面向低碳的车间生产过程数字孪生建模

Digital Twin Modeling of Low-Carbon Workshop Manufacturing Process

智能制造关键技术的不断突破及其在制造业的快速应用极大地改造着传统车间的生产模式.现代企业的生产方式逐渐朝着集成化、复杂化和智能化方向发展,给车间生产过程低碳运行目标带来挑战.为了提高车间生产过程能耗管理的质量与实时性,提出了一种面向车间能耗管理的数字孪生建模方法.基于物理生产车间的生产资源与车间布局方案搭建虚拟生产车间,通过物理与虚拟车间设备运动信息的交互为几何模型添加运动关系,并结合生产节拍建立车间运动规律的数字孪生模型;采集单机物理生产设备随加工参数变化的能耗数据,使用BP神经网络建立多设备的能耗模型,基于获取物理与虚拟车间实时交互的能耗数据建立车间能量流动规律的数字孪生模型.最终实现包含动态能耗信息的车间生产过程数字孪生建模.同时基于该数字孪生模型提出了一种面向车间生产过程的能耗优化方法,结合刀具寿命、机器人运动平稳性、生产时间等多源评价指标建立多目标优化函数,获取数字孪生模型中的动态能耗数据并应用蜂群算法实现对车间低碳生产需求下的多设备加工参数协同优化.最后以给定工件的生产过程为例,对所提数字孪生建模方法和建模效果进行验证,实验结果表明借助该数字孪生模型环境下的生产过程优化方案可以节省生产车间运行能耗21.77%,并提升了车间能耗信息的可视化表达效果.

With the monumental advancements in key technologies of intelligent manufacturing and their rapid applications in the manufacturing industry,the traditional workshop manufacturing mode is remarkably transitioning.The production system of modern enterprises is becoming more integrated,complex,and intelligent,which presents great challenges to the low-carbon operation goal of the workshop manufacturing process.To improve the quality and real-time level of energy consumption management in the workshop,a digital twin modeling approach for workshop energy consumption management is proposed.A virtual workshop geometric model is designed in the cyberspace based on the information derived from the physical workshop.Next,the kinematic relationships are added to the geometric model based on the interaction information obtained from the physical and virtual equipment within the workshop.Thus,the motion relationship-oriented digital twin model is built.Subsequently,an energy consumption model for multiple equipment systems is set up based on the BP neural network.This model will collect energy con-sumption data with different mechanical parameters from the physical equipment.The energy flow relationship-oriented digital twin model is based on the energy consumption information interaction between the physical and virtual workshops.As a result,the digital twin model of the workshop manufacturing process including the dynamic energy consumption information is realized.Subsequently,an energy consumption optimization method for the workshop manufacturing process based on the digital twin model is proposed.Furthermore,a multi-objective optimization function with evaluation indicators such as the life of cutting tools,smoothness of the robot movement,and manufacturing time,is established for the collaborative optimization of multi-machining parameters by the artificial bee colony algorithm,using the dynamic energy consumption data in the digital twin model.Finally,a practical machining case demonstrates that the proposed approach for the energy consumption digital twin model is effective.Results indicate that the digital twin model can save 21.77%of workshop energy consumption,and the real-time visualization expression degree of workshop energy consumption information is considerably improved.