Designing of Welding Defect Samples for Data Mining in Defect Detection and Classification using 3D Geometric Scanners (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.
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2017 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2017
Visual inspection process for weld defects still manually operated by human vision, so the result of the test still highly subjective. In this research, the visual inspection process will be done through image processing on the image sequence to make data accuracy more better. CNN as one of the image processing technique can determine the feature automatically which is suitable for this problem in order to classify the variation of each weld defect pattern. Classification using Convolution Neural Network (CNN) consist of two stages: extraction image using image convolution and image classification using neural network. Gaussian kernel used for blurring image, it helps the extraction of images without losing the main information from the original image, this filter also minimize the occurrence of interference or noise. Results of the classification used to get the category of weld defects with high accuracy as a variable of a weld inspection process whether the weld is pass the standard or not. The proposed system has obtained classification with validation accuracy of 95.83% for four different type of welding defect. The data input of this research is the result of images captured by a webcam.
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.
Artificial intelligence based defect classification for weld joints
IOP Conference Series: Materials Science and Engineering, 2018
This paper mainly deals with the development of a defect classification system that uses Artificial Neural Network (ANN) to classify weld defects based on ultrasonic test data. The system enables real-time identification of weld defects which finds application in testing of critical welding applications and also reduces dependency on skilled workforce for the function. The study mainly consists of three parts-(i) Weld defect detection using Ultrasonic Testing (UT) (ii) Implementation of ANN (iii) Defect classification. An ultrasonic test performed on welded samples shows different results for welds with and without defects and further between defects as well. The ultrasonic test data is fed into the ANN algorithm to train it to identify the various weld defects. An Artificial Neural Network (ANN) is an information processing paradigm that uses a large number of highly interconnected processing elements called neurons, working in unison to solve the specific problems. There are two types of neural network architectures that are used for classification-a back propagation network (BPN) and a probabilistic neural network (PNN). Back propagation network has been used for the purpose of this study. In order to test the performance of the back propagation neural network, four classes of defect namely porosity, lack of side wall fusion, lack of penetration and slag inclusion are considered.
Welding Defect Detection with Deep Learning Architectures
Welding Principles and Application [Working Title], 2022
Welding automation is a fundamental process in manufacturing industries. Production lines integrate welding quality controls to reduce wastes and optimize the production chain. Early detection is fundamental as defects at any stage could determine the rejection of the entire product. In the last years, following the industry 4.0 paradigm, industrial automation lines have seen the introduction of modern technologies. Although the majority of the inspection systems still rely on traditional sensing and data processing, especially in the computer vision domain, some initiatives have been taken toward the employment of machine learning architectures. This chapter introduces deep neural networks in the context of welding defect detection, starting by analyzing common problems in the industrial applications of such technologies and discussing possible solutions in the specific case of quality checks in fuel injectors welding during the production stage.
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The non-destructive testing methods offer great benefit in detecting and classifying the weld defects. Among these, infrared (IR) thermography stands out in the inspection, characterization, and analysis of the defects from the camera image sequences, particularly with the recent advent of deep learning. However, in IR, the defect classification becomes a cumbersome task because of the exposure to the inconsistent and unbalanced heat source, which requires additional supervision. In light of this, authors present a fully automated system capable of detecting defective welds according to the electrical resistance properties in the inline mode. The welding process is captured by an IR camera that generates a video sequence. A set of features extracted by such video feeds supervised machine learning and deep learning algorithms in order to build an industrial diagnostic framework for weld defect detection. The experimental study validates the aptitude of a customized convolutional neur...
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Welding Defect Detection and Classification Using Geometric Features
— In this paper we present a welding defect detection system using radiographic images. Main goal is to craft a dependable system because a human evaluator is not a stable evaluator besides other humanoid constraints. We present a novel technique for the detection and classification of weld defects by means of geometric features. Firstly noise reduction is done as radiographic images contain noise due to several effects. After this we tend to localize defects with maximum interclass variance and minimum intra class variance. Further we move towards extracting features describing the shape of localized objects in segmented images. Using these shape descriptors (geometric features) we classify the defects by Artificial Neural Network.
Weld Defect Detection Based on Deep Learning Method
Welding is an important joining technology but the defects in welds wreck the quality of the product evidently. Due to the variety of weld defects' characteristics, weld defect detection is a complex task in industry. In this paper, we try to explore a possible solution for weld defect detection and a novel image-based approach is proposed using small X-ray image data sets. An image-processing based data augmentation approach and a WGAN based data augmentation approach are applied to deal with imbalanced image sets. Then we train two deep convolutional neural networks (CNNs) on the augmented image sets using feature-extraction based transfer learning techniques. The two trained CNNs are combined to classify defects through a multi-model ensemble framework, aiming at lower false detection rate. Both of the experiments on augmented images and real world defect images achieve satisfying accuracy, which substantiates the possibility that the proposed approach is promising for weld defect detection.
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Laser welding is a key technology for many industrial applications. However, its online quality monitoring is an open issue due to the highly complex nature of the process. this work aims at enriching existing approaches in this field. We propose a method for real-time detection of process instabilities that can lead to defects. Hard X-ray radiography is used for the ground truth observations of the sub-surface events that are critical for the quality. A deep artificial neural network is applied to reveal the unique signatures of those events in wavelet spectrograms from the laser back-reflection and acoustic emission signals. The autonomous classification of the revealed signatures is tested on reallife data, while the real-time performance is reached by means of parallel computing. The confidence of the quality classification ranges between 71% and 99%, with a temporal resolution down to 2 ms and a computation time per classification task as low as 2 ms. This approach is a new paradigm in the digitization of industrial processes and can be exploited to provide feedbacks in a closed-loop quality control system.