A new fiber optic sensor for inner surface roughness measurement

2009 ◽  
Author(s):  
Xiaomei Xu ◽  
Shoubin Liu ◽  
Hong Hu
Keyword(s):  
Surface Roughness ◽  
Fiber Optic ◽  
Fiber Optic Sensor ◽  
Roughness Measurement ◽  
Surface Roughness Measurement ◽  
Optic Sensor ◽  
Inner Surface

A Reflective Fiber Optic Sensor for Surface Roughness In-Process Measurement

2002 ◽  
Vol 124 (3) ◽  
pp. 515-522 ◽  
Author(s):  
Jiancheng Liu ◽  
Kazuo Yamazaki ◽  
Yi Zhou ◽  
Sadayuki Matsumiya
Keyword(s):  
Surface Roughness ◽  
Light Intensity ◽  
Fiber Optic ◽  
Simulation Method ◽  
Experimental Results ◽  
Fiber Optic Sensor ◽  
Roughness Measurement ◽  
Surface Roughness Measurement ◽  
Optic Sensor ◽  
Sensor Probe

The paper deals with the development of a fiber optic sensor for surface roughness measurement. A new method for the calculation of reflection light intensity is proposed. By numerically counting the amount of reflection light rays from a measured surface, the relationship between the reflection light intensity and the surface roughness can be found. The simulation method is useful in understanding the effects of the sensor probe structure and the component parameters on the performance of the sensor such that an optimum sensor design can be obtained. A fiber optic sensor probe for surface roughness measurement was designed and fabricated using the results obtained by simulation. Experimental results show that the prototype sensor probe has high resolution and sensitivity for ground and milled surfaces with the roughness value (Ra) of 0.1μm∼3.2μm. The experimental results also show that the simulation method is accurate, and hence useful in designing fiber optic sensors. The simulation procedure and feasibility of the simulation method as well as the experimental results obtained from the prototype sensor probe are presented in this paper.

scholarly journals Computational neuroscience applied in surface roughness fiber optic sensor

2019 ◽  
Vol 10 (1) ◽  
pp. 70-75 ◽  
Author(s):  
Wei He
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Computational Neuroscience ◽  
Fiber Optic ◽  
Rbf Neural Network ◽  
Fiber Optic Sensor ◽  
Bottom Surface ◽  
Optic Sensor ◽  
Arbitrary Precision ◽  
The Stability

Abstract Computational neuroscience has been widely used in fiber optic sensor signal output. This paper introduces a method for processing the Surface Roughness Fiber Optic Sensor output signals with a radial basis function neural network. The output signal of the sensor and the laser intensity signal as the light source are added to the input of the RBF neural network at the same time, and with the ability of the RBF neural network to approach the non-linear function with arbitrary precision, to achieve the nonlinear compensation of the sensor and reduction of the effect of changes in laser output light intensity at the same time. The Surface Roughness Fiber Optic Sensor adopting this method has low requirements on the stability of the output power of laser, featuring large measuring range, high accuracy, good repeatability, measuring of special surfaces such as minor area, and the bottom surface of holed etc. The measurements were given and various factors that affect the measurement were analyzed and discussed.

LS-SVM-based surface roughness prediction model for a reflective fiber optic sensor

2017 ◽  
Vol 15 (9) ◽  
pp. 091201
Author(s):  
Li Fu Li Fu ◽  
Jun Luo Jun Luo ◽  
Weimin Chen Weimin Chen ◽  
Dong Zhou Dong Zhou ◽  
Zhongling Zhang Zhongling Zhang ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Fiber Optic ◽  
Fiber Optic Sensor ◽  
Surface Roughness Prediction ◽  
Optic Sensor ◽  
Roughness Prediction

Surface Roughness Measurement With Fiber-Optics

1983 ◽  
Vol 105 (4) ◽  
pp. 295-297 ◽  
Author(s):  
W. P. T. North ◽  
A. K. Agarwal
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
Fiber Optics ◽  
Fiber Optic ◽  
Incident Light ◽  
Average Surface Roughness ◽  
Object Surface ◽  
Roughness Measurement ◽  
Average Surface ◽  
Surface Roughness Measurement

This paper describes an experimental investigation of a technique for the measurement of surface roughness of “ground” surfaces using fiber-optics. A pair of fiber-optic bundles of similar specifications are used to carry both the incident light to and the reflected from the object surface at different angles of incidence. It has been shown that the ratio of reflected intensities is found to vary inversely as the cube of the average surface roughness.

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