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<p>With the increasing precision requirements of large and complex equipment, traditional assembly methods can no longer satisfy modern industrial production demands in terms of cost and efficiency. Balancing comprehensive assembly quality with the optimization of rework costs for structural components has become a critical challenge. To overcome this, we propose an assembly error analysis and optimization approach based on Digital Twin (DT) technology integrated with a genetic algorithm. Initially, we develop an error transfer and accumulation model (ETAM) using small displacement torsor (SDT) theory, which accounts for manufacturing errors and positional deviations. This model is further enhanced into a DT-based ETAM (DT-ETAM) by incorporating structural deformation data from finite element analysis (FEA) simulations along with actual measurement data. Building upon this framework, we introduce a comprehensive structural assembly accuracy analysis method that integrates multiple error sources. Additionally, to address assembly issues caused by significant deviations in structural features, we present an innovative optimization model that simultaneously considers assembly quality and rework costs. We employ a multifaceted integrated genetic algorithm (MIGA) to develop an optimization solution method for revising assembly feature schemes. The effectiveness of the proposed methods is demonstrated through a case study involving an actual adjustable sidewall frame (ASF). The results confirm that our approach achieves cost-effective, efficient, and high-precision structural reworking, thereby addressing the bottleneck in high-precision assembly processes.</p>

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Application of Digital Twin technology in high-precision assembly: structural accuracy analysis and rework optimization for large-scale equipment

  • 期刊论文

  • 发表日期：

    2025年4月30日

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  • Jihong Yan,
  • Huaqiu Ding,
  • Hanglong Liang

摘要

With the increasing precision requirements of large and complex equipment, traditional assembly methods can no longer satisfy modern industrial production demands in terms of cost and efficiency. Balancing comprehensive assembly quality with the optimization of rework costs for structural components has become a critical challenge. To overcome this, we propose an assembly error analysis and optimization approach based on Digital Twin (DT) technology integrated with a genetic algorithm. Initially, we develop an error transfer and accumulation model (ETAM) using small displacement torsor (SDT) theory, which accounts for manufacturing errors and positional deviations. This model is further enhanced into a DT-based ETAM (DT-ETAM) by incorporating structural deformation data from finite element analysis (FEA) simulations along with actual measurement data. Building upon this framework, we introduce a comprehensive structural assembly accuracy analysis method that integrates multiple error sources. Additionally, to address assembly issues caused by significant deviations in structural features, we present an innovative optimization model that simultaneously considers assembly quality and rework costs. We employ a multifaceted integrated genetic algorithm (MIGA) to develop an optimization solution method for revising assembly feature schemes. The effectiveness of the proposed methods is demonstrated through a case study involving an actual adjustable sidewall frame (ASF). The results confirm that our approach achieves cost-effective, efficient, and high-precision structural reworking, thereby addressing the bottleneck in high-precision assembly processes.