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.

scholarly journals Novel Fiber Optic Sensor Probe with a Pair of Highly Reflected Connectors and a Vessel of Water Absorption Material for Water Leak Detection

Sensors ◽  
2012 ◽  
Vol 12 (8) ◽  
pp. 10906-10919 ◽  
Author(s):  
Tae-Sik Cho ◽  
Ki-Sun Choi ◽  
Dae-Cheol Seo ◽  
Il-Bum Kwon ◽  
Jung-Ryul Lee
Keyword(s):  
Water Absorption ◽  
Fiber Optic ◽  
Leak Detection ◽  
Fiber Optic Sensor ◽  
Water Leak ◽  
Optic Sensor ◽  
Sensor Probe

Research on Fiber-Optic Strain Signal Processing Based on Demodulation Technique

2010 ◽  
Vol 44-47 ◽  
pp. 2569-2572 ◽  
Author(s):  
Xiao Wei Du ◽  
Li Rong Li
Keyword(s):  
Signal Processing ◽  
Fiber Optic ◽  
Sampling Rate ◽  
Fiber Optic Sensor ◽  
Sensor Signal ◽  
Optic Sensor ◽  
Processing Module ◽  
Sensor Probe ◽  
Signal Processing Module

A higher performance signal processing module is demanded because of the high sensitivity of fiber-optic sensor probe. Excellent signal processing module is expected have the function of restore the sensor signal and very little to the introduction of noise. In this paper, improved demodulation algorithm is proposed based on the general demodulation algorithm through changing certain parameters of the algorithm aimed to improve the quality of the signal after demodulation. The output signals of scene sensor probe is sampling, sampling data through filtering. By using the improved fiber optic sensor signal demodulation algorithm, the system can fully improve the quality of the signal after demodulation under the premise of maintains the minimum sampling rate.

Miniature multi-analyte fiber-optic sensor probe

2012 ◽  
Author(s):  
Lothar U. Kempen ◽  
Manal Beshay ◽  
Jesus A. Delgado
Keyword(s):  
Fiber Optic ◽  
Fiber Optic Sensor ◽  
Optic Sensor ◽  
Sensor Probe
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