Modeling Analysis of Intelligent Manufacturing System Based on Software Defined Networks

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1 Modeling Analysis of Intelligent Manufacturing System Based on Software Defined Networks Yun Bai Department of Information & Telecommunication Engineering Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications, Beijing , China Abstract Intelligent manufacturing today contains certain consisted of basic components such as storage, and ing resources. People develop defined (SDN) which is contemporary architecture. The separation of control layer and data forwarding layer, as well as programmable control system forms the concept to design a successful SDN. This paper analyzes the characteristics and mechanism of an intelligent manufacturing system in order to highlight the system s advantages. The combination of intelligent manufacturing systems with SDN is the core of our approach in aspects of technical connotation, equipment modeling, and intelligent enterprise solution for implementing today s manufacturing in Industrial 4.0. Keyword: Industrial Internet, Industrial Internet Emerges I. INTRODUCTION As the market changes faster and faster, the manufacturing sector is no longer the same as before mass production of the same types of products, but rather vary according to customer requirements. This requires manufacturers flexible manufacturing processes and equipment, flexible manufacturing, manufacturing, green manufacturing, intelligent manufacturing, global manufacturing production is increasingly becoming a new direction change. Intelligent Manufacturing is the advanced stage of development of the manufacturing sector, is an important part of advanced manufacturing. US industrial Internet [1], Germany's industrial 4.0, China's depth of industrial and internet integration is its concrete manifestation. In this paper will provide a general overview of the most important traditional technologies. We designed a novel SDN architecture to meet the data communications requirements of intelligent manufacturing systems. It will also discuss how manufacturing upgrades traditional, proprietary factor-floor s to a low-cost, secure, high-performance, scalable architecture. II. INTELLIGENT MANUFACTURING SYSTEM FEATURES AND IMPLICATIONS Advanced manufacturing entails the rapid transfer of new knowledge into industrial processes and products[2]. ICT is a key enabling technology to accelerate and improve productivity in manufacturing. Intelligent Manufacturing System (IMS) is a machine and human Combination, not all replaced by machines. It is intelligent machines and expert combination of human-computer integrated system. Throughout the production process computer simulation experts active on various production processes for analysis, judgment and decision making, instead of part of the human brain to achieve a living move. At the same time, the computer will get the production of large amounts of information, optimization transformation provided data support for expert. Intelligent manufacturing systems contain components using such techniques as expert systems, fuzzy logic, neural s, and machine learning. The number, the kind, and the complexity of intelligent components of an IMS can vary a lot from one system to another, and the components themselves can be combined in many ways, depending on the application. So far there is not an accurate definition of " intelligent manufacturing ", this paper summarized the two major description: 1. Intelligent manufacturing is kind of a system that capable of realizing smart manufacture; 2. Intelligent manufacturing is kind of a technology that capable of realizing smart manufacture. The IMS is the core of the intelligent manufacturing while the technology is the details composed of the intelligent manufacturing. Generally the IMS has the characteristics as follows: 1. The standardization of all the devices, so that the time consumed for installation/maintenance of the devices will decreased dramatically, also the often used, quick-wear part can be swapped easily, so that the cost will be reduced.

2 2. It is comprised of different modules. in this way different modules can be recomposed, so that different product lines can be switched quickly. 3. The closed loop. to realize this function, the system should include sensors for collecting the producing data, the control module for data process, data adjustment. the controller for the adjusted data output, the human-computer interaction module. Based on the needs of the production system, according to the intelligent manufacturing a key factor -> Function gradually refined, intelligent manufacturing drawn diagram shown in Figure 1. Information Process Energy Material key element2:traditional plant M2M Resources Network Infrastructure Key element1:intelligent manufacturing key element3:management factors Plan Supply and marketing Crafts Inside and outside the interconnection key element4:human factor MMI Acceptance and Security On-site management and training key element5:technology factor ModelingI Communications technology Smart production technology Figure1. Intelligent Manufacturing Chart III. III.SDN ADVANTAGES AND COMPOSITION The concept of SDN stems from the research of the operating system that provides a uniform and centralized programmatic interface to the entire. Software-defined (SDN) is a promising paradigm that separates the control plane and data plane in s such that switches become simple data forwarding devices and management is controlled by logically centralized servers [3] [4][5][6] based on Open Flow. It has shown great advantages in simplifying management such that administrators have central programmable control of traffic via controllers, and new functions can be easily supported without physical access to the switches [7][8][9]. SDN model is widely accepted three-tier structure, the application layer, controllers and infrastructure layers. As the first attempt of building a operating system at a large scale, NOX [10] achieves a simple programming model for control function based on Open Flow. Flow Visor is a virtualization layer. It is the first testbed for SDN, which slices the hardware by placing a layer between control plane and the data plane. IV. SDN APPLICATIONS IN INTELLIGENT MANUFACTURING To sum up, the intelligent manufacturing is a sophisticated system. In this system, not only to achieve a three-dimensional model based on product research and development, resource management, command scheduling, quality management should be realized, but the ed work, integrated control and management, unified scheduling and allocation of resources. Diverse technologies such as big data, advanced HMI, 3D models and simulations cloud computing, CPS, Internet of Things and Services, machine to machine, and smartization [11] can be applied in intelligent manufacturing solutions. So intelligent manufacturing systems has to face the problems such that the mass data, multi-path transfer, the transfer and arrangement of manufactures, automatic management, interfaces and so on [12]. V. TRADITIONALLY SEPARATE ARCHITECTURE Today, many manufacturing industries maintain separate s to support their factory floor operations and business operations(figure 2).Over the years, these were developed to respond to the different

3 information flows and control requirements involved with manufacturing process. office Maintain and services(e RP,MES,CAP P,PDM,etc) Control gateway actuator gateway Data server store M M I/O devices Production equipment Motors, Actuators Figure 2: Traditional Proprietary Architecture Each support for each traditional administrative functions and corporate applications, such as human resources, accounting and procurement, as well as disconnections between sites and internet connectivity. The is usually based on the Ethernet and IP suite of standards. The control- connects control and monitoring devices, including programmable automation controller, actuators, I/O racks,drives and human-machine interfaces (HMIs). This, which has not been based up on standard Ethernet and IP in the past, requires a router or, in most cases, a gateway to translate application-specific protocols to Ethernet-based protocols. This translation lets information pass between the control on the factory floor and the corporate infrastructure, but has limited functionality and bandwidth, and requires significant effort to keep up to date. The device-level links the controllers with the plant floor s I/O devices, including sensors such as transducers, photo-eyes, and flowmeters, and other automation and motion equipment, such as robotics, variable frequency drives, and actuators. Interconnectivity between these devices was traditionally achieved with a variety of fieldbuses such as DeviceNet, Profibus, and Modbus. Each fieldbus has specific power, cable, and communication requirements, depending on the factory application it supports. This has lead to a replication of multiple s in the same space and the need to have multiple sets of spares, skills, and support programs. Instead of using architectures composed of multiple separate s, Industrial Ethernet can unite a company s administrative, control-level, and device-level s to run over a single infrastructure. In an Industrial Ethernet, fieldbus-specific information that is used to control I/O devices and other manufacturing components are embedded into Ethernet frames. Because the technology is based on industry standards rather than on custom or proprietary standards, it is more interoperable with other equipment and s.

4 Faced with these problems, the traditional management methods cannot be achieved. The author first introduced to the SDN intelligent manufacturing systems, intelligent manufacturing systems architecture built an SDN-based, using some automated management architecture to meet the capacity needs of opening up intelligent manufacturing systems. The figure 3 shown intelligent manufacturing systems architecture diagram based on SDN architecture. SDN controller Traditional plant Cyber physical systems Industrial control industrial Internet IOT Resource Scheduling Human Raw materials Energy WLN application CAD/CAE/CAM 3D modeling Simulation Software Process development Figure 3. Intelligent manufacturing systems architecture based on SDN In this framework, the forward of the flow will across a manufactory system, this is different from OSPF, BGP or MPLS. This mechanism requires all the flow across the monitoring devices so as to improve the security, it also manage the routing for better performance. By introducing SDM into SM, with an open, standard interface, the control function and the data are separated, finally the devices marked by ellipse in fig.3will be more easy and common. After introducing SDN, the manager send orders across the south interface of the SDN, the design send orders to control system, the control system send orders to MES system and resource system, the orders among selling departments, all of them across the north interface. Since all the management control and dispatch are realized upon the controller of SDN, all the nodes are multicommunication enabled. VI. CONCLUSION This paper analyzes the characteristics and meaning of intelligent manufacturing systems, which leads to problems facing the Intelligent Manufacturing System, and then dispersing the benefits of SDN. Which made the combination of intelligent manufacturing systems SDN concept model. Despite the introduction of the concept of SDN it has an enormous effect, the current strategic vision documents and research literature provide a somewhat scattered view of intelligent manufacturing system in this context. This

5 article positions that the key role SDN intrinsic truly intelligent manufacturing systems and provides a general model, broad overview and points out specific directions and scenarios for future research. Intelligent manufacturing model based on SDN is a new and exciting areas of research in this system is linked to the next challenge of the industrial revolution. REFERENCES [1] M. Annunziata and P.C. Evans, Industrial Internet:Pushing the Boundaries of Minds and Machine, General Electric, [2] G. Ross, Advanced Manufacturing and Its Implications for Business, special seminar organized by the SPRI Basque Country Government, 22 Oct. 2013; [3] T. Koponen, M. Casado, N. Gude, J. Stribling, L. Poutievski,M. Zhu, R. Ramanathan, Y. Iwata, H. Inoue, T. Hama, and S. Shenker, Onix: a distributed control platform for large-scale production s, in Proceedings of the 9th USENIX conference on Operating systems design and implementation, OSDI 10, Berkeley,CA, USA: USENIX Association, 2010, pp [4] A. R. Curtis, J. C. Mogul, J. Tourrilhes, P. Yalagandula, P. Sharma,and S. Banerjee, Devoflow: Scaling flow management for highperformance s, in Proceedings of the ACM SIGCOMM 2011 Conference, ser. SIGCOMM 11. New York, NY, USA: ACM,2011, pp [5] D. Levin, A. Wundsam, B. Heller, N. Handigol, and A. Feldmann, Logically centralized?: State distribution trade-offs in defined s, in Proceedings of the First Workshop on Hot Topics in Software Defined Networks, ser. HotSDN 12. New York,NY, USA: ACM, 2012, pp [6] A. Monsanto, J. Reich, N. Foster, J. Rexford, and D. Walker, Composing -defined s, in Proceedings of the 10th USENIX Conference on Networked Systems Design and Implementation,ser. nsdi 13. Berkeley, CA, USA: USENIX Association, 2013,pp [7] S. Shenker et al., The future of ing, and the past of protocols, Open Networking Summit, [8] M. Reitblatt, N. Foster, J. Rexford, and D. Walker, Consistent updates for -defined s: Change you can believe in! in Proceedings of the 10th ACM Workshop on Hot Topics in Networks, ser. HotNets-X. New York, NY, USA: ACM, 2011, pp.7:1 7:6. [9] A. Voellmy, J. Wang, Y. R. Yang, B. Ford, and P. Hudak, Maple:Simplifying sdn programming using algorithmic policies, in Proceedings of the ACM SIGCOMM 2013 Conference on SIGCOMM,ser. SIGCOMM 13. New York, NY, USA: ACM, 2013, pp [10] N. Gude, T. Koponen, J. Pettit, B. Pfaff, M. Casado, N. McKeown,and S. Shenker, Nox: towards an operating system for s, SIGCOMM Comput. Commun. Rev., vol. 38, no. 3, pp , Jul [11] S. Heng, Industry 4.0 Upgrading of Germany s Industrial Capabilities on the Horizon, Deutsche Bank Research, [12] J. Lee et al., Recent Advances and Trends in Predictive Manufacturing Systems in Big Data Environment, Manufacturing Letters, vol. 1, no. 1,2013, pp [13] Y.C. Chi, J. Fang, G.B Cai, Center mutation based differential evolution, Systems Engineering and Electronics, 2010, 32(5): pp