精密铣削机床效能孪生模型构建及动态优化方法OA

A digital twin-based dynamic multi-objective optimization method for machining processes was proposed herein.By integrating historical machining data with real-time operational data,a digital twin system was established,comprising geometric,physical,behavioral,and rule-based sub-models.This system combined an Optuna-GBR model and an IMORIME to dynamically adjust machining param-eters.The cutting force fluctuations were monitored in real time by the digital twin system.When the fluc-tuations exceeded the adaptive threshold,a dynamic optimization process was triggered,during which a new Pareto solution set was regenerated and the optimal machining parameter combination was determined using the entropy-weighted technique for order preference by similarity to an ideal solution(TOPSIS)method.Experimental validation under actual machining conditions demonstrates that the dynamic optimi-zation method of the digital twin system achieves a 19.99%reduction in spindle energy consumption,a 29.02%reduction in specific cutting energy,and an 11.22%reduction in machining noise.These results indicate a significant improvement in machining efficiency and a remarkable reduction in spindle energy con-sumption and machining noises.