高速硬铣削加工刀具磨损监测研究

Monitoring of Tool Wear in Hard Milling Process

针对高速铣削淬硬钢加工中的刀具磨损问题,运用时域平均、离散小波分析以及希尔伯特变换等信号处理技术来提取切削力信号中与刀具磨损密切相关的特征量,并基于马氏距离法对刀具磨损状态进行监测识别。用硬铣削试验验证了该监测策略的有效性。

When high--speed milling （HSM） technology was applied to the cutting processes ot steels in their hardened states, drastical reduction of tool life is a big problem. In order to identify the state of the tool wear accurately and promptly, combinations of signal processing techniques, such as time--domain averaging, discrete wavelet transform and Hilbert spectrum analysis were adopted for extracting relevant features from the measured force signals. Tool conditions were identified directly through the recognition of these features by means of Mahalanobis distance method. Practical application results on a CNC vertical milling machine show that the proposed method is accurate for feature extraction and efficient for condition monitoring of the cutting tools.