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Data-driven intelligent control system in remanufacturing assembly for production and resource efficiency

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Abstract

Remanufacturing is one of the most effective ways to deal with the current global waste crisis; moreover, it can improve the production and resource efficiency of remanufacturing, which is an urgent need for global sustainable development. Therefore, in this study, a data-driven intelligent control system is proposed to improve the production and resource efficiency of remanufacturing assembly systems. An optimization model of the reassembly scheme is constructed to minimize quality loss and comprehensive cost. Then, based on the data acquisition and processing technology, the remanufacturing parts are measured, grouped, and coded, and the dimensional chain calculated. Then, a control method, which is a real-time monitoring and dynamic compensation response to abnormal quality, is proposed to achieve intelligent control of the remanufacturing assembly process. Moreover, some data-driven technologies of intelligent control systems that include information perception and fusion technology and real-time monitoring and dynamic compensation architecture are researched and implemented. Lastly, the intelligent control prototype system is used in a remanufacturing engine assembly workshop. Both theoretical and experimental results demonstrate that the data-driven intelligent control system in remanufacturing assembly is effective, thus providing a method and technical support for production and resource efficiency in reassembly systems.

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Funding

This research is supported by the General Program of Anhui Natural Science Foundation, (No.2008085ME150), Anhui Social Science Innovation and Development Research Project (2021CX069), Anhui Provincial Academic Funding Program for Top Disciplines (Specialties) in Colleges and Universities (gxbjZD2021083), and Anhui Province Teaching and Research Project (2021jyxm1502).

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Author notes

  1. Cuixia Zhang

    Present address: School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, 234000, China

Authors and Affiliations

  1. School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, 234000, China

    Conghu Liu

  2. Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai, 200030, China

    Conghu Liu & Fangfang Wei

  3. College of Engineering and Technology, Southwest University, Chongqing, 400715, China

    Wei Cai

  4. School of Economics and Management, Shanghai Polytechnic University, Shanghai, 201209, China

    Fangfang Wei

Authors
  1. Conghu Liu
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  2. Wei Cai
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  4. Fangfang Wei
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Contributions

Conghu Liu and Wei Cai contributed to the conception of the study; Conghu Liu and Cuixia Zhang performed the experiment; Conghu Liu and Fangfang Wei contributed significantly to the analysis and manuscript preparation; Conghu Liu and Wei Cai performed the data analyses and wrote the manuscript.

Corresponding authors

Correspondence to Wei Cai, Cuixia Zhang or Fangfang Wei.

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Liu, C., Cai, W., Zhang, C. et al. Data-driven intelligent control system in remanufacturing assembly for production and resource efficiency. Int J Adv Manuf Technol 128, 3531–3544 (2023). https://doi.org/10.1007/s00170-023-12080-y

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  • DOI: https://doi.org/10.1007/s00170-023-12080-y

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