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Optimising remanufacturing decision-making using the bees algorithm in product digital twins.

Mairi Kerin1, Natalia Hartono2,3, D T Pham2

  • 1Department of Mechanical Engineering, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham Edgbaston Campus, Birmingham, UK. m.e.kerin@bham.ac.uk.

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|January 13, 2023
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Summary
This summary is machine-generated.

This study introduces a digital twin (DT) model to optimize remanufacturing planning. By integrating Industry 4.0 (I4.0) technologies, the DT enhances decision-making for extending product lifecycles within the circular economy (CE).

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Area of Science:

  • Engineering
  • Computer Science
  • Sustainability Science

Background:

  • Remanufacturing is crucial for the circular economy (CE), extending product life but its integration with Industry 4.0 (I4.0) is underexplored.
  • The digital twin (DT) concept, an aggregation of I4.0 technologies, shows potential as a life-extending enabler for remanufacturing.

Purpose of the Study:

  • To design and demonstrate a digital twin (DT) model for optimizing remanufacturing planning.
  • To leverage product lifecycle data for enhanced remanufacturing strategies.

Main Methods:

  • Development of a DT model incorporating a neural network for remaining useful life (RUL) prediction.
  • Utilization of the Bees Algorithm for decision-making within the DT framework.
  • Validation of the model through a real-world case study.

Main Results:

  • The DT model successfully optimizes remanufacturing planning by utilizing lifecycle data.
  • Neural networks provided accurate RUL predictions, and the Bees Algorithm facilitated effective decision-making.
  • The case study confirmed the model's practical applicability and effectiveness.

Conclusions:

  • Intelligent tools within a DT framework can significantly improve remanufacturing decision-making.
  • Visibility into product status and reliable process information are key for DT-driven remanufacturing optimization.
  • This research highlights the synergistic potential of digital twins and remanufacturing for advancing the circular economy.