数字孪生驱动的机床能效监测方法研究  

Research on Digital Twin-driven Energy Efficiency Monitoring Method of Machine Tools

作　　者：谢阳 戴逸群 张超勇[2] 刘金锋[1] Xie Yang;Dai Yiqun;Zhang Chaoyong;Liu Jinfeng(School of Mechanical Engineering,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212000,China;State Key Laboratory Intelligent Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]江苏科技大学机械工程学院,江苏镇江212000 [2]华中科技大学智能制造装备与技术全国重点实验室,武汉430074

出　　处：《机电工程技术》2024年第9期37-41,103,共6页Mechanical & Electrical Engineering Technology

基　　金：国家自然科学基金资助项目(52205527,52075229);江苏省高校自然科学基金面上项目(22KJB460018);江苏省双创博士项目(JSSCBS20221286)。

摘　　要：能效提升作为数控机床绿色化、智能化发展的重要内容,如何在多源影响因素下实现准确监测与状态评估,成为激发能效提升潜力的关键问题。为此,提出了一种基于数字孪生的机床能效监测方法。首先,通过分析机床能量多源特性,建立各阶段能耗与能效模型。利用有限元仿真完成机床加工全过程的数字孪生体,并输出多源能耗仿真数据。根据多传感器的数据采集装备与集成学习算法,完成实时加工能耗的处理与分析,并实现对机床能效的准确监测以及运行状态的精准评估。结合孪生数据的三维映射关系,辅助生产过程中的决策优化,从而实现节能增效的目的。以多工况数控铣削加工为应用案例,结果表明能效监测误差总体小于7%,对机床运行状态预测准确率大于95%,验证了上述方法的可行性与有效性。As an important part of green and intelligent development of CNC machine tools,how to achieve accurate monitoring and status evaluation under multi-source influenced has become a key issue in stimulating the potential for energy efficiency improvement.To this end,an energy efficiency monitoring method of machine tools based on digital twin is presented.Firstly,the energy consumption and efficiency model for each stage is established by analyzing the multi-source energy characteristics of machine tools.Then,finite element simulation is utilized to complete the digital twin of the entire process of machine tools,where multi-source energy consumption simulation data is output afterwards.Real-time processing and analysis of processing energy consumption are completed by taking advantage of the multi-sensor data acquisition equipment and ensemble learning algorithm,meanwhile accurate monitoring of energy efficiency and precise evaluation of operating status are achieved.Finally,decision-making optimization in the production process is fulfilled combined with 3D mapping relationship of twin data,achieving the goal of energy conservation and efficiency improvement.Taking multi-condition CNC milling as an application case,the result reveals that the overall error of energy efficiency monitoring is less than 7%,and the accuracy of predicting the operating status of the machine tool is greater than 95%,verifying the feasibility and effectiveness of the above method.

关 键 词：数字孪生 能效监测 集成学习 状态评估 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TG659[自动化与计算机技术—控制科学与工程]