Optimization of Manufacturing Systems Using the Internet of Things
- 1st Edition - October 21, 2016
- Imprint: Academic Press
- Authors: Yingfeng Zhang, Fei Tao
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 0 9 9 1 0 - 0
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 0 9 9 1 1 - 7
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 Manufactu… Read more
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Request a sales quoteOptimization 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
Graduate students and researchers in manufacturing engineering and operations management; operations managers, control engineers, systems engineers at large manufacturers
- Preface
- Chapter 1: Introduction
- Abstract
- 1.1. The concept of IoT
- 1.2. Existing manufacturing paradigms and their 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
- Chapter 2: Overview of IoT-Enabled Manufacturing System
- Abstract
- 2.1. Introduction
- 2.2. Related work
- 2.3. Overall architecture of IoT-MS
- 2.4. Integration framework of real-time manufacturing information
- 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
- Abstract
- 3.1. Introduction
- 3.2. Related works
- 3.3. Overall architecture of real-time and multisource RMMISS
- 3.4. Deployment of multisensors
- 3.5. Multiple sensors manager
- 3.6. Multisource manufacturing information capturing and sharing
- 3.7. Case study
- Chapter 4: IoT-Enabled Smart Assembly Station
- Abstract
- 4.1. Introduction
- 4.2. Related works
- 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
- Abstract
- 5.1. Introduction
- 5.2. Related works
- 5.3. Overall architecture of manufacturing resources configuration method
- 5.4. Cloud machine model
- 5.5. MS-UDDI
- 5.6. Manufacturing service registration and publication
- 5.7. Task-driven manufacturing service configuration model
- Chapter 6: IoT-Enabled Smart Trolley
- Abstract
- 6.1. Introduction
- 6.2. Related works
- 6.3. Real-time information enabled material handling strategy
- 6.4. Overall architecture of optimization model for SMH
- 6.5. IoT-enabled smart trolley
- 6.6. Two-stage combination optimization method for move tasks
- Chapter 7: Real-Time Key Production Performances Analysis Method
- Abstract
- 7.1. Introduction
- 7.2. Related works
- 7.3. Overall architecture of real-time production performance analysis model
- 7.4. The event hierarchy of critical event
- 7.5. HTCPN-based critical event analysis
- 7.6. Real-time production anomaly diagnosis
- Chapter 8: Real-Time Information-Driven Production Scheduling System
- Abstract
- 8.1. Introduction
- 8.2. Related works
- 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
- Chapter 9: IoT-MS Prototype System
- Abstract
- 9.1. Configuration of a smart shop floor
- 9.2. The framework of the prototype system
- 9.3. The logical flow of the prototype system
- 9.4. Task driven manufacturing resource configuration module
- 9.5. Production scheduling/rescheduling module
- 9.6. IoT-enabled smart material handling module
- 9.7. IoT-enabled smart station
- 9.8. Real-time manufacturing information track and trace
- 9.9. Real-time key production performances monitor module
- Chapter 10: Conclusions and Future Works
- Abstract
- 10.1. Conclusions
- 10.2. Future works
- Index
- Edition: 1
- Published: October 21, 2016
- No. of pages (Paperback): 226
- No. of pages (eBook): 226
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780128099100
- eBook ISBN: 9780128099117
YZ
Yingfeng Zhang
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.
Affiliations and expertise
Northwestern Polytechnical University, ChinaFT
Fei Tao
Fei Tao is a Professor at the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. His research interests are service-oriented smart manufacturing, manufacturing service management and optimization, digital twin driven product design/manufacturing/service, green and sustainable manufacturing.
Affiliations and expertise
Professor, School of Automation Science and Electrical Engineering, Beihang University, ChinaRead Optimization of Manufacturing Systems Using the Internet of Things on ScienceDirect