A Metaheuristic Optimization Algorithm for energy efficiency in Digital Twins

This work aims to study the role of Digital Twins (DTs) technology combined with the Metaheuristic Optimization Algorithm in manufacturing energy efficiency optimization. Firstly,a machine tool model is established based on DTs technology to study the energy consumption of the milling process of Computer Numerical Machine Tools. Besides, the Particle Swarm Optimization (PSO) algorithm is introduced to optimize the milling parameters of the machining process. Meanwhile, the tool machining path is optimized by combining the Optimized Genetic algorithm and Simulated Annealing algorithm and find the optimal solution of the machining path. On the premise of ensuring the machining quality, this scheme improves the machining efficiency, reduces the energy consumption of the processing process, and improve the energy efficiency. The results demonstrate that the optimized milling parameters can ensure the lowest milling power while considering the maximum material removal rate. Take the plane model as an example. The Improved Genetic Algorithm-Simulated Annealing algorithm can significantly reduce the number of empty walking knife by adopting projection machining and helical machining. The total length of the milling path is reduced by 69.45 ​mm at most, a relative reduction of 10.01%. The average measured energy consumption of milling is reduced by 5.62W∗h compared with the empirical value; the measured average energy consumption of the optimized idle tool is reduced by 0.17W∗h; the total measured energy consumption of milling processing is reduced by 5.73W∗h compared with the empirical value. It can be seen that the optimization algorithm can improve the processing efficiency and reduce the energy consumption.