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

Abstract The processed surface contour shape is extracted with the finite element simulation software, and the difference value of contour shape change is used as the parameters of balancing surface roughness to construct the infinitesimal element cutting finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the central composite test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the finite element simulation model and surface roughness prediction model are verified and analyzed through experiment. The research results show that, compared with the experiment results, the maximum error of finite element simulation model and surface roughness prediction model is 30.9% and12.3%, respectively. So, the model in this paper is accurate and will provide the theoretical basis for optimization study of auxiliary milling process of supersonic vibration.

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Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-004-2110-y ◽

2005 ◽

Vol 26 (11-12) ◽

pp. 1202-1210 ◽

Cited By ~ 77

Author(s):

N. Suresh Kumar Reddy ◽

P. Venkateswara Rao

Keyword(s):

Surface Roughness ◽

Prediction Model ◽

End Milling ◽

Cutting Conditions ◽

Tool Geometry ◽

Surface Roughness Prediction ◽

Roughness Prediction ◽

Selection Of

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Computational neuroscience applied in surface roughness fiber optic sensor

Translational Neuroscience ◽

10.1515/tnsci-2019-0012 ◽

2019 ◽

Vol 10 (1) ◽

pp. 70-75 ◽

Cited By ~ 3

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.

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