Chatter occurs easily during robotic milling owing to the low structural stiffness of industrial robots and can degrade the machining quality or even cause robot failure. The accurate frequency response function (FRF) of the robot is essential for predicting chatter stability and selecting the appropriate process parameters. However, the FRF of a robot is affected by multiple factors, such as pose, operating state, and external excitation. In this study, an in-process FRF prediction method considering robot pose and feedrate was developed and used to predict chatter stability. Firstly, the static FRFs were obtained from the experimental modal analysis for different robot poses and used to train a Gaussian process regression (GPR) model. Subsequently, the static FRF predicted using GPR and the modal parameters identified by operational modal analysis (OMA) were used to calculate the in-process FRFs of the robot in the operation state. After removing the harmonic components of the vibration signals using a matrix notch filter, OMA was conducted using the least-squares complex frequency. Furthermore, the FRF of the robot was transformed from the robot flange coordinate system into the engagement coordinate system using the kinematics model and the tool path. The dynamic milling model, considering tool and robot modes was used for predicting stability. Finally, the proposed method was demonstrated by time-domain simulation of the robot-tool system and milling tests, and the effects of the running state and feed direction on chatter stability considering robot mode were analyzed.

由于工业机器人的结构刚度较低，在机器人铣削过程中容易发生颤振，会降低加工质量甚至导致机器人故障。机器人准确的频率响应函数 (FRF) 对于预测颤振稳定性和选择合适的工艺参数至关重要。然而，机器人的频响函数受姿态、运行状态、外部激励等多种因素的影响。在这项研究中，开发了一种考虑机器人位姿和进给率的过程中 FRF 预测方法，并将其用于预测颤振稳定性。首先，静态 FRF 从不同机器人姿态的实验模态分析中获得，并用于训练高斯过程回归 (GPR) 模型。随后，使用GPR预测的静态FRF和操作模态分析（OMA）识别的模态参数用于计算机器人在操作状态下的过程中FRF。在使用矩阵陷波滤波器去除振动信号的谐波分量后，使用最小二乘复数频率进行 OMA。此外，使用运动学模型和工具路径，将机器人的 FRF 从机器人法兰坐标系转换为接合坐标系。考虑工具和机器人模式的动态铣削模型用于预测稳定性。最后，通过机器人工具系统的时域仿真和铣削试验证明了所提出的方法，并分析了运行状态和进给方向对考虑机器人模式的颤振稳定性的影响。