The process correlation interaction construction of Digital Twin for dynamic characteristics of machine tool structures with multi-dimensional variables

Abstract

Digital Twin (DT) has become the foundation for intelligent manufacturing as a breakthrough application technology framework and can solve complex vibration problems in the machining process. However, this technology is mainly aimed at application development in a specific domain or a particular factor. The process correlation and interaction characteristics of the system have not been reflected, thus making it challenging to capture the dynamic characteristics of the machining process. This paper presents a system-oriented DT framework with process correlation interaction mechanism that integrates multiple models for dynamic process modeling. The data model is constructed as the core driving model of DT. Based on structural dynamics theory, the weak machine components are taken as digital threads of the data model to correlate position variables and cutting excitation variables. Then, the Frequency Response Function (FRF) corresponding to the principal mode of vibration at the weak machine component is used as an intermediate data for overall online process performance characterization and DT model interaction to enable mirror synchronization of the operation process. The results show that the presented DT can provide multifunctional applications such as cutting parameters optimization, process correlated variables visualization, and machining stability assessment.