Production events graphical deduction model enabled real-time production control system for smart job shop

Abstract

Under industry 4.0, the production control system of smart job shop should be able to real-time respond to various production events and effectively coordinate different kinds of manufacturing resources in good order according to their material flows. Production events enabled real-time production control system is good at responsibility and flexibility. Internet of things (IoT) can provide enormous real-time production events, which represent the change of material flows. However, some of production events seriously interfere with production control procedures. They sharply restrict the real-time capability of production control system. Thus, it is imperative that an efficient realization method of production control system, which is enabled by useful production events. Additionally, the control system should satisfy production control procedures visibility. For solving the problems, a production events graphical deduction model enabled real-time production control system for smart job shop is proposed in this article. Firstly, the manufacturing resources are divided into work in process related, operator-related, cutting tool-related, fixture tool-related, and measuring tool-related. And the material flows of different manufacturing resources in IoT-enabled smart job shop are described in detail. Secondly, the graphical deduction model of production event is put forward. Based on the model, the material flows of manufacturing resources in a process are segmented into several stages according to different production events. And then, the cooperation model of manufacturing resources is established by using the time and logical relationships between production events. Thirdly, the control model in a process is drawn from the cooperation model. Next, the entire control procedure of work in process production in IoT-enabled smart job shop is proposed. Finally, a small-scale IoT-enabled manufacturing system is used to verify the feasibility of the proposed model and methods.

Keywords

Production events graphical deduction model real-time production control system smart job shop IoT/RFID

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References

Persentili

Alptekin

. Product flexibility in selecting manufacturing planning and control strategy. Int J Prod Res 2000; 9: 2011–2021.

Liu MR, Zhang QL, Ni LM, et al. An RFID-based distributed control system for mass customization manufacturing. In: International conference on parallel and distributed processing and applications. Berlin Heidelberg: Springer, 2004.

Wang

Jiang

Ding

. A hybrid-data-on-tag-enabled decentralized control system for flexible smart workpiece manufacturing shop floors. Proc IMechE, Part C: J Mechanical Engineering Science 2015.

Wang

Jiang

. Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops. J Intell Manuf 2016.

Wang

. Some applications of RFID in manufacturing engineering. Int J Inform 2012.

Wang J, Luo Z, Wong EC, et al. RFID assisted object tracking for automating manufacturing assembly lines. In: IEEE international conference on e-Business engineering, 2007, pp.48–53. New York: IEEE.

Huang

Zhang

Jiang

. RFID-based wireless manufacturing for walking-worker assembly islands with fixed-position layouts. Robot Comput-Integr Manuf 2007; 23: 469–477.

Vogel L, Prabhu BS and Gadh R. Development and testing of an RFID-based cutting tools tracking application. In: 2nd HP RFID symposium, San Paulo, Brazil, 2007, pp.1–9.

Yang

Huang

, et al. A case of implementing RFID-based real-time shop-floor material management for household electrical appliance manufacturers. J Intell Manuf 2012; 23: 2343–2356.

10.

Jiang

Cao

. An RFID-driven graphical formalized deduction for describing the time- sensitive state and position changes of work-in-progress material flows in a job-shop floor. J Manuf Sci Eng 2013; 135: 1–14.

11.

Ding

Jiang

Sun

, et al. RFID-enabled physical object tracking in process flow based on an enhanced graphical deduction modeling method. IEEE Trans Syst Man Cybern Syst 2016.

12.

Ding K and Jiang P. A graphical formalized deduction for integrated production and logistic service flow monitoring in social manufacturing. In: Proceedings of the 6th international Asia conference on industrial engineering and management innovation, Tianjin, China, 2016, pp.603–613.

13.

Brusey

McFarlane

. Effective RFID-based object tracking for manufacturing. Int J Comput Integr Manuf 2009; 7: 638–647.

14.

Wang

Liu

. Complex RFID event processing. VLDB J 2009; 18: 913–931.

15.

Wang W, Sung J and Kim D. Complex event processing in EPC sensor network middleware for both RFID and WSN. In: 11th IEEE international symposium on object and component-oriented real-time distributed computing (ISORC), 2008, pp.165–169. New York: IEEE.

16.

Wang

Jiang

. Deep neural networks based order completion time prediction by using real-time job shop RFID data. J Intell Manuf 2017.

17.

Huang

Zhang

Chen

, et al. RFID-enabled real-time wireless manufacturing for adaptive assembly planning and control. J Intell Manuf 2008; 19: 701–713.

18.

Liu F and Miao Z. The application of RFID technology in production control in the discrete manufacturing industry. In: IEEE international conference on advanced video and signal based surveillance, Sydney, Australia, 22–24 November 2006.

19.

Chen

. Development of an agent-based system for manufacturing control and coordination with ontology and RFID technology. Expert Syst Appl 2009; 36: 7581–7593.

20.

Qiu

. RFID-enabled automation in support of factory integration. Robot Comput-Integr Manuf 2007; 23: 677–683.

21.

Småros

Lehtonen

Appelqvist

, et al. The impact of increasing demand visibility on production and inventory control efficiency. Int J Phys Distrib LogistManage 2003; 4: 336–354.

22.

Ramírez-Hernández

Cuamea-Cruz

. Real-time production visibility protocol using radiofrequency identification technology at a blinds manufacturing company. Athens J Technol Eng 2014; 1: 63–72.

23.

Guo

Ngai

EWT

Yang

, et al. An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment. Int J Prod Econ 2015; 159: 16–28.

24.

Kagermann H, Helbig J and Hellinger A. Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German Manufacturing Industry. Final Report of the Industrie 4.0 Working Group, Forschungsunion, 2013.

25.

Wang C and Jiang P. Cognitive computing based manufacturing data processing for internet of things in job-shop floor. In: 2015 IEEE international conference on mechatronics and automation (ICMA), 2015, pp.2521–2526. New York: IEEE.