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铣削振动是描述微细铣削加工状态的重要特征参数. 利用微小型车铣加工中心、压电加速度计和多通道电荷放大器建立了微细铣削振动测试系统,分别提取了不同铣削方式和铣削转速条件下微细铣削振动信号的时域特征参数和频域特征参数. 针对特征参数数量繁多且变化趋势不一致的特点,引入主成分分析方法,利用主成分分别对时域和频域特征参数进行替换,定量描述出振动信号的能量和差异,以及主频带位置和能量分散程度之间的关系. 分析结果表明,通过时域与频域特征参数主成分的综合运用,应用较少的参数即可描述微细铣削振动信号的主要特征,显著降低了原始数据维数; 主成分分析结果可用于铣削方式和铣削转速等微细铣削加工参数的优化.
Milling vibration is an important characteristic parameter to describe the state of micro-milling. Micro-milling vibration test system is established by micro-milling center, piezoelectric accelerometer and multi-channel charge amplifier. The micro- Time-domain and frequency-domain characteristic parameters of milling vibration signals.According to the characteristics of numerous characteristic parameters and inconsistent changing trend, the principal component analysis (PCA) method is introduced to replace the time-domain and frequency-domain characteristic parameters by principal components The energy and difference of the vibration signal and the relationship between the position of the main frequency band and the degree of energy dispersion.The analysis results show that with the combination of the main components of the time and frequency domain characteristic parameters, the application of fewer parameters can describe the micro milling vibration The main features of the signal significantly reduce the dimensionality of the original data. The principal component analysis can be used to optimize milling parameters such as milling method and milling speed.