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International Journal of Computer Integrated Manufacturing
Special Issue on New Frontiers in Smart Manufacturing Systems
Deadline: 31st December 2022

Guest Editors

Yuqian Lu (Managing Guest Editor), The University of Auckland, New Zealand. Email: yuqian.lu@auckland.ac.nz

Lihui Wang, KTH Royal Institute of Technology, Sweden

Aydin Nassehi, University of Bristol, UK

Jiafu Wan, South China University of Technology, China

Aims & Scope

Rapidly evolving global initiatives have highlighted a manufacturing future that is connected, smart, resilient, human-centric, and sustainable for rapidly producing high-value-added products and services defined by end-users. Manufacturing systems, therefore, have to change: (1) new manufacturing control strategies are required to enable flexible production of heterogeneous manufacturing jobs at dynamic batch sizes with mass efficiency (mass personalization); (2) factories need to be resilient and self-organizing to adapt to sudden disruptions in customer demands, factory conditions and supply chain changes; and (3) machines need to be sympathetic with its operators, to name a few.

These expected changes are possible, enabled by the recent advances in cybernetics, information theory, and sensing techniques. At the core, fundamental breakthroughs in how manufacturing systems and processes are modeled, designed, and controlled need to evolve towards a more adaptive, self-organizing, and contextual-dependent manner. Hence, this special issue welcomes original research work on smart manufacturing systems.

We are particularly interested in research work on the following topics:

  • Situational-aware manufacturing perception technologies

  • Cognitive reasoning and decision-making in manufacturing control and optimization

  • Plug-and-play in manufacturing

  • Self-organizing manufacturing algorithms

  • Reconfigurable, flexible, and resilient manufacturing methodologies

  • Machine-to-machine communication

  • Decentralized and adaptive control and automation

  • Reinforcement learning for dynamic manufacturing optimization

  • Agent-based manufacturing modeling, simulation, and control

  • Design and manufacturing process automation for mass personalization

  • Digital twin and digital thread for smart manufacturing systems

  • Standards-based integration for mass personalization

  • Knowledge extraction, management, and transfer in manufacturing automation

  • Human-automation symbiosis for enhanced shared autonomy

References

[1]: Lu, Y., Xu, X., & Wang, L. (2020). Smart manufacturing process and system automation–a critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems, 56, 312-325.

[2]: Wang, L., & Haghighi, A. (2016). Combined strength of holons, agents and function blocks in cyber-physical systems. Journal of manufacturing systems, 40, 25-34.

[3]: Qin, Z., & Lu, Y. (2021). Self-organizing manufacturing network: A paradigm towards smart manufacturing in mass personalization. Journal of Manufacturing Systems, 60, 35-47.

[4]: Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer networks, 101, 158-168.

[5]: Lu, Y., Zheng, H., Chand, S., Xia, W., Liu, Z., Xu, X., ... & Bao, J. (2022). Outlook on human-centric manufacturing towards Industry 5.0. Journal of Manufacturing Systems, 62, 612-627.

[6]: Ma, A., Nassehi, A., & Snider, C. (2021). Anarchic manufacturing: implementing fully distributed control and planning in assembly. Production & Manufacturing Research, 9(1), 56-80.