Laser Reflection as a Simple Prospect Tool for Nondestructive Quality Control of Charged Lapping Plates

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Rodrigo Mayen-Mondragon ◽  
Carlos Alberto Avila-Herrera ◽  
Alberto Herrera-Gomez ◽  
Jose Martin Yanez-Limon ◽  
Rafael Ramirez-Bon

In the present work, a simple laser/detector-based system was assembled and mounted in situ at the production line of the Hitachi hard-disk recording-head facilities in Mexico. The system was set to scan the surface of rotating lapping plates charged during time windows of varying lengths. The specular-reflection component was measured as a function of angular distance along the plate surface. The optical system showed enough sensitivity to follow the incorporation of abrasive into the plate surface. Moreover, two different charging stages were identified. Relevant topographical information could also be extracted. For example, the data distribution skewness was used to identify major surface defects, such as scratches. The reflection-signal spatial pattern was matched to that of the lapping plate topographer (LPT) surface profile by means of a coherence-spectrum analysis. The system assembly is relatively straightforward and it occupies little space. Thus, it could become a compact multitask substitute or complement of larger equipment at the manufacturing line. Note that even when the data analysis performed has led to promising results, the technique still needs to be fine-tuned in order to increase its precision and reliability.

BioTechniques ◽  
2002 ◽  
Vol 32 (5) ◽  
pp. 1051-1057 ◽  
Author(s):  
Jeffrey R. Shearstone ◽  
Norman E. Allaire ◽  
Michael E. Getman ◽  
Steven Perrin

Refractories ◽  
1994 ◽  
Vol 35 (1-2) ◽  
pp. 26-31
Author(s):  
Yu. M. Rapoport ◽  
V. G. Sloushch

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1376 ◽  
Author(s):  
Andrey Zhuravlev ◽  
Vladimir Razevig ◽  
Sergey Ivashov ◽  
Aleksey Skrebkov ◽  
Viktor Alekseev

The use of microwave holography for detecting rail surface defects is considered in this paper. A brief review of available sources on radar methods for detecting defects on metal surfaces and rails is given. An experimental setup consisting of a two-coordinate electromechanical scanner and a radar with stepped frequency signal in the range from 22.2 to 26.2 GHz is described, with the help of which experimental data were obtained. Fragments of R24 rails with surface defects in their heads were used as the object of study. The radar images of rail defects were obtained by the described method based on back propagation of a wavefront. It is shown that polarization properties of electromagnetic waves can be used to increase the contrast of small-scale surface defects. A method of estimating rail surface profile by radar measurements is given and applied to the experimental data. Comparison of the longitudinal rail head profiles obtained by radar and by direct contact measurements showed that the radar method gives comparable accuracy.


2015 ◽  
Vol 734 ◽  
pp. 543-547 ◽  
Author(s):  
Qing Hua Li ◽  
Di Liu

The aluminum plate surface defects recognition method of BP neural network is studied based on target detection .In order to detect the defects, the target image is binaried by adaptive threshold method. After binarizing the target image, three kinds of image feature, including geometric feature, grayscale feature and shape feature, are extracted from the target image and its corresponding binary image. The defects classification model based on back-propagation neural network utilizes three layers neural network structure model and the hyperbolic tangent function of S function as the activation function, the number of neurons in hidden layer is confirmed by experiments. The experimental results show that the classification accuracy of BP neural network classification model as high as 94%, this can meet our requirements.


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