A study of hough transform for weld extraction

The process of joining metals is called welding. At times, selecting a poor quality material or improper usage of welding technologies may cause defects in welded joints. Some of these welded joints have to be tested nondestructively, because their failure can cause lot of damage, for instance in power plants. Radiography is a very common method for non-destructive testing of welds. It is done by certified weld inspectors who have knowledge about weld flaws, looking at the radiograph of the welded joint with naked eye. The judgment of the weld inspector can be biased; subjective, because it is dependent on his/her experience. This manual method can also become very time consuming. Many researches were exploring computer aided examination of radiographic images in early 1990’s. With much advancement in computer vision and image processing technologies, they are being used to find more effective ways of automatic weld inspection. These days, fuzzy based methods are being widely used in this area too. The first step in automatic weld inspection is to locate the welds or find a Region of Interest (ROI) in the radiographic image [7]. In this thesis, a Standard Hough Transform (SHT) based methodology is developed for weld extraction. Firstly, we have done binarization of image to remove the background and non-welds. For binarization, optimal binary threshold is found by a metaheuristic –Simulated annealing. Secondly, we use SHT to generate the Hough Transform matrix of all non-zero points in the binary image. Thirdly, we have explored two different paths to find a meaningful set of lines in the binarized image that are welds. Finally, these lines are verified as weld using a weld-peak detection procedure. Weld-peak detection is also helpful to remove any non-welds that were remaining. We have used 25 digitized radiographic images containing 100 welds to test the method in terms of true detection and false alarm rate

Development Of A Computed Radiography-Based Weld Defect Detection And Classification System [RC78.7.D35 K75 2008 f rb].

Dalam penyelidikan ini, satu sistem bersepadu yang terdiri daripada satu peta kecacatan dan satu pengelas pelbagai rangkaian neural bagi peruasan, pengesanan dan pengesanan kecacatan kimpalan telah direkabentuk dan dibangun. In this research, an integrated system consisting of a flaw map and a multiple neural network classifier for weld defect segmentation, detection, and classification is designed and developed

Automated flaw detection method for X-ray images in nondestructive evaluation

Private, government and commercial sectors of the manufacturing world are plagued with imperfect materials, defective components, and aging assemblies that continuously infiltrate the products and services provided to the public. Increasing awareness of public safety and economic stability has caused the manufacturing world to search deeper for a solution to identify these mechanical weaknesses and thereby reduce their impact. The areas of digital image and signal processing have benefited greatly from the technological advances in computer hardware and software capabilities and the development of new processing methods resulting from extensive research in information theory, artificial intelligence, pattern recognition and related fields. These new processing methodologies and capabilities are laying a foundation of knowledge that empowers the industrial and academic community to boldly address this problem and begin designing and building better products and systems for tomorrow

Computer-aided weld inspection by fuzzy modeling with selected features

This thesis develops a computer-aided weld inspection methodology based on fuzzy modeling with selected features. The proposed methodology employs several filter feature selection methods for selecting input variables and then builds fuzzy models with the selected features. Our fuzzy modeling method is based on a fuzzy c-means (FCM) variant for the generation of fuzzy terms sets. The implemented FCM variant differs from the original FCM method in two aspects: (1) the two end terms take the maximum and minimum domain values as their centers, and (2) all fuzzy terms are forced to be convex. The optimal number of terms and the optimal shape of the membership function associated with each term are determined based on the mean squared error criterion. The fuzzy model serves as the rule base of a fuzzy reasoning based expert system implemented. In this implementation, first the fuzzy rules are extracted from feature data one feature at a time based on the FCM variant. The total number of fuzzy rules is the product of the fuzzy terms for each feature. The performances of these fuzzy sets are then tested with unseen data in terms of accuracy rates and computational time. To evaluate the goodness of each selected feature subset, the selected combination is used as an input for the proposed fuzzy model. The accuracy of each selected feature subset along with the average error of the selected filter technique is reported. For comparison, the results of all possible combinations of the specified set of feature subsets are also obtained

Development of an acoustic emission monitoring system for crack detection during arc welding

Condition monitoring techniques are employed to monitor the structural integrity of a structure or the performance of a process. They are used to evaluate the structural integrity including damage initiation and propagation in engineering components. Early damage detection, maintenance and repairs can prevent structural failures, reduce maintenance and replacement costs, and guarantee that the structure runs securely during its service life. Acoustic emission (AE) technology is one of the condition monitoring methods widely employed in the industry. AE is an attractive option for condition monitoring purposes, the number of industrial applications where is used is rising. AE signals are elastic stress waves created by the fast release of energy from local sources occurring inside of materials, e.g. crack initiating and propagating. The AE technique includes recording this phenomenon with piezoelectric sensors, which is mounted on the surface of a structure. The signals are subsequently analysed in order to extract useful information about the nature of the AE source. AE has a high sensitivity to crack propagation and able to locate AE activity sources. It is a passive approach. It listens to the elastic stress waves releasing from material and able to operate in real-time monitoring to detect both cracks initiating and propagating. In this study, the use of AE technology to detect and monitor the possible occurrence of cracking during the arc welding process has been investigated. Real-time monitoring of the automated welding operation can help increase productivity and reliability while reducing cost. Monitoring of welding processes using AE technology remains a challenge, especially in the field of real-time data analysis, since a large amount of data is generated during monitoring. Also, during the welding process, many interferences can occur, causing difficulties in the identifications of the signals related to cracking events. A significant issue in the practical use of the AE technique is the existence of independent sources of a signal other than those related to cracking. These spurious AE signals make the discovering of the signals from the cracking activity difficult. Therefore, it is essential to discriminate the signal to identify the signal source. The need for practical data analysis is related to the three main objectives of monitoring, which is where this study has focused on. Firstly, the assessment of the noise levels and the characteristics of the signal from different materials and processes, secondly, the identification of signals arising from cracking and thirdly, the study of the feasibility of online monitoring using the AE features acquired in the initial study. Experimental work was carried out under controlled laboratory conditions for the acquisition of AE signals during arc welding processing. AE signals have been used for the assessment of noise levels as well as to identify the characteristics of the signals arising from different materials and processes. The features of the AE signals arising from cracking and other possible signal sources from the welding process and environment have also collected under laboratory conditions and analysed. In addition to the above mentioned aspects of the study, two novel signal processing methods based on signal correlation have been developed for efficiently evaluating data acquired from AE sensors. The major contributions of this research can be summarised as follows. The study of noise levels and filtering of different arc welding processes and materials is one of the areas where the original contribution is identified with respect to current knowledge. Another key contribution of the present study is the developing of a model for achieving source discrimination. The crack-related signals and other signals arising from the background are compared with each other. Two methods that have the potential to be used in a real-time monitoring system have been considered based on cross-correlation and pattern recognition. The present thesis has contributed to the improvement of the effectiveness of the AE technique for the detection of the possible occurrence of cracking during arc welding

Detecção de Tubos em Imagens Radiográficas Digitais

Este artigo apresenta uma metodologia para a detecção do tubo em imagens radiográficas do tipo parede dupla vista dupla (PDVD) de tubulações condutoras de petróleo. O principal objetivo da proposta é reduzir a região de busca através da delimitação da área do tubo para a extração automática do cordão de solda auxiliando, desta forma, a posterior detecção de defeitos em juntas soldadas. O processo de detecção do tubo apresentado é totalmente automático e baseado em técnicas de processamento de imagens como ajustes no brilho e contraste, limiarização e análise das regiões identificadas para a segmentação do tubo. O processo foi aplicado em 167 imagens provenientes de três diferentes sistemas radiográficos obtendo um resultado de 90,4% de acerto na detecção do tubo. Foi realizada uma comparação com outra abordagem para a detecção do tubo em imagens radiográficas do tipo PDVD e a metodologia proposta apresentou melhora em relação ao trabalho anterior. Conclui-se, portanto que o método proposto pode ser usado como uma etapa que precede a detecção automática do cordão de solda

1992 NASA/ASEE Summer Faculty Fellowship Program

For the 28th consecutive year, a NASA/ASEE Summer Faculty Fellowship Program was conducted at the Marshall Space Flight Center (MSFC). The program was conducted by the University of Alabama and MSFC during the period June 1, 1992 through August 7, 1992. Operated under the auspices of the American Society for Engineering Education, the MSFC program, was well as those at other centers, was sponsored by the Office of Educational Affairs, NASA Headquarters, Washington, DC. The basic objectives of the programs, which are the 29th year of operation nationally, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate and exchange ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA centers

Técnicas automáticas para detecção de cordões de solda e defeitos de soldagem em imagens radiográficas industriais

This work proposes a new method for the automatic detection of the weld seam in radiographic images of pipeline welded joints. The proposed methodology is based on the optimization of parameters that are used to control and modify the position, size and shape of an image window, in order to enclose the image region that best matches the radiographic representation of a reinforced weld seam. The search for the best matching is managed by a genetic algorithm and uses an image similarity concept that is commonly applied on template matching procedures. The proposed technique solved weld seam detection problems for which no other automatic detection method was successful. The test results achieved an accuracy between 93% and 100%, regarding different tests circumstances, for a total of 478 radiographic images. The images data set included radiograph samples that cover all the recommended radiographic exposure techniques that are recommended, in the ASME V code, for inspection of pipeline welded joints. Meanwhile, a known image segmentation technique was modified and applied to perform the automatic detection of welding defects. For that test series, radiographic patterns from the International Institute of Welding (IIW) were used, including samples of the most popular classes of welding defects. After the used segmentation technique has been properly modified, defect detection samples were achieved for all the tested defect classes. Such results contribute with advancements in the automatic analysis of industrial radiographs and, as a final goal, aim at aggregating quality and efficiency to the radiographic inspection of welded joints.Este trabalho propõe um novo método para a detecção automática de cordões de solda em imagens radiográficas de juntas soldadas de tubulações. A metodologia proposta baseia-se na otimização de parâmetros que controlam e adaptam o posicionamento, tamanho e formato de uma janela de imagem, de modo a enquadrar a região da imagem que mais se assemelha à representação visual de um cordão de solda radiografado. A busca por parâmetros ótimos é conduzida por um algoritmo genético, que parte de soluções aleatoriamente geradas e as avalia com base em um conceito de similaridade entre imagens, oriundo de técnicas de casamento de protótipos. Além de se tratar de uma proposta inédita, a solução apresentada cobre uma diversidade de situações, incluindo problemas de detecção do cordão de solda para os quais ainda não havia sido encontrada uma solução automatizada que a literatura tenha referenciado. Os resultados dos testes realizados alcançaram um desempenho entre 93 e 100%, para um total de 478 imagens consideradas, que incluem exemplos de praticamente todas as técnicas de exposição radiográfica recomendadas pelo código ASME V, para inspeção de juntas soldadas de tubulações. Entrementes, uma técnica já existente de segmentação de imagens foi adaptada para desempenhar a detecção automática de defeitos de soldagem. Para tais testes, foram utilizados padrões radiográficos das principais classes de defeitos, provenientes do International Institute of Welding (IIW). Após modificações agregadas à técnica de segmentação utilizada, foi possível detectar exemplos de todas as classes de defeitos testadas. Tais resultados contribuem para a análise automática de radiografias industriais e visa melhorar a qualidade e eficiência na inspeção radiográfica de soldas

Index to NASA tech briefs, 1971

The entries are listed by category, subject, author, originating source, source number/Tech Brief number, and Tech Brief number/source number. There are 528 entries

Approaches to Industry 4.0 implementation for electron beam quality assurance using BeamAssure

Electron beam welding (EBW) is a complex process used in manufacturing high-value components in the aerospace and nuclear industries. The Fourth Industrial Revolution is a fusion of advances in artificial intelligence, sensing techniques, data science, and other technologies to improve productivity and competitiveness in fast-growing markets. Although the EBW process can be monitored by characterisation of the electron beams before welding or using backscattered electron signals (BSE), the noise and lack of understanding of these signal patterns is a major obstacle to the development of a reliable, rapid and cost-effective process analysis and control methodology. In this thesis a controlled experiment was designed to be relevant to those industries and improve understanding of the relationship between beam and weld quality. The welding quality control starts before welding, continue throughout the welding process, and is completed with examination after welding. The same workflow was followed in this thesis, focusing on aforementioned QC stages, starting with beam probing experiments, followed by monitoring weld pool stability using high dynamic range camera and BSE signals, and ending with metallographic inspection on sections. The rapid development of computer vision methods brought an idea of classifying beam probing data before welding, which is first QC stage. Dataset of 3015 BeamAssure measurements was used in combination with deep learning, and various encoding methods such as Recurrence Plots (RP), Gramian Angular Fields (GAF), and Markov Transition Fields (MTF). The segmentation and classification results achieved a remarkable rate of 97.6% of accuracy in the classification task. This part of the work showed that use of time-series images enabled identification of the beam focus location before welding and providing recommended focus adjustment value. To replicate in-process QC step, titanium alloy (Ti-6Al-4V) plates were welded with a gap opened in a stepwise manner, to simulate gap defects and introduce weld pool instability. Experiments were conducted to monitor the weld pool stability with a HDR camera and BSE detector designed for the need of this experiment. Signal and image analysis revealed occurrence of the weld defects and their locations, which was reflected by last QC stage, metallographic inspection on sections. This final part of the work proved that whatever method is used for gap defects monitoring, those joint misalignments can be easily registered by both methods. More interestingly, BSE monitoring allowed porosity and humping detection, which shapes and location were projected onto the BSE signal amplitude. Presented three stage QC method can contribute to a better understanding of beam probing and BSE signals patterns, providing a promising approach for quality assurance in EBW and could lead to higher weld integrity by improved process monitoring