Optimization of Manufacturing Systems Using the Internet of Things

 
 
Academic Press
  • 1. Auflage
  • |
  • erschienen am 21. Oktober 2016
  • |
  • 226 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-12-809911-7 (ISBN)
 

Optimization of Manufacturing Systems Using the Internet of Things extends the IoT (Internet of Things) into the manufacturing field to develop an IoMT (Internet of Manufacturing Things) architecture with real-time traceability, visibility, and interoperability in production planning, execution, and control. This book is essential reading for anyone interested in the optimization and control of an intelligent manufacturing system.

As modern manufacturing shop-floors can create bottlenecks in the capturing and collection of real-time field information, and because paper-based manual systems are time-consuming and prone to errors, this book helps readers understand how to alleviate these issues, assisting them in their decision-making on shop-floors..


  • Includes case studies in implementing IoTs for data acquisition, monitoring, and assembly in manufacturing.
  • Helps manufacturers to tackle the growing complexities and uncertainties of manufacturing systems in globalized business environments
  • Acts as an introduction to using IoT for readers across industrial and manufacturing engineering


Yingfeng Zhang is a professor at the department of Industrial Engineering of the Northwestern Polytechnical University, China. Currently, his research interests are the Internet of Manufacturing Things (IoMT) and real-time data driven production optimization.
  • Englisch
  • San Diego
  • |
  • USA
Elsevier Science
  • 20,20 MB
978-0-12-809911-7 (9780128099117)
0128099119 (0128099119)
weitere Ausgaben werden ermittelt
  • Cover
  • Title page
  • Copyright page
  • Contents
  • Preface
  • Chapter 1 - Introduction
  • 1.1 - The concept of IoT
  • 1.2 - Existing manufacturing paradigms and their limitations
  • 1.2.1 - Agile Manufacturing
  • 1.2.2 - Networked Manufacturing
  • 1.2.3 - Reconfigurable Manufacturing Systems
  • 1.2.3.1 - Modularity
  • 1.2.3.2 - Integrability
  • 1.2.3.3 - Customization
  • 1.2.3.4 - Convertibility
  • 1.2.3.5 - Scalability
  • 1.2.3.6 - Diagnosability
  • 1.2.4 - Product-Service System/Industrial Product-Service Systems
  • 1.2.5 - Manufacturing Grid
  • 1.2.6 - Cloud Manufacturing
  • 1.2.7 - Limitations
  • 1.3 - Applications of IoT in manufacturing system
  • 1.4 - The conception of IoT-MS
  • 1.5 - Key features and limitations of IoT-MS
  • 1.6 - Organization of the book
  • References
  • Chapter 2 - Overview of IoT-Enabled Manufacturing System
  • 2.1 - Introduction
  • 2.2 - Related work
  • 2.2.1 - Advanced Manufacturing Paradigms and Technologies
  • 2.2.2 - Manufacturing Information Standard and Share and Integration Method
  • 2.3 - Overall architecture of IoT-MS
  • 2.4 - Integration framework of real-time manufacturing information
  • 2.4.1 - Framework of Real-Time Manufacturing Information Sharing and Integration
  • 2.4.2 - Real-Time Manufacturing Data Processing, Sharing, and Exchanging Service
  • 2.5 - The worklogic of IoT-MS
  • 2.6 - Description of the core technologies of IoT-MS
  • References
  • Chapter 3 - Real-Time and Multisource Manufacturing Information Sensing System
  • 3.1 - Introduction
  • 3.2 - Related works
  • 3.2.1 - Real-Time Manufacturing Data Capturing
  • 3.2.2 - Sensor Management
  • 3.2.3 - Manufacturing Information Processing and Sharing
  • 3.3 - Overall architecture of real-time and multisource RMMISS
  • 3.3.1 - Deployment of Multiple Sensors
  • 3.3.2 - Multiple Sensors Manager
  • 3.3.3 - Multisource Manufacturing Information Processing and Sharing
  • 3.4 - Deployment of multisensors
  • 3.4.1 - Description of Multisource Manufacturing Information
  • 3.4.2 - Multiple Sensors Selection
  • 3.5 - Multiple sensors manager
  • 3.6 - Multisource manufacturing information capturing and sharing
  • 3.6.1 - Data Preprocessing
  • 3.6.2 - Information Encapsulation
  • 3.6.3 - Manufacturing Information Sharing
  • 3.7 - Case study
  • 3.7.1 - Hardware Device
  • 3.7.2 - Software System
  • References
  • Chapter 4 - IoT-Enabled Smart Assembly Station
  • 4.1 - Introduction
  • 4.2 - Related works
  • 4.2.1 - RFID-Based Applications in Assembly Line
  • 4.2.2 - Assistant Services for Assembly Line
  • 4.3 - Overall architecture of IoT-enabled smart assembly station
  • 4.4 - Real-time status monitoring
  • 4.5 - Real-time production guiding
  • 4.6 - Real-time production data sharing
  • 4.7 - Real-time production requeuing
  • References
  • Chapter 5 - Cloud Computing-Based Manufacturing Resources Configuration Method
  • 5.1 - Introduction
  • 5.2 - Related works
  • 5.2.1 - Cloud Manufacturing
  • 5.2.2 - Real-Time Production Information Perception and Capturing
  • 5.2.3 - Cloud Service Selection and Composition
  • 5.3 - Overall architecture of manufacturing resources configuration method
  • 5.4 - Cloud machine model
  • 5.4.1 - The Information Model of Manufacturing Service
  • 5.4.2 - The Ontology Model of Manufacturing Service
  • 5.5 - MS-UDDI
  • 5.5.1 - UDDI
  • 5.5.2 - The Framework of MS-UDDI
  • 5.6 - Manufacturing service registration and publication
  • 5.7 - Task-driven manufacturing service configuration model
  • 5.7.1 - Task-Driven Service Proactive Discovery
  • 5.7.2 - Service Optimal Configuration Method
  • References
  • Chapter 6 - IoT-Enabled Smart Trolley
  • 6.1 - Introduction
  • 6.2 - Related works
  • 6.2.1 - Material Handling
  • 6.2.2 - Real-Time Data Capturing in Manufacturing Field
  • 6.3 - Real-time information enabled material handling strategy
  • 6.4 - Overall architecture of optimization model for SMH
  • 6.5 - IoT-enabled smart trolley
  • 6.5.1 - Real-Time Information Capturing and Encapsulation
  • 6.5.2 - Real-Time Information Exchange
  • 6.5.3 - Workflow-Based Real-Time Guidance
  • 6.6 - Two-stage combination optimization method for move tasks
  • 6.6.1 - Real-Time Information Models of Move Tasks
  • 6.6.2 - Preoptimization for Candidate Tasks Set
  • 6.6.3 - AHP-Based Combination Optimization
  • References
  • Chapter 7 - Real-Time Key Production Performances Analysis Method
  • 7.1 - Introduction
  • 7.2 - Related works
  • 7.2.1 - Real-Time Production Monitoring Technique
  • 7.2.2 - Real-Time Production KPIs Analysis
  • 7.2.3 - Real-Time Production Anomaly Analysis
  • 7.2.4 - Research Gap
  • 7.3 - Overall architecture of real-time production performance analysis model
  • 7.3.1 - Configuration of Smart Sensors
  • 7.3.2 - Critical Event-Based Information Extracting Process
  • 7.3.3 - Real-Time Key Production Anomaly Analysis
  • 7.4 - The event hierarchy of critical event
  • 7.5 - HTCPN-based critical event analysis
  • 7.5.1 - Basic Concepts of HTCPN
  • 7.5.2 - HTCPN Model Construction
  • 7.5.3 - Connection Between HTCPN and Manufacturing Resources
  • 7.5.4 - Production Performance Extraction
  • 7.6 - Real-time production anomaly diagnosis
  • 7.6.1 - New Cases
  • 7.6.2 - Historical Cases
  • 7.6.3 - Decision Variables
  • 7.6.4 - Tree Builder
  • 7.6.5 - Anomaly Extraction and Causes Diagnosis
  • References
  • Chapter 8 - Real-Time Information-Driven Production Scheduling System
  • 8.1 - Introduction
  • 8.2 - Related works
  • 8.2.1 - Agent Technology and Applications in Manufacturing Field
  • 8.2.2 - Real-Time Production Scheduling
  • 8.2.3 - Manufacturing Information Monitor Technology
  • 8.3 - Overall architecture of real-time information-driven production scheduling system
  • 8.4 - Equipment agent
  • 8.5 - Capability evaluation agent model
  • 8.6 - Real-time scheduling agent model
  • 8.7 - Production execution monitor agent model
  • 8.8 - GA-based production scheduling algorithm
  • References
  • Chapter 9 - IoT-MS Prototype System
  • 9.1 - Configuration of a smart shop floor
  • 9.1.1 - Formation of the Production Task
  • 9.1.2 - Layout of the Shop Floor
  • 9.1.3 - Deployment of Hardware Devices
  • 9.2 - The framework of the prototype system
  • 9.2.1 - System Architecture
  • 9.2.2 - Information Model
  • 9.3 - The logical flow of the prototype system
  • 9.4 - Task driven manufacturing resource configuration module
  • 9.4.1 - Phase 1: MC Optimal Configuration
  • 9.4.2 - Phase 2: CMS Optimal Configuration
  • 9.5 - Production scheduling/rescheduling module
  • 9.5.1 - Quantifying the Tasks
  • 9.5.2 - The Scheduling and the Rescheduling Method
  • 9.6 - IoT-enabled smart material handling module
  • 9.6.1 - Task Description
  • 9.6.2 - Calculations for the Moving Tasks
  • 9.6.3 - User Interfaces of the Prototype System
  • 9.7 - IoT-enabled smart station
  • 9.7.1 - The Case Scenario
  • 9.7.2 - Operation Guidance From the System
  • 9.7.3 - Real-Time Queuing Under Exceptions
  • 9.8 - Real-time manufacturing information track and trace
  • 9.9 - Real-time key production performances monitor module
  • 9.9.1 - Details of the Case
  • 9.9.2 - The Hierarchy Timed Color Petri Net Model
  • References
  • Chapter 10 - Conclusions and Future Works
  • 10.1 - Conclusions
  • 10.2 - Future works
  • Index
  • Back cover

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