功率信息互相关法的刀具破损在线监测

为了在数控机床加工中对刀具破损进行有效监测以避免对工件及机床造成损坏,提出了一种利用机床加工功率特征信息和互相关算法的刀具破损在线监测方法。该方法通过Mallat多分辨分析小波算法提取工件正常加工时的主轴功率变化特征序列作为监测刀具状态的特征参考模板,在工件批量加工时采用改进的实时小波算法提取在加工工件的特征向量序列并与特征参考模板序列进行局部实时广义互相关系数计算,当刀具发生破损失效时,与正常情况相比在采样点计算时窗内的两特征向量子序列的相关性显著降低,将表征序列相关性的广义互相关系数定义为刀具状态系数(Tool Condition Index,TCI),对该系数设定合理的门限值即可监测刀具状态的异常。该方法在几种典型刀具破损的在线监测中均能准确识别刀具失效,实验结果表明其具有一定的实用性。

On line tool breakage monitoring method based on power information and cross-correlation algorithm

In order to reliably monitor unexpected tool failure and prevent workpiece or machine tool from possible damages in batch machining, a tool breakage on-line monitoring method based on power information and cross-correlation algorithm is proposed. In this method, wavelet coefficients of spindle-power signal are used as the characteristic vector of machining information, and then the vector sequence extracted from a normal machining process via Mallat wavelet is defined as the reference template for monitoring cutting tool condition. In batch machining, real-time characteristic vector of the workpiece in machining process is extracted via an improved real-time wavelet algorithm. The correlation of two vector sub-sequences within a sampling time window, which is described by generalized cross-correlation coefficient, decreases apparently when the tool is broken. The generalized cross-correlation coefficient is defined as tool condition index (TCI), and tool breakage can be detected by monitoring the TCI with a threshold value. Experiments show that the method can accurately identify tool breakage failures in normal machining condition, and thus it is practical.