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

A recognition algorithm to detect pipe weld defects

Uzimajući kao objekt istraživanja propuštanje magnetskog toka (MFL) za snimanje grešaka u zavarima cijevi, predložen je algoritam raspoznavanja slika grešaka u zavarima utemeljen na matrici istovremene pojave sivih tonova (GGCM) te klasterskoj analizi i matematičkoj morfologiji. Postignuto je raspoznavanje različitih vrsta grešaka u zavarima. Prvo je korišten sustav kontinuiranog bezdodirnog skeniranja MFL za prikupljanje trodimenzijskog propuštanja magnetskog toka (MFL), a nakon toga je trodimenzijski MFL signal pretvoren u dvodimenzijsku sliku sivih tonova. Zatim su karakteristike MFL slike za dvodimenzijsku sliku u sivim tonovima izdvojene pomoću GGCM. Na temelju izdvojenih značajki slike, analizira se karakteristična količina pomoću particioniranja k-sredine, a zatim kroz kombinaciju izjednačenja histograma, Otsuovu metoda binarizacija, morfološkog uklanjanja malih objekata, otkrivanja rubova, a zatim strukturiranja morfološki optimiziranog algoritma ekstrakcije ruba za otkrivanje rubova na sivim tonovima. Kombiniranjem nekoliko metoda, strukturira se novi algoritam za poboljšanje učinka otkrivanja. Rezultati su pokazali da je ova metoda prilagodljiva i praktična. Ovaj je algoritam riješio poteškoće vezane uz MFL metodu koja se koristi u ispitivanju zavara radi otkrivanja grešaka u zavarima cijevi, a nadilazi granice primjene tradicionalne tehnologije obrade signala.Taking magnetic flux leakage (MFL) imaging of pipe weld defects as the research object, a weld defect image recognition algorithm based on grey-gradient co-occurrence matrix (GGCM) and cluster analysis and mathematical morphology is proposed. Recognition of different types of welding defects was achieved. Firstly, a continuous non-contact scanning MFL system for the pipe weld was used to collect the three-dimensional MFL. Secondly, the three-dimensional MFL signal was converted to a two-dimensional greyscale image. Then the MFL image characteristics of the two-dimensional grayscale image were extracted using GGCM. Based on extracted image features, the characteristic quantity was analysed by using k-means clustering and then through the combination of histogram equalization, Otsu’s method of binaryzation, morphologically removing small objects, edge detection, and then structuring a morphologically optimized edge extraction method for edge detection on the grayscale. Through combination of several methods, a new algorithm to improve the detection effect was structured. The results indicated that this algorithm is adaptable and practical. This algorithm solved difficulties associated with the MFL method being used in the weld testing to realize the recognition of pipe weld defects and break through the applicable limitations of traditional signal processing technology

TEXTUAL DATA MINING FOR NEXT GENERATION INTELLIGENT DECISION MAKING IN INDUSTRIAL ENVIRONMENT: A SURVEY

This paper proposes textual data mining as a next generation intelligent decision making technology for sustainable knowledge management solutions in any industrial environment. A detailed survey of applications of Data Mining techniques for exploiting information from different data formats and transforming this information into knowledge is presented in the literature survey. The focus of the survey is to show the power of different data mining techniques for exploiting information from data. The literature surveyed in this paper shows that intelligent decision making is of great importance in many contexts within manufacturing, construction and business generally. Business intelligence tools, which can be interpreted as decision support tools, are of increasing importance to companies for their success within competitive global markets. However, these tools are dependent on the relevancy, accuracy and overall quality of the knowledge on which they are based and which they use. Thus the research work presented in the paper uncover the importance and power of different data mining techniques supported by text mining methods used to exploit information from semi-structured or un-structured data formats. A great source of information is available in these formats and when exploited by combined efforts of data and text mining tools help the decision maker to take effective decision for the enhancement of business of industry and discovery of useful knowledge is made for next generation of intelligent decision making. Thus the survey shows the power of textual data mining as the next generation technology for intelligent decision making in the industrial environment

A review of non-destructive testing on wind turbines blades

Wind energy, with an exponential growth in the last years, is nowadays one of the most important renewable energy sources. Modern wind turbines are bigger and complex to produce more energy. This industry requires to reduce its operating and maintenance costs and to increase its reliability, safety, maintainability and availability. Condition monitoring systems are beginning to be employed for this purpose. They must be reliable and cost-effective to reduce the long periods of downtimes and high maintenance costs, and to avoid catastrophic scenarios caused by undetected failures. This paper presents a survey about the most important and updated condition monitoring techniques based on non-destructive testing and methods applied to wind turbine blades. In addition, it analyses the future trends and challenges of structural health monitoring systems in wind turbine blades.Wind energy, with an exponential growth in the last years, is nowadays one of the most important renewable energy sources. Modern wind turbines are bigger and complex to produce more energy. This industry requires to reduce its operating and maintenance costs and to increase its reliability, safety, maintainability and availability. Condition monitoring systems are beginning to be employed for this purpose. They must be reliable and cost-effective to reduce the long periods of downtimes and high maintenance costs, and to avoid catastrophic scenarios caused by undetected failures. This paper presents a survey about the most important and updated condition monitoring techniques based on non-destructive testing and methods applied to wind turbine blades. In addition, it analyses the future trends and challenges of structural health monitoring systems in wind turbine blades

Challenges towards Structural Integrity and Performance Improvement of Welded Structures

Welding is a fabrication process that joint materials, is extensively utilized in almost every field of metal constructions. Heterogeneity in mechanical properties, metallurgical and geometrical defects, post-weld residual stresses and distortion due to non-linear welding processes are prime concerns for performance reduction and failures of welded structures. Consequently, structural integrity analysis and performance improvement of weld joints are important issues that must be considered for structural safety and durability under loading. In this study, an extensive experimental program and analysis were undertaken on the challenges towards structural integrity analysis and performance improvement of different welded joints. Two widely used welding techniques including solid-state “friction- stir- welding (FSW)” and fusion arc “gas tungsten arc welding (GTAW)” were employed on two widely utilized materials, namely aluminum alloys and structural steels. Various destructive and non-destructive techniques were utilized for structural integrity analysis of the welded joints. Furthermore, various “post-weld treatment (PWT)” techniques were employed to improve mechanical performances of weld joints. The work herein is divided into six different sections including: (i) Establishment of an empirical correlation for FSW of aluminum alloys. The developed empirical correlation relates the three critical FSW process parameters and was found to successfully distinguish defective and defect-free weld schedules; (ii) Development of an optimized “adaptive neuro-fuzzy inference system (ANFIS)” model utilizing welding process parameters to predict ultimate tensile strength (UTS) of FSW joints; (iii) Determination of an optimum post-weld heat treatment (PWHT) condition for FS-welded aluminum alloys; (iv) Exploration on the influence of non-destructively evaluated weld-defects and obtain an optimum PWHT condition for GTA-welded aluminum alloys; (v) Investigation on the influence of PWHT and electrolytic-plasma-processing (EPP) on the performance of welded structural steel joints; and finally, (vi) Biaxial fatigue behavior evaluation of welded structural steel joints. The experimental research could be utilized to obtain defect free weld joints, establish weld acceptance/rejection criteria, and for the better design of welded aluminum alloy and steel structures. All attempted research steps mentioned above were carried out successfully. The results obtained within this effort will increase overall understanding of the structural integrity of welded aluminum alloys and steel structures

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

Real-time Automated Weld Quality Analysis From Ultrasonic B-scan Using Deep Learning

Resistance spot welding is a widely used process for joining metals using electrically generated heat or Joule heating. It is one of the most commonly used techniques in automotive industry to weld sheet metals in order to form a car body. Although, industrial robots are used as automated spot welders in massive scale in the industries, the weld quality inspection process still requires human involvement to decide if a weld should be passed as acceptable or not. Not only it is a tedious and error- prone job, but also it costs industries lots of time and money. Therefore, making this process automated and real-time will have high significance in spot welding as well as the field of Non-destructive Testing (NDT). Research team in Institute of Diagnostic Imaging Research (IDIR) have developed technology to obtain grey-scale 2D images called ultrasonic b-scans in real-time during production in order to visualize the weld development with respect to time. They have demonstrated that by extracting and interpreting relevant patterns from these b-scans, weld quality can be determined accurately. However, current works combining conventional image and signal processing techniques are unable to extract those patterns from a wide variety of weld shapes with production-level satisfaction. Therefore, in this thesis, we propose to apply SSD, a single-shot multi-box detection based deep convolutional neural network framework for real-time embedded detection of components of cross-sectional weld shape from ultrasonic b-scans and interpret them to numeric parameters which are used as features to classify welds as good, bad or acceptable in real-time. Our proposed model has showed significant improvement in deciding weld quality compared to existing methods when tested on real industry facility