Preface

The manufacturing industry produces wealth, and it is one of the significant elements of the sustainability of the modern society. The complexity of products is increasing while their life cycles become shorter. Thus, the traditional manufacturing systems and their related management approaches are required to be improved to adapt to the aforementioned changes. Internet of things (IoT) provides the potentiality to collect and communicate real-time data within the manufacturing systems, thereby achieving dynamic optimization and control of production. It helps to develop a more intelligent manufacturing system with higher flexibility and transparency.

In this book, the authors apply the IoT technology to manufacturing system (IoT-MS) to capture manufacturing data actively. Based on sufficient data, real-time system monitoring and the optimization of the modern dynamic manufacturing systems could be achieved. It is expected that this research work could contribute to the cutting-edge development of modern intelligent manufacturing systems.

This work is a summary of the authors’ research works on the applications of IoT technology in manufacturing since 2010. The book includes10 chapters. Chapter 1 describes the newly advanced manufacturing technologies and intelligent manufacturing system, and then presents the conception of IoT and IoT-MS, and the challenges of IoT-MS. Chapter 2 proposes an overview of IoT-MS including the architecture, worklogic, and relevant core technologies. Chapter 3 describes the model and method of real-time and multisource manufacturing information perception. Chapter 4 presents the framework and the corresponding method of IoT-enabled smart assembly station. Chapter 5 describes the method and algorithm of cloud computing based manufacturing resources configuration. Chapter 6 describes the new strategy and method for IoT-enabled smart material handling. Chapter 7 presents the models and methods for real-time key production performances monitor. Chapter 8 presents the new strategy and method for real-time information-driven production scheduling. Chapter 9 illustrates the IoT-MS prototype system through a demo. Chapter 10 summarizes the conclusions and points out the future trends.

The contents of this book were planned and organized by Prof. Yingfeng Zhang and Prof. Fei Tao. Mr. Wenbo Wang, Mr. Geng Zhang, Mr. Sichao Liu, Mr. Dong Xi, Mr. Chen Qian, and Mr. Shan Ren assisted in the preparation of useful materials for Chapters 3–9.

This work was supported in part by the National Nature Science Foundation of China (NSFC) Under Grants No. 51175435, No. 51522501 and No. 51475032, the Doctoral Fund of Ministry of Education of China under Grant 20136102110022, and the 111 Project Grant of NPU under Grant B13044, and Beijing Nova Program and Beijing Natural Science Foundation (No.4152032).

The authors would like to acknowledge the graduate students Mr. Teng Yang, Mr. Jianxue Xu, Mr. Wei Du, Ms. Miao Li, and others for their contributions to the aforementioned research projects. In addition, the authors would like to acknowledge the contributors of the references used in this book.

The authors would like to express their special thanks to China Science Press and journal publishers. Some of the material has been published in IEEE Transactions on Automation Science and Engineering (TASE), IEEE Transactions on Industrial Informatics (TII), International Journal of Production Economics (IJPE), Computers & Industrial Engineering (CIE), International Journal of Production Research (IJPR), Journal of Intelligent Manufacturing (JIM), International Journal of Advanced Manufacturing (IJAMT), and International Journal of Computer Integrated Manufacturing (IJCIM).

The methodologies, technologies, and applications of IoT in manufacturing area are experiencing explosive development. This enables the optimization of dynamic production process that has drawn increasing attention from researchers worldwide. Hence, one of the purposes of the authors to deliver this book is to provide a platform to communicate with other researchers. The authors welcome any comments and suggestions.

Xi’an, July, 2016