Customized Production Through 3D Printing in Cloud Manufacturing explains how to combine the latest cloud manufacturing and additive manufacturing technology to find innovative solutions to important problems in research and industry.
The manufacturing industry strives constantly to improve levels of product personalization for its customers, who have become increasingly demanding in this respect in recent decades. Among the tools currently growing in use in the industry, there is great potential to address this demand. Cloud manufacturing maps manufacturing resources and capabilities to the cloud, adding the capacity to gather decentralized manufacturing resources and use manufacturing services on-demand, and 3D printing provides strong support for truly individualized manufactured components.
This is the first book to cover the whole lifecycle of 3D printing services in a cloud environment, including topics like: cloud servitization of 3D printers, 3D printing model design, supply-demand matching and scheduling, on-demand using and pricing, printing monitoring in cloud, and printing service evaluation. With a systematic introduction to this promising manufacturing paradigm, as well as coverage of models and service management to practical applications, this book will meet the needs of a broad range of researchers as well as practitioners.
- Provides readers with a unique combined technical overview of two rapidly developing technologies and how they interact in a modern manufacturing system
- Explores important challenges to security and privacy posed by these new technologies
- Draws on valuable knowledge of how these technologies have been applied in innovative industry settings
1. Review of Customized Manufacturing
2. Introduction to Cloud Manufacturing
3. 3D Printing with Cloud Manufacturing
4. Framework of 3D Printing Cloud Service
5. 3D Printing Modeling Technology
6. 3D Printer Resource Access and Servitization Technology
7. Supply-demand Matching and Task Scheduling Technology
8. Service Capability Evaluation Technology
9. Cloud 3D Printing Monitoring Technology
10. 3D Printing Service Credibility Evaluation Technology
11. Application Examples of 3D Printing Cloud Service Platform
12. Conclusion and Future Works
Lin Zhang is Professor of Computer and Systems Science at Beihang Unversity. He received the B.S. degree in 1986 from the Department of Computer and System Science at Nankai University, China. He received the M.S. degree and the Ph.D. degree in 1989 and 1992 from the Department of Automation at Tsinghua University, China. He served as the director of CIMS Office, China National 863 Program, from 1997 to 2001. From 2002 to 2005 he worked at the US Naval Postgraduate School as a senior research associate of the US National Research Council. Currently, he serves as the immediately past President of the Society for Modeling & Simulation International (SCS), a Fellow of the Federation of Asian Simulation Societies (ASIASIM), the executive vice president of Chinese Association for System Simulation (CASS), an IEEE senior member, a chief scientist of the 863 key projects, and associate Editor-in-Chief and associate editor of 5 peer-reviewed international journals. He has authored and co-authored 160 papers, 5 books and chapters. His research interests include service oriented modeling and simulation, agent based control and simulation, cloud manufacturing, model engineering. Longfei Zhou is currently a postdoc associate in the Carl E. Ravin Advanced Imaging Laboratories (RAI Labs) at Duke University. His research interests include cloud manufacturing, scheduling, modeling and simulation, and computer vision. Dr. Zhou received his Ph.D. degree in 2018 from Beihang University where he studied the dynamic scheduling problem in cloud manufacturing and proposed the simulation-based dynamic scheduling method to overcome uncertainties. In January 2019, Dr. Zhou joined MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) as a postdoc to study reinforcement learning-based scheduling and logistics scheduling problems. In October 2020, he joined Duke University as a postdoc to do machine learning-based medical imaging analysis. Xiao Luo is a researcher in the Dept of Information and Communication Engineering at Beihang University, China. His research interests are focused on cloud manufacturing.
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