scholarly journals A surface defects inspection method based on multidirectional gray-level fluctuation

2017 ◽  
Vol 14 (3) ◽  
pp. 172988141770311 ◽  
Author(s):  
Yunpeng Ma ◽  
Qingwu Li ◽  
Yaqin Zhou ◽  
Feijia He ◽  
Shuya Xi
Author(s):  
Farima Abdollahi Mamoudan ◽  
Sebastien Savard ◽  
Tobin Filleter ◽  
Clemente Ibarra-Castanedo ◽  
Xavier Maldague

It was recently demonstrated that a co-planar capacitive sensor could be applied to the evaluation of materials without the disadvantages associated with the other techniques. This technique effectively detects changes in the dielectric properties of the materials due to, for instance, imperfections or variations in the internal structure, by moving a set of simple electrodes on the surface of the specimen. An AC voltage is applied to one or more electrodes and signals are detected by others. This is a promising inspection method for imaging the interior structure of the numerous materials, without the necessity to be in contact with the surface of the sample. In this paper, Finite Element (FE) modelling was employed to simulate the electric field distribution from a co-planar capacitive sensor and the way it interacts with a non-conducting sample. Physical experiments with a prototype capacitive sensor were also performed on a Plexiglas sample with sub-surface defects, to assess the imaging performance of the sensor. A good qualitative agreement was observed between the numerical simulation and experimental result.


2013 ◽  
Vol 433-435 ◽  
pp. 2113-2116
Author(s):  
Zi Xin Chen ◽  
Feng Yu Xu

Machine vision based surface roughness inspection method is applied to assess different cylindrical grinding surfaces under LED illumination. Images directly recorded by a camera are analyzed by gray level co-occurrence matrix (GLCM) method to discover its texture information. It shows obviously relationship between feature values of the matrix and their corresponding surface roughness values. Uniform table are also designed to choose optimal parameters, which is five of distance between pixel pairs and ninety degree of angle to calculate GLCM. Entropy is chosen to represent different surface roughness images by comparison of correlation coefficients between the parameters and the corresponding surface roughness values.


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