scholarly journals Effectiveness of Non-Local Means Algorithm with an Industrial 3 MeV LINAC High-Energy X-ray System for Non-Destructive Testing

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2634 ◽  
Author(s):  
Kyuseok Kim ◽  
Jaegu Choi ◽  
Youngjin Lee

Industrial high-energy X-ray imaging systems are widely used for non-destructive testing (NDT) to detect defects in the internal structure of objects. Research on X-ray image noise reduction techniques using image processing has been widely conducted with the aim of improving the detection of defects in objects. In this paper, we propose a non-local means (NLM) denoising algorithm to improve the quality of images obtained using an industrial 3 MeV high-energy X-ray imaging system. We acquired X-ray images using various castings and assessed the performance visually and by obtaining the intensity profile, contrast-to-noise ratio, coefficient of variation, and normalized noise power spectrum. Overall, the quality of images processed by the proposed NLM algorithm is superior to those processed by existing algorithms for the acquired casting images. In conclusion, the NLM denoising algorithm offers an efficient and competitive approach to overcome the noise problem in high-energy X-ray imaging systems, and we expect the accompanying image processing software to facilitate and improve image restoration.

2017 ◽  
Vol 267 ◽  
pp. 248-252
Author(s):  
Alexey Tatarinov ◽  
Viktor Mironov ◽  
Dmitry Rybak ◽  
Pavels Stankevich

Possibilities of non-destructive testing (NDT) methods to assess the quality of permanent joints of powder metal parts were evaluated. Antifriction bushing-bushing couples used in transport braking systems were investigated. The parts made of bronze graphite were crimped by pulsed magnetic deformation by means of electromagnetic equipment with a maximum discharge energy of 30 kJ. The gap between joint parts in the couples was assessed by ultrasonic and radiographic methods. A standard ultrasonic flaw detector Krautkramer USM-25 with an Olympus 4MHz dual-element echo transducer and an industrial x-ray apparatus YXLON EVO 200D were used, correspondingly. In first trial, both methods were equally sensitive to tight and weak connection of joints.


Author(s):  
N. E. Staroverov

Introduction. Machine vision systems are increasingly used in industrial production, particularly for monitoring the quality of electronic components. Radiographic (Х-ray) inspection is currently one of the most popular types of non-destructive testing. Electronic components are typically characterized by a small size, hence, their radiographic inspection should be based on obtaining images and their further enlargement. X-ray equipment for performing such studies is designed such that there are relatively small input doses of X-ray radiation in the plane of the receiver, which leads to a higher image noise than that using conventional X-ray devices.Aim. To develop a method for automated object recognition on microfocus X-ray images.Materials and methods. A method for segmentation of X-ray images is proposed. In the first step, adaptive median filtering is performed followed by correction of the image background by subtracting the distorting function. Next, the contours of the objects in the image are identified using the Canny edge detector followed by recognition of the objects on the resulting image.Results. The developed method was tested for quality control of the installation of microcircuits and for determining the number of electronic components. The experiments confirmed the accuracy of the proposed method. When monitoring the quality of microcircuit installation, the number of detected defects differed from that verified by the operator by less than 10 %. The average error of the proposed method was less than 0.1% when determining the number of electronic components.Conclusion. The proposed method for object recognition on microfocus X-ray images demonstrated sufficient accuracy in typical tasks of non-destructive testing of electronic components.


2021 ◽  
Vol 11 (13) ◽  
pp. 6050
Author(s):  
Yangyi Yu ◽  
Ruiqin Zhang ◽  
Lu Lu ◽  
Yigang Yang

Both X-ray imaging and neutron imaging are essential methods in non-destructive testing. In this work, a bimodal imaging method combining neutron and X-ray imaging is introduced. The experiment is based on a small electron accelerator-based photoneutron source that can simultaneously generate the following two kinds of radiations: X-ray and neutron. This identification method utilizes the attenuation difference of the two rays’ incidence on the same material to determine the material’s properties based on dual-imaging fusion. It can enhance the identification of the materials from single ray imaging and has the potential for widespread use in on-site, non-destructive testing where metallic materials and non-metallic materials are mixed.


2021 ◽  
Author(s):  
Yangyi Yu ◽  
Ruiqin Zhang ◽  
Lu Lu ◽  
Yigang Yang

Abstract Both X-ray imaging and neutron imaging are essential methods in non-destructive testing. In this work, a bimodal imaging method combining neutron and X-ray imaging is introduced. The experiment is based on a compact electron accelerator that can simultaneously generate two kinds of radiation: X-ray and neutron. This identification method utilizes the attenuation difference of the two rays’ incidence on the same material to determine the material’s properties based on dual-imaging fusion. It can enhance the identification of the materials from single ray imaging and has the potential for widespread use in on-site, non-destructive testing where metallic materials and non-metallic materials are mixed.


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