Intelligent Manufacturing Oriented Digital Twin Factory Establishment Method And Applications

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.