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Heterogeneous demand–capacity synchronization for smart assembly cell line based on artificial intelligence-enabled IIoT

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Abstract

An assembly cell line (ACL) is one type of cell production practice, derived from the Toyota Production System in the electronics industry and rapidly spread to other fields. In this mode, the conveyor line is divided into assembly cells (ACs) where various parts and tools are placed closer to the workers, enabling them to perform multiple tasks throughout an entire product assembly from start to finish. In this way, ACL allows manufacturers to rapidly configure an appropriate heterogeneous capacity to match heterogeneous demands with diversified customer orders in the high-mix, low-volume (HMLV) environment, which is the spread of the Just-In-Time (JIT) philosophy from the material level to the organization level. However, due to the lack of real-time information sharing in the ACL workshop, especially the up-to-date individual capacity and asynchronous production processes within and between ACs, it is hard to coordinate the heterogeneous capacities of ACs to meet the HMLV demands in a complex manufacturing environment with uncertainties. In this context, this paper proposes a heterogeneous demand–capacity synchronization (HDCS) for smart ACL by using artificial intelligence-enabled IIoT (AIoT) technologies, in which computer vision (CV) is applied for up-to-date capacity analysis of ACs. Based on these, an AIoT-enabled Graduation Intelligent Manufacturing System (GiMS) with feedback loops is developed to support real-time information sharing for the synchronous coordination of the ACL operation, which also provides the basis for the implementation of the HDCS mechanism through a rolling scheduling approach. Finally, a real-life industrial case is carried out by a proof-of-concept prototype to verify the proposed approach, and the results show that the measures on shipment punctuality and production efficiency are both significantly improved.

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Funding

This work was supported by the RGC TRS Project (T32-707-22-N) and the National Natural Science Foundation of China (NSFC) granted project (72231005).

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Authors and Affiliations

  1. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China

    Shiquan Ling & Daqiang Guo

  2. Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China

    Shiquan Ling & Yiming Rong

  3. Centre for International Manufacturing, Institute for Manufacturing (IfM), Department of Engineering, University of Cambridge, Cambridge, UK

    Daqiang Guo

  4. School of Intelligent Systems Science and Engineering, Jinan University (Zhuhai Campus), Zhuhai, China

    Mingxing Li

  5. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    George Q. Huang

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  1. Shiquan Ling
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Contributions

SL: Conceptualization, Methodology, Prototype, Formal analysis and investigation, Writing-original draft, Writing-review and editing. DG: Conceptualization, Methodology, Writing-review and editing. ML: Conceptualization, Writing-review and editing. YR: Supervision, Funding acquisition, Writing-review and editing. GQH: Supervision, Funding acquisition, Writing-review and editing.

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Correspondence to Daqiang Guo or George Q. Huang.

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The authors declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere. There are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address (gqhuang@hku.hk) which is accessible by the Corresponding Author and which has been configured to accept emails from the editorial office.

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Ling, S., Guo, D., Li, M. et al. Heterogeneous demand–capacity synchronization for smart assembly cell line based on artificial intelligence-enabled IIoT. J Intell Manuf 35, 539–554 (2024). https://doi.org/10.1007/s10845-022-02050-8

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  • DOI: https://doi.org/10.1007/s10845-022-02050-8

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