Optimization of Manufacturing Systems Using the Internet of Things

Häftad, Engelska, 2016

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Beskrivning

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

Produktinformation

Utgivningsdatum:2016-10-21
Mått:152 x 229 x 15 mm
Vikt:390 g
Format:Häftad
Språk:Engelska
Antal sidor:226
Förlag:Elsevier Science
ISBN:9780128099100

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Mer om författaren

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. 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.

Innehållsförteckning

1. Introduction2. Overview of IoMT3. Real-time and multi-source manufacturing information perception4. IoT-enabled smart assembly station5. Cloud computing based manufacturing resources configuration method6. Real-time information-driven production-scheduling system7. IoT-enabled smart material handling8. Real-time key production performances monitor method9. IoMTprototype system10. Conclusions and future works