DWT Based Automated Weld Pool Detection and Defect Characterisation from Weld Radiographs

Industrial Radiography is the most widely accepted NDT technique for weld quality in industries. As it is an indirect method, defect type and nature must be obtained by analyzing the radiographs. Manual interpretation of radiographs is subjective in nature. So the paradigm shifted to automated weld defect detection system. Though considerable research is done in automated weld defect detection, an accurate domain specific technique has not yet been evolved due to noise, artifacts in radiographs, low contrast between the defect region and the background and difficulty in isolating the defect. The proposed work aims at developing an automated weld defect detection system that enhances the contrast between the object and the background and isolates the weld defect. In this work, real time weld radiographs are acquired and contrast enhancement is performed with DWT. Slag and Porosity are isolated and dimensionally characterized.

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Automatic Defect Detection in Spring Clamp Production via Machine Vision

Abstract and Applied Analysis ◽

10.1155/2014/164726 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Xia Zhu ◽

Renwen Chen ◽

Yulin Zhang

Keyword(s):

Neural Network ◽

Computer Vision ◽

Machine Vision ◽

Defect Detection ◽

Detection System ◽

Reliability And Validity ◽

Laser Sensor ◽

Defect Region ◽

Gigabit Ethernet ◽

Increasing Demand

There is an increasing demand for automatic online detection system and computer vision plays a prominent role in this growing field. In this paper, the automatic real-time detection system of the clamps based on machine vision is designed. It hardware is composed of a specific light source, a laser sensor, an industrial camera, a computer, and a rejecting mechanism. The camera starts to capture an image of the clamp once triggered by the laser sensor. The image is then sent to the computer for defective judgment and location through gigabit Ethernet (GigE), after which the result will be sent to rejecting mechanism through RS485 and the unqualified ones will be removed. Experiments on real-world images demonstrate that the pulse coupled neural network can extract the defect region and judge defect. It can recognize any defect greater than 10 pixels under the speed of 2.8 clamps per second. Segmentations of various clamp images are implemented with the proposed approach and the experimental results demonstrate its reliability and validity.

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Weld Inspection Based on Radiography Image Segmentation with Level Set Active Contour Guided Off-Center Saliency Map

Advances in Materials Science and Engineering ◽

10.1155/2015/871602 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Mohamed Ben Gharsallah ◽

Ezzeddine Ben Braiek

Keyword(s):

Level Set ◽

Defect Detection ◽

Active Contour ◽

Salient Feature ◽

Saliency Map ◽

Radiographic Images ◽

Low Contrast ◽

Small Defect ◽

Weld Defect ◽

Image Pixels

Radiography is one of the most used techniques in weld defect inspection. Weld defect detection becomes a complex task when uneven illumination and low contrast characterize radiographic images. In this paper we propose a new active contour based level set method for weld defect detection in radiography images. An off-center saliency map exploited as a feature to represent image pixels is embedded into a region energy minimization function to guide the level set active contour to defects boundaries. The aim behind using salient feature is that a small defect can frequently attract attention of human eyes which permits enhancing defects in low contrasted image. Experiment results on different weld radiographic images with various kinds of defects show robustness and good performance of the proposed approach comparing with other segmentation methods.

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An Independent Component Analysis Based Defect Detection for the OLED Display

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.605-607.724 ◽

2012 ◽

Vol 605-607 ◽

pp. 724-728 ◽

Cited By ~ 1

Author(s):

Zhi Liang Wang ◽

Jian Gao ◽

Chuan Xia Jian ◽

Yao Cong Liang ◽

Yu Cen

Keyword(s):

Independent Component Analysis ◽

Defect Detection ◽

Detection System ◽

Component Analysis ◽

Rapid Identification ◽

Independent Component ◽

Display Device ◽

Low Contrast ◽

Light Emitting ◽

Mixing Matrix

Organic Light Emitting Displays (OLED) is a new type of display device which has become increasingly attractive and popular. Due to the complex manufacturing process, various defects may exist on the OLED panel. These defects have the characteristics of fuzzy boundaries, irregular in shape, low contrast with background and they are mixed with the texture background increasing the difficulty of a rapid identification. In this paper, we proposed an approach to detect these defects based on the model of independent component analysis (ICA). The ICA model is applied to a perfect OLED image to determine the de-mixing matrix and its corresponding independent components (ICs). Through the choice of a proper ICi row vector, the new de-mixing matrix is generated which contains only uniform information and is used to reconstruct the OLED background image. The defect result can be obtained by the subtracting operation between the reconstructed background and the source images. The detection system is implemented in the Labview and the testing results show that the ICA based OLED defect detection method is feasible and effective.

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A novel extraction method for weld defect segmentation seeds using ANDM and clustering

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2019.61.12.706 ◽

2019 ◽

Vol 61 (12) ◽

pp. 706-713

Author(s):

Changying Dang ◽

Jiansu Li ◽

Wenhua Du ◽

Zhiqiang Zeng ◽

Rijun Wang

Keyword(s):

Defect Detection ◽

Extraction Method ◽

Detection Method ◽

High Accuracy ◽

Experimental Results ◽

Clustering Method ◽

Defect Region ◽

Radiographic Testing ◽

Weld Defect

To improve the accuracy and reliability in extracting defect segmentation seeds from a weld radiographic testing (RT) image, a novel extraction method (NESS) using clustering and a novel defect detection method (ANDM) that was presented in a previous paper by one of the authors is proposed in this paper. In the proposed NESS, firstly each column of the weld RT image is accurately analysed by ANDM to judge whether or not it really passes through weld defect regions. Most importantly, one or more defect seeds can be acquired if it passes through a defect region. Secondly, all the defect seeds (a defect seed group) of the RT image are extracted by analysing the entire image. Finally, a sorting-based clustering method is proposed to quickly and accurately search for defect segmentation seeds among all the defect seeds, which can solve the problems concerning the difficulty in determining defect segmentation seeds and the heavy calculational burden of defect segmentation. In order to evaluate the performance of the proposed NESS, some clustering and segmentation experiments have been performed. The experimental results reveal that the proposed NESS achieves high accuracy and reliability in extracting defect segmentation seeds from RT images and is helpful in defect segmentation.

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