| 基于BP神经网络的高效深磨工程陶瓷工件表面粗糙度的声发射预测 |
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| 引用本文： | 郭力.基于BP神经网络的高效深磨工程陶瓷工件表面粗糙度的声发射预测[J].湖南文理学院学报(自然科学版),2008,20(3). |
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| 作者姓名： | 郭力 |
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| 作者单位： | 湖南大学,国家高效磨削工程技术研究中心,湖南,长沙,410082;湖南大学,机械与汽车工程学院,湖南,长沙,410082 |
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| 基金项目： | 湖南省自然科学基金，教育部留学回国人员科研启动基金 |
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| 摘 要： | 对BP神经网络的原理、算法和公式进行了介绍,在对Matlab及其神经网络工具箱介绍的基础上,采用3个声发射特征值:即声发射信号有效值、FFT峰值和标准差作为输入,工件表面粗糙度作为输出,用BP神经网络的方法对高效深磨加工工程陶瓷Al2O3的工件表面粗糙度进行了训练、预测和分析. 结果表明,使用BP神经网络可以实现高效深磨加工工程陶瓷工件表面粗糙度的监测.
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| 关 键 词： | 表面粗糙度 BP神经网络 声发射 高效深磨 工程陶瓷 |
Predicts the roughness of workpiece surface of engineering ceramic in high efficiency deep grinding by use of acoustic emission based on the method of BP neural network |
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| GUO Li.Predicts the roughness of workpiece surface of engineering ceramic in high efficiency deep grinding by use of acoustic emission based on the method of BP neural network[J].Journal of Hunan University of Arts and Science:Natural Science Edition,2008,20(3). |
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| Authors: | GUO Li |
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| Institution: | GUO Li ( College of Mechanical , Automotive Engineering,National Engineering Research Center for High Efficiency Grinding,Hunan University,Changsha,Hunan,410082) |
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| Abstract: | Based on the Matlab software, The theory, methods and formula of BP Neural network was introduced under the condition of the inputs are three acoustic emission (AE) characteristic parameters, such as virtual value of AE signal, spike value of the FFT and the standard difference, and the output was roughness of workpiece surface, the roughness of workpiece surface of engineering ceramic Al2O3 under the condition of high efficiency deep grinding are analyzed. It was found that the roughness of workpiece surfa... |
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| Keywords: | surface roughness BP Neural network acoustic emission high efficiency deep grinding engineering ceramic |
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