Identification of flexible joint multibody dynamic models for machine tool feed drive assemblies via IMU and CNC data

High-accuracy modeling of machine tool dynamics is essential for advanced process planning and monitoring. However, modeling high-speed multi-axis machines is challenging due to the inherent coupled and nonlinear multibody dynamics and structural flexibility. This complex modeling task is addressed by a new approach in which the control dynamics and the open-loop plant dynamics are characterized by a multiple-input and multiple-output (MIMO) linear time-invariant (LTI) system coupled with a generalized disturbance, which is able to capture the open-loop coupled nonlinear dynamics. As a case study, different machine tool topologies of a flexible linear drive coupled with a rotary drive are systematically analyzed with the proposed modeling approach. The identification procedure for the proposed method requires capturing the internal structural vibration between the drives. This paper also presents a method to reconstruct the internal structural vibration using data from the embedded encoders as well as a low-cost microelectromechanical systems (MEMS) inertial measurement unit (IMU) mounted on the machine table. This modeling-building approach is non-intrusive and practical for industrial implementation. The experimental validation shows 2-6% error of predicting the tracking error and motor force/torque. Especially, the vibratory inter-axis coupling effect and posture-dependency are accurately predicted.