Chapter
1.2 - Existing manufacturing paradigms and their limitations
1.2.1 - Agile Manufacturing
1.2.2 - Networked Manufacturing
1.2.3 - Reconfigurable Manufacturing Systems
1.2.4 - Product-Service System/Industrial Product-Service Systems
1.2.5 - Manufacturing Grid
1.2.6 - Cloud Manufacturing
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
Chapter 2 - Overview of IoT-Enabled Manufacturing System
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
Chapter 3 - Real-Time and Multisource Manufacturing Information Sensing System
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
Chapter 4 - IoT-Enabled Smart Assembly Station
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
Chapter 5 - Cloud Computing-Based Manufacturing Resources Configuration Method
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
5.5.2 - 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
Chapter 6 - IoT-Enabled Smart Trolley
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
Chapter 7 - Real-Time Key Production Performances Analysis Method
7.2.1 - Real-Time Production Monitoring Technique
7.2.2 - Real-Time Production KPIs Analysis
7.2.3 - Real-Time Production Anomaly Analysis
7.3 - 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
7.6.3 - Decision Variables
7.6.5 - Anomaly Extraction and Causes Diagnosis
Chapter 8 - Real-Time Information-Driven Production Scheduling System
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
8.5 - 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
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
9.6.2 - 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
Chapter 10 - Conclusions and Future Works
Optimization of Manufacturing Systems Using the Internet of Things
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