【作者】 卢阳光；

【导师】 闵庆飞；

【作者基本信息】 大连理工大学 ， 管理科学与工程， 2020， 博士

【摘要】 数字孪生,与其它新兴技术诸如物联网、数据挖掘和机器学习一样,为当今制造模式向智能制造的转变提供了巨大的潜力。通过对智能制造研究成果量化分析、梳理和总结,可以发现数字孪生作为突破性的应用技术框架,将会成为实现信息物理系统乃至智能制造的必要方法,值得深入和全面地展开研究。现代制造业为了提升在效率、智能化和可持续性方面的管理水平,需要将工厂全生命周期各个阶段的数据与物理系统融合,体现在规划、生产控制和流程再造等各个阶段。现代工厂面临着快速变化的市场节奏,所以需要敏捷有效的规划方法;现代工厂的生产控制面对复杂环境和高实时性的要求,因此需要智能的生产控制优化手段;现代工厂面对全球化和新技术带来的机遇和挑战,需要灵活实用的精益制造和优化方法。新型的数字孪生信息技术方法有望帮助工厂更好地应对全生命周期的新问题和挑战。本文提出了面向工厂全生命周期构建数字孪生的方法框架,提出方法框架的构成核心即数字孪生实践环(Digital Twin Practice Loop,DTPL),并说明了 DTPL的组成要素和作用。在数字孪生方法框架的基础上,展开研究了面向制造型企业不同阶段的数字孪生工厂理论与应用方法,包括规划阶段、生产控制阶段、流程再造阶段。规划阶段的数字孪生工厂研究,为规划工作设计了一种新的快速仿真模型,称为效率验证分析(Efficiency Validate Analysis,EVA)模型,并基于工业物联网(Industrial Internetof Things,ⅡoT)和EVA,构建了一种敏捷规划的数字孪生方法,以在制造业规划工作中提升规划效率和降低规划成本。通过基于数字孪生的规划方法在汽车再制造工业中的实例,证明基于数字孪生的新方法比传统方法能更有效地支持制造业规划任务。生产控制阶段的数字孪生工厂研究,提出了通过ⅡoT和机器学习构建生产控制数字孪生的方法。通过工业大数据与机器学习持续训练和优化数字孪生模型,实现了用数字孪生实时优化生产控制,动态适应不断变化的环境,及时响应市场变化。通过数字孪生的生产控制方法应用于石化工厂的实例,验证了这种方法能够显著提高生产经济效益。流程再造阶段的数字孪生工厂研究,将传统的精益方法如价值流程图等,通过与ⅡoT和轻量级仿真模型有效整合,提出了一种生产流程再造的数字孪生方法。该方法基于数字孪生,为传统精益方法的定量分析提供了基础。通过将数字孪生的生产流程再造方法应用于中小型制造业工厂的实例,证明了该方法可以有效提升精益方法对生产流程再造工作的效果和精确度。更多还原

【Abstract】 Digital twins(DT),along with the internet of things(IoT),data mining,and machine learning technologies,offer great potential in the transformation of today’s manufacturing paradigm toward intelligent manufacturing.The research achievements about "intelligent manufacturing" are analyzed,combined and summarized,it can be found that as a breakthrough application technical framework,digital twin will become a necessary method to realize CPS(Cyber Physical Systems)and intelligent manufacturing,and its realization theory is worthy of in-depth and comprehensive research.In order to improve the efficiency,intelligence and sustainability level,modern manufacturing industry needs to integrate the data of all stages of the factory life cycle with the physical system,which is reflected in the planning,production control and process reengineering.Modern factories are faced with fast changing market rhythm,therefore they need agile and effective planning methods;production control of modern factories is faced with complex environment and high real-time requirements,therefore they need intelligent production control optimization methods;modern factories are faced with opportunities and challenges brought by globalization and new technology,therefore they need flexible and practical lean manufacturing and optimization methods.The new DT information technology methods are expected to help factories better cope with new problems and challenges in the whole life cycle.This dissertation puts forward a method framework of establishing digital twin for the whole life cycle of a factory,and its core components DTPL(Digital Twin Practice Loop),include the elements and functions of DTPL.Based on the DT method framework,this paper studies the theory and application methods of DT factory in different stages of manufacturing enterprises,including planning stage,production control stage and process reengineering stage.The research of DT factory in planning stage proposes a novel rapid simulation model for the production planning,named as EVA(Efficiency Validate Analysis).A DT method based on IIoT(Industrial Internet-of-Things)and EVA for agile planning is constructed to improve efficiency and reduce planning cost in the task of production planning.This novel approach is evaluated in an automobile remanufacturing case,the results show that the DT approach supports manufacturing process planning tasks more effectively than traditional methods.The research of DT factory in production control stage proposes an approache for constructing DT based on IIoT and machine learning to realize production control optimization.This novel approach integrates machine learning and real-time industrial big data to train and optimize DT models,for better dynamically adapt to the changing environment and respond in a timely manner to the market changes.This novel DT approache was evaluated by applying them in the production control optimization of a petrochemical factory,and the practice case shows this approach can significantly improve economic benefits.The research of DT factory in process reengineering stage proposes a production process optimization approache by constructing DT based on IIoT,light simulation technique,and traditional lean method,e.g.VSM(Value Stream Mapping).The DT based method provides the basis for quantitative analysis in traditional lean method.This novel approach was applied in a traditional manufacturing SMEs(Small and Medium Enterprises),and the case proved that it can effectively improve the effect and accuracy of lean methods in the production process reengineering tasks.更多还原