SPIE, 1991. http://dx.doi.org/10.1117/12.2297858.

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Ribolzi, Serge, and Antonio Pinti. "Optical correlation filter for detecting fabric defects." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by David P. Casasent and Tien-Hsin Chao. SPIE, 1995. http://dx.doi.org/10.1117/12.205789.

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Wu, Mingtao, Vir V. Phoha, Young B. Moon, and Amith K. Belman. "Detecting Malicious Defects in 3D Printing Process Using Machine Learning and Image Classification." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67641.

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3D printing, or additive manufacturing, is a key technology for future manufacturing systems. However, 3D printing systems have unique vulnerabilities presented by the ability to affect the infill without affecting the exterior. In order to detect malicious infill defects in 3D printing process, this paper proposes the following: 1) investigate malicious defects in the 3D printing process, 2) extract features based on simulated 3D printing process images, and 3) an experiment of image classification with one group of non-defect infill image and the other group of defect infill training image from 3D printing process. The images are captured layer by layer from the top view of software simulation preview. The data extracted from images is input to two machine learning algorithms, Naive Bayes Classifier and J48 Decision Trees. The result shows Naive Bayes Classifier has an accuracy of 85.26% and J48 Decision Trees has an accuracy of 95.51% for classification.
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Bison, P., M. Ceseri, and G. Inglese. "Detecting hidden defects on a thin metallic plate." In 2010 Quantitative InfraRed Thermography. QIRT Council, 2010. http://dx.doi.org/10.21611/qirt.2010.018.

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Sun, Xu, and Qijun Chen. "Defects detecting of gloves based on machine vision." In 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2016. http://dx.doi.org/10.1109/rcar.2016.7784020.

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Xie, Feng, Alexandra Uitdenbogerd, and Andy Song. "Detecting PCB component placement defects by genetic programming." In 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. http://dx.doi.org/10.1109/cec.2013.6557694.

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Reports on the topic "Detecting defects"

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Schad, Kristin C., Daniel L. Schmoldt, and Robert J. Ross. Nondestructive Methods for Detecting Defects in Softwood Logs. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 1996. http://dx.doi.org/10.2737/fpl-rp-546.

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Upchurch, Bruce, Ruth Ben-Arie, Amots Hetzroni, James Throop, and Farhad Geoola. Photometric Imaging for Detecting Surface and Internal Defects on Apples. United States Department of Agriculture, February 1993. http://dx.doi.org/10.32747/1993.7603830.bard.

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Mopsik, Frederick I., Edward F. Kelley, and Francoise D. Martzloff. A review of candidate methods for detecting incipient defects due to aging of installed cables in nuclear power plants. Gaithersburg, MD: National Bureau of Standards, 1988. http://dx.doi.org/10.6028/nbs.ir.88-3774.

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Chen, Pictiaw, Boaz Zion, and Michael J. McCarthy. Utilization of NMR Technology for Internal Nondestructive Quality Evaluation of Fruits and Vegetables. United States Department of Agriculture, September 1994. http://dx.doi.org/10.32747/1994.7568778.bard.

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Objective: The objective of this research was to investigate the potential use of NMR for evaluating various internal quality factors of fruits and vegetables, leading to the eventual development of practical techniques that are useful for future development of NMR sensors. Summary: Investigation on NMR imaging, one-dimension NMR projection, and single-pulse free-induction-decay (FID) spectrum led to the development of high-speed NMR techniques for real-time sensing of internal quality of selected fruits. NMR imaging can be used for detecting internal defects and various quality factors such as bruises, dry regions, worm damage, stage of ripeness, tissue breakdown, and the presence of voids, seeds, sprouts, and pits. The one-dimension (1-D) image profile technique, in which the 1-D projection of the NMR signal of a selected slice of the intact fruit is recorded, is suitable for detecting tissue breakdown regions, presence of pits, and other defects in fruits. The oil and sugar content of fruits can be determined from the single-pulse FID spectrum measurement, in which a surface coil is used to acquire the FID spectrum and the ratio of the resonance peaks is used as the quality index. The latter two techniques are suitable for high-speed sorting of fruits. The most important accomplishment is the successful development of high-speed NMR techniques for determining internal quality of fruits while they are moving at speed up to 30 cm/s. This accomplishment is an important step toward the development of NMR techniques for on-line sorting of fruits and vegetables.
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Cheadle, Nancy, Dennis Tackett, Robert Pierce, and Raymond de Lacaze. Automatic Detection of Radar Signature Defects,. Fort Belvoir, VA: Defense Technical Information Center, May 1999. http://dx.doi.org/10.21236/ada364069.

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Johnson, M. A., and G. E. Sommargren. Mask Blank Defect Detection. Office of Scientific and Technical Information (OSTI), February 2000. http://dx.doi.org/10.2172/15013535.

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Pantea, Cristian. Acoustic Wafer Defect Detection System. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1813835.

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Author, Not Given. A sequential detection algorithm for color printed pattern defects. Office of Scientific and Technical Information (OSTI), September 1995. http://dx.doi.org/10.2172/10129734.

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Cook, K. V., R. A. Jr Cunningham, W. A. Jr Simpson, and R. W. McClung. Ultrasonic detection of laminar-type defects in iridium alloy blanks. Office of Scientific and Technical Information (OSTI), July 1986. http://dx.doi.org/10.2172/5422629.

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Martzloff, F. D., E. Simmon, J. P. Steiner, and R. J. Van Brunt. Detection of incipient defects in cables by partial discharge signal analysis. Gaithersburg, MD: National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4487.

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