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.

1.
Liu
,
J.
,
Yamazaki
,
K.
,
1999
, “
Agile Production Realization Based on Autonomously Proficient CNC Controller Infrastructure
,”
J. Manuf. Syst.
,
29
(
5
), pp.
427
431
.
2.
Liu, J., 1996, “A Study on Fractal & Chaos Characterization for Engineering Surface Topography,” UMI Dissertation services, A Bell & Howell Company, ISNB4-8419-0216-3.
3.
Shoham
,
M.
,
Fainman
,
Y.
, and
Lenz
,
E.
,
1984
, “
An Optical Sensor for Real-Time Positioning, Tracking, and Teaching of Industrial Robots
,”
IEEE Trans. Ind.
IE-31
, pp.
159
163
.
4.
Jones
,
B. E.
,
1985
, “
Optical Fiber Sensors and Systems for Industry
,”
J. Phys. E
,
18
, pp.
770
781
.
5.
,
C.
,
Bohlmann
,
J.
, and
,
S.
,
1998
, “
A Fiber Optic Sensor for Surface Roughness Measurement
,”
ASME J. Manuf. Sci. Eng.
,
120
, pp.
359
367
.
6.
Salisbury, E. J., Moon, K. S., and Sutherland, J. W., 1994, “Nano-Surface Texture Measurement Using Laser Interferometry and Image Synthesis,” ASME Manufacturing Science and Engineering, PED-Vol. 68-1, pp. 183–191.
7.
Lin, P. P., Parvin, F., and Schoenig, F. C. 1991, “Optical Gaging of Very Short-Term Surface Waviness,” Trans of NAMRI/SME, Vol. 19, pp. 727–332.
8.
Vorburger
,
T. V.
, and
Teague
,
E. C.
,
1981
, “
Optical Techniques for On-Line Measurements of Surface Finish
,”
Precis. Eng.
,
3
(
2
), pp.
61
83
.
9.
Huynh
,
V. M.
, and
Fan
,
Y.
,
1992
, “
Surface-Texture Measurement and Characterization with Application to Machine Tool Monitoring
,”
The International Journal of Advanced Manufacturing Technology
,
7
, pp.
2
10
.
10.
Luk
,
F.
,
Huynh
,
V. M.
, and
North
,
W.
,
1989
, “
Measurement of Surface Roughness By a Machine Vision System
,”
J. Phys. E
,
22
, pp.
977
980
.
11.
Spurgeon, D., and Slater, R. A. C., 1974, “In-Process Indication of Surface Roughness Using a Fiber-Optics Transducer,” Proc. 15th International Machine Tool Design and Research, 15, pp. 339–347.
12.
North
,
W. P. T.
, and
Agarwal
,
A. K.
,
1983
, “
Surface Roughness Measurement with Fiber-Optics
,”
ASME J. Dyn. Syst., Meas., Control
,
105
, pp.
295
297
.
13.
Butler
,
C.
, and
Gregoriou
,
G.
,
1992
, “
A Novel Non-Contact Sensor for Surface Topography Measurement Using a Fiber Optic Principle
,”
Sens. Actuators A
,
A31
, pp.
68
74
.
14.
Zhang
,
K.
,
Butler
,
C.
, and
Yang
,
Q.
,
1997
, “
A Fiber Optic Sensor for the Measurement of Surface Roughness and Displacement Using Artificial Neural Networks
,”
IEEE Trans. Instrum. Meas.
,
46
(
4
), pp.
899
902
.
15.
Yamazaki
,
K.
,
Lee
,
S. K.
, and
Aoyama
,
H.
,
1993
, “
Non-contact Probe for Continuous Measurement of Surface Inclination and Position Using Dynamic Irradiation of Light Beam
,”
CIRP Ann.
,
42
(
1
), pp.
585
588
.
16.
Wang
,
Y.
, and
Wolfe
,
W. L.
,
1983
, “
Scattering from Microrough Surfaces: Comparison of Theory and Experiment
,”
J. Opt. Soc. Am.
,
73
, pp.
1596
1602
.