The framework design of smart factory in discrete manufacturing industry based on cyber-physical system

ABSTRACT A cyber-physical system (CPS) is a new trend in smart manufacturing-related research, where the physical system acts as a data access role with sensors and communication systems to collect real-world information and communicate to computation modules (i.e. cyber layer), which further analyse and notify the findings to the corresponding physical systems through multiple feedback loops. This paper develops a conceptual framework for a new paradigm called smart factory based on CPS by applying virtual-real mapping and fusion, digital twin, big data-driven, virtualisation, and edge-to-cloud service technology to the manufacturing system. The definitions, characteristics, architectures and previous case studies on smart factory based on CPS are explained in detail. In the paradigm, the digital twin merges the physical system modelling and cyberspace simulation to provide an intelligence solution simulation analysis ability. This generates a variety of digital designs of production processes, quality, cost, efficiency and environmental impact analysis about smart factory business mode in the preproduction phase based on big data which can be analysed, and provide authoritative solutions for actual physical space. The proposed system is applied and validated in a real case study from the circuit breaker production industry.

[1]  Kagermann Henning.  Recommendations for implementing the strategic initiative INDUSTRIE 4.0 , 2013 .

[2]  Jiafu Wan,et al.  Fuzzy feedback scheduling algorithm based on central processing unit utilization for a software-based computer numerical control system , 2010 .

[3]  Ming Luo,et al.  A wireless instrumented milling cutter system with embedded PVDF sensors , 2018, Mechanical Systems and Signal Processing.

[4]  Hafiz M. Hafeez,et al.  Four-axis trochoidal toolpath planning for rough milling of aero-engine blisks , 2019, Chinese Journal of Aeronautics.

[5]  Ying Tan,et al.  A prototype architecture for cyber-physical systems , 2008, SIGBED.

[6]  Fei Tao,et al.  IoT-Based Intelligent Perception and Access of Manufacturing Resource Toward Cloud Manufacturing , 2014, IEEE Transactions on Industrial Informatics.

[7]  Fei Tao,et al.  Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.

[8]  Daqiang Zhang,et al.  Cloud-Integrated Cyber-Physical Systems for Complex Industrial Applications , 2015, Mobile Networks and Applications.

[9]  Christian Diedrich,et al.  Cyber-physical systems alter automation architectures , 2014, Annu. Rev. Control..

[10]  Xiaoqing Frank Liu,et al.  Cyber-physical manufacturing cloud: Architecture, virtualization, communication, and testbed , 2017 .

[11]  Cong Zhang,et al.  Optimisation design of attribute control charts for multi-station manufacturing system subjected to quality shifts , 2016 .

[12]  Andrew Kusiak,et al.  Data-driven smart manufacturing , 2018, Journal of Manufacturing Systems.

[13]  Biqing Huang,et al.  Cloud manufacturing service platform for small- and medium-sized enterprises , 2012, The International Journal of Advanced Manufacturing Technology.

[14]  Yingfeng Zhang,et al.  Real-time information capturing and integration framework of the internet of manufacturing things , 2015, Int. J. Comput. Integr. Manuf..

[15]  Sidney C. Smith,et al.  A theoretical exploration of the impact of packet loss on network intrusion detection , 2014, 15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).

[16]  Athanasios V. Vasilakos,et al.  Data Mining for the Internet of Things: Literature Review and Challenges , 2015, Int. J. Distributed Sens. Networks.

[17]  Jiafu Wan,et al.  Implementing Smart Factory of Industrie 4.0: An Outlook , 2016, Int. J. Distributed Sens. Networks.

[18]  Lihui Wang,et al.  Current status and advancement of cyber-physical systems in manufacturing , 2015 .

[19]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[20]  Ravi Shankar,et al.  A big data driven sustainable manufacturing framework for condition-based maintenance prediction , 2017, J. Comput. Sci..

[21]  Fei Tao,et al.  Research on manufacturing grid resource service optimal-selection and composition framework , 2012, Enterp. Inf. Syst..

[22]  Hao Tang,et al.  Cloud-assisted interaction and negotiation of industrial robots for the smart factory , 2017, Comput. Electr. Eng..

[23]  Marco Zennaro,et al.  Successful deployment of a Wireless Sensor Network for precision agriculture in Malawi , 2012, NESEA.

[24]  Daqiang Zhang,et al.  Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination , 2016, Comput. Networks.

[25]  Dazhong Wu,et al.  Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.

[26]  Robert X. Gao,et al.  A new paradigm of cloud-based predictive maintenance for intelligent manufacturing , 2015, Journal of Intelligent Manufacturing.

[27]  Ray Y. Zhong,et al.  RFID-enabled real-time advanced planning and scheduling shell for production decision making , 2013, Int. J. Comput. Integr. Manuf..

[28]  Wu He,et al.  A state-of-the-art survey of cloud manufacturing , 2015, Int. J. Comput. Integr. Manuf..

[29]  Fei Tao,et al.  Cloud manufacturing: a computing and service-oriented manufacturing model , 2011 .

[30]  Qiang Liu,et al.  Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system , 2019, Int. J. Prod. Res..

[31]  Daqiang Zhang,et al.  Context-aware vehicular cyber-physical systems with cloud support: architecture, challenges, and solutions , 2014, IEEE Communications Magazine.

[32]  Paulo Leitão,et al.  Agent-based distributed manufacturing control: A state-of-the-art survey , 2009, Eng. Appl. Artif. Intell..

[33]  Alexandre Dolgui,et al.  A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0 , 2016 .

[34]  Wei Meng,et al.  Quality of service in manufacturing networks: a service framework and its implementation , 2012 .

[35]  Jay Lee,et al.  Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation , 2015 .

[36]  Yongkui Liu,et al.  Manufacturing Service Management in Cloud Manufacturing: Overview and Future Research Directions , 2015 .

[37]  Daqiang Zhang,et al.  VCMIA: A Novel Architecture for Integrating Vehicular Cyber-Physical Systems and Mobile Cloud Computing , 2014, Mobile Networks and Applications.

[38]  Jianhua Liu,et al.  Digital twin-based smart production management and control framework for the complex product assembly shop-floor , 2018, The International Journal of Advanced Manufacturing Technology.