【作者】 周明； 贾振元； 孙爱芹； 郭丽莎；

【Author】 Ming Zhou Zhenyuan Jia Aiqin Sun Lisha Guo Mechanical Engineering school of Dalian University of Technology, Dalian 116024

【机构】 大连理工大学机械工程学院；

【摘要】 针对微细电火花加工中的高频、微能、单一信号严重畸变等特点,提出了利用电压、电流信号互补性特点,运用模糊逻辑控制规则,得出采样点的状态值,然后采用LVQ神经网络的模式识别与分类功能,将采样点的状态值转化成电火花加工间隙状态中所属的状态矢量,最后通过统计分析,得出一组脉冲加工所处的加工状态。更多还原

【Abstract】 High frequency and small electric power of micro-electronic discharge machining (Micro-EDM) cause the waveforms of voltage and current highly distorted, thus indistinguishable by the commonly used EDM distinguishing methods. Upon such knowledge of this, a new method is presented in this paper, where the fuzzy logic rules are used to combine the complementary signals from voltage and current taken as the two inputs to the fuzzy system and deduce a value in a range representing the discharging state of the sampling point. Learning Vector Quantification (LVQ) neural network architecture is adopted to convert this value of the sampling point deduced from the fuzzy system to the corresponding discharging state vector. After the statistical generalization of the points in a set of pulses, the ratio between the elements in the vector clarifies the discharging state of the gap between electrode and workpiece.更多还原