胡敬文.基于BP神经网络的表面偏斜度和峰度预测建模[J].表面技术,2017,46(2):235-239.
HU Jing-wen.Predictive Modeling of Surface Skewness and Kurtosis Based on BP Neural Network[J].Surface Technology,2017,46(2):235-239 |
| 基于BP神经网络的表面偏斜度和峰度预测建模 |
| Predictive Modeling of Surface Skewness and Kurtosis Based on BP Neural Network |
| 投稿时间：2016-08-14 修订日期：2017-02-20 |
| DOI：10.16490/j.cnki.issn.1001-3660.2017.02.040 |
| 中文关键词: 表面偏斜度 表面峰度 磨削参数 神经网络` 预测建模 |
| 英文关键词:surface skewness surface kurtosis grinding parameters neural network predictive modeling |
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| 中文摘要: |
| 目的 当前实际生产中对表面形貌的表征主要利用表面算术平均偏差Sa，而通过不同加工方式获得的表面有时尽管具有相同的Sa值，而其表面纹理结构、表面轮廓幅度值的对称程度及凸峰尖锐程度往往存在较大的差异，所以此时引入表面偏斜度Ssk和表面峰度Sku来共同表征表面形貌更为精确可信。方法 利用正交试验和极差分析的方法研究各磨削参数如何影响表面偏斜度和表面峰度的变化。将BP神经网络引入到对表面偏斜度和表面峰度的预测建模中，利用其自学习的特性，有效克服了表面粗糙度建模的多输入、非线性复杂问题。结果 获得了磨削参数对表面偏斜度和表面峰度的影响规律，当vs=20 m/s, vf=27 m/min, f=5 mm/min, ap=0.005 mm时Ssk最小，当vs=29 m/s, vf=23 m/min, f=25 mm/min, ap=0.002 mm时Sku最小；分别建立了磨削参数对Ssk和Sku的精确神经网络预测模型。结论 vf和f对Ssk影响较大，而f和vs对Sku的影响最大。为获得凹谷较多、尖锐凸峰较少的表面，必须选择合适的磨削工艺参数。建立的预测模型可以对磨削工艺优化起到有效的指导作用。 |
| 英文摘要: |
| The work aims to introduce surface skewness Ssk and surface kurtosis Sku to jointly characterize surface topography in a more precise and reliable manner when the symmetric degree and profile peak sharpness of surface texture structures and surface profile amplitude values tend to be subject to large difference, even though the surfaces obtained through various processing methods sometimes have the same Sa value regarding the situation that the surface topography is characterized mainly with the surface arithmetic average deviation Sa in the actual production. Orthogonal experiment and range analysis were applied to study the influence of grinding parameters on the change in Ssk and Sku. On this basis, BP neural network was introduced in the predictive modeling of Ssk and Sku. The complex problem of multi-input and nonlinearity for surface roughness modeling was effectively solved due to the property of self-learning. The effect laws of grinding parameters on Ssk and Sku were achieved. Ssk would reach the minimum when vs=20 m/s, vf=27 m/min, f=5 mm/min and ap=0.005 mm, and Sku was the minimum when vs=29 m/s, vf=23 m/min, f=25 mm/min and ap=0.002 mm. And then, the accurate neural network prediction models for Ssk and Sku based on grinding parameters were built respectively. vf and f have a significant impact on Ssk. Similarly, f and vs impact Sku the most. It is necessary to select suitable grinding parameters to obtain the surface with more valleys and less acute profile peaks. Moreover, the prediction models built can guide the optimization of grinding process effectively. |
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