2014
DOI: 10.4028/www.scientific.net/amm.654.245
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Optical Fiber Sensor System for Detecting the Steel Ball Surface Quality

Abstract: The design of the optical fiber sensor system is used to detect the steel ball surface roughness and defects with displacement change such as cracks, pits, bumps and so on. Experiment results show that the sensor can distinguish between surfaces with different roughness. By establishment of the equivalent evaluation standard for surface quality, the system could detect whether the steel ball surface quality is qualified.

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Cited by 4 publications
(2 citation statements)
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“…(a) Single sensors: This kind of sensor is used to sense real information about HRRS defects. In HRRS DDSs, various single sensors have been used such as fiber optic sensors [11][12][13], an ultrasonic probe [14], capacitance sensors [ 15,16], vision sensors [3,4,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], etc. It is difficult to achieve rapid, accurate and comprehensive detection of HRRS defects using single sensor-based detection methods.…”
Section: Types Of Sensorsmentioning
confidence: 99%
See 1 more Smart Citation
“…(a) Single sensors: This kind of sensor is used to sense real information about HRRS defects. In HRRS DDSs, various single sensors have been used such as fiber optic sensors [11][12][13], an ultrasonic probe [14], capacitance sensors [ 15,16], vision sensors [3,4,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], etc. It is difficult to achieve rapid, accurate and comprehensive detection of HRRS defects using single sensor-based detection methods.…”
Section: Types Of Sensorsmentioning
confidence: 99%
“…(a) Air medium environment [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]: If there are no special requirements, HRRS defect detection is mostly performed by placing the measured workpiece in an air medium environment. (b) Other media environments [19,20]: If there are special requirements, HRRS defect detection can take place in other protective medium environments.…”
Section: Detection Environmentmentioning
confidence: 99%