采用遗传算法优化神经网络的铸铁表面粗糙度声发射预测

郭力; 邓喻

doi:10.13433/j.cnki.1003-8728.20180042

采用遗传算法优化神经网络的铸铁表面粗糙度声发射预测

doi: 10.13433/j.cnki.1003-8728.20180042

郭力,
邓喻

1.
湖南大学机械与运载工程学院, 长沙 410082

基金项目:

国家自然科学基金项目（51475157）资助

详细信息

作者简介:
郭力(1964-),教授,研究方向为智能磨削工艺与装备,guolihnu8@163.com

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- 文章访问数: 595
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- 被引次数: 0
出版历程
- 收稿日期: 2017-08-15
- 刊出日期: 2018-10-05

Acoustic Emission Monitor Grinding Surface Roughness of Cast Iron via BP Neural Networks and Genetic Algorithm

Guo Li,
Deng Yu

1.
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China

摘要

摘要: 表面粗糙度是汽车发动机曲轴精密磨削加工中的一个非常重要的指标，在线监测表面粗糙度是曲轴智能磨削成功的标志。应用美国声学物理公司PAC的PCI-2声发射实验仪器测量磨削声发射信号，采用遗传算法优化BP神经网络，以磨削声发射信号均方根和快速傅里叶变换峰值为特征值，对平面磨削曲轴球墨铸铁材料QT700-2表面粗糙度成功进行了预测。与表面粗糙度的实测结果表明相对误差可控制在6.22%以下。
- 曲轴磨削 /
- 声发射 /
- BP神经网络 /
- 遗传算法 /
- 表面粗糙度
Abstract: Surface roughness is a very important index in precision grinding of automotive engine crankshaft. On-line monitoring of surface roughness is a sign of intelligent grinding of the crankshaft. The PCI-2 acoustic emission experimental instrument made by PHYSICAL ACOUSTICS CORPORATION(PAC) was used in grinding test. By adopting a genetic algorithm for optimization of BP neural network and using grinding the acoustic emission signal root mean square(RMS) and fast Fourier transform peak as the characteristic values, the surface grinding crankshaft spheroidal graphite cast iron QT700-2 material surface roughness were predicted successfully. The relative error between the tested results and predicted is below 6.22%.
- crankshaft grinding /
- acoustic emission /
- BP neural network /
- genetic algorithm /
- surface roughness

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参考文献(15)

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