Hybrid Shape Descriptors for an Improved Weld Defect Retrieval in Radiographic Testing

2015 ◽  
pp. 127-135
Author(s):  
Nafaa Nacereddine ◽  
Djemel Ziou
Keyword(s):  
Shape Descriptors ◽  
Radiographic Testing ◽  
Weld Defect

Cracking of Heavy Wall Steam Separators During Fabrication: An Investigation

2012 ◽  
Author(s):  
Dilip Kumar ◽  
Luis Ganhao
Keyword(s):  
Cross Section ◽  
High Hardness ◽  
Radiographic Testing ◽  
Weld Defect ◽  
Section Viii ◽  
Carbon Arc ◽  
Recent Project ◽  
Weld Area ◽  
Non Destructive

On a recent project, four high pressure steam separator vessels were received from overseas after fabrication. There was suspicion on the quality of fabrication when non destructive examination (NDE) reports were reviewed. There were major concerns with the quality of radiographic films as they did not meet the ASMe Section VIII Div. 1 Code requirements as well as client specifications. Subsequent examination of welds using radiographic testing (RT) revealed crack-like features around nozzles in the region adjoining (but outside) the weld metal. Macro etching at the surface around nozzles showed that the weld area was extended beyond the apparent weld/base metal interface. Further examination of a cross section cut out from one vessel nozzle confirmed the initial doubts that weld repairs had been performed that were not reported. Metallography of the cross section indicated evidence of significant cracking associated with carbon contamination and very high hardness (up to 365 HV; in one particular case 609 HV) in affected areas. This was believed to be due to improper and incomplete cleaning by grinding after performing carbon arc or, flame gouging to remove a weld defect. Further detailed NDE was carried out using advanced ultrasonic testing (UT), i.e. phased array UT and time of flight diffraction (TOFD) and all defects (many new that were undetected by RT) were repaired per ASME Section VIII Div. 1 Code and client specification. This experience was a lesson for the design office and helped make a decision to be much more vigilant and to ask for greater quality surveillance on overseas fabrication of critical equipment for all future projects. The paper discusses the detailed investigation as well as findings.

Classification of weld defect based on information fusion technology for radiographic testing system

2016 ◽  
Vol 87 (3) ◽  
pp. 035110 ◽  
Author(s):  
Hongquan Jiang ◽  
Zeming Liang ◽  
Jianmin Gao ◽  
Changying Dang
Keyword(s):  
Information Fusion ◽  
Testing System ◽  
Fusion Technology ◽  
Radiographic Testing ◽  
Weld Defect

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

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.

Improving the robustness of DI and PVI further using fast guided filter on radiographic images

2021 ◽  
Vol 63 (7) ◽  
pp. 409-415
Author(s):  
Changying Dang ◽  
Jiansu Li ◽  
Zhiqiang Zeng ◽  
Wenhua Du ◽  
Rijun Wang
Keyword(s):  
Uniform Distribution ◽  
Guided Filter ◽  
Defect Index ◽  
Radiographic Images ◽  
Irregular Surfaces ◽  
Radiographic Testing ◽  
Desirable Outcome ◽  
Calculational Method ◽  
Weld Defect ◽  
Improvement Method

To further improve the robustness of the weld defect index (DI) and peak-valley index (PVI), which are key indices for detecting weld defects in radiographic testing (RT) images accurately and reliably, a robust improvement method is proposed, in which a fast guided filter (Fast-GF) is introduced and its effect on the DI and PVI is analysed. In this paper, the principle of the proposed robust improvement method, the related theory of Fast-GF, the definition and the calculational method of the DI and PVI are systematically analysed. Taking some practical RT images from industrial welding as an example, smoothing experiments with different filters and comparative computational experiments for the DI and PVI both with and without Fast-GF are carried out. The experimental results show that the robustness of the DI and PVI is further improved by the proposed robust improvement method, which is a desirable outcome. More specifically, the values of the DI and PVI are computed accurately and reliably regardless of some non-uniform distribution of grey levels, noise, irregular surfaces and artefacts in the RT images.

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