中国机械工程2026，Vol.37Issue(4)：875-884,10.DOI:10.3969/j.issn.1004-132X.2026.04.012
精密铣削机床效能孪生模型构建及动态优化方法
Digital Twin-driven Performance Modeling and Dynamic Optimization Methodology for Precision Milling Machines
摘要
Abstract
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.关键词
数字孪生/动态优化/基于Optuna优化的梯度提高回归/改进多目标雾凇优化算法/自适应阈值Key words
digital twin/dynamic optimization/Optuna-optimized gradient boosting regression(Optuna-GBR)/improved multi-objective rime optimization algorithm(IMORIME)/adaptive threshold分类
机械制造引用本文复制引用
梅术龙,谢阳,张超勇,吴剑钊,刘金锋..精密铣削机床效能孪生模型构建及动态优化方法[J].中国机械工程,2026,37(4):875-884,10.基金项目
国家自然科学基金(52205527,52075229) （52205527,52075229）
江苏省自然科学基金(22KJB460018) （22KJB460018）
