Automatic detection of welding defects using the convolutional neural network

Quality control of welded joints is an important step before commissioning of various types of metal structures. The main obstacles to the commissioning of such facilities are the areas where the welded joint deviates from acceptable defective standards. The defects of welded joints include non-welded, foreign inclusions, cracks, pores, etc. The article describes an approach to the detection of the main types of defects of welded joints using a combination of convolutional neural networks and support vector machine methods. Convolutional neural networks are used for primary classification. The support vector machine is used to accurately define defect boundaries. As a preprocessing in our work, we use the methods of morphological filtration. A series of experiments confirms the high efficiency of the proposed method in comparison with pure CNN method for detecting defects

Nondestructive techniques for characterizing mechanical properties of structural materials: An overview

An overview of nondestructive evaluation (NDE) is presented to indicate the availability and application potentials of techniques for quantitative characterization of the mechanical properties of structural materials. The purpose is to review NDE techniques that go beyond the usual emphasis on flaw detection and characterization. Discussed are current and emerging NDE techniques that can verify and monitor entrinsic properties (e.g., tensile, shear, and yield strengths; fracture toughness, hardness, ductility; elastic moduli) and underlying microstructural and morphological factors. Most of the techniques described are, at present, neither widely applied nor widely accepted in commerce and industry because they are still emerging from the laboratory. The limitations of the techniques may be overcome by advances in applications research and instrumentation technology and perhaps by accommodations for their use in the design of structural parts

Remote surface inspection system

This paper reports on an on-going research and development effort in remote surface inspection of space platforms such as the Space Station Freedom (SSF). It describes the space environment and identifies the types of damage for which to search. This paper provides an overview of the Remote Surface Inspection System that was developed to conduct proof-of-concept demonstrations and to perform experiments in a laboratory environment. Specifically, the paper describes three technology areas: (1) manipulator control for sensor placement; (2) automated non-contact inspection to detect and classify flaws; and (3) an operator interface to command the system interactively and receive raw or processed sensor data. Initial findings for the automated and human visual inspection tests are reported

Diagnosis of Bearing Fault Using Morphological Features Extraction and Entropy Deconvolution Method

It is observed that the bearing failure of rotating machinery is a pulse in the vibration signal, but it is mostly immersed in noise. In order to effectively eliminate this noise and detect pulses, a novel an image fusion technology based on morphological operators inference is proposed. The correctness of morphological operators lies in the correct selection of structural elements (SE). This report presents an effective algorithm for SE selection based on kurtosis, which makes the analysis free empirical method. When analyzing three different groups of faults, the results show that this method effectively and robustly generates impulse. It enables the algorithm to detect early faults too. Recently, minimum entropy deconvolution (MED) was introduced to the machine in the field of condition monitoring, to enhance the detection of rolling bearing and gear failures. MED analysis helps to extract these pulses and diagnose their source, namely defects bearing components. In this research, MED will be reviewed and reintroduced, Application in fault detection and diagnosis of rolling bearings. MED parameters are selected and its combination with pre-whitening. Test cases are presented to illustrate benefits of MED technology. The simulation has been done on MATLAB and a graphical user interface has been created for analysis of bearing and detection of bearing faults using morphological features

Signal processing and image restoration techniques for two-dimensional eddy current nondestructive evaluation

This dissertation presents a comprehensive study on the forward modeling methods, signal processing techniques, and image restoration techniques for two-dimensional eddy current nondestructive evaluation. The basic physical forward method adopted in this study is the volume integral method. We have applied this model to the eddy current modeling problem for half space geometry and thin plate geometry. To reduce the computational complexity of the volume integral method, we have developed a wavelet expansion method which utilizes the multiresolution compression capability of the wavelet basis to greatly reduce the amount of computation with small loss in accuracy. To further improve the speed of forward modeling, we have developed a fast eddy current model based on a radial basis function neural network. This dissertation also contains investigations on signal processing techniques to enhance flaw signals in two-dimensional eddy current inspection data. The processing procedures developed in this study include a set of preprocessing techniques, a background removal technique based on principal component analysis, and grayscale morphological operations to detect flaw signals. Another important part of the dissertation concerns image restoration techniques which can remove the blurring in impedance change images due to the diffusive nature of the eddy current testing. We have developed two approximate linear image restoration methods--the Wiener filtering method and the maximum entropy method. Both linear restoration methods are based on an approximate linear forward model formulated by using the Born approximation. To improve the quality of restoration, we have also developed nonlinear image restoration methods based on simulated annealing and a genetic algorithm. Those nonlinear methods are based on the neural network forward model which is more accurate than the approximate linear forward model

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

Visual Inspection Algorithms for Printed Circuit Board Patterns A SURVEY

The importance of the inspection process has been magnified by the requirements of the modern manufacturing environment. In electronics mass-production manufacturing facilities, an attempt is often made to achieve 100 % quality assurance of all parts, subassemblies, and finished goods. A variety of approaches for automated visual inspection of printed circuits have been reported over the last two decades. In this survey, algorithms and techniques for the automated inspection of printed circuit boards are examined. A classification tree for these algorithms is presented and the algorithms are grouped according to this classification. This survey concentrates mainly on image analysis and fault detection strategies, these also include the state-of-the-art techniques. Finally, limitations of current inspection systems are summarized

Application of mathematical morphology to the analysis of X-ray NDE images

Ever since the beginning, man has been in the relentless pursuit of perfection. From stone age to space age, from caves to condominiums, from carts to planes, trains and automobiles, his drive for consummation has grown considerably. The high quality products that are available in the market at the turn of the twenty first century are living legacies of his unyielding endeavor for excellence. But one fact that most people do not realize is the amount of time and money devoted to quality control and non- destructive evaluation (NDE) that is responsible for the high quality of products. In the past, people used to tap earthenware and other materials as a means of non destructive testing for defects in the material. They could sense the defects by the nature of the sound propagated through the material. The ultrasonic method of NDE is an extension of this principle

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