| 基于小波多分辨率分析和RBF神经网络的刀具磨损状态识别 |
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| 引用本文： | 汤为,王海丽,庄子杰,胡德金.基于小波多分辨率分析和RBF神经网络的刀具磨损状态识别[J].工具技术,2009,43(2):15-19. |
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| 作者姓名： | 汤为 王海丽 庄子杰 胡德金 |
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| 作者单位： | 上海交通大学 |
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| 摘 要： | 刀具磨损监测对于提高加工过程的精度和自动化程度具有重要意义。本文提出一种基于RBF函数神经网络的刀具磨损状态监测模式。该系统利用声发射传感器对切削过程进行监测，采用多分辨率小波分解技术从声发射信号中提取反映刀具磨损的特征向量，并输入RBF神经网络，实现了刀具磨损的自动识别。
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| 关 键 词： | 刀具磨损 声发射信号 小波分析 神经网络 状态监控 |
Tool Wear State Monitoring Based on Wavelet Multi-resolution Analysis and RBF Neural Network |
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| Tang Wei,Wang Haili Zhuang Zijie et al.Tool Wear State Monitoring Based on Wavelet Multi-resolution Analysis and RBF Neural Network[J].Tool Engineering(The Magazine for Cutting & Measuring Engineering),2009,43(2):15-19. |
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| Authors: | Tang Wei Wang Haili Zhuang Zijie |
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| Affiliation: | Department of Mechanical Manufacturing and Automation;Shanghai Jiao Tong University;Shanghai 200240;China |
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| Abstract: | The tool wear monitoring has a great significance to improve the accuracy and automation of manufacturing process.A model of tool wear state monitoring based on RBF neural network was proposed.In this system,the cutting process is monitored by means of acoustic emission sensors,and the feature vectors of tool wear are extracted from AE signals by using the wavelet multi-resolution decomposition technology and are input into RBF neural network,so that the automatic detection of tool wear state can be carried... |
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| Keywords: | tool wear acoustic emission signal wavelet analysis neural network state monitoring |
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