Extraction of weld defect from radiographic images using the level set segmentation without re-initialization

Ramou, N.; Halimi, M.

doi:10.1109/iccat.2013.6521998

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…al. [18] evaluates the radiographic images for the purpose of on line detects weld defects in the welding products. The image segmentation method are capture the weld process images to detects weld defects, in higher order accuracy of weld in the temporal discretization using Total Variation Diminishing (TVD) Runge Kutta (RK) methods.…”

Section: Existing Research Effortsmentioning

confidence: 99%

A Review on Analysis, Monitoring and Detection of Weld Defect Products

Kumar¹,

Somkuva²

2015

IJERT

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Weld defects detection method is generally evaluated on the basis of weld defects, weld disturbances, welding parameters and welding process monitoring system. The importance of weld defects detection is to identify the weld defects and weld disturbance in the welding process. The weld defect detection methods or devices to identify and monitoring the weld defects like internal and external cracks, lack of fusion, lack of penetration, porosity, blow holes, slag inclusion, lamellar tear and deformation etc. There are many approach or techniques to detect the weld defects basically DPT, MPI, RT, UT and VI for various materials. Reviews of various approaches compare relative advantages main, features and limitations of welding process. This paper analyzes the development done in the area of defect detection methods and monitoring systems in welding process.

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Section: Existing Research Effortsmentioning

confidence: 99%

A Review on Analysis, Monitoring and Detection of Weld Defect Products

Kumar¹,

Somkuva²

2015

IJERT

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“…Non-destructive testing (NDT) reduces human labor effort and increases the efficiency of weld fault detection and identifies different types of welding defects. N.Ramou and Al [137] evaluate x-ray images for line detection of weld defects welding. The image segmentation method is to capture images of the welding process for detecting welding defects in higher welding order accuracy in temporal discretization using Runge Kutta for Total Decrease of Variation (RST) (RK) methods.…”

Section: Chapter 3 Radiographic Image Analysis Methodologymentioning

confidence: 99%

Clasificación de defectos de soldadura: Un método basado en aprendizaje profundo para visión por computadora

Ajmi¹

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Welding techniques are frequently used in modern industrial facilities. However, the quality of weld is far from perfect. The weld produced may have different types of defects, hence the use of non-destructive testing (NDT) methods is ubiquitous to examine weld quality without causing any alteration in the weld structure. One of the most popular NDT methods used is radiography. Traditionally, the inspection of radiographic images is carried out by visual inspection, which leads to more time and labor-intensive process, as well as being prone to errors. The introduction of computer vision and machine learning techniques allow the interpretation of radiograms to be automated that makes the inspection of radiograms more reliable, reproducible and faster. In this doctoral thesis, new techniques for segmentation, detection and classification of welding defects are proposed to enable automated weld quality assessment. The new technology developed will contribute to both real time and offline automated analysis of weld quality, enabling better interpretation and a rapid correction of the defects. Therefore, a fundamental step to evaluate the quality of the weld is to identify perfectly the geometry of the welding defects.There are different stages for weld defects analysis. Among them, the image segmentation stage which is one of the most significant problems in the pattern recognition process due to the structural complexity of the radiographic images and the often insufficient contrast to extract the region of interest without any a priori knowledge of its shape and location. Given the influence of segmentation in the detection of defects, a section in this thesis is devoted for enhancing the performance of segmentation in the case of weld defect radigraphic images specifically for defects of porosity and lack of penetration, which are the subject of this investigation. This section is followed by the defects detection step which allows determination and interpretation of defect's shape. The final section focuses on determining effective models basing on deep learning technology making it possible to characterize defects so that they become easily identifiable class elements.

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