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<p>In the real manufacturing industry, the timely identification of weaknesses and the precise determination of optimization areas are crucial for efficient workshop production. Traditional plant simulation methods have been widely employed to support decision-making, but the accuracy of these decisions heavily depends on the reliability of the simulation results. Static simulations, which rely on manually set parameters often fall short of meeting these demands. To address this, dynamic simulations incorporating real-time data are essential for generating accurate and actionable insights. A worthwhile approach is proposed therefore in this paper by establishing a plant simulation environment in digital twin(DT) via a high-fidelity real-time coevolution model for intelligent manufacturing cells. To obtain the coevolutionary models in the DT system, data modeling and integration and the virtual-real event driven method are illustrated. This approach provides a true-to-life workshop simulation environment. By employing variable and customizable optimization strategies, diverse simulation outcomes can be generated, aiding in the selection of the most effective optimization strategies. The development of a coevolutionary DT for a manufacturing cell within an enterprise is presented as a case study, which demonstrates the effectiveness of dynamic and combo-strategy production simulations.</p>

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Dynamic simulation and optimization approach for coevolutionary intelligent manufacturing cell with digital twin

  • 期刊论文

  • 发表日期：

    2026年1月19日

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  • Sheng Leng,
  • Xianggui Yang,
  • Zenghua Jiang,
  • Yunpeng Wei,
  • Cong Wang

摘要

In the real manufacturing industry, the timely identification of weaknesses and the precise determination of optimization areas are crucial for efficient workshop production. Traditional plant simulation methods have been widely employed to support decision-making, but the accuracy of these decisions heavily depends on the reliability of the simulation results. Static simulations, which rely on manually set parameters often fall short of meeting these demands. To address this, dynamic simulations incorporating real-time data are essential for generating accurate and actionable insights. A worthwhile approach is proposed therefore in this paper by establishing a plant simulation environment in digital twin(DT) via a high-fidelity real-time coevolution model for intelligent manufacturing cells. To obtain the coevolutionary models in the DT system, data modeling and integration and the virtual-real event driven method are illustrated. This approach provides a true-to-life workshop simulation environment. By employing variable and customizable optimization strategies, diverse simulation outcomes can be generated, aiding in the selection of the most effective optimization strategies. The development of a coevolutionary DT for a manufacturing cell within an enterprise is presented as a case study, which demonstrates the effectiveness of dynamic and combo-strategy production simulations.