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<p>To achieve continuous improvement in production, it is essential to develop robust data management and process traceability for the assembly process. However, most existing studies on assembly processes struggle to implement refined data management and process traceability during the execution stage, particularly for complex products. To address this challenge, this paper proposes a workflow-based and multi-level data-driven digital twin system for the assembly process. First, a workflow-based data acquisition method is employed to facilitate systematic data collection. Subsequently, an OPC UA (Open Platform Communication Unified Architecture) information model is developed to enable dynamic data management, establishing a seamless connection between the physical and virtual assembly lines. Finally, deep learning techniques are incorporated to estimate the remaining assembly lead time, guided by multi-level data. Experimental validation is conducted in a real-world assembly workshop, and the results demonstrate that the proposed approach effectively manages hierarchical assembly processes and accurately predicts assembly progress.</p>

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A digital twin data management and process traceability method for the complex product assembly process

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月1日

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  • Xun Cheng,
  • Feihong Huang,
  • Qiming Yang,
  • Linqiong Qiu

摘要

To achieve continuous improvement in production, it is essential to develop robust data management and process traceability for the assembly process. However, most existing studies on assembly processes struggle to implement refined data management and process traceability during the execution stage, particularly for complex products. To address this challenge, this paper proposes a workflow-based and multi-level data-driven digital twin system for the assembly process. First, a workflow-based data acquisition method is employed to facilitate systematic data collection. Subsequently, an OPC UA (Open Platform Communication Unified Architecture) information model is developed to enable dynamic data management, establishing a seamless connection between the physical and virtual assembly lines. Finally, deep learning techniques are incorporated to estimate the remaining assembly lead time, guided by multi-level data. Experimental validation is conducted in a real-world assembly workshop, and the results demonstrate that the proposed approach effectively manages hierarchical assembly processes and accurately predicts assembly progress.