| 基于小波神经网络的刀具状态监测 |
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| 引用本文： | 舒服华.基于小波神经网络的刀具状态监测[J].组合机床与自动化加工技术,2006(1):69-70,78. |
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| 作者姓名： | 舒服华 |
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| 作者单位： | 武汉理工大学,机电工程学院,武汉,430070 |
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| 摘 要： | 建立了一种小波基函数神经网络的切削刀具磨损状态监测系统。通过提取反映刀具磨损状态的特征参数：声发射，主功率，进给电流为输入信号，利用Morlet解析小波神经网络的非线性模型，获得表示刀具磨损状态的特征量，来实现刀具磨损状态在线智能监测。它可以有效地提高系统识别的精确度和可靠性。
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| 关 键 词： | 刀具磨损 小波分析 神经网络 状态监测 |
| 文章编号： | 1001-2265（2006）01-0069-02 |
| 收稿时间： | 2005-07-26 |
| 修稿时间： | 2005-07-262005-09-19 |
Condition Detection of Cutting Tools Based on Wavelet Neural Network |
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| SHU Fu-hua.Condition Detection of Cutting Tools Based on Wavelet Neural Network[J].Modular Machine Tool & Automatic Manufacturing Technique,2006(1):69-70,78. |
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| Authors: | SHU Fu-hua |
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| Affiliation: | College of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan 430074, China |
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| Abstract: | It proposed a model of cutting tools wear condition detection.Extracting source feature parameters of cutting tools condition:acoustic emission,power,electric current as import signals and utilizing online model of Morlet wavelet neural network of obtaining parameters of showing cutting tools wear,It can realize online sate detection of cutting tools grinding and improve the detection rate of accurate and trustworthy |
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| Keywords: | cutting tool wear wavelet transform neural network condition detection |
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