Digital Twin Driven Service

Description

Digital Twin Driven Smart Service draws on the latest industry practice and research to explain how to implement digital twin service in a range of scenarios. It addresses relevant theory and methodologies, including product service, prognostic health management service, energy efficient service and testing service. Other sections discuss  key enabling technologies supported by cutting-edge case studies of implementation. Drawing on the work of researchers at the forefront of this technology, this book is the ideal guide for anyone interested in product services, manufacturing services and digital twin services. This book is one part of a trilogy on digital twins, the other titles being Digital Twin Driven Smart Design and Digital Twin Driven Smart Manufacturing. Front Cover Digital Twin Driven Service Digital Twin Driven Service Copyright Contents Contributors Preface 1 - From service to digital twin service 1.1 Introduction 1.2 The development and connotation of service 1.3 Product service and manufacturing service 1.3.1 Product service connotation 1.3.2 Manufacturing service and its management 1.3.2.1 Scope of manufacturing services 1.3.2.2 Manufacturing services running workflow 1.4 Digital twin service 1.4.1 Digital twin-driven services 1.4.2 Digital twin enhanced manufacturing service 1.4.3 Digital twin as services 1.4.4 Digital twin services applications 1.5 Summary Acknowledgments References 2 - Digital twin-driven service collaboration 2.1 Introduction 2.2 The concept of digital twin-driven service collaboration 2.2.1 Features of digital twin-driven service collaboration 2.2.2 Framework of digital twin-driven service collaboration 2.2.2.1 Manufacturing resources in physical space 2.2.2.2 Virtual model and twin data in virtual space Virtual model DT data 2.2.2.3 Manufacturing service platform 2.2.2.4 Interaction and connection 2.2.3 Procedures of digital twin-driven service collaboration 2.3 Enabling digital twin service collaboration 2.3.1 State aware service collaboration monitoring 2.3.1.1 Manufacturing service execution state monitoring 2.3.1.2 Manufacturing service collaboration state monitoring 2.3.2 Data-driven manufacturing service collaboration collaborative relationship evaluation 2.3.3 Manufacturing service collaboration failure prediction under virtual model 2.3.4 Service collaboration optimization and reconfiguration 2.4 Case study 2.5 Summary References 3 - Digital twin-driven production line custom design service 3.1 Introduction 3.2 Framework for digital twin-driven production line custom design service system 3.2.1 Connotation of digital twin-driven product design 3.2.2 Basic structure of digital twin-driven production line custom design service 3.2.3 Key theory and technology of digital twin-driven production line custom design service 3.3 Development method of digital twin-driven production line custom design service system 3.3.1 Design and development of modular system 3.3.2 Design and development of parametric customization system 3.3.3 Virtual simulation and modeling method of production line 3.3.4 Construction and debugging method of digital twin system 3.4 Case study 3.4.1 Design and simulation of intelligent welding production line for bicycle rear frame 3.4.2 Design of personalized fixture for automobile welding production line based on digital twin 3.5 Summary References 4 - Digital twin-enhanced product family design and optimization service 4.1 Introduction 4.1.1 Engineering product family design and optimization 4.1.2 Digital twin-enabled services for engineering solution re-/design 4.2 DT-enhanced services for ambient-based product family design 4.2.1 Benchmarking service for product family design 4.2.2 Interacting service for product family design 4.3 DT-enabled services for in-context product family optimization 4.3.1 Reconfiguration services for product family optimization 4.3.2 Reverse design services for product family optimization 4.4 Overall system framework 4.4.1 System architecture 4.4.2 Process flow 4.5 Case study 4.5.1 Advantages of context awareness in 3D printing services 4.5.2 Establishing a context-aware DT system 4.6 Summary References 5 - Digital twin-driven fault diagnosis service of rotating machinery 5.1 Introduction 5.2 The related works 5.3 Digital twin-driven fault diagnosis framework 5.3.1 Construction of digital twin models 5.3.2 Machinery interconnection and interoperability 5.3.3 Integrative virtual and real data analytics 5.3.4 Applications for fault diagnosis 5.4 Case studies 5.4.1 Digital twin for CNC interconnection and interoperability 5.4.2 Digital twin for rotating machinery diagnosis 5.5 Summary References 6 - Digital twin-driven energy-efficient assessment service 6.1 Introduction 6.2 Energy-efficient assessment for manufacturing capability 6.2.1 Energy-efficient assessment indicators 6.2.1.1 Principles for assessment indicator selection 6.2.1.2 Framework of assessment indicators 6.2.1.3 Extensible dynamic multidimensional assessment indicator framework 6.2.2 Energy-efficient assessment model 6.2.2.1 Dynamic assessment model 6.2.2.2 Dynamic assessment model based on correlation model 6.2.2.3 Concurrent assessment model 6.2.3 Dynamic energy-efficient assessment approach 6.2.3.1 Subjective assessment method 6.2.3.2 Objective assessment method 6.2.3.3 Combination weighting method 6.2.3.4 Dynamic assessment approach 6.3 Digital twin-driven assessment service 6.3.1 Framework of digital twin-driven assessment 6.3.2 Energy-efficient assessment as services 6.4 Case studies 6.4.1 Digital twin-driven assessment service platform 6.4.2 Energy-efficient assessment service for industrial robots 6.4.2.1 Physics-based energy modeling 6.4.2.2 Virtual industrial robot modeling 6.4.2.3 Unified digitized description model 6.4.2.4 Implementation 6.5 Summary References 7 - Digital twin-driven cutting tool service 7.1 Introduction 7.2 Related works 7.3 Framework of digital twin-driven cutting tool service 7.4 Enabling digital twin-driven cutting tool service 7.4.1 Digital model for cutting tools in machining process 7.4.2 Digital twin-driven cutting tool condition monitoring service 7.4.3 Digital twin-driven cutting tool condition forecasting service 7.4.4 Digital twin-driven cutting tool remaining useful life prediction service 7.4.5 Digital twin-driven cutting tool selection service 7.5 Case study 7.6 Summary References 8 - Digital twin-driven prognostics and health management 8.1 Introduction 8.2 Digital twin-driven PHM framework 8.2.1 Understanding of physical equipment 8.2.2 Construction of virtual equipment 8.2.3 Determination of PHM services (Ss) 8.2.4 Strategy of DT data synchronization 8.3 Case study 8.3.1 Example of aerospace control moment gyro 8.3.1.1 Problem description 8.3.1.2 The framework of DT-driven solution 8.3.1.3 Physical analysis of CMG 8.3.1.4 Construction of virtual control moment gyro Feature extraction and selection RUL calculation via CRNN 8.3.1.5 Data recovery via compressed sensing 8.3.1.6 Real bearing for RUL prediction services 8.3.2 Example of lithium-ion battery 8.3.2.1 Problem description 8.3.2.2 The framework of DT-driven solution 8.3.2.3 Physical analysis of battery health status 8.3.2.4 Construction of virtual battery 8.3.2.5 Realization of battery health estimation and prediction services 8.3.2.6 Experimental results using real flight data 8.3.3 Example of infrared imaging system 8.3.3.1 Problem description 8.3.3.2 The framework of DT-driven solution 8.3.3.3 Physical analysis of IR imaging system 8.3.3.4 Construction of virtual IR imaging system 8.3.3.5 Strategy of DT data synchronization Model inference based on the improved GPF Model selflearning based on DPMM 8.3.3.6 Realization of IR system estimation and prediction services Model inference based on the improved GPF algorithm Model structure updating based on DPMM 8.4 Summary References 9 - Production process management for intelligent coal mining based on digital twin 9.1 Introduction 9.2 Production process management and control framework of coal mining face based on digital twin 9.2.1 Digital twin model of coal mine production process management and control 9.2.2 Intelligent interaction mode of HCPS of mining equipment 9.3 Key technologies 9.3.1 Intelligent sensing 9.3.2 Position and attitude control 9.3.3 Running condition identification and abnormal diagnosis 9.3.4 DT-driven prognostics and health management for key CMEs 9.3.5 DT-driven adaptive control for intelligent mining 9.4 Typical application case 9.4.1 DT-driven PHM for shearer 9.4.2 DT-driven interaction between virtual and real in remote monitoring 9.4.3 Verification of the DT-driven MR-aided maintenance method 9.4.4 Hands-on training 9.4.4.1 VR roaming training 9.4.4.2 MR operation training 9.4.4.3 Robot remote control 9.5 Summary References 10 - Digital twin enhanced tribo-test service 10.1 Introduction 10.2 The related works 10.2.1 Research progress on tribo-test 10.2.2 Digital twin-driven test service 10.3 Understanding of tribo-test service 10.3.1 Scope of tribo-test service 10.3.2 Characteristic of tribo-test service 10.4 The framework of digital twin enhanced tribo-test service 10.4.1 Modeling of digital twin enhanced tribo-test service 10.4.1.1 Modeling of virtual tribo-test machine 10.4.1.2 Modeling of digital twin data 10.4.1.3 Modeling of tribo-test service 10.4.2 Procedures of application of DT framework 10.5 Case study 10.5.1 Tribo-test machine 10.5.1.1 Basic description 10.5.1.2 Experimental procedure Preparation Experiment Operation specification 10.5.2 Digital twin enhanced tribo-test service 10.5.2.1 Who needs to use 10.5.2.2 Why need to use 10.5.2.3 How to use 10.5.2.4 For clients 10.5.2.5 For providers 10.5.2.6 Other functions Tribo-test machine model view Real-time data monitoring Simulation analysis of tribological test 10.6 Summary Acknowledgments References Index A B C D E F G H I K L M N O P R S T U V W X Z Back Cover