Cut Process Research Articles

Bionic digital brain realizing the digital twin-cutting process

A digital twin (DT) is an effective means of achieving physical and information fusion and has great potential for achieving a new paradigm of cutting process (CP) intelligence. This paper traces the relevant studies of digital technology in manufacturing and proposes a bionic digital brain (BDB) as the intelligent core of a digital twin-cutting process (DTCP) framework. The BDB was built with digital neurons (DN) as the basic functional unit, and the reaction mechanism between the DN stimulated the BDB to compute intelligently in real time (RT). The left brain obtains the prophetic theoretical processing information through the DN. The right brain receives actual perceptive processing information through the DN. After the corpus callosum fuses the left and right brain information, the DN outputs the optimal control solutions in RT to ensure demand. The details of monitoring, predicting, optimizing, and controlling the CP based on this framework are described in detail through a case study, demonstrating the powerful information processing capability of the BDB and the RT precision intelligent machining effects. The developed DTCP system confirmed the feasibility and effectiveness of the proposed framework. It provided a theoretical and technical reference for applying and promoting DT technology in the CP.

Method of Reducing the Number of DOF in the Machine Tool-Cutting Process System from the Point of View of Vibrostability Analysis

The model of the mass-damping-spring (MDS) system of a machine tool is multi-degree of freedom model. The model of the cutting process (CP) has to correspond structurally to the MDS system model constituting the machine tool-cutting process (MT-CP) system model. This is often a model of time-variant parameters. Therefore, forecasting the vibrostability, i.e. resistance to the occurrence of self-excited vibrations, in the MT-CP system is a complicated task. To simplify analysis it is justified to reduce the number of degrees of freedom. The method presented simplifies significantly the computational model while the interpretation of the results applies to the whole construction. The reduced model carries information concerning vibration modes that determine vibrostability loss. The paper presents the mathematical fundamentals of the reduction of the machine tool MDS system model, analysis and selection of vibration modes considered in the reduced model and analysis of reduction errors.