Zhu Nan-Nan, Zhang Jun
Opt Express. 2016 Oct 31;24(22):25119-25128. doi: 10.1364/OE.24.025119.
The surface roughness prediction model based on a support vector machine was proposed and the multi-wavelength fiber optic sensor was established. The specimens with different surface roughness selected as the test samples were analyzed by using the prediction model when the incident wavelengths were 650 nm and 1310 nm, respectively. The working distance of 2.5 mm ~3.5 mm was chosen as the optimum measurement distance. The experimental results indicate that the error range of surface roughness is 0.74% ~7.56% at 650 nm, and the error range of surface roughness is 1.03% ~5.92% at 1310 nm. The average relative error is about 2.669% at 650 nm, while it is about 2.431% at 1310 nm. The error of roughness measurement is less than 3% by using the model, which is acceptable. The error of surface roughness based on the prediction model is smaller than that by using the characteristic curves between surface roughness and the scattering intensity ratio.
提出了基于支持向量机的表面粗糙度预测模型，并建立了多波长光纤传感器。选取具有不同表面粗糙度的试样作为测试样品，分别在入射波长为650nm和1310nm时，利用该预测模型进行分析。选择2.5mm3.5mm的工作距离作为最佳测量距离。实验结果表明，在650nm时表面粗糙度的误差范围为0.74%7.56%，在1310nm时表面粗糙度的误差范围为1.03%~5.92%。650nm时平均相对误差约为2.669%，而1310nm时约为2.431%。使用该模型进行粗糙度测量的误差小于3%，这是可接受的。基于预测模型的表面粗糙度误差小于利用表面粗糙度与散射强度比之间的特征曲线所得到的误差。
