高速铣削智能监测系统研究

An intelligent monitoring system for high-speed milling process

针对目前加工状态监测系统存在的依赖系统事先的"教学"或"训练"过程的问题,在对刀具磨损规律分析的基础上,提出一种针对高速加工的智能化实时刀具状态监测系统.引入自学习能力使该系统初步具备了智能性,自动进行不同刀具状态的识别和磨损程度的估计,较大程度上摆脱了对系统"教学"或"训练"过程的依赖.运用离散小波分解技术对铣削过程中的三向切削力信号进行时域以及各子频段的能量和变动特征的提取,并利用分析技术进行特征筛选.基于两个嵌套的循环运行过程构建了监测系统,进行特征量的线性拟合和马氏距离计算.高速铣削试验证明了所提出的智能刀具状态监测系统的有效性.

To avoid the pre-designed ＂ teaching＂ or ＂ training＂ phase for the condition monitoring system at present,an intelligent monitoring system for high-speed milling process is proposed based on the analysis of the tool wear rule and different tool wear stages.Self-learning was introduced to the system to automatically identify different tool wear states and estimate the wear value,which is independent of the pre-designed ＂ teaching＂ or ＂ training＂ phase.Three-direction components of the cutting force signals generated in high-speed milling process were processed using discrete wavelet decomposition technology.Features in different time and frequency domains were extracted and selected through correlation analysis method.The real-time intelligent monitoring system was built on the cycle process of linear fitting and Mahalanobis distance（MD） calculation.A series of experiments on a CNC vertical milling machine tool shows that the proposed method is accurate for feature extraction and efficient for condition monitoring of cutting tools.