Industrial Internet of Things-driven synchronization of production processes and sharing of resources for smart manufacturing

Nowadays, industrial enterprises are changing their business models to meet the ever rising personalized product demands with smart manufacturing capabilities [1]. By leveraging the power of advanced information technologies, the Industrial Internet of Things (IIoT) is about connecting all the industrial assets, including machines and control systems, with the information systems and business processes [2]. The rapid development of IIoT has brought up many novel paradigms/applications, such as cloud-based manufacturing [3], cyber physical production system [4,5], big data analytics for system design [6], digital twin-enabled product development [7], and smart product-service system [8].

Although IIoT has been preliminarily adopted in industry, its potentials for smart manufacturing implementations should be further investigated. Firstly, the smart manufacturing strategy puts forward high requirements for the dynamic interaction and collaboration of multiple production processes. Nevertheless, different production processes (e.g. production scheduling and logistics) are often investigated separately, which can then influence the result of real-time and smart decision making. How to use the IIoT to achieve the synchronization of production processes can be an essential issue. Meanwhile, industrial enterprises are suffering from a huge survival crisis due to the dynamic and changeable global manufacturing environment, such as the outbreak of pandemic and diverse global economic competition. Therefore, how to take advantages of IIoT to promote the resource sharing and improve the sustainability of industrial enterprises should be further studied. In addition, the servitization of resources and production processes has inevitably been a prevailing tendency since the manufacturing demands become ever increasingly personalized. Hence, to improve customers’ satisfaction, the novel service model of multiple resources and production processes should be considered in a sustainable manner. To address these issues, this special issue aims to present the state-of-the-art IIoT-driven methods, tools, systems, and cases to promote the synchronization of production processes and sharing of resources for smart manufacturing.

The topics of the special issue include, but are not limited to the following ones:

· Novel models/methodologies for IIoT-driven production synchronization

· Service discovery and allocation of shared manufacturing resources

· IIoT-enabled service management tools/systems

· Production planning and scheduling in smart manufacturing

· IIoT-driven reliability analysis of manufacturing resource management system

· Knowledge-based methods for smart manufacturing

· Human-cyber-physical system for smart manufacturing

· Resource synchronization and sharing for supply chain management

· Implementation and case studies of IIoT-driven production synchronization and resource sharing

Proposed Schedule

– Submission open: 01 April 2021

– Paper submission deadline: 30 September 2021

– First round review results: 31 December 2021

– Second round review results: 28 February 2022

– Notification of final decision: 31 March 2022

Submission of Paper

All papers forwarded for the special issue must use the new online submission and editorial system for Advanced Engineering Informatics (https://www.journals.elsevier.com/advanced-engineering-informatics). To ensure that your paper is correctly identified for inclusion into the special issue review, it is important that you select “IIoT-SoPP-SoR” when you reach the “Article Type” step of the submission process.

Manuscripts should be prepared in accordance with the format and guidelines found at https://www.elsevier.com/journals/advanced-engineering-informatics/1474-0346/guide-for-authors. Submitted papers should not have been previously published nor currently under consideration for publication elsewhere.

Guest Editors

Xun Xu, Professor

Department of Mechanical Engineering

University of Auckland, New Zealand

E-mail: [email protected]

Yingfeng Zhang, Professor

School of Mechanical Engineering,

Northwestern Polytechnical University, China

E-mail: [email protected]

Geng Zhang, Research Fellow

School of Electrical and Electronic Engineering,

Nanyang Technological University, Singapore

E-mail: [email protected]

References

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[2] E. Sisinni, A. Saifullah, S. Han, U. Jennehag, M. Gidlund, Industrial internet of things: Challenges, opportunities, and directions, IEEE Trans. Ind. Informatics. 14 (2018) 4724–4734. https://doi.org/10.1109/TII.2018.2852491.

[3] X. Xu, From cloud computing to cloud manufacturing, Robot. Comput. Integr. Manuf. 28 (2012) 75–86. https://doi.org/10.1016/j.rcim.2011.07.002.

[4] Y. Zhang, C. Qian, J. Lv, Y. Liu, Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor, IEEE Trans. Ind. Informatics. 13 (2017) 737–747. https://doi.org/10.1109/TII.2016.2618892.

[5] L. Wang, M. Törngren, M. Onori, Current status and advancement of cyber-physical systems in manufacturing, J. Manuf. Syst. 37 (2015) 517–527. https://doi.org/10.1016/j.jmsy.2015.04.008.

[6] A.J.C. Trappey, F. Elgh, T. Hartmann, A. James, J. Stjepandic, C. V. Trappey, N. Wognum, Advanced design, analysis, and implementation of pervasive and smart collaborative systems enabled with knowledge modelling and big data analytics, Adv. Eng. Informatics. 33 (2017) 206–207. https://doi.org/10.1016/j.aei.2017.01.001.

[7] F. Tao, F. Sui, A. Liu, Q. Qi, M. Zhang, B. Song, Z. Guo, S.C.Y. Lu, A.Y.C. Nee, Digital twin-driven product design framework, Int. J. Prod. Res. 57 (2019) 3935–3953. https://doi.org/10.1080/00207543.2018.1443229.

[8] P. Zheng, Z. Wang, C.H. Chen, L. Pheng Khoo, A survey of smart product-service systems: Key aspects, challenges and future perspectives, Adv. Eng. Informatics. 42 (2019) 100973. https://doi.org/10.1016/j.aei.2019.100973.