Feature Extraction and Classification of Flaws in Radio Graphical Weld Images Using ANN

In this paper, a novel approach for the detection and classification of flaws in weld images is presented. Computer based weld image analysis is most significant method. The method has been applied for detecting and discriminating flaws in the weld that may corresponds false alarms or all possible nine types of weld defects (Slag Inclusion, Wormhole, Porosity, Incomplete penetration, Under cuts, Cracks, Lack of fusion, Weaving fault Slag line), after being successfully tested on80 radiographic images obtained from EURECTEST, International scientific Association Brussels, Belgium, and 24 radiographs of ship weld provided by Technic Control Co. (Poland) were used, obtained from Ioannis Valavanis Greece.. The procedure to detect all the types of flaws and feature extraction is implemented by segmentation algorithm which can overcome computer complexity problem. Our problem focuses on the high performance classification by optimization of feature set by various selection algorithms like sequential forward search (SFS), sequential backward search algorithm (SBS) and sequential forward floating search algorithm (SFFS). Features are important for measuring parameters which leads in directional to understand image. We introduced 23 geometric features, and 14 texture features. The Experimental results show that our proposed method gives good performance of radiographic images

An automatic welding defects classifier system

Radiographic inspection is a well-established testing method to detect weld defects. However, interpretation of radiographic films is a difficult task. The reliability of such interpretation and the expense of training suitable experts have allowed that the efforts being made towards automation in this field. In this paper, we describe an automatic detection system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of geometrical features was proposed and extracted between defect candidates. In a third stage, an artificial neural network for weld defect classification was used under three regularisation process with different architectures. For the input layer, the principal component analysis technique was used in order to reduce the number of feature variables; and, for the hidden layer, a different number of neurons was used in the aim to give better performance for defect classification in both cases

Deep learning technology for weld defects classification based on transfer learning and activation features

Weld defects detection using X-ray images is an effective method of nondestructive testing. Conventionally, this work is based on qualified human experts, although it requires their personal intervention for the extraction and classification of heterogeneity. Many approaches have been done using machine learning (ML) and image processing tools to solve those tasks. Although the detection and classification have been enhanced with regard to the problems of low contrast and poor quality, their result is still unsatisfying. Unlike the previous research based on ML, this paper proposes a novel classification method based on deep learning network. In this work, an original approach based on the use of the pretrained network AlexNet architecture aims at the classification of the shortcomings of welds and the increase of the correct recognition in our dataset. Transfer learning is used as methodology with the pretrained AlexNet model. For deep learning applications, a large amount of X-ray images is required, but there are few datasets of pipeline welding defects. For this, we have enhanced our dataset focusing on two types of defects and augmented using data augmentation (random image transformations over data such as translation and reflection). Finally, a fine-tuning technique is applied to classify the welding images and is compared to the deep convolutional activation features (DCFA) and several pretrained DCNN models, namely, VGG-16, VGG-19, ResNet50, ResNet101, and GoogLeNet. The main objective of this work is to explore the capacity of AlexNet and different pretrained architecture with transfer learning for the classification of X-ray images. The accuracy achieved with our model is thoroughly presented. The experimental results obtained on the weld dataset with our proposed model are validated using GDXray database. The results obtained also in the validation test set are compared to the others offered by DCNN models, which show a best performance in less time. This can be seen as evidence of the strength of our proposed classification model.This work has been partially funded by the Spanish Government through Project RTI2018-097088-B-C33 (MINECO/FEDER, UE)

Machine Vision Application For Automatic Defect Segmentation In Weld Radiographs

Objektif penyelidikan ini adalah untuk membangunkan satu kaedah peruasan kecacatan kimpalan automatik yang boleh meruas pelbagai jenis kecacatan kimpalan yang wujud dalam imej radiografi kimpalan. Kaedah segmentasi kecacatan automatik yang dibangunkan terdir:i daripada tiga algoritma utama, iaitu algoritma penyingkiran label, algoritma pengenalpastian bahagian kimpalan dan algoritma segmentasi kecacatan kimpalan. Algoritma penyingkiran label dibangunkan untuk mengenalpasti dan menyingkirkan label yang terdapat pada imej radiograf kimpalan secara automatik, sebelum algoritma pengenalpastian bahagian kimpalan dan algortima segmentasi kecacatan diaplikasikan ke atas imej radiografi. Satu algoritma pengenalpastian bahagian kimpalan juga dibangunkan dengan tujuan mengenalpasti bahagian kimpalan dalam imej radiogaf secara automatik dengan menggunakan profil keamatan yang diperoleh daripada imej radiografi. The objective of the research is to develop an automatic weld defect segmentation methodology to segment different types of defects in radiographic images of welds. The segmentation methodology consists of three main algorithms. namely label removal algorithm. weld extraction algorithm and defect segmentation algorithm. The label removal algorithm was developed to detect and remove labels that are printed on weld radiographs automatically before weld extraction algorithm and defect detection algorithm are applied. The weld extraction algorithm was developed to locate and extract welds automatically from the intensity profiles taken across the image by using graphical analysis. This algorithm was able to extract weld from a radiograph regardless of whether the intensity profile is Gaussian or otherwise. This method is an improvement compared to the previous weld extraction methods which are limited to weld image with Gaussian intensity profiles. Finally. a defect segmentation algorithm was developed to segment the defects automatically from the image using background subtraction and rank leveling method

Using deep learning for defect classification on a small weld X-ray image dataset

This document provides a comparative evaluation of the performance of a deep learning network for different combinations of parameters and hyper-parameters. Although there are numerous studies that report on performance in deep learning networks for ordinary data sets, their performance on small data sets is much less evaluated. The objective of this work is to demonstrate that such a challenging small data set, such as a welding X-ray image data set, can be trained and evaluated obtaining high precision and that it is possible thanks to data augmentation. In fact, this article shows that data augmentation, also a typical technique in any learning process on a large data set, plus that two image channels, such as channels B (blue) and G (green), both are replaced by the Canny edge map and a binary image provided by an adaptive Gaussian threshold, respectively, gives to the network a 3% increase in accuracy, approximately. In summary, the objective of this work is to present the methodology used and the results obtained to estimate the classification accuracy of three main classes of welding defects obtained on a small set of welding X-ray image data.The authors wants to acknowledge the work of the rest of the participants in this project, namely: J.A. López-Alcantud, P. Rubio-Ibañez, Universidad Politécnica de Cartagena, J.A. Díaz-Madrid, Centro Universitario de la Defensa - UPCT and T.J. Kazmierski, University of Southampton. This work has been partially funded by Spanish government through project numbered RTI2018-097088-B-C33 (MINECO/FEDER,UE)

Friction Stir Welding Manufacturing Advancement by On-Line High Temperature Phased Array Ultrasonic Testing and Correlation of Process Parameters to Joint Quality

Welding, a manufacturing process for joining, is widely employed in aerospace, aeronautical, maritime, nuclear, and automotive industries. Optimizing these techniques are paramount to continue the development of technologically advanced structures and vehicles. In this work, the manufacturing technique of friction stir welding (FSW) with aluminum alloy (AA) 2219-T87 is investigated to improve understanding of the process and advance manufacturing efficiency. AAs are widely employed in aerospace applications due to their notable strength and ductility. The extension of good strength and ductility to cryogenic temperatures make AAs suitable for rocket oxidizer and fuel tankage. AA-2219, a descendent of the original duralumin used to make Zeppelin frames, is currently in wide use in the aerospace industry. FSW, a solid-state process, joins the surfaces of a seam by stirring the surfaces together with a pin while the metal is held in place by a shoulder. The strength and ductility of friction stir (FS) welds depends upon the weld parameters, chiefly spindle rotational speed, feedrate, and plunge force (pinch force for self-reacting welds). Between conditions that produce defects, it appears in this study as well as those studies of which we are aware that FS welds show little variation in strength; however, outside this process parameter “window” the weld strength drops markedly. Manufacturers operate within this process parameter window, and the parameter establishment phase of welding operations constitutes the establishment of this process parameter window. The work herein aims to improve the manufacturing process of FSW by creating a new process parameter window selection methodology, creation of a weld quality prediction model, developing an analytical defect suppression model, and constructing a high temperature on-line phased array ultrasonic testing system for quality inspection

Vision-based Monitoring System for High Quality TIG Welding

The current study evaluates an automatic system for real-time arc welding quality assessment and defect detection. The system research focuses on the identification of defects that may arise during the welding process by analysing the occurrence of any changes in the visible spectrum of the weld pool and the surrounding area. Currently, the state-of-the-art is very simplistic, involving an operator observing the process continuously. The operator assessment is subjective, and the criteria of acceptance based solely on operator observations can change over time due to the fatigue leading to incorrect classification. Variations in the weld pool are the initial result of the chosen welding parameters and torch position and at the same time the very first indication of the resulting weld quality. The system investigated in this research study consists of a camera used to record the welding process and a processing unit which analyse the frames giving an indication of the quality expected. The categorisation is achieved by employing artificial neural networks and correlating the weld pool appearance with the resulting quality. Six categories denote the resulting quality of a weld for stainless steel and aluminium. The models use images to learn the correlation between the aspect of the weld pool and the surrounding area and the state of the weld as denoted by the six categories, similar to a welder categorisation. Therefore the models learn the probability distribution of images’ aspect over the categories considered

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

Multistage adaptive noise cancellation and multi-dimensional signal processing for ultrasonic nondestructive evaluation

Ultrasonic signal processing presents several challenges with respect to both noise removal and interpretation. The interference of unwanted reflections from material grain structure can render the data extremely noisy and mask the detection of small flaws. It is therefore imperative to separate the flaw reflections from grain noise. The interpretation or classification of ultrasonic signals in general is relatively difficult due to the complexity of the physical process and similarity of signals from various classes of reflectors;Adaptive noise cancellation techniques are ideally suited for reducing spatially varying noise due to the grain structure of material in ultrasonic nondestructive evaluation. In this research, a multi-stage adaptive noise cancellation (MANC) scheme is proposed for reducing spatially varying grain noise and enhancing flaw detection in ultrasonic signals. The overall scheme is based on the use of an adaptive least mean square error (LMSE) filter with primary and reference signals derived from two adjacent positions of the transducers. Since grain noise is generally uncorrelated, in contrast to the correlated flaw echoes, adaptive filtering algorithms exploit the correlation properties of signals in a C-scan image to enhance the signal-to-noise ratio (SNR) of the output signal;A neural network-based signal classification system is proposed for the interpretation of ultrasonic signals obtained from inspection of welds, where signals have to be classified as resulting from porosity, slag, lack of fusion, or cracks in the weld region. Standard techniques rely on differences in individual A-scans to classify the signals. This thesis investigates the need for investigating signal features that incorporate the effects of beam spread and echo dynamics. Such effects call for data interpretation schemes that include a neighborhood of A-scans carrying information about a reflector. Several ultrasonic signal features based on the information in a two-dimensional array of ultrasonic waveforms, ranging from the estimation of statistical characteristics of signals to two and three-dimensional transform-based methods, are evaluated. A two-dimensional scan of ultrasonic testing is also represented in the form of images (B- and B\u27-scans). Multidimensional signal and image-processing algorithms are used to analyze the images. Two and three-dimensional Fourier transforms are applied to ultrasonic data that are inherently three-dimensional in nature (2 spatial and 1 time). A variety of transform-based features are then utilized for obtaining the final classification

Detection of crack-like indications in digital radiography by global optimisation of a probabilistic estimation function

A new algorithm for detection of longitudinal crack-like indications in radiographic images is developed in this work. Conventional local detection techniques give unsatisfactory results for this task due to the low signal to noise ratio (SNR ~ 1) of crack-like indications in radiographic images. The usage of global features of crack-like indications provides the necessary noise resistance, but this is connected with prohibitive computational complexities of detection and difficulties in a formal description of the indication shape. Conventionally, the excessive computational complexity of the solution is reduced by usage of heuristics. The heuristics to be used, are selected on a trial and error basis, are problem dependent and do not guarantee the optimal solution. Not following this way is a distinctive feature of the algorithm developed here. Instead, a global characteristic of crack-like indication (the estimation function) is used, whose maximum in the space of all possible positions, lengths and shapes can be found exactly, i.e. without any heuristics. The proposed estimation function is defined as a sum of a posteriori information gains about hypothesis of indication presence in each point along the whole hypothetical indication. The gain in the information about hypothesis of indication presence results from the analysis of the underlying image in the local area. Such an estimation function is theoretically justified and exhibits a desirable behaviour on changing signals. The developed algorithm is implemented in the C++ programming language and testet on synthetic as well as on real images. It delivers good results (high correct detection rate by given false alarm rate) which are comparable to the performance of trained human inspectors.In dieser Arbeit wurde ein neuer Algorithmus zur Detektion rissartiger Anzeigen in der digitalen Radiographie entwickelt. Klassische lokale Detektionsmethoden versagen wegen des geringen Signal-Rausch-Verhältnisses (von ca. 1) der Rissanzeigen in den Radiographien. Die notwendige Resistenz gegen Rauschen wird durch die Benutzung von globalen Merkmalen dieser Anzeigen erzielt. Das ist aber mit einem undurchführbaren Rechenaufwand sowie Problemen bei der formalen Beschreibung der Rissform verbunden. Üblicherweise wird ein übermäßiger Rechenaufwand bei der Lösung vergleichbarer Probleme durch Anwendung von Heuristisken reduziert. Dazu benuzte Heuristiken werden mit der Versuchs-und-Irrtums-Methode ermittelt, sind stark problemangepasst und können die optimale Lösung nicht garantieren. Das Besondere dieser Arbeit ist anderer Lösungsansatz, der jegliche Heuristik bei der Suche nach Rissanzeigen vermeidet. Ein globales wahrscheinlichkeitstheoretisches Merkmal, hier Schätzfunktion genannt, wird konstruiert, dessen Maximum unter allen möglichen Formen, Längen und Positionen der Rissanzeige exakt (d.h. ohne Einsatz jeglicher Heuristik) gefunden werden kann. Diese Schätzfunktion wird als die Summe des a posteriori Informationsgewinns bezüglich des Vorhandenseins eines Risses im jeden Punkt entlang der hypothetischen Rissanzeige definiert. Der Informationsgewinn entsteht durch die Überprüfung der Hypothese der Rissanwesenheit anhand der vorhandenen Bildinformation. Eine so definierte Schätzfunktion ist theoretisch gerechtfertigt und besitzt die gewünschten Eigenschaften bei wechselnder Anzeigenintensität. Der Algorithmus wurde in der Programmiersprache C++ implementiert. Seine Detektionseigenschaften wurden sowohl mit simulierten als auch mit realen Bildern untersucht. Der Algorithmus liefert gute Ergenbise (hohe Detektionsrate bei einer vorgegebenen Fehlalarmrate), die jeweils vergleichbar mit den Ergebnissen trainierter menschlicher Auswerter sind