【摘要】 加工稳定性是影响机床加工精度与效率的重要因素。为了实现对生产线设备不同加工工艺过程的健康状态进行智能监测,对切削过程健康状态的监测与评估进行了研究。基于快速傅里叶变换,确认机床加工失稳时信号在频域具有能量转移特性。考虑加工失稳时信号变化的复杂性,提出一种基于S变换与奇异值分解的特征提取方法,通过奇异值熵实时跟踪机床当前的加工状态。通过对切削数据的积累和学习统计,设定不同工艺过程的稳定阈值线,实现切削过程健康状态的实时监测。试验表明,所提出的方法能够有效应用于切削过程健康状态的监测与评估。更多还原

【Abstract】 Machining stability is an important factor affecting the machining accuracy and efficiency of the machine tool. In order to realize the intelligent monitoring of the health status of different machining processes of the equipment in the production line,the monitoring and evaluation of the health status of cutting process was studied. Based on the fast Fourier transform,it is confirmed that the signal has energy transfer characteristics in the frequency domain when the machining is unstable. Considering the complexity of signal changes when machining is unstable,a feature extraction method based on S-transform and singular value decomposition was proposed to track the current machining state of the machine tool in real time through singular value entropy. Through the accumulation,learning and statistics of cutting data,the stability threshold line of different technological processes was set to realize real-time monitoring of health status of cutting process. Tests show that the proposed method can be effectively applied to the monitoring and evaluation of the health status of cutting process.更多还原