摘要
切削加工中刀具状态是影响加工质量的关键因素,刀具的磨损直接影响工件的加工精度和表面粗糙度。选择加速度传感器监测切削加工中的振动信号,针对刀具状态变化时振动能量分布随之变化的特点,提取不同频段振动能量作为特征量,利用RBF神经网络进行聚类辨识。实验结果表明,该方法具有良好的识别效果和工程应用价值。
Tool condition is the key factor to affect the machining quality. The wear of cutting tool directly influences the machining accuracy and surface roughness of the workpiece. The acceleration sensor is used to monitor the machining vibration of cutting process. According to the change of energy distribution of vibration signals, the energy distribution of different frequency bands are extracted, and then RBF neural network is used to identify the cutting tool condition. Experimental results show that this approach has good recognition effect and engineering application value.
出处
《测控技术》
CSCD
2015年第10期154-156,共3页
Measurement & Control Technology
关键词
刀具状态
在线监测
智能识别
人工神经网络
tool condition
online monitoring
intelligent recognition
artificial neural network
