scholarly journals Fast γ Photon Imaging for Inner Surface Defects Detecting

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8134
Author(s):  
Min Yao ◽  
Guangdong Luo ◽  
Min Zhao ◽  
Ruipeng Guo ◽  
Jian Liu
Keyword(s):  
Maximum Likelihood ◽  
Surface Defects ◽  
Internal State ◽  
Structural Characteristics ◽  
Expectation Maximization Algorithm ◽  
Emission Computed Tomography ◽  
Detection Accuracy ◽  
Back Projection ◽  
Reconstruction Algorithms ◽  
Inner Surface

Only a few effective methods can detect internal defects and monitor the internal state of complex structural parts. On the basis of the principle of PET (positron emission computed tomography), a new measurement method, using γ photon to detect defects of an inner surface, is proposed. This method has the characteristics of strong penetration, anti-corrosion and anti-interference. With the aim of improving detection accuracy and imaging speed, this study also proposes image reconstruction algorithms, combining the classic FBP (filtered back projection) with MLEM (maximum likelihood expectation Maximization) algorithm. The proposed scheme can reduce the number of iterations required, when imaging, to achieve the same image quality. According to the operational demands of FPGAs (field-programmable gate array), a BPML (back projection maximum likelihood) algorithm is adapted to the structural characteristics of an FPGA, which makes it feasible to test the proposed algorithms therein. Furthermore, edge detection and defect recognition are conducted after reconstructing the inner image. The effectiveness and superiority of the algorithm are verified, and the performance of the FPGA is evaluated by the experiments.

Influence of Inner Surface Defects on the Fatigue Strength of Pipe Subjected to Cyclic Internal Pressure

1978 ◽  
Vol 100 (4) ◽  
pp. 360-368
Author(s):  
Y. Yazaki ◽  
S. Hashirizaki ◽  
S. Nishida ◽  
C. Urashima
Keyword(s):  
Fatigue Strength ◽  
Internal Pressure ◽  
Test Specimen ◽  
Surface Defects ◽  
Fatigue Crack Initiation ◽  
Internal Surface ◽  
Fatigue Tests ◽  
Inner Surface ◽  
Surface Notch ◽  
Medium Diameter

Cyclic internal oil pressure fatigue tests were carried out on medium-diameter ERW pipes of API 5LX - X60 in an attempt to determine the influence of surface defects on the fatigue strength. Experimental factors investigated were the depth and location of internal surface notch in relation to the axis of pipe. The specimen was subjected to cyclic internal pressure, the cyclic rate being 0.3–0.5 Hz. During the test, Acoustic Emission (AE) techniques were applied to detect the fatigue crack initiation. Along with the aforementioned fatigue tests, pulsating tension fatigue tests were carried out on specimens with the same surface notches as the cyclic internal pressure fatigue test specimen.

Influence of Geometrical Features of Material Defects on the Identification Level by the Eddy Current Method

2015 ◽  
Vol 237 ◽  
pp. 136-141
Author(s):  
Wojciech Jóźwik ◽  
Tomasz Samborski
Keyword(s):  
Eddy Current ◽  
Surface Defects ◽  
Roller Bearing ◽  
Current Method ◽  
Eddy Current Method ◽  
Cross Sectional ◽  
Tapered Roller Bearing ◽  
Geometrical Features ◽  
Inner Surface ◽  
The Face

The article presents the results of the influence of geometrical features of defects in materials on the level of identification by the eddy current method. The study involved the inner ring of the tapered roller bearing. Four test defects, located at a constant distance from the inner surface, and a subsurface marker defect were performed in the treadmill of the tested ring. The test defects had a constant cross-sectional area in a perpendicular direction to the surface of the eddy current head. The geometrical features of each defect were the following: shape, the perimeter of the defect projected onto the surface of the ring, and the width and height of the defect projected on the face of the measuring head. The study involved an inner surface (subsurface defect detection) and external surface (the study of surface defects). It has been shown that the shape of the defect affects the level of detection using the eddy current method.

scholarly journals Visual Inspection of the Aircraft Surface Using a Teleoperated Reconfigurable Climbing Robot and Enhanced Deep Learning Technique

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Balakrishnan Ramalingam ◽  
Vega-Heredia Manuel ◽  
Mohan Rajesh Elara ◽  
Ayyalusami Vengadesh ◽  
Anirudh Krishna Lakshmanan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Visual Inspection ◽  
Surface Defects ◽  
Learning Algorithm ◽  
Periodic Pattern ◽  
Surface Inspection ◽  
Detection Accuracy ◽  
Climbing Robot ◽  
Aircraft Skin ◽  
Surface Images

Aircraft surface inspection includes detecting surface defects caused by corrosion and cracks and stains from the oil spill, grease, dirt sediments, etc. In the conventional aircraft surface inspection process, human visual inspection is performed which is time-consuming and inefficient whereas robots with onboard vision systems can inspect the aircraft skin safely, quickly, and accurately. This work proposes an aircraft surface defect and stain detection model using a reconfigurable climbing robot and an enhanced deep learning algorithm. A reconfigurable, teleoperated robot, named as “Kiropter,” is designed to capture the aircraft surface images with an onboard RGB camera. An enhanced SSD MobileNet framework is proposed for stain and defect detection from these images. A Self-filtering-based periodic pattern detection filter has been included in the SSD MobileNet deep learning framework to achieve the enhanced detection of the stains and defects on the aircraft skin images. The model has been tested with real aircraft surface images acquired from a Boeing 737 and a compact aircraft’s surface using the teleoperated robot. The experimental results prove that the enhanced SSD MobileNet framework achieves improved detection accuracy of aircraft surface defects and stains as compared to the conventional models.

scholarly journals Visual Detection of Surface Defects Based on Self-Feature Comparison in Robot 3-D Printing

2019 ◽  
Vol 10 (1) ◽  
pp. 235 ◽  
Author(s):  
Hongyao Shen ◽  
Wangzhe Du ◽  
Weijun Sun ◽  
Yuetong Xu ◽  
Jianzhong Fu
Keyword(s):  
Feature Extraction ◽  
3D Printing ◽  
Defect Detection ◽  
Point Cloud ◽  
Fused Deposition Modeling ◽  
Surface Defects ◽  
Detection Accuracy ◽  
Feature Extraction Method ◽  
Fused Deposition ◽  
Detection Technology

Fused Deposition Modeling (FDM) additive manufacturing technology is widely applied in recent years. However, there are many defects that may affect the surface quality, accuracy, or even cause the collapse of the parts in the printing process. In the existing defect detection technology, the characteristics of parts themselves may be misjudged as defects. This paper presents a solution to the problem of distinguishing the defects and their own characteristics in robot 3-D printing. A self-feature extraction method of shape defect detection of 3D printing products is introduced. Discrete point cloud after model slicing is used both for path planning in 3D printing and self-feature extraction at the same time. In 3-D printing, it can generate G-code and control the shooting direction of the camera. Once the current coordinates have been received, the self-feature extraction begins, whose key steps are keeping a visual point cloud of the printed part and projecting the feature points to the picture under the equal mapping condition. After image processing technology, the contours of pictured projected and picture captured will be detected. At last, the final defects can be identified after evaluation of contour similarity based on empirical formula. This work will help to detect the defects online, improve the detection accuracy, and reduce the false detection rate without being affected by its own characteristics.

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