基于自适应变分模态分解与功率谱熵差的机器人铣削加工颤振类型辨识

Robotic Milling Chatter Types Detection Based on Adaptive VariationalMode Decomposition and Difference of Power Spectral Entropy

机器人铣削加工存在模态耦合颤振和再生颤振现象,有效地进行机器人铣削加工颤振类型的辨识是进行颤振精准抑制和保证加工质量的基础。为此,提出一种基于自适应变分模态分解与功率谱熵差的颤振类型辨识(AVMD-ΔPSE)方法。通过分析机器人铣削加工颤振特性和主导模态,将机器人铣削颤振分为机器人结构模态主导的模态耦合颤振和刀具-主轴结构模态主导的再生颤振两种类型。为了提取颤振敏感子信号,利用自适应变分模态分解方法对原始信号进行分解,根据功率谱熵和频率消除算法设计功率谱熵差颤振类型辨识指标,结合多组试验数据采用高斯混合模型自适应地确定辨识指标最佳分类阈值。颤振辨识试验表明机床铣削加工颤振辨识方法运用于机器人铣削加工中仅能识别颤振却无法区分不同的颤振类型,而AVMD-ΔPSE方法能准确有效地辨识和区分机器人铣削加工中的模态耦合颤振和再生颤振,为机器人铣削颤振的针对性抑制提供理论指导。

Robotic milling exists both mode coupling chatter and regenerative chatter.Effective chatter types detection in robotic milling is the basis for accurate chatter suppression and guarantee of machining quality.Therefore,a new method for chatter types detection based on adaptive variational mode decomposition and difference of power spectral entropy(AVMD-ΔPSE)is proposed.By analyzing the chatter characteristics and dominant modes of robotic milling,the chatter of robotic milling is divided into two types:mode coupling chatter dominated by robot mode and regenerative chatter dominated by tool-spindle mode.In order to extract the sensitive chatter signals,the original signals are decomposed by adaptive variational mode decomposition method.According to the power spectral entropy and frequency elimination algorithm,the chatter types detection indicators about difference of power spectral entropy is designed,and the optimal classification thresholds of the identification indicators are determined by using gaussian mixture model adaptively combined with many experimental data.Chatter detection experiments show that milling machine chatter detection method is applied to robotic milling can only identify chatter cannot distinguish between different types of chatter,and AVMD-ΔPSE method can accurately recognize and distinguish between mode coupling chatter and regenerative chatter,which provides theoretical guidance for the suppression of chatter in robotic milling.