Identification of a Practical Digital Twin for Simulation of Machine Tools
Tomoya Fujita*,, Tiandong Xi**, Ryosuke Ikeda*, Sebastian Kehne**, Marcel Fey**, and Christian Brecher**
*Advanced Technology R&D Center, Mitsubishi Electric Corporation
8-1-1 Tsukaguchi-honmachi, Amagasaki, Hyogo 661-8661, Japan
**Machine Tool Laboratory (WZL), RWTH Aachen University, Aachen, Germany
A practical digital twin for machine tools is proposed in this study. The proposed digital twin is capable of time-domain simulation of machine tools and consists of a controller model, machining process model, and machine dynamic model. To predict the quality of the machined surface after the finishing processes, a precise dynamic model is required. The developed dynamic model consists of an interaction force model, vibration model, and friction force model. A linear auto regressive with exogenous inputs (ARX) model is adopted for the interaction and vibration models. Based on a systematic analysis of the disturbance forces of the machine tool, the friction characteristics are extracted to a displacement-dependent friction model and velocity-dependent friction model. A nonlinear Hammerstein model is adopted to identify the friction. Online identification systems based on the recursive least-squares (RLS) method are developed and tested for each model.
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