基于广义BP神经网络的切削颤振识别研究

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摘要切削颤振将降低加工质量和加工效率，其一直是切削加工领域的一项重要研究课题。针对传统的切削颤振识别方法中，存在获取颤振加工信号中的测量不确定性问题及识别模型中的模型不确定性问题，提出基于广义区间理论的广义BP神经网络切削颤振识别模型，利用广义区间不确定性分析方法将测量不确定性量转换为广义区间量，并进行广义区间形式的时频特征提取，最后将广义区间化的特征量代入广义BP神经网络识别模型中，对切削加工状态进行识别。试验结果显示，提出的广义BP神经网络颤振模型比传统BP神经网络颤振模型有更高的识别率。

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关键词 ：广义区间, 神经网络, 颤振, 识别

Abstract：Cutting chatter reduces quality and efficiency of machining, and it is an important study topic in cutting processing field. There are problems of measurement uncertainty and recognition model uncertainty in the traditional cutting chatter recognition method. Here, a generalized BP neural network cutting chatter recognition model based on the generalized interval theory was proposed. The quantities with measurement uncertainty were converted into generalized intervals with the generalized interval uncertainty analysis method, and a time-frequency feature extraction in generalized interval form was performed. Finally, the features in generalized interval form were substituted into the generalized BP neural network recognition model, the cutting states were indentified. The test results showed that the proposed generalized BP neural network recognition model has a higher recognition rate than the traditional BP neural network recognition model does.

Key words： generalized interval neural network chatter recognition

收稿日期: 2016-10-10 出版日期: 2018-02-28

引用本文:

谢锋云，江炜文，陈红年，谢三毛，李雪萌，刘博文. 基于广义BP神经网络的切削颤振识别研究[J]. 振动与冲击, 2018, 37(5): 65-70.
XIE Feng-yun JIANG Wei-wen CHEN Hong-nian XIE San-mao LI Xue-meng LIU Bo-wen. Cutting chatter recognition based on generalized BP neural network. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(5): 65-70.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2018/V37/I5/65

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