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

		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	$- 0.5340$	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	$- 0.0440$	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	$- 4.2320$	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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Table 2. Time Comparison

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," Chin. Opt. Lett. 15, 091201 (2017)

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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)

DOI:10.3788/COL201715.091201

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology