The digital shopfloor industrial automation in the industry 4.0 era : performance analysis and applications
In today’s competitive global environment, manufacturers are offered with unprecedented opportunities to build hyper-efficient and highly flexible plants, towards meeting variable market demand, while at the same time supporting new production models such as make-to-order (MTO), configure-to-order (...
| Otros Autores: | , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Gistrup, Denmark :
River Publishers
2019.
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| Edición: | 1st ed |
| Colección: | River Publishers series in automation, control and robotics.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009703334406719 |
Tabla de Contenidos:
- Front Cover
- Half Title
- RIVER PUBLISHERS SERIES IN AUTOMATION, CONTROL AND ROBOTICS
- Title Page - The Digital Shopfloor: Industrial Automation in the Industry 4.0 Era Performance Analysis and Applications
- Copyright
- Contents
- Foreword
- Preface
- List of Contributors
- List of Figures
- List of Tables
- List of Abbreviations
- Chapter 1 - Introduction to Industry 4.0 and the Digital Shopfloor Vision
- 1.1 Introduction
- 1.2 Drivers and Main Use Cases
- 1.3 The Digital Technologies Behind Industry 4.0
- 1.4 Digital Automation Platforms and the Vision of the Digital Shopfloor
- 1.4.1 Overview of Digital Automation Platforms
- 1.4.2 Outlook Towards a Fully Digital Shopfloor
- 1.5 Conclusion
- References
- PART I
- Chapter 2 - Open Automation Framework for Cognitive Manufacturing
- 2.1 Introduction
- 2.2 State of the Play: Digital Manufacturing Platforms
- 2.2.1 RAMI 4.0 (Reference Architecture Model Industry 4.0)
- 2.2.2 Data-driven Digital Manufacturing Platforms for Industry 4.0
- 2.2.3 International Data Spaces
- 2.3 Autoware Framework for Digital Shopfloor Automation
- 2.3.1 Digital Shopfloor Evolution: Trends &
- Challenges
- 2.3.1.1 Pillar 1: AUTOWARE open reference architecture for autonomous digital shopfloor
- 2.3.1.2 Pillar 2: AUTOWARE digital abilities for automatic awareness in the autonomous digital shopfloor
- 2.3.1.3 Pillar 3: AUTOWARE business value
- 2.3.2 AUTOWARE Software-Defined Autonomous Service Platform
- 2.3.2.1 Cloud &
- Fog computing services enablers and context management
- 2.3.3 AUTOWARE Framework and RAMI 4.0 Compliance
- 2.4 Autoware Framework for Predictive Maintenance Platform Implementation
- 2.4.1 Z-BRE4K: Zero-Unexpected-Breakdowns and Increased Operating Life of Factories
- 2.4.2 Z-Bre4k Architecture Methodology
- 2.4.3 Z-BRE4K General Architecture Structure.
- 2.4.4 Z-BRE4K General Architecture Information Workflow
- 2.4.5 Z-BRE4K General Architecture Component Distribution
- 2.5 Conclusions
- References
- Chapter 3 - Reference Architecture for Factory Automation using Edge Computing and Blockchain Technologies
- 3.1 FAR-EDGE Project Background
- 3.2 FAR-EDGE Vision and Positioning
- 3.3 State of the Art in Reference Architectures
- 3.3.1 Generic Reference Architectures
- 3.3.2 RAMI 4.0
- 3.3.3 IIRA
- 3.3.4 OpenFog RA
- 3.4 FAR-EDGE Reference Architecture
- 3.4.1 Functional Viewpoint
- 3.4.1.1 Automation domain
- 3.4.1.2 Analytics domain
- 3.4.1.3 Simulation domain
- 3.4.1.4 Crosscutting functions
- 3.4.2 Structural Viewpoint
- 3.4.2.1 Field Tier
- 3.4.2.2 Gateway Tier
- 3.4.2.3 Ledger Tier
- 3.4.2.4 Cloud Tier
- 3.5 Key Enabling Technologies for Decentralization
- 3.5.1 Blockchain Issues
- 3.5.2 Permissioned Blockchains
- 3.5.3 The FAR-EDGE Ledger Tier
- 3.5.4 Validation use Cases
- 3.6 Conclusions
- References
- Chapter 4 - IEC-61499 Distributed Automation for the Next Generation of Manufacturing Systems
- 4.1 Introduction
- 4.2 Transition towards the Digital Manufacturing Paradigm: A Need of the Market
- 4.3 Reasons for a New Engineering Paradigm in Automation
- 4.3.1 Distribution of Intelligence is Useless without Appropriate Orchestration Mechanisms
- 4.3.2 Defiance of Rigid Hierarchical Levels towards the Full Virtualization of the Automation Pyramid
- 4.4 IEC-61499 Approach to Cyber-Physical Systems
- 4.4.1 IEC-61499 runtime
- 4.4.2 Functional Interfaces
- 4.4.2.1 IEC-61499 interface
- 4.4.2.2 Wireless interface
- 4.4.2.3 Wrapping interface
- 4.4.2.4 Service-oriented interface
- 4.4.2.5 Fieldbus interface(s)
- 4.4.2.6 Local I/O interface
- 4.5 The "CPS-izer", a Transitional Path towards Full Adoption of IEC-61499
- 4.6 Conclusions
- References.
- Chapter 5 - Communication and Data Management in Industry 4.0
- 5.1 Introduction
- 5.2 Industry 4.0 Communication and Data Requirements
- 5.3 Industrial Wireless Network Architectures
- 5.4 Data Management in Industrial Environments
- 5.5 Hierarchical Communication and Data Management Architecture for Industry 4.0
- 5.5.1 Heterogeneous Industrial Wireless Network
- 5.5.2 Hierarchical Management
- 5.5.2.1 Hierarchical communications
- 5.5.2.2 Data management
- 5.5.3 Multi-tier Organization
- 5.5.4 Architectural Enablers: Virtualization and Softwarization
- 5.5.4.1 RAN slicing
- 5.5.4.2 Cloudification of the RAN
- 5.6 Hybrid Communication Management
- 5.7 Decentralized Data Distribution
- 5.7.1 Average Data Access Latency Guarantees
- 5.7.2 Maximum Data Access Latency Guarantees
- 5.7.3 Dynamic Path Reconfigurations
- 5.8 Communications and Data Management within the AUTOWARE Framework
- 5.9 Conclusions
- References
- Chapter 6 - A Framework for Flexible and Programmable Data Analytics in Industrial Environments
- 6.1 Introduction
- 6.2 Requirements for Industrial-scale Data Analytics
- 6.3 Distributed Data Analytics Architecture
- 6.3.1 Data Routing and Preprocessing
- 6.3.2 Edge Analytics Engine
- 6.3.3 Distributed Ledger
- 6.3.4 Distributed Analytics Engine (DA-Engine)
- 6.3.5 Open API for Analytics
- 6.4 Edge Analytics Engine
- 6.4.1 EA-Engine Processors and Programmability
- 6.4.2 EA-Engine Operation
- 6.4.3 Configuring Analytics Workflows
- 6.4.4 Extending the Processing Capabilities of the EA-Engine
- 6.4.5 EA-Engine Configuration and Runtime Example
- 6.5 Distributed Ledger and Data Analytics Engine
- 6.5.1 Global Factory-wide Analytics and the DA-Engine
- 6.5.2 Distributed Ledger Services in the FAR-EDGE Platform
- 6.5.3 Distributed Ledger Services and DA-Engine.
- 6.6 Practical Validation and Implementation
- 6.6.1 Open-source Implementation
- 6.6.2 Practical Validation
- 6.6.2.1 Validation environment
- 6.6.2.2 Edge analytics validation scenarios
- 6.6.2.3 (Global) distributed analytics validation scenarios
- 6.7 Conclusions
- References
- Chapter 7 - Model Predictive Control in Discrete Manufacturing Shopfloors
- 7.1 Introduction
- 7.1.1 Hybrid Model Predictive Control SDK
- 7.1.2 Requirements
- 7.1.3 Hybrid System
- 7.1.4 Model Predictive Control
- 7.2 Hybrid System Representation
- 7.2.1 Piece-Wise Affine (PWA) System
- 7.2.2 Mixed Logical Dynamical (MLD) System
- 7.2.3 Equivalence of Hybrid Dynamical Models
- 7.3 Hybrid Model Predictive Control
- 7.3.1 State of the Art
- 7.3.2 Key Factors
- 7.3.3 Key Issues
- 7.4 Identification of Hybrid Systems
- 7.4.1 Problem Setting
- 7.4.2 State-of-the-Art Analysis
- 7.4.3 Recursive Two-Stage Clustering Approach
- 7.4.4 Computation of the State Partition
- 7.5 Integration of Additional Functionalities to the IEC 61499 Platform
- 7.5.1 A Brief Introduction to the Basic Function Block
- 7.5.2 A Brief Introduction to the Composite Function Block
- 7.5.3 A Brief Introduction to the Service Interface Function Block
- 7.5.4 The Generic DLL Function Block of nxtControl
- 7.5.5 Exploiting the FB DLL Function Block as Interfacing Mechanism between IEC 61499 and External Custom Code
- 7.6 Conclusions
- References
- Chapter 8 - Modular Human-Robot Applications in the Digital Shopfloor Based on IEC-61499
- 8.1 Introduction
- 8.2 Human and Robots in Manufacturing: Shifting the Paradigm from Co-Existence to Mutualism
- 8.3 The "Mutualism Framework" Based on IEC-61499
- 8.3.1 "Orchestrated Lean Automation": Merging IEC-61499 with the Toyota Philosophy
- 8.3.2 A Hybrid Team of Symbionts for Bidirectional Mutualistic Compensation.
- 8.3.3 Three-Dimensional Characterization of Symbionts' Capabilities
- 8.3.4 Machine Learning Applied to Guarantee Dynamic Adherence of Models to Reality
- 8.4 Technological Approach to the Implementation of Mutualism
- 8.4.1 "Mutualism Framework" to Sustain Implementation of Symbionts-Enhanced Manufacturing Processes
- 8.4.2 IEC-61499 Engineering Tool-Chain for the Design and Deployment of Real-Time Orchestrated Symbionts
- 8.4.3 AI-Based Semantic Planning and Scheduling of Orchestrated Symbionts' Tasks
- 8.4.4 Modular Platform for Perceptual Learning and Augmentation of Human Symbionts
- 8.4.5 Training Gymnasium for Progressive Adaptation andPerformance Improvement of Symbionts' Mutualistic Behaviours
- 8.5 The Potential to Improve Productivity and the Impact this Could Have on European Manufacturing
- 8.6 Conclusions
- References
- PART II
- Chapter 9 - Digital Models for Industrial Automation Platforms
- 9.1 Introduction
- 9.2 Scope and Use of Digital Models for Automation
- 9.2.1 Scope of Digital Models
- 9.2.2 Factory and Plant Information Modelling
- 9.2.3 Automation and Analytics Processes Modelling
- 9.2.4 Automation and Analytics Platforms Configuration
- 9.2.5 Cyber and Physical Worlds Synchronization
- 9.2.6 Dynamic Access to Plant Information
- 9.3 Review of Standards Based Digital Models
- 9.3.1 Overview
- 9.3.2 IEC 62264
- 9.3.3 IEC 62769 (FDI)
- 9.3.4 IEC 62453 (FDT)
- 9.3.5 IEC 61512 (Batch Control)
- 9.3.6 IEC 61424 (CAEX)
- 9.3.7 Business to Manufacturing Markup Language (B2MML)
- 9.3.8 AutomationML
- 9.4 FAR-EDGE Digital Models Outline
- 9.4.1 Scope of Digital Modelling in FAR-EDGE
- 9.4.2 Main Entities of Digital Models for Data Analytics
- 9.4.3 Hierarchical Structure
- 9.4.4 Model Repository Open Source Implementation
- 9.5 Simulation and Analytics Models Linking and Interoperability.
- 9.6 Conclusions.
