Chinese Optics Letters, Volume. 15, Issue 9, 091201(2017)
LS-SVM-based surface roughness prediction model for a reflective fiber optic sensor
Figures & Tables(5)
Fig. 1. Schematic diagram of surface roughness measurement using reflective fiber optic sensors.
Table 1. Experimental Results Comparison
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LS-SVM BPNN SVM Processing Methods Ra (μm) MRa (μm) MRE (%) SD (μm) MRa (μm) MRE (%) SD (μm) MRa (μm) MRE (%) SD (μm) Internal Grinded 1.6 1.5963 −0.2280 0.0137 1.6061 0.3870 0.0125 1.6005 0.0312 0.0015 0.8 0.7957 0.0096 0.7896 −1.2963 0.0274 0.7416 −7.2940 0.0879 0.4 0.4002 0.0534 0.0006 0.3882 −2.9266 0.0164 0.4098 2.4622 0.0151 0.2 0.1999 0.0002 0.2079 3.9568 0.0073 0.2002 0.1135 0.0039 Circular Grinded 0.8 0.8008 0.1034 0.0470 0.8100 1.2560 0.0539 0.7682 −3.9790 0.0731 0.4 0.4102 2.5709 0.0855 0.4231 5.7797 0.1018 0.4204 5.0916 0.0578 0.2 0.1915 0.0108 0.2109 5.4714 0.0118 0.2215 10.7345 0.0520 0.1 0.1000 0.0006 0.0002 0.0996 −0.3088 0.0064 0.1069 6.8799 0.0039
Table 2. Time Comparison
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Processing Methods Parameter LS-SVM (s) BPNN (s) SVM (s) Internal Grinding POT 2.135 16.938 399.346 OT 0.026 1.717 0.167 TPT 2.161 18.655 399.513 Circular Grinding POT 2.047 17.406 936.231 OT 0.052 1.947 0.573 TPT 2.099 19.353 936.804
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Li Fu, Jun Luo, Weimin Chen, Xueming Liu, Dong Zhou, Zhongling Zhang, Sheng Li. LS-SVM-based surface roughness prediction model for a reflective fiber optic sensor[J]. Chinese Optics Letters, 2017, 15(9): 091201
Paper Information
Category: Instrumentation, measurement, and metrology
Received: Feb. 27, 2017
Accepted: May. 25, 2017
Published Online: Jul. 19, 2018
The Author Email: Li Fu (310_fl@163.com)
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