Physics-based modeling and intelligent optimal decision method for digital twin system towards sustainable CNC equipment

With the continuous advancement of smart manufacturing technologies, traditional machine tool production is evolving toward greater integration and intelligence, particularly in addressing multi-objective optimization challenges such as energy efficiency, operational effectiveness, and noise reduction. To optimize machine tool performance under dynamically changing milling process parameters, a multi-objective optimization strategy for milling parameters based on digital twin technology is proposed. A virtual machine model is developed using the milling process parameters and data from the physical machine tool. Real-time data interaction between the physical and virtual machines is facilitated through edge gateways and the industrial internet, dynamically updating the motion relationships within the geometric model to establish a digital twin of the machine tool. An initial analysis of energy consumption characteristics in the milling process is conducted, followed by the construction of a multi-objective optimization model that incorporates interactions between the physical and virtual machine tools through digital twin technology. Through data exchange between the physical and virtual models, real-time operational data from the machine tool are gathered. The Optuna-optimized XGBoost algorithm (Optuna-XGBoost) is applied for target prediction, combined with an improved multi-objective rime optimization algorithm (IMORIME) to optimize the milling process. Finally, the TOPSIS decision analysis method is employed to evaluate the Pareto solution set, identifying the optimal combination of process parameters. Experimental results demonstrate that the digital twin-based optimization approach achieves significant reductions in spindle energy consumption by 11.96 %, specific cutting energy by 28.24 %, and noise levels by 11.38 % compared to traditional methods, while also enhancing the visualization of machine tool information.