数字孪生车间是智能制造背景下车间全息监控、精准分析、实时决策的有效手段.而实时高效的异构数据采集和融合,以及虚实车间的交互连接是驱动数字孪生车间运转的核心.为此,依托制造物联网和OPC UA通信技术,建立了数字孪生车间实时数据采集框架.采用分层融合的策略,去除原始数据中的信息冗余,匹配产生中间事件和生产过程数据,同时基于空间尺度构建实时数据空间对象模型.另外针对提高数据传输实时性的目的,按照信息分类—自适应压缩—节点合并的路线,缩减OPC UA信息模型节点数目和节点容量,设计了面向数字孪生车间的OPC UA信息建模方案.最后,以某航天结构件生产线为案例,为数字孪生车间开发了实时数据采集系统,并验证了此方案的可行性.

针对离散制造车间实时监控困难、调控能力差、管理不透明等问题,采用面向对象的方法,提出一种基于数字孪生的离散制造车间可视化实时监控方法.首先,搭建了基于数字孪生的离散制造车间可视化实时监控方法体系架构,明确了其关键实现流程;然后,分别围绕4个关键技术:基于AutomationML与OPC UA的数据建模及传输方法、事件驱动的虚实映射方法、基于复杂事件处理的车间逻辑建模方法、信息可视化及推送,详细阐述了该可视化实时监控的实现方法.最后,以某航天产品机加车间为应用案例,结合实际生产过程和开发的原型系统,验证了该监控方法的有效性.

Digital twin workshop (DTW) is an important embodiment of intelligent manufacturing in the workshop level, which enables the smart production control and management of the workshop. However, there still exist problems including data modeling and verification of digital model in the process of DTW construction. To solve these problem, multidimensional data modeling and model validation methods of DTW are proposed in this article. First, five-order tensor models for representing manufacturing elements are established to unify the data from physical workshop (PW) and virtual workshop (VW). Then, the mathematical method for verifying DTW twin model is proposed from the recessive and explicit perspective. Finally, a case study of an aerospace machining workshop is carried out to verify the operability and effectiveness of the proposed method. The case analysis shows that the proposed methods can effectively evaluate whether the twin model accurately provides the description of the actual behavior process of physical workshop, and the proposed methods have good performance.

针对离散制造车间生产调度存在的缺少信息反馈、动态响应能力弱、决策实时性不足等问题,提出了一种实时数据驱动的自适应调度方法.首先,以物联设备实时采集的大量生产数据为基础,提出一种数据驱动的“感知-调度-执行”闭环决策机制;其次,设计了一种改进的协同多智能体Q-learning算法,通过经验共享机制解决多智能体间的信息交互问题,在此基础上,采用基于学习次数的动态搜索策略来提高不同生产状态下算法的学习效率,从而实现对不同工位的自适应调度;最后,以某航天产品机加车间为案例,验证了所提方法的有效性和可行性.