An Optical Fiber Sensing Method for Measuring the Surface Flatness of an Object

Sensing of data or object is a mature research domain where intelligent devices, may possibly be embedded with the artificial intelligence-based techniques, are placed in the closed vicinity of the object or phenomenon. This sensing activity becomes more complex if it is related to the measurements...

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Bibliographic Details
Main Authors: Gu, Z. (Author), Jiang, P. (Author), Rao, Q. (Author), Zhang, W. (Author)
Format: Article
Language:English
Published: NLM (Medline) 2022
Online Access:View Fulltext in Publisher
LEADER 01724nam a2200169Ia 4500
001 10.1155-2022-8023271
008 220718s2022 CNT 000 0 und d
020 |a 16875273 (ISSN) 
245 1 0 |a An Optical Fiber Sensing Method for Measuring the Surface Flatness of an Object 
260 0 |b NLM (Medline)  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1155/2022/8023271 
520 3 |a Sensing of data or object is a mature research domain where intelligent devices, may possibly be embedded with the artificial intelligence-based techniques, are placed in the closed vicinity of the object or phenomenon. This sensing activity becomes more complex if it is related to the measurements of an object which is required to be accurate and precise. In this paper, the reflection interference spectrum method is used to measure the flatness of the surface of the object. The thickness of the tested object is between 0.4 and 16 μm. The optical fiber sensor is moved on the surface of the object, and the reflection spectrum of each point on the surface of the object is analyzed. The thickness of each point, and through the movement of the stepping motor, the thickness of different points on the object is continuously measured, so as to obtain the surface topography of the object. This method has no destructive effect on the surface of the object, has no lateral test range limitation, and has a simple test system structure, High test accuracy, and reliable test results. Copyright © 2022 Weijia Zhang et al. 
700 1 |a Gu, Z.  |e author 
700 1 |a Jiang, P.  |e author 
700 1 |a Rao, Q.  |e author 
700 1 |a Zhang, W.  |e author 
773 |t Computational intelligence and neuroscience  |x 16875273 (ISSN)  |g 2022, 8023271