基于改进小波包能量熵和阈值自适应的切削颤振在线监测

Research on online monitoring of milling chatter based on improved wavelet packet energy entropy and threshold adaptation

颤振是影响机床加工质量的重要原因之一,传统的颤振监测算法对颤振孕育阶段的感知灵敏度低,且监测阈值的设定不具备泛化性和实时性,针对该问题提出了一种能够自适应地识别早期颤振的在线监测方法。首先使用改进的小波包能量熵算法(IWPEE)提取颤振特征,在提高识别精度和鲁棒性的同时降低了计算量。其次基于改进的拉依达准则确定颤振监测阈值,使系统能够根据不同的加工条件自适应地计算颤振监测阈值。然后根据实际加工监测需求开发高效颤振在线监测软件,并且通过仿真信号和切削试验验证了本文所提算法的有效性。结果表明,IWPEE算法相较于传统熵值判定法,识别灵敏度提高了360%,改进的拉依达准则能自适应地确定阈值并成功在颤振孕育阶段将其监测出来,相较于传统阈值算法在阈值稳定性和适应性上有显著提升。

Chatter is considered as one of the important factors affecting the quality of machining processing,yet traditional chatter monitoring algorithms often lack sensitivity to chatter onset and struggle with real-time adaptability in setting monitoring thresholds.To tackle this challenge,we propose a self-adaptive online monitoring method for early chatter identification.The use of the improved wavelet packet energy entropy(IWPEE)algorithm enhances chatter feature extraction,thereby improving recognition accuracy,robustness,and efficiency.Moreover,an improved Pauta criterion dynamically determines the chatter monitoring threshold,enabling adaptive threshold calculation under varying processing conditions.Subsequently,we develop online chatter monitoring software to meet the practical monitoring demands of machining.Validation of the proposed algorithm through simulation signals and cutting experiments demonstrates a 360% increase in sensitivity compared to traditional entropy-based methods.Additionally,the adaptive determination of the threshold by the improved Pauta criterion ensures successful monitoring of chatter onset during its growth stage.Furthermore,significant enhancements in threshold stability and adaptability relative to traditional threshold algorithms are demonstrated.