An enhanced method for the identification of ferritic morphologies in welded fusion zones based on gray-level co-occurrence matrix: A computational intelligence approach (original) (raw)
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Automatic Classification of Welding Defects Using Neural Network and Image Processing Techniques
Albaha University Journal of Basic and Applied Sciences, 2017
Technological development accompanied the need to get a high-quality welding. In this research, an automatic technique is introduced to detect, recognize and classify welding defects in radiographic (x-ray) images, using texture features. Image processing techniques, including converting color images to grayscale, filtering and resizing images were applied to help in the image array of weld images and welding defect detection. Therefore, a proposed program was built in-house to automatically classify and recognize the most common types of welding defects met in practice. The introduced technique has been tested on eleven welding defects which are: center line crack, cap undercut, elongated slag lines, lack of interpass fusion, lack of root penetration, lack of side wall fusion, misalignment, root crack, root pass aligned, root undercut, and transverse crack (n = 35 for each). The overall average discrimination rate is about 94.29%. The introduced technique can find promising application of digital image processing technique to the field of welding defect inspection compared with traditional methods.
Bulletin of the Faculty of Engineering. Mansoura University
Technological development accompanied the need to get a high-quality welding. The important industries such as oil and auto industries and other important industries need to rely on reliable welding operations; collapse as a result of this welding may mean a great loss in lives and money. This paper aimed to produce an automatic system to detect, recognize and classify welding cases (defects and no defects) in radiography images was described depending upon image histogram technique. Two main steps to do that, In the first step, image processing techniques, including converting color images to gray scale, filtering image, and resizing were implemented to help in the image array of weld images and the detection of weld defects. The second step, a proposed program was build in-house depending upon Matlab to classify and recognize automatically six types of weld defects met in practice, it is Porosity-Undercut-Lac of fusion-Crack-Slag-Cavity, plus the non-defect type. It was clear from the results that it can rely on this method significantly, reaching rates as well as the appointment of defects and no defects to about 94.3%.
Classification of Welding Defects Using Gray Level Histogram Techniques via Neural Network
Technological development accompanied the need to get a high-quality welding. The important industries such as oil and auto industries and other important industries need to rely on reliable welding operations; collapse as a result of this welding may mean a great loss in lives and money. This paper aimed to produce an automatic system to detect, recognize and classify welding cases (defects and no defects) in radiography images was described depending upon image histogram technique. Two main steps to do that, In the first step, image processing techniques, including converting color images to gray scale, filtering image, and resizing were implemented to help in the image array of weld images and the detection of weld defects. The second step, a proposed program was build in-house depending upon Matlab to classify and recognize automatically six types of weld defects met in practice, it is Porosity – Undercut – Lac of fusion – Crack – Slag –Cavity, plus the non-defect type. It was c...
Automatic identification of different types of welding defects in radiographic images
Ndt & E International, 2002
Radiographic testing is a well-established non-destructive testing method to detect subsurface welding defects. In this paper, an automatic computer-aided identification system was implemented to recognize different types of welding defects in radiographic images. Imageprocessing techniques such as background subtraction and histogram thresholding were implemented to separate defects from the background. Twelve numeric features were extracted to represent each defect instance. The extracted feature values are subsequently used to classify welding flaws into different types by using two well-known classifiers: fuzzy k-nearest neighbor and multi-layer perceptron neural networks classifiers. Their performances are tested and compared using the bootstrap method. q
An Automatic Welding Defects Classifier System
Proceedings of the Third International Conference on Computer Vision Theory and Applications, 2008
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.
Metals
Automatic identification of metallurgical phases based on thresholding methods in microstructural images may not be possible when the pixel intensities associated with the metallurgical phases overlap and, hence, are indistinguishable. To circumvent this problem, additional visual information about the metallurgical phases, referred to as textural features, are considered in this study. Mathematically, textural features are the second order statistics of an image domain and can be distinct for each metallurgical phase. Textural features are evaluated from the gray level co-occurrence matrix (GLCM) of each metallurgical phase (ferrite, pearlite, and martensite) present in heat-treated ASTM A36 steels in this study. The dataset of textural features and pixel intensities generated for the metallurgical phases is used to train supervised machine learning classifiers, which are subsequently employed to predict the metallurgical phases in the microstructure. Naïve Bayes (NB), k-nearest ne...
2005
The interpretation of possible weld discontinuities in industrial radiography is ensured by human interpreters. Consequently, it is submitted to subjective considerations such as the aptitude and the experiment of the interpreter, in addition of the poor quality of radiographic images, due essentially to the exposure conditions. These considerations make the weld quality interpretation inconsistent, labor intensive and sometimes biased. It is thus desirable to develop computer-aided techniques to assist the interpreter in evaluating the quality of the welded joints. For the characterization of the weld defect region, looking for features which are invariant regarding the usual geometric transformations proves to be necessary because the same defect can be seen from several angles according to the orientation and the distance from the welded framework to the radiation source. Thus, a set of invariant geometrical attributes which characterize the defect shape is proposed. The principal component analysis technique is used in order to reduce the number of attribute variables in the aim to give better performance for defect classification. Thereafter, an artificial neural network for weld defect classification was used. The proposed classification consists in assigning the principal types of weld defects to four categories according to the morphological characteristics of the defects usually met in practice.
Welding is an excellent attachment or repair method. The advanced industries such as oil, automotive industries, and other important industries need to rely on reliable welding operations; collapse because of this welding may lead to an excessive cost in money and risk in human life. In the present research, an automatic system has been described to detect, recognize and classify welding defects in radiographic images. Such system uses a texture feature and neural network techniques. Image processing techniques were implemented to help in the image array of weld images and the detection of weld defects. Therefore, a proposed program was build in-house to automatically classify and recognize eleven types of welding defects met in practice. The proposed system is tested and verified on eleven welding defects as follows; center line crack, elongated slag lines, cap undercut, lack of interpass fusion, lack of side wall fusion, lack of root penetration, misalignment, root undercut, root crack, root pass aligned, and transverse crack. It was found that only two welding defects are failed in a total 3 from 308 images in the overall recognition. The lowest classification percent was found in case of lack of side wall fusion defect (92.9%). The overall average discrimination rate results from a combined technique of texture feature and neural network are about 99%.